City well lid multi-source disease intelligent identification and health state advanced management and control method and system

Abstract

The invention relates to an urban well lid multi-source disease intelligent identification and health state advanced management and control method and system. The method comprises the following steps: establishing a well lid multi-source disease classification system; the well lid multi-source disease intelligent recognition method comprises the steps that well lid basic information is collected; a well lid image is collected; well lid vibration sound is collected; carrying out data analysis on the well lid data obtained through intelligent identification, wherein the data analysis comprises the following steps: carrying out spatial feature analysis on well lid diseases; time characteristic analysis of well lid diseases is carried out; the well lid health state is managed and controlled in advance based on occurrence rules of well lid diseases in different space scenes and different time periods, and the method comprises the steps that a well lid service life space management and control scheme is established, and regional space control and optimization are conducted on well lid multi-source diseases; and establishing a well lid service life time-division and phase-division management and control scheme, and performing multi-time-period time control and optimization on well lid multi-source diseases. According to the method, the well lid health state can be accurately controlled, predictive maintenance is actively carried out, the disease occurrence frequency is reduced from the source, and the influence of well lid diseases on urban operation is reduced to the maximum extent.

Description

Technical Field

[0001] The present invention relates to the technical field of urban infrastructure operation and maintenance, in particular to the operation and maintenance of urban manhole covers, and specifically to a method and system for intelligently identifying multi-source diseases of urban manhole covers and proactively controlling their health status. Background Art

[0002] Urban manhole covers are an important part of urban infrastructure. However, in recent years, urban manhole cover defects have become frequent. Safety accidents caused by the lack of timely maintenance and repair of urban manhole covers have occurred frequently, seriously restricting the high-quality development of cities.

[0003] Currently, the operation and maintenance of urban manhole covers relies primarily on manual inspections and the identification of manhole cover defects. Limited by manual experience and skill, this identification efficiency is low, and there is a lack of intelligent, precise defect identification systems. Furthermore, the current operation and maintenance of urban manhole covers is not data-driven, and a mechanism for guiding maintenance using historical repair data has not yet been established. Research on the distribution patterns and influencing factors of manhole cover defects is lacking, making it impossible to develop precise, intelligent management and control of urban manhole covers at the spatiotemporal level, let alone effectively guide predictive maintenance.

[0004] In view of the existing deficiencies, there is an urgent need to reduce the frequency of disease occurrence from the source and effectively improve the safe operation level of urban manhole covers. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the main purpose of the present invention is to provide a method and system for intelligent identification of multi-source diseases of urban manhole covers and advanced management of their health status, so as to solve one or more problems in the prior art.

[0006] The technical solutions of the present invention are as follows:

[0007] A first aspect of the present invention provides a method for intelligently identifying multi-source diseases of urban manhole covers and proactively controlling their health status, the method comprising:

[0008] Establish a classification system for multi-source damage to manhole covers;

[0009] Based on the classification system of multi-source manhole cover diseases, intelligent identification of multi-source manhole cover diseases is carried out, including:

[0010] Collect basic information of manhole covers to match subsequent collected data with the basic information data of manhole covers;

[0011] Collect manhole cover images and determine whether the manhole cover is damaged and the type and severity of the damage based on the manhole cover image data;

[0012] Collect the vibration sound of the manhole cover and determine whether the manhole cover is damaged and the type and degree of damage based on the manhole cover vibration data;

[0013] Analyze the manhole cover data obtained by intelligent identification and establish a multi-source manhole cover disease database, including:

[0014] Conduct spatial characteristic analysis of manhole cover diseases to obtain the occurrence patterns of manhole cover diseases in different spatial scenarios;

[0015] Conduct time characteristic analysis of manhole cover diseases to obtain the occurrence patterns of manhole cover diseases in different time periods;

[0016] Based on the occurrence patterns of manhole cover diseases in different spatial scenarios and time periods, proactive control of manhole cover health status is carried out, including:

[0017] Establish a spatial management plan for the service life of manhole covers to conduct regional spatial control and optimization of multi-source defects of manhole covers;

[0018] Establish a time- and phase-based management and control plan for the service life of manhole covers, and conduct multi-period time control and optimization of multi-source defects of manhole covers.

[0019] Preferably, the manhole cover image is collected, and based on the manhole cover image data, whether the manhole cover is damaged and the type and degree of the damage are determined, specifically:

[0020] Manhole cover images are collected, and based on the collected manhole cover image data, it is determined whether the manhole cover has Class I diseases among multi-source diseases. Class I diseases include eight types of diseases: missing, disordered, displaced, damaged, damaged around the well, bulge, height difference, and subsidence of the manhole cover. The degree of damage of some or all of the Class I diseases is further determined.

[0021] Preferably, the manhole cover image is collected, and based on the collected manhole cover image data, it is determined whether the manhole cover has Class I damage among the multi-source damages. Class I damage includes eight types of damages: missing, disordered, displaced, damaged, damage around the well, bulge, height difference, and subsidence of the manhole cover. The degree of damage of some or all of the Class I damages is further determined, including:

[0022] Use image acquisition equipment to capture image data of the manhole cover and its surrounding environment in real time;

[0023] Establish a manhole cover defect recognition model to identify manhole cover image data and accurately determine the eight types of manhole cover defects in Class I.

[0024] The binary image method is used to convert the pictures collected on site into grayscale images to determine the severity of the two types of damage, namely, damage to the manhole cover and damage around the well, which are Class I damages.

[0025] Preferably, the method of collecting the vibration sound of the manhole cover and determining whether the manhole cover is damaged and the type and degree of the damage based on the vibration sound data is as follows:

[0026] The vibration sound of the manhole cover is collected, and based on the collected manhole cover vibration data, it is determined whether the manhole cover has a Class II disease among the multi-source diseases. Class II diseases include manhole cover vibration diseases, and the degree of Class II diseases is further determined.

[0027] Preferably, the method collects the vibration data of the manhole cover, and determines whether the manhole cover has a Class II disease among the multi-source diseases based on the collected manhole cover vibration data, where the Class II disease includes the vibration disease of the manhole cover, and further determines the degree of the Class II disease, including:

[0028] Vibration sensors are arranged on the manhole cover in the direction of vehicle flow, and the vibration sensors collect the vibration frequency of the manhole cover;

[0029] The vibration frequency of the manhole cover is collected by the vibration sensor, and the trend characteristics of the vibration frequency are used to determine whether the manhole cover has Class II disease, and the peak value of the vibration frequency is used to determine the severity of the Class II disease of the manhole cover.

