A MESH network-based intelligent inspection unmanned aerial vehicle applied to a comprehensive pipe gallery

Abstract

The utility model relates to a kind of based on MESH network's intelligent inspection unmanned aerial vehicle applied to comprehensive pipe gallery, and it is related to inspection unmanned aerial vehicle field, including: unmanned aerial vehicle body;Observation equipment is used to gather the multi-source information of unmanned aerial vehicle in comprehensive pipe gallery;Center controller realizes the control to observation equipment;MESH network communication equipment is used for the transfer of information;MESH network transfer equipment is used as transfer node, so that unmanned aerial vehicle keeps and remote network connection;When unmanned aerial vehicle is in flight inspection, carry out three-dimensional coordinate positioning by laying MESH node in pipe gallery interior;KNN algorithm is used to optimize unmanned aerial vehicle inspection route;Information is transferred using MESH network, and single node failure does not lead to data transmission failure, that is, the utility model has self-organizing self-recovery capability.The positioning effect of the inspection unmanned aerial vehicle is better, and information transmission is stable, and adaptability is stronger.

Description

Technical Field

[0001] This invention relates to an intelligent inspection and disaster relief drone, specifically an intelligent inspection drone based on MESH network connection technology, applied to intelligent inspection of integrated utility tunnels and disaster relief. Background Technology

[0002] The inspection of integrated utility tunnels is one of the most representative and challenging tasks in power systems. The proper functioning of electrical equipment and facilities directly impacts the safe and stable operation of the power system. With the rapid development of integrated utility tunnels in my country, line inspections face challenges such as high inspection difficulty, long inspection times, variable and sometimes harsh environments, diverse and complex inspection data, and a high reliance on specialized personnel. While regular inspections by professionals allow for timely resolution of issues and prevent escalation, manual inspections are burdensome due to their large workload and the inherent subjective factors that can reduce the reliability of results. This inefficient and costly inspection method is no longer adequate for the development and needs of modern power systems.

[0003] By using intelligent inspection and disaster relief drones, staff can promptly detect abnormal temperatures in equipment and facilities along the pipeline, arrange for maintenance, and eliminate potential safety hazards. The promotion and application of intelligent inspection and disaster relief drones reduces the demanding, periodic maintenance requirements for staff. Through various sensors deployed by the drones, the operational status of equipment and facilities in the integrated utility tunnel can be monitored in real time, effectively preventing personal injury accidents in extremely harsh or even dangerous environments.

[0004] Traditional inspection drones transmit camera video information via conventional Wi-Fi or upload data after the inspection is completed. In long distances and harsh environments, there is a risk of signal interruption and loss of connection, which cannot guarantee reliable real-time transmission of video signals. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, the present invention aims to provide an intelligent inspection and disaster relief drone based on a MESH network for use in integrated utility tunnels. This intelligent inspection and disaster relief drone has a good alarm effect, stable and real-time information transmission, and also has a self-repair node function and strong adaptability.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A MESH-based intelligent inspection drone for integrated utility tunnels includes a drone body, smoke sensors 7 located on the front, rear, left, and right sides of the drone body, a 3D positioning sensor 19 and fog lights 11 located on the front of the drone body, an infrared camera 12, a real-view camera 20, a robotic arm 6, a MESH network relay device 9, a stepper motor 37, a push plate 21, a compression spring 10, a baffle 38, and a battery pack 26 for providing power, four Mecanum wheels and four robotic arms 6 located on the drone body, propellers connected to the four robotic arms 6 via propeller holders 8 and telescopic arms 5, four DC drive motors connected to the telescopic arms 5 and the four propellers, and four DC drive motors connected to the four Mecanum wheels. The inspection drone is equipped with a central controller 31 connected to the MESH network relay device 9, the output of the smoke sensor 7, the output of the 3D positioning sensor 19, the adjustment end of the propeller holder 8, the output of the infrared camera 12, the output of the battery pack 26, and the control end of eight DC drive motors. A push plate 21 is fixed on the stepper motor 37, and a compression spring 10 is fixedly connected to a baffle 38. The MESH network relay device 9 is located between the push plate 21 and the baffle 38. The MESH network relay device 9 and the MESH nodes laid in the pipe gallery form a MESH communication network, which can meet the requirements of real-time communication between the drone and the ground. When the drone is conducting inspections, it uses the MESH nodes laid inside the pipe gallery to perform 3D coordinate positioning so that it can correct itself if it deviates from the route.

