32 results about "Specific model" patented technology

The application discloses an optical module, comprising: a first shell and a second shell, the first shell and the second shell surround to form a containing space, the containing space is used for containing a circuit board, a boss is arranged in the second shell, and the boss is located in the containing space, an adhesive is arranged on the boss, and the adhesive is used for fixedly connecting the circuit board and the second shell. The optical module does not need to additionally increase a jig, and the jig does not need to be maintained, so that equipment investment and maintenance cost can be reduced, the positioning precision of the jig can be avoided from being affected due to long-term use and abrasion, and the production rhythm can be avoided from being affected due to additional time required for installation and adjustment of the jig, so that the production efficiency can be improved. In addition, the jig is only suitable for a specific model, and the jig needs to be redeveloped when product design is changed, so that the development period is affected, and the boss can be compatible with circuit boards of different size specifications, and the production line flexibility is improved.

This invention discloses a rapid method for detecting the moisture content of fine aggregates used in concrete, belonging to the field of building materials testing technology. The method involves collecting samples of different types of fine aggregates, preparing multiple moisture content samples from oven-dry to saturated surface-dry states, and then measuring the resistivity values ​​after compaction under constant pressure to form a dataset. Based on this dataset, a specific model is established using nonlinear function fitting, and a general prediction model is established using machine learning algorithms. During on-site testing, the resistivity values ​​of the fine aggregates to be tested are measured after compaction under the same constant pressure, and the moisture content is calculated according to the type of aggregate using the appropriate model. This invention achieves rapid and non-destructive testing of the moisture content of fine aggregates, providing real-time data for adjusting water usage in concrete production.

This invention discloses a system and method for measuring the moisture content of cow manure during collection and storage, specifically relating to the field of agricultural waste treatment technology. It includes a sampling unit, a weighing unit, and a calculation and display unit. By weighing a fixed volume of wet cow manure sample, its wet density is calculated. Then, using a wet density-dry matter content relationship model and a specific model for cow manure, the moisture content of the collected cow manure is quickly calculated and displayed. This invention overcomes the bottleneck of time-consuming traditional drying methods and the disadvantage of electrical methods being susceptible to interference. It can complete accurate measurements within minutes at the production site, providing efficient and reliable data support for real-time control of cow manure collection, transportation, solid-liquid separation, composting, and other treatment processes.

The present application relates to the technical field of feature vector merging in large model inference acceleration, in particular to a feature vector similarity measurement method and device based on transformation domain, by constructing a new type of calculation path of "transformation-measurement", using an orthogonal transformation matrix to project the feature vector to the transformation domain and then calculating the absolute value error sum, effectively extracting the internal structured features, solving the defect of insufficient accuracy of traditional lightweight measurement indicators, designing a multiplierless hardware acceleration architecture supporting the algorithm, by strictly constraining the element values of the orthogonal transformation matrix within the integer set of {-1, 0, 1}, completely equivalent to the matrix multiplication into simple addition and subtraction operation, completely avoiding multiplication, division and square root operation, greatly reducing the chip area and power consumption overhead, in addition, the pareto optimality of model accuracy and hardware efficiency is also realized, and the configuration capability of searching and customizing the transformation matrix for specific models is also provided.

The application discloses a fine-grained sedimentary rock facies identification method based on deep learning, relates to the technical field of deep learning and sedimentary rock facies identification, and comprises the following steps: collecting facies curve data and microstructure image data and screening characteristic parameters, generating a standardized multi-modal feature set through a special analysis engine; calling a specific model to perform hierarchical feature extraction on the microstructure image to obtain a deep microstructure feature vector; inputting the two types of features into a special network to output a preliminary identification result through cross-modal attention weight distribution; and adopting a specific algorithm to perform inversion correction and dynamic adjustment on the preliminary result, combining feedback information to construct a facies classification system to complete accurate division. Through multi-source feature deep fusion, cross-modal correlation enhancement and multi-link collaborative optimization, the application realizes efficient and accurate identification of fine-grained sedimentary rock facies, adapts to complex lithological characteristics, and provides support for technical application in related fields.

This invention discloses a collaborative confidence fusion perception method for autonomous driving, belonging to the field of autonomous driving. This method eliminates the differences in perception models between different intelligent agents, makes full use of the advantages of sensor data from different modalities, and avoids the operation of forcibly converting visual images that are not good at extracting BEV features into BEV features in order to achieve feature unification. Based on fully leveraging the advantages of different modal data features, learning to adapt to the features that specific models are good at, and the inherent collaborative advantages of collaborative perception, it achieves better collaborative perception results.

