142 results about "Channel network" patented technology

The invention discloses an intelligent drill bit wear prediction system and method based on multi-source perception, and relates to the technical field of crossing of artificial intelligence and intelligent manufacturing, and the method comprises the steps: synchronously collecting vision, vibration, temperature, pressure and engineering parameter data through a multi-source sensor, and carrying out the time-space alignment and preprocessing; extracting each modal feature by a multi-channel network, generating high-dimensional joint representation through cross-modal attention fusion, inputting a time sequence model formed by a gating circulation unit and an adaptive residual block, and outputting a wear loss prediction sequence and a wear type probability; the system comprises a data acquisition module, a preprocessing module, a feature extraction module, a fusion module, a prediction module and a risk decision module, parameter instructions are optimized, and early warning is realized when a threshold value is triggered. Through multi-source cooperative sensing, dynamic feature fusion and closed-loop decision control, the prediction precision, robustness and real-time performance are remarkably improved, the drilling safety is guaranteed, and the operation and maintenance cost is reduced.

The invention relates to the technical field of image analysis, in particular to a cushion foaming forming quality detection method and system based on machine vision, and the method comprises the steps: collecting image data, carrying out the iterative screening of the image data, and selecting a detection sample; the method comprises the following steps: collecting a three-dimensional point cloud of a detection sample, calculating the flatness of a seat cushion by using the three-dimensional point cloud, carrying out modeling by using polarized light reflection to obtain surface cell uniformity, and carrying out feature extraction on the three-dimensional point cloud through a three-dimensional mapping model to generate a three-dimensional semantic model; extracting mechanical characteristics by using the time sequence pressure map, and generating a cushion digital model; variational self-coding is carried out on the cushion digital model, and an abnormal feature map is generated; performing information extraction and pixel-by-pixel multiplication on the abnormal feature map by using a learnable convolutional layer to generate an abnormal enhanced map; and inputting the abnormal enhancement graph and the seat cushion digital model into a multi-channel network model for final evaluation. According to the method, the image data is analyzed, and the performance of the cushion is subjected to index quantification, so that the deep detection of the quality of the cushion is realized.

The utility model discloses an electrolytic bath bipolar plate with micropore flow channels and an electrolytic bath, a micropore flow channel network is arranged on the side surface of an anode of a plate body, and the micropore flow channel network comprises a plurality of groups of micropore flow channel units which are arranged at intervals along the width direction of the plate body; each micropore flow channel unit comprises a main flow channel arranged in the length direction of the plate body and a plurality of first-stage branch flow channels arranged in the length direction of the main flow channel at intervals, a plurality of short branch flow channels are arranged on the two sides of each first-stage branch flow channel at intervals, and a jet hole is formed in the top of each short branch flow channel. The jet flow holes are formed in the direction perpendicular to the side surface of the anode, the length of the short branch runner is smaller than that of the first-stage branch runner, and the short branch runner is perpendicular to the first-stage branch runner. And the microporous flow channel network adopts a structure of combining tree fractal and T-shaped nodes, so that the mass transfer uniformity of water flow in the microporous flow channels can be improved, and the water-membrane contact efficiency is improved, thereby ensuring the hydrogen production efficiency of the bipolar plate.

The invention provides an optical and acoustic image registration method for underwater structure crack detection, and the method comprises the steps: collecting a multi-mode image through the vision field and time synchronization, building a one-to-one correspondence relation according to the pose and time metadata, and improving the image quality through the denoising, distortion correction and other means; optical and acoustic multi-scale features are respectively extracted through a dual-channel network, and cross-modal semantic alignment is realized in combination with a shared weight and a channel attention mechanism; generating a high-resolution scale offset field by using local cross-correlation and micro-upsampling, and introducing manifold regularization constraint to ensure that a vector field is smooth and continuous; according to the method, the local scale field and the affine parameters are combined, non-rigid space mapping is achieved through thin-plate spline interpolation, feedback iterative optimization based on edge structure consistency is assisted, the registration precision of a key structure is enhanced, the automatic registration effect of the underwater multi-modal image is improved, and the method has high robustness and practical application value.

