336 results about "Instruction stream" patented technology

The invention relates to an industrial control system security policy adaptive configuration method and device and a readable storage medium. According to the method, a control instruction stream, a process variable sampling value and link layer message metadata are synchronously collected, an association hypergraph fusing network semantics, control semantics and physical semantics is constructed to represent a system state, and a sampling period is divided into a plurality of time slots with fixed lengths. In each time slot, self-supervised learning is utilized to extract a topology embedded vector group, and a system operation rule is represented. A robustness envelope interval is generated by calculating a statistical distance between a current time slot and a historical baseline, and is used for driving a reinforcement learning model to generate a security policy parameter set. And executing corresponding access control, traffic shaping and integrity verification operations in the next time slot, generating receipt vector feedback model update according to an execution result, outputting a micro-policy patch with a hash signature and a timestamp, and sending the micro-policy patch to the edge security gateway to realize deployment, thereby realizing closed-loop adaptive optimization of the security policy in the dynamic environment.

The invention relates to the technical field of audio and video control, and discloses an audio and video player control method based on a voice instruction. The method comprises the steps that an original voice instruction stream of a user is collected, the instruction stream comprises a time domain audio signal sequence, an environment noise spectrum and user pronunciation characteristic parameters, and voice information can be comprehensively captured; multi-modal instruction analysis processing is carried out on the original voice instruction stream, a structured control instruction set containing acoustic control intention identification, semantic operation object description and context correlation parameters is generated, and the analysis precision is improved; then executing player state adaptation based on the set, generating a dynamic control response sequence containing an equipment state adjustment command, a media content positioning parameter and an interface interaction logic identifier, driving a player to execute a multi-dimensional control operation and generating real-time play control effect feedback data; and finally, multi-modal analysis parameters are optimized according to feedback data, a self-adaptive instruction analysis strategy is generated, and the control experience of a user on the audio and video player is optimized.

The invention relates to the technical field of cooperative control, in particular to a multi-station wind and light cooperative energy storage optimization control system, which comprises a wind and light output change identification module, an energy storage window state screening module, a linkage section intersection calibration module, a multi-station energy storage priority allocation module and a cooperative regulation and control instruction arrangement module. According to the method, the wind power climbing extreme value and the photovoltaic descending section are extracted, the matching relation between the power generation complementary characteristic and the energy storage response time is established, the time sequence scheduling precision is enhanced, the SOC and the operation state are fused to perform multi-dimensional screening on the energy storage units, the non-adjustable resources are eliminated, the execution reliability is improved, and the output fluctuation and energy storage window intersection is constructed; standardized scheduling time slices are generated, unified positioning of tasks is achieved, energy storage resource grouping and conflict elimination are completed based on available duration, priority ranking is clear, scheduling coordination is guaranteed, redundant conflict items are removed, instruction stream integrity and control coverage are improved, and execution efficiency of multi-station cooperative scheduling and system stability are enhanced.

The invention relates to the technical field of robot scheduling, and discloses a robot task management framework system based on asynchronous communication. The system comprises a task asynchronous analysis module for performing time sequence decoupling on an original task instruction stream of a heterogeneous robot terminal by means of a multi-channel buffer queue to separate out independent executable task units; the resource dynamic mapping module is used for establishing a variable granularity resource allocation mapping table according to cluster real-time load and task resource requirements, and recording various resource dynamic binding relationships; the task priority reconstruction module is used for constructing a directed acyclic graph scheduling topology based on task logic dependence and deadline constraint, and generating a weighted execution sequence through topological sorting; the exception isolation processing module captures exceptions during operation and triggers an isolation strategy; and the cross-node cooperation module guarantees the multi-robot cooperation task state consensus through a distributed consistency protocol. The system is suitable for heterogeneous robot cluster complex task scenes.

The invention relates to the technical field of chip design, and discloses a chip dynamic power consumption scheduling method and system based on an intelligent algorithm. The method comprises the following steps of: firstly, acquiring instruction stream data operated by a chip in real time, extracting a feature vector comprising an instruction dynamic change vector and context associated data, and determining a power consumption prediction mapping parameter according to the feature vector; and when the parameter exceeds a preset threshold value, an accurate power consumption prediction result is generated by adjusting the weight of the convolutional neural network. Subsequently, a synchronous timing demand is calculated based on the instruction switching frequency and the data dependency, and an initial power supply configuration is determined. By monitoring task load classification signals, the power consumption distribution proportion is adjusted when the signals are lower than a threshold value, the optimized power supply configuration is obtained, and the improvement index of the resource distribution efficiency is calculated according to the optimized power supply configuration. And finally, according to the index, dynamically adjusting a limiting condition of a scheduling period, and forming a self-adaptive optimization framework, thereby realizing accurate prediction and dynamic optimization scheduling of the chip power consumption.

