102 results about "Hardware software" patented technology

The embodiment of the invention provides a hot plug test board, a hot plug test system, a hot plug test method, hot plug test equipment and a medium, and relates to the field of internet big data. The hot plug test board comprises a logic module, a signal switch switching unit and a power supply detection unit, the logic module is connected with the signal switch switching unit and used for transmitting an NVME signal and controlling gating and cutting off of the signal switch switching unit so as to achieve hot removal or hot addition of the NVME hard disk, and the signal switch switching unit responds to control of the logic module and controls the power supply detection unit to detect the power supply of the NVME hard disk. And the NVME signal transmission to the NVME hard disk is gated or cut off, and the power supply detection unit is connected with the NVME hard disk and is used for detecting whether the NVME hard disk is normally powered off or powered on after hot plugging. Through the mode, the NVME hard disk hot plug test can be efficiently and conveniently carried out on the premise of not influencing the overall structure and the hardware and software logic of the original server.

The invention discloses a test environment availability intelligent monitoring method and system based on AI drive, and belongs to the technical field of test environment monitoring, in the invention, infrastructure indexes, application logs and network flow data are simultaneously acquired by the system through a multi-modal data fusion technology, and an omnibearing monitoring system covering hardware, software and a network layer is constructed. The AI model carries out deep analysis on time sequence data, and through combination with a space-time association algorithm, single index abnormity can be detected in real time, and a cross-node and cross-service composite fault mode can be identified. For example, through service call chain topology analysis, the system can accurately position a fault propagation path, and cascade problem diffusion is avoided. The closed-loop self-healing mechanism further converts the fault response from manual intervention into automatic processing, for example, automatic restarting of an abnormal container or adjustment of resource allocation, so that the fault repair time is greatly shortened, the environment availability reaches an extremely high level, and the strict requirement of the cloud native architecture for stability is met.

Technology is disclosed herein for enhanced similarity search. In an implementation, a search environment includes one or more computing hardware, software, and / or firmware components in support of enhanced similarity search. The one or more components identify a modality for a similarity search with respect to a query object. The components generate an embedding for the query object based on the modality and based on connections between the query object and neighboring nodes in a graph. The embedding for the query object provides the basis for the search for similar objects.

The invention discloses an adaptive two-stage matrix division method based on GEMM (General Empirical Matrix Modulation). The method comprises the following steps: determining the size of a block for the first time; carrying out first blocking processing on the input matrix from a global memory to a shared memory; loading the matrix partitioned for the first time to double buffer areas of a shared memory; determining the size of a secondary block; partitioning the partitioned matrix blocks from the shared memory to the register again; and loading the matrix blocks which are partitioned again to a buffer area of the register for calculation. The invention aims to provide a parallel optimization method based on hardware-software collaboration aiming at the bottlenecks of the current GPU-based GEMM in the aspects of data transmission efficiency, computing resource utilization rate, load balance and the like. By introducing an optimization scheme of a self-adaptive two-stage matrix partitioning mechanism in a matrix calculation process, the method aims to improve data locality, reduce memory bandwidth pressure and realize dynamic balance of loads among threads. According to the method, by reducing data transmission delay and optimizing allocation of computing resources, the efficiency of GEMM operation is improved from the source, and the efficient requirements of deep learning and large-scale scientific computing are met.

The invention discloses an intelligent system failure analysis method based on fault recognition, and relates to the technical field of intelligent system reliability analysis, and the analysis method comprises the specific steps: employing Modelica to construct a multi-physical domain digital twinborn model, combing historical data, and building a structured fault mode library; developing a fault injection algorithm to acquire data to form a standardized data set; preprocessing data to construct a causal link and a probability flow network; carding task scenes to construct a reliability evaluation model and training the reliability evaluation model; inputting actual parameters to trace faults, generating an optimization plan, and outputting an analysis report and a plan after verification; according to the method, the problem of insufficient fault correlation analysis caused by modeling scale splitting in a traditional method is solved by constructing a multi-physical domain digital twinborn model covering microcosmic, mesoscopic and macroscopic and establishing a structured fault mode library of a standardized feature description system containing three types of faults of hardware, software and environment interaction.

