296 results about "Quantum annealing" patented technology

The invention discloses an intelligent operation and maintenance system and method for a high-voltage switch cabinet based on digital twinning, relates to the technical field of intelligent power grids, and solves the problems of nonlinear effect modeling distortion, cross-spatio-temporal scale coupling deviation accumulation, time sequence real-time contradiction and insufficient extreme working condition adaptation in the prior art. Hysteresis parameters of the ferromagnetic material are dynamically calibrated through a quantum annealing optimization algorithm, and electromagnetic-thermal field strong coupling synchronous calculation is realized by combining multi-scale mesh generation and an implicit thermal field iterative algorithm; constructing an incremental transfer learning framework to fuse aging features and real-time data, and correcting boundary conditions of the model by adopting four-dimensional variational assimilation; establishing a hybrid verification platform to dynamically feed back extreme working condition parameters, and generating a credible operation and maintenance instruction in combination with a block chain; according to the method, the contact temperature rise prediction precision, the residual life evaluation reliability and the circuit breaker transient response real-time performance are remarkably improved, and active immune type intelligent operation and maintenance of the high-voltage switch cabinet under the extreme working condition are achieved.

The invention discloses an enterprise financial document unified management system and method based on distributed storage, and relates to the technical field of enterprise financial document management, and the system comprises a metadata processing module which is connected to a distributed storage node cluster and is used for converting the metadata attribute of an enterprise financial document into a high-dimensional sparse vector. According to the enterprise financial file unified management system and method based on distributed storage, efficient management of multi-dimensional financial file data is realized through dynamic metadata topology reconstruction and a dual-channel index optimization mechanism. A vectorization metadata packaging technology is adopted to convert traditional discrete attributes into high-dimensional association vectors, and quantum annealing path optimization and a three-section aggregation verification strategy are combined, so that the response time of multi-condition combination query is shortened compared with that of a traditional scheme, and the network bandwidth consumption is reduced.

The invention relates to the technical field of fault diagnosis of power electronic equipment, and particularly discloses a fault analysis and diagnosis method and system for a mining high-voltage frequency converter, and the method comprises the steps: collecting the skin effect depth and parasitic parameter drift distance in real time under a high-frequency working condition through a multi-physics field sensor array; constructing three-dimensional current density distribution of the conductor based on a non-Euclidean space mesh generation technology, and dynamically correcting the equivalent resistivity of the conductor in combination with a metamaterial database to calculate a dynamic eddy current loss characteristic value; optimizing and solving the parasitic parameter time-varying evolution equation by using a quantum annealing algorithm to obtain a voltage peak sensitivity characteristic value; inputting the characteristic value into a pre-trained deep residual shrinkage network diagnosis model for multi-modal fusion analysis, and outputting a fault risk assessment result; according to the method, the problem of parasitic parameter time-varying characteristic modeling in the vibration environment is innovatively solved, and early warning of hidden faults is achieved.

The invention relates to the technical field of glacier information processing, in particular to a glacier surface moraine identification method based on multi-source remote sensing parameters, which comprises the following steps: performing radiation-atmosphere joint correction and geometric-reprojection on optical, thermal infrared, synthetic aperture radar and digital elevation data to generate physical consistency data; deducing the surface temperature under the driving of short-wave albedo, downward radiation and turbulent flux by using a cross-modal visual conversion network and a symmetric neural operator, and obtaining a thickness-existence degree posterior through a normalized flow; an uncertainty field is constructed according to information entropy and variance, a representative pixel unmanned aerial vehicle radar sampling and value measurement backflow fine tuning model is selected by means of quantum annealing, and then a spatial continuous thickness field is output through anisotropic diagram diffusion. According to the method, the future thickness is integrated according to mass conservation in combination with day-by-day weather forecast, and the future thickness is coupled with the existence degree index to form a burst risk grid, so that high-precision and iterable moraine dam early warning is realized.

The invention relates to the technical field of heterogeneous unmanned aerial vehicle cluster task planning and intelligent scheduling, and discloses a heterogeneous unmanned aerial vehicle cluster intelligent task planning method, which is characterized by comprising task and unmanned aerial vehicle information initialization, task preliminary allocation based on a quantum annealing algorithm, real-time adjustment based on reinforcement learning, and a communication and cooperation mechanism. Executing and monitoring a task; the super-strong parallel computing capability and the quantum tunneling effect of a quantum annealing algorithm are utilized to quickly process a large-scale task allocation problem and find an approximate range of a globally optimal solution, and then an unmanned aerial vehicle cluster continuously learns and adjusts a task execution strategy according to real-time environment feedback in a task execution process by means of reinforcement learning, so that the task execution efficiency is improved. Therefore, the method adapts to dynamically changing scenes.