[0008] The central controller 31 is connected to the MESH network relay device 9 and the host computer through the MESH communication module 36.

[0009] The inspection drone body includes a top plate 13 and a bottom plate 14; a robotic arm 6 is mounted on the top plate 13, and four DC drive motors connected to the telescopic arm 5 and four propellers are mounted on the top plate 13; four Mecanum wheels and four DC drive motors connected to the four Mecanum wheels are mounted on the bottom plate 14, and a real-view camera 20 is set on the bottom plate 13.

[0010] During flight inspections, the UAV uses MESH nodes installed inside the pipe gallery for three-dimensional coordinate positioning to correct for deviations from its route. The specific steps are as follows:

[0011] Step 1. A signal is emitted from the MESH nodes laid inside the utility tunnel, and the propagation speed of the signal is known;

[0012] Step 2. After receiving the signal from the MESH node, the MESH network relay device 9 mounted on the drone calculates the time it takes for the signal to travel from the MESH node inside the pipe gallery to the drone.

[0013] Step 3. Calculate the relative distance between the UAV and the MESH node inside the utility tunnel based on the propagation speed and propagation time;

[0014] Step 4. Using trilateration, combined with relative distances and known MESH node positions, calculate the three-dimensional coordinate information of the UAV;

[0015] Step 5. Use three-dimensional coordinate information to correct the inspection route of the UAV.

[0016] In step 5, the inspection route of the UAV is corrected using three-dimensional coordinate information, and the KNN algorithm is used for optimization, as detailed below:

[0017] First, feature matching points are searched to establish feature point pairs between the actual position of the UAV and the position of the UAV calculated by the trilateration method;

[0018] Secondly, by finding the k reference nodes that have the greatest impact on the trilateration calculation results in the actual positioning area of ​​the UAV, and by weighting the reference node coordinates according to the degree of influence of different reference nodes on the actual position of the UAV, the UAV's actual position coordinates are made as close as possible to the actual position coordinates of the UAV.

[0019] When the position coordinates calculated by trilateration are within the positioning area, the formula for calculating the Euclidean distance between them and the k reference nodes to the coordinates calculated by trilateration is:

[0020]

[0021] In the formula, D is the Euclidean distance between the reference node and the coordinates calculated by the trilateration method, k is the number of reference nodes, and M is the distance between the reference node and the coordinates calculated by the trilateration method. i For the i-th reference node, M represents the calculated coordinate position information using the trilateration method;

[0022] A weighting factor is used to optimize the Euclidean distance, minimizing the distance between the calculated coordinates from the trilateration method and the actual coordinates of the UAV. Let D be the distance from the i-th reference node to the calculated coordinates from the trilateration method. i Then the weighting factor δ i Represented as:

[0023]

[0024] The KNN algorithm uses different weighting factors to reflect the varying impacts of different reference nodes on the actual coordinates of the UAV. The weighting factor δ... iThe introduction of this feature makes the final reference point coordinates approximate the actual coordinates of the UAV. The final reference point is represented as:

[0025]

[0026] in, P is the reference point coordinate of the weighted equivalent UAV actual coordinates obtained by the KNN algorithm. i The coordinates of each reference point.

[0027] The smoke sensor 7 on the drone detects smoke and gas information in the integrated utility tunnel. The central controller 31 collects on-site smoke and gas information through the smoke sensor 7. Once smoke is detected, the central controller 31 collects on-site video information and temperature information through the rotatable real-view camera 20 and infrared camera 12 mounted under the drone. Then, it sends the collected video information and temperature information to the host computer through the MESH communication module 36 using the MESH network, so that remote operators can view the real-time video in the tunnel and perform corresponding operations.

[0028] The real-view camera 20 and infrared camera 12 mounted under the drone can collect video information in the integrated utility tunnel in real time and send it to the host computer. When the host computer's automatic flame recognition program detects fire source information in the video, and the smoke sensor 7 and infrared camera 12 detect an anomaly, an alarm message is immediately issued. Remote maintenance personnel can obtain the location information of the fire site through the MESH network relay device 9 on the drone and the three-dimensional positioning coordinates of the positioning MESH nodes laid in the integrated utility tunnel.

[0029] The drone is equipped with an onboard backup MESH node.

