An intelligent inspection system for highway equipment

Abstract

The present invention discloses an intelligent inspection system for highway equipment, which belongs to the high-speed intelligent maintenance technology. The system first obtains a parameter Z that can reflect the aging speed of the highway through the historical vehicle information and temperature and humidity information running on the highway, and integrates the parameter Z with the maintenance density of each road section, and then further distributes the inspection times of each monitoring section on the highway on this basis. The present invention also analyzes the traffic flow information of each monitoring section in different time periods, and reasonably selects the scheme with the smallest traffic flow or the smallest overall traffic flow impact as the final inspection scheme, ensuring that the unmanned aerial vehicle inspection unit can reduce the obstruction of the road surface by the road vehicles when performing the inspection work, so that the unmanned aerial vehicle can collect and obtain the complete road surface information of each monitoring section as much as possible, avoiding the damaged part of the road surface being blocked by the vehicle and unable to be found, which affects the timely implementation of the road surface maintenance.

Description

Technical Field

[0001] The present invention belongs to the technical field of high-speed intelligent maintenance, and specifically relates to an intelligent inspection system for highway equipment. Background Art

[0002] Highway equipment inspection refers to the process of regular or irregular inspection of highways and their ancillary facilities such as toll booths, monitoring equipment, lighting systems, communication facilities, etc. to evaluate their operating conditions and safety. Inspections can promptly detect equipment failures and damages, prevent small problems from turning into big problems, thereby ensuring traffic safety, reducing maintenance costs, and extending the service life of equipment.

[0003] In the prior art, highway equipment inspection is performed by staff driving work vehicles to inspect highway equipment along the line one by one. This method is inefficient, and the acquisition of road surface information is greatly affected by vehicles traveling on the road, which affects the integrity of information acquisition and is not conducive to the timely discovery of road damage. Unmanned aerial vehicles are introduced in the prior art to acquire road surface information, which significantly improves efficiency. However, when acquiring road surface information through drones, there is still the influence of road vehicle occlusion, and the inspection frequency of highways is determined by experience, lacking scientific guidance. In order to solve the above problems, a method that can reasonably determine a highway road surface inspection plan is provided, and the present invention provides the following technical solutions. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent inspection system for highway equipment to solve the problems of low efficiency and susceptibility to obstruction by road vehicles in the prior art highway inspection.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An intelligent inspection system for highway equipment, comprising:

[0007] Camera units are distributed in each monitoring section and are used to collect image information of each monitoring section;

[0008] Temperature and humidity sensing units are distributed in each monitoring section of the highway to collect temperature and humidity information of each monitoring section;

[0009] The drone inspection unit includes a drone and a high-definition camera mounted on the drone. The drone is equipped with a high-definition camera and flies along the highway. The high-definition camera collects video image information of the highway road surface along the route and transmits it to the inspection control center.

[0010] The inspection control center is used to analyze the image information collected by the camera unit and the temperature and humidity information collected by the temperature and humidity sensors, so as to adjust the inspection frequency, specific inspection time and the number of drone inspection units participating in the inspection of each monitoring section.

[0011] Furthermore, the method for the inspection control center to obtain the inspection frequency of each monitoring area includes the following steps:

[0012] The first step is to divide the highway into several monitoring sections, and the same monitoring section has the same nature;

[0013] The second step is to obtain the flow information of various types of vehicles in each lane in the monitoring section;

[0014] The third step is to take the preset time t1 as a cycle, and then calculate the comprehensive aging coefficient Z1 of the corresponding monitoring section in one cycle according to the formula Z1=α1*S1+α2*S2+...+αn*Sn;

[0015] Where αi is the preset vibration influence coefficient corresponding to a vehicle type, and Si is the average vehicle flow corresponding to a corresponding vehicle type;

[0016] 1≤i≤n, n is the number of vehicle types;

[0017] Then, the sub-aging coefficient Z2j corresponding to each lane in the monitoring section in one cycle is calculated according to the formula Z2j=α1*S1j+α2*S2j+...+αn*Snj;

[0018] Sij is represented by the average flow of a corresponding type of vehicle on each corresponding lane;

[0019] 1≤j≤m, m is the number of lanes in the corresponding monitoring section;

