356 results about "Quantum annealing" patented technology

The invention discloses an equipment intelligent safeguard system based on an offline large model, and relates to the field of equipment management and safeguard, and the system comprises a collection module which collects an equipment surface defect image and a time sequence signal through an unmanned plane, collects a text log, processes the defect image, the time sequence signal and the text log, and generates a multi-modal data set; the feature fusion module is used for performing feature extraction on the multi-modal data set and fusing the three features to generate a multi-modal feature; the updating module is used for extracting a fault triple from the semantic vector, constructing a Bayesian causal graph according to the fault triple, dynamically updating node confidence and generating a knowledge graph; the maintenance strategy module is used for matching the multi-modal features with a knowledge graph and positioning a fault root cause; and the lightweight model deployment module is used for pre-training a lightweight model in an edge server, accelerating parameter aggregation by using a quantum annealing algorithm, adjusting and optimizing a global large model according to the aggregated parameters, and generating a strategy in combination with reinforcement learning.

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 abnormity early warning method and system based on artificial intelligence, and relates to the technical field of abnormity early warning. The method comprises the following components: S1, brain wave data acquisition and preprocessing, S2, emotional state recognition and feature extraction, S3, specific business data acquisition and arrangement, S4, data fusion and abnormal feature construction, and S5, abnormal early warning decision and notification. According to the method, the brain wave emotion features and the specific business data are fused, abnormal feature construction is carried out by adopting a fusion algorithm of multi-scale feature matching and a support vector machine algorithm based on quantum annealing optimization, the abnormal feature rule can be mined more accurately through the comprehensive data processing and analysis mode, and the accuracy of abnormal feature extraction is improved. Therefore, in the real-time data monitoring and analysis process, the accuracy of abnormal early warning is improved, meanwhile, the system can dynamically adjust the monitoring frequency according to the fluctuation characteristics of the data, the timeliness of early warning information is ensured, and related personnel can take countermeasures in time.

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 discloses a road bridge design method and system based on a BIM real scene model, and relates to the technical field of constructional engineering information, and the method comprises the steps: a BIM model carries out multi-objective optimization through a quantum annealing algorithm in combination with a bridge-crossing topology library, synchronously optimizes topological connectivity, structural stress and energy consumption indexes, generates a parameterized component, and feeds back the parameterized component to the BIM model; the model of the BIM model is loaded and optimized based on augmented reality equipment, the model is superposed to a reality scene through space anchoring, and the BIM model is corrected in combination with a conflict monitoring engine and a knowledge graph; the corrected BIM model is deployed to edge computer equipment, a dynamic deviation thermodynamic diagram is generated by scanning a construction surface, a lofting robot is driven to execute coordinate dotting, and meanwhile construction error data is transmitted back; according to the method, the problem that a global optimal solution is difficult to find when a traditional optimization algorithm is used for processing a complex multi-target problem is solved by utilizing an advanced quantum computing technology, and the economical efficiency and the structural safety of bridge design are greatly improved.

The invention belongs to the technical field of power systems, and particularly relates to a pressure sampler on-line unblocking control method and system, and the method comprises the steps: collecting the pressure difference fluctuation data of the inner wall of a pipeline in real time based on a quantum pressure difference sensor, associating historical flow field parameters through a pre-trained quantum neural network model, and dynamically predicting the multi-moment blocking risk level; a dynamic cleaning path is generated in combination with a quantum annealing algorithm, a genetic algorithm parameter library is called to match scene gene data, and cleaning priorities of a fourth pipe body and a seventh pipe body are determined; the control module generates an air cylinder driving instruction according to the cleaning path and the priority, and drives the spiral brush head to execute high-frequency inching unblocking operation, dynamic sensing and accurate cleaning of the blocking risk are achieved, and the data reliability and operation and maintenance safety of thermal power generation pressure measuring points are improved.

The invention relates to the technical field of marine environment monitoring, in particular to a marine physical data intelligent fusion and optimization processing method, which comprises the following steps: collecting marine physical data in real time through various sensors, including temperature, salinity, flow velocity, tide and sound wave characteristic data; and spatial-temporal feature extraction and spatial correlation analysis are carried out by using a depth residual-graph convolution hybrid model. And performing parameter optimization on the multi-source fusion data set by using a quantum annealing optimization algorithm to generate an optimal parameter set so as to improve the fusion precision of the data. And abnormal data nodes are identified through a self-adaptive threshold detection algorithm, so that the accuracy and reliability of the data are further ensured. And finally, constructing a three-dimensional visual marine environment model according to the optimized parameter set, and realizing a dynamic mapping relationship between model parameters and physical characteristics so as to realize real-time updating and visual display. According to the method, the processing precision of the marine environment data is effectively improved, accurate marine environment prediction is provided, and the method has a wide application prospect.

