166 results about "Random matrix" patented technology

The invention relates to the technical field of communication command, in particular to an intelligent emergency command system and method based on scene twinning. Comprising a data acquisition module, a scene twinborn management module, an emergency situation analysis module, an emergency response decision module and a resource scheduling command module. Multi-source data of an emergency scene are collected through a sensor network, a scene twinning model is constructed by using augmented reality and digital twinning technologies, and real-time rendering of a virtual scene is realized. According to the method, emergency situation analysis, risk detection and trend prediction are performed in combination with a random matrix theory and a graph neural network algorithm, an emergency response strategy is generated by using a decision tree based on an analysis report, resources are scheduled and allocated through a multi-objective optimization algorithm, and an optimal emergency command scheme is ensured. According to the invention, the efficiency and scientificity of emergency command are improved, and the rapidity and accuracy of emergency response are enhanced.

The invention discloses a data encryption and dynamic key management method based on a quantum security protocol, and the method comprises the steps: 1, selecting an initial quantum random seed and a classical entropy source seed, and generating a key at an initial moment; at each moment, forming an update increment through a nonlinear chaos increment and a fractal function, constructing a key update function, and updating the key; 2, preprocessing the original message into an integer vector; constructing a dynamic transformation matrix by using a secret key at the current moment, sampling from multi-dimensional Gaussian distribution to obtain a short vector, and constructing a public random matrix; encrypting the integer vector by combining the dynamic transformation matrix, the short vector and the public random matrix to obtain a ciphertext; and step 3, decrypting by using the secret key at the current moment and the dynamic transformation matrix which are synchronously updated, and recovering the original message. The invention provides a data encryption scheme with high security and quantum attack resistance.

The invention discloses a functional connection and dynamic model-based anatomical connection prediction method, which comprises the following steps of: firstly, constructing a neurodynamic model, taking a random matrix as initial input, combining functional connection data and the neurodynamic model, and generating a coupling matrix which is more in line with neurodynamic characteristics by iteratively optimizing the random matrix; and the coupling matrix is used for predicting anatomical connection and replacing anatomical connection data to construct a high-precision brain model. According to the method, the initial random matrix is iteratively optimized, so that the generated coupling matrix gradually approaches real anatomical connection, non-intrusive anatomical connection prediction is realized, dependence on intrusive structure connection data is remarkably reduced, and compared with an existing neurodynamics modeling method depending on dMRI data, the method has the advantages that the modeling efficiency is improved, and the modeling cost is reduced. The used DMF model achieves higher fitting degree in structure and function, has higher flexibility and adaptability, is beneficial to revealing deep connection between functional connection and anatomical connection, and provides more efficient technical support for brain network research.

The invention belongs to the field of magnetic resonance measurement signal noise filtering, and particularly relates to a ground magnetic resonance signal sparse denoising method of a combined dictionary, which comprises the following steps: reconstructing a power frequency harmonic component of a magnetic resonance signal according to frequency bands, and eliminating power frequency harmonics from the magnetic resonance signal to obtain a power frequency-removed signal; constructing a track matrix Z by adopting a Gaussian random matrix, constructing a track matrix Y by adopting a power frequency-removed signal, taking first K columns of the track matrix Z and normalizing to obtain an initial dictionary D, and using the track matrix Y as an original sample for constructing a sparse coefficient matrix X; updating the initial dictionary D and the sparse coefficient matrix X through K-SVD dictionary learning, and reconstructing a track matrix by adopting the updated dictionary # imgabs0 # and the sparse coefficient matrix # imgabs1 # to obtain a track matrix W; and obtaining a pure magnetic resonance signal by taking the first row of the track matrix W, and realizing effective removal of MRS signal noise in a complex electromagnetic interference scene under the condition of single signal acquisition.

The invention relates to the technical field of remote sensing image processing, in particular to an end-to-end neural network InSAR (Interferometric Synthetic Aperture Radar) phase unwrapping method based on mixed attention, which takes U-Net as a basic framework, extracts key features and improves resolution through down-sampling and up-sampling operations, thereby effectively recovering detail information. Specifically, a convolutional block attention module (CBAM) and two receptive field modules, namely cavity spatial pyramid pooling (ASPP) and a receptive field module (RFB) are combined to construct an RFAUNet network model, and then simulation data sets generated by two methods of digital elevation inversion and random matrix generation are used for training a neural network until a good unwrapping effect is obtained. And finally, carrying out a phase unwrapping experiment on simulation data and real data to verify the effectiveness and robustness of the RFAUNet network model.

