656 results about "Mapping algorithm" patented technology

The invention discloses a multi-modal diagnosis and treatment data analysis and auxiliary diagnosis method for internet medical treatment, which comprises the following steps: S1, performing feature extraction and fusion on multi-modal diagnosis and treatment data through a nonlinear mapping algorithm based on quantum state optimization; s2, constructing a self-adaptive dynamic relation network, and capturing a complex interaction relation between cross-domain features in the diagnosis and treatment data; s3, fusing implicit features in the sparse medical data with semantic association; s4, optimizing the diagnosis and treatment decision process by applying a causal inference technology; s5, processing anomalies in the diagnosis and treatment data by using a fusion generative adversarial network and sparse tensor decomposition technology; s6, constructing a decentralized encrypted federated learning network to protect the privacy of the patient; s7, predicting a disease evolution path in real time to form dynamic decision support; and S8, performing accelerated analysis on the high-dimensional feature mode in the diagnosis and treatment data in combination with quantum calculation. The method has the advantages of high data fusion capability, high diagnosis precision and high real-time decision support capability.

The invention relates to the technical field of fault detection control, and discloses an automobile fault detection method and system fusing sensor data. The method comprises the following steps: modeling a multi-system sensor network through an automobile system topology mapping algorithm to obtain a sensor topology incidence matrix; performing synchronous processing on the sensor topological incidence matrix according to a vehicle dynamics constraint equation to obtain a synchronous multi-sensor data set; inputting the synchronous multi-sensor data set into a fault propagation path sensing network for fusion to obtain a vehicle system fault feature vector; detecting the fault feature vector based on an automobile physical model constraint optimizer to obtain a fault state recognition result; and tracing and controlling a fault state identification result according to the fault propagation atlas model to obtain a system adjustment control signal. According to the invention, the source identification accuracy of the complex coupling fault and the pertinence of control and adjustment are improved.

A layout optimization method of water quality monitoring points based on a RF-C-SOM clustering algorithm includes: preprocessing collected water quality data to obtain preprocessed water quality data that are used as data, and using water quality categories as labels to train a random forest model to determine feature importance of water quality indicators; selecting important features based on the feature importance and model training accuracy, performing dimensionality reduction on the preprocessed water quality data to obtain dimension-reduced data; performing a fuzzy clustering on the dimension-reduced data to obtain a water quality section classification result, and based on it, determining initial weight values of a self-organizing mapping algorithm; initializing neurons and training a self-organizing mapping network model with the initial weight values; obtaining a point clustering result through the self-organizing mapping network model; and conducting a water quality index evaluation for the point clustering result before and after screening.

A distributed intelligent authentication method based on dynamic multi-modal fusion relates to the field of network security, and adopts an alliance chain + DAG hybrid block chain architecture, combines a threshold signature to realize secret key fragment management, and switches among PBFT, Raft and probabilistic algorithms through a dynamic consensus mechanism to improve authentication efficiency. The multi-mode authentication module is based on a dynamic weight distribution algorithm, integrates biological characteristics, behavior analysis, equipment fingerprints and environmental factors, and combines an LSTM-GAN model and a quantum random number driven challenge-response mechanism to realize zero-trust verification under environmental perception. The session management module generates a session key by using a chaotic mapping algorithm. In the aspect of privacy protection, CKKS homomorphic encryption, zero-knowledge proof and attribute-based encryption are fused. According to the method, the block chain technology, the secure multi-party computing technology, the machine learning technology and the quantum cryptography technology are fused, and a high-performance, high-security and strong-privacy-protection distributed authentication solution is provided.

The invention discloses a multifunctional magnetic stimulation treatment system and an application method thereof, relates to the technical field of medical equipment, and solves the defects of unstable curative effect and limited application range caused by individual difference in the existing system. The precision of the laser beam is automatically adjusted through a self-adaptive calibration algorithm, and it is ensured that a treatment area is accurately positioned; optimal stimulation parameters are calculated through a self-adaptive magnetic field depth optimization algorithm, and pertinence and effectiveness of treatment parameters are ensured; the treatment effect is predicted through a feedback mapping algorithm, and the treatment parameters are dynamically adjusted to ensure the safety and effectiveness of the treatment process; physiological data, symptom description and medical history information of a patient are comprehensively analyzed and recognized through the complex disease pattern recognition module, a detailed disease pattern recognition report is output, the application range of the system is expanded, and personalized disease prediction and risk assessment are provided; the applicability and the curative effect of an existing system are greatly improved.

