156 results about "Quantum neural network" patented technology

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 karst tunnel collapse early warning, in particular to a karst tunnel collapse intelligent early warning method, system and equipment based on micro-seismic multi-precursor characteristics. According to the technical scheme, the method comprises the steps of multi-scale micro-seismic monitoring network construction, multi-modal micro-seismic feature processing, quantum neural network early warning model construction and self-adaptive early warning and risk assessment. According to the invention, through omnibearing data acquisition of the multi-scale micro-seismic monitoring network, different depth sensors and an intelligent transmission mode, monitoring is accurate and stable, through combination of multi-modal feature extraction fusion, time domain, frequency domain and waveform multi-dimensional information mining, surrounding rock conditions are accurately judged, early warning analysis is enhanced by using quantum characteristics through the quantum neural network, and the early warning accuracy is improved. And self-adaptive threshold adjustment and multi-factor risk assessment are matched, accurate early warning is achieved, collapse is efficiently prevented, and tunnel construction safety is powerfully guaranteed.

The technology relates to enhancing performance of quantum sensor operation in a noisy environment. Quantum neural networks are used to pre- and post-process analog signals to which qubits of a quantum sensor are exposed. Intra-processing using additional quantum neural networks may also be employed. This approach filters out noise from both the input analog signal and the system itself to achieve a very high signal to noise ratio. This permits the system to detect induced dynamics associated during exposure of the qubits to the analog signals. This enables the quantum sensor to sense a change in a state of a system under test, which can be beneficial for areas such as imaging, magnetometry, sensing electric fields, optomechanical sensors, quantum radar and other areas where enhanced signal to noise ratios are desired.

The invention relates to a power equipment defect detection method and system. The method comprises the steps of firstly collecting historical defect image data of power equipment, and preprocessing to obtain a data set; s2, a deep learning model is built, the model architecture comprises a quantum neural network, a backbone network, a multi-scale feature fusion layer and a target position and category prediction layer, the deep learning model is trained by using the data set in S1, and a defect detection model is obtained; and finally, deploying the defect detection model to edge equipment, inputting the current target power equipment image into the defect detection model by the edge equipment, and outputting a power equipment defect detection result, thereby realizing power equipment defect detection. Compared with the prior art, the method has the advantages of being suitable for a complex inspection environment, accurate in detection, high in confidence degree and the like.

The invention relates to the technical field of GIS (Geographic Information System) equipment denoising, in particular to a GIS local interference source denoising method, which comprises the following steps of: constructing a hybrid architecture of a quantum convolutional neural network and a quantum recurrent neural network, extracting features by using a variable component sub-circuit as a convolution kernel in the quantum convolutional neural network, processing time sequence data by using quantum bit entanglement in the quantum recurrent neural network, and extracting a local interference source in the quantum recurrent neural network; normalization, coding and enhancement are carried out on electrical, electromagnetic and other multi-source data, a quantum stochastic gradient descent algorithm is used for training, a multi-modal fusion network based on an attention mechanism is also constructed, features of all modals are extracted through an independent convolutional layer, then splicing and weighted fusion are carried out, and finally a quantum reinforcement learning environment is constructed. An intelligent agent strategy network and a reward function are designed based on a quantum neural network, parameters are adjusted according to quantum Q learning, quantum calculation, deep learning and a multi-modal fusion technology are integrated, the GIS local interference source denoising effect is effectively improved, and stable operation of equipment is guaranteed.

The invention discloses a seal removing and document repairing method based on quantum state collaborative regulation and control, and relates to the field of document image processing and quantum computing cross technology, the method comprises the following steps: obtaining to-be-processed information, and carrying out quantum-classical feature collaborative preparation; the character stroke continuity is guaranteed through quantum entangled state modeling and entanglement degree constraint iteration, texture decoupling is achieved through quantum wavelet transform and Gram-Schmidt orthogonalization, and adaptive filling is conducted in combination with a quantum generative adversarial network; dynamic quantum phase adjustment is used for counteracting superposition interference of stamps with different transparency, and quantum neural network noise reduction and multi-scale quantum Fourier sharpening are used for optimizing image quality; checking the repair result, if the repair result does not reach the standard, returning to the edge sharpening link to perform decoupling and filling the edge sharpening link to readjust the parameter; and for special scenes such as inclination, multi-color overprinting and ultra-thin frames, quantum rotation correction, color channel separation and boundary annihilation operator processing are used, finally, high-precision, high-naturalness and high-adaptability restoration of seal removal is achieved, and high fidelity of results is guaranteed.

