13 results about "Hardware software" patented technology

This invention belongs to the field of intelligent agent large language model service technology, and discloses a chip computing system, device and application for intelligent agent large language model. Through the collaborative design of hardware architecture and software scheduling, it systematically solves the core technical challenges faced by intelligent agent large language model services. Specifically, it includes a hardware and software collaborative implementation scheme for resource partitioning, key-value cache management and runtime scheduling of pre-filling, decoding and tool call related workloads in a multi-chip encapsulation and inconsistent memory access environment.

An information handling system may include a main physical storage resource and a bootstrap physical storage resource. The information handling system may be configured to: boot from a bootstrap operating system (OS) stored on the bootstrap physical storage resource; install, by the bootstrap OS, a main OS onto the main physical storage resource; expose the bootstrap physical storage resource as a drive that is accessible from the main OS; and in response to a change in hardware, software, or firmware of the information handling system, update the bootstrap OS from the main OS.

The application discloses a kind of modular configurable tooling detection methods, belong to tooling detection field, specifically include: module configuration step, automatic detection step, detection result processing step.Before detection, detection group leader configures detection hardware according to detection plan, software platform.When the unit to be detected is placed in the area to be detected, detection personnel start main control machine, and start automatic detection process according to the configuration parameters of detection scheme module, detection strategy module and detection alarm module after authentication.After detection, whether the detection data is entered into database according to the parameters configured by data analysis module is processed by main control software;Whether detection report is saved or printed;The application is adapted to the detection needs of the unit to be detected constantly upgrading iteration by the modularization and configurability of detection hardware and software platform, and the problem that error reproduction is difficult, error chain is deep and difficult to locate is solved by error tracing module.

The application discloses a computing power optimization method and system in power data maintenance, and relates to the technical field of data center and power grid cooperation, which comprises the following steps: collecting and standardizing the hardware, software, energy consumption and power supply data of the computing power center through a unified interface protocol, and forming a data stream after security processing; constructing a comprehensive resource model of the integrated computing power supply and power demand in the region based on the data stream; then, establishing a digital twin model of the coupling of the computing power center and the power system, and realizing real-time mapping of the physical entity and the virtual model; generating a dynamic twin body supporting multi-dimensional visual display through synchronous real-time monitoring data; simulating different computing power scheduling strategies based on the twin body, and automatically generating a decision scheme of the cooperation optimization of the computing power resource and the power supply. The application realizes joint modeling and cooperative simulation optimization of the computing power and the power system.

This application discloses a method, apparatus, and device for security authentication, relating to the field of security protection technology. Before establishing a communication connection between the main control unit and the upper-layer software, the main control unit verifies the legitimacy of the upper-layer software's identity based on a software identity certificate. The upper-layer software verifies the legitimacy of the hardware circuit's identity based on a hardware identity certificate, and sends a random challenge value to the main control unit. The main control unit generates corresponding real-time response information based on the PUF circuit. Due to the physical non-cloning characteristic of the PUF circuit, cloned or simulated counterfeit hardware circuits cannot reproduce the correct real-time response information. Therefore, the upper-layer software can verify the authenticity of the hardware circuit's identity based on the real-time response information, preventing the hardware circuit from being a counterfeit product. Since the upper-layer software and hardware circuit are both legitimate, and the hardware circuit is authentic, a communication connection is established between the upper-layer software and the main control unit only when their identities are legitimate, preventing the access of unauthorized hardware / software.

Embodiments of the invention are directed to systems, methods, and devices for providing dynamic applicability management with respect to a testing framework. Executable code segments can be provided in / with various test procedures that include instructions for testing hardware, software, capabilities, features, functionalities, or protocols of a computing device. Each code segment can encapsulate logic that, when executed, identifies whether a corresponding test procedure is applicable to a device given that device's configuration. By executing each code segment, a set of applicable test procedures can be identified. Identifiers or the test procedures may be provided to the device to be tested or a testing platform configured to conduct the test of that device. Transmitting the identifiers and / or procedures configures those devices to perform the test through simulating a legitimate data exchange.

The application relates to a motor train unit CTCS-2 level ATP dynamic test system, which comprises a system hardware and a system software. In the aspect of hardware, the system is composed of a main control microcomputer and an electronic box, the main control microcomputer is a PC or a notebook computer, and the main control microcomputer communicates with the electronic box through an RS422 bus; the electronic box is internally provided with function modules such as digital IO, speed signals, track signals and transponder signals, each module takes an ARM chip as a main processor, is supplemented with an FPGA and a DDS chip, can output high-precision frequency and digital signals, and the electronic box is connected with a measured train-mounted control equipment through a matching cable. In the aspect of software, a program realizes functions such as transponder message generation, encoding and decoding, analysis, line data display, test parameter setting, flow control and data recording. The system can dynamically test a CTCS-2 level ATP equipment, supports 200H, 200C and 300 series ATP hosts, can detect hardware, software control logic and transponder message data of the ATP hosts, and can also be used as an ATP operation training simulation equipment. The system generates transponder messages according to track data, simulates transponder module output signals, drives ATP dynamic operation to complete maintenance test. The system adopts modular design, is small in size, convenient to carry and maintain on site, is high in integrated circuit board design, combines ARM, CPLD / FPGA and other technologies, reduces equipment size and weight, reduces power consumption, and improves portability and functional expansibility.

