164 results about "Spatial Autocorrelations" patented technology

The invention relates to the field of security surveying and mapping, in particular to a network security space surveying and mapping method, system and device based on multi-source data fusion, and the method comprises the steps: obtaining network security data in real time, and constructing a dynamic network topological graph; calculating a time-varying vulnerability score based on the topological graph and a historical attack log, and predicting an attack path and a propagation probability through a Bayesian network; performing cross-domain fusion on equipment, service and user behavior characteristics by adopting a federated learning framework to generate a dynamic asset portrait; generating a risk thermodynamic diagram in combination with spatial autocorrelation analysis and a multi-index fusion algorithm; a defense strategy effect is simulated based on an attack graph reconstruction engine, a Pareto optimal strategy combination is generated through an NSGA-II algorithm, and closed-loop verification and dynamic parameter correction are realized by utilizing honeypot deployment and flow traction. Therefore, the problems of topology update lag, single risk assessment dimension, cross-domain threat association fracture, defense strategy static stiffness, non-closed loop of a verification system and the like in the traditional technology are solved.

The invention discloses a three-dimensional point cloud data filtering method and system based on adaptive clustering segmentation and gradient compensation, and the method comprises the steps: carrying out the data preprocessing of three-dimensional point cloud data through employing a statistical filtering method, obtaining the preprocessed point cloud data, extracting the point cloud features in the preprocessed point cloud data, and obtaining the point cloud feature data; according to the invention, a function of establishing multi-dimensional features including point cloud density, spatial autocorrelation and local curvature features by using an adaptive clustering segmentation algorithm based on density spatial distribution and carrying out dynamic clustering segmentation on a ground feature-ground cluster is realized; and residual mixed point clouds can be further separated through a coarse-fine granularity grid grading processing strategy, so that ground point clouds are separated, and the technical limitations of high parameter dependence, insufficient terrain adaptability and poor real-time performance in the prior art are broken through; and the filtering precision and robustness in a complex scene are remarkably improved through multi-dimensional collaborative optimization, and the method is suitable for being widely popularized and used.

The invention provides a Beidou ionosphere random error autocorrelation model establishment and correction method and device, and the method comprises the steps: building a deep learning neural network model for predicting a Beidou ionosphere random error through combining a convolutional neural network, a long-short-term memory neural network and an attention mechanism, and carrying out the correction of the Beidou ionosphere random error based on the predicted Beidou ionosphere random error. And respectively fitting the time autocorrelation function of the Beidou ionosphere random error and the space autocorrelation function of the Beidou ionosphere random error, and establishing a Beidou ionosphere random error autocorrelation model. The Beidou ionosphere random error prediction method can predict the Beidou ionosphere random error under the condition of not knowing an accurate physical model, and provides technical support for high-precision prediction of the Beidou ionosphere model and analysis of the Beidou ionosphere random error.

The invention provides an urban groundwater inrush risk assessment method based on a three-dimensional grid model and an XGBoost algorithm, and belongs to the technical field of geological disaster early warning. The method comprises the following steps: acquiring geological, environmental and historical disaster data in real time through a multi-source heterogeneous data interface; constructing a heterogeneous grid unit based on three-dimensional geological modeling, and dynamically binding parameters such as underground water level, fault activity and lithologic permeability coefficient; an XGBoost algorithm is combined with second-order Taylor expansion to optimize gradient calculation, and a nonlinear risk assessment model is constructed through Bayesian hyper-parameter tuning and SHAP value feature interpretive analysis; identifying contiguous dangerous areas based on spatial autocorrelation analysis and an adaptive threshold segmentation algorithm; and a risk thermodynamic diagram, geological section superposition and real-time early warning are realized through a 3D GIS platform. According to the method, the problems that a traditional method depends on a linear model, data is single and local risk description is insufficient are solved, and the accuracy, efficiency and engineering practicability of risk assessment are remarkably improved.

