A large sewer culvert detection method based on multiple sensors

Abstract

The application relates to the technical field of urban drainage pipeline detection, and discloses a large drainage pipe culvert detection method based on multiple sensors, which comprises the following steps: a first pipeline parameter data of a drainage pipe culvert is acquired by an unmanned remote-controlled submersible vehicle carrying a pipeline sonar, a two-dimensional image sonar and a high-definition camera, so that a first abnormal position point in the drainage pipe culvert is obtained; a fiber is carried by the unmanned remote-controlled submersible vehicle, the fiber is fused with a distributed fiber temperature measurement system, and temperature space-time sequence data in the drainage pipe culvert is collected, so that a second abnormal position point in the drainage pipe culvert is obtained; a high-definition camera and a laser radar are carried by an unmanned aerial vehicle, second pipeline parameter data of the drainage pipe culvert is acquired, and a third abnormal position point in the drainage pipe culvert is obtained; and data information of the three abnormal position points is coupled, so that an abnormal position point in the drainage pipe culvert is obtained. The application provides a pipe culvert facility defect efficient detection method based on multiple sensor coupling, and high-precision detection of a large drainage pipe culvert is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban drainage pipeline detection, in particular to a large drainage pipe culvert detection method based on multiple sensors. BACKGROUND

[0002] With the acceleration of urbanization, the safety and reliability of urban infrastructure have become increasingly prominent, especially large box culverts as an important part of the urban drainage system, its safety and effectiveness are directly related to the city's flood control and drainage capacity and operation safety, however, due to the influence of long-term water erosion, soil pressure, geological changes and sewage corrosion and other factors, drainage pipe culverts are prone to structural damage, leakage, blockage and other problems. If these problems are not discovered and repaired in time, it may lead to urban waterlogging, environmental pollution and road collapse and other serious consequences, therefore, it is necessary to detect large drainage pipe culverts.

[0003] The traditional drainage pipe culvert detection method often relies on manual inspection inside the drainage pipe culvert, this method is not only inefficient, but also has safety risks in the operation inside the drainage pipe culvert, which is difficult to meet the needs of modern city management, and the traditional detection method uses a single sensor, which cannot comprehensively obtain pipeline information.

[0004] At the same time, with the development of urban construction, higher requirements are put forward for the accuracy, comprehensiveness and real-time of drainage pipe culvert detection.

[0005] In view of this, the present application provides a large drainage pipe culvert detection method based on multiple sensors to solve the above problems. SUMMARY

[0006] In view of this, the present application provides a large drainage pipe culvert detection method based on multiple sensors, which can more intelligently and efficiently and comprehensively and accurately detect the abnormal position of the drainage pipe culvert to improve the safety of the drainage pipe culvert.

[0007] In one aspect, the present application provides a large drainage pipe culvert detection method based on multiple sensors, comprising:

[0008] The first pipeline parameter data of the drainage pipe culvert is obtained by carrying the pipeline sonar, two-dimensional image sonar and high-definition camera on the unmanned remote-controlled submersible vehicle, and the first abnormal position point in the drainage pipe culvert is obtained according to the first pipeline parameter data;

[0009] The optical fiber is laid inside the drainage pipe culvert by carrying the optical fiber on the unmanned remote-controlled submersible vehicle, and the optical fiber is fused with the distributed optical fiber temperature measurement system;

[0010] Collecting the temperature spatiotemporal sequence data of the sewer culvert continuously monitored by the distributed optical fiber temperature measurement system; and obtaining a second abnormal position point in the sewer culvert according to the temperature spatiotemporal sequence data;

[0011] When the sewer culvert is in low water level operation and / or the sewer culvert pipeline is dry, a high-definition camera and a laser radar are carried by a drone to obtain second pipeline parameter data of the sewer culvert;

[0012] According to the second pipeline parameter data and the laser radar point cloud data, a three-dimensional model of the sewer culvert pipeline is constructed, and a third abnormal position point in the sewer culvert is obtained according to the three-dimensional model;

[0013] The data information of the first abnormal position point, the second abnormal position point and the third abnormal position point is coupled, and an abnormal position point in the sewer culvert is obtained according to the coupled data information.

