16 results about "Linear network" patented technology

The invention discloses a high-precision hybrid expert large model and a fine tuning method thereof. The hybrid expert large model is composed of a domain expert group and a shared expert group. A high-precision data transmission fine tuning module based on each linear network LN in the shared expert, wherein the fine tuning module comprises an extended linear network, a numerical value alignment network, a blocking controller and a precision converter; the extended linear network is of a replicated linear network (LN) structure and converts an input vector into a first data flow vector; the numerical value alignment network performs numerical value coding on the output data flow vector of the extended linear network to generate a second data flow vector; the blocking controller controls whether the extended linear network and the numerical alignment network participate in domain expert group reasoning or not according to binary data signals; the precision converter adjusts the precision of the data stream output by the numerical alignment network and the precision of the data of the shared expert linear network to be consistent; according to the invention, the flexibility and intelligent control capability of the high-precision hybrid expert large model can be enhanced.

This invention discloses a method and system for optimizing data interaction between intelligent terminals based on link state awareness, relating to the field of wireless communication data transmission technology. The method includes: constructing a directed acyclic graph topology; performing cooperative topology state discovery to generate a real-time link state communication topology; performing hierarchical packetization for transmission quality constraints; performing random linear network coding to obtain multiple linearly independent coded packets; performing distributed congestion-aware scheduling decisions to obtain multiple primary transmission links; performing differential coding and obfuscation transformation to obtain multiple obfuscated coded packets; and concurrently transmitting multiple obfuscated coded packets to a second terminal for Gaussian elimination decoding and reception. This invention solves the technical problems of weak topology adaptability, unreasonable congestion scheduling, and insufficient security protection in existing technologies, achieving efficient and secure data interaction between terminals and improving the real-time performance and transmission security of local area network data transmission.

This application discloses a method, device, terminal, and medium for assessing the synchronous stability of grid-connected wind power. The solution obtains the topology information of the grid-connected wind power combined transmission system and constructs an equivalent circuit. Then, combining the preset grid-connected wind power control logic and the principle of linear network superposition, it constructs an output active power relationship under different control conditions, including the coupling relationship between the output active power of the grid-connected wind power and the terminal voltage and output phase angle of the grid-connected wind power. Based on this output active power relationship, it generates the output phase angle characteristic curve of the grid-connected wind power and calculates the output phase angle acceleration area and the maximum deceleration area. Finally, based on the area comparison results, it determines the synchronous stability assessment result of the grid-connected wind power, thereby accurately assessing the synchronous stability of the grid-connected wind power in the grid-connected wind power combined transmission system, improving the reliability of the assessment results, and ensuring the safe and stable operation of the new energy transmission system.

The present disclosure provides a method and system for identifying individual procambarus clarkii of biological characteristics, relating to the technical field of deep learning, comprising: extracting local and global digital biological characteristics of each slice image and a target image, and fusing the local and global digital biological characteristics to obtain a digital biological characteristic matrix; inputting the digital biological characteristic matrix into an improved graph neural network model, generating a query and a key through an independent multi-layer perceptron, calculating edge weights through a scaling dot product of the query-key, and obtaining multiple node features after multi-head attention splicing; splicing different node features, generating an attention score for each node based on a gating mechanism aggregation module, and outputting a global representation vector of each group of point sets after global pooling operation; mapping the global representation vector to a fixed dimension through a linear network, and taking it as a final point set embedding, and finally outputting a procambarus clarkii individual identification result.

The application discloses a train network architecture based on Ethernet deep fusion, which comprises a red network switch topology, a blue network switch topology and train subsystems connected to the blue network switch topology and the red network switch topology; the train subsystems are redundantly designed and arranged on a head car, a middle car or a tail car of the train; the backbone network topologies of the red network and the blue network are not limited and are suitable for ring network topology, linear network topology or link aggregation network topology; the application saves switch hardware devices and communication cable materials; the whole train network data ports are centrally managed, so that traffic monitoring and fault positioning are facilitated; the application builds an intensive integrated network architecture and fully utilizes the data bearing capacity of a gigabit backbone network; and the application uniformly plans a train-ground safety transmission boundary, which is beneficial to the deployment and drilling of diversified safety strategies.

