66 results about "Hide node" patented technology

The invention provides a multi-source heterogeneous distributed computing power fusion scheduling method and system, and the method comprises the steps: carrying out the multi-dimensional collection and preprocessing of heterogeneous scheduling source data, generating a scheduling joint data set, carrying out the key demand prediction processing of a task set based on a preset quantum channel topology, generating a key pre-distribution matrix, and carrying out the key pre-distribution matrix, and generating a real-time task queue according to a task set of the scheduling joint data set, and performing multi-target matching degree calculation processing on the key pre-distribution matrix and the real-time task queue based on a dynamic priority quantification model to generate a distribution decision matrix. Then adaptive key supplement and migration path optimization processing are performed on the allocation decision matrix to obtain a migration task set, and finally hidden node resource prediction and preset quantum channel topology updating are performed on the migration task set, so that the rationality of key allocation is improved, the problem of compatible conflicts of resource scheduling is solved, and the resource scheduling efficiency is improved. Therefore, the task migration success rate is improved.

A system and method for scalable generations of synthetic <logical form, utterance> pairs for training a semantic parser is disclosed. A semantic parser is trained on pairs of <logical form, utterance>. An ontology graph is constructed and derived from a plurality of enterprise documents and provides relationships among the concepts or classes of an organization. One or more paths are traversed among a plurality of source and destination node pairs, facilitating comprehensive semantic representation. Attributed query subgraphs are generated of source nodes, destination nodes, and hidden nodes. Each path is recognized among a variety of possible paths between source and destination nodes. Each path in the ontology query subgraph is validated by considering a plurality of predicates and a knowledge graph generates a natural language utterance. The utterances are refined and rephrased using a large language model, enhancing their coherence and linguistic quality.

The invention discloses an anchoring crack rock deformation displacement lightweight prediction method and equipment, and belongs to the technical field of intelligent early warning of geotechnical engineering disasters. Constructing a prediction model by adopting a random configuration network method; partial hidden nodes with low contribution degree in the prediction model are eliminated by using a singular value decomposition method, the number of the hidden nodes in the prediction model is reduced, and the prediction model is lightened; excellent new hidden nodes are searched through a random hidden node increasing and replacing technology to replace original hidden nodes in the prediction model, a lightweight prediction model is reconstructed, and the prediction precision is improved; the lightweight prediction model is utilized to predict the deformation displacement of the anchoring crack rock; the method has simple steps, and can accurately predict the deformation displacement of the anchoring crack rock based on the position of the anchor rod and the load level of the anchoring crack rock, thereby ensuring the safety of geotechnical engineering.

The invention discloses a transformer area topological structure estimation method, system, equipment and medium based on multi-dimensional power utilization characteristics, and belongs to the technical field of power distribution transformer area topologies, and the method comprises the steps: collecting multi-source data, carrying out dynamic weight distribution and abnormal value correction, carrying out the characteristic extraction according to the collected multi-source data, and generating a high-dimensional characteristic vector; clustering user nodes, dividing cluster labels, performing topological modeling according to a cluster division result, generating a topological graph, and performing anomaly verification through multi-dimensional anomaly scoring and abnormal power utilization detection; and performing incremental model parameter correction and multi-objective optimization according to anomaly verification feedback, and generating dynamic topological graph rendering and multi-dimensional decision suggestions by integrating topological modeling, anomaly verification and optimization results. According to the method, accurate estimation of a topological structure is realized through dynamic clustering and hidden node recognition, real-time diagnosis of abnormal nodes and self-correction of a model are realized by means of a multi-dimensional abnormal scoring and self-adaptive optimization mechanism, and the accuracy and operation and maintenance efficiency of transformer area management are improved.

The invention relates to the technical field of underwater communication, in particular to a collision avoidance method suitable for digital underwater acoustic communication, which is characterized in that a neighbor propagation delay mapping database is maintained at each communication node, and inter-node propagation delay information is acquired through passive monitoring and active detection. When a node wants to send data, combining delay mapping to predict a channel occupation time slot, and judging whether the sending is conflicted with an RTS / CTS handshake stage, a data transmission stage and an ACK (Acknowledgement Character) confirmation stage which are carried out by a neighbor; if a conflict risk exists, the node delays to send the RTS and then initiates handshake until the security window is finished, so that collision caused by hidden nodes and exposed nodes is avoided, meanwhile, parallel transmission under the conflict-free condition is allowed, the channel space-time multiplexing efficiency is improved, the network throughput can be remarkably improved, the data packet collision rate is reduced, and the network performance is improved. And the node access fairness is improved.

