124 results about "Model application" patented technology

The invention discloses a multi-level detail automatic simplification method for an oblique photography live-action three-dimensional model, and relates to the technical field of three-dimensional model simplification and computer graphics, and the method comprises the steps: obtaining oblique photography original data and three-dimensional model basic information; preprocessing the model, performing adaptive Gaussian filtering denoising, improving RANSAC to remove outer points, compressing textures in a blocking manner, correcting mapping coordinates, and repairing a topological structure; extracting multi-scale features; constructing a simplified decision model, and determining a simplification rate and a priority by combining an observation distance, scene precision and hardware performance; performing hierarchical simplification, vertex hierarchical improved edge folding, patch hierarchical adaptive deletion and regional hierarchical grid reconstruction; performing multi-dimensional quality evaluation, and if the requirements are not met, performing backtracking adjustment; and outputting a simplified model stored according to the LOD hierarchy, wherein the simplified model comprises transition information and a simplified log. According to the method, the data quality is improved through refined preprocessing, the simplification pertinence is enhanced through multi-dimensional feature extraction and intelligent decision, and the application value of the model is improved.

Various embodiments include a system. The system comprises processing circuitry. The processing circuitry obtains an Application Programming Interface (API) call that is associated with a Large Language Model (LLM). The processing circuitry generates a feature vector that numerically represents data included in the API call associated with the LLM. The processing circuitry provides the feature vector to a security LLM trained to detect security threats to the LLM. The processing circuitry obtains an output from the security LLM that indicates a security threat to the LLM. The processing circuitry determines a security policy based on the security threat. The processing circuitry provides the security policy to a security proxy that screens the API call.

The invention relates to a method and apparatus for generating a project-specific network architecture. The invention relates to a method for generating a project-specific network architecture, said method comprising the steps of:-providing (S1) a base model, in particular a large language model, with LoRa network adaptation; providing (S2) a model library having training data pairs, each of which consists of input data having a model application and / or model and / or hardware and / or software specification, and output data having at least one network architecture belonging to the respective input data; -selecting (S3) item-specific training pairs on the basis of said library of models; and-training (S4) the LoRa network of the base model on the basis of the item-specific training pair for generating the item-specific network architecture.

The invention discloses a heterogeneity causal effect quantitative evaluation system and method for electric vehicle charging behaviors, and relates to the field of electric vehicle big data analysis, artificial intelligence and causal inference. The system comprises a data preparation and variable system construction module, a heterogeneity causal effect quantitative evaluation model construction module and a model application and result interpretation module. A dual machine learning framework is adopted, systematic deviation of high-dimensional hybrid factors is eliminated by calculating orthogonalization residual errors, a causal forest model is constructed on this basis, a conditional average processing effect is accurately and stably quantified based on a splitting criterion of maximizing effect heterogeneity, and a high-precision and high-precision effect is obtained. The problem of causal effect estimation deviation of a traditional method under the forms of high-dimensional data and complex functions is solved. In order to enhance the interpretability, an agent model based on a single decision tree is further constructed, and a complex causal forest conclusion is extracted into a group of visual and operable If-Then decision rules, so that a complete closed loop from data to robust and interpretable decisions is realized.

The invention relates to the technical field of prefabricated part modeling, in particular to an interaction system for prefabricated part steel bar parameter driving design and a modularization implementation method. Incrementally updating the engine; the model application module is used for acting the change patch sequence on the three-dimensional reinforcement model in a transaction mode and driving display refreshing; the system further comprises a rule base module and an interpretable verification module. The rule base module stores a steel bar arrangement rule and / or a standard verification rule in a constraint declaration form; the interpretable verification module is used for performing compliance verification on the three-dimensional reinforcement model or the derived quantity thereof based on the evidence data field, and outputting a violation item list; the interactive interface comprises parameter panels and a violation interactive area, and the parameter panels are displayed in groups according to component type parameters, geometric parameters, steel bar parameters and specification options and support interactive modification. According to the method, the script dependency is reduced, the dependency graph increment patch is updated in real time, the automatic specification matching and positioning are realized, and the low-cost expansion is governed by the plug-in.

The invention provides a human body motor function decline risk prediction method based on multi-modal data evaluation. The method comprises the following steps: S1, collecting multi-modal data; s2, data preprocessing; s3, multi-dimensional feature extraction is carried out; s4, labeling and quantifying; s5, feature coding and fusion; s6, model training; s7, evaluating and verifying the model; and S8, outputting the model application. Through multi-modal data fusion and intelligent modeling, the defects in the prior art are effectively overcome.

