175 results about "Degree distribution" patented technology

The invention discloses a real-time single-stage remote sensing image correction target detection method based on YOLOV8, and relates to the technical field of remote sensing image processing. According to the method, a deformable convolution dynamic prediction local geometric distortion parameter is embedded based on a YOLOv8 backbone network, an adaptive deformation field is generated, pixel-level real-time correction is realized, shallow details and high-level semantic features are fused through a bidirectional path aggregation network, and channel attention and a space gating mechanism are combined, so that the real-time correction of the image is realized. The small target detection capability is enhanced, background noise is suppressed, angle prediction is divided into discrete classification and continuous residual error regression tasks through a decoupling type rotation detection head, angle periodic errors are eliminated in combination with a direction sensitive loss function, and the rotation frame positioning precision is improved. And constructing a dynamic multi-task collaborative loss function, introducing gradient distribution consistency constraint to jointly optimize correction and detection tasks, and realizing feature semantic alignment and model self-enhancement through end-to-end closed-loop training. And the rotating target detection precision and the complex scene robustness are obviously improved.

The invention provides an on-line monitoring method and system for crack propagation of silicon-based new material equipment in a high-temperature environment, and relates to the technical field of crack detection, and the method comprises the steps: collecting stress distribution data through arranging a stress sensor, and generating a stress field distribution diagram by using an attention mechanism deep neural network model; identifying a stress concentration region based on a region growing algorithm, when a stress value exceeds a preset threshold value, acquiring temperature distribution data, extracting temperature distribution characteristics through a deep mixed probability model, performing multi-modal data fusion in combination with a stress field distribution diagram, and establishing a crack propagation prediction model by using a graph structure neural network. The crack propagation rate and direction are solved through a swarm intelligence optimization algorithm, when the crack propagation rate exceeds a preset threshold value, a control system adjusts equipment operation parameters, the sampling frequency of a sensor is adjusted according to the crack propagation direction, and model parameters are updated through an incremental learning method to achieve real-time monitoring.

The invention provides a network fault node automatic detection and roundabout path planning method, which comprises the following steps of: acquiring optical power and bit error rate of an optical fiber link, and recording abnormal time point and position information; obtaining a strong electromagnetic interference influence weight map; generating a deterioration trend curve; obtaining a health degree score of each optical fiber segment, and generating a health degree distribution map; probability distribution of fault nodes is determined, whether the nodes are fault points is judged according to the probability distribution, and a fault node position set is generated; obtaining a roundabout path planning scheme; and calculating a global network health state perception score to obtain an optimized global network health state diagram. The invention further discloses a corresponding storage medium. According to the invention, the reliability and stability of the optical fiber network can be effectively improved, and the efficiency and accuracy of fault detection and processing can be improved.

The invention provides a fresh food quality degradation pre-judgment method and system based on multi-modal dynamic coupling, and aims to solve the problem of prediction lag caused by data isolation and dynamic coupling deficiency in the traditional storage and transportation process. Specifically, sensory layer data, environment layer data and operation layer data are synchronously collected through a multi-source sensor, chromatic aberration quantification, chemical bond feature extraction and spatial chemical fusion processing are carried out on the sensory data, and a quality degradation feature tensor is generated; based on a graph network model and frequency domain analysis, constructing a dynamic coupling parameter set of quality degradation characteristics and environmental parameters; fusing the loading and unloading pulse trajectory tensor, the cold chain stability distribution and the coupling parameter set, and generating a degradation constraint distribution matrix through multivariable weight mapping; and finally, combining acoustic resonance mode coherence analysis and constraint matrix evolution, and outputting a four-stage quality label. According to the method, through multi-dimensional real-time sensing, dynamic coupling modeling and hierarchical management and control, the fresh food storage and transportation loss is remarkably reduced, and the cold chain management efficiency is improved.