The invention relates to the technical field of coal mine geological safety monitoring and disaster early warning, in particular to a coal mine disaster early warning method and system based on a geological model and a storage medium, and the method comprises the steps: firstly obtaining high fractal fracture information and monitoring data such as gas, hydrology and stress through chaotic wave excitation and fractal analysis; and a three-dimensional fractal grid with multi-level fracture representation is constructed. And discretizing a mechanical field and a fluid field by using a fractal partial differential equation operator, and globally solving a large-scale nonlinear equation through a quantum annealing mode or a quantum and classical combined annealing mode to form a quantum fractal coupling simulation result. And finally, integrating the grid units and monitoring data in the fuzzy hypergraph structure, carrying out fuzzy membership analysis on a multi-disaster element coupling sign, and outputting early warning information once the risk is judged to reach a threshold value. According to the invention, the precision and timeliness of coal mine geological disaster monitoring can be obviously improved.

The invention relates to the field of quantum storage and cloud computing fusion, and discloses a big data distributed storage method and system based on cloud computing, and the method comprises the steps: revealing a data intrinsic geometric structure through manifold embedding, and generating a manifold coordinate; secondly, constructing an Ising physical model based on the geometric structure, and solving a model ground state through quantum annealing to determine an optimal data dynamic fragmentation scheme; thirdly, homomorphic encryption is carried out on the data fragments, a Lie group optimization algorithm is used for solving on a special orthogonal group manifold, and a resource mapping matrix considering safety and efficiency is generated; and finally, instructing a fragmentation scheme, constructing a transmission tensor load fusing manifold information, data content and security mapping for each fragment, and optimizing transmission configuration. According to the method, multiple frontier technologies are fused, dynamic fragmentation of data structure sensing and safe and efficient resource mapping are achieved, and the overall performance and safety of the system are remarkably improved.

The invention relates to the technical field of intelligent forestry management, and particularly discloses a forest farm intelligent comprehensive management and control system, which comprises a space-air-ground collaborative sensing module, a fire danger dynamic modeling module and an intelligent decision execution module. The method comprises the following steps: acquiring temperature field data through multispectral satellite remote sensing, constructing a high-precision terrain model through a laser radar, extracting vegetation features through hyperspectral imaging, extracting a thermal anomaly structure by adopting topological continuous coherence analysis and a Morse-Smal complex method, and establishing an adaptive fire danger model in combination with a quantum annealing optimizer. The system can generate a fire danger thermodynamic diagram in real time and divide risk levels, and drives the unmanned aerial vehicle to perform priority patrol and obstacle avoidance path planning. The problems of difficulty in pseudo hot area identification, low fire danger prediction precision, non-intelligent path planning and the like in a complex environment are solved, and the intelligent level of forest farm fire prevention and control is improved.

An autonomous job scheduling system for distributed ledger technology is disclosed, leveraging photonic quantum computing and generative artificial intelligence (AI). The system collects transactional and operational data from various nodes within a distributed ledger network, focusing on transaction types, sizes, and priorities. This data is filtered for urgency and resource intensity, analyzed against real-time network conditions and business rules to ascertain job execution parameters. A photonic quantum computing system processes these parameters to optimize job scheduling strategies, including quantum annealing and simulations. The AI engine generates dynamic smart contracts deployed to distributed ledgers by a job orchestration engine. The system monitors job execution, gathering performance data and providing feedback for real-time adjustments. The job orchestration engine updates job scheduling in response to these adjustments, enhancing the efficiency and responsiveness of the blockchain network.

