149 results about "Quantum annealing" patented technology

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 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 dynamic response method of a network attack defense strategy based on multi-head attention fusion, and relates to the technical field of network security, and the method comprises the steps: collecting network flow data and equipment state data of IIOT equipment, carrying out the time alignment, obtaining statistical features and frequency domain features through feature extraction, and generating a structured feature matrix; calculating a cross-domain association weight of statistical features and frequency domain features in the structured feature matrix through an attention mechanism, generating a fusion feature vector, carrying out time sequence modeling by using an LSTM model, calculating an abnormal probability score, and generating an abnormal event report; the method comprises the following steps: mapping spatio-temporal information in an abnormal event report into a quantum bit coding scheme in a digital twin environment, generating a Pareto optimal solution set by using a quantum annealing algorithm, screening a final network attack defense strategy, and coding the final network attack defense strategy into a quantum decision instruction. According to the invention, the adaptive security protection capability of the industrial Internet of Things system in a complex attack scene is effectively improved.

Methods for optimizing path planning for agricultural vehicles are provided. In one embodiment, a field is defined, passes completely covering the field are created, the ends of each pass are transformed into nodes, distance and time matrices for the nodes are computed, and an optimization problem incorporating the time and distance matrices, agricultural constraints, and time window requirements is formulated. Prior to solving the optimization problem, verification that a feasible solution to the optimization problem exists is performed, and constraints are adjusted if a feasible solution does not exist. The optimization problem may be solved using a quantum annealing process to achieve an optimized route that performs well in an agricultural setting.

The invention relates to the technical field of new energy system fault diagnosis, in particular to a solar cell coupling hydrogen production system fault diagnosis method based on big data, and the method comprises the steps: collecting photovoltaic, electrolytic cell and control unit multi-source heterogeneous data, and detecting the abnormality through an abnormality detection core model; matching and repairing by combining historical similar sections to obtain operation re-repairing data; a multi-scale expansion convolutional neural network is adopted to extract time sequence fault features, a backbone network is adopted to extract infrared image high-order features, and heterogeneous aggregation features are obtained through fusion of a spiking neuron model and virtual dendritic branches; constructing dynamic graph nodes and attributes according to features and components, and integrating multiple indexes to fit a causal association strength weight; and multi-link diagnosis: obtaining a fault type through a quantum annealing algorithm, positioning a fault through causal discovery and a virtual dry prediction algorithm, integrating and reporting to a server. The method can improve the fault diagnosis accuracy and real-time performance, and is suitable for fault monitoring of the solar hydrogen production system.

The invention discloses a multi-source heterogeneous data assimilation-driven high-altitude atmospheric motion numerical simulation method, which comprises the following steps of: obtaining satellite microwave radiation brightness temperature data and ground dual-polarization radar differential reflectivity data, inputting the data into a quantum annealing collaborative inversion model, and solving three-dimensional humidity field distribution data; humidity gradient tensor field data are calculated, and the space coordinate range of the frontal area is judged based on a preset humidity gradient modulus length threshold value; carrying out vector included angle calculation on humidity gradient tensor field data of a frontal area and background wind field data, generating a cloud micro-physical parameterization regulation coefficient, and dynamically selecting a cloud phase change dominant mode; and inputting the three-dimensional humidity field distribution data and the cloud microphysical parameterized regulation and control coefficient into a four-dimensional variational assimilation system to solve optimal analysis field data, and outputting high-altitude wind field prediction data in a driving numerical mode. According to the method, the response sensitivity and the physical consistency of the cloud microphysical process in numerical simulation are enhanced, and the cloud phase evolution simulation precision in the severe weather process is improved.

