Urban road multi-device dynamic collaborative patrol method

Abstract

The invention discloses an urban road multi-device dynamic collaborative patrol method. The method comprises the following steps: S1, identifying road surface damage characteristics through a wide-area sensing layer and generating an event trigger signal; s2, analyzing the event trigger signal in real time through a digital intelligent decision-making layer, and generating a scheduling instruction including position coordinates and damage types; s3, the dynamic execution layer executes motor vehicle lane detection and driving vibration characteristics according to the scheduling instruction and executes sidewalk detection according to the scheduling instruction; s4, carrying out time domain-space domain correlation analysis on the image features and the vibration waveforms through a digital intelligent decision-making layer, and generating a detection result; and S5, the intelligent decision-making layer adjusts patrol paths and working parameters of the devices according to detection results of the terminals to form closed-loop feedback. According to the invention, the road patrol efficiency and quality are greatly improved, the optimal configuration of resources is realized, and the road quality problem can be found and solved more timely and comprehensively.

Description

Technical Field

[0001] The present invention relates to the technical field of road quality detection and automatic information collection, and in particular to a method for dynamic collaborative inspection of urban roads by multiple devices. Background Art

[0002] With the rapid development of urban transportation systems, road hazards have become a key factor affecting urban traffic safety and efficiency. Traditional methods for detecting road hazards rely primarily on manual inspections, which are not only time-consuming and labor-intensive, but also inefficient and difficult to achieve comprehensive monitoring of vast urban road networks. Therefore, the need for efficient and automated methods for detecting road hazards has become an urgent need to improve the level of urban road safety management. Furthermore, existing technologies suffer from problems such as independent equipment operation and lack of coordination, leading to irrational resource allocation, poor efficiency, and poor results. The present invention designs a task coordination mechanism based on event triggering. Summary of the Invention

[0003] In response to the shortcomings of the existing technology, the purpose of the present invention is to provide a method for dynamic collaborative inspection of urban roads by multiple devices, which greatly improves the efficiency and quality of road inspections, realizes the optimal allocation of resources, and can more timely and comprehensively discover and solve road quality problems. In order to achieve the above-mentioned purpose and other advantages of the present invention, a method for dynamic collaborative inspection of urban roads by multiple devices is provided, comprising:

[0004] S1, identifies road damage characteristics through the wide-area perception layer and generates event trigger signals;

[0005] S2. Analyze the event trigger signal in real time through the digital intelligent decision layer to generate a dispatch instruction including the location coordinates and damage type;

[0006] S3, the dynamic execution layer executes motor vehicle lane detection through the dispatching instruction, collects and analyzes road surface image data and driving vibration characteristics, and executes pedestrian sidewalk detection through the dispatching instruction, collects and analyzes sidewalk pavement surface image data and motion vibration waveform;

[0007] S4, performing time-domain-spatial correlation analysis on image features and vibration waveforms through the digital intelligent decision layer to generate detection results;

[0008] S5. The digital intelligent decision-making layer adjusts the inspection path and working parameters of each device according to the detection results of each terminal, forming a closed-loop feedback.

[0009] Preferably, the wide-area perception layer in step S1 includes a drone, which is equipped with a visible light / infrared composite optical sensor to conduct wide-area inspections, identify road damage characteristics and generate event trigger signals.

[0010] Preferably, the pavement damage characteristics include damage types and evaluation parameters, the damage types include pavement cracks, potholes, network cracks, repaired potholes, repaired network cracks, and repaired cracks, and the evaluation parameters include length / area evaluation parameters.

[0011] Preferably, the dynamic execution layer includes a patrol car and a mechanical dog, wherein the patrol car performs motor vehicle lane detection based on the dispatch instruction, collects and analyzes road surface image data and driving vibration characteristics;

[0012] The robot dog performs sidewalk detection based on the scheduling instruction, and collects and analyzes sidewalk pavement surface image data and motion vibration waveforms.

[0013] Preferably, the inspection vehicle is equipped with a multi-axis acceleration sensor array, and the vibration characteristic analysis includes detection of exposure duration and magnitude of abnormal vibration;

[0014] The vibration sensors carried on the limbs of the mechanical dog collect contact vibration waveforms of the paving bricks to determine the looseness of the paving bricks.

