14 results about "Adaptive hardware" patented technology

An adaptive hardware safety disconnect system for a motorized traffic vehicle may utilize configurable hardware logic circuitry to selectively disable a traffic vehicle system, such as a high voltage contactor, an airbag, or a low voltage power supply, in a deterministic manner. The first disconnect mechanism may disable the first set of systems, while the second disconnect mechanism disables the additional system to fully power down the traffic vehicle. The system may be configured for different use cases, such as emergency response, service, and development testing, while using common hardware components.

An adaptive hardware safety disconnect system for electric vehicles can use configurable hardware logic circuits to selectively and deterministically disable vehicle systems such as high-voltage contactors, airbags, or low-voltage current. A primary disconnect mechanism can disable an initial set of systems, while a secondary disconnect mechanism disables additional systems to completely shut down the vehicle. The system can be configured for various use cases, including emergency response, maintenance, and development testing, using common hardware components.

The application provides an intelligent detection system of an RGB camera combined with a deep learning algorithm, comprising a defect feature adaptive engine, a hardware component, an algorithm component and a feedback component; the defect feature adaptive engine serves as a core control unit, links the hardware component, the algorithm component and the feedback component to realize full-link closed-loop detection; the defect feature adaptive engine performs the following operations: identifying scene working conditions and core working condition pain points, extracting the subdivided features of target defects in the scene and matching the feature label library, dynamically calling the adaptive hardware configuration, algorithm combination and preprocessing strategy, optimizing the detection parameters in real time, allocating the computing power resources according to the defect risk level and triggering the corresponding early warning, realizing the intelligent identification, positioning, quantification and grading early warning of defects of equipment such as pressure pipelines and pressure vessels, and through the implementation of the above system, the problems of poor adaptability, insufficient precision and unreasonable resource allocation of the existing system are solved, and the industrial detection is realized in a scene-based, refined and efficient manner.

The present application relates to the technical fields of intelligent wearable device and health monitoring, in particular to a health detection control method of intelligent wearable watch, comprising: multi-source data acquisition step: synchronously acquiring physiological indexes and situation indexes, and acquiring apparent physiological data and situation characteristic data in real time through a sensor network; situation semantic analysis step: based on situation characteristic data analysis, calculating situation metabolic equivalent expected value through a motion dynamics model; compensation evaluation solving step: extracting heart rate variability and recovery rate characteristics, calculating the difference between actual metabolic level and situation metabolic equivalent expected value to obtain compensation deviation degree, and comprehensively generating compensation exhaustion index; adaptive hardware control step: dynamically generating control instruction sequence based on the numerical interval of compensation exhaustion index, and adjusting the working state of the sensor network; the present application significantly reduces the overall power consumption of the system, eliminates invalid false alarms, and improves the monitoring specificity and robustness in complex application environments.

The application discloses a kind of based on radio frequency and communication circuit calibration system, the present application relates to wireless communication transmission technical field, calibration system includes following module: environment perception module, intelligent algorithm module, adaptive hardware module and interactive verification module, the advantages of the present application are: reinforcement learning agent and cross-domain transfer learning model collaborative optimization calibration parameter, combine multi-modal environment perception data dynamic adjustment radio frequency circuit center frequency and impedance matching value, programmable hypersurface array is based on electromagnetic property reconstruction and realizes millisecond level beamforming, FPGA real-time loading optimization parameter, calibration cycle is greatly shortened, AR interactive interface is simplified artificial operation by high-precision gesture mapping and high-frequency calibration technology, overall calibration efficiency is improved by more than 40%, bit error rate is reduced to extremely low level, meet the real-time demand of 5G / 6G high-frequency communication, break through the efficiency bottleneck of traditional artificial debugging.

The embodiment of the invention provides a self-adaptive hardware abstraction layer configuration method and system based on hardware feature fingerprints, and belongs to the technical field of computers. The method comprises the following steps: collecting multi-dimensional feature information of hardware equipment; constructing a hardware feature fingerprint based on the multi-dimensional feature information; determining an HAL strategy according to the hardware feature fingerprint; determining interface configuration of each hardware device according to the HAL strategy; and packaging the hardware equipment according to the interface configuration. According to the method and the system, hardware feature fingerprints are captured from multi-dimensional feature information of hardware equipment, then unified interface configuration of the hardware equipment is determined according to a strategy matching mode, and finally packaging is performed based on the interface configuration, so that unified interface standardized operation of different hardware equipment is realized, and the operation efficiency is improved. Therefore, the compatibility of hardware is improved.

The invention provides a programmable logic controller based on a drag node. The method mainly comprises the following steps that S1, node dragging and node line connection can be conducted through a visual interface, parameters are set for corresponding nodes to finally complete writing of the whole control process, and the control process can be exported and stored as a file and imported to be regenerated; s2, process nodes with adaptive hardware and a visual interface are used for compiling man-machine interaction and an actual control process; s3, a set of complete downloading and uploading programs matched with the interface end and the hardware end can download the edited control flow into the logic controller hardware, and can upload the currently running control flow to an editing interface from the logic controller hardware; and S4, the logic controller hardware realizes the operation of the edited process, and realizes IO digital quantity control. The controller is provided with two 485 ports to realize a Modbus RTU slave station function, and can realize a Modbus RTU master station function by dragging node setting parameters; and S5, logic controller hardware has a 5-24V wide voltage power supply range to adapt to most of household and industrial power supply scenes, and a special external power supply and communication expansion module is designed for the serial port screen.