The embodiment of the invention provides a hull united workshop digital maturity evaluation method and system. The method is applied to the technical field of industrial big data, and comprises the steps of training a hull workshop maturity scoring model through historical data, and performing scoring and monitoring based on real-time data. Historical hardware facility, software system, personnel skill and management process data are collected, a first data set is generated, and a workshop maturity scoring model is trained in combination with a corresponding historical score and a preset total score calculation formula. And acquiring real-time hardware, software, personnel and management data, inputting the real-time hardware, software, personnel and management data into the model, and generating a real-time dimension score and an overall workshop maturity score. And comparing the real-time score with a preset standard, and if a substandard item exists, sending alarm information to a control center. According to the scheme, real-time monitoring and early warning can be realized, and problems can be timely found and quickly responded. The scoring model automatically calculates the maturity score, human intervention is reduced, and the evaluation efficiency and accuracy are improved.

The invention discloses a low-cost train operation control simulation system based on digital twinning and an SSA-DBO algorithm. The system is composed of hardware, software and a digital twinborn body. Firstly, a single chip microcomputer, a wireless communication module, a common cathode nixie tube, a yellow-green LED lamp, a motor driving module and the like are designed as hardware, and the modules form a physical entity; secondly, according to software design, input signals of a track circuit are monitored, and corresponding logic judgment and processing are carried out according to received information; and finally, the digital twin designs a virtual model based on a physical system, the virtual model comprises a train, a track, a sensor and other components, various state information of the physical system is collected in real time through a track circuit, the train sensor and the like of the solid model, the state information is uploaded to a server by utilizing the Internet of Things technology, and after data screening is carried out through an SSA-DBO algorithm, the state information of the physical system is obtained. The data and the model together drive the digital twin for monitoring, analysis, optimization, prediction and visualization.

The invention discloses a system and a method for behavior analysis and anomaly detection of an industrial personal computer, and relates to the technical field of behavior analysis of the industrial personal computer. A structured behavior sequence matrix is generated by collecting hardware, software and network multi-dimensional time series data and using parameters such as a state transition coefficient and a noise variance for noise reduction processing, so that the data accuracy is improved; a real-time updated normal behavior reference is constructed based on dynamic time warping and similarity calculation, behavior deviation is accurately evaluated, dynamic changes of an operation mode of the industrial personal computer are adapted, an anomaly severity score is calculated through multi-dimensional deviation value weighting, differential processing of slight and serious anomalies is achieved in combination with a hierarchical response strategy, and the safety of the industrial personal computer is improved. According to the method, excessive response or lag is avoided, key abnormal time sequence fragments and potential root causes are quickly positioned by means of gradient weight visualization, the accuracy of abnormal detection and fault processing efficiency of the industrial personal computer are remarkably improved, and the requirement for high reliability of an industrial control scene is met.

The invention discloses an industrial control system security protection capability assessment method and system. The method comprises the following steps: acquiring key chip models, operating systems, monitoring software version information and service port information of n selected industrial control systems; the method comprises the following steps: acquiring a vulnerability list under a decision-making layer index and a corresponding threat value, constructing a fuzzy consistency judgment matrix from a target layer to a decision-making layer and from the decision-making layer to a scheme layer, performing fuzzy consistency verification on the constructed judgment matrix, calculating the weight of the decision-making layer index for the matrix passing the consistency verification, forming a weight matrix, and determining the vulnerability list according to the weight matrix. The evaluation results of the safety protection capability of the selected n industrial control systems are calculated through a fuzzy analytic hierarchy process, an analytic hierarchy model including hardware, software, protocols and other multi-dimensional indexes is constructed, vulnerability database retrieval and field test data are combined, a quantitative evaluation system is established through the fuzzy analytic hierarchy process, and the safety protection capability of the industrial control systems is evaluated. The method has the advantages of improving evaluation index systematicness, realizing multi-dimensional safety factor quantitative analysis and improving evaluation result objectivity.

The invention discloses an intelligent automatic labeling method and system based on multi-variety mixed production, and relates to the technical field of intelligent labeling, precise labeling is achieved through three core processes, the first step is that a hardware-software-data three-layer intelligent operation system is constructed, and posture and position analysis is conducted on SKU commodities transmitted to labeling equipment; and marking the to-be-labeled commodities meeting the conditions. 2, a to-be-labeled commodity is transmitted to a visual collection area, an algorithm reasoning mode is evaluated, and appearance characteristic parameters and dynamic parameters of a single-module reasoning commodity are accurately extracted; and step 3, analyzing and generating a label template, labeling position coordinates, labeling pressure and labeling speed according to the extracted parameters, and finally integrating into a labeling execution parameter set containing specific numerical values, constraint conditions and execution priorities, so as to meet the efficient and accurate labeling requirements of multi-variety mixed production.

