Monitoring system and method based on Beidou positioning unmanned aerial vehicle cruise

Abstract

The invention relates to the technical field of unmanned aerial vehicle monitoring, in particular to a monitoring system and method based on Beidou positioning unmanned aerial vehicle cruise, and the system comprises an acquisition unit, an alignment unit, a credible unit, an identification unit, an early warning unit and an adjustment unit. According to the method, multi-source inherent observed quantities such as a multi-frequency positioning consistency parameter and a constellation visibility sequence are fused and calculated into positioning credibility and time traceability indexes, and a quantifiable quality reference is established for cruise data; the standard is used as a unified scale, cross-cycle conjoint analysis is carried out on the standard and synchronously-obtained engineering image changes, real deviation caused by engineering entity state changes and false deviation caused by signal shielding, data interruption and other evidence obtaining process anomalies can be accurately distinguished, and therefore the early warning accuracy is improved; the problems of low credibility and poor traceability of the cruise monitoring result of the unmanned aerial vehicle caused by lack of a unified time corresponding relation of multiple cruise monitoring results are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) monitoring technology, and in particular to a monitoring system and method for UAV patrol based on BeiDou positioning. Background Technology

[0002] With the increasing prevalence of drones in engineering operation monitoring and inspection, using drones for periodic monitoring of cross-regional projects has become an important means of improving regulatory efficiency and coverage. During multiple patrols, drones continuously acquire monitoring data related to the project's status, generating monitoring results that can be compared and traced. However, in actual monitoring, because patrol tasks are distributed across different times and responsibility areas, the monitoring results are prone to unclear temporal correspondences or insufficient consistency. This increases the difficulty of accurately judging and continuously monitoring changes in the project's status, posing a challenge to the reliable application of drone monitoring results.

[0003] Chinese Patent Application Publication No. CN113985921A discloses a 5G+BeiDou-based intelligent inspection system and method for unmanned aerial vehicles (UAVs). The system includes a data sensing system, a data processor, a 5G transmission system, and a cloud platform. All three systems are mounted on the UAV. The data sensing system comprises a 3D data sensing module and a video sensing module. The 3D data sensing module senses 3D data of the power transmission line corridor and transmits it to the data processor. The data processor constructs a 3D model of the power transmission line corridor and determines the UAV's flight path. The video sensing module captures images at a preset hovering position to obtain inspection videos, which are then transmitted to the cloud platform via the 5G transmission system. The preset hovering position is determined based on the calibrated UAV flight path and the BeiDou system's positioning. The cloud platform automatically identifies defects and potential hazards in each frame of the inspection video, generates maintenance work orders, and sends them to the operation and maintenance terminal, enabling parallel operation of inspection and image analysis.

[0004] Therefore, the existing technology has the following problems: While using BeiDou positioning and timing information to determine the inspection location and time, the existing technology does not organize the data generated during multiple patrols into segment-level time correspondences, which can easily lead to unclear time correspondences of monitoring data within the same responsibility segment; The existing technology takes a single inspection task as the basic processing object and does not establish a unified cross-cycle organization method for monitoring data generated in different inspection cycles, which can easily lead to a lack of clear cycle correspondences between different inspection results. Summary of the Invention

[0005] To address this, the present invention provides a monitoring system and method for UAV patrol based on BeiDou positioning, which standardizes the time organization and usage of UAV patrol monitoring results to overcome the problems of low reliability and poor traceability of UAV patrol monitoring results due to the lack of a unified time correspondence between multiple patrol monitoring results in the prior art.

[0006] To achieve the above objectives, on the one hand, the present invention provides a monitoring system for UAV patrol based on BeiDou positioning, comprising: The acquisition unit is used to acquire in real time the BeiDou positioning coordinates, BeiDou timing information, multi-frequency positioning consistency parameters, constellation visibility sequence, spatial coverage relationship between the cruise track and the preset section model, and engineering image data at the corresponding cruise time during the cruise mission of the UAV to perform cross-administrative boundary engineering, so as to obtain the cruise acquisition results. The alignment unit is used to perform time alignment processing on the cruise data in the cruise acquisition results based on the BeiDou timing information and the preset segment model, according to a preset alignment strategy, so as to generate cruise records for each of the responsible segments under the same BeiDou time reference. A trusted unit is used to calculate a trust index to characterize the location trust and time traceability of the cruise acquisition results based on the stability of the multi-frequency positioning consistency parameters, the constellation visibility sequence and the spatial coverage relationship in each of the responsibility segments in the cruise record. The identification unit is used to perform cross-cycle comparative analysis of the credibility index of the same responsibility section within a preset number of cruise cycles, so as to identify several deviation sections and determine the deviation type of the deviation section by combining the engineering image data corresponding to the deviation section. An early warning unit is used to generate early warning information based on the deviation type; An adjustment unit is used to adjust the preset alignment strategy based on all the credibility indicators of the responsible segment within the next preset adjustment number of cruise cycles.

[0007] Furthermore, the multi-frequency positioning consistency parameters include the planar position deviation, vertical altitude deviation, and timing deviation of different BeiDou frequency positioning results at the same cruise time, which are used to characterize the consistency level between the multi-frequency positioning calculation results.

[0008] Furthermore, the constellation visibility sequence includes the available satellite number change sequence, constellation geometric distribution factor change sequence, and occlusion state change sequence corresponding to each sampling time during the cruise mission.

[0009] Furthermore, the cruise record is a segment-level time series data set constructed based on the responsibility segment. The cruise record uses BeiDou time as a unified time axis and aggregates and associates positioning parameters, track coverage features, and engineering image data within the same responsibility segment.

[0010] Furthermore, in the trusted unit, the reliability index of the positioning reliability is calculated based on the fluctuation amplitude and stability statistical characteristics of the multi-frequency positioning consistency parameter within a preset time window.

[0011] Furthermore, the reliability index of time traceability is obtained by jointly evaluating the time continuity and time alignment integrity of the constellation visibility sequence and the BeiDou timing information in the cruise record.

[0012] Furthermore, the deviation types include engineering status change type deviation and cruise evidence collection type deviation, wherein the engineering status change type deviation is determined based on the cross-cycle changes of the engineering image data, and the cruise evidence collection type deviation is determined based on the abnormal fluctuation characteristics of the credibility index.