The invention discloses a micro-channel cooling method and system for semiconductor packaging, and belongs to the technical field of semiconductor packaging detection, and the method specifically comprises the steps: constructing a graded micro-channel network in a semiconductor packaging substrate, the graded micro-channel network is composed of a first-stage main channel and a second-stage branch channel with a smaller equivalent hydraulic diameter, and the first-stage main channel and the second-stage branch channel are communicated through a conical transition section; the pump pumps a cooling working medium into the second-stage branch channel through the first-stage main channel, and flow deviation caused by manufacturing tolerance is compensated through diameter difference. During boiling cooling, a cooling working medium absorbs heat in the second-stage branch channel to form a bubble core, and a periodic expansion or contraction structure in the channel regulates and controls bubble growth; a cooling working medium carrying bubbles enters an inertia damping structure comprising a sudden expansion cavity and a vortex generator for pressure stabilization and then enters an external condenser for phase change, the condensed liquid cooling working medium is injected into a primary main channel again through a pump, and efficient cooling circulation is achieved.

The invention discloses a small hole heat conduction and gas migration channel construction system and method based on seabed horizontal directional drilling, and relates to the technical field of ocean engineering and energy exploitation. A sea surface operation module is used for controlling all modules of the system to work cooperatively and supplying energy to the system; the seabed drilling guide module is used for realizing layer-entering drilling and channel pre-construction; the signal detection module is used for monitoring the spatial position and attitude parameters of the seabed drilling guide module in a seabed bottom layer in real time; the small hole perforation flow guide module is used for constructing a heat conduction and gas migration channel network after the drill bit enters a target hydrate layer; after the heat conduction and gas migration channel network is formed, the seabed drilling guide module continues to drill out of the seabed bottom layer, and a channel formed by the seabed drilling guide module is used for dragging a pipeline into a reservoir. A reusable mining channel suitable for the deep sea environment is constructed, the gasification efficiency is effectively improved, and stratum disturbance and energy consumption are reduced.

The invention discloses a hydrological sequence missing data complementation and trend prediction system and method, and belongs to the technical field of water conservancy information. The system comprises a data acquisition and preprocessing module, a spatial-temporal feature fusion module, a physical constraint completion module, an uncertainty prediction module, a credible evidence storage and verification module and a visual tracing module which are connected in sequence. The method comprises the following steps: collecting and preprocessing multi-source hydrological data; combining the spatial-temporal features through a dual-channel network to generate joint representation; generating and checking a completion sequence based on joint representation and coupling physical constraints such as a unit line method and a Manning formula; performing uncertainty trend prediction by using an integrated predictor; the key data of the whole process are linked and stored, and are automatically verified through an intelligent contract; and full-life-cycle visual tracing is provided. According to the method, the complementation robustness and accuracy in a high-missing-rate scene are effectively improved, the physical consistency of a complementation result is ensured, and credible evidence storage and transparent decision of the whole process are realized.

The invention discloses a seismic strong background noise removal method based on a two-channel network, and relates to the field of geophysical exploration data processing, and the method comprises the steps: selecting a noiseless signal and background noise when the noiseless signal and the background noise are not excited from a single-channel seismic record after a seismic source is excited, carrying out the superposition, generating noisy data, and carrying out the normalization processing; constructing a dual-channel constraint denoising network composed of a global constraint sub-network and a denoising sub-network; optimizing network parameters through the training sample set; and performing global constraint denoising and slice local denoising on the noisy seismic data by using the trained model, and finally splicing to obtain a complete denoising result. According to the method, the splicing effect problem caused by traditional blocking processing is effectively solved, the strong background noise removal effect is remarkably improved, and seamless high-quality denoising with the complete edge is achieved.

