642 results about "System level" patented technology

The invention provides a DCS early warning method and system based on intelligent AI visual identification, and relates to the technical field of DCS early warning, and the method comprises the steps: obtaining the real-time operation parameters of a DCS system, and obtaining the real-time operation parameter characteristics of a unified format; inputting the real-time operation parameter characteristics into a pre-constructed intelligent AI visual identification model, analyzing the operation state of the DCS system, identifying an abnormal mode of a system level, and comparing the abnormal mode with a normal operation mode in a preset normal operation mode library; a digital twinborn simulation technology based on a physical mechanism is adopted to predict a fault mode, a fault reason and an occurrence probability of the DCS system, early warning information of a corresponding level is automatically generated according to a prediction result in combination with a preset multi-level early warning decision matrix, and the early warning information is pushed to an intelligent operation and maintenance platform of the DCS system in real time. An operator is assisted in recognizing system risks in advance, and targeted preventive maintenance measures and suggestions are given.

The invention discloses a robot health state assessment method and system based on artificial intelligence. The method comprises the steps of data acquisition and fusion, digital twin health modeling, multi-scale predictive maintenance, fault tolerance control and health state assessment. The invention relates to the technical field of digital robot health analysis, and the method comprises the following steps: constructing an optimized feature set by fusing multi-source sensing data, realizing digital twin health modeling by adopting a dynamic dual-data flow neural differential equation combining physical constraint and wear evolution, and outputting a robot health degree parameter; quantum annealing optimization and a graph attention mechanism are introduced, a multi-level predictive maintenance architecture is constructed, fault propagation prediction and maintenance optimization from a part level to a system level are realized, and fault-tolerant control is realized in combination with model prediction control. And the accuracy, interpretability and practicability of health assessment of the robot are improved.

The invention relates to the technical field of control and regulation systems, in particular to a photovoltaic module and system, and the photovoltaic module comprises a sensor group, a dynamic feature extraction module, a prediction model module, a collaborative optimization control module and an instruction execution module. The sensor group collects the front irradiance, the back reflectivity and the temperature gradient data of the double-sided photovoltaic array, synchronously obtains the charge state and the charge and discharge rate of the energy storage system, and instructs the execution module to drive equipment to act and calibrate a noise filtering threshold through a feedback channel. The system layer integrates an edge computing node, a cloud scheduling center and a redundant communication network, realizes local real-time control and global resource optimization, and solves the problem of multi-time scale cooperative control of double-sided photovoltaic output dynamic fluctuation and energy storage response delay in combination with virtual sensing of a fault processing module, an off-network strategy and a case library self-learning mechanism. The energy utilization rate and the power grid frequency modulation precision are improved.

The invention discloses an automatic scheduling method and system for ship unloading equipment, and relates to the technical field of port automation. According to the method, a high-precision digital twinborn model for ship unloading operation is constructed, physical equipment is abstracted into a digital intelligent agent with an autonomous decision-making capability, real-time multi-dimensional data and historical data are utilized to perform deep fusion to drive system synchronization, and a future multi-step scheduling strategy is deduced in a parallel simulation manner in a virtual space based on rolling time domain control, so that the real-time multi-dimensional data and historical data are subjected to real-time multi-dimensional data synchronization driving system synchronization is realized. Dynamic evaluation and optimization are carried out by adopting a multi-objective evolutionary algorithm combined with a cooperative game mechanism, and conflicts and cooperation among equipment are effectively coordinated by defining an individual utility function and introducing cooperative game negotiation and a meta-controller to dynamically adjust target weights, so that system-level global optimal scheduling is realized under multiple objectives of efficiency, energy consumption, safety and the like, and the scheduling efficiency is improved. The intellectualization, the self-adaptability and the comprehensive operation benefit of port ship unloading operation are comprehensively improved.

The invention relates to the technical field of computer security, in particular to a security isolation method for an edge computing node of the Internet of Things, which comprises the following steps of: identifying a trust domain in the edge computing node of the Internet of Things, defining a sensitive data source, and generating a sensitive data identifier set, and based on the sensitive data identifier set, planning minimum central processing unit time and maximum memory bandwidth for each isolation domain to form an isolation domain resource limit, and establishing initial security isolation configuration. According to the method, the identification mechanism based on the sensitive data identification set is introduced into the edge computing node of the Internet of Things, the definition of the trust domain can be realized, and the resource limit is constructed in combination with the minimum central processing unit time and the maximum memory bandwidth of each isolation domain, so that the basic distribution mode of computing resources is restrained, and the computing efficiency is improved. And resources are prevented from being occupied by high-frequency low-priority tasks. After a data entry is positioned on a system level and stain marks are applied according to sensitive attributes, sensitive data images have identifiable features on a memory level.