The invention relates to the technical field of numerical weather forecast, in particular to a meteorological large model prediction method based on a data correction model, which comprises the following steps of: firstly acquiring multi-source atmospheric observation, screening observation by topology-optimal transmission quantum annealing, and constructing a weighted error covariance; applying mass, energy and earth rotation gradient, and generating a conservation assimilation field through diffusion implicit sampling; calculating a mutual information mask and coupling a cloud top optical flow fine tuning phase; cloud motion consistent field pulse codes are sent to the symplectic decomposition pulse neural network for neural form hardware reasoning, a pulse threshold is adjusted in a closed loop to control energy drift, and an uncertainty field is output through parallel disturbance reasoning. The method has the advantages of high resolution, low power consumption and probability prediction capability, and the extreme weather path and intensity prediction precision is obviously improved.

The invention discloses a cooperative control method and system for a charging pile and a photovoltaic energy storage system, and the method comprises the following steps: obtaining data of different sources, inputting the data into a fusion neural network model, carrying out the prediction of time series data through a deep learning model, carrying out the intelligent decision, and optimizing the electric energy dispatching; constructing an energy scheduling model of quantum calculation, processing a plurality of variables based on quantum calculation, calculating in a multi-target environment by using a quantum annealing algorithm, and solving a multi-target globally optimal solution; edge computing equipment is deployed, data processing and computing capabilities are transferred from a central server to local nodes of the charging pile, the energy storage system and the photovoltaic system, local data processing and real-time optimization are carried out, the multi-objective optimization problem is solved through quantum annealing and a quantum approximate optimization algorithm, the computing efficiency of the scheduling system can be greatly improved, and the scheduling efficiency is improved. And the calculation bottleneck faced by the traditional method is avoided. Data from different sources are integrated, intelligent decision making is carried out through deep learning, and the self-adaptive capacity of the system is enhanced.

The invention discloses a dynamic edge resource adaptive optimization method in a heterogeneous environment, which relates to the technical field of edge computing, and comprises the following steps: extracting a resource state feature set from a global resource state map through sliding segmentation, and carrying out coding mapping to generate a multi-dimensional resource state vector; according to the multi-dimensional resource state vector, executing interpolation operation, generating a dynamic resource thermodynamic diagram, performing comparative analysis on the dynamic resource thermodynamic diagram and historical edge resource data, and identifying a current high-load area; performing fusion analysis on the current high-load area and the matching relationship between the real-time monitoring task request and the node resource occupation to obtain a conflict task set, inputting the conflict task set into the quantum optimization model, executing quantum annealing calculation, and generating a task-node mapping table; unified modeling of distributed edge resources is realized, space-time relevance among the resources can be dynamically captured, and a high-precision global view is provided for subsequent load analysis and task scheduling.

The invention relates to the technical field of artificial intelligence, and discloses a feature screening method and device based on artificial intelligence, equipment and a storage medium, and the method comprises the steps: obtaining a feature factor which affects the pricing of vehicle insurance; preprocessing the feature factors to obtain features influencing car insurance pricing and risk category labels of the features; through a support vector machine and an arrangement importance algorithm, according to the features and the risk category labels of the features, candidate features are screened out from the features, and the importance degrees of the candidate features are obtained; and through a quantum annealing algorithm, according to the risk category labels and the importance degrees of the candidate features, target features influencing vehicle insurance pricing are screened out from the candidate features. The method can be applied to feature screening in a financial vehicle insurance pricing scene, the efficiency and quality of feature screening can be remarkably improved, and the feature screening difficulty is effectively reduced.

The invention discloses a carbon emission dynamic accounting method and system. The method comprises the following steps: collecting production equipment direct emission, logistics carbon emission, social media / satellite remote sensing and other abnormal event data; extracting unit yield carbon intensity and supply chain path sensitivity through principal component analysis; constructing a QUBO model and solving an optimal transportation path by using quantum annealing; training a prediction model in combination with the sudden emission risk index to output a carbon emission value; dynamically updating an emission factor library according to the cleanliness of the power grid, and accelerating generation of a confidence interval by adopting a quantum random number generator; and when the predicted value exceeds the threshold value and the lower limit of the confidence interval is higher than the threshold value, triggering carbon capture, carbon credit purchase or production regulation compensation according to the priority. The system correspondingly comprises a multi-mode acquisition module, a calculation optimization module, a dynamic accounting module and a carbon compensation module. Full-chain real-time carbon tracking and intelligent compensation are achieved, the problems of traditional accounting data isolation and compensation lag are solved, and the industrial carbon management accuracy is improved.