The invention belongs to the technical field of image encryption, and discloses a medical image compression and encryption method and system based on a memristor FHN neuron model, and the method comprises the steps: fusing n color or gray medical images of any size into an M * N * 3 fusion image; sparse representation is carried out on the fused medical image in combination with discrete wavelet transform, and redundant data are reduced; initial conditions and control parameters of an m-FHN chaotic system are generated through an SHA-512 hash value of an original image, and strong association of a secret key and a plaintext is ensured; the m-FHN system is iterated to generate three groups of chaos sequences, and the chaos sequences are quantized to obtain a pseudo-random matrix for diffusion and scrambling operation; performing forward diffusion, index scrambling and backward diffusion operation on the sparse image in sequence to obtain a final compressed and encrypted image; a receiving end acquires an initial parameter and a control parameter bound with a plaintext through a secure channel, and then performs inverse backward diffusion, inverse index scrambling, inverse forward diffusion and inverse discrete wavelet transform on a ciphertext image in sequence to obtain a decrypted image.

The invention discloses an ultra-harmonic rapid detection method based on compressed sampling and multiple measurement vectors, and solves the problems of high sampling rate, low reconstruction efficiency and insufficient spectrum resolution in the prior art. Sub-Nyquist compressed sampling is realized through a sparse random matrix, and the hardware burden is reduced; a dynamic step size sparse self-adaptive matching pursuit algorithm (DSSAMP) is adopted to reconstruct signals, the step size is adjusted in a self-adaptive mode in combination with residual energy changes, and the convergence speed and precision are balanced; an improved multi-measurement vector model (IMMV) is constructed through framing processing and introduction of interpolation factors and prior frequency point screening, and the calculation efficiency is improved; and finally, extracting super-harmonic frequency, amplitude and phase parameters based on an orthogonal matching pursuit (OMP) algorithm. According to the method, the high resolution is ensured, the sampling rate and the calculation complexity are remarkably reduced, and the real-time requirement is met.

The invention relates to a distributed resource portrait construction method based on RMT and deep learning fusion driving, and the method comprises the following steps: obtaining distributed resources from different sources in a power system, carrying out the preprocessing, and constructing an initial distributed resource portrait; based on the initial distributed resource portrait, performing distributed resource classification by adopting fuzzy clustering; based on a distributed resource classification result, key features are extracted by adopting a random matrix theory; and based on the key features, adopting a deep neural network to reconstruct the initial distributed resource portrait to obtain a final distributed resource portrait. Compared with the prior art, the method has the advantages of dynamically describing the state and characteristics of the distributed resources in multiple dimensions and the like.

The invention relates to the technical field of medical informatization, in particular to a ward distress call method and system based on intelligent medical treatment, and the method comprises the steps: obtaining patient state data collected by a plurality of sensors in a ward; receiving a ward help-calling instruction; constructing a multi-modal data fusion matrix based on the patient state data; executing random matrix transformation and feature extraction according to the multi-modal data fusion matrix; based on the extracted features, topological feature mapping and anomaly detection are carried out; executing group theory base risk assessment according to the topological features; calculating a chaotic power system early warning index based on a group theory risk assessment result; determining a call-for-help judgment result according to the early warning index; a corresponding response strategy is executed based on the call-for-help judgment result, the system can comprehensively grasp the state of the patient through the multi-modal data fusion technology, and the evaluation accuracy is greatly improved. And secondly, the robustness of the system is enhanced by random matrix transformation, so that the system can cope with a complex and changeable medical environment.

The invention discloses an accurate zero-knowledge proof method for a linear relation with a hidden product. The method comprises the following steps: converting a linear relation with the hidden product on an incomplete split ring # imgabs0 # into a linear relation on # imgabs1 #; an attached additional condition is converted into a gradual component product relation on # imgabs2; the secret evidence # imgabs4 # on the # imgabs3 # is inversely transformed into # imgabs6 # elements xi on the # imgabs5 # through NTT, and a uniform and random matrix and a plurality of vectors are selected on the # imgabs7 #; generating a commitment of the first stage, sampling a vector from discrete Gaussian distribution, and determining a public vector; selecting first challenge information; generating a new commitment, a blind element and a redundant item through the first challenge information; randomly sampling second challenge information c from the challenge space C; and c, determining a vector # imgabs9 # for blinding # imgabs8 # to verify whether the vector # imgabs10 # meets a preset condition or not through c, checking whether a series of equations are established or not, if all equations are established, accepting certification, and otherwise, refusing to accept.