This invention discloses a method and system for predicting the remaining lifespan of individual sensors based on Bayesian statistics, relating to the field of equipment health status monitoring and lifespan prediction technology. The invention provides a method comprising: establishing a general degradation model and prior distribution using historical degradation data; collecting monitoring data of a specific target sensor under real or accelerated stress in the field; updating the posterior distribution of model parameters based on Bayesian statistical inference and Gibbs sampling to generate a specific degradation model for that individual sensor; and calculating the remaining lifespan based on this specific model and a failure threshold. This invention achieves a breakthrough from "group lifespan assessment" to "accurate prediction of individual lifespan" through data-driven adaptive correction, significantly reducing over-maintenance costs and enhancing equipment operational safety.

Device for estimating the hot water consumption of a hot water production system, method and associated systems The present invention relates to a device (30) for estimating the hot water consumption of a hot water production system (10) over a time period comprising a tank (12) having a lower part (18) and a heating device (14), the estimating device (30) comprising: - an acquisition unit (34) measuring a temporal evolution of the water temperature of the lower part (18) during a period of use of the heating device (14), - an analysis unit (42) analyzing the variations of the temporal evolution to obtain a switching instant between a first sub-phase and a second sub-phase of a specific model of use of the heating device (14),and - a calculation unit (44) calculating a quantity representative of the heating energy of the volume of water consumed from the moment of switching. Figure for the abbreviation: figure 1,

This invention relates to a method for evaluating the reliability of train network control system software, comprising the following steps: Step 1: Calculating a reliability factor based on collected failure data; Step 2: Determining data trends based on the significance level under a normal distribution and the calculated reliability factors of the failure data; Step 3: Evaluating the reliability of the software based on multiple software reliability models; Step 4: Evaluating the predicted quality of each software reliability model based on the relative accuracy of the predicted software reliability. This method for evaluating the reliability of train network control system software, after obtaining failure data and before selecting a specific model, performs trend analysis on the data, and the analysis results can serve as guidance for model selection and the use of effective data; after evaluating the reliability of the software, it evaluates the predicted quality of the model based on the predicted effectiveness, thereby obtaining the optimal reliability evaluation result based on the rationality and relative accuracy of the reliability evaluation.

This application discloses a model training method and a cloud management platform based on a cloud management platform, which can improve model training efficiency. The method includes: a tenant sending a model training request for a specific model to the cloud management platform, whereby the tenant sets the number of queries, keys, and values ​​for the model. Based on this request, the cloud management platform determines multiple computing nodes. If the number of computing nodes exceeds the number of keys, the cloud management platform decomposes the model into multiple sub-models and deploys them on these computing nodes. These computing nodes can use these sub-models to process training data, obtaining the queries, subkeys, and subvalues. Then, these computing nodes can fuse the subkeys and subvalues ​​to obtain the keys and values. Subsequently, these computing nodes can use the queries, keys, and values ​​to complete the subsequent training of the sub-models.

This invention proposes a long-tail federated learning method and apparatus based on gradient cleanup and residual learning. The method includes: the server selecting clients and distributing global parameters; each client performing local training based on the global parameters to obtain updated local shared parameters, which are then uploaded to the server. During training, the task gradient and the balanced alignment gradient are calculated, and the task gradient is cleaned by projection to remove components opposite to the balanced alignment gradient, resulting in a cleaned gradient. The server aggregates the local shared parameters uploaded by each client to obtain global shared parameters and determines whether the current number of communication rounds has reached a preset limit. If so, the global shared parameters are distributed to all clients for personalized training to obtain client-specific models. During personalized training on each client, global branches and personalized branches are used to improve the long-tail category recognition rate.

The application discloses an industry-specific customized model generation method based on a large model, which comprises the following key steps: first, collecting structured and semi-structured multi-modal data containing key entities, concepts and relationships in the target industry; then, constructing a knowledge graph through a deep learning method; in the model construction stage, pre-training of a large language model is selected, and feature engineering and personalized adjustment of the model architecture are carried out according to the characteristics of the industry; on the basis of the constructed knowledge graph, the retrieval enhancement technology based on the knowledge graph is adopted to optimize the answer content of the large language model and inject the knowledge of the target industry field; finally, the reinforcement learning algorithm is used to optimize the model output by training the reward model. The application realizes the professional customization of the industry-specific model, significantly improves the question and answer ability of the large language model in the target industry field, and ensures the high accuracy and high relevance of the model when answering the problems related to the target industry.