[0030] Preferably, the establishment of a spatial management and control plan for the service life of manhole covers to regionally control and optimize multi-source defects of manhole covers includes:

[0031] Establish a spatial inspection plan for key points, key paths and key areas;

[0032] Smart manhole covers are deployed at key locations to monitor the health status of the manhole covers and the surrounding environment in real time. The monitoring frequency and sensitivity are automatically adjusted according to traffic flow and pedestrian flow to achieve key point control of the manhole covers.

[0033] Smart manhole covers are deployed along critical paths to collect and analyze the health status of manhole covers along the critical paths. The health status characteristics of manhole covers along the critical paths are summarized to predict the defects of ordinary manhole covers along the entire route and achieve critical path control of manhole covers.

[0034] Smart manhole covers are deployed in key areas, and the operating space of manhole covers is divided into pedestrian and vehicle areas. The patterns and causes of manhole cover defects in areas with dense pedestrian and vehicle flow are analyzed. Based on the characteristics of manhole cover defects in key areas, the monitoring frequency and inspection efforts are targetedly increased to achieve spatial regional control of manhole covers.

[0035] Preferably, the key points include intersections with heavy traffic and large passenger flow, and areas near subway stations; the key paths include main roads or important channels of urban traffic; and the key areas include areas with large passenger flow during the day, areas with large passenger flow during peak hours in the morning and evening, and areas with dense traffic.

[0036] Preferably, the establishment of a time- and stage-by-stage management and control plan for the service life of manhole covers, and the control and optimization of multi-source defects of manhole covers over multiple time periods, includes:

[0037] Choose different time periods: day / week, month / season;

[0038] Morning and evening peak traffic disease control: Analyze the impact of daily / weekly morning and evening traffic peaks on manhole covers, determine the types and causes of manhole cover diseases during these peaks, and control the health of manhole covers based on the disease characteristics caused by daily traffic peaks;

[0039] Tidal traffic damage control: Based on daily / weekly traffic data, the direction and time of tidal traffic are identified, and the impact of daily / weekly tidal traffic on manhole covers is analyzed. During peak tidal traffic periods, the types and causes of tidal traffic manhole cover damage are determined. Based on the characteristics of the damage caused by tidal traffic, the health of the manhole covers is controlled.

[0040] Seasonal disease control: Based on seasonal changes and climate characteristics, analyze the impact of monthly / quarterly seasonal changes and climate characteristics on manhole covers, determine the category characteristics and causes of manhole cover diseases each month / quarter, and control the health status of manhole covers based on the disease characteristics caused by seasonal changes and climate characteristics.

[0041] A second aspect of the present invention provides a city manhole cover health status advanced management and control system for the above method, comprising:

[0042] A manhole cover multi-source disease classification layer, which classifies multi-source diseases of urban manhole covers;

[0043] An intelligent recognition layer, which intelligently identifies multiple sources of damage to urban manhole covers, and determines whether a manhole cover has a damage, the type of damage, and the severity of the damage;

[0044] The data analysis layer analyzes the manhole cover data obtained by the intelligent recognition layer, analyzes the spatial and temporal characteristics of various manhole cover diseases, and obtains the occurrence patterns of manhole cover diseases in different spatial scenarios and different time periods;

[0045] The spatiotemporal control layer conducts proactive control over the health status of manhole covers, establishes a spatial control plan for the service life of manhole covers and a time- and stage-by-stage control plan for the service life of manhole covers, conducts regional spatial control and optimization of multi-source diseases of manhole covers, and conducts time control and optimization for multiple time periods.

[0046] Preferably, the smart recognition layer includes:

[0047] A manhole cover image intelligent collection and recognition system collects images of manhole covers in real-world scenarios and, based on the collected image data, determines whether the manhole covers have suffered Class I defects from multiple sources. Class I defects include missing, dislocated, displaced, damaged, damaged around the well, raised, height difference, and subsidence. The system also determines the severity of some or all of the Class I defects based on a binary image method.

[0048] A dynamic identification system for collecting and identifying manhole cover noises collects manhole cover noises and determines whether the manhole cover has a Class II disease among multi-source diseases based on the collected manhole cover noise data. Class II diseases include manhole cover noise diseases. At the same time, the severity of the Class II manhole cover disease is determined by the frequency of the manhole cover noise.

[0049] Preferably, the manhole cover image intelligent acquisition and recognition system uses image acquisition equipment installed at key locations to capture images of the manhole cover from multiple angles, including front and side views; and at the same time, with the help of a manhole cover disease recognition model, the manhole cover image data is recognized;

[0050] Preferably, the manhole cover vibration collection and dynamic identification system uses vibration sensors arranged on the manhole cover in the direction of vehicle flow, and the vibration sensors collect the vibration frequency of the manhole cover.

[0051] Preferably, the space-time control layer includes a space control system and a time control system, wherein:

[0052] The spatial control system includes a key point control unit, a key path control unit, and a key area control unit. The key point control unit monitors the status of manhole covers at key points in real time and automatically adjusts the monitoring frequency and sensitivity according to traffic flow and pedestrian flow to achieve key point control of manhole covers. The key path control unit predicts the defects of ordinary manhole covers on the key path to achieve key path control of manhole covers. The key area control unit analyzes the patterns and causes of manhole cover defects in areas with dense pedestrian and vehicle traffic, and increases the monitoring frequency and inspection intensity based on the characteristics of manhole cover defects in the key areas to achieve spatial regional control of manhole covers.

[0053] The time control system determines the category characteristics and causes of manhole cover defects during peak hours in the morning and evening, and based on the characteristics of the defects, conducts targeted manhole cover defect control, identifies the direction and time period of tidal traffic flow, and at the same time strengthens real-time monitoring and early warning of manhole covers in specific directions during peak tidal traffic flow. It also analyzes the category characteristics and causes of manhole cover defects on a monthly / quarterly basis, and based on the characteristics of the defects, formulates manhole cover defect control plans for different time periods and conducts seasonal maintenance and management of manhole covers.

[0054] The beneficial effects of the present invention relative to the prior art are:

[0055] (1) The method provided by the present invention can realize the accurate identification of multi-source diseases of manhole covers and the evolution of damage degree. It can accurately control the health status of manhole covers based on the occurrence pattern of manhole cover diseases in the region with the goal of increasing the life of manhole covers and extending the health status of manhole covers. It can actively carry out predictive maintenance, reduce the frequency of disease occurrence from the source, and minimize the impact of manhole cover diseases on urban operations.

[0056] (2) The method provided by the present invention establishes a classification system for multi-source diseases of manhole covers. According to the characteristics of the occurrence and existence of nine types of manhole cover diseases, they are divided into Class I diseases and Class II diseases, so as to facilitate the targeted collection of manhole cover data.

