3543 results about "Data synchronization" patented technology

The invention relates to a multi-modal data and RAG technology fused power design knowledge base construction method, and belongs to the technical field of power software development. The method comprises the following steps: carrying out collection and information extraction on multi-source heterogeneous original data; the method comprises the following steps of: constructing a multi-dimensional knowledge element structure containing parameters, specifications and case relationships by carrying out classification, specialized and precise processing and cross-modal association on data; based on a vector, graph and relational database mixed storage architecture, semantic vector efficient retrieval, knowledge graph relation management and business data synchronization are achieved respectively; and a dynamic optimization result is subjected to hybrid retrieval, a dual-drive reasoning mechanism outputs compliance conclusions and bases, and a retrieval enhancement generation service ensures that output contents conform to specifications. And systematic management and intelligent application of the electric power design knowledge are realized.

The invention relates to the technical field of civil engineering tunnel structure safety monitoring and intelligent detection, in particular to a multi-modal fusion tunnel structure apparent disease identification and risk assessment system, which comprises an image acquisition module used for acquiring continuous images of the inner wall of a tunnel lining; a laser point cloud acquisition module; a structure sensor acquisition module; a data synchronization and preprocessing module; the multi-modal feature extraction module is used for performing depth feature extraction on the image, the point cloud and the sensor data; the heterogeneous feature fusion and disease identification module is used for fusing each modal feature and outputting a disease type identification result; and the risk assessment module is used for carrying out size estimation and parameterized expression on the identified diseases. The problems that in an existing tunnel inspection technology, the detection means is single, appearance and internal information cannot be considered, and the disease size is difficult to quantify automatically are solved.

The invention discloses a quality management and control system for fabricated decoration construction, and the system comprises a sensing layer which constructs a multi-modal data collection network, carries out the three-dimensional real-time data synchronous collection through combining logistics API docking and an OCR recognition system, and constructs a construction process digital twin bottom plate; in the edge calculation layer, an edge node carries out lightweight processing on the original data; the cognitive layer is used for calling a Prolog rule through a process knowledge graph engine to reasone the feature snapshots, carrying out defect instant diagnosis and dynamic constraint propagation, outputting a root cause path with probability weight through three-stage verification, and quantifying intervention influence; the decision-making layer is used for constructing a dynamic prediction model based on a bidirectional LSTM and an attention mechanism, automatically activating a compensation mode when key interference is detected in combination with an anti-fact memory bank and a case-based reasoning compensator, and generating an alternative scheme of optimal cost / optimal construction period / comprehensive balance through a multi-target optimizer; and in the application layer, a Unity engine is utilized to develop the digital twinborn billboard.

The invention discloses a high-precision laser positioning unpacking path planning system, and belongs to the technical field of robot automatic control and industrial automation. The system comprises a data synchronization module used for multi-sensor hardware synchronization and data alignment; the high-precision positioning module is used for outputting a precise pose based on environment skeleton characteristics and sliding window optimization; the semantic map construction module is used for fusing vision and laser data to generate a dynamic semantic grid map; the global path planning module is used for planning a smooth path in the skeleton channel by utilizing a mixed potential field improved A * algorithm; the motion control module is used for realizing closed-loop motion control and safety monitoring through model prediction and tracking; and the operation execution module is used for finishing millimeter-level precise stopping of an operation point by adopting visual servo and triggering unpacking operation. According to the method, the positioning robustness under dynamic shielding is improved through the environmental skeleton features, the safety and the high efficiency of the path are ensured by utilizing semantic understanding and intelligent planning, and the full-process automation from navigation to precise operation is realized.