The invention provides a multi-image encryption method based on a four-dimensional memristor hyperchaotic system and a storage medium, and the method comprises the steps: constructing a new memristor model, introducing a three-dimensional chaotic system, and obtaining a four-dimensional memristor hyperchaotic system; generating hash values of a plurality of plaintext images, obtaining an initial value of a chaotic system through mathematical model conversion, and generating a chaotic sequence after iterative calculation; a cross-plane spiral traversal method is adopted, pixel positions of the image are rearranged to obtain a first scrambled image, and then a chaotic series generated by a chaotic system is utilized to realize secondary image index scrambling to obtain a scrambled image, so that correlation between adjacent pixels is reduced; performing cross-plane bidirectional diffusion modular addition operation on the scrambled image and a pseudo-random matrix generated by the chaos sequence, so that each pixel comprises information of more other pixels as much as possible, and obtaining a final ciphertext image; according to the method, the randomness of the sequence and the image processing efficiency can be improved, and meanwhile higher confidentiality and robustness are achieved.

The invention discloses a coal-fired power generation data encryption method and device, equipment and a medium, and relates to the technical field of encryption, and the method comprises the steps: carrying out the preprocessing of target coal-fired power generation data; converting the preprocessed target coal-fired power generation data into a two-dimensional matrix; performing multi-round iterative encryption on the two-dimensional matrix in a manner of generating a random matrix for encryption by using chaotic mapping to obtain an encrypted data matrix; carrying out steganography on the encrypted data matrix to obtain steganographic data; the steganographic data is used for storage or transmission. According to the invention, the data security can be improved.

The invention discloses an electronic circuit board defect detection intelligent method based on an RMT deep learning model, and relates to the technical field of electronic circuit board detection, and the method comprises the following steps: carrying out the image collection of an electronic circuit board through high-resolution camera shooting, carrying out the de-noising processing of an image, reducing the noise influence caused by environmental factors and equipment errors, and improving the detection precision of the electronic circuit board. And a clean input image is provided for subsequent analysis. According to the method, the convolutional neural network and the random matrix theory are combined, so that the accuracy and robustness of defect detection of the electronic circuit board are remarkably improved. The CNN accurately extracts image features, the RMT effectively filters noise, and the model stability is enhanced. The self-adaptive algorithm and the multi-task learning optimization training process reduce false detection and missing detection and improve the detection efficiency, thereby improving the automation level and the detection precision of the production line.

The invention discloses a load twinborn modeling and prediction method and system based on multi-source heterogeneous feature fusion, and the method comprises the steps: collecting original data from a user side, a power grid side and an external environment side, and carrying out the preprocessing, and obtaining multi-source heterogeneous feature data; extracting a structural spectrum index and a structural disturbance embedded spectrum entropy index of the multi-source heterogeneous feature data based on a random matrix theory; training a load prediction model based on the multi-source heterogeneous feature data, the structural spectrum index and the structural disturbance embedded spectrum entropy index; and constructing a virtual load twinborn model based on the trained load prediction model, the user static parameters and the dynamic variables, and performing multi-granularity load prediction. According to the method, the prediction precision and generalization ability can be remarkably improved, the interpretability and prediction precision of user load behaviors are improved, active perception and intelligent regulation and control of power system operation are supported, and the method has good engineering expandability and is suitable for dynamic management and control requirements of park-level, regional-level and distributed load resources.

The invention relates to a weak power distribution network fault rapid identification and positioning method, and belongs to the field of power distribution network fault identification. According to the method, a fault type identification method based on multi-point time-frequency matrix fusion analysis is provided, an improved adaptive noise complete set empirical mode decomposition method and a Hilbert band-pass filtering algorithm are utilized to construct time-frequency matrixes corresponding to three-phase voltage, current and zero-sequence voltage waveforms at a bus after a fault; performing singular value decomposition, obtaining a singular spectrum and a characteristic index corresponding to the singular spectrum, and inputting the singular spectrum and the characteristic index into a multi-classification SVM model to realize identification of a weak power distribution network fault type; the invention further provides a fault section positioning method based on synchronous waveform feature extraction and matrix analysis, synchronous waveform features at multiple measurement points are extracted from a multi-domain angle, and an analysis object is converted into a random matrix feature value from a fault feature matrix by introducing a random matrix theory. And determining a cluster center point of the fault section in the feature value space by using a historical sample, and further performing online fault positioning to determine the fault section.