[0030] The MESH communication network adopts a hierarchical distributed MESH network architecture to achieve stable and smooth network signal transmission within the utility tunnel. This hierarchical distributed MESH network architecture consists of multiple network layers, each of which consists of multiple MESH network routers, forming a distributed network topology. In this network, each router in each layer has the functions of data transmission and reception and network management. It can independently manage and maintain the routing table of its level and realize data transmission and reception functions through connections with other MESH routers.

[0031] The hierarchical distributed MESH network topology is capable of automatic blind spot filling and has self-organizing and self-recovering functions.

[0032] The present invention has the following beneficial effects:

[0033] In practical operation, the central controller acquires information on the smoke gas content in the environment through smoke sensors. Once smoke ignition is detected, the central controller collects video information from the scene using cameras and infrared sensors, and then transmits it to a remote host computer via a MESH network relay device. This allows remote operators to view the video inside the tunnel in real time and send commands to the central controller via the MESH network relay device to control it. Furthermore, the central controller acquires the ambient temperature through temperature sensors and determines whether equipment or facilities are on fire based on temperature changes detected by the temperature sensors. Once a fire is detected, the central controller triggers an alarm. Simultaneously, the distance between the fixed MESH node and the inspection drone is acquired using a 3D positioning sensor, and the drone's position is then determined using trilateration. Throughout these operations, the central controller continuously calibrates the drone's inspection route to ensure accurate inspection results. The layered distributed MESH network architecture adopted has a wide coverage area, is easy to deploy MESH network nodes, and ensures stable data transmission. Attached Figure Description

[0034] Figure 1 This is a top view of the UAV of the present invention;

[0035] Figure 2 This is a bottom view of the UAV of the present invention;

[0036] Figure 3 This is a front view of the UAV of the present invention;

[0037] Figure 4 This is a side view of the UAV of the present invention;

[0038] Figure 5 This is a schematic diagram of the coordinate calculation method.

[0039] Among them, 1 is the first DC drive motor, 2 is the second DC drive motor, 3 is the third DC drive motor, 4 is the fourth DC drive motor, 5 is the telescopic arm, 6 is the robotic arm, 7 is the smoke sensor, 8 is the propeller holder, 9 is the MESH network relay equipment, 10 is the compression spring, 11 is the fog light, 12 is the infrared camera, 13 is the top plate, 14 is the bottom plate, 15 is the fifth DC drive motor, 16 is the sixth DC drive motor, 17 is the seventh DC drive motor, 18 is the eighth DC drive motor, and 19 is the three-dimensional positioning device. Position sensor, 20 is real-view camera, 21 is push plate, 22 is left front Mecanum wheel, 23 is right front Mecanum wheel, 24 is left rear Mecanum wheel, 25 is right rear Mecanum wheel, 26 is battery pack, 27 is front board, 28 is rear board, 29 is left side board, 30 is right side board, 31 is central controller, 32 is left front propeller, 33 is right front propeller, 34 is left rear propeller, 35 is right rear propeller, 36 is MESH communication module, 37 is stepper motor, 38 is baffle. Detailed Implementation

[0040] The present invention will now be described in further detail with reference to the accompanying drawings:

[0041] refer to Figures 1 to 4 This invention relates to a MESH-based intelligent inspection drone for integrated utility tunnels, comprising an inspection drone body, a robotic arm 6, a central controller 31, a MESH network relay device 9, an infrared camera 12, a real-view camera 20, and a battery pack 26 for providing power. Smoke sensors 7 are installed on the front, rear, left, and right sides of the inspection drone body. A three-dimensional positioning sensor 19 and a fog light 11 are installed on the front of the inspection drone body. The infrared camera 12 and the robotic arm 6 are both installed on the inspection drone body. The MESH network relay device 9 is fixed to the inspection drone body. The central controller 31 is connected to the output terminals of the MESH network relay device 9, the smoke sensor 7, the three-dimensional positioning sensor 19, the adjustment terminal of the propeller holder 8, the output terminal of the infrared camera 12, the output terminal of the battery pack 26, and the control terminal of the inspection drone body.

