55 results about "Network completion" patented technology

The invention belongs to the technical field of legged robot control, and provides a quadruped robot robust motion control method and system based on joint learning, and the method comprises the steps: obtaining the body observation information and privileged observation information of a quadruped robot; extracting key features in the obtained privileged observation information by adopting a privileged encoder to obtain a first potential feature vector; obtaining a second potential feature vector matched with the first potential feature vector based on the obtained historical ontology observation information and an adaptive network; constructing a joint loss function according to the obtained first potential feature vector and the second potential feature vector, and performing strategy network updating training by taking the minimum joint loss function as a target; and robust motion control of the quadruped robot is completed according to the trained strategy network.

A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained on the training data set to determine, upon receiving the classification of the at least one of: the operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the value chain network. A VCN process may configure a robotic process automation system to execute the task to facilitate an improvement in the value chain network.

The invention provides a power equipment remote cooperative operation and maintenance method based on a satellite flash technology, and the method comprises the steps: deploying a satellite flash positioning tag on power equipment, building a satellite flash communication network based on a satellite flash gateway, and completing the three-dimensional coordinate calibration and networking of the power equipment; each satellite flash positioning tag transmits positioning data of the power equipment where the satellite flash positioning tag is located to the satellite flash gateway in real time; the handheld terminal obtains positioning data of the operation robot and the power equipment from the star flash gateway, generates a dynamic navigation path according to task requirements, guides the robot to move to an operation position, and issues an operation instruction to the operation robot; and the operation robot receives and executes the operation instruction, and feeds back an execution result in real time. The invention further discloses a corresponding method. By implementing the method, the positioning precision in remote operation and maintenance of the power equipment can be improved, the operation time delay can be reduced, and multi-equipment coordination can be realized, so that the safety and efficiency of power operation and maintenance are improved.

The invention relates to the technical field of structure health monitoring, and discloses a building structure health real-time monitoring method and system based on multi-sensor fusion, and the method comprises the steps: enabling the system to operate in a low-power-consumption passive monitoring mode, awakening sensor nodes in a target region according to the needs when a preset condition is met, and enabling the sensor nodes to be in a real-time state; and self-organizing to form a local diagnosis network and electing a dominant node. And then, the leading node coordinates the network to complete excitation-response type active diagnosis, cross validation and information extraction are carried out on original data locally, and only a generated structured diagnosis abstract is uploaded. And finally, the central processing unit fuses the abstract, the historical baseline and causal confidence analysis, performs weighted calculation and then outputs an evaluation result. Through a dynamic and static combined monitoring mode and distributed intelligent processing of a network edge, the contradiction between high-precision diagnosis and low-power-consumption operation is effectively solved, the communication overhead is reduced, and the real-time performance and reliability of an evaluation result are improved.

The invention discloses a global nerve drawing method and system based on a programmable rasterization engine, and belongs to the technical field of computer graphics, and the method comprises the steps: at the programmable rasterization engine, analyzing a rasterization descriptor according to a rasterization instruction, and extracting vector microoperation and control parameters; maintaining a task state machine according to the parameters and distributing a control signal, selecting an execution entry from a vector kernel table according to the control signal, and instantiating an operation into a parallel vector thread; in a vector thread execution process, tracking data dependence of a vector register and a synchronization state of a direct memory access unit, executing vector loading / storage operation so as to carry data between the register and an on-chip shared memory according to the data dependence and the synchronization state, and dynamically scheduling vector micro-operation to an execution component so as to complete rasterization calculation; and outputting a result to the neural rendering network to complete global neural rendering. According to the method, the multi-representation neural rendering load can be uniformly and efficiently supported on the AI accelerator, the memory access overhead is remarkably reduced, and the calculation efficiency is improved.