The invention provides a three-dimensional-TDOA positioning method based on distributed microphone array cooperative positioning, and relates to the technical field of TDOA positioning, and the method comprises the steps: constructing a self-adaptive dynamic microphone array; a quantum entangled state sensor is introduced into a microphone node signal acquisition front end, and the arrival time difference of a target signal is measured; constructing a deep learning model, predicting sound velocity change according to environmental factors, and dynamically adjusting a sound velocity value in TDOA measurement; the microphone array node optimizes a positioning strategy through a reinforcement learning algorithm according to self measurement data and interaction information with other nodes; fusing the data, and based on fusion result data, executing a TDOA estimation improved algorithm; based on a TDOA calculation result, performing three-dimensional space search by adopting a quantum annealing algorithm to solve an optimal position of the target; and multi-source information is fused to add constraint conditions for a positioning algorithm, and a positioning result is optimized. According to the method, sub-centimeter-level positioning precision can be realized, and application scenes with extremely high positioning precision requirements are met.

The invention discloses a multi-energy balance margin adaptive regulation and control method and device, and relates to the technical field of quantum computing crossover, and the method comprises the steps: carrying out the optimization of a four-dimensional input matrix through a quantum encoder, executing quantum annealing calculation to generate a ground state solution, integrating an inertia demand increment with the ground state solution through quantum bit entanglement, and carrying out the optimization of a four-dimensional input matrix; calculating a comprehensive inertia gap value, and generating a hierarchical scheduling instruction by adopting a dynamic threshold hierarchical strategy; and according to the hierarchical scheduling instruction, a topology reconstruction instruction is generated through a virtual inertia dynamic algorithm, dynamic monitoring is performed by applying multi-agent collaborative game optimization, and an intelligent regulation and control report is generated. After three-dimensional fault features and space-time dimensions of a multi-energy fluctuation ratio are aligned based on a dynamic time warping-covariance matching method, dynamic fusion is carried out on standard field phase fiber bundles by adopting aged-Simons topology weaving, and self-adaptive regulation and control of a multi-energy balance margin are realized.

The invention discloses a database index optimization method, system and equipment based on artificial intelligence. The method specifically comprises the steps of collecting performance data of a database in real time; performing preprocessing and feature extraction on the performance data to obtain a spatial-temporal feature matrix; taking the spatial-temporal characteristic matrix as input, performing index combination selection by using a hybrid solver formed by a quantum annealing algorithm and a classical genetic algorithm, and generating an index optimization strategy; on the basis of an index optimization strategy, potential performance risks are predicted according to an index change influence evaluation model, and an optimization decision explanation is generated through an SHAP value; and executing index change operation according to the index optimization strategy, evaluating database performance after index change, and feeding back to the hybrid solver for updating and optimization. According to the method, the database index is automatically and intelligently optimized, the database performance is improved, and the manual intervention cost is reduced.

The invention provides a domestic large model edge cloud collaborative management platform based on a quantum-classical hybrid architecture and an adaptive optimization method, and the platform comprises a quantum heuristic computing power optimization engine which carries out the heterogeneous computing power scheduling based on a quantum annealing algorithm, and enables a large model task to be divided into a quantum bit mapping problem capable of parallel computing, hybrid scheduling of classical calculation and quantum calculation is completed; the edge end dynamic knowledge distillation architecture is used for collecting real-time data through edge equipment, dynamically extracting cloud large model knowledge and compressing the cloud large model knowledge into a deployable edge small model; reverse synchronization of an incremental learning mechanism is set; the multi-modal data sovereignty management mechanism is used for realizing verifiable auditing of data use compliance in a model training process; the management platform realizes secure data input, completes cloud large model training and quantum acceleration, and establishes a bidirectional knowledge flow closed loop. According to the method, large model computing power efficiency and training bottlenecks are overcome, the problems of real-time performance, privacy and knowledge islands are solved, and it is guaranteed that data are safe and compliant.

