Well lid intelligent operation and maintenance management method and system

Abstract

The invention relates to a well lid intelligent operation and maintenance management method and system. The method comprises the steps of establishing a well lid data acquisition system; learning and identifying the collected well lid disease types; identification results are screened, if it is judged that the well lid is in the sub-health state, early warning is given out according to the sub-health state type, the risk level is predicted, and maintenance suggestions are further proposed according to previous maintenance data; the early warning information is manually audited and processed, if it is judged that the early warning information is correct through manual auditing, the risk level and the corresponding maintenance suggestion are confirmed, and if it is judged that the early warning information is correct through manual auditing and the state of the well lid is normal, early warning is cancelled; if the risk grade or the maintenance suggestion of the well lid is wrong, manually adjusting and confirming the risk grade and the corresponding maintenance suggestion; and the well lid structure is maintained. The invention provides a manhole cover structure disease perception-identification-early warning-response rapid feedback method, and the method meets the demands of urban tube well situation accurate perception and intelligent decision making.

Description

Technical Field

[0001] The present invention relates to the field of urban infrastructure operation and maintenance technology, and in particular to the maintenance management of manhole covers, and specifically to a method and system for intelligent operation and maintenance management of manhole covers. Background Art

[0002] Urban underground pipelines are important infrastructure and "lifelines" to ensure the operation of cities. Ensuring the safe, intelligent and efficient operation of urban underground pipelines is an important foundation for the high-quality development of cities. Manhole covers are an important part of urban underground pipelines. Managing problems such as abnormal opening of manhole covers, private penetration, and structural damage is of great significance to the management of underground pipelines.

[0003] With the improvement of urbanization level, the surge in the number of manhole covers has led to a significant increase in the number and frequency of manhole cover diseases, which has brought huge management pressure to the manhole cover management departments. The illegal penetration of urban pipe wells seriously threatens the safe operation of urban underground lifeline projects, threatens the production and life of residents and the safety of their personal and property, and causes economic losses to communication pipeline operation services.

[0004] In the early days, the management and maintenance of manhole covers were mainly based on manual maintenance. Later, the departments in charge of the manhole covers began to use computers for management and maintenance, but this was limited to computers replacing manual paper records. Currently, the main management mode of manhole covers is still based on manual inspection and investigation, with periodic inspection-recording-repair, low efficiency, high consumption of manpower and material resources, and the increase in the number of manhole covers will easily lead to the problem of saturation of human management.

[0005] As the pace of urbanization in my country accelerates, the number of urban pipe well lines and manhole covers has increased explosively. The traditional pipe well maintenance and inspection mode has become increasingly efficient, with inaccurate perception, inaccurate decision-making, and imprecise diagnosis. It cannot meet the needs of urban development. Therefore, there is an urgent need for an intelligent manhole cover management system that integrates perception, decision-making, and diagnosis. Summary of the invention

[0006] 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 operation and maintenance of manhole covers to solve one or more problems in the prior art.

[0007] The technical solution of the present invention is as follows:

[0008] A first aspect of the present invention provides a method for intelligent operation and maintenance management of manhole covers, comprising:

[0009] Establish a manhole cover data collection system, collect manhole cover related data, and establish a manhole cover database, wherein the manhole cover related data at least includes manhole cover disease information;

[0010] Study and identify the types of manhole cover diseases collected;

[0011] The recognition results are screened. If the manhole cover is judged to be in a healthy state, no warning is issued. If the manhole cover is judged to be in a sub-healthy state, a warning is issued and the risk level is predicted according to the sub-healthy state type, and maintenance suggestions are further made based on previous maintenance data.

[0012] Manual review and processing of warning information. If the manual review determines that the warning information is correct, the risk level and corresponding maintenance suggestions are confirmed. If the manual review determines that the warning information is incorrect, if the manhole cover is in normal condition, the warning is cancelled. If the risk level or maintenance suggestion of the manhole cover is incorrect, the risk level and corresponding maintenance suggestion are manually adjusted and confirmed.

[0013] Repair the manhole cover structure based on the final confirmed maintenance recommendations.

[0014] Optionally, the manhole cover related data includes manhole cover health data and personnel maintenance data, wherein:

[0015] The health data of the manhole cover includes: bulge, damage, warping, loss, surrounding environment, positioning and manhole cover function;

[0016] The personnel maintenance data includes: maintenance records, images before and after maintenance, maintenance personnel information, maintenance time, installation time and maintenance times;

[0017] And / or, the sub-health status types include: deformation or displacement, no fall protection function, damaged manhole cover, damage around the well, subsidence, bulge, manhole cover height difference, manhole cover disorder and vibration.

[0018] Optionally, the establishment of a manhole cover data collection system, collection of manhole cover related data, and establishment of a manhole cover database includes:

[0019] Install monitoring equipment at fixed locations to monitor key locations in real time and obtain the health status of the manhole cover and the surrounding environment in real time;

[0020] Build a dynamic inspection route system to conduct real-time monitoring on the inspection route and dynamically adjust the inspection route based on the maintenance frequency and periodic disease problems of the manhole covers;

[0021] Intelligent manhole cover inspection vehicles are deployed in the area to obtain images of road manhole covers and build a regional manhole cover health status image perception system.

