A method and system for intelligent low-altitude inspection of traffic facilities based on eVTOL

Abstract

This application belongs to the field of traffic facility inspection technology and discloses an intelligent low-altitude inspection method and system for traffic facilities based on eVTOL. By conducting task feasibility assessment locally on the eVTOL and interacting with the central control system based on the assessment results, it ensures that the eVTOL only executes tasks within its capabilities, effectively avoiding potential risks caused by information asymmetry. This method enables the eVTOL to autonomously judge the feasibility of tasks based on its actual physical state and energy reserves, and when a task is not feasible, it actively generates and reports alternative downgraded tasks, guiding the central control system to perform global replanning that is more in line with the actual situation, thereby significantly improving the reliability and safety of inspection tasks.

Description

Technical Field

[0001] This application relates to the field of traffic facility inspection technology, and more specifically, to an intelligent low-altitude inspection method and system for traffic facilities based on eVTOL. Background Technology

[0002] In the management and maintenance of modern urban infrastructure, deploying electric vertical takeoff and landing (eVTOL) fleets has become a key means of routine inspection of large transportation facilities. These aircraft undertake a variety of low-altitude inspection tasks, including inspecting structural health, identifying surface defects, checking equipment operational status, and collecting environmental data. To ensure the efficiency and coverage of the inspections, a central control system typically operates a sophisticated task decomposition and planning mechanism. This mechanism can refine complex inspection areas and tasks based on information such as the current location of each eVTOL, the type of sensors it carries, and the estimated remaining battery power, and rationally allocate them to different aircraft. Under ideal design conditions, the entire inspection system can operate efficiently, with good coordination between aircraft, ensuring that all tasks proceed smoothly as planned.

[0003] However, in real-world operating environments, some subtle factors can accumulate gradually, subtly impacting the system's normal function. For example, the physical contacts of eVTOL charging stations may undergo slow and uniform surface oxidation due to prolonged exposure to harsh environments, resulting in a generally stable decrease in actual charging efficiency of approximately 5% to 10% across all charging stations. This decrease is minute and continuous, making it difficult to identify as an anomaly during routine quick checks. When the eVTOL recharges at these environmentally affected charging stations, the logic used by its onboard battery management system to predict the "estimated charging time" may not be adaptively calibrated for this long-term, minute systemic environmental deviation. Consequently, the "estimated charging time" data reported to the central control system remains an optimistic prediction based on ideal operating conditions. This means that the central system receives the information that "this eVTOL will be fully charged in X minutes," while in reality, it takes much longer to reach the expected charge level.

[0004] When allocating tasks for the next round, the task decomposition and planning logic of the central control system relies entirely on the "estimated available time" data reported by each eVTOL. This planning is based on global optimization and efficiency maximization, but its decision-making foundation includes deviation information from local units. During an inspection mission, an eVTOL in the fleet might encounter a sudden strong crosswind. Its local flight control system will autonomously decide to temporarily reduce its speed and slightly deviate from its original flight path to avoid the risk. The aircraft coordination and safety distance management module in the central control system will immediately detect this deviation and recalculate and issue new coordination and avoidance routes for the surrounding eVTOLs. However, the new coordination route assigned to the eVTOL with the incorrect battery prediction will have a slightly longer total flight distance than the originally planned path due to the need for avoidance or detours, meaning it will consume more battery power. When the central control system's task decomposition and planning logic verifies the feasibility of this new route, it still relies on the erroneous battery data that the eVTOL "should have already charged to 85%". Based on this erroneous data, the system calculates that even after implementing new coordination adjustments, the aircraft "just happens" to have enough battery power to complete the mission and return safely. However, in reality, due to the systematic decline in charging efficiency, the eVTOL's actual battery level may only be 78%. This actual battery level will be insufficient to support the aircraft in completing new, longer coordination missions and returning safely to the charging point, and it may very well run out of power during the return journey. In this situation, the system faces a sharp contradiction: the central control system, from its global perspective, considers the dynamic mission adjustment based on coordination logic to be safe, effective, and optimal, and issues execution commands to the eVTOL. However, the eVTOL's local flight controller, based on its own real-time, accurate battery status and the new flight path plan, determines that this is a mission that cannot be completed safely. It possesses the most accurate local information, but its decision-making power is limited by the central command.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] The purpose of this application is to provide an intelligent low-altitude inspection method and system for traffic facilities based on eVTOL, which aims to solve the problems in the existing eVTOL traffic facility inspection system, where the central control system may rely on inaccurate local information during task planning, resulting in unreasonable task allocation, and the local system of eVTOL may not be able to effectively respond and coordinate with the central system during task execution, which may lead to the failure to complete the task safely.

[0007] Firstly, this application provides an intelligent low-altitude inspection method for traffic facilities based on eVTOL, applied to eVTOL, to perform low-altitude inspections of traffic facilities under the scheduling of a central control system, including:

[0008] A1. Receive task instructions issued by the central control system; the task instructions include flight routes, inspection targets, and inspection tasks for each of the inspection targets;

[0009] A2. Based on the body performance parameters of this eVTOL, calculate the total energy consumption required to execute the mission command, and evaluate the feasibility of the mission command based on the total energy consumption, the current actual remaining power, and the minimum power threshold for safe return.

[0010] A3. When the assessment determines that the task instruction is not feasible, at least one alternative degraded task is generated; the alternative degraded task contains fewer inspection tasks than the task instruction contains.

[0011] A4. The task instruction is not executable signal and the alternative degraded task plan are reported to the central control system so that the central control system can perform global replanning based on the alternative degraded task plan;

[0012] A5. Receive and execute new task instructions determined and issued by the central control system through global replanning.

[0013] Secondly, this application provides an intelligent low-altitude inspection system for traffic facilities based on eVTOL, used for low-altitude inspection of traffic facilities, including a central control system and multiple eVTOLs, each of which is communicatively connected to the central control system.

[0014] The central control system is used to perform global planning of inspection tasks, generate task instructions corresponding to each eVTOL, and send them to each eVTOL; the task instructions include flight routes, inspection targets, and inspection tasks for each of the inspection targets.

[0015] The eVTOL is used to perform:

[0016] Receive task instructions from the central control system;

[0017] Based on the body performance parameters of this eVTOL, calculate the total energy consumption required to execute the mission command, and evaluate the feasibility of the mission command based on the total energy consumption, the current actual remaining battery power, and the minimum battery power threshold for safe return.

[0018] When the assessment determines that the task instruction is not feasible, at least one alternative degraded task is generated; the alternative degraded task contains fewer inspection tasks than the task instruction.

[0019] The task instruction is not executable, and the alternative degraded task is reported to the central control system, so that the central control system can perform global replanning based on the alternative degraded task.

[0020] The central control system is also used to perform global replanning based on the degraded task alternative scheme when it receives a signal that the task instruction is not executable reported by the eVTOL and a degraded task alternative scheme, so as to generate new task instructions for the relevant eVTOL and send them to the relevant eVTOL.

[0021] The eVTOL is also used to execute the new task instruction when a new task instruction is received.

[0022] Beneficial Effects: This application provides an intelligent low-altitude inspection method and system for traffic facilities based on eVTOL. By introducing a local feasibility assessment mechanism for task instructions within the eVTOL, and combining it with the generation and reporting of degraded task alternatives, this effectively solves the problems in existing technologies where the central control system relies on inaccurate local information, leading to task planning deviations, and the inability of the eVTOL to effectively respond to emergencies during task execution. Specifically, this method enables the eVTOL to accurately calculate the total energy consumption required to execute a task based on its actual performance parameters, current remaining battery power, and minimum battery power threshold for safe return, and to assess the feasibility of the task instructions accordingly. When the assessment result indicates that the task is infeasible, the eVTOL no longer passively executes instructions that may lead to risks, but actively generates degraded alternatives containing fewer inspection tasks and reports them, along with the task infeasibility signal, to the central control system. Upon receiving this information, the central control system can perform global replanning based on the degraded alternatives provided by the eVTOL, thereby generating and issuing new, feasible task instructions. Attached Figure Description

[0023] Figure 1 A flowchart of an intelligent low-altitude inspection method for traffic facilities based on eVTOL provided in this application.