The invention relates to the field of artificial intelligence hardware, in particular to a neural network training method based on quantum annealing optimization, and the method comprises the steps: firstly solving a gradient at a graphics processor, and generating an energy coefficient through neuromorphic coding; dividing the weight into sub-blocks according to a coupling threshold value, outputting initial spinning by an optical coherence Isin optimizer, performing reverse annealing by a quantum annealing processor to obtain optimal spinning, and freezing scale parameters; then, a noise consistency optimizer is used for finely adjusting scale parameters, discrete weights are reconstructed according to spin-scale and written into the memristive cross array unit by unit, and hardware errors are eliminated through read-write-check cycle and column gain-row bias dual calibration; and generating a deployment model file and executing write protection after double-end multi-time sampling is carried out at a reference low temperature to confirm spin consistency. The method has high-speed convergence, low-energy-consumption writing and long-term reasoning stability, and the training and deployment efficiency of a large-scale neural network on light-quantum-memristive heterogeneous hardware is remarkably improved.

Determining a probability distribution for a solution is disclosed. A diagonal of a QUBO matrix is evaluated. When the diagonal is positive semidefinite, the QUBO may be solved using a simulated annealer and a solution can be sampled to determine a probability distribution. If the diagonal is not positive semidefinite, the QUBO may be sampled using a simulated annealer in an intermediate step. If a variance of a dispersion measure is too large, the QUBO is solved and sampled using a quantum annealer in order to determine the probability distribution.

The invention discloses a rail transit connection service quality evaluation and optimization method based on an intelligent traffic technology, and relates to the technical field of traffic connection, and the method comprises the steps: firstly collecting multi-source data, processing passenger complaint data through an AI model, constructing a dynamic knowledge graph, generating an optimized unmanned connection scheduling strategy set through a Diffusion Model and a PPO reinforcement learning algorithm, and carrying out the optimization of the unmanned connection scheduling strategy set; regional service planning information is obtained in combination with various data, then connection station positions and vehicle resource allocation information are obtained and optimized through the steps of clustering analysis and the like, a connection resource scheduling problem is modeled as a QUBO problem, a mixed quantum annealing strategy is adopted for solving, emergency connection path recombination is achieved, and the emergency connection path is optimized. The operation data is periodically obtained to evaluate the service quality index, resource configuration optimization adjustment is carried out on the stations exceeding the threshold value, the rail transit connection service can be comprehensively optimized, and the service quality is improved.

The application belongs to the technical field of data processing. An industrial equipment fault early warning method and system based on a pulse neural network are provided, vibration time series signals and temperature time series signals of industrial equipment are acquired, the vibration time series signals and the temperature time series signals are input into a pulse neural network optimized by using a quantum annealing algorithm, and output states of each neuron are obtained, a connection weight matrix of each neuron of the pulse neural network is calculated according to the output states of each neuron, a global energy is determined according to the output states of each neuron and the connection weight matrix, when the global energy is lower than a first set threshold value and a maintenance time is greater than a second set threshold value, it is determined that the industrial equipment has a fault and a fault early warning signal is generated, the quantum annealing algorithm in the field of quantum computing is used, the processing capacity of the pulse neural network for long-range dependence of the time series signals of the industrial equipment is improved, and the fault early warning precision is improved.

