CN-120811655-B - Unmanned aerial vehicle mapping data sharing method and system based on cloud computing

CN120811655BCN 120811655 BCN120811655 BCN 120811655BCN-120811655-B

Abstract

The present invention discloses a cloud computing based unmanned aerial vehicle surveying data sharing method and system, which relates to the field of unmanned aerial vehicle surveying technology. The method involves executing surveying data role declarations, determining user access types and generating access control contracts, collecting raw surveying image data, identifying geographic hotspots and establishing cache synchronization mechanisms, scheduling surveying image data to edge nodes based on predictive models, recording user access behavior and performing abnormal audit analysis, constructing surveying image data subscription channels and implementing dynamic cache configuration strategies. The present invention achieves efficient sharing of surveying image data among multiple nodes through multi role permission negotiation, edge preprocessing, hotspot area caching, predictive scheduling, access behavior auditing, and personalized subscription mechanisms, improving data transmission accuracy and timeliness, Reduced duplicate data collection and bandwidth usage, enhanced access security and controllable permissions, effectively solving the problems of high sharing latency, difficult collaboration, and inflexible permission management in existing technologies.

Inventors

QIN MENGQI
LI JIHUA
JIN DEXI
ZHANG HUANAN
CUI KE
YANG JIANFEI
RAO XIANMING
LIAO GUIPING

Assignees

广东飞亚创新技术有限公司

Dates

Publication Date: 20260505
Application Date: 20250709

Claims (8)

