Underground control ground-air patrol operation and maintenance early warning method

Abstract

The invention relates to the technical field of ground-air inspection, in particular to an underground control ground-air inspection operation and maintenance early warning method, which comprises the following steps: a ground detection point preliminarily analyzes a to-be-detected area, inspection tasks of different levels are generated, and an unmanned aerial vehicle plans an initial path according to the inspection tasks and calculates estimated power consumption; if the residual electric quantity is insufficient, whether charging is preferentially carried out is determined by combining the charging distance and the path inspection task level; the power consumption rate is adjusted in real time based on the meteorological threat index and the detection definition, and the power detection period is shortened. And if the residual electric quantity is lower than a return threshold value, an original return path or a nearby detection point is selected as a temporary return target, and safe endurance is ensured. By detecting and planning the inspection task level of the ground detection point and accurately judging the return path of the inspection unmanned aerial vehicle for the remaining power according to the meteorological threat index and the detection definition, the anti-risk capability of the inspection unmanned aerial vehicle is improved.

Description

Technical Field

[0001] The present invention relates to the field of ground and air inspection technology, and in particular to an underground control ground and air inspection operation and maintenance early warning method. Background Art

[0002] Forest fires are characterized by sudden onset, rapid spread, and significant destructiveness. Traditional manual inspections and single technical approaches are unable to meet the needs of large-scale, efficient monitoring. Therefore, integrated ground-to-air inspection technology (coordinated ground and air monitoring) has gradually become a key solution for forest fire prevention. In the early days, forest fire prevention primarily relied on the following methods, all of which had significant shortcomings: Ground inspections, including manual patrols (forest rangers patrolling on foot or in vehicles), were inefficient, had limited coverage, and lacked access to complex terrain. Lookout tower monitoring relied on observations from fixed vantage points, which were significantly affected by visibility and weather conditions and had numerous blind spots. Ground sensor networks deployed temperature, humidity, and smoke sensors, but these were costly to deploy, difficult to maintain, and susceptible to environmental interference. Aerial inspections, including helicopters or fixed-wing aircraft equipped with infrared / visible light equipment, offered wide coverage but were expensive, subject to airspace restrictions, and difficult to implement on a regular basis. Satellite remote sensing enabled large-scale monitoring, but had low resolution, long revisit cycles, and limited real-time warning capabilities.

[0003] Chinese Patent Publication No. CN113254076A discloses a method for automated monitoring of the control of an extraterrestrial surface rover. This method designs control instructions corresponding to each subsystem of the rover, establishes a table for interpreting the execution of the rover's control commands, and then generates a color-changing control process monitoring card for the rover's operation based on this table, thereby enabling automated monitoring of the rover's operation on the surface of an extraterrestrial body. The present invention enables automated monitoring of multiple exploration processes of the rover; provides real-time, automatic, and accurate interpretation of the execution of the rover's remote control commands and injected data, providing a reliable basis for formulating the next control plan; and provides real-time early warning of the rover's operating conditions and faults. This method significantly saves labor costs, improves work efficiency, and provides decision-making support for building a reliable platform for future high-intensity, long-term deep space exploration missions.

[0004] It can be seen that the existing technology has the following problems: due to the lack of inspection task levels that are detected and planned at ground inspection points, and the lack of judgment on whether the remaining power is sufficient for return trip based on the meteorological threat index and detection clarity, the inspection drone may be unable to return due to sudden weather changes and other reasons. Summary of the Invention

[0005] To this end, the present invention provides an underground control ground-air patrol operation and maintenance early warning method to overcome the problem in the existing technology that the patrol drone may be unable to return due to sudden weather changes due to the lack of inspection task levels through detection and planning of ground inspection points, and the judgment of whether the remaining power is sufficient for return based on the meteorological threat index and detection clarity.

[0006] To achieve the above-mentioned purpose, the present invention provides an underground control ground-air patrol operation and maintenance early warning method, comprising the following steps:

[0007] Perform preliminary inspections on the area to be inspected based on ground inspection points to obtain several inspection task levels for the inspection area. The inspection drone determines the initial planned path distance based on the inspection task levels for the inspection area.

[0008] Estimate the actual power consumption of the inspection drone when executing the initially planned path, and determine whether the path to be inspected meets the inspection requirements based on the remaining power;

[0009] If the remaining power consumption cannot meet the inspection requirements, the decision on whether to charge is made based on the charging distance and the inspection task level of the detection points on the charging path;

[0010] Obtaining a weather threat index and a detection clarity of the inspection drone in the area to be inspected, and determining a power consumption rate based on the weather threat index and the detection clarity;

[0011] Determine an initial power detection period based on the power consumption rate, and determine whether to return by the original route based on the comparison between the remaining power and the reserved power for the return trip, or select the nearest detection point and re-determine the return destination based on the remaining power;

[0012] The weather threat index is determined based on the wind speed and temperature in the inspection area.