The application relates to the technical field of fault diagnosis, and provides a high-frequency transformer fault diagnosis method and system, which comprises the following steps: collecting a target signal with a time stamp and extracting corresponding features, simultaneously relying on a transformer structure, material parameters and physical rules to build a digital twin model, simulating insulation and structure degradation equivalent working conditions, solving multi-physical field data and generating multi-physical field mechanism samples; then, the mechanism samples and field measured data are fused through a generative adversarial network to expand the fault sample data set and solve the sample scarcity problem; in the running stage, the digital twin model is updated in real time through parameter online inversion, a feature dynamic graph representing multi-physical coupling is constructed by combining the parameter deviation of internal mechanism degradation and the multi-source features of external working conditions, finally, the feature aggregation and time sequence reasoning are completed through the graph neural network and the time sequence neural network trained offline, the fault probability is output, and the optimal diagnosis result is determined; thereby, the fault recognition accuracy and the robustness in the running stage are improved.

This application relates to the field of flight control technology, specifically to a high-maneuverability flight control method for fixed-wing aircraft based on curriculum-based reinforcement learning. It constructs a closed-loop learning system comprising a curriculum scheduler, an agent, a simulation environment, and an experience buffer. The agent has a policy network. The state space of the aircraft and the normalized control surface and throttle action space output by the policy network are defined. A curriculum difficulty measurement model is established, quantifying task difficulty through weighted state deviation and envelope penalty terms. Based on the current curriculum level and this model, a safe and difficulty-matched training task set is dynamically generated. In the simulation environment, the policy network is iteratively updated through a two-layer loop training process. After completing all courses, a high-maneuverability flight control strategy is obtained. This strategy is deployed to the flight control system to achieve high-maneuverability flight control based on real-time state. This achieves efficient, safe, and adaptive flight control strategy training and deployment.

This invention discloses a traffic flow prediction method and system based on Transformer, belonging to the fields of intelligent transportation and deep learning technology. Addressing the technical problems of existing traffic flow prediction models, such as difficulty in simultaneously considering long-term and short-term dependencies, inability of static road network topology to characterize dynamic spatial heterogeneity, and poor modeling performance of spatiotemporal feature coupling, this invention proposes a multi-timescale adaptive graph attention Transformer model. This method first reconstructs the original traffic data at low, medium, and high time scales, and then aggregates spatiotemporal features through a temporal convolutional network and a compressed excitation network. Next, an adaptive data graph generation module learns node embedding vectors to generate an adaptive adjacency matrix that integrates static topology and dynamic associations. Finally, an encoder incorporating temporal one-dimensional convolutional multi-head attention and spatial graph attention, and a decoder integrating causal convolution and temporally gated convolution, are constructed to achieve high-precision multi-step prediction of traffic flow. This invention effectively captures the spatiotemporal dependencies of traffic flow, with prediction accuracy and generalization superior to mainstream models, and can be widely applied to urban intelligent traffic management, dynamic path planning, and traffic congestion mitigation scenarios.

The invention provides a strategy migration method and device based on multi-resolution simulation. The method comprises the following steps: constructing a first simulation environment and a second simulation environment in a target scene; in the first simulation environment, training the hierarchical agent based on the simulation data to obtain a teacher strategy; and based on the teacher strategy, guiding the student strategy network in the second simulation environment to perform initialization training to obtain an initial strategy of the target scene, and in the second simulation environment, performing adjustment training on the initial strategy to obtain a final strategy of the target scene. Large-scale training is carried out through high calculation efficiency of the first simulation environment to obtain a teacher strategy, and a student strategy network in the second simulation environment is guided to complete initialization and subsequent adjustment training, so that smooth transition from high-efficiency coarse-grained exploration to high-fidelity fine migration is realized; and when the intelligent agent is trained in the simulation environment, the accuracy of the generation strategy is improved while the intelligent agent training efficiency is guaranteed.

In order to solve the problems of long design time, low efficiency, high calculation cost and low prediction accuracy caused by information loss in the prior art, a resonance super surface design method and device based on spectrum perception improvement are provided.The method comprises the following steps: designing a GLSAT forward prediction network based on spectrum perception improvement;training and optimizing the GLSAT forward prediction network; designing a DNN reverse design network; cascading the DNN reverse design network and the trained GLSAT forward prediction network to obtain a cascaded reverse design network; inputting the ideal spectrum pretreated by GSSG into the cascaded reverse design network, training and optimizing the DNN; and completing the design of the resonance super surface by using the optimized DNN reverse design network.The method has the characteristics of short time consumption, high efficiency and low calculation cost while improving the design prediction accuracy.

