43 results about "Probability mapping" patented technology

The invention discloses a welding quality on-line detection method based on machine vision, and particularly relates to the field of welding quality detection.The welding quality on-line detection method comprises the steps that multi-dimensional basic data collection is conducted, the two-dimensional form, heat distribution, micro texture, three-dimensional topological data and welding process parameters of a welding seam are obtained, and a full-dimensional original data set with time-space synchronization is constructed; performing feature decoupling on the data, respectively constructing geometric morphology, thermal, texture and three-dimensional topological feature functions, and converting unstructured data into structured feature values; integrating multi-dimensional features through a nonlinear deep fusion model, introducing thermal probability mapping, texture attention weighting and a three-dimensional residual compensation mechanism, and outputting a weld comprehensive quality value; and finally, positioning dominant defect dimensions and identifying defect types in combination with a dynamic threshold model to finish quality grading judgment. Multi-dimensional accurate detection and dynamic grading of the welding quality are achieved, different scene requirements are met, and the detection efficiency and the judgment accuracy are improved.

The invention discloses an automobile casting multi-equipment detection data fusion judgment method, relates to the technical field of intelligent detection, and solves the problems that in an existing detection method, confidence output of multi-modal data is incomparable, misinformation is easily caused by evidence conflicts, and positioning offset is generated due to the fact that multi-modal registration is limited by a rigid model. According to the technical scheme, the method comprises the steps that surface images, point cloud and X-ray projection data are preprocessed, a detection response matrix is established, probability mapping is conducted on each modal score to form likelihood distribution and uncertainty representation, weighted fusion is completed in combination with a redundancy coefficient matrix, and a conflict judgment mark is generated; on the basis, hierarchical registration is executed, and a final judgment and recheck label is output; based on the technical scheme, the judgment accuracy and stability of multi-source detection data fusion of the automobile casting are remarkably improved.

The invention discloses an underwater Internet of Things destroy-resistant link optimization method combining self-guided graph representation and AMF. The method comprises the following steps: 1) generating a scale-free topology and a candidate link set according to node positions, residual energy and underwater acoustic channel quality; 2) inputting the topology into a self-guided graph representation learning model, and applying contrast constraint through unsupervised training to obtain robust node embedding representation; 3) on the basis of candidate link two-end embedding, combining node degree, betweenness centrality, bridge edge coefficient and link success rate to construct feature representation, and generating an existence probability matrix through a probability mapping function; 4) inputting the matrix into adaptive multi-layer filtering (AMF), reserving edges according to Top-k to form a connected skeleton, setting a hierarchical threshold according to node hierarchy and load, and maintaining approximate power law degree distribution; and 5) applying a global connectivity constraint, and outputting an optimized link structure. According to the method, link optimization can be realized without supervision labels, network connectivity and survivability are improved in an attack environment, and the method is suitable for ocean monitoring and security scenes.

The invention discloses a fracture network three-dimensional reconstruction and connectivity evaluation method and system based on Monte Carlo simulation, and belongs to the technical field of geotechnical engineering geological exploration and digital rock mass modeling. The method comprises the following steps: firstly, acquiring a two-dimensional fracture image of a drill hole, extracting geometric parameters, and acquiring three-dimensional plane initial parameters through a dimension conversion model; introducing Monte Carlo simulation, extending the deterministic plane model into a probability distribution model, and generating a large number of plane instances; calculating statistical characteristics of distances and included angles between fracture planes, and constructing a connectivity probability mapping function to evaluate a comprehensive connectivity probability; and finally, generating a global uncertainty cloud picture based on analysis results of all fracture pairs to identify a high-uncertainty region, and proposing an optimization and supplementation drilling scheme according to the high-uncertainty region. According to the method, the fracture network is transformed from deterministic reconstruction to probabilistic evaluation, connectivity can be quantified, uncertainty can be visualized, and a scientific basis is provided for engineering exploration decision making.