[0013] Furthermore, the early warning information includes a responsibility section identifier, a deviation type identifier, and a corresponding patrol cycle range, which is used to provide section-level risk warnings for cross-administrative region engineering supervision.

[0014] Furthermore, the adjustment unit includes: The index calculation module is used to perform a weighted summation calculation on the average value of the reliability index of all the positioning reliability and the average value of the reliability index of time traceability of each of the responsibility sections within the next preset number of cruise cycles, so as to obtain a comprehensive reliability index. The granularity adjustment module is used to adjust the preset time granularity corresponding to each responsibility segment in the preset alignment strategy according to the relative deviation between the comprehensive credibility index and the preset credibility index threshold when the comprehensive credibility index is less than the preset credibility index threshold.

[0015] On the other hand, the present invention also provides a monitoring method for UAV patrol based on BeiDou positioning, characterized in that it includes: The system acquires BeiDou positioning coordinates, BeiDou timing information, multi-frequency positioning consistency parameters, constellation visibility sequence, spatial coverage relationship between the cruise track and the preset section model, and engineering image data at the corresponding cruise time during the real-time cruise mission of the UAV performing cross-administrative boundary engineering projects, so as to obtain the cruise acquisition results. Based on the BeiDou timing information and the preset segment model, the cruise data in the cruise acquisition results are time aligned according to the preset alignment strategy to generate cruise records for each of the responsible segments under the same BeiDou time reference. A reliability index is calculated based on the stability of the multi-frequency positioning consistency parameter, the constellation visibility sequence, and the spatial coverage relationship in each of the responsibility segments in the cruise record, to characterize the positioning reliability and time traceability of the cruise acquisition results. Within a preset number of patrol cycles, the reliability index of the same responsible section is compared and analyzed across cycles to identify several deviation sections, and the deviation type of the deviation section is determined by combining the engineering image data corresponding to the deviation section. Based on the deviation type, generate an early warning message; The preset alignment strategy is adjusted based on all the credibility indicators of the responsible segment within the next preset adjustment number of cruise cycles.

[0016] Compared with existing technologies, the beneficial effects of this invention are that by fusing multi-source intrinsic observations such as multi-frequency positioning consistency parameters and constellation visibility sequences into positioning reliability and time traceability indicators, a quantifiable quality benchmark is established for cruise data. Using this benchmark as a unified standard, cross-cycle joint analysis with changes in synchronously acquired engineering images can accurately distinguish between real deviations caused by changes in the state of engineering entities and false deviations caused by abnormalities in the evidence collection process such as signal obstruction and data interruption, thereby improving the accuracy of early warning. Furthermore, based on the historical trend of reliability indicators, the system adaptively adjusts the key parameters in the data alignment strategy in reverse, so that the precision of data preprocessing can dynamically match the actual environmental complexity of different responsibility sections, forming an enhanced closed loop that optimizes front-end processing with data quality feedback. Ultimately, without human intervention, the reliability of monitoring conclusions is self-verified and continuously improved, effectively solving the problem of low reliability and poor traceability of UAV cruise monitoring results due to the lack of a unified time correspondence between multiple cruise monitoring results.

[0017] Furthermore, by systematically collecting BeiDou multi-frequency point positioning coordinates, timing information and their internal consistency parameters, constellation visibility sequences, track spatial coverage relationships, and synchronized images, a multi-dimensional observation set reflecting the spatial geometric strength, temporal continuity, and electromagnetic propagation environment was constructed. Among these, the position and timing deviations between multi-frequency points directly characterize the consistency level of propagation errors such as ionospheric delay; the number of satellites and geometric distribution factors in the constellation visibility sequence determine the accuracy potential of the positioning solution; and the occlusion status reflects the physical visibility conditions of the signal propagation path. These parameters collectively characterize the credible physical basis of the positioning results at a single moment. At the same time, the timing synchronization module unifies all observations to the BeiDou time reference, the track mapping module associates continuous position sequences with preset responsibility segments, and the image acquisition module binds spatiotemporal tags, placing all data under a unified spatiotemporal reference framework and providing a complete, synchronized, and traceable original data foundation.

[0018] Furthermore, the time mapping module eliminates the temporal misalignment caused by differences in sensor sampling frequencies, ensuring strict synchronization of positioning sequences, constellation observations, and image frames on the physical timeline, thus guaranteeing the causal consistency of event descriptions. The segment attribution determination module discretizes continuous tracks into segment event sequences based on geometric spatial relationships, giving all data a clear management dimension. The alignment and scheduling module aggregates data based on differentiated time granularities preset according to environmental characteristics. Essentially, it matches the optimal data fusion window based on the physical condition of signal propagation stability, smoothing random errors with a coarser granularity in open areas and capturing instantaneous anomalies with a finer granularity in complex areas. The final generated segment-level time series data set constitutes a unified observation fact basis in the three dimensions of time, space, and management, ensuring that any subsequent credibility calculations, change detections, and strategy adjustments based on this basis are established on a unified physical reference system.

[0019] Furthermore, by combining the fluctuations in multi-frequency positioning deviations, reflecting signal propagation errors, with constellation visibility and timing continuity, which reflect observation conditions, through trusted units, a core quantification of data quality is achieved. By statistically analyzing the variance of multi-frequency positioning deviations within a time window and the changing trends between windows, the instantaneous level and stability of errors such as ionospheric delay are quantified. Analysis of the continuous visibility duration of satellite signals and the completeness of timing information matching assesses the reliability of observation geometry and the time synchronization link. In index synthesis, the mean amplitude of positioning deviations is given a higher weight than its frequency of change, reflecting a stricter control principle for systematic errors compared to accidental jumps. Simultaneously, timing matching accuracy is given a higher weight than signal persistence, highlighting the fundamental role of a unified time reference in spatiotemporal data fusion. This embeds the physical constraints of signal propagation, spatial geometry, and clock synchronization logic into calculable indicators, making the quality of the data itself measurable and comparable, providing direct numerical evidence for accurately distinguishing between real engineering changes and anomalies in the evidence collection process.