The invention discloses a data processing method and system for micro-motion detection array station distribution, and relates to the technical field of micro-motion detection, and the method comprises the steps: according to the actually measured starting point coordinates and the azimuth angle of a measuring line, setting the measuring point distance, and the radius and number of nested triangle circumcircles, generating field station coordinates in batches; collecting and preprocessing station waveform data; calculating a spatial autocorrelation coefficient of any center station and a non-center station; dBSCAN clustering is carried out based on the station distance; performing spatial averaging on the spatial autocorrelation coefficient according to a clustering result; fitting the averaged spatial autocorrelation coefficient and a zero-order Bessel function to obtain a frequency dispersion spectrum, picking up a surface wave phase velocity in the frequency dispersion spectrum, and calculating an apparent shear wave velocity; inverting the shear wave velocity of the waveform data by adopting a self-adaptive gradient inversion strategy; and generating a velocity comprehensive profile of a depth domain and a frequency domain according to the surface wave phase velocity, the apparent shear wave velocity and the inverted shear wave velocity. The invention provides a new method for station distribution to data processing for micro-motion detection.

The invention relates to the technical field of education, in particular to a visual chart system for analyzing test question quality and test object ability, which comprises a dynamic data preprocessing module, a sliding window adaptive adjustment module, a multi-axis fluctuation intensity analysis module, a group trend disassembly module and an abnormal test question judgment module. According to the method, a difference absolute value algorithm is adopted to construct an adjacent test question physical correlation model, a test question serial number is mapped as a time axis variable to generate a thermodynamic diagram, the time-space continuity of score fluctuation is captured, a sliding window algorithm is adopted to establish dynamic correlation between window density and fluctuation amplitude, and normalized parameter difference degree is matched with sampling frequency and data features. A three-axis direction change rate is defined to construct a three-dimensional radar coordinate system, Kriging interpolation is combined to generate a distribution cloud picture, group trend departure and standard deviation distribution difference are analyzed, spatial autocorrelation modeling is used to enhance the anomaly recognition capability, and time sequence modeling, dynamic feedback and spatial vector analysis are used to cooperatively improve the detection and evaluation precision and reduce the misjudgment risk.

The invention relates to the technical field of data processing, and discloses an environment detection analysis method and system based on a simulation micro-motion signal excitation generator. The method comprises the following steps: generating a vertical vibration signal through an integrated structure of a constant-resistance vibration exciter and a special coupling base; performing multi-band signal excitation and station arrangement according to the frequency parameter combination; filtering and normalizing the original signal, and calculating a spatial autocorrelation coefficient; a frequency dispersion curve is extracted through an SPAC method; inverting an underground S-wave velocity structure by using a divergence genetic algorithm; and analyzing the speed abnormal region by adopting an edge detection algorithm, and determining the position and state of the box culvert body. According to the invention, high-precision non-destructive detection of the buried power pipeline box culvert body is realized in an urban complex underground environment.

The invention discloses a coal seam frequency resonance seismic exploration system and method based on deep learning. The method comprises the following steps: S1, arranging passive source seismic equipment to collect background waveform data; s2, performing spatial autocorrelation spectrum analysis, and extracting a frequency dispersion curve; s3, speed inversion is carried out, and a speed profile map is output; s4, constructing a DeepLabV < 3 + > model to identify geological anomalies; s5, performing image preprocessing to generate standardized input; s6, performing model reasoning to obtain a segmentation result graph; s7, generating a structure map and a risk map in combination with the coordinates; and S8, exporting an interpretation result in a structured format. According to the method, a seismic data processing flow combining a spatial autocorrelation method and a DeepLabV3 + model is adopted, and automatic extraction of coal seam frequency resonance information and intelligent recognition and visual interpretation of a geological structure are achieved.

