1997results about "Computer simulations" patented technology

Construct multiple retrieval augmented generation (RAG) databases for different related fields as needed to increase the recording capacity of additional information. By referring to the entire text or image of the original document page where relevant content is described based on the chunks obtained from RAG retrieval, eliminate reference omissions. At the same time, reflect the background information and related information that may be described around the retrieved chunks in the context of the question text. Databaseize the records of the question text and the answer text to make them searchable, and provide a large language model using retrieval augmented generation that eliminates the need for costly and time-consuming regenerating answers. 【Solution means】The RAG database includes a page image acquisition means, a page text database recording means, a related feature vector extraction means, and a feature vector database recording means.

Methods and systems for generating digital threads that connect models and software tools for an interconnected digital engineering and certification ecosystem are provided. In one embodiment, a process for generating a software-code-defined digital thread includes receiving a first model representation of a first engineering model, a second model representation of a second engineering model, and an intent input. Determining, using a machine learning (ML) model, one or more model endpoints in the first and the second model representations. Generating, using a script-generating ML model, a platform orchestration script that accomplishes the intent input by connecting the first model representation and the second model representation, using the one or more model endpoints. Finally, storing the platform orchestration script as the software-code-defined digital thread. The digital threading of digital engineering models, with and without AI assistance, leads to exponential increases in efficiency of designing, testing, certifying, manufacturing, and operating physical products, allowing physical products to be made at digital speeds.

The invention provides a deep learning self-calibration system for high-precision color management, and relates to the technical field of color calibration, a system architecture comprises an algorithm layer, a system architecture layer and a data processing and optimizing layer, through fusion of multiple advanced algorithms in the algorithm layer, the precision of color correction is effectively improved, and the accuracy of color correction is improved. The high consistency of colors of printed matters in the whole production process is ensured, the quality stability of printed products is greatly improved, data processing is carried out through all modules of the data processing and optimizing layer, real-time data in the printing production process are efficiently processed, a model is dynamically updated, and the printing production efficiency is improved. The design of the system architecture layer provides a comprehensive adaptation scheme for large-scale production, multiple links and multiple equipment materials in the printing industry, the universality and flexibility of the system are enhanced, and the system is suitable for large-scale production, multiple links and multiple equipment materials in the printing industry. The time and labor cost for readjusting the system due to the change of equipment materials is reduced, and the production strain capacity of enterprises is improved.

The invention relates to a high-efficiency image recognition system based on embedded edge equipment, which belongs to the technical field of computer application and comprises a hardware acceleration module, an image recognition module and an image recognition module, reasoning tasks are distributed to different cores and a model quantification module through dual-core cooperation, quantification converts high-precision floating-point number representation into low-precision integers through weight and activation in a deep learning model, an automatic deployment module automatically converts the trained deep learning model into a format suitable for an embedded hardware platform, and the embedded hardware platform is used for realizing real-time reasoning. The data processing module is used for preprocessing and enhancing input data and ensuring that the data meet the input requirements of the deep learning model, and the reasoning engine module is responsible for loading and operating the optimized deep learning model on embedded equipment. And the efficient and reliable image recognition function is realized while the calculation, storage and energy consumption limitation of the embedded equipment is met.

Techniques for optimizing and deploying deep neural network (CNN) machine learning models for inference using static analysis are described. A method includes obtaining a deep neural network (DNN) machine learning (ML) model, generating an intermediate representation for the ML model, the intermediate representation including one or more nodes corresponding to one or more operators utilized by the ML model, identifying, for at least one node of the intermediate representation, an optimized schedule for at least one operator corresponding to the at least one node using a static analysis that is based on a hardware-specific cost model, generating an optimized intermediate representation using the optimized schedule that is optimized for execution on a hardware platform, and generating code corresponding to the ML model based at least in part on the optimized intermediate representation, wherein the code is specific to the hardware platform.

