25 results about "Machine instruction" patented technology

Discloses are systems and method for detecting malicious code in a file. The system receives at least one file. The system analyzes the at least one file, during which offsets from a beginning of the at least one file are detected for data that is similar to code that assembles strings in memory from data included in machine instructions. The system performs emulation of the machine instructions included in the code according to the detected offsets, further comprising: checking an effect of each respective machine instruction of the machine instructions on a control flow, and extracting strings from virtual memory if the respective machine instruction affects the control flow. The system analyzes extracted strings, during which malicious code is detected using malicious code detection rules.

The embodiments of the present specification provide a method for generating a bytecode, a method for generating a machine code, and a device. The method for generating a bytecode comprises: obtaining a target source code, and applying a first process to the target source code, the first process comprising: replacing a target statement used for an integer operation in the target source code with a preset objective function; on the basis of the target source code after the first processing, generating a target bytecode, the target bytecode comprising a bytecode instruction generated on the basis of a preset function, and the bytecode instruction instructing to perform the integer operation corresponding to the target statement by means of a preset machine instruction sequence, and to perform an overflow check for the integer operation.

The application provides a runtime suspension detection and positioning method and device for parallel programs, and applies to the technical field of large-scale computing. The method comprises the following steps: constructing a program counter value queue of each target execution unit at each sampling time; recording a stagnation program counter value of a target execution unit in a stagnation state; clustering the stagnation program counter value of the target execution unit to determine a suspended target execution unit; based on a machine instruction corresponding to the stagnation program counter value of the suspended target execution unit, obtaining source file and line number information corresponding to the stagnation program counter value through debugging information; and when the machine code corresponding to the stagnation program counter value of the suspended target execution unit is a call instruction, obtaining function call information. In this way, non-intrusive and low-load monitoring of parallel programs is realized, normal synchronization waiting and abnormal suspension processes are effectively distinguished, and problems are positioned to source code lines.

The application relates to the technical field of computer data processing, and provides a high-performance operation method and system of an analytical database based on LLVM, which solves the technical problems of low hardware resource utilization and limited operation performance. The method comprises the following steps: obtaining a structured query statement sent by a computing device, and collecting pipeline pause data and cache miss rate of the computing device during operation; performing syntax and semantic analysis on the structured query statement, and calculating a load pressure value representing the current hardware resource occupation degree; determining a segmentation threshold value according to the load pressure value, and obtaining a plurality of fusion operator subgraphs; in the process of generating an intermediate representation based on an LLVM compiler architecture, an LLVM intermediate representation sequence is generated; the LLVM intermediate representation sequence is compiled by using a compiler of an LLVM back end, target machine instructions are generated, and the target machine instructions are loaded to the computing device for operation processing, so that the operation efficiency of the analytical database and the hardware resource utilization can be improved.

The invention relates to a method for executing a plurality of computer programs on a data processing system which has at least two separate computing units that share a common resource of the data processing system, wherein each computer program is executed by means of bytecode as program code means on one of the computing units of the data processing system, by converting the program code means of the respective computer program, which is present as bytecode, into the machine instructions relevant for the computing units by means of a code converter of the data processing system, characterized in that the code converter determines at least one interference value for each computer program as a function of a number of converted machine instructions per time step.

This invention discloses an ultra-high frequency carrier high-speed industrial motor drive control method and system, relating to the field of industrial motor drive control technology, including the following steps: S1: Obtain the instantaneous power fluctuation of the motor, obtain the vibration frequency of the motor spindle, obtain the load information value through the instantaneous power fluctuation and the vibration frequency of the motor spindle, obtain the instruction speed and instruction feed rate in the CNC system machining instructions, obtain the process quality value through the instruction speed and instruction feed rate, obtain the maximum allowable junction temperature and the current junction temperature of the power device, and obtain the temperature rise value through the maximum allowable junction temperature and the current junction temperature of the power device; thereby facilitating a more accurate judgment of the current grinding state, and further facilitating the subsequent adjustment of a more suitable carrier frequency based on the current grinding state, and further facilitating the automatic decision-making to switch the carrier frequency of the grinding motor based on the real-time grinding conditions, thereby improving the surface finish of the workpiece while reducing switching heating losses.