The invention discloses a low-delay slice resource intelligent allocation method for a 5G network, and the method comprises the steps: collecting the connection state and performance index of a network node in real time, dynamically adjusting the collection frequency according to the fluctuation of a network load, and generating structured topological data; calculating a node degree distribution entropy value based on the topological data, generating a virtual resource topological graph, and predicting resource requirements of network nodes; constructing a dynamic confidence interval by using the virtual resource topological graph and the entropy change rate, and designing a resource pre-allocation strategy; network burst traffic is detected, resource allocation is rapidly adjusted through incremental updating of the graph neural network, and it is ensured that the reconfiguration delay is lower than 1 millisecond; according to the invention, through topology perception driven by the graph neural network, prediction guided by entropy and an adaptive allocation strategy, low-delay resource allocation of the uRLLC slice is realized, resource waste is avoided, and strict performance requirements of a 5G network are met.

The invention discloses a bridge crack feature fine quantification method and system based on deep learning, and relates to the technical field of bridge detection, and the method comprises the steps: carrying out the preprocessing of bridge surface image data; inputting the preprocessed image data into a U-Net network fused with a multi-scale channel space attention module for semantic segmentation and outputting a crack segmentation mask graph; performing morphological refinement operation to obtain fracture skeleton line data, and calculating a trend angle and curvature distribution; measuring the crack width along the normal direction of the skeleton point to generate a width distribution thermodynamic diagram; accumulating skeleton point intervals to calculate the total length and identifying branch features; according to the method, the segmentation IoU reaches 85% or above, the width precision is superior to 0.05 mm, and the method has the crack development trend prediction capability.

The invention discloses a degree classification-based graph division method and system for a heterogeneous environment graph neural network, and the method comprises the steps: determining the classification threshold values of a high vertex and a low vertex according to the power law characteristics of the vertex degree distribution in an input graph neural network, and classifying the vertexes into the low vertex and the high vertex; performing initial sub-graph division on the low-degree vertex by using a constrained METIS method; performing initial subgraph division on the height vertex, calculating a locality score and a load balance score for dividing the height vertex into different partitions, performing weighted summation on the locality score and the load balance score to obtain a comprehensive score divided into the corresponding partition, and selecting the partition with the highest comprehensive score as a division result; each partition corresponds to one computing node in the heterogeneous environment. The invention aims to realize load balancing sensed by heterogeneous equipment while optimizing the edge cutting rate, and improve the overall performance and the resource utilization rate of a distributed graph neural network training system.

The invention relates to a nondestructive flaw detection method and system, and the method comprises the steps: carrying out the dynamic coupling wave mode decomposition of a response signal, and obtaining a basic wave mode component and an energy ratio; aiming at a plurality of discrete time-frequency scale units, constructing a multi-scale time-frequency energy flow graph cluster based on the basic wave mode component and the energy ratio; calculating the direction gradient of each energy flow graph in the energy flow graph cluster by taking the energy amplitude as the weight to obtain a gradient vector field; determining a suspicious region according to topological structure characteristics of the gradient vector field and an abnormal threshold value of a wave mode energy ratio in the energy flow graph cluster; determining the boundary of the suspicious region based on the trajectory of the gradient vector field; performing depth distribution inversion calculation on the suspicious region according to the energy attenuation gradient of the multi-scale energy flow diagram and the attenuation of each basic wave mode energy in the depth direction; and performing regularization tomography by using the energy ratio and spatial distribution of each fundamental wave mode as multi-channel input, and reconstructing the three-dimensional geometrical morphology of the suspicious region.