[0057] (3) The method provided by the present invention performs data analysis on manhole cover data, analyzes the spatial and temporal characteristics of various types of manhole cover diseases, finds the occurrence patterns of manhole cover diseases in different spatial scenes and different time periods, and establishes a manhole cover multi-source disease database.

[0058] (4) The method provided by the present invention conducts proactive control of the health status of manhole covers based on the occurrence patterns of manhole cover diseases in different spatial scenarios, establishes a spatial control plan for the service life of manhole covers (a spatial control system for multi-source diseases of manhole covers), establishes an inspection plan for key points, key paths and key areas, and proposes the layout principle of key points, key paths and key areas of smart manhole covers, so as to conduct regional spatial control and optimization of multi-source diseases of manhole covers.

[0059] (5) The method provided by the present invention is based on the occurrence pattern of manhole cover diseases in different time periods, and conducts advanced control of the health status of manhole covers. It establishes a time-based and stage-based control plan for the service life of manhole covers (a time control system for multi-source diseases of manhole covers), and proposes key control principles for manhole covers in different time periods, so as to achieve short-term (morning and evening peak hours) - tidal pedestrian and vehicle flow (tidal phenomenon formed by upward and downward movement due to urban planning) - seasonal (different damage modes in spring, summer, autumn and winter) disease control, and conducts time control and optimization of multi-time periods for multi-source diseases of manhole covers.

[0060] It should be understood that the contents described in the Summary of the Invention section are not intended to limit the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. In addition, the implementation of any embodiment of the present invention does not mean that multiple or all of the above-mentioned beneficial effects must be simultaneously possessed or achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can, without inventive effort, derive other implementation drawings based on the provided drawings.

[0062] The structures, proportions, sizes, etc. illustrated in this specification are intended solely to complement the contents disclosed herein and to facilitate understanding and reading by persons skilled in the art. They are not intended to limit the conditions under which the present invention may be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportions, or adjustments in sizes, provided they do not affect the efficacy and objectives of the present invention, shall remain within the scope of the technical contents disclosed herein.

[0063] Figure 1 A schematic flow chart of a method for intelligently identifying multi-source defects in urban manhole covers and proactively controlling their health status is shown according to some embodiments of the present invention;

[0064] Figure 2 A schematic diagram of the overall framework structure of a method for intelligently identifying multi-source defects in urban manhole covers and proactively controlling their health status according to some embodiments of the present invention is shown;

[0065] Figure 3 A schematic diagram of collecting manhole cover vibration data according to some embodiments of the present invention is shown;

[0066] Figure 4 A schematic diagram of manhole cover vibration frequency analysis according to some embodiments of the present invention is shown;

[0067] Figure 5 A schematic diagram of key point-key path-key area control according to some embodiments of the present invention is shown.

[0068] In the various drawings, the same or corresponding reference numerals denote the same or corresponding parts. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention more clearly understood, the embodiments of the present invention are further described in detail below in conjunction with the embodiments and drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0070] In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they may refer to fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0071] It should be understood that the terms "comprises / comprising," "consisting of," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a product, apparatus, process, or method that includes a list of elements includes not only those elements but also, if necessary, other elements not explicitly listed, or elements inherent to such product, apparatus, process, or method. In the absence of further limitations, elements defined by the phrases "comprises / comprising," "consisting of," do not preclude the presence of additional identical elements in the product, apparatus, process, or method that includes the elements.

[0072] It should also be understood that terms such as "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device, component or structure referred to must have a specific direction, be constructed or operate in a specific direction, and should not be understood as limiting the present invention.

[0073] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature identified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0074] The current technical shortcomings are as follows:

[0075] 1) The classification of manhole cover defects relies more on manual experience and is greatly influenced by individual experience. The maintenance goal is mainly to extend the life of the manhole cover and simplify the maintenance process, resulting in conservative maintenance and an increased replacement rate of manhole covers.

[0076] 2) Manual inspections are limited to a specific area, making it difficult to trace the source of a single manhole cover and observe the longitudinal evolution of a single manhole cover defect.

[0077] 3) The manhole cover disease data has not been classified in a targeted manner to find the patterns of disease occurrence and conduct targeted manhole cover health status control.

[0078] Therefore, there is an urgent need to accurately identify various manhole cover diseases, deeply explore manhole cover maintenance data, clarify the occurrence patterns and life models of manhole cover diseases, establish a maintenance data-driven model for precise maintenance of urban manhole covers, effectively improve the service life of manhole covers, and realize predictive maintenance of urban manhole covers, thereby reducing the frequency of disease occurrence from the source and effectively improving the safe operation level of urban manhole covers.

[0079] Based on this, the present invention believes that the control of manhole cover diseases should be carried out from two aspects: the first is to increase the service life of the manhole cover before the manhole cover disease occurs, based on the maintenance data of the manhole cover, find out the relationship between the manhole cover disease category and time and space from the data, find out the occurrence rules of various types of manhole cover diseases, and thus perform accurate health status control; the second is the current mainstream direction, through real-time monitoring of the health status of the manhole cover, identifying manhole cover diseases through machine learning algorithms, and performing point-to-point repairs on the diseases.

[0080] To this end, the present invention provides a method and system for intelligently identifying multi-source diseases of urban manhole covers and proactively controlling their health status to solve the above-mentioned problems.

[0081] The implementation of the present invention is described in detail below in conjunction with preferred embodiments.

[0082] See also Figure 1 The flow chart shown provides a method for intelligent identification of multi-source diseases of urban manhole covers and proactive control of their health status, the method comprising: S10, establishing a classification system for multi-source diseases of manhole covers; S20, intelligently identifying multi-source diseases of manhole covers based on the classification system for multi-source diseases of manhole covers; S30, performing data analysis on the manhole cover data obtained through intelligent identification and establishing a database for multi-source diseases of manhole covers; S40, proactively controlling the health status of manhole covers based on the occurrence patterns of manhole cover diseases in different spatial scenarios and in different time periods obtained through data analysis.

[0083] The following further combines Figure 2 The modular schematic diagram shown explains the specific implementation of each link in detail.

[0084] In the S10 stage, a classification system for multi-source diseases of manhole covers is first established.

[0085] According to relevant specifications, manhole cover defects can be divided into nine categories, namely: missing manhole cover (missing manhole cover seat), disordered manhole cover (manhole cover upside down, no prescribed markings on the surface), displacement of manhole cover (tilted, loose, deviated from the wellhead, warped, flipped), damaged manhole cover (cracks, notches, holes, deformations), damage around the well (cracks and potholes on the road surface within 1m from the edge), raised manhole cover (the top surface of the manhole cover or well seat is more than 10mm higher than the road surface around the well), height difference of manhole cover (the height difference between the top surface of the manhole cover and the well seat is more than 10mm), subsidence of manhole cover (the inspection well sinks relative to the ground, 15mm below the surface), and vibration of manhole cover (the vibration sound is loud when a vehicle rolls over the manhole cover, causing noise).

