CN118092458A - Highway tunnel monitoring, inspection and early warning integrated robot based on Internet of things - Google Patents
Highway tunnel monitoring, inspection and early warning integrated robot based on Internet of things Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
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Abstract
The invention belongs to the technical field of highway tunnel inspection, and particularly discloses a highway tunnel monitoring, inspection and early warning integrated robot based on the Internet of things.
Description
Technical Field
The invention belongs to the technical field of highway tunnel inspection, and particularly discloses a highway tunnel monitoring, inspection and early warning integrated robot based on the Internet of things.
Background
Road tunnels are an important component of traffic infrastructure, and their safety status is directly related to the life and property safety of driving personnel. The potential safety hazard in the tunnel can be found in time by regular inspection, and corresponding maintenance measures are taken to ensure the safe passing of the tunnel.
However, at present, the conventional inspection mode is manual inspection, the inspection mode is generally performed regularly, the inspection state is influenced by personal capacity and subjective factors of inspection personnel, and further the defects of untimely, out-of-place and large omission ratio exist, in addition, the manual inspection often requires the inspection personnel to enter the tunnel, and the inspection personnel often face double risks of traffic and environment in the whole inspection process. In order to avoid the defects of manual inspection, an intelligent inspection mode, such as robot inspection, is mostly adopted at present to improve the precision and the safety of tunnel monitoring.
However, in the prior art, although the inspection efficiency and the inspection safety of the robot are obviously improved, the following disadvantages exist: 1. the existing robot generally adopts uniform inspection speed for each tunnel section in the highway tunnel, and lacks pertinence, and because different tunnel sections may have different traffic conditions and environmental conditions, inspection at the uniform speed easily results in insufficient inspection for certain key tunnel sections in the tunnel, so that the problem of insufficient or missing inspection exists, and in addition, the inspection efficiency of the robot is reduced to a certain extent.
2. The existing robot generally strictly follows a preset inspection plan and a charging route in inspection in a highway tunnel, lacks flexible adjustment capability, particularly, under the condition of electric quantity limitation, particularly, when the robot monitors that the electric quantity is limited, the robot still performs inspection according to the original inspection plan until the electric quantity is insufficient, stops inspection and charging, the inspection plan and the charging route cannot be flexibly adjusted when the electric quantity is limited, monitoring of key areas is easily missed, problem deterioration or unnecessary loss is possibly caused, and when the electric quantity is limited, the robot continues to inspect according to the original plan, energy consumption is possibly uneven, so that the overall efficiency of inspection is reduced.
Disclosure of Invention
In view of the defects of the prior art, the invention aims to provide the integrated robot for monitoring, inspecting and early warning of the highway tunnel based on the Internet of things, which effectively solves the problems existing in the prior art.
The aim of the invention can be achieved by the following technical scheme: highway tunnel monitoring, inspection, early warning integrated robot based on thing networking, including following module: and the tunnel section dividing module is used for acquiring the length of the highway tunnel, so that the highway tunnel is uniformly divided, and a plurality of tunnel sections are obtained.
And the inspection speed planning module is used for calling the historical inspection log of the highway tunnel to plan the inspection speed corresponding to each tunnel section.
And the residual electric quantity monitoring module is used for monitoring the residual electric quantity in real time when the inspection robot executes an inspection plan along the inspection track according to the inspection speed corresponding to each tunnel section, wherein the inspection robot comprises monitoring equipment.
And the inspection plan dynamic adjustment module is used for identifying whether the charging is needed or not based on the residual electric quantity monitored in real time, recording the current inspection position of the inspection robot when the charging is needed, comparing the current inspection position with the position of the charging pile, determining an adaptive charging route and an adaptive travelling speed, and further adjusting the inspection plan.
And the inspection monitoring module is used for monitoring traffic road conditions and traffic environment by using monitoring equipment when the inspection robot executes an inspection plan according to the required inspection speed corresponding to each tunnel section.
And the reference database is used for storing the patrol abnormal occupation ratio intervals corresponding to the patrol abnormal grades and storing the required patrol speed corresponding to each patrol abnormal grade.
And the inspection anomaly identification module is used for identifying whether the traffic obstruction exists according to the traffic road condition monitoring result and identifying whether the traffic environment is bad according to the traffic environment monitoring result.
The traffic emergency guiding module is used for positioning the traffic obstacle and the tunnel section where the traffic environment is severe when the traffic obstacle or the traffic environment is severe in the analysis of the road tunnel, marking the corresponding tunnel section as a risk tunnel section, and simultaneously carrying out early warning, so as to analyze the centralized distribution position of the risk tunnel section and the continuous condition of the risk tunnel section, and accordingly, carrying out traffic emergency guiding by the inspection robot.
In an alternative embodiment, the operation of planning the patrol speed corresponding to each tunnel segment is as follows: and extracting the inspection results of each tunnel section in each inspection log from the historical inspection log, further selecting the inspection log with the abnormal inspection result as the abnormal inspection log, and recording the abnormal inspection log as the abnormal inspection log, thereby counting the number and the abnormal direction of the abnormal inspection log.
