60results about How to "Reduce calculation" patented technology

The invention discloses a federal recommendation privacy protection method based on ring signature and selective aggregation, which realizes anonymization privacy protection and selective aggregation in a federal learning process through an innovative double-server architecture and a hierarchical security mechanism. The method comprises the following steps: 1) deploying an aggregation server and a security server in a system initialization stage, and completing parameter initialization and key distribution; 2) the client executes local model training, and anonymization privacy protection uploading is realized by adopting a ring signature technology; 3) the security server performs selective aggregation of credibility verification, and completes grouping and parameter fusion based on user similarity; and 4) ensuring the completeness and source credibility of model updating through a hierarchical security parameter distribution mechanism. While the recommendation precision is ensured, the user identity leakage and privacy tracking are effectively prevented, the comprehensive security guarantee is provided for the federal recommendation system, and the method has the advantages of low calculation overhead and high communication efficiency.

This invention provides a method and system for UAV path planning based on a fusion algorithm, relating to the field of UAV autonomous navigation and path planning technology. The method includes the following steps: acquiring the starting coordinates, ending coordinates, and obstacle information in the flight environment of a flight mission, and constructing a flight environment spatial model; defining path constraints and optimization objectives, and generating an initial set of feasible paths based on the flight environment spatial model, path constraints, and optimization objectives; performing weighted fusion and guided enhancement on the initial set of feasible paths to obtain a weighted fused path and a guided enhanced path; employing a particle swarm optimization algorithm to construct an initial particle swarm based on the initial feasible path set, the weighted fused path, and the guided enhanced path, and iteratively optimizing the initial particle swarm to obtain a globally optimal particle; and decoding the globally optimal particle to obtain the final flight path. This invention significantly improves the efficiency and quality of UAV path planning in complex static obstacle environments.

This invention discloses an InSAR interferometric network optimization method based on a multi-factor coherence surrogate model. The method includes: constructing a multi-factor feature dataset containing SAR imagery, baseline data, and soil moisture data of the study area; constructing a candidate interferometric pair set, and randomly selecting a subset of sample interferometric pairs from the candidate set; constructing a coherence surrogate prediction model, which uses the multi-factor feature data corresponding to the sample interferometric pair subset and the ground truth values ​​of sample coherence to train the model parameters; inputting the multi-factor feature data corresponding to each interferometric pair in the candidate set into the trained coherence surrogate prediction model and outputting the predicted coherence of each interferometric pair and the optimized interferometric pair network. This invention improves the stability and accuracy of unwrapping and time-series inversion, while also reducing the difficulty of interferometric processing and coherence calculation under large-scale data, and can support the needs of large-scale, long-term, and near-real-time monitoring.

The application relates to the field of artificial intelligence and power equipment operation and maintenance data processing technology, and particularly discloses a power energy storage operation and maintenance health evaluation method based on deep learning, which comprises the following steps: a pre-processing unit first performs caliber unification according to a historical caliber change set and extracts single data in real time, eliminates equipment differences, makes the algorithm identify only one set of caliber, and reduces special cases and error sources; an edge gateway performs clock consistency test, aligns the time axis, and avoids wrong time misjudgment; if the test fails, the data is improved and data reliability is output, and the caliber change set is rewritten and corrected; then, the data and the reliability are sent to a model to perform health evaluation and physical consistency verification, and the correlation is improved to be explainable physical reliability; finally, the evaluation result is mapped to a standardized operation and maintenance disposal suggestion, and the operation and maintenance plan is synchronized to a platform; the standardized action and plan are synchronized to realize a closed loop of diagnosis, disposal and recovery, and the whole process is auditable and reproducible.

The application provides a road surface state recognition method and device, and solves the problems of single road surface state recognition category, coarse result granularity and inability to distinguish specific road surface abnormal types caused by dependence on simple threshold judgment and shallow features in the prior art. The method comprises the following steps: acquiring a vertical vibration acceleration data segment of a vehicle in operation; extracting a characteristic value according to a preset optimal feature subset, wherein the subset comprises a frequency domain peak value and a spectrum energy; inputting the characteristic value into a classification model to obtain a recognition result, wherein the model is obtained by training based on data comprising at least three road surface state labels. Through fixed feature optimization, the application realizes fine recognition of multiple states such as well frame difference, expansion joint and deceleration strip, and significantly improves the calculation efficiency while ensuring the accuracy.