[0020] Then according to the formula Z=θ3*(θ1*Z1+θ2*Z2j max ) calculates the aging detection coefficient Z of the corresponding monitoring section; wherein θ1 and θ2 are preset coefficients, and θ1+θ2=1, θ3 is a preset coefficient, and θ3 is related to the temperature and humidity of the corresponding monitoring section. The larger the average temperature and average humidity, the larger the corresponding θ3 value; Z2j max is the corresponding maximum sub-aging coefficient in the corresponding monitoring section;

[0021] Step 4: Calculate the aging detection coefficient Zx of each monitoring section in sequence according to the above steps; where 1≤x≤K, K is the number of monitoring sections;

[0022] In the fifth step, the inspection frequency is determined for each monitoring section in such a manner that the larger the aging detection coefficient Zx is, the larger the detection frequency of the corresponding monitoring section is.

[0023] Furthermore, the method for determining the detection frequency of each monitoring section is:

[0024] Obtain the number of maintenance times W of each monitoring section in the same preset time period t1 in the past;

[0025] With the aging detection coefficient Z as the horizontal axis and λ=W / L as the vertical axis, a rectangular coordinate system is established to fit the relationship curve between Z and W / L; where W is the number of maintenance times and L is the length of the corresponding monitoring section;

[0026] Obtain λx corresponding to each aging detection coefficient Zx according to the relationship curve between Z and λ;

[0027] According to the formula Calculate the number of inspections μx of each monitoring section within a cycle t1;

[0028] Q is a preset distance value that is less than the maximum distance Qmax that can be inspected within one cycle, and Lx is the mileage of the corresponding monitoring section.

[0029] Furthermore, the method for adjusting the specific inspection time of each monitoring section includes:

[0030] A1. Based on the data within a period, divide the length of a day into several time periods, and select several consecutive time periods as sample time periods;

[0031] A2. For a monitoring section, obtain the average flow corresponding to the same sample period of n days in the t1 period;

[0032] A3. Calculate the average flow rate of the corresponding monitoring section in each sample period in turn, and sort them in order from small to large average flow rate;

[0033] A4. When a monitoring station is responsible for monitoring only one monitoring section, the sample period with the smallest average flow rate is selected as the specific inspection period for the corresponding monitoring section;

[0034] A5. When a monitoring station is responsible for monitoring two or more consecutive monitoring sections, the sample time period numbers of the average flow from small to large corresponding to the corresponding monitoring sections are obtained;

[0035] Assuming that the starting sample period number of the inspection of the monitoring section corresponding to the starting point is 1, the sample period number of other monitoring sections is obtained when the drone inspection unit arrives at other continuous monitoring sections according to the inspection speed and the mileage of each monitoring section; then the sum of the serial numbers corresponding to the continuous monitoring sections is calculated;

[0036] The assumed starting points are adjusted in sequence to correspond to the start sample time period serial numbers of the monitoring section inspection, the sum of the corresponding serial numbers is calculated in sequence, and the scheme corresponding to the sum of the serial numbers is used as the final scheme.

[0037] Furthermore, the method for adjusting the number of drones inspecting each monitoring section includes:

[0038] B1. Obtain the average vehicle flow rate of each monitoring section corresponding to a monitoring site during the inspection time and the number of lanes in each monitoring section, and then further calculate the average vehicle flow rate per lane H of each monitoring section during the inspection time, and obtain the maximum value Hmax;

[0039] B2. Define several continuous value ranges within the range of 0 to Hy;

[0040] Where Hy means that when the average traffic volume of the lane reaches Hy, no matter how many drones are dispatched, they cannot obtain complete image information of the corresponding lane when flying at a specific speed;

[0041] B3. Determine the number of drones corresponding to the numerical range of Hmax, and dispatch a corresponding number of drone inspection units to inspect each monitoring section corresponding to the detection site during the inspection time.

[0042] Beneficial effects of the present invention:

[0043] 1. The present invention first obtains a parameter Z that can reflect the aging speed of the highway through the historical vehicle information and temperature and humidity information running on the highway, and integrates the parameter Z with the maintenance density of each section. Then, on this basis, the number of inspections of each monitoring section on the highway is further allocated. Furthermore, the actual number of inspections of each monitoring section can be considered, and the allocation of the number of inspections can be further optimized, taking into account the impact of accidental factors caused by geographical location, foundation and other conditions of each monitoring section on the aging of the highway.