Based on the number of the abnormal inspection logs and the abnormal direction corresponding to each abnormal inspection log, calculating the inspection abnormal occupation ratio corresponding to each tunnel section, wherein the calculation expression is thatIn/>Represents the/>Abnormal inspection log quantity corresponding to tunnel segment,/>Representing tunnel segment number,/>，/>Represents the/>Historical inspection log quantity of tunnel section,/>、/>Respectively represent the/>The abnormal direction in the abnormal inspection logs corresponding to the tunnel section is the number of the abnormal inspection logs of traffic road conditions and traffic environments, and the number of the abnormal inspection logs is/are equal to the number of the abnormal inspection logs of traffic conditions and traffic environments、/>Respectively represent the weight factors corresponding to traffic road conditions and traffic environments,/>Representing natural constants.
And matching the inspection abnormal occupation ratio corresponding to each tunnel segment with the inspection abnormal occupation ratio interval corresponding to each inspection abnormal grade in the reference database to obtain the inspection abnormal grade corresponding to each tunnel segment, and screening the required inspection speed corresponding to each tunnel segment from the reference database.
In an alternative embodiment, the、/>The acquisition of (2) is as follows: and extracting the abnormal processing time from the abnormal inspection log of each tunnel segment, and counting the average abnormal processing time corresponding to the abnormal direction as the traffic road condition and the traffic environment based on the abnormal direction.
The average abnormal processing time length corresponding to the traffic road condition and the traffic environment is calculated by the ratio of the abnormal points to the traffic road condition and the traffic environment、/>。
In an alternative embodiment, the determination of the adapted charging route is performed as follows:
And positioning the current inspection tunnel section of the inspection robot based on the current inspection position of the inspection robot, thereby determining the tunnel section to be inspected.
And comparing the current inspection position with the position of the charging pile, thereby planning a charging route.
Counting the travel distance of each planned charging route and the number of included tunnel segments to be detected;
and recording the serial numbers of the tunnel sections to be detected on each charging route, thereby obtaining the inspection abnormal occupation ratio corresponding to the tunnel sections to be detected, and further carrying out average value calculation on the inspection abnormal occupation ratio corresponding to the tunnel sections to be detected on each charging route.
And calculating the corresponding selected value of each charging route by combining the travel distance of each charging route with the number of the included tunnel segments to be detected and the routing inspection abnormal occupation ratio.
And selecting the charging route corresponding to the maximum selected value degree from the selected value degrees corresponding to the charging routes as an adaptive charging route.
In an alternative embodiment, the process of determining the adapted travel speed is as follows: and combining the travelling distance of the adaptive charging route with the residual electric quantity to obtain the limiting travelling speed of the inspection robot on the adaptive charging circuit line.
And acquiring the required inspection speed of each tunnel section to be inspected based on the serial number of each tunnel section to be inspected on the adaptive charging route.
And comparing the required inspection speed of each tunnel section to be inspected, selecting the minimum required inspection speed from the required inspection speeds, comparing the minimum required inspection speed with the limited advancing speed of the inspection robot on the adaptive charging circuit line, and taking the limited advancing speed as the adaptive advancing speed if the minimum inspection speed is lower than the limited advancing speed, otherwise taking the minimum inspection speed as the adaptive advancing speed.
In an alternative embodiment, the adjusted inspection plan is as follows: and extracting the tunnel section to be detected contained in the adaptive charging route from the current inspection tunnel section to be used as the current tunnel section to be detected.
In an alternative embodiment, the concentrated distribution location of the risk tunnel segments is analyzed as follows: and acquiring the serial numbers of the risk tunnel segments, and extracting the serial numbers of the initial tunnel segment, the middle tunnel segment and the ending tunnel segment from the serial numbers of the tunnel segments.
Comparing the serial numbers of the risk tunnel sections with the serial numbers of the initial tunnel section, the middle tunnel section and the tail tunnel section, and respectively counting the initial proximity corresponding to the risk tunnel sectionsMiddle segment proximity/>Tail proximityWherein/>、/>、/>In/>Represent the firstNumbering of risk tunnel segments,/>And the median number of the corresponding number of each tunnel segment is indicated.
Comparing the proximity of the risk tunnel section with the initial tunnel section, the middle tunnel section and the end tunnel section, and determining a modelObtaining the centralized distribution position/>, of the risk tunnel segments。
In an alternative embodiment, the risk tunnel segment continuity is analyzed as follows: and comparing the numbers of the risk tunnel segments, and further extracting the maximum number and the minimum number from the numbers.
Comparing the maximum number with the minimum number of the risk tunnel segment, and calculating the continuous index of the risk tunnel segmentThe computational expression is/>In/>、/>Respectively represent the maximum number and the minimum number of the risk tunnel segments,/>Representing the number of risk tunnel segments.
In an alternative embodiment, the following procedure is implemented by the inspection robot in the emergency guidance of traffic: (1) And if the centralized distribution position of the risk tunnel section is positioned in the initial section, the inspection robot is linked with the tunnel entrance information board to perform the sealing treatment.
