A control method of a tunnel inspection robot

Abstract

The application discloses a control method of a tunnel inspection robot, which comprises the following steps: the inspection robot performs multiple traffic flow data sampling in a current sampling period to form a traffic section average speed sample set, a traffic section occupancy rate sample set, an average speed difference sample set between lanes and an occupancy rate difference sample set between lanes; according to the sampling time sequence corresponding to the sample extreme value in each sample set, the road section average speed influence component, the road section occupancy rate influence component, the lane speed difference influence component and the lane occupancy rate influence component in the current sampling period are respectively obtained; and then the running direction and the running speed of the inspection robot in the next sampling period can be determined. The control method can determine the running speed and the running direction of the inspection robot in the next sampling period simultaneously, realizes self-help tracking and continuous monitoring on a road section with a safety hidden danger or a road section with a traffic accident in a tunnel.

Description

Technical Field

[0001] This invention relates to the field of intelligent traffic control, specifically to a control method for a tunnel inspection robot, used to control the operating speed and direction of the inspection robot inside the tunnel. Background Technology

[0002] The inspection work inside the tunnel mainly consists of drainage inspection, power supply inspection, hazardous materials inspection, equipment maintenance inspection, and traffic safety inspection. Among these, traffic safety inspection involves assessing the traffic flow status within the tunnel, searching for and monitoring sections with safety risks or where accidents have occurred, and promptly reporting these to the control center. Therefore, traffic safety inspection is a fundamental and crucial guarantee for driving safety within the tunnel. Currently, tunnel inspections are primarily conducted manually. However, due to the generally confined and enclosed space within tunnels, resulting in poor visibility, high vehicle speeds, and poor air quality, inspection personnel face safety risks, low efficiency, and communication difficulties. Therefore, manual inspections are gradually being replaced by robotic inspections. Inspection robots are typically fixed and operate on tracks at the tunnel ceiling, using onboard cameras to capture images of traffic flow within the tunnel, thereby identifying sections with driving safety risks or where accidents have occurred. Inspection robots are easy to operate, have controllable inspection speeds, and offer higher safety compared to manual inspections in the tunnel environment. Furthermore, robot inspection is unaffected by subjective or environmental factors, boasts high detection accuracy and consistency, and offers all-weather, high-efficiency inspection capabilities.

[0003] Most tunnel inspection robots currently operate on tracks at a preset speed, and they generally do not adjust their inspection speed according to whether the section of road being inspected is congested. Patent application number 202210224890.9 discloses a method for controlling the inspection speed of a tunnel robot for traffic safety. This method processes traffic flow data for the current operating interval of the robot and combines it with historical accident data of the road segment to determine the inspection speed of the robot for the next operating interval, thereby controlling the robot's inspection speed. Although this control method adjusts the robot's inspection speed according to the traffic flow status in the tunnel, it is limited to controlling the magnitude of the robot's inspection speed and cannot adjust the robot's inspection direction. In particular, when a severe congestion or traffic accident occurs in a certain section of the tunnel, the robot using this control method may first slow down its inspection speed. When the robot passes the severely congested or accident-prone section, the robot's camera can no longer capture the congested vehicles, so the robot will assume that the traffic is smooth and will accelerate again, ignoring the severely congested or accident-prone section. Therefore, this method cannot achieve autonomous tracking and continuous monitoring of severely congested or accident-prone sections in the tunnel. Summary of the Invention

[0004] To overcome the problems of existing technologies, this invention proposes a control method for a tunnel inspection robot. This method can control the running speed and direction of the tunnel robot according to the traffic flow status in the tunnel, so as to realize autonomous tracking and continuous monitoring of road sections with safety hazards in the tunnel.

[0005] The technical solution of the present invention is as follows:

[0006] A control method for a tunnel inspection robot, wherein the tunnel has a traffic section consisting of several lanes and a track for the inspection robot to run on, several vehicles travel on the traffic section, and the inspection robot collects traffic flow data within the tunnel, the method comprising:

[0007] The inspection robot performs multiple traffic flow data samplings within the current sampling period and records the position of the inspection robot at the end of the current sampling period, forming a traffic segment average speed sample set, a traffic segment occupancy sample set, a lane average speed difference sample set, and a lane occupancy difference sample set that contain samples corresponding to different sampling time sequences within the current sampling period.