[0014] Further, the step of obtaining a second abnormal position point in the sewer culvert according to the temperature spatiotemporal sequence data comprises:

[0015] A water temperature spatiotemporal graph is drawn according to the temperature spatiotemporal sequence data;

[0016] A background noise threshold of the current pipeline temperature data in the sewer culvert is obtained;

[0017] The background noise of the water temperature spatiotemporal graph is eliminated according to the background noise threshold;

[0018] The temperature abnormal point position is determined according to the water temperature spatiotemporal graph after eliminating the background noise.

[0019] Further, the drawing method of the water temperature spatiotemporal graph according to the temperature spatiotemporal sequence data comprises:

[0020] The measurement time sequence of the temperature spatiotemporal sequence data is taken as the vertical axis;

[0021] The fiber position of the temperature spatiotemporal sequence data is taken as the horizontal axis;

[0022] The measured temperature of the temperature spatiotemporal sequence data is drawn in the coordinate axis with different colors.

[0023] Further, when the three-dimensional model of the sewer culvert pipeline is constructed according to the second pipeline parameter data and the laser radar point cloud data, the generation step of the laser radar point cloud data comprises:

[0024] The laser radar emits a laser beam to the inner wall of the sewer culvert pipeline and receives the reflected light;

[0025] The reflection time of the reflected light reaching the laser radar is recorded;

[0026] According to the light speed and the reflection time difference, the distance and the direction of the inner wall of the culvert are calculated;

[0027] According to the distance and the direction of the inner wall of the culvert, the three-dimensional point cloud data of the inner wall surface of the culvert are generated.

[0028] Further, when the first pipe parameter data of the culvert is acquired by the unmanned remotely controlled submersible carrying the pipe sonar, the two-dimensional image sonar and the high-definition camera, the pipe sonar detects the information of the culvert structure under the water surface.

[0029] Further, when the first pipe parameter data of the culvert is acquired by the unmanned remotely controlled submersible carrying the pipe sonar, the two-dimensional image sonar and the high-definition camera, the high-definition camera detects the information of the culvert structure on the water surface.

[0030] Further, when the second pipe parameter data of the culvert is acquired by the unmanned aerial vehicle carrying the high-definition camera and the laser radar, the unmanned aerial vehicle remotely and non-contactly inspects the culvert.

[0031] Further, the temperature space-time sequence data include the sequence data of the measured temperature, the optical fiber position and the measurement time.

[0032] Further, the background noise threshold is determined according to the probability distribution of the temperature difference of two adjacent points in the culvert space.

[0033] Further, the pipe parameter data include the image data and the pipe defect information data of the culvert; and the pipe defect information data include the shape in the underwater culvert, the siltation condition of the bottom of the culvert and the damage defect on the structure of the culvert.

[0034] Compared with the prior art, the present application has the following advantages:

[0035] The present application realizes the real-time monitoring and early warning of the internal environment, the structure state and the potential risk of the culvert by the unmanned submersible and the unmanned aerial vehicle carrying the pipe sonar, the two-dimensional image sonar, the high-definition camera and the laser radar and other sensors.

[0036] The present application can generate complementary information or new information by integrating multiple sensor technologies, so that the detection of the drainage pipe culvert is more comprehensive, the coupling of the data information of multiple sensors detects the abnormal position points of the drainage pipe culvert, facilitates the timely discovery of abnormal conditions in the pipeline operation process, provides an important basis for pipeline maintenance and fault elimination, reduces the uncertainty of the entire drainage pipe culvert detection process, and improves the accuracy of the detection result. When a certain sensor fails, other sensors can provide information to eliminate the fault, which is beneficial to improve the stability and reliability of the detection. BRIEF DESCRIPTION OF DRAWINGS

[0037] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are included to provide a description of the preferred embodiments and are not intended to limit the scope of the present application. Moreover, the same reference numerals are used throughout the various drawings to designate identical parts. In the drawings:

[0038] Figure 1 A flowchart of a large drainage pipe culvert detection method based on multiple sensors is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0039] Exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0040] The conventional drainage pipe culvert detection method often relies on manual inspection inside the drainage pipe culvert. This method is not only inefficient, but also has safety risks in the operation inside the drainage pipe culvert, and is difficult to meet the needs of modern city management. Moreover, the traditional detection method uses a single sensor, which cannot comprehensively obtain pipeline information, etc.

[0041] At the same time, with the development of urban construction, higher requirements are put forward for the accuracy, comprehensiveness and real-time performance of drainage pipe culvert detection.