The invention discloses a micro-fluidic chip-based trace tumor nucleic acid low-damage enrichment method and system, and the method comprises the steps: constructing a virtual flow channel network digital twin model of a multi-stage micro-fluidic chip, the virtual flow channel network digital twin model comprises enrichment units connected in series, each enrichment unit is composed of a tapered convergence section, a periodic bending section and an aptamer modified amplification section; and executing real-time simulation of fluid dynamics based on the digital twinborn model, generating a dynamic pressure control instruction set, and adjusting the flow velocity distribution of the biological sample in the flow channel through a negative pressure system driven by a fuzzy PID (Proportion Integration Differentiation) controller. According to the invention, through digital twin dynamic reconstruction flow channel and multi-modal fusion regulation, shear stress accumulation is inhibited to maintain nucleic acid integrity, impurity adsorption is reduced based on dynamic balance of probe density and binding saturation, and multi-field collaborative optimization is realized.

The invention provides an intelligent audio power amplifier system and a working method thereof. The working method comprises the steps that a dynamic noise suppression module enables an input signal after reverse noise injection to enter a distortion compensation module; a distortion compensation signal is synthesized by using a distortion compensation module; the life cycle parameter self-calibration module calibrates parameters and inputs the parameters to the dynamic noise suppression module and the distortion compensation module; the dynamic noise suppression module is based on an intelligent learning method and adopts a frequency band channel network, a time sequence channel network and a fusion layer; the life cycle parameter self-calibration module provides the temperature and the working time for the dynamic noise suppression module to optimize the time domain channel network and provides the temperature and the working time for the distortion compensation module to update the predistortion coefficient of the dynamic nonlinear distortion model; the output of the dynamic noise suppression module is provided for the distortion compensation module; and the output of the distortion compensation module is fed back to the dynamic noise suppression module. The problems of high-frequency noise interference, nonlinear distortion degradation and insufficient long-term stability are solved at the same time.

The invention discloses a series arc fault detection method based on DSCAN and feature fusion. The method comprises the following steps: collecting source domain data and target domain data; preprocessing data of a source domain and a target domain to obtain multi-dimensional features such as a grayscale image after GASF conversion, an IMF component after VMD adaptive decomposition, a frequency domain component obtained by DT-CWT and the like; constructing a four-channel feature fusion module, inputting the original current signals and the converted multi-dimensional features into corresponding channel networks, extracting signal features, and splicing the signal features; and constructing a DSCAN network to learn domain invariant features of a source domain and a target domain, and detecting a series arc fault through a model. Through multi-dimensional feature fusion and cross-domain transfer learning, arc fault features in a complex load scene are effectively extracted, the robustness of the model to different load environments is enhanced, the problem that a traditional method is insufficient in detection precision under mixed loads is solved, efficient and accurate detection of series arc faults is achieved, and the fault detection efficiency is improved. And a reliable technical scheme is provided for electrical safety monitoring.

The invention discloses an employee performance evaluation method based on behavior data driving, and relates to the technical field of human resource management, and the method comprises the following steps: constructing a multi-channel network fluctuation prediction matrix, generating an expected behavior arrival time scale based on the transmission state of a task switching node, and taking the expected behavior arrival time scale as a baseline for behavior sequence gap recognition; and under the guidance of an expected behavior arrival time scale, performing sliding window scanning on the received behavior sequence, generating a gap identifier when detecting that a time slot exceeds a threshold value, and injecting a confidence coefficient weight descending label. According to the method, a behavior gap is accurately identified through network fluctuation prediction and sliding window scanning, behavior reconstruction is realized in combination with self-supervised generation and residual reasoning, and dynamic evaluation correction is completed by using confidence projection and an intelligent regulation engine, so that the data authenticity is guaranteed from the source, and the objectivity and robustness of performance evaluation and the resource allocation precision are improved.

