210 results about "Dbscan clustering" patented technology

The invention relates to the field of government affair work order intelligent processing, in particular to a government affair work order intelligent processing method and system based on space-time semantic clustering and a large language model. According to the scheme, unified data feature modeling is conducted on a work order to be processed, an improved DBSCAN clustering algorithm is executed on the work order through a weighted space-time semantic three-dimensional distance measurement formula, and combined clustering of space, time and semantic features is achieved; calculating priority scores of the work orders, and dynamically allocating scheduling resources according to the clustering scale and the priority of the work orders; based on a retrieval enhancement generation technology of an RAG framework and an FAISS vector retrieval library, historical similar work orders are matched, a few-sample learning case is generated, and two sets of differential treatment schemes are generated by controlling temperature parameters of a large language model; visual display and interactive analysis of work order clustering are realized through an interactive GIS platform; and establishing a quality feedback closed loop of work order reconstruction. The method is suitable for intelligent government affair work order processing.

The invention relates to the technical field of federated learning security, and discloses a federated learning poisoning defense method based on time-frequency spectrogram and comparative learning, which comprises the following steps: receiving model update uploaded by each client, grouping and vectorizing parameters according to model layers, and generating a time-frequency spectrogram by applying short-time Fourier transform to parameter vectors of each layer; based on the time-frequency spectrogram, constructing a positive sample pair through data enhancement, carrying out difficult negative sample mining, and training an encoder by using a contrast loss function to extract an embedded vector with high discriminant power; and performing unsupervised clustering on the embedded vector by using a DBSCAN clustering algorithm, judging the maximum cluster as a benign client, performing final judgment in combination with historical malicious records, and only aggregating model parameters of the benign client to update a global model. According to the invention, high-precision detection of attack features can be realized, and a more universal, more efficient and more practical federal learning poisoning attack defense method is realized.

The invention belongs to the technical field of integrated circuits, and particularly relates to a time mismatch extraction method based on parallel windowing autocorrelation. According to the method, firstly, clustering processing is carried out on signal points; thirdly, preliminarily screening out legal signals based on the time occupancy rate of the signals, and constructing an initial legal signal library; a similarity score between the signals is further calculated through an IDK algorithm, and adjacent frequency bands are divided through a Pettitt method; and finally, continuation of legal signals is realized by means of a DBSCAN clustering algorithm, and automatic construction of a legal signal library is completed. The method is not only suitable for scenes without prior information, but also can be applied under the condition with partial prior information, and can be dynamically updated according to actual requirements. The method is based on radio frequency spectrum monitoring big data, can construct a legal signal library with high reliability and strong generalization ability, provides effective support for subsequent illegal signal monitoring, and has important application value.

The invention provides a method for identifying a microscopic association body structure in a heavy oil molecule simulation trajectory, which comprises the following steps of: analyzing a molecular dynamics trajectory, identifying an aromatic ring structure in a heavy oil molecule component, calculating a geometric center of the aromatic ring structure, combining a periodic boundary condition, and applying an improved DBSCAN clustering algorithm to realize automatic identification and clustering of an association body; and performing post-processing optimization on the clustering result, merging the shared clusters to generate a final association body set, and analyzing the number, size distribution, morphological characteristics and dynamic evolution information of the association bodies based on the structural characteristics of the final association body set. The method can accurately capture pi-pi accumulation behaviors of heavy oil molecules, further reveals morphological characteristics and dynamic evolution laws of heavy oil microassociates and influences of the heavy oil microassociates on the apparent viscosity of heavy oil, provides theoretical support for heavy oil viscosity-causing mechanism analysis and viscosity reducer design, can complement experimental research, and has a wide application prospect. And efficient development and green utilization of a complex thickened oil system are promoted.