The application belongs to the technical field of power grid power supply, and particularly relates to a measurement method and system for tower grounding resistance, which comprises the following steps: constructing a chain linear network model, setting an engineering typical value of the grounding impedance of each tower, constructing a prior admittance matrix, inverting the prior admittance matrix to obtain a prior impedance matrix, injecting a mixed excitation current signal with multiple different frequencies into the grounding lead of the measured tower, synchronously collecting the measured voltage of all nodes, separating the measured voltage under each frequency, calculating an admittance deviation matrix and an impedance deviation matrix, establishing an approximate linear relationship between the impedance deviation matrix and the admittance deviation matrix and a voltage difference vector, solving the approximate linear relationship, obtaining the impedance deviation of the grounding impedance under each frequency relative to the engineering typical value of the grounding impedance, calculating the actual grounding impedance, and separating the actual grounding impedance to obtain the grounding resistance. The application realizes the inversion of the grounding resistance from the external port measurement value without disassembling any grounding lead.

The application discloses a preset performance synchronization control method of a nonlinear networked time-delay system based on state translation transformation, which transforms communication time delay of the system by using state translation transformation technology, designs a preset time synchronization control scheme through the system after translation transformation, analyzes the synchronization of the transformed system, indirectly obtains effectiveness of the original system under the preset time controller, and finally realizes preset time synchronization control of the nonlinear networked time-delay system. Based on the constructed Lyapunov function, sufficient conditions of preset time synchronization are obtained, under the conditions, even if the system is affected by communication time delay or preset performance index changes, the synchronization control of the nonlinear networked time-delay system can still be realized under the preset time requirement. Finally, the effectiveness of the synchronization control scheme is verified through numerical simulation.

This invention belongs to the fields of medical image processing, neural engineering, and artificial intelligence-assisted diagnosis. It provides a Parkinson's disease identification system, medium, and product based on brain network control energy characteristics. Based on the time series of brain region functional activities, it extracts brain states at rest and calculates the temporal dynamic characteristics of each brain state. Using the whole-brain structural connectivity matrix as the network topology, it constructs a linear network control model to calculate the optimal control energy characteristics for brain state transitions and the energy regulation capacity characteristics of whole-brain regions. Based on brain dynamics feature sets, brain network energy feature sets, and brain network graph theory feature sets, it extracts a feature set significantly related to Parkinson's disease and constructs a multi-dimensional fusion feature matrix. Using this multi-dimensional fusion feature matrix as input, it trains the Parkinson's disease identification model. Based on the multimodal brain imaging data of the subject and the trained Parkinson's disease identification model, it outputs the Parkinson's disease identification result. This achieves non-invasive, accurate, and early identification of Parkinson's disease.

The application discloses a new energy output risk early warning method and system based on power mutation feature recognition, comprising: the application collects output measurement values, meteorological forecast data and grid-connected operation state identifiers and generates operation observation records, reduces misjudgment caused by state switching, and improves early warning input reliability; the mutation feature vector is extracted by sliding window variable point detection and compared with the mutation threshold to generate a mutation trigger identifier, sudden climbing, sudden drop and other abnormalities are positioned in time, and the trigger condition is provided for subsequent risk inference; the mutation feature vector and the meteorological forecast data are input into the Bayesian linear network after triggering to output the future 60-minute output prediction interval and the out-of-limit probability, the quantitative expression of the mutation subsequent evolution uncertainty is realized; the risk intensity is calculated from the out-of-limit probability and the interval width, the risk grade is mapped, the early warning result and the disposal strategy are output, and the operation risk under high proportion of new energy is reduced.

This application belongs to the field of power grid supply technology, specifically a method and system for measuring the grounding resistance of power poles. The method includes constructing a chain-like linear network model, setting typical engineering values ​​for the grounding impedance of each power pole, constructing a priori admittance matrices, inverting the priori admittance matrix to obtain a priori impedance matrix, injecting multiple mixed excitation current signals of different frequencies into the grounding leads of the power pole under test, simultaneously acquiring the measured voltages of all nodes, separating the measured voltages at each frequency, calculating the admittance deviation matrix and impedance deviation matrix, establishing an approximate linear relationship between the impedance deviation matrix, admittance deviation matrix, and voltage difference vector, solving the approximate linear relationship to obtain the impedance deviation of the grounding impedance relative to the typical engineering value of the grounding impedance at each frequency, calculating the actual grounding impedance, and separating the actual grounding impedance to obtain the grounding resistance. This application enables the direct inversion of grounding resistance from external port measurements without disassembling any grounding leads.

