Unmanned aerial vehicle inspection system prototype verification method and system based on low-orbit satellite communication

Abstract

The invention discloses an unmanned aerial vehicle inspection system prototype verification method and system based on low orbit satellite communication, and the method comprises the steps: collecting complex terrain parameters, building an end edge cloud satellite full-link basic environment, and outputting deployment topology and environment features; verifying plug and play and fault self-recovery of the heterogeneous terminal, and outputting an anti-interference communication baseline; quantizing link performance of satellite-ground communication in an extreme scene, and outputting link performance quality; checking the reliability of the most core data uploading and instruction downlink performance of the system; a communication capability and an interaction function are integrated, and the unattended closed-loop mode response capability executed by the satellite return cloud analysis terminal is tested; and injecting a multi-mode failure scene and a limit load, verifying the survivability and the security boundary of the whole link, and outputting a verification report. According to the invention, through a multi-dimensional verification process, the cooperative work reliability of the remote intelligent inspection terminal and the low-orbit satellite communication system is ensured, and the effectiveness of a closed-loop inspection mode in a complex scene is verified.

Description

Technical Field

[0001] This invention relates to the field of remote operation and maintenance technology for power systems, and in particular to a prototype verification method and system for an unmanned aerial vehicle (UAV) inspection system based on low-orbit satellite communication. Background Technology

[0002] In the current field of power line inspection, the intelligent operation and maintenance of transmission lines in remote areas faces many challenges. Insufficient compatibility verification between existing remote inspection systems and communication modules leads to frequent equipment docking failures during actual deployment; the stability of satellite communication links in complex terrain lacks standardized verification procedures, resulting in frequent data transmission interruptions; and the functional integrity verification of closed-loop inspection modes is incomplete, making it difficult to ensure reliable operation in unattended scenarios.

[0003] Traditional verification methods focus on single-module performance testing, neglecting system-level collaborative verification. This leads to prototypes meeting laboratory standards but failing in the field during actual application. Therefore, a comprehensive verification method covering communication links, data interaction, and closed-loop modes is needed to ensure the stable operation of UAV inspection systems based on low-Earth orbit satellite communication in complex environments. This invention aims to ensure the reliability of collaborative operation between remote intelligent inspection terminals and low-Earth orbit satellite communication systems through a standardized, multi-dimensional verification process, verify the effectiveness of closed-loop inspection modes in complex scenarios, and provide a scientific basis for the large-scale application of the system. Summary of the Invention

[0004] This invention provides a prototype verification method for an unmanned aerial vehicle (UAV) inspection system based on low-Earth orbit (LEO) satellite communication, comprising:

[0005] Collect complex terrain parameters, build a full-link infrastructure environment for edge-cloud-satellite communication, and output deployment topology and environmental characteristics;

[0006] Based on environmental characteristics, verify the plug-and-play functionality and fault self-healing of heterogeneous terminals, and output an anti-interference communication baseline.

[0007] Based on the anti-interference baseline, the link performance of satellite-to-ground communication under extreme scenarios is quantified, and the link performance quality is output.

[0008] By utilizing link performance quality, the reliability of the system's core data upload and command downlink performance is verified;

[0009] Integrate communication capabilities and interactive functions to test the satellite-to-cloud analysis terminal's ability to execute unattended closed-loop mode response;

[0010] Based on the closed-loop model, multimodal failure scenarios and extreme loads are injected to verify the survivability and security boundaries of the entire link and output a verification report.

[0011] The above-described method for verifying a prototype of a UAV inspection system based on low-Earth orbit satellite communication includes: collecting complex terrain parameters, building a full-link infrastructure environment (edge-cloud-satellite), and outputting deployment topology and environmental characteristics, including:

[0012] By using multimodal sensing dynamic adaptive fusion technology, a dynamic terrain parameter model is constructed, core performance parameters of the end-edge-cloud-star full-link nodes are collected synchronously, and a multi-dimensional environmental feature dataset supporting full-link deployment is generated.

[0013] Based on a multi-dimensional environmental feature dataset, an edge-cloud-satellite data interaction channel is constructed. Through digital twin technology, the connection relationships and protocol adaptation logic of four-level nodes are mapped, and a dual-link intelligent switching architecture is built to output a full-link deployment topology and environmental feature report.

[0014] The above-described prototype verification method for a UAV inspection system based on low-Earth orbit satellite communication includes, according to environmental characteristics, verifying the plug-and-play functionality and fault self-healing of heterogeneous terminals, and outputting an anti-interference communication baseline, including:

[0015] Multiple models of remote intelligent inspection terminals were connected to low-orbit satellite terminals in a plug-and-play manner. Through packet capture analysis and hot-plug stress testing, the hot-plug stability test of satellite terminals in the hovering state of UAVs was carried out to verify the compatibility of heterogeneous devices.