The invention discloses a millimeter wave dual-polarization phased-array antenna beam forming method and device, and relates to the technical field of high-frequency communication, and the method comprises the following steps: obtaining a complex weight signal of each array element in an antenna array under a horizontal polarization channel and a vertical polarization channel, respectively carrying out amplitude response and phase response acquisition and calibration processing on the complex weight signal; performing dynamic extraction and modeling on amplitude errors and phase errors of each array element in different polarization channels; on the basis of error estimation, constructing a machine learning prediction module comprising a time sequence prediction model; performing joint estimation on a real-time error estimation result and a prediction error result; constructing a hardware-software collaborative calibration structure comprising a dynamic gain control module on the basis of the compensation weight; beam focusing of the target space area is realized; according to the invention, the hidden problems of beam offset, sidelobe lifting, gain loss and the like caused by inconsistency of polarization channels are solved.

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.

The invention discloses an AES encryption and decryption hardware implementation method based on an RISCV vector encryption and decryption expansion architecture, and belongs to the technical field of data encryption, and the method comprises the steps that an RISCV instruction set carries out vector expansion on AES encryption and decryption, in the AES encryption process, an initial round corresponds to a vaesz.vs instruction, a middle round corresponds to a vaesem.[ vv, vs] instruction, a final round corresponds to a vaesef.[ vv, vs] instruction, in the AES decryption process, the initial round corresponds to the vaesz.vs instruction, and in the AES decryption process, the middle round corresponds to the vaesef.[ vv, vs] instruction. The middle round corresponds to a vaesdm. [vv, vs] instruction, and the final round corresponds to a vaesdf. [vv, vs] instruction, so that the area of a chip can be reduced, the cost is reduced, the flexibility, modularization and extensible architecture design of hardware and software are enhanced, different platforms from embedded computing to high-performance computing can be easily adapted, and a solid foundation is provided for evolution and customization of a future cryptographic algorithm; and the encryption processor architecture is promoted to develop towards a more efficient, more open and more customizable direction.

An integrated computer network security and threat prevention and detection platform includes a central processor and a display operable to aggregate and present data from a plurality of network security applications in an integrated dashboard format to a system administrator. The network security applications may be hardware, software, or hybrid applications running on local machines, local networks, remote machines, or remote networks, in communication with the central processor. In one embodiment implementation of the integrated computer network security and threat prevention and detection platform is performed on premises, in an alternative embodiment the integrated computer network security and threat prevention and detection platform is provided in an Internet or cloud-based environment, in other embodiments the computer system security platform is a hybrid configuration having both on-premises and cloud base components.

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 invention relates to the technical field of traction protection device testing, and discloses an intelligent traction protection device testing system which comprises a cabinet body, a change-over switch, an intermediate relay, a knob, a button, a miniature circuit breaker, an indicating lamp, a 24V switching power supply module, a 110 / 220V switching power supply module, a measuring module and a man-machine interaction unit which are mutually connected to form a testing circuit. The intelligent traction protection device test system is used for testing various protection functions of a direct current traction protection device, simulates the opening and closing position of a circuit breaker, the position of a handcart and intertripping input and output signals through an intermediate relay, and is externally connected with an indicating lamp, so that an operator can quickly judge a test result according to the color of the indicating lamp; the fault signal can be positioned without checking a display screen and a protection device, so that the effects of quickly checking hardware / software faults of a direct-current traction protection measurement and control device product and improving the production inspection efficiency are achieved.

The application belongs to the technical field of solid-state remote power distribution device uniform type of aircraft electromechanical system, and particularly relates to a solid-state remote power distribution device uniform type design method and system. First, system requirements are collected, all power loads needing solid-state remote power distribution device secondary power distribution are divided according to locations, the rated current of all SSPC channels is obtained, and the rated current is upwardly compatible. The SSPC channels in the bus control mode and the SSPC channels in the override control mode are collectively classified as type 1. Each power output of the solid-state remote power distribution device is only bound with the power busbar input in the current location and is decoupled with the power supply on the aircraft. The uniform type is performed from four aspects of power distribution load area, SSPC channel current size, SSPC channel control mode and SSPC channel belonging busbar, so as to ensure that the hardware and software of the same type device are completely the same, the device is installed in different locations according to the recognition of the machine position, different software is executed, and the design cost, later stage support cost and management cost can be reduced.

