18 results about "Boltzmann machine" patented technology

The invention relates to the technical field of data processing, in particular to a multi-AI algorithm collaborative intelligent management system based on a large model, and the system comprises a data access module which collects original messages of terminal equipment of multiple manufacturers; the protocol adaptation module analyzes a device communication characteristic spectrum through a quantum entanglement separator, and outputs a standardized time-space event stream; a federation modeling module creates a parallel calculation instance, and a quantum tunneling mechanism is utilized to fuse the feature vectors to generate an equipment association topology model; the chaotic scheduling module generates an atomic task fractal network and assigns the atomic task fractal network to edge nodes; the interface optimization module calculates an interface field mapping relation through a quantum Boltzmann machine; and the closed-loop correction module triggers equipment communication characteristic spectrum re-calibration. Multi-source device communication differences are eliminated through quantum protocol analysis, federated quantum fusion breaks through algorithm collaboration barriers, chaotic fractal scheduling realizes resource dynamic optimization, a closed-loop collaboration system of device access to interface configuration is formed, and the problems of protocol incompatibility of multi-source heterogeneous devices and AI algorithm collaboration obstacles are solved.

The invention belongs to the technical field of data processing, and discloses a multi-modal project data analysis method, which comprises the following steps: collecting heterogeneous modal project data, carrying out denoising and timestamp correction on each modal project data based on an adaptive multi-scale filtering technology, carrying out cross-modal time alignment, and outputting a multi-modal signal. Constructing a multi-level Boltzmann machine energy network, and modeling the joint probability distribution of the multi-modal signals; a dynamic complexity measurement mechanism based on topological entropy is designed and used for representing project structure complexity, and a topological entropy dynamic field is generated; the topological entropy dynamic field is used as a constraint condition, a multi-dimensional covariant field frame is used, nonlinear coupling and space-time propagation between multi-modal signals are simulated, the multi-modal signals are abstracted into a string vibration mode, local topological defects in the topological entropy dynamic field are recognized, and an abnormal fluctuation map is generated; and the analysis of the complex multi-modal data in the project operation process is more intelligent and controllable.

The present application belongs to the technical field of data processing, and discloses a multi-modal project data analysis method, which comprises the following steps: collecting heterogeneous modal project data; based on adaptive multi-scale filtering technology, denoising and timestamp correction are performed on each modal project data, and cross-modal time alignment is performed, and a multi-modal signal is output; a multi-level Boltzmann machine energy network is constructed, and a joint probability distribution of the multi-modal signal is modeled; a dynamic complexity measurement mechanism based on topological entropy is designed, which is used for representing project structure complexity, and a topological entropy dynamic field is generated; the topological entropy dynamic field is used as a constraint condition, a multi-dimensional covariant field framework is used, nonlinear coupling and space-time propagation among the multi-modal signals are simulated, the multi-modal signals are abstracted into string vibration modes, local topological defects in the topological entropy dynamic field are identified, and an abnormal fluctuation atlas is generated; and the analysis of complex multi-modal data in the project running process is more intelligent and controllable.

The present invention belongs to the field of protein structure prediction technology and relates to a protein structure prediction method, system, and device based on an optical quantum computer. The method comprises: 1) obtaining a multiple sequence alignment (MSA) matrix of the target sequence based on the target sequence alignment of the protein to be predicted; 2) encoding the obtained MSA matrix into a {0,1} matrix; 3) converting the protein's amino acid interactions into an undirected graph model with {0,1} state nodes and training the model using an optical quantum computer using a Boltzmann machine training mechanism to obtain weight coefficients for the edges connecting the nodes in the undirected graph model; and 4) obtaining interaction coefficients between different amino acids in the protein based on the weight coefficients. This method improves the computational efficiency and solution results of protein structure prediction, and solves the problem that the existing technology is difficult to calculate and cannot obtain accurate solutions for complex scheduling problems.

