Intelligent well lid monitoring management system based on Internet of Things

Abstract

The invention discloses an intelligent well lid monitoring management system based on the Internet of Things, and the system comprises a data monitoring module which monitors a plurality of operation data of each intelligent well lid, and obtains the operation state of each intelligent well lid; when the operation state of one intelligent well lid is abnormal, judging that the current intelligent well lid is an abnormal well lid and generating a fault early warning instruction; the image calling module obtains a plurality of associated cameras corresponding to the abnormal well lid according to the position data of the abnormal well lid in the fault early warning instruction, and controls the associated cameras to collect a plurality of well lid abnormal images of the abnormal well lid; the fault analysis module confirms well lid fault data according to the plurality of well lid abnormal images and abnormal items of the abnormal well lid; and the task generation module generates a troubleshooting task according to the well lid fault data of the plurality of abnormal well lids in the preset troubleshooting period. The technical problem of low fault removal efficiency of the intelligent well lid in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent manhole cover management, and in particular to an intelligent manhole cover monitoring and management system based on the Internet of Things. Background Art

[0002] A smart manhole cover refers to a new type of manhole cover facility that uses the Internet of Things, sensors, communications and other technologies to upgrade the traditional manhole cover to be intelligent, so that it has functions such as real-time monitoring, data transmission, and abnormal alarm.

[0003] In the prior art, the smart manhole cover is mainly composed of the manhole cover body, sensor module, data transmission module, control module and power module. Through the collaborative work of these modules, the smart manhole cover can monitor the state of the manhole cover, such as whether the manhole cover is opened, shifted, tilted and other faults, and transmit relevant data to the management platform in a timely manner. So that managers can quickly respond to and handle abnormal situations to ensure the safe operation of urban infrastructure.

[0004] Although smart manhole covers have achieved certain development results in technology, market and application, they still face some challenges. For example, managers still need to arrive at the site to confirm the fault type and treatment method of the manhole cover, and take corresponding treatment measures to eliminate the fault of the smart manhole cover, which makes the troubleshooting efficiency of smart manhole covers low. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent manhole cover monitoring and management system based on the Internet of Things, which solves the technical problem of low efficiency of intelligent manhole cover troubleshooting in the prior art.

[0006] The present invention provides an intelligent manhole cover monitoring and management system based on the Internet of Things, the system comprising:

[0007] The data monitoring module is used to monitor several operation data of each smart manhole cover and obtain the operation status of each smart manhole cover; when the operation status of a smart manhole cover is abnormal, the current smart manhole cover is judged to be an abnormal manhole cover and a fault warning instruction is generated; the several operation data include vibration data, illumination data, tilt data and position data; the fault warning instruction includes the size data, abnormal items and position data of the abnormal manhole cover;

[0008] An image retrieval module is used to obtain a number of associated cameras corresponding to the abnormal manhole cover according to the position data of the abnormal manhole cover in the fault warning instruction, and control the several associated cameras to collect multiple abnormal images of the abnormal manhole cover;

[0009] A fault analysis module is used to confirm the manhole cover fault data based on multiple manhole cover abnormal images and abnormal items of the abnormal manhole cover; the manhole cover fault data includes the fault type and fault level;

[0010] The task generation module is used to generate troubleshooting tasks according to the manhole cover fault data of multiple abnormal manhole covers within a preset troubleshooting cycle, and the troubleshooting tasks include troubleshooting routes.

[0011] Furthermore, the data monitoring module monitors the operation data of each intelligent manhole cover to obtain the operation status of each intelligent manhole cover, including:

[0012] When several operation data of a smart manhole cover are within the corresponding preset operation range, the operation status of the smart manhole cover is normal;

[0013] When at least one of the several operating data of an intelligent manhole cover is not within the corresponding preset operating range, the operating state of the intelligent manhole cover is abnormal.

[0014] Furthermore, the image acquisition module obtains several associated cameras corresponding to the abnormal manhole cover according to the position data of the abnormal manhole cover in the fault warning instruction, including:

[0015] According to the location data of the abnormal manhole cover, a number of cameras within a preset geographical range are obtained, as well as the installation location and equipment parameters of each camera;

[0016] Based on the location data and size data of the abnormal manhole cover, the installation location and equipment parameters of each camera, the estimated acquisition image quality corresponding to each camera is obtained; the estimated acquisition image quality includes resolution and clarity;

[0017] A number of cameras whose collected image quality is expected to meet preset quality requirements are selected as cameras to be selected; the preset quality requirements include: a resolution higher than a preset resolution and a clarity higher than a preset clarity;

[0018] Based on a number of to-be-selected cameras, a target camera combination is obtained; the target camera combination includes no less than a first preset number of to-be-selected cameras; and each to-be-selected camera in the target camera combination is an associated camera.

