Electric power pipe gallery safety detection system based on intelligent vehicle and use method

Abstract

The invention discloses an electric power pipe gallery safety detection system based on an intelligent vehicle and a use method, and relates to the technical field of electric power pipe galleries, the electric power pipe gallery safety detection system comprises a plurality of online monitoring devices arranged on the inner wall of a pipe gallery body and a control center arranged in a main control room, and the online monitoring devices and the control center communicate with each other through a network. The pipe gallery further comprises an inspection vehicle device which is used for moving in the pipe gallery body according to the formulated obstacle avoidance path so as to replace inspection personnel to conduct inspection operation. The inspection vehicle device carries a plurality of data acquisition structures such as a laser radar, a gas acquisition unit and a temperature and humidity sensor, so that environment data can be acquired at high frequency, the inspection vehicle device can flexibly go deep into each area of the pipe gallery, and the problem of detection dead angles existing in a fixed online monitoring device is effectively solved; omnibearing and multi-dimensional data acquisition is carried out on the internal environment of the pipe gallery, the detection coverage range and precision are remarkably improved, and accurate recognition of the operation state of cable equipment and environmental hidden dangers is ensured.

Description

Technical Field

[0001] This invention relates to the field of power utility tunnel technology, specifically a power utility tunnel safety detection system and its usage method based on an intelligent vehicle. Background Technology

[0002] With the widespread application of cables in urban power grids, power utility tunnels have become one of the main channels for cable laying. The operating environment inside power utility tunnels is often affected by various objective factors, such as liquid leakage, site construction, and limited internal space, all of which directly affect the operation of cable equipment. Fixed online monitoring devices are often installed inside the tunnels to monitor the internal environment. However, online monitoring devices have problems such as blind spots, insufficient detection accuracy, and small range, making it difficult to conduct high-precision and efficient monitoring of the tunnels. Moreover, power utility tunnels are closed for a long time, and the composition of the internal air is unknown, posing safety hazards and hindering maintenance personnel from carrying out inspection work. Therefore, how to effectively monitor the operating status of power utility tunnels and improve inspection efficiency has become a research focus in the power field.

[0003] Based on this, a power utility tunnel safety detection system and its usage method based on intelligent vehicles are provided, which can eliminate the drawbacks of existing technical solutions. Summary of the Invention

[0004] The purpose of this invention is to provide a power utility tunnel safety detection system and its usage method based on intelligent vehicles, so as to solve the problems of insufficient detection accuracy and range of online monitoring devices and safety hazards when inspection personnel carry out inspection work in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A safety detection system for power utility tunnels based on intelligent vehicles includes several online monitoring devices installed on the inner wall of the tunnel body and a control center located inside the main control room. The online monitoring devices and the control center communicate with each other via a network.

[0007] It also includes an inspection vehicle device, which is used to move inside the utility tunnel body according to a predetermined obstacle avoidance path to replace inspection personnel in performing inspection operations. The inspection vehicle device is equipped with a communication module, a voice interaction module, a patrol module and a processor. The voice interaction module, the patrol module and the processor are all connected to the control center through the communication module.

[0008] The inspection vehicle device is equipped with a first acquisition module, a lidar and a first camera module on its outer side, and the first acquisition module, lidar and first camera module are all electrically connected to the processor.

[0009] Preferably, the online monitoring device is used to collect static images and physical quantity data inside the utility tunnel in real time, and includes several second acquisition modules and second camera modules:

[0010] The second acquisition module includes a smoke sensor, a temperature and humidity sensor, and a gas acquisition unit, used to sense various physical quantities. The second acquisition module has the same structural features as the first acquisition module.

[0011] The second camera module uses a high-definition dual camera with visible light thermal imaging to capture images and video data in real time. The structural features of the second camera module are consistent with those of the first camera module.