The invention discloses a dynamic electronic target sequence intention recognition method and system based on a time sequence diagram structure, and the method comprises the steps: obtaining multi-moment target data for a target object which dynamically changes in a real-time scene; performing sequence division on the multi-moment target data by adopting an FP-Growth algorithm to obtain an electronic target sequence set; modeling and coding each electronic target sequence in the electronic target sequence set by adopting a heterogeneous time sequence diagram model to obtain a heterogeneous time sequence diagram sequence; according to the heterogeneous time sequence diagram sequence, time sequence dynamic hidden relation prediction and time sequence dynamic feature prediction are carried out respectively, and a hidden relation and prediction features are obtained; the hidden relation comprises hidden nodes and hidden edges; and according to the hidden relationship and the prediction features, constructing graph data at the next moment, and performing intention recognition through a graph attention convolutional network. The problem that the time-space incidence relation of the target behavior changing along with time is difficult to capture in the prior art is solved, and the prediction and intention recognition precision is improved.

The invention discloses a water quality parameter measurement method driven by a multilayer deep learning model, which realizes high-precision measurement of key water quality parameters such as COD (Chemical Oxygen Demand), total nitrogen (TN), total phosphorus (TP), nitrate nitrogen (NO3-N), nitrite nitrogen (NO3-N), ammonia nitrogen (NH3-N), turbidity and chromaticity by fusing CNN (convolutional kernel 3 * 1 and kernel number 32 / 64), BiLSTM (hidden nodes 30-60) and Attention mechanism and combining WOA hyper-parameter optimization (learning rate 0.001-0.01). According to the method, Savitzky-Golay filtering (window 11, order 3) denoising is adopted, data quality is enhanced through normalization preprocessing, model robustness is improved through 10-fold cross validation and an Adam optimizer, the problem that a traditional model is poor in adaptability to complex spectrum time sequence data is solved, and the method is suitable for rapid online monitoring of water quality.

The present application belongs to the technical field of data processing and multi-source information fusion, and is used to solve the problem that the prior art cannot detect and effectively respond to the collective time drift caused by hidden node faults in the synchronization tree in real time. Specifically, it is a multi-source data correlation integration method based on artificial intelligence, which comprises: obtaining multiple data sources and their synchronization topology information; in response to a request for time sequence correlation of a first data source and a second data source, determining the data source with the smaller value between the first trust risk value and the second trust risk value as the reference source, and the other data source as the source to be calibrated; calculating the absolute value of the difference between the nominal time offset and the witness time offset as the consistency residual; and performing correlation determination based on the consistency residual. By introducing a physical reference benchmark independent of the synchronization tree and a periodic traceability prisoner test, the present application can accurately identify the collective time drift caused by hidden node faults in the synchronization tree, thereby effectively blocking the step-by-step transmission of errors along the synchronization tree.

The application provides a method for determining information and an electronic device. The method comprises enabling a first user equipment to provide auxiliary information, and determining the auxiliary information. The method for determining information provided by the application can be used to determine an auxiliary user equipment, and provide auxiliary information to other user equipments, so as to avoid problems such as hidden node or half-duplex problem.

The application provides a vehicle networking fairness transmission method based on convex optimization and reinforcement learning, and belongs to the technical field of vehicle networking and data transmission. The technical scheme is as follows: S1: an improved confirmation ACK frame and a data DATA frame are designed to realize hidden node detection; S2: mathematical modeling of the transmission fairness problem; S3: fairness transmission based on convex optimization and reinforcement learning. The application effectively discovers hidden nodes, alleviates transmission conflicts caused by hidden nodes by improving the transmission power and the 'continuation' field of the data frame DATA, and finally improves the throughput; the reinforcement learning and convex optimization method are used to jointly optimize the transmission strategy, solve the minimization problem of the difference between the transmission data amounts of fast and slow vehicles, and improve the data fairness of vehicle networking transmission.

