273 results about "Data decomposition" patented technology

The invention discloses a power grid power load intelligent prediction method, system and device and a medium, and relates to the technical field of power distribution network transmission optimization. Power load data is decomposed into a trend component, a periodic component and a random fluctuation component, and then the importance of different components is evaluated by using a feature channel attention layer; the time sequence attention layer captures key moments in each component time sequence and extracts features at the key moments, and then the features extracted based on the importance and the features extracted at the key moments are fused, so that the multi-scale features of the power load are captured; and then inputting the fused features into the dynamic gating residual connection LSTM network for prediction, and in the prediction process, improving the attention degrees of long-term sequences, short-term sequences and fluctuation sequences in different features through a residual modulation function, thereby more accurately capturing the multi-scale features of the power load and obtaining a prediction value of the power load.

The present invention relates to urban rainfall runoff pollution prediction in urban water systems, and provides an urban rainfall runoff pollution prediction method based on integrated rolling decomposition method and deep learning algorithm. A rolling decomposition method is firstly used to decompose rainfall runoff sequence data into different sub-sequences; then decomposition is sequentially performed on added data, and future data is excluded, to prevent information leakage; a recurrent neural network is used to model and predict the sub-sequences; and finally, predicted results of the sub-sequences are summed to obtain the predicted result of rainfall runoff pollution.

The invention discloses a wind power multi-scale decomposition prediction method. At present, single-point prediction is not comprehensive and accurate enough, and cannot adapt to quantitative accurate requirements of a wind power plant and a power grid dispatching mechanism in risk management. The method comprises the following steps of: forming an original wind power sequence from actually acquired wind power data, sequentially performing feature selection and data decomposition processing to form multi-scale modal data, and constructing a depth prediction model according to the multi-scale modal data; a probability prediction interval determination process is completed in the residual error distribution mode depth prediction model through adaptive bandwidth kernel density estimation; after actually obtained wind power data form an original wind power sequence, an initial model is established, feature selection processing is performed on the initial model, that is, weighted marginal contribution is calculated for each feature of the initial model according to all involved feature subsets by using an SHAP algorithm based on a Shapley value in a game theory, and the weighted marginal contribution of each feature of the initial model is calculated; and completing a feature data acquisition process of accurately quantifying interdependence and interaction effect between features.

The invention relates to the technical field of information platform construction, in particular to a construction method and system of a sports information intelligent service platform. The method comprises the following steps: collecting user motion image data and user health basic data; key body node position information in the movement process of the user is extracted; generating a user motion track curve based on the key node position information, and calculating a deviation angle and a deviation distance with a standard track template to obtain motion correctness data; constructing a user fitness risk assessment model according to the action correctness data and the user health basic data, analyzing the difference degree with a preset safety threshold, and generating layered health guidance data; and decomposing the layered health guidance data into quantifiable progress units, and generating progress tracking data. According to the method, the user motion image data and the health basic data are combined, the evaluation model capable of identifying individualized risk factors is constructed, and more accurate safety guidance is realized.

The invention discloses a multi-modal information interface complexity evaluation method and system, and the method comprises the steps: collecting multi-modal interface elements which comprise visual data, auditory data and interaction data; decomposing the interface complexity of the multi-modal interface, and dividing the interface complexity into information complexity, visual complexity, auditory complexity and cognitive complexity; respectively calculating an index of information complexity, an index of visual complexity, an index of auditory complexity and an index of cognitive complexity; respectively carrying out normalization processing on all the calculated indexes; allocating a weight to each index; and comprehensive score calculation: calculating a comprehensive score through the normalized index value and the weight corresponding to each index. According to the method, the multi-modal complexity is decomposed into four dimensions of information, vision, auditory sense and cognition, comprehensive analysis is carried out on multi-modal information fusion, a dynamic optimization process is embedded, the evaluation efficiency and the interface availability are remarkably improved, and the practicability is better.

The invention relates to the technical field of data center dynamic modeling, and discloses a twin model dynamic modeling method for a data center DCIM platform, which comprises the following steps: constructing a discrete state space model containing a thermal coupling matrix and a system matrix, collecting real-time power and temperature time sequence data, and calculating a cross-correlation function to lock hot air dynamic transmission lag time; calculating cut-off frequency based on physical attributes of the cabinet and decomposing data into high and low frequency components by using a complementary filter; according to the method, the model parameters are made to return to a physical source through a frequency domain decoupling mechanism, the problem of aliasing of airflow coupling and structural thermal inertia parameters in a traditional single-scale identification method is solved, and the method is suitable for large-scale identification. And the physical authenticity and prediction robustness of the twin model under a complex working condition are improved.