[0012] Preferably, the control center is used to monitor data information in real time and to establish a three-dimensional model of the inspection environment using the data, thereby realizing the safe inspection operation of the inspection vehicle device, including:

[0013] The database is used to collect, store, and provide historical data and design drawing data of the power utility tunnel environment, and to divide the corresponding data into several groups according to time, temperature, and regional parameters.

[0014] The server is used to receive and output information results from the control center, and to coordinate instructions according to the information results;

[0015] The data analysis module is used to analyze the collected data and determine whether an accident has occurred inside the utility tunnel.

[0016] The path planning module uses the database and collected data to build a 3D model and plan the obstacle avoidance path for the inspection vehicle device.

[0017] The display panel is used to present the collected data and generated model data to the user in a visual format.

[0018] Preferably, the data analysis module includes:

[0019] The data preprocessing module is used to perform preprocessing operations on the collected data. The preprocessing operations include, but are not limited to, noise reduction, data uniform conversion, correction and simplification of point cloud data.

[0020] The intelligent detection module is used to identify hazardous and corrosive gases, visually analyze whether there are defects on the surface of the equipment, and whether there are any abnormal conditions in the environment.

[0021] The output module is used to generate graded alarms based on the detection results and trigger the corresponding emergency response mechanism.

[0022] Preferably, the path determination module includes:

[0023] The real-time data synchronization module is used to receive several sets of pre-processed collected data and match the data with location coordinate information.

[0024] The environment modeling module is used to perform point cloud registration and 3D reconstruction on point cloud data to obtain a 3D model.

[0025] The path decision module is used to generate a path coordinate set and an initial obstacle avoidance path based on the utility tunnel data.

[0026] The fault detection module receives inspection data, automatically determines whether there are obstacles inside the utility tunnel, and corrects the obstacle avoidance path according to the range of data changes to obtain a safe obstacle avoidance route.

[0027] Preferably, both the main control room and the inspection vehicle device are equipped with audible and visual alarms on one side, and the audible and visual alarms communicate with the server and processor via a network.

[0028] Preferably, the gas acquisition unit is composed of several gas sensors electrically connected together, including but not limited to CO sensor, oxygen sensor, CH4 sensor and H2S sensor.

[0029] A method for using a power utility tunnel safety detection system based on an intelligent vehicle, the specific steps of which are as follows:

[0030] S1. Collect internal environmental data of the utility tunnel multiple times using online monitoring devices, existing data of the utility tunnel and data measurement devices. Collect data using camera modules and acquisition modules. Perform data preprocessing operations through data analysis modules and transmit the processed data to the path planning module in real time.

[0031] S2. Construct a 3D model of the utility tunnel through the path planning module, formulate an initial obstacle avoidance path, and the inspection vehicle device performs inspection operations according to the initial obstacle avoidance path. During the inspection, the real-time image of the inspection vehicle device is matched with the position of the online monitoring device, and the fault judgment module is used to analyze in real time whether there are obstacles and automatically update the safe obstacle avoidance route.

[0032] S3. The inspection vehicle continues its inspection operation along a safe obstacle avoidance route, and works with the online monitoring device to collect dynamic and static images and physical data inside the utility tunnel in real time, facilitating visual analysis of whether there are any abnormalities inside the utility tunnel.

[0033] Preferably, the inspection steps of the inspection vehicle device are as follows:

[0034] Step 1: Divide the pipe gallery to be inspected into several inspection areas. The length of the inspection areas is the length of the pipe gallery to be inspected. According to the inspection instructions obtained, adjust the position and posture of the inspection vehicle device so that the shooting angle of the inspection vehicle device can cover the width of a single inspection area.

[0035] Step 2: Control the inspection vehicle device to move along the inspection path from the starting point to the end point of any inspection area, collect images and physical quantity data in real time and transmit them to the control center;

[0036] Step 3: Readjust the inspection path of the inspection vehicle device so that the shooting angle of the inspection vehicle device can cover the width of another single inspection area. Repeat the above operation until several inspection areas have been inspected.