[0022] Optionally, the learning and identifying the collected manhole cover related data includes:

[0023] The collected manhole cover related data is uploaded to the cloud platform, which has a preset target monitoring deep learning image recognition algorithm for identifying and processing manhole cover related data. The images are annotated through manhole cover disease classification, and the computer is trained and learned based on the classification to establish a manhole cover disease image classification model for subsequent identification.

[0024] Optionally, the target monitoring deep learning image recognition algorithm adopts Yolov5 algorithm, including:

[0025] 1) Input preprocessing

[0026] Image scaling and normalization: Scale the input image to the size required by the network and perform normalization to facilitate network processing;

[0027] Data enhancement: Mosaic data enhancement technology is used to randomly scale, crop, and arrange multiple images to form a new image, enriching the data set and improving the robustness and training speed of the model.

[0028] 2) Feature extraction

[0029] Backbone network: Use CSPDarknet as the backbone network to improve the efficiency and accuracy of the network;

[0030] Feature map generation: The backbone network is used to extract features from the input image and generate a series of feature maps, each of which corresponds to objects of different scales.

[0031] 3) Anchor box generation

[0032] Adaptive anchor box calculation: During training, YOLOv5 adaptively calculates the best anchor box value based on the dataset to better match objects of different sizes and shapes;

[0033] 4) Prediction

[0034] Feature fusion: The Neck part of YOLOv5 performs multi-scale fusion of feature maps to enhance feature representation capabilities;

[0035] Prediction output: The Head part of the YOLOv5 algorithm converts the feature map into information such as the position, size, and category probability of the detection box through convolution operations. For each feature map, the detector predicts several detection boxes and calculates the IoU value between it and the anchor box to determine the final detection box;

[0036] 5) Post-processing

[0037] Non-maximum suppression: YOLOv5 uses an improved NMS algorithm to filter overlapping detection frames, remove redundant detection frames, and obtain the final detection results;

[0038] 6) Loss Function

[0039] GIOU_Loss: YOLOv5 uses GIOU as part of the loss function during training to better measure the similarity between the predicted box and the true box.

[0040] Optionally, if the determination that the manhole cover is in a healthy state further includes: assigning a normal mark to the manhole cover;

[0041] And / or, confirming the risk level and the corresponding maintenance suggestion also includes: publishing the maintenance suggestion to a cloud platform.

[0042] Optionally, the method further includes:

[0043] The manhole cover data before and after maintenance are recorded, and the manhole cover data collection system continues to collect manhole cover related data, and continuously tracks and feedbacks the health status of the manhole cover after maintenance based on the inspection cycle, so as to determine the maintenance effect and iteratively optimize the maintenance plan.

[0044] The second aspect of the present invention provides a manhole cover structure intelligent operation and maintenance management system, comprising:

[0045] The sensing module has a manhole cover data collection system and is configured to collect manhole cover related data, wherein the manhole cover related data at least includes manhole cover disease information;

[0046] The identification module has a cloud platform, in which an algorithm for identifying and processing manhole cover related data is preset, and the manhole cover related data collected can be learned and identified;

[0047] The early warning module is configured to screen the recognition results. If the manhole cover is judged to be in a healthy state, no early warning is issued. If the manhole cover is judged to be in a sub-healthy state, an early warning is issued and the risk level is predicted according to the sub-healthy state type, and maintenance suggestions are further made based on previous maintenance data.

[0048] The response module is configured to manually review and process the warning information. If the manual review determines that the warning information is correct, the risk level and corresponding maintenance suggestions are confirmed. If the manual review determines that the warning information is incorrect, such as the state of the manhole cover, the warning is revoked. If the risk level or maintenance suggestions of the manhole cover are incorrect, the risk level and corresponding maintenance suggestions are manually adjusted and confirmed, and the manhole cover structure is repaired according to the final confirmed maintenance suggestions.

[0049] A third aspect of the present invention provides an electronic device, the device comprising:

[0050] a memory for storing one or more computer programs; and

[0051] The processor is used to execute one or more of the computer programs to implement the method described in the first aspect of the present invention.

[0052] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in the first aspect of the present invention.

[0053] The beneficial effects of the present invention relative to the prior art are:

[0054] 1. A smart manhole cover management system with perception, decision-making and diagnosis functions has been built, providing a rapid feedback method for manhole cover structure disease perception-identification-warning-response, establishing the underlying architecture for building smart management of manhole cover diseases, achieving full-process control of massive communication pipelines, comprehensive control of resource information, real-time warning of resource status and timely response to risks, meeting the needs of accurate perception and smart decision-making of urban manhole situation.

[0055] 2. A comprehensive data collection system of "point-line-surface" has been established to improve management efficiency. Originally, one operation required 24 hours of inspection and 48 hours of maintenance. With the method provided by the present invention, the image transmission and recognition are almost real-time, and the time from issuing an early warning to the arrival of maintenance personnel is only 30 minutes. In other words, the inspection cycle is increased to 30 minutes, which can quickly sense-identify-warn-respond.

[0056] 3. Improve the tracking and feedback efficiency after manhole cover maintenance, and increase the control over maintenance results.

[0057] 4. Improve recognition accuracy, achieve precise maintenance, and form maintenance standards.

[0058] 5. Establish a hierarchical early warning management system, which is divided into two systems: patrol early warning system and monitoring early warning system. The early warning level is divided into three levels of red, orange and yellow according to the severity of the disease, so as to maximize the utilization of maintenance resources.