The invention discloses a network data intelligent management system and method based on artificial intelligence, and relates to the technical field of network data management, original sensor data is projected to a sparse domain through an auto-encoder, whether the sparsity of the original sensor data meets a compressed sensing condition is verified, if yes, a Gaussian random matrix is constructed, and if not, a Gaussian random matrix is constructed; the Gaussian random matrix maps sparse signals to a low-dimensional space, a server side uses a basis tracking reconstruction algorithm to recover original sensor data according to the Gaussian random matrix, and after performance data in actual deployment are collected, the performance score of the deployment is calculated and obtained according to the evaluation model. And a corresponding optimization strategy is generated according to a performance score calculation result. According to the invention, in an application scene with limited bandwidth and limited equipment energy consumption, the data quality is ensured, the cost of data acquisition and transmission is significantly reduced, and the overall efficiency and intelligent level of network management are improved.

The invention provides an image encryption method, system and device based on an improved random matrix interpolation iteration sequence and a medium, and relates to the technical field of image encryption, a plaintext image is acquired, and a two-dimensional digital matrix of preset pixels is generated; setting an initial coordinate value and a key parameter matrix; on the basis of a Monte Carlo sampling driven dynamic iteration mechanism, a two-channel encryption sequence is formed after multiple times of iteration; performing analog-to-digital operation and numerical value normalization operation on the encrypted sequence to generate a standardized digital matrix; after the first encryption transformation is completed, generating a primary ciphertext matrix; processing the primary ciphertext matrix to generate a final ciphertext; and outputting the final ciphertext, and performing format packaging to generate an encrypted output file. By performing format conversion processing on the plaintext image to generate the two-dimensional digital matrix of the preset pixel, images of different formats and sizes can be processed in a unified manner, the compatibility and universality of an encryption algorithm are improved, and the method can be applied to various types of image encryption scenes.

The invention relates to the field of intelligent automobile data processing, and discloses an automobile user behavior prediction method based on a data model, and the method comprises the following steps: 1, collecting user multi-modal data which comprises a user driving track, a purchase record, a vehicle-mounted video and a voice instruction; 2, carrying out standardization processing on the multi-modal data, and constructing a data matrix; 3, calculating a covariance matrix of the data matrix, and improving the stability of the covariance matrix through an optimization algorithm; step 4, eliminating noise features in the covariance matrix based on a random matrix theory, and extracting features with relatively high signal intensity; and 5, optimizing the cooperative relationship between the signal features by adopting a feature interaction modeling method. According to the method, the technical scheme of combining multi-modal data acquisition and standardized processing is adopted, and the fusion of high-dimensional and diversified data under the same analysis framework is realized by unifying the scale and distribution of different modal data.

The invention relates to the technical field of financial risk control systems, in particular to a supply chain financial intelligent risk control system and method. The random matrix construction module is in communication connection with the data acquisition module; constructing a random matrix based on the supply chain knowledge graph data; the topological feature extraction module is in communication connection with the random matrix construction module; the risk propagation model module is in communication connection with the topological feature extraction module; the nonlinear risk quantification module is in communication connection with the risk propagation model module; the quantum probability risk prediction module is in communication connection with the nonlinear risk quantification module; based on a nonlinear risk quantification result, performing quantum probability risk prediction; the risk assessment result output module is in communication connection with the quantum probability risk prediction module; the enterprise risk assessment report is generated, the overall efficiency of supply chain financial risk management is remarkably improved, the current risk condition can be accurately assessed, and risk events possibly occurring in the future can be predicted.

The invention provides an improved heterogeneous pedestrian trajectory prediction method under a federated learning framework. The method comprises the following steps: firstly, dividing a pedestrian data set through a random matrix to ensure that each client obtains heterogeneous data so as to increase the diversity of the data; next, local training is carried out on each client, the client can adopt a social space-time diagram convolutional neural network (Social-STGCNN) or a social attention network (STGAT), the adaptability of the model to local data is improved through fine adjustment of a private data set, and meanwhile similarity calculation is added into a loss function to maintain the stability of the model; and then, dynamically allocating weights by using a dynamic weight aggregation algorithm according to contribution indexes such as an average displacement error (ADE) and a final displacement error (FDE) so as to realize knowledge sharing of the heterogeneous model. Next, performing weighted average on client model parameters in the same group (namely, the same model type), updating a global model in the group, and transmitting the global model to each client in the group to ensure the consistency of initial parameters of the next round of training; and then knowledge distillation is carried out among different groups, and the consistency of prediction results when the heterogeneous model processes the same data is ensured by aligning model output. And finally, continuously optimizing the global model through multiple rounds of training until a preset training target is reached, and performing evaluation on the test set to improve the prediction precision and robustness of the model.