The invention discloses a dense goods allocation warehouse-in and warehouse-out method and system based on an MAP map and a medium, and relates to the technical field of intelligent warehousing, and the method comprises the following steps: obtaining MAP map data, extracting channel information of a dense goods allocation area based on the MAP map data, and obtaining a channel network graph; constructing an AGV initial path based on the channel network diagram, and performing path oscillation evaluation on the AGV initial path to obtain an oscillation conflict point; building an obstacle avoidance area in the channel network diagram based on the channel position of the oscillation conflict point; discretizing the AGV initial path in the obstacle avoidance area by using an ORCA algorithm to obtain an optimized moving path; the AGV is controlled to execute the warehouse-in and warehouse-out operation of the dense goods allocation based on the optimized moving path, and the problem that in the dense goods allocation environment, path oscillation and collision are likely to happen when the AGV executes the warehouse-in and warehouse-out operation is solved.

The invention discloses a station hub passenger flow load and flow composition prediction method and system, and the method comprises the steps: generating OD demands from an exit to a destination of each vehicle based on the flow of each exit and a traffic mode sharing rate predicted by a comprehensive hub prediction model; obtaining channel basic data in the hub, constructing a directed graph representing a channel network, and calculating a channel capacity attribute of each edge; for each OD pair in the OD demand, calculating corresponding OD trajectory data in combination with the directed graph; calculating the load rate of the channel according to the load attribute of the edge in the OD track data and the channel capacity attribute of the edge; and calculating the flow composition of the passenger flow according to the flow information of the nodes in the OD trajectory data. According to the method, the whole dynamic flow process from arrival to departure of the passenger flow in the station and traffic mode conversion can be predicted, so that the passenger flow distribution and dispersion can be better performed during congestion according to the prediction result, the passenger flow load pressure in the station in the peak period is reduced, and the occurrence probability of safety accidents is reduced.

The invention discloses a method for quickly constructing a transient hydraulic model of a canal network. The method comprises the following steps: firstly, numbering open channel natural sections in a channel network, respectively determining hydraulic parameters of different open channel natural sections, and uniformly summarizing section numbers of different open channel natural sections and node numbers corresponding to the section numbers; determining a topological structure form and a boundary change condition of the channel network; then according to the topological form of the channel network, respectively determining a corresponding structure topological incidence matrix, an open channel water depth incidence matrix, a water depth incidence matrix in a branch point continuity equation and a flow incidence matrix in the branch point continuity equation; and finally, constructing a transient hydraulic mathematical model of the canal network, giving boundary change conditions, and simulating the hydraulic transition process state of the canal network based on the transient model. The method provided by the invention is high in applicability, can theoretically simulate the transient transition process change of any topological structure channel network, and has a great application prospect.

This invention belongs to the field of artificial intelligence and system response calculation technology, and provides a method and system for calculating system response based on a generalizable physical information neural network. The technical solution involves establishing a family of differential equations consisting of multiple governing equations based on the physical characteristics of the system to be analyzed, and obtaining the corresponding dataset; constructing a neural network model consisting of a main network and multiple channel networks, where the main network learns the basis functions representing the system response and shares them with all channel networks; each channel network processes a specific differential equation task within the task family and learns the basis function coefficients corresponding to that task; inputting the dataset into the neural network model, training the model by minimizing a loss function containing multiple constraints, and obtaining the optimal solution for the system response under different stimuli based on the trained model. This method can solve complex differential equations more accurately and improve the model's generalization performance on unseen tasks.

The invention discloses a three-dimensional flexible distributed strain sensor and a strain interpretation method for temperature compensation thereof, and relates to the technical field of intelligent sensors. The three-dimensional flexible distributed strain sensor comprises a sensor body and a signal conditioning module; wherein the sensor body comprises an Ecoflex flexible substrate, a liquid metal microchannel network and an annular electrode array; the Ecoflex flexible substrate is internally provided with a three-dimensional communicated micro-channel structure formed by a three-dimensional printing sacrificial template process; the liquid metal micro-channel network is composed of gallium-indium alloy filled in the three-dimensional communicating micro-channel structure and comprises an X-direction micro-channel group extending in the X direction, a Y-direction micro-channel group extending in the Y direction and a Z-direction micro-channel group extending in the Z direction. The strain monitoring precision in a complex environment is remarkably improved, errors caused by temperature drift are effectively restrained, and the strain monitoring device has the advantages of being high in flexibility, high in stability and good in adaptability.