[0037] Preferably, the specific steps of using S2 are as follows:

[0038] S21. Collect data on the inspection environment of the inspection vehicle device, obtain several sets of data points, acquire data records, calculate the amount of data collected, and obtain the standard deviation of the data collected and the observation difference of the data collected.

[0039] S22. Based on the observation difference and standard deviation of the collected data, the standard score of the collected data is obtained. After filtering and preprocessing, the first point cloud data is obtained.

[0040] S23. Use the environment modeling module to perform three-dimensional reconstruction of the first point cloud data, establish a three-dimensional model of the inspection environment, and obtain the path coordinate set and obstacle avoidance path.

[0041] S24. Real-time acquisition of three-dimensional data during the operation of the inspection vehicle device to obtain second point cloud data. The second point cloud data is analyzed and compared with the first and second point cloud data. The range of data change is obtained through the fault judgment module. An adaptive obstacle avoidance algorithm is introduced to correct the obstacle avoidance path according to the data range and obtain a safe obstacle avoidance route.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] 1. This invention provides a power utility tunnel safety detection system based on an intelligent vehicle, which breaks through the limitations of traditional detection methods. By using an inspection vehicle equipped with multimodal data acquisition structures such as lidar, gas sampling units, and temperature and humidity sensors, the inspection vehicle can collect environmental data at high frequency and flexibly penetrate into various areas of the utility tunnel. This effectively solves the detection blind spot problem of fixed online monitoring devices, and performs comprehensive and multi-dimensional data collection on the internal environment of the utility tunnel, significantly improving the detection coverage and accuracy, and ensuring accurate identification of the operating status of cable equipment and environmental hazards.

[0044] 2. This invention facilitates the construction of a utility tunnel model by performing three-dimensional reconstruction on the collected data, which enables the development of inspection paths for the inspection vehicle device. At the same time, the route is dynamically adjusted based on the obstacle avoidance path based on real-time data feedback, avoiding the blindness and repetitiveness of manual inspection, automatically avoiding obstacles, avoiding collision risks, and continuously monitoring the air quality and harmful gas concentration in the utility tunnel, which facilitates real-time assessment of the environmental safety level and significantly reduces the safety hazards for personnel. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the structure of the present invention.

[0046] Figure 2 This is a schematic diagram of the inspection vehicle device of the present invention.

[0047] Figure 3 This is a schematic diagram of the online monitoring device and control center of the present invention.

[0048] Figure 4 This is a schematic diagram of the data analysis module of the present invention.

[0049] Figure 5 A schematic diagram of the path design module of the present invention.

[0050] Figure reference numerals: Online monitoring device 100, second acquisition module 110, second camera module 120, control center 200, database 210, server 220, data analysis module 230, data preprocessing module 231, intelligent detection module 232, output module 233, path planning module 240, real-time data synchronization module 241, environment modeling module 242, path decision module 243, fault diagnosis module 244, display panel 250, inspection vehicle device 300, communication module 310, voice interaction module 320, patrol module 330, processor 340, first acquisition module 350, lidar 360, first camera module 370. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0052] In this embodiment, as Figures 1-5As shown, a power utility tunnel safety detection system based on intelligent vehicles includes several online monitoring devices 100 installed on the inner wall of the tunnel body and a control center 200 installed inside the main control room. The online monitoring devices 100 and the control center 200 communicate with each other through a network. The online monitoring devices 100 can be installed at key nodes inside the tunnel, such as the top, side walls, or near the fire compartment isolation doors. They can be installed in areas with dense cables, areas prone to overheating of joints, ventilation openings, etc., as needed, so as to achieve full coverage monitoring of the power utility tunnel without blind spots. The control center 200 has good computing power and storage resources, which is convenient for large-scale data processing and analysis, ensuring the accuracy and reliability of the model, and facilitating unified management and maintenance of modules. This allows users to observe accidents remotely without entering the tunnel site, reducing the probability of safety accidents. The system also includes data measurement devices, such as laser rangefinders and scanning instruments, to perform preliminary measurements of the data inside the tunnel.