[0019] Furthermore, the image acquisition module controls a number of associated cameras to collect multiple abnormal manhole cover images of the abnormal manhole cover, including:

[0020] Based on the location data of the abnormal manhole cover, the installation location and device parameters of each associated camera, the image acquisition parameters of each associated camera are obtained;

[0021] Based on the image acquisition parameters of each associated camera, each associated camera is adjusted to acquire images of abnormal manhole covers to obtain multiple images to be selected;

[0022] The multiple images to be selected collected by each associated camera are screened to obtain multiple manhole cover abnormal images that meet the preset image requirements; the preset image requirements include: a resolution higher than a preset resolution and a clarity higher than a preset clarity.

[0023] Furthermore, the fault analysis module confirms the manhole cover fault data based on multiple manhole cover abnormal images and abnormal items of the abnormal manhole cover, including:

[0024] Based on the mapping of abnormal items of abnormal manhole covers and preset associated detection items, a number of associated detection items are obtained; the associated detection items include basic data items and feature data items;

[0025] Based on multiple abnormal manhole cover images of the abnormal manhole cover, a three-dimensional manhole cover model of the abnormal manhole cover is constructed;

[0026] Based on the three-dimensional manhole cover model and several related detection items, several manhole cover basic data and several manhole cover feature data are obtained;

[0027] Based on several basic data of manhole covers and several characteristic data of manhole covers, the fault type and fault level are confirmed; the fault types include: breakage fault, displacement fault, lift fault and well seat fault.

[0028] Furthermore, based on a number of candidate cameras, a target camera combination is obtained, including:

[0029] S1: taking a first preset number as the number of cameras to be selected, and obtaining a number of combinations to be selected to form a set to be selected by permutation and combination;

[0030] S2: Determine whether there is a combination in the current selection set that meets the preset combination requirements; if so, execute step S3; if not, execute step S4;

[0031] S3: taking any candidate combination that meets the preset combination requirements as the target camera combination;

[0032] S4: Adjust the number of candidates for selection, and obtain several candidate combinations to form a candidate set through the permutation and combination method, and return to step S2.

[0033] Furthermore, the preset combination requirements include:

[0034] There are no less than a second preset number of shooting points where the cameras to be selected are arranged along the circumference of the abnormal manhole cover, and the interval angles of adjacent shooting points are within the preset angle range;

[0035] There are at least two to-be-selected cameras corresponding to no less than a third preset number of shooting points, and the multiple to-be-selected cameras corresponding to the same shooting point meet the camera constraint condition.

[0036] Furthermore, based on a number of manhole cover basic data and a number of manhole cover characteristic data, the fault type and fault level are confirmed, including:

[0037] Obtain a manhole cover detection data relationship library, which includes manhole cover basic data, manhole cover feature data, fault type, fault level, and a mapping relationship between the manhole cover basic data, manhole cover feature data, fault type, and fault level;

[0038] Use the manhole cover detection database to train the neural network model and obtain the fault analysis model;

[0039] A number of manhole cover basic data and a number of manhole cover characteristic data are input into the fault analysis model to obtain the fault type and fault level.

[0040] Furthermore, the task generation module generates troubleshooting tasks according to the manhole cover fault data of multiple abnormal manhole covers within a preset troubleshooting period, including:

[0041] Based on the fault types and fault levels of multiple abnormal manhole covers within a preset troubleshooting period, several troubleshooting priorities are determined; each troubleshooting priority has at least one abnormal manhole cover;

[0042] Obtain the obstacle removal sub-routes between several abnormal manhole covers corresponding to each obstacle removal priority in descending order of obstacle removal priority;

[0043] Obtain an obstacle removal route according to the obstacle removal sub-routes between a number of manhole covers corresponding to each obstacle removal priority.

[0044] Furthermore, according to the order of troubleshooting priorities from high to low, the troubleshooting sub-routes between a number of abnormal manhole covers corresponding to each troubleshooting priority are obtained in sequence, including:

[0045] SS1: Based on the location data of several abnormal manhole covers of the current obstacle removal priority, obtain the current obstacle removal sub-route and the route end node of the current obstacle removal sub-route;

[0046] SS2: Use the end node of the current obstacle removal sub-route as the start node of the next obstacle removal priority route and return to SS1;

[0047] SS3: Repeat steps SS1 to SS2 until the lowest troubleshooting priority is reached.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] In the present invention, the operation state of the intelligent manhole cover is monitored by the data monitoring module, and when the operation state of the intelligent manhole cover is abnormal, it is confirmed as an abnormal manhole cover to generate a fault warning instruction; so that the image retrieval module retrieves multiple abnormal manhole cover images of the abnormal manhole cover; the fault analysis module is based on the abnormal manhole cover images and abnormal items to accurately confirm the fault type and fault level of the abnormal manhole cover; the task generation module confirms the troubleshooting route based on the manhole cover fault data of multiple abnormal manhole covers within a preset troubleshooting cycle, so that the route planning in the troubleshooting process is more reasonable. The technical problem of low troubleshooting efficiency of intelligent manhole covers in the prior art is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 Shown is a schematic diagram of the structure of an intelligent manhole cover monitoring and management system based on the Internet of Things provided in an embodiment of the present application.