The invention discloses a facial expression detection method based on a Transform dual-channel network and related equipment, and can be applied to the technical field of deep learning. Static features of a video sequence to be processed are extracted through a video sequence feature extractor to form a video sequence feature map carrying global information and local information, and dynamic features of an optical flow sequence to be processed are extracted through an optical flow sequence feature extractor to form an optical flow sequence feature map. Fusing features of different levels in the video sequence feature map through a multi-level feature fusion module to obtain multi-level static fusion features, and carrying out feature refinement on the optical flow sequence feature map through a self-adaptive convolution attention module to obtain space channel refinement dynamic features; the two features are fused through a cross attention fusion module to obtain a cross fusion feature, and then facial expression recognition is performed according to the cross fusion feature, so that the accuracy of expression detection can be effectively improved.

The invention discloses a production workshop-oriented multi-device wireless communication optimization method and device, and relates to the related field of wireless communication networks, and the method comprises the steps: executing real-time task calling and interpreting a production service intention through the interaction between a standard interface and a workshop service system, and carrying out the QoS parameter coding to generate a communication KPI; according to the communication KPI, assisting a slice manager embedded in a communication center controller, dynamically generating a network slice, performing protocol element reading coupling under multiple communication protocols, and determining a communication element condition set; and for the communication element condition set, dimension joint optimization based on network resource scheduling is carried out, a resource configuration strategy is determined, communication management is carried out on task-related equipment interaction terminals, temporary communication isolation is established between non-associated task terminals, and a loose channel network is used as alternative configuration. The technical problem that existing production workshop multi-device wireless communication is insufficient in stability and reliability is solved, and the technical effect of improving the stability and reliability of communication is achieved.

The present application relates to a kind of multi-priority differential fault prediction and active prevention method for vehicle-mounted fiber channel network, belong to vehicle-mounted network communication technical field.The present application method includes: receiving the state report frame that each network node periodically reports, obtain the state information of each network node connected link from the state report frame, based on each state information, the network overall health index value corresponding to it, the bayesian probability anomaly value and the bayesian probability anomaly trend value of each network node are calculated;Based on each state information, network overall health index value, bayesian probability anomaly value and bayesian probability anomaly trend value, network fault risk prediction is carried out;Based on the predicted fault risk, the network optimization instruction frame carrying network optimization instruction is issued to optimize network.The present application method realizes the advance accurate prediction and active prevention of vehicle-mounted fiber channel network fault, effectively improves the reliability and security of network operation, improves the safety of vehicle operation.

The application relates to an image processing method, device and equipment for medical images and a storage medium, and relates to the technical field of medical treatment. The method comprises the following steps: calling a first channel network to process a first sample image, obtaining a prediction type result corresponding to the first sample image and an image feature; calling a second channel network to process a second sample image and the image feature of the first sample image, and obtaining a prediction classification result; based on the prediction type result, the prediction classification result, a type label of the first sample image and a type label of the second sample, training the first channel network and the second channel network, and obtaining a channel network after the training of the first channel network is completed, and using the channel network to construct an image classification model. Through the above method, the overfitting phenomenon caused by the limited number of samples in a small sample learning scene is avoided, the training effect of the model is improved, and the classification accuracy of the obtained image classification model is improved.