The invention provides an embedded operating environment-oriented AI model cross-platform deployment method and system. The method comprises the following steps: generating a model configuration file conforming to a heterogeneous model configuration information standard format to realize model decoupling; the running state of the heterogeneous processor is collected in real time and abstracted as a reasoning resource node, and static or dynamic deployment is executed according to the priority and the running state for efficient resource scheduling; bottom layer differences are shielded by calling a unified packaged standardized inference engine service interface, and model inference is completed; and starting an AI model file dynamic updating process in application software operation, and dynamically switching the AI model file under the condition that the system does not need to be restarted through legality, content change and check value consistency check. According to the method, the cross-platform portability, the heterogeneous resource utilization efficiency and the system reliability of the AI model application software are remarkably improved.

The invention provides a banana nutrient deficiency detection method based on a multi-modal enhanced collaborative multi-scale module. The banana nutrient deficiency detection method comprises a data enhancement module which generates new data conforming to real pathological distribution based on a Stable Diffusion technology; the data processing module is used for extracting image multi-resolution features based on a multi-scale feature fusion network; the model optimization module is used for improving the element deficiency differentiation feature capturing capability based on a channel attention mechanism; the model training module dynamically focuses the difficult sample based on a mixed loss function of focus loss and cross entropy, and optimizes model training in a sample imbalance scene; the model application module is used for deploying a model to a WeChat applet based on model lightweight design to realize convenient detection of a mobile terminal; finally, the method is based on an MS-ConvNeXt model constructed by the modules, so that the accuracy of leaf element deficiency detection is greatly improved, and the convenience of field real-time detection is met.

The invention discloses a geological disaster susceptibility evaluation method and system based on sample enhancement, and the method comprises the steps: collecting original disaster sample data, carrying out the spatial expansion of a positive sample through the analysis of the spatial features of a disaster influence range, and generating a new enhanced sample set; introducing an influence factor contribution degree as a weight coefficient, and respectively calculating weighted cosine similarity between the expanded sample and the original positive sample; constructing a credibility evaluation index based on a weighted cosine similarity result; performing sample screening according to a preset credibility threshold value; using the screened sample set to train an XGBoost model; and applying the trained model to a target research area. The system comprises a sample enhancement module, a model training module and a model application module. According to the invention, the accuracy and reliability of geological disaster susceptibility evaluation are effectively improved. The method can be widely applied to the field of disaster prediction.

The invention relates to the technical field of digital twinning, in particular to a digital twinning driven equipment information model construction system. Comprising a data acquisition and preprocessing unit; a digital twin modeling unit; the model verification and optimization unit adopts a virtual and real data real-time comparison mechanism and is combined with a self-adaptive optimization algorithm driven by deviation characteristics; and a model application and updating unit. A dynamic weight distribution algorithm is adopted in a model verification and optimization unit, the initial weight is adjusted based on the current load rate and the accumulative operation duration of equipment, and instantaneous fluctuation and trend deviation are distinguished through a two-dimensional fusion judgment algorithm in combination with the deviation duration and the trend slope. The problem of insufficient accuracy of virtual and real data comparison in the prior art is solved; according to different deviation properties, an adaptive genetic algorithm is adopted to generate an optimization strategy, and it is guaranteed that the digital twin equipment information model is consistent with the physical characteristics of an equipment entity for a long time.

The embodiment of the invention relates to a data display method and device, equipment and a medium, and the method comprises the steps: responding to a model triggering operation, and displaying a first model page which comprises real-time analysis data, and the real-time analysis data comprises at least one piece of data; in response to a trigger operation on first data in the at least one piece of data, determining a corresponding first output result based on first input information corresponding to the first data; and displaying the first input information and the first output result on the first model page. According to the embodiment of the invention, the real-time analysis data is displayed on the model home page, the model processing can be quickly triggered to obtain the result by triggering a certain data, and the real-time data display is associated in the model application scene, so that the data acquisition efficiency is improved, and the problem processing efficiency is further improved.

The application provides a method and device for generating a proxy model in a virtual scene, a computer device and a storage medium. The method comprises the following steps: obtaining potential visible set information of a virtual scene and object models in the virtual scene; obtaining a visible bounding box of the object models based on the potential visible set information of the virtual scene; obtaining a model bounding box of the object models; clustering the object models according to the model bounding box and the visible bounding box of the object models to obtain a plurality of target object clusters; generating a visible bounding box of the target object clusters according to the visible bounding box of each object model under the target object clusters; generating a proxy model corresponding to the target object clusters; and determining a model application position point of the proxy model based on the visible bounding box of the target object clusters. The proxy model corresponding to each target object cluster and the application position range thereof are used to reduce DrawCall and game picture loading overhead while ensuring the picture effect of the game picture and avoiding the degradation of the game picture.