The invention discloses a data security transmission method and device, equipment and a storage medium, and relates to the technical field of information security, and the method comprises the steps: obtaining a data transmission parameter generated based on a ring signature in a block chain network, generating a target key pair of each transaction party, generating corresponding hidden address information based on the data transmission parameter and a public key of a receiver, and transmitting the hidden address information to the block chain network; encrypting the to-be-transmitted data according to the waiting party public key, the hidden address information and the transmitting party key pair; generating data supervision information of the to-be-transmitted data and identity supervision information of the receiver public key based on the supervisor public key; data transmission content is constructed according to the hidden address information, the to-be-transmitted data ciphertext, the data supervision information and the identity supervision information, the supervision party verifies the data transmission content based on the supervision party private key and the supervision list, and after it is judged that the identities of the data transmission party and the data receiving party are correct, the data transmission party completes data transmission through the block chain network. Privacy protection and supervision auditing of data can be balanced, and recovery and supervision of transmission data are realized.

The invention provides a global intelligent remote 5G joint sharing system and method. The system comprises a remote access and session management module which is configured to complete access, identity authentication and authority distribution of remote medical participants through a 5G network; the multi-modal data transmission and processing module performs multi-modal transmission, coding and decoding processing and synchronous management; the intelligent analysis and personalized content generation module is used for generating personalized interaction content and auxiliary prompt and risk early warning information; the cooperative arrangement and terminal control module is used for executing corresponding meeting control and control operation on the remote medical terminal; and data transmission and cooperative processing are carried out through the 5G access network and the edge computing node. Thus, in combination with 5G network characteristics and intelligent analysis capability of artificial intelligence, fusion processing can be performed on multi-modal medical data such as medical images, medical record data and vital sign monitoring data, and stable and efficient collaborative services are realized.

The application relates to the technical field of flight control, in particular to a fixed-wing aircraft high-maneuver flight control method based on course reinforcement learning, a closed-loop learning system containing a course scheduler, an intelligent agent, a simulation environment and an experience buffer is constructed, and the intelligent agent has a strategy network; a state space of an aircraft and a normalized rudder and throttle action space output by the strategy network are defined. A course difficulty measurement model is established, the task difficulty is quantified by weighting the state deviation and the envelope penalty term; based on the current course level and the model, a training task set that is difficult to match and safe is dynamically generated. In the simulation environment, the strategy network is iteratively updated through double-loop training, and a high-maneuver flight control strategy is obtained after all courses are completed; the strategy is deployed to the flight control system to realize high-maneuver flight control based on real-time state. Efficient, safe and adaptive flight control strategy training and deployment are realized.

The application discloses an aspect-level sentiment analysis method and device based on a cross-modal syntax-visual graph convolutional network, containing a cross-modal graph structure construction module and a type-sensitive graph convolutional network for updating features between modes. Feature vector representations of pictures and texts are obtained through a pre-training model, a graph structure representation of the text is obtained through a syntax analysis method, a new dependency relationship is constructed to integrate the picture feature vector into the graph to form a new integrated cross-modal graph structure, a type-sensitive graph convolutional network is used to update the feature vector corresponding to the aspect word, and a full-connection neural network is used for final aspect-level sentiment detection of the group of comments. The pre-training model is used to obtain basic feature representations containing prior knowledge, syntax analysis and construction of the new cross-modal graph structure are used for fine-grained division and combination of the cross-modal comments, a graph network is used for fine-grained fusion, and finally, sentiment classification detection is completed.

The application discloses an infrared weak small target detection method based on line-by-line detection, and solves the problems of high detection delay and large resource consumption caused by global image caching in the prior art. The method skips the traditional "read-out-caching-global detection" process and performs real-time detection while reading out the infrared image data line by line. The method comprises the following steps: performing first-order and second-order differential calculation on a single-line image vector, extracting and fusing the intra-line spatial features; inputting the continuous multi-line features into an inter-line fusion module based on a self-attention mechanism in parallel, and restoring the high-dimensional features of the target; then, completing target detection through a U-Net network, and adapting the dimension through a line expansion and line compression module; and in the training, adaptively selecting a loss function according to whether the current line block contains a target, so as to improve the training efficiency. The application realizes parallel processing of detection and data reading, significantly improves the timeliness of detection, and reduces the demand for cache and computing resources of edge devices.

