104 results about "Network formation" patented technology

A method for quality inspection of an etching paste material based on a spectral response difference, including: identifying a position of density mutation boundary through spectral collection and density gradient analysis. Applying a specific frequency beam to obtain the resonance response parameter, generating stress field reconstruction data. Performing a reverse optical tracking to obtain a defect formation path, generating degradation prediction data. Establishing a self-organizing optical monitoring grid, to form a hierarchical spectral fingerprint library. Constructing a multi-point linked defect blocking network, to form an interconnected optical energy field. Monitoring a cooperative response to obtain network stability data, Identifying abnormal position coordinates based on a material quality grade and the network stability data, obtaining the abnormal position coordinates. Performing a phase adjustment to obtain a phase difference spectral set, conducting a phase comparison marking on the abnormal position coordinates, completing the quality inspection of the etching paste material.

The invention discloses a cross-chain transaction-oriented smart contract collaboration method, and belongs to the technical field of cross-chain contract collaboration, and the method specifically comprises the steps: deploying a log collection assembly at each cross-chain transaction smart contract node, and recording log information in real time; based on a cross-chain collaborative typical risk scene, various risks are converted into quantifiable detection rules, and a risk rule model reads node log streams in real time; when the risk rule model detects an exception, forming a complete cross-chain calling graph according to the transaction association identifier in the node log; extracting operation features of nodes in the cross-chain calling atlas, comparing the operation features with a normal node feature library, and screening out suspected fault nodes of which the operation features deviate from a threshold value; the suspected fault node abnormal data, the graph fragments and the log information are integrated, a unique responsibility affirmation abstract is generated, and the unique responsibility affirmation abstract and complete materials are stored in a shared evidence storage network to form a non-tampering certificate; and after the voucher is generated, calling a responsibility investigation intelligent contract, and executing cross-chain responsibility investigation operation according to the fault node identifier and the responsibility terms.

The invention discloses a digital media content element accurate screening method based on artificial intelligence image recognition, and relates to the technical field of digital media, and the method comprises the steps: building a distributed capture network to form a dynamic content pool, and building a metadata index database; calling a multi-modal image perception engine to generate a double-layer characteristic spectrum containing dominant and recessive elements; constructing a distributed recognition model cluster based on federated learning; converting the user demand into a screening parameter set and generating a decision tree; screening and secondarily verifying an output result through a double-path matching mechanism; and constructing a reinforcement learning reward function based on user behaviors, and driving the model and the decision tree to co-evolve. Multi-source heterogeneous content full-dimension analysis is achieved, the recognition comprehensiveness and depth are improved, knowledge barriers and privacy risks are solved, the screening accuracy and flexibility are improved, the system is endowed with the continuous optimization capacity, and the method is suitable for efficient and accurate digital media content screening scenes.

The invention discloses a medicine sales management method and system based on information monitoring, and relates to the technical field of medicine sales management, and the method comprises the steps: collecting medicine sales time sequence data, carrying out the cleaning and structural processing, and generating standardized sales data; based on the standardized sales data, constructing a sales behavior time sequence causal network, embedding virtual sales intervention nodes, and generating a time sequence causal graph; the intervention strategy is coded to the virtual intervention node in the time sequence causal atlas, the graph neural network is utilized to simulate time sequence propagation of the intervention strategy, and a sales prediction result is obtained; and combining the sales prediction result with the real-time inventory, constructing a multi-level distributed sales dependency network, and forming the sales dependency network. According to the method, the preset intervention strategy is embedded into the causal structure in the node form, so that dynamic modeling of the multivariable causal relationship in the sales behavior is realized, and the prediction result not only reflects the historical trend, but also can respond to the possible influence of different intervention strategies.

The invention belongs to the technical field of computer hardware, and discloses a self-adaptive voltage regulation method for dynamic power consumption management of a notebook computer, which comprises the following steps: collecting multi-source data, generating a state label, and constructing to obtain a four-dimensional situation map; task association strength and load types are analyzed through space-time association, and double tags are generated in combination with user behaviors; component security boundaries are divided, multi-type credits are distributed, and a DAO smart contract set is formed by constructing a component node network; a state circulation rule is formulated in combination with the real-time load characteristics of the components; in combination with the V-F curve, a voltage regulation parameter table is generated in the sub-regulation scene, a synchronous regulation group is reconstructed, a synchronous regulation group configuration table is generated, then component voltage regulation and state circulation are executed, and a state circulation completion signal is generated; generating a parameter optimization scheme through three-level feedback, and deducing and quantifying the expected effect of the scheme to form an evaluation report; and executing a parameter optimization scheme, and synchronously updating to a preorder link as a new reference.

