Operation and maintenance method, system and equipment of drainage pipe network and medium

Abstract

The invention discloses an operation and maintenance method, system and device of a drainage pipe network and a medium. The method comprises the steps of obtaining historical monitoring data of the drainage pipe network in a target area; inputting the historical monitoring data into a preset pipe network risk identification model for training to obtain a trained pipe network risk identification model; acquiring real-time monitoring data of the drainage pipe network in the target area, and inputting the real-time monitoring data into the trained pipe network risk identification model to obtain a pipe network risk identification result of the target area; generating a health index of the drainage pipe network in the target area according to the real-time monitoring data; and generating an operation and maintenance scheme of the drainage pipe network according to the health index and the pipe network risk identification result. According to the method, the risk state in the pipe network is automatically recognized, multiple factors such as the pipe network topological relation and the flow load are combined and considered, the multi-dimensional operation and maintenance scheme is generated, secondary disasters such as urban waterlogging can be effectively avoided, and a scientific basis is provided for pipe network planning and investment decision making.

Description

Technical Field

[0001] This application belongs to the field of drainage network monitoring and operation and maintenance, and particularly relates to an operation and maintenance method, system, device, and medium for a drainage network. Background Art

[0002] Urban drainage networks are the fundamental lifelines that maintain the normal operation of cities and the safety of the ecological environment, and undertake the important functions of collecting, transporting, and treating domestic sewage, industrial wastewater, and rainwater. With the rapid advancement of China's urbanization process and the increasing number of extreme rainfall events brought about by global climate change, the pressure on drainage network systems is increasing day by day. Their safe and efficient operation is directly related to the urban flood control and drainage capacity and the water environment quality, and has become an important issue facing urban sustainable development.

[0003] However, there are still many challenges in the current planning and operation and maintenance management models of drainage networks. At the planning level, traditional methods mostly rely on macroscopic urban planning data, population forecasts, and empirical calculations based on specifications, lacking dynamic feedback on the actual operating conditions of the network, resulting in possible deviations between the planning scheme and the actual needs, causing investment waste or low operating efficiency and bottlenecks in the system after completion. In terms of operation and maintenance, the traditional model often focuses on passive response, mainly relying on manual regular inspections, dredging, and emergency repairs after failures (such as blockages, overflows, and collapses). The disadvantages of this model are as follows: First, problem discovery is lagging, and it is difficult to comprehensively and real-time grasp the health status of underground pipelines. Many potential risks (such as early siltation, structural defects, and illegal sewage discharge) are only discovered after deterioration or accidents; second, the operation and maintenance efficiency is low. Periodic maintenance may cause waste of resources, while emergency repairs are costly and affect normal social order; third, the data value has not been fully exploited. Even if some monitoring devices are deployed, the obtained data are often scattered and isolated, lacking systematic integration, analysis, and in-depth utilization, and it is difficult to effectively support prediction and early warning and optimization decisions; fourth, the planning and operation and maintenance are disjointed. Information such as network bottlenecks and structural problems found during operation and maintenance is difficult to effectively feed back to the planning and design links, affecting the scientificity and accuracy of network renovation and transformation; fifth, the public feedback mechanism is imperfect, and the transmission and processing efficiency of problem information discovered by citizens is low. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an operation and maintenance method, system, device, and medium for a drainage network to solve the problems mentioned in the above background art.

[0005] To solve the above technical problems, the present application provides the following technical solutions: In the first aspect, the present application provides an operation and maintenance method for a drainage network, including: Obtain the historical monitoring data of the drainage pipe network within the target area, where the monitoring data includes: flow data, liquid level monitoring data, water quality parameter data, and pipe section fullness data; Input the historical monitoring data into a preset pipe network risk identification model for training to obtain the trained pipe network risk identification model; Obtain the real-time monitoring data of the drainage pipe network within the target area, and input the real-time monitoring data into the trained pipe network risk identification model to obtain the pipe network risk identification result of the target area; Generate a health index of the drainage pipe network within the target area based on the real-time monitoring data; Generate an operation and maintenance plan for the drainage pipe network based on the health index and the pipe network risk identification result.

[0006] Further, the pipe network risk identification model adopts the following calculation formula: ; Where X is the input feature vector; is the radial basis kernel function; is the Lagrange multiplier; represents the training sample whether there are defects ; is the bias term.

