489 results about "Instruction stream" patented technology

The invention provides a network security defense method and system based on intrusion modeling trapping, and the method comprises the steps: constructing a dynamic virtual asset atlas based on a real network topology, generating a virtual node and an interaction link through protocol fingerprint confusion, implanting an interface deformation rule in combination with a session context, and constructing a protocol stack controllable dynamic trapping interface. Extracting topology correlation degree parameters, triggering node recombination and attack path shunting rules, using a protocol stack mirror image tunnel confusion instruction flow path, constructing a dynamic attack path confusion network, and directionally transmitting an intrusion instruction to a trapping interface protocol stack. And when the parameters reach a threshold value, a node recombination strategy and an interface deformation rule are updated, a dynamic attack and defense game mechanism is formed, the attack behavior induction and feature capture capability is enhanced, and active interference and accurate traceability of network intrusion behaviors are realized. According to the technical scheme provided by the embodiment of the invention, active shunting of the attack path and accurate induction of the trapping interface can be realized, and the attack detection rate is improved.

A microprocessor includes execution units that execute macro-operations (MOPs), a decode unit that decodes architectural instructions into MOPs, an instruction fetch unit (IFU) having an instruction cache that caches architectural instructions and a macro-operation cache (MOC) that caches MOPs into which the architectural instructions are decoded. A prediction unit (PRU) predicts a series of fetch blocks (FBs) in a program instruction stream to be fetched by the IFU from the MOC if hit or from the instruction cache otherwise. A branch target buffer (BTB) caches information about previously fetched and decoded FBs. A counter of each BTB entry is incremented when the entry predicts the associated FB is present again. For each FB in the series, the PRU indicates whether the counter has exceeded a threshold for use deciding whether to allocate the MOPs into the MOC in response to an instance of decoding the instructions into the MOPs.

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 information processing, in particular to an electronic information dynamic scheduling and management system and method, and the system comprises a data flow dynamic monitoring module, a dynamic window adjustment module, a data distribution module and an instruction flow recombination module. According to the invention, the adaptability and flexibility of data processing are improved by monitoring the data flow in the electronic information in real time and carefully analyzing the size and type change, and the dynamic adjustment of the data processing window is realized by identifying the peak value and valley value of the flow and analyzing the flow fluctuation amplitude. By analyzing the dependency relationship and potential delay between execution instructions and re-arranging the instruction execution sequence, the system can more effectively distribute the load of a processor core, reduce the delay and enhance the processing speed, thereby not only optimizing the management of electronic information flow, but also enhancing the adaptive capacity of the system to a complex data environment, and improving the user experience. And a more efficient and safer information processing platform is provided.

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 provides a computer hardware performance evaluation method and system based on an analogue simulation model, and relates to the technical field of computer performance evaluation. According to the method, hardware component models such as a processor, a memory and I / O are constructed by analyzing a system structure description file, semantic analysis is performed on an instruction stream or a parallel computing graph of a to-be-evaluated application, and a task load model is formed. The model is input into an event-driven scheduling module, simulation is executed on the hardware component model, task-resource mapping is generated, and task time delay, communication traffic and power consumption are recorded. And calculating average response time delay, data bandwidth and unit energy consumption according to the simulation data, and outputting an evaluation report compared with the performance baseline. According to the method, real load-oriented multi-dimensional performance prediction can be realized, and the method can be used for architecture optimization and scheme selection.

The invention relates to the technical field of network security and vulnerability detection, in particular to an intelligent contract vulnerability detection method and system based on hybrid fuzzy test optimization, and the method comprises the steps: carrying out the static analysis of compiled EVM byte codes, generating a first test case set, carrying out the fuzzy test, and introducing taint analysis to track the transmission of taints in an instruction stream; in the fuzzy testing process, according to the length change of the instruction counting set within the execution frequency threshold value, symbol execution processing is triggered when the length change value is 0, in the symbol execution processing process, a constraint cache reuse strategy is adopted to solve an obtained constraint expression, and a solving result is cached and reused, so that a fuzzy test result is obtained; a use case value reaching a complex branch can be quickly generated through symbolic execution and is fed back to the fuzzy engine to assist the fuzzy engine in exploring a new path to capture more deep vulnerabilities; and meanwhile, test predictions are designed for three intelligent contract vulnerabilities of authority control, transaction sequence dependence and denial of service, and vulnerability detection is more accurate.

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 discloses a lithium battery industry MES system production scheduling method based on deep learning. The method comprises the following steps: S1, collecting and preprocessing lithium battery production data; s2, constructing a double-tower Siamese network to encode the standardized sequence, and generating a similarity matrix; s3, modeling long-term dependence by utilizing Transform-XL, and extracting a cross-cycle production scheduling relationship; s4, constructing the production scheduling relation into an association weight tensor, inputting the association weight tensor into a neural Turing machine, and generating an original production scheduling instruction stream through read-write operation; s5, performing conflict detection and dependency rearrangement based on a controller memory comparison mechanism, and iteratively updating the state of the neural Turing machine; and S6, feeding back the candidate scheme to an MES execution layer and updating model parameters. According to the method, a multi-model structure is fused, intelligent modeling and dynamic optimization of lithium battery scheduling are realized, and the scheduling efficiency, the response speed and the system adaptive capacity are effectively improved.