The invention relates to the technical field of engineering management, in particular to an engineering progress quality space-time tracing system based on a digital twin sand table, which comprises a behavior factor extraction module for acquiring equipment operation records, process instruction streams, personnel tracks and quality detection data; constructing a construction behavior factor including an operation type, a trigger source identifier, an execution node and an execution duration; a behavior driving chain reconstruction module uses the behavior factors as nodes, identifies causal paths between adjacent or cross-processes, constructs a behavior driving chain, and maps the behavior driving chain with a construction quality result to generate a behavior quality association chain; the space-time twinborn projection and inversion module maps the association chain to a digital twinborn sand table, generates a tracing view based on a time axis and space coordinates, and supports inversion of an operation chain and responsibility factor information forward from a quality abnormal point. According to the method, high-precision, verifiable and responsibility-traceable time-space visual tracing of the abnormal quality events in the whole construction process is realized.

The embodiment of the invention discloses a test instruction generation method and device, electronic equipment, a medium and a product. The method comprises the steps that hardware constraint information of a specified processor architecture is obtained, and the hardware constraint information comprises a plurality of threads supported by the specified processor architecture, the number of operators to be executed by each thread and an address space corresponding to each thread; for each thread, according to the number of operators to be executed by the thread, calling different operators through each execution engine, and generating a target operator queue corresponding to the thread; determining a test instruction stream corresponding to a target operator queue of the thread according to the address space corresponding to the thread; wherein each target operator in the target operator queue corresponds to one test instruction in the test instruction stream, and an output address of at least one test instruction in the test instruction stream is an input address of a subsequent test instruction; and generating a test instruction file of the specified processor architecture according to the test instruction stream corresponding to each thread.

The invention provides a single instruction stream multi-fiber programming model and a construction method thereof. The method comprises the following steps: constructing a task, thread and fiber three-level concurrent unit; different code logic units are configured for each task, and one task is mapped to a plurality of threads; mapping the threads and processor cores one by one, and mapping one thread to a plurality of threads; the threads within the thread are caused to share all registers on the processor core. According to the single-instruction-stream multi-fiber-thread programming model and the construction method thereof, the programming model is constructed based on the fiber threads, concurrent execution of asynchronous data streams is achieved, the resource utilization rate is effectively increased, and redundant calculation is eliminated.

The invention discloses an industrial robot multi-machine cooperative interaction method and system based on artificial intelligence. The method comprises the following steps: S1, collecting and preprocessing interaction data of an industrial robot; s2, constructing a cooperative relation graph formed by industrial robot nodes and interaction edges; s3, disassembling the target task by adopting a priority mechanism and generating an execution weight matrix; s4, in combination with the interaction data and the weight matrix, generating a cooperative action vector through a multi-target reinforcement learning algorithm; s5, action conflict detection is executed, path overlapping and resource conflicts are eliminated, and a candidate action sequence is obtained; s6, generating a path and a control instruction, and forming a distributed execution instruction stream; and S7, driving the robot to execute the task, collecting feedback, updating the atlas, and circularly executing until the task is completed. According to the invention, task cooperation, path obstacle avoidance and dynamic optimization control among multiple industrial robots are realized, and the scheduling efficiency, the execution stability and the system intelligence level are effectively improved.

The embodiment of the invention discloses a processor front-end branch prediction and instruction fetching verification method and device and a storage medium, relates to the technical field of processor function verification, and can reduce the complexity of instruction scene construction and improve the verification efficiency. The method comprises the following steps: acquiring instruction fetching information and a test vector; the fetch information comprises a fetch address range and branch prediction information of the target instruction block, and jump instruction marking information is configured in the test vector; according to the fetch address range and the jump instruction mark information, generating an instruction boundary of each cache line in the fetch address range; respectively associating the branch prediction information and the jump instruction mark information to the instruction boundary of each cache line, and constructing to obtain an instruction stream; the instruction stream is used for simulating execution logic of processor front-end branch prediction and instruction fetching; and decoding simulation verification is carried out based on the instruction stream, and processor front-end branch prediction and instruction fetching accuracy is determined. The method is suitable for a verification scene of processor front-end branch prediction and instruction fetching.