The invention discloses a resource awareness and task migration method and system for industrial edge nodes. The method comprises the following steps: constructing an edge node resource dynamic monitoring system, and sensing, calculating, storing and network resource states in real time; establishing a node health degree evaluation model, and predicting a potential fault risk; designing an intelligent task migration decision-making mechanism based on a resource state; the guarantee of data consistency and service continuity in the task migration process is realized; and constructing a distributed task scheduling optimization framework. The system comprises a resource monitoring module, a health assessment module, a migration decision module, a data synchronization module and a scheduling optimization module. According to the method, the problem of unstable task execution caused by dynamic resource change in an industrial edge computing environment is solved, and intelligent resource management and efficient task migration of the edge nodes are realized.

The invention relates to the technical field of offshore wind turbine inspection, and discloses an offshore wind turbine inspection monitoring system based on digital twinning. A twinborn data synchronization module of the system collects fan structure state data and environmental hydrological data in real time through a multi-source sensor cluster, and constructs an offshore fan digital twinborn body; the routing inspection path dynamic generation module analyzes a fan fragile area and risk distribution based on the digital twinborn body, and generates a multi-unmanned aerial vehicle cooperative routing inspection path in combination with real-time marine weather forecast; the unmanned aerial vehicle cooperative control module schedules an unmanned aerial vehicle group to execute a task according to the cooperative inspection path, and dynamically responds to real-time data updated by the twin data synchronization module; and the real-time abnormity identification module fuses and analyzes unmanned aerial vehicle returned data, identifies fan defects, outputs abnormal information, calculates relative space deviation, generates an attitude control instruction, and sends the attitude control instruction to the unmanned aerial vehicle cooperative control module to realize unmanned aerial vehicle attitude real-time adjustment.

The invention discloses a non-power-grid sporadic material emergency purchase response system considering risk priority. The system comprises a sporadic material demand trend pre-judgment module, a risk perception purchase adaptation module, a risk income balance optimization module, an emergency purchase scheduling module, a non-power-grid material response module and a module cooperative control module. According to the system, data such as historical purchase and consumption rate are integrated through a demand trend pre-judgment module to output a demand prediction result, risk and performance parameters are coupled through a risk perception purchase adaptation module to generate an adaptation scheme, and an optimal purchase strategy is calculated through a risk income balance optimization module. A scheduling instruction is generated through an emergency purchase scheduling module according to the risk priority, a non-power-grid material response module executes inventory checking, transportation planning and other operations, and a module cooperation control module achieves whole-process data synchronization and parameter dynamic adjustment. According to the system, the decision-making scientificity and response efficiency of non-power-grid sporadic material emergency purchase are improved, and emergency purchase burstiness and complexity requirements are met.

The invention relates to the technical field of bearing fault diagnosis, in particular to a multi-bearing fault positioning and diagnosing method, which comprises the following steps of: acquiring bearing vibration signals of a plurality of sensing points, extracting peak time information to complete synchronous calibration, extracting high-frequency band energy and constructing a feature vector, screening deviation features and clustering and grouping, and identifying an energy abnormal channel, positioning a bearing position, extracting time domain and frequency domain features, comparing fault modes, and outputting a fault type diagnosis result. According to the method, the consistency of multi-channel data synchronization is realized by extracting the peak point of the vibration signal and calibrating the time offset, the high-frequency energy change rate is utilized to match the weight coefficient, the detection sensitivity of tiny fault features is enhanced, and the separability of a composite fault state is improved by combining feature relative deviation rate screening and density trend clustering. A frequency section energy proportion change recognition channel is adopted, accurate positioning of a fault bearing is achieved, and accurate judgment of a fault type is achieved in combination with a multi-feature comparison mode.

The invention discloses an L-shaped conveyor belt path dynamic planning and congestion prediction system, which relates to the technical field of automatic control, and constructs a conveying path node diagram by collecting material weight, speed and spacing information of each node on a conveying path, and calculates a congestion index of each path section; the method comprises the following steps: combining a light-mass material jumping sensitivity model, identifying a high-risk node and marking the high-risk node as a congestion prediction area, constructing a dynamic optimization model based on path risk distribution and a material flow direction, generating and evaluating a plurality of path adjustment strategies, executing an optimal strategy according to priority, and realizing path speed adjustment, standby path switching and feeding rhythm control; through a feedback mechanism, a strategy effect is evaluated in real time, a path state is updated, operation data is synchronized to a historical database, a learnable conveying operation file is formed, the stability and the intelligent response capability of a conveying system can be effectively improved, and the method is suitable for high-speed sorting and automatic packaging scenes.