(2) And if the centralized distribution position of the risk tunnel sections is positioned in the middle section, comparing the continuous indexes of the risk tunnel sections with a limit value, if the continuous indexes of the risk tunnel sections are larger than the limit value, sending alarm information by the inspection robot to bypass and guide vehicles existing in the tunnel, and if the continuous indexes of the risk tunnel sections are smaller than or equal to the limit value, advancing to each risk tunnel section by the inspection robot to conduct local guide.
(3) And if the centralized distribution position of the risk tunnel section is positioned at the tail section, the inspection robot sends a deceleration passing voice prompt to the vehicles at the initial section and the middle section.
In an alternative embodiment, the traffic emergency guiding module further comprises a fan working based on the tunnel section where the traffic environment is bad and linked with the corresponding tunnel section by the inspection robot.
Compared with the prior art, the invention has the following beneficial effects: (1) According to the invention, road sections are divided by the highway tunnel, the historical inspection log of each tunnel section is called, so that the inspection abnormal occupation ratio corresponding to each tunnel section is calculated based on the inspection result of the historical inspection log, the inspection speed corresponding to each tunnel section is planned according to the inspection abnormal occupation ratio, the targeted planning of the inspection speed on each tunnel section is realized, the inspection speed of a robot can be accelerated on the tunnel section which is relatively simple and smooth, the inspection time and energy are saved, the inspection efficiency is improved, the inspection speed can be reduced in the key tunnel section, the robot can inspect the areas more carefully, the potential problems are found, the inspection accuracy and the inspection comprehensiveness are improved, the potential safety hazards possibly occurring in the inspection process are reduced to the maximum extent while the inspection efficiency is improved, and the inspection quality is improved.
(2) According to the invention, when the inspection robot executes the inspection plan along the inspection track, the residual electric quantity is monitored in real time, so that the current inspection position of the inspection robot is recorded when the inspection robot is identified to be charged, the adaptive charging route is determined, the inspection plan is adjusted, the pertinence and flexibility adjustment of the inspection plan and the charging route in the inspection process are realized, the monitoring of the key areas can be considered in the route to charge, the working time of the robot can be utilized to the greatest extent, the time consumed by the robot in the route due to the insufficient electric quantity is avoided, the inspection efficiency and coverage range are improved, the missed monitoring of the key areas can be avoided, and the potential problems or abnormal situations can be found in time.
(3) According to the invention, when the charging is required, the adaptive charging route is determined by recording the current inspection position of the inspection robot, and then the travelling speed planning on the way of the charging circuit is performed according to the tunnel section to be monitored existing on the adaptive charging route, so that the requirement of the inspection speed of the tunnel section to be monitored can be met while the remaining electric quantity can be supported to travel to the charging position in the charging route, the condition that the robot can effectively monitor more tunnel sections before charging can be ensured, the inspection efficiency and quality are improved, the requirement of charging and the requirement of inspection can be met, and the practical value is higher.
(4) According to the invention, when the risk tunnel section is identified according to the traffic road condition and the traffic environment monitoring result of each tunnel section, the centralized distribution position of the risk tunnel section and the continuous condition analysis of the risk tunnel section are increased, so that the routing inspection robot is utilized to travel to the corresponding processing position to conduct traffic emergency guidance in a targeted manner, accurate road condition information and traffic advice can be provided for a driver, the risk area is helped to be avoided or proper measures are taken, the accident risk is reduced, the road safety is ensured, the driver can be helped to pass through the risk tunnel section more quickly and more safely, traffic jam and delay are reduced, the traffic efficiency is improved, the optimization of tunnel traffic management is realized, and the overall operation efficiency and the safety of a tunnel traffic system are promoted.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the connection of the modules of the system according to the present invention.
Fig. 2 is a schematic diagram showing the comparison of the interval division length and the monitoring coverage area in the present invention.
Reference numerals: 1-interval dividing length, 2-monitoring coverage area, 3-monitoring equipment.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a highway tunnel monitoring, inspection and early warning integrated robot based on the Internet of things, which comprises a tunnel section dividing module, an inspection speed planning module, a residual electric quantity monitoring module, an inspection plan dynamic adjustment module, an inspection monitoring module, a reference database, an inspection abnormality identification module and a traffic emergency guiding module.
Referring to fig. 1, the above-mentioned middle tunnel segment dividing module is connected with an inspection speed planning module, the inspection speed planning module is connected with a remaining power monitoring module and an inspection monitoring module respectively, the remaining power monitoring module is connected with an inspection plan dynamic adjustment module, the inspection monitoring module is connected with an inspection abnormality identification module, the inspection abnormality identification module is connected with a traffic emergency guiding module, and the reference database is connected with the inspection speed planning module.
The tunnel section dividing module is used for obtaining the length of the highway tunnel, so that the highway tunnel is uniformly divided, a plurality of tunnel sections are obtained, and the divided tunnel sections are numbered along the direction from the starting point to the end point of the highway tunnel.