The invention discloses a defect repair method based on digital twinning and friction stir welding technologies, and relates to the technical field of intelligent manufacturing and digital twinning, and the defect repair method comprises the following steps: synchronously capturing full-dimensional data of a welding area through a multi-mode sensor array integrated by an actuator; secondly, segmenting defect boundaries by adopting a deep learning algorithm, constructing a dynamic twin model in combination with thermal-force field coupling simulation, and accurately mapping defect three-dimensional features; generating a repair track according to the twinborn model, converting the repair track into a robot joint instruction through a curved surface parameterization mapping algorithm, and implanting real-time anti-collision constraint; in the repairing process, based on reinforcement learning control of the material rheological resistance and the temperature gradient, the rotating speed, the advancing speed and the down force of the tool are dynamically adjusted; and after repairing, micro-focus CT scanning is started immediately, actually measured data is compared with twinborn prediction, and when the deviation exceeds a threshold value, a re-repairing process is triggered automatically. The method solves the problems that a traditional method depends on manual intervention and the precision of a sensor is easily interfered by the environment.

The invention discloses a battery cluster level fire-fighting linkage control method and system based on a BMS, and the method comprises the steps: carrying out the thermal runaway prediction through a multi-source feature fusion network according to the real-time voltage, temperature and gas concentration data of a battery cluster, and outputting a thermal runaway prediction level; on the basis of the thermal runaway prediction level, a dynamic priority mapping algorithm is utilized, and a control instruction set containing execution priority and spatial positioning is output according to the battery cluster topological structure; according to the control instruction set, a dual-channel redundant signal verification mechanism is adopted to execute a fire-fighting action, and a real-time action feedback signal is output; and on the basis of the real-time action feedback signal and the residual state data of the battery cluster, updating the parameter weight of the multi-source feature fusion network through a reinforcement learning model so as to be used for fire-fighting linkage control in the next period, and forming a closed-loop control link. According to the embodiment of the invention, battery cluster-level fire-fighting linkage control can be realized, so that the safety protection capability of a battery system is improved.

The invention discloses a precise interference avoidance method and device in a radio system, and relates to the field of signal processing, and the method comprises the steps: constructing a sensing matrix through sensing node data, and capturing a transient interference signal through aperiodic scanning; performing tensor decomposition on the signal data to extract time domain, frequency domain, space domain and modulation domain features, constructing a dual-mode spectrum analysis model, reconstructing an instantaneous spectrogram by using compressed sensing, and predicting an interference mode through LSTM; after the instantaneous spectrogram and the predicted interference graph are fused, threat assessment is carried out through a multi-stage interference classification model; according to the interference category and the threat level, beam forming is optimized, adaptive null is generated, a power density optimization model is constructed, and the transmitting power is dynamically adjusted; an anti-interference frequency hopping sequence is generated based on a chaotic mapping algorithm, and spectrum camouflage and tracking interference resistance are realized. The method has the advantages that accurate identification and dynamic avoidance of interference are realized through multi-dimensional perception, intelligent prediction and adaptive beam forming, and the interference avoidance capability of a wireless system is improved.

The invention discloses a railway office system heterogeneous database integration method based on a data conversion technology, and relates to the technical field of data integration, the method realizes seamless integration of heterogeneous databases in a railway office system by uniformly designing a route data standard model and combining an AI intelligent mapping algorithm; according to the scheme, a distributed architecture is adopted to process data conversion tasks in parallel, node loads, conversion speeds and parallel speed-up ratios are monitored dynamically, and the system stability in a high-load scene is ensured; the universal adaptation layer supports integration of multiple protocols and multiple interfaces, and the overall performance is effectively improved; meanwhile, a multi-level security mechanism and consistency verification are introduced in the scheme, dynamic encryption, authority management and data redundancy backup are combined, the integrity and security of data circulation are guaranteed, and the problems of complex data integration, poor compatibility and performance bottleneck are solved.

The invention discloses a driving style quantitative evaluation method based on multi-modal perception and a knowledge graph, and the method comprises the steps: employing a multi-modal data source, which comprises various sensor data, vehicle motion data, and physiological reaction and behavior data (such as heart rate change and operation intervention frequency during emergency braking) of a driver and a passenger in a real scene; a user emotion mapping algorithm is constructed by the model, subjective experience is converted into quantifiable numerical indexes (such as a trust index and a comfort score), a driving style deviation index (SDI) is calculated by using an algorithm in combination with a constructed driving behavior knowledge graph, quantitative evaluation of brand driving genes is realized, and a data driving basis is provided for function and algorithm optimization. And a personalized intelligent driving mode is realized. Therefore, the driving style quantitative evaluation method based on the multi-modal perception and the knowledge graph has the advantages of being high in intelligent degree and high in individuation degree.

