865 results about "Calculated data" patented technology

The invention relates to the technical field of network communication, and discloses a vehicle-mounted edge computing data recording system and method based on protocol adaptive analysis, and the system comprises a multi-protocol network access controller which is used for capturing an original data frame and extracting a physical feature triple; the identification analysis engine is used for executing hash operation on the triple to generate a physical feature code and retrieving a physical logic address mapping table; the direct memory access controller is used for responding to a target memory address pointer hit by retrieval and directly writing a data load into an input buffer area of the functional operation module, and by constructing a direct addressing mechanism based on Hash mapping, thorough decoupling of vehicle-mounted heterogeneous network physical topology and edge computing logic is achieved.

A method is provided for constructing decision trees. The method includes analyzing a dataset to determine data distribution metrics; calculating feature importance scores for features in the dataset; dynamically adjusting tree depth and node pruning criteria based on the determined data distribution metrics and feature importance scores; initializing a decision tree structure based on the adjusted tree depth and node pruning criteria; and iteratively refining the decision tree by applying heuristic adjustments to improve splits based on updated data distribution metrics and feature importance scores.

The invention relates to the technical field of data processing, and discloses an intelligent monitoring method and system for cyanobacterial bloom outbreak. The method comprises the following steps: collecting a water surface spectrum and underwater particle size data, carrying out atmospheric correction, calculating a normalized algae index and a blue-green wave band ratio, inputting the normalized algae index and the blue-green wave band ratio into a U-Net network to obtain a water bloom coverage area, carrying out integral interpolation on the particle size data to obtain a vertical section distribution curve, and calculating a surface layer enrichment degree and a floating trend index, and establishing a water surface-underwater association relationship through random forest regression training, and inputting the multi-dimensional features into a CNN-LSTM model to predict a water bloom outbreak probability and determine an early warning level. According to the method, the problem that the cyanobacterial bloom three-dimensional structure cannot be comprehensively described due to the lack of effective fusion of the water surface spectral data and the underwater vertical section data is solved, the problem that the early warning timeliness of cyanobacterial bloom outbreak is insufficient due to the lack of a multi-source data time sequence analysis model is solved, and the spatial integrity and early warning advance of cyanobacterial bloom monitoring are improved.

The invention provides an automatic data management method and system based on a multi-modal large model, and the method comprises the steps: collecting multi-source heterogeneous industrial data, and carrying out the standardization processing, and forming standardized multivariable time series data; constructing a process knowledge base, and performing semantic embedding coding on a process knowledge text and storing the process knowledge text; constructing and finely tuning a KTSF multi-modal large model, and fusing process knowledge semantics and multivariable time sequence data through a cross-modal attention mechanism to generate joint semantic representation; based on prediction of a KTSF multi-mode large model, outputting a residual error with actual data, and dynamically identifying abnormal data; performing attribution analysis; based on an attribution result, calling a KTSF multi-mode large model to generate a repair value, and performing intelligent correction on the abnormal data; the design quality evaluation and feedback learning module is used for calculating a data quality score and driving incremental updating of the model; and the design rule self-learning module is used for automatically extracting the governance rule through clustering analysis and updating the knowledge base.

The invention relates to the field of edge computing data security, in particular to an integrity auditing method and system in an edge computing environment. The problem that an existing cloud auditing model is high in delay and large in bandwidth consumption in an edge scene is mainly solved. A lightweight audit challenge is generated through random sampling, data block aggregation operation is executed at an edge node, Merkel Hash tree path evidence is extracted, and integrity proof of dual verification is formed; and the aggregated evidence and the path evidence are verified by using bilinear mapping, so that efficient and credible auditing is realized. Dynamic data updating and accurate positioning of damaged blocks are supported, communication overhead is remarkably reduced, and real-time credibility of edge data is guaranteed.

The invention belongs to the technical field of artificial intelligence, and discloses an artificial intelligence model training resource adaptive distribution system. The method comprises the following steps: acquiring and calculating graph structure data and hardware resource state data in real time, and calculating a data reuse rate and generating a candidate operator fusion scheme by constructing an operator execution time sequence constraint matrix and identifying an operator cluster of data locality characteristics; a resource competition hotspot prediction mechanism is introduced, memory bandwidth occupation fluctuation characteristics are analyzed, a resource conflict probability is calculated for a fusion scheme, and a dynamic balance optimization model of fusion income and resource conflicts is constructed. An optimal operator fusion decision sequence and a resource allocation strategy are generated through iterative solution, and accurate dynamic adjustment of computing resources in the training process is achieved. The training efficiency and the resource utilization rate are improved, the energy consumption is reduced, and the system stability is enhanced.