[0051] Figure 2 Shown is a flow chart of a method for obtaining a target camera combination provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0053] like Figure 1 As shown in the figure, the intelligent manhole cover monitoring and management system based on the Internet of Things includes:

[0054] The data monitoring module is used to monitor several operation data of each smart manhole cover and obtain the operation status of each smart manhole cover; when the operation status of a smart manhole cover is abnormal, the current smart manhole cover is judged to be an abnormal manhole cover and a fault warning instruction is generated; the several operation data include vibration data, illumination data, tilt data and position data; the fault warning instruction includes the size data, abnormal items and position data of the abnormal manhole cover;

[0055] An image retrieval module is used to obtain a number of associated cameras corresponding to the abnormal manhole cover according to the position data of the abnormal manhole cover in the fault warning instruction, and control the several associated cameras to collect multiple abnormal images of the abnormal manhole cover;

[0056] A fault analysis module is used to confirm the manhole cover fault data based on multiple manhole cover abnormal images and abnormal items of the abnormal manhole cover; the manhole cover fault data includes the fault type and fault level;

[0057] The task generation module is used to generate troubleshooting tasks according to the manhole cover fault data of multiple abnormal manhole covers within a preset troubleshooting cycle, and the troubleshooting tasks include troubleshooting routes.

[0058] The specific implementation process of this embodiment includes:

[0059] In this embodiment, the operation state of the intelligent manhole cover is monitored by the data monitoring module, and when the operation state of the intelligent manhole cover is abnormal, it is confirmed as an abnormal manhole cover to generate a fault warning instruction; so that the image retrieval module retrieves multiple abnormal manhole cover images of the abnormal manhole cover; the fault analysis module is based on the abnormal manhole cover images and abnormal items to accurately confirm the fault type and fault level of the abnormal manhole cover; the task generation module confirms the troubleshooting route based on the manhole cover fault data of multiple abnormal manhole covers within the preset troubleshooting cycle, so that the route planning in the troubleshooting process is more reasonable. The technical problem of low efficiency of troubleshooting of intelligent manhole covers in the prior art is solved.

[0060] In this embodiment, the data monitoring module monitors the operation data of each smart manhole cover to obtain the operation status of each smart manhole cover, including:

[0061] When several operation data of a smart manhole cover are within the corresponding preset operation range, the operation status of the smart manhole cover is normal;

[0062] When at least one of the several operating data of an intelligent manhole cover is not within the corresponding preset operating range, the operating state of the intelligent manhole cover is abnormal.

[0063] In this embodiment, the vibration data of the smart manhole cover is collected by a vibration sensor or an acceleration sensor; the illumination data is collected by an illumination sensor; the tilt data is collected by a tilt sensor; and the position data is collected by a GPS positioning module or a Beidou positioning module. The vibration data, illumination data, tilt data, and position data are sent to the data monitoring module via the NB-IoT network base station through the NB-IoT communication module set at the bottom of the smart manhole cover;

[0064] The data monitoring module monitors vibration data, illumination data, tilt data and position data. In this embodiment, a corresponding preset operating range is set for each intelligent manhole cover vibration data, illumination data, tilt data and position data;

[0065] In this embodiment, the vibration data is used to monitor whether the smart manhole cover is vibrating. When the vibration amplitude or vibration frequency is too large, it may cause damage to the manhole cover structure and loose connection parts;

[0066] Light data is used to monitor the light intensity at the bottom of the smart manhole cover. When the light intensity at the bottom of the smart manhole cover changes significantly in a short period of time, it may be that the smart manhole cover is opened or damaged.

[0067] The tilt data is used to monitor the tilt angle of the smart manhole cover. When the tilt angle of the smart manhole cover changes in a short period of time, it may be that the smart manhole cover is opened or damaged; when the tilt angle of the smart manhole cover is too large for a long time, it may be that the smart manhole cover is tilted or collapsed;

[0068] The location data is used to monitor the location of the smart manhole cover. When the location of the smart manhole cover changes, it may be that the smart manhole cover has been displaced or stolen.