[0024] Figure 2 A schematic diagram of an intelligent low-altitude inspection system for traffic facilities based on eVTOL provided in this application.

[0025] Labeling explanation: 1. Central control system; 2. eVTOL. Detailed Implementation

[0026] The technical model of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0027] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] refer to Figure 1 This application proposes an intelligent low-altitude inspection method for transportation facilities based on eVTOL (electric vertical takeoff and landing aircraft) to perform low-altitude inspections of transportation facilities under the scheduling of a central control system, including:

[0029] A1. Receive task instructions issued by the central control system; the task instructions include flight routes, inspection targets, and inspection tasks for each of the inspection targets;

[0030] A2. Based on the body performance parameters of this eVTOL, calculate the total energy consumption required to execute the mission command, and evaluate the feasibility of the mission command based on the total energy consumption, the current actual remaining power, and the minimum power threshold for safe return.

[0031] A3. When the assessment determines that the task instruction is not feasible, at least one alternative degraded task is generated; the alternative degraded task contains fewer inspection tasks than the task instruction contains.

[0032] A4. The task instruction is not executable signal and the alternative degraded task plan are reported to the central control system so that the central control system can perform global replanning based on the alternative degraded task plan;

[0033] A5. Receive and execute new task instructions determined and issued by the central control system through global replanning.

[0034] In traditional eVTOL-based traffic facility inspection methods, the central control system typically relies on predictive or non-real-time data from the eVTOL during task planning. This leads to discrepancies between the issued task instructions and the eVTOL's actual execution capabilities. For example, when the eVTOL's actual battery level is lower than expected due to decreased charging efficiency or unforeseen circumstances (such as strong crosswinds causing flight path adjustments), the central system may still issue tasks exceeding its safe execution capacity. Without addressing this issue, the eVTOL may face the risk of battery depletion during task execution, affecting the completion and safety of the inspection task. To address this, this application proposes an intelligent low-altitude inspection method for traffic facilities based on eVTOL. By conducting task feasibility assessments locally on the eVTOL and interacting with the central control system based on the assessment results, it ensures that the eVTOL only performs tasks within its capabilities, effectively avoiding potential risks caused by information asymmetry. This method enables eVTOL to autonomously determine the feasibility of a task based on its actual physical state and energy reserves. When a task is not feasible, it can proactively generate and report alternative downgraded task plans, guiding the central control system to perform a more realistic global replanning, thereby significantly improving the reliability and safety of inspection tasks.

[0035] Specifically, in step A1, after receiving the task instruction issued by the central control system, the task instruction is parsed to obtain the flight path, inspection targets, and inspection tasks for each inspection target. This receiving process can be implemented through a wireless communication module between the eVTOL and the central control system, for example, by receiving data packets via Wi-Fi, cellular networks, or satellite communication.

[0036] Subsequently, in step A2, the total energy consumption required to execute the mission command is calculated based on the eVTOL's airframe performance parameters. These airframe performance parameters may include at least one of the eVTOL's weight, aerodynamic characteristics, motor efficiency, battery capacity, etc. The calculation of the total energy consumption can be based on a preset energy consumption model that takes into account factors such as flight path length, inspection mission type, and duration. For example, flight energy consumption can be simply obtained by multiplying the flight distance by a unit distance energy consumption coefficient; mission energy consumption can be accumulated based on the estimated power consumption and duration of different inspection missions (such as high-definition photography, infrared scanning, data transmission, etc.). The calculated total energy consumption result, along with the current actual remaining battery power and a preset minimum safe return battery power threshold, is used to evaluate the feasibility of the mission command. This evaluation can be simply performed by comparing whether the current actual remaining battery power is greater than or equal to the sum of the total energy consumption and the minimum safe return battery power threshold.

[0037] In step A3, when the assessment determines that the task instruction is infeasible, at least one degraded task alternative is generated. The degraded task alternative aims to provide a feasible replacement, containing fewer inspection tasks than the original task instruction. For example, some task removal rules can be pre-defined, such as prioritizing the removal of inspection tasks with the lowest importance, or removing non-critical tasks that have the least impact on the overall inspection objective. The generation of the degraded task alternative can be an iterative process, removing one or more tasks each time, until the calculated required energy consumption meets the feasibility conditions.

[0038] Next, in step A4, the mission command inexecutable signal and the degraded mission alternatives are reported to the central control system. This reporting process is also implemented through the eVTOL's communication module, ensuring that the central control system can promptly obtain the eVTOL's local evaluation results and suggestions. Upon receiving this information, the central control system will perform a global replanning based on the degraded mission alternatives to generate new mission commands. The central system will select the optimal option from these alternatives as the new mission command for the eVTOL, or use these options as new constraints for a faster and more targeted global replanning to obtain the new mission command for the eVTOL, based on global mission priorities (e.g., main tower structure inspection has higher priority than bridge deck coating inspection), the current status of other eVTOLs (e.g., whether other aircraft have sufficient energy to take over the mission), and overall fleet coordination requirements. For example, if eVTOL-A reports a plan that "can only complete the first half of the mission", the central system may use this plan as a new mission instruction for eVTOL-A and issue it to eVTOL-A, while assigning another eVTOL-B with sufficient power to take over the second half of the mission (i.e. generating a new mission instruction for eVTOL-B and issuing it to eVTOL-B), thereby ensuring the continuity of the overall mission and avoiding the interruption of the entire fleet's mission due to a problem with a single aircraft.

[0039] Finally, in step A5, the eVTOL receives and executes the new task instructions determined and issued by the central control system through global replanning. These new task instructions are tasks reassigned to the eVTOL by the central control system after comprehensively considering global resources and the status of each eVTOL, and are deemed feasible through local evaluation. Thus, the eVTOL can safely and effectively complete its assigned inspection tasks.

[0040] This application's solution significantly improves the reliability and safety of traffic facility inspection by introducing a task feasibility assessment and degradation task alternative generation mechanism locally on the eVTOL. Compared to existing technologies where the central control system may issue tasks unidirectionally based on inaccurate information, this application's solution empowers the eVTOL to make autonomous judgments based on its true, real-time local status. This introduction of two-way interaction and local intelligent decision-making effectively solves the task execution risks caused by factors such as charging efficiency fluctuations and sudden environmental changes, preventing the eVTOL from failing to complete a task or returning safely due to insufficient power during the mission. By feeding back the local assessment results and degradation schemes to the central control system, this application's solution allows global planning to better adapt to local realities, thereby achieving more robust and adaptive inspection task scheduling and ensuring the stable and efficient operation of the entire inspection system.

[0041] In some implementations, step A2 includes:

[0042] A201. Calculate the flight energy consumption based on the flight path and the airframe performance parameters;

[0043] A202. Based on each inspection target and its corresponding inspection task, calculate the task energy consumption for each inspection target and summarize the total task energy consumption.

[0044] A203. Calculate the safety reserve energy consumption required to cope with emergencies based on the importance of each inspection target and the priority of each inspection task.

[0045] A204. The total energy consumption is obtained by adding the flight energy consumption, the total mission energy consumption, and the safety reserve energy consumption;

[0046] A205. Compare the current actual remaining battery power with the sum of the total energy consumption and the minimum battery power threshold for safe return to determine whether the mission instruction is feasible.