The invention discloses a U.2 interface-based server intelligent integrated hierarchical storage system and method, relates to the technical field of computer server storage, and is used for solving the problem of optimization of multi-type SSD data access efficiency and storage resource utilization rate. Firstly, a multi-protocol parallel channel is established by redefining U.2 interface reserved pins, signal integrity parameters are monitored in real time and fused with equipment I / O access features, and an I / O feature sequence of physical layer state information is generated; then, based on a time sequence convolutional neural network, extracting access mode multi-scale features, calculating a data block access heat probability, mapping and determining a target storage hierarchy, and generating a scheduling instruction containing a source physical address, the target hierarchy and a migration priority; the migration execution module realizes zero-copy migration of the data block between the shared DRAM pool and the SSD according to the instruction; and the resource coordination module monitors system load in real time, dynamically adjusts migration concurrency and an I / O sampling strategy, and realizes self-adaptive optimization of storage performance and resource utilization.

The invention discloses a data transaction security assessment method and system, and relates to the technical field of data security and intelligent assessment. The method comprises the steps of performing feature analysis on a data field, determining a sensitivity weight and generating a sensitivity vector; the fields are graded according to the sensitivity vectors, and dynamic adjustment is carried out in combination with transaction related information to obtain a grading result; calculating a data content risk, a field association risk, and a transaction environment risk based on the sensitivity feature set; determining a weight according to a transaction scene, and weighting various risks to obtain a comprehensive score; and judging the risk level according to the comprehensive score and a threshold value dynamically determined based on historical data. According to the invention, through hierarchical sensitivity modeling and dynamic risk calculation, fine-grained security assessment of different types and sensitivity data is realized, and the risk result can be adaptively adjusted along with changes of data contents, transmission paths and transaction environments, so that the accuracy, real-time performance and scene adaptability of assessment are improved, and the risk assessment efficiency is improved. The defects of a static evaluation method in the prior art are overcome.

The invention provides a vehicle NVH comfort assessment method and device based on sound and vibration fusion and electronic equipment. The method comprises the following steps: performing in-vehicle interference noise identification processing on window-level data in a candidate window set to obtain a candidate effective window set; calculating a sound and vibration consistency index between the in-vehicle audio signal and the in-vehicle vertical acceleration signal for the window-level data in the candidate effective window set, and screening an external noise dominant window to obtain an effective window set; respectively calculating an acoustic index and a vibration index based on the effective window set, and performing calibration compensation on the acoustic index and the vibration index based on the sensor calibration information; and performing fusion calculation based on the data volume information of the effective window set, the working condition coverage information, the uncertainty information corresponding to the sensor calibration information and the proportion information of the effective window set in the candidate window set to obtain a comprehensive confidence index. According to the method, the evaluation accuracy and stability can be improved, the cross-equipment comparability is enhanced, and the result credibility is improved.

The invention discloses an intelligent monitoring and early warning system and method for project progress and cost, and relates to the technical field of constructional engineering management. The streaming data fusion module adopts an Apache Flink engine and applies a dynamic time warping algorithm to carry out time alignment on multi-source asynchronous data to realize semantic unification; the space-time diagram neural network prediction module constructs a space-time association diagram, integrates global features such as weather and supply chain fluctuation, and adaptively learns an influence weight by using a graph attention mechanism; the self-adaptive early warning module adopts a Bayesian online learning framework, an early warning threshold value is dynamically adjusted according to a historical false alarm rate and a missing report rate, the method comprises the steps of data acquisition, fusion, mapping, joint prediction and self-adaptive early warning, the problem of data islands is solved, complex space-time association is accurately captured through a space-time diagram neural network, and real-time early warning is achieved. Joint prediction of progress and cost risk is realized, false report and missing report are reduced, and real-time performance, accuracy and decision-making efficiency of engineering management and control are improved.