This invention discloses an automatic mining and classification method for private data, comprising the following steps: First, receiving private unstructured documents, performing preprocessing and semantic alignment, and using sliding window technology to segment the documents into continuous text blocks; then loading a locally deployed general basic model and an industry-specific model, calculating the perplexity of each text block, and determining the scarcity of the text block by comparing the differences in the model output; next, constructing a statistical model based on the perplexity distribution of the enterprise's historical documents and dynamically setting a threshold, calculating the value score for text blocks exceeding the threshold; finally, projecting the scarcity and value score onto a preset classification decision matrix to automatically determine the secret level of the text block and trigger corresponding security handling strategies. This invention achieves automated and intelligent classification of private data, improving the efficiency and accuracy of data security management.

The application discloses a kind of energy storage auxiliary thermal power unit deep reinforcement learning load frequency control methods, including establishing the deep reinforcement learning environment of power system load frequency control problem, to simulate the characteristics and control demand of each component in actual power system;For lithium ion battery energy storage system in power system component, design the energy storage system output control strategy considering power system frequency deviation and energy storage system state of charge;The power system load frequency control problem is modeled as Markov decision model, combined with Markov decision model and energy storage system output control law, build the deep reinforcement learning load frequency control framework based on Actor-Critic architecture;Combined with deep deterministic policy gradient (DDPG) algorithm and random network distillation technology, design the load frequency controller of double output, including agent state, action and multi-objective dynamic adaptive reward function;Design agent solving algorithm based on dynamic weight adjustment.The load frequency control method provided by the application does not depend on specific model, has strong environmental adaptability, compared with the traditional energy storage output constant control strategy, the complementary coordination control of energy storage system and thermal power unit can be realized, and the frequency modulation effect of power system is effectively improved.

A method for assessing driver ability is presented, belonging to the field of driving behavior detection and assessment technology. It addresses the technical problems of existing driving ability assessment technologies, such as limited assessment dimensions and the lack of specific models for individuals with impaired neurological function. By constructing a multimodal data acquisition platform involving humans, vehicles, and the environment, driving behavior data is collected under preset simulated driving conditions. Statistical feature values ​​are extracted, driving behavior distribution feature values ​​are constructed, and cluster analysis and classification neural networks are used to complete the driving ability level assessment. Corresponding driving suggestions and auxiliary prompts are automatically output, thereby achieving a scientific, systematic, and multidimensional assessment of the ability of individuals with impaired neurological function to regain driving ability.

Processors, systems and techniques to perform different inferencing tasks using a unified multi-task inferencing mode are disclosed. In at least one embodiment, multiple task-specific models are created through fine tuning of a common pre-trained model, a single unified model is derived from the multiple task-specific models, and a task-specific model is then reconstructed from the unified model according to a type of inferencing task to be performed.

The application discloses a device self-healing type fault diagnosis system based on a biological heuristic algorithm and a digital twin model, belongs to the technical field of specific models of computers, and comprises the following modules: a digital twin model construction module, a biological heuristic type sensor network deployment optimization module, a fault mode identification and learning module, and a fault evolution path and maintenance strategy generation module.The device self-healing type fault diagnosis system based on the biological heuristic algorithm and the digital twin model can realize full-process closed-loop intelligent operation and maintenance from mapping of equipment states to intelligent maintenance decisions, can enable the system to identify hidden dangers in advance, accurately diagnose faults and automatically formulate optimal maintenance schemes, and can comprehensively realize digitalization, intelligentization, self-healing and active management of equipment operation and maintenance, and significantly improve the reliability, stability and operation and maintenance efficiency of equipment.

Generating a location-specific three-dimensional model in response to a location query can provide users with a better understanding of a location through providing better interactivity, better perspective, and better understanding of dimensionality. Generation of the models can be enabled by leveraging a three-dimensional asset database and segmentation methods. The location-specific models can provide further utility by further including situation specific simulated effects, such as simulated weather or traffic.

This invention provides a method and system for extracting abnormal entity information based on a large language model. The method includes: constructing a dedicated knowledge base containing an entity determination rule base, a typical behavior pattern base, and a related data resource base; fine-tuning a general large language model based on the dedicated knowledge base using a retrieval enhancement generation strategy to obtain a domain-specific model; using the domain-specific model in conjunction with the entity determination rule base to perform subject-level extraction of the data to be processed, identifying initiating entities, participating entities, and assisting entities; and using the domain-specific model in conjunction with the typical behavior pattern base to perform entity attribute mining on the extracted entities, identifying implicit abnormal activity-related feature attributes to determine whether an entity is an abnormal entity. This invention achieves accurate identification and attribute mining of abnormal entities.