Be applied to above-mentioned embodiment, still should patrol and examine the monitoring coverage that the robot carried monitoring facilities when carrying out the division with highway tunnel, through the model specification that obtains monitoring facilities in the inspection robot, obtain the monitoring coverage of monitoring facilities from this, and then with the interval division length of even division and monitoring coverage contrast, if the interval division length of even division is less than monitoring coverage, then need adjust the interval division length, make it be greater than monitoring coverage or make the interval division length be the integral multiple of monitoring coverage, can ensure like this that every tunnel section is all covered entirely, the emergence of monitoring blind area and overlapping area has been avoided, and then monitoring effect can be optimized to the maximum extent, ensure that the monitoring coverage to whole tunnel is abundant and even, moreover can avoid repetition monitoring to cause the wasting of resources.
The comparison of the intermediate partition length and the monitoring coverage is schematically shown in fig. 2.
In further examples, the monitoring devices carried by the inspection robot include, but are not limited to, cameras, various types of sensors (e.g., temperature sensors, humidity sensors, smoke sensors, gas sensors), navigational positioning devices, and the like.
The inspection speed planning module is used for calling the historical inspection log of the highway tunnel to plan the inspection speed corresponding to each tunnel section, and the specific operation is as follows: and extracting the inspection results of each tunnel section in each inspection log from the historical inspection logs, further selecting the inspection log with the abnormal inspection result as the abnormal inspection log, and counting the number and abnormal directions of the abnormal inspection logs, wherein the abnormal directions are traffic road conditions or traffic environments.
The method is characterized in that the current time is limited when the historical inspection logs are extracted, the situation that the current tunnel condition cannot be reflected due to the fact that the extracted historical inspection logs are too long is avoided, the reference value for the inspection speed planning is further lost, the number of the extracted historical inspection logs is not too small, the number of the historical inspection logs extracted is not less than 5 in an exemplary mode, and the influence on the inspection speed planning accuracy caused by too small inspection result data is avoided.
Based on the number of the abnormal inspection logs and the abnormal direction corresponding to each abnormal inspection log, calculating the inspection abnormal occupation ratio corresponding to each tunnel section, wherein the calculation expression is thatIn/>Represents the/>Abnormal inspection log quantity corresponding to tunnel segment,/>Representing tunnel segment number,/>，/>Represents the/>Historical inspection log quantity of tunnel section,/>、/>Respectively represent the/>The abnormal direction in the abnormal inspection logs corresponding to the tunnel section is the number of the abnormal inspection logs of traffic road conditions and traffic environments, and the number of the abnormal inspection logs is/are equal to the number of the abnormal inspection logs of traffic conditions and traffic environments、/>Respectively represent the weight factors corresponding to traffic road conditions and traffic environments,/>Represents a natural constant, wherein/>、/>The acquisition of (2) is as follows:
extracting an abnormal processing time length from an abnormal inspection log of each tunnel section, and counting the average abnormal processing time length corresponding to the abnormal direction as the traffic road condition and the traffic environment based on the abnormal direction;
the average abnormal processing time length corresponding to the traffic road condition and the traffic environment is calculated by the ratio of the abnormal points to the traffic road condition and the traffic environment 、/>。
In the above example, the average exception handling duration corresponding to the traffic condition and traffic environment is the exception direction is the traffic condition、/>Then/>，/>。
The reason that the abnormal processing time length of the traffic road condition and the traffic environment in the history inspection log is taken as the corresponding weight factors of the traffic road condition and the traffic environment is that the abnormal processing time length can reflect the actual influence degree of the traffic road condition and the traffic environment, and the longer abnormal processing time length usually indicates that the road condition or the environment problem is serious and possibly has great influence on traffic, so that the abnormal processing time length can be taken as an important index of the weight factors, and in addition, the abnormal processing time length is an objective and quantifiable index and can intuitively reflect the conditions of the traffic road condition and the traffic environment. The actual situation can be converted into a digital index by taking the abnormal processing time length as a weight factor, so that quantitative evaluation and comparison analysis are facilitated.
It is to be noted that the history inspection log includes the monitoring results of the traffic road condition and the traffic environment, when the traffic road condition is abnormal, the abnormality points to the traffic road condition, and when the traffic environment is abnormal, the abnormality points to the traffic environment, and in addition, the history inspection log also includes the processing time for monitoring the abnormality.
And matching the inspection abnormal occupation ratio corresponding to each tunnel segment with the inspection abnormal occupation ratio interval corresponding to each inspection abnormal grade in the reference database to obtain the inspection abnormal grade corresponding to each tunnel segment, and screening the required inspection speed corresponding to each tunnel segment from the reference database.
In the example of the above scheme, the inspection anomaly level has three levels, and the required inspection speed corresponding to the first level inspection anomaly level > the required inspection speed corresponding to the second level inspection anomaly level > the required inspection speed corresponding to the third level inspection anomaly level.
