Hinged well lid bounce risk early warning method for urban drainage shaft

Abstract

The invention discloses a hinged well lid bounce risk early warning method for an urban drainage vertical shaft. The method comprises the steps that firstly, in-well air pressure data is collected in real time and filtered; then, based on an established multi-moment balance dynamic control equation, the rotating angle response of the well lid is solved in real time through the air pressure data; and finally, early warning is triggered according to a comparison result of the predicted corner and a safety threshold. The control equation comprehensively considers a pneumatic excitation torque driven by air pressure, a gravity recovery torque generated by the dead weight of the well lid, and an inertia torque, a nonlinear friction torque and a structural elastic torque which represent the rotation characteristics of the well lid. According to the method, the well lid movement is accurately modeled into the rotating process around the hinge shaft, efficient numerical solution is combined, the defects that an existing fixed threshold value method is poor in adaptability, the calculation amount of a complex model is large, and simplified model prediction is inaccurate are overcome, and high-precision and high-efficiency bounce risk dynamic early warning is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban drainage safety, and particularly relates to a method for predicting the risk of springing of a hinged manhole cover by monitoring the gas pressure in a shaft and using a mechanical model. BACKGROUND

[0002] In the operation process of modern urban municipal drainage pipe networks, especially under the working conditions of encountering heavy rainfall or experiencing dramatic changes in upstream flow, the water flow in the pipe interacts with the air, and it is extremely easy to form a stagnant air mass in local spaces such as inspection shafts. In complex pipe network nodes or areas with a high density of shafts, the dynamic characteristics of this gas-liquid two-phase flow are particularly complex, often showing rapid compression, release and high-frequency oscillation of the air mass, thereby producing severe transient pressure fluctuations in the shaft. When the upward force generated by this internal pressure is large enough, it will cause the sudden opening and even springing of the manhole cover, constituting a serious public safety hazard, which not only directly threatens the safety of vehicles and pedestrians on the road, but also may exacerbate the risk of urban waterlogging in the local area due to the open manhole. Therefore, in-depth research on the mechanism of manhole cover springing and the establishment of an accurate and reliable risk warning mechanism are key technical links to ensure the safe operation of municipal drainage systems.

[0003] However, the existing technical means for dealing with the risk of manhole cover springing have obvious limitations, mainly in the following aspects: Early warning method based on fixed pressure threshold: This is the most common early warning method at present. This method sets a constant critical pressure value as the early warning trigger condition. The core defect of this method is that it oversimplifies the opening process of the manhole cover and completely ignores the influence of many dynamic physical parameters on the opening characteristics, such as the mass and geometric size of the manhole cover, the friction at the hinged structure, and the sealing state between the manhole cover and the well ring, etc. In actual working conditions, these parameters are not constant, and their changes will directly cause the actual opening critical pressure of the manhole cover to change. Therefore, this early warning method using a fixed threshold cannot adapt to the dynamically changing working conditions, resulting in insufficient accuracy of the early warning, with a large number of false negatives and false positives, making it difficult to meet the needs of precise safety control.

[0004] Numerical simulation method based on computational fluid dynamics (CFD): In order to more accurately describe the springing process of the manhole cover, researchers use complex numerical simulation methods such as the fluid-structure interaction (FSI) model to simulate the interaction between the gas-liquid flow in the well and the movement of the manhole cover. Although this method can highly reproduce the physical process in theory, its engineering applicability is extremely poor. Firstly, the model establishment requires accurate geometric parameters, fluid physical parameters and boundary conditions, which are often difficult to accurately obtain and calibrate in the field environment; secondly, complex numerical calculations require a large amount of computing resources and time, which cannot meet the urgent needs of rapid and real-time analysis and early warning of risks in engineering sites.

[0005] Analysis methods based on simplified mechanical models: To balance accuracy and computational efficiency, some simplified mechanical models exist. However, these existing models generally simplify the motion of the manhole cover to the vertical translation of a rigid body. This simplification ignores the most crucial motion characteristic of articulated manhole covers—rotation about the hinge axis. It cannot accurately describe the key dynamic effects introduced by the hinge structure, such as the moment of inertia, nonlinear frictional torque, and gravitational restoring torque, nor does it consider the nonlinear characteristics when the manhole cover contacts the manhole ring. Therefore, this oversimplification of kinematics and dynamics leads to a significant deviation between its predictions of the manhole cover's dynamic response and the actual situation.

