An unmanned aerial vehicle-based underground management and control patrol operation and maintenance early warning method

Abstract

The present application relates to the technical field of underground management and control, and particularly relates to an underground management and control patrol operation and early warning method based on a UAV, comprising: using a UAV to patrol an underground passage with initial parameters, collecting pipeline data and environmental data in the passage and completing storage; dividing a patrol route into several road sections of different risk levels according to the comprehensive risk value of the risk event type in the passage and determining the parameters of UAV patrol; comparing the current data in a single road section with historical data to determine a data change rate and determining the storage level of the single road section data according to the data change rate; determining a data storage optimization strategy for the single road section according to the data change index of the single road section after a preset number of patrols; and determining a response strategy based on the comprehensive data change rate of the single road section after several patrols at an increased patrol frequency. The present application improves the intelligent level and emergency response efficiency of underground management and control patrol operation.

Description

Technical Field

[0001] This invention relates to the field of underground control technology, and in particular to an underground control, inspection, maintenance and early warning method based on unmanned aerial vehicles (UAVs). Background Technology

[0002] With the acceleration of urbanization, the scale of underground spaces (such as integrated utility tunnels, subway tunnels, and underground pipe networks) is constantly expanding, posing severe challenges to their safe operation and maintenance. Traditional underground inspections mainly rely on manual inspections or fixed sensor monitoring, which suffers from problems such as low efficiency, incomplete coverage, and delayed response. For example, manual inspections are difficult to cover complex and hidden areas and cannot acquire data in real time; while fixed sensors are costly to deploy, lack flexibility, and are difficult to dynamically adapt to the monitoring needs of different risk levels.

[0003] Chinese Patent Application Publication No. CN116128466A discloses a method and system for the coordinated management and control of flood risks in underground utility tunnels, relating to the field of flood early warning technology. The method includes: determining monitoring objects that characterize the flood situation in the external surface area of ​​the underground utility tunnel, monitoring objects that characterize the flood situation inside the underground utility tunnel, and monitoring indicators corresponding to each monitoring object, based on existing standards and specifications for underground utility tunnels and historical flood case causes; determining the flood source of the underground utility tunnel based on the monitoring objects and corresponding monitoring indicators; determining the flood risk level of the underground utility tunnel based on the four-level risk assessment principle, the monitoring objects, and the corresponding monitoring indicators; and determining emergency response measures based on the flood risk level and the flood source of the underground utility tunnel.

[0004] However, the existing technology has the following problems: it cannot dynamically adjust the risk assessment strategy based on real-time monitoring data, resulting in a lag in response to sudden risks or gradual hidden dangers. Summary of the Invention

[0005] To address this issue, the present invention provides an underground control, inspection, maintenance, and early warning method based on unmanned aerial vehicles (UAVs) to overcome the problem of delayed early warning response caused by the inability to achieve dynamic monitoring and control in existing technologies.

[0006] To achieve the above objectives, this invention provides a method for underground control, inspection, maintenance, and early warning based on unmanned aerial vehicles (UAVs), comprising:

[0007] Step S1: Use a drone to inspect the underground passage with initial parameters, collect pipeline data and environmental data in the passage and store them. The pipeline data includes pipeline distribution density and corrosion area at pipeline connections. The environmental data includes humidity, passage crack size, water depth and vibration frequency.

[0008] Step S2: Divide the patrol route into several sections with different risk levels based on the comprehensive risk value of the types of risk events in the passage and determine the parameters for the drone patrol, wherein the parameters include speed and clarity;

[0009] Step S3: Compare the current data in a single road segment with historical data to determine the data change rate, and determine the storage level of the single road segment data based on the data change rate;

[0010] Step S4: Determine the data storage optimization strategy for a single road segment based on the data change index after a preset number of inspections. This strategy includes reducing the inspection comparison frequency, adjusting the inspection parameters, and increasing the inspection frequency.

[0011] Step S6: Determine the response strategy based on the rate of change of comprehensive data of a single road segment after several inspections following the increase in inspection frequency.