[0042] The inspection drone body includes a base plate 14, and at the bottom of the base plate 14 are a left front Mecanum wheel 22, a right front Mecanum wheel 23, a left rear Mecanum wheel 24, a right rear Mecanum wheel 25, a fifth DC drive motor 15, a sixth DC drive motor 16, a seventh DC drive motor 17, and an eighth DC drive motor 18. The output shaft of the fifth DC drive motor 15 is connected to the right rear Mecanum wheel 25, the output shaft of the sixth DC drive motor 16 is connected to the left rear Mecanum wheel 24, the output shaft of the seventh DC drive motor 17 is connected to the right front Mecanum wheel 23, and the output shaft of the eighth DC drive motor 18 is connected to the left front Mecanum wheel 24. The control terminals of the fifth DC drive motor 15, the sixth DC drive motor 16, the seventh DC drive motor 17, and the eighth DC drive motor 18 are connected to the central controller 31. A front plate 27 is provided at the front end of the base plate 14, a rear plate 28 is provided at the rear end of the base plate 14, a left side plate 29 is provided at the left end of the base plate 14, and a right side plate 30 is provided at the right end of the base plate 14. Each smoke sensor 7 is provided on the front plate 27, the rear plate 28, the left side plate 29, and the right side plate 30. A three-dimensional positioning sensor 19 and a fog light 11 are provided on the front plate 27, and a real-view camera 20 is provided on the base plate 13.

[0043] The inspection drone body includes a top plate 13, a left front propeller 32, a right front propeller 33, a left rear propeller 34, and a right rear propeller 35 mounted on a robotic arm 6 on the top plate 13, and a first DC drive motor 1, a second DC drive motor 2, a third DC drive motor 3, and a fourth DC drive motor 4 mounted on the top plate 13. The output shaft of the first DC drive motor 1 is connected to the left front propeller 32, the output shaft of the second DC drive motor 2 is connected to the right front propeller 33, and the third DC drive motor 4... The output shaft of motor 3 is connected to the left rear propeller 34, and the output shaft of the fourth DC drive motor 4 is connected to the right rear propeller 35. The control terminals of the first DC drive motor 1, the second DC drive motor 2, the third DC drive motor 3, and the fourth DC drive motor 4 are connected to the central controller 31. A front plate 27 is provided at the front end of the top plate 13, a rear plate 28 is provided at the rear end of the top plate 13, a left side plate 29 is provided at the left end of the top plate 13, and a right side plate 30 is provided at the right end of the top plate 13.

[0044] The left front propeller 32, right front propeller 33, left rear propeller 34, and right rear propeller 35 are connected to the robotic arm 6 via the propeller retainer 8 and the telescopic arm 5, respectively. The telescopic arm 5 and the left front propeller 32, right front propeller 33, left rear propeller 34, and right rear propeller 35 are connected to the first DC drive motor 1, the second DC drive motor 2, the third DC drive motor 3, and the fourth DC drive motor 4, respectively, via wires.

[0045] Battery pack 26 is a lithium battery pack; the central controller 31 is connected to the MESH network relay device 9 and the host computer through the MESH communication module 36; a push plate 21 is fixed on the stepper motor 37; the compression spring 10 is fixedly connected to the baffle 38; and the MESH network relay device 9 is set between the push plate 21 and the baffle 38.

[0046] Specific work process

[0047] 1) Automatic navigation inspection work

[0048] Due to the complex and variable environment inside the utility tunnel, when drones fly along the pre-programmed route for inspection, they need to use MESH nodes laid inside the tunnel for three-dimensional coordinate positioning so that they can correct themselves if they deviate from the route.

[0049] The following are the steps for UAV 3D coordinate positioning using the Time of Arrival (TOA) algorithm:

[0050] 1. A signal is emitted from a MESH node laid inside the utility tunnel, and the propagation speed of the signal is known.

[0051] 2. After receiving the signal from the MESH node, the MESH network relay device 9 mounted on the drone calculates the time it takes for the signal to travel from the MESH node inside the pipe gallery to the drone.

[0052] 3. Calculate the relative distance between the drone and the MESH node inside the utility tunnel based on the propagation speed and propagation time.

[0053] 4. Using the trilateration method, combined with the relative distance and the known MESH node positions, the three-dimensional coordinate information of the UAV is calculated.

[0054] 5. Use this three-dimensional coordinate information to correct the inspection route of the UAV.

