61 results about "Network composition" patented technology

The invention discloses an enterprise insurance risk control method and device, computer equipment and a storage medium, and belongs to the field of financial science and technology and the field of artificial intelligence. The method is applied to an enterprise insurance risk control scene, an enterprise insurance risk control system composed of a gradient strategy network, an action generation network and an action evaluation network is constructed, and risk feature vectors of an enterprise in specific time are extracted; inputting the risk features into a gradient strategy network to generate an initial risk control strategy combination; the action generation network generates a risk control action combination matched with the initial strategy, and evaluates the expected income of the risk control action combination through a dominant function in the action evaluation network; and according to an evaluation result, dynamically adjusting gradient strategy network parameters, further optimizing a risk control strategy, and outputting an optimal risk control strategy. The invention further relates to the technical field of block chains. The enterprise risk feature vectors are stored in nodes of a block chain network. According to the invention, the calculation overhead and training time of the enterprise insurance risk control model can be reduced, and the stability of the model is improved.

The invention relates to the technical field of computers, and discloses a multi-agent reinforcement learning fault diagnosis method based on edge-center hybrid optimization, and the method comprises the following steps: S1, collecting and preprocessing equipment data; s2, layered fault diagnosis: an intelligent diagnosis agent adopts a layered reinforcement learning structure and is composed of a high-layer strategy network and a low-layer strategy network; s3, edge-center hybrid optimization: designing a center strategy optimization agent, training a high-layer strategy network and a low-layer strategy network in stages, and performing compression and distillation on the trained strategy networks by adopting a teacher-student strategy structure; executing strategy fusion and global updating based on the received strategy execution information; and S4, strategy migration. According to the method, continuous learning and strategy updating are carried out in a scene in which fault samples are extremely scarce, diagnosis knowledge sharing, strategy synchronization and cross-device migration are realized by using a multi-agent cooperation mechanism, efficient deployment and operation at an edge device end are supported, and field state change is adapted in real time.

The invention discloses a central air conditioner control method, system and device, a medium and a program product, and relates to the technical field of data processing and control systems.The method comprises the steps that a central air conditioner system control model is built; the method comprises the following steps: constructing an actor network, and embedding an optimization control model consisting of a global critic network and a local critic network of a GAT (Graphics Attention Transform); and training the optimization control model, and controlling the refrigeration temperature set value and the heating temperature set value of each area according to the current state observation variable of each area by using the trained actor network. A critic network is designed into a global critic network and a local critic network, the problem that regional thermal comfort awards and central air conditioning system total energy consumption awards conflict with each other in the training process is relieved, a graph attention network is fused into the global critic network, coupling information in the system is fully extracted, and the optimization control performance is improved.

The invention relates to the technical field of e-commerce, and relates to a digital shopping mall management SaaS system, and the system comprises the steps: collecting shopping mall call and network events, and generating an embedded vector through optical reserve wavelength division multiplexing nonlinear mapping; under the constraint of the causal structure in the last round, the de-noising diffusion model generates twinborn events, an updated causal graph is formed through incremental Bayesian information criterion learning, and a causal feature tensor is obtained through random walk embedding; a reinforcement learning framework formed by the neural morphological execution network and the GPU evaluation network fuses tensors with inventory, price and promotion business data into a value state, and outputs a price adjustment-discount-replenishment strategy; the strategy is compiled into a WebAssembly micro contract, and the components are subjected to hot replacement without shutdown through a BPF sandbox; generating a zero-knowledge proof for the micro contract and storing the proof on the side chain of the block chain; in the gray stage, profit, inventory and cost feedback is collected and written back to a data link to complete self-adaptive closed loop; according to the invention, millisecond-level strategy iteration, second-level security release and extreme scene robust operation are realized.