[0006] In summary, existing technologies for early warning of manhole cover bounce risk fail to effectively balance accuracy, real-time performance, and model universality. Whether it's an overly simplistic fixed threshold method, an overly complex numerical simulation method, or a simplified translational model that inaccurately describes the physical process, none of these methods can meet the demands of refined and intelligent safety management in current urban drainage systems. Therefore, there is an urgent need to develop a new dynamic early warning method that can fully consider the rotational characteristics of articulated manhole covers and the influence of multiple physical parameters, while ensuring computational efficiency to adapt to on-site engineering applications. Summary of the Invention

[0007] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0008] In view of the problems existing in the above-mentioned articulated manhole cover bounce risk warning technology, this invention is proposed.

[0009] Therefore, the technical problem solved by this invention is to address the difficulty in balancing accuracy, real-time performance, and universality in existing articulated manhole cover bounce risk warning technologies.

[0010] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for early warning of the bounce risk of articulated manhole covers in urban drainage shafts, comprising the following steps: data acquisition and processing step: real-time acquisition of gas pressure signal P(t) in the shaft below the articulated manhole cover, and filtering of the acquired pressure signal; model solving and state prediction step: based on a preset dynamic control equation describing the rotation of the articulated manhole cover about its hinge axis, using the filtered pressure signal P(t) as input, solving the equation in real time to predict the rotation angle θ(t) of the articulated manhole cover at a future time; risk judgment and early warning step: comparing the predicted rotation angle θ(t) with a preset safety angle threshold θsafe The comparison is made when the predicted rotation angle θ(t) reaches or exceeds the safety angle threshold θ. safe When the warning signal is triggered, the dynamic control equation includes at least the aerodynamic excitation torque term generated by the gas pressure, the gravity recovery torque term generated by the gravity of the manhole cover itself, and the rotational inertia torque term characterizing the rotational inertia of the manhole cover.

[0011] As a preferred embodiment of the articulated manhole cover bounce risk warning method for urban drainage vertical shafts described in this invention, the dynamic control equation is: Where J is the moment of inertia of the manhole cover about the hinge axis, C is the damping coefficient, F is the equivalent frictional torque, K is the stiffness coefficient, A is the effective area, m is the total mass of the manhole cover, and R is the radius of gravity.

[0012] As a preferred embodiment of the method for early warning of bouncing risk of articulated manhole covers for urban drainage shafts as described in this invention, the standard deviation σ during filtering is set to 0.5.

[0013] As a preferred embodiment of the method for early warning of bouncing risk of articulated manhole covers for urban drainage shafts as described in this invention, the parameter calibration of the dynamic control equation is completed by applying a counterweight of 0-30kg, and each working condition is repeated 3 times to take the average value.

[0014] As a preferred embodiment of the method for early warning of bouncing risk of articulated manhole covers for urban drainage shafts described in this invention, the rotation angle θ(t) is solved in real time using the fourth-order Runge-Kutta method, with an adaptive step size ranging from 0.001 to 0.01 s.

[0015] As a preferred embodiment of the method for early warning of bouncing risk of articulated manhole covers for urban drainage shafts described in this invention, wherein: the safety angle threshold θ safe Set to 1.5°.

[0016] This invention provides a method for early warning of the bounce risk of articulated manhole covers for urban drainage shafts, which has the following beneficial effects: 1. Significantly Improved Accuracy and Adaptability of Early Warning: Addressing the shortcomings of traditional fixed-threshold early warning methods, which suffer from insufficient reliability due to neglecting dynamic parameter changes, this invention abandons the simplistic assumption of constant critical pressure. By constructing a dynamic model based on the principle of torque balance, incorporating multiple coupled parameters such as rotational inertia, nonlinear frictional torque, and structural stiffness, this invention can accurately quantify the impact of key physical parameters such as manhole cover mass and articulated friction state on its opening characteristics. This model dynamically predicts the manhole cover's motion response through real-time solution, thus transforming the early warning mechanism from static threshold judgment to dynamic process prediction. Therefore, this method can accurately adapt to complex and varied actual working conditions such as different specifications of manhole covers, different degrees of aging, and different sealing states, effectively solving the core problems of poor adaptability and low accuracy of the fixed-threshold method.