The invention discloses a conflict detection processing method and system based on a BIM model, a medium and a product, and relates to the technical field of constructional engineering, and the method comprises the steps: obtaining a construction project design scheme; based on a preset model framework, determining a BIM model according to the construction project design scheme; predicting potential conflict points in the BIM model based on a preset conflict mode library; matching the potential conflict points with the building structure, the electromechanical pipeline and the equipment layout by using a space mapping algorithm, and determining possible conflict areas; on the basis of a natural language processing technology, operable solution suggestions are generated according to the possible conflict areas; after the operable solution suggestions are sent to the user, the solution suggestions determined by the user are received; and adjusting the BIM model based on the solution suggestion. By implementing the method, the accuracy and intelligence of conflict detection can be improved.

The invention provides a hybrid parallel and dynamic scheduling method of a hybrid expert model based on 3D near-memory processing. The method comprises the following steps: establishing a joint performance analysis model; performing off-line linear programming optimization expert distribution; performing Bayesian optimization on physical node mapping; performing online reasoning; carrying out online dynamic priority detection; and an expert pre-broadcast and communication-friendly lexical element distribution strategy with optimal efficiency is provided. According to the method, node balancing optimization is realized through offline linear programming, and the problem of load imbalance of 3D NMP calculation is remarkably improved; in combination with a Bayesian optimization mapping strategy of link balance, the communication speed-up ratio is increased, and NoC link congestion is reduced; a dynamic scheduling strategy adapts to dynamic changes of expert activation in real-time reasoning through calculation load prediction and a pre-broadcast mechanism. Through cooperation of the offline automatic hybrid parallel mapping algorithm and the online dynamic scheduling strategy, the calculation load and the communication overhead are effectively balanced, and the reasoning efficiency of the hybrid expert model MoE on the 3D near-memory processing architecture is remarkably improved.

The invention relates to the field of measurement, in particular to a subway tunnel secondary lining section limit measurement method based on SLAM mobile scanning, which comprises the following steps: step 1, tunnel point cloud acquisition and target clustering point cloud acquisition; step 2, CPIII control point coordinate extraction; step 3, point cloud coordinate conversion; 4, performing point cloud processing, and removing noisy points and non-secondary-lining target point cloud data in the tunnel secondary-lining point cloud; and step 5, performing three-dimensional limit deviation analysis, performing spatial comparative analysis on the point cloud under the construction coordinate system and the subway secondary lining tunnel design three-dimensional model by adopting a voxelization deviation mapping algorithm, extracting a continuous deviation region through voxel clustering, and generating a three-dimensional deviation thermodynamic diagram. According to the method, the two-lining tunnel point cloud obtained by SLAM mobile scanning is accurately converted into a construction coordinate system by combining CPIII high-precision control points in the tunnel, rapid, visual and three-dimensional contrastive analysis with design of a three-dimensional model can be achieved, and visual and reliable data support is provided for line and slope adjustment, track laying and defect improvement.

The invention belongs to the technical field of data processing, and particularly discloses a dynamic path planning method and system for an inspection robot, and the method comprises the steps: collecting field data through a sensor, processing a laser scanning point cloud and a camera image through a positioning mapping algorithm, and fusing multi-source information to generate a real-time map. Obtaining a three-dimensional environment model containing a dynamic obstacle position; according to the three-dimensional environment model, obstacle features are extracted, a deep learning model is adopted to identify moving personnel and randomly placed goods, obstacle types and movement tracks are judged, and a classified obstacle data set is obtained; through the classified obstacle data set, the distance and the relative speed between the obstacle and the current position of the robot are calculated, and if the distance is smaller than a preset threshold value and the speed is larger than zero, high-priority interference is marked; according to the invention, the problem of inaccurate dynamic obstacle identification and obstacle avoidance in a complex scene in the prior art is solved.