The invention provides a signal decoding method based on a PSI5 interface, and relates to the technical field of signal decoding, and the decoding method comprises the following steps: processing and adjusting a received PSI5 signal to obtain an accurate identification signal; and extracting a clock signal from the PSI5 signal, designing a clock compensation algorithm according to the quantum neural network, and dynamically adjusting the clock signal to obtain clock synchronization data. And determining a data frame boundary, and decoding the accurate identification signal to obtain original data. And performing comparative analysis on the original data and the PSI5 signal to obtain comparative difference data, judging whether the comparative difference data accords with a preset error type or not, and performing corresponding processing. According to the invention, fractional calculus processing is carried out on the received signal, then signal distortion is corrected by using a channel equalization method, and the quantum neural network is constructed to dynamically adjust the clock signal, so that clock synchronization is realized, a reliable time reference is provided for accurate processing of the signal, and the accuracy of signal decoding is improved.

The invention discloses an intelligent temperature control system of an aluminum alloy plate punch forming die, which relates to the field of aluminum alloy plate processing and comprises a temperature acquisition module, a data transmission module, an analysis processing module, a control decision module and a heating and cooling execution module. Various sensors and self-adaptive layout are used for temperature acquisition, so that data accuracy is guaranteed; data transmission is combined with multiple technologies and block chain authentication to ensure stability; analyzing and processing introduced quantum neural network and causal analysis mining features; the control decision is combined with multiple algorithms and multiple parameters are considered, so that intelligent decision making is realized; a novel device and a phase-change material are adopted for heating and cooling, and the temperature is accurately controlled by combining an advanced control strategy. According to the invention, accurate temperature acquisition, multi-technology guarantee data transmission and quantum calculation are realized to improve the analysis decision ability, and accurate temperature control is realized by the novel device and the control strategy; the die state can be monitored, faults can be diagnosed, stamping quality and efficiency are improved, the service life of the die is prolonged, and cost is reduced.

The invention provides a multi-mode environment cooperative regulation and control system for poultry egg preservation. The system comprises a sensor network and a server, wherein the sensor network is used for collecting poultry egg data; the server is used for constructing a digital twinborn model according to the poultry egg data; perceptual data output by the digital twin model are input into a quantum neural network model, and a predicted fresh-keeping result and an optimal environment regulation and control parameter combination are obtained; according to the optimal environment regulation and control parameter combination, driving a regulation and control actuator group and feeding back actually executed regulation and control parameter values to the digital twinborn model; comparing the difference between the actual fresh-keeping effect reflected by the digital twinborn model and the predicted fresh-keeping result, and optimizing a mapping strategy from the current poultry egg state to the optimal regulation and control parameter by taking the fresh-keeping period extension rate as a reward function; the change condition of biochemical indexes is monitored through a digital twinborn model, and a monitoring result serves as a feedback signal to be input into the reinforcement learning module for strategy adjustment. The fresh-keeping period can be prolonged, intelligent prediction is achieved, quality traceability is guaranteed, and the energy consumption cost is reduced.

The invention discloses a large model fine tuning method and device based on LoRA, equipment and a medium, and belongs to the technical field of quantum computing, the method comprises the following steps: decomposing target data by using a tensor decomposition technology to obtain a result representing that the target data passes through a first low-rank matrix as first data, the target data being training data of a target large model, and the training data being training data of the target large model; the first low-rank matrix is represented by a core tensor used for decomposing the target data; based on the first data, obtaining second data through a quantum neural network used for extracting high-dimensional features; based on the second data, a tensor decomposition technology is utilized to obtain a result representing the target data passing through a first low-rank matrix and a second low-rank matrix, the result serves as third data, and the second low-rank matrix is represented by a core tensor utilized by current decomposition; and respectively updating the core tensor representing the first low-rank matrix and the core tensor representing the second low-rank matrix by using the third data. By applying the embodiment of the invention, more efficient model fine tuning can be realized.

