Intelligent tunnel inspection robot inspection method

Abstract

The invention relates to the field of tunnel inspection, in particular to an intelligent tunnel inspection robot inspection method which comprises the steps that S1, an inspection robot is started; s2, the background central control system sends the initial operation parameters of the inspection robot through a network; s3, the inspection robot starts inspection operation according to the initial operation parameters set by the background central control system and calculates the passage condition of the tunnel, and calculation is carried out by collecting air quality data, fire detection data, the road condition, the fire fighting equipment state and vehicle collision data; s4, the inspection robot automatically moves reversely when moving to the farthest end; and S5, repeating the steps S2-S4 according to the set initial operation parameters. The method has the advantages that all-weather intelligent daily inspection of the tunnel can be achieved, the difficulty of manual daily inspection is reduced, robot inspection is more efficient, and the tunnel detection efficiency is improved.

Description

[Technical field]

[0001] The present invention relates to the field of tunnel inspection, and in particular to an inspection method of an intelligent tunnel inspection robot. [Background technology]

[0002] Tunnels have a large volume of vehicles and are closed environments, which makes vehicle collision accidents more likely to occur. Because it is difficult to determine the specific location of the accident, accident handlers are unable to handle it in a timely manner, causing congestion and affecting vehicle traffic. Tunnel robots are used for daily inspections to optimize daily manual inspections, improve inspection efficiency and safety index, and quickly and effectively prevent and handle accidents, thereby gaining precious time for later rescue, reducing losses and harm, and even saving more lives.

[0003] However, when the tunnel robot is performing tasks such as line patrol, it needs to execute functions such as arriving at the accident location for monitoring and arriving at the designated location for charging. At this time, it is necessary to configure it with an intelligent tunnel inspection robot inspection method, which can enable all-weather intelligent daily inspection of the tunnel, reduce the difficulty of manual daily inspection, make robot inspection more efficient, and improve tunnel detection efficiency. [Summary of the invention]

[0004] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide an intelligent tunnel inspection robot inspection method, which can enable all-weather intelligent daily tunnel inspections, reduce the difficulty of manual daily inspections, make robot inspections more efficient, and improve tunnel detection efficiency.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0006] An intelligent tunnel inspection robot inspection method comprises the following steps:

[0007] S1: The inspection robot starts;

[0008] S2: The backend central control system sends the initial operating parameters of the inspection robot through the network;

[0009] S3: The inspection robot starts the inspection operation according to the initial operating parameters set by the background central control system, and calculates the tunnel traffic conditions. By collecting air quality data, it intelligently detects the toxic and harmful air gas environment to ensure the healthy breathing environment of drivers. Fire detection data such as car collision fire, road conditions such as foreign objects on the road blocking the car and pond conditions, fire equipment status such as intelligent detection of abnormal conditions of important equipment, and vehicle collision data are calculated. The tunnel traffic condition calculation formula is:

[0010]

[0011] In the formula, Z is the tunnel traffic condition, n is the tunnel road grade, m is i is the tunnel road weight of tunnel level i, a i is the tunnel air quality data of tunnel grade i, b i is the fire detection data of tunnel level i, c i is the road condition data of tunnel level i, d i is the fire-fighting equipment status of tunnel level i, ei is the vehicle collision data of tunnel level i, and Q is the ecological corridor weight of the road with road level i. Multiple detections are performed on the air quality data and fire detection data, road conditions, fire-fighting equipment status and vehicle collision data in the tunnel to improve the accuracy of tunnel detection.

[0012] S4: The inspection robot moves to the farthest end and automatically moves in the reverse direction. In this embodiment, there is one inspection robot, which moves from the leftmost end to the bottom and then returns from the rightmost end, repeating the cycle. It is worth mentioning that two inspection robots can be set to reduce the phenomenon that the leftmost end or the rightmost end is not in the inspection state;

[0013] S5: Repeat S2-S4 according to the set initial operating parameters.

[0014] Preferably, the stability of the inspection robot running on the track is calculated according to the running angle of the inspection robot relative to the track, and the calculation formula is:

[0015]

[0016] Among them, M ij represents the mass of the i-th tunnel channel and the j-th tunnel robot; z ij represents the running angle of the jth tunnel robot on the track in the i-th tunnel channel; l m Indicates the height of the vehicle body from the ground; m indicates the specific number of vehicle bodies connected to the tunnel robot; F indicates the objective function; min indicates taking the minimum value of the objective function. The tunnel robot is composed of multiple vehicle bodies. The vehicle bodies are checked at different angles. When a vehicle body is at risk of falling, it is easy to cause the tunnel robot as a whole to be driven to fall. The minimum stability is calculated. When the minimum stability is higher than the risk of falling, the tunnel robot will not fall and will perform normal inspections on the track.