[0017] 2. Achieving a balance between engineering practicality and computational accuracy: Addressing the limitations of complex numerical simulation methods, such as high computational resource consumption and difficulty in real-time application in engineering fields, this invention abstracts the rotation process of the manhole cover into a second-order dynamic control system with clear physical meaning and concise mathematical form. This model, while ensuring an accurate description of the manhole cover rotation mechanism, avoids the enormous overhead of complex fluid-structure interaction calculations. By employing the computationally efficient and numerically stable Runge-Kutta method for real-time solution, and combining Gaussian filtering to preprocess sensor data to effectively suppress on-site noise interference, this method can achieve millisecond-level rapid response on ordinary embedded hardware. This organic combination of "accurate mechanism" and "efficient computation" successfully solves the technical bottleneck of complex models being difficult to apply in practice, giving it strong engineering practicality.

[0018] 3. Achieves universality and integration of the early warning mechanism: Addressing the problem of dynamic response prediction deviations caused by simplifying manhole cover motion to translation in existing simplified models, this invention fundamentally corrects the errors in the kinematic description of articulated structures by establishing control equations based on rotational characteristics. This precise physical model description makes the early warning method of this invention highly universal and widely applicable to articulated manhole covers of various specifications and installation conditions in urban drainage systems. Its high reliability and efficiency allow it to be easily integrated as a standard functional module into existing urban flood monitoring platforms or smart municipal management systems, providing unified and efficient bounce risk early warning capabilities for high-risk scenarios such as complex pipe network nodes, and providing strong technical support for ensuring the safe operation of urban drainage systems and public safety. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 The flowchart illustrates the overall method for early warning of bouncing risk of articulated manhole covers for urban drainage shafts provided by this invention.

[0020] Figure 2 This is a schematic diagram of the architecture and field deployment of the manhole cover bounce early warning system provided by the present invention.

[0021] Figure 3 The torque balance analysis diagram of the articulated manhole cover provided by the present invention.

[0022] Figure 4 This is a comparison chart of the model predictions and measured values ​​provided by the present invention. Detailed Implementation

[0023] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0024] Existing articulated manhole cover bounce risk warning technologies generally suffer from the following three key bottlenecks: First, the traditional fixed threshold method treats the critical opening pressure as a constant value, ignoring the dynamic changes in working parameters such as manhole cover mass and articulation friction, resulting in poor adaptability and insufficient reliability of warnings in complex and variable field environments. Second, while complex numerical simulation methods such as computational fluid dynamics (CFD) can accurately describe physical processes, their application is limited because model parameter calibration is difficult and computational resources are consumed in large quantities, making it difficult to meet the real-time requirements of engineering sites. Third, most existing simplified mechanical models simplify the manhole cover motion to rigid body translation, failing to accurately characterize the unique rotational kinematics and dynamics of the articulated structure, leading to significant deviations in the prediction of the manhole cover's dynamic response.

[0025] To overcome the aforementioned technical bottlenecks, this invention provides a novel method and system for early warning of the bounce risk of articulated manhole covers based on a mechanical mechanism model. The technical concept of this invention does not follow traditional threshold judgment or pure numerical simulation, but rather establishes a dynamic prediction model that can accurately characterize the physical movement process of articulated manhole covers while simultaneously achieving high computational efficiency. Specifically, this invention achieves a systematic solution to the above problems through the following technical paths: First, addressing the problem of inaccurate descriptions of manhole cover motion in existing simplified models, this invention no longer simplifies the manhole cover motion to translation. Instead, based on rigid body rotation theory, it establishes a dynamic control equation with torque balance as its core. This equation accurately describes the motion of the manhole cover as a rotational process about its hinge axis, comprehensively incorporating multiple key physical effects such as the aerodynamic excitation torque driving the manhole cover to open, the gravity restoring torque hindering its opening, and the nonlinear frictional damping torque and rotational inertia torque reflecting its motion characteristics. This modeling approach based on rotational torque balance fundamentally solves the deficiency of traditional translational models in describing the rotational characteristics of hinged structures, laying a solid physical foundation for accurately predicting the dynamic response of manhole covers.