According to the invention, road sections are divided by the highway tunnel, the historical inspection log of each tunnel section is called, so that the inspection abnormal occupation ratio corresponding to each tunnel section is calculated based on the inspection result of the historical inspection log, the inspection speed corresponding to each tunnel section is planned according to the inspection abnormal occupation ratio, the targeted planning of the inspection speed on each tunnel section is realized, the inspection speed of a robot can be accelerated on the tunnel section which is relatively simple and smooth, the inspection time and energy are saved, the inspection efficiency is improved, the inspection speed can be reduced in the key tunnel section, the robot can inspect the areas more carefully, the potential problems are found, the inspection accuracy and the inspection comprehensiveness are improved, the potential safety hazards possibly occurring in the inspection process are reduced to the maximum extent while the inspection efficiency is improved, and the inspection quality is improved.
The residual electric quantity monitoring module is used for monitoring the residual electric quantity in real time when the inspection robot executes an inspection plan along the inspection track according to the required inspection speed corresponding to each tunnel section, wherein the inspection robot comprises monitoring equipment.
The inspection plan dynamic adjustment module is used for identifying whether charging is needed or not based on the residual electric quantity monitored in real time, specifically, comparing the monitored residual electric quantity with the set safe residual electric quantity, if the monitored residual electric quantity is smaller than the set safe residual electric quantity, identifying that charging is needed, and exemplarily, the safe residual electric quantity is 30%, recording the current inspection position of the inspection robot when the charging is needed, comparing the current inspection position with the position of the charging pile, and therefore determining an adaptive charging route and an adaptive travelling speed, and further adjusting the inspection plan.
Preferably, determining the adapted charging route is implemented as follows: the method comprises the steps that a current inspection tunnel section and an inspected tunnel section of the inspection robot are positioned based on the current inspection position of the inspection robot, so that the tunnel section to be inspected is determined, and particularly, the inspection robot inspects according to an inspection plan, generally inspects from a tunnel starting point, the tunnel section before the current inspection tunnel section is the inspected tunnel section, and at the moment, the tunnel section to be inspected can be determined according to all the divided tunnel sections and the inspected tunnel section.
Comparing the current inspection position with the position of the charging pile, so as to plan a charging route, wherein a plurality of charging routes are planned.
And counting the travel distance of each planned charging route and the number of included tunnel segments to be detected.
And recording the serial numbers of the tunnel sections to be detected on each charging route, thereby obtaining the inspection abnormal occupation ratio corresponding to the tunnel sections to be detected, and further carrying out average calculation on the inspection abnormal occupation ratio corresponding to the tunnel sections to be detected on each charging route, so as to obtain the average inspection abnormal occupation ratio.
Calculating the corresponding selected value of each charging route by combining the travel distance of each charging route with the number of the included tunnel segments to be detected and the average routing inspection abnormal occupation ratio, wherein the selected value is calculated by the number of the included tunnel segments to be detected; The shorter the travel distance of the medium charging route is, the more the number of the included tunnel segments to be detected is, the larger the average inspection abnormal occupation ratio is, and the greater the selection value of the charging route is, wherein the medium total travel distance is the sum of the travel distances of the charging routes.
And selecting the charging route corresponding to the maximum selected value degree from the selected value degrees corresponding to the charging routes as an adaptive charging route.
Further preferably, the determination of the adapted travel speed is as follows: and combining the travel distance of the adaptive charging route with the residual electric quantity to obtain the travel limiting speed of the inspection robot on the adaptive charging circuit, wherein the travel limiting speed is the lowest travel speed of the inspection robot under the residual electric quantity, namely, the inspection robot cannot support to travel to the charging position under the travel speed.
And acquiring the required inspection speed of each tunnel section to be inspected based on the serial number of each tunnel section to be inspected on the adaptive charging route.
And comparing the required inspection speed of each tunnel section to be inspected, selecting the minimum required inspection speed from the required inspection speeds, comparing the minimum required inspection speed with the limited advancing speed of the inspection robot on the adaptive charging circuit line, and taking the limited advancing speed as the adaptive advancing speed if the minimum inspection speed is lower than the limited advancing speed, otherwise taking the minimum inspection speed as the adaptive advancing speed.
According to the invention, when the charging is required, the adaptive charging route is determined by recording the current inspection position of the inspection robot, and then the travelling speed planning on the way of the charging circuit is performed according to the tunnel section to be monitored existing on the adaptive charging route, so that the requirement of the inspection speed of the tunnel section to be monitored can be met while the remaining electric quantity can be supported to travel to the charging position in the charging route, the condition that the robot can effectively monitor more tunnel sections before charging can be ensured, the inspection efficiency and quality are improved, the requirement of charging and the requirement of inspection can be met, and the practical value is higher.
Still further preferably, the patrol plan is adjusted to: and extracting the tunnel section to be inspected contained in the adaptive charging route from the current inspection tunnel section to serve as the current tunnel section to be inspected, and performing inspection according to the inspection plan instead of the office work.