[0047] Specifically, in step A201, the calculation of flight energy consumption can be based on the eVTOL's flight path and airframe performance parameters. The flight path can include information such as flight distance, flight altitude, and flight speed profile, while the airframe performance parameters can include the eVTOL's lift-to-drag ratio, motor efficiency, battery energy density, and maximum takeoff weight. By combining these parameters, the energy consumption required by the eVTOL to complete a specified flight path can be accurately estimated. For example, based on the flight distance and average flight speed, combined with the eVTOL's power consumption model under different flight states (such as takeoff, cruise, hovering, and landing), the total flight energy consumption can be calculated.

[0048] In step A202, calculating the total task energy consumption involves summarizing the energy consumption of each inspection target and its corresponding inspection task. Specifically, each inspection task (e.g., high-definition image acquisition, LiDAR scanning, thermal imaging detection, etc.) has its specific energy consumption pattern and duration. For example, when the eVTOL hovers above a specific inspection target and operates a high-resolution camera to acquire data, its energy consumption will increase significantly. Therefore, the individual energy consumption of each inspection task can be calculated based on its type, duration, and the power consumption of the onboard equipment used. The total task energy consumption is then obtained by summing the energy consumption of all individual tasks.

[0049] In step A203, the calculation of safety reserve energy consumption aims to address potential unforeseen circumstances during the inspection process. This energy consumption calculation can comprehensively consider the importance of each inspection target and the priority of each inspection task. For example, for inspection tasks of high importance or high priority, more safety reserve energy consumption can be allocated to ensure that even in unexpected situations (such as sudden weather changes, equipment failures, etc.), the eVTOL still has sufficient energy to complete critical tasks or return safely.

[0050] In step A204, the total energy consumption is obtained by simply adding the flight energy consumption, total mission energy consumption, and safety reserve energy consumption calculated above. This summation method ensures a comprehensive consideration of the total energy required for the mission.

[0051] In step A205, the feasibility of the task instruction is determined by comparing the current actual remaining battery power with the sum of the total energy consumption and the minimum battery power threshold for safe return. If the current actual remaining battery power is greater than or equal to the total required energy (including task execution energy consumption and safe return energy consumption, i.e., the sum of total energy consumption and the minimum battery power threshold for safe return), the task instruction is determined to be feasible; otherwise, it is determined to be infeasible.

[0052] This application's solution refines the calculation of total energy consumption into three components: flight energy consumption, total mission energy consumption, and safety reserve energy consumption. By comprehensively considering the current actual remaining battery power and the minimum battery power threshold for safe return, it enables a more accurate and comprehensive assessment of the feasibility of mission commands. The calculation of flight energy consumption ensures that the energy requirements of the flight path itself are fully considered; the calculation of total mission energy consumption covers the energy consumption of all inspection operations; and the introduction of safety reserve energy consumption provides necessary energy redundancy to cope with unforeseen emergencies. Therefore, eVTOL can obtain a more reliable energy consumption prediction before executing a mission, effectively avoiding mission interruptions or safety risks due to insufficient energy.

[0053] Through the above technical solution, this application can significantly improve the accuracy and reliability of feasibility assessment for eVTOL traffic facility inspection tasks. Compared to methods that only roughly estimate total energy consumption, this solution refines the energy consumption composition and introduces safety reserve energy consumption, enabling the eVTOL to more accurately determine whether its energy reserves are sufficient to support the completion of the entire task during the task planning stage, and to reserve energy to cope with emergencies. This not only helps optimize task scheduling and resource allocation, but also effectively reduces the risk of accidents caused by the eVTOL running out of power during task execution, thereby improving the overall safety and efficiency of inspection operations.

[0054] Preferably, step A203 may include:

[0055] Obtain the importance of each inspection target and the priority of each inspection task;

[0056] Based on the importance and priority, a safety reserve coefficient is determined for each of the inspection tasks;

[0057] For each inspection task, the individual safety reserve energy consumption of the inspection task is calculated based on the task energy consumption and the safety reserve coefficient.

[0058] The sum of the individual safety reserve energy consumptions is calculated as the safety reserve energy consumption.

[0059] The acquisition of the importance of each inspection target and the priority of each inspection task refers to the inherent importance level of inspection targets (such as bridges, tunnels, roads, etc.) related to traffic facilities, which is pre-set by the system or operators or dynamically acquired, as well as the urgency or priority of inspection tasks (such as structural scanning, visual inspection, thermal imaging, etc.) performed on these targets. For example, a bridge on a main road may be assigned a "high" importance level, while an urgent safety inspection task on it may be assigned a "highest" priority level.

[0060] Furthermore, based on the importance and priority, a safety reserve coefficient is determined for each inspection task. This safety reserve coefficient is a multiplier factor used to quantify the proportion of additional energy reserved to cope with potential emergencies (such as sudden changes in severe weather, temporary equipment failures, and repeated data collection needs) when performing a specific inspection task. This coefficient can be dynamically generated based on a preset rule base (such as a lookup table), an expert experience system, or a machine learning model to ensure that tasks with high importance or high priority are allocated higher safety reserves.

[0061] Therefore, for each inspection task, the individual safety reserve energy consumption of that inspection task is calculated based on the task's energy consumption and the safety reserve coefficient. Specifically, the individual safety reserve energy consumption is obtained by multiplying the task's expected energy consumption by its corresponding safety reserve coefficient. This item-by-item calculation method ensures that the risk and importance of each task are considered independently, avoiding a crude "one-size-fits-all" estimation.

[0062] Finally, the sum of the individual safety reserve energy consumptions is calculated as the total safety reserve energy consumption. This means that all independently calculated individual safety reserve energy consumptions are added together to form a comprehensive and precise safety reserve energy value for the entire mission instruction.

[0063] This application's solution achieves precise calculation of safety reserve energy consumption by introducing a refined consideration of the importance of each inspection target and the priority of each inspection task, and determining differentiated safety reserve coefficients accordingly. Specifically, by acquiring and utilizing this key information, the system can allocate an additional energy reserve ratio to each specific inspection task that matches its risk level and importance. Thus, by multiplying the actual energy consumption of each task by its corresponding safety reserve coefficient, the individual safety reserve energy consumption required for that task can be calculated. This method of calculating item by item and finally summing ensures that the calculation of total safety reserve energy consumption is no longer based on a general, potentially inaccurate estimate, but rather on a precise quantification of the specific needs of each task. It is precisely because of this refined calculation method that eVTOL can more accurately reserve the energy needed to cope with emergencies when performing inspection tasks, significantly improving the reliability of task feasibility assessment.

[0064] Through the above technical solution, this application provides a more accurate and reliable method for calculating safety reserve energy consumption. Compared to estimations based solely on general priorities or importance, this solution introduces a task-specific safety reserve coefficient and calculates and summarizes the individual safety reserve energy consumption for each inspection task, making the estimation of safety reserve energy consumption more closely aligned with actual needs. This not only improves the accuracy of task feasibility assessment and effectively avoids the risk of task interruption or failure due to insufficient energy estimation, but also enables eVTOL to complete traffic facility inspection tasks more efficiently and safely, especially when facing high-risk or high-value inspection targets, providing more sufficient energy assurance, thereby improving the overall reliability and safety of inspection operations.

[0065] Furthermore, the step of determining the safety reserve coefficient for each inspection task based on its importance and priority may include:

[0066] Obtain the target type information of each of the inspection targets and the task type information of each of the inspection tasks;

[0067] Based on the task type information of each inspection task and the target type information of the corresponding inspection target, the risk level of each inspection task is determined.