[0055] The principle of the trilateration method is as follows: According to Figure 5 As shown in the diagram, after obtaining the distances d1, d2, and d3 between the three MESH nodes A, B, and C inside the utility tunnel and the corresponding point O of the MESH network relay device 9 on the drone, let the position coordinates of A, B, and C be (x1, y1), (x2, y2), and (x3, y3) respectively, and the coordinates of point O on the drone be (x, y). Then, according to the distance formula:

[0056]

[0057]

[0058]

[0059] The coordinates of point O on the drone are:

[0060]

[0061] Due to the objects inside the utility tunnel and the actual conditions of the tunnel, the results of the trilateration method may be subject to certain errors. Therefore, this invention optimizes the results of the trilateration method using the KNN algorithm to reduce the error between the UAV position calculated by the trilateration method and the actual position of the UAV. First, feature matching points are searched to establish feature point pairs between the actual position of the UAV and the position calculated by the trilateration method. Second, by finding the k reference nodes in the actual positioning area of ​​the UAV that have the greatest impact on the trilateration method's calculated value, and weighting the coordinates of the reference nodes according to the degree of influence of different reference nodes on the actual position of the UAV, the coordinates are made as close as possible to the actual position coordinates of the UAV. When the position coordinates calculated by the trilateration method are within the positioning area, the Euclidean distance between the calculated coordinates and the k reference nodes is calculated using the following formula:

[0062]

[0063] In the formula, D is the Euclidean distance between the reference node and the coordinates calculated by the trilateration method, k is the number of reference nodes, and M is the distance between the reference node and the coordinates calculated by the trilateration method. i M represents the information of the i-th (1≤i≤k) reference node, and M represents the calculated coordinate position information of the trilateration method.

[0064] A weighting factor is used to optimize the Euclidean distance, minimizing the distance between the calculated coordinates from the trilateration method and the actual coordinates of the UAV. Let D be the distance from the i-th reference node to the calculated coordinates from the trilateration method. i Then the weighting factor δ i Represented as:

[0065]

[0066] Using the KNN algorithm, different weighting factors reflect the different influences of different reference nodes on the actual coordinates of the UAV. Therefore, the weighting factor δ... i The introduction of this reference point makes the final reference point coordinates approximate the actual coordinates of the UAV, and can ultimately be expressed as:

[0067]

[0068] in, P is the reference point coordinate of the weighted equivalent UAV actual coordinates obtained by the KNN algorithm. i The coordinates of each reference point are given. A weighting factor δ is introduced into the calculation of the positioning coordinates using the trilateration method. iThis parameter ensures the accuracy of the actual location of the drone and improves the precision of the positioning.

[0069] Because this invention uses MESH nodes to transmit signals, it offers advantages over traditional wireless sensor signal transmission: 1. When a MESH node fails, the host computer can obtain the fault information immediately, while traditional sensor faults cannot be detected immediately, leading to incorrect inspection routes. 2. It has powerful non-line-of-sight transmission capabilities. MESH technology makes positioning and configuration easy, greatly expanding the application areas and coverage of wireless broadband. 3. MESH networks can extend the "hotspot" coverage of traditional WLANs to a wider "hot zone" coverage, eliminating the bandwidth degradation caused by increasing distance in traditional WLANs. 4. MESH network technology also provides greater redundancy and communication load balancing capabilities. Devices can connect to the network simultaneously through different nodes, thus not causing a decrease in system performance.

[0070] 2) Information collection work

[0071] The smoke sensor 7 on the drone detects smoke and gas information in the integrated utility tunnel. The central controller 31 collects on-site smoke and gas information through the smoke sensor 7. Once smoke is detected, the central controller 31 collects on-site video information and temperature information through the rotatable real-view camera 20 and infrared camera 12 mounted under the drone. Then, it sends the collected video information and temperature information to the host computer through the MESH communication module 36 using the MESH network, so that remote operators can view the real-time video in the tunnel and perform corresponding operations.

[0072] 3) Alarm Operation

[0073] The real-view camera 20 and infrared camera 12 mounted on the underside of the drone can collect video information from the integrated utility tunnel in real time and send it to the host computer. When the host computer's automatic flame recognition program detects a fire source in the video, and the smoke sensor 7 and infrared camera 12 detect an anomaly, an alarm is immediately issued. Remote maintenance personnel can obtain the location information of the fire site through the MESH network relay device 9 on the drone and the three-dimensional positioning coordinates of the positioning MESH nodes laid in the integrated utility tunnel, so as to quickly extinguish the fire and prevent the disaster from spreading.