[0059] 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 easy to understand 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 possessed or achieved at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0061] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment in size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0062] Figure 1 A schematic diagram showing the overall framework structure of a manhole cover intelligent operation and maintenance management method according to some embodiments of the present invention is shown;

[0063] Figure 2 A schematic diagram of the process flow of a manhole cover intelligent operation and maintenance management method according to some embodiments of the present invention is shown;

[0064] Figure 3 shows a manhole cover health status image collected according to some embodiments of the present invention;

[0065] Figure 4 A schematic diagram of a manhole cover database established according to some embodiments of the present invention is shown;

[0066] Figure 5 A schematic diagram of collecting data related to a manhole cover according to some embodiments of the present invention is shown;

[0067] Figure 6 A schematic diagram of a manhole cover disease classification early warning management system according to some embodiments of the present invention is shown;

[0068] Figure 7 A schematic diagram of a manhole cover defect repair system according to some embodiments of the present invention is shown;

[0069] Figure 8 A schematic block diagram of an intelligent operation and maintenance management system for a manhole cover structure according to some embodiments of the present invention is shown;

[0070] Fig. 9 A schematic block diagram of an electronic device according to some embodiments of the present invention is shown;

[0071] Fig.10 A block diagram of a computing device capable of implementing various embodiments of the present invention is shown.

[0072] In the various drawings, the same or corresponding reference numerals represent the same or corresponding parts. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical solution 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 description are used to explain the present invention, but are not intended to limit the present invention.

[0074] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0075] It should be understood that the terms "include / comprise", "consist of..." or any other variations are intended to cover non-exclusive inclusion, so that a product, device, process or method that includes a series of elements includes not only those elements, but also may include other elements not explicitly listed when necessary, or also includes elements inherent to such product, device, process or method. In the absence of more restrictions, the elements defined by the sentence "include / comprise...", "consist of..." do not exclude the presence of other identical elements in the product, device, process or method that includes the elements.

[0076] It is also necessary to understand that terms such as "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside" and "outside" to indicate 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 referred device, component or structure must have a specific direction, be constructed or operate in a specific direction, and should not be understood as a limitation on the present invention.

[0077] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0078] As mentioned in the background technology, the core issues of manhole cover management are:

[0079] 1. How to accurately and timely obtain the health status of manhole covers? The current point-to-point maintenance record method is mainly the "inspection point" method. The inspection cycle of this method is at least 24 hours (one day), and the maintenance cycle is at least 48 hours (one inspection discovery (24 hours) and the second inspection and maintenance (24 hours)). For the current urban traffic volume (passenger and vehicle flow), the 48-hour maintenance cycle is too long, there are too many safety hazards, and there is a lack of an efficient manhole cover perception system that covers the entire area.

[0080] 2. How to provide targeted suggestions for manhole cover maintenance? Currently, manhole cover maintenance is mainly based on the experience and judgment of front-line workers. The maintenance effect is not guaranteed, and maintenance errors and waste of equipment resources are uncontrollable. There is a lack of accurate identification of the health status of manhole covers, classification and stage damage standards, and corresponding maintenance technical guidelines.

[0081] 3. If the control of the effect status after maintenance is not precise, the same manhole cover disease problem may occur again after maintenance, and the maintenance effect is not thorough. It is necessary to accurately track and feedback the health status of the manhole cover to improve the process management capability of manhole cover maintenance.

[0082] To this end, the present invention provides a rapid feedback method for manhole cover structure disease perception-identification-warning-response, constructs an intelligent manhole cover management system with perception, decision-making and diagnosis functions, establishes an underlying architecture for the construction of intelligent management of manhole cover diseases, realizes full-process control of massive communication pipelines, comprehensive control of resource information, real-time warning of resource status and timely response to risks, and meets the needs of accurate perception of urban manhole situation and intelligent decision-making.

[0083] The implementation of the present invention is described in detail below in conjunction with preferred embodiments.

[0084] The technology of the present invention is realized mainly by the distribution of manhole cover structure disease perception-identification-warning-response and the cyclic iteration of manhole cover warning and maintenance.

[0085] For details, see Figure 1 , Figure 1 The specific step-by-step implementation of perception-identification-warning-response is shown, in which:

[0086] 1) Perception layer: mainly collects relevant data of manhole covers.

[0087] The traditional single data collection mode cannot fully and accurately collect the data of relevant manhole cover diseases, but the perception level of the present invention effectively solves this problem. The present invention establishes a comprehensive manhole cover data collection system covering "points, lines and surfaces".

[0088] Specifically, "point" refers to installing monitoring equipment at fixed locations, real-time monitoring of key points at key locations, real-time monitoring of the health status of manhole covers and the surrounding environment, and achieving "point perception". "Line" refers to building a dynamic inspection path system, flexibly adjusting the inspection route based on the maintenance frequency and periodic disease problems of manhole covers, and ensuring the efficiency and accuracy of data collection. "Surface" refers to obtaining road manhole cover images through intelligent manhole cover inspection vehicles in the region to build a regional manhole cover health status image perception system. Through the coordination and cooperation of "point-line-surface", each group of data complements each other and complements each other, making the data collection system of manhole covers more efficient and accurate. Real-time monitoring at the point level provides detailed local data, dynamic inspection path optimization at the line level ensures the systematicness and flexibility of the inspection process, and the regional image perception system at the surface level provides a comprehensive macro perspective. The organic combination of each level effectively improves the efficiency and accuracy of data collection.