The present application relates to the technical field of property transaction, in particular to an intelligent contract automatic execution and supervision system for the whole process of property transaction, comprising: an intelligent contract generation unit; a contract automatic execution unit, which maps the intellectual property right transfer and transaction settlement process agreed in the standardized intelligent contract into a multi-stage execution phase sequence of corresponding performance nodes, and each execution phase is subject to the necessary condition of performance risk assessment; a contract supervision and verification unit; and an intellectual property right ownership verification unit.The present application builds a whole-process intelligent contract automatic execution and supervision system, uses a time sequence attention mechanism and an improved lightweight residual time sequence convolution network to complete dynamic risk assessment, combines hierarchical execution and reversible rollback to realize abnormal disposal, forms an execution, supervision and ownership verification closed-loop architecture, can monitor and dispose transaction abnormalities in real time, ensures stable transaction execution, realizes whole-link ownership tracing and compliance supervision.

The invention discloses a deep learning assisted waveform index modulation single carrier communication method, and belongs to the technical field of wireless communication. According to the method, the frequency spectrum efficiency and the system performance are improved by fully utilizing the waveform freedom degree of the single-carrier system. Cooperative transmission of index information and symbol information is realized by jointly optimizing a constellation mapping set, a shaping filter bank and a Bi-LSTM detection network of a receiving end. Specifically, a sending end divides information bits into symbol bits and index bits, the index bits dynamically select a constellation mapping set and a shaping filter, and the symbol bits generate a time domain waveform through the selected constellation and filter. And after a receiving end adopts frequency domain equalization and matched filtering, joint detection of indexes and symbols is completed through a Bi-LSTM network. According to the method, bit mutual information can be achieved through end-to-end training optimization, the signal power and the spectrum template are constrained at the same time, a high-performance and low-complexity index modulation implementation method is provided for a single-carrier system, and the method is suitable for a future high-spectrum-efficiency communication scene.

A method and system provide the ability to interpolate fluids. At least two keyframes are produced, for a physics based fluid simulation. The keyframes are within a continuous-time framework and separated by a defined interval. Each keyframe includes one or more fluid elements having a corresponding state. Data is prepared utilizing a pre-trained transformer-based network by: (i) handling a tokenization process in a physics-adapted context; and (ii) generating temporal embeddings for states of the one or more fluid elements. Based on the prepared data, a time-continuous density is prepared for substeps between the two keyframes using a density network.

The invention provides a data transmission method and device and related equipment, and relates to the technical field of computers, and the method comprises the steps that first electronic equipment receives a first request sent by terminal equipment; the first electronic equipment sends a second request to the second electronic equipment and the terminal equipment, and receives first information fed back by the second electronic equipment and second information fed back by the terminal equipment; the first electronic equipment determines a first transmission safety coefficient of the second electronic equipment based on the first information, and determines a second transmission safety coefficient of the terminal equipment based on the second information; under the condition that the first transmission safety coefficient is greater than the second transmission safety coefficient, the first electronic equipment sends a channel reconstruction request to the SDN network; and under the condition that the SDN completes channel reconstruction, the first electronic device sends first data to the second electronic device and sends second data to the terminal device. Through the method, the data can be quickly, safely and remotely acquired from the electronic equipment.

This application relates to a method, apparatus, equipment, and medium for scheduling engineering vehicles in complex road network environments. The method includes: acquiring static geographic data of physical nodes in the construction road network, engineering vehicle operating status data, and construction node operational status data to construct an initial dynamic spatiotemporal map for the current simulated scheduling cycle; obtaining the vehicle's global perception latent state vector through spatiotemporal map interactive attention network encoding; inputting the graph attention game network to complete game decision-making, generating the optimal response action command for the engineering vehicles, and aggregating the optimal response action commands into a joint scheduling strategy; iteratively optimizing the network model parameters based on the data updated in the simulated scheduling, and repeating the above steps in subsequent simulated scheduling cycles until the joint scheduling strategy reaches a Nash equilibrium state, outputting the joint scheduling strategy as an engineering vehicle scheduling scheme. This method can dynamically adjust the vehicle paths during the process, achieving the optimal scheduling goal of low energy consumption and short travel time.