[0012] Furthermore, the risk event types include one or more of the following:

[0013] The pipeline distribution density is greater than the preset density;

[0014] Alternatively, the area of ​​corrosion at the pipeline connection is greater than the preset area ratio;

[0015] Alternatively, the humidity inside the channel is higher than the preset humidity;

[0016] Alternatively, the channel crack size is larger than the preset size;

[0017] Or, the water depth is greater than the preset depth;

[0018] Alternatively, the channel vibration frequency is greater than the preset frequency.

[0019] Furthermore, the risk level of the road segment is determined based on the comprehensive risk value of the types of risk events within the passage, and the parameters for drone patrol are determined. If the comprehensive risk value is less than or equal to a first preset risk value, the road segment is determined to be a low-risk road segment, and the speed of drone patrol is determined to be a first preset speed, and the resolution is determined to be a first preset resolution.

[0020] If the overall risk value is greater than the first preset risk value and less than or equal to the second preset risk value, then the road segment is determined to be a medium-risk road segment, and the speed of the drone patrol is determined to be the second preset speed and the clarity is determined to be the second preset clarity.

[0021] If the overall risk value is greater than the second preset risk value, the road segment is determined to be a high-risk road segment, and the speed of the drone patrol is determined to be a third preset speed and the resolution to be a third preset resolution.

[0022] The overall risk value is determined by the number of risk event types and their weighting coefficients.

[0023] Furthermore, the storage level of the single-segment data is determined based on the data change rate. If the data change rate is less than the first preset change rate, the storage level of the single-segment data is determined to be level three, and level three storage is high-compression storage.

[0024] If the data change rate is greater than or equal to the first preset change rate and less than the second preset change rate, then the storage level of the single-segment data is determined to be level two, and level two storage is low-compression storage.

[0025] If the data change rate is greater than or equal to the second preset change rate, then the storage level of the single-segment data is determined to be Level 1, and Level 1 storage is lossless storage.

[0026] Furthermore, a data storage optimization strategy for a single road segment is determined based on the data change index in the single road segment after a preset number of inspections. If the data change index is less than a first preset change index, the data storage optimization strategy for the single road segment is determined to be to reduce the data comparison frequency based on the difference between the first preset change index and the data change index.

[0027] If the data change index is greater than or equal to the first preset change index and less than the second preset change index, then the data storage optimization strategy for a single road segment is determined to be to adjust the patrol parameters according to the risk proportion characteristic value of the risk event type to which the changed data belongs.

[0028] If the data change index is greater than or equal to the second preset change index, then the data storage optimization strategy for a single road segment is determined to be to upgrade the storage level of the road segment and increase the patrol frequency according to the difference between the data change index and the second preset change index.

[0029] Furthermore, the data comparison frequency is positively correlated with the difference in the change index, wherein the difference in the change index is the difference between the first preset change index and the data change index.

[0030] Furthermore, the patrol parameters are adjusted according to the risk proportion characteristic value. If the risk proportion characteristic value is less than the preset risk proportion characteristic value, the patrol speed is reduced according to the difference between the preset risk proportion characteristic value and the risk proportion characteristic value.

[0031] If the risk proportion feature value is greater than or equal to the preset risk proportion feature value, then under the condition of low patrol speed, the patrol clarity is increased according to the difference between the risk proportion feature value and the preset risk proportion feature value.

[0032] Furthermore, the risk proportion feature value is determined based on the proportion of risk event types to which the change data belongs, wherein the proportion includes a first-class proportion and a second-class proportion;

[0033] The risk event types mentioned above include pipeline distribution density greater than preset density, humidity in the channel greater than preset humidity, and channel vibration frequency greater than preset frequency.

[0034] The risk event types of the second category include the proportion of rust area at pipeline connections being greater than the preset proportion, the size of channel cracks being greater than the preset size, and the depth of water accumulation being greater than the preset depth.

[0035] Furthermore, several adjustment methods are set for the patrol frequency, and each adjustment method has a different adjustment range for the patrol frequency.

[0036] Furthermore, a response strategy is determined based on the comprehensive data change rate of a single segment in several inspections after increasing the inspection frequency. If the comprehensive data change rate is less than a first preset change rate, the response strategy is to downgrade the storage level.