This invention relates to the field of AI model applications, specifically to a method and system for assembling model input parameters based on multidimensional variable expressions. The method includes: using a first multidimensional variable expression to filter and select various types of user-input data based on a pre-built system context variable dataset to obtain multiple corresponding computational datasets; using a second multidimensional variable expression to preprocess the data in the computational datasets to obtain multiple corresponding assembly datasets; using a third multidimensional variable expression to merge and assemble the multiple assembly datasets to obtain an input parameter dataset; parsing a pre-built input parameter JSON template and solving it based on the input parameter dataset to obtain the input parameter JSON for the AI ​​model. This invention, based on a multi-level, multidimensional vector expression-based automated process, can achieve real-time dynamic construction of input parameter JSON.

The invention provides a model acceleration method based on a face recognition model application. The method comprises the following steps: sampling a face picture; two different data enhancement modes are adopted for the face image, and a first distortion image and a second distortion image are obtained respectively; performing feature extraction on the first distortion graph through an original model, and outputting a first embedded vector; performing feature extraction on the second distortion graph through a simplified model, and outputting a second embedded vector; measuring a cross-correlation matrix of the first embedded vector and the second embedded vector by adopting a contrastive learning thought; and constraining the distance between the cross-correlation matrix and the unit matrix through the relative loss function, repeating until the output of the relative loss function meets the preset stability condition, and replacing the original model with the simplified model. According to the method, knowledge distillation from a heavy-weight network to a lightweight-weight network is completed, and the function of replacing a heavy-weight original model by a lightweight-weight simplified model is realized, so that hardware facilities of embedded equipment are adapted, and the real-time detection efficiency of face recognition is improved under limited computing resources.

In various embodiments, an inverse kinematic (IK) modeling application generates models that are used to solve IK problems for object animations. The IK modeling application generates configuration vectors based on a set of joint parameters associated with a joint chain. The IK modeling application executes forward kinematic operation(s) on the joint chain based on the configuration vectors to generate target vectors. Each target vector includes data associated with at least one of a position or an orientation for an end-effector associated with the joint chain. The IK modeling application performs one or more machine learning (ML) operations on an ML model based on the configuration vectors and the target vectors to generate a trained ML model that computes a predicted joint vector associated with the joint chain based on at least one of a target position or a target orientation for the end-effector.

The embodiment of the application provides a kind of artificial intelligence native application ecosystem, and related method and product;Artificial intelligence native application ecosystem includes: big model application module and multiple application programs;Big model application module is used to determine user intent information, and according to user intent information, determine target application program from multiple application programs;With target application program carries out data interaction, to provide target application program corresponding service to user.By the embodiment of the application, the application program of third party can be called, so that AI application can provide more rich functions to user, to reach the performance ability of human assistant.

The invention provides a model training and application method and device, equipment and a storage medium, and the method comprises the steps: determining a training sample set which comprises at least one group of sample data, and the sample data comprises first information and / or second information; wherein the first information is channel state information determined by a sensing signal receiving end based on a received sensing signal, and the first information is used for determining a sensing result of a sensing target; the second information is actual state information of the sensing target; training a first model based on the training sample set; wherein the first model can determine at least one type of sensing result of the sensing target based on the first information. According to the method and the device, the technical problems of limited application scene, relatively high complexity, relatively poor sensing accuracy under the condition of multiple sensing targets and the like when the sensing result is calculated by a sensing algorithm are solved, the sensing accuracy is ensured, the sensing complexity is reduced, and the sensing application scene is widened.

The application discloses a kind of based on large language model multi-agent cooperation's key raw material procurement decision optimization method, system, equipment and medium, it is related to supply chain intelligent decision, procurement optimization, big model application and multi-agent cooperation technical field, including the following steps: acquisition key raw material procurement related multi-source data;Multi-source data are standardized and time caliber alignment, generate uniform purchase state vector;Build purchase influence factor causal diagram and generate price prediction result package;Generate candidate procurement strategy task;Generate candidate procurement plan;Risk verification is carried out to candidate procurement plan;Determine execution procurement plan and reference procurement plan.The application adopts above-mentioned one based on large language model multi-agent cooperation's key raw material procurement decision optimization method, system, equipment and medium, through "observation-decision-optimization-feedback" closed-loop cooperation mechanism, improve the dynamic adaptability, explainability and execution feasibility of key raw material procurement decision.