[0020] Furthermore, by establishing a joint discrimination mechanism between credibility indicators and changes in engineering images, the problem of distinguishing between genuine engineering changes and data acquisition defects in traditional monitoring is solved. Anomalies in credibility indicators, reflecting signal propagation quality and time synchronization accuracy, are compared with image differences, reflecting changes in the geometric and textural structure of the entity's surface, within the same decision-making framework. When image features change significantly while the credibility indicator remains stable, it indicates that the change originates from a change in the physical state of the engineering entity itself. When image features remain unchanged while the credibility indicator fluctuates abnormally, it indicates that the change originates from abnormal physical processes affecting data acquisition quality, such as satellite signal obstruction or multipath interference. By verifying the dual evidence through preset quantification thresholds, automated and accurate classification of deviations from the root cause can be achieved without human intervention, significantly improving the reliability of monitoring conclusions.

[0021] Furthermore, by mapping the different deviation types determined by the identification unit into early warning information containing clear root cause classifications, efficient conversion from monitoring conclusions to regulatory actions is achieved. Early warnings are categorized based on differences in the physical causes of the underlying data. For deviations caused by changes in the physical state of the engineering entity itself, which are considered engineering state change deviations, the early warning information directly relates to specific sections and change cycles, pointing to the engineering entity requiring on-site verification. For deviations triggered by abnormalities in the physical processes of the data acquisition link, such as signal propagation and time synchronization, which are considered patrol and evidence collection deviations, the early warning information points to sections and cycles where data credibility has decreased, indicating the need to check equipment or the environment. Through this early warning mechanism based on physical cause classification, regulators can directly use the type identifier in the early warning to initially determine whether the problem originates from the "engineering object" or the "observation process," thereby adopting drastically different response strategies. This significantly improves the accuracy and efficiency of response in cross-administrative region collaborative supervision.

[0022] Furthermore, by fusing the indicators characterizing spatial positioning accuracy with those characterizing temporal synchronization integrity using a 70 / 30 weighting to form a comprehensive reliability index, this weighting reflects the design consideration that positioning results, as a spatial reference, have a more decisive impact than temporal continuity. When the comprehensive index falls below a threshold reflecting the system's minimum performance requirements, the unit adjusts the temporal granularity of data alignment using a formula mitigated by an adjustment coefficient, based on the relative deviation between the index and the threshold. This attributes the decrease in reliability to excessive signal noise or asynchronous errors being smoothed out within the current time window, thereby re-examining and fusing data with a finer temporal resolution to match the instantaneous characteristics of signal propagation in complex environments. This allows the precision of data processing to be dynamically adjusted based on the quality evaluation of its own output, ultimately ensuring that the system can autonomously maintain stable, highly reliable data output when facing changing external observation conditions.

[0023] Furthermore, by acquiring and uniformly aligning multi-source spatiotemporal data, a highly reliable segment-level cruise record was constructed. Then, a quantitative reliability index was calculated using the inherent physical characteristics of the data, such as consistency and continuity, as a judgment benchmark. Based on this benchmark and combined with image changes, anomalies with two different physical roots—engineering entity changes and data acquisition defects—were intelligently distinguished. Finally, based on the continuous feedback of the reliability index, the data alignment strategy was adaptively optimized, forming a complete closed loop from data quality assessment and accurate anomaly identification to self-optimization of processing parameters. This significantly improved the automation level, reliability of conclusions, and adaptability of cross-administrative region engineering monitoring. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the monitoring system for UAV patrol based on BeiDou positioning in this embodiment; Figure 2 This is a schematic diagram of the acquisition unit in this embodiment; Figure 3 This is a schematic diagram of the alignment unit in this embodiment; Figure 4 This is a flowchart of the monitoring method for UAV patrol based on Beidou positioning in this embodiment. Detailed Implementation

[0025] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0026] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0027] Please see Figure 1 As shown, this is a schematic diagram of a monitoring system for UAV patrol based on BeiDou positioning in this embodiment. This embodiment provides a monitoring system for UAV patrol based on BeiDou positioning, including: Acquisition Unit 1 is used to acquire in real time the BeiDou positioning coordinates, BeiDou timing information, multi-frequency positioning consistency parameters, constellation visibility sequence, spatial coverage relationship between the cruise track and the preset section model, and engineering image data at the corresponding cruise time during the cruise mission of the UAV to perform cross-administrative boundary engineering, so as to obtain the cruise acquisition results. Alignment unit 2, which is connected to the acquisition unit, is used to perform time alignment processing on the cruise data in the cruise acquisition result based on the BeiDou timing information and the preset segment model, according to the preset alignment strategy, so as to generate cruise records for each of the responsibility segments under the same BeiDou time reference. Trusted unit 3, which is connected to the acquisition unit and the alignment unit respectively, is used to calculate a trust index to characterize the positioning trust and time traceability of the cruise acquisition results based on the stability of the multi-frequency positioning consistency parameters, the constellation visibility sequence and the spatial coverage relationship in each of the responsibility segments in the cruise record. The identification unit 4, which is connected to the trust unit, is used to perform cross-cycle comparative analysis of the trust index of the same responsibility section within a preset number of cruise cycles, so as to identify several deviation sections and determine the deviation type of the deviation section by combining the engineering image data corresponding to the deviation section. The early warning unit 5, which is connected to the identification unit, is used to generate early warning information based on the deviation type. The adjustment unit 6 is connected to the alignment unit, the early warning unit, and the trust unit, respectively, and is used to adjust the preset alignment strategy based on all the trust indicators of the responsible segment within the next preset adjustment number of cruise cycles.

[0028] In this embodiment, cross-administrative boundary projects refer to geographical entities whose physical facilities or construction activities continuously span two or more different administrative jurisdictions, such as inter-provincial or inter-city power transmission lines, oil and gas pipelines, highways, railways, water conservancy facilities, ecological protection zones, and other linear or strip-shaped projects. The UAV flies continuously along the project corridor according to a preset route, and its onboard BeiDou multi-frequency receiver calculates and outputs positioning coordinates and precise timing information in real time, while the visibility status of the satellite constellation is recorded simultaneously.