This invention relates to a power data sparsity compression observation method for non-intrusive load monitoring, comprising the following steps: Step 1, acquiring power monitoring data and determining its electricity consumption behavior pattern; Step 2, initializing wavelet sparse basis for sample data based on simple power behavior patterns; Step 3, based on the determination results of the electricity consumption behavior pattern of the completed training samples and the results after wavelet basis initialization in Steps 1 and 2, performing improved K-SVD training and generating sparse basis; Step 4, training the sample dataset Y based on Step 3 to obtain a sparse dictionary D, and performing power monitoring data compression observation based on the improved sparse basis.

The invention discloses a heavy-load train group operation control system hybrid energy storage management method and device, and the method comprises the steps: obtaining the speed and position of all heavy-load trains in a group, planning the speed and acceleration of each train, and calculating the future power demand of each train; according to the future power requirement of the train, online simulation is carried out through cloud digital twin, and a sliding mode surface parameter, a control law parameter and an event triggering threshold value are output; collecting a train bus voltage, and generating a total reference current required by future power by combining a given bus voltage and adopting a PI (Proportional Integral) controller; acquiring real-time states of the super capacitor and the lithium battery, and comparing the real-time states with an event triggering threshold value to judge whether to start a sliding mode controller or not; if the sliding mode controller is started, the total reference current is distributed to the lithium battery and the super capacitor, and a corresponding DC / DC driving control signal is generated. According to the method, the vehicle-mounted calculation and communication frequency can be reduced, and the flexibility and robustness of heavy-load train group energy management are improved.

This invention discloses a hierarchical federated learning method and system based on split meta-learning. The method specifically includes: a cloud server initializing a global model and distributing it to various edge servers; the edge server layer distributing the global model to its respective clients; each client updating the feature extraction module of the global model locally based on local data, while simultaneously freezing the classifier module of the global model; the edge server layer aggregating the updated parameters of the feature extraction modules from multiple clients and performing meta-optimization on the classifier module using a local validation dataset to generate an edge local model; and the cloud server layer periodically collecting model parameters from multiple edge servers and updating the global model using a gradient-sensitive momentum aggregation strategy. This invention, through a three-layer "vehicle-edge-cloud" architecture and a two-level collaborative optimization mechanism, achieves coordinated optimization between privacy protection, communication efficiency, and model adaptability in a vehicle-to-everything (V2X) environment.

This invention discloses an adaptive self-triggered optimal control method for catalytic rod reactors, specifically comprising: a temperature field model of the catalytic rod reaction process described by a high-dissipation partial differential equation system based on the control input of the cooling material under time triggering, and obtaining a low-order slow subsystem characterizing the high-dissipation partial differential equation system after order reduction processing; constructing a corresponding Hamiltonian function based on the performance index of the low-order slow subsystem, obtaining the optimal control strategy under time triggering control based on the Hamiltonian function, and then obtaining the optimal control strategy under self-triggered control, converting the Hamiltonian function into HJB equations; constructing a self-triggered controller based on an evaluation-execution dual-network structure using data-driven adaptive dynamic programming, and outputting the adaptive self-triggered optimal strategy based on the optimal value function of the HJB equations.

The application discloses a multi-agent dynamic real-time path planning method and device based on signal timing logic, and relates to the technical field of control methods. The method comprises the following steps: acquiring multi-robot path planning task information and a preset space-time constraint task demand; constructing a multi-robot task specification based on signal timing logic, and defining a corresponding robustness evaluation function; constructing a comprehensive path cost function based on the robustness evaluation function; introducing the path cost function into a path expansion and reconnection process based on a real-time rapid expansion random tree algorithm, updating a search tree structure, and generating a candidate path meeting environment constraints and timing constraints; planning paths for robots in sequence according to a preset multi-robot cooperative planning strategy, taking the path information of the robots that have been planned as dynamic obstacles and performing progressive cooperative obstacle avoidance, and outputting a conflict-free trajectory adaptive to a dynamic environment. The application can solve the path planning problem in a multi-agent scene.