The invention discloses an AGV scheduling strategy generation method and device based on multi-agent reinforcement learning, relates to the technical field of AGV scheduling, and solves the problem that cooperative scheduling of multiple AGVs in a large-scale, high-dynamic and flexible workshop is difficult to effectively solve in the prior art. The method comprises the following steps: constructing a data set and a multi-agent decision model consisting of a GNN network, an Actor network and a Critic network, carrying out centralized training and distributed execution on the multi-agent decision model, generating a scheduling strategy for each AGV, and introducing the GNN network to help the Actor network and the Critic network to understand a relationship between a plurality of agents in an environment and the intelligence of a to-be-selected task set. And the Actor network makes strategy judgment for a plurality of intelligent agents. The Critic network not only evaluates the individual value of the intelligent agent in each step, but also evaluates the contribution of each intelligent agent to the global value, so that the intelligent agent not only realizes the individual optimization, but also needs to achieve the global optimization.

The invention discloses a point cloud up-sampling method based on a lightweight neural network, and the method comprises the steps: building the lightweight neural network composed of a teacher network and a student network, the teacher network comprises a global context retainer used for obtaining a teacher feature map and a Transform up-sampler used for obtaining the global perception of a teacher dense point cloud block; the student network comprises a local geometric retainer used for acquiring a student feature map and a Mama enhancer used for acquiring local perception of a student dense point cloud block; carrying out network training on the lightweight neural network by utilizing a training set comprising a plurality of pairs of sparse point cloud blocks and true value dense point cloud blocks, firstly training a teacher network, freezing parameters of the teacher network and then training a student network to obtain a student network training model; performing up-sampling on each sparse point cloud block in the test set by using a student network training model to obtain a student dense point cloud block corresponding to each sparse point cloud block in the test set; the method has the advantages that the sampling performance of the point cloud can be effectively improved, and computing resources are saved.

The invention discloses a short-time traffic flow prediction method based on a traffic-air interactive hybrid convolutional network, and belongs to the technical field of traffic flow prediction. According to the method, historical traffic flow and air quality data are collected and preprocessed, a fusion feature sequence is constructed through cross-modal time-space sequence interleaving recombination, and real-time traffic flow prediction is achieved through training optimization of a mean square error loss function and an RMSprop optimizer by adopting a model composed of a convolutional interaction network and a bidirectional long-short term memory network. According to the method, the space-time coupling relation of the two kinds of explicit modeling is recombined through cross-modal space-time sequence interleaving, differentiation feature extraction and two-way dependence mining are combined, the problem that real-time interactive modeling cannot be achieved through a traditional method is solved, and robustness and adaptability in the complex environment are improved.

The invention relates to an AMT signal denoising method and device based on self-adaptive multi-level U-Net. According to the method, noise segment classification is performed on a noisy AMT signal, a corresponding position mask of the clean AMT signal is recorded, and noise positioning information is provided for training of a denoising model, so that the training effect of the denoising model is improved, and loss of non-noise segments can be avoided; the method also uses two U-Net networks to form a U-Net cascade structure, the first network performs preliminary denoising, the second network processes fragment data output by the first network, generates a noise intensity probability by using a residual error, uses the noise intensity probability as a driving signal, distributes a corresponding weight value, and outputs the driving signal to the U-Net cascade structure. According to the method and the system, the second network is dynamically controlled to selectively absorb and fuse fragment data output by the first network, an area with remarkable residual noise can be adaptively concerned, complementation and optimization of a feature level are realized, and finally, the denoising effect of the denoising model on the noisy AMT signal is improved.

The invention discloses a self-supervised monocular depth estimation method based on an enhanced multi-scale pose network, and relates to the field of computer vision. The problems that in an existing self-supervision monocular depth estimation method, the pose network structure is simple, the time sequence modeling capacity is insufficient, and geometric constraints are missing are solved. According to the method, a self-supervision joint training framework composed of a depth estimation sub-network and an enhanced pose estimation sub-network is constructed; wherein the pose estimation sub-network extracts multi-scale spatial structure features through a layer-by-layer feature fusion encoder, and adopts a context fusion decoder based on time sequence attention to model a motion dependency relationship between continuous frames; meanwhile, a self-supervised pose consistency loss function is introduced, geometric continuity of a camera track is enhanced through forward and reverse transformation consistency constraint and closed-loop geometric constraint, and collaborative optimization of depth prediction and pose estimation is realized. The method is also suitable for the application fields of automatic driving, robot perception, augmented reality and the like.