A method of determining ecological risk with spatial aggregation and regional imbalance level applicable for use in a river basin which involves geographic information, ecology, environmental engineering and computer technology includes the steps of: dividing a river basin into a plurality of fishnets; obtaining a basin landscape disturbance index and a basin landscape vulnerability index; obtaining a basin ecological risk index based on the basin landscape disturbance index and the basin landscape vulnerability index; and determining and evaluating the basin ecological risk in spatial aggregation and the regional imbalance with reference to a spatial autocorrelation value and a basin ecological risk imbalance value obtained by using the basin ecological risk index. The ecological risk in spatial aggregation and the regional imbalance is output and evaluate so that the ecological risk in river basin can be assessed comprehensively with high evaluation accuracy.

The invention provides an airfield pavement detection method based on an earthquake background noise imaging technology, and relates to the technical field of airfield pavement detection. Comprising the following steps: acquiring seismic background noise data of an airport pavement to be measured; preprocessing the seismic background noise data to obtain preprocessed noise data; carrying out cross-correlation calculation on the preprocessed noise data and carrying out empirical Green function extraction; according to the empirical Green function, a surface wave group velocity frequency dispersion curve is extracted by adopting spatial autocorrelation and multiple filtering methods; inverting shear wave velocity distribution of each layer of the pavement based on the surface wave group velocity dispersion curve, and constructing a three-dimensional velocity model; determining a shear wave velocity distribution diagram according to the three-dimensional velocity model; and determining a detection result of the airfield pavement to be detected according to the transverse wave velocity distribution diagram. According to the invention, the problems of low detection precision and poor real-time performance of a common detection technology in the prior art are solved.

The invention discloses a deep geothermal target area intelligent positioning method based on a machine learning algorithm, and relates to the technical field of geothermal resource exploration. Comprising the following steps: S1, multi-source heterogeneous data fusion acquisition and preprocessing; s2, geological feature entropy quantification and spatial autocorrelation analysis are carried out; s3, deep feature extraction and multi-modal feature fusion are carried out; s4, performing multi-model adaptive integration and dynamic weight optimization; s5, reinforcement learning dynamic adjustment participation abnormal threshold determination; and S6, performing intelligent positioning and risk assessment on the three-dimensional geothermal target area. According to the positioning technology, geological structure, geophysical field, geochemistry and remote sensing data are integrated through a machine learning algorithm, high-dimensional feature vectors are constructed, the limitation that the prior art depends on physical detection singly is solved, reinforcement learning is introduced to dynamically optimize model parameters, real-time geothermal well data updating is combined, and the positioning accuracy is improved. The model can adapt to geological condition changes, and the prediction precision is greatly improved compared with a traditional method.

The invention relates to the technical field of medical elastography and material multi-scale mechanical measurement, in particular to an elastography method and system based on an instantaneous scattering displacement field, and the method comprises the steps: driving a passive / active method scattering displacement field excitation device through a frequency domain controllable sine wave signal, and generating a scattering displacement field on a target material, an instantaneous displacement field is obtained through high spatial resolution measurement, then sliding window local interception, two-dimensional autocorrelation calculation and nonlinear fitting based on the Rayleigh / shear wave spatial autocorrelation theory are performed on the single-frame instantaneous displacement field, shear wave velocity distribution is obtained, and then the viscoelasticity modulus of a target area is inverted to achieve comprehensive characterization of the viscoelasticity of the material. According to the scheme, the single-frame scattering displacement field is excited and collected, and the elasticity inversion is performed based on the instantaneous displacement field, so that the dependence of traditional elasticity imaging on high-frame-rate equipment is broken through, the material viscoelasticity evaluation can be efficiently completed at low cost, and the application prospect in the fields of clinical diagnosis and mechanical measurement is wide.