The invention relates to an edge computing network-oriented micro-service and distributed database collaborative deployment system. The system comprises a database copy number dynamic optimization unit which obtains a dynamic optimization strategy of the copy number of a database based on a queuing gradient projection elastic scaling algorithm; a deployment strategy dynamic adjustment unit calculates end-to-end time delay based on the micro-service routing path and the database routing path; calculating a data inconsistency measurement index based on the number of each copy of the database; dynamically adjusting the deployment strategy based on the resource constraint between the end-to-end time delay and the data inconsistency measurement index and the dynamic optimization strategy of the number of copies of the database; the deployment strategy comprises micro-service instance deployment, database copy deployment and optimal routing path selection; according to the MEC scene-oriented distributed database design and fine-grained collaborative deployment optimization method, a message queue, a queuing theory and a Canal database incremental updating mechanism are integrated, and the micro-service application performance and the data query efficiency / reliability are improved.

The invention relates to the technical field of visual monitoring, and discloses a novel bridge visual monitoring method, which comprises the following steps: S1, arranging targets, installing monitoring targets and reference targets, S2, erecting a system, erecting a customized visual monitoring system in a stable area, S3, establishing a coordinate system, and S4, carrying out visual monitoring. The method comprises the following steps: S1, arranging a standard target at the bottom of a structure to be measured, performing three-dimensional coordinate measurement on the standard target at the bottom of the structure to be measured in the step S1 by adopting a total station, and establishing a standard coordinate system O-XYZ, S4, acquiring and processing an image, and vertically and upwards shooting an image of the target mounted on the inner side of each structure to be measured, S5, calculating displacement and perpendicularity, and S6, transmitting data. According to the method, an environmental parameter compensation mechanism is deeply integrated into the whole process of coordinate measurement and deformation calculation, and unified reference coordinate system establishment and target layout specifications are combined, so that data deviation caused by environmental factors and operation differences in different monitoring scenes is eliminated, long-term monitoring data has systematicness and continuity, and the method is suitable for large-scale popularization and application. The problem of inconsistent results caused by human differences in traditional measurement is solved.

The invention discloses a method for calculating the influence sensitivity of multiple stations and multiple control parameters on the short-circuit current of an alternating current system, and the method comprises the steps: building a calculation data model of the short-circuit current of a power grid, and enabling the model to comprise at least one new energy or energy storage device equivalent to a controlled current source; based on the calculation data model and the independent variable selected by each station, utilizing an iteration method to calculate the short-circuit current increment of each station; calculating the short-circuit current of each station fault point according to the short-circuit current increment; and calculating a sensitivity index of the independent variable relative to the short-circuit current of the specified fault point according to the short-circuit current.

The invention discloses a child abnormal breathing early warning and remote cooperation system for a family environment, and relates to the technical field of breathing data processing, and the child abnormal breathing early warning and remote cooperation system comprises a data acquisition module, a first data calculation module, a first data judgment module, a second data judgment module, a second data calculation module, a third data judgment module and a data output module. The system sequentially performs primary risk assessment, hidden risk mining and potential decompensation index calculation on the basis of a multi-modal signal processing technology by synchronously acquiring a breathing sound signal, a non-contact thoracoabdominal movement signal and a contact thoracoabdominal movement signal, so that graded early warning is realized, and a remote cooperation mechanism is automatically triggered according to the early warning level. According to the method, the hidden risk in the early stage of respiratory compensation of children can be effectively identified, and advanced early warning and timely intervention of abnormal respiration can be realized in a family environment.

The invention relates to the technical field of AI chips, in particular to a calculation method of an AI chip based on RISC-V CPU Core and the chip. The AI chip comprises a scheduling core and a plurality of computing cores based on an RISC-V instruction set architecture, and the method comprises the following steps: the scheduling core analyzes an AI task to determine one or more computing operation types required by the AI task, and selects one or more computing cores capable of executing the computing operation types from the plurality of computing cores according to the computing operation types; the scheduling core allocates the AI task to a calculation core corresponding to the calculation operation type, and activates the calculation core; and the calculation cores obtain respective kernel codes and required calculation data so as to execute respective calculation tasks. It can be understood that according to the technical scheme, the scheduling core and the computing core are both based on the RISC-V instruction set architecture, the ecological barrier problem is solved, flexible iteration of the architecture and optimal configuration of computing resources are achieved, and the method has the beneficial effects of ecological openness, high flexibility, remarkable cost effectiveness and the like.