[0089] 2) Recognition layer: mainly responsible for learning and identifying the data collected by the perception layer.

[0090] The recognition layer is one of the key parts of the present invention. It also plays the role of a transportation hub and is a transit station for data transmission in the system of the present invention. At this level, all data collected by the perception layer are uploaded to the cloud platform. The platform has algorithms for identifying and processing manhole cover related data (time, coordinates, angles, health status photos). The recognition layer uses machine learning to identify manhole cover data more accurately and efficiently, and effectively shortens the time for data processing and transmission.

[0091] 3) Early warning layer: It is mainly used to further screen the recognition results obtained from the previous layer of machine learning, and to issue early warnings and make maintenance suggestions when the manhole cover is in sub-healthy condition.

[0092] The early warning layer is also one of the key parts of the present invention, and the combined effect of the early warning layer and the identification layer constitutes the core of the present invention. According to the different results of the machine learning recognition and judgment in the previous layer, the cloud platform will release different information. When the manhole cover is in a healthy state and the early warning state is normal, the platform will not issue an early warning and assign a normal mark to the manhole cover. When the manhole cover is judged to be in a sub-healthy state, the algorithm in the platform will issue an early warning and predict the risk level based on the sub-healthy state type of the manhole cover, and then make maintenance suggestions based on previous maintenance data. At the same time, if the state of the manhole cover in the next cycle after maintenance is judged to be healthy, the early warning will be revoked. The realization of this level of function further screens the types of manhole cover structural diseases and accurately evaluates the manhole cover diseases.

[0093] In addition, this layer continues to track and feedback the status of the manhole cover before and after maintenance, and timely judges the maintenance status of the manhole cover so that subsequent maintenance work can be carried out efficiently. The organic combination of the early warning layer and the identification layer jointly lays the foundation and key of the entire invention system.

[0094] 4) Response layer: mainly responsible for manual feedback and execution of warnings issued by the platform.

[0095] In the actual operation and maintenance of manhole covers, the abnormal state of a manhole cover is often caused by a variety of reasons. For example, the sinking of the manhole cover is often accompanied by damage around the well. At this time, the importance of manual work is reflected. After the warning and risk level of the manhole cover in the warning layer are determined, the platform staff will make the final processing of the warning layer information. After the manual review determines that the warning information is correct, the risk warning level is finally determined and the maintenance suggestions are published to the cloud platform. When the manual review determines that the warning information is incorrect, if the manhole cover is in normal condition, the warning will be revoked. If there is a problem with the risk level or maintenance suggestion of the manhole cover, the information will be published on the platform after manual adjustment. The response layer effectively improves the fault tolerance rate of machine learning, greatly reduces the misjudgment and misjudgment of the health status of the manhole cover, makes the maintenance of the manhole cover more accurate, avoids unnecessary waste of manpower and material resources, and reduces operation and maintenance costs.

[0096] Based on the maintenance suggestions finally received by the cloud platform, on-site workers carry out repairs according to the maintenance suggestions and re-upload the repaired manhole cover data after the repair, including the manhole cover image and other data. These data continue to be extracted by the manhole cover data collection system of the perception layer, thereby providing feedback on the status of the manhole cover after repair, so that subsequent maintenance work can be carried out efficiently.

[0097] Based on the above implementation, the manhole cover intelligent operation and maintenance management method 100 provided by the present invention is as follows: Figure 2 , mainly including: S101. Establish a manhole cover data collection system, collect manhole cover related data, and establish a manhole cover database, where the manhole cover related data at least includes manhole cover disease information; S103. Learn and identify the collected manhole cover related data; S105. Screen the identification results. If the manhole cover is judged to be in a healthy state, no warning will be issued. If the manhole cover is judged to be in a sub-healthy state, a warning will be issued according to the sub-healthy state type and the risk level will be predicted, and maintenance suggestions will be further made based on previous maintenance data; S107. Manually review and process the warning information. If the manual review determines that the warning information is correct, the risk level and corresponding maintenance suggestions will be confirmed. If the manual review determines that the warning information is incorrect, if the manhole cover is in normal condition, the warning will be revoked. If the risk level or maintenance suggestion of the manhole cover is incorrect, the risk level and corresponding maintenance suggestion will be manually adjusted and confirmed; S109. According to the finally confirmed maintenance suggestion, the manhole cover structure is repaired.

[0098] In some specific embodiments, step S101 first establishes a manhole cover data collection system, collects manhole cover related data, and establishes a manhole cover database, wherein the manhole cover related data at least includes manhole cover disease information.

[0099] Manhole cover defects refer to the unsafe, uneven, unquiet, and irregular turns of underground pipeline inspection wells and manhole cover facilities that may affect the safe operation of pipelines, the safety of personnel and vehicles, the comfort of road traffic, and the daily life of residents. They include missing, damaged, displaced, vibrating, subsidence, bulges, height differences of manhole covers, damage around the well, disordered manhole covers, and no fall protection function.

[0100] like Figure 3 As shown, Figure 3 The figure shows the health status images of some manhole covers collected daily. It is clearly visible that some manhole covers in use are damaged, displaced, and have problems that may affect the passage of people and traffic safety.