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 discloses a noise monitoring point site selection method and system based on a noise map, and relates to the technical field of environmental noise monitoring, and the method comprises the steps: constructing a three-dimensional topology network based on a three-dimensional noise intensity matrix, calculating an optimal sound propagation path through employing a quantum heuristic topology optimization algorithm, and obtaining an optimal sound propagation path; the weights of nodes in the three-dimensional topology network are optimized in combination with quantum annealing, and an acoustic topology characteristic spectrum is generated; extracting multi-scale noise propagation features through a convolutional neural network according to the acoustic topological feature map, constructing a gridding space demand matrix in combination with a monitoring equipment parameter library and coverage constraint conditions, and generating an optimal noise monitoring network site selection scheme according to the gridding space demand matrix; according to the invention, a quantum heuristic topology optimization algorithm is combined with quantum annealing to optimize the node weight of the three-dimensional topology network, and intelligent optimization and dynamic adjustment of the sound propagation path are realized.

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 a numerical control equipment cooperative scheduling method and system based on a heterogeneous computing architecture, and aims to solve the core problems of multi-brand numerical control equipment protocol isomerism, weak digital twin generalization ability, low multi-objective optimization efficiency, quantum era communication security and the like in the industrial internet. Real-time analysis and unified format conversion of 18 industrial protocols are realized through a PDSA-Chip hardware-level protocol conversion module; in combination with a transfer learning digital twinning technology, cross-brand equipment parameter self-calibration is completed within 72 hours; a quantum annealing and NSGA-III collaborative optimization algorithm is adopted, the machining parameter optimization period is shortened to 1 / 3 of that of a traditional method, energy consumption, precision and the service life of a tool are dynamically balanced, and the service life of the tool is prolonged by 37% in a high-load scene; an NTRU lattice encryption and chaos confusion fused anti-quantum security communication system is constructed, the G code confusion degree is larger than 98%, and SL3-level quantum attacks are defended.

The invention discloses an earthquake prediction method and system based on multi-physics coupling and quantum optimization, and relates to the technical field of earthquake prediction, and the method comprises the following steps: obtaining earth surface deformation observation data, geomagnetic observation data, gravity gradient data, and infrasonic wave monitoring data, and carrying out the preprocessing; performing multi-physics field coupling calculation on the preprocessed earth surface deformation observation data, geomagnetic observation data, gravity gradient data and infrasonic wave monitoring data; optimizing the fault network by adopting a D-Wave quantum annealing technology to find a lowest energy state to obtain a quantum optimization result; and generating an earthquake probability distribution diagram, an earthquake magnitude-time window table and a fault stress field three-dimensional model, thereby greatly expanding the breadth and depth of data, and remarkably improving the comprehensiveness of earthquake prediction.

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 discloses a multi-stage task processing method and system based on an intelligent Agent model, and relates to the technical field of task processing, and the method comprises the steps: collecting heterogeneous data streams through a distributed sensor network, and generating a dynamic feature vector through a feature encoder inspired by quantum annealing; calculating a path expected utility value by using a Bayesian optimization algorithm, and projecting a high-order task space to a Kupman space; starting multi-thread asynchronous calculation, collecting execution state data in real time and constructing a causal graph model; the short-term execution logs are integrated through a neural Turing machine, edge computing nodes are called for distributed knowledge extraction, a multi-mode interpretable report is generated, and a long-term memory library is updated. According to the method, by starting multi-thread asynchronous calculation, the execution state data are collected in real time, the causal graph model is constructed, the execution result matrix with the confidence score is output, and the stability and reliability of task execution are improved.