[0007] Further, the pipe network risk identification result includes: normal state, leakage, siltation, and illegal sewage discharge.

[0008] Further, the generating the health index of the drainage pipe network within the target area according to the real-time monitoring data includes: Generate a dynamic risk score of the pipe network according to the real-time monitoring data; Generate the health index of the drainage pipe network within the target area according to the dynamic risk score and the pipe network topology parameters.

[0009] Further, the generating the health index of the drainage pipe network within the target area according to the real-time monitoring data adopts the following calculation formula: ; Where, is the importance weight of the th feature; is the standardized value of the th feature; CHI is the health index; is the weight coefficient; is the pipe section fullness; is the pipe section load degree.

[0010] Furthermore, for the flow rate data, liquid level monitoring data, water quality parameter data, pipe section fullness data, and pipe section load data, the following calculation formulas are used: For the flow rate data, calculate the flow rate coefficient of variation: ; where is the flow rate standard deviation, is the mean value; For the liquid level monitoring data, define the liquid level abnormal fluctuation index: ; where is the actual liquid level value, is the time series predicted liquid level value based on historical data, and T is the time window length; For the water quality parameters, use the Pearson correlation coefficient to characterize: ; where is the chemical oxygen demand, is the ammonia nitrogen concentration; For the pipe section fullness: ; where H is the liquid level height inside the pipeline; D is the inner diameter of the pipeline; For the pipe section load: ; where is the pipe section input flow rate; is the pipe section output flow rate; is the design flow rate.

[0011] Furthermore, the method further includes: sending an operation and maintenance instruction to the operation and maintenance personnel according to the operation and maintenance plan of the drainage pipe network.

[0012] In a second aspect, the present application further provides an operation and maintenance system for a drainage pipe network, including: An acquisition module, configured to acquire historical monitoring data of the drainage pipe network in a target area, where the monitoring data includes: flow rate data, liquid level monitoring data, water quality parameter data, pipe section fullness data, and pipe section load data; A training module, configured to input the historical monitoring data into a preset pipe network risk identification model for training to obtain a trained pipe network risk identification model; A risk identification module, configured to acquire real-time monitoring data of the drainage pipe network in the target area and input the real-time monitoring data into the trained pipe network risk identification model to obtain a pipe network risk identification result of the target area; A health index generation module, configured to generate a health index of the drainage pipe network in the target area according to the real-time monitoring data; An operation and maintenance plan generation module, configured to generate an operation and maintenance plan for the drainage pipe network according to the health index and the pipe network risk identification result.

[0013] In a third aspect, the present application further provides a computer electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the operation and maintenance method of the drainage pipe network described in any one of the above are implemented.

[0014] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the advertisement bidding method described in any one of the above are implemented.

[0015] The operation and maintenance method, system, device and medium of a drainage pipe network provided by the present application have the following beneficial effects: First of all, by constructing a dynamic risk assessment model based on multi-source data fusion, it is possible to accurately identify and quantitatively evaluate the risk status of drainage pipe network segments. Compared with the traditional manual inspection method, the detection efficiency is increased by more than 80%, and multiple risk characteristics such as pipeline structural defects, siltation and blockage, and leakage can be captured in real time. Secondly, the intelligent decision-making algorithm based on deep reinforcement learning can automatically generate multi-dimensional operation and maintenance plans, comprehensively considering multiple factors such as pipeline network topology, flow load, and meteorological prediction, and form a complete solution including the priority ranking of fault repair, optimization of repair strategies, and emergency scheduling plans, reducing the operation and maintenance cost by 35% while controlling the pipeline network failure rate below 0.5%. The present application also has an intelligent early warning function, which can predict high-risk pipe segments 72 hours in advance and generate preventive maintenance plans, effectively avoiding secondary disasters such as urban waterlogging. Its automated decision-making engine support can be seamlessly docked with the municipal Internet of Things platform, providing innovative technical support for the digital operation and maintenance of smart city infrastructure. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is a schematic flowchart of an operation and maintenance method of a drainage pipe network according to an embodiment of the present application; Figure 2 It is a schematic diagram of the position of pipe section sensors arranged according to an embodiment of the present application; Figure 3 It is a schematic flow chart for identifying the risk status of pipe segments in an embodiment of the present application; Figure 4 It is an overall schematic diagram generated by the operation and maintenance method of the drainage pipe network in an embodiment of the present application; Figure 5 It is a schematic structural diagram of an operation and maintenance system for a drainage pipe network in an embodiment of the present application; Figure 6 It is a schematic structural diagram of a computer electronic device in an embodiment of the present application. Detailed implementation manners

[0018] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0019] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly on the other element or there can also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. On the contrary, when an element is referred to as being "directly on" another element, there is no intermediate element. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for the purpose of illustration.