The invention discloses a reverse BIM modeling method, system and equipment for an existing building space rod system composite structure, and belongs to the technical field of BIM modeling. In order to solve the problem that in the prior art, point cloud processing software lacks a special recognition and reconstruction tool for a'ball node-rod piece 'class space rod system combined structure, and efficient and accurate reverse generation of a rod piece axis is difficult to achieve, through curvature analysis, DBSCAN clustering, RANSAC spherical surface fitting and other technologies, the position of a ball node is accurately recognized from actually measured point cloud data; and finally generating a high-precision BIM model of a space rod system combined structure according with engineering practice by combining a two-stage geometric filtering strategy according to a sphere center connecting line of sphere nodes and the space constraint of the rod piece point cloud data. According to the method, the problem that actually measured point cloud data is discrete and difficult to process is effectively solved, and reliable technical support is provided for health monitoring, digital filing and accurate modeling of the existing building space rod system composite structure.

The invention discloses a multi-level water supply and drainage balance monitoring method and system for the water affair industry, and relates to the technical field of water affair monitoring. The method comprises the following steps: deploying a multi-level monitoring network; the method comprises the following steps: collecting real-time data of a multi-level monitoring network, executing space-time alignment operation, and preprocessing the data; constructing a quarterly three-dimensional water balance equation, and dynamically estimating through Kalman filtering; dBSCAN clustering is implemented based on pipe network topology, and leakage positioning is carried out to generate a leakage probability thermodynamic diagram; and establishing a hierarchical response mechanism to carry out multi-level early warning linkage, and rendering a multi-layer perspective view in a GIS (Geographic Information System) engine. According to the method, a quarterly three-dimensional water balance equation is constructed through a city-region-enterprise three-level dynamic weight model and a hierarchical penetration algorithm, and dynamic estimation is performed through Kalman filtering; dBSCAN clustering is implemented based on the pipe network topology, leakage positioning is performed, a leakage probability thermodynamic diagram is generated, the water affair monitoring efficiency is improved, and the manual troubleshooting workload is reduced.

The invention provides a three-dimensional point cloud change detection method based on Siamese AdaptConv, and belongs to the field of intelligent scene reconstruction. The method comprises the following steps: firstly, acquiring three-dimensional point cloud data of a scene to be detected at two different time points, and constructing an adaptive neighborhood for each point; secondly, inputting the preprocessed three-dimensional point cloud data into a Siamese AdaptConv double-branch network, fusing the multi-scale features of each layer to obtain respective corresponding fused features, further calculating the change probability of the same position point, mapping the change probability into a candidate change point set, executing DBSCAN clustering, and outputting a candidate region; and finally, performing semantic verification on the candidate region, pre-constructing a geographic knowledge graph, and calculating semantic relevancy: when the semantic relevancy exceeds a set threshold value, considering that the three-dimensional point cloud change of the candidate region is consistent with the existing entity semantics in the knowledge graph, and judging that the change is reasonable. According to the invention, end-to-end multi-time sequence feature alignment and change identification are realized.

The invention provides an electrical load release method based on clustering analysis, and relates to the technical field of electrical load adjustment. The electrical load release method based on clustering analysis comprises the steps of data collection, collection of historical data of electrical loads in a target area through a power grid load power monitoring system, data preprocessing, elimination of abnormal values in the collected electrical data in the target area, and extraction of static characteristics and dynamic characteristics of power data. And the data features are standardized. According to the method, the K-means clustering algorithm and the DBSCAN clustering algorithm are adopted, the convergence speed of the algorithms and the stability of the clustering result are effectively improved by combining the characteristics of the two algorithms, and the load release strategy which is high in pertinence and feasible is formulated based on different electrical load categories obtained through clustering analysis.