[0086] In some feasible embodiments, the present invention divides the above nine types of manhole cover defects into Class I defects and Class II defects based on the characteristics of the defects. Class I defects include eight types of defects, namely, missing, disordered, displaced, damaged, damaged around the well, bulge, height difference, and subsidence of the manhole cover. Class II defects include vibration defects of the manhole cover. With the goal of accurately detecting the categories and identifying the degree of damage of the above nine types of defects, appropriate discrimination methods are selected for each of the nine types of defects, which will be elaborated in detail later.

[0087] In the S20 stage, based on the established classification system for multi-source diseases of manhole covers, multi-source diseases of manhole covers are intelligently identified.

[0088] This step first includes: S201, collecting basic information of the manhole cover to match the subsequent collected data with the basic information data of the manhole cover.

[0089] Specifically: through basic design, construction, and acceptance data, basic information about the manhole cover, such as its name and number, geographical location, time of commissioning, and connectivity between the well chamber and other wells, is obtained to match the above basic information with subsequent collected data at the manhole cover location.

[0090] The S20 step also includes: S202, collecting the manhole cover image, and determining whether the manhole cover is damaged and the type and degree of the damage based on the manhole cover image data.

[0091] Specifically, the manhole cover image is collected, and based on the collected manhole cover image data, it is determined whether the manhole cover has Class I disease among multi-source diseases, and the degree of disease of some or all of the Class I diseases is further determined.

[0092] The eight Class I defects (missing, dislocated, displaced, damaged, perimeter damage, bulge, height difference, and subsidence) are determined based on the high efficiency of image recognition. Machine learning target detection technology is used to develop image recognition algorithms for manhole cover defects (Yolo, RetinaNet, Faster R-CNN, and Cascade R-CNN) through machine image recognition to accurately determine the eight types of manhole cover defects. In determining the severity of the eight types of manhole cover defects, the characteristics of the defects are fully considered. Missing, dislocated, and displaced manhole covers do not require a severity assessment. However, damage to the manhole cover, perimeter damage, bulge, height difference, and subsidence require a severity assessment.

[0093] In some specific embodiments, the present invention can use binary imaging to identify manhole cover damage, converting images collected on-site into grayscale images. This allows the determination of the extent of damage to the manhole cover and surrounding areas, tracing the progression of damage, and developing targeted repair plans. This creates an intelligent manhole cover image acquisition and recognition system.

[0094] The S20 step also includes: S203, collecting the vibration sound of the manhole cover and determining whether the manhole cover is damaged and the type and degree of the damage based on the manhole cover vibration data.

[0095] Specifically, the vibration sound of the manhole cover is collected and based on the collected manhole cover vibration data, it is determined whether the manhole cover has Class II disease among multi-source diseases. Class II diseases include vibration diseases of the manhole cover, and the degree of Class II diseases is further determined.

[0096] To address rattling manhole cover defects, this invention fully considers the limitations of image detection. Two-dimensional images cannot accurately determine spatial issues. Taking into account the characteristic of rattling manhole covers that cause unusual noises when vehicles and people pass through, the invention uses the rattling frequency to determine whether a manhole cover has rattling defects, and the magnitude of the rattling frequency to determine the severity of the defect. This forms a dynamic identification system for manhole cover rattling defects.

[0097] In some specific embodiments, Figure 3 As shown, vibration sensors are arranged on the manhole cover in the direction of vehicle flow. The figure shows the position of a vibration sensor, and the vibration sensor collects the vibration frequency of the manhole cover respectively;

[0098] like Figure 4 As shown in the figure, the vibration frequency collected by the vibration sensor is used to determine whether the manhole cover has Class II disease based on the trend characteristics of the vibration frequency, and the severity of the Class II disease of the manhole cover is determined by the peak value of the vibration frequency.

[0099] In the S30 stage, data analysis is performed on the manhole cover data obtained by intelligent identification to establish a multi-source disease database for manhole covers.

[0100] This link can be implemented in two aspects, space and time, according to the types and characteristics of various manhole covers, including:

[0101] S301, performing spatial feature analysis of manhole cover diseases, analyzing the spatial characteristics of various types of manhole cover diseases, and obtaining the occurrence patterns of manhole cover diseases in different spatial scenarios;

[0102] Specifically, we classified nine types of defects, including missing manhole covers (missing manhole covers and seats), disordered manhole covers (manhole covers turned upside down, with no prescribed markings on the surface), displaced manhole covers (tilted, loose, deviated from the wellhead, warped, and flipped), damaged manhole covers (cracks, notches, holes, and deformations), damaged manhole areas (broken pavement and potholes within 1m from the edge), raised manhole covers (the top surface of the manhole cover or seat is more than 10mm higher than the pavement around the well), height difference of manhole covers (the height difference between the top surface of the manhole cover and the seat is more than 10mm), sunken manhole covers (the inspection wells are sunken relative to the ground, 15mm below the surface), and vibrating manhole covers (the vibration sound is loud when a vehicle runs over the manhole cover, causing noise). We analyzed the scenarios in which various manhole cover defects occur, such as main roads, traffic intersections, parking lots, and office buildings, etc., summarized the spatial characteristics of various manhole cover defects, and found the occurrence patterns of various manhole cover defects in different spatial scenarios.

[0103] S302, performing time characteristic analysis of manhole cover diseases, analyzing the time characteristics of various manhole cover diseases, and obtaining the occurrence patterns of manhole cover diseases in different time periods;

[0104] Specifically, we analyze the occurrence time of various manhole cover diseases, analyze the occurrence characteristics of various diseases in different time periods (such as morning and evening peak hours, holidays and weekdays, different months and different seasons, etc.), and summarize the high-frequency occurrence patterns of various manhole cover diseases in different time periods.

[0105] Finally, in the S40 stage, based on the occurrence patterns of manhole cover diseases in different spatial scenarios and different time periods obtained through data analysis, the health status of manhole covers is proactively controlled.