The invention relates to the technical field of new energy pipelines, in particular to an anti-cracking hydrogen-doped pipeline system and a performance degradation monitoring method thereof. The hydrogen-doped pipeline system comprises a pipeline assembly, a supporting assembly, an operation assembly, a power assembly, a storage battery pack, a system control cabinet and a power generation assembly, the phenomena of hydrogen permeation and gas stratification are effectively inhibited through innovative pipeline structure design and material optimization in the system level, the hydrogen embrittlement risk is reduced from the source, and the energy consumption is reduced. According to the method, the hydrogen atom diffusion tracking and crack initiation prediction are combined, and a multi-scale collaborative monitoring system is constructed, so that the whole-process monitoring from hydrogen atom diffusion tracking to crack initiation prediction is realized, and particularly, a quantum dot marking technology is combined with a traditional sensing network, so that the service life of the pipeline can be effectively prolonged, and the reliability and the maintenance efficiency of safety monitoring are improved.

The invention relates to the technical field of construction management, and discloses a railway temporary facility construction progress optimization method and system based on artificial intelligence. According to the invention, through heterogeneous data fusion and conflict graph modeling, accurate quantification of construction contradictions is realized; a multi-agent auction mechanism is combined with a virtual credit system, so that the fairness and efficiency of resource allocation are improved; the self-healing mechanism of the spiking neural network and quantum genetic optimization break through the limitation of the traditional algorithm from local and global levels respectively. In the system level, an edge computing node and a distributed consensus engine guarantee real-time decision reliability, a dynamic digital sand table realizes visual prediction of risks, and a self-organizing control tower guarantees standard compliance. Practical verification shows that the number of times of resource conflicts is reduced, the early warning accuracy of major risks is higher during emergency response, and the construction efficiency and safety are remarkably improved.

Various systems and methods are presented regarding monitoring and controlling operation of a process. A visual representation of the process can be created based on a supermodel comprising models (representing one or more devices) and nodes (representing respective device variables and constraints). Further, the process can be represented by levels, wherein devices at each level can be self-aware and have onboard artificial intelligence, such that a device at any level can auto-configure itself in accordance with a requirement placed upon it. Field-level devices (IFLDs) can be smart devices which auto-configure based upon a requirement from a higher-level device. Accordingly, system awareness can be incorporated across all levels of the process enabling overall and device-specific optimization of the process. IFLDs can auto-configure to collect and transmit data in accordance with an instruction from a higher-level device, leading to efficient data collection, reduced data bandwidth / processing, and expedited system optimization.

Aspects of the present disclosure are directed to a two-dimensional (2D) user interface (UI) content overlay for an artificial reality (XR) environment. In some implementations, the content overlay can be a default home screen when a user launches into an immersive XR application that helps the user navigate and control immersive destinations, and complete relevant and frequent actions related to the XR application. In some implementations, the content overlay can serve dynamic content based on the user's context to help them, e.g., A) find and play with other users, B) learn relevant actions that can be taken, C) quickly travel to a destination, D) see relevant content, etc. The content overlay can provide these functions 2D cards in the XR environment, which can be specific to the executing XR application, or can be system-level for multiple providing content XR applications.

The invention provides a voice replication and low-delay streaming voice synthesis method and system based on an ultra-short sample, relates to the technical field of artificial intelligence, and is suitable for intelligent interaction, outbound service and multi-mode communication scenes. Deep personalized customization of intelligent voice interaction and real-time generation of ultra-low delay are realized, and bidirectional streaming interaction of a system level is supported, so that the fluency and response speed of dialogues are improved; an ultra-short sample sound duplicating module specially designed for processing ultra-short audio samples and a speech synthesis engine with ultra-low delay and bidirectional streaming output capability are integrated, and the capabilities of the ultra-short sample sound duplicating module and the speech synthesis engine are applied to a real-time and interactive intelligent speech interaction process to form a complete and efficient solution. The industrial pain point is directly solved in a targeted manner, and the method has important commercial application value.