This invention discloses a multimodal emotion intervention data protocol and a collaborative repositioning evaluation method based on neural mapping parameters. The method defines a standardized Emotional State Data Structure (ESDS), atomically binding Role ID, Embodied Action Code, and Narrative Tag based on self-distance theory, thus addressing the problems of unstructured intervention instructions and the disconnect between cultural prototypes and machine instructions in existing systems. Simultaneously, this invention constructs a multimodal collaborative repositioning evaluation algorithm, quantifying the degree of emotion calming using the formula Q_return=alpha × Norm(S_bio) + beta × Norm(S_audio) + gamma × Sync(S_visual) + delta × Decay(S_time), where the weight alpha of the biological signal derived from real physical electrical signals (HRV / GSR) is ≥ 0.5. This invention establishes an industrial-grade emotion intervention interface, achieving cross-device interoperability, and ensures the objectivity and scientific rigor of the repositioning evaluation through high-weighted physiological indicators.

This disclosure relates generally to systems and methods to systematically curate knowledge of industrial process(es) from various sources and generate process ontology via meta model(s). Root cause graph (RCG) is created wherein the RCG corresponds to process and root cause and failure modes in the process. The RCG is then transformed to machine instructions which are executed for root cause analysis in real time. The created graphs / knowledge also help in identifying conflicting knowledge or redundant knowledge. Present disclosure enables root cause analysis as soon as a failure occurs or as the systems show or indicate a tendency towards failure.

This invention relates to the field of CNC machining technology for building timber structure components, specifically to a parametric CNC machining method and system for timber structure component nodes. The method involves dividing the timber structure component into multiple typical component nodes based on the node connection method; setting a reference point for each typical component node and extracting machining parameters; inputting the machining parameters into a PLC controller and converting them into I / O variables; pre-setting a VE variable array corresponding to each typical component node and writing the I / O variables into the VE variable array; calling the MAIN main program through a computer numerical control system to read the VE variable array; jumping to the corresponding node subroutine based on the typical component node in the VE variable array, assigning the VE variable array values ​​to local machining parameters, and generating machining instructions through CNC program processing; and controlling the machining equipment to perform machining according to the machining instructions. The method proposed in this invention effectively improves the production efficiency and accuracy of timber structure component machining, and reduces the production costs for enterprises.

A computer readable medium having non-transitory memory for storing instructions that are to be executed by a computer. The machine instructions when executed by the computer implement the following functions within an application layer to dynamically scale network interfaces: transmitting, at a proxy consumer, a network interface owner request in response to a proxy application programming interface (API) starting the proxy consumer associated with a proxy; transmitting, at the proxy consumer, a proxy request in response to the proxy API starting the proxy consumer associated with the proxy; and receiving, at the proxy consumer, target content after the first and second transmitting functions are performed.

This invention discloses an automatic generation method for instruction matching templates based on LLVM intermediate representation, relating to the field of computer technology. It includes the following steps: Step 1, obtaining and parsing the LLVM IR instruction description file to generate a directed acyclic graph (DAG) of instruction operation behavior; Step 2, performing instruction behavior legalization processing on the DAG, analyzing types and operations incompatible with the target architecture in the DAG, and replacing them with operation sequences supported by the target architecture. This invention automatically generates instruction selection rules matching the target machine instruction set by analyzing the function semantic features, operand dependencies, and target architecture instruction set constraints of LLVM IR. These rules are used in the code generation stage of the LLVM compiler backend, solving the problems of low development efficiency, poor cross-hardware platform adaptability, and compiler code performance degradation caused by manual coding errors in existing technologies that involve manually writing instruction matching templates.