Provided in the present invention is a parameter-efficient large-language fine-tuning federated learning framework, comprising the following steps: performing modeling on LoRA adapters of different edge clouds; since different weights exhibit different average performances on the LoRA adapters, using singular values to quantify the importance of the weights, and therefore, before each round of independent training of the LoRA adapters using N edge clouds, using a matrix singular value to decompose a BA matrix in the LoRA adapter for each trainable weight; configuring heterogeneous LoRA adapters on the basis of the importance of the weights; and using different numbers of quantization bits to quantize a pre-trained model, and performing high-precision inverse quantization on the pre-trained model only when matrix multiplication is executed, wherein the pre-trained model is quantized to the maximum number of quantization bits on the basis of the memory budget of the edge clouds. The present invention has the following beneficial effects: the present invention determines the optimal fine-tuning model structure, thereby improving the performance of LLM fine-tuning, and adapts to heterogeneous and resource-constrained edge clouds.

The invention discloses a model training configuration parameter determination method and device and a storage medium, and relates to the technical field of computers, and the method comprises the steps: dividing configuration items into fixed configuration items and undetermined configuration items, and under the constraint that the fixed configuration items adopt specified configuration, determining the undetermined configuration items; predicting the predicted training performance of the to-be-trained model when the to-be-trained configuration item is trained by adopting different configuration parameters, and selecting a target configuration parameter from multiple configuration parameters of a group of to-be-trained configuration items based on the predicted training performance corresponding to the multiple configuration parameters, when the to-be-trained model is subjected to model training, the target configuration parameters are selected by one group of to-be-determined configuration items, so that the cost required by model training can be remarkably reduced, the resource utilization efficiency is maximized, the resource waste is reduced, the training efficiency is improved, and the problems of high hardware resource demand and high cost in a large model training stage in the related technology are solved.

A method, an apparatus, and a computer readable medium for storing instructions are described for a user terminal and a base station for updating an AI / ML configuration in case of a handover. The method performed by a user equipment comprising operating a first AI / ML configuration in a coverage area of a first base station; receiving an AI / ML configuration information indicating a second AI / ML configuration; operating the second AI / ML configuration indicated by the AI / ML configuration information in the coverage area of a second base station. Operating a first AI / ML configuration comprises operating a first AI / ML Model or a first AI / ML Model with a first AI / ML Model configuration in the coverage area of the first base station. The first AI / ML Model is associated with a first AI / ML Model identifier and the first AI / ML Model configuration is associated with a first AI / ML Model configuration identifier. Operating the second AI / ML configuration comprises operating a second AI / ML Model or the first AI / ML Model with a second configuration in the coverage area of the second base station. The second AI / ML Model is associated with a second AI / ML Model identifier and the second AI / ML Model configuration is associated with a second AI / ML Model configuration identifier. Indicating the second AI / ML configuration comprises indicating a second AI / ML Model identifier and / or a second AI / ML Model configuration identifier.

The invention relates to the field of machine learning, and discloses a charging pile fault diagnosis method, which comprises the following steps: S1, preparing an environment, building a deep learning development environment, and providing a necessary technical environment for the training and deployment of a subsequent model; and S2, data acquisition and processing: installing a current sensor, a voltage sensor and a temperature sensor on a new energy automobile charging detection vehicle, and acquiring operation data of the charging pile in real time. And S3, model definition: a fault diagnosis model based on deep learning is constructed, a fully connected neural network (DNN) is adopted, and the model comprises an input layer, a hidden layer and an output layer. The key operation data of the charging pile are collected in real time through the sensor, the fault state is intelligently judged in combination with the deep learning model, rapid and accurate fault positioning and diagnosis are achieved, and the maintenance efficiency of the charging pile is remarkably improved. The system can automatically extract data features and analyze fault types, and manual intervention is reduced.