[0074] 4) Automatic blind spot filling

[0075] In a mesh network, any device that belongs to or can become part of the network is called a mesh network node, and the starting transmitting node in the mesh network is called the root node. When the mesh communication module 36 receives a signal strength from a mesh network node in the upper layer that is lower than the corresponding set threshold (the threshold depends on the distance between the mesh network node and the root node) or when a node fails, the connection between the nodes will be broken. At this time, the mesh network node will search for a neighboring node to rejoin the mesh network. This not only ensures that the signal strength of the entire network is above a certain level, but also ensures that signal connection and transmission can be quickly guaranteed after a node failure.

[0076] After a neighboring node is found again, a signal is sent to the central controller 31. The central controller 31 controls the stepper motor 37 to push the push plate 21 a certain distance, which in turn pushes the MESH network relay device 9 out through the baffle 38. The pushed-out MESH network relay device 9 automatically operates, acting as a relay node to ensure stable signal transmission. Subsequently, the baffle 38 closes again under the action of the compression spring 10, and the central controller 31 controls the stepper motor to move the push plate 21 the same distance, returning it to its original position. This allows the detection device to continue detection via a remote network connection and sends data information to remote personnel to ensure timely replacement of faulty nodes.

[0077] 5) Self-organizing and self-recovering function

[0078] After establishing the root node in a MESH network, MESH node devices with signal strength greater than a threshold between them and the root node are selected as Layer 2 network transmission devices. Similarly, Layer 3 devices are selected from MESH node devices with signal strength greater than a threshold between them and Layer 2 devices, and so on. To prevent excessive data transmission delays due to a large number of layers between MESH network nodes and the root node, a maximum allowed number of layers can be set in the root node. Once a node at the maximum layer connects to the network, other nodes cannot connect to the last layer node.

[0079] This invention utilizes a MESH network for information transmission, ensuring that data transmission will not fail even if a single node fails. Furthermore, airborne backup MESH nodes can automatically replace faulty fixed MESH nodes in the utility tunnel, demonstrating self-organizing and self-recovering capabilities. This invention is a highly adaptable alarm drone capable of automatically detecting abnormal states, triggering simultaneous alarms from multiple nodes to minimize losses, and participating in a self-organizing and self-recovering network.

Claims

1. A MESH-based intelligent inspection drone for integrated utility tunnels, characterized in that: The system includes the inspection drone body, smoke sensors (7) located on the front, rear, left and right sides of the inspection drone body, a three-dimensional positioning sensor (19) and fog light (11) located on the front of the inspection drone body, an infrared camera (12), a real-view camera (20), a robotic arm (6), a MESH network relay device (9), a stepper motor (37), a push plate (21), a compression spring (10), a baffle (38) and a battery pack (26) for providing power, four Mecanum wheels and four robotic arms (6) located on the inspection drone body, propellers connected to the four robotic arms (6) respectively via propeller retainers (8) and telescopic arms (5); four DC drive motors connected to the telescopic arms (5) and the four propellers respectively, and four DC drive motors connected to the four Mecanum wheels respectively; and a system for the inspection drone body. The central controller (31) on the main body is connected to the MESH network relay device (9), the output end of the smoke sensor (7), the output end of the three-dimensional positioning sensor (19), the adjustment end of the propeller holder (8), the output end of the infrared camera (12), the output end of the battery pack (26), and the control end of the eight DC drive motors; a push plate (21) is fixed on the stepper motor (37), and a compression spring (10) is fixedly connected to the baffle (38). The MESH network relay device (9) is set between the push plate (21) and the baffle (38); the MESH network relay device (9) and the MESH nodes laid in the pipe gallery form a MESH communication network, which can meet the requirements of real-time communication between the UAV and the ground; when the UAV is conducting flight inspection, it performs three-dimensional coordinate positioning through the MESH nodes laid inside the pipe gallery so as to correct the UAV when it deviates from the route; During flight inspections, the UAV uses MESH nodes installed inside the pipe gallery for three-dimensional coordinate positioning to correct for deviations from its route. The specific steps are as follows: Step 1. A signal is emitted from the MESH nodes laid inside the utility tunnel, and the propagation speed of the signal is known; Step 2. After receiving the signal from the MESH node, the MESH network relay device (9) mounted on the UAV calculates the time it takes for the signal to travel from the MESH node inside the pipe gallery to the UAV. Step 3. Calculate the relative distance between the UAV and the MESH node inside the utility tunnel based on the propagation speed and propagation time; Step 4. Using trilateration, combined with relative distances and known MESH node positions, calculate the three-dimensional coordinate information of the UAV; Step 5. Use 3D coordinate information to correct the inspection route of the UAV; In step 5, the inspection route of the UAV is corrected using three-dimensional coordinate information, and the KNN algorithm is used for optimization, as detailed below: First, feature matching points are searched to establish feature point pairs between the actual position of the UAV and the position of the UAV calculated by the trilateration method; Secondly, by finding the k reference nodes that have the greatest impact on the trilateration calculation results in the actual positioning area of ​​the UAV, and by weighting the reference node coordinates according to the degree of influence of different reference nodes on the actual position of the UAV, the UAV's actual position coordinates are made as close as possible to the actual position coordinates of the UAV. When the position coordinates calculated by trilateration are within the positioning area, the formula for calculating the Euclidean distance between them and the k reference nodes to the coordinates calculated by trilateration is: In the formula D To calculate the Euclidean distance between the reference node and the coordinates using the trilateration method, k For reference node number, For the first i Information on each reference node, M This provides the coordinate position information for calculation using the trilateration method. Weighting factors are used to optimize the Euclidean distance, minimizing the distance between the calculated coordinates from the trilateration method and the actual coordinates of the UAV. Let the first... i The distance from each reference node to the coordinates calculated by the trilateration method is: Then the weighting factor Represented as: The KNN algorithm uses different weighting factors to reflect the varying impacts of different reference nodes on the actual coordinates of the UAV. The introduction of this reference point makes the final reference point coordinates approximate the actual coordinates of the UAV. The final reference point is represented as: in, The reference point coordinates for the weighted equivalent of the UAV's actual coordinates using the KNN algorithm. The coordinates of each reference point.