[0053] It also includes an inspection vehicle device 300, which is used to move inside the utility tunnel body according to a predetermined obstacle avoidance path to replace inspection personnel in inspection operations. The inspection vehicle device 300 is equipped with a communication module 310, a voice interaction module 320, a patrol module 330 and a processor 340. The communication module 310 adopts 5G / Wi-Fi 6 low latency communication to ensure real-time data transmission. The voice interaction module 320, the patrol module 330 and the processor 340 are all connected to the control center 200 through the communication module 310.

[0054] The patrol vehicle device 300 is equipped with a first acquisition module 350, a lidar 360 and a first camera module 370 on its outer side. The first acquisition module 350, lidar 360 and first camera module 370 are all electrically connected to the processor 340.

[0055] Specifically, the inspection vehicle device 300 can be selected as a tracked or wheeled mobile intelligent inspection vehicle according to the actual environment. It can move autonomously by using LiDAR and a navigation and positioning system. The surface of the vehicle is equipped with a first camera module 370, which is mounted on the inspection vehicle device 300 via a pitch-adjustable bracket to facilitate adjustment of the camera range. The data collection range of the inspection vehicle device 300 and the online monitoring device 100 partially overlaps. The inspection vehicle device 300 is used to fill blind spots and achieve dynamic data capture, which can achieve 24 / 7 uninterrupted monitoring, replacing manual inspection. The inspection vehicle device 300 is also equipped with a charging module to achieve battery life.

[0056] Among them, such as Figures 1-3 As shown, the online monitoring device 100 is used to collect static images and physical quantity data inside the utility tunnel in real time, and includes several second acquisition modules 110 and second camera modules 120:

[0057] The second acquisition module 110 includes a smoke sensor, a temperature and humidity sensor, and a gas acquisition unit, used to sense various physical quantities. The second acquisition module 110 has the same structural features as the first acquisition module 350.

[0058] The second camera module 120 adopts a high-definition dual camera with visible light thermal imaging for real-time capture of image and video data. The second camera module 120 has the same structural features as the first camera module 370.

[0059] Specifically, the second acquisition module 110 and the second camera module 120 are both equipped with electromagnetic shielding structures to avoid strong electromagnetic interference in the power corridor. The temperature and humidity sensor can monitor the ambient temperature and humidity in real time. The gas acquisition unit can detect toxic and harmful gases such as carbon monoxide and hydrogen sulfide generated by electrical equipment failure. The smoke sensor can quickly detect smoke and reduce the probability of fire.

[0060] Among them, such as Figure 1 and Figure 3 As shown, the control center 200 is used to monitor data information in real time and to establish a three-dimensional model of the inspection environment using the data, thereby enabling the safe inspection operation of the inspection vehicle device 300, including:

[0061] Database 210 is used to collect, store and provide historical data and design drawing data of the power pipeline corridor environment. It divides the data in the corresponding information into several groups according to time, temperature and regional parameters, stores the collected data according to the preset format and structure, ensures the integrity and traceability of the data, and supports historical data backtracking and fast query.

[0062] Server 220 is used to receive and output information results from control center 200, and coordinate instructions according to the information results. Server 220 receives multi-source data such as temperature and humidity, gas concentration, images, and lidar point clouds from online monitoring device 100 and inspection vehicle device 300 in real time, and coordinates instructions for various components in the system. For example, when data analysis module 230 detects an abnormal situation in the pipe gallery, server 220 will coordinate inspection vehicle device 300 to adjust the inspection path and prioritize going to the abnormal area for detailed inspection. Or, after receiving a new path plan generated by path planning module 240, server 220 will accurately transmit the path information to inspection vehicle device 300 to ensure that it can perform safe inspection operations according to the planned path.

[0063] Data analysis module 230 is used to analyze the collected data and determine whether an accident has occurred inside the utility tunnel.