[0013] Furthermore, the process of performing preliminary detection on the area to be detected based on the ground detection points includes:

[0014] The area to be inspected contains several ground inspection points, and the area to be inspected is divided into several inspection areas according to the areas surrounded by the ground inspection points. The inspection task level of the inspection area is determined according to the temperature and wind speed in the inspection area.

[0015] The inspection task levels include primary inspection task level, intermediate inspection task level and advanced inspection task level.

[0016] Furthermore, the process of determining the initial planned path distance of the inspection drone according to the inspection task level of the detection area includes:

[0017] The inspection unmanned aerial vehicle determines an inspection priority according to an inspection task level and an inspection task distance, and determines an inspection sequence of the ground detection points according to the inspection priority.

[0018] Further, the process of determining whether the to-be-inspected path meets the inspection requirement according to the remaining power consumption includes,

[0019] The inspection task distance is determined according to the inspection sequence, the actual power consumption is estimated according to the inspection task distance, the remaining power is detected by the power detection device, and it is determined whether the remaining inspection task distance can be met according to the remaining power.

[0020] Further, the process of determining whether to charge according to the charging distance and the inspection task level of the detection points on the charging path includes,

[0021] The charging distance is obtained by detecting the distance from the inspection unmanned aerial vehicle to the charging ground detection area, and for the case that the inspection task level of the detection points on the charging path is a high-level inspection task level, the corresponding detection area is preferentially inspected;

[0022] For the case that the inspection task level of the detection area on the charging path is a primary inspection task level, the inspection unmanned aerial vehicle is preferentially charged;

[0023] For the case that the inspection task level of the detection area on the charging path is a middle-level inspection task level, it is determined whether to directly charge according to the middle-level inspection task level and the charging distance.

[0024] Further, the process of determining whether to directly charge according to the middle-level inspection task level performance parameter and the charging distance performance parameter includes,

[0025] The charging index is generated according to the middle-level inspection task level performance parameter and the charging distance performance parameter, the charging index is compared with the standard index, and it is determined whether to directly charge according to the index comparison result;

[0026] For the case that the charging index is less than or equal to the standard index, it is determined to directly charge;

[0027] For the case that the charging index is greater than the standard index, it is determined to preferentially inspect the detection area corresponding to the middle-level inspection task level.

[0028] Further, the process of determining the power consumption rate according to the weather threat index and the detection clarity includes,

[0029] The weather threat index is determined according to the wind speed and temperature in the inspection area, the detection clarity of the inspection unmanned aerial vehicle is obtained, and the power consumption rate is determined according to the weather threat index and the detection clarity;

[0030] The detection clarity is determined according to the inspection task level.

[0031] Further, according to the comparison between the remaining power and the return reservation power, it is determined whether to return along the original path, or the process of selecting the nearest detection point comprises,

[0032] For the case that the remaining power is greater than the return reservation power, it is determined to return along the original path;

[0033] For the case that the remaining power is less than or equal to the return reservation power, the nearest detection point is selected for return;

[0034] The return reservation power is determined according to the distance of the initial planning path.

[0035] Further, the process of re-determining the return target point according to the remaining power comprises,

[0036] The return distance of the real-time monitoring patrol unmanned aerial vehicle and the nearest detection point is monitored, and it is determined whether to perform return in-flight inspection according to the comparison result of the return distance and the corresponding remaining distance of the remaining power.

[0037] Further, the process of determining whether to perform return in-flight inspection according to the comparison result of the return distance and the corresponding remaining distance of the remaining power comprises,

[0038] For the case that the return distance is greater than the remaining distance, it is determined to directly determine the nearest ground detection point as the return target point;

[0039] For the case that the return distance is less than or equal to the remaining distance, it is determined whether to inspect the ground detection points passed according to the in-path inspection task level.

[0040] Compared with the prior art, the beneficial effects of the present application are that the multi-dimensional dynamic decision mechanism significantly improves the efficiency and safety of large-scale patrol tasks such as forest fire prevention. Through the preprocessing of the ground detection points, the invalid flight of the unmanned aerial vehicle is reduced (the primary task point can be skipped), and the task coverage rate is effectively improved. The grid detection point layout (without overlap) reduces the deployment cost of the equipment, reduces the number of sensors compared with the traditional random point layout, and saves the cost. The dynamic power model (including task level weight) controls the power prediction error within a preset range, avoiding the estimation deviation of the traditional fixed power consumption rate. When the power is insufficient, the low-priority task is automatically terminated to reduce the risk of crashing. The high-priority task execution strategy ensures the key fire processing rate, and the charging decision algorithm combined with the task level reduces the number of invalid return charging times and charging time. The weather threat index (wind speed / temperature) and the detection clarity are linked to adjust the power consumption rate, and the return rate is maintained in extreme weather. The return target point real-time reselection mechanism improves the success rate of emergency landing. When the power is sufficient, the return in-flight inspection is automatically performed to improve the in-flight task utilization rate and avoid data omission.