[0016] A distributed heartbeat detection and link quality prediction algorithm is adopted to simulate complex fault scenarios and trigger link switching and node reconstruction mechanisms. The self-healing response time and data continuity are recorded, and an anti-interference communication baseline is output through dynamic threshold adjustment and time series modeling.

[0017] The above-described prototype verification method for a UAV inspection system based on low-Earth orbit satellite communication includes, based on an anti-interference baseline, quantifying the link performance of satellite-to-ground communication in extreme scenarios and outputting the link performance quality, including:

[0018] Based on fixed-point channel monitoring terminals and combined with the dynamic flight trajectory of UAVs, a benchmark database of link performance in conventional scenarios is constructed using a spatiotemporal adaptive correlation algorithm.

[0019] An extreme scenario simulation platform is built based on the anti-interference baseline. Combined with on-site extreme weather tests, intelligent time series analysis is used to quantify the performance fluctuation pattern, quantify the link anti-interference capability, and output a multi-dimensional link quality assessment report.

[0020] The above-described prototype verification method for a UAV inspection system based on low-Earth orbit satellite communication includes verifying the reliability of the system's core data upload and command downlink performance by utilizing link performance quality, including:

[0021] Based on link quality, the coding strength of low-density parity-check code is dynamically adjusted to test the forward error correction effect of core data upload and output the uplink transmission performance index.

[0022] Based on the uplink transmission performance index, and through instruction redundancy coding and dynamic retransmission strategies, the accuracy of downlink response to emergency instructions is verified by priority scheduling algorithm when the link fluctuates.

[0023] The above-described prototype verification method for an unmanned aerial vehicle (UAV) inspection system based on low-Earth orbit (LEO) satellite communication includes, but is not limited to, integrating communication capabilities and interactive functions, and testing the response capability of the satellite-transmitted cloud analysis terminal in executing an unattended closed-loop mode, comprising:

[0024] The system integrates satellite backhaul, cloud-based intelligent inference, and terminal execution into a real-time linkage chain. It constructs a closed-loop process for defect capture, satellite backhaul, cloud analysis, command generation, and terminal secondary inspection, and tests the full-process response time and closed-loop response efficiency of typical defects.

[0025] Based on the spatiotemporal load perception dynamic scheduling algorithm, multi-machine load balancing and data fusion are achieved in unmanned operation. The autonomous processing capability is verified through the intelligent grading mechanism for abnormal events, and the effectiveness of the closed-loop mode is quantified.

[0026] The above-described prototype verification method for a UAV inspection system based on low-Earth orbit satellite communication includes, based on a closed-loop mode, injecting multimodal failure scenarios and extreme loads to verify the survivability and security boundaries of the entire link, and outputting a verification report, including:

[0027] Based on the closed-loop mode injection of multimodal failure scenarios, the triggering speed and recovery effect of the system's self-healing mechanism are monitored to verify the end-to-end damage resistance and survivability.

[0028] The gradient pressure method is used to test the limit of multi-machine concurrent transmission. The resource threshold is determined by combining satellite communication frame structure fusion encryption. A comprehensive verification report containing survivability boundary and security threshold is output.

[0029] A prototype verification system for an unmanned aerial vehicle (UAV) inspection system based on low-Earth orbit (LEO) satellite communication, comprising:

[0030] The anti-interference baseline generation module is used to verify the plug-and-play and fault self-healing capabilities of heterogeneous terminals based on environmental characteristics, and output an anti-interference communication baseline.

[0031] The satellite-to-ground link quantization module is used to quantify the link performance of satellite-to-ground communication in extreme scenarios based on the anti-interference baseline, and output the link performance quality.

[0032] The data interaction verification module is used to verify the reliability of the system's core data upload and command downlink performance by utilizing link performance quality.

[0033] The closed-loop response test module is used to integrate communication capabilities and interactive functions to test the satellite-to-cloud analysis terminal's ability to execute unattended closed-loop response mode.

[0034] The survival boundary verification module is used to verify the survivability and security boundary of the entire link based on closed-loop mode, by injecting multimodal failure scenarios and extreme loads, and outputting a verification report.

[0035] A computer-readable storage medium comprising one or more program instructions, the one or more program instructions being executed by a processor as described in any one of claims 1-7, a prototype verification method for an unmanned aerial vehicle (UAV) inspection system based on low-Earth orbit satellite communication.

[0036] The beneficial effects achieved by this invention are as follows:

[0037] Establish a standardized verification system for end-edge-cloud-satellite collaboration to accurately capture communication characteristics under complex terrain and provide quantitative basis for equipment selection; innovate heterogeneous terminal compatibility and fault self-healing mechanisms to reduce the difficulty of cross-vendor integration and accelerate the deployment cycle; ensure the high reliability of unattended inspection through extreme scenario and closed-loop mode testing, and the results can be extended to remote operation and maintenance in multiple fields, combining engineering practicality and scenario adaptability. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0039] Figure 1 This is a flowchart of a prototype verification method for an unmanned aerial vehicle (UAV) inspection system based on low-orbit satellite communication, provided in Embodiment 1 of this application.