The invention relates to a cache management method and device, computer equipment and a storage medium, and relates to the technical field of software development, the cache logic and service codes are thoroughly decoupled through AOP and annotation, the conciseness and maintainability of the codes are improved, and the cache management efficiency is improved through a version control mechanism. All the associated caches can be in batch failure only by updating one global version number, the performance impact caused by traversing and deleting the caches in a traditional scheme is avoided, the cache cleaning efficiency is improved, meanwhile, lazy updating and intelligent multiplexing of the caches are achieved through version comparison, unnecessary database access and calculation are reduced, the system load is effectively reduced, and the system performance is improved. And the overall response speed is improved.

The invention discloses a point cloud data processing method based on a point cloud Transform network, and the method comprises the steps: carrying out the local sampling and normalization processing of point cloud data, and generating an enhanced point cloud feature sequence; then, the data are input into a point cloud Transform network into which an adapter module is embedded; gating vectors generated by geometric conditions are introduced into an adapter module, and cross-layer collaborative updating is executed among different Transform coding layers by using cross-layer memory vectors, so that deep modulation of features and continuous transmission of interlayer information are realized; and finally, decoding the final point feature sequence through a downstream task head to output a classification or segmentation result. According to the method, the fixed trunk parameter set does not participate in updating in the training stage, only the adapter and task head parameters are updated, the training cost is reduced, and meanwhile the precision of the point cloud task is effectively improved.

The application discloses an automatic focusing method, system and medium, the automatic focusing method comprises the following steps: identifying the magnification of an objective lens, screening the defocus threshold corresponding to the magnification; constructing the theoretical motion model, the measured motion model and the measured variance of the focusing process parameters in the focusing process, and predicting the focusing process parameters at the next moment; analyzing the deviation between the measured value and the predicted value of the focusing process parameters at the same moment, determining whether the deviation of at least one focusing process parameter is greater than the set deviation threshold, if there is, dynamically adjusting the theoretical motion model; if not, driving the objective lens to focus when the predicted defocus is less than the defocus threshold. The application determines whether to dynamically adjust the theoretical motion model by the deviation of the predicted and measured focusing process parameters, eliminates the error, realizes fast focusing, meets the focusing requirements of objective lenses with different magnifications, and improves the focusing efficiency.

This invention discloses a 3D target detection method based on sparse dynamic attention and star-shaped interaction. The method obtains basic voxel features from the original LiDAR point cloud through voxelization and sparse convolution, then introduces a sparse dynamic parallel attention module. This module achieves efficient enhancement of global context and channel dimensions through dynamic attention branches and parallel channel interaction branches. A sparse star-shaped interaction module is then used to construct a star-shaped neighborhood interaction structure with a central voxel, completing local geometric modeling and nonlinear feature interaction only on non-empty voxels. Finally, keypoint sampling, RoI pooling, and a detection head output the 3D detection box, category, and confidence score. This invention, through the synergistic complementarity of SDPA and SSB, significantly improves the detection accuracy of long-distance, small-scale, and occluded targets while maintaining linear growth in computational complexity and meeting real-time requirements. It achieves balanced performance optimization across multiple categories, including vehicles, pedestrians, and cyclists, and is suitable for 3D perception scenarios with high precision and real-time requirements, such as autonomous driving.

This application provides a 3D Gaussian rendering optimization system and method based on CPU hierarchical splitting and GPU driving, relating to the field of 3D graphics rendering technology. This application divides the 3D Gaussian scene into multiple bounding boxes on the CPU side, determines the view distance level, protection level, and reconstruction cost level of each bounding box, and uses the highest of these three levels as the lower limit of the target level. The scheduling module adjusts the current rendering level frame by frame, determining the summary state, partial unfolding mode, or unfolded state based on screen error and GPU budget. On the GPU side, representative Gaussian points and unfolded 3D Gaussian points are uniformly sorted and rendered, and the frame rate and the total number of activated 3D Gaussian points are fed back. This reduces the amount of data submitted, takes into account the protection of key areas, achieves smooth hierarchical transitions, and forms a rendering feedback loop, improving the frame rate stability and resource utilization of real-time rendering.