The invention discloses a dynamic optimization system and method for a compound essential oil ratio, and aims to solve the problems that the traditional technology depends on artificial experience, the optimization efficiency is low and the quality tracing is insufficient. According to the method, raw material molecular fingerprint data are collected in real time through a high-precision spectrum sensor array and a micro-fluidic chip, a production environment field domain is constructed in combination with a computational fluid mechanics model, a formula optimization problem is encoded into an Ising model by using a quantum annealing algorithm, and global search is realized through a quantum tunneling effect. The system integrates digital twin bodies, covers molecular dynamics simulation, phase equilibrium prediction and olfactory receptor activation probability simulation, and accurately maps a physical system state; a multi-modal data fusion network and a reinforcement learning framework are adopted to dynamically adjust formula parameters, and an NSGA-III algorithm is combined to optimize efficacy collaboration degree, cost and stability indexes. And the execution control module realizes nanoscale flow regulation through the piezoelectric micro-injection valve array, and realizes non-tampering evidence storage of formula parameters and quality data based on a block chain technology. Dynamic closed-loop optimization of the compound essential oil ratio is achieved, the production efficiency, the product consistency and the supply chain transparency are remarkably improved, and the method is suitable for the field of intelligent manufacturing of high-end essential oil.

The invention discloses a travel itinerary navigation method, device and equipment and a storage medium, and the method comprises the steps: collecting user biological rhythm parameters, cultural heritage activation indexes and multi-dimensional data of microclimate evolution prediction in real time, carrying out the feature weight dynamic distribution of the fusion of the multi-dimensional data through a quantum annealing algorithm, and constructing a four-dimensional decision space; based on a four-dimensional decision space, the tourist density, the cultural relic protection critical value and the commercial facility bearing capacity are mapped to an Isin spin system, a spin state evolution path is optimized in combination with a quantum walking algorithm, and an optimal solution set containing multiple candidate paths is generated; bio-rhythm parameters, emotional changes and scenic spot environment data of tourists are collected in real time, a quantum annealing algorithm and a quantum walking algorithm are adopted to perform deep fusion and optimization on multi-dimensional data, and tourism recommendation routes, information pushing frequency and feedback intensity are dynamically adjusted according to instant demands of the tourists. And the tourism experience of tourists is optimized.

The invention discloses a livestock feed proportioning method based on livestock growth, and relates to the technical field of feed proportioning, and the livestock feed proportioning method comprises the following steps: collecting the body temperature and motion track data and environmental temperature and humidity data of livestock individuals, monitoring the concentration of amino acids in digestive tracts, preprocessing the collected data, and generating a standardized biological data set; analyzing the compensation amount of vitamins and trace elements according to the metabolic fingerprint spectrum, and constructing a multi-objective optimization function in combination with an individual growth curve; solving the multi-objective optimization function through a quantum annealing algorithm, and matching local raw material inventory data of block chain evidence storage to generate an optimization strategy; and executing the optimization strategy and detecting the mixing uniformity, and when the mixing uniformity index exceeds a preset mixing uniformity threshold, starting the compensation device to adjust the optimization strategy. According to the invention, through a cooperation mechanism of dynamic nutritional requirement identification and multi-constraint optimization, the precise regulation and control capability of livestock breeding is significantly improved.

The invention relates to an intelligent path planning technology, and discloses an industrial robot multi-target path planning method based on a quantum annealing algorithm, and the method comprises the steps: obtaining the environment information of an industrial site; path planning is carried out based on the environment information, and a quantum Hamiltonian is constructed according to an optimization target of a path planning problem; encoding each optional path in the path planning space by adopting quantum bits, and initializing the quantum bits to be in a uniform superposition state; quantum system energy is gradually reduced through a quantum annealing algorithm, and an optimal solution of quantum bits is searched; after quantum annealing is finished, a final path is extracted; carrying out smooth optimization processing on the extracted final path; and generating a motion instruction of the industrial robot according to the optimized final path. The invention further discloses a control system, the industrial robot and a computer readable storage medium. According to the method, the global optimality of path planning is improved while multi-target path planning is efficiently carried out on the industrial robot.

The invention discloses a beam control optimization method and system for a millimeter wave network, and relates to the technical field of beam control optimization, and the method comprises the following steps: employing a DTW algorithm dynamic time warping algorithm to match multi-hop path time delay characteristics between a base station and user equipment, and screening an optimal reflection path sequence; obtaining path topology, constructing a secondary unconstrained binary optimization model, and obtaining discrete phase distribution through quantum annealing solution; calculating a quantization error of the discrete phase and performing frequency domain compensation by adopting an interpolation algorithm to obtain a phase compensation vector; loading the compensation vector to programmable metasurface hardware to form a reflection link, and constructing a phase error covariance matrix; correcting the weight parameter increment of the optimization model based on the matrix; and finally, a target function is constructed in combination with the correction weight and the compensation vector, channel capacity optimization is realized, the problems of difficult multi-hop path selection, insufficient phase error compensation and high beam optimization complexity in millimeter wave communication are solved, and the channel capacity and the communication quality are improved.