The invention relates to the technical field of data analysis, in particular to a traffic flow prediction method and system based on an interpretable graph and a multi-scale time trunk. The method comprises the following steps: processing historical traffic flow information of a target road network according to a prediction model to obtain traffic flow prediction information of the target road network in a target time period; the prediction model is a composite model formed by fusing a space backbone network and a time backbone network, the space backbone network comprises a graph neural network, the time backbone network comprises an improved PatchTST model, and the improved PatchTST model comprises a multi-time scale patch. The multi-time scale patch comprises a scale patch corresponding to short-term fluctuation, a scale patch corresponding to intra-day rhythm and a scale patch corresponding to cross-day trend. According to the method, the real constraint of each node in the road network can be combined, the complex time sequence characteristics of the historical traffic flow information of the road network under each time dimension can be fully mined, and more accurate and reliable traffic flow prediction information can be obtained.

The invention discloses an unmanned aerial vehicle complex airspace cooperative passage method based on a graph neural network, and the method comprises the steps: collecting the airspace data of an unmanned aerial vehicle complex airspace, and generating standardized airspace data; constructing a mixed queuing network, and forming arrival, queue length and service capability state of each service node and service link; mapping to obtain graph nodes, graph edges and graph features, and constructing an airspace graph; inputting the airspace graph into the graph neural network, and carrying out constraint updating on the mixed queuing network; integrating the service capability state, the queue length state and the congestion propagation parameters to form a network passing cost relationship; and executing a Frank-Wolfe algorithm, and generating a cooperative passing instruction for each unmanned aerial vehicle. According to the invention, by introducing the mixed queuing network and the Frank-Wolfe algorithm, efficient and stable cooperative passage scheduling of multiple unmanned aerial vehicles in a complex airspace environment is realized.

The invention relates to a subgrade slope stability intelligent prediction method based on deep learning, and belongs to the technical field of data processing, and the method comprises the following steps: 1, constructing a multi-field coupling data collection network, and forming a dynamic coupling data set with synchronous time response; 2, a meta learning-dynamic graph Transform hybrid model is constructed, rapid adaptation of a new slope scene is achieved through meta learning, the dynamic graph Transform updates rock mass unit mechanical association in real time, the dynamic graph Transform captures multi-field coupling data features, and a Hoek-Brown criterion and a damage evolution equation are embedded to serve as double physical constraint layers; 3, a causal discovery algorithm is introduced to identify key stability factors, a multi-objective evolutionary algorithm is adopted to optimize meta-learning-dynamic graph Transform hybrid model hyper-parameters, and a dynamic loss function is constructed by using project full life cycle risk cost; 4, inputting real-time monitoring data, outputting data, and cooperatively updating multi-engineering data; the method has the beneficial effects that the causal effect in a group is quantified, hyper-parameter optimization is guided, and the prediction performance is improved.

The present application belongs to the technical field of computer hardware, and discloses a self-adaptive voltage regulation method for dynamic power management of a notebook computer, comprising: collecting multi-source data, generating a state label, and constructing a four-dimensional situation map; analyzing the task correlation strength and load type through space-time correlation, generating double labels in combination with user behavior; dividing the component safety boundary and allocating multiple types of credit, then forming a DAO smart contract set by constructing a component node network; and formulating state flow rules in combination with the real-time load characteristics of the components; generating a voltage regulation parameter table in combination with a V-F curve, reconstructing a synchronous regulation group and generating a synchronous regulation group configuration table, then executing component voltage regulation and state flow, and generating a state flow completion signal; generating a parameter optimization scheme through three-level feedback, and deducing the expected effect of the quantization scheme to form an evaluation report; and executing the parameter optimization scheme and synchronously updating it to the previous link as a new benchmark.