[0008] Based on the sampling time sequence corresponding to the extreme values ​​of each sample set, the influence components of average speed of road segment, road segment occupancy rate, lane speed difference, and lane occupancy rate within the current sampling period are obtained respectively.

[0009] Based on the influence components of average road speed, road occupancy, lane speed difference, and lane occupancy corresponding to the current sampling period, the operating direction and speed of the inspection robot in the next sampling period are determined.

[0010] Furthermore, the traffic flow data includes vehicle type, speed, and lane information; wherein, in the k-th sampling time series, the type of the j-th vehicle in the i-th lane is p. k,i,j The speed is v k,i,j ,in,

[0011] Furthermore, the average speed sample set of the traffic segment is A = [v1,...,v...]. k ,...,v N ]; where v1,...,v k ,...,v N These represent the average speeds of traffic segments in the sampling time series of the 1st, ..., 1kth, ..., 1Nth sampling periods; where... v k,i,j Let m be the speed of the j-th vehicle in the i-th lane at the k-th sampling time sequence, m be the total number of lanes, and n be the speed of the j-th vehicle in the i-th lane. k,i This represents the number of vehicles in the i-th lane during the k-th sampling time series.

[0012] The traffic segment occupancy sample set B = [o1,...,o] k ,...,o N ]; where o1,...,o k ,...,o N These represent the traffic segment occupancy rates under the sampling time series of the 1st, ..., 1kth, ..., 1Nth sampling periods, where... In the formula o k,i Let i be the lane occupancy rate of lane i. l t Standard length for trucks; l p L is the standard length of the bus; L is the data collection range of the inspection robot, L = 2Htanα; H is the vertical distance between the inspection robot and the traffic section; α is half of the shooting angle of the inspection robot's camera;

[0013] The average speed difference sample set between lanes C = [v d,1 ,...,v d,k ,...,v d,N ]; where v d,1 ,...,v d,k ,...,v d,N Let be the average speed difference between lanes in the sampling time series of the 1st, ..., 1kth, ..., 1Nth sampling times, where In the formula,

[0014] The sample set of lane occupancy differences D = [o d,1 ,...,o d,k ,...,o d,N ]; where o d,1 ,...,o d,k ,...,o d,N Let represent the lane occupancy differences in the sampling time series of the 1st, ..., 1kth, ..., 1Nth sampling periods, where o d,k The difference in lane occupancy is represented by the k-th sampling time sequence.

[0015] Furthermore, the average speed influence component of the road segment includes a speed influence component and a direction influence component, which are obtained as follows:

[0016] For the Kth sampling period, let the sampling time sequence of the samples be denoted as 1 to N. Then the sampling time sequences corresponding to the minimum and maximum values ​​of the average speed samples in the traffic segment sample set are k and N, respectively. v,min and k v,max ;

[0017] If k v,max <kv,min <N or 1 < k v,min <k v,max Then the velocity-affected component Directional influence component D v =-1;

[0018] Otherwise, the speed-affected component Where f v The average speed influence coefficient for the road segment, and the directional influence component.

[0019] Furthermore, the road segment occupancy influence components include speed influence components and direction influence components, which are obtained as follows:

[0020] For the Kth sampling period, let the sampling time sequence of the samples be denoted as 1 to N. Then the sampling time sequences corresponding to the minimum and maximum values ​​of the traffic segment occupancy sample set are respectively k o,min and k o,max ;

[0021] If k o,min <k o,max <N or 1 < k o,max <k o,min Then the velocity-affected component Directional influence component D o =-1;

[0022] Otherwise, the speed-affected component Where f o The road segment occupancy influence coefficient, directional influence component

[0023] Furthermore, the lane speed difference influence component includes a speed influence component and a direction influence component, which are obtained as follows:

[0024] For the Kth sampling period, let the sampling time sequence of the samples be denoted as 1 to N. Then, the sampling time sequences corresponding to the minimum and maximum values ​​of the average speed difference between lanes in the sample set are respectively... and

[0025] like or The velocity-affected component Direction affects components

[0026] Otherwise, the speed-affected component in The influence coefficient for lane speed difference, and the influence component for direction.