The invention relates to the field of new energy power systems, and discloses a multi-dimensional index-based new energy base network construction capability evaluation method, which comprises the following steps of: constructing five-dimensional dynamic tensor fusion multi-source data, optimizing low-rank decomposition by combining a quantum annealing algorithm, and embedding a power grid topology hole feature constraint; adopting a federated learning framework to realize distributed parameter optimization, and introducing a quantum noise gradient to eliminate resolution difference; based on a causal graph model and back door adjustment stripping hybrid bias, generating an unbiased evaluation index; and dynamically correcting quantum annealing parameters through a closed-loop feedback mechanism. According to the method, multi-dimensional data fusion, topological vulnerability perception, privacy security calculation, unbiased evaluation and adaptive optimization are realized, and the precision and reliability of new energy base networking capability evaluation are improved.

The invention relates to the field of power transmission line channel risk hidden danger monitoring, and provides a data fusion power transmission line channel risk hidden danger monitoring method and system, and the method comprises the steps: collecting the multi-modal sensing data of a power transmission line channel, and generating a multi-modal data flow of a unified time-space coordinate; constructing a three-dimensional space point cloud through a phase unwrapping and stereo matching fusion algorithm, and fusing multi-modal data to generate a space probability tensor; extracting risk semantic latent variables, constructing a Bayesian network and identifying potential risks; performing tensor product on the potential risk and the environmental data to generate a dynamic risk enhancement feature matrix, and constructing a nonlinear dynamic threshold curved surface through quantum annealing and Gaussian process regression; a mechanical equation is constructed, Gaussian kernel density estimation and numerical simulation are combined, the evolution trajectory of the risk in the space-time dimension is predicted, and a risk thermodynamic diagram and early warning information are generated; and generating a structured risk early warning report by adopting a natural language processing method. And the accuracy of power transmission line channel risk hidden danger monitoring is improved.

The invention discloses a hydrogen fuel cell ship power switching system, which relates to the technical field of hydrogen fuel cell ship hybrid power, and comprises a topological optimization module for optimizing a series-parallel topological structure of a fuel cell stack through a quantum annealing algorithm based on a multi-modal data set, the reconfigurable bipolar plate magnetic control switch is triggered to execute millisecond topology switching, and a topology state coding signal is synchronously generated; and the compensation module is used for inputting the topological state coding signal into the LSTM model, predicting a fault risk value, correcting the time step length of a leakage diffusion equation according to the real-time switching frequency of a magnetic control switch, and calculating a ship inclination angle correction term according to the corrected time step length. According to the invention, through an accurate fault prediction and compensation mechanism, operation interruption and maintenance cost caused by unpredictable faults are greatly reduced.

The invention discloses a motor equipment intelligent supervision method and system based on big data. The method comprises the following steps: collecting initial multi-dimensional data; extracting semantic features and spatial-temporal features in the initial multi-dimensional data by using a pre-trained BERT language model and an STGN spatial-temporal convolutional network, performing weighted fusion on the physical parameters, the semantic features and the spatial-temporal features through an attention mechanism, constructing a dynamic graph network based on multi-dimensional information feature representation, and performing time sequence modeling in combination with a TCN time convolutional network; and carrying out multi-objective optimization by using an NSGA-III algorithm, carrying out local search by using a quantum annealing algorithm, outputting an equipment operation state report, and carrying out motor equipment fault early warning according to the equipment operation state report. And the fault detection accuracy and the early warning response time are improved.

The invention discloses a cable operation environment optimization method based on cable trench collection information, and relates to the technical field of intelligent operation and maintenance of a power system, and the method comprises the steps: collecting an original parameter data set in a cable trench; a pulse current signal is collected at a cable grounding wire connection point, and a standard pulse discharge capacity sequence is generated through band-pass filtering and amplitude integral conversion; based on the original relative humidity measurement value and the environment temperature value, calculating a compensated relative humidity value through a multi-scale heat conduction-diffusion coupling equation; calculating a cable insulation risk index through a quantum annealing algorithm by using the compensated relative humidity value and the standard pulse discharge capacity sequence; the dynamic measurement deviation of the temperature and humidity sensor is subjected to double compensation of thermal radiation and molecular adsorption by constructing a multi-scale heat conduction-diffusion coupling equation, so that the reliability of environmental state sensing is greatly improved, and the operation safety, stability and intelligent operation and maintenance level of a power cable system are effectively enhanced.