[0101] In some specific embodiments, Figure 4 As shown, the manhole cover related data in the manhole cover database include manhole cover health data and personnel maintenance data, among which: manhole cover health data include: bulge, damage, lift, loss, surrounding environment, positioning and manhole cover function; personnel maintenance data include: maintenance records, images before and after maintenance, maintenance personnel information, maintenance time, installation time and number of maintenance. It should be noted that the installation time refers to the first installation time, the time when the manhole cover begins to serve, and the service life ends when a new manhole cover is replaced. The maintenance time refers to the number of maintenance during the service process, and the time point of each maintenance is recorded. Through these two aspects of manhole cover related data, the potential risks of the manhole cover can be determined.

[0102] like Figure 5 As shown, Figure 5 The collected information of the database is shown in FIG, which includes information such as manhole cover health data, personnel and maintenance data.

[0103] In some specific embodiments, the present invention establishes a comprehensive manhole cover data collection system covering "points, lines, and surfaces". The use of the comprehensive manhole cover data collection system to collect manhole cover related data includes:

[0104] Install monitoring equipment at fixed locations to monitor key locations in real time, obtain the health status of manhole covers and surrounding environment in real time, and achieve "point perception";

[0105] Build a dynamic inspection route system to conduct real-time monitoring on the inspection route, and dynamically adjust the inspection route based on the maintenance frequency and periodic disease problems of the manhole covers to achieve "line perception";

[0106] Intelligent manhole cover inspection vehicles are deployed in the area to obtain images of road manhole covers, build a regional manhole cover health status image perception system, and achieve "surface perception".

[0107] Real-time monitoring at the point level provides detailed local data, dynamic inspection path optimization at the line level ensures the systematicness and flexibility of the inspection process, and the regional image perception system at the surface level provides a comprehensive macro perspective. Through the all-round three-dimensional coordination and cooperation of "point-line-surface", the efficiency and accuracy of data collection are effectively improved.

[0108] In some specific embodiments, step S103 learns and identifies the collected manhole cover related data, including:

[0109] The collected data on manhole covers is uploaded to the cloud platform, which has a preset target monitoring deep learning image recognition algorithm for identifying and processing manhole cover related data. The target monitoring deep learning image recognition algorithm annotates images by classifying manhole cover defects, and then the computer performs training and learning based on the classification to establish a manhole cover defect image classification model for identification. Machine learning makes identification of manhole cover data more accurate and efficient, and also effectively shortens the time for data processing and transmission.

[0110] In some specific embodiments, the target monitoring deep learning image recognition algorithm adopts the Yolov5 algorithm. YOLOv5 is a deep learning algorithm for target detection, which is the latest version of the YOLO (You Only Look Once) series of algorithms. The YOLOv5 algorithm can be summarized into the following main steps for identifying and processing manhole cover related data:

[0111] 1) Input preprocessing

[0112] Image scaling and normalization: Scale the input image to the size required by the network (such as 608x608) and perform normalization to facilitate network processing.

[0113] Data enhancement: YOLOv5 uses Mosaic data enhancement technology, which enriches the data set and improves the robustness and training speed of the model by randomly scaling, randomly cropping, and randomly arranging four pictures to splice them into a new picture.

[0114] 2) Feature Extraction (Backbone)

[0115] Backbone network: YOLOv5 uses CSPDarknet as the backbone network, which combines the ideas of residual network and feature pyramid network, and adds CSP (Cross-Stage Partial Connections) module to improve the efficiency and accuracy of the network.

[0116] Feature map generation: The backbone network is used to extract features from the input image and generate a series of feature maps, each of which corresponds to objects of different scales.

[0117] 3) Anchor generation

[0118] Adaptive anchor box calculation: During the training process, YOLOv5 will adaptively calculate the best anchor box value based on the data set to better match objects of different sizes and shapes. This function is embedded in the code and executed every time training is performed.

[0119] 4) Prediction (Neck and Head)

[0120] Feature fusion: The Neck part of YOLOv5 adopts the FPN (Feature Pyramid Networks) + PAN (Path Aggregation Network) structure to perform multi-scale fusion of feature maps to enhance feature representation capabilities.

[0121] Prediction output: In the head part, the feature map is converted into information such as the position, size, and category probability of the detection box through convolution operations. For each feature map, the detector predicts several detection boxes and calculates the IoU value between it and the anchor to determine the final detection box.

[0122] 5) Post-processing

[0123] Non-maximum suppression (NMS): Use the NMS algorithm to filter overlapping detection boxes, remove redundant detection boxes, and obtain the final detection results. YOLOv5 can use an improved NMS algorithm, such as DIOU_nms, to improve detection performance.

[0124] 6) Loss Function

[0125] GIOU_Loss: YOLOv5 uses GIOU as part of the loss function during training to better measure the similarity between the predicted box and the true box.

[0126] In some specific embodiments, step S105 screens the recognition results, and determines whether to issue a warning and predict the risk level according to the specific situation.