The invention discloses a passenger motion sickness prediction method based on multi-modal information fusion, and belongs to the technical field of vehicle comfort control. The model comprises a data acquisition system, a data preprocessing module and a multi-modal fusion deep learning model based on CNN + BiLSTM + Attention. The data acquisition system acquires electroencephalogram, myoelectricity, galvanic skin, electrocardio physiological signals and three-axis acceleration data; the data preprocessing module carries out filtering, feature extraction and data synchronization on the signals; the deep learning model carries out fusion processing on physiological signals and motion parameters through a modal specific feature extraction layer, a time sequence feature extraction layer, an attention mechanism layer, a feature fusion layer and a prediction layer, and motion sickness risk prediction is achieved. The model further comprises a personalized adaptation mechanism, the motion sickness risk can be predicted 30-60 seconds in advance, and linkage with a vehicle comfort control system is achieved through a grading early warning mechanism. The invention further provides a vehicle-mounted motion sickness early warning system, a training method and a lightweight method based on the model, the riding comfort can be effectively improved, and the risk of motion sickness is reduced.

The invention discloses a material supply and resource recovery collaborative scheduling method and system, and belongs to the technical field of logistics scheduling, and the method comprises the steps: obtaining a material supply instruction and a garbage recovery instruction, carrying out the image recognition of a building garbage picture, obtaining the garbage attribute, and generating a demand instruction library; the method comprises the following steps: establishing a feature knowledge base, setting a spatial clustering method, carrying out spatial clustering and time window screening, carrying out geographic coordinate analysis and time sequence sorting on material demand points and recovery points, generating an initial task pool, setting a loading matching method, and generating a material supply and recovery loading schematic diagram according to building material and garbage attributes; a circulation path is planned, a path planning method is set, an ant colony algorithm is used for initial path planning, a monitoring feedback method is set, data are returned in real time, the running state of the vehicle is monitored, and an alarm is given out immediately once abnormity is found; and the sorted and regenerated aggregate data is synchronized to a building material database, a resource feedback method is set, a reverse transportation task is identified, and a material supply and resource recovery closed loop is formed.

The invention relates to the technical field of cultural transmission content auditing, and discloses a cultural transmission content auditing method, which comprises an auditing system and comprises the following operation steps: S1, a preparation stage: initializing and configuring a module; the method comprises the following steps: S101, dynamic strategy engine deployment: accessing a federal learning model, configuring a cross-regional data synchronization rule (for example, dividing data cooperative training groups according to cultural transmission coverage regions), initializing a dynamic risk feature library, importing features such as basic sensitive words and cultural taboo symbols, and associating'cultural sensitivity parameters' of a cultural dimension quantizer; according to the cultural transmission content auditing method, system sustainability is guaranteed through a process closed loop, auditing accuracy is improved through technology fusion, cultural transmission complexity is dynamically adapted and coped with, the requirement for compliance auditing of the cultural transmission content can be met, protection of cultural diversity and innovativeness can be considered, and the method is suitable for popularization and application. And the method is suitable for auditing challenges of emerging scenes such as short videos, cross-culture transmission and meta universe.