[0042] Therefore, the present application proposes a large drainage pipe culvert detection method based on multiple sensors to solve the above problems.

[0043] Referring to Figure 1 As shown in the drawings, in some embodiments of the present application, a large drainage pipe culvert detection method based on multiple sensors includes:

[0044] S1. Obtaining first pipeline parameter data of the drainage culvert using a pipeline sonar, a two-dimensional imaging sonar, and a high-definition camera equipped on an unmanned remotely operated submersible; and determining a first abnormal location point within the drainage culvert based on the first pipeline parameter data;

[0045] S2. Using an unmanned remotely operated submersible to carry an optical fiber, laying the optical fiber inside the drainage culvert, and fusing the optical fiber to the distributed optical fiber temperature measurement system;

[0046] S3. Collecting the temperature spatiotemporal sequence data in the drainage culvert continuously monitored by the distributed optical fiber temperature measurement system; and obtaining a second abnormal location point in the drainage culvert based on the temperature spatiotemporal sequence data;

[0047] S4. When the drainage culvert is operating at a low water level and / or the drainage culvert is dry, obtain the second pipe parameter data of the drainage culvert by using a drone equipped with a high-definition camera and a laser radar;

[0048] S5. Constructing a three-dimensional model of the drainage culvert according to the second pipeline parameter data and the laser radar point cloud data, and obtaining a third abnormal location point in the drainage culvert according to the three-dimensional model;

[0049] S6. Couple the data information of the first abnormal location point, the second abnormal location point, and the third abnormal location point, and obtain the abnormal location point in the drainage culvert according to the coupled data information.

[0050] Specifically, pipeline sonar uses cross-sectional sonar.

[0051] Specifically, when the drainage culvert is operating at high water levels, a full-coverage cross-sectional inspection and defect detection are performed underwater using 2D imaging and cross-sectional sonar. This checks the integrity of the culvert's inner wall, confirming any defects and bottom siltation. Above water, a high-definition camera captures images of the top of the drainage culvert pipe. Combining this cross-sectional, defect, and image information, a comprehensive assessment can be made to identify a preliminary abnormal location, known as the first abnormal location.

[0052] Specifically, the distributed fiber optic temperature measurement system continuously monitors drainage culvert pipes over a monitoring length of 360 meters and a monitoring time of 48 hours.

[0053] Specifically, the distributed fiber optic temperature measurement system includes an optical time domain reflectometer, a data interpretation module, a temperature-sensing optical fiber, and a distributed fiber optic thermometer.

[0054] It can be understood that by analyzing the correlated fluctuations of the sewage temperature time series in the drainage culvert pipe through the distributed fiber optic temperature measurement system, dynamic monitoring of the internal conditions of the box culvert pipe and identification of abnormal points can be achieved, and then the second abnormal location point can be obtained.

[0055] Specifically, the unmanned underwater vehicle can support a plurality of external sensor devices, and each component device can be replaced or repaired.

[0056] Specifically, the propulsion and control system of the unmanned underwater vehicle is connected by network communication to facilitate the operation of the external sensor and the collection of data detected by the external sensor.

[0057] It can be understood that when the drainage pipe culvert is operated at a low water level and / or the drainage pipe culvert is dry, the water quantity is small, and the unmanned underwater vehicle is not needed, and the unmanned aerial vehicle can be used to carry a high-definition camera and a laser radar to detect the drainage pipe culvert.

[0058] It can be understood that the unmanned remote control underwater vehicle carries a pipe sonar, a two-dimensional image sonar and a high-definition camera to detect the drainage pipe culvert and obtain the first abnormal position point; the distributed optical fiber temperature measurement system continuously detects the drainage pipe culvert to obtain the second abnormal position point; and the unmanned aerial vehicle carries a high-definition camera and a laser radar to detect the drainage pipe culvert to obtain the third abnormal position point.

[0059] It can be seen that the method of the present application uses the unmanned underwater vehicle and the unmanned aerial vehicle to carry a plurality of sensors such as a pipe sonar, a two-dimensional image sonar, a high-definition camera and a laser radar to realize real-time monitoring and early warning of the internal environment, structural state and potential risks of the drainage pipe culvert.