The invention discloses a cardiovascular disease risk assessment system based on big data analysis, which relates to the technical field of medical big data and quantum and comprises a data acquisition module, a preprocessing module, a feature extraction module, a model training module, a risk assessment module and a result display module. Data acquisition integrates multi-source heterogeneous data, quantum encryption is used to guarantee security, and preprocessing is carried out by deep reinforcement learning cleaning and adaptive normalization; feature extraction is combined with quantum principal component analysis and an auto-encoder, and a quantum attention mechanism is introduced; the model training adopts quantum neural network ensemble learning of quantum annealing optimization; introducing fuzzy logic reasoning to correct probability and layering in risk assessment; results are displayed through virtual reality and augmented reality technologies, and report suggestions are automatically generated. The method has the advantages of prominent advantages, comprehensive multi-modal data acquisition, accurate advanced technology preprocessing and feature extraction, efficient and accurate quantum optimization model training, and combination of real-time monitoring and a knowledge graph, provides a basis for prevention and treatment of cardiovascular diseases, and promotes medical intellectualization and precision.

The invention relates to the technical field of 8G, and discloses a nuclear fusion plasma real-time control method and system based on a quantum neural network and 8G communication. The method comprises the following steps: transmitting plasma multi-dimensional electromagnetic parameters at a rate greater than or equal to 1Tbps through an 8G terahertz communication network; quantum dimension reduction (the dimension compression ratio is greater than or equal to 98%) and classical time sequence prediction are executed at the edge quantum computing nodes; a control instruction is transmitted to the superconducting magnet array through the photon integrated interface, so that the adjustment of a magnetic field smaller than or equal to 1nm is realized; physical layer quantum encryption is adopted to guarantee communication security, and the secret key distribution rate is larger than or equal to 20 Mbps.

The invention relates to the technical field of digital twinborn simulation, and particularly discloses a digital twinborn simulation training system for the whole process of administrative law enforcement, and the system comprises an intelligent sensing layer which is used for collecting the multi-modal data of a law enforcement site in real time, carrying out the data transmission, preprocessing and encryption, and obtaining the monitoring data of the emotional response and physiological state of a law enforcement officer; the twinborn modeling layer is used for constructing a digital twinborn model of a law enforcement scene based on a quantum neural network and simulating a multi-agent game process in a law enforcement process; the scene generation layer is used for intelligently generating a law enforcement scene in combination with legal provisions and cases, simulating the evolution process of emergencies and performing rule constraint on physical interaction in a virtual environment; according to the simulation training system, various dynamic changes in a complex law enforcement environment can be reflected more accurately, and a more vivid and efficient training environment is provided for administrative personnel, so that the law enforcement ability and the ability of coping with complex conditions of the law enforcement personnel are effectively improved.

The invention relates to the technical field of quantum computing, and discloses a quantum variational circuit and a data processing method, and the method comprises the steps: applying an H gate to each quantum bit to create a superposition state of the quantum bits, and coding input data into quantum coding data; an input revolving door is applied to each quantum bit in the superposition state; applying a combined U gate between every two adjacent quantum bits to form a quantum variational circuit; performing multiple rounds of quantum evolution training processing on the quantum coding data based on the quantum variational line; the combined U gate comprises a CNOT gate configured between two adjacent quantum bits, an RX rotating door and an RZ rotating door configured on a first quantum bit in the two adjacent quantum bits, and an RY rotating door configured on a second quantum bit in the two adjacent quantum bits. According to the invention, by constructing the quantum variation line, a quantum neural network is formed, so that fitting and classification tasks can be realized.

The invention discloses a machine room operation and maintenance management automatic inspection system based on intellectualization, and belongs to the technical field of intellectualization. Comprising a multi-source data fusion processing module, a hidden fault probability distribution module, a fault root cause analysis module, a dynamic adaptive baseline generation module, a multivariate strategy self-healing decision module, a cooperative control execution module and a feedback optimization module. According to the method, a full-link intelligent system of multi-source data fusion, quantum neural network prediction, graph neural network traceability, dynamic baseline generation, reinforcement learning decision, transactional scheduling execution and closed-loop optimization is constructed, the hidden fault recognition capability is improved through quantum classical hybrid calculation, and accurate root cause positioning is realized in combination with a causal atlas; and an optimal repair scheme is generated based on a dynamic baseline and a reinforcement learning strategy library, and finally, the reliability of cross-system operation is ensured through transactional scheduling, so that the fault prediction accuracy is remarkably improved.