The invention provides a power distribution network early fault section identification method and system based on multi-dimensional feature probability mapping, and belongs to the technical field of power system power distribution network fault detection. The method comprises the following steps: collecting zero-sequence voltage of a power distribution network bus and zero-sequence current signals of head ends of all feeder lines, and obtaining derivative signals of the zero-sequence voltage and the zero-sequence current signals; calculating the third harmonic energy of the zero-sequence current of each feeder line, and when the third harmonic energy exceeds a set threshold value, judging that the feeder line has an early fault; processing the zero-sequence current signal, and extracting an IMF1 component; calculating a correlation characteristic quantity and a dot product characteristic quantity for each section formed by the current difference values of the adjacent measuring devices based on the IMF1 component; and respectively carrying out standardized transformation on the correlation characteristic quantity and the dot product characteristic quantity, substituting the correlation characteristic quantity and the dot product characteristic quantity into a Laplacian cumulative distribution function, calculating a probability value of a fault in each section, and judging a fault section according to a set probability threshold value. The method can effectively and reliably identify the early fault section of the power distribution network.

The invention relates to a long straight control cable space radiation field effect calculation and evaluation method, system and device and a medium. The method comprises the following steps: constructing a partial differential equation constraint model based on a cable geometric structure and electromagnetic transient characteristics; fusing an actually measured magnetic field and an additional physical rule through a physical information neural network, and generating a complete electromagnetic data set to compensate for electric field data missing; a multi-dimensional interference factor is calculated by combining time-frequency distribution characteristics, equipment sensitive parameters and cable equivalent inductance, and the radiation field effect complexity is objectively quantified; establishing a probability mapping model of interference indexes and effect states by adopting multi-dimensional Gaussian process regression to realize risk probability distribution prediction; and finally, generating a visual probability curved surface of an electric field-magnetic field plane, and identifying a high-risk area to guide electromagnetic protection optimization. According to the method, the evaluation precision is improved in a data limited scene, and a technical closed loop from multi-dimensional feature fusion to probabilistic risk early warning is realized.

The invention discloses an interpretable lightweight automatic driving lane changing decision method and system based on fuzzy structure adaptive configuration, and relates to the technical field of automatic driving lane changing decision, and the method comprises the steps: obtaining a six-dimensional state of a unified aperture, building an adaptive fuzzy structure for each dimension, automatically determining the precondition division according to the data distribution, and carrying out the self-adaptive configuration of a fuzzy structure. Underfitting or redundancy caused by manual setting is avoided; rules are activated and normalized, and a first-order TS linear consequent is matched to form traceable segmented approximation for a continuous state space, so that a lane changing probability source is clear, and an explanation path is clear; end-to-end learning is carried out through probability mapping and a consistent training target, so that the judgment accuracy and the output stability are improved; after training is completed, based on joint evaluation of rule activation and category deviation, only effective rules are reserved for online reasoning, the calculation scale and memory occupation are remarkably compressed, and therefore a decision link is controlled within vehicle-mounted time delay and resource constraint.

The invention relates to the technical field of data processing, and relates to an intelligent pre-declaration risk management and control method for cross-border e-commerce customs clearance. Through the multi-dimensional data dynamic verification and automatic declaration mechanism, real-time compliance verification of customs clearance pre-declaration data is realized, the rejection rate caused by data errors is reduced, and the customs clearance period is shortened; by constructing a multi-dimensional risk scoring model of commodity compliance, transportation abnormality, document completeness and flow tax compliance and combining a risk score-rejection probability mapping table, the rejection probability of a commodity to which a target cross-border e-commerce belongs is determined, the customs clearance risk prediction accuracy is improved, and high-risk declaration can be avoided in advance; by automatically triggering data correction and resubmission for high-risk declaration and quickly releasing low-risk declaration, intelligent allocation of customs clearance resources is realized, and reduction of enterprise declaration labor cost is facilitated.