[0040] Figure 2 This is a schematic diagram of a prototype verification system for an unmanned aerial vehicle (UAV) inspection system based on low-orbit satellite communication, provided in Embodiment 2 of this application. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Example 1

[0043] like Figure 1As shown, Embodiment 1 of this application provides a prototype verification method for an unmanned aerial vehicle (UAV) inspection system based on low-Earth orbit satellite communication, including:

[0044] S1: Collect complex terrain parameters, build the basic environment of the entire edge-cloud-star link, and output the deployment topology and environmental characteristics;

[0045] The process includes collecting complex terrain parameters, building a full-link infrastructure environment for edge-cloud-satellite communication, and outputting deployment topology and environmental characteristics, comprising the following sub-steps:

[0046] S11: Through multimodal sensing dynamic adaptive fusion technology, a dynamic terrain parameter model is constructed, core performance parameters of the entire link nodes of edge, cloud and star are collected synchronously, and a multi-dimensional environmental feature dataset supporting the deployment of the entire link is generated.

[0047] Raw terrain data is simultaneously collected by a drone equipped with lidar, high-definition cameras, and infrared sensors. Dynamic adaptive fusion technology is used to preprocess the multi-source sensor data, eliminating noise and redundant information. Simultaneously, spatiotemporal coordinates obtained from the BeiDou positioning module are combined to perform spatiotemporal calibration on the satellite remote sensing data, achieving precise alignment of multi-scale data. This allows for the construction of a dynamic terrain parameter model, accurately depicting terrain features. During this process, a full-link node parameter acquisition mechanism is simultaneously activated to acquire core parameters in real time, including the sensing response characteristics of end-side terminals, the computing power capacity of edge nodes, the storage and scheduling performance of the cloud platform, and the channel transmission characteristics of satellite communication. After data consistency verification and dimensional normalization, these parameters are integrated into a multi-dimensional environmental feature dataset.

[0048] S12: Based on a multi-dimensional environmental feature dataset, construct an edge-cloud-star data interaction channel, map the four-level node connection relationship and protocol adaptation logic through digital twin technology, build a dual-link intelligent switching architecture, and output a full-link deployment topology and environmental feature report.

[0049] Based on a multi-dimensional environmental feature dataset, a four-layer physical deployment architecture of edge-cloud-satellite nodes is planned. This involves sequentially deploying an intelligent inspection terminal on the edge, an edge protocol conversion gateway, a cloud management platform, and a low-Earth orbit satellite transceiver terminal to construct a full-link data interaction channel. Digital twin technology is introduced to map the computing power configuration, communication interface types, and protocol adaptation logic of physical nodes to a virtual space, establishing a digital model of the relationships between nodes. Simultaneously, a dual-link intelligent switching mechanism is configured, achieving protocol compatibility between satellite and fiber optic links through the protocol conversion gateway, monitoring link status in real time, and triggering dynamic switching logic. Finally, based on the digital twin model, a full-link deployment topology map containing node connection topology, protocol adaptation rules, and link switching strategies is generated, and an environmental feature report is output based on the environmental feature analysis results.

[0050] S2: Based on environmental characteristics, verify the plug-and-play functionality and fault self-healing of heterogeneous terminals, and output an anti-interference communication baseline;

[0051] The process, based on environmental characteristics, verifies the plug-and-play functionality and fault self-healing capabilities of heterogeneous terminals, and outputs an anti-interference communication baseline, including the following sub-steps:

[0052] S21: Connect multiple models of remote intelligent inspection terminals to low-orbit satellite terminals in a plug-and-play manner. Through packet capture analysis and hot-swap stress testing, conduct hot-swap stability testing of satellite terminals in the hovering state of UAVs to verify the compatibility of heterogeneous devices.

[0053] Based on environmental characteristics and deployment topology, multiple models of remote intelligent inspection terminals and low-Earth orbit satellite terminals were selected and connected according to the plug-and-play specification. A distributed interface monitoring tool was activated to capture hardware interface response signals in real time, record connection establishment time and status feedback information, and verify interface compatibility. Simultaneously, the system enabled a protocol packet capture and analysis engine to parse the instructions and data frames transmitted between terminals, extract protocol field matching results, and quantify the completeness of driver and terminal model matching. A hot-swap stress test mechanism was triggered for drone hovering scenarios. While maintaining the operational status of the inspection terminals, the plugging and unplugging operations of the satellite terminals were repeatedly performed, simultaneously monitoring data transmission continuity and terminal operational stability. Through anomaly log capture and status verification, the compatibility verification of heterogeneous devices in dynamic scenarios was completed.