Implementations for receiving, by a GAI integration platform, a request from an application executed by an enterprise system of an enterprise, the application being executed remotely from the GAI integration platform, processing, through a control tier of the GAI integration platform, at least a portion of the request through a set of modules to generate a prompt that is responsive to the request, the set of modules including one or more of a prompt template module, a prompt quality module, and a personally identifiable information (PII) detection module, transmitting, by the GAI integration platform, the prompt to a GAI system of a plurality of GAI systems, receiving, by the GAI integration platform, a response from the GAI system, the response comprising content generated by the GAI system in response to the prompt, and transmitting, by the GAI integration platform, the response to the application.

The methods, systems, and computer networking apparatuses described herein enable language models to receive input (e.g., a query or a request) from a user or application, and without any additional training data or instructions, determine to generate a function call based on the received input from the user and generate the function call based on the determination. In some embodiments, a language model may further access an external tool or application to request an output as a response to the generated function call. The disclosed methods, systems, and networking apparatuses improve the technical field by incorporating language model capabilities within the function calling process, and allowing for function-related information to be provided to a language model via input received in any number of formats or types, including structured or unstructured input, language or non-language input, or any combination thereof.

A method is provided for processing data in a neural network system. The method includes receiving input data; processing the input data through a first set of neural network layers configured to perform data processing using MatMul-free techniques to produce intermediate data; further processing the intermediate data through a second set of neural network layers configured to simulate spiking neural network (SNN) functionalities using MatMul-free techniques; and outputting a result based on the processed data from the second set of neural network layers.

The invention discloses a topographic surveying and mapping method based on artificial intelligence. The topographic surveying and mapping method comprises the steps of (1) multi-source data fusion acquisition; (2) intelligently extracting topographic features; (3) three-dimensional reconstruction optimization; (4) dynamic terrain evolution modeling; the invention also discloses a system for implementing the topographic mapping method based on artificial intelligence, and the system comprises a data acquisition and preprocessing module, a topographic feature intelligent extraction module, a three-dimensional reconstruction optimization module, a dynamic topographic evolution modeling module and an interactive visualization and decision platform. The data acquisition and preprocessing module, the terrain feature intelligent extraction module, the three-dimensional reconstruction optimization module, the dynamic terrain evolution modeling module and the interactive visualization and decision-making platform are connected in sequence, and each module communicates through a gRPC interface and supports independent upgrading.

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform certain acts. The acts can include generating a digital twin network simulation of a physical computer network controlled through a software-defined-network (SDN) control system. The acts also can include training a routing agent model on the digital twin network simulation using a reinforcement-learning model on traffic that flows through nodes of the digital twin network simulation. The routing agent model includes a machine-learning model. The acts additionally can include deploying the routing agent model, as trained, from the digital twin network simulation to the SDN control system of the physical computer network. Other embodiments are described.

Workloads for executing a graph neural network (GNN) may be divided among various processing units, such as a central processing unit (CPU) and a neural processing unit (NPU). The NPU may include a data processing unit (DPU) and a digital signal processor (DSP). The CPU may perform precomputation, model optimization, hardware optimization, and compilation. For example, the CPU may precompute a parameter matrix and use the parameter matrix as internal parameters of a GNN. The CPU may also perform node padding, approximation computation, or transfer of DSP operations to DPU to optimize the GNN. The CPU may also perform sparsity data compute and storage, vertical fusion of DSP operations and DPU operations, or data quantization to optimize performance of the NPU. The compiled GNN may be provided to the NPU, and the DPU and DSP may perform the operations in the compiled GNN to produce a prediction of the GNN.

Evaluate the accuracy of the RAG system's answers. [Solution] The information processing method disclosed herein includes the steps of: (f) after all Q&A data corresponding to multiple target data satisfy a predetermined standard, a user terminal inputs a first question of a pair of a first question and a first answer to the first question contained in the Q&A data into a RAG system and has the RAG system provide a second answer to the first question; and (g) outputting a second judgment result regarding the accuracy of the second answer to the first answer.