The invention discloses a multi-dimensional distribution offset adaptive threshold detection method and system, and the method comprises the steps: obtaining real-time data, extracting a multi-dimensional statistical feature, and obtaining a feature vector; based on historical normal data, using an expectation maximization algorithm to train a Gaussian mixture model, and determining parameters to obtain a normal distribution model; inputting the feature vector into the model, and calculating a probability value of the feature vector belonging to normal distribution as a first offset judgment index; based on the real-time data distribution of a plurality of detection objects in the same group, the distribution difference of any two objects is calculated by using a Wasserstein distance, and the similarity between the objects is obtained; and constructing a similarity network and calculating connectivity as a second offset judgment index. Setting a fixed-length sliding window, dynamically updating two indexes in the window, and obtaining a first self-adaptive threshold value and a second self-adaptive threshold value; and when any index is lower than a corresponding threshold value, determining distribution offset and giving an alarm, and updating model parameters in real time by using an incremental expectation maximization algorithm. According to the invention, accurate detection and intelligent analysis of data distribution offset are realized.

The invention provides a construction safety risk grading method and system combined with fuzzy clustering, and belongs to the technical field of building construction safety risk management.The method comprises the steps that firstly, a real-time risk feature set of a construction scene is obtained, and environmental influences, equipment operation and personnel operation features of a construction area are covered; secondly, fuzzy clustering preprocessing is conducted on the real-time risk feature set, fuzzy membership degree distribution and a feature correlation degree matrix of all risk features are obtained, a risk transmission network is constructed based on the result, nodes are risk features, edges are correlation degree parameters between the features, and risk diffusion coefficients of all the nodes are calculated through the risk transmission network; and generating a risk grade division result according to a preset grading rule, and finally outputting a construction safety grading instruction containing the risk area identifier and the corresponding management and control strategy, thereby dynamically and accurately evaluating the construction safety risk.

The invention discloses an online anomaly monitoring method and system for a linear movement cutting ore pulp sampler, and relates to the technical field of industrial automation, and the method comprises the steps: executing frequency band energy separation and sliding window statistical analysis on a working condition data set of the ore pulp sampler, and obtaining a multi-dimensional feature matrix; performing weight distribution and dynamic weighted aggregation on the multi-dimensional feature matrix to form a space-time analysis data packet, performing collaborative analysis on the space-time analysis data packet, and outputting a trend collaborative interaction matrix; and performing risk quantification and contribution degree distribution on the trend collaborative interaction matrix by using an entropy weight method to generate an abnormal quantification parameter, and performing confidence coefficient weighted calculation on the abnormal quantification parameter to form an abnormal probability value. According to the method, the working condition data set of the ore pulp sampler is fully fused through the sliding window statistical analysis and the entropy weight method, and meanwhile, deep feature mining and spatial relation fusion are performed through the dynamic causal atlas and the space-time convolutional neural network model, so that the reliability of anomaly monitoring is improved.

The invention belongs to the technical field of PID control, and particularly relates to a PID parameter adaptive algorithm, system and program based on a multi-dimensional fuzzy rule. A PID parameter adaptive algorithm based on a multi-dimensional fuzzy rule comprises the following steps: normalizing system error data and an error change rate by taking a maximum change value as a normalization factor; performing fuzzification processing on the normalized error data and the normalized error change rate by adopting a triangular membership function of a plurality of linguistic variables to obtain fuzzy error data and a membership distribution matrix of the fuzzy error change rate; orthogonal design is carried out on the multiple linguistic variables, and a fuzzy rule base is constructed; and according to fuzzy error data in the membership degree distribution matrix and the distribution of the fuzzy error change rate, traversing the fuzzy rule base, forming a plurality of matched mapping results, performing weighted average calculation, and performing fixed amplitude limiting and dynamic amplitude limiting to obtain a final proportional gain # imgabs0 #, a final integral gain # imgabs1 # and a final differential gain # imgabs2 #.

The invention discloses a context poisoning attack defense method oriented to an RAG application, and relates to the technical field of RAG. the method comprises the following steps: inputting a target query statement, and retrieving the target query statement to obtain multiple pieces of context information; taking representative sentences in the retrieved context information, and identifying and filtering potential malicious template clusters; the big language model gives all candidate answers according to existing context information, the logarithmic probability of all contexts to different candidate answers is calculated, and after the influence of parameter knowledge of the big language model is removed from the logarithmic probability, the support degree of all contexts to different candidate answers is obtained; the whole logarithmic probability vector is used as a support degree distribution condition of the context to the candidate answers; identifying a single piece of harmful information from the support degree distribution condition of the context to the candidate answers through a logistic regression model so as to filter wrong answers; according to the attack defense method provided by the invention, centralized injection of multiple malicious texts and sparse injection of a small number of malicious texts can be defended.