[0041] Further, the monitoring efficiency is improved, and the non-overlapping coverage ensures that there is no blind area in monitoring, and the monitoring efficiency is improved compared with the random distribution scheme. Each detection point focuses on detecting the corresponding area, reduces the data processing amount, and improves the response speed. Multi-parameter fusion judgment reduces the false alarm rate, and the composite detection mode of temperature + smoke + image improves the early fire identification rate, and the priority processing of high-level tasks reduces the fire loss. The system realizes the closed-loop management of accurate monitoring-quick response-optimal disposal through innovative grid deployment, intelligent grading and dynamic path planning, and provides a revolutionary solution for the field of forest fire prevention.

[0042] Further, the three-level task dynamic response system adopts a strong interruption-immediate response mechanism for high-level tasks (red warning), improves the response speed, introduces a quantitative evaluation model for middle-level tasks (yellow warning), and realizes full-automatic charging optimization for primary tasks (green normal), reduces invalid patrol mileage, increases the service life of the unmanned aerial vehicle, and significantly improves the response efficiency. The priority of high-level task disposal improves the early fire control rate. The technical scheme has formed a complete intellectual property system, and sets a new technical standard for the field of unmanned aerial vehicle intelligent inspection. The dynamic energy management algorithm of the system makes the operation time effectively prolonged under the same battery capacity, and redefines the industry efficiency benchmark.

[0043] Further, the three-variable coupling algorithm (basic power consumption x meteorological coefficient x task coefficient) is adopted to reduce the power estimation error, the dynamic threat index calculation fuses real-time wind speed (0.6 weight) and temperature parameters (0.4 weight) to improve the warning accuracy, the task level and the clarity linkage mechanism, the high-level task 4K infrared detection, the dynamic power threshold management, the return reserve quantity calculation introduces a 1.2 safety factor, and through the deep coupling of meteorological data, task demand and power management, an intelligent power supply system with autonomous decision-making ability is constructed. Practical application proves that the scheme can improve the working efficiency of the unmanned aerial vehicle in complex environment, and control the operation risk at the lowest level in the industry, and provides reliable technical support for unmanned inspection. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 The flowchart of the underground control and ground-air patrol operation and maintenance early warning method in the embodiment;

[0045] Figure 2 The process flowchart of the underground control and ground-air patrol operation and maintenance early warning method in the embodiment for determining whether to charge according to the charging distance and the task level of the detection point inspection on the charging path;

[0046] Figure 3 The process flowchart of the underground control and ground-air patrol operation and maintenance early warning method in the embodiment for determining whether to directly charge according to the middle-level inspection task level performance parameter and the charging distance performance parameter;

[0047] Figure 4 The process flow chart for determining whether to return along the original path according to the comparison between the remaining electric quantity and the return reserved electric quantity in the underground pipe control and ground-air patrol operation and maintenance early warning method in the embodiment. DETAILED DESCRIPTION

[0048] In order to make the objects and advantages of the present application clearer, the present application will be further described below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0049] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. It should be understood by those skilled in the art that the embodiments are only used to explain the technical principles of the present application and are not used to limit the protection scope of the present application.

[0050] It should be noted that, in the description of the present application, the terms of "upper", "lower", "left", "right", "inner", "outer" and the like indicating the direction or positional relationship are based on the direction or positional relationship shown in the drawings, which is only for the convenience of description and does not indicate or imply that the device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0051] In addition, it should also be noted that, in the description of the present application, unless otherwise explicitly specified and limited, the terms of "mounting", "connecting", "connection" should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or the internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0052] Please refer to Figures 1-4 as shown, Figure 1 The process flow chart for determining whether to return along the original path according to the comparison between the remaining electric quantity and the return reserved electric quantity in the underground pipe control and ground-air patrol operation and maintenance early warning method in the embodiment. Figure 2 The process flow chart for determining whether to return along the original path according to the comparison between the remaining electric quantity and the return reserved electric quantity in the underground pipe control and ground-air patrol operation and maintenance early warning method in the embodiment. Figure 3 The process flow chart for determining whether to return along the original path according to the comparison between the remaining electric quantity and the return reserved electric quantity in the underground pipe control and ground-air patrol operation and maintenance early warning method in the embodiment. Figure 4 The process flow chart for determining whether to return along the original path according to the comparison between the remaining electric quantity and the return reserved electric quantity in the underground pipe control and ground-air patrol operation and maintenance early warning method in the embodiment.