[0029] By fusing multi-source intrinsic observations such as multi-frequency positioning consistency parameters and constellation visibility sequences into positioning reliability and time traceability indicators, a quantifiable quality benchmark is established for cruise data. Using this benchmark as a unified standard, cross-cycle joint analysis with changes in synchronously acquired engineering images can accurately distinguish between true deviations caused by changes in the state of engineering entities and false deviations caused by abnormalities in the evidence collection process such as signal obstruction and data interruption, thereby improving the accuracy of early warnings. Furthermore, based on the historical trend of reliability indicators, the system adaptively adjusts key parameters in the data alignment strategy in reverse, enabling the precision of data preprocessing to dynamically match the actual environmental complexity of different responsibility areas. This forms an enhanced closed loop that optimizes front-end processing through data quality feedback. Ultimately, without human intervention, the system achieves self-verification and continuous improvement of the reliability of monitoring conclusions, effectively solving the problem of low reliability and poor traceability of UAV cruise monitoring results due to the lack of a unified time correspondence between multiple cruise monitoring results.

[0030] Specifically, the multi-frequency positioning consistency parameters include the planar position deviation, vertical altitude deviation, and timing deviation of different BeiDou frequency positioning results at the same cruise time, which are used to characterize the consistency level between the multi-frequency positioning calculation results.

[0031] Specifically, the constellation visibility sequence includes the sequence of changes in the number of available satellites at each sampling time during the cruise mission, the sequence of changes in the constellation geometric distribution factor, and the sequence of changes in the occlusion status.

[0032] Please see Figure 2 As shown, this is a schematic diagram of the acquisition unit in this embodiment. In this embodiment, the acquisition unit 1 includes: The positioning acquisition module 11 is electrically connected to the airborne Beidou multi-frequency receiver to acquire the Beidou positioning coordinates of the UAV during the cruise mission in real time, and to parse and extract the real-time position information of the UAV from the navigation message output by the receiver in real time. The Beidou positioning coordinates include at least longitude, latitude and altitude. The timing synchronization module 12 is connected to the positioning acquisition module and the airborne clock. It is used to decode the BeiDou system time from the navigation information in the airborne clock and use this as a reference to assign a unified and accurate timestamp to the coordinates output by the positioning acquisition module, the waypoints provided by the UAV flight control system, and the data packets output by the image acquisition module. The consistency calculation module 13 is connected to the positioning acquisition module to receive and process independent results from different frequency positioning calculation channels within the same Beidou receiver in parallel. For the same timing time, it calculates the planar position deviation and vertical height deviation between the calculation results of different frequencies, and calculates the timing deviation between the timing values ​​of each frequency, thereby outputting multi-frequency positioning consistency parameters that characterize the consistency level of the multi-frequency positioning calculation results at that moment. The constellation monitoring module 14 is connected to the timing synchronization module and the receiver status interface. It records and outputs sequence data according to the sampling time. The constellation visibility sequence specifically includes the available satellite number change sequence consisting of the number of satellites effectively locked at each sampling time, the constellation geometric distribution factor change sequence consisting of the position accuracy attenuation factor calculated based on the satellite spatial geometric distribution at each time, and the occlusion status change sequence obtained after judging the signal visibility based on the UAV attitude and terrain data. The track mapping module 15 is connected to the positioning acquisition module to receive the cruise track composed of continuous BeiDou positioning coordinates with timestamps, as well as the pre-loaded preset segment model defined in geographic vector polygon format. By performing spatial point-surface inclusion relationship analysis, it determines the specific responsibility segment where each coordinate point on the track is located, and thus outputs the spatial coverage relationship of the cruise track to each responsibility segment in the preset segment model. The image acquisition module 16 is used to control the airborne shooting equipment so that its acquisition action is synchronized with the time reference provided by the timing synchronization module. Each frame of engineering image is bound to the corresponding acquisition time Beidou timing information and Beidou positioning coordinates to form engineering image data with strict spatiotemporal correspondence.

[0033] In this embodiment, the preset segment model refers to a spatial reference model of responsibility segments that is digitally defined and stored in the monitoring system in advance, based on the spatial span and management requirements of the monitored cross-administrative boundary projects. This model uses a geodetic coordinate system consistent with BeiDou positioning coordinates as a spatial reference benchmark, and logically divides the continuous engineering corridor or monitoring area into a series of continuous or discontinuous responsibility segments along its direction or according to administrative management boundaries. Each responsibility segment is an independent spatial unit, defined in the model at least by its unique segment code and a set of spatial boundary coordinates that accurately describe its geographical range in the form of a closed polygon or linear buffer. It can also be associated with and stored the engineering attributes, management responsibility entities, or preset inspection standard metadata of the segment. During the execution of the patrol mission, the system's track mapping module calls the boundary data of the preset segment model and performs spatial calculations with the real-time acquired patrol track to determine the spatial coverage relationship of the UAV track to each specific responsibility segment, providing a spatial basis for subsequent segmented data processing and analysis.

[0034] By systematically collecting BeiDou multi-frequency point positioning coordinates, timing information and their internal consistency parameters, constellation visibility sequences, track spatial coverage relationships, and synchronized imagery, a multi-dimensional observation set reflecting signal spatial geometric strength, temporal continuity, and electromagnetic propagation environment was constructed. Among these, the position and timing deviations between multi-frequency points directly characterize the consistency level of propagation errors such as ionospheric delay; the number of satellites and geometric distribution factors in the constellation visibility sequence determine the accuracy potential of the positioning solution; and the occlusion status reflects the physical visibility conditions of the signal propagation path. These parameters collectively characterize the reliable physical basis of the positioning results at a single moment. At the same time, the timing synchronization module unifies all observations to the BeiDou time reference, the track mapping module associates continuous position sequences with preset responsibility segments, and the image acquisition module binds spatiotemporal tags, placing all data under a unified spatiotemporal reference framework and providing a complete, synchronized, and traceable original data foundation.

[0035] Specifically, the cruise record is a segment-level time series data set constructed based on the responsibility segment. The cruise record uses BeiDou time as a unified time axis and aggregates and associates positioning parameters, track coverage features and engineering image data within the same responsibility segment.