The invention provides a fault root cause analysis and disposal scheme generation method based on a large model, and relates to the technical field of artificial intelligence and fault diagnosis and operation and maintenance, and the method comprises the steps: generating a fault feature vector sequence fusing spatio-temporal topological semantics; inputting the fault feature vector sequence into a pre-trained large model to construct a fault propagation chain, and judging whether the system has a fault or not based on an abnormal strength threshold value; if the fault exists, abnormal nodes are identified through a large model, and a correlation score of each abnormal node is calculated based on a time sequence change feature and a causal attention mechanism; calculating to obtain responsibility degree distribution of each abnormal node through a large model according to the node topological relation and the correlation score; and adjusting the responsibility degree value according to the responsibility degree distribution and the depth of the abnormal node in the call chain, and outputting a fault root cause analysis and disposal scheme recommendation result according to a responsibility degree threshold. Intellectualization of root cause analysis is achieved, a targeted recovery operation instruction is provided, and the fault recovery time is greatly shortened.

The invention discloses a flow field pressure gradient prediction method based on a graph attention network guided by a physical operator, and aims to rapidly and accurately predict the gradient distribution of a flow field. According to the method, a grid computational domain of computational fluid dynamics (CFD) is expressed as a graph structure, a graph neural network is adopted to carry out feature learning on grid nodes and adjacency relations, and a physical operator (such as a fluid control equation difference operator) is introduced in a model training process to carry out guide constraint on the network. Through the above technical scheme, the method can greatly improve the efficiency of flow field gradient prediction on the premise of ensuring the prediction precision and physical consistency, has the advantages of fast model calculation speed, strong adaptability to complex boundary conditions, and the prediction result meets the law of conservation of fluid mechanics, and is suitable for popularization and application. The method can be used for rapidly predicting the flow field gradient in the fields of aerospace fluid simulation, wind engineering and the like.

The invention discloses an intelligent positioning method and system for a microseism event. Intelligent positioning of the microseism event is realized by combining a convolutional neural network and probability density function mapping. Comprising the following steps: according to a work area speed model, generating four-dimensional training data representing detector coordinates and seismic phase arrival time and a three-dimensional label representing a seismic source position through Poisson disk sampling and a Gaussian probability density function; designing a convolutional neural network structure, optimizing network weight through back propagation, and constructing a micro-seismic event intelligent positioning model; and inputting actual data into the positioning model to obtain probability density distribution of the seismic source in a three-dimensional space, and finally outputting a high-precision seismic source positioning result through peak value extraction. According to the method, the observation system is creatively integrated into network input, and the strong nonlinear feature extraction capability of the convolutional neural network is combined, so that the generalization capability of the positioning model to different observation systems and the robustness to detector coordinate deviation and arrival time error are remarkably improved.

The present invention belongs to the technical field of power system operation and management, and provides a load-side supply and demand balancing control method and system based on multi-dimensional reliability assessment. In terms of the access mechanism, by comprehensively evaluating key factors such as the node distribution, type, capacity and dispatchability of load-side flexibility resources, scientific and reasonable evaluation standards and access thresholds are designed, which can effectively screen out high-quality and high-reliability resources to participate in the regulation, ensure that the access resources have a good regulation basis, and greatly improve the quality of the overall regulation resources; at the same time, in the centralized control mode, a global optimal control strategy is formulated to achieve efficient and unified scheduling of resources. In the distributed autonomous mode, local information perception and modeling are used, and each distributed resource optimizes local strategies based on maximizing its own interests, which can give full play to the autonomy and flexibility of distributed resources and enhance the system's ability to respond quickly to local changes.