[0054] S22: Employs distributed heartbeat detection and link quality prediction algorithms to trigger link switching and node reconstruction mechanisms when simulating complex fault scenarios, records self-healing response time and data continuity, and outputs anti-interference communication baseline through dynamic threshold adjustment and time series modeling.

[0055] To acquire compatibility data and environmental characteristics of heterogeneous devices, a distributed heartbeat detection mechanism is employed. Detection signals are periodically sent to each terminal via edge nodes to collect real-time link connectivity and response time data. A link quality prediction algorithm is run to predict link stability trends based on historical transmission parameters and current channel characteristics. Complex fault scenarios such as disconnection and packet loss are simulated to trigger link switching and node reconstruction mechanisms on the edge gateway, dynamically selecting backup communication paths and re-establishing node connections.

[0056] During the process, the response time from fault triggering to normal recovery was continuously recorded, and data integrity and continuity during transmission were monitored using data verification tools. Based on the results of multiple rounds of testing, the system adopted dynamic threshold adjustment and timing modeling methods to transform self-healing performance indicators into quantitative parameters, and finally output an anti-interference communication baseline that includes link switching standards, fault recovery timeliness, and other content.

[0057] S3: Based on the anti-interference baseline, quantify the link performance of satellite-to-ground communication in extreme scenarios and output the link performance quality;

[0058] The process, which relies on an anti-interference baseline, quantifies the link performance of satellite-to-ground communication under extreme scenarios and outputs the link performance quality, includes the following sub-steps:

[0059] S31: Based on fixed-point channel monitoring terminals and combined with the dynamic flight trajectory of UAVs, a benchmark database of link performance in conventional scenarios is constructed through a spatiotemporal adaptive correlation algorithm.

[0060] Channel monitoring terminals are deployed at fixed locations within the inspection area to continuously collect basic parameters of the satellite-to-ground communication link. Simultaneously, the system schedules drones to perform dynamic flight missions along preset trajectories, synchronously recording the drones' real-time location information and link status data captured by onboard sensors. A spatiotemporal adaptive correlation algorithm is employed to align the static monitoring data from fixed locations with the drones' dynamic trajectory data spatiotemporally. Through time series matching and spatial coordinate mapping, correlations between communication parameters at different locations and time periods are established. Feature extraction and noise filtering are performed on indicators such as signal strength and transmission rate to eliminate abnormal fluctuations caused by environmental interference. The processed parameters are categorized and stored according to scenario type. Statistical analysis generates link performance benchmark values ​​for typical scenarios, constructing a link performance benchmark database containing multi-dimensional indicators.

[0061] S32: Based on the anti-interference baseline, an extreme scenario simulation platform is built. Combined with on-site extreme weather tests, intelligent time series analysis is used to quantify the performance fluctuation pattern, quantify the link anti-interference capability, and output a multi-dimensional link quality assessment report.

[0062] Based on a link performance benchmark database and an anti-interference communication baseline, an extreme scenario simulation platform was built to simulate complex environmental conditions such as strong electromagnetic interference and signal blockage through parameter configuration. Tests were conducted in conjunction with real-world extreme weather scenarios, with drones scheduled to fly along preset routes and satellite-to-ground communication terminals simultaneously collecting real-time transmission data. Using intelligent time-series analysis algorithms, parameters such as signal strength and transmission delay under extreme scenarios were compared with the benchmark database to quantify the amplitude and duration of performance fluctuations and identify link vulnerabilities.

[0063] By trend fitting and feature extraction, the performance degradation patterns under different extreme conditions are summarized, and the link's anti-interference capability is evaluated. The formula for the link's anti-interference capability is as follows:

[0064]

[0065] K anti S represents the link's anti-interference capability index, which comprehensively characterizes the link's anti-interference performance in extreme scenarios; actual S represents the measured signal strength under extreme conditions.base The reference value for signal strength in the benchmark database is represented by α; α represents the weighting coefficient of the influence of signal strength on the overall anti-interference capability; T base T represents the transmission delay reference value in the benchmark database; actual L represents the measured transmission delay under extreme scenarios; β represents the weighting coefficient of the impact of transmission delay on the overall anti-interference capability; γ represents the weighting coefficient of the impact of packet loss rate on the overall anti-interference capability; loss t represents the packet loss rate in extreme scenarios; start Indicates the starting time of an extreme event; t end The extreme event ends at time t; D(t) represents the link performance degradation function at time t; D th This represents the acceptable link performance degradation threshold; δ represents the acceleration factor for performance degradation, used to quantify the rate at which the degradation level changes over time; f event R represents the frequency of extreme events occurring per unit of time. recover This represents the ratio of link recovery time to the duration of the extreme event.

[0066] The system performs consistency checks and outlier removal on multiple rounds of test data, and integrates the analysis results to generate a multi-dimensional link quality assessment report that includes indicators such as anti-interference level and performance degradation threshold.