[0026] Secondly, addressing the problem of poor adaptability of traditional fixed threshold early warning methods to dynamic working conditions, the dynamic model proposed in this invention is a coupled system incorporating multiple physical parameters such as manhole cover mass, geometric dimensions, hinge friction coefficient, and contact stiffness. These parameters (such as moment of inertia J and equivalent friction torque F) can all be obtained through on-site calibration or calculation, enabling the model to quantify the impact of key parameters on the opening characteristics of the manhole cover under different working conditions. Therefore, this method no longer relies on a fixed critical pressure value, but instead dynamically predicts the motion state (turning angle) of the manhole cover by solving the dynamic equation in real time, thereby achieving adaptive and accurate judgment of the bounce risk for different manhole cover specifications, different aging degrees, and different sealing states.

[0027] Finally, addressing the issue of poor engineering applicability of complex numerical simulation methods, this invention abstracts the rotation process of the manhole cover into a second-order nonlinear ordinary differential equation system described by lumped parameters. While ensuring the accuracy of the core mechanical mechanism, this system boasts a concise mathematical form and can be solved in real-time using efficient numerical algorithms (such as the fourth-order Runge-Kutta method) on low-power embedded computing units. This strategy of combining "mechanism modeling with efficient computation" significantly reduces the demand for computing resources, making continuous prediction and early warning at the second or even millisecond level possible in engineering sites, perfectly solving the problem of the difficulty in applying complex models in practical applications.

[0028] In summary, this invention achieves high-precision and timely early warning of manhole cover bounce risk by constructing a multi-parameter coupled and computationally efficient dynamic model that accurately describes the rotation characteristics of articulated manhole covers, effectively overcoming the shortcomings of existing technologies. The technical solution of this invention will be described in detail below through specific embodiments.

[0029] For details, please refer to Figure 1 This invention provides a method for early warning of the bounce risk of articulated manhole covers for urban drainage shafts, comprising the following steps: Data acquisition and processing steps: Real-time acquisition of gas pressure signal P(t) in the vertical shaft below the hinged manhole cover, and filtering of the acquired pressure signal; Model solving and state prediction steps: Based on a preset dynamic control equation describing the rotation of the articulated manhole cover about its hinge axis, the filtered pressure signal P(t) is used as input to solve the equation in real time to predict the rotation angle θ(t) of the articulated manhole cover in the future. Risk assessment and early warning steps: Compare the predicted rotation angle θ(t) with a preset safety angle threshold θ safe The comparison is performed when the predicted rotation angle θ(t) reaches or exceeds the safety angle threshold θ. safe When this happens, a warning signal is triggered; The dynamic control equations include at least the aerodynamic excitation torque term generated by gas pressure, the gravity restoring torque term generated by the weight of the manhole cover itself, and the rotational inertia torque term characterizing the rotational inertia of the manhole cover.