The invention belongs to the technical field of communication, and particularly relates to a router system service exception self-healing method based on cloud AI, which comprises the following steps: deploying an exception detection module at a router end, collecting system logs and state data in real time and generating a standardized exception report; uploading the report to a cloud AI server through an encrypted MQTT protocol; the cloud calls a hybrid analysis model composed of a rule matching engine, a machine learning classifier and a reinforcement learning decision network to generate a self-healing strategy instruction packet; the router end receives and executes the strategy, completes service restart, configuration rollback or hotfix loading and other operations, and verifies the self-healing effect; when communication interruption exceeds a threshold value, an embedded loopback self-healing subsystem is automatically activated, and abnormity is independently handled based on a local strategy library. According to the technical scheme, millisecond-level abnormal response and high-success-rate autonomous recovery can be achieved, the network availability and the service continuity are remarkably improved, and the disaster recovery self-healing capacity is still achieved when the cloud end is disconnected.

The invention provides an operator learning-based large-scale grid intelligent generation method and related equipment, and is applied to the technical field of grid generation. The method is applied to a double-branch shared backbone network architecture, and the double-branch shared backbone network architecture is composed of a shared backbone network, a first branch network and a second branch network. According to the method, global features of computational domain coordinates are extracted through a shared backbone network, meanwhile, transverse and longitudinal distribution characteristics of boundary coordinates are learned through a double-branch network, multi-dimensional feature coupling is achieved through operator learning of multivariate mapping, and finally a physical domain grid is generated through coordinate mapping. On the premise of ensuring accurate capture of boundary features, efficient and high-quality grid generation is realized through sharing feature multiplexing and multivariable decoupling processing, and generalization to different geometric shapes can be realized, so that collaborative optimization of complex geometric adaptability, multivariable coupling capability and calculation efficiency is achieved.

The application provides a video summarization method based on multi-dimensional features and fine-grained hierarchical modeling, and relates to the technical field of video processing. In practical application, the video summarization technology can facilitate large-scale video retrieval and browsing. The method comprises the following steps: firstly, frame extraction is performed on an input video to obtain a frame sequence, and a multi-dimensional feature extraction network composed of a 2D network and a 3D network is used to extract multi-dimensional features; then, hierarchical temporal modeling is performed to complete the modeling process of the temporal dependence of the entire video sequence; finally, a regression network is used to obtain the importance score of each frame and generate a video summary. The application further explores the influence of the spatiotemporal features extracted by 3D feature extractors with different spatiotemporal complexities on the video summary result. The application shows excellent performance on the video summary datasets SumMe and TVSum. Whether from the application scene or the performance index, the application has strong practical value.

The invention discloses a self-service guide explanation system and method based on mobile phone NFC, and belongs to the technical field of intelligent services of museums and exhibition venues. The system is composed of an NFC tag array, a WeChat applet client, a cloud management server and a content distribution network. A user holds a mobile phone to touch an NFC label close to an exhibit, so that a WeChat applet can be automatically opened, 'e touch-to-play 'can be achieved, label identification, content pulling and multimedia playing can be completed in a zero-configuration mode, and offline and online mixed display of audios and videos, pictures and texts and AR / VR is supported; meanwhile, the system records user behaviors and generates a personalized visiting report. The method solves the problems that in the prior art, special equipment needs to be rented, operation is complex, interaction is single, and fusion with a social platform cannot be achieved, and has the advantages of being low in cost, fast in deployment, zero in learning cost, rich in experience, easy to maintain and the like.

The application relates to a fine-grained traffic classification method based on an improved residual convolutional network in an SDN environment and belongs to the software technical field. In order to provide finer-grained application-aware traffic classification, let network operators better analyze network composition and manage and schedule network resources, fine-grained classification of the specific application programs is very important. Traditional methods tend to classify traffic based on protocols, which is coarse-grained classification. Inspired by the research in computer vision, the method of the residual convolutional network is applied to the identification and classification of network traffic. The method solves the network degradation problem that occurs in the process of fine-grained network traffic identification by traditional deep learning methods with the increase of network depth, can effectively learn deeper network features, and further realizes fine-grained network traffic classification.