The invention discloses an underground mine pipeline type point cloud completion method based on axial slicing, which relates to the technical field of point cloud data processing, is used for performing data completion on pipeline type point clouds, and comprises the steps of axially slicing preprocessed pipeline point cloud data, performing dimension reduction on three-dimensional point clouds to two-dimensional plane processing, and calculating related geometric parameters of pipelines. The method comprises the following steps: extracting a pipeline center line based on improved DBSCAN clustering and least square circle fitting, constructing a skeleton model under geometric topology constraints, combining local geometric features and global topology constraints, realizing geometric reconstruction of a missing region through rotating surface generation and a local interpolation algorithm, and realizing robust repair of the missing region. According to the method, a convex hull and an RANSAC method are combined to extract a pipeline center line and geometric parameters, pipeline point cloud data complementation is realized through a continuous rotation correction method, and the pipeline point cloud complementation quality is remarkably improved.

The invention particularly relates to a point cloud self-supervision quality evaluation and completion method, which relates to the technical field of point cloud processing and computer vision, and comprises the following steps of: constructing a normal data set and simulating refined defects; performing double-branch network training and normal feature deep learning; carrying out cloud processing and completion reasoning on the point to be detected; and carrying out residual calculation and multi-dimensional anomaly positioning analysis. According to the invention, the two-dimensional analysis of the point-by-point residual error and the voxelization residual error comprehensively depicts the single-point abnormal intensity and the density and position difference of the local area, and the candidate abnormal points are screened in combination with the optimization threshold, so that the false drop rate is effectively reduced; a continuous abnormal area is further obtained through DBSCAN clustering, the residual coefficient and the two-dimensional volume coefficient are fused to calculate the abnormal index, accurate classification of the anomalies is achieved, and the problem that classification of different types of anomalies is not accurate through a traditional method is solved.

The invention relates to the technical field of gas turbine unit data optimization processing, and provides a gas turbine unit data anomaly identification method based on group decision, which comprises the following steps: step 1, preprocessing original operation data; 2, performing data detection by adopting multiple detection methods to obtain an abnormal score of each detection method; 3, adopting a voting mechanism to judge abnormal points; and step 4, outputting all abnormal points which are finally judged. The detection method comprises but not limited to a standard deviation method, an isolated forest method, a DBSCAN clustering method, an OCSVM method, an LOF method, a CBLOF method, a KNN method and an HBOS method. According to the method, a plurality of different abnormal data identification methods can be integrated, the idea of majority voting is used, an abnormal value is selected by a proposed group decision-making method, and the abnormal value can be better detected.

The invention relates to the field of data processing, in particular to an archive information management method and system based on artificial intelligence, and the method comprises the steps: according to the analysis of classified historical archives, extracting the features of word vectors, sentence vectors, time vectors, place vectors and the like, and calculating the feature classification accuracy and information entropy to determine the attention weight. And calculating the density by using the cosine similarity of the feature vectors of the archives to be classified, fusing the feature vectors to obtain a target vector and the comprehensive density, and adaptively adjusting the initial neighborhood radius of the DBSCAN clustering algorithm. And finally, based on the adjustment radius and the Euclidean distance, clustering classification and archiving are carried out on the archives, and the classification accuracy and the system intelligence level are improved. According to the method, multiple features are comprehensively utilized, and the clustering radius is adaptively adjusted, so that the accuracy and adaptability of archive classification are improved, the performance of a clustering algorithm is optimized, and the local structure of data is reflected more accurately.

The invention discloses a gallium oxide crystal storage environment monitoring system and method based on artificial intelligence, and relates to the technical field of intelligent industrial monitoring, and the method comprises the steps: inputting a multi-source coupling data set into a space-time hypergraph model, carrying out the multi-source data coupling of a hypergraph construction layer, and carrying out the spatial dependence capture and time sequence feature extraction of a space-time convolution layer, the method comprises the steps of forming an environment parameter prediction matrix, performing damage quantification and probability mapping on a gallium oxide crystal damage image, obtaining a reference damage distribution diagram, performing coupling association on the environment prediction matrix and the reference damage distribution diagram, outputting an environment-damage weight matrix, and performing defect positioning on the reference damage distribution diagram according to the environment-damage weight matrix. And obtaining a damage sensitive area. According to the invention, through the space-time hypergraph model, the DBSCAN clustering analysis and the risk grade division strategy, the accuracy and the real-time performance of the monitoring scheme are enhanced.