[0053] In this embodiment, an underground control ground-to-air patrol operation and maintenance early warning method is used to patrol the area to be detected during forest fire prevention. It can patrol the fire situation in the area to be detected due to increased ambient temperature or human factors, and ensure that the possible fire locations in the area to be detected are discovered and checked in time around the clock to avoid loss of life and property.

[0054] This embodiment provides an underground control ground-air patrol operation and maintenance early warning method, including the following steps:

[0055] Step S1: Perform preliminary inspections on the area to be inspected based on ground inspection points to obtain several inspection task levels for the inspection areas. The inspection drone determines the initial planned path distance based on the inspection task levels for the inspection areas.

[0056] Step S2: estimating the actual power consumption of the inspection drone when executing the initially planned path, and determining whether the path to be inspected meets the inspection requirements based on the remaining power;

[0057] Step S3: If the remaining power consumption cannot meet the inspection requirements, whether to charge is determined based on the charging distance and the inspection task level of the detection points on the charging path;

[0058] Step S4, obtaining a weather threat index in the area to be inspected and a detection clarity of the inspection drone, and determining a power consumption rate based on the weather threat index and the detection clarity;

[0059] Step S5, determining an initial power detection period based on the power consumption rate, and determining whether to return by the original route based on a comparison of the remaining power and the reserved power for the return trip, or selecting the nearest detection point and re-determining the return destination based on the remaining power;

[0060] The weather threat index is determined based on the wind speed and temperature in the inspection area.

[0061] This solution significantly improves the efficiency and safety of large-scale inspection missions, such as forest fire prevention, through a multi-dimensional dynamic decision-making mechanism. Pre-processing ground inspection points reduces ineffective drone flights (primary mission points can be skipped), effectively improving mission coverage. A grid-based inspection point layout (no overlap) reduces equipment deployment costs and reduces the number of sensors compared to traditional random deployment, saving costs. A dynamic power model (including task-level weights) keeps power prediction errors within a preset range, avoiding the estimation bias of traditional fixed power consumption rates. Low-priority tasks are automatically terminated when the battery is low, reducing the risk of crashes. A high-priority task priority execution strategy ensures that critical fires are handled at the required rate. Combined with a task-level charging decision algorithm, this reduces ineffective round-trip charging and reduces charging time. The power consumption rate is adjusted based on the weather threat index (wind speed / temperature) and detection clarity to maintain a high return rate in extreme weather. A real-time reselection mechanism for the return target improves the success rate of emergency landings. Automatically executing inspections en route when the battery is sufficient improves task utilization and prevents data omissions.

[0062] Specifically, the process of conducting preliminary inspection of the area to be inspected based on the ground inspection points includes:

[0063] The area to be inspected contains several ground inspection points, and the area to be inspected is divided into several inspection areas according to the areas surrounded by the ground inspection points. The inspection task level of the inspection area is determined according to the temperature and wind speed in the inspection area.

[0064] The inspection task levels include primary inspection task level, intermediate inspection task level and advanced inspection task level.

[0065] Specifically, the process of determining the initial planning path distance of the inspection drone according to the inspection task level of the detection area includes:

[0066] The inspection drone determines the inspection priority according to the inspection task level and the inspection task distance, and determines the inspection order of the ground inspection points according to the inspection priority.

[0067] Specifically, the process of determining whether the inspection path meets the inspection requirements based on the remaining power consumption includes:

[0068] The inspection task distance is determined according to the inspection sequence, the actual power consumption is estimated according to the inspection task distance, the remaining power is detected by the power detection device, and it is determined whether the remaining inspection task distance can be met according to the remaining power.

[0069] A number of ground detection points (such as Internet of Things sensors or infrared smart cameras) are deployed in the area to be detected (such as a forest fire prevention area), and the detection point in the lower left corner of the area to be detected is selected as the origin, with its due north direction as the X-axis and its due east direction as the Y-axis. A detection point is set every 10 meters in the due north direction, and a detection point is set every 10 meters in the due east direction. The number of detection points is determined according to the area of ​​the area to be detected. The area formed by four detection points is a detection area. The temperature and wind speed in the detection area are collected, and the inspection task level in the area is determined according to the temperature and wind speed detected in any detection area. The inspection task parameters are determined according to the temperature and wind speed. In this embodiment, the inspection task level includes primary inspection task level, intermediate inspection task level and advanced inspection task level.