The invention discloses a fracture seepage prediction method combining a yield criterion and network topology, and belongs to the field of rock mass engineering construction, and the method comprises the following steps: carrying out topology analysis on a fracture network based on a complex network theory, and determining the degree centrality of fracture network nodes and a fracture shear stress transfer coefficient; establishing a shear stress equation based on a Mohr-Coulomb yield criterion, and determining a criterion of fracture network damage leakage according to the yield criterion and a seepage stress coupling relationship; considering degree distribution and clustering coefficients of fracture network nodes, and establishing a damage evolution equation comprehensively considering node importance and fracture damage characteristics; and based on the established damage evolution equation, carrying out fracture seepage damage evolution to predict the percolation region. According to the method, the importance of the fracture network nodes is identified by using complex network indexes, and failure calculation is performed on fracture edges and the nodes in combination with the yield criterion and the seepage-stress coupling relationship condition, so that the fracture network damage evolution and seepage prediction efficiency is effectively improved.

The invention discloses a multi-scene risk perception method based on a knowledge graph and deep learning, and the method comprises the following steps: collecting multi-source heterogeneous data, carrying out the unified standardization of the multi-source heterogeneous data into a multi-modal data vector, converting the data vector into polar coordinate representation with direction and amplitude characteristics, and carrying out the inter-modal fusion through a polar coordinate transformation network; the fusion result is matched with a preset risk knowledge graph, and a dynamic graph structure containing risk nodes and edge weights is constructed; by introducing a reverse stochastic differential equation, the state of risk evolution along with time is simulated; and analyzing the risk density distribution in different directions, generating a dynamic vector for controlling risk reasoning, dynamically activating a risk channel in an asynchronous mode, and comprehensively outputting a unified multi-scene risk perception map. According to the invention, continuous and interpretable identification and prediction of the risk state in a complex environment are realized.

The invention discloses a robot group detection scheduling method and system, and relates to disaster monitoring: scene reconstruction is carried out according to collected real-time sensing information, a three-dimensional digital model of a disaster scene is generated, and a discrete monitoring grid based on sampling points is established; constructing a disaster risk assessment model on the discrete monitoring points, and for gas and fire, calculating the risk value of each monitoring point by using actual measurement data to form a discrete risk distribution map; calculating the space change trend of the risk degree by adopting an adjacent point difference method, and calculating a directed gradient through the risk degree difference value and the distance of adjacent monitoring points; when the risk degrees of any two disasters are increased at the same time, marking the risk points as potential coupling risk points; identifying a propagation path of the risk degree based on connectivity analysis of the monitoring network; and scheduling the robot group by adopting a risk avoiding path planning algorithm. According to the method, the spatial distribution and evolution trend of the disaster field are accurately reconstructed through local gradient estimation under the sparse sampling condition.

The invention provides a 3D Gaussian sputtering method based on depth feature fusion. The 3D Gaussian sputtering method comprises the steps that multi-view features and monocular depth features are extracted through a multi-view Transform network and a pre-trained monocular depth estimation model respectively; dynamically fusing the two types of features through a content attention guiding module; performing deep regression on the fused features by using a 2D U-Net network to obtain robust depth distribution; back-projecting the depth distribution to a 3D space to obtain a Gaussian center, and predicting other Gaussian parameters through a 2DU-Net network; and rendering a high-quality three-dimensional model through rasterization according to all Gaussian parameters. According to the 3D Gaussian sputtering method, by combining the complementary advantages of multi-view feature matching and monocular depth priori, the reconstruction quality of a complex scene is remarkably improved, and the problem that the scene reconstruction quality is low due to inaccurate matching or missing matching information in the complex scene in a traditional feature matching method is solved.