The invention discloses a task-oriented dialogue system reward function optimization method and system, and belongs to the technical field of natural language processing. The method comprises the following steps: acquiring an expert dialogue track from a dialogue system data set, extracting a state-action-reward triple, training an initial reward function by using a maximum entropy inverse reinforcement learning framework, and initializing a strategy network; an Actor-Critic reinforcement learning algorithm is adopted to train a strategy network, and a suboptimal trajectory is collected to dynamically update a reward function; and taking the dynamic reward function as a unified evaluation signal, and optimizing the strategy network to form a dialogue strategy model. Through dynamic reward function optimization, manual rule dependence is reduced, the generalization ability, the task completion rate and the stability of a dialogue system are improved, and the method is suitable for complex dialogue scenes in multiple fields.

This invention relates to the field of segmentation algorithm technology, and in particular to a lung nodule segmentation algorithm based on TransUNet. The steps include: replacing the Transformer layers in the TransUNet network with MaxViT networks to form an MM-TransUNet network as the segmentation model; training the segmentation model using the MM-TransUNet network; after each training round, saving the segmentation model parameters for that round; comparing the detection results obtained by the segmentation model with the actual results to calculate the loss; adjusting the parameters of the segmentation model based on the loss; reading the optimal parameters saved during the training phase; substituting the optimal parameters into the segmentation model to obtain the optimal lung nodule segmentation model; applying the MM-TransUNet network as the segmentation model can effectively improve the segmentation accuracy of lung nodules; MM-TransUNet combines residual mechanisms, CNNs, and multi-axial self-attention mechanisms as an encoder module, playing a role in more completely extracting lung nodule features.

A system is provided for wireless online processing using a peer to peer network. In particular, the system may comprise a plurality of wireless computing devices in nearby proximity to one another that may form the peer to peer (“P2P”) network through a shared wireless communication channel. The system may identify the network connection strengths of each of the plurality of wireless computing devices and subsequently designate one or more devices as the hubs of the P2P network. Each of the wireless computing devices may have one or more online processes to be completed through the network connections formed by the P2P network. The processes may be propagated to the devices within the P2P network, where the data used to complete the processes may be encrypted within the memory or storage devices of each of the computing devices.

A satellite space target cooperative observation distributed planning method based on multi-agent reinforcement learning, comprising: constructing an on-board Actor network and a ground centralized Critic network to form a centralized training and distributed execution architecture; designing a structured mixed space action, training the Actor-Critic network through a large number of ground simulations and using an improved MAHPPO algorithm and a curriculum learning technique; deploying the trained Actor network on each satellite of the constellation executing the cooperative observation task to realize distributed execution; after uploading the list of space targets to be observed to each satellite through satellite-ground communication, each satellite does not need to communicate with other satellites, each satellite executes the observation target of each time slice decided by the on-board Actor network, and controls the satellite to implement the observation task, thereby realizing the space target cooperative observation task. The present application enables each satellite to complete the cooperative observation without communication during the on-orbit execution, and can be extended to large-scale constellation applications.

The invention discloses a fish gelatin jelly gel performance evaluation method based on multi-scale correlation analysis, which comprises the following steps: extracting multi-scale characteristics of molecular characteristics, network structures and macro performance of three fish gelatin jellies, performing data correlation based on three dimensions of molecular characteristics-network structures-macro performance, and combining a stoichiometric analysis method to evaluate the gel performance of the fish gelatin jellies. And a multi-level systematic evaluation system is constructed. According to the method, data of molecular characteristics (secondary structure ratio, particle size, Zeta potential and amide A wave number), network structure (aperture size) and macroscopic performance (imino acid content and gel strength) are integrated to construct the relevance of molecular characteristics-network structure-macroscopic performance, and full-dimensional analysis of the quality of the fish gelatin jelly is realized for the first time. The method breaks through the limitation that the traditional method only depends on single indexes such as protein or collagen content and the like, and clarifies the regulation and control of molecular aggregation behavior, imino acid content and electrostatic interaction on the formation of a gel network.