The application provides a revit model direct analysis and rendering method and system, the method comprises the following steps: extracting model original data through a plug-in API; analyzing the model original data to generate model analysis data; transmitting the analysis data to a server end for scene processing, including reconstructing the spatial structure of the model, generating hierarchical scene features, and establishing an index to form a model scene file; loading the model scene file through a multi-threaded download mechanism of a front-end page; and rendering the downloaded model scene file in real time. The application realizes direct analysis and rendering of a revit model by targeting a revit plug-in API, provides basic data support for flexible use of model data throughout the life cycle, has good reusability and expandability, greatly improves the stability of revit analysis and the efficiency of rendering, and provides a new method and idea for building an independent revit model application platform.

The invention belongs to the technical field of mechanical equipment fault diagnosis, and particularly relates to an equipment fault diagnosis method based on BO and an intelligent model, and the method comprises the following steps: S1, analyzing application scene data; s2, intelligent model selection of anomaly detection and fault identification; s3, optimization parameters are determined; s4, data set division; s5, determining model parameters based on Bayesian optimization; s6, testing an optimal parameter model; s7, determining an output fusion scheme of the anomaly detection and fault recognition model, and performing overall testing; s8, optimal parameter model application; s9, an engineer performs maintenance according to conditions; and S10, updating the model when the data type or scale changes. Test verification shows that the scheme has a good effect on diagnosis of various faults under variable working conditions.

The application relates to the technical field of big model computing power scheduling, in particular to a computing power cooperative intelligent scheduling method and system based on a big model application, which comprises the following steps: obtaining a global computing power task request containing multiple big model inference tasks, extracting multi-dimensional features according to task constraint conditions and forming a task demand vector. An improved greedy algorithm is adopted to match the task demand vector and a state vector of a dynamic heterogeneous computing power resource pool, a preliminary scheduling instruction set is generated through weighted distance, a multi-step forward-looking evaluation model with computing power resource balance and task communication overhead as joint targets is constructed, the running state of tasks and resources in multiple time slices is simulated, the scheduling instruction is corrected, and finally the instruction is issued to a hardware computing unit to complete task execution. The method can improve the matching accuracy of tasks and computing power resources, optimize the balance of resource allocation, and reduce the data communication overhead between tasks.

The application relates to a dimension reduction method, device and storage medium of a model of a new energy grid-connected system, and the method comprises the following steps: S1, a linear dynamic model of a new energy unit grid-connected system is established; S2, an eigenvalue analysis method is used to analyze oscillation modes generated by the model, characteristic roots and participation factors existing in the system are calculated, and state variables mainly participated by the system are obtained; S3, the state variables are removed based on a time scale method, fast decay modes are omitted, dominant eigenvalues are reserved, a state equation of a dimension reduction system is established, and a dynamic response curve is analyzed based on the state equation of the dimension reduction system. Compared with the prior art, the application has the advantages of effectively realizing model dimension reduction of the new energy grid-connected system while guaranteeing model application effects and calculation accuracy, and reducing calculation amount.

This application provides a model evaluation method, apparatus, device, storage medium, and program product. The method includes: acquiring multi-dimensional evaluation quantitative data, wherein the multi-dimensional evaluation quantitative data includes at least one of the following: task adaptability of a first model, model deployment feasibility, and model application value; and determining the maturity of the first model deployment applied to the target device based on the multi-dimensional evaluation quantitative data. This method, by utilizing model maturity, allows for reasonable model selection in the early stages of model deployment, ensuring that the model deployed to the device matches actual needs and avoiding resource waste caused by blindly and recklessly using large models.

The invention discloses a high-quality data set quality evaluation method based on data and model collaboration. The technical problems that in the prior art, the evaluation dimension is single, decoupling with the model effect is achieved, and the real application value of data is difficult to quantify are solved. The method comprises four core stages of evaluation tool preparation, data quality evaluation, reference model verification and data set and model packaging. A logic closed loop is formed among the stages, and a complete high-quality data set comprehensive evaluation system is jointly formed. By introducing model application features such as data content diversity, labeling accuracy and model suitability, a data-model integrated quality evaluation system is constructed. According to the method, the scientificity and practicability of high-quality data set evaluation are improved, and a new technical support is provided for high-quality data set construction and optimization.