The invention discloses a multi-scale space-time fusion water quality prediction and anti-counterfeiting method based on a dynamic graph neural network. Comprising the following steps: 1) collecting water quality index hour data of a plurality of monitoring stations in a drainage basin; 2) decomposing the data into a plurality of intrinsic mode functions through variational mode decomposition; 3) constructing a dynamic graph neural network spatial feature extraction module, and generating a discrete dynamic graph structure; 4) constructing a multi-scale time feature extraction module, and synchronously capturing short-term fluctuation and long-term trend; 5) designing a residual fusion mechanism to integrate the spatial-temporal characteristics, and outputting a water quality prediction result through a full connection layer; and 6) calculating a path distance between the input data and a prediction result through a dynamic time warping algorithm, and comparing residual distribution by combining K-S to realize authenticity discrimination of the input data. The method can fully excavate the spatial and temporal characteristics of the basin water quality under the condition that the geographical spatial distribution of the sites is unknown, improves the prediction precision, carries out the authenticity recognition of the water quality data of an unknown source, and prevents the data from being tampered.

The invention relates to the technical field of mine safety, in particular to a rock burst prediction method based on dynamic frequency domain decomposition, relative event coding and dynamic sparse attention. According to the method, data decomposition of Fourier transform, adaptive frequency domain noise reduction, relative event coding and a dynamic sparse attention mechanism are combined, key features in micro-seismic data can be effectively extracted, and the accuracy and robustness of rock burst prediction are improved. The method is excellent in performance in rock burst prediction, the problems of noise interference, error accumulation and the like can be effectively solved, and reliable technical support is provided for accurate early warning of rock burst.

The invention discloses a security knowledge graph construction method and system based on behavior tracks, and relates to the technical field of network security. The method comprises the following steps: carrying out operation link aggregation according to user operation records to obtain a standardized behavior track chain; calling and accessing heterogeneous behavior trajectory data by taking the standardized behavior trajectory chain as a constraint to obtain multi-source behavior trajectory data; decomposing the standardized behavior track chain to obtain a link node time sequence operation relation and M link nodes; performing time sequence dependence modeling based on the graph convolutional network to generate a security knowledge graph; decomposing the multi-source behavior track data according to the M link nodes to obtain M pieces of multi-source behavior node data; and taking the M multi-source behavior node data as training data, and executing local parameter adaptive optimization of the security knowledge graph through a meta-learning framework. The technical problem of insufficient adaptability of the knowledge security graph in the prior art is solved, and the technical effect of dynamic adaptive optimization of the security knowledge graph is realized.

The invention provides a fall detection algorithm model for a wearable device of a stroke patient, and relates to the technical field of health monitoring and posture detection, and the technical key points are as follows: a construction method of the model is as follows: S1: obtaining posture information of a wearer by using an inertial sensor IMU in the wearable device, after data collection is completed, performing low-pass filtering processing on original data to obtain sensor data; s2, decomposing the inertial sensor data obtained at the t moment into a trend component, a season component and a residual component by using LOESS according to an STL (Standard Template Library) cyclic trend decomposition method, and then carrying out normalization processing on the data; and S3, performing time sequence modeling on each inertial sensor component by using LSTM (Long Short Term Memory). According to the fall detection algorithm model for the wearable device of the stroke patient, collaborative optimization of multi-physical-quantity coupling feature decoupling and time sequence dynamic modeling is realized, and medical-level fall monitoring performance is realized under resource constraints of the wearable device.

The invention discloses a power carbon emission measurement and calculation data anomaly identification method and system. The method comprises the steps of obtaining power carbon emission original data and performing preprocessing; the method comprises the following steps: decomposing data into a plurality of internal mode functions and residual sequences by adopting an adaptive variable scale weighted noise enhancement CEEMDAN method, and effectively separating a time sequence trend from high-frequency fluctuation; constructing an improved box plot model based on the residual sequence, and adaptively adjusting an abnormal boundary by introducing a median deviation coefficient and a dynamic abnormal detection coefficient; and identifying abnormal data points by using the optimized boundary, and performing correction or elimination processing. According to the method, the problem that a traditional method is poor in adaptability to non-stationary and multi-scale electrical carbon data is solved, normal periodic fluctuation is prevented from being misjudged to be abnormal, the accuracy and robustness of anomaly detection are remarkably improved, and a reliable data basis is provided for accurate measurement and calculation of carbon emission of an electric power system.