[0070] Set the inspection task parameter range corresponding to the primary inspection task level to [1,3];

[0071] The inspection task parameter range corresponding to the intermediate inspection task level is [4,6];

[0072] The inspection task parameter range corresponding to the advanced inspection task level is [7,9];

[0073] Set the compensation parameter for the effect of temperature on inspection task parameters to 0.1, and the compensation parameter for the effect of wind speed on inspection task parameters to 0.2;

[0074] The inspection task parameters are determined according to the sum of the initial inspection task parameters, the product of the temperature and the compensation parameter for the temperature's influence on the inspection task parameters, and the product of the wind speed and the compensation for the wind speed's influence on the inspection task parameters, wherein the initial inspection task parameters are set to 1.

[0075] Calculate the inspection task parameters of the detection area,

[0076] For example,

[0077] The current temperature in the detection area is 10°C, the wind speed is 5m / s, and the corresponding inspection task parameters are 1+10×0.1+5×0.2=3. The corresponding inspection task level is the primary inspection task level.

[0078] The current temperature in the detection area is 30°C, the wind speed is 10m / s, and the corresponding inspection task parameters are 1+30×0.1+10×0.2=6. The corresponding inspection task level is intermediate.

[0079] The current temperature in the detection area is 40°C, the wind speed is 15 m / s, and the corresponding inspection task parameters are 1 + 40 × 0.1 + 15 × 0.2 = 8. The corresponding inspection task level is the advanced inspection task level.

[0080] The unmanned aerial vehicle flies to the area corresponding to the high-level inspection task level according to the inspection task level distribution, and then the medium-level inspection task level and the primary inspection task level are connected according to the priority, to form the shortest coverage path, and for the same inspection task level, the unmanned aerial vehicle is inspected from large to small according to the value of the inspection task parameter corresponding to the area.

[0081] For the case that the inspection areas have the same inspection task parameter, the unmanned aerial vehicle is inspected from near to far according to the inspection task distance from the unmanned aerial vehicle to the inspection area, wherein the inspection task distance is determined according to the position of the unmanned aerial vehicle and the position of the center point in the detection area.

[0082] The inspection priority of each ground detection area is determined by the inspection task parameter and the inspection task distance, and the weight coefficient of the inspection task parameter is set to 0.7 and the weight coefficient of the inspection task distance is set to 0.3 in this embodiment.

[0083] The inspection task level includes primary inspection task level, medium-level inspection task level and high-level inspection task level corresponding to level values 1, 2 and 3 respectively.

[0084] The inspection sequence is generated in descending order of priority,

[0085] The ground detection area A corresponds to the high-level inspection task level, the corresponding level value is 3, the inspection task distance is 20m, and the priority is 0.7*3+0.3*20=8.1, corresponding to the sorting 1.

[0086] The ground detection area B corresponds to the inspection task level, the corresponding level value is 2, the inspection task distance is 21.5m, and the priority is 0.7*2+0.3*21.5=7.85, corresponding to the sorting 2.

[0087] The ground detection area C corresponds to the primary inspection task level, the corresponding level value is 1, the inspection task distance is 21.0m, and the priority is 0.7*1+0.3*21=7, corresponding to the sorting 3.

[0088] The unmanned aerial vehicle departs from the base, visits the detection points in turn according to the sorting, to form the initial planning path (such as from the base to the A detection area, from the A detection area to the B detection area, from the B detection area to the C detection area, and from the C detection area back to the base).

[0089] Power consumption estimation:

[0090] The power consumption of a single flight is related to the inspection task distance and the inspection task level, and the basic power consumption rate per kilometer is set to 5%, and the power consumption per kilometer is 200mAh,

[0091] From detection area A to detection area B, the inspection task distance is 1.5 km, the inspection task level is intermediate, the actual power consumption rate from detection area A to detection area B is 5% × 1.5 = 7.5%, and the actual power consumption from detection area A to detection area B is 300 mAh;

[0092] Continue to estimate the actual power consumption from detection area B to detection area C and from detection area C back to base.

[0093] If the estimated actual power consumption is greater than or equal to the remaining power, the inspection drone needs to be charged;

[0094] If the estimated actual power consumption is less than the remaining power, the inspection drone will continue to perform the inspection task;

[0095] For example:

[0096] If the estimated actual power consumption is less than the remaining power, the inspection drone will continue to perform the inspection task. If the power detection device detects that the remaining power is 80% of the total power of the inspection drone, and the cumulative estimated power consumption is 45% of the total power of the inspection drone, the inspection drone will continue to perform the inspection task.

[0097] If the estimated actual power consumption is greater than or equal to the remaining power, the inspection drone determines the nearest charging point for charging.

[0098] Monitoring efficiency is improved, and non-overlapping coverage ensures no blind spots, improving monitoring efficiency compared to random point placement solutions. Each detection point focuses on detecting the corresponding area, reducing data processing and improving response speed. Multi-parameter fusion judgment reduces false alarm rates, and the composite detection mode of temperature, smoke, and imagery improves the early identification rate of fires, prioritizing high-level tasks to reduce fire losses. Through innovative grid deployment, intelligent classification, and dynamic path planning, this system achieves a closed-loop management system of precise monitoring, rapid response, and optimal disposal, providing a revolutionary solution for fields such as forest fire prevention.