[0044] 2. The present invention analyzes the traffic flow information of each monitoring section in different time periods, and reasonably selects the plan with the smallest traffic flow or the smallest impact on the overall traffic flow as the final inspection plan, to ensure that the UAV inspection unit can reduce the obstruction of the road surface by vehicles on the road surface when performing inspection work, so that the UAV can collect and obtain complete road surface information of each monitoring section as much as possible, to avoid the damaged part of the road surface being blocked by vehicles and unable to be discovered, which affects the timely implementation of road maintenance.

[0045] 3. The present invention predicts the average flow rate of a single lane in each monitoring section during the inspection, and further adjusts the number of drone inspection units during the inspection based on the average flow rate of a single lane, thereby ensuring that complete road surface image information of each monitoring section can be obtained during one inspection, reducing the impact of vehicle density on subsequent image analysis, and ensuring the integrity of image acquisition. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention will be further described below in conjunction with the accompanying drawings.

[0047] Figure 1 This is a schematic diagram of the framework structure of a highway equipment intelligent inspection system.

[0048] Figure 2 It is a flowchart of the method for the inspection control center to obtain the inspection frequency of each monitoring area;

[0049] Figure 3 It is a flowchart of a method for adjusting the specific time for patrol inspection of each monitoring section. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Example 1

[0052] An intelligent inspection system for highway equipment, such as Figure 1 As shown, including:

[0053] Camera units are distributed in each monitoring section and are used to collect image information of each monitoring section;

[0054] It should be noted that the camera unit should be located away from special locations on the highway, such as curves with large arcs and near intersections, so as to ensure that the images collected by the camera unit can reflect the distribution level of vehicle types and numbers in each lane in the entire monitoring section;

[0055] Temperature and humidity sensing units are distributed in each monitoring section of the highway to collect temperature and humidity information of each monitoring section;

[0056] The drone inspection unit includes a drone and a high-definition camera mounted on the drone. When working, the drone is equipped with a high-definition camera and flies along the highway. The high-definition camera collects video image information of the highway pavement, guardrails and other facilities along the highway and transmits it to the inspection control center.

[0057] The inspection control center is used to analyze the image information collected by the camera unit and obtain the vehicle quantity information of various types of vehicles in each monitoring section;

[0058] It is also used to further analyze the image information collected by the camera unit and the temperature and humidity information collected by the temperature and humidity sensor, so as to adjust the inspection frequency of each monitoring section, the specific inspection time and the number of drone inspection units participating in the inspection.

[0059] Example 2

[0060] like Figure 2 As shown, the method for the inspection control center to obtain the inspection frequency of each monitoring area includes the following steps:

[0061] The first step is to segment the highway. Specifically, the highway is divided into several monitoring sections, and only the two ends of the same monitoring section are monitoring nodes.

[0062] The monitoring nodes include both ends of a bridge, both ends of a tunnel, intersections of roads, etc.;

[0063] In addition, when the monitoring section is too long, it can be reasonably divided into two or more monitoring sections as needed;

[0064] The second step is to collect image information of each monitoring section through the camera unit;

[0065] By analyzing the image information of each monitoring section, the traffic flow information of each lane in the monitoring section and the traffic flow information of various types of vehicles are obtained;

[0066] Vehicle types include sedans / compact cars, SUVs / off-road vehicles, MPVs / minivans, mid-sized trucks, large trucks / trucks, etc.;

[0067] The third step is to take the preset time t1 as a cycle, and then calculate the comprehensive aging coefficient Z1 of the corresponding monitoring section in one cycle according to the formula Z1=α1*S1+α2*S2+...+αn*Sn;

[0068] Where αi is the vibration influence coefficient corresponding to a type of vehicle, which is a preset value. Generally speaking, the heavier the vehicle, the larger the corresponding vibration influence coefficient. Si is the average traffic volume corresponding to a type of vehicle;

[0069] 1≤i≤n, n is the number of vehicle types;

[0070] Similarly, the sub-aging coefficient Z2j corresponding to each lane in the monitoring section in one cycle is calculated according to the formula Z2j=α1*S1j+α2*S2j+...+αn*Snj;

[0071] Where Sij represents the average flow of a corresponding type of vehicle on each corresponding lane;

[0072] 1≤j≤m, m is the number of lanes in the corresponding monitoring section;