The invention discloses a metering box abnormal power consumption behavior diagnosis method and system based on edge calculation, and belongs to the technical field of power monitoring, and the method comprises the steps: collecting the voltage, current and time mark data of a metering box in real time through an edge end, synchronously obtaining the physical state information of a box door state and the like, generating a space-time feature matrix through an electric parameter space-time coupling analysis method, and carrying out the calculation of the time-space feature matrix; inputting an electric parameter feature mask pruning algorithm to obtain an optimized feature set, triggering an edge-sensing sleep wake-up linkage device when current abnormity is monitored, and collecting magnetic field data; the edge end calculates a total-branch electric energy difference value, combines the magnetic field data and a box door state, and constructs an electricity consumption abnormity classification determination tree through a metering error-electricity stealing behavior coupling diagnosis method to distinguish electricity stealing behaviors; an intermittent abnormal trajectory splicing algorithm is adopted to process fragmented abnormal data to generate an electricity stealing trajectory, and the electricity stealing trajectory is fed back to an electric parameter space-time coupling analysis method to dynamically update the space-time feature weight; according to the invention, the abnormity diagnosis precision and real-time performance are improved, and the energy consumption is reduced.

The invention provides an mbse-based system full life cycle management method and system, and relates to the technical field of data synchronization, and the method comprises the steps: obtaining to-be-synchronized data of a source system, constructing a semantic graph structure, generating a mapping strategy based on implicit association between semantic matching learning systems, executing transactional data transmission, and constructing a time sequence traceability tree to track change propagation. And adaptive reconstruction is carried out when a constraint conflict is detected. According to the method, the problem of data synchronization among heterogeneous systems is solved, and the model consistency maintenance capability and the adaptive conflict processing efficiency are improved.

The invention belongs to the technical field of sensor error correction, and discloses a data synchronization and error correction method based on multi-source gas detection, and the method comprises the steps: firstly achieving the time-space synchronization of a multi-source sensor through the combination of GPS / Beidou hardware time service and a gas diffusion transmission model; non-negative matrix factorization (NMF) is adopted to decouple sensor cross interference, and an LSTM neural network is utilized to predict a sensor drift amount caused by environmental temperature and humidity changes; and finally, carrying out dynamic weight fusion based on the real-time confidence of each sensor, and outputting a high-precision gas concentration value. The problem of data distortion caused by time sequence dislocation, cross sensitivity and environment drift in a multi-sensor system is effectively solved, the accuracy of detection data and the reliability of the system are remarkably improved, and the method is particularly suitable for multi-component gas detection scenes in the fields of chemical engineering, environment monitoring and the like.

The invention relates to the technical field of computer data processing, and discloses an automatic format conversion and incremental synchronization method for cross-platform heterogeneous document data. The method comprises the following steps: capturing an atomic data change event from a source system transaction log; aggregating into logic transaction units according to the transaction identifiers; converting into a unified intermediate normal form; generating a target system operation instruction set based on the metadata mapping rule; the synchronization sites are executed and persisted in a transactional manner. According to the method, through log-level capture and transactional synchronization, data integrity, sequence and high consistency are ensured, and efficient and reliable data synchronization between heterogeneous systems is supported.

The invention relates to the technical field of terminal monitoring, in particular to an intelligent security terminal collaborative monitoring system with edge computing capability. Comprising a multi-mode sensing unit; the edge calculation processing unit is used for distributing CPU and FPGA operation tasks by using a dynamic characteristic distillation technology and a hardware load feedback technology on the basis of multi-mode perception data; a distributed cooperative communication unit; and an intelligent decision-making unit. Visual, voiceprint and environmental physical signals of a security area are collected through the multi-modal sensing unit, unified time stamps and spatial coordinate association are added for multi-dimensional signals by means of the data synchronization module, and cooperation of the feature distillation module and the model optimization module in the edge calculation processing unit is combined. According to the method, multi-modal features are mapped to a unified feature space, high-value features are screened, and redundant convolution kernels are identified and removed, so that effective cooperative processing of multi-modal security and protection data can be realized, and the processing efficiency of edge-side sensing data is improved.