[0067] S4: Utilize link performance quality to verify the reliability of the system's core data upload and command downlink performance;

[0068] Among them, using link performance quality to verify the reliability of the system's core data upload and command downlink performance includes the following sub-steps:

[0069] S41: Based on link quality, dynamically adjust the low-density parity check code encoding strength, test the forward error correction effect of core data upload, and output the uplink transmission performance index.

[0070] Access link performance quality data serves as input for adjusting the low-density parity-check code (LDPC) coding strength. The system activates a dynamic coding control module to establish a mapping relationship between link quality and coding strength: when link quality deteriorates, coding redundancy is automatically increased to enhance error correction capabilities; when the link is stable, coding complexity is reduced to optimize transmission efficiency. The system selects core inspection data (such as device status parameters and high-definition images) for upload testing, simultaneously recording the error correction rate, data integrity verification results, and transmission latency during transmission. Through multiple sets of test data under different link conditions, the system uses a weighted algorithm to comprehensively evaluate the forward error correction effect, integrating the quantified results into an uplink transmission performance index, providing an uplink performance benchmark for downlink command verification.

[0071] The formula for calculating the uplink transmission performance index is as follows:

[0072]

[0073] P up R represents the uplink transmission performance index, which comprehensively characterizes the core data upload performance; fix The error correction rate (ω1) represents the proportion of errors corrected by low-density parity-check codes out of the total number of errors; ω1 represents the weighting coefficient of the error correction rate; I data ω2 represents the data integrity check value, reflecting the completeness of the uploaded data; θ represents the error correction effectiveness amplification factor, used to enhance the combined impact of error correction and integrity; T actual T represents the measured transmission delay, which refers to the actual time it takes for data to be uploaded from the terminal to the receiving end; base σ represents the baseline transmission delay, a standard delay reference value when the link is stable; λ represents the delay penalty coefficient, used to quantify the negative impact of delay deviation from the baseline value; error represents the standard deviation of the error correction rate across multiple test groups, reflecting the stability of the correction effect; i is the test group number, and n is the total number of test groups; C red,i C represents the coding redundancy of the i-th test group; base Indicates the baseline coding redundancy, the standard redundancy configuration when the link is stable; E trans,i denoted as the transmission efficiency of the i-th test group, i.e., the ratio of actual throughput to theoretical bandwidth; μ represents the penalty coefficient for excessive adjustment of coding redundancy, used to suppress the negative impact of unreasonable increase in redundancy on performance.

[0074] S42: Relying on the uplink transmission performance index, and through instruction redundancy coding and dynamic retransmission strategies, the downlink response accuracy of emergency instructions is verified by priority scheduling algorithm when the link fluctuates.

[0075] The system receives the uplink transmission performance index to determine the current link stability level. Downlink commands are categorized and labeled, distinguishing between routine operation commands and emergency response commands. Differentiated strategies are implemented based on link level: multi-level redundant coding is configured for emergency commands, and anti-interference capabilities are improved through data fragmentation and parity bit embedding; routine commands use a basic coding scheme to save channel resources. A dynamic retransmission mechanism is also implemented, dynamically setting retransmission thresholds based on the uplink transmission performance index. When an abnormal command transmission is detected, the rapid retransmission process for emergency commands is triggered first. A priority scheduling algorithm is run, allocating higher channel occupancy weights to emergency commands when link fluctuations lead to resource constraints. By comparing the execution results fed back by the terminal with the expected command objectives, the downlink response accuracy of emergency commands is quantified, verifying the reliability of command interaction under dynamic link changes.

[0076] The formula for calculating response accuracy is as follows:

[0077]

[0078] A resp This represents the downlink response accuracy index for emergency commands, which comprehensively characterizes the accuracy of emergency commands from issuance to execution at the terminal; V red The redundancy check score represents the structural integrity of emergency instructions after multi-level redundancy coding processing; N retry Indicates the actual number of retransmissions, referring to the total number of actual retransmissions when a command transmission error occurs; N max ξ represents the maximum allowed number of retransmissions; ξ represents the retransmission penalty coefficient, used to quantify the negative impact of the number of retransmissions on accuracy; P parse η represents the terminal instruction parsing accuracy rate, which is the proportion of instructions that the terminal successfully parses; η represents the structural deviation impact weight, which is used to adjust the degree of impact of instruction transmission structural deviation on accuracy.

[0079] Δ struct Indicates the deviation between the actual transmitted structure and the expected structure of the instruction, reflecting the integrity of the instruction format; T act Indicates the actual response time, referring to the actual time elapsed from when the instruction is issued to when it is completed on the terminal; T exp This indicates the expected response time, a standard reference value for the response time of emergency commands when the link is stable; W prio This indicates the priority weight of emergency commands, reflecting their priority level in scheduling; k is the sequence number of regular commands, and m is the total number of concurrent regular commands; I reg,k This represents the channel occupancy rate of the k-th regular instruction, reflecting the proportion of channel resources occupied by regular instructions; I emer This represents the channel occupancy rate of emergency commands, reflecting the proportion of channel resources occupied by emergency commands; ζ k σ represents the interference coefficient of the k-th regular instruction on priority scheduling, quantifying the impact of regular instructions on the transmission of emergency instructions; resp It represents the standard deviation of response accuracy across multiple rounds of testing, reflecting the stability of response accuracy.