[0027] Furthermore, the lane occupancy influence component includes a speed influence component and a direction influence component, which are obtained as follows:

[0028] For the Kth sampling period, let the sampling time sequence of the samples be denoted as 1 to N. Then, the sampling time sequences corresponding to the minimum and maximum values ​​of the lane occupancy difference sample set are respectively... and

[0029] like or The velocity-affected component Direction affects components

[0030] Otherwise, the speed-affected component in The influence coefficient of lane occupancy difference, and the directional influence component.

[0031] Furthermore, for the Kth sampling period, the speed influence component in the average speed influence component of the road segment is denoted as V. v The directional influence component is D v The speed component in the road segment occupancy influence component is V. O The directional influence component is D O The speed influence component in the lane speed difference component is: The directional influence component is The speed component in the lane occupancy influence component is: The directional influence component is

[0032] The recommended speed for the (K+1)th sampling period

[0033] In the formula, w v w o , and These are the weighting coefficients for the component affected by average speed of road segment, the component affected by road segment occupancy, the component affected by lane speed difference, and the component affected by lane occupancy, respectively.

[0034] For the (K+1)th sampling period, the direction of movement of the inspection robot The operating speed V of the inspection robot K+1 =min(V K+1,rec V K+1,lim ),

[0035] In the formula, V K+1，lim The inspection robot's speed is limited during the K+1th sampling period;

[0036]

[0037] In the formula, D is the tunnel length; K and D K+1 These represent the running directions of the inspection robot during the Kth and K+1th sampling periods, respectively; Nt is the sampling period length; X K This represents the position of the inspection robot at the end of the Kth sampling period.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] This invention provides a control method for a tunnel inspection robot. This control method can simultaneously determine the operating speed and direction of the inspection robot in the next sampling cycle by sampling traffic flow data multiple times in the current sampling cycle and performing time-series analysis and calculation on the sampling results. This enables autonomous tracking and continuous monitoring of road sections with safety hazards or where traffic accidents have occurred within the tunnel.

[0040] The control method of this invention performs time-series analysis on the collected traffic flow data samples and comprehensively considers the impact of average speed of traffic segments, traffic segment occupancy, average speed difference between lanes, and occupancy difference between lanes on traffic operation safety. This provides scientific and reasonable judgment and decision support for the inspection robot to determine its inspection targets, enabling it to more efficiently focus on tunnel congestion or potentially dangerous road sections, thus ensuring tunnel traffic safety. Attached Figure Description

[0041] Figure 1 The flowchart shows the control method for the tunnel inspection robot.

[0042] Figure 2 This is a diagram illustrating the operation of the inspection robot in the embodiment. Detailed Implementation

[0043] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] Example 1:

[0045] The present invention provides a control method for a tunnel inspection robot, such as... Figure 2 As shown, the tunnel contains a traffic section consisting of several lanes and a track for inspection robots. Several vehicles travel in each lane of the traffic section. The inspection robots collect traffic flow data within the tunnel, including vehicle type, speed, lane, and location information. Figure 1 As shown, the control method of the present invention includes:

[0046] S1. The inspection robot performs multiple traffic flow data samplings within the current sampling period and records the robot's position at the end of the current sampling period, forming a traffic segment average speed sample set, a traffic segment occupancy sample set, a lane average speed difference sample set, and a lane occupancy difference sample set containing samples from different sampling time sequences within the current sampling period. Among these, the traffic segment average speed sample set includes traffic segment average speed samples from different sampling time sequences, the traffic segment occupancy sample set includes traffic segment occupancy samples from different sampling time sequences, the lane average speed difference sample set includes lane average speed difference samples from different sampling time sequences, and the lane occupancy difference sample set includes lane occupancy difference samples from different sampling time sequences.

[0047] S2. Based on the sampling time sequence corresponding to the extreme values ​​of samples in each sample set, obtain the road segment average speed influence component, road segment occupancy influence component, lane speed difference influence component, and lane occupancy influence component within the current sampling period.

[0048] S3. Based on the road segment average speed influence component, road segment occupancy influence component, lane speed difference influence component, and lane occupancy influence component corresponding to the current sampling period, determine the running direction and running speed of the inspection robot in the next sampling period.

[0049] This control method can simultaneously determine the operating speed and direction of the inspection robot in the next sampling cycle by sampling traffic flow data multiple times in the current sampling cycle and performing time-series analysis and calculation on the sampling results. This enables autonomous tracking and continuous monitoring of road sections with safety hazards or where traffic accidents have occurred within the tunnel.