The invention relates to the field of water environment pollution detection and analysis, in particular to a water environment pollution detection and analysis system and method based on the Internet of Things, and solves the problems of sampling redundancy, missing detection, high-dimensional data over-fitting, difficulty in traceability and early warning lag in traditional detection. The system comprises a monitoring acquisition module, a water quality anomaly detection module, a pollution tracing module, a pollution early warning module and a database. The monitoring acquisition module adopts chaotic adaptive sampling to adjust sensor frequency, and entropy optimization water quality data is obtained by combining permutation entropy and collaborative filtering denoising; the water quality anomaly detection comprises the following steps: standardizing data into high-dimensional point clouds, calculating a Betti number and a non-equilibrium state entropy, and fusing to generate a comprehensive pollution topological entropy index; according to pollution traceability, a dynamic graph and a diffusion kernel matrix are constructed by using GNN, and the optimal source intensity is solved through quantum annealing; the range is predicted through a diffusion equation for early warning, and information is integrated to generate a report to be pushed to users. The method corresponds to a module flow and provides support for pollution prevention and control.

The invention relates to the technical field of glass hot working control, and discloses a rapid prototyping control system and a rapid prototyping control method for tempered glass production. Comprising a thermal coupling module, a temperature control decision module, a flow field solving module, a photoelastic stress analysis module, a quantum annealing optimization module and a time domain synchronous control module. According to the system, a three-dimensional thermal-stress field is constructed on the basis of physical properties and thermal boundaries of glass, heating power is predicted through reinforcement learning, flow field simulation and stress image analysis are combined, control parameter self-adaptive adjustment is achieved through quantum annealing optimization, beats of all subsystems are coordinated through a synchronization module, and an efficient closed-loop control structure is formed. By introducing the quantum annealing optimization module, parameter adjustment in the control system is optimized, the technical effect of improving the precision of complex control decisions is achieved, and the optimization speed and the decision quality of the system are improved.

The invention discloses a deep learning-based mixed-shooting image correction method and system, and relates to the field of computer vision, and the method comprises the steps: carrying out the illumination normalization processing of a mixed-shooting image group, and constructing a Gaussian pyramid image group; inputting the Gaussian pyramid image group into a ResNet-34 network to extract a multi-scale feature map, and generating spatial position and channel statistical information; and converting the spatial position and the channel statistical information into a DNA sequence fragment through a DNA base mapping rule. Illumination normalization is achieved through Retinex decomposition and Gaussian filtering, the exposure difference of different devices is effectively eliminated, the input quality of follow-up processing is guaranteed, traditional feature description is converted into biological sequence comparison through DNA base coding, geometric parameters are optimized in combination with quantum annealing, and the accuracy of the method is improved. The matching precision of a low-texture area and the correction effect of a large-view-angle-difference scene are remarkably improved, and the sub-pixel-level matching precision is achieved based on weighted RANSAC and multi-scale pyramid fusion.

The invention discloses a spare power automatic switching device test switch simulation test system, and belongs to the technical field of power grid simulation test. Probability distribution generated through kernel density estimation covers a fault scene of a 99% confidence interval, and the characterization capability of an extreme scene is enhanced; the time delay precision of a switch action sequence is controlled timely and reliably by combining the global search capability of quantum annealing and the local fine tuning of the particle swarm algorithm, and equipment damage caused by time sequence dislocation is avoided by combining the UWB technology and the phase compensation algorithm; through an objective function weight coefficient and a closed-loop feedback mechanism, an optimization objective can be dynamically adjusted according to a fault type, through real-time comparison of a physical device and digital twinning and through logic criterion goodness of fit analysis, control logic consistency can be guaranteed, and through loop response time difference analysis and two-dimensional index quantification, verification precision can be guaranteed. And the strict requirements of the power system on correct logic and quick action of the spare power automatic switching device are met.