The invention discloses an online monitoring method and system for hot-sell commodities, and relates to the technical field of e-commerce, and the method comprises the steps: collecting consumer behavior data, and carrying out the preprocessing of the data, and forming a consumer behavior data set; based on the consumer behavior data set, constructing a consumer behavior popularity model, and obtaining a popularity score of the commodity; performing geographic position analysis on the sales data of the potential hot commodities through a geographic information technology, generating a regional commodity sales thermodynamic diagram, and identifying and marking regional hot commodities; linking the identified regional hot-sell commodities with a supply chain management system, obtaining commodity inventory data, and calculating an inventory consumption rate and inventory depletion time; according to a calculation result, triggering inventory early warning and generating replenishment suggestions, and automatically adjusting inventory management; a regional commodity sales thermodynamic diagram is generated in combination with a geographic information technology, and spatial autocorrelation analysis is performed by using a Moran index, so that the capability of linkage of an enterprise internal supply chain management system is effectively improved.

The invention discloses a fish potential spawning site identification and positioning method based on habitat similarity and spatial autocorrelation, and belongs to the technical field of river ecological protection and habitat evaluation. The method comprises the following steps: step 1, obtaining state values of hydrodynamic force, water quality and substrate indexes of a typical target fish spawning site and target fish distribution information; 2, determining egg laying habitat requirements of target fishes; 3, performing comprehensive habitat suitability evaluation on the typical target fish spawning site; 4, constructing a target fish potential spawning site identification model combining habitat similarity and spatial autocorrelation; and 5, evaluating and verifying the feasibility and accuracy of the method. According to the method, habitat similarity and spatial autocorrelation are combined, a potential spawning site of target fishes in a drainage basin is identified and positioned, and feasibility and accuracy of the method are verified by combining habitat suitability evaluation and spatial position coincidence.

The invention relates to the technical field of regional ecological risk assessment and space management and control, in particular to a regional ecological risk assessment and partitioning method based on a multi-system spatio-temporal dynamic relationship, which comprises the following steps: collecting and preprocessing multi-source spatio-temporal data; a natural ecological risk index and a human social risk index are fused, and a comprehensive ecological risk assessment model based on a social-ecological system is constructed; divariate spatial autocorrelation analysis and cold and hot spot analysis are adopted, and a region is divided into three-level subareas including a management and control area, a monitoring area and a prevention area and six types of subareas; and analyzing the partition dominant driving factor by using the optimal parameter geographic detector, and generating a differentiated control strategy. The method realizes quantitative coupling of natural ecology and human social system risk spatio-temporal dynamic relationships, solves the problems of strong subjectivity, insufficient system comprehensiveness and lack of dynamic association mechanisms of a traditional method through multi-dimensional index fusion and spatial analysis technologies, and provides systematic technical support for accurate management of regional ecological risks.

The invention discloses a water resource optimization scheduling method based on water and soil resource space coupling, and the method comprises the steps: dividing an agricultural land of a target region into grid units, collecting the basic data of each grid unit, calculating a water and soil resource space coupling evaluation index value based on the grid units and the basic data, and carrying out the water and soil resource space coupling evaluation index value. The water and soil resource coupling degree of each grid unit is calculated, and a hot spot area and a cold spot area of water and soil resource space coupling are identified by adopting a spatial autocorrelation analysis method; on the basis of hot spot region and cold spot region division distribution, determining a water resource optimization scheduling region and a regulation and control method by combining a water land resource space coupling evaluation index value; fine management and efficient utilization of water and soil resources are achieved, and the method has important significance in improving the utilization efficiency of agricultural water resources and promoting agricultural sustainable development.

The invention provides a sensor-based seasonal frozen soil monitoring method and system, and the method comprises the steps: dividing a monitored region into grids, deploying a sensor group in the monitored region, obtaining the geographic parameters of the monitored region, and obtaining the terrain impact factors of seasonal frozen soil according to the geographic parameters; acquiring heat flux between two points of the sensor monitoring point i and the sensor monitoring point j in the monitored area; calculating a final weight matrix, and calculating spatial autocorrelation among the sensor monitoring points based on the terrain influence factors, the heat flux and the final weight matrix; unnecessary monitoring points are screened out according to spatial autocorrelation, the monitoring points are sequenced according to spatial autocorrelation values, the monitoring points with larger absolute values are sequenced in the front, and the monitoring points with higher sequencing are preferentially monitored. According to the method and the corresponding system, redundant monitoring points in seasonal frozen soil monitoring are screened, so that the deployment number and the maintenance cost of the sensors can be reduced, and the operation cost of the whole monitoring system is reduced.