[0017] Preferably, determining the operating angle specifically includes the following steps:

[0018] Step S20, obtaining the maximum vertical distance between the tunnel robot and the track under different tunnel road grades;

[0019] Step S21, obtaining the minimum horizontal distance between the tunnel robot and the track under different tunnel road grades;

[0020] Step S22, determining the running angle according to the obtained maximum vertical distance and minimum horizontal distance.

[0021] Preferably, S2 specifically includes that the initial operation parameters include inspection speed, power threshold, and round-trip movement interval time.

[0022] Preferably, the method further includes S6: the tunnel robot enters the cleaning mechanism to clean the vehicle body;

[0023] Preferably, the method further includes S7: if the battery power of the tunnel robot is lower than a set threshold during a normal inspection process, the tunnel robot automatically returns to the location of the wireless charger for charging;

[0024] Preferably, S8 is also included: during normal inspection, a camera is used to identify pedestrians who accidentally run into a tunnel or a car accident. If a pedestrian accidentally runs into a tunnel or a car accident occurs, the tunnel robot tracks the pedestrian and gives a voice reminder.

[0025] Preferably, the real-time distance between the vehicle and the accident scene is obtained, and when the real-time distance between any vehicle and any accident scene is less than the safe distance, a warning alarm is issued.

[0026] An intelligent tunnel inspection robot inspection system, using the above-mentioned intelligent tunnel inspection robot inspection method, comprises:

[0027] A switch module is used to control the inspection robot to turn on the power switch;

[0028] A parameter setting module is used to control the backend central control system to send the initial operating parameters of the inspection robot through the network;

[0029] The calculation module is used to control the inspection robot to start the inspection operation according to the initial operating parameters set by the background central control system, and calculate the tunnel traffic conditions by collecting air quality data and fire detection data, road conditions, fire equipment status and vehicle collision data. The tunnel traffic condition calculation formula is:

[0030]

[0031] In the formula, Z is the tunnel traffic condition, n is the tunnel road grade, m is i is the tunnel road weight of tunnel level i, a i is the tunnel air quality data of tunnel grade i, b i is the fire detection data of tunnel level i, c i is the road condition data of tunnel level i, d i is the status of fire-fighting equipment in tunnel level i, e iis the vehicle collision data of tunnel grade i, Q is the ecological corridor weight of the road with road grade i;

[0032] The reciprocating motion module is used to control the vehicle body to move to the farthest end and then automatically move in the reverse direction.

[0033] Preferably, when the number of vehicles passing through the tunnel is 0, the intelligent tunnel inspection robot inspection system suspends operation.

[0034] The beneficial effects of the present invention are as follows:

[0035] 1. The inspection robot starts the inspection operation according to the initial operating parameters set by the background central control system, and calculates the tunnel traffic conditions. By collecting air quality data, it intelligently detects the toxic and harmful air gas environment to ensure the healthy breathing environment of drivers. Fire detection data such as car collision fire, road conditions such as foreign objects on the road blocking the car and pond conditions, fire equipment status such as intelligent detection of abnormal conditions of important equipment, and vehicle collision data are calculated. The tunnel traffic condition calculation formula is:

[0036]

[0037] In the formula, Z is the tunnel traffic condition, n is the tunnel road grade, m is i is the tunnel road weight of tunnel level i, a i is the tunnel air quality data of tunnel grade i, b i is the fire detection data of tunnel level i, c i is the road condition data of tunnel level i, d i is the status of fire-fighting equipment in tunnel level i, e i is the vehicle collision data of tunnel grade i, Q is the ecological corridor weight of the road with road grade i, and multiple tests are performed on the air quality data and fire detection data, road conditions, fire-fighting equipment status and vehicle collision data in the tunnel to improve the accuracy of tunnel detection.

[0038] 2. The inspection robot automatically moves in the opposite direction when it moves to the farthest end. In the present embodiment, there is one inspection robot, which moves from the leftmost end to the bottom and then returns from the rightmost end, repeating the cycle. It is worth mentioning that there can be two inspection robots to reduce the phenomenon that the leftmost or rightmost end is not in the inspection state.

[0039] 3. Determine the running angle based on the maximum vertical distance and minimum horizontal distance obtained. The calculation formula is maximum vertical distance / minimum horizontal distance. The running angle is calculated to determine whether there is a risk of falling. If there is a risk of falling, the vehicle will brake automatically and send an alarm to the background central control system, and maintenance personnel will be dispatched in time to carry out relevant maintenance.