1. The unmanned aerial vehicle mapping data sharing method based on cloud computing is characterized by comprising the following steps of: Step S1, mapping data role declaration and authority negotiation are executed, user access types are determined, and access control contracts are generated; s2, acquiring original mapping image data and performing edge preprocessing and attribute labeling; step S3, identifying a geographic hot spot area and establishing a cache synchronization mechanism; Step S4, mapping image data are scheduled to edge nodes based on a prediction model; s5, recording the access behaviors of the user and executing abnormal audit analysis; s6, constructing a mapping image data subscription channel and implementing a dynamic cache configuration strategy; the step S3 includes the following sub-steps: Step S301, obtaining historical uploading frequency, edge node access density and task execution times of original mapping image data, and constructing a geographic hot spot identification map according to the historical uploading frequency, the edge node access density and the task execution times of the image data, wherein each region in the geographic hot spot identification map corresponds to a space heat value; step S302, when a region with a spatial heat value exceeding a set threshold exists, marking the region as a hot spot region, starting a shared mapping image data caching mechanism by an edge node, and carrying out unified caching management on acquired data in the hot spot region, wherein the acquired data comprises a time tag, a spatial coordinate, image resolution information and a compression coding mode; Step S303, pushing the hot spot area cache to a cloud main storage system according to a synchronous period, and executing comparison processing on redundant mapping image data fragments except original mapping image data corresponding to the hot spot area according to a redundant fusion strategy, and generating single-version mapping image data by combining a time stamp, space coordinates, image definition and source consistency; The step S4 includes the following sub-steps: Step S401, loading mapping image data access prediction model modules by edge nodes, wherein the prediction model modules take historical mapping task tracks, access frequencies of users to different geographic areas, hot spot area labels generated based on area statistics and established access authority control strategies as input data, and calculate a prediction set of mapping image data segments to be requested in the next period; Step S402, carrying out item-by-item comparison verification according to the prediction set and the current user authority boundary, carrying out edge pre-scheduling operation on the mapping image data segment successfully matched and transmitting the mapping image data segment to a corresponding edge cache node; in step S403, if the user accesses the requested mapping image data segment without hitting the called edge cache in the actual operation, the fast data transfer module is started, and the corresponding requested mapping image data segment is called out from the cloud main storage.
2. The unmanned aerial vehicle mapping data sharing method based on cloud computing as claimed in claim 1, wherein the step S1 comprises the following sub-steps: step S101, a mapping task instruction is received, mapping data role declarations are generated according to user identity information, users are divided into mapping data providers, mapping data users and mapping data supervisors, and role type data are output; step S102, a user history task record, a geographical operation area and an established access control strategy are called, and an access matching matrix is constructed by combining a preset authority rule; And step S103, calling a cloud collaborative proxy module to negotiate rights among the role type data, generating an access control contract according to the negotiation result, and recording the access control contract to a rights log database.
3. The unmanned aerial vehicle mapping data sharing method based on cloud computing as claimed in claim 2, wherein the step S2 comprises the following sub-steps: Step S201, acquiring original mapping image data of a designated area according to an input mission plan and corresponding time labels, space coordinates and gesture parameters; Step S202, after receiving original mapping image data, an edge node executes preprocessing operation, wherein the preprocessing operation comprises image noise filtering, image resolution resampling, unified coding format conversion and block compression processing; Step S203, the preprocessed original mapping image data is classified and stored according to the corresponding time labels and the corresponding space coordinate indexes, and a complete data identification field is set, wherein the data identification field comprises an acquisition equipment identification, a data type, a compression mode and a storage path.
4. The unmanned aerial vehicle mapping data sharing method based on cloud computing as claimed in claim 3, wherein the step S5 comprises the following sub-steps: Step S501, log recording is carried out on user access mapping image data, wherein the recorded content of the log recording comprises user identity, access time stamp, data segment number, data segment storage path, access operation type and return status code; Step S502, performing cluster analysis, frequency statistics and behavior track modeling on the recorded content periodically, and outputting a user access behavior portrait model; And step S503, if it is determined that the data segment storage path and the access operation type have abnormal indexes in the running process of the user access behavior portrait model generated in the step S502, triggering an authority suspension instruction and generating an access abnormal audit record.
5. The unmanned aerial vehicle mapping data sharing method based on cloud computing as claimed in claim 4, wherein the step S6 comprises the sub-steps of: Step S601, calling a log behavior analysis module, calling a target user historical access log, typical task area distribution, task type codes and authority level labels, generating user mapping image data subscription interest portraits according to access frequency, task concentration and geographic block preference, and combining current task parameters to construct a user data label set, wherein the user data label set is used for identifying a data range potentially focused by a user, and label fields of the user data label set comprise geographic block numbers, task time windows, image types, spatial resolution requirements and authority level identifiers; Step S602, according to the user data label set generated in the step S601, a mapping image data subscription channel is established, the mapping image data subscription channel is bound to a designated edge cache node, and a cache capacity allowance, a data push priority sequence and an access rate control parameter are configured.
6. The unmanned aerial vehicle mapping data sharing method based on cloud computing as claimed in claim 5, wherein the access matching matrix in step S102 is composed of organization attribute categories, historical task ranges, current region ranges and authority historical compliance scores, and generates dynamic access decision basis.
7. The unmanned aerial vehicle mapping data sharing method based on cloud computing as claimed in claim 6, wherein the redundancy fusion strategy in step S303 includes extracting mapping image data version numbers, calculating image space content overlap ratios, analyzing image resolution definition indexes, and outputting final main storage reserved versions after consistency verification of acquisition equipment identification fields.
8. The unmanned aerial vehicle mapping data sharing system based on cloud computing is applied to the unmanned aerial vehicle mapping data sharing method based on cloud computing as claimed in any one of claims 1-7, and is characterized by comprising a user type confirmation module, a data acquisition processing module, a hot spot area identification module, an image data scheduling module, a user behavior analysis module and a user dynamic configuration module; the user type confirmation module is used for executing mapping data role declaration and authority negotiation, determining user access types and generating access control contracts; the data acquisition processing module is used for acquiring original mapping image data and carrying out edge preprocessing and attribute labeling; The hot spot area identification module is used for identifying a geographic hot spot area and establishing a cache synchronization mechanism; The image data scheduling module is used for scheduling mapping image data to edge nodes based on a prediction model; the user behavior analysis module is used for recording user access behaviors and executing abnormal audit analysis; The user dynamic configuration module is used for constructing a mapping image data subscription channel and implementing a dynamic cache configuration strategy.