The invention discloses a periodic variable star light variable curve classification method based on a neural network, and relates to the field of astronomical photometric data classification and identification, and the method comprises the steps: employing an intelligent neural network composition principle, training a plurality of small networks, enabling each small network to be responsible for the identification of a variable star type, enabling all small networks to form a large intelligent neural network, and enabling all small networks to be responsible for the identification of a variable star type; and all variable star types are identified. According to the method, the amplitude of the harmonic wave in the Fourier spectrum of the optical variation curve is used as a characteristic value, supervised learning is performed on the small network, and the learning and identification efficiency of the whole network is improved by combining the symbol logic of the intelligent neural network. In addition, the method has good expandability, for example, when a to-be-recognized variable star type needs to be newly added in the future, only a corresponding small net needs to be added, the small net is added into a large net, and then a small amount of training is iteratively carried out.

The invention provides a matching generation method, device and equipment of a passive device network and a network group, a medium and a computer product, the passive device network is generated according to a specified matching parameter value, and the passive device network is composed of a first passive device network and a second passive device network which are connected in series or in parallel; the first passive device network and the second passive device network are both composed of a plurality of passive devices of the same type; based on the distribution condition of the equivalent electrical parameter values, determining a corresponding high-density distribution range; the matching parameter values are decomposed through the high-density distribution range; the parameter parts falling into the range are divided into decimal parameters, and the rest are divided into integer parameters; the first passive device network is provided with unit passive devices with an integer parameter number; and searching a series-parallel network matched with the decimal parameter, and constructing a second passive device network. The problem that in the prior art, an automatic tool is lacked to efficiently and accurately design a passive device network with overall equivalent parameters meeting expected design requirements is solved.

The present invention provides a control method, device, equipment and medium based on the grid-following ratio and grid-forming ratio, which relates to the technical field of power systems. The method includes: obtaining the system capacity and device capacity of the power system; wherein, the system capacity refers to the sum of the rated capacities of various hub substations in the power system, and the device capacity refers to the installed capacity of the equipment integrating the grid-following function and grid-forming function within a preset range in the power system; determining the short-circuit ratio according to the system capacity and device capacity; determining the grid-following ratio and grid-forming ratio according to the short-circuit ratio; obtaining the three-phase voltage, three-phase current, DC-side voltage and DC-side reference voltage of the power system; and performing pulse-width control according to the three-phase voltage, three-phase current, DC-side voltage, DC-side reference voltage, grid-following ratio and grid-forming ratio. The technical solution of the present invention controls and adjusts the pulse width of the power system comprehensively by determining the grid-following ratio and grid-forming ratio and based on the grid-following ratio and grid-forming ratio.

The application discloses a network space simulation construction and analysis display method, which comprises the following steps: editing and setting multiple types of network entity models and network relationship models, storing the network entity models and the network relationship models into a database, outputting the network entity models and the network relationship models from the database, constructing corresponding network simulation entities and a simulation target network, and performing hierarchical display on the simulation target network and the network simulation entities. The method can effectively realize simulation construction of the network space, can display the network architecture as a whole and display network composition details, and is favorable for intuitive network analysis and feature presentation.

The present invention belongs to the technical field of radar true and false target recognition, and specifically relates to a radar true and false target recognition method based on a multi-modal deep network. First, the invention preprocesses the one-dimensional range profile data of radar true and false targets as the input of the multi-modal deep network. The multi-modal deep network consists of 3 parallel one-dimensional convolutional sub-networks to extract multi-modal features from the one-dimensional range profile. Then, a stacked autoencoder with 3 layers is cascaded to further extract recognition features from the multi-modal features. Finally, a softmax classification layer is used to complete the target type recognition. Since multiple convolutional sub-networks with different convolutional kernel sizes are used for preliminary feature extraction, and then a stacked autoencoder is used to further extract recognition features, more refined and comprehensive non-linear features can be extracted, improving the target recognition performance. Simulation experiments are carried out on the one-dimensional range profile data of four types of simulated targets, and the experimental results verify the effectiveness of the method.