The invention discloses a power transmission line traveling wave fault positioning method and system based on data analysis, and relates to the technical field of big data analysis, and the method comprises the steps: firstly collecting fault original records, temperature, humidity and weather states, and building a historical library; carrying out time-frequency domain conjoint analysis on the traveling wave signal, and calculating a phase difference and a time difference; the signals are adaptively decomposed to different frequency bands to calculate fractal dimensions, and advanced features are extracted in combination with a deep convolutional neural network; associating the phase difference, the instantaneous electric field intensity and the magnetic field rotation direction to construct a three-dimensional matrix rotation graph structure, and forming a high-dimensional feature vector by using a graph attention network; and calculating a fault posterior probability according to the high-dimensional vector, dynamically adjusting a threshold value according to an environment parameter, and generating an adversarial network virtual sample to match a fault type when the probability is insufficient. The system comprises a data acquisition module, a time-frequency analysis module, a feature extraction module, a feature association module and a fault determination module, all the modules cooperate to realize data acquisition, feature analysis and fault positioning, and the positioning reliability is enhanced.

This invention discloses a multi-agent collaborative decision-making method for ramp merging based on spatiotemporal interaction capture and action priority, belonging to the field of intelligent transportation technology. The method includes: acquiring a sequence of observation information from multiple agents and obtaining a spatiotemporal interaction code through temporal encoding and self-attention interaction encoding; gating the spatiotemporal interaction code based on ramp merging features (TTMP); inputting the enhanced code into a policy priority ranking network, sampling the action generation order using Plackett-Luce, and then reordering the enhanced code, before inputting it into a decoding policy network with cross-attention to form a spatiotemporal interactive Plackett-Luce multi-agent reinforcement learning model. The model then autoregressively generates actions for each agent according to the reordered order and integrates them to form a joint action, generating vehicle control commands to control vehicles to perform collaborative driving in the ramp merging area. This invention can dynamically model the multi-vehicle interaction relationship and the action priority dependency between individual decisions in a strong interaction scenario of ramp merging, thereby improving the stability of collaborative decision-making and traffic efficiency.

The invention provides a mooring load automatic prediction method and system based on wind wave big data, and the method comprises the steps: firstly collecting the wind wave action data of a target sea area to construct a wind wave action chain, and forming a wind wave action sequence set; then, a load conduction network is constructed according to the structural component distribution of the mooring equipment, and a load conduction model is formed; inputting the wind wave action chain into a load conduction network, and establishing a dynamic response mapping model in combination with historical data; and acquiring real-time wind wave action data, converting the data into a real-time wind wave action chain, inputting the real-time wind wave action chain into the dynamic response mapping model to obtain a real-time load conduction distribution result, and determining a mooring load prediction value. And finally, a load regulation and control strategy is generated according to the mooring load prediction value and is sent to the control module to realize dynamic load adjustment, so that the accuracy and the real-time performance of mooring load prediction are improved, and the ocean engineering safety is guaranteed.

The application discloses a kind of bare rock slope ecological restoration scheme demonstration method based on VR simulation, comprising: obtaining the multi-source data of bare rock slope, fusion processing generates three-dimensional digital model and ecological parameter distribution chart.Key nodes in slope are identified, and the connection network between key nodes is constructed, forming preliminary resource allocation scheme and network topology structure.With the aid of VR simulation technology, the water resource flow and vegetation growth process are simulated, and the resource allocation scheme and water flow distribution prediction map are optimized.Combining the vegetation coverage target and the characteristics of matrix material, the optimal solution of resource allocation is obtained.According to the distribution table of key nodes, a visual model of the slope restoration process is generated using three-dimensional rendering and dynamic simulation, and the ecological response data is analyzed to optimize the restoration scheme, and finally a complete slope ecological restoration scheme is formed.The application realizes the intelligent generation and optimization of bare rock slope ecological restoration scheme, and improves the restoration efficiency and accuracy.

The application discloses a kind of asphalt pavement technical literature mining methods based on TongYiQianWan semantic graph, comprising: obtaining road engineering-computer vision cross field academic literature data, carries out multidimensional preprocessing and forms standardized data set;Improved double-tower semantic coding network is constructed, the uniform mapping of keyword and title semantic features is realized;Optimized GraphSAGE spatial feature extraction network is constructed;CNN-LSTM hybrid architecture is used to construct timing feature extraction network, and form a multidimensional fusion feature representation system;Weighted random forest classifier with class weight balance strategy is introduced;Key word co-occurrence prediction and technology trend quantitative prediction system is established, finally, multi-module collaborative Qwen-SGCL intelligent model is obtained;The training, verification and comprehensive performance evaluation of the model are completed.The application provides an efficient intelligent tool for intelligent mining and technology trend quantitative analysis of literature in the cross field of road engineering and computer vision.