[0030] It should be noted that, for reference Figure 2 and Figure 3 The system upon which this method relies consists of four main modules: a multi-source sensing module, a data processing module, a model calculation module, and an early warning decision module. The multi-source sensing module includes a high-frequency pressure sensor installed inside the shaft and a high-speed industrial camera facing the manhole cover (the multi-source sensing module includes a high-frequency pressure sensor positioned at the top of the shaft, as close as possible to the bottom of the manhole cover, and a high-speed industrial camera installed outside the shaft with its lens aimed at the manhole cover; the pressure sensor should have waterproof and corrosion-resistant encapsulation, and its signal line interface should be waterproof and sealed; the high-speed industrial camera should be placed in a waterproof protective cover to cope with harsh field environments); the data processing module is responsible for signal filtering and feature extraction; the model calculation module runs the core dynamic model; and the early warning decision module implements tiered early warnings based on the model output. See also... Figure 1 Inside the well is a pressure sensor used to measure the pressure in real time and transmit the pressure data to an integrated terminal. Outside the well is a high-speed camera used to capture the current angle of the manhole cover and transmit the angle data to the integrated terminal. The integrated terminal uses the collected pressure and angle data to automatically optimize and identify the undetermined parameter set [C, F, K] in the control equation using the experimental data inversion method according to this patent. After determining all the coefficients in the control equation, the angle of the manhole cover can be predicted using only the pressure sensor data. Corresponding early warnings are then triggered based on pre-set criteria.

[0031] Furthermore, the dynamic governing equations are: Where J is the moment of inertia of the manhole cover about the hinge axis, C is the damping coefficient, F is the equivalent frictional torque, K is the stiffness coefficient, A is the effective area, m is the total mass of the manhole cover, and R is the radius of gravity.

[0032] It should be noted that this invention establishes a complete dynamic model of the hinged manhole cover based on rigid body rotation theory and the principle of torque balance. Considering the force characteristics of the manhole cover during actual operation, the model takes into account the torque components corresponding to the following six key physical effects: a. Rotational inertia torque term: Where J is the moment of inertia of the manhole cover about the hinge axis, which can be calculated from the geometric dimensions and mass distribution of the manhole cover. This term characterizes the inertial effect of the manhole cover during rotation and is a fundamental term in the dynamic model.

[0033] Where J is the moment of inertia of the manhole cover about the hinge axis (unit: kg·m²). For common circular hinged manhole covers, the moment of inertia can be calculated using the formula J=mR² / 3, where m is the mass of the manhole cover and R is the radius of the manhole cover.

[0034] Compared to translational models that treat manhole covers as point masses or simplified models that ignore structural stiffness, this invention, for the first time, abstracts articulated manhole covers into a second-order dynamic system that considers rotational inertia, nonlinear friction, and contact stiffness. This allows for a precise description of the rotation process around the articulation axis and enables accurate prediction.

[0035] In this model, θ(t) represents the real-time rotation angle (in radians rad) of the manhole cover around the hinge axis at time t. Here, t is the time variable (in seconds s), and the rotation angle θ is a function of time t.

[0036] b. Viscous damping moment term: The damping coefficient C includes various dissipation effects such as air resistance and hinge friction, and its value is determined by inversion from experimental data.

[0037] It should be noted that for lumped parameters such as the damping coefficient C, equivalent frictional torque F, and stiffness coefficient K, which cannot be directly calculated theoretically, their values ​​are determined through an experimental data inversion method. The specific steps are as follows: a. Data acquisition: Apply different counterweights to the manhole cover and conduct an air-filled bouncing test. Simultaneously record the pressure data P(t) inside the well and the true rotation sequence θ(t) of the manhole cover obtained through high-speed video analysis.

[0038] b. Parameter identification: Using the measured P(t) as input and θ(t) as the fitting target, optimization algorithms such as the least squares method are used to automatically identify and optimize the set of undetermined parameters [C, F, K] in the control equation, so as to minimize the sum of squared errors between the predicted rotation angle and the measured rotation angle output by the model.

[0039] c. Assignment and Determination: Using the above method, a set of dynamic parameters for a specific manhole cover can be obtained. For example: moment of inertia J = 8.2 kg·m², damping coefficient C = 12.5 N·m·s / rad, equivalent frictional torque F = 6.8 N·m.

[0040] c. Nonlinear frictional torque term: This model uses a sign function to describe the Coulomb friction characteristics, where F is the equivalent frictional torque (unit: N·m), which characterizes the torque effect generated by the combined Coulomb dry friction at the manhole cover hinge shaft and the friction between the manhole cover and the manhole ring contact surface. This term can accurately characterize the abrupt frictional changes during the opening and closing of the manhole cover.