The application relates to an intelligent AR sticker making system and method and belongs to the technical field of augmented reality. The system realizes user registration and sticker template PNG background picture configuration through a management module; an AR function module sets a white edge parameter of 15 pixels based on an object recognition unit to realize accurate positioning; a PC beautifying module adopts a bilateral filtering algorithm with a spatial domain standard deviation sigma d = 15 pixels and a color domain standard deviation sigma r = 0.3xs v Dynamic real-time beautifying is adjusted, and the frame rate is greater than or equal to 30 fps under CUDA acceleration; a cutting machine instruction format is controlled by a CRC32 check protocol, the instruction format is an instruction header, a length, data and a CRC32 check code, and a cutting error is less than or equal to 1 mm; a recall module supports two-dimensional code sharing of electronic stickers. The system realizes full-process automation, a sticker registration error is less than or equal to 0.1 pixels, and a dynamic tracking displacement error is less than or equal to 1 cm. The application realizes integrated control of AR sticker creation, beautifying parameter optimization and printing calibration through a centralized management module, effectively solves the problem of AR special effect and printing output color distortion, and supports efficient operation of multiple devices in a commercial scene.

The application relates to a power equipment diagnosis method, device, equipment, storage medium and program product. The method comprises the following steps: analyzing a obtained multi-modal diagnosis task by using a multi-modal large model, generating a task path corresponding to the multi-modal diagnosis task, and the task path comprises a plurality of machine instructions; based on the task path, interacting with an external module corresponding to each machine instruction respectively to obtain external information corresponding to each machine instruction; inputting the multi-modal diagnosis task and the external information into the multi-modal large model to instruct the multi-modal large model to align the external information with internal knowledge of the multi-modal diagnosis task, verify the external information and the internal knowledge, and generate a structured diagnosis report and an operation and maintenance decision under the condition that the verification is passed. Based on the multi-modal large model, the limitation that the multi-modal large model only relies on the internal knowledge fixed in the training stage is compensated by combining the external information provided by the external module, and the accuracy and professionalism of power equipment diagnosis are significantly improved.

The application relates to the technical field of mixed reality, and discloses an intention-driven mixed reality content automatic generation method and system and a storage medium, which comprise the following steps: analyzing structured intention data to generate a machine-generated parameter set; calling a multi-modal large model matrix to process the machine-generated parameter set to generate a multi-modal asset set; reducing the dimension of an original three-dimensional entity file, writing the optimized three-dimensional entity file and a spatial audio file into a local work area directory and extracting an absolute path; constructing a logical chain skeleton according to parameters and the absolute path, compiling to generate a directed graph state machine; and encapsulating and packaging to push to a mixed reality end to perform space rendering. The application converts development intentions into machine instructions and outputs multi-modal assets, uses the absolute path to establish an addressing identifier to improve correlation accuracy, simultaneously establishes a bottom-layer hot replacement channel in a runtime environment, maps a file modification operation to a rendering pipeline across a compiling and packaging link, and shortens feedback time of scene iteration and rendering preview.

The present application relates to the technical field of artificial intelligence and polymer material synthesis, and solves the problems of lack of adaptive adjustment ability and dependence on manual trial and error of the existing automatic synthesis platform. A polymer material automatic synthesis system and method are disclosed: a target formula is received, and a literature analysis intelligent agent is used to extract process parameters; the parameters are cross-modally compiled into machine instructions to drive hardware by a device control intelligent agent; during the reaction, physicochemical state data is collected in real time by an in-situ characterization device; when an abnormality occurs, a reaction planning intelligent agent generates an online dynamic parameter adjustment strategy based on a proxy optimization algorithm, and the strategy is converted into an execution code to correct the reaction conditions. The present application is used for intelligent unmanned research and development of polymer materials, and realizes adaptive error correction and closed-loop control of the synthesis process.