The invention discloses a data cleaning scheme generation method and device and a readable storage medium, and the method comprises the steps: carrying out the grouping of pre-training task data through employing a clustering mode, and obtaining a plurality of data groups; extracting data characteristics of each data packet based on a large model; generating a data cleaning scheme for the corresponding data groups by using the pre-training model in combination with data characteristics, and executing the data cleaning scheme; calculating a data cleaning index, and storing the data characteristics, the data cleaning scheme and the data cleaning index as short-term memory into a short-term memory module; evaluating the overall cleaning quality evaluation score of the data cleaning scheme by using a reflection module to obtain a reflection result; the data characteristics, the data cleaning scheme, the overall cleaning quality evaluation score, whether data cleaning succeeds or not and the reflection result serve as long-term memory and are stored in a long-term memory module, the data cleaning process can have traceability, and the data cleaning scheme adjusting efficiency is improved.

The invention discloses a robot remote adaptive data acquisition control system and method based on dynamic value evaluation. According to the system, dynamic information value factors of data points are calculated in real time through a local processing unit of the robot, and the factors integrate the data change rate, the uncertainty, the task context and the abnormal probability. Based on the factor, the system dynamically adjusts the sampling frequency, the compression strategy and the transmission priority. The remote center is responsible for data reconstruction and fusion and utilizes machine learning closed-loop optimization to acquire parameters. According to the invention, the problems of resource waste and key information acquisition omission caused by fixed frequency sampling are solved, and the optimal balance of data acquisition efficiency and quality under a limited bandwidth is realized.

The invention discloses a sensing target motion compensation method, system and device based on high-precision positioning information and a medium, and mainly relates to the technical field of target motion compensation. The method is used for solving the problems that an existing motion compensation method mainly depends on chassis information of a vehicle, a wheel speed meter depended by the existing motion compensation method is prone to being affected by external factors, and accumulated drifting generated by purely depending on integral calculation of pose changes cannot meet the long-period and high-precision sensing requirements of an intelligent driving system. Comprising the steps of calculating a relative motion transformation matrix from a data acquisition moment to a data processing moment, and further calculating coordinates of a sensing target at the data processing moment; inputting the covariance matrix and the moment state at the data acquisition moment into a Kalman filter to obtain a predicted moment state and a predicted covariance matrix at the data processing moment; and calculating the moment state of the sensing target after data processing moment compensation by using the coordinates of the sensing target at the data processing moment, the predicted moment state at the data processing moment and the predicted covariance matrix.

The invention provides a data receiving and processing method based on serial port idle interruption and memory direct transmission. The method comprises the following steps of: S1, initializing a multi-stage buffer area, enabling idle interruption and DMA (Direct Memory Access) by a UART S2, serial port receiving and DMA transmission are started; s3, automatically transmitting data to an active buffer area by a DMA background, identifying frame ending with 1.5 times of character idle duration through idle interruption, and detecting whether communication of a serial port is abnormal or not in combination with a heartbeat packet and overtime detection design; s4, the effective length of the data is calculated through the address offset, the data is copied to the to-be-processed buffer area, the buffer area is switched, and finally the application layer is notified; s5, the application layer carries out CRC verification on the data, the data is processed if the CRC verification passes, and the data is retransmitted if the CRC verification fails; and S6, initializing the serial port and the DMA when the serial port communication is judged to be abnormal. According to the method, through idle interruption, DMA, a multi-level buffer area and exception recovery, efficient receiving of the variable-length data is achieved, CPU occupation is reduced, and communication reliability is improved.

The invention relates to the technical field of hydraulic engineering safety monitoring and operation control, in particular to a hydraulic engineering information management system based on digital twinning, which comprises a data acquisition module used for acquiring operation state parameters of a hydraulic engineering entity, real-time water level data of a reservoir area and simulation node calculation data generated by a digital twin engine are collected; the first processing module is used for determining a fidelity deviation index between the physical monitoring data and the digital simulation data; the second processing module is used for calculating accumulated risk potential energy reflecting the equivalent exposure duration of the high-risk working condition; the scheduling decision module is used for generating a gate opening degree adjusting instruction for eliminating the accumulated risk through proportional-differential control logic according to the accumulated risk potential energy and a preset safety potential energy threshold value so as to drive a physical gate to act to form a feedback closed loop; according to the method, the technical problem that multi-source heterogeneous data is difficult to fuse and check is effectively solved, data features with small numerical values are prevented from being covered, and the simulation credibility of the system is improved.