[0060] Referring to Figure 1 In some embodiments of the present application, the step of obtaining a second abnormal position point in the drainage pipe culvert according to the temperature space-time sequence data comprises:

[0061] S51, drawing a water temperature space-time graph according to the temperature space-time sequence data.

[0062] S52, obtaining a background noise threshold of the current drainage pipe culvert pipe temperature data.

[0063] S53, eliminating background noise of the water temperature space-time graph according to the background noise threshold.

[0064] S54, judging a temperature abnormal point according to the water temperature space-time graph after the background noise is eliminated.

[0065] Specifically, the water temperature spatiotemporal map can display the water temperature at different locations in the drainage culvert through color coding. Warm colors represent high-temperature areas, and cold colors represent low-temperature areas. By visualizing the temperature data in both time and space dimensions, the abstract temperature data can be converted into intuitive graphics.

[0066] It is understandable that normal water temperature distribution and changes follow a certain pattern. Once an abnormality occurs, the water temperature at a certain location on the map suddenly rises or falls, and the inspectors can quickly find the abnormal area of ​​temperature distribution.

[0067] Specifically, historical temperature data from drainage culverts during normal operation, across different seasons and time periods, was collected. This data was then cleaned using a computer algorithm to remove outliers caused by sensor failures and data transmission errors. The data characteristics of this historical temperature data were then calculated, and the background noise threshold range was determined based on these characteristics.

[0068] Under normal operating conditions, the temperature inside the drainage culvert fluctuates within a certain range, and background noise is random interference within this normal fluctuation range. Statistical analysis of historical data can help us understand the distribution characteristics of normal temperatures and determine a reasonable threshold range for defining noise.

[0069] Specifically, the temperature data points corresponding to the water temperature spatiotemporal map are determined to determine whether they are within the background noise threshold. If the temperature data point is within the threshold, it is treated as background noise and smoothed using a filtering algorithm. If the temperature data point is outside the threshold, its original value is retained.

[0070] Understandably, data points within the background noise threshold are considered noise. This noise, likely due to minor sensor errors or random environmental interference, does not help reflect the true temperature anomalies in drainage culverts. By removing or smoothing this noise data, the spatiotemporal water temperature map can more clearly reflect actual temperature changes, highlighting potential temperature anomaly signals and providing a more reliable data foundation for subsequent accurate identification of temperature anomaly points.

[0071] Specifically, after removing background noise, the processed data is re-examined. Data points that fall outside the corrected threshold range are marked as possible temperature anomalies. The threshold range can be adjusted based on actual conditions, and the threshold range can be appropriately narrowed to improve detection sensitivity.

[0072] Reference Figure 1 As shown, in some embodiments of the present application, a method for drawing a water temperature spatiotemporal map based on the temperature spatiotemporal sequence data includes:

[0073] The measurement time sequence of the temperature spatiotemporal sequence data is taken as the vertical axis.

[0074] The fiber position of the temperature spatiotemporal sequence data is taken as the horizontal axis.

[0075] The measurement temperature of the temperature spatiotemporal sequence data is plotted in the coordinate axis in different colors.

[0076] Referring to Figure 1 As shown in the figure, in some embodiments of the present application, when constructing a three-dimensional model in the culvert pipe according to the second pipe parameter data and the laser radar point cloud data, the generation step of the laser radar point cloud data includes:

[0077] The laser radar emits a laser beam to the inner wall of the culvert pipe and receives reflected light.

[0078] The reflection time of the reflected light reaching the laser radar is recorded.

[0079] The distance and orientation of the inner wall of the culvert pipe are calculated according to the speed of light and the reflection time difference.

[0080] According to the distance and orientation of the inner wall of the culvert pipe, three-dimensional point cloud data of the inner wall surface of the culvert pipe is generated.

[0081] It can be understood that the second pipe parameter data is matched with the three-dimensional point cloud data of the inner wall surface of the culvert pipe, so that the second pipe parameter data is fused into a unified three-dimensional point cloud data set, and the fused point cloud data is constructed into a triangular mesh model using a triangular meshing algorithm, and a three-dimensional surface model of the inner wall surface of the culvert pipe is preliminarily formed.

[0082] Referring to Figure 1 As shown in the figure, in some embodiments of the present application, the pipe sonar detects information of the culvert pipe structure under the water surface.

[0083] Referring to Figure 1 As shown in the figure, in some embodiments of the present application, the high-definition camera detects information of the culvert pipe structure on the water surface.