The invention discloses a real-time data stream processing method for multi-device collaborative management of a dome cinema, which relates to the technical field of cinema device data management, and comprises the following steps of: establishing communication connection between a central management node and a plurality of playing devices, collecting device identifiers, projection area numbers and geometric position parameters of the playing devices, and establishing a central management node; generating an equipment mapping relation table; realizing local clock correction of the playing equipment based on a unified time synchronization protocol; the central management node generates a control instruction stream according to the equipment mapping relation and the playing content, the instruction stream comprises space frame distribution information and fusion control parameters, and the space frame distribution information and the fusion control parameters are distributed to the playing equipment through communication channel numbers; and the playing device completes image geometric transformation and edge fusion processing according to the received control instruction and then executes synchronous playing. According to the invention, multi-device automatic mapping is realized, the time synchronization precision is improved, the image playing is coordinated and consistent, and the collaborative display capability and the playing stability of the dome cinema are improved.

The invention provides an AI semantic model and robot interconnection method and system based on an MCP protocol and a medium. A natural language instruction is received, and a structured operation sequence is generated through a semantic analysis model; extracting a semantic tag dynamic matching service engine in the sequence, and generating an operation request containing a tool identifier and a parameter template; if the operation authority is verified based on the verification security token, triggering secondary authorization of the user, and if the authority is passed, packaging the operation request into a security instruction according to an encryption rule; a transmission protocol channel is dynamically selected based on the real-time flag bit, an executable instruction stream is generated, and therefore the target tool is driven to execute operation and feed back execution state data; according to the method, the problem of AI and robot instruction heterogeneity is solved through dynamic matching of semantics and robot tools, the unauthorized operation is intercepted based on authority verification, data security is guaranteed through field-level encryption, and a safe and efficient man-machine interaction framework is provided for intelligent manufacturing.

The invention relates to the technical field of simultaneous interpretation, and discloses a simultaneous interpretation data processing method and system based on a POE microphone array. The method comprises the following steps: synchronously acquiring multi-language original audio streams and meeting place environment noise spectrum features through a distributed microphone array powered by the Ethernet; after time domain framing is carried out on the audio stream, adaptive filtering is carried out by using a dynamic noise reduction weight coefficient to obtain a primary pure voice segment; dividing the multi-language speech endpoint detection model into independent speech units with language labels through a pre-trained multi-language speech endpoint detection model, and matching a corresponding acoustic model to generate a phoneme-level time alignment sequence; comparing and outputting a term replacement instruction stream in real time in combination with a simultaneous transfer term library, and generating an intermediate semantic representation vector after fusion; and the low-delay encoder converts the voice parameter sequence into a target language voice parameter sequence, and drives the waveform synthesizer to generate final simultaneous transmission audio. The method optimizes the whole process processing, gives consideration to the simultaneous transmission accuracy and real-time performance, and is suitable for a multilingual meeting place scene.

The invention discloses a power grid industrial control system security protection scheme based on a block chain, and belongs to the field of power grid automation security. Aiming at the problems of high centralized authentication risk, weak physical layer protection and the like of a traditional industrial control system, a trusted execution environment and intelligent contract response system is constructed through fusion of a block chain distributed account book and a hardware security module (HSM). According to the scheme, equipment physical identity binding and vibration monitoring are achieved through an HSM, the consensus efficiency is optimized through an improved PBFT algorithm, instruction legality, parameter compliance and execution logic rationality are dynamically verified in combination with an intelligent contract, a security baseline is generated in real time, and the instruction stream deviation degree is monitored. The method can dynamically balance the security and real-time performance of the instruction, reduce manual intervention, improve the abnormal response efficiency to a second level, is suitable for industrial control systems such as SCADA (Supervisory Control And Data Acquisition) and the like, remarkably improves the protection capability in scenes such as DDoS attack resistance and physical tampering detection, and provides an extensible intelligent protection normal form for power grid security.

The invention discloses a double-seat cooperative safety control method and system for engineering machinery, relates to the technical field of engineering machinery control, and provides the following scheme that the method comprises the steps that a first operation instruction stream of a main driving seat and a second operation instruction stream of a co-driving seat are collected in real time, and a working condition parameter set of the engineering machinery is synchronously obtained; the operation instruction stream comprises all degree-of-freedom motion parameters of the engineering machinery, inputting the first operation instruction stream into a pre-constructed long-short-term memory model, outputting a prediction operation instruction stream in a future time window, and based on a difference vector of the first operation instruction stream and a second operation instruction stream on each degree-of-freedom motion parameter. According to the method, through the index curve fitting model and Simpson integral calculation, the defect that the co-pilot intervention weight is difficult to quantify is overcome, the dynamic intervention weight based on the physical equidistant interval absolute integral value is realized, and the accuracy of the control signal of the engineering machinery is improved.