The present application relates to the field of data processing technology, and discloses an intelligent fire and wind prevention linkage method and system based on meteorological perception of port quay cranes. The method includes: collecting equipment operation data through a multi-dimensional sensing unit, constructing a perception feature vector and calculating the working condition load index; according to the feature vector and the load index, the protection margin coefficient is calculated through a boundary mapping algorithm to generate an operation toughness value; based on the toughness value, a correlation analysis is performed to obtain the wind-fire coupling risk index and the disaster cascade impact; the prediction value is calculated according to the risk index and the impact, and the response matching degree is output; the strategy coefficient is adjusted according to the matching degree and fed back to the feature vector. The present application establishes a dynamic protection mechanism based on the actual bearing capacity of the equipment, so that it can maximize the operating time of the equipment while ensuring safety, while improving the system's ability to cope with complex disasters.

The invention discloses an intelligent monitoring method and system for the construction progress of a building decoration curtain wall. The method comprises the steps that multi-source construction data of a construction site are collected through Internet of Things equipment and a high-definition camera; constructing a multi-dimensional tensor model and extracting data association features by applying a local reversible mapping algorithm to generate a standardized feature data set; performing parallel computing through shared memory hierarchical process mapping by utilizing the standardized feature data set, and outputting a project progress state evaluation result; constructing a decision diagram based on the project progress state evaluation result, and generating a construction progress deviation report; predicting a construction progress trend by adopting a sample optimal private regression algorithm and generating an optimization scheme; and visually displaying the construction progress prediction report and carrying out early warning. According to the invention, the problems of low data integration efficiency, delayed decision response and weak multi-dimensional data association analysis capability of traditional curtain wall construction monitoring are solved, and intelligent and efficient monitoring of the construction progress of the building decoration curtain wall is realized.

The invention relates to a multi-person motion capture real-time digital human driving method based on a skeleton-muscle interaction model, and belongs to the field of artificial intelligence and ubiquitous universe. According to the method, the key points of the human body are detected through YOLO-Pose, the positions of the shielding points are obtained through a double-stage dynamic shielding point compensation method, and the accuracy of the key points is guaranteed. And outputting the three-dimensional coordinates of the key points of the human body by adopting a three-dimensional key point depth extractor based on multi-dimensional exclusive-shared feature reasoning in combination with an MT ransPose model. A real person-skeleton model-digital person real-time mapping method is provided, three-dimensional key points are mapped to a standard human body skeleton model, and a digital person role and the skeleton model are bound through a weight mapping algorithm. Based on a skeleton-muscle interaction model, the digital person accurately reflects a real motion posture by introducing the mechanical action of a skeleton rigid body dynamic model and a muscle force line model, and the process of driving the digital person in real time through multi-person motion capture is achieved.

The invention relates to the technical field of network security and edge computing, in particular to a trusted asymmetric encryption communication protocol of an edge computing gateway driven by a certificate chain, which comprises the following steps of: generating node characteristic data based on edge node hardware characteristics, generating dynamic certificate chain data through edge node cooperation, and recording the dynamic certificate chain data to a block chain to form global trust data; generating global random number data in combination with the dynamic certificate chain data and a chaotic mapping algorithm, and verifying the integrity of the global random number data; generating quantum initialization vector data in combination with the global random number data and the hardware noise data, and performing block encryption on the original communication data to generate block encrypted data; a secret sharing algorithm is used to distribute a dynamic session key to a highly trusted node to ensure the security of the key; and dynamically optimizing subsequent communication parameters according to a result of decrypting the communication data. According to the method, the security, robustness and adaptability of data transmission in an edge computing scene are improved by introducing a dynamic certificate chain, a chaotic mapping algorithm, block encryption and a feedback optimization mechanism.

The invention discloses a network traffic anomaly detection system based on artificial intelligence, and relates to the technical field of network security, and the system comprises the following components: a plurality of agent modules, a cooperative communication module, a decision fusion module and a model training and updating module. According to the invention, by integrating a plurality of agent modules, the multi-dimensional features of the network traffic can be comprehensively and deeply analyzed, and each agent module adopts advanced algorithms and technologies, such as a multi-dimensional feature mapping algorithm, a behavior pattern mining algorithm, a protocol semantic parsing algorithm and a periodic fluctuation decomposition algorithm. The algorithms can accurately capture the abnormal behavior of the network flow, and meanwhile, the decision fusion module adopts an adaptive dynamic weight fusion algorithm, comprehensively considers the detection result of each agent, obtains a final abnormal detection conclusion, and further improves the detection accuracy.