The invention discloses a pre-hospital emergency rescue command and dispatch optimization method and system, and relates to the technical field of intelligent medical first aid, and the method comprises the steps: collecting the position coordinates of a patient and a first aid resource state, carrying out the medical resource matching through combining with the injury condition level, and constructing a three-dimensional resource matrix; converting the three-dimensional resource matrix into discrete decision topology, and performing multi-state parallel evolution through a quantum annealing processor to generate an optimal resource allocation scheme; based on the optimal resource allocation scheme, path optimization is carried out in combination with real-time road conditions, sudden obstacles and environment changes, and a driving route optimization instruction is generated; and in the process of executing the driving route optimization instruction, monitoring the vehicle driving state deviation and the multi-vehicle collaborative collision risk in real time, triggering a multi-dimensional perception collaborative optimization mechanism, and generating a dynamic driving route. According to the method, the global optimal solution of the high-dimensional resource combination is realized through quantization mapping from the three-dimensional resource matrix to the discrete decision topology in combination with the polymorphic parallel evolution and energy level transition of the superconducting quantum bit group.

The invention provides a product production quality inspection management method and system based on quantum sensing, and relates to the technical field of industrial automation and quantum detection, and the method comprises the steps: collecting the quantum phase difference, spin relaxation time and energy density data of a product through a quantum sensing array; performing quantum state decoupling processing on the multi-dimensional field data, and extracting a quantum hue consistency coefficient and a structure anomaly index; constructing a multi-scale convolutional neural network to generate a three-dimensional quality feature tensor; dynamically optimizing a defect judgment threshold value based on a quantum annealing algorithm; and generating a visual thermodynamic diagram and triggering a process control instruction. The system comprises a quantum sensing module, an edge computing unit, a dynamic decision engine, a man-machine interaction terminal and a feedback control module, and closed-loop management is realized through multi-physics field synchronous acquisition and real-time control. According to the method, traditional detection resolution limitation is broken through by utilizing quantum sensing, micro defects are accurately identified, and complex working conditions are adapted through dynamic threshold optimization.

The invention relates to the field of intelligent security and path planning, and discloses a weapon escorting route dynamic optimization and risk early warning platform, which comprises a multi-modal data acquisition module, integrates traffic flow, geographic elevation and threat public opinion data through space-time alignment and tensor filling, and constructs a multi-channel space-time feature tensor; the dynamic risk assessment module is used for solving and generating a dynamic risk field based on an adversarial game model and Nash equilibrium, and quantifying a risk gradient under an attacker strategy; the quantum collaborative optimization module is used for generating quantum parameters through tensor hidden variable decomposition and solving a multi-target optimal route in combination with a quantum annealing algorithm; the hypergraph emergency response module is used for reconstructing a mutual exclusion redundant path and feeding back and updating a strategy space when a threshold exceeding risk is detected; and the strategy collaborative updating module fuses the risk gradient and the game utility to generate a joint reward signal, and drives the reinforcement learning model to dynamically optimize the decision strategy. The safety, efficiency and reliability of the escorting task are improved.

An optimal route can be determined for delivering an item through a logistics system where vehicles have multiple stops when traveling along routes. The optimal route is determined using a hybrid system employing a classical computing device and a quantum annealer. The classical device reduces the search space that allows the quantum annealer to determine a more optimal solution. In an aspect, a routing graph comprising nodes and edges is populated from routes of vehicles, where the nodes identify locations and the edges represent routing data. At least a portion of the nodes and edges is removed to form a refined routing graph. For an origin-destination input, the refined routing graph can be filtered according to a first set of routing constraints to form a reduced route search space. A quantum annealer is invoked according to an objective and a different second set of routing constraints.

The invention discloses a pressure adjusting method and system for a pressure air gun for drying, and relates to the technical field of pressure air gun adjusting.The method comprises the steps that pressure distribution data and drying effect parameters of the surface of a dried object are collected in real time; the drying effect parameters are input into a reinforcement learning and quantum annealing collaborative decision model, a dynamic pressure adjusting strategy is generated, and the dynamic pressure adjusting strategy comprises control parameters of a pressure value, a waveform frequency and a nozzle direction; an air gun is controlled to output a target pressure waveform according to the dynamic pressure adjusting strategy, the target pressure waveform is generated based on the 3D geometrical characteristics of the drying object, and the impedance of an internal flow channel of the air gun is adjusted in real time through a magnetorheological fluid strategy; in the drying process, three-section mode switching is triggered according to the real-time water content and surface roughness data, chaos frequency modulation pulse waves are output in the initial stage to peel off water, fractal diffusion waves are switched in the middle stage to achieve uniform coverage, and nanometer resonance waves are adopted in the final stage to complete drying.