[0080] S5: Integrates communication capabilities and interactive functions to test the satellite-to-cloud analysis terminal's ability to execute unattended closed-loop mode response;

[0081] The integration of communication capabilities and interactive functions, and testing the satellite-to-cloud analysis terminal's ability to execute unattended closed-loop mode response, includes the following sub-steps:

[0082] S51: Integrates a real-time linkage chain of satellite backhaul, cloud-based intelligent inference, and terminal execution to construct a closed-loop process for defect capture, satellite backhaul, cloud-based analysis, command generation, and terminal secondary inspection. Tests the full-process response time and closed-loop response efficiency of typical defects.

[0083] Based on multi-dimensional link quality, uplink transmission performance, and downlink command accuracy, a satellite backhaul module, a cloud-based intelligent inference engine, and a terminal execution unit are integrated to construct a real-time linkage chain of terminal perception, satellite transmission, cloud analysis, and command feedback. Typical defect data of the inspection area is captured by the UAV's sensing unit and transmitted back to the cloud via satellite using an optimized encoding strategy. This triggers the cloud inference engine to perform feature recognition and defect level determination, automatically generating a secondary inspection command containing verification areas and parameter adjustment requirements. The command is then sent down to the terminal through a priority scheduling mechanism. The entire process from defect capture to the terminal initiating the secondary inspection is recorded synchronously. By comparing the consistency of the two inspection data and the command execution accuracy, the response efficiency of the closed-loop process is calculated, verifying the continuity of the linkage chain in unattended scenarios.

[0084] S52: Based on the spatiotemporal load perception dynamic scheduling algorithm, it realizes multi-machine load balancing and data fusion in unmanned operation, verifies autonomous processing capabilities through an intelligent grading mechanism for abnormal events, and quantifies the effectiveness of the closed-loop mode.

[0085] Based on a closed-loop framework, a spatiotemporal load-aware dynamic scheduling algorithm is implemented to collect real-time location information, remaining computing power, and link occupancy status of multiple UAV terminals. Based on the spatiotemporal load distribution characteristics, inspection tasks and data transmission time slots are dynamically allocated to achieve load balancing among multiple UAVs and edge-level fusion of collected data, reducing redundant transmission. Simultaneously, an intelligent anomaly event classification mechanism is activated, automatically prioritizing processing based on defect severity and link status, triggering corresponding autonomous response processes (such as local recording, cloud alarms, or terminal-linked handling). By continuously monitoring the task completion rate, data fusion accuracy, and anomaly handling timeliness under unmanned operation, the overall operational efficiency of the closed-loop mode is tested, verifying the end-to-end autonomous operation and maintenance capabilities.

[0086] S6: Based on closed-loop mode, inject multimodal failure scenarios and extreme loads to verify the survivability and security boundaries of the entire link and output a verification report.

[0087] The process, based on a closed-loop model, involves injecting multimodal failure scenarios and extreme loads to verify the survivability and security boundaries of the entire link, and outputting a verification report, including the following sub-steps:

[0088] S61: Based on closed-loop mode injection of multimodal failure scenarios, monitor the triggering speed and recovery effect of the system's self-healing mechanism to verify the end-to-end damage resistance and survivability performance.

[0089] Leveraging closed-loop operational efficiency, a multi-machine concurrent test environment was constructed to simulate scenarios where multiple UAV terminals simultaneously execute inspection tasks. A distributed monitoring module collects real-time resource load data from each node across the edge, cloud, and satellite systems, including edge node computing power utilization, satellite link bandwidth utilization, and cloud storage response latency. Combined with dynamic scheduling logic, the number of concurrent terminals is gradually increased, triggering load balancing mechanisms and resource preemption strategies. Task execution success rate, data transmission integrity, and node failure probability are recorded under different load intensities. When key indicators exceed the reliability threshold verified by S4, a fault tracing mechanism is initiated to locate bottleneck nodes and analyze the causes of overload. Multiple rounds of testing verify the system's adaptive adjustment capabilities under high-concurrency scenarios, quantifying the basic parameters of the survivability boundary and providing a practical basis for threshold setting.

[0090] S62: The gradient pressure method is used to test the limit of multi-machine concurrent transmission. The resource threshold is determined by combining satellite communication frame structure fusion encryption. A comprehensive verification report containing survivability boundary and security threshold is output.