The invention discloses a portable operation and maintenance gateway management and control method and system based on dynamic monitoring feedback, and relates to the field of portable operation and maintenance gateway management and control, and the method comprises the steps: enabling a portable operation and maintenance gateway to be connected between an operation and maintenance terminal and a power control system in series, and carrying out the dual authentication of personnel and equipment; after authentication is passed, operation and maintenance are started, identity, time, position and gateway state of operation and maintenance personnel are collected and monitored in real time, reference information is matched to obtain a matching result, operation and maintenance behaviors are monitored in real time, multi-source data are collected, a management and control strategy is dynamically generated based on risk assessment, and a safety response is triggered; when the equipment is abnormal, an emergency repair mode is switched, non-core authority is relaxed, and whole-course auditing is carried out; the data in the whole operation and maintenance process is uploaded to a remote management platform after structured arrangement, and multi-channel synchronization and delayed uploading are supported when the network is abnormal. The system comprises an authentication module, a risk assessment module, a strategy execution module and a data synchronization module, power operation and maintenance whole-process safety management and control are achieved, efficiency and safety are both considered, and operation traceability is guaranteed.

The invention relates to the technical field of air leakage big data analysis, in particular to a compressed air pipeline air leakage intelligent analysis system and method based on LLM. A plurality of monitoring nodes in a compressed air pipeline are analyzed, and data normal range parameters and adaptive acquisition frequency are preset for each node in the pipeline operation stage; the clear definition standard avoids misjudgment of original data due to fuzzy threshold values, then data noise is effectively reduced by analyzing the data synchronization degree of each monitoring node and intelligently repairing the problems of missing, deviation and the like, the repaired data better fits the actual operation state of the compressed air pipeline, the missed detection and mispositioning risks are reduced, and the reliability of the compressed air pipeline is improved. And finally fusing the data of the gas leakage node and the adjacent node to supplement multi-dimensional information, and optimizing the fusion data quality. The LLM model depends on high-quality fusion data, deep analysis is carried out in combination with compressed air pipeline multi-monitoring-node correlation characteristics and historical experience, deviation caused by data defects is reduced, and the accuracy of an intelligent air leakage analysis result is greatly improved.

The invention discloses a data synchronization method and device, a storage medium and a program product, and relates to the technical field of databases, and the method comprises the following steps: determining a start site for collecting change data in a source database, the source database comprising a main database and at least one standby database; target change data in the standby library are obtained according to the start site, a first logic replication slot in the standby library is called to decode the target change data, decoded data are obtained, and the first logic replication slot is obtained through synchronization of a second logic replication slot in the main library; and synchronizing the decoded data to the target storage unit. According to the method, the main resource consumption of real-time data synchronization is unloaded from the core service main library to the standby library, so that the performance interference of the data synchronization task on the main library is avoided to the greatest extent, and the continuity and response capability of the GaussDB main library service are ensured.

The invention relates to the technical field of industrial digital twinning, in particular to a digital twinning-oriented real-time data synchronization and consistency verification method, which comprises the following steps of: S1, acquiring multi-source heterogeneous data from a physical entity and a service system, and performing timestamp alignment based on a unified time reference to generate a standardized data stream; and S2, based on the standardized data stream, carrying out data cleaning, aggregation and preprocessing, verifying the integrity and time sequence of the data stream through a transmission verification mechanism, and outputting high-fidelity credible data. According to the method, a bidirectional consistency verification mechanism between a physical entity and a digital twinborn model is constructed, real-time deviation is quantified into a loss function, an online learning engine is driven to adaptively adjust physical parameters in the model, and a complete closed loop from data acquisition to parameter optimization is formed; the digital twinborn model can continuously adapt to dynamic conditions such as material aging and working condition change of a physical entity, and the tedious process that a traditional system depends on manual regular calibration is avoided.