The invention provides a Q / R dual-adaptive hybrid quantum filtering method for an OTFS (Over the The Over the File System) communication and inductance integrated system, and the method comprises the following steps: constructing an OTFS communication and inductance integrated signal model, and obtaining a target measurement vector; establishing a state vector for describing a target motion state, and constructing a state transition equation and a measurement equation; the method comprises the following steps: constructing a Q / R mixed quantum adaptive adjustment module based on dual-channel features to construct a dual-channel feature input vector, generating a dual-path adjustment factor through a mixed quantum neural network, and constructing an adaptive Q matrix and an adaptive R matrix; and performing adaptive state prediction and updating at each time step by using the adaptive Q and R matrixes constructed in the step 3 to realize tracking of the motion state of the target. According to the method, the hybrid quantum neural network is introduced for driving, collaborative optimization of filter parameters is achieved, and therefore the precision, robustness and response speed of target tracking are remarkably improved in a complex dynamic noise environment.

The invention relates to a power transmission system fault identification method based on a quantum neural network. The method comprises the steps that electrical data are collected and preprocessed; mapping the preprocessed electrical data to a quantum calculation space to obtain quantum state data; and inputting the quantum state data into the quantum neural network, and outputting a fault identification result. Compared with the prior art, the method has the advantages of high recognition accuracy, high reliability and the like.

The invention discloses a quantum-classical machine learning hybrid wind power prediction method, which comprises the following steps of: acquiring historical wind power, a fan sensor and meteorological data, and setting the wind power as a learning target; the method comprises the following steps: decomposing formatted time characteristics, generating 6-dimensional periodic coding time characteristics through sine-cosine function mapping, splicing the 6-dimensional periodic coding time characteristics with meteorological characteristics, selecting first n characteristic vectors according to importance scores, coding the first n characteristic vectors into a quantum state, and coding a mapping angle by using a parameterized revolving door; and constructing a parameterized quantum circuit to capture high-dimensional nonlinear characteristics, obtaining an output expected value through terminal measurement, calculating a mean square error as a loss function, adjusting a parameter to minimize the loss function, and predicting wind power by using the trained quantum neural network. The technical problems that an existing model is large in parameter quantity, complex in hyper-parameter space, slow in training convergence, high in model black box performance and the like are solved.

The invention provides a network attack detection model training method, a training device, a detection method, a storage medium and computer equipment. The method comprises the following steps: acquiring a network attack training data set; preprocessing the attack training data set and then performing quantum state coding to form a first input quantum state; inputting the first input quantum state into the variable component sub-line, forming an optimal parameter through dynamic adjustment, and obtaining a corresponding first detection result based on the optimal parameter to represent whether a network attack behavior exists in the network flow data; endowing the optimal parameter to a variable component sub-neural network model to form a network attack detection model; the optimal parameters obtained through training are input into the variable component sub-neural network model to form the network attack detection model, the variable component sub-neural network model can be flexibly designed, and high detection precision can be achieved under the condition that a small number of sub-bits are used.

The invention discloses a high-reliability aircraft communication encryption method based on a quantum neural network. The aircraft communication encryption method comprises the following steps: collecting and preprocessing aircraft communication original data; the method comprises the following steps: initializing a quantum neuron state by using a quantum state superposition principle, and determining an initial connection weight value of a quantum neural network by calculating probability amplitude values of different quantum gate operations so as to construct an initial quantum neural network structure; generating an encryption key according to quantum bits of the output layer of the quantum neural network; for aircraft communication data to be transmitted, an encrypted data ciphertext is obtained according to the encryption key, a quantum key distribution technology is utilized to generate and distribute the key, and the ciphertext and the key are transmitted through a communication channel; and the receiving end receives the ciphertext and decrypts and verifies the security in the ciphertext transmission process. According to the method, the aircraft communication data is encrypted based on the quantum neural network, and the security in the aircraft communication process is improved.