[0106] Accurate control of manhole cover lifespan requires accurate identification of multiple source disease categories and damage levels. Therefore, after accurately identifying the various characteristics of manhole cover diseases, in order to control the health status of manhole covers and extend their service life, we plan to optimize the spatial layout of urban manhole covers and implement multi-cycle and multi-frequency time control through two levels of spatial disease control and temporal disease control, so as to improve the control of the operating status of manhole covers and achieve refined management of manhole covers. Specifically, this includes:

[0107] S401: Establish a spatial management and control plan for the service life of manhole covers to conduct regional spatial control and optimization of multi-source manhole cover diseases;

[0108] In response to the problem that the inspection cycle range of urban manhole covers is fixed, resulting in discrete, inefficient and unfocused control of manhole covers, the present invention establishes a spatial management plan for the service life of manhole covers, proposes key inspection and control areas, increases the inspection frequency, and deploys high-standard bearing-capacity manhole covers in areas with high risk of disease occurrence in view of the differences in manhole cover lifespan in different areas. In addition, the frequency of health status collection and inspection frequency is increased during the maintenance phase.

[0109] (1) First, establish a key point-key path-key area spatial inspection plan, analyze the high-frequency points of manhole cover diseases through the category and location data of manhole cover diseases, connect the high-frequency points into lines, and thus form a network area. Through the frequency analysis of manhole cover diseases, establish the key inspection path to achieve local regional control;

[0110] In an embodiment of the present invention, key points include intersections with heavy traffic and large passenger flow, and areas near subway stations; key paths include main roads or important channels of urban traffic; key areas include areas with large passenger flow during the day, areas with large passenger flow during peak hours in the morning and evening, and areas with dense traffic. For example, areas near subways have large passenger flow during peak hours in the morning and evening; areas around Beijing's second, third, and fourth ring roads are areas with dense traffic; places with many office buildings such as Zhongguancun have large passenger flow during the day, and so on.

[0111] (2) Then, based on the key point-key path-key area division characteristics, smart manhole covers with the function of real-time monitoring of the manhole cover's own health status and the surrounding environment are deployed at the key points of manhole cover disease control. The manhole cover status at the key points is monitored in real time, and the monitoring frequency and sensitivity are automatically adjusted according to the traffic flow and pedestrian flow, thus realizing the key point control of the manhole cover;

[0112] like Figure 5 As shown, Figure 5 It shows that smart manhole covers are laid out according to the laws of diseases occurring at different points, paths, and areas. They can be laid out in areas with high frequency of diseases. Figure 5 Targeted key point arrangements are adopted for cross-shaped, Y-shaped, L-shaped, T-shaped and other paths, so as to realize the control of key points, paths and areas. At the same time, the occurrence pattern of disease types can be determined during the inspection process to realize targeted repairs of manhole cover diseases.

[0113] (3) Based on the characteristics of traffic roads, smart manhole covers are deployed on key paths, at road intersections and at the ends of roads. By deploying smart manhole covers at both ends and in the middle of the road and analyzing the development trend of manhole cover defects, the defects of ordinary manhole covers on the entire route can be predicted, thereby achieving key path control of manhole covers;

[0114] In realizing the critical path control of manhole covers, it can be linked with the traffic management system. By sharing traffic data in real time and based on road traffic flow data, manhole covers with different load-bearing standards can be deployed in sections and areas with high traffic volume and high risk of manhole cover damage. At the same time, the frequency of inspections and health status monitoring can be increased to enhance the resistance of manhole covers.

[0115] (4) Based on the characteristics of the manhole cover layout, the manhole cover operation space is divided into pedestrian area and vehicle area, the characteristics of the manhole cover operation area of ​​pedestrian and vehicle flow are distinguished, the law of manhole cover damage in areas with high density of pedestrian and vehicle flow is identified, the causes of damage to pedestrian and vehicle flow are analyzed, and the impact of relevant factors such as pedestrian density and vehicle flow speed on manhole covers is analyzed. Targeted reinforcement of manhole covers is carried out according to the causes of the damage, thereby achieving key area control of manhole covers.

[0116] S402: Establish a time-based and phased management and control plan for the service life of manhole covers, and conduct multi-period time control and optimization of multi-source defects of manhole covers.

[0117] In order to solve the problem that the inspection cycle of manhole covers is fixed, resulting in uneven and unstable inspection work, which is sometimes high and sometimes low, the present invention establishes a time- and stage-by-stage management and control plan for the service life of manhole covers, and establishes a multi-factor and multi-scenario manhole cover life model to achieve early maintenance and proactive maintenance. Predictive maintenance measures are taken in advance when there is a trend of disease occurrence, and proactive maintenance is achieved before the disease occurs.

[0118] (1) First, select different time periods: day / week, month / season;

[0119] (2) Management of defects during morning and evening peak traffic: Analyze the impact of daily / weekly morning and evening traffic peaks on manhole covers, determine the category characteristics and causes of manhole cover defects during morning and evening peak traffic, and manage the health of manhole covers based on the characteristics of defects caused by daily traffic peaks;

[0120] (3) Tidal traffic hazard control: Based on the characteristics of urban planning distribution, for example, Beijing's urban planning causes tidal phenomena during peak hours in the morning and evening. In the morning, the traffic and pedestrian flow from north to south is denser, while in the evening, the traffic flow from south to north is denser. Therefore, the probability of hazard occurrence in the uplink and downlink areas of traffic roads is different within a day. Analyze daily / weekly traffic data to identify the direction and time period of tidal traffic flow. During the peak period of tidal traffic flow, strengthen real-time monitoring and early warning of manhole covers in specific directions;

[0121] (4) Seasonal disease control: Based on seasonal changes and climate characteristics, analyze the category characteristics and causes of manhole cover diseases in each month / quarter, identify the weather in the period (the manhole cover floating and missing diseases are more likely to occur during the rainy season in July and August), population density (Beijing's floating population decreased in the first quarter, the number of diseases decreased, and the inspection frequency can be appropriately reduced), temperature changes (temperature changes in spring, summer, autumn and winter, seasonal changes in thermal expansion and contraction cause cracks around the wells, such as cracks around the wells are more likely to occur in winter, and damage to the well ring and manhole covers is more likely to occur due to compression and damage in summer), and formulate manhole cover disease control plans for different time periods based on the characteristics of the diseases, and carry out seasonal maintenance and management of manhole covers.

[0122] Continue to see Figure 2 The figure shows a modular schematic diagram of a system for proactively managing the health status of urban manhole covers, which implements and realizes the above-mentioned method. The system includes a manhole cover multi-source disease classification layer, an intelligent identification layer, a data analysis layer, and a spatiotemporal control layer.