According to the invention, when the inspection robot executes the inspection plan along the inspection track, the residual electric quantity is monitored in real time, so that the current inspection position of the inspection robot is recorded when the inspection robot is identified to be charged, the adaptive charging route is determined, the inspection plan is adjusted, the pertinence and flexibility adjustment of the inspection plan and the charging route in the inspection process are realized, the monitoring of the key areas can be considered in the route to charge, the working time of the robot can be utilized to the greatest extent, the time consumed by the robot in the route due to the insufficient electric quantity is avoided, the inspection efficiency and coverage range are improved, the missed monitoring of the key areas can be avoided, and the potential problems or abnormal situations can be found in time.
The inspection monitoring module is used for monitoring traffic road conditions and traffic environment by using monitoring equipment when the inspection robot executes an inspection plan according to the required inspection speed corresponding to each tunnel section, wherein the traffic road conditions are specifically monitored by the inspection robot by using a camera to carry out image shooting on the traffic of the tunnel section, so that whether traffic is jammed, traffic accidents exist or not is identified, and the traffic environment is specifically monitored by the inspection robot by using various sensors.
The reference database is used for storing patrol abnormal occupation ratio intervals corresponding to various patrol abnormal grades and storing required patrol speed corresponding to each patrol abnormal grade.
The inspection anomaly identification module is used for identifying whether traffic obstruction exists according to the traffic road condition monitoring result, and identifying whether the traffic environment is bad according to the traffic environment monitoring result.
The traffic emergency guiding module is used for positioning the traffic obstacle and the tunnel section where the traffic environment is severe when the traffic obstacle or the traffic environment is severe in the road tunnel is identified, marking the corresponding tunnel section as a risk tunnel section, and simultaneously carrying out early warning, so that the centralized distribution position of the risk tunnel section and the continuous condition of the risk tunnel section are analyzed, and the traffic emergency guiding is carried out by the inspection robot according to the situation.
In the innovative implementation of the scheme, the concentrated distribution position of the risk tunnel segments is analyzed as follows: acquiring the serial numbers of the risk tunnel segments, and extracting the serial numbers of the initial tunnel segment, the intermediate tunnel segment and the ending tunnel segment from the serial numbers of the tunnel segments, wherein the serial numbers of the initial tunnel segment are 1, and the serial numbers of the intermediate tunnel segment are 1Ending tunnel segment number n, in one particular example when n is 9,/>When n is 10,/>。
Comparing the serial numbers of the risk tunnel sections with the serial numbers of the initial tunnel section, the middle tunnel section and the tail tunnel section, and respectively counting the initial proximity corresponding to the risk tunnel sectionsMiddle segment proximity/>Tail proximityWherein/>、/>、/>In/>Represents the/>Numbering of risk tunnel segments,/>And the median number of the corresponding number of each tunnel segment is indicated.
Comparing the proximity of the risk tunnel section with the initial tunnel section, the middle tunnel section and the end tunnel section, and determining a modelObtaining the centralized distribution position/>, of the risk tunnel segments。
In a further innovation of the above scheme, the risk tunnel segment continuity is analyzed as follows: and comparing the numbers of the risk tunnel segments, and further extracting the maximum number and the minimum number from the numbers.
Comparing the maximum number with the minimum number of the risk tunnel segment, and calculating the continuous index of the risk tunnel segmentThe computational expression is/>In/>、/>Respectively represent the maximum number and the minimum number of the risk tunnel segments,/>The number of the risk tunnel segments is represented, wherein the smaller the interval between the maximum number and the minimum number of the risk tunnel segments is, the larger the risk tunnel segment continuous index is.
Still further, the implementation process of the traffic emergency guidance by the inspection robot is as follows: (1) If the centralized distribution position of the risk tunnel section is located in the initial section, the patrol robot is linked with the tunnel entrance information board to conduct sealing treatment, so that a driver can be warned in advance to avoid the risk tunnel section, the possibility of accident occurrence is reduced, in addition, the sealing treatment is conducted in the initial section through the information board, and traffic interference after vehicles enter a tunnel can be reduced. Traffic jam and congestion caused by sealing treatment in the tunnel are avoided, and smooth traffic of other traffic vehicles is ensured.
(2) And if the centralized distribution position of the risk tunnel sections is positioned in the middle section, comparing the continuous index of the risk tunnel sections with a limit value preset by a system, wherein the limit value is 0.6 in an exemplary manner, if the continuous index of the risk tunnel sections is larger than the limit value, the inspection robot sends alarm information to conduct detouring guidance on vehicles existing in the tunnel, and if the continuous index of the risk tunnel sections is smaller than or equal to the limit value, the inspection robot advances to each risk tunnel section to conduct local guidance.
(3) And if the centralized distribution position of the risk tunnel section is positioned at the tail section, the inspection robot sends a deceleration passing voice prompt to the vehicles at the initial section and the middle section.