[0064] The path planning module 240 uses the database 210 and the collected data to build a three-dimensional model, and plans the obstacle avoidance path of the inspection vehicle device 300. Using three-dimensional modeling technology, it constructs an accurate three-dimensional model of the power tunnel inspection environment, accurately presenting information such as various facilities, equipment, and obstacles in the tunnel in the three-dimensional model. Taking into account factors such as inspection task requirements, equipment distribution, and obstacle location, it plans the obstacle avoidance path of the inspection vehicle device 300 to avoid collisions with obstacles in the tunnel during the inspection process, while ensuring that all areas that need to be inspected are covered.

[0065] Display panel 250 is used to display the collected data and generated model data to users in a visual form. It adopts a variety of intuitive display methods such as graphics and charts to facilitate staff to keep abreast of the operation status of the utility tunnel.

[0066] Among them, such as Figures 2-4 As shown, the data analysis module 230 includes:

[0067] Data preprocessing module 231 is used to perform preprocessing operations on the collected data. The preprocessing operations include, but are not limited to, noise reduction, data uniform conversion, correction and simplification of point cloud data.

[0068] The intelligent detection module 232 is used to identify hazardous and corrosive gases, visually analyze whether there are defects on the surface of the equipment and whether there are abnormal conditions in the environment. Through the gas acquisition unit installed in the pipe gallery, it monitors the gas composition in the pipe gallery in real time. Hazardous gases are identified by threshold comparison (such as triggering an alarm when H2S ≥ 10ppm). Once hazardous gases such as carbon monoxide and hydrogen sulfide or corrosive gases are detected, an alarm can be issued quickly. The camera module is used to observe the surface of the equipment in detail to identify whether there are defects such as cracks and wear, as well as whether there are abnormal conditions such as water accumulation and foreign object accumulation in the environment, so as to facilitate the timely detection of potential safety hazards in the pipe gallery.

[0069] The output module 233 is used to generate graded alarms based on the detection results and trigger the corresponding emergency response mechanism, simultaneously triggering the audible and visual alarms and emergency notifications.

[0070] Among them, such as Figures 2-5 As shown, the path designation module 240 includes:

[0071] The real-time data synchronization module 241 is used to receive several sets of preprocessed collected data and match the data with the location coordinate information.

[0072] The environment modeling module 242 is used to perform point cloud registration and 3D reconstruction on point cloud data to obtain a 3D model.

[0073] Path decision module 243 is used to generate a path coordinate set and an initial obstacle avoidance path based on the utility tunnel data;

[0074] The fault detection module 244 is used to receive inspection data, automatically determine whether there are obstacles inside the pipe gallery, and correct the obstacle avoidance path according to the range of data changes to obtain a safe obstacle avoidance route.

[0075] Specifically, by using existing drawings of the utility tunnel and data measurement devices, multiple sets of environmental data inside the utility tunnel were collected, resulting in several sets of data points a. i = [t, T, ..., x, y, z], where the spatial coordinates of the corresponding point are P. i (x i ,y i ,z i The spatial coordinate information of multiple sets of collected data points is placed into the same pipe gallery coordinate system. The pipe gallery entrance is taken as the origin, the direction of the pipe gallery is set as the x-axis, the vertical height direction is set as the z-axis, and the direction perpendicular to the pipe gallery is set as the y-axis. Collected data with the same coordinate points in different sets are recorded, that is, multiple measurement data of the same spatial location.

[0076] Data collection and recording: The data measurement device collects environmental data inside the pipe gallery multiple times, and obtains several sets of data collection datasets [t,T,···,d,x,y,z] containing time, sensor data, and measurement values, where t is time, T is temperature, d is distance, and x, y, and z are coordinate values;

[0077] Calculate the amount of data collected: Count the total number of samples N for all collected data;

[0078] Calculate the standard deviation of the collected data: For a certain collected data A = {a1, a2, ..., a...} N}, its mean is The formula for calculating standard deviation is:

[0079] Calculate the observation difference of the collected data: the difference Δa between two adjacent data collections. i =a i+1 -a i Where i = 1, 2, ..., N-1, and records the set of all observed differences {Δa1, Δa2, ..., Δa...} N-1}

[0080] Calculate the standard score of the collected data for each data point a. i The standard score is calculated using the following formula: in Let σ be the mean of the data, and σ be the standard deviation.