The application provides a kind of robust laser SLAM repositioning and mapping method for embodied intelligent robot in the technical field of robot autonomous navigation, comprising: receiving original laser radar point cloud and IMU data, pre-processing point cloud to extract feature points;IMU pre-integration provides initial pose constraint;Loop closure detection is through the two-stage cascade of ScanContext global search and LinK3D local matching, obtains repositioning constraint;Front-end odometry matches feature points with local voxel map, combines IMU optimization to output pose estimation, filters key frame to generate laser odometry factor;With repositioning constraint as initial value, use improved ICP to accurately register to obtain repositioning factor;Finally, construct factor graph, jointly optimize IMU, laser odometry and repositioning factor, solve global consistent pose sequence and map.The application has the advantages that: it greatly improves the system robustness, repositioning accuracy and global map consistency of quadruped robot in severe motion and large-scale scene, while meeting the real-time constraint of embedded platform.

The present disclosure provides a new energy station power prediction method and system based on longitudinal federal decision tree, relates to the field of federated learning and information security technology, and comprises the following steps: acquiring real-time environmental characteristic data; inputting the environmental characteristic data into a power load prediction model to output a predicted power value; wherein the power load prediction model is a longitudinal federal decision tree model, and the training process of the longitudinal federal decision tree model comprises the following steps: each participant generates a key and a hyperparameter for each column of characteristics according to a security requirement, generates a random topological mapping for each column of characteristics, constructs a nonlinear conversion function based on each mapping relationship, encrypts the data in a cascade manner using the generated nonlinear conversion function and a lightweight order-preserving encryption, concentrates the encrypted data in a coordination party for ciphertext state model training, and obtains the longitudinal federal decision tree model after the training is completed. The present disclosure can improve the accuracy of joint power prediction modeling and realize more accurate scheduling optimization.

The invention provides a Pyraform-based irradiance rolling prediction method, and the method is divided into four core parts: a data preprocessing module, a multi-source feature fusion module, a Pyraform prediction model, and a rolling prediction module. According to the invention, a Pyraformer model captures a plurality of different scale time sequence dependencies through a coarse scale construction module and a pyramid attention module; according to the method, the prediction precision and the prediction operation rate under the complex weather condition are remarkably improved, and the technical defect that a traditional method cannot respond to future weather sudden change due to dependence on historical data is overcome; the method can be widely applied to the fields of photovoltaic power generation power regulation and control, intelligent power grid dispatching, solar energy resource evaluation and the like, and has high precision, strong adaptability and wide application value.

The application belongs to the technical field of pattern matching and mining under the hypergraph data model, and particularly relates to an incremental dynamic hypergraph matching method centered on overlap, which comprises: on the basis of OHMiner, when the data of a pattern hypergraph changes, partial updating is performed on OIG; when a hyperedge is inserted, new overlap relationships brought by the new hyperedge are calculated, and the new overlap relationships are added to OIG as new nodes in an incremental manner, and the same nodes are merged; when the data of a target hypergraph changes dynamically, DDAL is updated incrementally, and pattern matching is performed in an incremental manner; when a new hyperedge is added, the existing partial matching affected by the new hyperedge is searched in a partial matching state storage library, each partial matching instance is incrementally expanded, and the size of a new overlap is calculated; the size of the new overlap is compared with a requirement in an execution plan to verify, and invalid candidates are discarded, which is incremental pruning. The application introduces a brand-new computing paradigm for dynamic hypergraph matching, and optimizes the performance of dynamic pattern matching.

The application discloses a multi-modal satire detection method based on a large language model, and comprises the following steps: constructing a large-scale high-quality text data set; constructing a pre-training language model; adopting a supervised fine-tuning strategy to optimize parameters of the pre-training language model, training the pre-training language model through a cross-entropy loss function of self-recurrence language modeling, and obtaining a multi-modal large language model; and inputting the large-scale high-quality text data set into the multi-modal large language model for processing, and obtaining a detection result.