The invention discloses a dynamic multi-dimensional fast track association method and device based on a millimeter wave traffic radar. The method comprises the following steps: acquiring point cloud data acquired by a millimeter wave traffic radar; clustering the point cloud data through a DBSCAN clustering algorithm to obtain a point cloud clustering result; adopting a centroid algorithm to complete the condensation of trace points in the point cloud clustering result, and obtaining the condensed trace points; and carrying out association calculation on the condensed trace points and the currently managed track or historical trace points based on an association algorithm of an optimal association coefficient to obtain matched track information.

The invention relates to the technical field of construction progress monitoring, in particular to a construction progress multi-dimensional dynamic monitoring method based on the Internet of Things and BIM, and the method comprises the following steps: obtaining a component number and a planned time period through the BIM, collecting an action label, a three-dimensional coordinate and space projection matching time, and generating a component behavior set through DBSCAN clustering; the method comprises the following steps: slicing an action sequence to calculate frequency and coverage proportion; classifying component states to generate progress deviation; matching the deviation with a Hungary algorithm to calculate a coupling map; extracting a mechanical track and path included angle, normalizing and correcting the included angle to generate a deviation sequence, and grading track segments to generate a dynamic monitoring index set. According to the method, time-space association is established based on component numbers and coordinate projection, a classification model is constructed by using time slice coverage to identify progress deviation, task chain coupling is analyzed through bidirectional matching, a deviation sequence is generated through included angle correction, a delay amount and coverage rate evaluation system is established, and progress deviation tracing and abnormal early warning are achieved.

The invention discloses a linear guide rail lightweight optimization design method and device, a medium and a program product, and the method comprises the steps: (1) constructing a lightweight optimization mathematical model on the basis of a linear guide rail structure and load characteristics under the condition of satisfying frictional resistance and first-order modal constraints; (2) quantizing a parameter effect based on a PCE model and constructing an effect space to generate a population; (3) designing an evolutionary strategy guided by a bidirectional information individual; (4) constructing a double-layer Stacking integration model based on DBSCAN clustering, and screening an optimal filial generation guide rail; and (5) if the current optimal filial generation guide rail meets the optimization requirement, outputting an optimal guide rail parameter value, otherwise, returning to the step (3) until the optimization requirement is met. According to the method, the weight of the guide rail can be minimized for the effect space of high-effect guide rail parameters, transition optimization for redundancy and low-effect parameters is avoided, and better lightweight performance is achieved.

The invention discloses a line loss positioning method based on phase feature clustering, belongs to the technical field of power system line loss treatment, and constructs a three-dimensional feature space through high-frequency collection of HPLC carrier signal phase offset, current ripple frequency spectrum features and intelligent electric meter load rate to comprehensively characterize the equipment operation state. An improved DBSCAN clustering algorithm is utilized, the neighborhood radius is dynamically calculated according to the feature space density, the minimum sample number MinPts is adaptively set according to the transformer area equipment scale, and clustering parameter adaptation is achieved; meanwhile, a noise filtering mechanism is introduced, secondary verification is carried out on initial clustering noise points, effective abnormal points are screened out by calculating local density and overall average density, and the misjudgment rate is reduced. The method can quickly and accurately position the position of the line loss abnormal equipment, greatly improves the line loss abnormal processing efficiency, can be widely applied to power distribution network line loss management, effectively improves the line loss analysis accuracy and treatment efficiency, and provides powerful technical support for reducing the power grid loss and improving the power supply reliability.