The invention relates to the technical field of constructional engineering quality detection, and discloses a method for testing the apparent quality of mirror-surface bare concrete. The method comprises the following steps: acquiring a multiband multispectral image of the surface of a component; through image fusion and feature decoupling, an independent specular glossiness distribution diagram, a microscopic fluctuation topological diagram and a chromaticity coordinate mapping diagram are obtained; registering and associating the three images to construct a three-dimensional total element apparent characteristic field; performing gridding scanning on the characteristic field to extract glossiness uniformity, texture roughness and chromaticity stability indexes of each grid; inputting a pre-trained apparent quality analysis network to output a local quality evaluation value and a defect type label of each grid; and integrating and generating an overall quality index and generating a repair guidance map according to the defects. According to the method, multi-dimensional, full-field and intelligent fine detection of the apparent quality is realized, the gloss, the texture and the color can be independently and quantitatively analyzed, and the defect can be accurately positioned.

The embodiment of the invention provides a multi-account concurrent processing management system and a multi-account concurrent processing management method, which are applied to the technical field of multi-account high-concurrency business processing, and are used for analyzing a plurality of received account operation requests into a micro-operation set, packaging micro-operations into an operation object identifier, an operation type and a dependency relationship, and storing the operation object identifier, the operation type and the dependency relationship. Comprising resource dependence, granularity dependence and business logic dependence; determining a lock execution path based on the operation object identifier, the operation type and the dependency relationship; constructing a dependency tensor model, performing conflict density mapping to form conflict density distribution, and performing tensor conflict operation on the microoperation in combination with a lock execution path to obtain a conflict intensity value; a high-conflict area is identified based on conflict density distribution, a potential deadlock graph is constructed, a micro-operation directed acyclic graph is generated, nodes with zero in-degree and out-degree are identified to form a first execution sequence, a second execution sequence is constructed according to residual micro-operations, a micro-operation execution sequence is formed through splicing, and efficient parallel scheduling and throughput capacity improvement are achieved.

The invention provides an intelligent work order automatic classification system, and relates to the technical field of work order management. According to the system, work order texts are analyzed through an LLM technology, core business phenomena are identified, auxiliary descriptions are eliminated, and an initial phenomenon mark set is generated. And further establishing a cross-sentence association analysis mechanism, dynamically inserting context associators with directivity between discrete phenomena based on syntactic dependency and semantic role labeling, and implementing dynamic weight assignment based on context proximity. The system generates a causal rule set with version identification, and a multi-decision trigger unit processes weighted phenomenon marks in parallel to generate a differentiated candidate root cause set. And the decision conflict quantization unit calculates rule matching degree distribution, and activates rule backtracking verification to ensure decision stability. And finally outputting a root cause classification work order entity with a rule version traceability identifier, thereby realizing automatic work order classification with high accuracy and stability.

The invention relates to a supply chain demand prediction and risk early warning method and system based on multi-source data. The method comprises the steps of collecting multi-source heterogeneous data and constructing a feature vector set; constructing a supply chain network topology structure; inputting the feature vector set and the supply chain network topology structure into a risk propagation model to obtain a risk space-time distribution diagram; inputting the feature vector set and the risk space-time distribution diagram into a demand prediction model to obtain collaborative prediction result information; based on a supply chain network topology structure, generating a node criticality distribution map, comparing, identifying high-risk nodes, obtaining risk level information, and generating graded risk early warning information; according to the method, the feature vector set and the supply chain network topology structure are constructed by integrating the multi-source heterogeneous data, combined optimization of dynamic risk simulation and demand prediction is realized by combining cooperative calculation of the risk propagation model and the demand prediction model, and the method has the advantages of improving prediction accuracy, dynamically identifying high-risk nodes and generating hierarchical early warning information.