This invention discloses a parametric generation method for structured meshes of a three-dimensional pipeline system, comprising: acquiring the geometric structure of the three-dimensional pipeline system; dividing the geometric structure into multiple parts and acquiring the geometric parameters of each part; acquiring the coordinates of the starting section and the coordinates of the center point of the starting section in the coordinate system based on the diameter of the starting section of the geometric structure; calculating the coordinates and normal vector of the center point of the starting section of the geometric structure; performing a spatial matrix transformation on the coordinates of the starting section of the geometric structure based on the geometric parameters and the normal vector of the starting section of the geometric structure to obtain the interface coordinates and the center coordinates; acquiring the coordinates of the interface coordinates; the interface of the geometric structure includes the starting section of the geometric structure; generating a dictionary file of the interface coordinates and corresponding geometric parameters of each part of the geometric structure in the three-dimensional pipeline system according to the blockMeshDict rule using Python; and executing the dictionary file to generate the structured mesh of the three-dimensional pipeline system.

The invention provides a visualization method and system for self-adaptive matching of a display object resolution and a fusion processing mode. The method provided by the invention comprises the following steps: before to-be-displayed content reaches a video physical port of a visualization system, determining a display template according to a plurality of to-be-controlled visualization system display sizes; selecting a target visualization system according to the content density of the to-be-displayed content; determining a display object type in the to-be-displayed content, determining a fusion processing mode of the display object according to the display object type, determining a fused display position of each display object according to the display scene, supplementing a display template corresponding to the target visualization system, and obtaining a target display template; different types of display objects are obtained by utilizing a video physical port, each display object is processed by utilizing a video processor according to a fusion processing mode of each display object, and a plurality of processed to-be-fused display objects are displayed in the same canvas according to a fused display position in a target display template to obtain a fused canvas; and displaying in the target visualization system.

The application belongs to the field of cryptography, and particularly relates to a revocable lattice ring signature method with forward security, which constructs a ring signature system with quantum resistance based on a lattice cryptography, and comprises a trusted agency, a user and a verification party; the working process of the ring signature system comprises six main stages: a parameter initialization stage, a lattice key generation stage, a forward security update stage, a ring signature generation stage, a signature revocation verification stage and a signature correctness verification stage; the application realizes periodic update and revocation mechanism of short-term keys, and enhances the security and practicability of the signature system.

Embodiments of the present application provide a network attack tracing analysis method and related device, which relate to the network security technical field, and are used to perform homology tracing analysis on a network attack object, determine a network attack organization, ensure network security, and reduce losses. In the method, a target feature value of a feature field is extracted from network traffic data of a to-be-identified object, the to-be-identified object is a first source device whose access behavior satisfies a network attack behavior condition, and the feature field at least includes a transport layer field and an application layer field; based on a field type of the feature field, a similarity recognition mode matched with the feature field is determined; based on the similarity recognition mode, a similarity between the target feature value and a historical feature value is determined, the historical feature value is obtained from a knowledge base constructed according to a historical network attack object based on the feature field; the historical network attack object is a second source device that has a network attack behavior; and based on the similarity, a network attack organization to which the to-be-identified object belongs is determined.

This application discloses a method, apparatus, and computer device for wind farm data source authentication based on SVM-UGR. It includes: acquiring the original feature set of multimodal physical signals generated by wind turbines; normalizing the original feature set to a preset feature space using a static SVM model to obtain low-dimensional static features of the multimodal physical signals; extracting dynamic coupling features of the multimodal physical signals at different time dimensions from the original feature set using a dynamic UGR model; fusing the low-dimensional static features and dynamic coupling features corresponding to different modal physical signals to form a physical fingerprint of the wind turbines; and authenticating the target wind farm data source based on the first similarity between the target wind farm data and the physical fingerprint. The method of this application constructs a "physical fingerprint" of the wind turbine's operational characteristics through a model and achieves trusted data source authentication based on operational behavior and physical state through comparison with new data, without relying on network identity.