The invention relates to a human body action recognition method and device based on heterogeneous space-time diagram convolution, and belongs to the technical field of computers, and the method comprises the steps: obtaining motion video data, and extracting a frame sequence from the motion video data based on a fixed time interval; extracting a joint target point and a target object of each video frame in the frame sequence, and constructing a first heterogeneous space-time diagram based on the joint target point and the target object; generating feature vectors for replacing shielded nodes according to the trained diffusion model and the shielded nodes in the first heterogeneous space-time diagram, and obtaining a second heterogeneous space-time diagram; and inputting the second heterogeneous space-time diagram into the graph neural network model to obtain a target semantic feature, and determining an action category based on the target semantic feature. According to the method, the accuracy and robustness of human body action recognition in a complex real scene (especially in the presence of shielding) are remarkably improved.

The present invention relates to the field of graph representation data analysis technology, and in particular to a method and system for bidirectional matching of scientific research talents and R&D tasks based on graph representation. A knowledge graph is constructed, and the knowledge graph is pre-trained based on the weight attributes of scientific research talents and the weight attributes of R&D tasks, and the node relationships hidden in the knowledge graph are captured to obtain a representation method of the knowledge graph; the characteristic data of scientific researchers are integrated and the relationship is mined according to the representation method of the knowledge graph to obtain a fusion representation of the professional and technical capabilities of scientific researchers; a bidirectional LSTM encoder is used to independently encode meta-paths from the perspective of scientific research talents and the perspective of R&D tasks, and knowledge graph information is modeled from the perspective of scientific research talents and the perspective of R&D tasks; a bidirectional recommendation training is performed through a bilateral quadruple loss function to obtain a bidirectional matching model, and this method can improve the recommendation matching accuracy of scientific research talents and R&D tasks.

A method of operating a neural network. The input layer of the network may have n input nodes connected to output nodes via a hidden layer. The hidden layer may include m hidden nodes. The n input nodes may connect to a subset of k nodes of the m hidden nodes via respective synaptic connections, to which training weights are associated, which form an n×k input matrix Win, whereas a subset of m−k nodes of the hidden layer are not connected by any node of the input layer. Running the network may include performing a first matrix vector multiplication between the input matrix Win and a vector of values obtained in output of the input nodes and a second matrix vector multiplication between a fixed matrix Wrec of fixed weights and a vector of values obtained in output of the m nodes of the hidden layer.

A method includes receiving a training data set, which includes one or more operating parameters of a plasma processing system and ion energy distribution function (IEDF) features corresponding to the one or more operating parameters. The one or more operating parameters include at least one process setpoint from a process recipe, at least one measured operating parameter, or at least one simulated operating parameter. The IEDF features include at least one measured IEDF feature or at least one simulated IEDF feature. The method further includes training an artificial neural network (ANN) using the one or more operating parameters as input, and the IEDF features as output. The ANN includes an input layer including one or more input nodes corresponding to the one or more operating parameters, a single hidden layer including one or more hidden nodes, and an output layer including output nodes corresponding to the IEDF features.

The invention discloses a rapid simulation generation method and system for a dynamic attribute graph, and relates to the technical field of attribute graph generation, and the method comprises the steps: obtaining hidden variables based on variational inference; generating an attribute graph snapshot according to the hidden variable and the hidden node state; updating the hidden node state of the attribute graph snapshot by using a gating loop unit; carrying out model optimization by utilizing logarithm likelihood, and constructing a VRDAG model; and generating a dynamic attribute graph based on the VRDAG model. The invention provides an efficient dynamic attribute graph generation framework VRDAG, which effectively solves the problems of efficiency and accuracy in dynamic attribute graph data generation, especially in the aspect of a complex mode that a capture graph structure and node attributes evolve together with time. The dynamic attribute graph generation framework VRDAG achieves the effects of high efficiency and high quality in dynamic attribute graph generation.