[0091] By integrating concurrent load data and end-to-end performance metrics, a multi-dimensional security boundary model is constructed. Model inputs include core parameters such as maximum concurrent terminal count, encryption processing latency, and satellite link anti-interference threshold. Correlation analysis algorithms identify the constraints between parameters (e.g., the positive correlation between concurrency and transmission latency). Based on the model output, the system initiates extreme condition tests, gradually approaching resource capacity limits (e.g., continuously increasing encryption strength to link congestion, increasing task density to computing power saturation), recording the critical values ​​of each parameter and the corresponding system response status (e.g., degraded operation, partial interruption). Statistical analysis determines the safe operating range, integrating key thresholds (e.g., maximum stable concurrency, upper limit of encryption latency) into quantitative indicators, and outputting a comprehensive report containing information on survivability boundaries and safe operating ranges.

[0092] Example 2

[0093] like Figure 2 As shown, Embodiment 2 of this application provides a prototype verification system for an unmanned aerial vehicle (UAV) inspection system based on low-Earth orbit satellite communication, comprising:

[0094] Full-link environment construction module 21: Used to collect complex terrain parameters, build the end-edge-cloud-star full-link basic environment, and output deployment topology and environmental characteristics;

[0095] Anti-interference baseline generation module 22: used to verify the plug-and-play and fault self-healing capabilities of heterogeneous terminals based on environmental characteristics, and output an anti-interference communication baseline;

[0096] Satellite-to-ground link quantization module 23: Used to quantify the link performance of satellite-to-ground communication in extreme scenarios based on the anti-interference baseline, and output the link performance quality;

[0097] Data interaction verification module 24: Used to verify the reliability of the system's core data upload and command downlink performance by utilizing link performance quality;

[0098] Closed-loop response test module 25: used to integrate communication capabilities and interactive functions, and to test the satellite backhaul cloud analysis terminal's ability to execute unattended closed-loop mode response;

[0099] Survival Boundary Verification Module 26: Based on closed-loop mode, it injects multimodal failure scenarios and extreme loads to verify the survivability and security boundary of the entire link and outputs a verification report.

[0100] Corresponding to the above embodiments, the present invention provides a computer storage medium, including: at least one memory and at least one processor;

[0101] The memory is used to store one or more program instructions;

[0102] A processor for running one or more program instructions to execute a prototype verification method for a drone inspection system based on low-Earth orbit satellite communication.

[0103] Corresponding to the above embodiments, this embodiment of the invention provides a computer-readable storage medium containing one or more program instructions, which are executed by a processor to provide a prototype verification method for an unmanned aerial vehicle (UAV) inspection system based on low-Earth orbit satellite communication.

[0104] The embodiments disclosed in this invention provide a computer-readable storage medium storing computer program instructions. When the computer program instructions are executed on a computer, the computer executes the above-described prototype verification method for an unmanned aerial vehicle (UAV) inspection system based on low-Earth orbit satellite communication.

[0105] In this embodiment of the invention, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0106] The various methods, steps, and logic diagrams disclosed in the embodiments of this invention can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor reads information from the storage medium and, in conjunction with its hardware, completes the steps of the above methods.

[0107] The storage medium can be memory, such as volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0108] Among them, non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0109] Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (Synchlink DRAM, SLDRAM), and direct memory bus RAM (DRRAM).

[0110] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0111] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this invention can be implemented using a combination of hardware and software. When applied as software, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0112] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. A prototype verification method for an unmanned aerial vehicle (UAV) inspection system based on low-Earth orbit satellite communication, characterized in that, include: Collect complex terrain parameters, build a full-link infrastructure environment for edge-cloud-satellite communication, and output deployment topology and environmental characteristics; Based on environmental characteristics, verify the plug-and-play functionality and fault self-healing of heterogeneous terminals, and output an anti-interference communication baseline. Based on the anti-interference baseline, the link performance of satellite-to-ground communication under extreme scenarios is quantified, and the link performance quality is output. By utilizing link performance quality, the reliability of the system's core data upload and command downlink performance is verified; Integrate communication capabilities and interactive functions to test the satellite-to-cloud analysis terminal's ability to execute unattended closed-loop mode response; Based on the closed-loop model, multimodal failure scenarios and extreme loads are injected to verify the survivability and security boundaries of the entire link and output a verification report.

2. The prototype verification method for an unmanned aerial vehicle (UAV) inspection system based on low-orbit satellite communication according to claim 1, characterized in that, Collect complex terrain parameters, build a complete edge-cloud-satellite infrastructure environment, and output deployment topology and environmental characteristics, including: By using multimodal sensing dynamic adaptive fusion technology, a dynamic terrain parameter model is constructed, core performance parameters of the entire link nodes of edge, cloud and star are collected synchronously, and a multi-dimensional environmental feature dataset supporting the full-link deployment is generated. Based on a multi-dimensional environmental feature dataset, an edge-cloud-satellite data interaction channel is constructed. Through digital twin technology, the connection relationships and protocol adaptation logic of four-level nodes are mapped, and a dual-link intelligent switching architecture is built to output a full-link deployment topology and environmental feature report.