The invention discloses a processing method for industrial internet identification analysis data, and the method specifically comprises the following steps: 1, carrying out the data processing near a device or a sensor, reducing the delay and bandwidth consumption, analyzing the data in real time, generating feedback, achieving the real-time decision through edge calculation, reducing the dependence on a cloud end, and improving the response speed; 2, dynamically adjusting a data stream processing strategy according to a real-time analysis result, preferentially processing important data, reducing unnecessary data processing burden, processing the data by adopting an event-driven mode, and triggering corresponding processing logic based on a specific event; the data transmission delay is reduced, the response time is shortened, the real-time processing capability is improved, unnecessary calculation is reduced, and the resource utilization rate is improved; the data security is enhanced by adopting a block chain technology, and the non-tampering and traceability of the data are ensured; the decision quality is improved through self-adaptive data stream processing, and the strategy is dynamically adjusted through real-time data.

The invention discloses a flow table state synchronization and packet processing method based on an intelligent network card, and belongs to the technical field of computer network communication, and the method comprises the steps: obtaining flow table item meta-information, local historical matching data and an external state prompt signal, and generating multi-dimensional prediction input; based on the multi-dimensional prediction input, a synchronization urgency index is obtained through calculation of a local flow table behavior predictor, and a precise synchronization request is generated and sent according to the synchronization urgency index; performing arbitration processing based on the local flow table mirror image and the shadow flow table area on the received data packet to generate a data packet processing result; and receiving pre-execution confirmation feedback generated based on the data packet processing result, and adjusting the calculation parameters of the local flow table behavior predictor according to the pre-execution confirmation feedback, so that the low-delay consistency synchronization of the flow table state and the continuous linear speed processing of the data packet can be realized while the control signaling overhead is remarkably reduced.

This invention belongs to the field of path planning, specifically relating to a method for obtaining the shortest path between nodes. The method first obtains the sets of cut vertices on the original graph through several traversals, and then maps the cut vertex set subgraphs corresponding to these sets of cut vertices into a high-level tree of nodes. Preprocessing and parallel shortest path queries are then performed using the high-level tree with the cut vertex set subgraphs as the smallest unit, thereby obtaining the overall shortest path. This method transforms the shortest path analysis for a large number of nodes in a topologically complex original graph into a shortest path analysis for cut vertex sets. It eliminates a large number of useless nodes when obtaining the shortest path, saving related calculations and reducing computational load, thus improving the speed of shortest path acquisition. Furthermore, preprocessing is performed on the subgraphs formed by cut vertex sets, avoiding the computationally cumbersome problem of directly obtaining the shortest path for complex topological structures of large subgraphs.

This invention discloses a distributed multi-view visual radar UAV positioning method and system integrating sensing and control, including radar, a visual camera, a data transmitter, a data processor, a distributed sensing terminal, and a computing platform. It designs a sensor control method and a multi-view collaborative fusion wide-area UAV positioning algorithm, utilizing multi-view modalities for UAV positioning to achieve collaborative fusion of distributed cross-modal sensing data and high-precision wide-area UAV positioning. The technical solution provided by this invention can avoid UAV sensing failures and meets the needs of wide-area applications, significantly improving the positioning range and accuracy of UAVs.

This invention provides a fault information conversion and extraction device and a vehicle. The fault information conversion and extraction device includes: a fault diagnosis module, an upload decision module, a fault information conversion module, an on-board bus, and an on-board terminal. The fault diagnosis module is used to diagnose vehicle faults and generate diagnostic messages; the upload decision module is used to send diagnostic messages; the fault information conversion module is used to convert the diagnostic messages sent by the upload decision module into application messages and send the application messages; the on-board bus is used to transmit the application messages sent by the fault information conversion module; and the on-board terminal is used to extract application messages from the on-board bus and send application messages to a cloud server. This fault information conversion and extraction device can process and convert pre-collected vehicle data, converting diagnostic messages into application messages, ensuring data validity, saving bandwidth, facilitating cloud data analysis and rapid response, and reducing additional computation by functional controllers or the cloud.