[0050] Example 2:

[0051] This embodiment, based on Embodiment 1, is further designed in that: the traffic flow data collected by the inspection robot in this example includes vehicle type, speed, lane, and location information; wherein, the type of the j-th vehicle in the i-th lane at the k-th sampling time sequence is p. k,i,j The speed is v k,i,j ,in,

[0052] The resulting average speed sample set A = [v1,...,v] is formed on traffic segments. k ,...,v N ]; where v1,...,v k ,...,v N These represent the average speeds of traffic segments in the sampling time series of the 1st, ..., 1kth, ..., 1Nth sampling periods; where... v k,i,j Let m be the speed of the j-th vehicle in the i-th lane at the k-th sampling time sequence, m be the total number of lanes, and n be the speed of the j-th vehicle in the i-th lane.k,i This represents the number of vehicles in the i-th lane during the k-th sampling time series.

[0053] The resulting traffic segment occupancy sample set B = [o1,...,o] k ,...,o N ]; where o1,...,o k ,...,o N These represent the traffic segment occupancy rates under the sampling time series of the 1st, ..., 1kth, ..., 1Nth sampling periods, where... In the formula o k,i Let i be the lane occupancy rate of lane i. l t Standard length for trucks; l p L is the standard length of the bus; L is the data collection range of the inspection robot, L = 2Htanα; H is the vertical distance between the inspection robot and the traffic section; α is half of the shooting angle of the inspection robot's camera.

[0054] The resulting sample set of average speed differences between lanes, C = [v d,1 ,...,v d,k ,...,v d,N ]; where v d,1 ,...,v d,k ,...,v d,N Let be the average speed difference between lanes in the sampling time series of the 1st, ..., 1kth, ..., 1Nth sampling times, where In the formula,

[0055] The resulting sample set of lane occupancy differences D = [o d,1 ,...,o d,k ,...,o d,N ]; where o d,1 ,...,o d,k ,...,o d,N Let represent the lane occupancy differences in the sampling time series of the 1st, ..., 1kth, ..., 1Nth sampling periods, where o d,k The difference in lane occupancy is represented by the k-th sampling time sequence.

[0056] Example 3:

[0057] This embodiment, based on Embodiment 1 or Embodiment 2, is further designed in that: the average speed influence component of the road segment in this example includes a speed influence component and a direction influence component, which are obtained as follows:

[0058] For the Kth sampling period, let the sampling time sequence of the samples be denoted as 1 to N. Then the sampling time sequences corresponding to the minimum and maximum values ​​of the average speed samples in the traffic segment sample set are k and N, respectively. v,min and k v,max ;

[0059] If k v,max <k v,min <N or 1 < k v,min <k v,max Then the velocity-affected component Directional influence component D v =-1;

[0060] Otherwise, the speed-affected component Where f v This is the average speed influence coefficient for the road segment, which falls within the recommended range of 0.1 to 0.5 based on empirical thresholds. The directional influence component...

[0061] The components affecting road segment occupancy include speed and direction components, which are obtained as follows:

[0062] For the Kth sampling period, let the sampling time sequence of the samples be denoted as 1 to N. Then the sampling time sequences corresponding to the minimum and maximum values ​​of the traffic segment occupancy sample set are respectively k o,min and k o,max ;

[0063] If k o,min <k o,max <N or 1 < k o,max <k o,min Then the velocity-affected component Directional influence component D o =-1;

[0064] Otherwise, the speed-affected component Where f o This is the road segment occupancy influence coefficient, which falls within the recommended value range of 10-50 based on empirical thresholds, and includes the directional influence component.

[0065] The lane speed difference influence components include speed influence components and direction influence components, which are obtained as follows:

[0066] For the Kth sampling period, let the sampling time sequence of the samples be denoted as 1 to N. Then, the sampling time sequences corresponding to the minimum and maximum values ​​of the average speed difference between lanes in the sample set are respectively... and

[0067] like or The velocity-affected component Direction affects components

[0068] Otherwise, the speed-affected component Where f vd This is the influence coefficient for lane speed differences, which falls within the recommended empirical threshold range of 0.3 to 0.8, and includes the directional influence component.