[0123] Manhole cover multi-source disease classification layer:

[0124] The classification layer of multi-source disease of manhole covers classifies multi-source diseases of urban manhole covers, and classifies them into nine types of diseases: missing manhole cover (missing manhole cover and seat), disordered manhole cover (manhole cover is upside down, and there is no prescribed marking on the surface), displacement of manhole cover (tilted, loose, deviated from the wellhead, lame, flipped), damaged manhole cover (cracks, notches, holes, deformation), damage around the well (cracks and potholes on the road surface within 1m outside the edge), raised manhole cover (the top surface of the manhole cover or well seat is more than 10mm higher than the road surface around the well), height difference of manhole cover (the height difference between the top surface of the manhole cover and the well seat is more than 1m), and the like. 0mm), manhole cover subsidence (the inspection well sinks relative to the ground, 15mm below the surface), and manhole cover vibration (the vibration sound is loud when a vehicle rolls over the manhole cover, causing noise) are divided into Class I and Class II diseases. Class I diseases include eight types of manhole cover defects: missing, disordered, dislocated, damaged, damaged around the well, bulge, height difference, and subsidence; Class II diseases include manhole cover vibration defects. The purpose is to facilitate targeted and accurate detection of the types and damage degrees of the nine types of diseases, and to select appropriate identification methods for the nine types of diseases.

[0125] Intelligent recognition layer:

[0126] The intelligent recognition layer intelligently identifies multiple sources of damage to urban manhole covers, including the use of image acquisition, spatial inspection, image classification and recognition technologies to determine whether the manhole cover has damage, the type of damage, and the severity of the damage.

[0127] In some feasible embodiments, the intelligent recognition layer includes: a manhole cover image intelligent collection and recognition system, and a manhole cover vibration collection and dynamic recognition system.

[0128] The intelligent manhole cover image acquisition and recognition system captures images of manhole covers and their surroundings in real time, performing two types of identification: damage classification and damage severity. Based on the collected manhole cover image data, it determines whether the manhole cover has suffered from Class I damage, a multi-source damage category. Class I damage includes eight types of damage: missing, misaligned, displaced, damaged, damage around the manhole, bulges, height differences, and subsidence. The intelligent manhole cover image acquisition and recognition system utilizes image acquisition equipment (such as high-definition cameras) installed in strategic locations to capture images of the manhole cover from multiple angles, including front and side views. The system then pre-processes and analyzes the images using image processing techniques such as edge detection and feature extraction. Through image segmentation, noise reduction, and enhancement, the system can identify defects such as missing, misaligned, displaced, and damaged manhole covers, and then label and classify them.

[0129] On the other hand, the manhole cover image intelligent collection and recognition system is based on the binary image method and combines image processing technologies such as crack binary image and grayscale image to determine the degree of damage of some or all of the Class I manhole cover diseases. Specifically, it can accurately identify the degree of damage to the manhole cover and the surrounding area of ​​the well, and conduct a quantitative assessment of the degree of manhole cover damage.

[0130] The manhole cover vibration collection and dynamic identification system is used to detect sound signals generated by vehicles running over or ground vibrations. This system also performs two types of identification: damage classification and vibration severity. Firstly, it collects manhole cover vibration data and determines whether the manhole cover has a Class II multi-source damage condition, which includes vibration damage. Secondly, it determines the severity of Class II damage based on the frequency of the vibration. Specifically, the frequency trend determines whether a Class II damage condition exists, and the peak value of the vibration frequency determines the severity of the Class II damage condition.

[0131] The manhole cover vibration collection and dynamic identification system includes three types of sensors: 1) Accelerometer: used to measure changes in acceleration of the manhole cover and its surrounding environment, thereby sensing the intensity and frequency of vibration. 2) Sound sensor: used to collect the sound signal of the manhole cover vibration and convert it into an electrical signal for processing. 3) Vibration sensor: installed directly on the manhole cover, it can monitor the vibration of the manhole cover in real time and output a corresponding electrical signal. The data collected by these sensors is processed through signal amplification, filtering, digitization, and other processing steps before being transmitted to the system's analysis module. The analysis module uses advanced signal processing techniques (such as spectrum analysis and time-frequency analysis) to analyze the signals to identify whether the manhole cover has abnormal vibration and the corresponding vibration disease.

[0132] Data analysis layer:

[0133] The data analysis layer analyzes the manhole cover data obtained by the intelligent recognition layer, analyzes the spatial and temporal characteristics of various manhole cover diseases, and obtains the occurrence patterns of manhole cover diseases in different spatial scenarios and different time periods, laying a foundation for manhole cover health life control measures;

[0134] The data analysis layer mainly serves as data support for the subsequent spatiotemporal control layer. Based on the manhole cover disease data obtained by the intelligent recognition layer, it conducts statistical analysis on the time and space of the occurrence of manhole cover diseases, finds the spatiotemporal laws of the occurrence of various manhole cover diseases, and counts the frequency of occurrence of various manhole cover diseases (missing, disordered, displaced, damaged, damaged around the well, bulge, height difference, subsidence, vibration) in different spaces (main roads, traffic intersections, parking lots and office buildings, etc.) and in different time periods (morning and evening rush hours within 24 hours a day, holidays and weekdays within a week, different seasons of spring, summer, autumn and winter, etc.). It analyzes the characteristics of various manhole cover diseases in different time and space, and lays the foundation for the subsequent spatiotemporal control layer to establish targeted control measures for various manhole cover diseases.

[0135] Space-time control layer:

[0136] The spatiotemporal control layer conducts proactive control of the health status of manhole covers based on the identification results and actual conditions, establishes a spatial control plan for the service life of manhole covers and a time- and stage-by-stage control plan for the service life of manhole covers, conducts regional spatial control and optimization of multi-source diseases of manhole covers, and conducts time control and optimization for multiple time periods.

[0137] The spatiotemporal control layer plays a crucial role in the proactive management and control system for the health status of urban manhole covers. It implements targeted spatiotemporal control strategies based on the data and analysis results provided by the intelligent recognition and data analysis layers. These strategies are divided into two aspects: spatial control and temporal control.

[0138] In the construction of smart cities, spatial hazard control of smart manhole covers is a key link in ensuring the safe and efficient operation of urban infrastructure. This includes three aspects: key point control, critical path control, and key area control.

[0139] Key point control of manhole covers is achieved by monitoring the status of manhole covers in real time and automatically adjusting the monitoring frequency and sensitivity based on traffic and pedestrian flow. Smart manhole covers are deployed at key locations with heavy traffic and high pedestrian flow, such as intersections and near subway stations. These manhole covers not only monitor their status in real time but also automatically adjust the monitoring frequency and sensitivity based on traffic and pedestrian flow. If an anomaly is detected, such as cover displacement or damage, the system will immediately issue an alarm and automatically transmit the information to the management center for rapid response and resolution.