[0069] Therefore, when the vibration data, light data, tilt data and position data are all operating within the corresponding preset operating range, the operating status of the smart manhole cover is judged to be normal; when one or more of the vibration data, light data, tilt data and position data are not within the corresponding preset operating range, the operating status of the smart manhole cover is judged to be abnormal.

[0070] In this embodiment, the image acquisition module obtains several associated cameras corresponding to the abnormal manhole cover according to the position data of the abnormal manhole cover in the fault warning instruction, including:

[0071] Step 1: According to the location data of the abnormal manhole cover, obtain a number of cameras within a preset geographical range, as well as the installation location and equipment parameters of each camera;

[0072] In this embodiment, the camera includes a road condition monitoring camera, a traffic monitoring camera, and other monitoring cameras; the preset geographical range includes a circular area with the location of the abnormal manhole cover as the center and a preset length as the radius; the preset radius is 200 meters;

[0073] By connecting to existing cameras to collect image data of abnormal manhole covers, the utilization rate of existing camera equipment is improved while reducing the cost of image collection;

[0074] In this embodiment, a spatial rectangular coordinate system is constructed with the center of the abnormal manhole cover as the origin; the installation position of the camera includes the coordinates in the spatial rectangular coordinate system; the device parameters include resolution, number of pixels, focal length adjustment range and aperture adjustment range.

[0075] Step 2: Based on the location data and size data of the abnormal manhole cover, the installation location and equipment parameters of each camera, obtain the expected acquisition image quality corresponding to each camera; the expected acquisition image quality includes resolution and clarity;

[0076] In this embodiment, based on the installation position of each camera and the position data of the abnormal manhole cover, the shooting distance data of the camera relative to the abnormal manhole cover is obtained. In this embodiment, the shooting distance data includes the distance from the coordinates of the camera to the X-axis, Y-axis and Z-axis in the spatial rectangular coordinate system.

[0077] Based on the device parameters of the camera, an image quality relationship database is obtained; the image quality relationship database includes size data, shooting distance data, expected acquisition image quality, and a mapping relationship between the shooting distance data and the expected acquisition image quality;

[0078] Using the image quality relationship database to train the neural network model, and obtain the image quality analysis model;

[0079] The shooting distance data of the current camera and the size data of the abnormal manhole cover are input into the image quality analysis model to obtain the expected collected image quality.

[0080] In this embodiment, during training, the image quality relationship database is divided into a training set and a verification set, and the neural network model is trained through the training set; the image quality analysis model is obtained through training, and the image quality analysis model is verified through the verification set. When the verification accuracy rate is greater than 98%, it is judged that the image quality analysis model verification has passed.

[0081] Step 3: A number of cameras whose image quality is expected to meet preset quality requirements are selected as cameras to be selected; the preset quality requirements include: a resolution higher than a preset resolution and a clarity higher than a preset clarity;

[0082] In this embodiment, a camera having a resolution higher than a preset resolution and a definition higher than a preset definition is selected as a camera to be selected;

[0083] Screening the cameras within a preset geographical range can prevent cameras that collect abnormal manhole covers that do not meet preset quality requirements from participating in the collection of abnormal manhole cover images, thereby reducing the occupancy of such cameras.

[0084] Step 4: Based on a number of cameras to be selected, a target camera combination is obtained; the target camera combination includes no less than a first preset number of cameras to be selected; each camera to be selected in the target camera combination is an associated camera.

[0085] In this embodiment, by obtaining a target camera combination, several candidate cameras are further screened to obtain associated cameras, thereby avoiding incomplete acquisition of abnormal images of abnormal manhole covers due to unreasonable settings of associated cameras.

[0086] In this embodiment, based on a number of cameras to be selected, a target camera combination is obtained, including:

[0087] S1: taking a first preset number as the number of cameras to be selected, and obtaining a number of combinations to be selected to form a set to be selected by permutation and combination;

[0088] In this embodiment, the first preset number includes 6, the number of candidates is 6, when the number of cameras to be selected is n, 6 cameras are randomly selected from the n cameras to be selected to form a selection combination, then there are a total of C in the selection set. 6 n A combination of candidates;

[0089] S2: Determine whether there is a combination in the current selection set that meets the preset combination requirements; if so, execute step S3; if not, execute step S4;

[0090] S3: taking any candidate combination that meets the preset combination requirements as the target camera combination;

[0091] S4: Adjust the number of candidates for selection, and obtain several candidate combinations to form a candidate set through the permutation and combination method, and return to step S2.