[0068] Calculate the importance score of each inspection task based on its priority and the importance of the corresponding inspection target.

[0069] Based on the risk level and the importance score of the inspection task, the safety reserve coefficient of each inspection task is determined.

[0070] The target type information can refer to the category of traffic facilities, such as bridges, tunnels, highways, railways, traffic lights, road signs, etc. The task type information can refer to the specific content of the inspection task, such as visual inspection, thermal imaging detection, structural stress analysis, equipment function testing, etc. This information can be pre-stored in the eVTOL's local database or issued by the central control system upon receiving task instructions.

[0071] Specifically, risk levels can be determined based on pre-defined rules or lookup tables. For example, a task involving structural stress analysis of critical transportation facilities such as bridges or tunnels might be classified as "high" risk; while a routine visual inspection of ordinary road sections might be classified as "low" risk. The classification of risk levels aims to quantify the degree of uncertainty or potential danger that may be encountered during task execution.

[0072] The importance score is designed to comprehensively measure the urgency of the task and the criticality of the objective. For example, it can be calculated using weighted averages, table lookups, or piecewise functions. A high-priority emergency inspection task targeting a high-importance transportation hub will have a higher importance score.

[0073] The safety reserve coefficient is a key parameter used to calculate the energy consumption of a single safety reserve. This coefficient can be determined based on a combination of risk level and importance score. For example, a two-dimensional lookup table can be constructed, where one dimension is the risk level (high, medium, low) and the other dimension is the importance score (e.g., 0-100). Tasks with higher risk levels and higher importance scores have larger safety reserve coefficients, meaning more energy needs to be reserved to cope with unforeseen circumstances.

[0074] This application's solution refines the process of determining the safety reserve coefficient, making the calculation of safety reserve energy consumption more accurate and reasonable. Specifically, by acquiring information on the target type of the inspection target and the task type of the inspection mission, the inherent risks of the mission can be assessed more comprehensively. Based on this, an importance score is calculated by combining the mission's priority and the importance of the inspection target, further quantifying the strategic significance and urgency of the mission. Finally, by combining the risk level with the importance score, the safety reserve coefficient can be dynamically adjusted, allowing eVTOL to reserve appropriate energy reserves when performing inspection missions of different types, importance levels, and risk levels. This method avoids the energy waste or insufficient reserves that may result from a uniform safety reserve coefficient.

[0075] Through the above technical solution, this application enables refined management of safety reserve energy consumption. Compared to simply calculating safety reserves based on task energy consumption or a fixed proportion, this solution considers the risk level and importance score of the inspection task, making the calculation of safety reserve energy consumption more targeted and adaptable. Therefore, eVTOL can intelligently allocate energy resources according to the complexity and importance of the actual task, ensuring a safety margin in emergency situations while avoiding unnecessary energy consumption, thereby improving the energy utilization efficiency of eVTOL and the reliability of task execution.

[0076] In some embodiments described above in this application, an intelligent low-altitude inspection method for traffic facilities based on eVTOL is proposed. This method can assess the feasibility of task instructions and generate alternative degraded tasks when the task instructions are not feasible. However, in practical applications, how to systematically and efficiently generate these alternative degraded tasks to ensure that they are both feasible and retain the value of the original task to the greatest extent possible is a problem that needs further resolution. Simply removing tasks randomly or indiscriminately may result in the generated degraded solutions still being infeasible, or the removed task may happen to be a critical task, causing unnecessary value loss.

[0077] Therefore, in some preferred embodiments, step A3 includes:

[0078] A301. Obtain the important scores of each inspection task in the task instruction and the dependencies between the inspection tasks;

[0079] A302. Based on the importance score and the dependency relationship, identify removable inspection tasks from the task instructions;

[0080] A303. Initialize the task removal quantity n to 1;

[0081] A304. Select n removable inspection tasks as removal tasks in sequence, and remove the removal tasks from the original task instructions to obtain the downgraded task instructions;

[0082] A305. Calculate the total energy consumption required to execute each degradation task instruction, and use it as the total degradation energy consumption;

[0083] A306. Compare the current actual remaining power with the sum of the total energy consumption for downgrade and the minimum power threshold for safe return to determine whether each downgrade task instruction is feasible;

[0084] A307. If a feasible downgrade task instruction exists, end the loop and use at least one feasible downgrade task instruction as the downgrade task candidate; otherwise, increment n by 1 and return to step A304.

[0085] Specifically, in step A301, the importance score can be a quantitative assessment of the criticality, priority, or expected benefit of each inspection task within the overall inspection task instruction. For example, inspection tasks for the main structure of a bridge or key parts of a tunnel can be assigned a higher importance score, while inspection tasks for secondary road surfaces or auxiliary facilities can be assigned a lower importance score; this importance score can be obtained through the preceding steps. The dependency relationship refers to the logical or data association that may exist between different inspection tasks. For example, the execution of some inspection tasks may depend on the completion of other tasks, or the data collected by them may be the basis for the analysis of other tasks. This information can be pre-configured or dynamically calculated based on task type and inspection target attributes.

[0086] In step A302, based on the obtained importance scores and dependencies, the system can intelligently identify removable inspection tasks that, if removed, would have little impact on the overall task objective or would not disrupt the critical task chain. For example, tasks with lower importance scores and not serving as prerequisites for other high-importance tasks are more likely to be identified as removable tasks.

[0087] In step A303, the task removal quantity n is initialized to 1, which means that the system first tries to find a feasible degradation solution by removing the minimum number of tasks in order to preserve the original task content to the greatest extent.

[0088] In step A304, the system sequentially selects n tasks from the identified removable inspection tasks as task combinations to be removed. For example, when n is 1, the system attempts to remove each removable task one by one, generating multiple downgraded task instructions that remove only one task. When n is 2, the system attempts to remove all possible combinations of two tasks, and so on. By removing these selected tasks from the original task instructions, the corresponding downgraded task instructions are obtained.

[0089] In step A305, for each generated downgrade mission command, the system recalculates the total energy consumption required to execute that downgrade mission command. This includes recalculating flight energy consumption, total mission energy consumption, and safety reserve energy consumption to reflect the actual energy requirements after the mission scope is reduced.

[0090] In step A306, the current actual remaining battery power is compared with the sum of the recalculated total energy consumption for downgrade and the minimum battery power threshold for safe return. If the current actual remaining battery power is greater than or equal to this sum, the downgrade task instruction is deemed feasible.

[0091] In step A307, the system checks if at least one feasible degrade task instruction exists. If it does, the loop ends, and these feasible degrade task instructions are reported as final degrade task alternatives. If no feasible degrade task instruction is found for the current task removal quantity n, the value of n is incremented by 1, and the system returns to step A304 to continue trying to remove more tasks until a feasible solution is found or all reasonable options are exhausted.

[0092] This application's solution, by incorporating consideration of the importance score and dependencies of inspection tasks and employing an iterative strategy of increasing task removal amounts, can systematically and intelligently generate alternative solutions for degraded tasks. The method first attempts to find feasible solutions with the minimum task removal amount, thereby preserving the integrity of the original task to the greatest extent possible. By performing precise energy consumption calculations and feasibility assessments for each potential degraded solution, it ensures that the generated alternative solutions are practically executable.

[0093] Through the aforementioned technical solution, when the initial task instruction of eVTOL becomes infeasible due to insufficient energy, the system can automatically and efficiently generate a series of optimized degraded task alternatives. This mechanism avoids blind or random task removal, ensuring that the proposed degraded solutions are not only feasible but also retain the key objectives and value of the original task to the greatest extent possible, thereby significantly improving the resilience and efficiency of eVTOL in complex and constrained environments. Furthermore, this method reduces the need for manual intervention and improves the level of automation and response speed in decision-making.