[0127] In some specific embodiments, Figure 6As shown, the present invention has established a hierarchical early warning management system. The left side is the overall architecture of the early warning system, and the right side is the corresponding early warning level given according to the classification of multi-source diseases of manhole covers. The overall architecture of the early warning system is divided into two systems: the patrol early warning system and the monitoring early warning system. The patrol early warning refers to the early warning of problems in daily inspections, and the monitoring early warning refers to the health status early warning for a single manhole cover in response to problems in the point, line, and surface monitoring system. The early warning level is divided into three levels of red, orange, and yellow according to the severity of the disease. If there is missing, damaged, or displaced, a yellow early warning needs to be reported: arrange for a special person to repair immediately; if there is damage around the well and the manhole cover is disordered, an orange early warning is reported, and maintenance is arranged on the same day; if there is a height difference, vibration, or subsidence of the manhole cover, a yellow early warning is reported, and maintenance is arranged during the next periodic inspection. By hierarchical management of early warnings, the utilization of maintenance resources can be maximized.

[0128] In some specific embodiments, step S107 manually reviews and processes the warning information to confirm the risk level and corresponding maintenance suggestions.

[0129] In some specific embodiments, Figure 7 As shown, the present invention establishes a corresponding defect maintenance system based on maintenance experience and regional manhole cover maintenance standards. The defect maintenance system provides manhole cover damage judgment standards and corresponding manhole cover defect management methods, thereby establishing a "manhole cover disease-health status-maintenance suggestion" full-process intelligent management and control method. After artificial intelligence makes a maintenance suggestion, a maintenance work order is dispatched after manual review, ultimately realizing precise and institutionalized manhole cover maintenance.

[0130] The manhole cover intelligent operation and maintenance management method 100 provided by the present invention further includes: S111. Recording the data of the manhole cover after maintenance, and continuing to extract manhole cover related data by the manhole cover data collection system of the perception layer, tracking and feedback on the health status of the manhole cover after maintenance, so as to determine the maintenance effect and iteratively optimize the maintenance plan.

[0131] From the above description, it can be seen that the management method and system of the present invention have the following two periodic cycles:

[0132] 1) Manhole cover early warning cycle: When an abnormal manhole cover is warned and repaired, the data of the repaired manhole cover is extracted and learned again. If the manhole cover is in normal condition at this time, the warning status is cancelled and the manhole cover is given a normal mark. If the manhole cover is still abnormal at this time, the warning is retained and the risk level and maintenance suggestions are adjusted according to the degree of on-site maintenance, so as to carry out subsequent maintenance work. This is a complete early warning cycle. During the cycle, the maintenance status of the manhole cover is fed back in a timely and effective manner. The original manhole cover maintenance cycle is based on the manhole cover inspection cycle, which requires all manhole covers in the area to be inspected together. During this process, a single manhole cover is repaired, and the maintenance cycle of a single manhole cover is relatively long. After adding real-time disease identification management, point-to-point disease identification and maintenance can be performed for the disease of a single manhole cover, shortening the maintenance cycle of a single manhole cover and speeding up the maintenance progress. At the same time, from the warning of the health status of the manhole cover to the cancellation of the warning, it is necessary to go through layers of screening, which greatly reduces the error rate and ensures the safety and reliability of the operation and maintenance of urban underground pipelines.

[0133] 2) Manhole cover maintenance cycle: The manhole cover data collected by the manhole cover data collection system of the perception layer is submitted to the cloud platform for machine learning recognition in the recognition layer. The recognition data is screened and judged in the early warning layer, and abnormal manhole covers are warned. Finally, after the early warning information is manually reviewed, the risk level is finally determined and the maintenance suggestions are uploaded to the cloud platform. The manhole covers are repaired according to the maintenance suggestions. After the maintenance is completed, the repaired manhole cover data is submitted to the perception layer for data extraction, and then the previous steps are performed, and the cycle is repeated to complete the iteration of the manhole cover maintenance cycle. The original inspection method was to inspect all manhole covers and then arrange maintenance. The current manhole cover health status monitoring method can repair individual manhole covers, while monitoring the maintenance progress and subsequent maintenance quality of individual manhole covers. This cycle is smarter than conventional practices. It warns of the health status of the manhole covers, and repairs are carried out manually after receiving the warning. Photos of the repaired manhole covers are then uploaded to complete the cycle. This greatly shortens the time compared to manual inspections, and can efficiently and quickly track the maintenance progress and quality of manhole cover repairs, facilitate communication and cooperation between on-site personnel and managers, play an important role in the safe operation and maintenance of important urban pipelines, and effectively ensure the smooth flow of urban roads.

[0134] The manhole cover intelligent operation and maintenance management method and system provided by the present invention can at least solve the following technical problems and achieve corresponding beneficial effects:

[0135] 1. The present invention constructs an intelligent manhole cover management system with perception, decision-making and diagnosis functions, provides a rapid feedback method for perception-identification-warning-response of manhole cover structure defects, establishes an underlying architecture for building intelligent management of manhole cover defects, realizes full-process control of massive communication pipelines, comprehensive control of resource information, real-time warning of resource status and timely response to risks, and meets the needs of accurate perception and intelligent decision-making of urban manhole status.

[0136] 2. In view of the current problems of long inspection cycles, inaccurate and untimely acquisition of the health status of manhole covers, this invention establishes a comprehensive and three-dimensional perception system of "point-line-surface", establishes a monitoring system for the health status of manhole covers at fixed locations and the surrounding environment, and conducts real-time monitoring of key points at key locations to achieve "point perception"; establishes a dynamic inspection path system to dynamically adjust the inspection route of manhole covers based on the maintenance frequency of manhole covers and the occurrence of periodic diseases (diseases that are prone to occur in spring, summer, autumn and winter, morning, noon and evening) to achieve "line perception"; uses intelligent manhole cover inspection vehicles in the region to acquire images of road manhole covers, establishes a regional manhole cover health status image perception system, and achieves "surface perception". An organic combination is formed between each level, which effectively improves the efficiency and accuracy of data collection.