The invention discloses a Beidou time service synchronous multi-source data fusion monitoring device, system and method, and aims to solve the core problems of low meteorological factor-ground surface settlement causal correlation analysis precision and poor early warning reliability caused by multi-source data time asynchronization due to sensor clock independence. The device directly drives each sensor in the multi-source data acquisition module to perform physical-level synchronous acquisition through a hardware synchronous trigger pulse generated by the Beidou time service module, and ensures that settlement, weather and positioning data have the same timestamp at the source; and time axis calibration and dynamic weight fusion based on real-time data quality are carried out through the preprocessing module. The system integrates the devices, a cloud analysis model and an early warning engine. According to the invention, a closed loop from hardware-level synchronous acquisition, space-time alignment to intelligent analysis is realized, a multi-source data synchronization error is reduced from a second level to a millisecond level, and the accuracy and timeliness of water and soil conservation monitoring are remarkably improved.

The invention discloses a heat supply pipe network operation fault diagnosis method based on digital twinning, and the method comprises the following steps: S1, constructing a digital twinning architecture of a heat supply system, and constructing a complete digital twinning system based on a physical entity, a virtual model, a data synchronization layer and a decision control layer; s2, multi-source data fusion and state mapping; s3, a fault prediction and diagnosis mechanism is adopted, an SVM-LSTM joint model is adopted to carry out time sequence anomaly detection and fault prediction, an XGBoost classifier is optimized in combination with an improved sparrow optimization algorithm, and a classification result is output; and S4, performing self-healing control and cooperative regulation and control. According to the invention, a complete digital twin architecture is constructed, through a five-layer system of a physical entity, a sensing and execution network, a data synchronization layer, a virtual model layer and a decision control layer, a full-link closed loop from data acquisition, model driving to intelligent decision is realized, and systematic support is provided for fault diagnosis and self-healing control.

The invention discloses a campus intelligent consumption analysis system and method based on multi-dimensional data fusion, and relates to the technical field of electric digital data processing. The system comprises a terminal feature fusion module, an anomaly identification analysis processing module and a classification verification and synchronization module. The terminal feature fusion module fuses terminal feature data and time feature data in the original multi-dimensional data to generate behavior association features, and data dispersion is reduced; the anomaly identification analysis processing module performs anomaly identification and processing on the behavior association features to form a feature data set and verify the validity, so that the detection accuracy is improved; and the classification verification and synchronization module uploads the verified classification result as a core data set to a specified service platform and synchronizes the core data set to a database of the specified service platform, so that the platform processing efficiency is improved. Through cooperation of multiple modules, efficient fusion, accurate anomaly detection and data synchronization of campus consumption data are realized, and comprehensiveness of campus consumption analysis and processing efficiency of a service platform are improved.

The invention relates to the technical field of pollution source monitoring, in particular to a stationary pollution source automatic monitoring method and system and a digital intelligent construction method thereof. S2, in a real-time monitoring stage, a multi-dimensional sensing array collects equipment health degree data and working condition parameters at high frequency, and a data collection device inputs real-time data into the ternary coupling reference model; s3, starting deviation traceability, and correcting equipment drift or working condition interference; s4, synchronously uploading the data to a remote operation and maintenance platform, pre-judging an equipment fault risk based on an equipment health degree change rate, and generating a maintenance work order 48 hours in advance; s5, for the predictive maintenance work order; and S6, automatically iterating the model monthly, incorporating the current-month effective monitoring data and maintenance records into the ternary coupling reference model, and optimizing the association weight. The method effectively guarantees the accuracy of the monitoring data of the stationary pollution source by constructing the ternary coupling reference model and combining the comparison and correction of the real-time monitoring data and the predicted value.

The invention discloses an abnormal sleep signal identification method for an intelligent sleep monitoring pillow, and relates to the technical field of abnormal sleep signal identification. Comprising the following steps of collecting multi-modal sleep physiological data, preprocessing the multi-modal sleep physiological data, extracting multi-dimensional features from the preprocessed data, constructing a fusion analysis model to judge abnormal sleep signals, triggering a hierarchical intervention mechanism and outputting associated intervention suggestions. According to the method, the time dimension alignment technology and the sliding window matching technology are adopted, the problem of data synchronization caused by multi-sensor sampling frequency difference in a traditional abnormal sleep signal recognition technology is solved, multi-source features are fused through deep learning, the accuracy of abnormal sleep signal recognition is remarkably improved, and the recognition accuracy of abnormal sleep signals is improved. An intelligent analysis model based on deep learning multi-network fusion is constructed, deep extraction and fusion judgment of complex sleep features are realized by utilizing cooperative processing of a convolutional neural network, a recurrent neural network and an attention mechanism, and the reliability of abnormal sleep signal recognition is improved.