The invention discloses an NRLC enhanced two-stage DBSCAN clustering rock discontinuous surface identification method, and relates to the field of rock mass structural surface intelligent identification, and the method comprises the steps: S1, identifying concave, convex and boundary feature points in a rock point cloud; s2, calculating a rock point cloud normal vector based on local fitting; s3, based on the rock point cloud normal vector, identifying a group discontinuous surface through DBSCAN clustering; s4, DBSCAN clustering is carried out based on the group discontinuous surfaces to obtain all discontinuous surfaces in the point clouds, automatic recognition and accurate representation of the rock mass discontinuous surfaces are achieved through normal vector clustering analysis, a set of efficient and accurate rock mass structural surface intelligent recognition technology system is established, and reliable three-dimensional data support is provided for rock mass stability evaluation.

The invention discloses an AI marking closed-loop method based on active learning and difficult case mining, and the method comprises the steps: carrying out the parallel calculation of uncertainty, representativeness and diversity three-dimensional value indexes after training an initial model, and carrying out the dynamic weighting screening of a high-value sample through a closed-loop feedback controller; difficult cases are recognized, DBSCAN clustering is adopted, similar sample expansion is retrieved, and the samples are stored in a dynamic pool; annotations are distributed, intelligent judgment is triggered for dispute samples after consistency verification, and labels are determined by fusing node reputation, model confidence and feature similarity; a mixed loss function is adopted for incremental training, and if the performance does not reach the standard, sampling, difficult examples and training parameters are adjusted in a linkage mode and then retry is conducted; the effectiveness of the difficult cases is evaluated after each round of iteration, the invalid difficult cases are eliminated, redundant clusters are combined, and the pool capacity is adaptively maintained; and the performance of the monitoring model is iteratively optimized until the standard is reached. According to the method, the labeling cost is reduced, and the boundary sample and long tail category recognition capability is improved.

The invention discloses a tunnel blasting lumpiness and muck pile form two-dimensional correlation evaluation method and system, and constructs a set of complete blasting effect intelligent evaluation system by introducing a low illumination enhancement algorithm based on Retinex, improving a YOLOv8 instance segmentation model and a three-dimensional point cloud reconstruction technology. According to the system, an explosion-proof binocular camera is used for collecting muck pile images, point cloud data is generated through stereo matching after image enhancement, rock block recognition and mask extraction, the three-dimensional size of rock blocks is calculated through DBSCAN clustering and PCA analysis, parameters such as the particle size and the uniformity index are calculated, and the quality of the muck pile is improved. Meanwhile, the overall form of the muck pile is reconstructed to extract form parameters such as the stacking height, the looseness and the form expansion degree, and finally a correlation model of the blasting fragmentation and the muck pile form is established, so that all-around and automatic quantitative evaluation of the blasting'crushing 'and'throwing' effects is realized; the innovative instance segmentation and point cloud processing algorithm effectively solves the detection problem in complex scenes such as rock adhesion and overlapping, and the detection precision and reliability are remarkably improved.

The invention provides a comprehensive analysis method for compaction quality of a lime treated soft soil roadbed based on intelligent compaction. The method comprises the following steps: S1, acquiring vibration modulus data corresponding to a plurality of detection units respectively; s2, calculating a compaction degree passing rate and a stability passing rate based on the vibration modulus data corresponding to the plurality of detection units, obtaining a uniformity passing rate by adopting a multi-scale variation structure analysis method, and calculating an abnormal aggregation index through a DBSCAN clustering algorithm; s3, determining the weight of each index, wherein the weight comprises the weight of the compaction degree passing rate, the weight of the uniformity passing rate and the weight of the stability passing rate; s4, calculating a compaction quality value based on each index and the corresponding weight; s5, the roadbed compaction quality grade is judged based on the compaction quality value, and roadbed repairing is conducted based on the roadbed compaction quality grade and the abnormal aggregation index. Compared with the prior art, the method has the advantages of improving the spatial resolution and defect identification precision of uniformity detection of the roadbed compactness and the like.