The invention discloses a model training system, a model training task execution method, a model training task execution device and a medium, a management cluster in the model training system can obtain real-time state data when a training cluster executes a model training task, and the model training task is executed through the real-time state data. The state of the training cluster during execution of a model training task in a set time period in the future is predicted, the management cluster determines simulators corresponding to the devices included in the training cluster, and the virtual training cluster corresponding to the training cluster is initialized through the simulators. The management cluster generates a plurality of execution strategies for the virtual training cluster according to the prediction state, simulates and executes the model training task through the virtual training cluster according to at least a part of the execution strategies in the execution strategies, obtains performance indexes corresponding to the at least a part of the execution strategies, determines a target strategy according to the obtained performance indexes, and sends the target strategy to the virtual training cluster. And the model training task is executed according to the target strategy, so that the efficiency of the whole training cluster is effectively improved.

The invention provides a large model active tool calling method and system based on multi-step reasoning, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting tool information, generating a structured tool description set, automatically generating an executable code sample and corresponding natural language explanation based on the tool description set, and constructing a reverse training data set; the method comprises the following steps of: marking tool use necessity tags in a real problem, prompting a large model to generate a multi-step reasoning path combined with a natural language and codes, verifying and filtering the correctness of the reasoning path, and constructing a forward training data set; and performing three-stage training on the large model based on the reverse training data set and the forward training data set. According to the method, the problem that the active tool calling capability of a large model is insufficient is solved, and the code execution rate and problem solving efficiency of a complex reasoning task are improved.

Systems and methods of the present disclosure may receive, from a user computing device, a user-provided data record query including a natural language request for information associated with one or more data sources. User persona attributes of the user may be determined, such as a user role or security parameters or both. Based on the user persona attributes a context query may be generated to obtain context attributes associated with the user-provided query. The natural language request and the context attributes are input into the model orchestration large language model (LLM) to output instructions to machine learning (ML) agents based on the context attributes. The ML agents output responses associated with the user-provided data record query based on the instructions, and the responses are input into the model orchestration LLM to output to the user computing device a natural language response based on the context attributes.

The embodiment of the invention provides a multi-agent arrangement method and device based on a service grid and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: generating corresponding domain-specific language description information based on workflow information of a plurality of agents; and based on the domain-specific language description information, generating routing information corresponding to the workflow information of the plurality of agents, the routing information being used for defining a calling relationship of the plurality of agents in the service grid. According to the scheme, the labor cost is reduced, and the arrangement efficiency is improved.

An illustrative method includes monitoring operations performed with respect to a storage system by an entity using an identity associated with a particular role, the particular role providing the entity with a set of permissions associated with the storage system; determining, based on the monitoring, that one or more operations of the operations deviate from an expected activity profile associated with the role by more than a threshold; and performing, based on the determining that the one or more operations deviate from the expected activity by more than the threshold, a remedial action with respect to the entity.

The invention discloses an equipment operation and maintenance agent implementation method based on a small-parameter large language model, and the method comprises the steps: constructing an equipment fault diagnosis knowledge base, and providing a large-parameter-magnitude large language model capable of completing an Agent complex task; abnormal information of the historical data is collected and processed; analyzing abnormal information in the historical data, identifying and extracting trend characteristics of the measuring points; the method comprises the following steps: constructing an Agent Prompt template library, and generating a plurality of Agent Prompts; constructing an Agent fine tuning training data set based on a plurality of Agent Prompts; and performing Agent fine tuning on the small-parameter large-language model to form a fine-tuned small-parameter large-language model. A small-parameter large language model is deployed in a localized manner, so that the privacy and security of data are ensured; by migrating the Agent capability to the small-parameter large language model, a fault diagnosis task can be quickly processed in a localized environment, and the diagnosis efficiency is improved.

The present application discloses a parallel strategy optimal selection method, an electronic device, a device, and a neural network solver training method and apparatus. The parallel strategy optimal selection method comprises: on the basis of a neural network solver and a heuristic candidate strategy, quickly acquiring a plurality of candidate hybrid parallel strategies; predicting overhead values of the candidate hybrid parallel strategies; and using the predicted overhead values as guidance to perform optimal selection among the candidate hybrid parallel strategies so as to quickly determine a hybrid parallel strategy. The present application improves the parallel training efficiency of large models, and reduces energy consumption.