The invention discloses an aeromagnetic interference compensation method based on a radial basis function network. The method comprises the following steps: taking a 16-term coefficient matrix as input data of a deep neural network; a hidden space is constructed for data in hidden nodes in a hidden layer by using a radial basis function, the hidden layer performs certain conversion on input data, and low-dimensional mode input data is converted into a high-dimensional space, so that a linear inseparable problem in the low-dimensional space becomes linearly separable in the high-dimensional space; training a sample set of aeromagnetic data to obtain a network center and other weight parameters meeting supervision requirements; fitting the aeromagnetic interference by using the obtained weight parameter information to obtain predicted magnetic interference; and calculating the compensated standard deviation according to the predicted magnetic interference. The anti-interference capability of the model can be effectively improved.

The invention discloses a social network inference method and a security monitoring method, and relates to the technical field of network identification. A social network inference method comprises the following steps: collecting income time sequence characteristic data of all nodes in a game dynamics system; calculating a reference degree sequence formed by all node reference degrees; inhibiting the behavior of the node i, and obtaining new income time sequence characteristic data of all nodes after the node i is inhibited; obtaining a new reference degree sequence based on new income time sequence feature data of all nodes obtained after the suppression of the node i; and comparing a new reference degree sequence obtained after the node i is inhibited with the reference degree sequence of the nodes, finding out the nodes with changed reference degrees so as to obtain network nodes connected with the node i, judging the number of hidden nodes connected with the node i, and finally inferring the network structure of the whole social network. A complex network structure in a nonlinear dynamic system is captured, connection information between nodes is deeply mined, and accuracy and comprehensiveness are improved.

Embodiments of this application provide a method and an apparatus for accessing a network by a node. The method is applied to a power line communication system, and the method includes: A first node sends first request information, where the first request information is used to request a child node in a first domain to become a proxy node of the first node. The first node receives first response information, where the first response information indicates a second node to serve as the proxy node of the first node in the first domain. According to the method provided in this application, a case in which a master in the first domain periodically traverses all nodes in the first domain to determine whether there is a hidden node is avoided. In this application, the first node actively sends the first request information to request the child node in the first domain to become the proxy node of the first node in the first domain, so that a case in which the master traverses all the nodes in the first domain is avoided, a network access delay of the hidden node is reduced, a resource waste caused by periodic traversal is avoided, and communication performance is improved.

The application relates to the technical field of control cabinets, in particular to a control cabinet remote intelligent diagnosis method and system based on multi-source data fusion, which comprises the following steps: acquiring a visible light image, infrared thermal image data and loop current of a target control cabinet; based on semantic analysis of the visible light image, a hidden node that is shielded is identified, a heat conduction agent area is locked and a central skeleton path is extracted; the heat conduction agent area is analyzed to determine a temperature rise gain strategy, the infrared thermal image data is compensated to generate corrected temperature rise data; a temperature gradient vector of the infrared thermal image data along the central skeleton path is calculated, and a heat source position feature is output based on the directivity of the temperature gradient vector relative to the hidden node; a theoretical heat production potential is generated based on the loop current, and a logic interlocking criterion is constructed. The application can realize accurate reverse attribution of hidden contact faults under non-line-of-sight conditions through multi-modal logic decoupling.

Disclosed are a transmission or reception method and apparatus for reducing interference of a hidden node in an OBSS in a wireless LAN system. According to one embodiment of the present disclosure, a method performed by a first station (STA) in a wireless LAN system comprises the steps of: monitoring, by a first STA associated with a first access point (AP) of a first basic service set (BSS), an inter-BSS physical layer protocol data unit (PPDU) transmitted from a second STA belonging to a second BSS or a second STA; transmitting, to the first AP, report information including information for indicating that the inter-BSS PPDU is received or not received; and receiving, from the first AP, scheduling information of the first STA based on the reporting information.

The present invention provides a hidden node expansion method, device, computing device and storage medium, wherein the method includes: when receiving an expansion initiation instruction for a target shrink node in a point-line relationship diagram, obtaining the attribute information of the target shrink node; determining the maximum level to which the target shrink node can be expanded according to the attribute information, and responding to the expansion initiation instruction according to the maximum level; receiving an expansion operation instruction for the target shrink node, determining the expansion level that does not exceed the maximum level required for the target shrink node according to the expansion operation instruction; and expanding the hidden nodes on the expansion level under the target shrink node according to the attribute information of the target shrink node. This solution can improve the flexibility and controllability of the selection of the expansion level, thereby improving the user experience.