The invention provides a medical entity alignment method and system based on knowledge representation learning and constraint clustering, and belongs to the field of medical knowledge maps. The method comprises the steps of preprocessing a to-be-aligned medical knowledge graph; obtaining structure embedding, semantic embedding and attribute embedding of a medical knowledge graph entity; a multi-head attention mechanism is adopted to fuse structure embedding, attribute embedding and semantic embedding, and final entity embedding is obtained; calculating the cosine similarity between the final entity embedding, performing pre-clustering by adopting a Canopy clustering algorithm, and dividing the final entity embedding into a plurality of overlapped clusters; carrying out secondary clustering in the clusters by adopting a constrained DBSCAN clustering method, and gathering similar entities into the same cluster to form a cluster set; and extracting a one-to-one alignment result of the entities in each cluster by using a Hungary algorithm, and outputting an aligned entity pair set. The problems of insufficient annotation data, isomerism and difficulty in alignment of long-tail entities can be effectively solved, and the accuracy of entity alignment is improved.

The application relates to an improved random subspace identification method based on MCKF and DBSCAN, and belongs to the technical field of structural health monitoring and signal processing, which comprises an MCKF denoising pretreatment module, a COV-SSI identification module and a DBSCAN clustering post-processing module, and comprises the following steps: S1, directly analyzing a measurement response signal by using the COV-SSI identification module to estimate an initial system state matrix F and a measurement matrix H of MCKF; S2, setting parameters of MCKF in the MCKF denoising pretreatment module, and realizing the reduction of measurement noise in the measurement response signal in step S1 by using the MCKF denoising pretreatment module; S3, identifying structural parameters by using the COV-SSI identification module, inputting the measurement response signal pretreated in step S2, and recalculating the system state matrix and the measurement matrix according to step S1; and S4, adopting the DBSCAN clustering post-processing module to cluster the result identified in step S3; the method can effectively reduce measurement noise, improve data quality and signal quality, and improve the robustness and accuracy of modal parameter identification.

The embodiment of the invention provides a call quality detection method and device, electronic equipment and a storage medium, and is applied to the technical field of communication. The method provided by the embodiment of the invention is applied. The method comprises the following steps: acquiring a single call MOS value, a single call packet loss rate, single call average jitter, single call average delay, single call maximum jitter, a single call packet loss rate peak value, a single call codec type, a single call network type and a single call duration of each call of a user in a preset historical duration; and calculating the first data and constructing a user feature vector to obtain the user feature vector representing the call quality of the user. And respectively analyzing the feature vectors of the users through a K-means clustering algorithm and a DBSCAN clustering algorithm, identifying abnormal call users, and clustering the users, so that the users with the same or similar call quality are clustered in the same cluster. Therefore, the long-term call quality of the user is judged.

The invention relates to the field of intelligent automobile control, in particular to a driving condition recognition method based on adaptive clustering. According to the driving condition identification method based on adaptive clustering provided by the invention, the driving condition is comprehensively represented through 10 characteristic parameters, and the DBSCAN clustering and the BP neural network are combined, so that high-speed keeping, high-speed overtaking, urban congestion, urban non-congestion, suburb common working conditions and suburb uphill and downhill complex driving conditions can be accurately identified; the application range is wide, the response speed is high and the recognition precision is high; through standardization and PCA dimension reduction processing, redundant information can be reduced, the calculation complexity can be reduced, the model training and recognition speed can be increased, and the method is suitable for real-time scene driving condition recognition; the adaptive clustering method is adopted, the number of types does not need to be preset, different driving environments can be flexibly coped with, a training set and a test set are reasonably distributed, and the model generalization ability is good.

The application discloses a kind of multi-beam outlier automatic filtering methods combined with uncertainty and density clustering algorithm, belong to multi-beam sounding data processing technical field.The application constructs grid node absorbable water depth point selection model according to CUBE filtering algorithm, and gridding is carried out to sounding data;The one-dimensional clustering of node absorbable water depth value is carried out using DBSCAN clustering algorithm, a plurality of water depth hypotheses are constructed according to the difference between water depth values, then the water depth hypothesis and uncertainty of node are updated using Kalman filter;Finally, the water depth hypothesis with higher reliability is selected from a plurality of water depth hypotheses as the real water depth value of node.The application can provide a kind of multi-beam sounding data automatic cleaning algorithm selection for domestic multi-beam sounding data processing software.