The invention discloses an earth and rockfill dam material space autocorrelation detection and sampling point optimization method and system based on sampling simulation, and the method comprises the steps: collecting the whole-section surface basic data of the compaction quality of a test bin surface: selecting a construction bin surface which is similar to the working condition needing the compaction quality evaluation as the test bin surface, discrete random sampling points are arranged in a uniform point distribution mode, the compaction quality of the test bin surface is detected, and related basic data are collected; detecting the spatial autocorrelation of the earth and rockfill dam material by using a Monte Carlo method; detecting the significance of the spatial autocorrelation of the earth and rockfill dam material by comparing the difference between the accuracy rates of no statistical misjudgment when the spatial autocorrelation of the earth and rockfill dam material is considered and not considered; and reasonably optimizing discrete random sampling point arrangement by utilizing an aggregation state fuzzy comprehensive evaluation model considering multi-source information fusion. The method provided by the invention is more visual, and can truly reflect the obvious influence of the earth and rockfill dam material space autocorrelation on the characterization precision of the compaction quality.

The invention specifically discloses a long-time-sequence high-frequency ecological environment quality space-time differentiation and driving analysis method, and relates to the technical field of remote sensing and ecological environment. The method comprises the following steps: determining an evaluation area, and constructing a macroscopic ecological safety risk evaluation framework; ecological indexes of greenness, humidity, temperature and dryness are calculated, and a time sequence data set is constructed; reconstructing a time sequence data set and constructing a remote sensing ecological index model based on the reconstructed time sequence data set; verifying the precision of the model, and evaluating the reconstruction precision by taking the screened high-quality pixels as true values; analyzing the spatial and temporal change trend and significance output by the model by using slope estimation and trend test, outputting future change continuity by using a Hurst index analysis model, and measuring spatial autocorrelation output by the model by using a Moran index; remote sensing ecological index evolution factors are researched by means of an optimal parameter geographic detector. According to the invention, the precision and timeliness of ecological assessment are improved, and decision support is provided for ecological management.

The invention relates to the technical field of machine learning, and particularly discloses a spatial local XGBoost machine learning model based on multi-scale geographic weighting, and the model comprises a feature decoupling module which is used for constructing a two-channel input structure of geographic features and non-geographic features, and achieves the feature decoupling and fusion through a multi-scale spatial weight matrix; the bandwidth allocation module dynamically determines the bandwidth scale in the XGBoost tree splitting process, and establishes a dynamic weight bound by tree levels; the constraint gain module generates split points with scale marks; a contribution decoupling module extracts split features and associates geographic and non-geographic features to realize salinization prediction and contribution index extraction; and the verification optimization module optimizes model parameters through a multi-scale thermodynamic diagram and spatial autocorrelation analysis. The model can capture geospatial effects of different scales, improves the spatial local prediction precision, and is applied to soil salinization analysis scenes with spatial heterogeneity.

The invention discloses a loss prediction method and system for a light building material. The method comprises the steps of data acquisition, data screening, loss prediction modeling and loss prediction of the light building material. The invention belongs to the field of data processing, and particularly relates to a loss prediction method and system for a light building material.According to the scheme, an environment difference measurement function is introduced for sub-region merging, and local loss characteristics are captured; a local space autocorrelation index is introduced, and abnormal loss data is identified based on local abnormal value scoring optimization, so that the quality of the loss data is effectively improved; an environment penalty correction factor is introduced to weight an abnormal environment region sample, so that a prediction error in an environment dramatic change region is amplified, and the sensitivity to serious loss is improved; a compensation correction term is designed, grading punishment is carried out on different loss grades through the loss gradient, and therefore the model has better distinguishing power when the light loss, the medium loss and the serious loss are predicted; and the final material loss prediction effect is improved.