[0081] Set a standard score threshold, remove data points whose standard scores exceed the threshold, and normalize the remaining data according to the minimum-maximum normalization formula to finally obtain the first point cloud data.

[0082] Using the first point cloud data, a 3D model of the inspection environment is established using a 3D reconstruction algorithm, and the path coordinate set P = {p1, p2, ..., p...} from the starting point to the target point is planned. N}; where P i =(x i ,y i ,z i Based on the obstacle information within the utility tunnel, an initial obstacle avoidance path is generated, with the safe distance between the path and the obstacle set as d. safe ;

[0083] Real-time acquisition and second point cloud data: During operation, the inspection vehicle collects three-dimensional environmental data in real time. Every 10 meters the inspection vehicle device 300 moves, it scans the surrounding environment with LiDAR 360 to obtain second point cloud data. Using point cloud registration technology, the first point cloud data and the second point cloud data are placed in the same reference coordinate system. The matching degree between the current point cloud data and the three-dimensional model is compared, and the spatial position and attribute information of the corresponding points are compared to obtain the range of data changes.

[0084] Calculate the range of data change: For each corresponding point, calculate its spatial position change Δp = (Δx, Δy, Δz), where Δx = x2 - x1, Δy = y2 - y1, and Δz = z2 - z1;

[0085] If the data change range is zero or exists but does not exceed the threshold, continue executing the initial obstacle avoidance path;

[0086] If the data change range exists and exceeds the threshold (±5cm), an adaptive obstacle avoidance algorithm is introduced to dynamically adjust the obstacle avoidance path according to the data change range. If the data change Δp in a certain inspection area exceeds the threshold, it means that a new obstacle has appeared in the inspection area or the environment has changed. The path planning algorithm is called again to modify the original obstacle avoidance path and generate a safe obstacle avoidance route to ensure the safe driving of the intelligent inspection vehicle.

[0087] Among them, such as Figure 1 As shown, both the main control room and one side of the inspection vehicle device 300 are equipped with audible and visual alarms. The audible and visual alarms communicate with the server 220 and processor 340 through the network, enabling real-time early warning and rapid response to anomalies, ensuring the safety of power corridor inspections and improving operation and maintenance efficiency. For example, if a CO concentration ≥ 50 ppm or a temperature rise rate ≥ 5℃ / min is detected, the audible and visual alarm will flash yellow. If an H2S concentration ≥ 10 ppm or an open flame signal is detected, the audible and visual alarm will flash red.

[0088] Among them, such as Figure 1 As shown, the gas acquisition unit is composed of several gas sensors electrically connected together. The sensors include, but are not limited to, CO sensors, oxygen sensors, CH4 sensors and H2S sensors, which are convenient to work with the intelligent detection module 232 to identify dangerous gases and corrosive gases.

[0089] In use, the online monitoring device 100, existing data of the utility tunnel, and data measurement devices are used to collect environmental data inside the utility tunnel multiple times. The camera module and acquisition module are used to collect data, and the data analysis module 230 performs data preprocessing operations. The processed data is then transmitted to the path planning module 240 in real time. The path planning module 240 constructs a three-dimensional model of the utility tunnel and formulates an initial obstacle avoidance path. The inspection vehicle device 300 performs inspection operations according to the initial obstacle avoidance path. During the inspection, the real-time images of the inspection vehicle device 300 are matched with the position of the online monitoring device 100. The fault judgment module 244 analyzes in real time whether there are obstacles and automatically updates the safe obstacle avoidance route. The inspection vehicle device 300 continues to perform inspection operations according to the safe obstacle avoidance route and cooperates with the online monitoring device 100 to collect dynamic and static images and physical quantity data inside the utility tunnel in real time, which facilitates visual analysis of whether there are any abnormalities inside the utility tunnel.