The application provides a new view synthesis method based on Gaussian probability distribution and feature regularization, relates to the technical field of new view synthesis, and comprises the following steps: extracting a feature tensor from a preprocessed input image, performing Gaussian probability calculation based on the mean and standard deviation of the feature tensor; introducing an elastic net regularization loss to constrain the feature tensor, performing mean calculation on the Gaussian probability of the feature tensor, substituting the calculation result into a loss function, minimizing the loss function, obtaining an optimized Gaussian point set, performing multi-scale densification and pruning, and generating a new view. The application solves the technical problem that, due to overfitting in a sparse scene, the number of Gaussian points is too small and the transparency is low, which further affects the accuracy of view synthesis, and improves the modeling effect and the accuracy of view synthesis in a sparse view angle by introducing Gaussian probability mapping and feature regularization, thereby increasing efficiency and improving precision.

The application discloses a mountainous city park CES space probability mapping method considering population differences, and steps include: constructing a space feature classification system; dividing population categories according to population difference characteristics and obtaining space perception data; generating CES value space vector data and frequency data through data cleaning; generating environment layer grid data set and park boundary; performing space density interpolation to output CES value density distribution map; training and verifying using a space probability prediction model, performing probability prediction taking population categories as grouping variables, converting and standardizing mapping probability data and maximum value index to generate a CES space distribution map considering population differences; and statistically analyzing to generate a frequency feature map, a correlation diagram and a flow direction diagram. The technical scheme realizes precise mapping of CES space distribution of population differences, reveals the correlation mechanism of landscape elements and CES value, and provides scientific support for mountainous park renewal design.

This application relates to a training method and apparatus for improving the information acquisition capability of an artificial intelligence model. The method includes: determining the probability of performing different semantic enhancement modes on any first training sample based at least on first training samples and processing parameters corresponding to the artificial intelligence model; performing random probability mapping on at least one first training sample according to the probabilities corresponding to each semantic enhancement mode; determining the semantic enhancement mode corresponding to each first training sample based on the random probability mapping results; performing corresponding reconstruction processing on at least a portion of each first training sample according to the semantic enhancement mode corresponding to each first training sample to obtain second training samples; and training the artificial intelligence model based on at least one second training sample. This method can improve the model's ability to output accurate answers.

The application discloses a low-altitude airspace operation supervision method and system for highway maintenance inspection, and relates to the field of traffic road maintenance inspection control.The application iteratively clusters and merges new and old crack influencing factor data, continuously optimizes mapping model parameters, and makes crack probability prediction more in line with actual road conditions.A crack probability model and an accident level model form a "prevention-emergency" double link, the former predicts crack risks, and the latter controls vehicle speed in real time, covering the whole maintenance cycle.According to crack probability mapping results, the height of unmanned aerial vehicle (UAV) inspection is automatically adjusted, high-risk road sections are preferentially investigated, and inspection efficiency is improved.The accident level model fuses vehicle dynamic speed and crack static characteristics to generate graded alarm instructions, avoiding single factor misjudgment.According to the contribution degree of influencing factors, the crack source is repaired in a targeted manner, and invalid construction is reduced.Through accurate inspection and repair priority sorting, the flight time of the UAV and the frequency of manual inspection are reduced, and maintenance costs are reduced.

The present disclosure provides a large three-dimensional tunnel surrounding rock parameter random field model construction method and system, relates to the tunnel surrounding rock modeling technical field, and includes: fitting and constructing a probability distribution model reflecting surrounding rock parameters; selecting multiple surrounding rock parameters as clustering features, quantitatively classifying the global geological structure in the tunnel surrounding rock, and decomposing the global tunnel model into continuous subdomains containing overlapping buffer zones; for the first subdomain started in any direction of the global tunnel model, the KL decomposition method is used for covariance matrix spectral decomposition to generate the first subdomain random field, and the random field value of the overlapping area between the subdomains is extracted as the boundary condition data of the recursive generation process; based on the boundary condition data, the conditional random field of the subsequent subdomain is generated layer by layer; the conditional random fields of each subdomain recursively generated are seamlessly spliced into the global parameter random field, the probability mapping method is used to convert the probability distribution, the cross-platform data interface engine is developed to encapsulate instructions, and the construction process of the parameter random field model is realized.