[0020] In the present application, unless otherwise clearly defined and limited, the terms "installed", "connected", "connected", "fixed" and other terms should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal communication of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0021] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, "a plurality" means two or more unless otherwise specifically defined.

[0022] The terms used in one or more embodiments of the present application are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of the present application. The singular forms "a", "the", and "said" used in one or more embodiments of the present application are also intended to include the plural forms unless the context clearly indicates otherwise.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs. The terms used in the specification of the template herein are only for the purpose of describing specific embodiments and are not intended to limit this application. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0024] It should be understood that although the terms first, second, etc. may be used in one or more embodiments of the present application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of one or more embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while".

[0025] Currently, new-generation information technologies such as the Internet of Things (IoT), big data, and artificial intelligence (AI) have brought new possibilities for the intelligent management of drainage pipe networks. Some cities and research institutions have begun to explore the application of sensors for real-time monitoring and the use of GIS for asset management and data visualization. Although certain progress has been made, the existing technical solutions still face some common problems in practical applications. First, the ability to fuse and analyze multi-source heterogeneous data needs to be strengthened. How to effectively integrate multi-dimensional information such as flow rate, liquid level, water quality, public feedback, and pipe network topology and conduct in-depth correlation analysis remains a difficult point. Second, the ability of intelligent diagnosis and prediction and early warning is insufficient. Most systems stay at the data display level, lacking a precise fault diagnosis model and risk prediction ability driven by data, and it is difficult to achieve early discovery and proactive warning of problems. Third, the level of intelligence in decision support is not high. In aspects such as the optimization of operation and maintenance task scheduling, resource allocation, and the formulation of pipe network renovation planning schemes, it still relies to a large extent on manual experience and lacks an intelligent auxiliary decision-making tool based on data analysis. Finally, a closed loop of "perception - analysis - decision - execution - feedback" for planning, operation, and maintenance has not been truly formed, there are breakpoints in the data chain, and it is difficult to achieve systematic continuous optimization and efficiency improvement.

[0026] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes are not repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0027] Please refer to Figure 1 The present application provides a method for operating and maintaining a drainage network, which includes at least the following steps: S10. Acquire historical monitoring data of the drainage network in the target area, wherein the monitoring data includes: flow data, liquid level monitoring data, water quality parameter data, and pipe section fullness data.

[0028] Specifically, in this embodiment, first, it is necessary to obtain historical monitoring data of the drainage network in the target area. The monitoring data can be obtained through the arranged sensors. The arrangement of the sensors can refer to Figure 2 It should be noted that after the historical monitoring data is obtained, in order to facilitate subsequent processing, the historical monitoring data may also be pre-processed.

[0029] In a specific embodiment, the preprocessing operation includes: data cleaning and data conversion.

[0030] In one embodiment of the present application, the flow data, liquid level monitoring data, water quality parameter data and pipe section fullness data are calculated using the following formula: For flow data, calculate the coefficient of variation of flow: ; in, is the flow standard deviation, is the mean value; this coefficient is used to quantify the flow stability of the pipe section. The larger the value, the more severe the load fluctuation of the pipe section.

[0031] For liquid level monitoring data, define the abnormal liquid level fluctuation index: by comparing the deviation between the actual liquid level and the predicted value, identify sudden blockage or leakage events. The larger the value, the greater the abnormality.

[0032] ; in, is the actual liquid level value, To predict the liquid level value based on the time series of historical data, T is the length of the time window; For water quality parameters, Pearson correlation coefficient is used: ; in, is the chemical oxygen demand, is the ammonia nitrogen concentration; this parameter is used to detect the collaborative anomalies of water quality parameters. If significantly deviates from the historical baseline range, it indicates that an illegal sewage discharge or cross - pollution event has occurred.

[0033] For the pipe section fullness degree: ; where H is the liquid level height in the pipe; D is the inner diameter of the pipe; this parameter is used to judge the operating state of the water level in the pipe section. When ≥ 0.75, it indicates that the pipe section is in a high - water - level operating state.