[0037] If the rate of change of the comprehensive data is greater than or equal to the first preset rate of change and less than the second preset rate of change, the response strategy is determined to be to activate a low-level warning. The strategy of the low-level warning is to remind the operation and maintenance personnel to pay attention to the data changes of the road segment and to arrange regular inspections.

[0038] If the rate of change of the comprehensive data is greater than or equal to the second preset rate of change, the response strategy is determined to be to activate an advanced early warning. The strategy of the advanced early warning is to call maintenance personnel to immediately go to the abnormal section for detailed inspection and to carry out emergency repairs or maintenance.

[0039] Compared with the prior art, the beneficial effects of the present invention are that the present invention uses drones to inspect and collect pipeline data and environmental data in the channel, obtains the data change parameters based on the comparison results of real-time data and historical data, and determines the operation and maintenance response strategy according to the parameters, thereby improving the intelligence level and emergency response efficiency of underground management, inspection and maintenance.

[0040] Furthermore, this invention divides the inspection route into several sections with different risk levels based on the comprehensive risk value of the types of risk events within the passage and determines the parameters for drone inspection, enabling the adoption of appropriate inspection strategies for sections with different risk levels, thereby improving inspection efficiency and targeting.

[0041] Furthermore, this invention determines the data change rate by comparing the current data of a single road segment with historical data, and then determines the data storage level, realizing hierarchical storage of data with different degrees of change, which is conducive to the rational use of storage resources.

[0042] Furthermore, the present invention determines a data storage optimization strategy based on the single-segment data change index after a preset number of inspections, including reducing the inspection comparison frequency, adjusting inspection parameters, and increasing the inspection frequency, thereby further optimizing the data storage and inspection strategy and improving the system's operating efficiency.

[0043] Furthermore, this invention adjusts the inspection parameters based on the risk proportion characteristic value, comprehensively considering the impact of different risk event types on the inspection parameters, which can more reasonably adjust the inspection strategy and improve the inspection effect.

[0044] Furthermore, this invention determines response strategies based on the rate of change of comprehensive data of a single road segment after several inspections with increased inspection frequency. These strategies include downgrading storage level, initiating low-level early warning, and initiating high-level early warning. The invention clarifies the response measures under different circumstances and can handle abnormal situations in underground passages in a timely and effective manner. Attached Figure Description

[0045] Figure 1 This is a flowchart of an embodiment of the underground control, inspection, maintenance, and early warning method based on unmanned aerial vehicles (UAVs) according to the present invention.

[0046] Figure 2 This is a flowchart illustrating the process of determining the storage level of single-segment data in an embodiment of the present invention;

[0047] Figure 3 A flowchart illustrating the data storage optimization strategy for a single segment in an embodiment of the present invention;

[0048] Figure 4 A flowchart for determining the underground control and response strategy in an embodiment of the present invention. Detailed Implementation

[0049] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0050] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0051] It should be noted that the data in this embodiment are all derived from a comprehensive analysis and evaluation of historical test data and corresponding historical test results from the three months prior to this test. Those skilled in the art will understand that the determination of the above-mentioned parameters for any single item in this invention can be achieved by selecting the value with the highest percentage based on the data distribution as the preset standard parameter, using weighted summation to obtain the value as the preset standard parameter, substituting each historical data point into a specific formula and using the value obtained from that formula as the preset standard parameter, or other selection methods, as long as the invention can clearly define different specific situations in the single-item judgment process through the obtained values.

[0052] Please see Figures 1 to 4 The flowcharts shown are respectively: a flowchart of the underground control, inspection, operation and maintenance early warning method based on UAVs according to an embodiment of the present invention; a flowchart of determining the storage level of single-segment data according to an embodiment of the present invention; a flowchart of determining the data storage optimization strategy of a single-segment according to an embodiment of the present invention; and a flowchart of determining the underground control response strategy according to an embodiment of the present invention.

[0053] This invention provides an underground control, inspection, maintenance, and early warning method based on unmanned aerial vehicles (UAVs), comprising:

[0054] Step S1: Use a drone to inspect the underground passage with initial parameters, collect pipeline data and environmental data in the passage and store them. The pipeline data includes pipeline distribution density and corrosion area at pipeline connections. The environmental data includes humidity, passage crack size, water depth and vibration frequency.