The invention discloses a method, a system and a related device for controlling a motor based on an adaptive algorithm, which are used for improving the accuracy of motor control. The method comprises the following steps: acquiring initial data of a motor; decomposing the initial data into intrinsic mode function components, and performing time-frequency transformation on the intrinsic mode function components; dynamic time-frequency features of the intrinsic mode function component are extracted, cross-band weighted fusion is carried out on the dynamic time-frequency features through a preset attention weighting mechanism, and a multi-dimensional feature vector is generated; inputting the multi-dimensional feature vector into a preset deep learning model, and constructing a target prediction model in combination with a loss function and an adaptive momentum optimization algorithm; transmitting the target prediction model to an analysis module of the motor to obtain a load change trend and a confidence coefficient; dynamically adjusting the proportion of the weight of the PID controller according to the load change trend and the confidence coefficient, and optimizing the parameters of the unit of which the proportion is adjusted through a gradient descent method; and dynamically regulating and controlling the motor based on the PID controller.

The invention discloses a reservoir flood forecasting method based on physical constraint and space-time double-flow coupling. The reservoir flood forecasting method comprises the following steps: S1, acquiring multi-source hydrometeorological data; s2, decomposing the multi-source hydro meteorological data into a historical state sequence and a future driving sequence; s3, performing feature extraction on the historical state sequence data through the physical enhanced long-short term memory network of the historical inertial feature extraction branch to obtain historical inertial features, and performing feature extraction on a future driving sequence through the time domain convolutional network of the future forced feature extraction branch to obtain future forced features; s4, performing weighted fusion on the historical inertial features and the future forced features to generate fusion features; and S5, inputting the fused features into a decoder to obtain a predicted water level increment, and superposing the predicted water level increment to the current water level to obtain a predicted value. The prediction timeliness is improved, the prediction precision of the water recession stage is improved, and the physical consistency of the prediction result is enhanced.

The invention discloses a multi-element hydrological information prediction method and system. The method comprises the following steps: acquiring water level, flow velocity, flow and temperature time sequence data of a target water area through a high-frequency acoustic tomography system, and constructing a multivariate hydrological data set containing sampling duration and hydrological parameter vectors at all moments; preprocessing the collected multivariate hydrological data set to generate preprocessed data, and extracting timestamp features of a sequence; and decomposing the preprocessed data into a plurality of intrinsic mode functions and the like by adopting a variational mode decomposition method. The problems of gradient disappearance and local dependence of a traditional RNN model are solved, redundant noise interference is effectively eliminated while the multi-time-scale characteristic of hydrological variables is reserved, the prediction generalization ability under the complex coupling relation is improved, and high-precision technical support is provided for dynamic planning of water resources and disaster early warning.

The invention relates to the field of computer systems based on specific calculation models, and discloses an intelligent water meter data driving leakage identification early warning system based on a neural network model, comprising a learnable decoupling module, parameters of which are determined by a collaborative adversarial training rule, and which is used for decomposing data into a baseline data stream and a transient data stream; the system is provided with a baseline channel and a transient channel in parallel; according to the system and the method, a behavior mode monitor, a transient mode analyzer and an abnormal arbitration module are arranged, through information cooperation of the three modules, accurate identification of different leakage states and normal mode drifting is achieved, through the architecture, the fundamental problem that a calculation model is shielded by a high-amplitude normal water use signal is solved, and the system and the method have the advantages of being high in practicability and high in practicability. According to the method, high sensitivity of the system to tiny baseline leakage is recovered, and meanwhile, active adaptation to user habit changes and effective compensation for a high-frequency leakage blind area are achieved through cross-channel information collaboration.