[0073] Then according to the formula Z=θ3*(θ1*Z1+θ2*Z2j max ) Calculate the aging detection coefficient Z of the corresponding monitoring section;

[0074] Wherein θ1 and θ2 are both preset coefficients, and θ1+θ2=1, θ3 is a preset coefficient, and θ3 is related to the temperature and humidity of the corresponding monitoring section. The area with higher average temperature and average humidity has a larger corresponding θ3 value;

[0075] Specifically, in one embodiment, the calculation method of θ3 is θ3=γ1*average temperature+γ2*average humidity, where γ1 and γ2 are both preset proportional coefficients, such as γ1 is 0.7 and γ2 is 0.3; and the average temperature and average humidity are dimensionless during calculation, and the average humidity is calculated as absolute humidity.

[0076] ZGar max is the maximum sub-aging coefficient in the corresponding monitoring section.

[0077] Step 4: Calculate the aging detection coefficient Zx of each monitoring section in sequence according to the above steps; where 1≤x≤K, K is the number of monitoring sections;

[0078] According to the historical maintenance records of each monitoring section, the number of maintenance times W of each monitoring section in the same preset time period t1 in the past is obtained;

[0079] With the aging detection coefficient Z as the horizontal axis and λ=W / L as the vertical axis, a rectangular coordinate system is established to fit the relationship curve between Z and W / L; where W is the number of maintenance times and L is the length of the corresponding monitoring section;

[0080] Obtain the aging detection coefficient Z of each monitoring section, and then obtain the λx corresponding to each aging detection coefficient Zx according to the relationship curve between Z and λ;

[0081] According to the formula Calculate the number of inspections μx of each monitoring section within a cycle t1;

[0082] Wherein Q is a preset distance value less than the maximum distance Qmax that can be inspected within a cycle. In one embodiment of the present invention, Q is Qmax*80%, and Lx is the mileage of the corresponding monitoring section;

[0083] During execution, when the inspection number μx is not an integer, it is rounded up; and when μx is less than 1, it is taken as 1.

[0084] In one embodiment of the present invention, when the actual number of maintenance times Ws of a monitoring section in the past cycle is greater than Wy*σ, the actual number of inspection times μxs of the corresponding monitoring section is adjusted to μx*Ws / Wy;

[0085] Where Wy is the predicted maintenance times (actually the average maintenance times) obtained based on the relationship curve between Z and W / L; σ is a preset value greater than 1, such as 1.5;

[0086] The present invention first obtains a parameter Z that can reflect the aging speed of the highway through the historical vehicle information and temperature and humidity information running on the highway, and integrates the parameter Z with the maintenance density of each section. Then, on this basis, the inspection times of each monitoring section on the highway are further allocated. Furthermore, the actual inspection times of each monitoring section can be considered, and the allocation of inspection times can be further optimized, taking into account the impact of accidental factors caused by geographical location, foundation and other conditions of each monitoring section on the aging of the highway.

[0087] Example 3

[0088] like Figure 3 As shown, this embodiment further describes a method for adjusting the specific time for patrol inspection of each monitoring section based on the embodiment 2, which is as follows:

[0089] A1. Based on the data in one cycle (i.e., the time period of the past duration is t1), the duration of a day is divided into several time periods, and several consecutive time periods in which natural lighting can meet the video shooting requirements are selected as sample time periods;

[0090] A2. For a monitoring section, obtain the average flow corresponding to the same sample period of n days in the t1 period;

[0091] A3. Calculate the average flow rate of the corresponding monitoring section in each sample period in turn, and sort them in order from small to large average flow rate;

[0092] A4. When a monitoring station is responsible for monitoring only one monitoring section, the sample period with the smallest average flow rate is selected as the specific inspection period for the corresponding monitoring section;

[0093] A5. When a monitoring station is responsible for monitoring two or more consecutive monitoring sections, the sample time period serial numbers (in ascending order) of the average flow corresponding to the corresponding monitoring sections are obtained;

[0094] Assuming that the starting sample period number of the inspection of the monitoring section corresponding to the starting point is 1, the sample period number of other monitoring sections is obtained when the drone inspection unit arrives at other continuous monitoring sections according to the inspection speed and the mileage of each monitoring section; then the sum of the serial numbers corresponding to the continuous monitoring sections is calculated;

[0095] The assumed starting points are adjusted in sequence to correspond to the start sample time period serial numbers of the monitoring section inspection, the sum of the corresponding serial numbers is calculated in sequence, and the scheme corresponding to the sum of the serial numbers is used as the final scheme.