[0055] Step S2: Divide the patrol route into several sections with different risk levels based on the comprehensive risk value of the types of risk events in the passage and determine the parameters for the drone patrol, wherein the parameters include speed and clarity;

[0056] Step S3: Compare the current data in a single road segment with historical data to determine the data change rate, and determine the storage level of the single road segment data based on the data change rate;

[0057] Step S4: Determine the data storage optimization strategy for a single road segment based on the data change index after a preset number of inspections. This strategy includes reducing the inspection comparison frequency, adjusting the inspection parameters, and increasing the inspection frequency.

[0058] Step S5: Determine the response strategy based on the rate of change of comprehensive data of a single road segment after several inspections following the increase in inspection frequency.

[0059] Specifically, the initial parameters in step S1 include: a patrol speed of 0.5 m / s and a resolution of 4 k.

[0060] Specifically, the length of a single road segment in step S2 is 10m.

[0061] Specifically, the risk event types include one or more of the following:

[0062] Pipeline distribution density is greater than the preset density of 15 pipelines / m 2 ;

[0063] Alternatively, the area of ​​corrosion at the pipeline connection is more than 10% larger than the preset area.

[0064] Alternatively, the humidity within the channel is greater than the preset humidity of 85%RH;

[0065] Alternatively, the channel crack size is larger than a preset size, where the preset size includes a width greater than 5mm or a length greater than 0.5m;

[0066] Or, the water depth is greater than the preset depth by 10cm;

[0067] Alternatively, the channel vibration frequency is greater than the preset frequency of 50Hz.

[0068] In this embodiment of the invention, the preset density is 15 strands / m². 2 The preset area ratio is 10%, the preset humidity is 85%RH, the preset depth is 10cm, and the preset frequency is 50Hz. However, the above values ​​are not limited to these values, and those skilled in the art can adjust the above values ​​according to actual needs.

[0069] Specifically, the drone is equipped with a high-resolution camera to acquire pipeline distribution density and channel crack size; a multispectral camera to identify corrosion area at pipeline connections; a miniature weather sensor to acquire humidity within the channel; an ultrasonic sensor to acquire water depth; and an acoustic sensor to acquire channel vibration frequency.

[0070] Specifically, the risk level of a road segment is determined based on the comprehensive risk value of the types of risk events within the passage, and the parameters for drone patrol are determined accordingly. If the comprehensive risk value is less than or equal to a first preset risk value of 0.1, the road segment is determined to be a low-risk road segment, and the drone patrol speed is determined to be a first preset speed of 1 m / s and the resolution is determined to be a first preset resolution of 1080p.

[0071] If the comprehensive risk value is greater than the first preset risk value and less than or equal to the second preset risk value of 0.3, then the road segment is determined to be a medium-risk road segment, and the speed of the drone patrol is determined to be the second preset speed of 0.7 m / s, and the resolution is determined to be the second preset resolution of 2k.

[0072] If the comprehensive risk value is greater than the second preset risk value, the road segment is determined to be a high-risk road segment, and the speed of the drone patrol is determined to be the third preset speed of 0.5m / s and the resolution is determined to be the third preset resolution of 3k.

[0073] The overall risk value is determined by the number of risk event types and their weighting coefficients.

[0074] Specifically, the comprehensive risk value is the sum of the products of the number of risk event types and the type weight coefficients.

[0075] Specifically, the weighting factor for pipeline distribution density is 0.05, the weighting factor for corrosion area is 0.1, the weighting factor for humidity is 0.2, the weighting factor for crack size is 0.05, the weighting factor for water accumulation depth is 0.3, and the weighting factor for vibration frequency is 0.3.

[0076] In this embodiment of the invention, the first preset risk value is 0.1 and the second preset risk value is 0.3. However, the above values ​​are not limited to these. Those skilled in the art can adjust the above values ​​according to actual needs.

[0077] Specifically, the storage level of single-segment data is determined based on the data change rate. If the data change rate is less than the first preset change rate of 10%, the storage level of single-segment data is determined to be level three, and level three storage is high-compression storage.

[0078] If the data change rate is greater than or equal to the first preset change rate and less than the second preset change rate of 20%, then the storage level of the single-segment data is determined to be level two, and level two storage is low-compression storage.