The invention discloses a nonlinear system response prediction method based on a time sequence convolutional network and a main and auxiliary double-network structure, and belongs to the technical field of nonlinear system modeling and prediction. The method comprises the following steps: constructing a training data set, processing an excitation sequence and a response sequence of a nonlinear system through a sliding window method, and generating a plurality of samples; a main prediction network is constructed, the main prediction network is composed of a main time sequence convolution sub-network and a main condition sub-network, and the main condition sub-network outputs condition parameters used for carrying out condition normalization modulation on the time sequence convolution sub-network so as to directly output a future response prediction sequence; training the main prediction network to minimize the prediction error; the topological structure of the auxiliary correction network is the same as that of the main network, but parameters of the auxiliary correction network are independent, and a sequence obtained after main network prediction residual normalization serves as a training target for training; in the prediction stage, new excitation and historical response are input into the trained main network and auxiliary network, main prediction output and auxiliary output are obtained, and a final prediction result is obtained through formula calculation. According to the method, through time sequence convolution, condition normalization and cooperative work of the main network and the auxiliary network, the precision and robustness of nonlinear system response prediction are effectively improved.

The application discloses a kind of based on wireless self-organizing network topology's blockchain consensus method.According to the current topology of wireless self-organizing network, relay broadcast link is constructed;According to relay broadcast link, reliable broadcast network and asynchronous binary consensus network are constructed, and blockchain network is composed;According to asynchronous binary consensus network, consensus group is constructed;Periodically, the content proposed by each node is consensus, including two types of user data and network topology change information;After consensus, report to application layer, or update local network topology and re-perform system construction.The method inherits the decentralization characteristics of blockchain in network layer, avoids single point failure problem;Using the broadcast nature of wireless communication, an efficient broadcast mechanism based on network topology is used, which reduces the communication overhead in the blockchain consensus process;A blockchain construction mechanism based on network topology is used, which solves the mismatch problem of low dynamicity of traditional private chain consensus algorithm and high dynamicity of wireless self-organizing network.

A fuzz testing method for unstructured data combines the Sequence2Sequence network and the Char-RNN network from deep learning to generate test cases based on the relationship between the unstructured data to be tested and the execution path triggered by it in the corresponding target program. The Sequence2Sequence network model is weighted and optimized for the unstructured data to be tested using an Attention mechanism. A Scheduled Sampling model consisting of two decoder networks is used to mix real sequence elements with test sequence elements to improve the final prediction sequence of the Sequence2Sequence network. This method can perform high-quality fuzz testing on test cases for unstructured data, while improving the path coverage of the target program and detecting more anomalies.

The application discloses a kind of multi-graph hierarchical road network representation methods based on graph neural network, in the method, the structure perception graph neural network of spectral clustering and graph attention network is formed, modeling is carried out to road network representation of different levels, two kinds of virtual nodes, namely structure area and functional area, are introduced, multi-graph mechanism is used to guide virtual node to correspond with the structure area and functional area of real world, structural similarity is established using road type attribute, functional similarity between road segments is defined using city POI information, message sharing is then performed at high level, then updated information is propagated to low level nodes, and functional attributes of road network are supplemented;Using the method, road network representation containing structural features and functional features can be obtained;It is convenient to determine road network structure and functional role;It is helpful to reveal the functional area of city, helpful to route planning and time of arrival estimation and position prediction, and conducive to the construction of intelligent transportation system.

The invention belongs to the technical field of image processing, and relates to a self-priori-guided image restoration method and system, and the method comprises the following steps: S1, constructing a damaged image data set; step S2, a step of network construction; step S3, a step of network training and optimization; step S4, a network performance test step; according to the technical scheme of the invention, aiming at the problems of content repetition, color distortion and the like which are easy to occur when a traditional image restoration method is used for processing large-area defect of a continuous region or reference content is insufficient, a two-stage image restoration method consisting of a semantic prior estimation network and a semantic prior guide restoration network is adopted; and residual semantic information in the damaged image is fully mined and utilized, so that stronger robustness is realized while the visual naturalness and detail accuracy of a restoration result are improved.