[0096] The present invention analyzes the traffic flow information of each monitoring section in different time periods, and reasonably selects the plan with the smallest traffic flow or the smallest impact on the overall traffic flow as the final inspection plan, to ensure that the UAV inspection unit can reduce the obstruction of the road surface by vehicles on the road surface when performing inspection work, so that the UAV can collect and obtain complete road surface information of each monitoring section as much as possible, to avoid the damaged part of the road surface being blocked by vehicles and unable to be discovered, which affects the timely implementation of road surface maintenance.

[0097] Example 4

[0098] Based on Example 3, this example further describes a method for adjusting the number of drones inspecting each monitoring section, as follows:

[0099] B1. Based on the specific inspection time determined in Example 3, the average vehicle flow rate of each monitoring section corresponding to a monitoring station during the inspection time and the number of lanes in each monitoring section are obtained, and then the average vehicle flow rate per lane H of each monitoring section during the inspection time is further calculated, and the maximum value Hmax is obtained;

[0100] B2. Define several continuous value ranges within the range of 0 to Hy;

[0101] Where Hy means that when the average traffic volume of the lane reaches Hy, no matter how many drones are dispatched, they cannot obtain complete image information of the corresponding lane when flying at a specific speed;

[0102] Each numerical range corresponds to a number of drones, and the larger the value, the larger the corresponding number of drones;

[0103] B3. Determine the number of drones corresponding to the numerical range of Hmax, and dispatch a corresponding number of drone inspection units to inspect each monitoring section corresponding to the detection station during the inspection time. Two adjacent drones maintain a certain distance along the highway to avoid highly similar images collected by the two drones at the same time.

[0104] The present invention predicts the average flow rate of a single lane in each monitoring section during the inspection, and further adjusts the number of drone inspection units during the inspection according to the average flow rate of the single lane, thereby ensuring that complete road surface image information of each monitoring section can be obtained during one inspection process, reducing the impact of vehicle density on subsequent image analysis, and ensuring the integrity of image acquisition.

[0105] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

Claims

1. A highway equipment intelligent inspection system, characterized in that: include: Camera units are distributed in each monitoring section and are used to collect image information of each monitoring section; Temperature and humidity sensing units are distributed in each monitoring section of the highway to collect temperature and humidity information of each monitoring section; The drone inspection unit includes a drone and a high-definition camera mounted on the drone. The drone is equipped with a high-definition camera and flies along the highway. The high-definition camera collects video image information of the highway road surface along the route and transmits it to the inspection control center. The inspection control center is used to analyze the image information collected by the camera unit and the temperature and humidity information collected by the temperature and humidity sensor, so as to adjust the inspection frequency, specific inspection time and the number of drone inspection units participating in the inspection in each monitoring section; The method for the inspection control center to obtain the inspection frequency of each monitoring area includes the following steps: The first step is to divide the highway into several monitoring sections, and the same monitoring section has the same nature; The second step is to obtain the flow information of various types of vehicles in each lane in the monitoring section; The third step is to take the preset time t1 as a cycle, and then calculate the comprehensive aging coefficient Z1 of the corresponding monitoring section in one cycle according to the formula Z1=α1*S1+α2*S2+...+αn*Sn; Where αi is the preset vibration influence coefficient corresponding to a vehicle type, and Si is the average vehicle flow corresponding to a corresponding vehicle type; 1≤i≤n, n is the number of vehicle types; Then, the sub-aging coefficient Z2j corresponding to each lane in the monitoring section in one cycle is calculated according to the formula Z2j=α1*S1j+α2*S2j+...+αn*Snj; Sij is represented by the average flow of a corresponding type of vehicle on each corresponding lane; 1≤j≤m, m is the number of lanes in the corresponding monitoring section; Then according to the formula Z=θ3*(θ1*Z1+θ2*Z2j max ) calculates the aging detection coefficient Z of the corresponding monitoring section; wherein θ1 and θ2 are preset coefficients, and θ1+θ2=1, θ3 is a preset coefficient, and θ3 is related to the temperature and humidity of the corresponding monitoring section. The larger the average temperature and average humidity, the larger the corresponding θ3 value; Z2j max is the corresponding maximum sub-aging coefficient in the corresponding monitoring section; Step 4: Calculate the aging detection coefficient Zx of each monitoring section in sequence according to the above steps; where 1≤x≤K, K is the number of monitoring sections; In the fifth step, the inspection frequency is determined for each monitoring section in such a manner that the larger the aging detection coefficient Zx is, the larger the detection frequency of the corresponding monitoring section is.