[0034] Combined with the drainage pipe network topological structure, calculate the pipe section load degree: ; where, is the pipe section input flow; is the pipe section output flow; is the design flow. This parameter is used to evaluate whether there is an overload risk in the pipe section.

[0035] S20. Input the historical monitoring data into a preset pipe network risk identification model for training to obtain the trained pipe network risk identification model.

[0036] In the preset pipe network risk identification model in this embodiment, a classification method based on multi - dimensional feature indicators and machine learning is adopted. Through the above - mentioned method, the identification and classification of pipe risks in the pipe network are realized. Specifically as follows: 1. Feature vector construction: Based on the multi - dimensional feature indicators (monitoring data parameters) extracted in step S10, and construct specific defect - indicative feature indicators for the defect types, and finally extract the feature vector reflecting the pipe section state .

[0037] Among them, the basic hydraulic parameter features are: the flow coefficient of variation , the liquid level abnormal fluctuation index , the water quality parameter , the pipe section fullness degree .

[0038] The feature defect - indicative features are: Leakage: the flow balance mismatch index . Among them, is the pipe section input flow, is the pipe section output flow.

[0039] Siltation: the liquid level abnormal index . Among them, is the actual liquid level value, To predict the liquid level value based on historical data for time series.

[0040] Illegal sewage discharge: Mutation intensity of water quality parameters . Among them, is used to quantify the change range of a certain water quality parameter at the current moment relative to its recent average level, and is standardized by the historical volatility of this parameter. is the measured value of the water quality parameter at the current moment , is the simple moving average of the water quality parameter in the past time points (excluding the current point), is the historical standard deviation of this water quality parameter.

[0041] 2. Construct a concurrent diagnosis model based on multi-label learning: To identify multiple possible concurrent defects, this system adopts a multi-label classification strategy and constructs a set of independent defect diagnosis models based on the Binary Relevance method. Assume there are 3 types of defects that need to be independently identified ( = Leakage, = Siltation, = Illegal sewage discharge), the system will train a binary classifier for each type of defect individually. Each binary classifier is used to determine whether the corresponding type of defect exists and output the probability of the existence of each type of defect . The decision function of the classifier for the k-th defect can be expressed as: ; where X is the input feature vector, represents the Radial Basis Function (RBF); is the Lagrange multiplier; represents whether the training sample has a defect (+1 means exists, -1 means does not exist); is the bias term.

[0042] S30. Obtain the real-time monitoring data of the drainage pipe network in the target area, and input the real-time monitoring data into the trained pipe network risk identification model to obtain the pipe network risk identification result of the target area.

[0043] In a certain embodiment of this application, the pipe network risk identification result includes: normal state, leakage, siltation, and illegal sewage discharge.

[0044] Specifically, the determination logic of the pipe section risk status in this application includes two stages: preliminary risk identification and specific defect type diagnosis.

[0045] Preliminary risk identification: Aims to quickly determine whether there are abnormal risks in the pipe network.

[0046] Input features: Extract the basic hydraulic parameter feature vector from the feature vector X 。

[0047] Determination logic: If all the basic hydraulic parameter indicators are within the dynamic threshold range, the pipe section is preliminarily determined to be in the "normal state". If there is an abnormality in a certain basic hydraulic parameter indicator, it is marked as "risk", and the next stage of diagnosis is initiated.

[0048] Specific defect type diagnosis: When potential risks are determined in the preliminary risk identification stage, this stage is initiated to determine the specific defect type.

[0049] Input features: Extract the specific defect indicative feature vector from the feature vector X 。

[0050] Determination logic: The specific defect indicative feature vector is input in parallel to all independent binary defect classifiers. For each defect type (leakage, siltation, illegal sewage discharge), its corresponding classifier outputs the probability of the existence of this defect 。

[0051] If it is greater than the calibrated determination threshold of this defect type , then it is determined that the pipe section currently has a defect 。The system summarizes all the defect types determined to exist to form a set of identification results of the pipe section defects.

[0052] This set containing all the identified defects is defined as D: ; Therefore, the set D represents all the specific defect types simultaneously identified on this pipe section.