The application discloses a kind of water transport ship traffic collaborative control methods based on deep learning, specifically includes: obtaining ship operation data set, time index alignment is carried out, and state vector sequence is generated;State vector sequence is encoded to feature, and deep boltzmann machine model is constructed;First conditional energy function, second conditional energy function and energy coupling matrix are established between model layer, and form hierarchical conditional energy function set;Hierarchical conditional energy function set is carried out to hidden variable probability inference, and hidden layer probability state vector sequence is generated;Parameter flow shape is divided to energy function parameter, and policy optimization is carried out under the constraint of confidence domain, and updated energy function parameter set is generated;Updated energy function parameter is carried out to energy calculation, and state probability distribution sequence is generated;State probability distribution sequence is carried out to strategy mapping, and ship traffic collaborative control strategy is generated.The application constructs hierarchical energy coupling model, realizes the collaborative control of multiple ships state.

The present application relates to the technical field of data processing, and more particularly to a multi-AI algorithm collaborative intelligent management system based on a large model, comprising a data access module that collects original messages of multiple manufacturer terminal devices; a protocol adaptation module that analyzes device communication characteristic spectrum through a quantum entanglement separator and outputs standardized space-time event flow; a federal modeling module that creates parallel computing instances and generates a device correlation topology model by fusing feature vectors using a quantum tunneling mechanism; a chaotic scheduling module that generates an atomic task fractal network and dispatches it to an edge node; an interface optimization module that calculates interface field mapping relationships through a quantum Boltzmann machine; and a closed-loop correction module that triggers device communication characteristic spectrum recalibration. The quantum protocol analysis eliminates differences in multi-source device communication, federal quantum fusion breaks through algorithm collaboration barriers, chaotic fractal scheduling realizes dynamic optimization of resources, and a closed-loop collaborative system is formed from device access to interface configuration, solving the problem of incompatible protocols of multi-source heterogeneous devices and AI algorithm collaboration obstacles.

The present invention relates to the technical field of intelligent elderly care, and further relates to an intelligent elderly care service management method based on in-depth information integration. The method includes: Step 1: Collect the health data of each elderly person through various sensors; take the health data of the same elderly person collected at the same moment as a sample, and for each sample, extract the features of the sample; form all the samples into a sample data set; form the features of all the samples into a feature data set; Step 2: Perform data fusion on the sample data set to obtain a fused data set; Step 3: Perform matrix feature analysis on each element in the time series to screen out the elderly people with health problems and send information to the elderly people to prompt them that there are health problems. The present invention performs health data fusion through a multi-layer Boltzmann machine, provides continuous monitoring, personalized services, and timely problem warnings, so as to improve the scientific nature and effectiveness of intelligent elderly care services.

The application discloses a production business process node vector representation method fusing structure and behavior characteristics. The method comprises the following steps: firstly, structure characteristics of a production business process are extracted by using a random walk algorithm and a Skip-gram model; then, behavior characteristics of the production business process are extracted by using the Skip-gram model; finally, a deep Boltzmann machine is introduced to fuse the structure characteristics and the behavior characteristics of the production business process, thereby automatically performing vector representation on nodes of the production business process, extracting and fusing various characteristics to improve the accuracy of the node vector representation, and further improving the accuracy of subsequent production business process recommendation, optimization and the like. The application has the advantages that the graph structure characteristics of the production business process itself and the behavior characteristics based on historical execution logs are fused, deep learning technology is used to strengthen the production business process model, the node vector representation which simply depends on the historical execution logs is supplemented and improved, and the accuracy of the node vector representation is improved.