An LLM-based method for profiling chatbot users' technical proficiency and performing adaptation in the chatbot response, comprising the step of receiving encountered problem by the troubleshooting chatbot via a user prompt; using a profile inference LLM to obtain inferred profiles of chatbot users' technical proficiency regarding cybersecurity troubleshooting, by analyzing current prompt as entered by the user, from a data storage; the most recently updated user profile, which encapsulates the past user data; troubleshooting-oriented cybersecurity taxonomy consisting of proficiency domains and subdomains; a collection of inferred profiles of all users; hardware, software or networking-related data collected from the user's device; generating a vector of profile scores that quantify troubleshooting-oriented cybersecurity proficiency subdomains; updating the user's inferred profile by sending the inferred profile to the collection; replacing the older profile of the user by an updated profile; sending the inferred profile to a response adaptation LLM, along with the response generated by the chatbot; adapting the technical instructions, produced by the chatbot to the inferred user's profile; sending the profile-adapted response to the user, to follow the chatbot's instructions.

The present application belongs to the technical field of computer performance detection, and discloses a computer performance degradation prediction and anomaly detection method based on multi-source data fusion, and the specific steps are as follows: S1. Multi-source data acquisition: collect hardware sensor data, software performance counter data, and user behavior and system log data of the computer. By fusing hardware, software and system log multi-source data, the one-sidedness of a single data source is avoided, the computer performance is accurately and comprehensively reflected, the detection accuracy is improved, and the false alarm and missed alarm rates are reduced; based on the historical data of the health state, a personalized model is trained to build a health region, which is adapted to different user habits, solves the drawbacks of fixed threshold one-size-fits-all, and enhances adaptability; the health degree trend is predicted through the LSTM model, the change from "post-detection" to "pre-prediction" is realized, and the user is helped to carry out preventive maintenance in advance; with the help of the SHAP method, the abnormal root cause is located, the fault diagnosis and repair time is shortened, and the operation and maintenance efficiency is improved.

The embodiment of the invention discloses a processing method for a network attack behavior in a high-confrontation scene and a high-confrontation intelligent analysis system, and the method comprises the steps: responding to a detected network attack request of an attacker terminal, and obtaining a session ID corresponding to the network attack request based on the session ID corresponding to the network attack request; querying a current unified state object corresponding to the session ID from a session-state binding manager; the intelligent analysis module constructs prompt words according to the network attack request, the current unified state object and historical context information; the large language model carries out reasoning based on the cue word and generates a simulation response instruction; the intelligent analysis module analyzes the simulation response instruction and distributes the simulation response instruction to the three-layer collaborative simulation engine; and the three-layer co-simulation engine performs co-simulation based on three dimensions of hardware, software and network traffic, feeds back a simulation response generated by simulation to the attacker terminal, and synchronizes a state change after simulation execution to the session-state binding manager, so that the session-state binding manager updates a current unified state object.

This invention relates to the field of warehousing and logistics technology, specifically to a control system and method for an integrated intelligent palletizing and storage robot for grain storage. The control system is based on multi-source heterogeneous multimodal sensor data fusion technology, constructing a four-level hierarchical decision-making architecture. It integrates a distributed and centralized hybrid collaborative control system to achieve intelligent integrated closed-loop management of palletizing, storage, and transfer operations across all grain storage scenarios. This invention solves the problems of traditional palletizing robots, such as poor adaptability in complex grain storage conditions, rigid path planning, low efficiency of multi-machine collaboration, lag in dynamic condition response, and susceptibility to environmental interference in operational accuracy. Through deep coupling of multiple algorithms and three-dimensional collaboration of hardware, software, and digital twins, it significantly improves the intelligence level, operational efficiency, and safety and reliability of grain storage operations, adapting to the full-process operational needs of multi-variety, multi-specification, and large-batch grain storage.

This invention relates to the field of digital education management and Data Envelopment Analysis (DEA) technology, and discloses a method for improving the efficiency of educational technology based on DEA, including the following steps: S1: Constructing a multi-dimensional input-output indicator system for educational technology efficiency adapted to digital education scenarios. The indicator system includes input indicators, expected output indicators, and unexpected output indicators. This method for improving the efficiency of educational technology based on DEA, by constructing an input indicator system covering four dimensions—hardware, software, human resources, and capital—establishes a full-factor input-output evaluation framework adapted to all scenarios of digital education. Simultaneously, it employs a super-efficient SBM-DEA model incorporating unexpected output to complete single-cycle static efficiency calculations, and combines this with the Malmquist-Luenberger index to achieve multi-cycle dynamic efficiency tracking and factor decomposition. This allows for accurate classification of decision-making unit efficiency levels, selection of industry benchmarks, and precise identification of core constraints to inefficiency.