The application provides a scene-based home linkage control method and system of a health-care robot, the control method comprising: collecting multi-modal sensing data of a health-care scene, and outputting a latent mean vector and a variance vector of each time step by means of a variational autoencoder; inputting the mean vector into a probability mapping model to obtain an initial probability distribution of an atomic predicate, and generating a space-time compound predicate by fusing according to a space-time co-occurrence frequency and a weighted point mutual information; constructing an initial probability graph by taking the atomic predicate, the space-time compound predicate and a preset scene as nodes, correcting a conditional probability by combining a state uncertainty factor and a time sequence tightness factor to obtain a probability graph model, completing Bayesian inference by relying on a real-time predicate probability to obtain a scene posterior probability vector, generating an initial control point by a strategy network, and combining a control cost function neighborhood optimization to decode and output a home linkage control instruction and execute the same.

The invention relates to the technical field of geological disaster monitoring and early warning, and discloses a geological disaster probability assessment method based on multi-source big data fusion analysis. The method comprises the steps of three-dimensional coordinate system layout and multi-point sensor acquisition, standardization, memory kernel construction, time sequence convolution fusion, anomaly recognition, probability mapping and spatial interpolation, and high-risk area output. By unifying a spatial reference system, the spatial consistency of data of each monitoring point is ensured; the whole-process data quality inspection and standardization are realized, and the dimension unification and comparability of the multi-source heterogeneous data are realized. A finite memory weight model and time sequence weighted convolution are innovatively adopted, the historical cumulative effect is comprehensively reflected, the anomaly discrimination robustness is improved, and the defect that only short-term data is used is overcome. Probabilistic mapping and feature distance-based spatial smoothing endow the risk expression with continuity and adaptivity, and realize scientific determination of regional high-risk partitions.

The invention discloses a fault propagation analysis and root cause identification method for an early design stage of a complex system, and the method comprises the steps: carrying out failure mode and influence analysis, and identifying a potential failure mode and fault representation of the complex system; determining a fault propagation path through fault tree analysis, and quantifying the causal strength of fault propagation by using a Bayesian network; fusing the element, the fault propagation Bayesian network and the fault representation, and developing a causal enhanced multilayer Bayesian network for quantifying a probability mapping relation between a component fault mode and the fault representation; providing two importance degree models based on the multilayer Bayesian network: cascade failure Birnbaum importance degree and element Birnbaum importance degree, which are respectively used for identifying a key root cause and a key component; based on the above analysis, a fault feature probability matrix is constructed for fault propagation analysis and root cause identification. According to the method, weak links of a complex system can be effectively identified, and potential fault propagation paths and fault root causes can be identified.

The application discloses a multi-exposure image fusion method and system based on cross-layer random walk, and the method comprises the following steps: acquiring multi-exposure images, establishing a cross-layer random walk model corresponding to the exposure images, wherein the cross-layer random walk model is a double-layer topological structure; obtaining optimal probability mapping corresponding to the exposure images based on the cross-layer random walk model; and obtaining a fusion image through weighted fusion according to the exposure images and the optimal probability mapping corresponding to the exposure images, so that the technical problem of low multi-exposure image fusion quality is solved, the details of the brightest / darkest area in the scene under different exposure conditions can be revealed, color distortion in the fusion image is avoided, and a high-quality fusion image is obtained.