[0137] 3. In view of the problem that the current manhole cover maintenance process relies mostly on manual experience and the maintenance effect is not guaranteed, a manhole cover health status database is established (for example, a collection of health status images of all manhole covers in a certain area in the past few years, and images collected from subsequent manhole cover maintenance records in each quarter). The deep learning method based on the attention mechanism is used to accurately identify the health status of manhole covers. A cloud platform-based automatic identification method for manhole cover health status based on Yolov5 is proposed. According to maintenance experience and regional manhole cover maintenance standards, a corresponding disease maintenance system is established, such as Figure 7 As shown in the figure, a smart management and control method for the entire process of "manhole cover disease - health status - maintenance suggestions" is established. After artificial intelligence makes maintenance suggestions, maintenance work orders are dispatched after manual review, ultimately achieving precise and institutionalized manhole cover maintenance.

[0138] 4. In order to solve the problems of imprecise control of the maintenance status of manhole covers after maintenance and incomplete maintenance effects, we established image collection data for the entire maintenance process of a single manhole cover, implemented iterative management of "maintenance warning-maintenance suggestions-worker maintenance-maintenance effect judgment" for the repaired manhole covers, established a health status prediction system based on deep learning, proposed an accurate evaluation of the repair effect of manhole cover diseases, and continuously iterated and optimized the maintenance plan.

[0139] Figure 8 FIG. 3 is a schematic block diagram of a manhole cover structure intelligent operation and maintenance management system 300 according to some embodiments of the present invention. Figure 8As shown, the intelligent operation and maintenance management system 300 for manhole cover structures includes a perception module 301 , an identification module 303 , an early warning module 305 , and a response module 307 .

[0140] The sensing module 301 has a manhole cover data collection system and is configured to collect manhole cover related data;

[0141] The identification module 303 has a cloud platform, in which an algorithm for identifying and processing manhole cover related data is preset, and the collected manhole cover related data can be learned and identified;

[0142] The warning module 305 is configured to screen the recognition results. If the manhole cover is judged to be in a healthy state, no warning is issued. If the manhole cover is judged to be in a sub-healthy state, a warning is issued according to the type of sub-healthy state and the risk level is predicted, and maintenance suggestions are further made according to previous maintenance data.

[0143] The response module 307 is configured to manually review and process the warning information. If the manual review determines that the warning information is correct, the risk level and maintenance suggestions are confirmed. If the manual review determines that the warning information is incorrect, such as the state of the manhole cover, the warning is revoked. If the risk level or maintenance suggestions of the manhole cover are incorrect, the risk level and maintenance suggestions are manually adjusted and confirmed, and the manhole cover structure is repaired according to the final confirmed maintenance suggestions.

[0144] Fig. 9 The schematic block diagram of an example electronic device 500 that can be used to implement an embodiment of the present invention is shown. The electronic device 500 includes a memory and a processor, wherein the memory is used to store one or more computer programs, and the processor is used to execute one or more of the computer programs. The electronic device 500 can be used to implement, for example Figure 1 The operations in the manhole cover intelligent operation and maintenance management method 100 are shown.

[0145] Electronic device 500 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 500 may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown in the present invention, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required in the present invention.

[0146] like Fig.10As shown, the electronic device 500 is a computing device, or a computer system, which may include a CPU 501 (computing unit), which can perform various appropriate actions and processes according to a computer program stored in a ROM 502 (read-only memory) or a computer program loaded from a storage unit 508 into a random access RAM 503 (memory). In the RAM 503, various programs and data required for the operation of the electronic device 500 may also be stored. The CPU 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An I / O interface 505 (input / output interface) is also connected to the bus 504.

[0147] Multiple components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0148] CPU 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of CPU 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. CPU 501 performs the various methods and processes described above, such as the intelligent operation and maintenance management method 100 for manhole covers. For example, in some embodiments, the intelligent operation and maintenance management method 100 for manhole covers may be implemented as a computer software program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 508.

[0149] In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the CPU 501, one or more steps of the manhole cover intelligent operation and maintenance management method 100 described above may be performed. Alternatively, in other embodiments, the CPU 501 may be configured to execute the manhole cover intelligent operation and maintenance management method 100 in any other appropriate manner (e.g., by means of firmware).

[0150] Various embodiments of the systems and techniques described above in the present invention can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general programmable processor, which can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0151] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0152] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may be a machine-readable signal medium or a machine-readable storage medium. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. More specific examples of computer-readable storage media may include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0153] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0154] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0155] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and the present invention is not limited here.

[0156] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for intelligent operation and maintenance of manhole covers, characterized in that: include: Establish a manhole cover data collection system, collect manhole cover related data, and establish a manhole cover database, wherein the manhole cover related data at least includes manhole cover disease information; Study and identify the types of manhole cover diseases collected; The recognition results are screened. If the manhole cover is judged to be in a healthy state, no warning is issued. If the manhole cover is judged to be in a sub-healthy state, a warning is issued and the risk level is predicted according to the sub-healthy state type, and maintenance suggestions are further made based on previous maintenance data. Manual review and processing of warning information. If the manual review determines that the warning information is correct, the risk level and corresponding maintenance suggestions are confirmed. If the manual review determines that the warning information is incorrect, if the manhole cover is in normal condition, the warning is cancelled. If the risk level or maintenance suggestion of the manhole cover is incorrect, the risk level and corresponding maintenance suggestion are manually adjusted and confirmed. Repair the manhole cover structure based on the final confirmed maintenance recommendations.