[0040] These features and advantages of the present invention will be disclosed in detail in the following specific embodiments and drawings.

Brief Description of the Drawings

[0041] The present invention will be further described below in conjunction with the accompanying drawings:

[0042] Figure 1 It is a schematic diagram of the process of the present invention. [Specific implementation method]

[0043] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0044] The following first describes the concepts involved in this application in conjunction with the accompanying drawings. It should be noted that the following description of each concept is only to make the content of this application easier to understand and does not limit the scope of protection of this application.

[0045] Embodiment 1:

[0046] An intelligent tunnel inspection robot inspection method, such as Figure 1 As shown, the following steps are included:

[0047] S1: The inspection robot starts and turns on the power switch;

[0048] S2: The backend central control system sends the initial operating parameters of the inspection robot through the network;

[0049] S3: The inspection robot starts the inspection operation according to the initial operating parameters set by the background central control system, and calculates the tunnel traffic conditions. By collecting air quality data, it intelligently detects the toxic and harmful air gas environment to ensure the healthy breathing environment of drivers. Fire detection data such as car collision fire, road conditions such as foreign objects on the road blocking the car and pond conditions, fire equipment status such as intelligent detection of abnormal conditions of important equipment, and vehicle collision data are calculated. The tunnel traffic condition calculation formula is:

[0050]

[0051] In the formula, Z is the tunnel traffic condition, n is the tunnel road grade, m is i is the tunnel road weight of tunnel level i, a i is the tunnel air quality data of tunnel grade i, b i is the fire detection data of tunnel level i, c iis the road condition data of tunnel level i, d i is the status of fire-fighting equipment in tunnel level i, e i is the vehicle collision data of tunnel grade i, Q is the ecological corridor weight of the road with road grade i, and multiple tests are performed on the air quality data and fire detection data, road conditions, fire-fighting equipment status and vehicle collision data in the tunnel to improve the accuracy of tunnel detection.

[0052] It is also possible to perform data fusion with multi-dimensional weights, integrate the data from each sensor, and obtain fused environmental data. The calculation formula is as follows:

[0053]

[0054] Among them, x f (t,f) is the fused environmental data, is the subband signal of the sth level after preliminary denoising and filtering of sensor i at time t and frequency f, and N is the total number of sensors. Through multi-dimensional weight optimization, the information of each sensor is integrated to ensure the accuracy and consistency of the data.

[0055] The fused environmental data is adaptively denoised using a noise filtering algorithm based on recursive Bayesian estimation and information entropy constraints. The noise filtering algorithm continuously updates noise parameters through recursive Bayesian estimation and optimizes adaptive adjustment using information entropy constraints. The specific implementation process is as follows:

[0056] The noise parameters are updated through recursive Bayesian estimation to achieve dynamic adjustment. The calculation formula is as follows:

[0057]

[0058]

[0059] P t =(IK t H t ) t-1

[0060] in, is the noise parameter estimated at the current time t, which represents the estimation of the current noise characteristics; is the noise parameter estimated at the previous time t-1; K t is the gain estimated by recursive Bayesian estimation at the current time t, and is the weight factor calculated based on the prediction error covariance and the observation noise covariance; P t is the prediction error covariance at the current time t, which represents the covariance matrix of the estimation error at the current time t; H t is the measurement matrix, which is used to map the noise parameters to the observation space. is the predicted observation value, which is the result of the noise parameter at the previous moment transformed by the observation matrix; R t is the observation noise covariance at the current time t, which represents the noise characteristics of the observation data; x f (t,f) is the fused environment data; I is the unit matrix, which is used to maintain the dimensional consistency of the matrix; P t-1 is the prediction error covariance at the previous time t; T represents the transpose of the matrix. Through the recursive Bayesian estimation update rule, the noise parameters are dynamically adjusted to adapt to environmental changes.

[0061] S4: The inspection robot moves to the farthest end and automatically moves in the reverse direction. In this embodiment, there is one inspection robot, which moves from the leftmost end to the bottom and then returns from the rightmost end, repeating the cycle. It is worth mentioning that two inspection robots can be set to reduce the phenomenon that the leftmost end or the rightmost end is not in the inspection state;

[0062] S5: Repeat S2-S4 according to the set initial operating parameters.

[0063] It also includes S6: the tunnel robot enters the cleaning mechanism to clean the vehicle body. There is a lot of dust in the tunnel, which causes blurring of the shooting lens of the tunnel robot. Timely cleaning is conducive to improving the inspection clarity.