1. Name of this product design: Action Camera Clip (Go Ultra). 2. Purpose of this design: This design is used for a back clip of a specific model of miniature action camera. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

This invention relates to the field of welded support plate reinforcement, specifically a support plate chamfer recognition and analysis method based on 3D vision. The specific steps are as follows: S1, reading point cloud; S2, point cloud preprocessing; S3, point cloud segmentation; S4, point cloud planar projection; S5, constructing a kd-tree; S6, edge detection; S7, line fitting and intersection calculation; S8, chamfer feature search; S9, whether it is a chamfer feature; S10, constructing a chamfer inner edge search direction group; S11, end. This invention is universal for support plates with chamfers. Even when the workpiece changes, as long as the support plate has a chamfer structure, it can accurately locate and identify it, and provide relevant parameters. This overcomes the problem that deep learning requires training on specific models and lacks universality.

This disclosure provides a product competition analysis method, apparatus, electronic device, and storage medium. The method includes: determining each user's attention percentage for each product based on behavioral data of each user towards each product; wherein the attention percentage indicates the user's attention percentage or mental share of a specific model of a product under a target brand within a target industry; determining a first attention index for the target product based on the attention percentage, and determining a second attention index for competing products by users who are interested in the target product; the first attention index indicates the user's purchase intention for the target product, and the second attention index indicates the user's purchase intention for the competing product; determining a competition index for the target product based on the first attention index, and determining a competition index for competing products based on the second attention index; and performing competitive analysis on the target product based on the competition indices.

The application relates to a CAN bus-based embedded system end-to-end scheduling exception detection method and belongs to the technical field of simulation. The application establishes mathematical models of a system and components of an embedded system interconnected by a CAN bus, especially a CAN bus data frame model and a transmission time calculation method, and builds a simulation framework of the system. In addition, the application analyzes the operation mechanism of the system in detail, clarifies the interaction relationship between the components, combines a non-destructive arbitration mechanism and a priority-based preemptive scheduling strategy, and respectively designs state update functions of tasks and messages, so that the application provides a basis for system state updating in a simulation process. Compared with an existing method, the method directly gives a specific model of the CAN bus, is easy to apply, and is high in expandability.

In an approach to assessment of cognitive performance, a method includes attaching a plurality of portable physiological sensors to a body of a user; collecting data from the plurality of portable physiological sensors; processing the data using a cognitive performance algorithm and a pre-trained subject-specific model; and reporting a results of the cognitive performance algorithm.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating outputs using machine learning models. One of the methods includes receiving input data for a machine learning task; processing the input data to generate a respective output at each of one or more iterations, comprising, at each of the one or more iterations: processing an input for the iteration derived from the input data using a trained model that has been trained to perform one or more machine learning tasks; processing the input for the iteration using a task-specific model to generate a task-specific representation of the input for the machine learning task; for each adapting layer in a set of multiple adapting layers, processing an adapting layer input to generate a candidate output for the iteration; and generating the output for the iteration from the candidate outputs for the iteration generated by the adapting layers.

This application relates to the field of X-ray diffraction technology, and discloses an intelligent X-ray diffractometer and its control method based on skill transferability. By introducing a large language model unit, the operator's multi-stage operational experience and logic can be extracted into structured skill information and encapsulated in a skill file that can be copied across devices. The skill transfer and execution unit supports the physical transfer of skill files between different instruments, and can intelligently adapt and convert the instructions generated by the large language model unit according to the specific model and hardware characteristics of the target instrument, thereby realizing the transformation from fuzzy operation relying on personal experience to standardized, transferable, and iterative intelligent skills. This solves the problem in the prior art that skill transfer is limited to the same model of instrument and cannot reuse complex operational logic, lowers the operation threshold, improves the consistency and comparability of analysis results between different instruments, and supports the continuous optimization and evolution of skills.

This invention discloses an AI-based intelligent clinical pharmacy monitoring and management system, belonging to the field of medical information technology. The system adopts a hierarchical cloud-edge collaborative architecture, including: a patient-side lightweight perception and feature extraction module, deployed on a smart terminal, for local fusion and privacy protection processing of multi-source health data and patient complaints; a cloud-based individualized modeling and robust optimization module, used to dynamically update patient-specific models using Bayesian methods and generate multiple future physiological perturbation paths using conditional variational autoencoders, performing robust optimization based on scenario-based methods to output robust treatment plans; a human-machine collaborative intelligent contract execution module, which automatically performs graded execution or clinical adjudication of plans based on pre-signed intelligent contracts, and stores the entire operation on the blockchain; and a panoramic interaction and audit traceability module, providing interactive and audit interfaces for multiple users. This invention significantly improves medication safety and monitoring efficiency.