The invention relates to the technical field of unmanned scheduling, in particular to a task scheduling method and system based on an unmanned forklift cloud platform. The method comprises the following steps: obtaining a vehicle state flow and an operation instruction flow of an unmanned forklift group, and constructing a vehicle state task set and an operation instruction task set according to a timestamp; generating a scheduling decision feature set based on the vehicle state task set and the operation instruction task set; performing priority mapping on the scheduling decision feature set to generate a first decision instruction set; performing resource adaptation on the scheduling decision feature set to generate a second decision strategy set; therefore, by constructing a data-driven scheduling mechanism based on time sequence alignment, feature extraction, dual-channel decision and closed-loop correction, the problems of data isolation, single feature, decision lag and lack of feedback optimization in the traditional unmanned forklift scheduling process are solved; and the accuracy of task allocation, the stability of system operation and the execution efficiency of the whole operation are improved.

Embodiments of the invention provide an instruction processing method and apparatus, an electronic device and a readable storage medium. The method comprises the steps of obtaining a target instruction stream and target instruction boundary information from a cache according to a memory access request; the target instruction boundary information is used for indicating boundary information of each instruction in the target instruction stream; determining the boundary of each instruction in the target instruction stream according to instruction starting information and the target instruction boundary information; the instruction starting information is used for representing whether a target field in the target instruction stream is a complete instruction; and according to the boundary of each instruction, segmenting the target instruction stream, and processing each instruction obtained by segmentation. As the target instruction boundary information is pre-stored in the storage space in the cache, the delay when the instruction is read is reduced, and the efficiency of determining the instruction boundary is improved.

The invention discloses a dynamic resource demand characterization method and system for a task life cycle, and belongs to the technical field of artificial intelligence computing power resource management.The method comprises the steps that when a task is started, a monitoring agent is deployed, the task life cycle is dynamically divided through a GPU instruction sudden increase inflection point, and a CPU instruction stream is synchronously monitored; task semantic features, CPU / GPU hardware indexes and interaction time delay data are collected, key features are extracted after space-time alignment, and collaborative efficiency indexes are calculated; constructing a cross-stage resource demand coupling matrix based on a historical task library, and quantifying CPU / GPU resource conduction coefficients in adjacent stages; constructing a double-flow prediction model, predicting a resource demand in combination with the coupling matrix, and generating a three-dimensional demand matrix; and encoding the demand matrix into a dynamic vector, introducing a stage transition resource change intensity enhancement vector, and finally outputting an enhancement vector sequence to trigger CPU / GPU cooperative scheduling.

The invention provides a performance analysis method and device for a cross-architecture semantic equivalence instruction stream and a storage medium, and the method comprises the steps: taking a high-level language program as input, aiming at a plurality of target architectures, generating a multi-dimensional semantic equivalence instruction stream set from a high-level language form to an intermediate representation and then to an assembly code through a compiler; based on semantic equivalence instruction streams of code units with different granularities and compiler debugging information, establishing a mapping relationship between a target region in a source code and an intermediate representation and assembly codes, and forming a cross-hierarchy equivalence region fully-connected graph; and based on the semantic equivalence instruction stream set and the cross-level equivalence region fully-connected graph, automatically embedding a performance monitoring code at the boundary of the target region, and carrying out performance test on the elastic region. According to the method, the accurate performance test of the cross-architecture semantic equivalence instruction stream can be realized.

The invention discloses a real-time software deformation interaction method based on a physical engine in an environment. The real-time software deformation interaction method comprises the following steps: S1, establishing a virtual environment and a software model; s2, constructing an interactive perception and collision detection network; s3, receiving and analyzing a multi-modal interaction instruction; s4, real-time deformation calculation based on a physical engine; s5, performing multi-user collaborative state synchronization and rendering feedback; and S6, carrying out fault state simulation on the software object. According to the method, real-time and high-precision simulation of software deformation is realized through deep fusion of software dynamics calculation of a physical engine and an interaction instruction stream, and through hierarchical physical attribute binding and client prediction, the network synchronization data volume is greatly reduced while the millimeter-level deformation precision is ensured, so that the network synchronization efficiency is improved. And low-delay and high-consistency experience under multi-person cooperative operation is ensured.