[0099] Specifically, the process of determining whether to charge based on the charging distance and the inspection task level of the detection points on the charging path includes:

[0100] The distance between the inspection drone and the charging ground inspection area is measured to obtain the charging distance. If the inspection task level of the inspection point on the charging path is a high-level inspection task level, the corresponding inspection area is inspected first.

[0101] If the inspection task level of the detection area on the charging path is the primary inspection task level, the inspection drone will be charged first;

[0102] If the inspection task level of the detection area on the charging path is the intermediate inspection task level, whether to directly charge is determined based on the intermediate inspection task level and the charging distance.

[0103] Specifically, the process of determining whether to directly charge based on the intermediate inspection task level performance parameters and the charging distance performance parameters includes:

[0104] Generate charging indicators based on the performance parameters of the intermediate inspection task level and the charging distance performance parameters, compare the charging indicators with the standard indicators, and determine whether to directly charge based on the indicator comparison results;

[0105] If the charging index is less than or equal to the standard index, it is determined to charge directly;

[0106] When the charging index is greater than the standard index, it is determined that the inspection area corresponding to the intermediate inspection task level is inspected first.

[0107] When the inspection task level of the detection point on the charging path is the advanced inspection task level, the corresponding inspection area will be inspected first;

[0108] When the inspection task level of the detection point on the charging path is the primary inspection task level, the inspection drone will be charged first;

[0109] When the inspection task level of the detection point on the charging path is the intermediate inspection task level, the charging index is determined based on the intermediate inspection task level and the compensation parameter for the impact of the intermediate inspection task level on the charging index, and the charging distance and the compensation parameter for the impact of the charging distance on the charging index. The charging index is compared with the standard index. If the charging index is less than or equal to the standard index, it is determined that charging is directly carried out;

[0110] When the charging index is greater than the standard index, it is determined that the inspection area corresponding to the intermediate inspection task level is inspected first.

[0111] Among them, the standard index is an indicator that reflects whether the inspection drone should be charged directly on the charging path. It is determined according to the usage time of the inspection drone. The standard index is negatively correlated with the usage time of the inspection drone. The proportion parameter of the negative correlation between the standard index and the usage time of the inspection drone is determined according to the preset proportion parameter.

[0112] In this embodiment, the compensation parameter for the impact of the intermediate inspection task level on the charging index is set to 0.7; the compensation parameter for the impact of the charging distance on the charging index is set to 0.3; the initial standard index is set to 1.5, and the negative correlation ratio parameter for the inspection drone usage time is set to 0.02.

[0113] For example:

[0114] When the charging distance is 4km, the charging index is 0.7×2+0.3×4=2.6.

[0115] If the charging index is greater than the initial standard index, the inspection area corresponding to the intermediate inspection task level is determined to be prioritized.

[0116] If the drone has flown for 100 hours, the standard index is 1.5+100×0.02=3.5.

[0117] If the charging indicator is lower than the standard indicator, it is determined that charging is to be performed directly.

[0118] The three-level dynamic response system uses a strong interruption-immediate response mechanism for high-level tasks (red alerts) to improve response speed, a quantitative assessment model for intermediate-level tasks (yellow alerts), and fully automatic charging optimization for primary tasks (green normal). This reduces ineffective inspection mileage, increases the service life of drones, and significantly improves response efficiency. The handling priority of high-level tasks increases the initial control rate of fires. This technical solution has formed a complete intellectual property system and set a new technical standard for drone intelligent inspection. The system's unique dynamic energy management algorithm effectively extends operating time with the same battery capacity, redefining the industry's performance benchmark.

[0119] Specifically, the process of determining the power consumption rate according to the weather threat index and the detection clarity includes:

[0120] Determine the weather threat index based on the wind speed and temperature in the inspection area, obtain the inspection clarity of the inspection drone, and determine the power consumption rate based on the weather threat index and detection clarity;

[0121] The detection clarity is determined according to the inspection task level.

[0122] Specifically, the process of determining whether to return by the original route or select the nearest detection point based on the comparison between the remaining power and the reserved power for the return trip includes:

[0123] If the remaining power is greater than the reserved power for the return trip, it is determined to choose to return by the original route;

[0124] If the remaining battery power is less than or equal to the return reserve power, the nearest detection area will be selected for return flight.

[0125] The reserved power for the return trip is determined based on the distance of the initial planned path.