[0094] In some of the embodiments described above in this application, a method is proposed to identify removable inspection tasks and generate alternative downgraded tasks when task instructions are not feasible. However, in actual implementation, if removable inspection tasks are simply identified based on importance scores and dependencies, the comprehensive impact of task removal on the overall inspection task completion, data collection integrity, and remaining energy consumption may not be fully considered. This could lead to excessive loss of value for the removed tasks, affecting the final inspection results.

[0095] Therefore, in some preferred embodiments, step A302 includes:

[0096] B1. Based on the importance score and the dependency relationship, identify a preliminary set of removable inspection tasks from the task instructions;

[0097] B2. For each inspection task in the preliminary removable inspection task set, obtain the evaluation results of the task instruction completion rate, data collection integrity, and remaining energy consumption after the inspection task is removed;

[0098] B3. Based on the evaluation results, assess the value loss caused by removing the inspection tasks in the preliminary removable inspection task set;

[0099] B4. Select the inspection tasks with the lowest value loss assessment results as the removable inspection tasks.

[0100] Specifically, in step B1, the preliminary set of removable inspection tasks refers to the initial screening of inspection tasks based on their importance scores and dependencies. These tasks are those whose removal would have a relatively small impact on the overall task or would not disrupt the critical task chain. For example, non-core tasks with low importance scores, or tasks that are not prerequisites for other critical tasks, can be initially identified as removable tasks.

[0101] In step B2, for each inspection task in the initial set of removable inspection tasks, it is necessary to obtain assessment results of its task instruction completion rate, data acquisition integrity, and remaining energy consumption after removal. Task instruction completion rate measures the overall completion rate of the original task instructions after removal; data acquisition integrity assesses whether removing the task will lead to the loss of critical data or a break in the data chain; and remaining energy consumption refers to the total energy consumption required to execute the remaining tasks after removal. These assessment results provide a quantitative basis for subsequent value loss assessment.

[0102] In step B3, based on the above evaluation results, the value loss caused by removing each inspection task from the preliminary set of removable inspection tasks is assessed. The assessment of value loss can be a comprehensive consideration; for example, it can be calculated using weighted summation or multi-objective optimization algorithms based on multiple dimensions such as the degree of decline in task instruction completion, the degree of damage to data acquisition integrity, and the degree of energy saving. The purpose is to quantify the negative impact of removing a specific task on the overall inspection task.

[0103] In step B4, the inspection tasks with the lowest value loss assessment results are finally selected as removable inspection tasks. This means that the system will prioritize removing those tasks that have the least impact on the overall inspection tasks and result in the least loss, thereby maximizing the preservation of the value of the inspection tasks while satisfying energy constraints.

[0104] This application's solution optimizes the identification process of removable inspection tasks by introducing an assessment mechanism for the value loss caused by task removal. Specifically, it first narrows down the range of tasks to be evaluated through preliminary screening (step B1). Subsequently, for each initially removable task, the system conducts a detailed assessment of its post-removal task completion, data integrity, and energy consumption (step B2), making the consideration of the impact of task removal more comprehensive and quantifiable. Based on these assessment results, the system can accurately calculate the value loss caused by removing each task (step B3). Finally, by selecting the task with the lowest value loss for removal (step B4), it ensures that energy balance can be achieved at the lowest cost when tasks are downgraded, thereby preserving the core value and effectiveness of the inspection task to the greatest extent. This mechanism avoids the significant value loss that may result from blindly or simply removing tasks.

[0105] Through the above technical solution, this application overcomes the problem of excessive value loss that may occur when traditional methods degrade tasks. By comprehensively evaluating the task instruction completion rate, data collection integrity, and remaining energy consumption after task removal, and quantifying the value loss based on this, eVTOL can make more intelligent and optimized decisions when faced with insufficient energy and the need to degrade tasks. This ensures that even in the event of task degradation, the execution of critical inspection tasks can be preserved to the maximum extent, maintaining the integrity and continuity of data collection. This significantly improves the efficiency and reliability of traffic facility inspections, reduces the negative impact of task degradation, and enhances the robustness and adaptability of the system.

[0106] In some preferred embodiments, a specific example is given below. Suppose an eVTOL is assigned a traffic facility inspection task instruction, which includes inspecting bridges, tunnels, and streetlights on a highway. The bridge inspection task has the highest importance score because it relates to structural safety; the tunnel inspection task is next because it relates to lighting and ventilation; and the streetlight inspection task has the lowest importance score because it primarily relates to lighting functionality. Furthermore, the bridge inspection task is a crucial prerequisite for subsequent data analysis, while the streetlight inspection task is relatively independent.

[0107] When eVTOL assessment finds that the current power is insufficient to complete all tasks, it is necessary to generate alternative degraded task plans.

[0108] First, in step B1, the system will initially identify street light inspection tasks and tunnel inspection tasks as potential initial removable inspection task sets based on importance scores and dependencies. This is because bridge inspection tasks have the highest importance and critical dependencies, and should not be initially removed.

[0109] Next, in step B2, the system will evaluate the removal of street light inspection tasks and the removal of tunnel inspection tasks respectively:

[0110] 1. If the street light inspection task is removed: the evaluation results show that the task instruction completion rate decreases slightly, but the data collection integrity is almost unaffected (not involving critical structural data), and the remaining energy consumption is significantly reduced;

[0111] 2. If the tunnel inspection task is removed: The evaluation results show that the completion rate of task instructions drops significantly, the integrity of data collection is affected to some extent (which may lead to the loss of lighting data inside the tunnel), and the remaining energy consumption is also significantly reduced.

[0112] Then, in step B3, the system assesses the value loss caused by removing these two tasks based on the above evaluation results. For example, by setting weights, the loss of data collection integrity is weighted higher than the decrease in task completion. Therefore, the overall value loss caused by removing the street light inspection task is assessed as the lowest, while the value loss of removing the tunnel inspection task is relatively high.

[0113] Finally, in step B4, the system selects the street light inspection task with the lowest value loss assessment result as the removable inspection task. Therefore, when generating alternative degraded task solutions, removing the street light inspection task is given priority. This ensures that while meeting energy constraints, more critical inspection tasks such as bridges and tunnels are preserved to the greatest extent possible, guaranteeing the achievement of core inspection objectives and the integrity of key data.

[0114] Preferably, step B2 may include sequentially selecting each inspection task in the preliminary removable inspection task set as the target inspection task and executing it:

[0115] B201. Obtain the task intensity of each inspection task in the task instruction and the original total task intensity of the task instruction;

[0116] B202. Based on the task intensity of the target inspection task, calculate the total remaining task intensity of the remaining inspection tasks after removing the target inspection task;

[0117] B203. Determine the task instruction completion rate based on the ratio of the remaining total task intensity to the original total task intensity;

[0118] B204. Obtain the type, spatial coverage, and function of the data collected by the target inspection task;

[0119] B205. Based on the type, spatial coverage, and function of the data collected by the target inspection task, determine whether removing the target inspection task will result in the loss of key data types, interruption of data coverage in key areas, or affect the continuity of overall data, thereby determining the integrity of the data collection.

[0120] B206. Based on the set of remaining inspection tasks after removing the target inspection task, recalculate the flight energy consumption required to execute the set of remaining inspection tasks, and denot it as the remaining flight energy consumption;

[0121] B207. Based on the set of remaining inspection tasks, recalculate the task energy consumption corresponding to each inspection target, and summarize to obtain the new total task energy consumption, which is recorded as the remaining total task energy consumption.