[0015] Preferably, the step of detecting the looseness of paving bricks by the mechanical dog specifically includes:

[0016] 1) Perform gait traversal in the target area and collect vibration signals in the X / Y biaxial directions;

[0017] 2) Construct an attenuation model of vibration energy in the brick joint area;

[0018] 3) Locate the three-dimensional coordinates of the loose bricks according to the phase mutation point of the attenuation model.

[0019] 7. A method for dynamic collaborative inspection of urban roads by multiple devices as described in claim 6, characterized in that the digital intelligent decision-making layer includes a cloud control center, and the cloud control center has a built-in data fusion module, which performs time-space correlation analysis on the damage distribution detected by drones, the vibration time series characteristics of inspection vehicles, and the loose positioning data of mechanical dog bricks.

[0020] 8. A method for dynamic collaborative inspection of urban roads by multiple devices as described in claim 1, characterized in that the cloud control center includes a damage degradation trend prediction module, which is used to analyze the loss of driving / walking comfort that may be caused by the detected disease and the damage degradation trend at the location in combination with historical data, and then generate a composite inspection report including repair priority.

[0021] Compared with existing technologies, the present invention offers the following advantages: Its event-triggered task coordination mechanism offers numerous significant benefits. First, it significantly improves the efficiency of urban road inspections. Through the collaborative work of various terminals, duplicate inspections and resource waste are avoided, enabling larger-scale inspections to be completed in a shorter time. Second, it significantly enhances the accuracy and comprehensiveness of inspections. The combination of large-scale initial screening by drones and precise detection by patrol vehicles and robotic dogs ensures that no potential road problems are missed, providing a reliable basis for timely repair and maintenance. Furthermore, it effectively reduces road maintenance costs. Early detection and resolution of problems prevents further deterioration of road damage, thereby reducing the cost of later large-scale repairs. Furthermore, it enhances the flexibility and adaptability of road inspections. The tasks and coordination methods of various terminals can be dynamically adjusted according to varying road conditions and needs, adapting to various complex urban road environments. Finally, it improves the safety of urban roads. Timely detection and resolution of road defects reduces the risk of traffic accidents caused by road quality issues and ensures the safety of citizens. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Schematic diagram of the system architecture and equipment coordination of the method for dynamic collaborative inspection of multiple devices on urban roads according to the present invention;

[0023] Figure 2 The X / Y alternating travel path of the mechanical dog in the orthogonal gait of the multi-device dynamic collaborative inspection method for urban roads according to the present invention;

[0024] Figure 3 This is an example of mechanical dog vibration data according to the method for dynamic collaborative inspection of urban roads by multiple devices of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] Reference Figure 1 , a method for dynamic collaborative inspection of urban roads by multiple devices, comprising:

[0027] S1. Identify road damage characteristics through a wide-area perception layer and generate an event trigger signal. The wide-area perception layer in step S1 includes an unmanned aerial vehicle (UAV), equipped with a visible light / infrared composite optical sensor, to conduct wide-area inspections, identify road damage characteristics, and generate an event trigger signal. The road damage characteristics include damage types and evaluation parameters, including cracks, potholes, cracked meshes, repaired potholes, repaired cracked meshes, and repaired cracks. The evaluation parameters include length / area evaluation parameters. The UAV performs high-altitude patrols along a planned route, identifies road damage types and evaluation parameters through real-time image analysis, and generates a trigger signal containing geographic location and preliminary damage determination.

[0028] S2. Analyze the event trigger signal in real time through the digital intelligent decision layer to generate a dispatch instruction including the location coordinates and damage type;

[0029] S3. The dynamic execution layer executes motor vehicle lane detection based on the dispatch instruction, collects and analyzes road surface image data and driving vibration characteristics, and executes pedestrian sidewalk detection based on the dispatch instruction, collects and analyzes sidewalk pavement surface image data and motion vibration waveforms. The dynamic execution layer includes a patrol vehicle and a robotic dog. The patrol vehicle executes motor vehicle lane detection based on the dispatch instruction, collects and analyzes road surface image data and driving vibration characteristics. The patrol vehicle is equipped with a multi-axis acceleration sensor array, and the vibration characteristic analysis includes the exposure duration and magnitude detection of abnormal vibration.