2. The intelligent inspection system for highway equipment according to claim 1 is characterized in that: The method for determining the detection frequency of each monitoring section is: Obtain the number of maintenance times W of each monitoring section in the same preset time period t1 in the past; With the aging detection coefficient Z as the horizontal axis and λ=W / L as the vertical axis, a rectangular coordinate system is established to fit the relationship curve between Z and W / L; where W is the number of maintenance times and L is the length of the corresponding monitoring section; Obtain λx corresponding to each aging detection coefficient Zx according to the relationship curve between Z and λ; According to the formula Calculate the number of inspections μx of each monitoring section within a cycle t1; Q is a preset distance value that is less than the maximum distance Qmax that can be inspected within one cycle, and Lx is the mileage of the corresponding monitoring section.

3. The intelligent inspection system for highway equipment according to claim 2 is characterized in that: The value of Q is Qmax*80%.

4. The intelligent inspection system for highway equipment according to claim 2 is characterized in that: During execution, when the inspection number μx is not an integer, it is rounded up; and when μx is less than 1, μx takes the value of 1.

5. The intelligent inspection system for highway equipment according to claim 2 is characterized in that: When the actual maintenance times Ws of a monitoring section in the past cycle is greater than Wy*σ, the actual inspection times μxs of the corresponding monitoring section is adjusted to μx*Ws / Wy; Where Wy is the predicted maintenance times obtained based on the relationship curve between Z and W / L; σ is a preset value greater than 1.

6. The intelligent inspection system for highway equipment according to claim 5 is characterized in that: The value of σ is 1.

5.

7. The intelligent inspection system for highway equipment according to claim 2 is characterized in that: Methods for adjusting the specific inspection time of each monitoring section include: A1. Based on the data within a period, divide the length of a day into several time periods, and select several consecutive time periods as sample time periods; A2. For a monitoring section, obtain the average flow corresponding to the same sample period of n days in the t1 period; A3. Calculate the average flow rate of the corresponding monitoring section in each sample period, and sort them in order from the smallest to the largest average flow rate; A4. When a monitoring station is responsible for monitoring only one monitoring section, the sample period with the smallest average flow rate is selected as the specific inspection period for the corresponding monitoring section; A5. When a monitoring station is responsible for monitoring two or more consecutive monitoring sections, the sample time period numbers of the average flow from small to large corresponding to the corresponding monitoring sections are obtained; Assuming that the starting sample period number of the inspection of the monitoring section corresponding to the starting point is 1, the sample period number of other monitoring sections is obtained when the drone inspection unit arrives at other continuous monitoring sections according to the inspection speed and the mileage of each monitoring section; then the sum of the serial numbers corresponding to the continuous monitoring sections is calculated; The assumed starting points are adjusted in sequence to correspond to the start sample time period serial numbers of the monitoring section inspection, the sum of the corresponding serial numbers is calculated in sequence, and the scheme corresponding to the sum of the serial numbers is used as the final scheme.

8. The intelligent inspection system for highway equipment according to claim 7 is characterized in that: Methods for adjusting the number of drones inspecting each monitoring section include: B1. Obtain the average vehicle flow rate of each monitoring section corresponding to a monitoring site during the inspection time and the number of lanes in each monitoring section, and then further calculate the average vehicle flow rate per lane H of each monitoring section during the inspection time, and obtain the maximum value Hmax; B2. Define several continuous value ranges within the range of 0 to Hy; Where Hy means that when the average traffic volume of the lane reaches Hy, no matter how many drones are dispatched, they cannot obtain complete image information of the corresponding lane when flying at a specific speed; B3. Determine the number of drones corresponding to the numerical range of Hmax, and dispatch a corresponding number of drone inspection units to inspect each monitoring section corresponding to the detection site during the inspection time.

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