[0122] B208. Based on the set of remaining inspection tasks, recalculate the safety reserve energy consumption required to deal with emergencies, and record it as the remaining safety reserve energy consumption;

[0123] B209. Add the remaining flight energy consumption, the remaining total mission energy consumption, and the remaining safety reserve energy consumption to obtain the remaining energy consumption.

[0124] The task intensity can be understood as a quantitative indicator of the workload or resource consumption required to complete a specific inspection task. For example, it can be comprehensively evaluated based on factors such as inspection duration, data collection volume, and sensor usage frequency. The original total task intensity refers to the total task intensity of all inspection tasks in the original task instruction. The task instruction completion rate aims to quantify the overall completion degree of the original task instruction after removing a specific inspection task.

[0125] Furthermore, the types of data collected by the target inspection task may include, but are not limited to, high-definition video, infrared images, and lidar point cloud data; the spatial coverage refers to the geographical area or facility range covered by the data; the function refers to the specific uses and importance of the data in areas such as traffic facility inspection, fault diagnosis, and maintenance planning. The data collection integrity aims to assess whether removing a specific inspection task will cause the loss or interruption of key data, thereby affecting the accuracy of subsequent analysis and decision-making. For example, the loss of key data types may mean that removing the task results in the inability to obtain certain necessary data (such as infrared images of bridge structural cracks); the interruption of key area data coverage may mean that removing the task results in gaps in data collection for an important area (such as a tunnel entrance); and the impact on the continuity of overall data may mean that removing the task results in discontinuities in time-series or spatial-series data.

[0126] Furthermore, the calculation methods for the remaining flight energy consumption, the remaining total mission energy consumption, and the remaining safety reserve energy consumption are similar to those for the original mission command energy consumption calculation, but only apply to the set of remaining inspection missions after the removal of the target inspection mission. The remaining energy consumption is an assessment of the total energy required to execute the remaining missions after removing a specific inspection mission, providing basic data for subsequent feasibility assessments of downgraded mission commands.

[0127] This application's solution conducts a detailed removal impact assessment of each inspection task in the initial set of removable inspection tasks. This allows for accurate assessment of the task instruction completion rate, data acquisition integrity, and remaining energy consumption after the task's removal. Specifically, by acquiring the task intensity and calculating the total remaining task intensity, the loss of task completion rate can be quantified; by analyzing data type, spatial coverage, and function, it can be determined whether data integrity has been compromised, particularly to avoid the loss or interruption of critical data; and by recalculating various energy consumption parameters, the actual energy consumption required after task removal can be accurately assessed. Therefore, this provides a comprehensive and quantitative basis for subsequent assessment of the value loss caused by task removal.

[0128] The aforementioned technical solution enables a refined impact assessment of each potential removable inspection task. It not only considers the reduction in workload but also deeply analyzes potential risks to data integrity and changes in actual energy consumption. This multi-dimensional and quantitative assessment approach allows for a more accurate weighing of the pros and cons of task removal when identifying removable inspection tasks. It avoids the loss of critical information or a significant reduction in inspection effectiveness due to indiscriminate task removal, thereby improving the rationality and effectiveness of alternative task degradation plans. This ensures that the core value of inspection tasks is preserved to the maximum extent possible even under resource constraints.

[0129] When generating multiple feasible degradation task instructions, simply selecting one or a few as alternatives without a mechanism for in-depth evaluation and optimization of these alternative task options may lead to eVTOL failing to select the option with the least loss of value to the original task during task degradation, thus affecting the overall efficiency and data integrity of the inspection task.

[0130] Therefore, in some preferred embodiments, step A307 includes:

[0131] If no feasible downgrade task instruction exists, increment n by 1 and return to step A304;

[0132] When only one feasible degradation task instruction exists, that feasible degradation task instruction shall be used as the alternative degradation task.

[0133] When multiple feasible degradation task instructions exist, execute:

[0134] For each feasible degradation task instruction, obtain the evaluation results of the task instruction completion rate, data acquisition integrity and remaining energy consumption corresponding to the feasible degradation task instruction, in order to evaluate the value loss of the feasible degradation task instruction.

[0135] Based on the value loss of each feasible degradation task instruction, at least one feasible degradation task instruction is selected as the alternative degradation task.

[0136] Specifically, when the system iteratively removes tasks and calculates energy consumption, and finds no feasible degraded task instructions, the task removal quantity *n* is increased, and the process returns to step A304 to continue attempting to remove more tasks in order to find a feasible solution. When only one feasible degraded task instruction is found, that instruction is directly adopted as a degraded task alternative. However, when multiple feasible degraded task instructions exist, further evaluation of these instructions is necessary to ensure that the selected degraded solution is optimal.

[0137] The "task instruction completion rate" refers to the proportion of remaining inspection tasks to the original task instructions after removing some inspection tasks, reflecting the completeness of the task. For example, it can be determined by the ratio of the total intensity of the remaining tasks to the original total task intensity. The calculation process for this task instruction completion rate is similar to steps B201-B203 above.

[0138] "Data collection integrity" refers to whether removing some inspection tasks will lead to the loss of key data types, interruption of data coverage in key areas, or impact on the continuity of overall data. Its purpose is to ensure that even if a task is downgraded, the core value and usability of the collected data are preserved to the greatest extent possible. The calculation process for this data collection integrity is similar to steps B204-B205 described earlier.

[0139] The "remaining energy consumption" refers to the energy that eVTOL is expected to consume after executing the degradation task instruction. This can be used as an indicator to measure the execution efficiency of the scheme and the flexibility of future potential tasks. The calculation process for this remaining energy consumption is similar to steps B206-B209 mentioned above.

[0140] In practical applications, the above evaluation results are comprehensively used to assess the "value loss" of each feasible degradation task instruction. Value loss is a comprehensive indicator designed to quantify the negative impacts caused by task degradation, such as reduced task completion, compromised data integrity, and changes in energy efficiency. By calculating the value loss for each feasible degradation task instruction, the system can quantify the merits of different degradation schemes (the evaluation method for value loss can be found in step B3 above). Finally, based on these value loss evaluation results, at least one feasible degradation task instruction with the lowest value loss is selected as the final degradation task candidate.

[0141] This application addresses the problem of selecting the optimal degradation solution from multiple feasible options when energy is insufficient by introducing a value loss assessment mechanism for multiple feasible degradation task instructions. Specifically, when the eVTOL's current actual remaining power is insufficient to support the original task instructions, the system attempts to generate a series of degradation task instructions containing fewer inspection tasks. Among these degradation task instructions, there may be multiple energy-feasible options. To avoid random or suboptimal selection, this solution performs multi-dimensional evaluation on each feasible degradation task instruction, including task instruction completion, data acquisition completeness, and remaining energy consumption. These evaluation results are used to calculate the value loss of each degradation task instruction. By comparing the value losses of different degradation task instructions, the system can identify the option that has the least impact on the original task objective and the highest retention value while satisfying energy constraints. It is precisely this value loss-based quantitative assessment and selection mechanism that enables the eVTOL to make more intelligent and optimized task degradation decisions when facing energy limitations.

[0142] Through the aforementioned technical solution, this application ensures that when eVTOL energy is insufficient and task degradation is necessary, the selected degradation task alternatives are optimized and screened, rather than randomly chosen. This significantly improves the intelligence and accuracy of task degradation decision-making, avoiding the omission of critical inspection tasks or interruption of important data collection due to blind degradation. Therefore, with limited energy resources, eVTOL can maximize the completion of valuable inspection tasks, ensuring the overall efficiency and data quality of traffic facility inspections. Furthermore, this mechanism enhances eVTOL's autonomous adaptability and task resilience in complex and dynamic environments, reduces the need for manual intervention, and improves system reliability and operational efficiency.