The invention relates to the technical field of large language models, in particular to a large language model multi-agent cooperative working method, which comprises the following steps: S1, designing a plurality of agents to simulate social division and cooperation, so that each agent executes different professional abilities, the plurality of agents including a planning agent, a tool agent and a reflection agent; s2, when task input is obtained, the planning agent filters retrieved tools, and a complex task is decomposed into subtasks easy to understand in a code and annotation mode; s3, performing targeted tool enhancement on the filtered tool set by the tool agent, and executing specific operation planned by the planning agent to obtain a subtask result; s4, evaluating a subtask result by the reflection agent, and planning the task from different angles by combining static planning and dynamic planning; and S5, the planning agent feeds back a summary result according to execution of the subtasks.

The invention provides a cost-aware efficient tool planning method based on a large model. According to the method, efficiency and cost optimization of tool scheduling is realized by constructing a CATP-LLM framework. The method comprises the steps that a tool planning language TPL is designed, a non-linear multi-branch parallel scheme is generated through structured Token support, and the task execution efficiency is remarkably improved; in combination with a cost-aware offline reinforcement learning CAORL algorithm, a tool selection strategy is dynamically optimized based on historical data, and task performance and resource consumption are balanced; and the balance relationship between the performance and the cost is quantified through the QoP of the scheme quality index, and dynamic adjustment is realized to output an efficient and low-cost planning scheme. Compared with the prior art, parallel tool calling, dynamic cost optimization and complex task adaptability are supported, the execution cost is remarkably reduced while the task quality is ensured, and an efficient solution is provided for multi-scene task planning.

A machine learning model compilation optimization method comprises the following steps: identifying nodes in a computational graph of a machine learning model to identify sparse nodes; determining an optimal sparse implementation scheme of the sparse nodes, wherein the optimal sparse implementation scheme comprises memory layout of the sparse nodes, configuration parameters and one or more of sparse formats of weight tensors; if the performance data of the sparse node is superior to target performance data under the optimal sparse implementation scheme, generating a first node based on the optimal sparse implementation scheme, and replacing the sparse node with the first node in the computational graph, the target performance data being the performance data of the sparse node under the original sparse implementation scheme; and performing code generation on the computational graph to obtain an execution code of the computational graph. Therefore, by considering the performance benefits of the nodes in the computational graph under different sparse implementations, the sparse nodes in the computational graph are optimized on the basis of the optimal sparse implementation, and the performance benefits of a machine learning model related to the computational graph are improved.

The invention relates to a method for improving the calculation speed of a model based on a mercuric chloride AI processor. The method comprises the following steps: deploying the mercuric chloride AI processor and a deep learning model in a server, and carrying out adaptation and optimization on the mercuric chloride AI processor; before the data in the first buffer area is read, predicting and preloading the data to be processed, and loading the data from the global memory to the second buffer area in advance; a double-buffer mechanism is arranged in the mercuration AI processor, interrupt and event trigger points are set, the operation of loading data to a next buffer area is immediately started when a specified calculation stage is finished, and the time sequence of data circulation is accurately controlled; and a control parameter is automatically adjusted based on monitoring data of the real-time monitoring module, the calculation process is decomposed into a plurality of stages, different buffer areas are allocated for each stage, and access to the shared memory is optimized by setting a specified cache replacement strategy and the size of a cache line. According to the process, the calculation speed of the model in the deep learning field is improved, the resource utilization rate is improved, and the defect of manual adjustment and optimization is overcome.

A processor-implemented method includes obtaining a benchmark execution result, receiving input data comprising a neural network model subject to prediction and analysis requirement information, receiving information on hardware of a device in which the neural network model is run, building a prediction model based on the benchmark execution result and the hardware information, extracting layer information respectively corresponding to a plurality of layers configuring the neural network model, and predicting either one or both of operation performance information and energy efficiency information respectively corresponding to the plurality of layers by inputting the analysis requirement information and the layer information to the prediction model.