[0079] If the data change rate is greater than or equal to the second preset change rate, then the storage level of the single-segment data is determined to be Level 1, and Level 1 storage is lossless storage.

[0080] Specifically, the data change rate is the proportion of changed data to all data. In this embodiment of the invention, the first preset change rate is 10%, and the second preset change rate is 20%. However, the above values ​​are not limited to these, and those skilled in the art can adjust the above values ​​according to actual needs.

[0081] Specifically, the compression rate of the third-level storage is 50%, and the compression rate of the second-level storage is 30%.

[0082] Specifically, the data storage optimization strategy for a single road segment is determined based on the data change index in a single road segment after 10 preset inspections. If the data change index is less than the first preset change index of 0.05, the data storage optimization strategy for the single road segment is to reduce the data comparison frequency based on the difference between the first preset change index and the data change index.

[0083] If the data change index is greater than or equal to the first preset change index and less than the second preset change index of 0.15, then the data storage optimization strategy for a single road segment is determined to be to adjust the patrol parameters according to the risk proportion characteristic value of the risk event type to which the changed data belongs.

[0084] If the data change index is greater than or equal to the second preset change index, then the data storage optimization strategy for a single road segment is determined to be to upgrade the storage level of the road segment and increase the patrol frequency according to the difference between the data change index and the second preset change index.

[0085] Specifically, the data change index is the variance of the rate of change of each data point, which is calculated and obtained through the data analysis module.

[0086] In this embodiment of the invention, the preset number of times is 10 times, the first preset change index is 0.05, and the second preset change index is 0.15. However, the above values ​​are not limited to these, and those skilled in the art can adjust the above values ​​according to actual needs.

[0087] Specifically, upgrading the storage level involves reducing the level number by one, and for Level 1 storage, the upgrade strategy is to extend the storage duration to one month.

[0088] Specifically, the first preset change index is a parameter obtained after the data changes normally. The present invention reduces the data comparison frequency based on the condition that the data change index is less than the first preset change index, which can improve the control efficiency.

[0089] Specifically, the data comparison frequency is positively correlated with the difference in the change index. If the difference in the change index is less than the first preset difference in the change index of 0.01, the data comparison frequency is reduced to the corresponding value using the first frequency adjustment coefficient of 0.5.

[0090] If the difference in the change index is greater than or equal to the first preset difference in the change index and less than the second preset difference in the change index of 0.03, then the data comparison frequency is reduced to the corresponding value using the second frequency adjustment coefficient of 0.2.

[0091] If the difference in the change index is greater than or equal to the second preset difference in the change index, then the data comparison frequency is reduced to the corresponding value using a third frequency adjustment coefficient of 0.1.

[0092] The change index difference is the difference between the first preset change index and the data change index.

[0093] In this embodiment of the invention, the initial frequency of the data comparison is one comparison per inspection. The first preset change index difference is 0.01, and the second preset change index difference is 0.03. However, the above values ​​are not limited to these values, and those skilled in the art can adjust the above values ​​according to actual needs.

[0094] Specifically, the patrol parameters are adjusted according to the risk proportion characteristic value. If the risk proportion characteristic value is less than a preset risk proportion characteristic value of 0.5, the patrol speed is reduced according to the difference between the preset risk proportion characteristic value and the risk proportion characteristic value.

[0095] If the risk proportion feature value is greater than or equal to the preset risk proportion feature value, then under the condition of low patrol speed, the patrol clarity is increased according to the difference between the risk proportion feature value and the preset risk proportion feature value.

[0096] In this embodiment of the invention, the preset risk ratio feature value is 0.5, the patrol speed is adjustable from 0.3 to 1.0 m / s, and the resolution is adjustable from 1080P to 4K.

[0097] Specifically, the risk proportion feature value is determined based on the proportion of risk event types to which the change data belongs, wherein the proportion includes a first-class proportion and a second-class proportion;

[0098] The risk event types mentioned above include pipeline distribution density greater than preset density, humidity in the channel greater than preset humidity, and channel vibration frequency greater than preset frequency.