The invention provides a similarity retrieval system and method based on vector modulus length sorting. The system comprises a vectorization module, an index construction module, a self-adaptive candidate set generation module and a precise sorting module. Relates to the technical field, and the method comprises the following specific steps: S1, calculating a vector modulus length value of a database vector, generating a globally sorted ordered index according to the vector modulus length value, and constructing a density distribution diagram; s2, calculating the query modulus length of the query vector; s3, extracting a candidate vector set from the ordered index; s4, performing accurate similarity calculation on each candidate vector and the query vector, and outputting a retrieval result; according to the method, rapid coarse positioning is carried out by constructing a one-dimensional modular length ordered index, and the size of a candidate set is dynamically adjusted according to the local density of data by utilizing an adaptive modular length adjacent window calculation process, so that intelligent and adaptive regulation and control of a retrieval process are realized.

The invention discloses a hotspot sensing hierarchical storage and computing resource scheduling method oriented to multi-modal data, and belongs to the technical field of data center resource management. The core of the method is as follows: firstly, monitoring characteristics, access modes and computing node loads of multi-modal data in real time; secondly, a comprehensive popularity evaluation model is used for dynamically calculating the comprehensive popularity value of the data object, and the model comprehensively considers the data type, the access frequency, the data size and the task priority; then, intelligently scheduling the data to different performance levels of the hierarchical storage system according to the popularity value; and finally, when a calculation task is scheduled, synchronously sensing the heat and the position of required data, and carrying out collaborative scheduling by taking optimal data locality and load balance as targets. According to the method, storage and computing resources are managed cooperatively through the unified heat index, the problem of resource scheduling disjunction in a multi-modal data environment is effectively solved, and the system throughput rate, the response speed and the resource utilization efficiency are remarkably improved.

The invention discloses a DC-DC converter parameter identification method based on digital twinning, and the method comprises the following steps: firstly building a Buck converter simulation model, collecting needed data from the simulation model, building a mathematical model according to a Buck converter circuit structure, KCL and KVL, and carrying out the calculation of the Buck converter simulation model. And solving a differential equation of the mathematical model by using a Runge-Kutta method to obtain calculation data, substituting the collected simulation data and calculation data into a target function, performing iterative optimization on the target function value by using a Kirchhoff's law optimization algorithm, and outputting an optimal solution of a to-be-identified parameter, namely an identification value of the circuit parameter. According to the method, the parameter identification error of the DC-DC converter can be reduced, and the accuracy is improved.

The invention provides a urine HCG full-automatic detection and urine routine combined detection system, which comprises a host shell, an interactive interface, a sample interface, a USB interface, a sampling and sample introduction module, a urine detection module, a urine detection module, a urine detection module and a urine routine detection module, the microfluidic and reagent module is used for automatically distributing and reacting urine samples; the multifunctional automatic detection module is used for performing optical detection and electrochemical detection and completing combined detection of HCG and urine routine; the data processing module is used for collecting original signals and carrying out data processing and calculation to obtain HCG concentration, urine routine multi-parameter results and a detection report; and the intelligent diagnosis support module is used for performing medical correlation analysis and medical record intelligent diagnosis. Through combined detection of HCG and urine routine, HCG and urine routine results can be obtained at the same time, the detection efficiency is improved, and the detection accuracy and sensitivity are enhanced through optical detection and electrochemical detection processes.

The invention relates to the technical field of passive perception and behavior recognition, and discloses a passive attitude monitoring method and system based on deep learning, and the method comprises the steps: obtaining a target object monitoring image sequence collected by a plurality of visual angle cameras, and generating a multi-dimensional shielding mark tensor; calculating a data validity weight based on the occlusion mark tensor, and constructing a sparse multi-view observation tensor; decomposing the observation tensor into a low-rank background tensor, a sparse change tensor and a noise tensor by using a sparse time sequence tensor decomposition algorithm; inputting the decomposed low-rank background tensor and sparse observation features into a conditional variation auto-encoder, and reconstructing a complete time sequence track; and through physical constraint optimization and Bayesian reasoning, a high-confidence continuous attitude monitoring sequence and a gradual change abnormity early warning signal are generated. The technical problem of accurately monitoring the slow change of the posture of the illuminating lamp under the condition of frequent shielding is solved.