[0036] Please see Figure 3 As shown, this is a schematic diagram of the alignment unit in this embodiment. In this embodiment, the alignment unit 2 includes: The time reference parsing module 21 is used to parse the BeiDou time identifier from the BeiDou time information and establish this identifier as the unified clock reference for the entire system to perform data time alignment processing. The time mapping module 22 is connected to the time reference parsing module. It is used to map and unify the original timestamps of all input data to the unified BeiDou time reference through interpolation, resampling or timestamp conversion algorithms, so as to form a standardized timestamp sequence with a consistent BeiDou time axis. In this embodiment, a linear interpolation algorithm is preferably used to map non-BeiDou time reference data to the unified BeiDou time axis. The segment attribution determination module 23 is used to perform segment mapping calculations for each spatial coordinate point with a timestamp under a unified time axis. Based on the segment boundary set defined in the preset responsibility segment model, it calculates the spatial relationship between the coordinate point and each responsibility segment boundary at the spatial location corresponding to each timestamp according to the geometric determination criterion of point-to-surface inclusion relationship, and determines the responsibility segment number to which the coordinate point belongs based on the determination result, thereby realizing the dual labeling of cruise data in the time dimension and the responsibility segment dimension. The alignment and scheduling module 24 is connected to the time mapping module and the segment attribution determination module. It is used to aggregate, align and associate all types of cruise data that belong to the same responsibility segment and are in the same or adjacent time window, based on the responsibility segment as the basic organizational unit, BeiDou time as the order, and according to the preset time granularity corresponding to each responsibility segment in the preset alignment strategy. The record generation module, connected to the alignment and scheduling module, receives data packets aligned by responsibility segment and time, and stores or encapsulates them in a structured manner as independent data sets indexed by responsibility segment. The cruise records of each responsibility segment constitute a segment-level time series data set. In this set, BeiDou time is the unique and continuous time axis, and all time-aligned positioning parameters, track coverage features, and corresponding engineering image data indexes or metadata within the segment are aggregated and associated together.

[0037] The preset time granularity configured for each responsibility segment in the preset alignment strategy depends on the complexity of the historical positioning environment and the accuracy requirements of engineering monitoring for that segment, and can typically be set differently between 0.5 seconds and 2.0 seconds. In this embodiment, for open and flat segments with stable signal quality, the preset time granularity can be set to a coarser 2.0 seconds; for complex segments such as urban canyons, forest areas, or areas with severe signal obstruction, the initial value of the preset time granularity is set to a finer 0.5 seconds to ensure the accuracy of spatiotemporal data alignment. This differentiated configuration allows for matching appropriate data processing rhythms for responsibility segments with different characteristics during system initialization, serving as a benchmark for subsequent adaptive adjustments.

[0038] The time mapping module eliminates the temporal misalignment caused by differences in sensor sampling frequencies, ensuring strict synchronization of positioning sequences, constellation observations, and image frames on the physical timeline, thus guaranteeing the causal consistency of event descriptions. The segment attribution determination module discretizes continuous tracks into segment event sequences based on geometric spatial relationships, giving all data a clear management dimension. The alignment and scheduling module aggregates data based on differentiated time granularities preset according to environmental characteristics. Essentially, it matches the optimal data fusion window based on the physical condition of signal propagation stability, smoothing random errors with coarser granularity in open areas and capturing instantaneous anomalies with finer granularity in complex areas. The final segment-level time series data set constitutes a unified observation fact basis in the three dimensions of time, space, and management, ensuring that any subsequent credibility calculations, change detections, and strategy adjustments based on this basis are established on a unified physical reference system.

[0039] Specifically, in the trusted unit, the credibility index of the positioning credibility is calculated based on the fluctuation amplitude and stability statistical characteristics of the multi-frequency positioning consistency parameter within a preset time window.

[0040] Specifically, the reliability index of time traceability is obtained by jointly evaluating the time continuity and time alignment integrity of the constellation visibility sequence and the BeiDou timing information in the cruise record.

[0041] In this embodiment, the trusted unit includes: The multi-frequency consistency calculation module is used to perform consistency calculation on the multi-frequency positioning results obtained in the same responsibility segment under a unified BeiDou time axis. Specifically, it includes calculating the spatial deviation of the positioning coordinates of each frequency point within a preset time window, and calculating the maximum deviation value, mean deviation value and deviation variance within the time window based on the deviation value sequence, and generating multi-frequency consistency parameters to characterize the consistency of multi-frequency positioning. The positioning stability statistics module is connected to the multi-frequency consistency calculation module. It is used to perform statistical analysis on the changes of the multi-frequency consistency parameters within a preset time window of a continuous preset number of statistics. Specifically, it includes calculating the change amplitude and frequency of the multi-frequency consistency parameters between adjacent preset time windows, and generating a stability statistical feature quantity to characterize the positioning stability based on the change amplitude and change frequency. The stability statistical feature quantity consists of two sub-feature quantities: the mean change amplitude Amean and the change frequency Fvar. The visibility continuity analysis module is used to analyze the changes in the number of continuously visible satellites in each responsibility segment based on the constellation visibility sequence in the cruise record and according to the BeiDou time sequence. It calculates the number of interruptions, continuous holding duration, and visibility fluctuation amplitude of the number of visible satellites at a preset time granularity, and generates the temporal continuity feature of constellation visibility. The temporal continuity feature is specifically represented by the proportion of continuous holding duration, Pcontinuity. The timing alignment integrity calculation module is connected to the visibility continuous analysis module. It is used to perform point-by-point alignment calculation between the timestamps corresponding to the constellation visibility sequence and the BeiDou timing information. It statistically analyzes the proportion of time points with effective timing matching, the distribution of time offset, and the maximum time offset value in the cruise record, and generates alignment integrity feature quantities to characterize the degree of time alignment integrity. Specifically, the alignment integrity feature quantity is represented by the effective timing matching ratio Pmatch. The credibility index generation module is connected to both the positioning stability statistics module and the timing alignment integrity calculation module. It receives stability statistical features and alignment integrity features, and performs normalized combination calculations on each feature according to a preset weighting rule to generate credibility indices representing the positioning credibility and time traceability of the cruise acquisition results. The positioning credibility index Cpos is calculated using the formula Cpos = 100 × [wa × (1...]). N(Amean)) + wf × (1 The reliability index Ctime for time traceability is calculated using the formula Ctime = wm × Pmatch + wc × Pcontinuity. In the formula, N() represents the linear normalization function, N(x) = (x - Xmin) / (Xmax - Xmin), where Xmin and Xmax are the theoretical reasonable lower and upper limits of the feature (such as Amean) determined by historical data statistics during the system initialization phase. The weights wa, wf, wm, and wc are preset constants that satisfy wa + wf = 1 and wm + wc = 1. Among them, the weight wa depends on the relative importance of the mean change amplitude in location consistency, and is usually set between 0.5 and 0.8. In this embodiment, it is set to 0.6, which can give the change amplitude feature a moderate dominant weight when synthesizing the location reliability index. The weight wf depends on the relative importance of the change frequency in location consistency. The weight is typically set between 0.2 and 0.5, and in this embodiment it is set to 0.4, which can work in conjunction with the weight wa to form a complete evaluation of positioning stability; the weight wm depends on the criticality of timing alignment integrity in time traceability, and is typically set between 0.6 and 0.9, and in this embodiment it is set to 0.7, which can emphasize the core contribution of timing matching accuracy to the establishment of a unified time reference; the weight wc depends on the fundamental role of satellite visibility continuity in time traceability, and is typically set between 0.1 and 0.4, and in this embodiment it is set to 0.3, which can reflect the supporting role of continuous and stable observation conditions in ensuring the integrity of the time series data chain.