The invention provides a data synchronization method and device based on Spring Batch and a large model. Synchronously initializing a large model micro service and a Spring Batch framework, and establishing a model service hot standby mechanism; obtaining data source features through a metadata scanning engine driven by a large model, and generating an adaptive synchronization strategy; optimizing a query plan based on the synchronization strategy, dynamically controlling data fragmentation and executing streaming preprocessing and semantic mapping conversion; a target table structure is adapted, a three-level conflict detection mechanism is adopted to eliminate data conflicts, and meanwhile transaction processing and batch writing logic are optimized; the data quality is verified through three-level verification, performance parameters are dynamically adjusted and optimized, and fault-tolerant recovery and resource elastic expansion and contraction are achieved; metadata are synchronized, an execution report is generated, knowledge base self-learning is completed based on task full-link indexes, and a data consanguinity map and a reusable processing case library are constructed. According to the scheme, diversified and dynamic enterprise data synchronization requirements can be met.

The invention discloses a multi-scene adaptive AGV scheduling management system and a path planning method. Comprising a multi-scene perception and environment modeling module, a multi-AGV task scheduling and resource allocation module, a dynamic path planning and optimization module, a scene adaptive decision module, a multi-sensor fusion obstacle avoidance module, a heterogeneous communication and data synchronization module and a whole-process monitoring and fault diagnosis module. According to the invention, full-process adaptive control from environment modeling to decision optimization is realized. An SVM classification algorithm and dynamic matching degree calculation are adopted, so that the system can accurately identify characteristics of heterogeneous scenes such as warehousing, workshops and logistics hubs, and an optimal scheduling strategy is automatically switched; and a multi-dimensional cost function including path length, obstacle risk, smoothness and congestion degree is constructed, and seamless connection of global path pre-planning and local dynamic adjustment is realized.

The invention provides a roadway surface and internal defect synchronous sensing method based on point cloud registration. The roadway surface and internal defect synchronous sensing method comprises the following steps: S1, multi-sensor joint calibration; s2, synchronously acquiring data; s3, data preprocessing; s4, projecting a camera image to the point cloud; s5, surface defect identification: S5.1, geometric defect identification based on point cloud; s5.2, performing visual defect identification based on the image, and marking an identification result in the point cloud; s6, integrating the geometric defect point cloud and the visual defect point cloud; s7, identifying internal defects; s8, determining an internal confirmation three-dimensional position; s9, marking an internal defect point cloud; and S10, uniformly visualizing the surface defects and the internal defects. According to the invention, through cooperative work of the laser radar, the ground penetrating radar and the camera, the same perception of tunnel defect surface and internal defects is realized through point cloud visualization.

The invention relates to the technical field of laser displacement meters, discloses a calibration method of a laser displacement meter for a Beidou device based on multi-data coupling, and effectively solves the problems of time delay compensation and data synchronization of cooperative work of multiple devices in a dynamic environment. According to the invention, when a carrier moves at a high speed or environmental parameters suddenly change, submillimeter-level positioning precision can still be maintained, meanwhile, data processing delay is controlled within a millisecond-level range, the reliability of a measurement system under a complex working condition is remarkably improved, and through double marking of a GNSS timestamp and a local clock, the measurement precision is greatly improved. And an accurate time deviation observation value is provided for a subsequent time delay compensation algorithm, so that a time-space unified data basis is established in a data preprocessing stage, and the feasibility of multi-source heterogeneous data fusion is remarkably improved.