The present invention relates to a quantum machine learning method for multi-class classification, and the method comprises the steps of: applying a Quantum Convolution Neural Network (QCNN) quantum circuit to input data having q qubits, and outputting a feature vector based on Pauli-Z measurement; and applying a Quantum Neural Network (QNN) quantum circuit to the feature vector, and outputting a multi-class prediction vector with scalability increased compared to q qubits based on basis measurement.

The invention discloses a high-precision quantum sensing environment physical quantity real-time monitoring system and method, and belongs to the technical field of environment monitoring. The system comprises a quantum sensing front end, a signal excitation and regulation module, a quantum classical hybrid computing engine, an edge cloud collaborative transmission module and a terminal application module. The quantum sensing front end adopts a quantum entanglement sensing array integrated by NV color center diamond and a rubidium atom gas chamber to synchronously sense a magnetic field, temperature, pressure and vibration; the signal excitation and regulation module optimizes regulation parameters and inhibits noise through a quantum optimal control algorithm; a quantum-classical hybrid calculation engine is combined with a quantum neural network and classical multi-algorithm collaboration to realize efficient resolving and data fusion of quantum state features. According to the invention, the monitoring precision, the real-time response speed and the anti-interference capability are obviously improved, synchronous monitoring of multiple physical quantities is supported, and the system is suitable for various scenes such as industry, scientific research and deep space exploration, and has wide application value.

The invention discloses an intelligent power transmission and transformation monitoring and fault early warning system based on Internet of Things, which relates to the technical field of power transmission and transformation monitoring and Internet of Things and comprises a data acquisition layer, a network transmission layer, a data processing layer and an early warning display layer. The functions of quantum encryption communication and self-diagnosis repair are achieved; multi-element networking is adopted for network transmission, quantum key distribution and a block chain are combined, an SDN and SD-WAN fusion architecture is used, a three-layer architecture is used for data processing, a quantum neural network and knowledge graph assistance is used, an MR technology and a brain-computer interface are used for early warning display, and all layers collaboratively guarantee efficient operation of the system. The system has obvious advantages, data acquisition is comprehensive and safe, and the sensor can be self-diagnosed and repaired; network transmission is stable and reliable, and data security is guaranteed; data processing is accurate and efficient, and early warning accuracy is improved; the early warning display interactivity is high, the equipment can be scientifically evaluated, the maintenance decision is optimized, the cost is reduced, and the stable operation of the power transmission and transformation system is ensured.

The invention provides a focusing optimization method, device and equipment based on quantum deep learning. The method comprises the following steps: acquiring a continuous shooting image sequence of a target object; the method comprises the following steps: shooting a target object by using photographic equipment to obtain a continuous shooting image sequence; quantum image features corresponding to the continuous shooting image sequence are extracted; wherein the quantum image features comprise quantum bits, quantum dynamic trajectories and quantum depth-of-field distribution; inputting the quantum image features into a pre-trained quantum neural network to obtain a focus prediction result of the target object; and adjusting the focal length of the photographic equipment according to the focus prediction result. Through the above method, the focusing speed and precision are improved, so as to meet the shooting requirements in ultra-high-speed photography and complex scenes.

The invention discloses a light quantum chip which is combined with high-dimensional coding, is provided with two data coding layers and two trainable entanglement layers, is compatible with a data recoding technology, and can be used for variable component subtasks such as a quantum neural network. The chip structure comprises a cascaded Mach-Zehnder interferometer binary tree array, d groups of photon pair sources, d groups of controlled unitary gate modules, a multi-quantum bit control Z gate, four d-dimensional adjustable linear networks and two state tomography linear networks. The cascaded MZI binary tree array and the controlled unitary group jointly form a first coding layer and a parameter-containing entanglement layer, and the adjustable optical linear network and the multi-quantum bit control Z gate jointly form a second coding layer and a parameter-containing entanglement layer. The two state chromatography linear networks can further improve the computing power of the chip in a mode of changing a measurement basis or executing quantum state chromatography. According to the method, the expression ability of the light quantum neural network is improved, and the development of a light quantum computing chip and the application of the light quantum computing chip in actual tasks are facilitated.