According to the dynamic coverage area-CPU load balancing scheduling method oriented to the low-orbit satellite system, multi-dimensional load evaluation and dynamic family migration are adopted, the CPU load state can be sensed in real time, continuous coverage areas can be accurately divided, the overload CPU load is rapidly transferred to idle CPUs, overload of a computing power unit is effectively avoided, and the resource utilization rate is increased; adjacent coverage areas are preferentially aggregated to the same CPU, so that the cross-CPU interference coordination overhead is reduced, spectrum multiplexing is supported, the system interference complexity is reduced, and the spectrum utilization rate is improved; the user context is kept complete in the migration process, and a cooling mechanism is set, so that service interruption and frequent migration jitter are avoided, the QoS stability is guaranteed, and the user communication experience is improved; the method supports periodic global load rebalancing and preferentially recovers computing power resources with the lowest load, so that idle CPUs can be dynamically closed, hardware architecture and track characteristics can be self-adapted, energy efficiency optimization is achieved, and the method has high expansibility and compatibility.

The invention provides an operator lightweight design method, device and equipment for a humanoid robot and a medium, and relates to the technical field of robots. According to the method, through scene modeling and quantitative analysis, the minimum necessary set of operator functions is extracted from the interaction behavior data, and function redundancy is avoided. And according to the operator demand document, function cohesion analysis and independent function analysis are performed on the operator, so that the system complexity is reduced. Under the constraint of the modular architecture framework, algorithm redundancy reduction is carried out on each operator function module, and algorithm redundancy is reduced. Data interaction parameters are optimized and adjusted, and data transmission delay and redundancy are reduced. Physical parameter and functional performance verification is carried out in an adaptive hardware platform, and the performance and stability of system operation are ensured. Technical document data and configuration information are packaged into a reusable operator lightweight design scheme, so that reuse and iteration of the scheme are facilitated, and the reliability and sustainability of the humanoid robot in a medical care application scene are further improved.

The invention relates to an AI inference acceleration architecture and a security isolation method, and relates to the technical field of industrial real-time control, and the architecture ensures the real-time performance of a key task through hardware-level time window allocation, dynamically reconstructs the computational logic of a field programmable gate array based on load characteristics, and improves the security of the field programmable gate array. Physical-level security isolation of the model weight is realized in a trusted execution environment constructed by a kylin operating system and a boron hardware platform; the security isolation method comprises the following steps: dividing an encrypted memory area in a kernel layer of an operating system, implementing real-time data protection by a hardware encryption engine, and blocking unauthorized access in combination with memory access control and a physical protection mechanism; according to the invention, collaborative breakthrough of deterministic real-time response, adaptive hardware optimization and multi-level safety protection of artificial intelligence reasoning in an industrial control system is realized, and autonomous and controllable technical support is provided for intelligent upgrading of high-end equipment.

An adaptive hardware safety disconnect system for electrified vehicles may utilize configurable hardware logic circuits to selectively disable vehicle systems, such as high voltage contactors, airbags, or low voltage power in a deterministic manner. A first disconnect mechanism may disable a first set of systems, while a second disconnect mechanism disables additional systems to fully power down the vehicle. The system can be configured for different use cases such as emergency response, servicing, and development testing while using common hardware components.

The invention discloses a network card hardware function configuration method based on software definition, which belongs to the technical field of network security, and comprises the following steps: monitoring logic layer and physical layer characteristics generated when a network card processes a data stream, comparing the logic layer and physical layer characteristics with a normal behavior baseline, and triggering defense reconstruction when deviation occurs; an evolution reference sample set is generated according to the abnormal traffic, and initial hardware logic configuration is retrieved and combined from a hardware module library according to the threat type; iteratively optimizing and testing the configuration in an evolution sandbox isolated in the network card to generate a superior hardware logic configuration; extracting a dynamic session state from the current main path hardware and transferring the dynamic session state to a superior configuration; and during the operation period of the superior configuration, the idle programmable logic unit is driven to execute randomization calculation to generate coverage physical noise, so that adaptive hardware-level precise defense for unknown threats can be realized, the service continuity and the line speed processing performance are guaranteed, and meanwhile, the protection capability for bypass channel attacks is enhanced.

The application discloses a cross-patient seizure detection method based on a graph-enhanced pulse state space model, first, the brain electrical signal data of a patient is acquired and an electrode space topology graph is constructed; then, a pulse state space model is used to extract the time sequence features of physiological channels, and a pulse graph convolution network is used to perform message aggregation between adjacent nodes in a discrete pulse domain across time steps; finally, feature aggregation is performed through a global average pooling operation at a graph level, and a two-class prediction probability is output through a fully connected classifier. The application follows the design logic of "first time and then space", the pulse state space model models the potential brain state dynamics of the time sequence change, and realizes stable long context feature processing through the cyclic updating mode of the adaptive hardware; the pulse graph neural network completes the information transmission and fusion between channels based on the electrode correlation graph, can capture the spatial dependence relationship which is crucial for the representation of seizures, reduces the calculation energy consumption, and improves the overall performance.

This invention discloses an adaptive, hardware-friendly hybrid quantization method based on deep neural networks. By analyzing the sensitivity of different network layers to quantization errors, it adaptively selects the optimal quantization strategy for each layer, thereby reducing the model's storage and computational resource consumption while maximizing model accuracy. The method includes: obtaining a full-precision deep neural network model to be quantized; extracting the weight parameters and activation parameters of each network layer; performing statistical analysis on the weight parameters and activation parameters of each network layer to construct an evaluation index for measuring quantization error; performing quantization sensitivity analysis on different network layers based on the evaluation index; adaptively determining the quantization method corresponding to each network layer based on the quantization sensitivity analysis results; performing hybrid quantization processing on the weight parameters and activation parameters according to the quantization method to obtain a hybrid precision quantization model; and completing the deep neural network inference computation based on the quantized weight parameters and activation parameters.