The application discloses a kind of based on data-driven wasserstein fuzzy set active distribution network distribution robust day-ahead scheduling method, realize the construction compact DRO fuzzy set, and effectively reduce the conservativeness of day-ahead scheduling result;The method constructs the wasserstein condition generated adversarial network model (CWGAN-GP) based on gradient penalty norm, for wind, light output day-ahead scene generation, and proposes the abnormal sample identification method of improved DBSCAN clustering combined with CNN-BiGRU automatic encoder, to improve the credibility of generated scene set;Adopt the compact boundary of data support set determined based on non-parametric kernel density estimation (NKDE) confidence interval, and combined with wasserstein metric to construct DRO fuzzy set.The application compared with the existing DRO day-ahead scheduling method, realizes the deep combination of data-driven method and DRO model, effectively reduces the conservativeness of fuzzy set, and improves the economy of day-ahead scheduling scheme and the adaptability of coping with new energy output uncertainty under the premise of guaranteeing decision robustness.

The application provides a kind of intelligent government affair hotline mass appeal analysis, detection method, the method includes the following contents: pull daily work order data regularly;The work order content in work order data is vectorized coding;For vectorized work order data, using DBSCAN clustering algorithm, obtain classification cluster;Traversal classification cluster, utilize the address feature of classification cluster, complaint feature, with the data not included in any cluster, again using DBSCAN clustering algorithm expands classification cluster, for obtaining more accurate complaint quantity and complaint number information;Obtain the complaint summary of each classification cluster through large model;Data is written into data.The application can realize mass appeal detection considering semantics and spatial information, replace the redundancy, repetitive work of original manual statistics in an intelligent way, can effectively help to identify and integrate similar appeals, so as to improve the processing efficiency, optimize the service quality, quickly and efficiently provide daily mass event list.

The invention belongs to the technical field of constructional engineering measurement, and discloses a constructional engineering measurement system based on segmentation and clustering, which comprises a multi-source data acquisition module, a data processing and analysis module and a control and display module. The multi-source data acquisition module is composed of a three-dimensional laser radar, unmanned aerial vehicle oblique photography equipment, a total station and GPS positioning equipment, and is used for acquiring and fusing multi-source data. And the data processing and analysis module realizes automatic and accurate identification and measurement of building components by adopting a process of data preprocessing, point cloud segmentation based on deep learning and combination of DBSCAN clustering and energy function optimization. And the control and display module is used for system control and result three-dimensional visualization. Through combination of multi-source data fusion and an intelligent algorithm, the problems of low efficiency, insufficient precision and poor automation degree of a traditional measurement method are solved, and the intelligent level and the operation efficiency of constructional engineering measurement are improved.

The application discloses a kind of road width automatic calculation and quality checking method of road network data, it is related to road network data processing technical field, to solve the problem of low efficiency of multi-source data fusion in existing method;The application is through collecting multi-source road network data and pre-standardization processing, unified coordinate system mapping and metadata annotation generate labeled original dataset;Perform noise adaptive filtering and occlusion detection, utilize U-Net semantic segmentation to identify interference area, and apply GAN local completion output purification dataset;Construct the multi-modal network calculation dynamic fusion weight of attention mechanism, and parallel integration forms fusion feature vector;Through Canny optimization edge extraction and DBSCAN clustering calculation width parameter, embedding topological structure;Use multi-scale consistency index to check result, if threshold backtracking optimization;Output result and iteratively update parameter, form closed loop mechanism.The method improves fusion efficiency and calculation accuracy, applicable to traffic navigation and urban planning, supports real-time dynamic application.