The invention relates to the technical field of confidential computing, and discloses a data credibility-oriented vehicle-mounted integrated security computing system and credibility construction method, which comprises the following steps of: establishing a static trust chain by using a hardware trust root, shielding external interruption in a credible execution environment, and controlling a performance monitoring unit; synchronously collecting real-time hardware micro-architecture events of the key business algorithm to generate a runtime feature vector; according to the method, the instruction stream during operation is anchored by utilizing the physical characteristics of the micro-architecture, the hijacking attack of the control stream is accurately identified in a ciphertext environment, the hijacking attack of the control stream is accurately identified, the hijacking attack of the control stream is accurately identified, the hijacking attack is accurately identified, and the hijacking attack of the control stream is accurately identified. Strong coupling verification of a calculation result and an execution behavior is realized, and logic safety of a vehicle-mounted platform is ensured.

The invention discloses a test spline identity tracing method and system based on a block chain. The method comprises the following steps: collecting full-life-cycle multi-modal data of a test spline embedded with a PUF chip for quality evaluation and semantic mapping; generating an attack judgment mark and an abnormal conduction path so as to embed an encrypted watermark in the control instruction; carrying out block chain evidence storage on the standardized traceability data set; constructing a test spline dynamic knowledge graph to generate a knowledge enhancement traceability data set; analyzing a control instruction stream carrying a watermark, monitoring a deviation value between an execution result and an expected physical behavior, generating an execution deviation value and watermark feedback data, enhancing a traceability data set in combination with base knowledge, generating attack source coordinate information and a traceability path map, and pushing a traceability report which cannot be tampered. The space-time calibration of the multi-source heterogeneous data is realized, and the integrity and consistency of the traceability data are ensured; a PUF chip is adopted, and watermark information which cannot be tampered is embedded in a control instruction, so that double protection from a physical layer to a data layer is realized.

Embodiments of the present disclosure provide a processor, an instruction processing method, and an electronic device including the processor. The processor includes a plurality of processor cores and an accelerator module, wherein each of the plurality of processor cores is configured to execute a first type of instruction; the accelerator module includes at least one accelerator engine configured to execute a second type of instruction different from the first type of instruction; the accelerator module is coupled with and shared by the plurality of processor cores, and each of the plurality of processor cores dispatches an object instruction belonging to the second type of instruction in an instruction stream processed by itself to the accelerator module for execution and receives an execution result message of the object instruction from the accelerator module. The processor can have higher accelerator module usage efficiency.

The invention discloses a statistical data downloading method for supply chain finance, which relates to the technical field of finance and comprises an acceptance permission judgment module, a query resource control module and a desensitization packaging evidence storage module. The acceptance permission judgment module is used for uniformly accessing the statistical application, completing identity verification, main body range binding, index range binding, time range binding and area range binding, and generating an acceptance bill and a judgment bill; the query resource control module is used for generating a query plan under the index aperture mapping dictionary, the partition information and the resource quota, binding a concurrent quota, a throttling quota and a priority queue, and outputting an executable instruction stream; and the desensitization packaging evidence storage module is used for executing differential desensitization according to the main body type and the sensitivity level, performing aggregation calculation and deduplication execution, completing result set packaging, digital watermarking and downloading delivery, generating a downloading certificate and writing a certificate abstract and an evidence index into an evidence storage chain. The method has the characteristic of high consistency.

The invention relates to a neural network accelerator, in particular to an instruction flow control system applied to the neural network accelerator, which comprises a first-stage instruction flow control unit, a second-stage instruction flow control unit, a third-stage instruction flow control unit, a fourth-stage instruction flow control unit and a fourth-stage instruction flow control unit, and is characterized in that the first-stage instruction flow control unit is responsible for prefetching, decoding, executing and distributing instructions of the neural network accelerator and responding to pause influence of debugging events and synchronization events on instruction distribution; the second-level instruction flow control unit is located in each execution unit, extracts execution parameters from the second-level execution instructions distributed by the first-level instruction flow control unit, is responsible for translating and maintaining the instruction execution dependency relationship among the execution units, and ensures that the execution units execute the instructions in sequence according to the instruction execution dependency relationship among the execution units; according to the technical scheme, the defect that in the prior art, it is difficult to efficiently execute instructions with the execution dependency relationship between the execution units can be effectively overcome.