This invention belongs to the field of computer application technology. It discloses an information aggregation and retrieval system and method based on artificial intelligence, comprising: receiving user task objectives; combining associated resource sets and search preferences; calling a semantic model to generate semantic anchors; thereby generating a university-specific contextual blueprint to form a standardized information foundation within the university; parallel scheduling of various information interfaces within the university according to permissions; expanding query intents; filtering and sorting results to form a blueprint-bound retrieval result set; performing university-specific scenario analysis; generating dynamic aggregation summaries and difference comparison tables; grouping according to preset logic and performing visualization processing to form a structured presentation view; collecting user interaction data in the structured presentation view to generate a university user feedback dataset; and optimizing the university-specific contextual blueprint and university-specific knowledge unit network to form a closed-loop link.

The invention discloses a self-adaptive impedance matching device and method for a multi-gun parallel charging gun end of an electric vehicle, and the device comprises a pile end controller TCU, at least two charging guns and a vehicle battery management system BMS which are connected through a shared CAN bus network. Each charging gun is provided with a charging control unit (CCU), a matched resistor and a controlled resistor switching module. The method specifically comprises the steps that a TCU appoints a main charging gun; the main charging gun CCU controls a matched resistor of the main charging gun CCU to access a bus, and sends a detection frame to the auxiliary charging gun CCU for networking after communication verification is passed; and the slave charging gun CCU controls the resistor to be disconnected and responds to networking. In the charging process, only the matched resistor of the main charging gun is connected to the CAN bus. When a non-serious fault occurs in the main charging gun, the main charging gun stops charging, the resistor is kept connected, and the main charging gun is disconnected until all the auxiliary charging guns finish charging, so that normal work of other parts of the system is guaranteed to the greatest extent, and the stability and safety of multi-gun parallel charging are improved.

The invention relates to an optical neural network topology adaptive mode division multiplexing communication system and a training method, and the method provided by the invention is applied to a mode division multiplexing communication system, and is used for solving the problem that the transmission or calculation performance is reduced due to dynamic coupling crosstalk of a spatial mode caused by environmental disturbance. The method comprises the following steps: monitoring the output of an optical neural network in real time, and generating a state matrix representing mode crosstalk; extracting matrix features to construct an environment vector; through a pre-trained deep reinforcement learning network, a reconstruction action for controlling the adjustable photonic device is decided and generated according to the vector; the driving device dynamically adjusts the network physical topology to compensate crosstalk; and finally, optimizing the strategy network on line based on the reconstructed performance evaluation result to form a closed loop. According to the method, the optical neural network in the mode division multiplexing has online self-adaptive capability, the dynamic mode crosstalk can be continuously inhibited, and the stability and high performance of the system in actual deployment are guaranteed.

To improve the diffusion effect of contents in a communication network.SOLUTION: The content diffusion support system stores community information that is information related to a plurality of communities formed via a communication network, diffusion effect calculation information that is information used to calculate a diffusion effect of content in the communication network, and information related to a related community that is a community related to a recommended content that is a content to be diffused in the communication network, identifies an influencer in each of the related communities, and selects a community in which a diffusion effect of the recommended content is equal to or greater than a first threshold value set in advance as a participation recommended community when the influencer of the related community participates in the community. Information for urging the influencer to participate in the participation recommendation community is generated as diffusion support information.SELECTED DRAWING: Figure 1

This invention relates to a battery pack balancing control method and system. The method includes: Step S1: treating the battery pack as an intelligent agent network and converting the battery pack balancing problem into a Markov game that can be handled by the DRL algorithm; Step S2: generating online expert data using the FCS-MPC algorithm; Step S3: training the intelligent agent network and a hybrid network based on different intelligent agent networks using the FCS-MPC-QMIX algorithm, and dynamically fusing the expert data into the objective functions of the intelligent agent network and the hybrid network during training to accelerate network convergence; Step S4: using the trained intelligent agent network and the hybrid network to achieve battery pack balancing control. This invention can provide balancing control for distributed battery energy storage systems.