The invention provides an energy storage battery state simulation and prediction system and method based on digital twinning, and aims to solve the problems that an existing energy storage system is inaccurate in state modeling, insufficient in sensing granularity, unexplained in prediction, lagged in response and the like. According to the method, an equivalent thermal model, an electrochemical model and an aging mechanism model are adopted for modeling a battery, and a self-adaptive gating parameter fusion mechanism and data characteristics are introduced, so that cross-scale state mapping and characteristic reconstruction from a battery cell to a system level are realized; generating a baseline trajectory by using an electric-thermal-aging coupling relationship, realizing multi-time-domain closed-loop prediction by combining a data-driven residual correction mechanism, and identifying potential faults and risks in advance through a multi-time-domain anomaly judgment system; and constructing a three-dimensional topology model to dynamically display the state parameters, generating a graded alarm signal and providing operation suggestions. The system constructed by the invention has high precision, interpretability and prediction and visualization capabilities, and can significantly improve the intelligent operation and maintenance level of the energy storage battery system.

The invention relates to the technical field of chip testing, and discloses a clock reset system-level verification method and device based on general verification methodology, and the method comprises the steps: firstly analyzing a clock topological relation defined in a system control unit architecture document, and generating a clock reset information table; on the basis of the information table, an executable verification environment component and a virtual interface component are automatically generated through a script, the verification environment component comprises an excitation forwarding and processing component and can generate clock and reset signals matched with framework definition, and the virtual interface component can generate actual clock and reset waveforms; and finally, constructing a verification platform based on the components, calling the excitation sequence in the parameterized test sequence library, and carrying out system-level clock reset verification on the digital chip. And meanwhile, the parameterized test sequence library supports flexible adjustment of a verification scene and ensures that a test case fits a clock frequency defined by an architecture document, and a reset mechanism can improve the efficiency, accuracy and reliability of digital chip clock reset system-level verification.

The embodiment of the invention provides a multi-system login method and device, a storage medium and a program product, and relates to the technical field of security authentication. The method comprises the steps of receiving a token sent by a server in response to successful passing of identity verification of a digital portal, displaying system entrances of a plurality of systems associated with the digital portal, and responding to a touch operation for a system entrance of a target system in the system entrances of the plurality of systems, determining a system verification level of the target system according to the permission identifier, if the system verification level of the target system is a first system level, logging in a preset page of the target system, and if the system verification level of the target system is a second system level, logging in a preset page of the target system; if yes, first system verification information input by the user based on verification prompt information corresponding to the second system level is verified, and if verification succeeds, a preset page of the target system is logged in. According to the method provided by the embodiment of the invention, the convenience and security of multi-system login are improved.

The invention provides a new energy equipment system maintenance decision-making method and system based on a fault coupling model, and relates to the technical field of equipment predictive maintenance, and the method comprises the steps: collecting system structure information and system fault data of a complex new energy equipment system, and carrying out the maintenance decision-making of the complex new energy equipment system according to the system structure information and the system fault data; respectively constructing a function model view, a system level fault model view and each flow fault model view; constructing a fault coupling model corresponding to the complex new energy equipment system; probability estimation is carried out on the system fault data according to a kernel density estimation method, the fault probability of each fault node in the complex new energy equipment system is obtained, and key fault nodes in all the fault nodes and key node information corresponding to the key fault nodes are obtained; and mapping the key node information into a risk level corresponding to the key node information, and obtaining an optimal maintenance decision based on a decision tree analysis method and a plurality of maintenance decisions. According to the method, multi-dimensional analysis of a complex system can be realized, and fault modes of different components and subsystems can be identified more clearly, so that the fault detection and diagnosis efficiency is improved.

The invention discloses a multi-unmanned-aerial-vehicle cooperative hoisting method and system, and the method comprises the steps: firstly, enabling each unmanned aerial vehicle to operate an extended state observer, regarding internal and external disturbances (such as airflow, unknown load parameters, internal coupling force and the like) as a lumped disturbance, and carrying out the real-time estimation; and then, through a communication network operation consensus algorithm between the unmanned aerial vehicles, fusing the local disturbance estimation values of the unmanned aerial vehicles into a global consistent system-level disturbance estimation. And finally, each unmanned aerial vehicle actively counteracts the influence of disturbance by using the accurate disturbance estimation value in a feedforward compensation controller, and accurately tracks the respective target trajectory in combination with feedback control. According to the method, active observation of disturbance, distributed fusion and feed-forward compensation are combined, dependence on an accurate system dynamic model is eliminated, and trajectory tracking accuracy, stability and stability of the system in a complex environment are remarkably improved.