[0140] Critical path control is achieved by predicting the defects of ordinary manhole covers along critical paths. Critical paths are usually the main arteries or important passages of urban traffic, and the condition of the manhole covers on them directly affects the safety and smooth flow of the roads. Smart manhole covers are also deployed on critical paths. By deploying smart manhole covers at both ends and in the middle of the road, the development trend of manhole cover defects can be analyzed. In addition to deploying smart manhole covers for real-time monitoring, they can also be linked with traffic management systems. By sharing traffic data in real time and based on road traffic flow data, manhole covers of different load-bearing standards are deployed in sections and areas with high traffic volume and high risk of manhole cover defects. At the same time, the frequency of inspections and health status monitoring is increased to enhance the resistance of manhole covers.

[0141] Critical area control analyzes the patterns and causes of manhole cover defects in areas with high pedestrian and vehicle traffic. Based on the characteristics of manhole cover defects in these key areas, monitoring frequency and inspection efforts are targeted to achieve spatially zoning control of manhole covers. Critical area control also utilizes smart manhole covers, deployed within key areas. Smart manhole covers are deployed more densely along paths and at points within these key areas to achieve more precise awareness of these areas.

[0142] The time disease control system includes two aspects: daily / weekly disease control and monthly / quarterly disease control. Daily / weekly disease control includes morning and evening peak disease control and tidal traffic flow disease control.

[0143] First, control and manage hazards during peak hours: The system analyzes historical traffic data to identify peak hours, determine the types and causes of manhole cover defects during these periods, and implement targeted manhole cover defect control based on these characteristics. During these periods, real-time monitoring and early warning of manhole covers are strengthened to ensure they remain stable and safe despite increased traffic pressure. By collaborating with traffic management departments and based on road traffic data, manhole covers of varying load-bearing standards are deployed in high-traffic areas and sections of roads with high traffic risk, reducing traffic congestion and safety hazards caused by manhole cover problems.

[0144] Second, tidal traffic hazard management: Based on the characteristics of urban planning and distribution, the system analyzes traffic data to identify the direction and time period of tidal traffic. During peak tidal traffic periods, real-time monitoring and early warning of manhole covers in specific directions are strengthened to ensure that these manhole covers can withstand the heavy traffic pressure. At the same time, based on the actual tidal traffic situation, manhole cover inspections and health monitoring are arranged on corresponding roads to effectively monitor the health of manhole covers.

[0145] Third, seasonal disease control: Based on seasonal changes and climate characteristics, we analyze the types, characteristics, and causes of manhole cover diseases each month and quarter. Based on the characteristics of the diseases, we develop manhole cover disease control plans for different time periods and conduct seasonal maintenance and management of manhole covers. For example, before the rainy season, we strengthen inspections of manhole cover drainage functions to prevent manhole cover diseases caused by accumulated water; during cold seasons, we increase inspections of manhole cover anti-freeze measures to ensure that the covers are not damaged by ice. At the same time, the system adjusts the monitoring frequency and warning thresholds based on seasonal changes to adapt to the changing characteristics of manhole cover diseases in different seasons.

[0146] In short, daily / weekly traffic damage control (tidal traffic damage control) is mainly based on the location of manhole covers during the morning and evening rush hours. Due to the characteristics of the city, traffic tidal phenomena will occur. Taking Beijing as an example, the morning rush hour is more from north to south, and the evening rush hour is more from south to north. Therefore, the imbalance of two-way traffic in the morning and evening rush hours will cause the probability of manhole cover damage in the morning rush hour from north to south to increase. Therefore, it is necessary to focus on controlling the manhole covers distributed on the upward traffic routes before and after the morning rush hour. Similarly, focus on controlling the manhole covers distributed on the downward traffic routes before and after the evening rush hour.

[0147] The monthly / quarterly disease control unit is based on the monthly and quarterly disease occurrence data, the seasonal characteristics of manhole cover diseases and the different characteristics of diseases in each month. For example, the disease characteristics of manhole covers are different in spring, summer, autumn and winter. They are prone to sinking and protrusion in winter and spring, and prone to damage in summer and autumn, etc. Control is strengthened every month (such as important months such as January-February for the Spring Festival, September-October for the Mid-Autumn Festival and National Day), and the frequency of control can be reduced on Saturdays and Sundays when there are fewer people near the CBD of office buildings.

[0148] The above control measures are aimed at reducing the probability of manhole cover damage, lowering the cost of manhole cover maintenance and saving human resources, ensuring the safe and stable operation of urban infrastructure, and providing citizens with a safer and more convenient travel environment.

[0149] While various embodiments of the present invention have been described above, the above description is intended to be illustrative, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

[0150] Although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present invention. Certain features described in the context of separate embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented in multiple implementations individually or in any suitable sub-combination.

Claims

1. A method for intelligently identifying multi-source diseases of urban manhole covers and preemptively controlling health status, characterized in that: The method includes: Establish a classification system for multi-source diseases of manhole covers; Based on the classification system of multi-source diseases of manhole covers, multi-source diseases of manhole covers are intelligently identified, including: Collect basic information of manhole covers to match subsequent collected data with the basic information data of manhole covers; Collect manhole cover images, and determine whether the manhole cover is damaged and the type and degree of damage based on the manhole cover image data; Collect the vibration sound of the manhole cover, and determine whether the manhole cover is damaged and the type and degree of damage based on the vibration data; Analyze the manhole cover data obtained by intelligent identification and establish a multi-source disease database for manhole covers, including: Conduct spatial characteristic analysis of manhole cover diseases to obtain the occurrence patterns of manhole cover diseases in different spatial scenarios; Conduct time characteristic analysis on manhole cover diseases to obtain the occurrence patterns of manhole cover diseases in different time periods; Based on the occurrence patterns of manhole cover diseases in different spatial scenarios and time periods, the health status of manhole covers is controlled in advance, including: Establish a spatial management and control plan for the service life of manhole covers, and conduct regional spatial control and optimization of multi-source diseases of manhole covers; Establish a time- and phase-based management and control plan for the service life of manhole covers, and conduct multi-time period time control and optimization of multi-source diseases of manhole covers.

2. The method according to claim 1, characterized in that The collecting of manhole cover images and determining whether the manhole cover is damaged and the type and degree of the damage based on the manhole cover image data are specifically as follows: The manhole cover image is collected, and based on the collected manhole cover image data, it is determined whether the manhole cover has Class I disease among multi-source diseases. Class I diseases include eight types of diseases: missing, disordered, displaced, damaged, damaged around the well, bulge, height difference, and subsidence of the manhole cover. The degree of damage of some or all of the Class I diseases can be further determined.