The application discloses a color polarized image restoration method based on a three-dimensional convolution-attention joint mechanism. A three-dimensional convolution-attention joint polarization restoration model is adopted, including a space-polarization three-dimensional attention module, a space-polarization three-dimensional convolution module, and each module is connected and fused to form a multi-scale hierarchical network; a polarization restoration loss function based on a Stokes physical model is designed to train the network; three-dimensional features are extracted, fused and polarization information is solved, so that high signal-to-noise ratio and high-resolution color visible light and polarization information are obtained from low signal-to-noise ratio and low-resolution images. The application utilizes three-dimensional feature calculation to perform collaborative feature extraction and fusion on multiple dimensions of space, polarization and color, forms a multi-scale convolution-attention joint mechanism through a hierarchical connection network, combines the advantages of convolution in local detail feature extraction and the advantages of the attention mechanism in long-range dependence modeling, and thus improves the color polarized image restoration effect.

DC network (100), comprising: a DC busbar (110, 220); a transformer (104, 210) with a primary side and a secondary side; a switching device (106, 206) arranged on the primary side of the transformer, wherein the transformer can be electrically connected on its primary side to an AC network (102, 202); a bidirectional AC / DC converter (108, 218) arranged on the secondary side of the transformer, wherein the AC / DC converter is electrically connected to the transformer on its AC side and to the DC busbar on its DC side; a voltage measuring device (230, 232) configured to detect a voltage on a side of the switching device facing the AC network and a voltage on a side of the switching device facing the transformer;and a control device (226) which is configured: to provide electrical energy on the DC busbar when the switching device is open, to operate the AC / DC converter as a network-forming voltage source and to magnetize the transformer from the secondary side by means of a voltage ramp.

The invention relates to the field of information monitoring, and discloses a dough processing information monitoring management method and system, which are used for providing accurate monitoring and intelligent management for a dough processing process. Comprising the following steps: acquiring time sequence data of torque, temperature and rotating speed in real time, generating an original data packet, processing the data to generate a state index representing processing energy efficiency and system order degree, and inputting the state index into a constitutive relation model based on a viscoelastic memory effect to obtain a real-time maturity parameter reflecting a gluten network forming state. And predicting the final pore structure and fermentation stability of the dough, and generating microstructure prediction characteristics as quality indexes for generating a fermentation termination instruction, a quality evaluation report and a production line scheduling strategy. Key parameters are monitored in real time, a parameter adjusting mechanism is started when abnormity occurs, the process is recorded, an optimization suggestion table is formed to adapt to different raw material characteristics, and it is guaranteed that the dough processing quality is stable.

The invention discloses a purchase file checking method based on a large language model, and the method comprises the following steps: receiving a to-be-checked purchase file, carrying out the content analysis of the purchase file through a large language model, and generating a purchase content set; obtaining a checking rule set and a historical checking case set, and generating a rule entry set and a case knowledge set; constructing a unified semantic representation set based on the purchase content set, the rule entry set and the case knowledge set; inputting the unified semantic representation set into the improved end-to-end memory network to form multi-slot memory representation; constructing a semantic query representation vector, inputting the semantic query representation vector into the improved end-to-end memory network, and generating an audit reasoning result; based on the audit reasoning result, outputting a verification conclusion of the purchase file; and updating the improved end-to-end memory network according to the manual auditing feedback, the newly added checking rule or the newly added auditing case. According to the invention, a large language model and a memory network are adopted to realize intelligent semantic verification of purchase files.

The embodiment of the invention provides a data transmission method and device, a storage medium and an electronic device. The method comprises the following steps: after data transmission service deployed at a vehicle end is initialized, obtaining service data of the data transmission service; channel state reports of a plurality of transmission channels between the vehicle terminal and the cloud terminal are obtained, and the plurality of transmission channels are formed between the vehicle terminal and the cloud terminal through a plurality of networks; arbitrating from the plurality of transmission channels to obtain a target transmission channel of which the channel state report is matched with the service data; sending the service data as to-be-verified data to the cloud through the target transmission channel; and when the verification result fed back by the cloud for the service data indicates that verification fails, re-determining the transmission strategy of the service data. Therefore, the success rate and the reliability of data transmission are remarkably improved, and the transmission quality of the service data between the vehicle end and the cloud end is improved.