This invention discloses a method and platform for single- and dual-channel EEG decoding and physical device control based on heterogeneous computing power, relating to the field of brain-computer interfaces. Addressing the shortcomings of existing systems, such as reliance on multi-channel acquisition, limited computing power, and lack of dynamic safety assessment in control, this invention trains a cross-paradigm neural decoding large-scale model based on a domestically produced NPU heterogeneous architecture. Under simplified single- and dual-channel input, it extracts weak EEG features by combining knowledge distillation and spatiotemporal attention mechanisms. The decoding intent confidence is compared with a dynamic threshold generated by an MLP network based on parameters such as risk level. Finally, it converts the decoded intent into machine instructions through a hierarchical instruction interface to control external devices (such as XR, wheelchairs, etc.). This invention overcomes the bottleneck of missing spatial information in low-channel environments, significantly reducing hardware costs while achieving calibration-free high-precision decoding and a low-latency, highly reliable physical collision avoidance control closed loop.

The application relates to the computer technical field, in particular to a register-allocated instruction scheduling method and device, a computer device and a storage medium. The method comprises the following steps: obtaining a machine instruction sequence to which a physical register has been allocated; determining scheduling dependency information and scheduling characteristic information of each instruction corresponding to the machine instruction sequence; constructing a global instruction scheduling model according to the scheduling dependency information and the scheduling characteristic information; the global instruction scheduling model comprises scheduling constraint information determined according to the scheduling characteristic information; solving the global instruction scheduling model to obtain target transmission periods corresponding to the instructions, with the scheduling constraint information being a solving condition, the transmission clock period of each instruction being a solving variable, and the earliest clock period in which all the instructions are executed completely being a solving target; and allocating and scheduling the instructions in a register-allocated instruction scheduling stage based on the target transmission periods. The application provides an instruction scheduling method with better scheduling effect.

The application provides a numerical control turning and milling processing path optimization and prediction feedback method, and belongs to the technical field of numerical control lathe processing. A digital twin model is determined based on a three-dimensional model of a part to be processed and process parameters according to a confidence level; a machining instruction is obtained by optimizing and solving a tool path for a plurality of control cycles in the future based on the digital twin model with a multi-objective function; a plurality of source data are synchronously collected by a plurality of source sensors, and data fusion is performed; the plurality of source data include position, force, vibration, and temperature; an actual state is obtained based on the plurality of source data by Kalman filtering; a predicted state is obtained based on the fused data by the digital twin model; a deviation between the actual state and the predicted state is calculated, and the machining instruction is corrected by a hybrid controller; and the machining is completed by using the corrected machining instruction. By introducing adaptive digital twin model and dynamic weight model prediction control, real-time dynamic optimization of the machining path is realized, and the intelligent level and adaptability of the machining are improved.

The application discloses a BPF trampoline implementation method and device based on a domestic CPU, and the implementation method comprises the following steps: S1, a receiving step: receiving a BPF program mounting request, the BPF program has been compiled into LoongArch machine code; S2, a generating step: dynamically generating a trampoline code compatible with a LoongArch instruction set ABI; S3, a mounting step: modifying a starting instruction of a target kernel function, so that the execution flow jumps to the generated trampoline machine instruction sequence; S4, an executing step: when the target kernel function is called, the BPF program is transparently scheduled and executed. The trampoline machine instruction sequence of the LoongArch ABI dynamically constructs an execution environment in the kernel, correctly calls the JIT-compiled BPF program, replaces the entry instruction of the target kernel function with the instruction jumping to the trampoline through atomic operation, thereby realizing seamless hijacking and transparent scheduling of the execution flow, and enabling the efficient dynamic tracking capability of the BPF to be realized in the LoongArch ecology.

The embodiment of the specification discloses a kind of time series database security test method, device and equipment, the method comprises: the target program code in the time series database to be tested is compiled and inserted into the processing, the intermediate file corresponding to the target program code after inserting into is obtained, the intermediate file after inserting into is converted into the machine instruction code file after inserting into;Generation meets the time sequence rule corresponding to the time series database Time sequence SQL statement, the time sequence SQL statement is input into the target equipment running the machine instruction code file after inserting into, by the target equipment in the process of running the machine instruction code file after inserting into execution time sequence SQL statement, capture the abnormal information that appears in the process of running the time series database;Based on the captured abnormal information, determine the test result of the time series database security.