The invention relates to the technical field of remote sensing, in particular to a cultivated land non-grain intelligent identification method based on multi-modal satellite remote sensing data and integrated learning. Comprising the following steps: remote sensing data preprocessing: processing a multi-source satellite image by using a cloud platform to generate a time-space aligned data set; constructing a sample data set: based on a high-resolution historical image, establishing a food crop positive and negative sample library through artificial visual interpretation; and multi-dimensional feature engineering: extracting spectral features and time sequence features from the image according to the growth cycle features of the food crops, and performing feature wave band screening. Drawing a grain planting probability: training a classification model based on an ensemble learning strategy and outputting a probability result of grain crop identification; constraint calibration based on actual data: screening grain planting pixels according to local statistical data; and non-grain discrimination: determining whether the image is non-grain according to the time sequence change characteristics of the pixels, and finally outputting a non-grain mapping result. According to the scheme, the non-grain identification of the cultivated land can be realized quickly and accurately.

The application relates to the technical field of industrial data processing, and discloses a vehicle-mounted mobile pump station abnormal data detection method and system. The method comprises the following steps: acquiring real-time vibration parameters and pressure fluctuation data, and obtaining a preliminary feature vector through preprocessing; clustering the preliminary feature vector to obtain grouped data clusters; training a classifier to obtain a decision boundary, and obtaining an abnormal probability score through probability mapping; if the threshold is exceeded, analyzing the feature contribution degree and matching a mapping dictionary to obtain a preliminary abnormal label; performing cross-validation and confidence evaluation by fusing historical environmental factor data to obtain a refined abnormal source classification; combining real-time feedback to adjust grouping parameters to obtain an optimized data grouping model; and using the optimized model to perform abnormal quantitative analysis on real-time data flow, and if the attribution probability exceeds an early warning limit, a fault early warning signal is generated and a blocking logic is triggered. The method can realize high-precision abnormal detection and real-time early warning of a vehicle-mounted mobile pump station under a complex environment.

The invention discloses a coupling analysis method for cutterhead mud cake formation and tunneling parameters, belongs to the technical field of cutterhead intelligent control, and aims to solve the problems that early warning of shield tunneling machine cutterhead mud cake formation is lagged, and tunneling parameter adjustment is blindness. A dynamic digital twin cutterhead model is constructed based on the cutterhead three-dimensional model; constructing a three-dimensional parameter space to generate a representative working condition point, obtaining a simulation result set, extracting a mud cake forming degree index, and constructing a standard simulation result library containing multiple parameters and the mud cake forming degree index; on the basis of the actual working environment parameters and the tool abrasion interval, the representative working condition point set is retrieved and matched, and preliminary target tunneling parameters are generated in combination with the mud cake forming degree index and the similarity; driving the cutterhead to work, quantizing a mud cake forming degree index in real time, constructing three-level correlation characteristics, predicting the mud cake forming probability according to the three-level correlation characteristics and a probability mapping relation library, and dynamically adjusting the initial target tunneling parameters when the mud cake forming probability exceeds a threshold value. Advanced early warning of the mud cake forming risk of the cutterhead and accurate adaptation of tunneling parameters are achieved.

The invention relates to the technical field of industrial vision, and discloses a wire harness surface defect detection method and system based on visual inspection, and the method comprises the steps: obtaining a multi-view image set of a bending part of a wire harness, and obtaining a space corresponding relation between images; generating a wire harness three-dimensional point cloud model, and extracting surface normal vector and curvature features to obtain a geometric feature map; performing convolutional neural network processing on the geometric feature map, and extracting multi-scale feature vectors covering crack linear extension and stain diffusion dimensions and fusing depth information; associating the point cloud coordinates to generate initial defect probability mapping, and marking a potential defect area; and point cloud extraction features are traced back, defect type classification labels and defect distribution fused with depth information are obtained through integration, if the crack depth is abnormal, feature vectors are adjusted to judge the extension trend to obtain a final result, and then the bending degree is determined according to the stain diffusion dimension to obtain a complete label. The method realizes the accurate detection of the surface defects of the wire harness, and reduces the misjudgment rate.