The invention discloses an LDPC (Low Density Parity Check) code check matrix construction method, which is used for processing a low earth orbit satellite ground foundation zone, and comprises the following steps of: A1, determining degree distribution of a basis matrix under a high code rate by adopting an extrinsic information transfer graph EXIT, and realizing construction of the basis matrix under the constraint of optimal degree distribution through extrinsic information transfer P-EXIT based on a basic model graph; a2, based on the code length and the code rate of FEC coding of the DVB-S2 protocol, determining the size and the expansion factor of a basis matrix corresponding to each code rate; a3, determining a finite field and a generator based on an expansion factor, and constructing a linearly independent shift value row vector and cycle coefficient table; a4, selecting row vectors from the cyclic coefficient table to construct a cyclic shift matrix, and performing short ring identification and elimination on the cyclic shift matrix; and A5, performing matrix hashing on the basis of the basis matrix and the cyclic shift matrix to obtain an LDPC check matrix.

This invention discloses a method for constructing a natural-artificial water network model and analyzing its connectivity, including: collecting basic data; generalizing nodes and edges of the natural and artificial water networks and adding weights to the edges; constructing a natural-artificial water network model based on complex networks; constructing the degree distribution of structure and flow based on node degrees; and calculating the structural connectivity and flow connectivity of the natural-artificial water network based on the degree distribution. This invention combines complex network theory to model natural-artificial water networks and analyze their connectivity, solving the technical problem of lacking comprehensive modeling and connectivity assessment in existing water network analysis methods, and providing new analytical ideas and technical support for water resource management and water network structure optimization.

The present disclosure belongs to the technical field of network security, and particularly relates to a network anomaly monitoring method, device and program product based on traffic fingerprint learning, which comprises the following steps: extracting metadata irrelevant to encryption of a target network; the metadata comprises five-tuple, timestamp, length and protocol flag information of a data packet; the metadata is aggregated with a source IP address as a primary key to define a network entity; session feature extraction is performed on a session of the target network entity and other network entities; the extracted session features comprise timing features, operation sequence features and scale distribution features; the operation sequence features comprise Shannon entropy of a continuous request and response type pair; an action fingerprint of the target network entity is constructed according to the session features; and whether the target network and the target network entity are abnormal is judged according to a deviation degree of a current action fingerprint of the target network entity and a fingerprint baseline. The present disclosure can realize accurate identification of network anomalies.

The invention relates to the technical field of modern communication, and discloses a wireless network card power adaptive control method and system. The method comprises the following steps: acquiring real-time interference data, performing spectrum change according to the real-time interference data to obtain an interference intensity distribution diagram, performing interference correlation analysis and power optimization calculation according to the interference intensity distribution diagram to obtain a power configuration scheme, performing parameter updating according to the power configuration scheme to obtain a preliminary power correction result, and performing power optimization calculation according to the preliminary power correction result. The method comprises the steps of obtaining a preliminary power correction result, carrying out interference comparison evaluation according to the preliminary power correction result to obtain deviation information, carrying out power optimization according to the deviation information to obtain an optimized power configuration scheme, and carrying out synchronous configuration and index feedback analysis according to the optimized power configuration scheme to obtain a final regulation command set. According to the method, the problem that interference chain reaction is easily caused by equipment power adjustment can be solved, so that stable communication and interference suppression in a complex network environment are realized.

The present application relates to the field of engineering detection technology, in particular to a deep compaction degree nondestructive testing method and system for large-thickness water-stable base course. A multi-source heterogeneous dataset of a to-be-detected area is obtained using an integrated detection device; a construction process compliance index and a process uniformity index of the to-be-detected area are obtained; each data source in the multi-modal nondestructive testing data is deeply corrected to obtain a deep correction compaction degree estimation set of each data source; the deep correction compaction degree estimation set of each data source is input into an adaptive weight fusion model to obtain a preliminary compaction degree distribution map; a fusion compaction degree distribution map of the to-be-detected area and a dynamic confidence score of each spatial point are calculated; the compaction quality of the to-be-detected area is determined, and a detection strategy optimization instruction is generated for the area with a confidence score lower than a confidence threshold. The present application can realize high-precision, high-confidence and engineering-actual nondestructive testing of deep compaction degree of large-thickness water-stable base course.