The application provides a plug-and-play network access method and system of an intelligent gateway based on an RS485 bus, relates to the field of industrial bus communication, and through an N-frame collision avoidance algorithm, the method can effectively deal with bus conflict conditions through a random backoff mechanism; a Nonce identification system is established, a 4-byte random number identification domain is set in a communication frame, a temporary session channel is established, precise device identification without address pre-configuration is realized; a bidirectional handshake protocol is set; through the interaction of a network access request frame and a network access response frame, a network ID is dynamically allocated, and device identity binding is completed. Through the use of the method, a sub-device can actively initiate a network access application, plug-and-play and plug-and-network access are realized, and due to the existence of the Nonce code, all sub-devices do not need to perform address configuration operations before network access, thereby greatly simplifying the configuration process.

The application provides a multi-modal automatic modeling and fusion method and device based on neural architecture search, and relates to the technical field of computer science. The method comprises the following steps: acquiring multi-modal data and a task type; automatically generating a corresponding optimal unit architecture for each modal data based on neural architecture search, and performing feature extraction on each modal data respectively; generating a dynamically updated correlation heat map according to the inter-modal dependency relationship in the feature level of each modal data, obtaining a specific fusion strategy according to a fusion strategy decision maker, and constructing an optimal fusion network architecture; automatically adjusting an output layer and a loss function according to the task type, obtaining a network suitable for task requirements, and completing different downstream tasks. The application can realize end-to-end joint optimization of single-modal feature extraction and multi-modal fusion strategy, significantly reduce the cost of manual intervention and GPU resource consumption, and improve the task performance and model generalization ability in complex scenarios.

This invention relates to the field of smart property facility operation and maintenance management technology, specifically to an IoT-based intelligent predictive management system for property facilities. This system includes a multi-domain causal network construction module, a risk quantification and causal inference module, and a risk resource joint optimization and decision execution module. This invention integrates prior knowledge of property operation and maintenance with multi-source heterogeneous IoT data to construct a dynamic, directed acyclic causal network covering all elements, uncovering causal relationships between variables. Based on the causal network, it performs risk transmission inference for abnormal events, quantifies fault risks, and identifies intervened nodes, matching corresponding operation and maintenance intervention measures. Coupled with constraints on all operation and maintenance elements, it constructs a multi-objective optimization model to solve for the globally optimal maintenance scheduling scheme and achieve dynamic closed-loop scheduling, enabling proactive risk prevention and efficient allocation of operation and maintenance resources for property facilities.

The invention provides a virtual navigator path planning method based on SAC-RRT *, which innovatively introduces formation geometric envelope parameters to map multi-agent entity constraints into virtual navigator geometric constraints, replaces traditional RRT uniform sampling with non-deterministic Gaussian sampling of an SAC algorithm, and realizes adaptive step length extension and formation-level collision detection in combination with a Critic network. Meanwhile, the path cost optimization quantity of RRT rewiring is creatively converted into rewards to be fed back to the SAC network to complete backward fusion, network training is enhanced in cooperation with a real and virtual dual-experience playback strategy, formation trafficability factors are fused in a state space, a multi-dimensional reward function optimization decision is designed, and finally, the formation trafficability is improved through three times of B spline curve smoothing processing. The multi-agent formation path planning considering safety, optimality and convergence efficiency is realized, and the problems that a traditional method ignores formation geometric constraints, sampling blindness is large, and reinforcement learning and sampling algorithm fusion is not deep are effectively solved.

This invention discloses a malware detection method integrating federated learning and quantum hybrid convolutional networks, belonging to the field of computer network security technology. The method includes: on the client side, firstly, preprocessing the malware sample and inputting it into a classical convolutional module to extract local spatial features, obtaining a first feature vector; subsequently, segmenting the first feature vector and inputting it into a quantum convolutional module, mapping the features to quantum states through quantum circuit initialization and parameter encoding, evolving using trainable variable quantum layers and measuring expected values ​​to obtain a second feature vector; finally, concatenating and fusing the two feature vectors, and completing classification through a fully connected network. Each client uploads the trained non-sensitive parameters to the server for aggregation and updating. This invention utilizes the superposition and entanglement properties of quantum computing to enhance the representation ability of high-dimensional sparse and evasive features, while ensuring data privacy and significantly improving malware detection performance.