3. The method according to claim 2, characterized in that The manhole cover image is collected, and based on the collected manhole cover image data, it is determined whether the manhole cover has Class I disease among the multi-source diseases. Class I diseases include eight types of diseases: missing, disordered, displaced, damaged, damaged around the well, bulge, height difference, and subsidence of the manhole cover. The degree of disease of some or all of the diseases in Class I is further determined, including: Use image acquisition equipment to capture image data of the manhole cover and its surrounding environment in real time; A manhole cover disease recognition model was established to identify manhole cover image data and accurately determine eight types of manhole cover disease in Class I. The binary image method is used to convert the pictures collected on site into grayscale images to determine the severity of the two types of damage, namely, damage to the manhole cover and damage around the well, which are Class I damages.

4. The method according to claim 1, characterized in that: The collecting of the manhole cover vibration data and determining whether the manhole cover is damaged and the type and degree of the damage based on the manhole cover vibration data are specifically as follows: The vibration sound of the manhole cover is collected, and based on the collected manhole cover vibration data, it is determined whether the manhole cover has Class II disease among multi-source diseases, Class II disease includes the vibration disease of the manhole cover, and the degree of the Class II disease is further determined.

5. The method according to claim 4, characterized in that The collecting of the manhole cover vibration data and determining whether the manhole cover has a Class II disease among the multi-source diseases based on the collected manhole cover vibration data, the Class II disease includes the manhole cover vibration disease, and further determining the degree of the Class II disease, including: Arrange vibration sensors on the manhole cover in the direction of vehicle flow, and use the vibration sensors to collect the vibration frequency of the manhole cover; The vibration frequency of the manhole cover is collected by the vibration sensor, and the trend characteristics of the vibration frequency are used to determine whether the manhole cover has Class II disease, and the peak value of the vibration frequency is used to determine the severity of the Class II disease of the manhole cover.

6. The method according to claim 1, characterized in that The establishment of a spatial management and control plan for the service life of manhole covers to regionally control and optimize multi-source diseases of manhole covers includes: Establish a spatial inspection plan for key points, key paths and key areas; Smart manhole covers are deployed at key points to monitor the health status and surrounding environment of the manhole covers at key points in real time, and the monitoring frequency and sensitivity are automatically adjusted according to traffic flow and pedestrian flow to achieve key point control of the manhole covers; Smart manhole covers are deployed on critical paths, the health status of manhole covers on critical paths is collected and analyzed, and the health status characteristics of manhole covers on critical paths are summarized to predict the defects of ordinary manhole covers on the entire route and realize critical path control of manhole covers; Smart manhole covers are deployed in key areas, and the manhole cover operating space is divided into pedestrian and vehicle areas. The patterns and causes of manhole cover defects in areas with dense pedestrian and vehicle flow are analyzed. Based on the characteristics of manhole cover defects in key areas, the monitoring frequency and inspection efforts are increased in a targeted manner to achieve spatial regional control of manhole covers.

7. The method according to claim 6, characterized in that The key points include intersections with heavy traffic and large passenger flow, and areas near subway stations; the key paths include main roads or important channels of urban traffic; the key areas include areas with large passenger flow during the day, areas with large passenger flow during peak hours in the morning and evening, and areas with dense traffic.

8. The method according to claim 1, characterized in that The establishment of a time-based and stage-based management and control plan for the service life of manhole covers to control and optimize multi-source defects of manhole covers over multiple time periods includes: Choose different time periods: day / week, month / season; Morning and evening peak traffic disease control: Analyze the impact of daily / weekly morning and evening traffic peaks on manhole covers, determine the category characteristics and causes of manhole cover diseases during morning and evening peaks, and control the health status of manhole covers based on the disease characteristics caused by daily traffic peaks; Tidal traffic disease control: Based on daily / weekly traffic data, identify the direction and time period of tidal traffic, analyze the impact of daily / weekly tidal traffic on manhole covers, determine the category characteristics and causes of tidal traffic manhole cover diseases during the peak period of tidal traffic, and control the health status of manhole covers based on the disease characteristics caused by tidal traffic; Seasonal disease control: According to seasonal changes and climate characteristics, analyze the impact of monthly / quarterly seasonal changes and climate characteristics on manhole covers, determine the category characteristics and causes of manhole cover diseases every month / quarter, and control the health status of manhole covers based on the disease characteristics caused by seasonal changes and climate characteristics.

9. An advanced control system for the health status of urban manhole covers used in the method according to any one of claims 1 to 8, characterized in that: include: A manhole cover multi-source disease classification layer, wherein the manhole cover multi-source disease classification layer classifies multi-source diseases of urban manhole covers; An intelligent recognition layer, which intelligently recognizes multi-source diseases of urban manhole covers, determines whether the manhole covers are damaged and the type and degree of the damage; The data analysis layer performs data analysis on the manhole cover data obtained by the intelligent recognition layer, analyzes the spatial and temporal characteristics of various manhole cover diseases, and obtains the occurrence patterns of manhole cover diseases in different spatial scenes and different time periods; The space-time control layer conducts advanced control over the health status of manhole covers, establishes a spatial control plan for the service life of manhole covers and a time- and stage-by-stage control plan for the service life of manhole covers, conducts regional spatial control and optimization of multi-source diseases of manhole covers, and conducts time control and optimization over multiple time periods.

10. The system according to claim 9, characterized in that The smart recognition layer includes: A manhole cover image intelligent collection and recognition system, which collects manhole cover images in actual scenes and determines whether the manhole cover has Class I disease among multi-source diseases based on the collected manhole cover image data. Class I diseases include eight types of diseases: missing, disordered, displaced, damaged, damaged around the well, bulge, height difference, and subsidence of the manhole cover. At the same time, the degree of some or all of the Class I diseases of the manhole cover is determined based on the binary image method; A dynamic identification system for collecting and identifying the vibration of a manhole cover collects the vibration of the manhole cover and determines whether the manhole cover has a Class II disease among multi-source diseases based on the collected manhole cover vibration data. Class II diseases include vibration diseases of the manhole cover. At the same time, the degree of the Class II disease of the manhole cover is determined by the size of the manhole cover vibration frequency.

11. The system according to claim 10, characterized in that The manhole cover image intelligent acquisition and recognition system uses image acquisition equipment installed at key positions to capture images of the manhole cover from multiple angles, including front and side views. At the same time, the manhole cover image data is recognized with the help of a manhole cover disease recognition model. And / or, the manhole cover vibration collection dynamic identification system uses vibration sensors arranged on the manhole cover according to the direction of vehicle flow, and the vibration sensors collect the vibration frequency of the manhole cover.

Images