[0042] The preset time window depends on the real-time requirements and statistical significance requirements of the positioning consistency analysis, and is usually set between 30 and 120 seconds. In this embodiment, it is set to 60 seconds, which can capture the short-term fluctuation characteristics of multi-frequency positioning results while providing a statistically significant basic calculation unit. The preset statistical quantity depends on the time series sample length and statistical significance requirements required for the system to evaluate positioning stability, and is usually set between 5 and 15. In this embodiment, it is set to 10, which can ensure the robustness of statistical results while taking into account the ability to respond promptly to changes in positioning conditions.

[0043] By combining the fluctuations in multi-frequency positioning deviations, reflecting signal propagation errors, with constellation visibility and timing continuity, which reflect observation conditions, through a trusted unit, the core quantification of data quality is achieved. Statistical analysis of the variance of multi-frequency positioning deviations within a time window and the trend of changes between windows quantifies the instantaneous level and stability of errors such as ionospheric delay. Analysis of the continuous visibility duration of satellite signals and the completeness of timing information matching assesses the reliability of observation geometry and the time synchronization link. In index synthesis, the mean amplitude of positioning deviations is weighted higher than its frequency of change, reflecting a stricter control principle for systematic errors compared to accidental jumps. Simultaneously, timing matching accuracy is weighted higher than signal persistence, highlighting the fundamental role of a unified time reference in spatiotemporal data fusion. This embeds the physical constraints of signal propagation, spatial geometry, and clock synchronization logic into calculable indicators, making the quality of the data itself measurable and comparable, providing direct numerical evidence for accurately distinguishing between real engineering changes and anomalies in the evidence collection process.

[0044] Specifically, the deviation types include engineering status change type deviation and cruise evidence collection type deviation. The engineering status change type deviation is determined based on the cross-cycle changes of the engineering image data, while the cruise evidence collection type deviation is determined based on the abnormal fluctuation characteristics of the credibility index.

[0045] In this embodiment, the identification unit includes: The cross-cycle alignment module is used to perform cycle-level alignment processing on the credibility indicators corresponding to the same responsibility segment in multiple cruise cycles with a preset number of cruise cycles under a unified BeiDou time reference. It constructs a cross-cycle sequence of credibility indicators for the responsibility segment according to the cruise cycle order, so that the credibility indicators in different cruise cycles are comparable in terms of time and segment dimensions. An abnormal fluctuation detection module, which is connected to the cross-cycle alignment module, is used to perform statistical analysis on the cross-cycle sequence of the credibility index, calculate the change in the credibility index between adjacent cruise cycles, and, based on a preset fluctuation threshold, identify the responsible segment where the change in the credibility index exceeds the preset fluctuation threshold, and generate a set of candidate segments for cruise evidence collection deviation. The image change calculation module is used to register and align engineering image data acquired in different cruise cycles for the same responsibility section. Based on pixel differences, structural feature differences, or changes in target detection results, it calculates the change index of engineering images in the cross-cycle dimension and generates image change feature quantities to characterize the degree of change in the state of engineering entities. In this embodiment, the preferred registration method is based on ORB feature point matching and homography matrix transformation. Then, the structural similarity index SSIM between the registered images is calculated, and (1-SSIM) is used as the image change feature quantity. The deviation type determination module, connected to the abnormal fluctuation detection module and the image change calculation module, is used to jointly compare the changes in the corresponding image change feature quantity and the credibility index in the deviation candidate segment. When the image change feature quantity exceeds the preset image change threshold, the deviation segment is marked as an engineering state change type deviation. When the image change feature quantity does not exceed the preset image change threshold and the credibility index has abnormal fluctuation characteristics, that is, it shows a monotonically decreasing trend in the most recent three cruise cycles, the deviation segment is marked as a cruise evidence collection deviation.

[0046] The preset fluctuation threshold depends on the system's tolerance for the stability of cruise data quality and the requirements for false alarm control. It is usually set between 0.10 and 0.20. In this embodiment, it is set to 0.15, which can effectively filter out random small fluctuations and sensitively capture medium to high confidence degradation that characterizes abnormal evidence collection environment or equipment status. The preset image change threshold depends on the sensitivity requirements of engineering monitoring to changes in entity status and the level of scene background noise. It is usually set between 0.20 and 0.40. In this embodiment, it is set to 0.25, which can reliably detect substantial engineering progress or abnormal status changes while suppressing interference from non-engineering changes such as illumination and seasons.

[0047] By establishing a joint discrimination mechanism between credibility indicators and changes in engineering images, the problem of distinguishing between real engineering changes and data acquisition defects in traditional monitoring is solved. Anomalies in credibility indicators, reflecting signal propagation quality and time synchronization accuracy, are compared with image differences, reflecting changes in the geometry and texture of the entity's surface, within the same decision-making framework. When image features change significantly while the credibility indicator remains stable, it indicates that the change originates from a change in the physical state of the engineering entity itself. When image features remain unchanged while the credibility indicator fluctuates abnormally, it indicates that the change originates from anomalies in physical processes affecting data acquisition quality, such as satellite signal obstruction or multipath interference. By verifying this dual evidence through preset quantification thresholds, automated and accurate classification of deviations from the root cause can be achieved without human intervention, significantly improving the reliability of monitoring conclusions.