[0069] The lane occupancy impact components include speed impact components and direction impact components, which are obtained as follows:

[0070] For the Kth sampling period, let the sampling time sequence of the samples be denoted as 1 to N. Then, the sampling time sequences corresponding to the minimum and maximum values ​​of the lane occupancy difference sample set are respectively... and

[0071] like or The velocity-affected component Direction affects components

[0072] Otherwise, the speed-affected component in This is the influence coefficient of lane occupancy difference, which falls within the recommended range of 30-80 based on empirical thresholds, and includes the directional influence component.

[0073] Example 4:

[0074] This embodiment, based on Embodiment 1, Embodiment 2, or Embodiment 3, further designs the following: This example provides a detailed explanation of the method for determining the running direction and speed of the inspection robot in the next sampling cycle.

[0075] For the Kth sampling period, the speed influence component in the average speed influence component of the road segment is denoted as V. v The directional influence component is D v The speed component in the road segment occupancy influence component is V. O The directional influence component is D O The speed influence component in the lane speed difference component is: The directional influence component is The speed component in the lane occupancy influence component is: The directional influence component is

[0076] The recommended speed for the (K+1)th sampling period

[0077] In the formula, w v w o , and These are the weighting coefficients for the components of average speed on road segments, road segment occupancy, lane speed difference, and lane occupancy, respectively. They are all empirical thresholds and a value of 0.25 is recommended.

[0078] For the (K+1)th sampling period, the direction of movement of the inspection robot The operating speed V of the inspection robot K+1 =min(V K+1,rec V K+1,lim ),

[0079] In the formula, V K+1，lim The inspection robot's speed is limited during the K+1th sampling period;

[0080]

[0081] In the formula, D is the tunnel length; K and D K+1 These represent the running directions of the inspection robot during the Kth and K+1th sampling periods, respectively; Nt is the sampling period length; X K This represents the position of the inspection robot at the end of the Kth sampling period.

[0082] Application Example 1:

[0083] This example uses the control method of the tunnel inspection robot of the present invention to control the running speed and running direction of the inspection robot in an exemplary simulated tunnel. In the simulated tunnel, the inspection robot inspects along the top track of the tunnel parallel to the traffic section. The vertical distance between the inspection robot and the traffic section is H = 9m, the robot camera shooting angle is 2α = 132°, the total number of lanes is m = 2, the tunnel length is D = 5000m, and the acquisition range of the inspection robot is L = 2Htanα = 40m.

[0084] The maximum operating speed V of the inspection robot max =30km / h, sampling period length Nt=120s, sampling time interval t=10s, traffic flow data is sampled 12 times in each period, and the running speed and running direction are changed once after collecting 12 traffic flow data.

[0085] The following uses the 12th data sampling of the inspection robot in a certain sampling period as an example to illustrate the traffic flow data. The traffic flow data collected in the 12th sampling period is shown in Table 1:

[0086] Table 1

[0087] Vehicle number Lane number Speed ​​(km / h) Vehicle type 12-1-1 1 50 0 12-1-2 1 41 1 12-1-3 1 41 0 12-1-4 1 48 0 12-2-1 2 58 0 12-2-2 2 50 0 12-2-3 2 45 0 12-2-4 2 55 0

[0088] In Table 1, the data entry with vehicle number 12-1-4, lane number 1, speed 48 km / h, and vehicle attribute 0 indicates that the fourth vehicle in lane 1 at the 12th sampling time series is a passenger car with a speed of 48 km / h; the number of vehicles n in lanes 1 and 2 at this sampling time series... 12,1 =n 12，2 =4, total number of vehicles n 12 =8; Based on the data in Table 1, the following information can be obtained:

[0089] Average speed of lane 1 Average speed of lane 2 Average speed of road section

[0090] Lane occupancy of lane 1 Among them l t =12m,l p =7m; Lane occupancy of the second lane Road segment lane occupancy

[0091] Average speed difference between lanes on the road segment Difference in average lane occupancy

[0092] The inspection robot sampled traffic flow data 12 times during this period. The traffic segment occupancy, average speed of traffic segments, difference in occupancy between lanes, and difference in average speed between lanes obtained from each sampling are shown in Table 2.