The application relates to the technical field of game animation, and particularly provides a game animation self-adaption method based on emotion perception, which comprises the following steps: collecting multi-source signals of a player and performing pretreatment, simultaneously obtaining a current game context vector, inputting the pretreated multi-source signals and the game context vector into an emotion decoupling model, and outputting a four-dimensional emotion vector E(t) with a confidence score C(t); calculating an animation behavior identifier A id and an animation adjustment parameter set A params according to the emotion vector E(t); a game engine completes animation rendering according to the animation behavior identifier A id and the animation adjustment parameter set A params , and outputs the result to a screen; and the game engine dynamically calculates a fluency index by analyzing new player data flow generated after executing animation adjustment, and reversely fine-tunes parameters of the emotion decoupling model and a probability mapping diagram according to the fluency index. Through the above scheme, the problems of high delay, poor robustness and lack of coupling with a game context in animation self-adaption are solved.

The invention relates to the technical field of game animations, in particular to a game animation self-adaption method based on emotion perception, which comprises the following steps: collecting and preprocessing a multi-source signal of a player, simultaneously obtaining a current game context vector, inputting the processed multi-source signal and the game context vector into an emotion decoupling model, outputting a four-dimensional emotion vector E (t) with the confidence score C (t); calculating an animation behavior identifier Aid and an animation adjustment parameter set Aparams according to the emotion vector E (t); the game engine completes animation rendering according to the animation behavior identifier Aid and the animation adjustment parameter set Aparams, and outputs a result to a screen; and analyzing a newly generated player data stream after executing animation adjustment, dynamically calculating a fluency index, and reversely and finely adjusting parameters of the emotion decoupling model and the probability mapping graph according to the fluency index. By means of the scheme, the problems that animation self-adaption is high in delay, poor in robustness and lack of game context coupling are solved.

This invention discloses a method for evaluating the operational status of a mining drilling rig system based on an echo-state Bayesian neural network. This method addresses the challenges of dynamic feature extraction, model overfitting, and the lack of uncertainty quantification in deterministic assessments of drilling rig status under conditions of strong vibration and high noise in underground environments. It employs an echo-state network with a leakage integral mechanism to suppress noise and extract slowly varying dynamic features from multi-source sensor time-series data, constructing a high-dimensional feature vector through dual-view feature aggregation. A Bayesian neural network with Concrete Dropout is introduced to achieve state probability mapping based on variational inference, and Monte Carlo sampling and prediction entropy are combined to quantify cognitive uncertainty and provide early warning of high-entropy anomalies. This invention effectively filters out high-frequency noise, suppresses overfitting in small samples, and enables proactive early warning of abnormal operating conditions. It achieves high classification accuracy on hard coal seam drilling datasets, and its assessment accuracy and decision reliability are significantly superior to traditional deep learning models.

The present application relates to the field of human-computer interaction and intelligent control technology, in particular to a flexible somatosensory interaction system and method based on intention probability mapping. By setting up a flexible somatosensory acquisition module, an intention modeling module, a flexible-rigid mapping field construction module, a dynamic intention screening module and an execution control module, the multi-modal somatosensory information of the operator is collected, the probability distribution model of the action intention is established, and the flexible-rigid mapping field is constructed based on the information retention and minimum semantic loss principle, the continuous action space of the human body is converted into a limited control instruction set executable by the mechanical arm. The system dynamically screens the optimal control instruction under multiple candidate intentions using the confidence driving mechanism, and realizes the precise response of the mechanical arm to the operator's action through closed-loop feedback. The problem of mismatch between flexible action and rigid control freedom and intention recognition ambiguity in the prior art is effectively solved.

The invention relates to a moving target prediction method and system based on Gaussian mixture and Bayesian reasoning, and belongs to the technical field of robots. The method comprises three parts of Gaussian mixture prior model, Bayesian online reasoning and prediction step length dynamic adjustment. And estimating a Gaussian component parameter of each target point by using a Gaussian mixture model, and establishing a target-observation probability mapping relation. And calculating the posterior probability of the current data belonging to each target point based on a naive Bayes method, and outputting the target point corresponding to the maximum posterior probability. And a prediction step length dynamic adjustment method based on the motion speed is introduced, and the prediction step length is dynamically adjusted according to the real-time motion speed. And carrying out a motion target prediction experiment in man-machine cooperation. The problem that prediction accuracy is reduced due to moving target change and redundant historical data in contact type man-machine cooperation is solved, the provided prediction method shows good performance in the aspects of target change adaptability, accuracy and real-time performance, and target prediction can be effectively achieved.