[0048] Specifically, the warning information includes the responsibility section identifier, deviation type identifier, and corresponding patrol cycle range, which is used to provide section-level risk warnings for cross-administrative region engineering supervision.

[0049] In this embodiment, the early warning unit includes: The engineering early warning module is used to generate early warning information reflecting changes in the status of engineering entities based on the changes in the corresponding engineering image data within adjacent or cross-cruise cycles for the responsible sections identified by the identification unit as deviations in engineering status. The early warning information includes at least the responsible section identifier, the deviation in engineering status identifier, and the range of the changed cruise cycle. The evidence collection and early warning module is used to generate early warning information reflecting the decline in the reliability of cruise evidence collection based on the abnormal fluctuation characteristics of the credibility index in the cruise cycle sequence for the responsible section determined by the identification unit to be a deviation from the cruise evidence collection. The early warning information includes at least the responsible section identifier, the cruise evidence collection deviation identifier, and the corresponding cruise cycle range.

[0050] By mapping different deviation types identified by the identification unit into early warning information containing clear root cause classifications, efficient conversion from monitoring conclusions to regulatory actions is achieved. Early warnings are categorized based on differences in the underlying physical causes of the data. For deviations caused by changes in the physical state of the engineering entity itself, which are considered engineering state change deviations, the early warning information directly associates with specific sections and change cycles, pointing to the engineering entity requiring on-site verification. For deviations triggered by abnormalities in the physical processes of data acquisition links such as signal propagation and time synchronization, which are considered patrol and evidence collection deviations, the early warning information points to sections and cycles where data credibility has decreased, indicating the need to check equipment or the environment. This early warning mechanism based on physical cause classification allows regulators to directly determine whether the problem originates from the "engineering object" or the "observation process" based on the type identifier in the early warning, thus enabling them to adopt drastically different response strategies. This significantly improves the accuracy and efficiency of response in cross-administrative region collaborative supervision.

[0051] Specifically, the adjustment unit includes: The index calculation module is used to perform a weighted summation calculation on the average value of the reliability index of all positioning reliability and the average value of the reliability index of time traceability within the next preset adjustment number of cruise cycles for each of the responsibility sections, so as to obtain a comprehensive reliability index, V=(a×R1+b×R2) / (a+b), where V is the comprehensive reliability index, a is the preset positioning reliability weight, R1 is the average value of all positioning reliability indicators, b is the preset traceability weight, and R2 is the average value of all time traceability indicators. The granularity adjustment module is used to adjust the preset time granularity corresponding to each responsibility segment in the preset alignment strategy according to the relative deviation between the comprehensive credibility index and the preset credibility index threshold when the comprehensive credibility index is less than the preset credibility index threshold. T'=T×[1-k×(V0-V) / V0], where T' is the preset time granularity after adjustment, T is the preset time granularity before adjustment, k is the preset adjustment coefficient, and V0 is the preset credibility index threshold.

[0052] The preset location credibility weight depends on the fundamental role of location data in the comprehensive credibility evaluation, and is usually set between 0.5 and 0.8. In this embodiment, it is set to 0.7, which gives higher decision weight to the fundamental dimension of location quality in the comprehensive evaluation. The preset traceability weight depends on the key role of time data in ensuring the integrity of the evidence chain, and is usually set between 0.2 and 0.5. In this embodiment, it is set to 0.3, which works in conjunction with weight 'a' to form a complete quantification of data credibility in the two core dimensions of spatiotemporal dimensions. The preset adjustment coefficient depends on the sensitivity of the system to the decrease in credibility, and is usually set between 0.1 and 0.5. In this embodiment, it is set to 0.3, which can achieve a robust and non-aggressive adjustment range and avoid excessive oscillation of the strategy due to a single fluctuation. The preset credibility index threshold depends on the minimum data quality level required for the system to maintain stable and reliable operation, and is usually set between 0.75 and 0.90. In this embodiment, it is set to 0.80, which can provide a clear and reasonable performance baseline for triggering the adaptive optimization mechanism.

[0053] By fusing indicators characterizing spatial positioning accuracy with indicators of temporal synchronization integrity using a 70 / 30 weighting to form a comprehensive reliability index, this weighting reflects the design consideration that positioning results, as a spatial reference, have a more decisive impact than temporal continuity. When the comprehensive index falls below a threshold reflecting the system's minimum performance requirements, the unit adjusts the temporal granularity of data alignment using a formula mitigated by an adjustment coefficient, based on the relative deviation between the index and the threshold. This attributes the decrease in reliability to excessive signal noise or asynchronous errors being smoothed out within the current time window, thereby re-examining and fusing data with a finer temporal resolution to match the instantaneous characteristics of signal propagation in complex environments. This allows the precision of data processing to be dynamically adjusted based on the quality evaluation of its own output, ultimately ensuring that the system can autonomously maintain stable, highly reliable data output when facing changing external observation conditions.

[0054] Please see Figure 4 The diagram shown is a flowchart of the monitoring method for UAV patrol based on BeiDou positioning in this embodiment. Furthermore, this embodiment also provides a monitoring method for UAV patrol based on BeiDou positioning, including: The system acquires BeiDou positioning coordinates, BeiDou timing information, multi-frequency positioning consistency parameters, constellation visibility sequence, spatial coverage relationship between the cruise track and the preset section model, and engineering image data at the corresponding cruise time during the real-time cruise mission of the UAV performing cross-administrative boundary engineering projects, so as to obtain the cruise acquisition results. Based on the BeiDou timing information and the preset segment model, the cruise data in the cruise acquisition results are time aligned according to the preset alignment strategy to generate cruise records for each of the responsible segments under the same BeiDou time reference. A reliability index is calculated based on the stability of the multi-frequency positioning consistency parameter, the constellation visibility sequence, and the spatial coverage relationship in each of the responsibility segments in the cruise record, to characterize the positioning reliability and time traceability of the cruise acquisition results. Within a preset number of patrol cycles, the reliability index of the same responsible section is compared and analyzed across cycles to identify several deviation sections, and the deviation type of the deviation section is determined by combining the engineering image data corresponding to the deviation section. Based on the deviation type, generate an early warning message; The preset alignment strategy is adjusted based on all the credibility indicators of the responsible segment within the next preset adjustment number of cruise cycles.