[0090] The inspection procedures for the inspection vehicle device 300 are as follows:

[0091] The pipe gallery to be inspected is divided into several inspection areas, the length of which is the length of the pipe gallery to be inspected. According to the inspection instructions, the position and posture of the inspection vehicle device 300 are adjusted so that the shooting angle of the inspection vehicle device 300 can cover the width of a single inspection area. The inspection vehicle device 300 is controlled to move along the inspection path from the starting point to the end point of any inspection area, and images and physical quantity data are collected in real time and transmitted to the control center 200. The inspection path of the inspection vehicle device 300 is readjusted so that the shooting angle of the inspection vehicle device 300 can cover the width of another single inspection area. The above operation is repeated until all inspection areas have been inspected.

[0092] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A power utility tunnel safety detection system based on intelligent vehicles, comprising a plurality of online monitoring devices (100) installed on the inner wall of the tunnel body and a control center (200) installed inside the main control room, wherein the online monitoring devices (100) and the control center (200) communicate with each other via a network: Its features are, It also includes an inspection vehicle device (300), which is used to move inside the pipe gallery body according to a predetermined obstacle avoidance path to replace the inspection personnel in performing inspection operations. The inspection vehicle device (300) is equipped with a communication module (310), a voice interaction module (320), a patrol module (330) and a processor (340). The voice interaction module (320), the patrol module (330) and the processor (340) are all connected to the control center (200) through the communication module (310). The inspection vehicle device (300) is equipped with a first acquisition module (350), a lidar (360) and a first camera module (370) on its outer side. The first acquisition module (350), lidar (360) and first camera module (370) are all electrically connected to the processor (340).

2. The power utility tunnel safety detection system based on intelligent vehicles according to claim 1, characterized in that, The online monitoring device (100) is used to collect static images and physical quantity data inside the pipe gallery in real time, including several second acquisition modules (110) and a second camera module (120): The second acquisition module (110) includes a smoke sensor, a temperature and humidity sensor and a gas acquisition unit, used to sense various physical quantities. The second acquisition module (110) has the same structural features as the first acquisition module (350). The second camera module (120) adopts a high-definition dual camera with visible light thermal imaging for real-time capture of images and video data. The second camera module (120) has the same structural features as the first camera module (370).

3. The power utility tunnel safety detection system based on an intelligent vehicle according to claim 2, characterized in that, The control center (200) is used to monitor data information in real time and to establish a three-dimensional model of the inspection environment using the data, thereby realizing the safe inspection operation of the inspection vehicle device (300), including: Database (210) is used to collect, store and provide historical data and design drawing data of the power tunnel environment, and divide the corresponding data into several groups according to time, temperature and regional parameters; The server (220) is used to receive and output information results from the control center (200) and coordinate instructions according to the information results; The data analysis module (230) is used to analyze the collected data and determine whether an accident has occurred inside the utility tunnel; The path planning module (240) uses the database (210) and the collected data to build a three-dimensional model and plan the obstacle avoidance path of the inspection vehicle device (300); The display panel (250) is used to present the collected data and generated model data to the user in a visual form.

4. The power utility tunnel safety detection system based on an intelligent vehicle according to claim 3, characterized in that, The data analysis module (230) includes: The data preprocessing module (231) is used to perform preprocessing operations on the collected data, including but not limited to noise reduction, data uniform conversion, correction and simplification of point cloud data; The intelligent detection module (232) is used to identify hazardous and corrosive gases, visually analyze whether there are defects on the surface of the equipment, and whether there are any abnormal conditions in the environment; The output module (233) is used to generate graded alarms based on the detection results and trigger the corresponding emergency response mechanism.