[0143] In some embodiments described above, a method is proposed to report a task instruction non-executable signal and a degraded task alternative to the central control system, enabling the central control system to perform global replanning based on the degraded task alternative. However, during implementation, when the central control system receives the degraded task alternative reported by the eVTOL, it may lack sufficient information to evaluate the reliability and generation basis of the alternative, thus affecting the efficiency and accuracy of global replanning. To address this, this application further proposes to simultaneously report the confidence level information of the degraded task alternative when reporting the task instruction non-executable signal and the degraded task alternative to the central control system, thereby assisting the central control system in performing more effective global replanning.

[0144] Therefore, in some preferred embodiments, step A4 includes:

[0145] A401. Obtain the generation logic information of the downgrade task alternative scheme and the local status data on which the downgrade task alternative scheme is based; the local status data includes the current actual remaining battery power and the device performance parameters;

[0146] A402. Based on the generated logic information and the local state data, the credibility of the alternative degradation task scheme is evaluated to obtain the credibility evaluation result;

[0147] A403. Based on the credibility assessment results, generate confidence information for the alternative degradation task solutions;

[0148] A404. The task instruction non-executable signal, the degraded task alternative scheme, and the confidence information are reported to the central control system.

[0149] The generated logic information can be understood as the algorithms, rules, or strategies adopted by the eVTOL when generating alternative degraded task solutions. For example, it may include priority rules for task removal, energy consumption calculation models, and task feasibility assessment criteria. The local state data refers to the real-time or near-real-time data that the eVTOL relies on when assessing task feasibility and generating alternative degraded task solutions. For example, the current actual remaining battery power is the actual remaining battery power of the eVTOL, and the system performance parameters are inherent attributes of the eVTOL.

[0150] Furthermore, the credibility assessment of the proposed degradation task alternatives refers to quantitatively or qualitatively judging their reliability, accuracy, or applicability based on the logic and data upon which the alternatives were generated. For example, the assessment can be based on factors such as the completeness of the generation logic, the real-time nature and accuracy of the local state data, and the stability of the computation process. This yields a credibility assessment result, which can be a numerical value (e.g., a score between 0 and 1) or a level (e.g., high, medium, low).

[0151] Based on the credibility assessment results, confidence information for the alternative degraded task is generated. This confidence information directly represents the reliability of the alternative and is reported to the central control system along with the task instruction non-executable signal and the alternative degraded task. Upon receiving this information, the central control system can combine the confidence information to decide how to adopt or adjust the alternative degraded task, thereby performing more intelligent global replanning.

[0152] The proposed solution, when the eVTOL reports alternative degradation tasks, includes its generation logic information, local state data, and confidence information assessed based on this information. This allows the central control system to more comprehensively understand the background and reliability of the alternative proposed by the eVTOL. Specifically, the generation logic information and local state data provide transparency to the central control system, enabling it to trace the generation process of the degradation plan; the confidence assessment and confidence information generation transmit the eVTOL's local judgment to the central control system in a standardized manner. Because the central control system obtains confidence information about the alternative degradation tasks, it can more accurately weigh the value and risk of the alternative during global replanning, avoiding blind adoption or unnecessary recalculation, thereby optimizing the overall scheduling decision-making process.

[0153] Through the above technical solution, this application effectively addresses the problem that the central control system may lack sufficient information to assess the reliability of degrade task alternatives reported by the eVTOL. By providing the generation logic information, local state data, and confidence information of the degrade task alternatives, the central control system can gain a deeper understanding and more accurate evaluation of the alternatives proposed by the eVTOL. This significantly improves the efficiency and accuracy of the central control system's global replanning, reduces decision-making risks caused by information asymmetry, and thus enhances the robustness and intelligence level of the entire traffic facility inspection system, ensuring the smooth execution of inspection tasks and the optimal allocation of resources.

[0154] In some preferred embodiments, a specific example is given below. Suppose that an eVTOL, while performing a routine inspection task, discovers that its current actual remaining battery power is lower than the total energy consumption required to execute the original task command. Therefore, it determines that the task is not feasible and generates a degraded task alternative. Before reporting this information to the central control system, the eVTOL performs the following steps:

[0155] First, obtain the generation logic information for generating the alternative solution for the degradation task. For example, the solution is generated by removing the inspection task with the lowest importance and no dependencies. At the same time, obtain the local status data on which the solution is based, including the current actual remaining battery power (e.g., 25%) and the eVTOL's body performance parameters (e.g., energy consumption per kilometer, energy consumption of inspection equipment, etc.).

[0156] Secondly, based on this generated logic information and local state data, the credibility of the alternative degradation task is evaluated. For example, if the sensor reading of the current actual remaining battery power is stable and has a small error range, and the algorithm for generating the degradation plan has been rigorously verified, then the credibility evaluation result is likely to be high. Conversely, if the sensor reading fluctuates significantly, or if the degradation plan generation process involves many uncertainties, then the credibility evaluation result is likely to be low. Assuming that after evaluation, the credibility evaluation result of this alternative is 0.85 (out of 1.0).

[0157] Finally, based on the confidence assessment result of 0.85, corresponding confidence information is generated. For example, it can be converted into a "high confidence" level or directly represented by the value 0.85. Ultimately, eVTOL reports the task instruction non-executable signal, the alternative degraded task, and the "high confidence" or "0.85" confidence information to the central control system. Upon receiving this information, the central control system can prioritize the alternative degraded task based on the high confidence information and quickly perform global replanning, thereby avoiding unnecessary delays and resource waste.

[0158] refer to Figure 2 This application provides an intelligent low-altitude inspection system for traffic facilities based on eVTOL, which is used to perform low-altitude inspection of traffic facilities. It includes a central control system 1 and multiple eVTOLs 2, and each eVTOL is communicatively connected to the central control system.

[0159] The central control system 1 is used to perform global planning of inspection tasks, generate task instructions corresponding to each eVTOL, and send them to each eVTOL; the task instructions include flight routes, inspection targets, and inspection tasks for each of the inspection targets.

[0160] The eVTOL is used to perform:

[0161] Receive task instructions issued by the central control system 1 (for details, please refer to step A1 above).

[0162] Based on the performance parameters of this eVTOL, calculate the total energy consumption required to execute the mission command, and evaluate the feasibility of the mission command based on the total energy consumption, the current actual remaining battery power, and the minimum battery power threshold for safe return (for details, please refer to step A2 above).

[0163] When the assessment determines that the task instruction is not feasible, at least one alternative degraded task is generated; the alternative degraded task contains fewer inspection tasks than the task instruction contains (for details, please refer to step A3 above).

[0164] The task instruction is not executable signal and the alternative degraded task plan are reported to the central control system so that the central control system 1 can perform global replanning according to the alternative degraded task plan (the specific process can be referred to step A4 above).

[0165] The central control system 1 is also used to perform global replanning according to the degraded task alternative scheme when it receives the task instruction non-executable signal reported by the eVTOL and the degraded task alternative scheme, so as to generate new task instructions for the relevant eVTOL and send them to the relevant eVTOL.

[0166] The eVTOL is also used to execute the new task instruction when a new task instruction is received (for details, please refer to step A5 above).