[0092] In this embodiment, adjusting the number of parameters of the cameras to be selected includes adding 1 to the current number of candidates each time to obtain a new number of candidates; for example, if the current number of candidates is 6 and there is no candidate combination that meets the preset combination requirements in the candidate set, the current number of candidates is added 1 to obtain a new number of candidates 7; 7 cameras are randomly selected from n candidates to form a candidate combination, and there are a total of 7 candidates in the candidate set. If there is still no combination that meets the preset combination requirements in the current selection set, the number of candidates is adjusted to 8, and 8 cameras are randomly selected from the n cameras to form a combination. There are a total of The above steps are repeated until the number of candidates is n.

[0093] It should be noted that, in this embodiment, when the number of cameras to be selected at an abnormal manhole cover is less than the first preset number, or the target camera combination does not exist among several cameras to be selected; based on the abnormal items of the abnormal manhole cover and the data values ​​of the abnormal items, the troubleshooting priority of the abnormal manhole cover is confirmed through a pre-set priority mapping.

[0094] In this embodiment, the preset combination requirements include:

[0095] There are no less than a second preset number of shooting points where the cameras to be selected are arranged along the circumference of the abnormal manhole cover, and the interval angles of adjacent shooting points are within a preset angle range; in this embodiment, the preset angle range includes 30-90°. In this embodiment, the interval angle is the angle formed by the projection points of the two shooting points on the horizontal reference plane and the center of the abnormal manhole cover as the angular vertex;

[0096] There are at least two to-be-selected cameras corresponding to no less than a third preset number of shooting points, and the multiple to-be-selected cameras corresponding to the same shooting point meet the camera constraint condition.

[0097] In this embodiment, the camera constraint condition is that the difference in shooting height between any two to-be-selected cameras is greater than a preset height difference;

[0098] It should be noted that when the interval angle between any two of the multiple cameras to be selected is less than the preset interval angle, the multiple cameras to be selected are determined to correspond to the same shooting point. In this embodiment, the preset interval angle includes 5°.

[0099] In this embodiment, the interval angle between the cameras to be selected is the angle formed by the projection points of the two cameras to be selected on the horizontal reference plane and the center of the abnormal manhole cover as the angular vertex;

[0100] It should be noted that, in this embodiment, the first preset number>the second preset number>the third preset number.

[0101] According to another embodiment of the present invention, the image acquisition module controls a plurality of associated cameras to collect a plurality of abnormal manhole cover images of the abnormal manhole cover, including:

[0102] Based on the location data of the abnormal manhole cover, the installation location and device parameters of each associated camera, the image acquisition parameters of each associated camera are obtained;

[0103] In this embodiment, the current light data is obtained according to the position data of the abnormal manhole cover, and the exposure parameters and color parameters are confirmed based on the light data; the focal length parameters are confirmed according to the associated camera and the position data; the image parameters of each associated camera include several parameter combinations, and each parameter combination is set with corresponding exposure parameters, color parameters and focal length parameters.

[0104] Based on the image acquisition parameters of each associated camera, each associated camera is adjusted to acquire images of abnormal manhole covers to obtain multiple images to be selected;

[0105] In this embodiment, each associated camera collects multiple images to be selected according to the corresponding parameter combination;

[0106] The multiple images to be selected collected by each associated camera are screened to obtain multiple manhole cover abnormal images that meet the preset image requirements; the preset image requirements include: a resolution higher than a preset resolution and a clarity higher than a preset clarity.

[0107] In this embodiment, during the process of the associated camera collecting abnormal manhole covers, the abnormal manhole covers may be blocked by vehicles, pedestrians, animals, etc., resulting in incomplete abnormal manhole covers in the selected image.

[0108] In this embodiment, the preset image requirement also includes that the manhole cover is intact. Determining whether the manhole cover in the selected image is intact includes:

[0109] The selected image is processed by edge extraction technology to obtain edge data, and the manhole cover contour, manhole cover corner points, and edge size are obtained based on the edge data;

[0110] The manhole cover is considered complete when the contour of the manhole cover is continuous and closed, the number of corner points of the manhole cover is consistent with the number of actual corner points, and the edge size is consistent with the actual size.

[0111] In this embodiment, the fault analysis module confirms the manhole cover fault data based on multiple manhole cover abnormal images and abnormal items of the abnormal manhole cover, including:

[0112] Based on the mapping of abnormal items of abnormal manhole covers and preset associated detection items, a number of associated detection items are obtained; the associated detection items include basic data items and feature data items;

[0113] In this embodiment, each abnormal item is correspondingly provided with a number of associated detection items, and a preset associated detection item mapping is constructed based on the correspondence between the abnormal items and the associated detection items; the correspondence between the abnormal items and the associated detection items is obtained through big data and expert experience. For example, when the vibration data is abnormal, the associated detection items include the size of the manhole cover, the size of the manhole seat, and the gap data between the edge of the manhole cover and the manhole seat; among which, the size of the manhole cover and the size of the manhole seat are basic data items; the gap data between the edge of the manhole cover and the manhole seat is a characteristic data item;

[0114] For another example, when the illumination data is abnormal, the associated detection items include the size of the manhole cover, the size of the manhole seat, whether the manhole cover is open, and the damaged area of ​​the manhole cover; among them, the size of the manhole cover and the size of the manhole seat are basic data items; whether the manhole cover is open and the damaged area of ​​the manhole cover are characteristic data items.