The invention discloses a processing method and device for time-sensitive events in a complex working environment and a storage medium. The method comprises the following steps: determining a corresponding time budget value; carrying out heterogeneous decoupling on the multi-source resource data to obtain a resource capability vector; calculating a time margin; calculating system resistance; determining a resource release probability and a resource newly-added occupation probability, and establishing a probability mapping function for the time disturbance quantity; calculating a prediction correction item through a probability mapping function, wherein the prediction correction item is determined according to the resource release probability, the resource newly-added occupation probability and the corresponding income amount and loss amount; calculating a reachability value of the to-be-processed event according to the time margin, the system resistance and the prediction correction term through a preset reachability function, and comparing the reachability value with a preset threshold set; and determining a target processing strategy from the multiple event processing strategies according to the comparison result.

The application discloses a cross-domain intelligent wireless sensing method based on federated learning and block chain, relates to the technical field of wireless sensing, solves the problems of efficient cooperation and privacy protection of cross-domain heterogeneous data, insufficient generalization ability of multi-target sensing model under a dynamic environment, and contradiction between real-time sensing and calculation efficiency under resource constraints, and the application constructs a FL-BLC cooperative security framework based on lightweight federated learning and block chain to perform distributed training on a local client model; on the basis of the framework, a DB-SE-Yolov8 sensing algorithm model is constructed to extract and reserve global and local fine-grained information by adopting a parallel double-branch network structure, to dynamically weight and fuse multi-scale feature information through a gating mechanism, to perform regression statistics on the extracted feature information by using a full-connection linear classifier, to perform probability mapping by using a Softmax activation function, and to output a sensing result; while improving the precision of cross-domain wireless sensing, the privacy protection capability of cross-domain heterogeneous data is effectively improved.

The invention discloses a multi-dimensional constellation probability shaping method based on dimensional energy statistics and entropy constraint, which belongs to the technical field of communication and comprises the following steps: acquiring coordinates of constellation points to perform statistical analysis on energy distribution characteristics of each dimension to obtain energy statistics of each dimension so as to construct a dimensional weight; on the basis of the dimension weights and coordinate components of the constellation points on all dimensions of the multi-dimensional modulation constellation, constructing dimension weighted energy measurement of the constellation points, providing a probability mapping function for constructing the constellation points, mapping the dimension weighted energy measurement into probability weights of the constellation points, and normalizing the probability weights to obtain initial constellation point probability distribution; determining a search interval of a probabilistic shaping strength parameter under the constraint condition of the target information entropy; according to the initial constellation point probability distribution, information entropy is calculated, a search interval is updated, shaping intensity parameters meeting entropy constraints are searched, constellation point probability distribution is calculated, and the problem that the probability shaping effect is limited due to the fact that energy distribution of different dimensions in an existing multi-dimensional modulation constellation is unbalanced is solved.

The invention provides an urban three-dimensional water network construction method for water safety guarantee, and relates to the technical field of urban water networks. According to the method, a city multi-source feature matrix fusing digital elevation, land utilization, meteorological prediction, hydrological actual measurement and statistical data is constructed, and spatial clustering analysis is carried out to obtain a refined city partition set; further constructing and solving a generalized linear mixed effect model, and accurately determining a threshold set of water network object attributes of each city partition meeting a preset water safety target probability; carrying out probability mapping by taking the attribute threshold set as a constraint to generate a plurality of sets of candidate water network topological structures; and comprehensively evaluating the candidate schemes through a multi-scene efficiency scoring method, and determining an optimal water network construction scheme with the highest comprehensive efficiency score. The accuracy, reliability and comprehensive adaptability of urban water safety guarantee are improved.