The invention relates to the technical field of communication, and provides a time synchronization network operation quality comprehensive evaluation method comprising the following steps: constructing a time synchronization network operation quality evaluation index system based on time synchronization network composition and a quality target; determining the weight of each level of evaluation index by combining qualitative analysis and quantitative calculation; based on the time synchronization network operation basic data and in combination with a time synchronization network operation quality evaluation index system, calculating evaluation index values of all levels; calculating a total score of the operation quality of the time synchronization network according to the evaluation index values of all levels; determining the operation quality grade of the time synchronization network based on the total score of the operation quality of the time synchronization network; and comprehensively evaluating the network operation quality based on the total score of the time synchronization network operation quality and the quality grade, and outputting a quality evaluation report. According to the invention, the network operation maintenance management efficiency and level can be improved, and the time service guarantee efficiency of the time synchronization network is improved.

This invention relates to a robust image tampering localization method based on consistency-guided learning, belonging to the fields of image content tampering forensics and deep learning technology. The method employs an alternating training strategy based on epochs, constructing a consistency-guided learning framework consisting of a target network and a source network: the target network takes the unprocessed image as input and performs joint optimization through localization loss and contrast loss; the source network takes the post-processed image as input and learns by fitting the response of the target network. This invention improves the model's robustness to post-processing operations while minimizing interference with the model's learning of tampering traces.

The invention discloses a content traffic prediction method and system, and the method comprises the steps: obtaining analysis data of target content, and inputting the analysis data into a pre-trained prediction model; selecting expert network combination analysis based on the analysis data of the target content by a top gating network in the prediction model, and outputting a selection result vector; wherein each expert network combination is composed of each expert network in the prediction model; analyzing the current weight of each expert network in the selected target expert network combination based on the analysis data of the target content and the splicing result of the selection result vector by a bottom layer gating network in the prediction model; and fusing the flow information predicted by each expert network in the target expert network combination based on the analysis data of the target content according to the current weight of the flow information to obtain a current flow prediction result of the target content.

The present invention discloses a method for fault diagnosis of rotating equipment based on partial domain adaptation and knowledge distillation, which belongs to the field of fault identification. In terms of network architecture, it is composed of two parallel migration networks with the same structure, and each migration network is composed of three basic units: feature extractor, domain discriminator and classifier. Among them, the feature extractor is constructed using the Vision Transformer network, and the domain discriminator and classifier are constructed using two independent three-layer fully connected neural networks. In terms of network parameter updating, a weighted balancing mechanism and partial adversarial training are constructed to constrain the feature alignment process of the source domain and the target domain in the two migration networks, so as to achieve edge distribution alignment in the shared label space of the source domain and the target domain; the knowledge distillation method is used to alternately update the internal parameters of the two migration networks to reduce the prediction error of the two networks, so as to achieve state distribution alignment in the shared label space of the source domain and the target domain.

The present disclosure relates to the field of computer vision image dehazing and discloses a single image dehazing method based on detail recovery. The method includes step 1: constructing a training dataset; step 2: constructing a backbone dehazing network to achieve preliminary image dehazing: based on the U-Net network model, introduce a residual module based on pixel attention mechanism in the encoding region, and an feature enhancement module in the decoding area; step 3: constructing a detail recovery network for image detail recovery, and introducing residual shrinkage module and spatial attention mechanism; step 4: training an overall network model composed of the backbone dehazing network and the detail recovery network; step 5: Testing. The present disclosure can effectively remove fog while improving the problem of edge detail information loss, reducing the blurring of the edges of the dehazing image, and generating a higher-quality dehazing image.

The application belongs to the technical field of decoding, and discloses an LDPC decoding system based on dynamic hypernetwork and two-stage learning, which is composed of a main decoding network and a dynamic hypernetwork, the dynamic hypernetwork is composed of a plurality of hypernetwork units corresponding to each layer of the main decoding network one by one, and the dynamic hypernetwork and the main decoding network are a controller-executor relationship; the dynamic hypernetwork dynamically outputs key parameters of the main decoding network for each iteration, and the main decoding network uses the key parameters to complete message passing and LLR updating; the input of the first hypernetwork unit is a log-likelihood ratio (LLR) value vector received by a channel, and the vector carries key information of a received signal; starting from the second hypernetwork unit, the input of the tth hypernetwork unit is the output of the t-1th layer of the main decoding network. The application significantly improves decoding performance, greatly improves decoding efficiency, enhances environmental adaptability, and reduces calculation and hardware costs.