[0167] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for intelligent low-altitude inspection of traffic facilities based on eVTOL, applied to eVTOL, to perform low-altitude inspection of traffic facilities under the scheduling of a central control system, characterized in that... include: A1. Receive task instructions issued by the central control system; the task instructions include flight routes, inspection targets, and inspection tasks for each of the inspection targets; A2. Based on the body performance parameters of this eVTOL, calculate the total energy consumption required to execute the mission command, and evaluate the feasibility of the mission command based on the total energy consumption, the current actual remaining power, and the minimum power threshold for safe return. A3. When the assessment determines that the task instruction is not feasible, at least one alternative degraded task is generated; the alternative degraded task contains fewer inspection tasks than the task instruction contains. A4. The task instruction is not executable signal and the alternative degraded task plan are reported to the central control system so that the central control system can perform global replanning based on the alternative degraded task plan; A5. Receive and execute new task instructions determined and issued by the central control system through global replanning; Step A2 includes: A201. Calculate the flight energy consumption based on the flight path and the airframe performance parameters; A202. Based on each inspection target and its corresponding inspection task, calculate the task energy consumption for each inspection target and summarize the total task energy consumption. A203. Calculate the safety reserve energy consumption required to cope with emergencies based on the importance of each inspection target and the priority of each inspection task. A204. The total energy consumption is obtained by adding the flight energy consumption, the total mission energy consumption, and the safety reserve energy consumption; A205. Compare the current actual remaining battery power with the sum of the total energy consumption and the minimum battery power threshold for safe return to base to determine whether the mission instruction is feasible; Step A3 includes: A301. Obtain the important scores of each inspection task in the task instruction and the dependencies between the inspection tasks; A302. Based on the importance score and the dependency relationship, identify removable inspection tasks from the task instructions; A303. Initialize the task removal quantity n to 1; A304. Select n removable inspection tasks as removal tasks in sequence, and remove the removal tasks from the original task instructions to obtain the downgraded task instructions; A305. Calculate the total energy consumption required to execute each degradation task instruction, and use it as the total degradation energy consumption; A306. Compare the current actual remaining power with the sum of the total energy consumption for downgrade and the minimum power threshold for safe return to determine whether each downgrade task instruction is feasible; A307. If there is a feasible downgrade task instruction, end the loop and use at least one feasible downgrade task instruction as the downgrade task candidate; otherwise, add 1 to n and return to step A304. Step A307 includes: If no feasible downgrade task instruction exists, increment n by 1 and return to step A304; When only one feasible degradation task instruction exists, that feasible degradation task instruction shall be used as the alternative degradation task. When multiple feasible degradation task instructions exist, execute: For each feasible degradation task instruction, obtain the evaluation results of the task instruction completion rate, data acquisition integrity and remaining energy consumption corresponding to the feasible degradation task instruction, in order to evaluate the value loss of the feasible degradation task instruction. Based on the value loss of each feasible degradation task instruction, at least one feasible degradation task instruction is selected as the alternative degradation task.

2. The intelligent low-altitude inspection method for traffic facilities based on eVTOL according to claim 1, characterized in that, Step A203 includes: Obtain the importance of each inspection target and the priority of each inspection task; Based on the importance and priority, a safety reserve coefficient is determined for each of the inspection tasks; For each inspection task, the individual safety reserve energy consumption of the inspection task is calculated based on the task energy consumption and the safety reserve coefficient. The sum of the individual safety reserve energy consumptions is calculated as the safety reserve energy consumption.

3. The intelligent low-altitude inspection method for traffic facilities based on eVTOL according to claim 2, characterized in that, The step of determining the safety reserve coefficient for each inspection task based on its importance and priority includes: Obtain the target type information of each of the inspection targets and the task type information of each of the inspection tasks; Based on the task type information of each inspection task and the target type information of the corresponding inspection target, the risk level of each inspection task is determined. Calculate the importance score of each inspection task based on its priority and the importance of the corresponding inspection target. Based on the risk level and the importance score of the inspection task, the safety reserve coefficient of each inspection task is determined.

4. The intelligent low-altitude inspection method for traffic facilities based on eVTOL according to claim 1, characterized in that, Step A302 includes: B1. Based on the importance score and the dependency relationship, identify a preliminary set of removable inspection tasks from the task instructions; B2. For each inspection task in the preliminary removable inspection task set, obtain the evaluation results of the task instruction completion rate, data collection integrity, and remaining energy consumption after the inspection task is removed; B3. Based on the evaluation results, assess the value loss caused by removing the inspection tasks in the preliminary removable inspection task set; B4. Select the inspection tasks with the lowest value loss assessment results as the removable inspection tasks.

5. The intelligent low-altitude inspection method for traffic facilities based on eVTOL according to claim 4, characterized in that, Step B2 includes sequentially selecting each inspection task in the initial set of removable inspection tasks as the target inspection task and executing the following: B201. Obtain the task intensity of each inspection task in the task instruction and the original total task intensity of the task instruction; B202. Based on the task intensity of the target inspection task, calculate the total remaining task intensity of the remaining inspection tasks after removing the target inspection task; B203. Determine the task instruction completion rate based on the ratio of the remaining total task intensity to the original total task intensity; B204. Obtain the type, spatial coverage, and function of the data collected by the target inspection task; B205. Based on the type, spatial coverage, and function of the data collected by the target inspection task, determine whether removing the target inspection task will result in the loss of key data types, interruption of data coverage in key areas, or affect the continuity of overall data, thereby determining the integrity of the data collection. B206. Based on the set of remaining inspection tasks after removing the target inspection task, recalculate the flight energy consumption required to execute the set of remaining inspection tasks, and denot it as the remaining flight energy consumption; B207. Based on the set of remaining inspection tasks, recalculate the task energy consumption corresponding to each inspection target, and summarize to obtain the new total task energy consumption, which is recorded as the remaining total task energy consumption. B208. Based on the set of remaining inspection tasks, recalculate the safety reserve energy consumption required to deal with emergencies, and record it as the remaining safety reserve energy consumption; B209. Add the remaining flight energy consumption, the remaining total mission energy consumption, and the remaining safety reserve energy consumption to obtain the remaining energy consumption.

6. The intelligent low-altitude inspection method for traffic facilities based on eVTOL according to claim 1, characterized in that, Step A4 includes: A401. Obtain the generation logic information of the downgrade task alternative scheme and the local status data on which the downgrade task alternative scheme is based; the local status data includes the current actual remaining battery power and the device performance parameters; A402. Based on the generated logic information and the local state data, the credibility of the alternative degradation task scheme is evaluated to obtain the credibility evaluation result; A403. Based on the credibility assessment results, generate confidence information for the alternative degradation task solutions; A404. The task instruction non-executable signal, the degraded task alternative scheme, and the confidence information are reported to the central control system.

7. An intelligent low-altitude inspection system for traffic facilities based on eVTOL, used for low-altitude inspection of traffic facilities based on the intelligent low-altitude inspection method for traffic facilities based on any one of claims 1-6, characterized in that, It includes a central control system and multiple eVTOLs, each of which is communicatively connected to the central control system. The central control system is used to perform global planning of inspection tasks, generate task instructions corresponding to each eVTOL, and send them to each eVTOL; the task instructions include flight routes, inspection targets, and inspection tasks for each of the inspection targets. The eVTOL is used to perform: Receive task instructions from the central control system; Based on the body performance parameters of this eVTOL, calculate the total energy consumption required to execute the mission command, and evaluate the feasibility of the mission command based on the total energy consumption, the current actual remaining battery power, and the minimum battery power threshold for safe return. When the assessment determines that the task instruction is not feasible, at least one alternative degraded task is generated; the alternative degraded task contains fewer inspection tasks than the task instruction. The task instruction is not executable, and the alternative degraded task is reported to the central control system, so that the central control system can perform global replanning based on the alternative degraded task. The central control system is also used to perform global replanning based on the degraded task alternative scheme when it receives a signal that the task instruction is not executable reported by the eVTOL and a degraded task alternative scheme, so as to generate new task instructions for the relevant eVTOL and send them to the relevant eVTOL. The eVTOL is also used to execute the new task instruction when a new task instruction is received.

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