[0084] As can be seen, the pipe sonar and the high-definition camera can realize comprehensive detection of the culvert pipe structure, and improve the accuracy of detection.

[0085] Referring to Figure 1 As shown in the figure, in some embodiments of the present application, the unmanned aerial vehicle remotely and non-contactingly inspects the culvert pipe.

[0086] Referring to Figure 1In some embodiments of the present application, the temperature spatiotemporal sequence data includes: sequence data of measured temperature, fiber position and measurement time.

[0087] Referring to Figure 1 In some embodiments of the present application, the background noise threshold is determined according to the probability distribution of the temperature difference between adjacent two points in the sewer culvert space.

[0088] Specifically, in the sewer culvert space, a series of adjacent points are selected along the length of the pipeline or other spatial dimensions, the temperature values of each pair of adjacent points are measured and recorded, and then the temperature difference between them is calculated. A large number of adjacent point temperature difference data are collected, the frequency of different temperature difference is counted, and then the probability distribution of the temperature difference is obtained. Based on the obtained probability distribution, a temperature difference corresponding to a certain probability is selected as the background noise threshold. The temperature difference corresponding to the probability can be selected as the temperature difference corresponding to the place where the probability density starts to sharply decrease.

[0089] It can be understood that the temperature difference exceeding the background noise threshold is considered as non-background noise factor, and the temperature difference within the background noise threshold is considered as normal background noise fluctuation.

[0090] Referring to Figure 1 In some embodiments of the present application, the pipeline parameter data includes: image data of the sewer culvert and pipeline defect information data.

[0091] Specifically, the pipeline defect information data includes: the shape inside the underwater sewer culvert, the bottom siltation condition inside the sewer culvert, and the damage defect on the structure inside the sewer culvert.

[0092] It can be understood that the pipeline sonar can detect the shape inside the underwater sewer culvert, the bottom siltation condition inside the sewer culvert, and the damage defect on the structure inside the sewer culvert; the high-definition camera can detect the image data of the sewer culvert.

[0093] It can be seen that the method of the present application realizes real-time monitoring and early warning of the internal environment, structural state and potential risks of the sewer culvert by using the unmanned underwater vehicle and the unmanned aerial vehicle to carry various sensors such as pipeline sonar, two-dimensional image sonar, high-definition camera and laser radar. The distributed optical fiber temperature measurement system analyzes the correlation fluctuation of the sewage temperature time sequence in the sewer culvert to realize dynamic monitoring and abnormal point identification of the internal conditions of the sewer culvert. The intelligent efficiency of the detection process is improved, which provides strong technical support for the safety management of urban infrastructure.

[0094] The application can generate complementary information or new information through the integration of multiple sensor technologies, so that the detection of the drainage pipe culvert is more comprehensive, the data information of the coupled multiple sensors is used for detecting the abnormal position point of the drainage pipe culvert, the abnormal condition in the pipe operation process is found in time, important basis is provided for pipe maintenance and fault elimination, the uncertainty of the entire drainage pipe culvert detection process is reduced, and the accuracy of the detection result is improved. When a certain sensor fails, other sensors can provide information to eliminate the fault, which is beneficial to improve the stability and reliability of the detection.

[0095] Those skilled in the art will appreciate that embodiments of the application can be provided as methods, systems or computer program products. Accordingly, the application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the application can be embodied in the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk memory, CD-ROM, optical memory, etc.) embodying computer usable program code.

[0096] The application is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 the functions specified in one or more blocks.

[0097] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction horticulture device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 the functions specified in one or more blocks.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1steps of the functions specified in the one or more blocks.

[0099] It should be noted that the above-mentioned embodiments are only used to illustrate the technical solutions of the present application, but not limit the present application. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the technical solutions of the present application can still be modified or equivalent replaced without departing from the spirit and scope of the present application, and any modification or equivalent replacement should be covered in the protection scope of the claims of the present application.