The invention provides a cognitive function evaluation method and system based on data fusion, and relates to the technical field of data processing. The method comprises the following steps: acquiring multiple medical examination data of a to-be-evaluated patient; extracting data features of the multiple medical examination data; fusing the data features of the multiple medical examination data through a linear perception fusion algorithm to obtain fused features; according to the fusion features, the cognitive function of the patient to be evaluated is evaluated through a quantum neural network. According to the method, the data features of the multiple medical examination data are fused, then the cognitive function of the patient to be evaluated is evaluated through the quantum neural network according to the fused features, the cognitive state of the patient can be reflected more comprehensively, and improvement of the accuracy of cognitive function evaluation is facilitated.

The invention discloses a thermal imaging technical method, device, equipment and medium, and relates to the field of thermal imaging temperature drift compensation correction, and the method comprises the steps: collecting the original temperature data of an infrared sensor in the thermal imaging equipment; preprocessing the original temperature data, and converting the preprocessed temperature data into a superposition state and an entanglement state of quantum; according to the loss function and gradient of the quantum neural network, constructing a temperature drift compensation model based on the quantum neural network; and inputting the converted superposition state and entanglement state of the quantum into a temperature drift compensation model, and outputting data of the thermal imaging equipment after temperature drift compensation correction during operation. Therefore, quantum calculation is introduced into a dynamic temperature drift compensation and correction algorithm of the thermal imaging equipment, the precision of a temperature drift compensation model is enhanced by using the parallel calculation capability of a quantum superposition state and an entangled state, and the reaction rate of the thermal imaging equipment in a high-temperature environment is efficiently improved; and the problems of operation blockage, image distortion, abnormal shutdown and the like caused by excessive heating of the equipment are solved.

The invention discloses an anti-quantum computing attack data encryption method based on a deep quantum neural network, which is applied to the technical field of data processing, and comprises the following steps: obtaining quantum associated data and security constraint parameters in the fields of government affairs and finance, and completing classical data quantum bit coding, high-dimensional feature extraction and noise filtering through quantization preprocessing; generating high-dimensional quantum state data adaptive to the quantum neural network; a deep quantum neural network encryption architecture is constructed, a dynamic key pool is generated based on quantum superposition-entanglement characteristics, and a layered encryption scheme is formed in combination with a weight sub-update mechanism; encryption parameters are optimized through a quantum neural network reinforcement learning framework, robustness is improved, and a self-learning optimization strategy is generated; an evolution trajectory is monitored by means of a quantum neural network to construct an encryption execution closed loop, and finally comprehensive evaluation information containing indexes such as a training convergence rate and key efficiency is generated through a quantum gate inverse operation learning model and a Hash verification neural network, so that the quantum attack resistance is enhanced.

The invention discloses a remote sensing image recognition method, system, device and product based on quantum migration, and relates to the technical field of remote sensing image recognition, and the method comprises the steps: obtaining a pre-trained deep learning model used for recognizing a preset target, and extracting the high-level features of the deep learning model; encoding the high-level features into a quantum state through angle encoding, and constructing a multi-bit entangled state taking a specified quantum bit as a center to form a quantum neural network; forming a hybrid migration model according to the pre-trained deep learning model and the quantum neural network; and training the hybrid migration model, inputting the remote sensing image to be identified into the trained hybrid migration model, and identifying to obtain a preset target. By adopting the embodiment of the invention, the parameter quantity of the model can be reduced, and meanwhile, the accurate recognition requirement of the remote sensing image is met by utilizing the computing power of the classic computer and the global characteristics of the quantum computer.

The invention relates to the technical field of neural networks and vehicle professional language processing, in particular to a vehicle professional language text classification method and system based on a quantum recurrent neural network, and the method comprises the steps: enabling a word to be embedded into a pre-trained hybrid calculation model with a vector as an input feature, the hybrid calculation model comprises a quantum recurrent neural network uploaded in batches and a classical neural network, and the quantum recurrent neural network uploaded in batches is fused into the classical neural network; the quantum neural network uploaded in batches is characterized in that an input word embedding vector is divided into a plurality of batches according to a predefined quantum bit number, and the batches are connected through a variable component sub-circuit, so that a coding-variable layering hybrid model is formed; and running the hybrid calculation model, and outputting the text classification corresponding to the to-be-processed corpus data. According to the method, limited data resources are better utilized to carry out classification tasks, and meanwhile, the understanding ability of the model for complex vehicle professional language expression is improved.