According to the invention, when the risk tunnel section is identified according to the traffic road condition and the traffic environment monitoring result of each tunnel section, the centralized distribution position of the risk tunnel section and the continuous condition analysis of the risk tunnel section are increased, so that the routing inspection robot is utilized to travel to the corresponding processing position to conduct traffic emergency guidance in a targeted manner, accurate road condition information and traffic advice can be provided for a driver, the risk area is helped to be avoided or proper measures are taken, the accident risk is reduced, the road safety is ensured, the driver can be helped to pass through the risk tunnel section more quickly and more safely, traffic jam and delay are reduced, the traffic efficiency is improved, the optimization of tunnel traffic management is realized, and the overall operation efficiency and the safety of a tunnel traffic system are promoted.
And further optimally, the traffic emergency guiding module further comprises a fan which is linked with the corresponding tunnel section by the inspection robot to work based on the tunnel section where the traffic environment is severe, so that traffic conditions such as smoke removal and harmful gas removal can be improved, and traffic safety is improved.
The foregoing is merely illustrative and explanatory of the principles of this invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of this invention or beyond the scope of this invention as defined in the claims.
Claims (10)
1. Highway tunnel monitoring, inspection, early warning integration robot based on thing networking, its characterized in that includes following module:
The tunnel section dividing module is used for obtaining the length of the highway tunnel, so that the highway tunnel is uniformly divided, and a plurality of tunnel sections are obtained;
The routing inspection speed planning module is used for calling the historical routing inspection log of the highway tunnel to plan the routing inspection speed corresponding to each tunnel segment;
The system comprises a residual electric quantity monitoring module, a monitoring module and a control module, wherein the residual electric quantity monitoring module is used for monitoring the residual electric quantity in real time when a patrol robot executes a patrol plan along a patrol track according to the required patrol speed corresponding to each tunnel section, and the patrol robot comprises monitoring equipment;
The inspection plan dynamic adjustment module is used for identifying whether the charging is needed or not based on the residual electric quantity monitored in real time, recording the current inspection position of the inspection robot when the charging is needed, comparing the current inspection position with the position of the charging pile, determining an adaptive charging route and an adaptive travelling speed, and adjusting the inspection plan;
The inspection monitoring module is used for monitoring traffic road conditions and traffic environments by using monitoring equipment when the inspection robot executes an inspection plan according to the required inspection speed corresponding to each tunnel section;
The reference database is used for storing patrol abnormal occupation ratio intervals corresponding to various patrol abnormal grades and storing required patrol speeds corresponding to the patrol abnormal grades;
the inspection abnormality identification module is used for identifying whether traffic obstruction exists according to the traffic road condition monitoring result and identifying whether the traffic environment is bad according to the traffic environment monitoring result;
The traffic emergency guiding module is used for positioning the traffic obstacle and the tunnel section where the traffic environment is severe when the traffic obstacle or the traffic environment is severe in the analysis of the road tunnel, marking the corresponding tunnel section as a risk tunnel section, and simultaneously carrying out early warning, so as to analyze the centralized distribution position of the risk tunnel section and the continuous condition of the risk tunnel section, and accordingly, carrying out traffic emergency guiding by the inspection robot.
2. The internet of things-based highway tunnel monitoring, inspection and early warning integrated robot as set forth in claim 1, wherein: the operation of planning the inspection speed corresponding to each tunnel segment is as follows:
Extracting the inspection results of each tunnel section in each inspection log from the historical inspection logs, further selecting the inspection log with the abnormal inspection result as the abnormal inspection log, and recording the abnormal inspection log as the abnormal inspection log, thereby counting the number and the abnormal direction of the abnormal inspection log;
based on the number of the abnormal inspection logs and the abnormal direction corresponding to each abnormal inspection log, calculating the inspection abnormal occupation ratio corresponding to each tunnel section, wherein the calculation expression is that In/>Represents the/>Abnormal inspection log quantity corresponding to tunnel segment,/>Representing tunnel segment number,/>，/>Represents the/>Historical inspection log quantity of tunnel section,/>、/>Respectively represent the/>The abnormal direction in the abnormal inspection logs corresponding to the tunnel section is the number of the abnormal inspection logs of traffic road conditions and traffic environments, and the number of the abnormal inspection logs is/are equal to the number of the abnormal inspection logs of traffic conditions and traffic environments、/>Respectively represent the weight factors corresponding to traffic road conditions and traffic environments,/>Representing natural constants;
And matching the inspection abnormal occupation ratio corresponding to each tunnel segment with the inspection abnormal occupation ratio interval corresponding to each inspection abnormal grade in the reference database to obtain the inspection abnormal grade corresponding to each tunnel segment, and screening the required inspection speed corresponding to each tunnel segment from the reference database.