5. A power utility tunnel safety detection system based on an intelligent vehicle according to claim 4, characterized in that, The path designation module (240) includes: The real-time data synchronization module (241) is used to receive several sets of preprocessed collected data and match the data with the location coordinate information; The environment modeling module (242) is used to perform point cloud registration and 3D reconstruction on point cloud data to obtain a 3D model; The path decision module (243) is used to generate a path coordinate set and an initial obstacle avoidance path based on the utility tunnel data; The fault detection module (244) is used to receive inspection data, automatically determine whether there are obstacles inside the pipe gallery, and correct the obstacle avoidance path according to the range of data changes to obtain a safe obstacle avoidance route.

6. A power utility tunnel safety detection system based on an intelligent vehicle according to claim 5, characterized in that, The main control room and the inspection vehicle device (300) are each equipped with an audible and visual alarm. The audible and visual alarm communicates with the server (220) and the processor (340) through a network.

7. A power utility tunnel safety detection system based on an intelligent vehicle according to claim 6, characterized in that, The gas acquisition unit is composed of several gas sensors electrically connected together, including but not limited to CO sensor, oxygen sensor, CH4 sensor and H2S sensor.

8. A method of using the intelligent vehicle-based power utility tunnel safety detection system according to any one of claims 1-7, characterized in that, The specific usage steps are as follows: S1. Collect internal environmental data of the utility tunnel multiple times using online monitoring device (100), existing data of the utility tunnel and data measurement device, collect data using camera module and acquisition module, perform data preprocessing operation through data analysis module (230), and transmit the processed data to path planning module (240) in real time. S2. Construct a three-dimensional model of the pipe gallery through the path planning module (240) and formulate an initial obstacle avoidance path. The inspection vehicle device (300) performs inspection operations according to the initial obstacle avoidance path. During the inspection, the real-time image of the inspection vehicle device (300) is matched with the position of the online monitoring device (100). The fault judgment module (244) is used to analyze in real time whether there are obstacles and automatically update the safe obstacle avoidance route. S3. The inspection vehicle device (300) continues to carry out inspection operations according to the safe obstacle avoidance route, and cooperates with the online monitoring device (100) to collect dynamic and static images and physical quantity data inside the pipe gallery in real time, so as to facilitate visual analysis of whether there are any abnormalities inside the pipe gallery.

9. The method of using a power utility tunnel safety detection system based on an intelligent vehicle according to claim 8, characterized in that, The inspection steps of the inspection vehicle device (300) are as follows: Step 1: Divide the pipe gallery to be inspected into several inspection areas. The length of several inspection areas is the length of the pipe gallery to be inspected. According to the inspection instructions obtained, adjust the position of the inspection vehicle device (300) so that the shooting angle of the inspection vehicle device (300) can cover the width of a single inspection area. Step 2: Control the inspection vehicle device (300) to move from the starting point to the end point of any inspection area along the inspection path, collect images and physical quantity data in real time and transmit them to the control center (200); Step 3: Readjust the inspection path of the inspection vehicle device (300) so that the shooting angle of the inspection vehicle device (300) can cover the width of another single inspection area. Repeat the above operation until several inspection areas have been inspected.

10. The method of using a power utility tunnel safety detection system based on an intelligent vehicle according to claim 9, characterized in that, The specific steps for using S2 are as follows: S21. Collect data on the inspection environment of the inspection vehicle device (300), obtain several sets of data points, acquire data records, calculate the amount of data collected, and obtain the standard deviation of the data collected and the observation difference of the data collected. S22. Based on the observation difference and standard deviation of the collected data, the standard score of the collected data is obtained. After filtering and preprocessing, the first point cloud data is obtained. S23. Use the environment modeling module (242) to perform three-dimensional reconstruction of the first point cloud data, establish a three-dimensional model of the inspection environment, and obtain the path coordinate set and obstacle avoidance path; S24. Real-time acquisition of three-dimensional data during the operation of the inspection vehicle device (300) to obtain the second point cloud data. The second point cloud data is analyzed and compared with the first point cloud data. The range of data change is obtained through the fault discrimination module (244). An adaptive obstacle avoidance algorithm is introduced. The obstacle avoidance path is corrected according to the data range to obtain a safe obstacle avoidance route.

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