[0115] By building a preset associated detection item mapping, more data can be provided for analysis.

[0116] There are duplicate items between the associated detection items corresponding to each abnormal item. The duplicate items are used as basic data items, and the remaining non-duplicate items are used as feature data items.

[0117] Based on multiple abnormal manhole cover images of the abnormal manhole cover, a three-dimensional manhole cover model of the abnormal manhole cover is constructed;

[0118] In this embodiment, by screening the cameras around the abnormal manhole cover, a number of associated cameras are obtained; a plurality of abnormal manhole cover images that meet the preset image requirements are obtained through each associated camera to construct a three-dimensional manhole cover model of the abnormal manhole cover. The construction process of the three-dimensional manhole cover model includes:

[0119] The image processing step includes converting the format of the abnormal manhole cover image and cropping it; converting each abnormal manhole cover image into the same PNG format or JPEG format; and removing irrelevant background parts in the abnormal manhole cover image and placing the abnormal manhole cover at the center of the image;

[0120] In the model building step, the processed abnormal manhole cover images and corresponding image acquisition parameters are imported into the 3D modeling software for feature extraction to obtain edge features and corner features; in this embodiment, the 3D modeling software includes MeshLab, 3DMAX, etc.;

[0121] Based on edge features and corner features, each abnormal manhole cover image is matched, the matching relationship is obtained and the positional relationship of each edge feature and each corner feature in different abnormal manhole cover images is calculated; based on the positional relationship of each edge feature and each corner feature in different abnormal manhole cover images, the three-dimensional spatial structure of the manhole cover is obtained; point cloud generation operations, mesh generation operations and texture mapping operations are performed on the three-dimensional spatial structure in sequence; and a three-dimensional basic model of the abnormal manhole cover is obtained;

[0122] In the model optimization step, the three-dimensional basic model is smoothed and denoised, and the three-dimensional basic model is calibrated based on the factory data of the manhole cover to obtain a three-dimensional manhole cover model.

[0123] Based on the three-dimensional manhole cover model and several related detection items, several manhole cover basic data and several manhole cover feature data are obtained;

[0124] Data of related inspection items can be collected based on the three-dimensional manhole cover model, making the data more accurate and intuitive. Maintenance personnel can confirm the corresponding troubleshooting methods through the three-dimensional manhole cover model.

[0125] Based on several basic data of manhole covers and several characteristic data of manhole covers, the fault type and fault level are confirmed; the fault types include: breakage fault, displacement fault, lift fault and well seat fault.

[0126] In this embodiment, the fault levels are arranged from high to low into five levels: A, B, C, D and E.

[0127] In this embodiment, based on a number of manhole cover basic data and a number of manhole cover characteristic data, the fault type and fault level are confirmed, including:

[0128] Obtain a manhole cover detection data relationship library, which includes manhole cover basic data, manhole cover feature data, fault type, fault level, and a mapping relationship between the manhole cover basic data, manhole cover feature data, fault type, and fault level;

[0129] Use the manhole cover detection database to train the neural network model and obtain the fault analysis model;

[0130] A number of manhole cover basic data and a number of manhole cover characteristic data are input into the fault analysis model to obtain the fault type and fault level.

[0131] In this embodiment, the manhole cover detection data relationship library is divided into a fault analysis training set and a fault analysis verification set. The neural network model is trained with the fault analysis training set to obtain the fault analysis model; the fault analysis model is verified with the fault analysis verification set to obtain the fault analysis accuracy. When the fault analysis accuracy is greater than 98%, it is determined that the fault analysis model verification has passed.

[0132] According to another embodiment of the present invention, the task generation module generates a troubleshooting task according to the manhole cover fault data of a plurality of abnormal manhole covers within a preset troubleshooting period, including:

[0133] Based on the fault types and fault levels of multiple abnormal manhole covers within a preset troubleshooting period, several troubleshooting priorities are determined; each troubleshooting priority has at least one abnormal manhole cover;

[0134] In this embodiment, a troubleshooting priority query table is pre-set, and the troubleshooting priority is queried in the troubleshooting priority query table according to the fault type and the corresponding fault level; when maintenance personnel troubleshoot, they perform troubleshooting work in order of troubleshooting priority from high to low.

[0135] In this embodiment, the preset troubleshooting cycles include 8 hours, 12 hours and 24 hours; the preset troubleshooting cycle is set according to the number of abnormal manhole covers in daily life.