The invention discloses a landslide early warning method based on multi-source fusion and an intelligent algorithm. The landslide early warning method comprises the following steps: acquiring meteorological and hydrological monitoring data, remote sensing image data and landslide displacement monitoring data of a target landslide area; performing data decomposition on the landslide displacement monitoring data to obtain a trend term and a periodic term; extracting ground monitoring data composed of a plurality of factors and response time of the plurality of factors from the meteorological and hydrological monitoring data; performing fusion processing on the remote sensing image data and the ground monitoring data to obtain key disaster-causing factors; constructing a prediction model used for predicting the landslide displacement rate; triggering an early warning signal based on the predicted value of the landslide displacement rate; according to the method, the landslide multi-scale response rule and the lag effect can be identified more accurately, and the dynamic prediction and early warning capability of the landslide is improved.

The invention discloses a battery pack thermal runaway early warning method and system, and relates to the technical field of battery management. The battery pack thermal runaway early warning method comprises the following steps: acquiring temperature and voltage data of each battery unit in a battery pack in real time; performing standardization processing on the temperature and voltage data to eliminate dimensional differences; synchronously extracting a space basis function and a time coefficient based on data decomposition; calculating a spatial entropy based on the spatial basis function; calculating a time entropy based on the time coefficient; the spatial entropy and the time entropy are fused into improved information entropy based on weighted parameters; outputting a prediction information entropy in response to the improved information entropy input time sequence prediction model; and triggering graded early warning based on comparison between the predicted information entropy and a preset information entropy threshold. On-line decomposition is carried out on multi-source data of a battery pack, space and time characteristics are extracted, an improved information entropy is constructed, early warning of thermal runaway is realized in combination with a trend prediction technology, and accuracy and real-time performance are improved.

The invention relates to the technical field of industrial visual intelligent detection, in particular to a magnetic attraction robot inspection method and system based on weld visual tracking. The specific implementation process comprises the following steps: performing illumination equalization processing on continuous frame original images, decomposing and filtering texture layer data by using total variation regularization, and retaining structural layer data; predicting a region of interest by using Kalman filtering, and inputting the region of interest into a double-flow feature fusion semantic segmentation network; extracting welding seam features by utilizing a spatial detail branch and a context semantic branch, and generating a welding seam mask through an attention gating mechanism; performing skeleton vectorization on the mask image, calculating a curvature change rate and a perspective gradient rate, and constructing a mapping model; and generating a visual tracking control vector in combination with the transverse position deviation and the angle deviation. According to the method, by means of total variation regularization decomposition and perspective gradient analysis, the welding seam features can be extracted under the complex industrial texture background, and robot inspection robustness and tracking precision are improved.

The invention discloses an equipment residual life prediction method fusing dynamic decomposition and a Koopman operator, and the method comprises the steps: decomposing the preprocessed multi-sensor time sequence data into a trend term and an unstable term through a time sequence decomposition module, and forming a comprehensive trend representation; establishing a Koopman-GRU module, extracting time sequence dependence characteristics in the comprehensive trend representation through the GRU module, and performing linear modeling on the time sequence dependence characteristics in a high-dimensional space by using a Koopman operator module to obtain a space-time state tensor; on the basis of a channel interactive learning module, global core representation between channels is used for capturing the dependency relationship between the sensors, and the feature expression ability is enhanced; generating a final RUL prediction result by using a linear layer of a residual structure in the prediction module; compared with the prior art, the method has the advantages that the feature representation of the multi-sensor time series data is more sufficient, dynamic aggregation and redistribution of the spatial-temporal features are achieved, and then accurate prediction of the nonlinear degradation process of multi-source data monitoring is achieved.

The invention relates to the technical field of pollution discharge intelligent sensing monitoring and early warning systems, in particular to a drainage pipe network pollution monitoring and advanced early warning system based on the Internet of Things. The method comprises the steps of firstly obtaining water quality data of a current monitoring period; further identifying a water quality fluctuation event according to fluctuation characteristics of data in each water quality data dimension, and extracting an event center moment and an event waveform characteristic vector; further constructing a matching deviation matrix; further obtaining a matching deviation parameter based on the optimal matching result, comparing the matching deviation parameter with a preset historical mode statistical baseline, and obtaining a mode deviation degree corresponding to the COD data dimension and each non-COD data dimension; and finally, based on the mode deviation degree, decomposing the COD data and extracting a real pollution load, and generating an early warning report based on the real pollution load, so that the false alarm rate is reduced, and the early warning accuracy is improved.