The present application discloses a method for making patrol path decisions of a swarm of unmanned aerial vehicles (UAVs) under resource constraints, which relates to the technical field of UAV path planning. The method comprises: discretizing the actual physical locations to be patrolled to construct an undirected topological graph; generating a steady-state distribution of each patrol node according to the topological constraints and the importance of the nodes; generating a plurality of transfer matrices with the same steady-state distribution but different transfer characteristics according to a multi-stage entropy-driven random matrix optimization algorithm; initializing the position of a navigator and determining the path selected by it according to the transfer matrix; implementing adaptive active positioning decisions under positioning constraints according to the navigator's reference path to ensure path tracking effects; according to the navigator's path selection and tracking, the followers form a humanoid grouping cluster with the navigator through a reward function; according to the multi-state transfer matrix and the humanoid grouping, automatically switching to the next transfer matrix when a transfer number threshold is reached, thereby realizing intelligent patrol path decisions of the UAV swarm under resource constraints.

The invention relates to the technical field of medical artificial intelligence, and discloses a brain disease prediction method based on double encoders and a diffusion model, and the method comprises the steps: carrying out the preprocessing of a functional magnetic resonance image, and constructing a brain function network; data enhancement of semantic preservation is achieved through a diffusion model, a dual random matrix and a cosine scheduling strategy are adopted in the noise adding process, and a GraphTransform neural network containing global topological features is utilized in the denoising process; the spatial features of the brain network and the time dynamic features of the BOLD signals are respectively extracted by using double encoders; designing a triple contrast learning mechanism to optimize cross-dimension feature interaction; and finally migrating to a downstream classification task to realize disease prediction. Small sample overfitting is relieved through diffusion enhancement, and the diagnosis reliability is improved; fusing spatial-temporal characteristics to assist multi-dimensional pathological analysis; and the cross-site adaptability of the model is enhanced, and collaborative analysis of multi-center heterogeneous data is supported. The method is suitable for auxiliary diagnosis of cerebral diseases such as infantile autism.

The present disclosure describes techniques of releasing data while protecting individual privacy. A dataset may be compressed by applying a first random matrix. The dataset may be owned by a party among a plurality of parties and there may be a plurality of datasets owned by the plurality of parties. A noise may be added by applying a random Gaussian matrix to the compressed dataset to obtain a processed dataset. The processed dataset ensures data privacy protection. The processed dataset may be released to other parties.

The invention discloses a body fat prediction system and method based on a multi-band impedance signal, and relates to the technical field of big data analys.The method comprises the steps that impedance data of different segments under different frequency bands, the body fat amount, the lean body weight and the human body weight are collected; integrity verification is carried out on the impedance data, missing values are filled with mean values, abnormal values are removed and corrected, and meanwhile timestamp alignment is carried out; extracting features to construct an original impedance vector, and mapping the original impedance vector to a high-dimensional space through a random matrix to generate a random mapping function; constructing a time sequence regression model, taking the embedding dimension as an input layer, taking the body fat amount and the lean body mass as an output layer, and using mean square error calibration and back propagation updating; and through a mean square error weighting evaluation model, outputting a performance standard result. The system comprises a data acquisition module, a data preprocessing module, a random distribution embedding module, a time sequence model training module and a display module. The method can adapt to impedance characteristics of different crowds, is suitable for portable terminal or smart home body measurement, and can be used at high frequency in daily life.

The application provides a multi-AUV system formation coordination control method with non-convex control input constraints, converts the coordination control of the multi-AUV system formation without a leader into a formation consistency problem, and defines the consistency state of the multi-AUV system formation without a leader. In the case that the water acoustic communication bandwidth is limited, the application selects a double-layer communication channel mainly composed of position and speed information. Considering the existence of communication delay and non-convex control input constraints, a discrete-time leaderless formation consistency constraint controller algorithm with communication delay is designed by introducing a constraint operator. By using the properties of graph theory, random matrix and SIA matrix, and selecting appropriate controller parameters, the multi-AUV system formation can reach the defined consistency state and maintain the stability of the formation shape.