Disclosed are an electronic device and a method for generating an executable file of a heterogeneous computing platform. The electronic device comprises a processor configured to determine a plurality of first source codes corresponding to a neural network model and second source codes representing dependency relationships between the plurality of first source codes; determine first intermediate representations of the first source codes under a same intermediate representation framework; convert the first intermediate representations into first machine instructions suitable for execution on first hardware units matched with the first source codes, respectively; and link at least the first machine instructions based on the second source codes to generate an executable file corresponding to the neural network model. Embodiments of the present disclosure can realize training and deployment of a neural network model and pre / post processing thereof based on one-time development, thereby reducing the amount of code that needs to be developed and reducing development costs.

The application belongs to the technical field of rudder machine instruction processing, and specifically discloses a rudder machine instruction smoothing speed limiting method based on a differential step length. The method comprises the following steps: receiving an instruction frame sent by an upper computer according to a communication protocol, and analyzing the instruction frame to determine a rudder control instruction; determining a closed-loop instruction by using the rudder control instruction; obtaining potentiometer data, and processing the potentiometer data to obtain rudder feedback data, wherein the potentiometer is used to obtain the displacement of a motor output shaft of a rudder machine driven by a driver; processing the closed-loop instruction and the rudder feedback data according to a rudder machine mathematical model to obtain a control signal, which is used for driver control. The scheme solves the three core problems in the control of a traditional electric rudder machine, i.e., the technical difficulties of instantaneous current impact, mechanical vibration and overshoot, and back electromotive force hazards.

The application discloses an ALU operation fusion processing module and method suitable for a neural network, and the module comprises: a control unit, which is used for receiving and decoding machine instructions, managing the execution flow of inner and outer loops and microinstruction loops, and generating control signals of each stage of a microinstruction pipeline; a microinstruction buffer, which is used for storing a microinstruction sequence generated in advance by a neural network compiler; a register file, which is used for storing source operands and results of ALU operations; each entry of the register file is composed of a valid bit, a tag bit and a data bit; an ALU calculation core, which adopts a SIMD architecture and comprises multiple parallel arithmetic logic function units; a Load / Store unit, which is used for processing data exchange between the register file and a local buffer; and a data selection interface, which is used for selecting a data source or a target buffer according to a storage tag field of a microinstruction. The application can effectively balance the efficiency and flexibility of the ALU unit in a neural network hardware accelerator.

A system and methods are disclosed for end-to-end provable guarantees for safety critical computer platforms. The technical problem addressed by this invention is the need to provide assurance that a complex safety-critical computer platform behaves as intended and is secure, particularly in safety-critical contexts where errors or malicious attacks can have devastating effects. Despite advances in methodologies and toolchains for high-assurance software production, there remains a significant gap between theoretical guarantees and practical implementation at the operation instruction level (e.g., Assembly language instruction, byte-code instruction, digital circuit state-machine instruction etc.). The present invention overcomes this limitation by providing a system for verifying key security properties for safety-critical computer platforms at the operation instruction level without reliance on custom generation pipelines or certified compilers, thereby enabling the practical development of more secure and reliable computer platforms.

This invention provides a method, apparatus, and electronic device for training and analyzing large language models, belonging to the field of artificial intelligence technology. The method includes: constructing a training sample set, where each training sample includes task instructions as model input, intermediate representation code sequences, and label information; using the training sample set to perform supervised fine-tuning training on an initial large language model to obtain a target large language model; the intermediate representation code sequences are obtained by converting machine instructions in binary file samples of any processor architecture into semantic operation sequences independent of the processor architecture. This invention innovatively uses platform-independent intermediate representation code as training corpus, unifying the instruction sets of different hardware platforms at the semantic level. This solves the technical problem that existing models cannot be universally applied across platforms due to their dependence on specific architectures, thus achieving the effect of one-time training and universal application across multiple architectures, significantly improving the model's generalization ability and applicability in the field of binary program analysis.