[0136] Obtain the obstacle removal sub-routes between several abnormal manhole covers corresponding to each obstacle removal priority in descending order of obstacle removal priority;

[0137] In this embodiment, there is no requirement for the order of fault dispatch work between multiple abnormal manhole covers with the same troubleshooting priority; the shortest path that passes through the locations of multiple abnormal manhole covers with the same troubleshooting priority one by one is used as the troubleshooting sub-route.

[0138] Obtain an obstacle removal route according to the obstacle removal sub-routes between a number of manhole covers corresponding to each obstacle removal priority.

[0139] The specific implementation process of this embodiment includes:

[0140] In this embodiment, according to the order of the troubleshooting priority from high to low, the troubleshooting sub-routes between the abnormal manhole covers corresponding to each troubleshooting priority are obtained in sequence, including:

[0141] SS1: Based on the location data of several abnormal manhole covers of the current obstacle removal priority, obtain the current obstacle removal sub-route and the route end node of the current obstacle removal sub-route;

[0142] For several abnormal manhole covers with the highest troubleshooting priority, the maintenance site is used as the route starting node of the troubleshooting sub-route during troubleshooting; in this embodiment, when the number of abnormal manhole covers with the same troubleshooting priority is less than the preset number of abnormal manhole covers, all paths passing through the manhole covers one by one are obtained by exhaustive enumeration, and the shortest path among them is used as the troubleshooting sub-route; in some embodiments, when the number of abnormal manhole covers with the same troubleshooting priority is greater than the number of abnormal manhole covers, the troubleshooting sub-route corresponding to each troubleshooting priority is obtained by the ant colony algorithm;

[0143] SS2: Use the end node of the current obstacle removal sub-route as the start node of the next obstacle removal priority route and return to SS1;

[0144] SS3: Repeat steps SS1 to SS2 until the lowest troubleshooting priority is reached.

[0145] It should be noted that, in this embodiment, the troubleshooting task also includes a three-dimensional manhole cover model of each abnormal manhole cover; maintenance personnel can perform corresponding preparations by viewing the three-dimensional manhole cover model of the next abnormal manhole cover on the way to the next abnormal manhole cover.

[0146] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0147] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The intelligent manhole cover monitoring and management system based on the Internet of Things is characterized by: The system includes: A data monitoring module is used to monitor several operating data of each intelligent manhole cover and obtain the operating status of each intelligent manhole cover; When the operation state of a smart manhole cover is abnormal, the current smart manhole cover is judged to be an abnormal manhole cover and a fault warning instruction is generated; the several operation data include vibration data, illumination data, tilt data and position data; the fault warning instruction includes the size data, abnormal items and position data of the abnormal manhole cover; An image retrieval module is used to obtain a number of associated cameras corresponding to the abnormal manhole cover according to the position data of the abnormal manhole cover in the fault warning instruction, and control the several associated cameras to collect multiple abnormal images of the abnormal manhole cover; A fault analysis module is used to confirm the manhole cover fault data based on multiple manhole cover abnormal images and abnormal items of the abnormal manhole cover; the manhole cover fault data includes the fault type and fault level; The task generation module is used to generate troubleshooting tasks according to the manhole cover fault data of multiple abnormal manhole covers within a preset troubleshooting cycle, and the troubleshooting tasks include troubleshooting routes.

2. The intelligent manhole cover monitoring and management system based on the Internet of Things as claimed in claim 1, characterized in that: The data monitoring module monitors the operating data of each intelligent manhole cover and obtains the operating status of each intelligent manhole cover, including: When several operation data of a smart manhole cover are within the corresponding preset operation range, the operation status of the smart manhole cover is normal; When at least one of the several operating data of an intelligent manhole cover is not within the corresponding preset operating range, the operating state of the intelligent manhole cover is abnormal.

3. The intelligent manhole cover monitoring and management system based on the Internet of Things as claimed in claim 1, characterized in that: The image acquisition module obtains several associated cameras corresponding to the abnormal manhole cover according to the position data of the abnormal manhole cover in the fault warning instruction, including: According to the location data of the abnormal manhole cover, a number of cameras within a preset geographical range are obtained, as well as the installation location and equipment parameters of each camera; Based on the location data and size data of the abnormal manhole cover, the installation location and equipment parameters of each camera, the estimated acquisition image quality corresponding to each camera is obtained; the estimated acquisition image quality includes resolution and clarity; A number of cameras whose image quality is expected to meet preset quality requirements are selected as cameras to be selected; the preset quality requirements include: a resolution higher than a preset resolution and a clarity higher than a preset clarity; Based on a number of to-be-selected cameras, a target camera combination is obtained; the target camera combination includes no less than a first preset number of to-be-selected cameras; and each to-be-selected camera in the target camera combination is an associated camera.