This invention relates to a blockchain network formation method, belonging to the field of blockchain networking technology. During network formation, this invention first selects one node from the nodes to be networked as a blockchain resource server, and the remaining nodes as blockchain node servers. Then, a blockchain infrastructure server is installed on the blockchain resource server, storing the tested blockchain software and creating the identity and certificate of the organization to which the node server belongs. On the node servers, an infrastructure client and blockchain software are installed, and identity registration is performed through the blockchain software. After obtaining the registration information of the node servers based on the blockchain software, the resource server sends a network formation command. The blockchain node servers create their own node certificates and network communication certificates based on the received network formation command, start the blockchain network, and complete the blockchain network formation. The network formation process is simple and convenient, greatly improving network formation efficiency.

The application relates to the technical field of fault diagnosis, and provides a high-frequency transformer fault diagnosis method and system, which comprises the following steps: collecting a target signal with a time stamp and extracting corresponding features, simultaneously relying on a transformer structure, material parameters and physical rules to build a digital twin model, simulating insulation and structure degradation equivalent working conditions, solving multi-physical field data and generating multi-physical field mechanism samples; then, the mechanism samples and field measured data are fused through a generative adversarial network to expand the fault sample data set and solve the sample scarcity problem; in the running stage, the digital twin model is updated in real time through parameter online inversion, a feature dynamic graph representing multi-physical coupling is constructed by combining the parameter deviation of internal mechanism degradation and the multi-source features of external working conditions, finally, the feature aggregation and time sequence reasoning are completed through the graph neural network and the time sequence neural network trained offline, the fault probability is output, and the optimal diagnosis result is determined; thereby, the fault recognition accuracy and the robustness in the running stage are improved.

The invention relates to the technical field of digital media interaction, in particular to a dynamic media interaction method and system based on digital virtualization, and the method comprises the steps: constructing a virtual space representing a media interaction process, and initializing an interaction state sensing network; obtaining an original interaction flow and performing event slicing, generating an interaction fragment with a time overlapping window, distributing a temporary space-time anchor point and mapping the temporary space-time anchor point to a virtual space to form an interaction data unit; inputting the interaction data unit into the sensing network, and calculating a potential motion track formed by the time sequence prediction anchor points; comparing the distance between the predicted anchor point and the existing mapping position, and if the distance is smaller than a convergence threshold value, marking a stable anchor point and binding a solidified interactive data unit; and updating sensing network parameters according to the solidified data and the anchor points, completing mapping and anchor point prediction through the updated network when a new interaction flow is input, and realizing media dynamic interaction modeling. According to the method, the space-time modeling continuity of media interaction is optimized through space-time anchor point and trajectory convergence judgment.

The invention discloses a high mountain avalanche element feature stepped coupling discrimination method, and relates to the technical field of mountain disaster space intelligent evaluation. The method comprises the following steps: firstly, constructing a'terrain-earth surface-weather '12.5 m resolution factor grid; then carrying out balanced sampling under an altitude-gradient double-step framework to form a 1: 2 positive and negative training set; then outputting a first-level posterior probability in parallel through an extreme random tree calibrated by Platt, a CatBoost ternary heterogeneous learning device and an XGBoost ternary heterogeneous learning device, and splicing the first-level posterior probability with the original features to form high-dimensional element features; inputting the meta-features into a'factorization machine + residual MLP 'dual-channel lightweight deep network to complete secondary coupling and noise suppression; and finally, a 12.5 m avalanche susceptibility probability graph is generated through enhancement during multi-scale testing. According to the method, the GIS interpretability is reserved, meanwhile, the precision and robustness in small-sample, high-dimension and strong-autocorrelation scenes are remarkably improved, and the method can be widely applied to avalanche risk management of western traffic corridors, ski fields and national defense channels.