[0064] It also includes S7: during normal inspection, if the battery power of the tunnel robot is lower than the set threshold, the tunnel robot automatically returns to the location of the wireless charger for charging to prevent insufficient power from affecting the inspection;

[0065] It also includes S8: During normal inspections, cameras are used to identify pedestrians who mistakenly enter the tunnel or car accidents. If pedestrians mistakenly enter the tunnel or car accidents occur, the tunnel robot will track them and give voice reminders and commands to reduce congestion.

[0066] For example, when an accident occurs, the tunnel robot will automatically move to the scene and remind users to pull over and evacuate, and report to the backend. It will also turn on the red and blue warning lights and play built-in voice messages, reminding users to drive slowly and carefully if there is an accident ahead. It will also issue sound and light warnings to vehicles behind, and link up with the information board at the tunnel entrance to strengthen the warning.

[0067] It can also handle simple accidents and improve handling efficiency. It takes time for staff to be dispatched to the site, but through the tunnel robot, the back-end staff can identify the accident through remote voice intercom mode, quickly deal with it quickly, and remotely command and handle it, effectively improving the efficiency of handling accidents and reducing the probability of congestion.

[0068] According to the running angle of the inspection robot relative to the track, the stability of the inspection robot running on the track is calculated. The calculation formula is:

[0069]

[0070] Among them, M ij represents the mass of the i-th tunnel channel and the j-th tunnel robot; z ij represents the running angle of the jth tunnel robot on the track in the i-th tunnel channel; l m Indicates the height of the vehicle body from the ground; m indicates the specific number of vehicle bodies connected to the tunnel robot; F indicates the objective function; min indicates taking the minimum value of the objective function. The tunnel robot is composed of multiple vehicle bodies. The vehicle bodies are checked at different angles. When a vehicle body is at risk of falling, it is easy to cause the tunnel robot as a whole to be driven to fall. The minimum stability is calculated. When the minimum stability is higher than the risk of falling, the tunnel robot will not fall and will perform normal inspections on the track.

[0071] Determine the running angle, specifically including the following steps:

[0072] Step S20, obtaining the maximum vertical distance between the tunnel robot and the track under different tunnel road grades, the tunnel robot includes multiple vehicle bodies, the vertical distance between each vehicle body and the track is different, and the maximum vertical distance between the vehicle body and the track is obtained;

[0073] Step S21, obtaining the minimum horizontal distance between the tunnel robot and the track under different tunnel road grades, the tunnel robot includes multiple vehicle bodies, the horizontal distance between each vehicle body and the track is different, and obtaining the maximum horizontal distance between the vehicle body and the track;

[0074] Step S22, determine the running angle according to the maximum vertical distance and the minimum horizontal distance obtained, the operation formula is maximum vertical distance / minimum horizontal distance, so as to calculate the running angle, and judge whether there is a risk of falling. If there is a risk of falling, the car will brake automatically, send an alarm to the background central control system, and send maintenance personnel to carry out relevant maintenance in time.

[0075] The real-time distance between the vehicle and the accident scene is obtained, and when the real-time distance between any vehicle and any accident scene is less than the safe distance, a reminder alarm is issued.

[0076] Embodiment 2:

[0077] An intelligent tunnel inspection robot inspection system, using the above-mentioned intelligent tunnel inspection robot inspection method, comprises:

[0078] A switch module is used to control the inspection robot to turn on the power switch;

[0079] A parameter setting module is used to control the backend central control system to send the initial operating parameters of the inspection robot through the network;

[0080] The calculation module is used to control the inspection robot to start the inspection operation according to the initial operating parameters set by the background central control system, and calculate the tunnel traffic conditions by collecting air quality data and fire detection data, road conditions, fire equipment status and vehicle collision data. The tunnel traffic condition calculation formula is:

[0081]

[0082] In the formula, Z is the tunnel traffic condition, n is the tunnel road grade, m is i is the tunnel road weight of tunnel level i, a i is the tunnel air quality data of tunnel grade i, b i is the fire detection data of tunnel level i, c i is the road condition data of tunnel level i, d i is the fire-fighting equipment status of tunnel level i, e i is the vehicle collision data of tunnel grade i, Q is the ecological corridor weight of the road with road grade i;

[0083] The reciprocating motion module is used to control the vehicle body to move to the farthest end and then automatically move in the reverse direction.

[0084] When the number of vehicles passing through the tunnel is 0, the intelligent tunnel inspection robot inspection system will stop working.