4. The intelligent manhole cover monitoring and management system based on the Internet of Things as claimed in claim 3 is characterized by: The image acquisition module controls several associated cameras to collect multiple abnormal manhole cover images, including: Based on the location data of the abnormal manhole cover, the installation location and equipment parameters of each associated camera, the image acquisition parameters of each associated camera are obtained; Based on the image acquisition parameters of each associated camera, each associated camera is adjusted to acquire images of abnormal manhole covers to obtain multiple images to be selected; The multiple images to be selected collected by each associated camera are screened to obtain multiple manhole cover abnormal images that meet the preset image requirements; the preset image requirements include: a resolution higher than a preset resolution and a clarity higher than a preset clarity.

5. The intelligent manhole cover monitoring and management system based on the Internet of Things as claimed in claim 4, characterized in that: The fault analysis module confirms the manhole cover fault data based on multiple manhole cover abnormal images and abnormal items, including: Based on the mapping of abnormal items of abnormal manhole covers and preset associated detection items, a number of associated detection items are obtained; the associated detection items include basic data items and feature data items; Based on multiple abnormal manhole cover images of the abnormal manhole cover, a three-dimensional manhole cover model of the abnormal manhole cover is constructed; Based on the three-dimensional manhole cover model and several related detection items, several manhole cover basic data and several manhole cover feature data are obtained; Based on several basic data of manhole covers and several characteristic data of manhole covers, the fault type and fault level are confirmed; the fault types include: breakage fault, displacement fault, lift fault and well seat fault.

6. The intelligent manhole cover monitoring and management system based on the Internet of Things as claimed in claim 3, characterized in that: Based on several candidate cameras, obtain the target camera combination, including: S1: taking a first preset number as the number of cameras to be selected, and obtaining a number of combinations to be selected to form a set to be selected by permutation and combination; S2: Determine whether there is a combination in the current selection set that meets the preset combination requirements; if so, execute step S3; if not, execute step S4; S3: taking any candidate combination that meets the preset combination requirements as the target camera combination; S4: Adjust the number of candidates for selection, and obtain several candidate combinations to form a candidate set through the permutation and combination method, and return to step S2.

7. The intelligent manhole cover monitoring and management system based on the Internet of Things as claimed in claim 6, characterized in that: The preset combination requirements include: There are no less than a second preset number of shooting points where the cameras to be selected are arranged along the circumference of the abnormal manhole cover, and the interval angles of adjacent shooting points are within the preset angle range; There are at least two to-be-selected cameras corresponding to no less than a third preset number of shooting points, and the multiple to-be-selected cameras corresponding to the same shooting point meet the camera constraint condition.

8. The intelligent manhole cover monitoring and management system based on the Internet of Things as claimed in claim 5, characterized in that: Based on some basic data of manhole covers and some characteristic data of manhole covers, the fault type and fault level are confirmed, including: Obtain a manhole cover detection data relationship library, which includes manhole cover basic data, manhole cover feature data, fault type, fault level, and a mapping relationship between the manhole cover basic data, manhole cover feature data, fault type, and fault level; Use the manhole cover detection database to train the neural network model and obtain the fault analysis model; A number of manhole cover basic data and a number of manhole cover characteristic data are input into the fault analysis model to obtain the fault type and fault level.

9. The intelligent manhole cover monitoring and management system based on the Internet of Things as claimed in claim 1, characterized in that: The task generation module generates troubleshooting tasks according to the manhole cover fault data of multiple abnormal manhole covers within a preset troubleshooting period, including: Based on the fault types and fault levels of multiple abnormal manhole covers within a preset troubleshooting period, several troubleshooting priorities are determined; each troubleshooting priority has at least one abnormal manhole cover; Obtain the obstacle removal sub-routes between several abnormal manhole covers corresponding to each obstacle removal priority in descending order of obstacle removal priority; Obtain an obstacle removal route according to the obstacle removal sub-routes between a number of manhole covers corresponding to each obstacle removal priority.

10. The intelligent manhole cover monitoring and management system based on the Internet of Things as claimed in claim 9, characterized in that: Obtain the obstruction removal sub-routes between several abnormal manhole covers corresponding to each obstruction removal priority in descending order, including: SS1: Based on the location data of several abnormal manhole covers of the current obstacle removal priority, obtain the current obstacle removal sub-route and the route end node of the current obstacle removal sub-route; SS2: Use the end node of the current obstacle removal sub-route as the start node of the next obstacle removal priority route and return to SS1; SS3: Repeat steps SS1 to SS2 until the lowest troubleshooting priority is reached.

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