The invention discloses a cable-stayed bridge cross brace optimization system based on system reliability and an intelligent algorithm, which relates to the technical field of bridge engineering, and comprises a multi-source parameter input module used for collecting and processing data such as a main beam section, stay cable parameters, a strain sequence, a vibration sequence, a satellite cloud picture, a traffic monitoring flow, laser point cloud and the like; and outputting the structured design data, the monitoring data flow, the dynamic load spectrum, the probability distribution model and the construction error correction parameters. According to the invention, all-dimensional data such as design, monitoring, load, materials and construction errors are integrated through the multi-source parameter input module, and multi-level and dynamic reliability evaluation is carried out on the component, the subsystem and the system level by using the system reliability analysis module, so that the component failure probability, the system reliability index time sequence and the like can be output; therefore, the real reliability level of the cross brace of the cable-stayed bridge in the whole life cycle is reflected more accurately, and the one-sidedness of a traditional method is avoided.

The invention relates to a four-body coupling vehicle base vibration noise prediction optimization method and system, and relates to the technical field of rail transit operation and maintenance optimization, and the method comprises the steps: obtaining a vehicle base multi-physics field correlation monitoring data set; carrying out system level assembly in a multi-physics field co-simulation environment to generate a coupled system state parameter matrix; generating sound ray bending trajectory data; loading the vehicle operation condition time sequence data to a wave equation solver, and outputting vibration noise propagation characteristic field distribution data; building a network topology structure to form a prediction model, and executing a back propagation algorithm to train network parameters to a convergence state; designing a vehicle scheduling scheme chromosome coding structure to generate a candidate solution population; and performing selection, crossover and variation genetic operations on the primary solution set, and outputting a vehicle base operation and maintenance scheduling real-time optimization scheme. The method has the beneficial effects that the precision and generalization ability of vibration noise prediction in the future time period are greatly improved, and the fundamental transformation from passive vibration isolation treatment to active prediction optimization is realized.

A system and method for comprehensive data loss prevention and compliance management designed to identify and prevent cybersecurity attacks on modern, highly-interconnected networks, to identify attacks before data loss occurs, using a combination of human level, device level, system level, and organizational level monitoring and protection.

Various embodiments include techniques for managing cache memory in a computing system. The disclosed techniques include a cache policy manager that monitors activity of various components that access a common cache memory. The cache policy manager establishes cache rules that determine what data remains stored in cache memory and what data is removed from cache memory in order to make room for new data. As the activities of these components change over time, cache rules that work well for a previous activity profile may no longer work well for the current activity profile. Therefore, the cache policy manager dynamically modifies the cache rules as the activity profile changes in order to select cache rules at any given time that work well with the current activity profile. These techniques are advantageous over conventional approaches that employ static cache rules that work well only for specific activity profiles.

The invention provides a system efficiency evaluation method based on OODA ring closing, and the method comprises the steps: dividing an efficiency index system into three dimensions: a task, an OODA ring flow, and a killing chain, and constructing the overall dimension of the efficiency index system; establishing a multi-layer efficiency index system of task-OODA ring process-killing chain sub-chain-efficiency index by analyzing the OODA ring process and killing chain closing; and acquiring expert comprehensive weights of each node in the task layer, the OODA ring flow layer and the killing chain layer and each efficiency index in the efficiency index layer, and performing layer-by-layer upward recursion by adopting a weighted summation method according to the expert score and the expert comprehensive weight of each node or index in each layer, thereby determining a comprehensive efficiency value of the system, and completing efficiency evaluation of the cross-domain collaborative system. According to the method, the system efficiency index is constructed based on OODA ring closing, the richness and integrity of task requirements are ensured, and meanwhile, the finally obtained efficiency index can more comprehensively and effectively represent the capability of the system on the system level from the perspective of OODA ring closing.

The invention relates to the technical field of calculation, and discloses a slag metal recovery process optimization method based on digital twinning, which comprises a data acquisition module, a data driving model construction module, a digital twinning construction module, a synchronous operation module, an online calibration module, a simulation optimization module, an online operation module and a model self-learning module. According to the method, through prospective simulation optimization, the recovery rate of each batch of materials is directly pushed to the theoretical limit; cost reduction and benefit increase are realized from the system level through a hybrid model and multi-objective optimization; through digital twin and automatic execution, process standardization and intelligent control are realized, and the stability and consistency of product quality are significantly improved. By adjusting model parameters and process constraints, smelteries with different scales and different raw material sources can be rapidly adapted, universality is high, standardization and productization are easy, and the method has high commercial expansion potential and huge market space.