[0055] By acquiring and uniformly aligning multi-source spatiotemporal data, a highly reliable segment-level cruise record was constructed. Then, a quantitative reliability index was calculated using the inherent physical characteristics of the data, such as consistency and continuity, as a judgment benchmark. Based on this benchmark and combined with image changes, anomalies with two different physical roots—engineering entity changes and data acquisition defects—were intelligently distinguished. Finally, based on continuous feedback from the reliability index, the data alignment strategy was adaptively optimized, forming a complete closed loop from data quality assessment and accurate anomaly identification to self-optimization of processing parameters. This significantly improved the automation level, reliability of conclusions, and system adaptability of cross-administrative region engineering monitoring.

[0056] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A monitoring system based on Beidou positioning unmanned aerial vehicle cruise, characterized in that, The method comprises the following steps: an acquisition unit is configured to acquire, in real time, Beidou positioning coordinates, Beidou time service information, multi-frequency positioning consistency parameters, constellation visibility sequences, a spatial coverage relationship between a cruise track and a preset section model, and engineering image data corresponding to a cruise time during a cruise task performed by a UAV across administrative boundary engineering, to obtain cruise acquisition results; an alignment unit is configured to perform time alignment processing on cruise data in the cruise acquisition results according to a preset alignment strategy based on the Beidou time service information and the preset section model, to generate cruise records of each responsibility section under the same Beidou time reference; a credibility unit is configured to calculate a credibility index for representing positioning credibility and time traceability of the cruise acquisition results according to a stability degree of the multi-frequency positioning consistency parameters, the constellation visibility sequences, and the spatial coverage relationship within each responsibility section in the cruise records; an identification unit is configured to perform cross-cycle comparative analysis on the credibility index of the same responsibility section within a preset number of cruise cycles, to identify a plurality of deviation sections, and determine a deviation type of a deviation section in combination with the engineering image data corresponding to the deviation section; a warning unit is configured to generate a warning prompt information based on the deviation type; an adjustment unit is configured to adjust the preset alignment strategy based on all the credibility indexes of the responsibility sections within a next preset number of cruise cycles.

2. The monitoring system based on the Beidou positioning unmanned plane cruise according to claim 1, characterized in that, The multi-frequency positioning consistency parameters comprise plane position deviation, vertical height deviation, and time service deviation of different Beidou frequency positioning results at the same cruise time, to represent a consistency level between multi-frequency positioning solution results. 3.The monitoring system based on the Beidou positioning unmanned plane cruise according to claim 2, characterized in that, The constellation visibility sequence comprises a change sequence of the number of available satellites, a change sequence of constellation geometric distribution factors, and a change sequence of occlusion states corresponding to each sampling time during the cruise task.

4. The monitoring system based on the Beidou positioning unmanned plane cruise according to claim 3, characterized in that, The cruise record is a section-level time series data set constructed based on responsibility sections, and the cruise record takes Beidou time as a unified time axis, aggregates and associates positioning parameters, track coverage features, and engineering image data within the same responsibility section.

5. The monitoring system based on Beidou positioning unmanned plane cruise according to claim 4, characterized in that, In the credibility unit, the credibility index of the positioning credibility is calculated based on fluctuation amplitude and stability statistical characteristics of the multi-frequency positioning consistency parameters within a preset time window.

6. The monitoring system based on the Beidou positioning unmanned plane cruise according to claim 5, characterized in that, The credibility index of the time traceability is obtained based on joint evaluation of time continuity and time alignment integrity in the cruise record of the constellation visibility sequence and the Beidou time service information.

7. The monitoring system based on the Beidou positioning unmanned plane cruise according to claim 6, characterized in that, The deviation type comprises an engineering state change type deviation and a cruise evidence deviation, wherein the engineering state change type deviation is determined based on cross-cycle changes of the engineering image data, and the cruise evidence deviation is determined based on abnormal fluctuation characteristics of the credibility index.

8. The monitoring system based on the Beidou positioning unmanned plane cruise according to claim 7, characterized in that, The warning prompt information comprises responsibility section identification, deviation type identification, and corresponding cruise cycle range, to provide a section-level risk prompt for cross-administrative engineering supervision.

9. The monitoring system based on the Beidou positioning unmanned plane cruise according to claim 8, characterized in that, The adjustment unit comprises: an index calculation module configured to calculate a comprehensive trust index by performing a weighted sum calculation on an average of trustworthiness indexes of all the positioning trustworthiness and an average of trustworthiness indexes of the time traceability of each of the responsibility sections in the next preset number of cruise periods; a granularity adjustment module configured to adjust a preset time granularity corresponding to each of the responsibility sections in the preset alignment strategy according to a relative deviation between the comprehensive trust index and a preset trust index threshold when the comprehensive trust index is less than the preset trust index threshold. 10.A method for monitoring the cruise of a UAV based on Beidou positioning, applied to the system for monitoring the cruise of a UAV based on Beidou positioning according to any one of claims 1-9, characterized in that, comprise: real-time acquisition of Beidou positioning coordinates, Beidou time information, multi-frequency positioning consistency parameters, constellation visibility sequences, spatial coverage relationships between cruise trajectories and preset section models, and engineering image data corresponding to cruise time points during execution of a cruise task of a UAV for a cross-administrative boundary project, to obtain cruise acquisition results; time alignment processing of cruise data in the cruise acquisition results according to a preset alignment strategy based on the Beidou time information and the preset section models, to generate cruise records of each of the responsibility sections under the same Beidou time reference; calculation of trustworthiness indexes for representing positioning trustworthiness and time traceability of the cruise acquisition results according to a stability degree of the multi-frequency positioning consistency parameters, the constellation visibility sequences, and the spatial coverage relationships in each of the responsibility sections; cross-period comparison and analysis of the trustworthiness indexes of the same responsibility section in a preset number of cruise periods, to identify a plurality of deviation sections, and determination of deviation types of the deviation sections in combination with the engineering image data corresponding to the deviation sections; generation of early warning prompt information based on the deviation types; adjustment of the preset alignment strategy based on all the trustworthiness indexes of the responsibility sections in a next preset adjustment number of cruise periods.

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