The invention provides a digital human lip shape synchronization method based on voice driving. The method comprises the following steps: acquiring a voice signal; voice features related to lip shape changes in the voice signals are extracted; mapping the extracted voice features into lip shape change data; and applying the lip shape change data to the digital human model and carrying out real-time rendering to finish lip shape synchronization. According to the method, the voice signal characteristics are directly analyzed, subtle changes of pronunciation can be more accurately captured, and more natural and smooth lip-shaped actions are generated; the method is suitable for a multi-language environment, and especially improves the processing effect of tone languages such as Chinese and the like. An optimized signal processing and mapping algorithm improves the lip shape synchronization speed, and the real-time interaction requirement is met; a complicated phoneme identification process is not needed, and the technical implementation difficulty and the maintenance cost are reduced; through more accurate and natural lip shape synchronization, the overall performance and interaction naturalness of the digital human are remarkably improved.

The present invention discloses a distributed heuristic unmanned cluster brain-like swarm intelligence fusion search and capture method, comprising: step 1: completing single target detection on the basis of a Yolov5 network, and predicting the position of a next frame of target on the basis of a DeepSort algorithm; step 2, on the basis of the distance difference and the area difference, determining, by mapping, navigation end point coordinates formula (1) of an intelligent agent in a physical space; step 3, constructing a diffusion-contraction coordinated chaotic mapping algorithm, and mapping the update of the navigation end point coordinates into a uniformly distributed chaotic space; step 4: on the basis of target detection confidence decision information and regional position information of all intelligent agents, fusing and generating global search information formula (3) of a search space formula (2); step 5: on the basis of the intelligent agent global search information, fusing various sensing data in a multi-agent system, so as to estimate target position information; and step 6: performing measurement on the basis of a real-time position of the intelligent agent and an obstacle position. The present invention has high capture efficiency and can accurately update a capture end point of the intelligent agent to complete a task of encircling a moving target.

The invention provides a linear dimension measurement method and device for the total length of a road guardrail stand column, and relates to the field of linear dimension measurement, the device is deployed on a detection vehicle, and comprises a control module, a ground penetrating radar measurement module, a 3D camera measurement module, a sensor module, a lower computer and an upper computer; according to the invention, accurate data acquisition is realized through multi-module cooperative control, and the lower computer is combined with the AI image recognition technology to realize accurate acquisition of contour data; the lower computer transmits radar original data (reflected waves and diffracted waves) and 3D contour data (column cap space coordinates) to the upper computer, the upper computer achieves data fusion through a hierarchical velocity model (time-depth conversion) and a coordinate system mapping algorithm (3D-road coordinate system), and the accuracy of the total length measurement error is greatly improved compared with that of single module detection. The method solves the problems of low efficiency, large destructiveness and poor accuracy of traditional manual detection, and has the advantages of method innovation and engineering landing.

The invention belongs to the technical field of artificial intelligence, is applied to the field of financial science and technology and the field of medical health, and discloses a rule engine configuration method, device and equipment based on machine learning and a storage medium. The method comprises the steps that historical calculation data and performance indexes in historical calculation tasks are collected, and a training data set is constructed; a machine learning model is trained, and algorithm parameters of the rule engine are dynamically optimized through the training data set; configuring an expression and a user-defined function of a rule engine, and realizing analysis and execution of scripts through JEXL (JavaScript Exchange Library); establishing a dynamic mapping relationship between the algorithm configuration library and the business variables, mapping algorithm variable names and business variable names, and generating configurable algorithm rules; and analyzing task load and resource requirements based on a pre-trained machine learning model, distributing system resources, and repairing abnormal behaviors in a calculation process. According to the invention, the problems of low processing efficiency, high maintenance cost, unreasonable resource allocation and poor expandability in the prior art are solved.

The invention relates to the technical field of intelligent driving, and discloses a defogging embedded system and method for an intelligent driving vehicle-mounted camera, and the method comprises the steps: obtaining original image data collected by the vehicle-mounted camera, and analyzing the brightness distribution, color shift and texture features in an image; based on the image degradation feature information, performing preliminary enhancement processing on the image by adopting an edge guided filtering and brightness adaptive mapping algorithm to generate an initial enhanced image; carrying out fog distribution estimation on the initial enhanced image, carrying out detail restoration and color compensation in combination with a lightweight image reconstruction strategy, and dynamically improving the image quality through an iterative optimization mode; performing definition evaluation on the optimized image, and measuring the fuzzy degree change trend and the structural similarity; and in combination with the finally output clear image, performing target identification and feature extraction operation, and performing accuracy evaluation on an identification result. The method has the advantage of improving the definition of the intelligent driving image.

A page identification technique is configurable to select regions of a screen for optical character recognition. The size of the regions may be selected to reduce the CPU resources needed to identify a page. Pre-processing and post-processing may be performed to improve the accuracy of the optical character recognition. A word mapping algorithm may include logical condition to determine a page name.