[0030] The robot dog performs sidewalk detection based on the dispatch instruction, collecting and analyzing sidewalk pavement surface image data and motion vibration waveforms. The vibration sensors on the robot dog's limbs collect contact vibration waveforms of paving bricks to determine whether the paving bricks are loose. The steps for the robot dog to detect loose paving bricks specifically include:

[0031] 1) Perform gait traversal in the target area and collect vibration signals in the X / Y biaxial directions;

[0032] 2) Construct an attenuation model of vibration energy in the brick joint area;

[0033] 3) Locate the three-dimensional coordinates of the loose bricks according to the phase mutation point of the attenuation model.

[0034] Furthermore, the robot dog sidewalk pavement detection method is as follows:

[0035] 1. Gait control and data acquisition:

[0036] Adopt the orthogonal gait traversal algorithm based on the grid map, as shown in the following method: Figure 2 , ensuring 100% coverage of the detection area;

[0037] The foot-end embedded vibration detection array is used to collect the z-axis vibration waveform at 5s intervals at the moment of contact with the ground, such as Figure 3 As shown;

[0038] 2. Vibration signal modeling and positioning:

[0039] After performing wavelet denoising on the original signal, short-time Fourier transform is used to extract frequency domain features;

[0040] Construct a vibration energy attenuation model:

[0041] 3. Loose positioning and 3D mapping

[0042] Abnormal displacement of brick joints is identified through phase mutation point detection algorithm.

[0043] S4, performing time-domain-spatial correlation analysis on image features and vibration waveforms through the digital intelligent decision layer to generate detection results;

[0044] S5. The digital intelligent decision-making layer adjusts the inspection path and operating parameters of each device based on the detection results of each terminal, forming a closed-loop feedback loop. The digital intelligent decision-making layer includes a dynamic planning module, which implements the following optimization strategies:

[0045] (1) Density adjustment of the UAV’s subsequent patrol path;

[0046] (2) Matching of patrol vehicle detection speed and sensor working mode;

[0047] (3) Dynamic balance between the gait parameters of the mechanical dog and the detection accuracy.

[0048] Furthermore, the digital intelligence decision-making layer includes a cloud-based control center with a built-in data fusion module. This module performs spatiotemporal correlation analysis on the damage distribution detected by drones, the vibration time series characteristics of patrol vehicles, and the location of loose bricks detected by the robot dog. The end-to-end control center also has a built-in multi-source data fusion engine.

[0049] Furthermore, the cloud control center includes a damage degradation trend prediction module, which is used to analyze the loss of driving / walking comfort that may be caused by the detected disease, and the damage degradation trend at that location in combination with historical data, and then generate a composite detection report including repair priority.

[0050] Furthermore, intelligent collaboration and dynamic optimization are as follows:

[0051] 1. Multi-device task scheduling:

[0052] Assign detection equipment based on damage type: when a drone triggers a crack larger than 2 cm, a patrol vehicle is dispatched first; when a dislocated joint is triggered, a robotic dog is dispatched;

[0053] Establish a device status monitoring dashboard to display parameters such as workload, remaining power, and inspection progress in real time;

[0054] 2. Closed-loop feedback control:

[0055] The dynamic programming module trains a detection efficiency optimization model based on historical data and implements three core strategies, namely the optimization strategies implemented by the dynamic programming module:

[0056] The drone automatically adjusts the aerial photography overlap rate according to the damage density (20%-80% dynamically adjustable);

[0057] The patrol car matches the speed, i.e. 5-30km / h, and the sampling frequency, i.e. 10-100Hz, based on the complexity of the road conditions;

[0058] The robot dog switches between high-precision mode and fast mode. The high-precision mode is a low-speed step; the fast mode is a high-speed step.

[0059] 3. Emergency Response Mechanism:

[0060] After receiving the municipal emergency command, the priority interruption protocol is activated. That is, when an emergency inspection command is received from the municipal system, the nearest idle equipment is prioritized to generate a response team, and the optimal scheduling chain is constructed according to the GPS location to ensure that a response plan is generated within 30 seconds.