[0126] The weather threat index reflects the adverse impact of the environment in the detection area on drone flight and is determined by wind speed and temperature:

[0127] The compensation parameter for the effect of wind speed on the meteorological threat index is set to 0.6, and the compensation parameter for the effect of temperature on the meteorological threat index is set to 0.4;

[0128] The weather threat index is 0.6×(current wind speed / maximum safe wind speed)+0.4×|current temperature-reference temperature| / |temperature limit-reference temperature|. The maximum safe wind speed is set to 12m / s (the upper limit of the drone's wind resistance), the reference temperature is 25°C (ideal operating temperature), and the temperature limit is 50°C (extreme high temperature threshold).

[0129] The weather threat index is less than 0.3, which is considered a low threat (the power consumption rate remains unchanged);

[0130] If the weather threat index is greater than or equal to 0.3 and less than 0.7, it is considered a medium threat (the power consumption rate is 1.2 times the basic power consumption rate);

[0131] If the weather threat index is greater than or equal to 0.7, it is considered a high threat (the power consumption rate is 1.5 times the basic power consumption rate);

[0132] The detection clarity determines the extra power consumption of drone hovering, zooming, data transmission, etc.

[0133] The advanced task level corresponds to ultra-high definition detection requirements (4K+infrared), and the power consumption impact coefficient is set to 1.5;

[0134] The intermediate task level corresponds to the detection definition requirement of ultra-high definition (1080P), and the power consumption impact coefficient is set to 1.2;

[0135] The low task level corresponds to the detection definition requirement of standard definition (720P), and the power consumption impact coefficient is set to 1.0;

[0136] The final power consumption rate is determined by the basic power consumption rate, weather threat index and detection clarity.

[0137] The basic power consumption rate is set to 200mAh / km, the current wind speed is 8m / s, the temperature is 38°C, and the weather threat index is 0.6×(8 / 12)+0.4×|38-25| / |50-25|=0.608.

[0138] The reserved power for return flight is dynamically adjusted based on the return distance of the initially planned route and the current power consumption rate.

[0139] Set the return distance to 5km and the current power consumption rate to 300mAh / km.

[0140] The reserve power for return is 5×300=1800mAh;

[0141] If the remaining power is sufficient, it will return along the original route;

[0142] If the remaining battery power is insufficient, initiate emergency return:

[0143] Scene setting, the remaining battery of the drone is 4000mAh, the current inspection task level is the advanced inspection level, the wind speed is 6m / s, the temperature is 40℃, the return distance is 8km, the nearest detection point is 3km (with charging function), the execution process,

[0144] The weather threat index is 0.6×(6 / 12)+0.4×(40-25) / (50-25)=0.54, which corresponds to the advanced inspection level.

[0145] The power consumption is 200×(1+0.54)×1.5=462mAh / km, and the reserve power for return is 8×462×1.2=4435.2mAh. If the remaining power is less than the reserve power, emergency return is initiated.

[0146] The power consumption at the nearest detection point is 3×462=1386mAh, which is less than the remaining power, so the system flies to the detection point and lands.

[0147] A triple-variable coupling algorithm (basic power consumption × meteorological coefficient × mission coefficient) is used to reduce power estimation errors. Dynamic threat index calculations integrate real-time wind speed (0.6 weight) and temperature parameters (0.4 weight) to improve warning accuracy. A mechanism for linking mission level and clarity is implemented, along with 4K infrared detection for advanced missions, dynamic power threshold management, and a 1.2 safety factor for return-to-home reserve calculations. By deeply coupling meteorological data, mission requirements, and power management, this solution establishes an intelligent power supply system with autonomous decision-making capabilities. Practical applications have proven that this solution can improve drone performance in complex environments while keeping operational risks to the lowest level in the industry, providing reliable technical support for unmanned inspections.

[0148] Specifically, the process of re-determining the return target point based on the remaining power includes:

[0149] Monitor the return distance between the inspection drone and the nearest inspection point in real time, and determine whether to conduct an inspection on the way back based on the comparison between the return distance and the remaining distance corresponding to the remaining power.

[0150] Specifically, the process of determining whether to conduct a return inspection based on the comparison result of the return distance and the remaining distance corresponding to the remaining power includes:

[0151] If the return distance is greater than the remaining distance, the nearest ground detection point is directly determined as the return target point;

[0152] If the return distance is less than or equal to the remaining distance, it is determined whether to inspect the ground inspection points passed by based on the inspection task level within the return path.

[0153] If the remaining power is insufficient to return to any inspection point, the inspection drone sends a detailed location and a distress signal to the terminal.