The application discloses an image transmission method and device, a storage medium and a program product. In the method, a predetermined random matrix and an ordering value mapping table are acquired from a server; an original image matrix is transformed based on the random matrix to obtain an intermediate matrix; and finally, each pixel value in the intermediate matrix is replaced based on the ordering value mapping table to obtain an encrypted image. According to the technical scheme, each pixel in the original image matrix is subjected to matrix transformation and ordering reset, so that the original image is encrypted, the data processing amount of the image data in the encryption process is reduced, and the security of the image data in the plaintext transmission process is effectively improved.

The invention discloses a compressed sensing method and system based on a guaranteed-dimension semi-tensor product, a medium and equipment. The method comprises the following steps: acquiring a vector expression form of an original image; converting the obtained vector into a sparse vector representation through discrete cosine transform; generating a Gaussian random matrix, and performing dimensionality-preserving semi-tensor product operation through all-one vectors with weights to construct a measurement matrix; sampling the sparse vector by using the measurement matrix to obtain a compressed transmission vector; and the Gaussian random matrix and the transmission vector are sent to a receiving end, the receiving end reconstructs and recovers the original image based on an l1 norm optimization algorithm, and compressed sensing transmission is completed. According to the method, the measurement matrix is improved based on the semi-tensor product of the guaranteed dimension, and the measurement matrix construction method can reduce the size of the generated Gaussian random matrix, so that the memory is saved, the speed in the transmission process is higher, and the bandwidth cost is saved.

The invention discloses a power grid harmonic wave and inter-harmonic wave detection method and device based on distributed multi-frequency measurement data, equipment and a medium. A sine wave method comprises the following steps: detecting harmonic wave and inter-harmonic wave components existing at each sine wave measurement point based on a random matrix theory; determining the frequency and amplitude of each harmonic wave and inter-harmonic wave of each sine wave measurement point by using a rotation invariant technology; clustering the frequency of each harmonic wave and inter-harmonic wave on each measurement point by using a density-based noise application space clustering method to obtain the target frequency of each frequency cluster; and determining harmonics and inter-harmonics of the power grid according to the target frequency and the amplitude. Each harmonic and inter-harmonic component existing on a measurement point is detected through a random matrix theory and a rotation invariant technology, accurate frequencies and amplitudes of the harmonic and the inter-harmonic are obtained, and more accurate frequencies of the harmonic and the inter-harmonic of a power grid are obtained by fusing data of a plurality of distributed measurement points through a clustering method. Therefore, the detection capability of harmonic waves and inter-harmonic waves of a power grid is improved.

The invention discloses an RGB-D video encryption and decryption method based on a hyper-chaos technology and octlet fractional order Hartley transformation, and the encryption method comprises the following steps: S1, constructing a two-dimensional hyper-chaos system ICCHM based on ICMIC chaos mapping and Chebyshev chaos mapping, and generating a random matrix and a random sequence required by encryption by using the two-dimensional hyper-chaos system ICCHM; s2, defining octlet fractional order Hartley transformation and inverse transformation of the octlet fractional order Hartley transformation; and S3, carrying out RGB-D video encryption processing. According to the method disclosed by the invention, integral encryption of RGB-D is realized, two types of data with difference can be processed at the same time, and relatively high video encryption efficiency, relatively large key space and relatively high security are achieved; in addition, the encryption key management and transmission of the method are very convenient, and common cryptographic attacks can be effectively resisted.

The application discloses a bit data transmission scheduling method based on 6G space-ground integrated quantum network, a storage medium and equipment, and comprises the following steps: constructing a 6G space-ground integrated quantum communication network; searching for nodes meeting network scheduling task requirements in the quantum communication network, constructing a candidate node set, and screening out a determined best emission node; sending a quantum state to a receiving end through a quantum key distribution protocol at the best emission node, calculating a quantum bit error rate, selecting a candidate relay node from the candidate node set with the minimum quantum bit error rate as the target, and forming a quantum transmission channel; and the quantum transmission channel prepares a quantum key by using an entangled state, generates a random matrix density, generates a ciphertext through the random matrix density, and transmits the ciphertext to the receiving end. The application can reasonably schedule network resources, ensure data security through quantum communication, and the like.