Claims

1. A large-scale drainage culvert detection method based on multiple sensors, characterized in that: include: Acquire first pipeline parameter data of the drainage culvert using a pipeline sonar, a two-dimensional imaging sonar, and a high-definition camera carried by an unmanned remotely operated submersible; and obtain a first abnormal location point in the drainage culvert based on the first pipeline parameter data; Using an unmanned remotely operated submersible to carry an optical fiber, the optical fiber is laid inside a drainage culvert, and the optical fiber is fused with a distributed optical fiber temperature measurement system; Collecting the temperature spatiotemporal sequence data in the drainage culvert continuously monitored by the distributed optical fiber temperature measurement system; and obtaining a second abnormal position point in the drainage culvert based on the temperature spatiotemporal sequence data; When the drainage culvert is running at a low water level and / or the drainage culvert is dry, the second pipe parameter data of the drainage culvert is obtained by using a drone equipped with a high-definition camera and a lidar; Constructing a three-dimensional model of the drainage culvert according to the second pipeline parameter data and the laser radar point cloud data, and obtaining a third abnormal location point in the drainage culvert according to the three-dimensional model; The data information of the first abnormal location point, the second abnormal location point and the third abnormal location point are coupled, and the abnormal location point in the drainage culvert is obtained according to the coupled data information.

2. A large-scale drainage culvert detection method based on multiple sensors according to claim 1, characterized in that: The step of obtaining a second abnormal location point in the drainage culvert according to the temperature spatiotemporal series data includes: Drawing a water temperature spatiotemporal map according to the temperature spatiotemporal series data; Get the background noise threshold of the current pipe temperature data in the drainage culvert; Eliminating background noise of the water temperature spatiotemporal map according to the background noise threshold; The temperature anomaly points are determined based on the water temperature spatiotemporal map after background noise elimination.

3. A large-scale drainage culvert detection method based on multiple sensors according to claim 2, characterized in that: The method for drawing a water temperature spatiotemporal map based on the temperature spatiotemporal sequence data includes: Taking the measurement time series of the temperature spatiotemporal series data as the vertical axis; The optical fiber position of the temperature spatiotemporal sequence data is taken as the horizontal axis; The measured temperatures of the temperature spatiotemporal series data are plotted in different colors on the coordinate axis.

4. A large-scale drainage culvert detection method based on multiple sensors according to claim 1, characterized in that: When constructing a three-dimensional model of the interior of the drainage culvert according to the second pipeline parameter data and the laser radar point cloud data, the step of generating the laser radar point cloud data includes: The laser radar is used to emit a laser beam to the inner wall of the drainage culvert and receive the reflected light; Recording the reflection time of the reflected light reaching the laser radar; Calculating the distance and direction of the inner wall of the drainage culvert according to the speed of light and the reflection time difference; According to the distance and orientation of the inner wall of the drainage culvert pipe, three-dimensional point cloud data of the inner wall surface of the drainage culvert pipe is generated.

5. The large-scale drainage culvert detection method based on multiple sensors according to claim 1 is characterized in that: When obtaining the first pipe parameter data of the drainage culvert through an unmanned remote-controlled submersible equipped with a pipe sonar, a two-dimensional image sonar and a high-definition camera, the pipe sonar performs information detection on the pipe structure of the drainage culvert under the water surface.

6. A large-scale drainage culvert detection method based on multiple sensors according to claim 1, characterized in that: When obtaining the first pipe parameter data of the drainage culvert through the unmanned remote-controlled submersible equipped with a pipe sonar, a two-dimensional image sonar and a high-definition camera, the high-definition camera performs information detection on the pipe structure of the drainage culvert on the water surface.

7. The large-scale drainage culvert detection method based on multiple sensors according to claim 1 is characterized in that: When the second pipe parameter data of the drainage culvert is obtained by using a drone equipped with a high-definition camera and a laser radar, the drone performs a long-distance, non-contact inspection of the drainage culvert pipeline.

8. The large-scale drainage culvert detection method based on multiple sensors according to claim 2 is characterized in that: The temperature spatiotemporal sequence data includes sequence data of measured temperature, optical fiber position and measurement time.

9. The large-scale drainage culvert detection method based on multiple sensors according to claim 2 is characterized in that: The background noise threshold is determined according to the probability distribution of the temperature difference between two adjacent points in the drainage culvert pipe space.

10. The large-scale drainage culvert detection method based on multiple sensors according to claim 1 is characterized in that: The pipeline parameter data includes: image data of the drainage culvert pipeline and pipeline defect information data; the pipeline defect information data includes: the shape of the underwater drainage culvert, the siltation condition at the bottom of the drainage culvert, and damage defects on the structure of the drainage culvert.

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