[0154] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0155] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A ground-to-air patrol and maintenance early warning method for underground control, characterized in that: The following steps are included: Perform preliminary inspections on the area to be inspected based on ground inspection points to obtain several inspection task levels for the inspection area. The inspection drone determines the initial planned path distance based on the inspection task levels for the inspection area. Estimate the actual power consumption of the inspection drone when executing the initially planned path, and determine whether the path to be inspected meets the inspection requirements based on the remaining power; If the remaining power consumption cannot meet the inspection requirements, the decision on whether to charge is made based on the charging distance and the inspection task level of the detection points on the charging path; Obtaining a weather threat index and a detection clarity of the inspection drone in the area to be inspected, and determining a power consumption rate based on the weather threat index and the detection clarity; Determine an initial power detection period based on the power consumption rate, and determine whether to return by the original route based on the comparison between the remaining power and the reserved power for the return trip, or select the nearest detection point and re-determine the return destination based on the remaining power; The weather threat index is determined based on the wind speed and temperature in the inspection area.

2. The underground control ground-air patrol operation and maintenance early warning method according to claim 1 is characterized in that: The process of preliminary inspection of the area to be inspected based on ground inspection points includes: The area to be inspected contains several ground inspection points, and the area to be inspected is divided into several inspection areas according to the areas surrounded by the ground inspection points. The inspection task level of the inspection area is determined according to the temperature and wind speed in the inspection area. The inspection task levels include primary inspection task level, intermediate inspection task level and advanced inspection task level.

3. The underground control ground-air patrol operation and maintenance early warning method according to claim 2 is characterized in that: The process of determining the initial planning path distance of the inspection drone according to the inspection task level of the detection area includes: The inspection drone determines the inspection priority according to the inspection task level and the inspection task distance, and determines the inspection order of the ground inspection points according to the inspection priority.

4. The underground control ground-air patrol operation and maintenance early warning method according to claim 3 is characterized in that: The process of determining whether the inspection path meets the inspection requirements based on the remaining power consumption includes: The inspection task distance is determined according to the inspection sequence, the actual power consumption is estimated according to the inspection task distance, the remaining power is detected by the power detection device, and it is determined whether the remaining inspection task distance can be met according to the remaining power.

5. The underground control ground-air patrol operation and maintenance early warning method according to claim 4 is characterized in that: The process of determining whether to charge based on the charging distance and the inspection task level of the detection points on the charging path includes: The distance between the inspection drone and the charging ground inspection area is measured to obtain the charging distance. If the inspection task level of the inspection point on the charging path is a high-level inspection task level, the corresponding inspection area is inspected first. If the inspection task level of the detection area on the charging path is the primary inspection task level, the inspection drone will be charged first; If the inspection task level of the detection area on the charging path is the intermediate inspection task level, whether to directly charge is determined based on the intermediate inspection task level and the charging distance.

6. The underground control ground-air patrol operation and maintenance early warning method according to claim 5 is characterized in that: The process of determining whether to directly charge based on the intermediate inspection task level performance parameters and charging distance performance parameters includes: Generate charging indicators based on the performance parameters of the intermediate inspection task level and the charging distance performance parameters, compare the charging indicators with the standard indicators, and determine whether to directly charge based on the indicator comparison results; If the charging index is less than or equal to the standard index, it is determined to charge directly; When the charging index is greater than the standard index, it is determined that the inspection area corresponding to the intermediate inspection task level is inspected first.

7. The underground control ground-air patrol operation and maintenance early warning method according to claim 6 is characterized in that: The process of determining the power consumption rate according to the weather threat index and the detection clarity includes: Determine the weather threat index based on the wind speed and temperature in the inspection area, obtain the inspection clarity of the inspection drone, and determine the power consumption rate based on the weather threat index and detection clarity; The detection clarity is determined according to the inspection task level.

8. The underground control ground-air patrol operation and maintenance early warning method according to claim 7 is characterized in that: The process of determining whether to return by the original route or select the nearest detection point based on the comparison between the remaining power and the reserved power for the return trip includes: If the remaining power is greater than the reserved power for the return trip, it is determined to choose to return by the original route; If the remaining battery power is less than or equal to the reserved power for the return trip, the nearest detection point will be selected for return. The reserved power for the return trip is determined based on the distance of the initial planned path.

9. The underground control ground-air patrol operation and maintenance early warning method according to claim 8 is characterized in that: The process of re-determining the return target point based on the remaining power includes: Monitor the return distance between the inspection drone and the nearest inspection point in real time, and determine whether to conduct an inspection on the way back based on the comparison between the return distance and the remaining distance corresponding to the remaining power.

10. The underground control ground-air patrol operation and maintenance early warning method according to claim 9 is characterized in that: The process of determining whether to conduct a return inspection based on the comparison result of the return distance and the remaining distance corresponding to the remaining power includes: If the return distance is greater than the remaining distance, the nearest ground detection point is directly determined as the return target point; If the return distance is less than or equal to the remaining distance, it is determined whether to inspect the ground inspection points passed by based on the inspection task level within the return path.

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