[0099] The risk event types of the second category include the proportion of rust area at pipeline connections being greater than the preset proportion, the size of channel cracks being greater than the preset size, and the depth of water accumulation being greater than the preset depth.

[0100] Specifically, the risk proportion feature value is determined by a first-class proportion evaluation value and a second-class proportion evaluation value, wherein the first-class proportion evaluation value is the product of the first-class proportion of the risk event type to which the changing data belongs and the first weight coefficient 0.5;

[0101] The evaluation value of the second category proportion is the product of the second category proportion of the risk event type to which the change data belongs and the first weight coefficient of 0.5;

[0102] The risk proportion characteristic value is obtained by summing the first-class proportion evaluation value and the second-class proportion evaluation value.

[0103] Specifically, there are several adjustment methods for the inspection frequency, and each adjustment method has a different adjustment range for the inspection frequency. The inspection frequency is positively correlated with the difference of the second change index, which is the difference between the data change index and the second preset change index.

[0104] In this embodiment of the invention, the patrol frequency can be adjusted from once a day to once a week.

[0105] Specifically, the response strategy is determined based on the comprehensive data change rate of a single segment in three inspections after increasing the inspection frequency. If the comprehensive data change rate is less than the first preset change rate of 0.02, the response strategy is to downgrade the storage level.

[0106] If the rate of change of the comprehensive data is greater than or equal to the first preset rate of change and less than the second preset rate of change by 0.1, then the response strategy is determined to be to activate a low-level warning. The strategy of the low-level warning is to remind the operation and maintenance personnel to pay attention to the data changes of the road segment and to arrange regular inspections.

[0107] If the rate of change of the comprehensive data is greater than or equal to the second preset rate of change, the response strategy is determined to be to activate an advanced early warning. The strategy of the advanced early warning is to call maintenance personnel to immediately go to the abnormal section for detailed inspection and to carry out emergency repairs or maintenance.

[0108] Specifically, the comprehensive data change rate is the average of the ratios of the change rates of each data point to a unit of time (h).

[0109] In this embodiment of the invention, the first preset change rate is 0.02% / h, and the second preset change rate is 0.1. However, the above values ​​are not limited to these values, and those skilled in the art can adjust the above values ​​according to actual needs.

[0110] Specifically, downgrading a storage level involves incrementing the storage level number by one, and for level 3 storage, the downgrading strategy involves increasing the compression ratio to 70%.

[0111] The technical solution of the present invention has been described above with reference to 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 can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0112] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for underground control, inspection, maintenance, and early warning based on unmanned aerial vehicles (UAVs), characterized in that, include: Step S1: Use a drone to inspect the underground passage with initial parameters, collect pipeline data and environmental data in the passage and store them. The pipeline data includes pipeline distribution density and corrosion area at pipeline connections. The environmental data includes humidity, passage crack size, water depth and vibration frequency. Step S2: Divide the patrol route into several sections with different risk levels based on the comprehensive risk value of the types of risk events in the passage and determine the parameters for the drone patrol, wherein the parameters include speed and clarity; Step S3: Compare the current data in a single road segment with historical data to determine the data change rate, and determine the storage level of the single road segment data based on the data change rate; Step S4: Determine the data storage optimization strategy for a single road segment based on the data change index after a preset number of inspections. This strategy includes reducing the data comparison frequency, adjusting the inspection parameters, and increasing the inspection frequency. If the data change index is less than the first preset change index, then the data storage optimization strategy for a single road segment is determined to be to reduce the data comparison frequency based on the difference between the first preset change index and the data change index. If the data change index is greater than or equal to the first preset change index and less than the second preset change index, then the data storage optimization strategy for a single road segment is determined to be to adjust the patrol parameters according to the risk proportion characteristic value of the risk event type to which the changed data belongs. If the data change index is greater than or equal to the second preset change index, the data storage optimization strategy for a single road segment is determined to be to upgrade the storage level of the road segment and increase the patrol frequency according to the difference between the data change index and the second preset change index. The data change index is the variance of the rate of change of each data point. Step S5: Determine the response strategy based on the rate of change of comprehensive data of a single road segment after several inspections following the increase in inspection frequency.