A method for surveying an environment by a movable surveying instrument with a progressional capturing of 2D-images by at least one camera and applying a visual simultaneous location and mapping algorithm (VSLAM) or a visual inertial simultaneous location and mapping algorithm (VISLAM) with a progressional deriving of a sparse evolving point cloud of at least part of the environment, and a progressional deriving of a trajectory of movement. The method comprises a progressional matching of the sparse evolving point cloud with a previously derived 3D-geometry, with a minimizing of a function configured to model a distance between the sparse point cloud and the previously derived 3D-geometry and deriving a spatial localization and orientation of the surveying instrument. At least one surveying measurement value of the environment by a spatial measurement unit is combined with the sparse point cloud or the previously derived 3D-geometry.

The invention relates to the technical field of chip compiling and mapping, in particular to a coarse-grained reconfigurable chip mapping method and device based on pre-scheduling. The method comprises the following steps: compiling an intermediate representation through the front end of a compiler according to an input program; constructing a data flow diagram structure according to the intermediate representation; carrying out pre-scheduling according to the data flow diagram and the reconfigurable chip structure information to obtain an operation time period; searching the mapping result according to a pre-allocated time period, and determining a final mapping scheme; configuration information for execution on the coarseness reconfigurable chip is generated. According to the method, the complexity of a mapping algorithm can be effectively reduced, the execution performance of the task on the coarseness reconfigurable chip is improved, heterogeneous resources on the coarseness reconfigurable chip are fully considered in the mapping process, the method is suitable for multi-node and complex-structure data flow diagram mapping in complex tasks, and the mapping efficiency is remarkably improved.

The invention relates to the technical field of bridge engineering structure health monitoring, in particular to a service life prediction method and system for a bridge connection part, and the method comprises the steps: collecting the strain of a grouting sleeve, a reinforcement-grouting interface ultrasonic reflection signal and environment temperature and humidity data through a distributed sensor array; a three-dimensional dynamic void rate model is generated through fusion of a spatial topological mapping algorithm, a correlation function of load circulation and void expansion is established, the dynamic void rate is input into a bond strength coupling degradation model, after strength attenuation is quantized in combination with steel bar corrosion data and bond failure early warning is triggered, stress redistribution is calculated through multi-physics coupling simulation, and the dynamic void rate is obtained. Early warning is taken as a starting node, the actual load spectrum and temperature and humidity data are combined, Monte Carlo sampling is adopted to simulate rigidity degradation and output the residual life probability, the system comprises a multi-source collaborative sensing unit, a micro-void evolution analysis unit and a life prediction decision unit, and the safety control precision of bridge connection parts is improved.

The invention provides an automobile seat massage adjusting method and adjusting system, and belongs to the technical field of automobile seat intelligent massage control. The method comprises the steps that seat multi-area pressure sensing data are collected, and a pressure feature matrix and a massage mode classification model set are constructed; setting a user body type parameter adjustment category set, and establishing a parameter fusion association model based on historical use data; current pressure and vibration characteristic parameters are extracted, and an optimized parameter set is generated through characteristic optimization; and updating massage control logic and calibrating a parameter matching position. The system comprises a data acquisition module, a model construction module, a feature optimization module and a calibration module. By dynamically fusing real-time data and historical parameters, self-adaptive adjustment of massage intensity is achieved, the problem that a traditional fixed mode cannot be matched with personalized requirements is solved, meanwhile, the parameter calibration precision is improved through a coordinate mapping algorithm, and user experience is improved.

The invention relates to the technical field of resource allocation, in particular to an edge computing resource allocation method and system, and the method comprises the following steps: collecting network node information, recognizing key nodes through embedded learning, and analyzing a communication mode. A graph convolutional network and a self-organizing mapping algorithm are utilized to analyze and encode node connectivity and resource states, and a network structure is iteratively optimized. And monitoring and updating a network state, adjusting a resource allocation strategy, and generating a dynamic resource allocation model. According to the method, the data processing task is performed on the near side of the data source, so that the data transmission distance and delay are remarkably reduced, the processing speed is increased, and the application performance is directly improved. Simulation mapping and dynamic adjustment of resource states and demands enable resource allocation to be more suitable for actual demands, and resource waste is reduced. Self-adaptive adjustment of resource allocation is carried out according to network real-time data, and real-time performance and accuracy of resource allocation are guaranteed. The instant response and prediction capability optimizes the operation of the whole network.