1. The name of the design product: graphical user interface (2) of big data intelligent application platform for electronic equipment. 2. The use of the design product: for an electronic equipment. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best indicates the design points: front view. 5. The product carrier is the usual design, and other views are omitted. 6. The use of the graphical user interface: for querying and managing the big data intelligent application platform. 7. The human-computer interaction mode of the graphical user interface: click "XX convergence clue intelligent screening", "model application XX supervision", "professional assistance empowerment XX", and "situation analysis scientific management" in the front view, and enter the information display page of each module. 8. Other circumstances that need to be explained: the symbol "X" in the front view represents changeable or replaceable text or letters.

1. Name of this design product: Graphical User Interface for Large-Scale Evaluation of Electronic Equipment. 2. Purpose of this design: An electronic device. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key points: the front view. 5. GUI (Graphical User Interface), omitting the rear view, left view, right view, top view, and bottom view. 6. Purpose of the graphical user interface: To implement various evaluations throughout the lifecycle of a large model. 7. Human-computer interaction method of graphical user interface: Select or enter version information from the "Specific Version" field in the main view, and click "Submit for Review" below to enter interface change state diagram 1. Click "Next" in interface change state diagram 1 to enter interface change state diagram 2. Click "Start Evaluation" in interface change state diagram 2 to enter interface change state diagram 3. Click "Next" in interface change state diagram 3 to enter interface change state diagram 4. Enter the model application service image (group), model ID, and application model packaging description in the text box in interface change state diagram 4, and click "Next Evaluation" in interface change state diagram 4 to enter interface change state. Figure 5, click "Start Timeliness Evaluation" in the interface change state diagram 5, enter the interface change state diagram 6, click "Next" in the interface change state diagram 6, enter the interface change state diagram 7, click "Start Performance Evaluation" in the interface change state diagram 7, enter the interface change state diagram 8, click "Next" in the interface change state diagram 8, enter the interface change state diagram 9, click "Submit Application Experience Evaluation" in the interface change state diagram 9, enter the interface change state diagram 10, click "Generate Report" in the interface change state diagram 10, enter the interface change state diagram 11, click "Next" in the interface change state diagram 11, enter the interface change state diagram 12. 8. Other situations requiring explanation: The upper left corner of the interface is the content screen.

This application discloses a task execution method, apparatus, and electronic device. Relating to the field of data processing, the method includes: upon receiving a training task for a target model, determining the model application scenario of the target model; acquiring single-turn dialogue data within the model application scenario, and constructing multi-turn dialogue data based on the single-turn dialogue data, wherein the single-turn dialogue data includes a question-answer pair, and the question-answer pair includes two dialogue rounds; and using the multi-turn dialogue data to execute the training task of the target model. This application solves the problem in related technologies where the processing method of piling up single-turn dialogue data results in low data validity and efficiency of multi-turn dialogue data, leading to low task execution efficiency for model training.

A communication method and apparatus, and relates to the field of communication technologies. In the method, after indicating a first state of one or more first models to a model management device, a model application device may retain the first state within a first time window, so that the model application device does not frequently report the first state of the one or more first models to the model management device, to reduce information exchange between the model application device and the model management device.

The invention discloses an AI modeling agent integrated with a three-dimensional application and a task planning and execution method thereof. The method comprises the following steps: S1, establishing a three-dimensional model; s2, calling a built-in domain knowledge base and a built-in parametric modeling rule base by the AI modeling agent, and mapping the analyzed instruction into a code segment; s3, the system automatically executes the generated code segment through a three-dimensional modeling software interface, constructs a corresponding parameterized model in a three-dimensional modeling environment in real time, and renders a visual result in real time for a user to preview; and S4, establishing an interactive design environment supporting iterative feedback and continuous optimization. The method is integrated with a three-dimensional model application, supports a user to output a simulation code and call an application through demand description and parameter input after AI analysis, and automatically generates a three-dimensional model for the user to browse.

The invention discloses an intelligent driving collaborative optimization method, system and device and a storage medium, and relates to the technical field of intelligent driving, and the method comprises the steps: determining a teacher model based on a pre-constructed multi-module mixed search space; carrying out multi-scale combined distillation training on the teacher model by utilizing a knowledge distillation strategy and an early leaving mechanism to obtain a student model; according to the embodiment of the invention, a student model is enabled to calculate and adjust a driving path according to scene complexity and a vehicle state in combination with a preset branch scheduling mechanism, the diversity and expansibility of an architecture can be improved while real-time performance and sensing precision are guaranteed, and the model application requirements of a complex driving scene are met.