2. The method for underground control, inspection, maintenance, and early warning based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The risk event types include one or more of the following: The pipeline distribution density is greater than the preset density; Alternatively, the area of ​​corrosion at the pipeline connection is greater than the preset area ratio; Alternatively, the humidity inside the channel is higher than the preset humidity; Alternatively, the channel crack size is larger than the preset size; Or, the water depth is greater than the preset depth; Alternatively, the channel vibration frequency is greater than the preset frequency.

3. The method for underground control, inspection, maintenance, and early warning based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The risk level of a road segment is determined based on the comprehensive risk value of the types of risk events within the passage, and the parameters for drone patrol are determined accordingly. If the comprehensive risk value is less than or equal to a first preset risk value, the road segment is determined to be a low-risk road segment, and the speed of the drone patrol is determined to be a first preset speed, and the resolution is determined to be a first preset resolution. If the overall risk value is greater than the first preset risk value and less than or equal to the second preset risk value, then the road segment is determined to be a medium-risk road segment, and the speed of the drone patrol is determined to be the second preset speed and the clarity is determined to be the second preset clarity. If the overall risk value is greater than the second preset risk value, the road segment is determined to be a high-risk road segment, and the speed of the drone patrol is determined to be a third preset speed and the resolution to be a third preset resolution. The overall risk value is determined by the number of risk event types and their weighting coefficients.

4. The method for underground control, inspection, maintenance, and early warning based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The storage level of a single segment of data is determined based on the data change rate. If the data change rate is less than the first preset change rate, the storage level of the single segment of data is determined to be level three, and level three storage is high-compression storage. If the data change rate is greater than or equal to the first preset change rate and less than the second preset change rate, then the storage level of the single-segment data is determined to be level two, and level two storage is low-compression storage. If the data change rate is greater than or equal to the second preset change rate, then the storage level of the single-segment data is determined to be Level 1, and Level 1 storage is lossless storage.

5. The method for underground control, inspection, maintenance, and early warning based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The data comparison frequency is positively correlated with the difference in the change index, wherein the difference in the change index is the difference between the first preset change index and the data change index.

6. The method for underground control, inspection, maintenance, and early warning based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The patrol parameters are adjusted according to the risk proportion characteristic value. If the risk proportion characteristic value is less than the preset risk proportion characteristic value, the patrol speed is reduced according to the difference between the preset risk proportion characteristic value and the risk proportion characteristic value. If the risk proportion feature value is greater than or equal to the preset risk proportion feature value, then under the condition of low patrol speed, the patrol clarity is increased according to the difference between the risk proportion feature value and the preset risk proportion feature value.

7. The method for underground control, inspection, maintenance, and early warning based on unmanned aerial vehicles (UAVs) according to claim 6, characterized in that, The risk proportion feature value is determined based on the proportion of risk event types to which the change data belongs, wherein the proportion includes the proportion of Category I and Category II; The risk event types mentioned above include pipeline distribution density greater than preset density, humidity in the channel greater than preset humidity, and channel vibration frequency greater than preset frequency. The risk event types of the second category include the proportion of rust area at pipeline connections being greater than the preset proportion, the size of channel cracks being greater than the preset size, and the depth of water accumulation being greater than the preset depth.

8. The method for underground control, inspection, maintenance, and early warning based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, There are several adjustment methods for the patrol frequency, and each adjustment method has a different adjustment range for the patrol frequency.

9. The method for underground control, inspection, maintenance, and early warning based on unmanned aerial vehicles (UAVs) according to claim 1, characterized in that, The response strategy is determined based on the comprehensive data change rate of a single segment after several inspections with increased inspection frequency. If the comprehensive data change rate is less than a first preset change rate, the response strategy is to downgrade the storage level. If the rate of change of the comprehensive data is greater than or equal to the first preset rate of change and less than the second preset rate of change, the response strategy is determined to be to activate a low-level warning. The strategy of the low-level warning is to remind the operation and maintenance personnel to pay attention to the data changes of the road segment and to arrange regular inspections. If the rate of change of the comprehensive data is greater than or equal to the second preset rate of change, the response strategy is determined to be to activate an advanced early warning. The strategy of the advanced early warning is to call maintenance personnel to immediately go to the abnormal section for detailed inspection and to carry out emergency repairs or maintenance.

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