The invention discloses a virtual character behavior processing method and system based on semantic decoupling and elastic coupling and a computer readable storage medium. According to the method, unstructured action data is decomposed into physical layer intention descriptors, measurable style descriptors and high-dimensional semantic layer features by utilizing a parallel physical calculation engine and an AI semantic analysis engine through a double-track feature decoupling mechanism. Besides, an elastic coupling mechanism between intentions and styles is introduced, style parameters are dynamically clamped based on a physical priority principle, and physical topology collapse caused by style overload is prevented. According to the method, a physical-semantic dual index system is constructed, database autonomous evolution based on manifold density analysis is supported, and the problems that in the prior art, virtual character action generation is poor in physical controllability, semantic understanding is lacked, and cross-scene generalization ability is weak are effectively solved.

The invention provides a KPIs anomaly detection method based on MVMD decomposition. The KPIs anomaly detection method comprises the following steps: S1, obtaining multi-dimensional key performance index KPIs time sequence data in a micro-service system; s2, decomposing the normalized multivariable KPIs time sequence data into K intrinsic mode function (IMF) components; s3, dividing the time sequence of each IMF component into a plurality of fixed-length subsequences through a sliding window; s4, respectively constructing and training a variational auto-encoder VAE model, and calculating a reconstruction error of each IMF component at each time point; s5, obtaining a comprehensive abnormal score of each time point; s6, based on a grid search method, determining an optimal anomaly score threshold value for anomaly judgment; and S7, comparing the comprehensive abnormal score with an optimal abnormal score threshold value, and if the comprehensive abnormal score at a certain time point exceeds the threshold value, judging that the KPIs at the time point is abnormal. According to the invention, the modeling capability of abnormal modes of different frequency components can be enhanced, so that the accuracy of overall anomaly detection is improved.

The invention belongs to the technical field of water quality prediction, and particularly relates to a water quality dynamic prediction method and system in a marine ranch culture environment, and the method comprises the steps: collecting historical water quality time sequence data, and carrying out the standardization processing; decomposing the standardized water quality time sequence data into a trend component and a season / disturbance component, and performing multi-scale feature coding; based on the seasonal multi-scale features, dynamically adjusting the frequency domain resolution through fractional Fourier transform, and obtaining time domain enhancement features after inverse transformation in combination with multi-head self-attention; based on trend multi-scale features, calculating a Pearson correlation matrix to form a mask matrix, and calculating output channel frequency domain enhancement features in combination with a mask attention mechanism; utilizing a global time sequence attention mechanism to obtain a globally enhanced trunk feature; and performing dynamic gating fusion on the three paths of features to obtain a fused spatial-temporal feature, inputting the fused spatial-temporal feature into a prediction head, and outputting a future value of the water quality time sequence data.

The invention discloses an ocean sound velocity field prediction method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the quantitative diagnosis of key driving factors affecting the evolution of an ocean sound velocity field of a target sea area from the ocean environment data, and building a causal network diagram based on the information flow of the key driving factors and a structural equation model, obtaining a core feature set; the ocean sound velocity field sequence data in the core feature set are decomposed into data in a space-time coherent mode through dynamic mode decomposition, and then the data are mapped into low-dimensional space-time features; learning the evolution dynamic state of the ocean sound velocity field according to the low-dimensional spatial-temporal characteristics by using a KAN network, and outputting an ocean sound velocity field prediction result at a future moment; future guide learning is used in the training stage of the KAN network, and real observation data in future set time is used as an auxiliary supervision signal; and using a dynamic feedback mechanism to calibrate the ocean sound velocity field prediction result based on the real-time observation data in the online prediction stage. The accuracy of predicting the ocean sound velocity can be improved.

The invention discloses a travel assistant system. The travel assistant system comprises the steps that the format of input multi-modal data is converted into to-be-recognized data of a uniform format; decomposing to-be-identified data of the user into at least one sub-query task, and extracting task parameters of each sub-query task from the task calling mapping table; calling task parameters of the sub-query tasks through the task calling interface to execute task retrieval; executing the sub-query task based on the task parameters of the sub-query task, and obtaining multi-modal data related to the sub-query task in the spatial grid; scoring the retrieval result according to the preference information of the user; planning the output of the system at least according to the scoring result; and generating a standardized output template, performing structured assembly on the retrieval result or the reasoning result of the large language model based on the standardized output template, and outputting an answer. The comprehensive performance of the intelligent travel assistant in the aspects of personalized service, response real-time performance, result accuracy, complex travel problem processing capacity and the like is improved.