Description

Unmanned aerial vehicle mapping data sharing method and system based on cloud computing Technical Field The invention relates to the technical field of unmanned aerial vehicle mapping, in particular to an unmanned aerial vehicle mapping data sharing method and system based on cloud computing. Background With the development of unmanned aerial vehicle remote sensing technology, unmanned aerial vehicle survey and drawing is widely applied in fields such as natural resource investigation, engineering construction monitoring and agricultural fine management. Traditional unmanned aerial vehicle survey and drawing system mainly relies on the mode of edge collection and high in the clouds centralized processing, and survey and drawing data is uploaded to central platform after gathering, and is distributed to different users by the platform unification. At present, the Chinese patent application No. CN202410600375.5 discloses a data management system based on unmanned aerial vehicle mapping, by determining the intensity and the range of electromagnetic radiation and classifying the electromagnetic radiation, and combining the real-time operation data and the flight test result of the unmanned aerial vehicle, the operation states of the unmanned aerial vehicle under different environments are comprehensively evaluated, and further the abnormal states, possible abnormal states and normal states of the unmanned aerial vehicle operation are classified, and the unmanned aerial vehicle mapping data is intelligently managed through a prediction management module under the possible abnormal states, so that the abnormal perception and processing efficiency is improved, the quality of the data and the reliability of subsequent analysis application are further ensured, the task execution result is optimized, and the safety of equipment and personnel is improved. The technology is difficult to realize efficient sharing, dynamic authority management and intelligent edge scheduling of unmanned aerial vehicle mapping data under multi-role and multi-region task scenes. Disclosure of Invention The technical problem solved by the invention is that the efficient sharing, dynamic authority management and intelligent edge scheduling of unmanned aerial vehicle mapping data under multi-role and multi-region task scenes are difficult to realize in the prior art. In order to solve the technical problems, the invention provides the following technical scheme: The unmanned aerial vehicle mapping data sharing method based on cloud computing comprises the following steps: Step S1, mapping data role declaration and authority negotiation are executed, user access types are determined, and access control contracts are generated; s2, acquiring original mapping image data and performing edge preprocessing and attribute labeling; step S3, identifying a geographic hot spot area and establishing a cache synchronization mechanism; Step S4, mapping image data are scheduled to edge nodes based on a prediction model; s5, recording the access behaviors of the user and executing abnormal audit analysis; and S6, constructing a mapping image data subscription channel and implementing a dynamic cache configuration strategy. Preferably, the step S1 includes the following sub-steps: step S101, a mapping task instruction is received, mapping data role declarations are generated according to user identity information, users are divided into mapping data providers, mapping data users and mapping data supervisors, and role type data are output; step S102, a user history task record, a geographical operation area and an established access control strategy are called, and an access matching matrix is constructed by combining a preset authority rule; And step S103, calling a cloud collaborative proxy module to negotiate rights among the role type data, generating an access control contract according to the negotiation result, and recording the access control contract to a rights log database. Preferably, the step S2 includes the following sub-steps: Step S201, acquiring original mapping image data of a designated area according to an input mission plan and corresponding time labels, space coordinates and gesture parameters; Step S202, after receiving original mapping image data, an edge node executes preprocessing operation, wherein the preprocessing operation comprises image noise filtering, image resolution resampling, unified coding format conversion and block compression processing; Step S203, the preprocessed original mapping image data is classified and stored according to the corresponding time labels and the corresponding space coordinate indexes, and a complete data identification field is set, wherein the data identification field comprises an acquisition equipment identification, a data type, a compression mode and a storage path. Preferably, the step S3 includes the following sub-steps: Step S301, obtaining historical uploading frequency, edge node access