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 discloses an extended-range hydrogen fuel power ship propulsion system, which relates to the technical field of ship propulsion and comprises an acquisition module, a quantum annealing optimization module and a quantum chip, the acquisition module is used for eliminating noise interference of acquired hydrogen energy ship power parameters through a Kalman filtering algorithm and generating a standardized data packet, and the quantum chip is used for receiving the standardized data packet and outputting the standardized data packet. The method comprises the following steps: mapping hydrogen energy ship power parameters into an Isin model, generating a control instruction according to a hydrogen fuel consumption rate, an equipment life attenuation coefficient and a navigation task priority weight, inputting the control instruction into a simulation model of a proton exchange membrane by a proton membrane regulation and control module, and dynamically regulating a hydrogen production rate and fuel cell power, according to the system, the efficient hydrogen energy closed-loop monitoring unit and the three-level safety protocol are adopted, the response speed and precision are improved, the stability and safety are remarkably enhanced, it is ensured that a ship can reliably operate under the complex working condition, and potential safety risks are reduced.

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 bridge and tunnel inspection and maintenance management system and method based on a three-dimensional panorama and a GIS map, and relates to the field of bridge and tunnel inspection and maintenance. The method comprises the following steps: processing and fusing collected bridge and tunnel multi-dimensional environment data to obtain preliminary three-dimensional map data, performing super-resolution enhancement on a turbid shadow area to construct a high-precision three-dimensional digital model, and fusing the model with GIS data to obtain a digital twin data volume; microcosmic spectrum features are extracted, spectrum power authenticity judgment is carried out, and a health state time sequence is generated; planning an autonomous inspection path of an inspection machine, and introducing a quantum annealing algorithm to optimize scheduling and solve an optimal path; and constructing a knowledge graph associated historical maintenance record and an expert experience library, and mining a hidden association rule to generate a maintenance suggestion. Through fusion of multi-source data and intelligent analysis and optimization technologies, high-precision inspection, accurate damage evaluation and intelligent maintenance decision making are realized, and the intelligent level of bridge and tunnel management is improved.

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 belongs to the technical field of risk assessment, and discloses a risk assessment method based on digital economy. The method comprises the following specific steps: S1, cross-modal data federal acquisition; s2, risk factor dynamic deconstruction; s3, dynamic modeling of game theory enabling is carried out; s4, generating a real-time risk thermodynamic diagram; s5, an edge intelligent decision center; s6, dynamic knowledge graph evolution; and S7, sandbox verification and anti-fact deduction are carried out. According to the method, all-around innovation is brought to risk management and control, first, data processing is performed, block chains, 5G and NLP technologies are utilized, multi-source data are collected, public opinions are insighted, privacy compliance is guaranteed based on federal learning, and a safety footstone is built, and second, market strategies are provided, and by means of reinforcement learning, Monte Carlo deduction and a quantum annealing algorithm, subject behaviors are simulated, policy impact is pre-estimated, and a model is optimized, so that risk management and control are realized. And thirdly, knowledge application, leading-edge technology labeling new risk database establishment, federal learning optimization atlas and enhancement of practical operation to cope with complex risks are realized.

The invention relates to the field of optical material defect detection, and discloses a PC lens defect classification and identification method and system, and the method comprises the following steps: asynchronously collecting lens multi-modal data through polarized light imaging, thermal imaging and an acoustic emission sensor, and constructing a five-order asymmetric tensor containing polarization angle, spectrum, time, space and defect features; performing dynamic dimension reduction on the tensor by using an improved Tucker decomposition method, and extracting a core tensor and a factor matrix; establishing a polarization angle-time correlation coding model based on inverse problem solution of a light field state equation, deducing defect distribution characteristics and optimizing a core tensor; mapping the optimized tensor to the QUBO Hamiltonian of a quantum annealing machine, and carrying out optimization solution; and finally, fusing the multi-mode confidence coefficients of polarized light, thermal imaging and acoustic emission signals, generating defect classification labels and inverting geometric and mechanical parameters of the defects. The lens flaw detection efficiency and the classification accuracy are remarkably improved, and the method is suitable for industrial-grade high-precision quality control scenes.

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 the technical field of numerical control machining, in particular to a CNC cutting path optimization method and system based on quantum computing. The optimization method comprises the following steps: acquiring a three-dimensional geometric model and a cutting process parameter set of a target workpiece, wherein the cutting process parameter set comprises a cutter diameter, a feeding rate, a cutting depth and a material removal rate threshold value; and constructing a multi-objective optimization model with the purposes of minimizing the total length of the path, maximizing the load balance degree of the cutter and minimizing the idle stroke time. According to the method, a quantum-classical coupled multi-objective optimization model is constructed, the spatial correlation of cutting path points is mapped into the entanglement strength between quantum bits, and the curse limitation of dimensionality of a traditional optimization algorithm is broken through by using the parallel tunneling capability of a quantum annealing processor.

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.