2. The method according to claim 1, characterized in that: The manhole cover related data includes manhole cover health data and personnel maintenance data, where: The health data of the manhole cover includes: bulge, damage, warping, loss, surrounding environment, positioning and manhole cover function; The personnel maintenance data includes: maintenance records, images before and after maintenance, maintenance personnel information, maintenance time, installation time and maintenance times; And / or, the sub-health status types include: deformation or displacement, no fall protection function, damaged manhole cover, damage around the well, subsidence, bulge, manhole cover height difference, manhole cover disorder and vibration.

3. The method according to claim 1, characterized in that: The establishment of a manhole cover data collection system, collecting manhole cover related data, and establishing a manhole cover database includes: Install monitoring equipment at fixed locations to monitor key locations in real time and obtain the health status of the manhole cover and the surrounding environment in real time; Build a dynamic inspection route system to conduct real-time monitoring on the inspection route and dynamically adjust the inspection route based on the maintenance frequency and periodic disease problems of the manhole covers; Intelligent manhole cover inspection vehicles are deployed in the area to obtain images of road manhole covers and build a regional manhole cover health status image perception system.

4. The method according to claim 1, characterized in that: The learning and identification of the collected manhole cover related data includes: The collected manhole cover related data is uploaded to the cloud platform, which has a preset target monitoring deep learning image recognition algorithm for identifying and processing manhole cover related data. The images are annotated through manhole cover disease classification, and the computer is trained and learned based on the classification to establish a manhole cover disease image classification model for subsequent identification.

5. The method according to claim 4, characterized in that The target monitoring deep learning image recognition algorithm adopts the Yolov5 algorithm, including: 1) Input preprocessing Image scaling and normalization: Scale the input image to the size required by the network and perform normalization to facilitate network processing; Data enhancement: Mosaic data enhancement technology is used to randomly scale, crop, and arrange multiple images to form a new image, enriching the data set and improving the robustness and training speed of the model. 2) Feature extraction Backbone network: Use CSPDarknet as the backbone network to improve the efficiency and accuracy of the network; Feature map generation: The backbone network is used to extract features from the input image and generate a series of feature maps, each of which corresponds to objects of different scales. 3) Anchor box generation Adaptive anchor box calculation: During training, YOLOv5 adaptively calculates the best anchor box value based on the dataset to better match objects of different sizes and shapes; 4) Prediction Feature fusion: The Neck part of YOLOv5 performs multi-scale fusion of feature maps to enhance feature representation capabilities; Prediction output: The Head part of the YOLOv5 algorithm converts the feature map into information such as the position, size, and category probability of the detection box through convolution operations. For each feature map, the detector predicts several detection boxes and calculates the IoU value between it and the anchor box to determine the final detection box; 5) Post-processing Non-maximum suppression: YOLOv5 uses an improved NMS algorithm to filter overlapping detection frames, remove redundant detection frames, and obtain the final detection results; 6) Loss Function GIOU_Loss: YOLOv5 uses GIOU as part of the loss function during training to better measure the similarity between the predicted box and the true box.

6. The method according to claim 1, characterized in that The step of determining that the manhole cover is in a healthy state further includes: assigning a normal mark to the manhole cover; And / or, confirming the risk level and the corresponding maintenance suggestion also includes: publishing the maintenance suggestion to a cloud platform.

7. The method according to claim 1, characterized in that The method further includes: The manhole cover data before and after maintenance are recorded, and the manhole cover data collection system continues to collect manhole cover related data, and continuously tracks and feedbacks the health status of the manhole cover after maintenance based on the inspection cycle, so as to determine the maintenance effect and iteratively optimize the maintenance plan.

8. A smart operation and maintenance management system for manhole cover structures, characterized in that: include: The sensing module has a manhole cover data collection system and is configured to collect manhole cover related data, wherein the manhole cover related data at least includes manhole cover disease information; The identification module has a cloud platform, in which an algorithm for identifying and processing manhole cover related data is preset, and the manhole cover related data collected can be learned and identified; The early warning module is configured to screen the recognition results. If the manhole cover is judged to be in a healthy state, no early warning is issued. If the manhole cover is judged to be in a sub-healthy state, an early warning is issued and the risk level is predicted according to the sub-healthy state type, and maintenance suggestions are further made based on previous maintenance data. The response module is configured to manually review and process the warning information. If the manual review determines that the warning information is correct, the risk level and corresponding maintenance suggestions are confirmed. If the manual review determines that the warning information is incorrect, such as the state of the manhole cover, the warning is revoked. If the risk level or maintenance suggestions of the manhole cover are incorrect, the risk level and corresponding maintenance suggestions are manually adjusted and confirmed, and the manhole cover structure is repaired according to the final confirmed maintenance suggestions.

9. An electronic device, comprising: a memory for storing one or more computer programs; as well as A processor, configured to execute one or more of the computer programs to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the method according to any one of claims 1 to 7 when executed by a processor.

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