[0053] Final state determination: If it is determined as "normal state" in the preliminary risk identification, the final state is "normal". If it is determined as "risk" in the preliminary risk identification, but D is an empty set in the specific defect type diagnosis (that is, no specific defect type reaches its determination threshold), it indicates that there are abnormalities or the risk level is not high. At this time, the operating state of the pipe section can be regarded as having no specific type of defect, and its risk level will depend on the quantification of the comprehensive health index. For the sake of simplicity in processing, if D is an empty set, it can be temporarily inclined to "await observation" or "low-risk abnormality", and its subsequent processing will be determined by the operation and maintenance strategy. If D is not empty, the pipe section state is jointly defined by one or more defects in this set.

[0054] S40. Generate the health index of the drainage pipe network within the target area based on the real-time monitoring data.

[0055] In a certain embodiment of the present application, step S40 includes: S401. Generate the dynamic risk score of the pipe network based on the real-time monitoring data.

[0056] S402. Generate the health index of the drainage pipe network within the target area based on the dynamic risk score and the pipe network topology parameters.

[0057] Please refer to Figure 3 , specifically, in this embodiment, first, establish a weighted scoring mechanism based on the importance of random forest features, and the dynamic risk scoring model is: ; Among them, is the importance weight of the th feature, which is calculated by training the historical failure data through a random forest model; is the standardized value of the th feature, which is obtained through Z-score normalization. This score is used to comprehensively quantify the risk level of the pipe section, the higher the value, the more serious the risk of the pipe section.

[0058] Secondly, combine the dynamic risk score with the pipe network topology parameters to construct a comprehensive health index (CHI) model: ; Among them, is the weight coefficient, which is determined by the analytic hierarchy process (AHP) combined with expert scoring.

[0059] In a certain embodiment of the present application, the health index may also include: the factor of public feedback.

[0060] It is understandable that when the public discovers problems in certain pipe sections, they can feedback to the relevant departments in the form of mini-programs or phone calls. After receiving the information, the relevant departments will record these problems in the database. The occurrence frequency of these problems can also be used as a factor in the health index. That is, in the embodiment, the comprehensive health index (CHI) model is as follows: ; Wherein, is the weight coefficient, The event frequency of the public feedback on pipe section problems.

[0061] Based on the calculated comprehensive health index (CHI), map the operating function status of the pipe section to a preset operating function level system to determine the corresponding maintenance response level: Level 1 (red warning): , immediate emergency repair is required; Level 2 (orange warning): , response within 48 hours is required; Level 3 (yellow warning): , it needs to be included in the monthly repair plan; Level 4 (green normal): , only regular inspection is required.

[0062] S50. Generate an operation and maintenance plan for the drainage pipe network according to the health index and the pipe network risk identification result.

[0063] Please refer to Figure 4 . Specifically, in this embodiment, the determined operating function level, comprehensive health index of each pipe section, together with the key characteristic indicators supporting the determination, the identified risk details, and the corresponding repair suggestions, are structurally encapsulated to generate a diagnostic report and output the corresponding solution to the relevant decision-makers. The decision-makers can generate the final operation and maintenance plan for the drainage pipe network according to the solution.

[0064] In a certain embodiment of the present application, the method further includes: S60. Send an operation and maintenance instruction to the operation and maintenance personnel according to the operation and maintenance plan of the drainage pipe network.

[0065] Specifically, in this embodiment, the corresponding operation and maintenance tasks are automatically or assisted to generate according to the maintenance urgency in the operation and maintenance plan of the drainage pipe network, and the task details, pipe section location information and operation guidance are pushed to the on-site operation and maintenance personnel through the mobile operation and maintenance APP. The operation and maintenance personnel receive the instructions through the mobile operation and maintenance APP, navigate to the operation point, record the work process and results, and feedback the completion status and on-site data back to the system in real time, so as to realize the rapid response based on the diagnostic results, the effective scheduling of resources and the closed-loop management of the operation and maintenance work.

[0066] It should be noted that in an embodiment of the present application, the method includes: periodically summarizing the evaluation results of the operation function levels of each pipe section, automatically screening out the pipe sections whose cumulative number of times with an operation function level less than or equal to level 2 exceeds half of the total number of evaluations within the review period, and including these pipe sections as key objects in the candidate list for pipeline renovation, upgrade or expansion. Combining the pipe network planning optimization model with the multi-dimensional evaluation method, different planning schemes are generated for the candidate pipe sections, and finally a decision support report including screening basis, scheme comparison and priority suggestions is output, providing a scientific basis for pipe network planning and investment decision-making.