[0061] The number of devices and processing scales described herein are intended to simplify the description of the present invention, and the application, modification, and variation of the present invention will be apparent to those skilled in the art. Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiment. They can be applied to various fields suitable for the present invention. For those skilled in the art, additional modifications can be easily implemented. Therefore, the present invention is not limited to the specific details and illustrations shown and described herein without departing from the general concept defined by the claims and their equivalents.

Claims

1. A method for dynamic collaborative inspection of multiple devices on urban roads, characterized in that: The following steps are involved: S1, identifies road damage characteristics through the wide-area perception layer and generates event trigger signals; S2. Analyze the event trigger signal in real time through the digital intelligent decision layer to generate a dispatch instruction including the location coordinates and damage type; S3, the dynamic execution layer executes motor vehicle lane detection through the dispatching instruction, collects and analyzes road surface image data and driving vibration characteristics, and executes pedestrian sidewalk detection through the dispatching instruction, collects and analyzes sidewalk pavement surface image data and motion vibration waveform; S4, performing time-domain-spatial correlation analysis on image features and vibration waveforms through the digital intelligent decision layer to generate detection results; S5. The digital intelligent decision-making layer adjusts the inspection path and working parameters of each device according to the detection results of each terminal, forming a closed-loop feedback.

2. A method for dynamic collaborative inspection of urban roads by multiple devices according to claim 1, characterized in that: The wide-area perception layer in step S1 includes a drone, which is equipped with a visible light / infrared composite optical sensor to conduct wide-area inspections, identify road damage characteristics and generate event trigger signals.

3. A method for dynamic collaborative inspection of urban roads by multiple devices according to claim 2, characterized in that: The pavement damage characteristics include damage types and evaluation parameters. The damage types include pavement cracks, potholes, network cracks, repaired potholes, repaired network cracks, and repaired cracks. The evaluation parameters include length / area evaluation parameters.

4. A method for dynamic collaborative inspection of urban roads by multiple devices according to claim 1, characterized in that: The dynamic execution layer includes a patrol car and a mechanical dog, wherein the patrol car performs motor vehicle lane detection based on the dispatch instruction, collects and analyzes road surface image data and driving vibration characteristics; The robot dog performs sidewalk detection based on the scheduling instruction, and collects and analyzes sidewalk pavement surface image data and motion vibration waveforms.

5. A method for dynamic collaborative inspection of urban roads by multiple devices according to claim 4, characterized in that: The patrol vehicle is equipped with a multi-axis acceleration sensor array, and the vibration characteristic analysis includes the exposure duration and magnitude detection of abnormal vibration; The vibration sensors carried on the limbs of the mechanical dog collect contact vibration waveforms of the paving bricks to determine the looseness of the paving bricks.

6. A method for dynamic collaborative inspection of urban roads by multiple devices according to claim 5, characterized in that: The steps of the mechanical dog detecting the looseness of the paving bricks specifically include: 1) Perform gait traversal in the target area and collect vibration signals in the X / Y biaxial directions; 2) Construct an attenuation model of vibration energy in the brick joint area; 3) Locate the three-dimensional coordinates of the loose bricks according to the phase mutation point of the attenuation model.

7. A method for dynamic collaborative inspection of urban roads by multiple devices according to claim 6, characterized in that: The digital intelligent decision-making layer includes a cloud control center, which has a built-in data fusion module. The data fusion module performs time-space correlation analysis on the damage distribution detected by the drone, the vibration time series characteristics of the inspection vehicle, and the loose positioning data of the mechanical dog bricks.

8. The method for dynamic collaborative inspection of urban roads by multiple devices according to claim 1, characterized in that: The cloud control center includes a damage degradation trend prediction module, which is used to analyze the loss of driving / walking comfort that may be caused by the detected disease and the damage degradation trend in combination with historical data, and then generate a composite detection report including repair priority.

9. A method for dynamic collaborative inspection of urban roads by multiple devices according to claim 7, characterized in that: In step S5, the digital intelligent decision-making layer includes a dynamic programming module, which executes the following optimization strategy: (1) Density adjustment of the UAV’s subsequent patrol path; (2) Matching of patrol vehicle detection speed and sensor working mode; (3) Dynamic balance between the gait parameters of the mechanical dog and the detection accuracy.

Images