The invention discloses a prospective preprocessing method and system in six-axis mechanical motion automatic control, particularly relates to the field of six-axis mechanical motion automatic control, and is used for solving the problem of insufficient track prediction and control precision in a load sudden change scene. The inertia tensor is updated in real time in combination with dual-channel Kalman filtering, dynamic inertia data are accessed, and the sensitivity of trajectory prediction to load change is improved; double-domain feature extraction of time-domain thermal stress density and frequency-domain energy dispersion is adopted, acceleration pulse layout is dynamically optimized by using a complementary integral mechanism, and the overload risk of a driver is reduced; meanwhile, the instruction dispatcher avoids cache overflow through advanced rhythm regulation and control, and continuity of instruction streams is guaranteed; the inertia model is continuously refreshed through closed-loop feedback, and synchronous evolution of inertia change and trajectory prediction is achieved; the overload shutdown risk caused by inertia lag of six-axis mechanical motion is reduced, and the adaptive capacity of the prospective preprocessing method in a dynamic complex operation environment is enhanced.

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.

The invention relates to a gateway based on a general description language and control logic for efficient redundancy removal tasks. The gateway comprises a file library in an HTDL form, a compiler, an instruction composer and a task scheduler, a terminal task description file is written through HTDL and stored in a file library, a task scheduler selects the task description file needing to be sent from the file library and transmits the task description file to a compiler, and the compiler compiles data preprocessing statements and sensor control statements in the task description file into continuous binary instruction streams and sends the continuous binary instruction streams to an instruction composer. The instruction arranger arranges task instruction data and control instruction data and outputs binary data flow which can be identified by the terminal to the communication interface, and the task scheduler predicts long downlink communication of the system through a multi-terminal task prediction mechanism and performs coordinated management on terminal tasks. According to the invention, the architecture is lighter, and the general task description language is used in the gateway, so that the development process is faster, and the architecture redundancy is reduced.

The invention discloses a resource scheduling method and device and a server, and relates to the technical field of communication. Under the condition that an execution request of a first application program is received, a first instruction stream feature of the first application program is obtained, and the first instruction stream feature indicates features of a plurality of first instructions included in the first application program; determining a first resource allocation mode of the first application program according to the first instruction stream feature and a physical resource called by the first application program; and optimizing the physical resources called by the first application program based on the first resource allocation mode. The resource allocation mode determined through the instruction stream characteristics of the application program is matched with the multiple instructions included in the application program, and then the physical resources called by the application program optimized based on the resource allocation mode are matched with the multiple instructions, so that resource scheduling based on application program driving is realized, the granularity of resource scheduling is relatively fine, and the resource scheduling efficiency is improved. And the resource utilization rate for different application programs is 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 provides a jump instruction jump address selection method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: generating an instruction stream, determining instruction information of a jump instruction in the instruction stream, determining a first difference value between addresses of adjacent instructions in the instruction stream, and according to the first difference value and the instruction information of the jump instruction, selecting a jump address of the jump instruction, determining candidate jump addresses of the jump instructions and constraint conditions for the jump addresses of all the jump instructions according to the instruction information of the jump instructions and the number of the instructions in the instruction stream, and when the candidate jump addresses of the jump instructions do not conform to the constraint conditions, determining a jump address range according to the first difference value, the instruction information of the jump instructions and the number of the instructions in the instruction stream, and determining a target jump address of the jump instruction from the jump address range, and performing instruction jump according to the target jump address when the jump instruction is executed. The probability that the jump instruction jumps to a reasonable range can be improved, and verification covers all modules of the processor as much as possible.

The invention provides an RDP graphic rendering instruction stream high-risk operation real-time blocking method under a Loongson architecture, and relates to the technical field of computers, and the method comprises the following steps: intercepting a graphic rendering instruction stream transmitted by an RDP protocol in real time, analyzing each graphic rendering instruction in the graphic rendering instruction stream one by one, generating structural features, and storing the structural features in a database; comprising single instruction features and context-level features, calling and traversing a static rule base, matching the single instruction feature of each graphic rendering instruction with the static rule base, and if at least one single instruction feature is successfully matched, judging that the operation is a high-risk operation and triggering blocking; and if the matching is not successful, performing dynamic behavior analysis on the graphic rendering instruction stream based on the context-level features, and if a judgment result of the dynamic behavior analysis is abnormal, triggering blocking. According to the scheme, the static rule base and the dynamic analysis work cooperatively, so that the risk of missed judgment is reduced, excessive dependence on static rules is avoided, and the protection capability on system-level high-risk operation 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.