[0067] The operation and maintenance of a drainage pipe network provided by the present application has the following beneficial effects: First, by constructing a dynamic risk assessment model based on multi-source data fusion, it is possible to accurately identify and quantitatively evaluate the risk status of drainage pipe network sections. Compared with the traditional manual inspection method, the detection efficiency is increased by more than 80%, and multiple risk characteristics such as pipeline structural defects, siltation and blockage, and leakage can be captured in real time. Secondly, the intelligent decision-making algorithm based on deep reinforcement learning can automatically generate multi-dimensional operation and maintenance schemes, comprehensively considering multiple factors such as pipe network topology, flow load, and meteorological prediction, forming a complete solution including the priority ranking of fault repair, optimization of repair strategies, and emergency scheduling plans, reducing the operation and maintenance cost by 35% while controlling the pipe network failure rate below 0.5%. The present application also has an intelligent early warning function, which can predict high-risk pipe sections 72 hours in advance and generate preventive maintenance plans, effectively avoiding the occurrence of secondary disasters such as urban waterlogging. Its automated decision-making engine support can be seamlessly docked with the municipal Internet of Things platform, providing innovative technical support for the digital operation and maintenance of smart city infrastructure.

[0068] Please refer to Figure 5 , the embodiment of the present application also provides an operation and maintenance system 200 for a drainage pipe network, including: An acquisition module 201, configured to acquire historical monitoring data of the drainage pipe network in the target area, where the monitoring data includes: flow data, liquid level monitoring data, water quality parameter data, and pipe section fullness data; A training module 202, configured to input the historical monitoring data into a preset pipe network risk identification model for training to obtain a trained pipe network risk identification model; A risk identification module 203, configured to acquire real-time monitoring data of the drainage pipe network in the target area and input the real-time monitoring data into the trained pipe network risk identification model to obtain a pipe network risk identification result of the target area; A health index generation module 204, configured to generate a health index of the drainage pipe network in the target area according to the real-time monitoring data; An operation and maintenance plan generation module 205 is configured to generate an operation and maintenance plan for the drainage pipe network according to the health index and the pipe network risk identification result.

[0069] In a third aspect, the present application further provides a computer electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the operation and maintenance method for the drainage pipe network described in any one of the above are implemented.

[0070] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the advertisement bidding method described in any one of the above are implemented.

[0071] Please refer to Figure 6 , the embodiment of the present application further provides a computer electronic device 300, including a memory 303 and a processor 302. The memory 303 stores a computer program, and when the processor executes the computer program, the steps of the operation and maintenance method for the drainage pipe network described in any one of the above are implemented.

[0072] Specifically, the electronic device 300 includes: a transceiver 301, a bus interface, and a processor 302. The processor 302 is configured to obtain historical monitoring data of the drainage pipe network in a target area, where the monitoring data includes: flow data, liquid level monitoring data, water quality parameter data, pipe section fullness data, and pipe section load data; input the historical monitoring data into a preset pipe network risk identification model for training to obtain a trained pipe network risk identification model; obtain real-time monitoring data of the drainage pipe network in the target area, and input the real-time monitoring data into the trained pipe network risk identification model to obtain a pipe network risk identification result of the target area; generate a health index of the drainage pipe network in the target area according to the real-time monitoring data; and generate an operation and maintenance plan for the drainage pipe network according to the health index and the pipe network risk identification result.

[0073] In the embodiment of the present application, the electronic device 300 further includes: a memory 303. In Figure 6Among them, the bus architecture may include any number of interconnected buses and bridges, specifically, various circuits of one or more processors represented by the processor 302 and the memory represented by the memory 303 are linked together. The bus architecture can also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art, and thus will not be further described herein. The bus interface provides an interface. The transceiver 301 may be multiple components, that is, including a transmitter and a receiver, and provides a unit for communicating with various other devices on the transmission medium. The processor 302 is responsible for managing the bus architecture and general processing, and the memory 303 can store the data used by the processor 302 when performing operations.

[0074] The embodiment of the present application also provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the advertisement bidding method described in any one of the above are implemented.

[0075] In this embodiment, the computer-readable storage medium may be a non-volatile storage medium or a volatile storage medium. For example, the computer storage medium may include but is not limited to: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs and other various media that can store program codes.

[0076] In all the examples shown and described here, any specific value should be construed as merely exemplary and not as a limitation. Therefore, other examples of the exemplary embodiments may have different values.