A network connection control system and method is provided. The network connection control system includes user equipments, base stations, a server and first and second processing units. Each user equipment transmits a network parameter between it and every base station to the server through the base station connected therewith. The first processing unit assigns CIO set values corresponding to the base station. The network connection control system is configured to perform an optimizing procedure. In the optimizing procedure, according to the CIO set values, the network parameters, the throughput objective function and connection and network resource constraints of all the user equipments and base stations, the first and second processing units processes based on classical algorithm and quantum annealing algorithm respectively to obtain the optimized connection configuration.

The application relates to the field of intelligent manufacturing and printing processes, and discloses a digital printing equipment image optimization system based on dynamic texture mapping, which comprises the following steps: constructing a dynamic manifold representation space of printing parameters, realizing global optimization through a curvature-driven quantum annealing algorithm; designing an online correction mechanism of a connection coefficient and a multistage fault-tolerant protocol, establishing a differential homeomorphism mapping from quantum optimization results to execution instructions; extracting optimization features by using a manifold adaptive wavelet transform, and combining deep reinforcement learning to generate pulse shaping control quantities. The method breaks through the local extremum limitation of traditional optimization, realizes adaptive control under material property drift, solves the control mismatch problem caused by multi-physical field coupling, and improves the precision stability and working condition adaptability of complex texture printing.

Methods, systems, and computer storage media for solving optimization problems are disclosed. Problem parameters of an optimization problem are received where the optimization problem has a solution space larger than a solution space threshold. Clustering is performed to reduce the solution space below a solution space threshold, and routing is performed to determine the routing options using the clusters. Solutions are received using simulated annealing on the clusters with the reduced solution space, the one or more solutions are recombined to generate a solution, and the solution is provided via a user interface.

A method includes receiving a learning requirement for a user. A preliminary learning recommendation is generated based on the learning requirement of the user and learning histories of a plurality of users. The preliminary learning recommendation is filtered to generate a filtered learning recommendation. A problem of finding an improved learning recommendation from the filtered learning recommendation is mapped to a quantum Ising model. A quantum annealing process is performed one or more times to determine one or more final quantum states from an initial quantum state. One or more energies of the quantum Ising model are determined for the one or more final quantum states. A minimum energy is determined among the determined energies. A target quantum state that corresponds to the minimum energy is determined among the determined final quantum states. The improved learning recommendation is determined from the target quantum state and is sent to the user.

A computer-implemented method for an encryption for secure communication between devices during quantum annealing or QUBO-based routines, which are executed on a quantum device with configurations determined or supported by at least one classical computing device, wherein the encryption and decryption of data are managed by the classical computing device, the method characterized by within a QUBO based routine that includes set of pre-trained weak learners that provide output for test values and weight adjustment process for these weak learners and after the discretized to model it as a QUBO; using different encoding depths for different weights with the assigning the weights randomly according to the correlation in between the weak learners with the registration of this encoding information; shuffling the order of the variables randomly with the registering this order information; converting the QUBO matrix, which is stored in at least one storage medium and generating a string which includes two different number whose length matches the solution space dimensionality and registering the strings with spin flipping these numbers; decryption of the data which is received from at least one quantum device with encrypted by classical computing device before the transfer procedure with using encoding information, order information, spin flipped result, combination order information or suitable combination of them according to the encryption.

The application provides an automatic turning method and system, and relates to the technical field of power station maintenance, comprising: obtaining first information and second information, the first information is a sensor deployment scheme, sensor data and corresponding turning effect evaluation index of historical turning operation, and the second information is real-time monitoring data; obtaining third information by quantum annealing processing according to the first information; obtaining fourth information by Kalman filtering processing according to the first information and the second information; obtaining fifth information by dynamic programming processing according to the third information and the fourth information; and obtaining sixth information by fuzzy logic reasoning processing according to the first information and the fifth information. Based on quantum annealing processing and dynamic programming processing, combined with graph construction, random forest algorithm and shortest path algorithm and other technologies, the application realizes intelligent design of the optimal control strategy of the turning operation, can automatically adjust the turning operation according to the real-time monitoring data and the turning effect evaluation index, and improves the turning efficiency and precision.