A surface deformation monitoring method and systems based on time-series InSAR (TS-InSAR) are provided. The method includes acquiring Synthetic Aperture Radar (SAR) data; performing differential interferometry; performing a robust two-tier multi-temporal InSAR method for detection of Persistent Scatterer (PS) and Distributed Scatterer (DS) candidates to acquire time series data for surface deformation monitoring; performing a variational mode decomposition (VMD) method to decompose TS-InSAR data into a plurality of components; reconstructing time series data; performing a gated recurrent unit (GRU) method; extracting trend of surface deformation; and performing continuous large-scale deformation monitoring. The InSAR-based deformation monitoring method integrates VMD and GRU, offering significant improvements in robustness and accuracy over the existing methods.

The invention relates to the technical field of data processing, in particular to a cleaning fusion system for urban traffic multi-source heterogeneous data, which comprises a space-time correlation graph construction module used for creating a unique node identifier for each GPS data record, roadside camera data record and coil data record according to multi-source heterogeneous traffic data, and establishing a spatial adjacent relation edge by judging whether road segment identifiers to which multiple nodes belong are the same or not. According to the method, context awareness evaluation of data source reliability is achieved, meanwhile, when traffic time series data are processed, the data are decomposed into a low-frequency approximation coefficient representing a long-term trend and a high-frequency detail coefficient capturing a sudden change event, and the approximation coefficients of the multi-source data are fused through the obtained context awareness credibility score; meanwhile, a maximum value selection strategy is adopted for detail coefficients, key signal features caused by real traffic events can be reserved, and the fidelity of fused traffic data is improved.

The invention relates to the technical field of electric power, provides an electric power transaction strategy generation method and system based on Monte Carlo simulation and deep learning, and solves the problem of poor scientificity and adaptability of an optimal transaction strategy of a multi-region electric power market in the prior art. The method comprises the following steps: acquiring historical price data of a multi-region power market, decomposing and extracting trends, periods and random components, and constructing a multi-dimensional feature set; the method comprises the following steps: receiving unstructured data by using a block link, packaging the unstructured data into a structured data packet through Hash verification and a smart contract, and converting the structured data packet into a multi-dimensional semantic feature vector based on a preset mapping table; associating the two types of features through a deep learning multi-modal model, and outputting price trend prediction probability distribution data with confidence; and generating a plurality of contract power splitting schemes based on Monte Carlo simulation, combining price prediction, searching a global optimal solution by using a particle swarm optimization algorithm, and generating an optimal power transaction strategy. According to the invention, the scientificity and adaptability of the optimal transaction strategy of the multi-region power market are improved.

The invention provides a rainwater collection pipe network blockage risk early warning method, which comprises the steps of collecting multi-point flow and environmental parameter data, fusing water level and rainfall intensity, and realizing unified processing of multi-source data through timestamp alignment and mean filtering; using an empirical mode decomposition algorithm to decompose the flow time sequence data into trend, period and high-frequency fluctuation components, and identifying abnormal segments through sliding window energy calculation and dynamic threshold gating; according to the method, the change point detection algorithm and the Gaussian mixture model are combined, energy accumulation sudden change and trend attenuation are verified in a layered mode, and then structured early-stage blockage risk early warning is generated, the accuracy and timeliness of anomaly detection are effectively improved, the pipe network blockage supervision and maintenance cost can be reduced, and the operation reliability of an urban drainage system is enhanced.

The invention relates to the technical field of topographic surveying and mapping, and discloses a topographic surveying and mapping method and system based on an unmanned aerial vehicle, equipment and a medium. The system comprises a topographic data acquisition module, a feature extraction module, a data decomposition module and a parameter distribution module. Wherein the topographic data acquisition module collects topographic original data of a target area by means of a multi-source sensor carried by an unmanned aerial vehicle; the feature extraction module performs density clustering processing on the original data to obtain a topographic feature point cloud data set; the data decomposition module separates the feature point cloud data set into high-frequency terrain detail data and low-frequency terrain contour data through adaptive wavelet decomposition; the parameter distribution module analyzes user surveying and mapping instruction semantics, generates a task weight vector, and distributes surveying and mapping parameters between the laser scanning system and the optical imaging system according to the task weight vector. The system enhances the adaptability and comprehensive effect of topographic surveying and mapping by means of multi-source acquisition, feature extraction, data decomposition and dynamic parameter distribution, and can meet diversified requirements.