[0085] Those of ordinary skill in the art will appreciate that the units (or modules, steps, etc., the same below) of the examples described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0086] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0087] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0088] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nly Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. An intelligent tunnel inspection robot inspection method, characterized in that: The following steps are involved: S1: The inspection robot starts; S2: The backend central control system sends the initial operating parameters of the inspection robot through the network; S3: The inspection robot starts the inspection operation according to the initial operation parameters set by the background central control system, and calculates the tunnel traffic condition by collecting air quality data and fire detection data, road conditions, fire equipment status and vehicle collision data. The tunnel traffic condition calculation formula is: In the formula, Z is the tunnel traffic condition, n is the tunnel road grade, m is i is the tunnel road weight of tunnel level i, a i is the tunnel air quality data of tunnel grade i, b i is the fire detection data of tunnel level i, c i is the road condition data of tunnel level i, d i is the fire equipment status of tunnel level i, ei is the vehicle collision data of tunnel level i, and Q is the ecological corridor weight of the road with road level i; S4: The inspection robot moves to the farthest end and automatically moves in the reverse direction; S5: Repeat S2-S4 according to the set initial operating parameters.

2. The intelligent tunnel inspection robot inspection method according to claim 1, characterized in that: According to the running angle of the inspection robot relative to the track, the stability of the inspection robot running on the track is calculated. The calculation formula is: Among them, M ij represents the mass of the i-th tunnel channel and the j-th tunnel robot; z ij represents the running angle of the jth tunnel robot on the track in the i-th tunnel channel; l m Represents the height of the vehicle from the ground; m represents the specific number of vehicles connected to the tunnel robot; F represents the objective function; min represents taking the minimum value of the objective function.

3. The intelligent tunnel inspection robot inspection method according to claim 2, characterized in that: Determining the operating angle specifically includes the following steps: Step S20, obtaining the maximum vertical distance between the tunnel robot and the track under different tunnel road grades; Step S21, obtaining the minimum horizontal distance between the tunnel robot and the track under different tunnel road grades; Step S22, determining the running angle according to the obtained maximum vertical distance and minimum horizontal distance.

4. The intelligent tunnel inspection robot inspection method according to claim 1, characterized in that: Specifically, the S2 includes that the initial operation parameters include the inspection speed, the power threshold, and the round-trip movement interval time.

5. The intelligent tunnel inspection robot inspection method according to claim 1, characterized in that: The process also includes S6: the tunnel robot enters a cleaning mechanism to clean the vehicle body.

6. The intelligent tunnel inspection robot inspection method according to claim 1, characterized in that: It also includes S7: during the normal inspection process, if the battery power of the tunnel robot is lower than the set threshold, the tunnel robot automatically returns to the location of the wireless charger for charging.

7. The intelligent tunnel inspection robot inspection method according to claim 1, characterized in that: It also includes S8: during normal inspection, the camera is used to identify pedestrians who accidentally run into the tunnel or a car accident collision. If a pedestrian accidentally runs into the tunnel or a car accident collision occurs, the tunnel robot tracks the pedestrian and gives a voice reminder.

8. The intelligent tunnel inspection robot inspection method according to claim 1, characterized in that: The real-time distance between the vehicle and the accident scene is obtained, and when the real-time distance between any vehicle and any accident scene is less than the safe distance, a reminder alarm is issued.

9. An intelligent tunnel inspection robot inspection system, using an intelligent tunnel inspection robot inspection method as claimed in any one of claims 1 to 8, characterized in that: include: A switch module is used to control the inspection robot to turn on the power switch; A parameter setting module is used to control the backend central control system to send the initial operating parameters of the inspection robot through the network; The calculation module is used to control the inspection robot to start the inspection operation according to the initial operating parameters set by the background central control system, and calculate the tunnel traffic conditions by collecting air quality data and fire detection data, road conditions, fire equipment status and vehicle collision data. The tunnel traffic condition calculation formula is: In the formula, Z is the tunnel traffic condition, n is the tunnel road grade, m is i is the tunnel road weight of tunnel level i, a i is the tunnel air quality data of tunnel grade i, b i is the fire detection data of tunnel level i, c i is the road condition data of tunnel level i, d i is the status of fire-fighting equipment in tunnel level i, e i is the vehicle collision data of tunnel grade i, Q is the ecological corridor weight of the road with road grade i; The reciprocating motion module is used to control the vehicle body to move to the farthest end and then automatically move in the reverse direction.

10. The intelligent tunnel inspection robot inspection method according to claim 9, characterized in that: When the number of vehicles passing through the tunnel is 0, the intelligent tunnel inspection robot inspection system will stop working.

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