The invention discloses a personalized learning path recommendation method based on deep learning, and the method comprises the following steps: S1, collecting and standardizing a learning log, resource data and a knowledge point first repair relation, and generating a training data set and a knowledge graph; s2, constructing a deep Boltzmann machine model based on the training data set and initializing parameters; s3, performing model improvement based on meta learning; s4, training a strategy prior network and a value network; s5, screening resources according to the knowledge graph and the learner state to generate a candidate set; s6, adopting a Monte Carlo tree with prior guidance to search and optimize path selection, and generating candidate learning paths; and S7, selecting an optimal path according to the candidate path scores, and outputting final model parameters after judging convergence. According to the method, accurate recommendation of personalized learning paths is realized, and the learning efficiency and the intelligent adaptability of the model are improved.

This invention relates to an entangled Boltzmann machine apparatus and method. An entangled Boltzmann machine apparatus may include: a probability bit node array comprising a plurality of probability bit nodes, each probability bit node being used to generate a true random number with a desired probability; a random sampling device for sampling the current state of the plurality of entangled probability bit nodes in the probability bit node array; a probability calculation device for calculating the entanglement probability distribution of the plurality of entangled probability bit nodes based on their current states; a decision device for determining whether to change the current state of the plurality of entangled probability bit nodes to a target state based on the entanglement probability distribution of the plurality of entangled probability bit nodes; and a driving device for adjusting the driving signals of the plurality of entangled probability bit nodes to achieve the target state in response to the decision device's decision to change the current state of the plurality of entangled probability bit nodes to the target state.

The application discloses a self-supervised deep representation method based on contrast learning, comprising the following steps: S1, obtaining a data set V to be trained; S2, training N pieces of data in one batch, and performing data enhancement on each piece of data v; S3, feeding the data v and the enhanced data vs into a restricted Boltzmann machine for forward propagation, and obtaining corresponding data h and data hs in the hidden layer; S4, performing contrast learning to improve the similarity between positive samples; S5, performing back propagation to obtain reconstructed data v' of the data v in the visual layer; S6, minimizing the distance between the data v' and the data v; S7, repeating steps S2-S6 until the reconstruction loss function converges, and realizing the training of the restricted Boltzmann machine; and S8, using the trained restricted Boltzmann machine in S7 to perform deep representation learning on the data set V, and obtaining deep feature representation thereof. The application can overcome the purposeless learning defect of the traditional restricted Boltzmann machine, thereby improving the representation capability of the model.

The application discloses a kind of multi-modal characteristic fusion's intelligent target identification method, on the basis of constructing typical aerial target high-resolution range image, infrared image sample library, respectively using attention bidirectional gate recurrent unit model from high-resolution range image time series sample, using light convolutional neural network model from infrared image sample learning extraction aerial target multi-modal deep feature representation;Using Gaussian distribution limited Boltzmann machine realizes aerial target multi-modal feature abstract fusion, removes redundancy, forms more discernibility, representative multi-modal feature joint representation;Using kernel ultra-limit learning machine with strong generalization ability under small sample condition, fast training speed and high classification accuracy as classifier, realize aerial target type identification.The application uses heterogeneous deep learning model to abstract extraction and fusion aerial target feature information in high-resolution range image, infrared image for target identification, improve recognition precision.

This invention discloses an AI-based intelligent patrol method and system for unmanned aerial vehicles (UAVs), comprising the following steps: collecting multi-source perception data from the UAV and preprocessing it; performing multi-modal feature fusion and spatial topology modeling using an improved multi-modal deep Boltzmann machine; constructing a three-dimensional coordinate reference system to determine tower locations, conductor directions, ground feature heights, and navigable airspace; performing path search and constraint solving to generate an initial inspection route; performing end-side inference based on a pruned quantization multi-modal deep Boltzmann machine and updating the unified environmental spatial representation; incrementally updating the three-dimensional spatial modeling results and correcting the trajectory to generate an optimized inspection route; collecting multi-source perception data along the optimized inspection route, performing association labeling and data uploading to generate an inspection dataset. This invention achieves intelligent patrol of UAVs based on multi-modal deep learning and three-dimensional spatial modeling, possessing advantages such as accurate environmental perception, adaptive route optimization, and high-quality management of inspection data.