The invention discloses an ecological vulnerability evaluation method based on road domain landscape pattern optimization, and the method comprises the steps: obtaining expressway road domain environment data as training data, and obtaining ecological environment data; calculating an ecological environment quality index of the ecological environment data, and obtaining landscape structure features in the target area; integrating the landscape pattern index into an ecological sensitive factor, and constructing an ecological vulnerability evaluation model by adopting an SRP structure; performing spatial autocorrelation analysis on space-time evolution of the ecological vulnerability of the road domain according to the ecological vulnerability evaluation index, establishing an ecological vulnerability multi-scene simulation model, performing overlay analysis on an ecological vulnerability evaluation result and a land utilization type, obtaining a specific restoration strategy, tracking space-time evolution characteristics of the ecological vulnerability, and performing road region ecological vulnerability evaluation. Ecological vulnerability influence factors are adjusted based on different scenes, future possible ecological vulnerability risks are simulated, specific restoration strategies are made according to different partition standards, and effective configuration and use efficiency of resources are ensured.

The invention discloses a high-standard farmland construction time sequence optimization method, and relates to the technical field of agricultural information, and the method comprises the steps: superposing a permanent basic farmland distribution diagram layer and a high-standard farmland distribution diagram layer, and recognizing a permanent basic farmland region which is not constructed into a high standard; obtaining multi-source basic data in the region, and representing the grain yield by adopting an LAI average value after S-G filtering; constructing a high-standard farmland construction ecological toughness evaluation index system; the combined weight of each evaluation index is calculated according to the combination of an entropy weight method and a CRITIC method, and a set pair analysis model is established to calculate the farmland ecological toughness; a spatial interaction relationship between grain yield and farmland ecological toughness is obtained based on a bivariate local spatial autocorrelation analysis method, a preferential plot of high-standard farmland construction is identified in combination with a self-organizing mapping neural network and a K-means method, and a construction time sequence arrangement and spatial layout optimization scheme is formed. According to the invention, the problem of high-standard farmland construction priority can be solved.

The invention discloses a communication and inductance integrated channel modeling method with space consistency, which comprises the following steps: 1) setting an initialization scene, and configuring related parameters of an antenna; 2) generating powers, azimuth angles and pitch angles of the proprietary clusters and the common clusters and distances between the first and the last passing clusters and a transmitting and receiving end, and then generating azimuth angles, pitch angles and propagation distances of sub-paths in the clusters; 3) calculating the probability of existence of the proprietary cluster and the common cluster on the antenna array; 4) calculating and generating channel impulse response (CIR) of the sensing channel and the communication channel; and 5) simulating and calculating the existence condition of the clusters on the array surface, a time autocorrelation function (TACF) and a space autocorrelation function (SCCF), and judging whether the channel modeling method has accuracy and space consistency or not. The channel modeling method disclosed by the invention is based on a common cluster and a proprietary cluster of a sensing channel and a communication channel, the relationship between the two channels is considered, the channel space consistency is comprehensively analyzed, and the channel modeling method has important significance for guiding the design of an ISAC system.

The invention discloses a brain connection coupling analysis method based on grey matter morphological similarity-white matter fiber bundles, relates to the technical field of neuropsychiatric imaging, and has the technical key points that the method can be used for analyzing grey matter-white matter brain connection group coupling modes of the whole brain level, the level in brain network communities and the level between the brain network communities; the integration characteristics of structural tissues of the brain on different scales can be understood; the reliability of statistical analysis is improved by adopting a spatial autocorrelation correction method; based on a specific machine learning fusion model, the clinical application value of the model is verified.