The application discloses a flow channel network inversion method based on distributed optical fiber monitoring heat-flow response and belongs to the field of energy exploitation. The method comprises the following steps: a, carrying out periodic water injection test, adopting distributed optical fiber temperature sensing to acquire temperature monitoring data of a target layer section, and adopting distributed optical fiber acoustic wave sensing to acquire acoustic wave monitoring data of the target layer section; b, interpreting the temperature monitoring data, identifying and extracting temperature response data at different injection / production positions; interpreting the acoustic wave monitoring data, identifying and extracting flow monitoring data at different injection / production positions; c, based on the real-time acquired temperature response data and flow monitoring data at different positions, combining an inversion method, inverting the geometric structure and parameters of the flow channel network, reconstructing the distribution of the flow channel network, and completing the inversion of the flow channel network. The application can improve the accuracy of the inversion result and realize more fine depiction of the underground thermal reservoir flow channel network structure.

The invention discloses a PEM water electrolysis hydrogen production bipolar plate and a hydrogen production unit. The PEM water electrolysis hydrogen production bipolar plate comprises a plate body, a bionic fractal microchannel network is formed on the anode side of the plate body, adopts a three-stage fractal branch channel structure, and comprises a plurality of main channels, a plurality of secondary branch channels and a plurality of micropore nozzles; the main channel is arranged along the length direction of the plate body, one end of the main channel is connected to a water flow inlet of the water inlet area, the secondary branch channels are respectively connected with the main channel at one end and cover the reaction area of the whole anode side, and the micropore nozzles are respectively and uniformly distributed on the secondary branch channels and are vertically arranged relative to the plate body; and the water flow in the secondary branch channel can be ejected in the vertical direction through the micropore nozzle. A three-dimensional fractal-stepped micro-channel structural design is adopted on the anode side, three-dimensional turbulent flow is constructed on the anode side, the water-membrane contact efficiency is improved, and oxygen bubbles are rapidly stripped; a design of coupling a fishbone-microrib open runner and a gradient porous confluence area is adopted on the cathode side, and a hydrogen confluence path with low pressure drop and high uniformity is formed on the cathode side, so that the hydrogen production efficiency and the performance of the hydrogen production unit are effectively improved.

Disclosed are implantable devices, systems, and methods of fabricating, testing, optimizing, and / or implanting such devices. An implantable device includes a body, a first intrinsic channel network formed in the body and configured to receive cells, and a second intrinsic channel network formed in the body and configured to allow for active flow of a fluid to support viability and / or function of the cells in the first intrinsic channel network. The first intrinsic channel network comprises a first channel and the second intrinsic channel network comprises a second channel, with at least segments of the first and second channels parallel to and spaced apart from each other. Cells may be suspended in a collagen matrix and introduced to the first intrinsic channel network.

The invention provides a business data prediction method. The method comprises the following steps: acquiring business transaction data; extracting business related characteristics, and updating the business related characteristics in real time to obtain business time sequence data; in the offline prediction process, business time sequence data and semantic information thereof are collected, the business time sequence data and the semantic data are input into the large language model and the prediction model respectively, and semantic features and local time sequence features are obtained; in the online prediction process, service time sequence data and semantic information thereof are input into a two-channel network, and global influence features are obtained through output; and feature weights of the semantic features, the local time sequence features and the global influence features are adjusted according to historical prediction accuracy, and a service prediction result is obtained. According to the method, off-line prediction and on-line prediction are combined at the same time, the prediction requirement for minute-level services can be met, and service changes can be responded in time. The invention also provides a business data prediction system, a computer readable storage medium and electronic equipment, which have the above beneficial effects.

The application discloses a collaborative office network security management method and system based on artificial intelligence and a medium, belongs to the technical field of artificial intelligence network security, and is used for solving the technical problem that when the existing collaborative office network has network abnormal conditions or network security events, the emergency mechanism mainly depends on manual intervention, and the response efficiency is low when data leakage or system paralysis is caused. The method comprises the following steps: through an edge computing node and based on an artificial intelligence engine architecture, performing edge preprocessing on initial office network data about adversarial samples to obtain a network feature matrix based on the initial office network data; through the artificial intelligence engine architecture, performing double-channel network anomaly detection processing on the network feature matrix to obtain network anomaly detection result data; and according to the network anomaly detection result data, performing corresponding network risk response actions and automatically generating a network anomaly solution strategy.