The application discloses a brain tumor image feature extraction method and system, electronic equipment and computer readable storage medium, and belongs to the technical field of brain tumor image feature extraction; the deep belief network composed of three consecutive Boltzmann machines effectively extracts the depth features of the image; then the dimensionality reduction is performed through the local linear embedding, and under the premise of reducing the redundant features, the sufficient expression of the image is effectively ensured, and the space is saved. The problems that the brain tumor image features are difficult to extract, and the image features extracted by the traditional convolutional neural network (CNN) are accompanied by redundant information or noise are solved.

This invention provides an ADAS intelligent control method based on full-vehicle camera perception, comprising the following steps: S1: Rigid body spatiotemporal synchronization and online joint calibration of the full-vehicle surround-view camera group based on Lie group and Lie algebra; S2: Pixel-level alignment and full-domain static-dynamic bi-branch semantic mask generation; S3: Hierarchical confidence perception and target full attribute decoupling extraction based on variational Bayesian inference; S4: Spatiotemporal coupled risk field modeling based on lattice Boltzmann machine; S5: Multi-constraint hierarchical decision-making and expected behavior sequence generation; S6: Actuator control quantity calculation and feedforward compensation. This invention breaks down the independent barriers between each link in traditional schemes by deeply integrating the Lie group and Lie algebra online calibration algorithm, the variational Bayesian decoupled perception algorithm, and the lattice Boltzmann machine risk field modeling algorithm, achieving end-to-end collaborative optimization and closed-loop iteration from front-end calibration to end-to-end control.

The invention discloses a DBM-SGMD-based train network resonance characteristic identification method and system, and relates to the technical field of new energy power generation control, and the method comprises the steps: collecting target train network system data, and carrying out the preprocessing; constructing a filter bank by adopting a least square method, and obtaining optimal vehicle network resonance voltage and current data; calculating an embedded dimension by using a symplectic geometry method, and reconstructing symplectic geometry components; and classifying the symplectic geometric components through a Gaussian depth Boltzmann machine. According to the method, the vehicle network resonance phenomenon is identified through the training model, and the identification accuracy and efficiency can be improved. The deep learning algorithm can extract more complex features through multi-level data processing, thereby improving the identification capability of vehicle network resonance. Vehicle network resonance identification is not only a key for ensuring safe and stable operation of an electrified railway system, but also an important link for improving the power supply quality of a traction power supply system.

The application discloses a kind of based on Boltzmann's model neural network prediction method, comprising: by Boltzmann machine model creation with symmetrical connection right random neural network and deploy log server;With log, the network topology of Boltzmann machine is handled in layers, obtains first matrix and second matrix;First matrix and second matrix are respectively put into Boltzmann machine state transition Markov chain and are predicted, if the difference of predicted value is less than 10% weighted average, then matrix predicted value is final predicted value;If the difference of predicted value is greater than 10% weighted average, then take the final predicted value of the proportion of high core business data of matrix data. The application guarantees the timeliness of data processing, and highlights the position of artificial intelligence in the field of big data processing.

The present invention relates to the technical field of protein structure prediction, and relates to an optical quantum computer-based protein structure prediction method, system and apparatus. The method comprises: 1) obtaining a multiple sequence alignment (MSA) matrix of a target sequence on the basis of target sequence alignment of a protein needing to be predicted; 2) encoding the obtained MSA matrix into a (0,1) matrix; 3) converting interaction of amino acids of the protein into an undirected graph model of (0,1) state nodes, and using a Boltzmann machine training mechanism and an optical quantum computer to perform training to obtain weight coefficients of edges connecting nodes in the undirected graph model; and 4) obtaining coefficients of interaction between different amino acids of the protein on the basis of the weight coefficients. The present invention improves the computation efficiency and solution result of protein structure prediction, and solves the problems in the prior art that it is difficult to perform computation for complex scheduling problems and accurate solutions cannot be obtained.