[0093] Table 2

[0094]

[0095] Based on the above-formed sample set of average speeds for traffic segments, A = [47.17, 49.96, 62.22, 46.06, 57.38, 37.74, 59.90, 63.69, 59.59, 40.43, 59.22, 48.50];

[0096] The resulting traffic segment occupancy sample set B = [0.17, 0.18, 0.17, 0.17, 0.16, 0.19, 0.17, 0.18, 0.16, 0.18, 0.20, 0.76];

[0097] The sample set of average speed differences between lanes is C = [20.58, 7.93, 5.66, 20.29, 8.56, 22.02, 4.79, 6.62, 3.24, 25.54, 3.72, 9.34];

[0098] The resulting sample set of lane occupancy differences is D = [0.03, 0.03, 0.03, 0.04, 0.01, 0.02, 0.05, 0.04, 0.02, 0.05, 0.02, 0.10].

[0099] Extreme values ​​of average speed in traffic segments (v) max =63.69km / h, k K,v,Max =8, v min = 37.74 km / h, k K,v,Min =6;

[0100] Extreme values ​​of average speed differences between lanes in the sample set v d,max =25.54km / h, v d,min =3.24km / h,

[0101] Extreme values ​​of traffic segment occupancy sample set o max =0.76, k K,o,Max =12, o min =0.16, k K,o,Min =5;

[0102] Extreme values ​​of the sample set of differences in lane occupancy d,max =0.10, o d,min =0.01,

[0103] Based on the above extreme values, the following components are obtained for the road segment average speed influence, road segment occupancy influence, lane speed difference influence, and lane occupancy influence within the current sampling period:

[0104] The first operation change interval, the robot's direction D before the operation change. K =1, velocity V K =20.00km / h and position X K =1200m;

[0105] The speed influence component V in the calculation of the average speed influence component of the road segment v And the direction affects the component D v :

[0106] Satisfying k v,max <k v,min If the value is less than N, then the speed affects the recommended speed. And D v =-1;

[0107] The speed influence component V in the calculation of the road segment occupancy influence component o And the direction affects the component Do :

[0108] Not satisfied with k o,min <k o,max <N and 1<k o,max <k o,min Therefore, market share affects recommendation speed. and

[0109] The speed influence component V in the calculation of the lane speed difference influence component vd And direction affects the components

[0110] satisfy The speed difference affects the recommended speed. and

[0111] The speed component in the calculation of lane occupancy influence components And direction affects the components

[0112] Not satisfied and The difference in market share affects the recommendation speed. and

[0113] Therefore, the recommended speed for the inspection robot in the next sampling cycle

[0114]

[0115] The direction of the inspection robot in the next sampling cycle

[0116] The inspection robot will have its speed limited in the next sampling cycle.

[0117] Therefore, the speed V of the inspection robot in the next sampling cycle K+1 =min(V K+1,rec V K+1,lim = 18.17 km / h.