The invention discloses a method for predicting the material demand of a deep learning model based on swarm intelligence, and aims to solve the problems that a conventional hyper-parameter optimization method is low in efficiency and is liable to fall into local optimum. The method comprises the following steps: firstly, constructing a deep learning model containing the number of layers, the number of hidden nodes, a learning rate and regularization parameters, and defining an optimization interval of each hyper-parameter; secondly, designing an improved sparrow search algorithm (ISSA), enhancing global search capability through Cauchy variation disturbance, balancing exploration and development of a refraction reverse learning strategy, and adjusting a self-adaptive early warning value to improve convergence stability so as to optimize a hyper-parameter combination; further taking a verification set RMSE as a fitness target, combining with parallelization calculation to accelerate model performance evaluation, and screening optimal hyper-parameter configuration; and finally, predicting material demand data by using the optimized model, performing transverse comparison with LSTM, ARIMA, GRU, GCN and other models, and verifying the effectiveness of the model through multiple indexes such as RMSE, MAPE and R. Experiments show that the ISSA optimization method provided by the invention significantly reduces prediction errors, the optimized model has higher accuracy and robustness in a complex material demand scene, and meanwhile, the reliability of performance improvement is confirmed through statistical significance test.

The application relates to a data transmission protection method based on combination of an HMAC-MD5 algorithm and key pre-distribution, which combines a key pre-distribution scheme and an HMAC-MD5 algorithm based on symmetric cryptography and integrates a key pre-distribution management scheme based on a hash mechanism; that is, before data information participates in communication encryption transmission, the key held by a node is preloaded, and the one-way characteristic of a hash function is utilized to hide part of the key information of the node; the application utilizes the one-way characteristic of the hash function and the key pre-distribution to hide part of the key information of the node to a certain extent, that is, to reduce the number of key leaks of the node, and then to reduce the possibility of destruction of other links of the network by an attacker, so that the security of node communication is improved.

Embodiments of the present disclosure relate to communication. According to embodiments of the present disclosure, a first terminal device receives first control information indicating a first set of resources reserved by a second terminal device and second control information indicating a second set of resources reserved by a third terminal device, respectively. If the first set of resources and the second set of resources overlap with each other, the first terminal device whether a condition associate with coordination of terminal devices is satisfied. If the condition is satisfied, the first terminal device transmits an indication to at least one of the second and third terminal devices. In this way, collisions in resources can be avoided. Further, it also can solve the hidden node problem.

The invention relates to the technical field of financial credit data processing and distributed storage, and discloses a distributed storage method and system for dynamic index query of a debtor relation graph. The method comprises the following steps: processing existing multi-source associated data by using a scene attribute completion and cross-source conflict resolution method, and generating a debtor associated node panoramic attribute matrix; constructing a debtor multi-level associated value directed graph based on the matrix; generating a hidden key node multi-dimensional evaluation table through an on-graph hidden node value migration evaluation algorithm; and generating a debtor associated node communication coping scheme through a communication coping suggestion matching algorithm in combination with distributed fragmentation storage and dynamic index service, and archiving the debtor associated node communication coping scheme. According to the method, the problem of priority misjudgment of the implicit associated nodes is solved, accurate integration of multi-source data is achieved, and distributed efficient storage and query are achieved.

The embodiment of the present application provides a configuration method, compensation method, display module and device for a brightness compensation coefficient. The configuration method includes: obtaining the number of preset DBV parameters to be configured in the chip, the number of preset DBV parameters being N; obtaining the node values ​​of N DBV nodes and the corresponding compensation coefficients and configuring them accordingly as the preset DBV parameters. Obtaining the node values ​​of N DBV nodes and the corresponding compensation coefficients includes: obtaining the node values ​​of N DBV nodes and the node values ​​of hidden nodes; the N DBV nodes include a first node and a second node, the node value of the first node is zero, the node value of the second node is greater than zero and less than the node values ​​of the remaining DBV nodes of the N DBV nodes; the node value of the hidden node is greater than the node value of the first node and less than the node value of the second node; obtaining the compensation coefficients corresponding to the second node and the hidden node; obtaining the compensation coefficient corresponding to the first node based on the compensation coefficient corresponding to the second node and the compensation coefficient corresponding to the hidden node.