2. The intelligent inspection drone based on a MESH network for integrated utility tunnels according to claim 1, characterized in that: The central controller (31) is connected to the MESH network relay device (9) and the host computer through the MESH communication module (36).

3. The intelligent inspection drone based on a MESH network for integrated utility tunnels according to claim 1, characterized in that: The inspection drone body includes a top plate (13) and a bottom plate (14); a robotic arm (6) is mounted on the top plate (13), and four DC drive motors connected to the telescopic arm (5) and four propellers are mounted on the top plate (13); four Mecanum wheels and four DC drive motors connected to the four Mecanum wheels are mounted on the bottom plate (14), and a real-view camera (20) is set on the bottom plate (13).

4. The intelligent inspection drone based on a MESH network for integrated utility tunnels according to claim 1, characterized in that: The smoke sensor (7) mounted on the drone detects smoke gas information in the integrated utility tunnel. The central controller (31) collects on-site smoke gas information through the smoke sensor (7). Once smoke is detected, the central controller (31) collects on-site video information and temperature information through the rotatable real-view camera (20) and infrared camera (12) mounted under the drone. Then, the collected video information and temperature information are sent to the host computer through the MESH communication module (36) using the MESH network, so that remote operators can watch the real-time video in the tunnel and make corresponding operations.

5. The intelligent inspection drone based on a MESH network for integrated utility tunnels according to claim 1, characterized in that: The real-view camera (20) and infrared camera (12) mounted under the drone can collect video information in the integrated utility tunnel in real time and send it to the host computer. When the automatic flame recognition program of the host computer finds fire source information in the video, and the smoke sensor (7) and infrared camera (12) detect an abnormality, an alarm message is immediately issued. Remote maintenance personnel can obtain the location information of the fire location through the MESH network relay device (9) on the drone and the three-dimensional positioning coordinates of the positioning MESH node laid in the integrated utility tunnel.

6. The intelligent inspection drone based on a MESH network for integrated utility tunnels according to claim 1, characterized in that: The drone is equipped with an onboard backup MESH node.

7. The intelligent inspection drone based on a MESH network for integrated utility tunnels according to claim 1, characterized in that: The MESH communication network adopts a hierarchical distributed MESH network architecture to achieve stable and smooth network signal transmission within the utility tunnel. This hierarchical distributed MESH network architecture consists of multiple network layers, each of which consists of multiple MESH network routers, forming a distributed network topology. In this network, each router in each layer has the functions of data transmission and reception and network management. It can independently manage and maintain the routing table of its level and realize data transmission and reception functions through connections with other MESH routers.

8. The intelligent inspection drone based on a MESH network for integrated utility tunnels according to claim 7, characterized in that: The hierarchical distributed MESH network topology is capable of automatic blind spot filling and has self-organizing and self-recovering functions.

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