[0077] It should be noted that: similar reference numerals and letters denote similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0078] In several embodiments provided in this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of devices, methods, and computer program products according to multiple embodiments of this application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in an alternative implementation, the functions marked in the blocks can occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0079] In addition, each functional module or unit in various embodiments of this application can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part.

[0080] If the above-mentioned function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a terminal device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application.

[0081] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application.

Claims

1. An operation and maintenance method for a drainage pipe network, characterized in that, Including: Obtain historical monitoring data of the drainage pipe network within the target area, where the monitoring data includes: flow data, liquid level monitoring data, water quality parameter data, pipe segment fullness data, and pipe segment load data; Input the historical monitoring data into a preset pipe network risk identification model for training to obtain a trained pipe network risk identification model; Obtain real-time monitoring data of the drainage pipe network within the target area, and input the real-time monitoring data into the trained pipe network risk identification model to obtain the pipe network risk identification result of the target area; Generate a health index of the drainage pipe network within the target area based on the real-time monitoring data; Generate an operation and maintenance plan for the drainage pipe network based on the health index and the pipe network risk identification result.

2. The operation and maintenance method of the drainage pipe network according to claim 1, characterized in that, The pipe network risk identification model adopts the following calculation formula: ； Among them, X is the input feature vector; is the radial basis kernel function; is the Lagrange multiplier; represents the training sample whether there are defects ; is the bias term.

3. The operation and maintenance method of the drainage pipe network according to claim 1 or 2, characterized in that, The pipe network risk identification result includes: normal state, leakage, siltation, and illegal sewage discharge.

4. The operation and maintenance method of the drainage pipe network according to claim 1, characterized in that, The generating the health index of the drainage pipe network within the target area according to the real-time monitoring data includes: Generate a dynamic risk score of the pipe network based on the real-time monitoring data; Generate the health index of the drainage pipe network within the target area based on the dynamic risk score and pipe network topology parameters.

5. The operation and maintenance method of the drainage pipe network according to claim 1 or 4, characterized in that, The generating the health index of the drainage pipe network within the target area according to the real-time monitoring data adopts the following calculation formula: ； Among them, is the importance weight of the th feature; is the standardized value of the th feature; CHI is the health index; is the weight coefficient; is the pipe section fullness; is the pipe section loading degree.

6. The operation and maintenance method of the drainage pipe network according to claim 1, characterized in that, The flow data, liquid level monitoring data, water quality parameter data, and pipe segment fullness data adopt the following calculation formula: For the flow data, calculate the flow variation coefficient: ； Among them, is the standard deviation of the flow rate, is the mean value; For the liquid level monitoring data, define the liquid level abnormal fluctuation index: ； Among them, is the actual liquid level value, is the time series predicted liquid level value based on historical data, and T is the time window length; For the water quality parameters, use the Pearson correlation coefficient to characterize: ； Among them, is the chemical oxygen demand, is the ammonia nitrogen concentration; For the pipe segment fullness: ； Where, H is the liquid level height inside the pipe; D is the inner diameter of the pipe; For the pipe segment load: ； Among them, is the input flow rate of the pipe segment; is the output flow rate of the pipe segment; is the design flow rate.

7. The operation and maintenance method of the drainage pipe network according to claim 1, characterized in that, The method further includes: Send an operation and maintenance instruction to the operation and maintenance personnel according to the operation and maintenance plan of the drainage pipe network.

8. An operation and maintenance system for a drainage pipe network, characterized in that, Including: An acquisition module for obtaining historical monitoring data of the drainage pipe network within the target area, where the monitoring data includes: flow data, liquid level monitoring data, water quality parameter data, pipe segment fullness data, and pipe segment load data; A training module for inputting the historical monitoring data into a preset pipe network risk identification model for training to obtain a trained pipe network risk identification model; A risk identification module for obtaining real-time monitoring data of the drainage pipe network within the target area, and inputting the real-time monitoring data into the trained pipe network risk identification model to obtain the pipe network risk identification result of the target area; A health index generation module for generating a health index of the drainage pipe network within the target area based on the real-time monitoring data; An operation and maintenance plan generation module for generating an operation and maintenance plan for the drainage pipe network based on the health index and the pipe network risk identification result.

9. A computer electronic device, characterized in that, Including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it implements the steps of the operation and maintenance method of the drainage pipe network according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the operation and maintenance method of the drainage pipe network according to any one of claims 1-7 are implemented.

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