3. The internet of things-based highway tunnel monitoring, inspection and early warning integrated robot as set forth in claim 2, wherein: the said、/>The acquisition of (2) is as follows:
extracting an abnormal processing time length from an abnormal inspection log of each tunnel section, and counting the average abnormal processing time length corresponding to the abnormal direction as the traffic road condition and the traffic environment based on the abnormal direction;
the average abnormal processing time length corresponding to the traffic road condition and the traffic environment is calculated by the ratio of the abnormal points to the traffic road condition and the traffic environment 、。
4. The internet of things-based highway tunnel monitoring, inspection and early warning integrated robot as set forth in claim 1, wherein: the determining of the adapted charging route is implemented as follows:
Positioning a current inspection tunnel section of the inspection robot based on the current inspection position of the inspection robot, thereby determining a tunnel section to be inspected;
Comparing the current inspection position with the position of the charging pile, thereby planning a charging route;
Counting the travel distance of each planned charging route and the number of included tunnel segments to be detected;
Recording the serial numbers of the tunnel sections to be detected on each charging route, thereby obtaining the inspection abnormal occupation ratio corresponding to the tunnel sections to be detected, and further carrying out average calculation on the inspection abnormal occupation ratio corresponding to the tunnel sections to be detected on each charging route to obtain the average inspection abnormal occupation ratio;
calculating the corresponding selected value of each charging route by combining the travel distance of each charging route with the number of the included tunnel segments to be detected and the average inspection abnormal occupation ratio;
and selecting the charging route corresponding to the maximum selected value degree from the selected value degrees corresponding to the charging routes as an adaptive charging route.
5. The internet of things-based highway tunnel monitoring, inspection and early warning integrated robot as set forth in claim 4, wherein: the adaptive travel speed is determined as follows:
Combining the travel distance of the adaptive charging route with the residual electric quantity to obtain the limited travel speed of the inspection robot on the adaptive charging circuit line;
Acquiring the required inspection speed of each tunnel section to be inspected based on the serial number of each tunnel section to be inspected on the adaptive charging route;
And comparing the required inspection speed of each tunnel section to be inspected, selecting the minimum required inspection speed from the required inspection speeds, comparing the minimum required inspection speed with the limited advancing speed of the inspection robot on the adaptive charging circuit line, and taking the limited advancing speed as the adaptive advancing speed if the minimum inspection speed is lower than the limited advancing speed, otherwise taking the minimum inspection speed as the adaptive advancing speed.
6. The internet of things-based highway tunnel monitoring, inspection and early warning integrated robot as set forth in claim 4, wherein: the adjustment inspection plan is as follows:
and extracting the tunnel section to be detected contained in the adaptive charging route from the current inspection tunnel section to be used as the current tunnel section to be detected.
7. The internet of things-based highway tunnel monitoring, inspection and early warning integrated robot as set forth in claim 1, wherein: the centralized distribution position of the risk tunnel segments is analyzed as follows:
acquiring the serial numbers of the risk tunnel segments, and extracting the serial numbers of the initial tunnel segment, the serial numbers of the middle tunnel segment and the serial numbers of the ending tunnel segment from the serial numbers of the tunnel segments;
Comparing the serial numbers of the risk tunnel sections with the serial numbers of the initial tunnel section, the middle tunnel section and the tail tunnel section, and respectively counting the initial proximity corresponding to the risk tunnel sections Middle segment proximity/>Tail proximityWherein/>、/>、/>In/>Represents the/>Numbering of risk tunnel segments,/>A median number representing the corresponding number of each tunnel segment;
comparing the proximity of the risk tunnel section with the initial tunnel section, the middle tunnel section and the end tunnel section, and determining a model Obtaining the centralized distribution position/>, of the risk tunnel segments。
8. The internet of things-based highway tunnel monitoring, inspection and early warning integrated robot as set forth in claim 7, wherein: the risk tunnel segment continuity is analyzed as follows:
Comparing the numbers of the risk tunnel sections, and further extracting the maximum number and the minimum number from the numbers;
Comparing the maximum number with the minimum number of the risk tunnel segment, and calculating the continuous index of the risk tunnel segment The computational expression is/>In/>、/>Respectively represent the maximum number and the minimum number of the risk tunnel segments,/>Representing the number of risk tunnel segments.
9. The internet of things-based highway tunnel monitoring, inspection and early warning integrated robot as set forth in claim 8, wherein: the implementation process of the routing inspection robot for carrying out the passing emergency guidance is as follows:
(1) If the centralized distribution position of the risk tunnel section is positioned in the initial section, the inspection robot is linked with the tunnel entrance information board to perform the channel sealing treatment;
(2) If the centralized distribution position of the risk tunnel sections is positioned in the middle section, comparing the continuous indexes of the risk tunnel sections with a limit value, if the continuous indexes of the risk tunnel sections are larger than the limit value, sending alarm information by the inspection robot to bypass and guide vehicles existing in the tunnel, and if the continuous indexes of the risk tunnel sections are smaller than or equal to the limit value, moving to each risk tunnel section by the inspection robot to conduct local guide;
(3) And if the centralized distribution position of the risk tunnel section is positioned at the tail section, the inspection robot sends a deceleration passing voice prompt to the vehicles at the initial section and the middle section.
10. The internet of things-based highway tunnel monitoring, inspection and early warning integrated robot as set forth in claim 1, wherein: the emergency guidance module for the traffic also comprises a fan which is operated by the inspection robot and is linked with the corresponding tunnel section based on the tunnel section where the traffic environment is severe.
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