Claims

1. A control method for a tunnel inspection robot, wherein the tunnel has a traffic section consisting of several lanes and a track for the inspection robot to run on, several vehicles travel on the traffic section, and the inspection robot collects traffic flow data within the tunnel, characterized in that: The method includes: The inspection robot performs multiple traffic flow data samplings within the current sampling period and records the position of the inspection robot at the end of the current sampling period, forming a traffic segment average speed sample set, a traffic segment occupancy sample set, a lane average speed difference sample set, and a lane occupancy difference sample set that contain samples corresponding to different sampling time sequences within the current sampling period. Based on the sampling time sequence corresponding to the extreme values ​​of each sample set, the influence components of average speed of road segment, road segment occupancy rate, lane speed difference, and lane occupancy rate within the current sampling period are obtained respectively. Based on the influence components of average speed of road segment, road segment occupancy, lane speed difference, and lane occupancy corresponding to the current sampling period, determine the running direction and speed of the inspection robot in the next sampling period; The average speed influence component of the road segment includes a speed influence component and a direction influence component, which are obtained as follows: For the Kth sampling period, let the sampling time sequence of the samples be denoted as 1 to N. Then the sampling time sequences corresponding to the minimum and maximum values ​​of the average speed samples of traffic segments are k and N, respectively. v,min and k v,max ; If k v,max < k v,min < N or 1 < k v,min < k v,max , then the velocity influence component Direction influence component D v = -1; Otherwise, the speed-affected component Where f v The average speed influence coefficient for the road segment, and the directional influence component. or, For the Kth sampling period, let the sampling time sequence of the samples be denoted as 1 to N. Then the sampling time sequences corresponding to the minimum and maximum values ​​of the traffic segment occupancy sample set are respectively k o,min and k o,max ; If k o,min <k o,max <N or 1 < k o,max <k o,min Then the velocity-affected component Directional influence component D o =-1; Otherwise, the speed-affected component Where f o The road segment occupancy influence coefficient, directional influence component or, For the Kth sampling period, let the sampling time sequence of the samples be denoted as 1 to N. Then the sampling time sequences corresponding to the minimum and maximum values ​​of the average speed difference between lanes in the sample set are respectively... and like or The velocity-affected component Direction affects components Otherwise, the speed-affected component in The influence coefficient for lane speed difference, and the directional influence component. or, For the Kth sampling period, let the sampling time sequence of the samples be denoted as 1 to N. Then, the sampling time sequences corresponding to the minimum and maximum values ​​of the lane occupancy difference sample set are respectively... and like or The velocity-affected component Direction affects components Otherwise, the speed-affected component in The influence coefficient of lane occupancy difference, and the directional influence component. For the Kth sampling period, the speed influence component in the average speed influence component of the road segment is denoted as V. v The directional influence component is D v The speed component in the road segment occupancy influence component is V. O The directional influence component is D O The speed influence component in the lane speed difference component is: The directional influence component is The speed component in the lane occupancy influence component is: The directional influence component is The recommended speed for the (K+1)th sampling period In the formula, w v w o , and These are the weighting coefficients for the component affected by average speed of road segment, the component affected by road segment occupancy, the component affected by lane speed difference, and the component affected by lane occupancy, respectively. For the (K+1)th sampling period, the direction of movement of the inspection robot The operating speed V of the inspection robot K+1 =min(V K+1,rec V K+1,lim ), In the formula, V K+1，lim The inspection robot's speed is limited during the K+1th sampling period; In the formula, D is the tunnel length; K and D K+1 These represent the running directions of the inspection robot during the Kth and K+1th sampling periods, respectively; Nt is the sampling period length; X K This represents the position of the inspection robot at the end of the Kth sampling period.

2. The control method for the tunnel inspection robot according to claim 1, characterized in that: The traffic flow data includes vehicle type, speed, and lane information; wherein, in the k-th sampling time series, the type of the j-th vehicle in the i-th lane is p. k,i,j The speed is v k,i,j ,in, 3. The control method for the tunnel inspection robot according to claim 1, characterized in that: The average speed sample set of the traffic segment is A = [v1,...,v...]. k ,...,v N ]; Where, v1,...,v k ,...,v N These represent the average speeds of traffic segments in the sampling time series of the 1st, ..., 1kth, ..., 1Nth sampling periods; where... v k,i,j Let m be the speed of the j-th vehicle in the i-th lane at the k-th sampling time sequence, m be the total number of lanes, and n be the speed of the j-th vehicle in the i-th lane. k,i This represents the number of vehicles in the i-th lane during the k-th sampling time series. The traffic segment occupancy sample set B = [o1,...,o] k ,...,o N ]; where o1,...,o k ,...,o N These represent the traffic segment occupancy rates under the sampling time series of the 1st, ..., 1kth, ..., 1Nth sampling periods, where... In the formula o k,i Let i be the lane occupancy rate of lane i. l t Standard length for trucks; l p L is the standard length of the bus; L is the data collection range of the inspection robot, L = 2Htanα; H is the vertical distance between the inspection robot and the traffic section; α is half of the shooting angle of the inspection robot's camera; The average speed difference sample set between lanes C = [v d,1 ,...,v d,k ,...,v d,N ]; where v d,1 ,...,v d,k ,...,v d,N Let be the average speed difference between lanes in the sampling time series of the 1st, ..., 1kth, ..., 1Nth sampling times, where In the formula, The sample set of lane occupancy differences D = [o d,1 ,...,o d,k ,...,o d,N ]; where o d,1 ,...,o d,k ,...,o d,N Let represent the lane occupancy differences in the sampling time series of the 1st, ..., 1kth, ..., 1Nth sampling periods, where o d,k The difference in lane occupancy is represented by the k-th sampling time sequence.

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