The invention provides a Baijiu production method and system based on machine learning, and the method comprises the steps: constructing a multi-source heterogeneous data collaborative collection system, and collecting various data in a Baijiu production process; carrying out intelligent processing on the collected data; a hybrid model architecture and a transfer learning strategy are adopted, a dynamic adaptive model optimization mechanism is established, and the model performance is monitored in real time. Compared with the prior art, on the aspect of data, a multi-source heterogeneous acquisition system is constructed, and an advanced data management technology is adopted, so that the data quality and the value mining capability are improved; in the aspect of the model, the adaptability is enhanced by the hybrid architecture, the rapid customization of the model is realized, a dynamic adaptive optimization mechanism is established, and the continuous and efficient operation of the model is ensured; cost and risks are reduced in multiple dimensions such as hardware, software and a decision-making mechanism, and feasibility and reliability of technology application are enhanced.

The utility model relates to the technical field of control development boards, in particular to a C language teaching development board based on a microcontroller technology, and aims to closely combine C language teaching with hardware development so as to overcome the limitation of C language teaching in a traditional computer software environment. The development board comprises a power supply module, a microcontroller module, an LED module, a key module, a USB-UART module, a nixie tube module, a rotary encoder module, an EEPROM module and a temperature sensor module. Through the modules, students can learn how to use the C language to perform programming control on hardware in practice, so that understanding of a hardware-software interaction mechanism is deepened. In addition, the development board further supports teaching of communication protocols such as SPI and IIC, serial port communication between the development board and a computer is allowed, and program debugging and data exchange are facilitated. According to the utility model, not only is the teaching effect of C language improved, but also the opportunity of practical practice is provided for students, and the combination of theoretical knowledge and practical application is promoted.

Embodiments of the present disclosure relate to a unified framework for multi-modal similarity search. Disclosed herein is a technique for enhanced similarity search. In implementations, a search environment includes one or more computing hardware, software, and / or firmware components to support enhanced similarity search. The one or more components identify a modality of a similarity search relative to the query object. The component generates an embedding for the query object based on the modality and based on connections between the query object and neighboring nodes in the graph. The embedding of the query object provides a basis for the search of similar objects.

A convenient real-time acoustic wave ranging system with high sampling rate and high precision, comprising: a board hardware, an FPGA software, and a real-time ranging software. The board hardware includes a USB sound card chip, a DAC, a MEMS digital microphone, and an FPGA module. The USB sound card chip is a dedicated sound card chip with a sampling rate of 192 kHz, communicating with the operating system; the DAC is a high-dynamic-range audio decoding chip, converting into an analog signal and outputting it to the speaker; the MEMS digital microphone has a wide-band response range; the FPGA module is a GW1N series chip, connecting to the MEMS digital microphone. The FPGA software realizes signal acquisition, low-pass and decimation filtering of the MEMS digital microphone, transmits the data to the USB sound card chip via an IIS encoder, and also generates precise clocks required for PDM and IIS signals, achieving consistent transceiver sampling rates. The real-time ranging software is implemented on the operating system, including real-time transceiver, cross-correlation, cross-correlation peak jitter elimination, and distance conversion modules. The present invention has a high sampling rate of 192 kHz with consistent transceiver, and can conveniently perform high-precision real-time ranging.

Computerized systems and methods are described for managing multi-tiered subscription-based models of technology products, including hardware, software, and cloud services. The system performs automated optimizing of subscription models and pricing using a Real-Time Data Mesh (RTDM) and Advanced Analytics and Machine Learning (AAML) Module. The system employs a Single Pane of Glass User Interface (SPoG UI) to enhance user interaction and subscription management. Automated processes facilitate dynamic adjustment of subscription terms, pricing, and configurations based on real-time data and analytics. API integrations for perform data exchange for comprehensive management of multi-tiered subscriptions within a unified ecosystem.

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 invention belongs to the technical field of cloud platform operation, and particularly relates to a method, a system, a terminal and a storage medium for a cloud platform to automatically adapt to a computing node, comprising the following steps: automatically collecting hardware configuration information and software configuration information of a to-be-adapted computing node through a pre-stored multi-dimensional feature fingerprint identification method; constructing a feature vector which uniquely identifies the computing node; according to the method, hardware data are acquired cooperatively through a Redfish protocol and a system-level diagnosis command, and layered feature vectors containing known feature layers, unknown feature layers and confidence weights are constructed in combination with software stack layered detection and software-hardware association mapping, so that global unique identification of multi-architecture and multi-manufacturer computing nodes is realized. Through matching with similarity comparison of an incremental fingerprint knowledge base, unknown hardware / software features can be accurately identified, the adaptive coverage range of heterogeneous computing nodes is greatly expanded, and the problem that a traditional method is insufficient in adaptation to novel hardware and small software environments is solved.