The invention relates to the technical field of supply chain risk management, and particularly discloses a comprehensive risk early warning method and system fusing a supply chain graph and machine learning, and the method comprises the steps: collecting supply chain multi-modal heterogeneous data and prior risk knowledge, dynamically constructing and updating a sequential supply chain knowledge graph, and carrying out the early warning of the risk of a supply chain. Performing node-level, edge-level and system-level multi-level anomaly detection, generating an anomaly score and a network vulnerability index, when the anomaly score exceeds a dynamic threshold value, deducing risk propagation and node sweep probability by improving an SEIR model, calculating a dynamic fusion weight in combination with data source authority and the like, and performing weighted fusion on multi-dimensional indexes to generate a comprehensive risk score, and dynamically adjusting a threshold value to generate graded early warning, an optimization model, a map and a priori knowledge base. According to the method, the defects of lag, isolation and stiffness of a traditional method can be overcome, risk perception in advance, comprehensive coverage and accurate early warning are achieved, the method has the self-evolution capacity, the method adapts to dynamic changes of a supply chain, and powerful support is provided for safety and stability of an industrial chain.

The invention discloses a laser wireless energy transfer dynamic MPPT control method based on an RL-PSO hybrid algorithm and a reconfigurable array system, and aims to solve the problems of power oscillation and mismatch loss caused by light spot offset of a photovoltaic array under laser dynamic irradiation. According to the method, reinforcement learning (RL) and particle swarm optimization (PSO) are fused, dynamic parameters such as the light spot moving speed and irradiance are sensed in real time through the RL, the inertia weight and learning factors of the PSO are adjusted in a self-adaptive mode, and collaborative optimization of global search and local tracking is achieved. According to the system level, a reconfigurable photovoltaic array is designed, and according to data of a light spot position sensor, battery series-parallel topology is dynamically adjusted through a switch matrix, and current matching is optimized. According to the method, double-loop control is adopted, wherein an outer-layer RL decision module dynamically switches PSO working modes according to the environment, and an inner-layer PSO module optimizes the working voltage. The system efficiency in a dynamic scene can be improved by 15%-30%, mismatch loss is reduced by 40% or above, and the method is suitable for unsteady-state energy supply scenes such as unmanned aerial vehicles and mobile robots.

The invention discloses a hierarchical database system tuning method and system based on a large language model, and relates to the technical field of database system tuning. The method comprises the steps that expert knowledge in the database field is processed based on a large language model, a cue word integration algorithm is used for converting the knowledge into structured knowledge capable of being used by a computer so as to optimize a search space, and a coarse granularity-fine granularity Bayesian optimization algorithm is used for determining optimal configuration, so that configuration optimization of a system level is completed; a parameter screening algorithm, a space pruning algorithm and a context Bayesian optimization algorithm are utilized, and an optimal execution plan is generated by selecting an appropriate optimization prompt for each query in the complete workload, so that configuration optimization of query levels is completed. According to the method, the search space is optimized by utilizing expert knowledge, the optimized space is explored by utilizing a Bayesian optimization algorithm, multi-level optimization is performed on the database system from a system configuration level and a query level, and the optimization efficiency and the system performance are improved.

The invention relates to an intelligent pushing monitoring system for a steel grid beam, and the system is characterized in that a data processing module constructs a processing system covering a data collection-washing-storage full link, and provides a high-confidence data base for an intelligent deviation rectification module; through synchronous monitoring of steel grid beam pushing distance, attitude parameters, guide beam front end elevation, temporary pier settlement and transverse and longitudinal corners by the geometric monitoring module, and through combination of data acquisition of stress deformation of key components by the structure monitoring module, omnibearing real-time monitoring of spatial geometric parameters and mechanical states in a construction process is realized. The intelligent deviation rectifying module receives the preprocessed geometric monitoring parameters in real time, automatically generates a jack stroke adjusting instruction and directly issues and executes the jack stroke adjusting instruction, one-key adjustment is achieved, the defects of operation delay and deviation rectifying discontinuity caused by manual calculation and segmented adjustment are overcome, the pushing synchronism control precision is effectively improved, and the working efficiency is improved. The stress balance of the beam body in the pushing process is guaranteed from the system level, and the safety risk is reduced.