3. The prototype verification method for an unmanned aerial vehicle (UAV) inspection system based on low-orbit satellite communication according to claim 1, characterized in that, Based on environmental characteristics, the plug-and-play functionality and fault self-healing capabilities of heterogeneous terminals are verified, and an anti-interference communication baseline is output, including: Multiple models of remote intelligent inspection terminals were connected to low-orbit satellite terminals in a plug-and-play manner. Through packet capture analysis and hot-plug stress testing, the hot-plug stability test of satellite terminals in the hovering state of UAVs was carried out to verify the compatibility of heterogeneous devices. A distributed heartbeat detection and link quality prediction algorithm is adopted to simulate complex fault scenarios and trigger link switching and node reconstruction mechanisms. The self-healing response time and data continuity are recorded, and an anti-interference communication baseline is output through dynamic threshold adjustment and time series modeling.

4. The prototype verification method for an unmanned aerial vehicle (UAV) inspection system based on low-orbit satellite communication according to claim 1, characterized in that, Based on the anti-interference baseline, the link performance of satellite-to-ground communication under extreme scenarios is quantified, and the link performance quality is output, including: Based on fixed-point channel monitoring terminals and combined with the dynamic flight trajectory of UAVs, a benchmark database of link performance in conventional scenarios is constructed using a spatiotemporal adaptive correlation algorithm. An extreme scenario simulation platform is built based on the anti-interference baseline. Combined with on-site extreme weather tests, intelligent time series analysis is used to quantify the performance fluctuation pattern, quantify the link anti-interference capability, and output a multi-dimensional link quality assessment report.

5. The prototype verification method for an unmanned aerial vehicle (UAV) inspection system based on low-orbit satellite communication according to claim 1, characterized in that, By utilizing link performance quality, the reliability of the system's core data upload and command downlink performance is verified, including: Based on link quality, the coding strength of low-density parity-check code is dynamically adjusted to test the forward error correction effect of core data upload and output the uplink transmission performance index. Based on the uplink transmission performance index, and through instruction redundancy coding and dynamic retransmission strategies, the accuracy of downlink response to emergency instructions is verified by priority scheduling algorithm when the link fluctuates.

6. The prototype verification method for an unmanned aerial vehicle (UAV) inspection system based on low-orbit satellite communication according to claim 1, characterized in that, Integrating communication capabilities and interactive functions, the test assesses the satellite-to-cloud analysis terminal's ability to execute unattended closed-loop response mode, including: The system integrates satellite backhaul, cloud-based intelligent inference, and terminal execution into a real-time linkage chain. It constructs a closed-loop process for defect capture, satellite backhaul, cloud analysis, command generation, and terminal secondary inspection, and tests the full-process response time and closed-loop response efficiency of typical defects. Based on the spatiotemporal load perception dynamic scheduling algorithm, multi-machine load balancing and data fusion are achieved in unmanned operation. The autonomous processing capability is verified through the intelligent grading mechanism for abnormal events, and the effectiveness of the closed-loop mode is quantified.

7. The prototype verification method for an unmanned aerial vehicle (UAV) inspection system based on low-Earth orbit satellite communication according to claim 1, characterized in that, Based on a closed-loop model, multimodal failure scenarios and extreme loads are injected to verify the survivability and security boundaries of the entire link, and a verification report is output, including: Based on the closed-loop mode injection of multimodal failure scenarios, the triggering speed and recovery effect of the system's self-healing mechanism are monitored to verify the end-to-end damage resistance and survivability. The gradient pressure method is used to test the limit of multi-machine concurrent transmission. The resource threshold is determined by combining satellite communication frame structure fusion encryption. A comprehensive verification report containing survivability boundary and security threshold is output.

8. A prototype verification system for an unmanned aerial vehicle (UAV) inspection system based on low-Earth orbit satellite communication, characterized in that, include: The anti-interference baseline generation module is used to verify the plug-and-play and fault self-healing capabilities of heterogeneous terminals based on environmental characteristics, and output an anti-interference communication baseline. The satellite-to-ground link quantization module is used to quantify the link performance of satellite-to-ground communication in extreme scenarios based on the anti-interference baseline, and output the link performance quality. The data interaction verification module is used to verify the reliability of the system's core data upload and command downlink performance by utilizing link performance quality. The closed-loop response test module is used to integrate communication capabilities and interactive functions to test the satellite-to-cloud analysis terminal's ability to execute unattended closed-loop response mode. The survival boundary verification module is used to verify the survivability and security boundary of the entire link based on closed-loop mode, by injecting multimodal failure scenarios and extreme loads, and outputting a verification report.

9. A computer-readable storage medium, characterized in that, It includes one or more program instructions, which are executed by a processor as described in any one of claims 1-7, to verify the prototype of a UAV inspection system based on low-Earth orbit satellite communication.

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