The invention discloses an intelligent task scheduling and collaborative execution method and a heterogeneous computing system, and the method comprises the steps: receiving and analyzing a computing task, and extracting the computing, data and constraint features of the computing task; decomposing the task into sub-tasks with a dependency relationship; acquiring load, computing power and power consumption states of the NPU, the GPU and the CPU in real time; on the basis of task characteristics and real-time states, multiple allocation schemes are evaluated through a pre-constructed cost model, and an optimal scheduling decision for allocating the sub-tasks among the heterogeneous computing units is dynamically generated; a flow line collaborative execution plan across at least two computing units is arranged according to a decision, sub-task execution is scheduled, meanwhile, cross-unit data flow and synchronization are managed, and a corresponding system comprises an NPU, a GPU, a CPU, a system memory and an intelligent heterogeneous resource scheduler for executing the method. According to the method, fine scheduling, self-adaptive load balancing and multi-objective optimization of heterogeneous computing resources are realized, and the comprehensive efficiency and energy efficiency of the system are remarkably improved.

The invention relates to a cloud particle data target detection method based on image complexity. The method comprises the following steps: firstly, calculating weighted information entropy, weighted texture complexity and weighted local standard deviation of data of each cloud particle image, and evaluating the complexity of the image data; and marking according to the complexity of the image data, further calculating the comprehensive complexity of the data group, and judging the uniformity degree of complexity distribution of the data group. On the basis, different splitting and combining strategies are adopted according to the overall complexity distribution uniformity and marks of the data sets, and it is ensured that the deep learning detection model can select the most suitable strategy according to the complexity. Through the method, the computing resources and the detection precision of the deep learning model can be optimized in the face of cloud particle image data with different complexities, the image processing efficiency is improved, and the method is suitable for large-scale cloud particle data sets with large complexity differences.

The embodiment of the invention discloses a database security situation assessment and prediction method based on a multi-dimensional index, and the method comprises the steps: constructing a multi-level index system, collecting the asset data and operation data of a target database, determining the first weight of the asset data and the second weight of the operation data based on a dynamic weight rule, calculating a database security value through a situation assessment model, obtaining predicted asset data and predicted operation data of a target database through a situation prediction model, and calculating a database predicted security value through the situation assessment model; and based on the database safety value and the database prediction safety value, according to an early warning rule, initiating early warning. The method covers multi-dimensional security data of the database, adopts a dynamic weight rule, adapts to different application scenes and threat environments, can effectively improve the accuracy and adaptivity of database security situation assessment, and improves the perspectiveness of database security protection through cooperation of a prediction mechanism and an early warning mechanism.

The invention relates to the technical field of data transmission, and discloses a speed reducer remote monitoring and parameter configuration system, which comprises the following modules: a calculation module used for establishing QUIC protocol connection between a speed reducer terminal and a cloud server to realize communication; the speed reducer terminal collects vibration, temperature and load data, extracts a main frequency amplitude and calculates a comprehensive index of the health state of the equipment; the scheduling module is used for determining the priority of the current data for the cloud server to perform flow scheduling; the reporting module is used for calculating the size of a data aggregation window by the speed reducer terminal in combination with the smooth round-trip time and priority of the QUIC connection; a plurality of time sequence telemetering floating point values collected in the window size are converted into fixed-length binary sequences, and after the fixed-length binary sequences are spliced to form aggregated data blocks, bit compression coding is carried out, and the aggregated data blocks are reported; and the feedback module is used for realizing asynchronous feedback of an instruction execution state after the speed reducer terminal executes the instruction. According to the invention, the real-time performance of data transmission and the reliability of connection are improved.

The invention belongs to the technical field of three-dimensional geometric measurement, and particularly relates to a three-coordinate detection data processing method and system for automobile parts, and the method comprises the steps: obtaining an actual point cloud and a standard point cloud; by calculating the maximum angle gap of the data points, screening to obtain actual and standard feature point sets; anti-noise geometric descriptors based on tangential weights are constructed for the feature points in the two sets of feature point sets respectively; matching the two groups of feature points based on the descriptors and generating an initial matching set; solving global optimal transformation from the initial matching set by adopting a random sampling consistency algorithm; and the transformation is applied to the actual point cloud, fine registration is carried out by using an iterative nearest point algorithm, and finally deviation analysis is completed. The technical problem that traditional registration depends on initial alignment and is prone to falling into local optimum is solved, and the robustness and accuracy of registration are improved.