The invention discloses a heat dissipation control method, device and system based on an array chip and a medium. The method comprises the steps that the basic valve opening degree is determined based on the temperature value of any partition and a valve opening degree mapping table; determining a target valve opening degree of each electric valve based on the basic valve opening degree and a spray coverage overlapping model, and sending the target valve opening degree to each electric valve; calculating a target total flow based on all target valve opening degrees, adjusting the rotating speed of a liquid supply pump through a frequency converter, and enabling a measured value of a flow sensor to be the target total flow; determining a target pressure value based on the saturation temperature of the cooling liquid, and adjusting the power of the vacuum pump or the three-way vacuum electromagnetic valve to enable the pressure value of the evaporation cavity to be the target pressure value. According to the method, three-layer linkage logic of partition precise temperature control, system flow coordination and global pressure setting is established, scattered devices are integrated into a whole, overall adjustment of the system level is achieved during heat dissipation control, instructions do not need to be sent frequently to control each valve, and the electric energy consumption of the system is reduced.

The embodiment of the invention provides a network simulation evaluation method and device, a storage medium and computer equipment, and the method comprises the steps: obtaining a simulation model obtained for network system simulation, and the simulation model comprises different simulation levels; determining to select a simulation index from each simulation hierarchy; determining a simulation evaluation value of each simulation index according to the index attribute characteristic of each simulation index; and performing index aggregation on the simulation evaluation value of each simulation index to obtain a total simulation evaluation value. Compared with the prior art that simulation evaluation is carried out from the similarity of the whole system level, and the evaluation accuracy is poor, different hierarchies can be simulated during simulation, different simulation indexes are set for different hierarchies, the simulation evaluation values of different simulation indexes are comprehensively evaluated, the final total simulation evaluation value is obtained, and the evaluation accuracy is improved. And evaluation accuracy is improved.

The invention provides a computer cross-scale integrated analysis method based on a multi-physics field coupling mechanism, and the method comprises the steps: constructing a multi-level finite element model from a system level to a micro-area level, and integrating thermal-mechanical-electric coupling modeling; therefore, the technical problems that in a traditional method, multi-physics field coupling lacks, local area modeling is not fine, and electrical performance evolution is not introduced are solved, high-precision simulation of a typical device failure mechanism is achieved, and the method is suitable for device packaging reliability evaluation in special electronic systems such as military and high-reliability civil use and the like. The method can be widely popularized in microelectronic structures such as a power supply module, a processor and a memory, and can improve the evaluation precision of the high-frequency signal transmission performance of complex electronic equipment, thereby improving the precision of predicting the service life.

The invention discloses an architecture system and method for large-scale task allocation, and belongs to the field of spaceflight measurement and control, and the system comprises a large-scale hardware device which is used for deploying processing units with different performances and various heterogeneous physical communication link resources; the hardware resource dynamic modeling and visualization framework is used for realizing a dynamic modeling system based on multi-source heterogeneous hardware topology perception; the resource topology awareness database captures and synchronizes system deployment changes by maintaining the dynamic topological relation of hardware resources, presents the life cycle state of the hardware resources, and supports system-level resource situation visualization and decision making; a versioning management engine of component arrangement is applied, a multi-version executable unit is packaged based on an atomization component, and an application instance is completed through a combined modeling component; and deploying a scheduling decision system for deploying a scheduling decision. The method can adapt to task deployment requirements of a current large-scale spaceflight measurement and control system.

The invention discloses a branch prediction unit power consumption management method and device, and the method comprises the steps: setting a multi-stage branch predictor and a low-power-consumption control logic coupled with the multi-stage branch predictor in a processor core micro-architecture, and enabling the multi-stage branch predictor to comprise three stages of predictors which are sequentially accessed in an assembly line, the hardware resource consumption and the prediction accuracy of each predictor are gradually increased from front to back; monitoring the prediction condition of each predictor in real time through low-power-consumption control logic, and closing all predictors behind the first predictor meeting the prediction requirement within a first preset time according to a front-to-back sequence; branch jump addresses are divided into high-order address fields and low-order address fields by BTBs in the multiple levels of predictors except the first-level predictor to be stored in different storage blocks, and label registers used for taking the high-order addresses as label indexes are arranged. The power consumption unit of each predictor can be finely controlled, the energy consumption is greatly reduced, system-level cooperation is not needed, and the universality is high.