The invention relates to the technical field of 3D Gaussian sputtering reconstruction and geometric accuracy optimization, in particular to a building three-dimensional reconstruction method and system based on 3D Gaussian sputtering, and the method comprises the steps: recognizing a building right-angle component through a two-channel network, outputting a semantic segmentation map with geometric mutation features, and generating non-uniform Gaussian kernel distribution; the method comprises the following steps: reconstructing an isotropic kernel into an ellipsoidal anisotropic kernel aligned with a normal direction, establishing an independent texture channel, segmenting a cross-plane Gaussian kernel, clearing the texture weight of an overlapping region, introducing a right-angle coplanarity loss function to correct kernel coplanarity deviation, and carrying out gradient attenuation on kernel density in a wall surface normal direction; an auxiliary kernel is implanted in an edge low-confidence high-deviation area, coordinates are optimized through back projection calibration, the system comprises a building component semantic analysis unit, a self-adaptive kernel distribution control unit, a parallel texture processing unit and a geometric precision enhancement unit, and finally a high-precision reconstruction model which is free of right-angle distortion and high in edge resolution is output to meet the building construction drawing restoration requirement.

The invention relates to the field of preparation of energy storage materials, and discloses an ultrasonic-assisted preparation method of a paraffin / nano-metal composite phase change energy storage material. Comprising the following steps: heating paraffin to a temperature 10-20 DEG C higher than the phase transition temperature of the paraffin to melt the paraffin, and adding nano metal particles subjected to surface modification into the molten paraffin to form a uniform suspension system; a multi-field coupling field composed of an ultrasonic frequency band sound field and an alternating electric field is applied in a molten state, and particles construct a heat conduction channel network at a phase boundary through low-frequency pulse dispersion, medium-frequency sweep frequency chained orientation and high-frequency stable frequency standing wave aggregation in sequence; paraffin molecules are induced by high temperature and high pressure generated by cavitation collapse to react with the metal surface to generate a composite interface layer; graded cooling is carried out in a high-frequency standing wave sound field, so that the particles form concentration decreasing distribution in the vertical direction, and a chain structure is stabilized through a weak alternating electric field; and after curing, carrying out constant-temperature annealing to stabilize the interface layer and eliminate residual stress.

The present application relates to a kind of fiber channel network intelligent response scheduling method, belong to the fault response and adaptive control technical field in vehicle integrated electronic system.Method includes: center management node carries out network fault risk prediction based on the state report frame received in current processing period and historical state information;Determine the multiple candidate optimization strategies of each network node based on the predicted fault risk;The strategy score corresponding to each candidate optimization strategy is calculated, and the global optimal strategy combination is determined based on each candidate optimization strategy, each strategy score and strategy conflict avoidance rule;Strategy instruction chain is determined based on global optimal strategy combination;Strategy instruction chain is issued to carry out global optimization to network.The present application method solves the problem such as single fault response strategy, scheduling mode rigidity, execution mechanism solidification in large-scale fiber channel network, significantly improves the intelligent level of fault control, response accuracy and execution flexibility of vehicle fiber channel network under abnormal situation.

The invention provides a voice generation network constructed based on LLM, and the network comprises a reasoning network constructed based on LLM, and the reasoning network is used for carrying out the reasoning calculation of input voice data, so as to generate a text lexical element sequence corresponding to the voice data in an autoregression manner; a generation network adopting a multi-channel network architecture and used for generating a plurality of voice lexical elements corresponding to the text lexical elements in the text lexical element sequence; the plurality of voice lexical elements have voice features of multiple dimensions learned from the voice data; the plurality of voice word elements are used for decoding to generate second voice data serving as voice response corresponding to the first voice data.