The application discloses a kind of wind power grid-connected system transient overvoltage evaluation methods considering unit dynamic characteristics, it is related to new energy grid-connected technology field.The method first establishes the wind turbine model considering grid following control and low voltage ride through dynamics, deduces the dynamic constraint condition of reactive current support coefficient;Second, construct the equivalent admittance of unit during fault and embed system original admittance matrix, obtain the modified system impedance matrix;Further based on dynamic constraint condition, the reactive current support coefficient value is revised and impedance matrix is updated, the transient voltage increment caused by unit injection current during fault is calculated;Finally, traverse fault scene output evaluation result.The application realizes decoupling solution with linear network by converting the nonlinear dynamics of unit into equivalent admittance, while significantly improving the calculation efficiency, effectively describes the dynamic mechanism such as direct current bus voltage constraint, and guarantees the accuracy of transient overvoltage evaluation.

The application relates to the technical field of energy storage power station equipment updating, and discloses a batch firmware updating method and system for energy storage power station equipment, which comprises the following steps: using a random linear network coding technology to fragment and encode and combine a firmware updating burning file, so as to obtain an encoding package corresponding to the firmware updating burning file; dynamically adjusting the priority of executing a firmware updating task in combination with the task emergency degree, system idleness, firmware updating emergency and firmware updating deadline in the current energy storage power station; and performing batch firmware updating through the encoding package according to the priority of the firmware updating task. The application can improve the efficiency of firmware updating, is simple and safe, and has high practicability.

This invention discloses a spatiotemporal traffic flow prediction method based on dual-flow decoupling. The method first decomposes the temporal data of road network traffic flow into trend and periodic components using the exponential moving average method. Then, features are extracted and fused using a deep linear network and a local feature hybrid network with temporal block embedding to obtain the global temporal prediction component. After the traffic flow temporal data is encoded in the temporal domain by gated dilated convolution, multi-view spatial aggregation is performed using forward, backward, and normalized graph convolutions with adaptive adjacency matrices to obtain the local spatiotemporal prediction component. Finally, dynamic weights are generated by a gated network, and the two types of components are weighted and fused to obtain the final prediction result. This invention accurately separates long-term and short-term traffic flow features, adaptively balances global patterns and local details, improves the accuracy and robustness of long-term temporal prediction, and reduces computational complexity, making it suitable for intelligent traffic flow management scenarios.

The application provides a handwriting prediction method based on an enhanced hybrid linear network and semantic prior constraint, comprising: obtaining original point reporting data and performing preprocessing and normalization to obtain time sequence data; inputting the time sequence data into a trajectory classification model to output a probability distribution of a semantic category to which a current writing trajectory belongs; inputting the time sequence data into a trajectory prediction model and introducing the probability distribution as a prior constraint into an inference process of the trajectory prediction model, and outputting a plurality of predicted point reporting coordinates from the trajectory prediction model; and correcting and smoothing the predicted point reporting coordinates to generate a predicted writing trajectory for real-time display. The method can achieve smooth writing experience without high-end hardware, the predicted trajectory has high adhesion to the real handwriting, and stable prediction accuracy can be maintained in challenging scenarios such as fast writing and stroke turning, thereby significantly improving the writing hand feeling of the touch device and the user experience.

The application discloses a kind of based on multiple label co-occurrence network discrimination electric larceny type detection method and system, comprising: obtaining historical electric larceny sample data;User electricity process characteristics are mined using GRU network modeling, and the hidden state representation of current time step is output as user electricity process evolution characteristics;Based on the historical electric larceny sample data, establish electric larceny label co-occurrence relationship network graph;Based on the electric larceny label co-occurrence relationship network graph, the associated structure information between electricity features is mined, and the relationship characteristics between electric larceny labels are obtained;Determine the attention score weighting of user electricity process evolution characteristics, the relationship characteristics between electric larceny labels and user attribute characteristics, and after linear network layer processing, obtain the comprehensive characteristics of user electric larceny, and based on softmax activation function the comprehensive characteristics of user electric larceny are converted into probability value by output, to determine electric larceny behavior type based on probability value.