The invention discloses a method for constructing vTEE and certification in an AMD SEV environment, provides a refined communication and fusing sequence diagram for the vTEE for the first time, and defines a module interaction sequence under two scenes of normal task execution and anomaly detection. According to the method, a trusted cloud security management platform initiates a request, a protection component coordinates a calculation component to collect an SEV-SNP report and vTEE key data, signature combination and uploading verification are carried out, and finally, the platform compares with a reference value to determine whether the vTEE is trusted or not. According to the method, a double-layer certification mechanism of SEV hardware certification and vTEE equipment measurement is adopted, an SEV-SNP certification report proves that a hardware environment, a VM mirror image and a vTEE operation environment are not tampered, vTEE key data measurement supports complete trust chain verification of equipment firmware, instruction streams and strategy states, and end-to-end certification is achieved. Equipment-level measurement, strategy issuing, abnormal fusing and remote certification are realized through an external protection part, and the system has important commercial landing value and strong system expandability.

A power consumption prediction and management method and system based on a solid state disk, a medium and a product relate to the field of solid state disks. The method comprises the following steps: predicting the workload intensity of a host in a first preset time window based on an I / O (Input / Output) instruction stream to obtain an expected host workload curve, and determining an expected host idle time period; calculating an expected host power consumption value in the expected host idle time period; extracting internal state parameters of the solid state disk according to the historical operation log data; establishing a background task triggering probability model, and calculating the triggering probability of starting the high-power-consumption background task by the solid state disk in a first preset time window; performing power consumption conflict judgment according to the expected host power consumption value and the triggering probability, and generating a comprehensive predicted power consumption value; and based on the comprehensive predicted power consumption value, determining an optimal target power consumption state, and instructing the solid state disk to be switched to the optimal target power consumption state. By implementing the technical scheme provided by the invention, the stability of state switching of the solid state disk is improved.

A microprocessor includes a prediction unit (PRU) that predicts a sequence of fetch blocks (FBlks) in a program instruction stream and a macro-op (MOP) cache (MOC) that comprises MOC entries (MEs). An ME holds MOPs into which architectural instructions of one or more FBlks are decoded. The PRU receives a detection of a first instance in which execution of an ME caused a need for an abort and in response resets a counter of the ME. Subsequently, the PRU increments the counter when the PRU predicts the ME is present, invalidates the ME in response to detecting a predetermined number of instances in which execution of the ME caused a need for an abort before the counter reaches a threshold, and retains the ME in the MOC if the counter reaches the threshold before detecting the predetermined number of instances in which the ME caused a need for an abort.

A microprocessor includes a prediction unit (PRU) that predicts a sequence of fetch blocks (FBlks) in a program instruction stream, a macro-op (MOP) cache (MOC) that comprises MOC entries (MEs), a decode unit, and a fusion engine. Each ME indicates whether it is a single-FBlk ME (SF-ME) that holds MOPs associated with a single FBlk whose architectural instructions have been decoded into the MOPs of the SF-ME or a multi-FBlk ME (ME-ME) that holds MOPs associated with multiple FBlks whose architectural instructions have been decoded into the MOPs of the MF-ME. For each FBlk of one or more FBlks in the program instruction stream: the decode unit decodes the architectural instructions of the FBlk into MOPs, and the fusion engine builds a SF-ME in the MOC using the decoded MOPs, and the fusion engine builds a MF-ME in the MOC using the MOPs of a series of SF-MEs.

The invention relates to the technical field of instruction set conversion, and discloses an instruction set conversion method of a GPU heterogeneous environment based on simulation software. According to the method, the key instruction stream in the simulation task is accurately captured through the dynamic instrumentation method, and a solid foundation is provided for subsequent processing; the method comprises the following steps: dividing an original instruction stream into a plurality of instruction blocks to be converted through a classification mechanism according to instruction semantic features and hardware suitability, and creating conditions for parallel conversion; a lightweight front-end translator is adopted to efficiently convert classified instruction blocks into architecture-independent intermediate representations, the degree of parallelism and the data dependency relationship between instructions are reserved, the intermediate representation instructions are deeply optimized, and the method comprises the key steps of instruction selection, register allocation, instruction recombination, SIMT mapping, instruction coding and the like. And generating a native instruction of the target GPU architecture through accelerated translation of the target GPU thread block. According to the method, the instruction conversion period is shortened, and the correctness and the execution efficiency of the conversion result are improved.