The invention relates to the technical field of land utilization change evaluation, in particular to a carbon emission spatial autocorrelation evaluation method, model and system based on a land gradient utilization model and a medium. Calculating the carbon absorption amount of the to-be-evaluated area from the natural vegetation dimension, the crop dimension and the water area dimension; calculating the carbon emission of the to-be-evaluated area from the energy consumption dimension, the industrial production process dimension, the waste treatment dimension, the agricultural dimension, the respiration dimension and the water area carbon volatilization dimension; determining a carbon emission net value of the to-be-evaluated region according to a difference value between the carbon absorption amount and the carbon emission amount; determining a global spatial autocorrelation index and a local spatial autocorrelation index of the to-be-evaluated region according to the carbon emission net value; and determining a carbon emission spatial autocorrelation evaluation result of the to-be-evaluated region according to the global spatial autocorrelation index and the local spatial autocorrelation index. The objective of the invention is to solve the problem of how to carry out spatial autocorrelation evaluation on areas with obvious vertical zone performance and altitude gradient performance.

The invention belongs to the field of tunnel engineering, and relates to a tunnel defect detection data spatial resolution enhancement method and device based on a Vine Copula dependent structure and a conditional random field, a computer readable medium and a computer program product. The method comprises the following steps: firstly, separating an overall trend and random fluctuation from original tunnel defect monitoring data; then establishing a Vine Copula multivariable dependence model to represent a statistical correlation relationship among the plurality of defect indexes; and then generating defect distribution data with high spatial resolution by using a conditional random field interpolation simulation technology in combination with a spatial autocorrelation analysis result. According to the invention, while the accuracy of the existing measuring point data is ensured, the spatial correlation structure and multivariable joint distribution characteristics of the defect data are maintained, and the precision of spatial interpolation prediction is improved.

The invention discloses an expressway bridge construction state real-time monitoring method and system, and relates to the technical field of bridge construction monitoring. An expressway bridge construction state real-time monitoring system comprises a data acquisition and processing module, a data expansion and analysis module, a multi-point data fusion module, a state comprehensive evaluation module and a construction scheme adjustment module. According to the method, data interpolation and expansion are carried out on the preprocessed vibration monitoring data through the Kriging interpolation method, the spatial autocorrelation between the vibration monitoring data can be fully utilized, and the monitoring data are expanded, so that the problem of insufficient coverage of monitoring points can be effectively solved, and the integrity and precision of the monitoring data are improved; the change trend and the distribution rule of vibration in space can be reflected more accurately, a more comprehensive and reliable basis can be provided for evaluation of the highway bridge construction state, and the state evaluation accuracy of the highway bridge construction state real-time monitoring method and system is improved.

The invention provides an integrated circuit hot spot detection method based on multi-modal feature fusion and dynamic optimization, which relates to the technical field of integrated circuits, and comprises the following steps: acquiring multi-modal feature data, executing progressive block clustering to extract multi-scale features, fusing the features by using a multi-head attention mechanism, and constructing a hot spot feature map. A hot spot area is determined by combining spatial autocorrelation analysis, and a detection result is optimized based on synchronization analysis of a morphological evolution and energy evolution sequence. The hot spot detection accuracy and robustness can be improved, the false detection rate can be reduced, and the potential hot spot area in the complex layout can be effectively identified.

The invention discloses a carbon emission monitoring method and device considering a spatial topological structure, and a carbon emission reduction evaluation method. The carbon emission monitoring method comprises the following steps: S1, constructing a multi-dimensional data set of a clean energy base; s2, detecting spatial autocorrelation in the data by using a Moran index; s3, constructing a clean energy base output project weight matrix; s4, constructing a clean energy base carbon emission spatial feature change prediction model considering spatial dependency; and S5, predicting the future carbon emission of the target area by using the prediction model, and judging the spatial autocorrelation of the target area by using the Moran index. According to the invention, prediction can be carried out based on the carbon emission spatial characteristic change of multi-dimensional data, the carbon emission change trend of the clean energy base under different working conditions can be accurately predicted, and the spatial distribution characteristics of carbon emission in different areas and different scenes can be comprehensively analyzed.