The invention discloses a cultural creative robot based on an AI large model and ROS2 and a control method. The cultural creative robot comprises a robot body, a hardware module arranged on the robot body and a software module built in the hardware module. The hardware module comprises a sensor module and an execution module which are connected with the main controller; the software module comprises a motion control unit, a voice function unit, an ROS2 unit and an interaction unit. The control method comprises steps S1 to S7. Through the design of the hardware, the software and the mechanical structure of the creative and creative robot, innovation of the robot in creative and creative and functional aspects is provided, and the emotional value and functional requirements of people are further met.

The invention relates to a large secondary active cockpit operation and maintenance method based on a power distribution automation system, and belongs to the field of automatic operation and maintenance of power systems. According to the method, a'hardware-software-terminal-service link 'full-dimension stereoscopic monitoring system is constructed. Specifically, an SNMPv3 protocol and edge calculation are adopted to collect equipment operation data in real time, and a core index calculation formula (such as a distribution network secondary active defect elimination rate and a morning operation application rate) is adopted to realize data quantitative analysis; a four-level alarm mechanism is constructed based on a severity-urgency matrix, and potential faults are pre-warned 72 hours in advance in combination with an LSTM prediction model; a defect closed-loop management and control process is innovated, master station type defect one-key defect elimination and terminal type defect cross-platform linkage are supported, and the defect disposal period is shortened by 60% or above. Meanwhile, the operation and maintenance state is visually presented through a system health index quantitative model (basic score 100-alarm score deduction), data can be seamlessly connected with a cloud platform, and intelligent operation and maintenance support is provided for a novel electric power system.

The invention discloses a damage early warning device capable of automatically scanning, detecting and evaluating space complex electromagnetic radiation. Through integration of a broadband antenna, a two-degree-of-freedom holder and a real-time safety evaluation alarm module, three core advantages of broadband domain high-precision measurement, dynamic scanning real-time evaluation and one-button portable operation are realized through hardware-software collaborative innovation, and national standard compliance detection requirements in complex electromagnetic environments such as 5G and Internet of Things are met.

A cybersecurity event validation service provides a user-friendly scheme for detecting a cyberattack or threat. The cybersecurity event validation service accepts very simple, high-level, user-friendly descriptions of the cyberattack or threat. A user of the cybersecurity event validation service thus need not input detailed hardware / software events that specify the potential cyberattack or threat. The cybersecurity event validation service, instead, validates the user's very simple descriptions for correctness. If the user's very simple descriptions conform to basic rules or requirements, then the cybersecurity event validation service elegantly fills in the deep hardware and software details using context and inferences. The cybersecurity event validation service thus elaborates and enhances the user's very simple descriptions by supplying specific hardware / software details needed to detect the cyberattack or threat. The user thus need not be versed in the intricate programming / configurational details for defining the cyberattack or threat.

Embodiments of the present disclosure relate to artificial intelligence (AI) for hardware / software co-design of accelerators and machine learning models. Systems and methods are provided for iteratively co-designing hardware and software elements of a device configuration to optimize it. The process may use a machine learning process that simulates how the device configuration is to execute to predict how the software and hardware parameters are to execute in the device configuration. Corresponding inputs (e.g., software / hardware parameters) and outputs (e.g., model accuracy estimates for software parameters and hardware cost estimates for hardware parameters) determined from a machine learning process may be used for various purposes, including being used to train a machine learning (ML) model to select an optimized device configuration in consideration of various constraints.

The invention provides a hardware and software intelligent alarm method for warehouse ex-warehouse and warehouse-in equipment, which comprises the following steps of: firstly, collecting hardware data such as equipment temperature and running speed and software data such as system response time through a sensor and a system interface, sampling once every 10 milliseconds, and storing by utilizing an SQL (Structured Query Language) program; secondly, cleaning the data according to a 3 sigma principle and performing Z-score standardization processing by applying an SQL query statement and a related function; and then an anomaly detection model is constructed based on a hidden Markov model (HMM) and support vector data description (SVDD), the HMM is trained by normal data to determine a state transition and observation probability matrix, a minimum hypersphere is found by the SVDD, and parameters are optimized through cross validation. In the real-time monitoring link, the SQL continuously obtains a new data input model, the HMM calculates probability distribution, the SVDD measures the distance, the sliding window technology is combined for comprehensive judgment, when abnormal conditions are met, voice alarm is triggered, abnormal information is displayed at the same time, abnormal events can be recorded and analyzed to improve the system, and high efficiency and stability of warehousing operation are effectively guaranteed.