The invention discloses a novel power grid adjustable resource demand response method based on wide-area measurement data, and relates to the technical field of power regulation and control, and the method comprises the steps: building a gauge tensor through metric mapping based on a slice data set, and generating cluster topological data through a Riemann clustering algorithm according to a geodesic distance; constructing Hamiltonian by adopting quantum mapping based on cluster topological data, and generating a resource scheduling strategy through a quantum annealing algorithm; based on a resource scheduling strategy, multi-modal response verification is carried out on the multi-source measurement data through topology dynamic verification, and an instruction response verification report is generated; and according to the instruction response verification report, counting the proportion of the number of abnormal nodes in the cluster to generate a topology recombination decision, and optimizing a resource scheduling strategy through consistency verification. The intelligent degree gauge tensor is constructed by fusing the physical characteristics of the power grid and the communication topology, deep coupling cooperation of response clusters is achieved, and the bottleneck of large-scale real-time decision making is broken through.

The invention provides a personalized ear mold 3D printing technology and an application system thereof. The personalized ear mold 3D printing technology comprises the following printing steps: S1, synchronously acquiring multi-modal data: synchronously finishing by using an intelligent ear canal scanner: (1) physiological data: detecting a high-precision ear canal 3D model and a soft tissue elasticity modulus; according to the 3D ear mold printing technology, a non-European acoustic waveguide structure is optimized through multi-modal data fusion and a quantum annealing algorithm, millimeter-level anatomical matching and dynamic acoustic impedance tuning of the ear canal are achieved, and the acoustic impedance spectrum and the personalized frequency response preference of the ear canal of a user are obtained. According to the invention, femtosecond laser coloring is combined to construct a nano grating color developing layer and StyleGAN3 to generate microvascular bionic textures, so that the traditional single adaptive dimension is broken through, the ear mold has the lifelong evolution ability of acoustic self-adaption, aesthetic self-matching and physiological self-optimization, and a static hearing aid device is upgraded to an intelligent biological function extension body.

The invention discloses a circular area sensor network communication method based on quantum-classical polarization entanglement, relates to the technical field of sensing communication, and solves the problem of how to realize efficient and reliable quantum communication in a circular area scene. The method comprises the following steps: initializing a network, and carrying out self-positioning on each sensor node; dividing the circular area into a plurality of annular subareas; electing part of sensor nodes as cluster heads; clustering other sensor nodes; establishing a quantum communication channel and verifying the quantum communication channel; taking the cluster head as a relay node, and performing quantum enhanced communication between the central node and any sensor node; and dynamically maintaining a cluster head and a sensor cluster thereof. In a word, the quantum polarization entanglement and the circular sensor network are deeply fused for the first time, through three innovations of spatial topological optimization, quantum annealing clustering and annular entanglement distribution, the problems of energy holes, multipath interference and quantum safety of circular area communication are solved, and a high-reliability anti-quantum-attack communication normal form is provided for the industrial Internet of Things.

The invention discloses an online education consultation system and method based on big data, and relates to the technical field of intelligent education, and the method comprises the steps: analyzing a real-time consultation session audio and video stream, generating an emotion signal and a cognitive load pause ratio, and carrying out the feature level fusion with a user behavior feature vector, constructing an educational causal graph through a Do-Calculus algorithm, converting the educational causal graph into a root cause coding report, and generating a root cause knowledge point dependence path set; encoding the root cause knowledge point dependent path set into a quantum bit state, and generating an anti-interference learning path sequence through a quantum annealing algorithm based on a quantum constraint matrix converted by a root cause knowledge point ID; and analyzing the anti-interference learning path sequence, extracting knowledge point state and sequence data, generating a structured teaching suggestion, and converting the structured teaching suggestion into an executable consultation report. According to the method, the pertinence, intelligence and intervention effect of educational consultation are remarkably improved, and the method is widely applied to online learning platforms and personalized tutoring scenes.