[0041] It should be noted that, to illustrate the physical meaning of this item, the following example is given: Suppose that the equivalent frictional torque of a manhole cover is determined to be F = 5.0 N·m through parameter identification.

[0042] When the manhole cover is opened, its angular velocity dθ / dt > 0, at which point sgn(dθ / dt) = 1. Therefore, the nonlinear friction torque term is: This torque is positive and opposite to the direction of rotation, thus hindering the opening of the manhole cover.

[0043] When the manhole cover falls back down, its angular velocity dθ / dt < 0, at which point sgn(dθ / dt) = -1. Therefore, the nonlinear friction torque term is: This torque is negative and flows in the opposite direction to the fall, thus hindering the closing of the manhole cover.

[0044] When the manhole cover is stationary, dθ / dt = 0, and at this time sgn(dθ / dt) = 0. Therefore, the nonlinear friction torque term... .

[0045] d. Structural elastic restoring moment term: Among them, the stiffness coefficient K reflects the elastic characteristics of the contact surface between the manhole cover and the manhole ring, which ensures the accuracy of the model under small turning angle conditions.

[0046] e. Pneumatic excitation torque term: Where A is the effective working area, which is determined by the geometry of the manhole cover and the location of the vent, and P(t) is the real-time air pressure data after Gaussian filtering.

[0047] It should be noted that A represents the effective area of ​​pneumatic pressure (unit: m²), which physically means the effective area on the manhole cover where gas pressure is equivalently applied. This area A is determined by the geometry of the manhole cover, the location and size of the vent holes, and its value can be obtained by calculating the projected area of ​​the pressure-bearing surface of the manhole cover and subtracting the area of ​​the vent holes. For a standard circular manhole cover, its effective area can be expressed as... Where R is the radius of the manhole cover. This refers to the total effective pressure relief area of ​​the vent.

[0048] It should be noted that the Gaussian filter is a linear smoothing filter used to suppress high-frequency measurement noise in the pressure signal. Its processing is represented in the discrete system as the original pressure signal P. raw Convolution of (t) with a Gaussian kernel function: Wherein, the Gaussian kernel function G(σ, t-τ)=(1 / (√(2π)σ))×exp(-(t-τ)² / (2σ²)) Here, P filtered Let (t) be the filtered signal, and G(σ, t-τ) be the Gaussian kernel function centered at time τ with a standard deviation of σ. In practical applications, the standard deviation σ can be selected according to the noise characteristics to achieve a balance between denoising effect and signal response speed.

[0049] f. Gravitational restoring moment term: This term reflects the restoring torque generated by the weight of the manhole cover, and is an important stability term of the system. Here, m is the total mass of the manhole cover, and R is the radius of gravity, which mechanically represents the perpendicular distance from the center of mass of the manhole cover to the hinge axis. For a manhole cover of uniform material and regular shape, its center of mass is located at its geometric center; therefore, R is the distance from the geometric center of the manhole cover to the hinge axis.

[0050] Where g is the acceleration due to gravity, which is usually taken as 9.81 m / s².

[0051] Based on the principle of torque balance, the complete control equations of the system are obtained: This invention predicts the future rotation angle of the manhole cover by solving the aforementioned control equations. The specific solution and early warning process is as follows: 1. Model Solving and State Prediction The governing equations described above are second-order ordinary differential equations. To solve them numerically, they are first transformed into a system of first-order equations: Let the angular velocity ω = dθ / dt.

[0052] The original equation then becomes: The system employs the fourth-order Runge-Kutta method to iteratively solve the equations. Its final output is the predicted manhole cover rotation angle θ(t) over future time series. At each time step, the algorithm uses the current pressure P(t) and state [θ,ω] to calculate the state [θ{n+1},ω{n+1}] for the next time step, thus achieving full-process tracking of the manhole cover's trajectory.

[0053] 2. Adaptive time step mechanism To ensure computational efficiency and accuracy, an adaptive strategy is employed for the time step Δt. This mechanism is manifested in the calculation process as follows: The system monitors the rate of change dP / dt of the air pressure signal P(t) in real time. A threshold δ (50 Pa / s) is set for the rate of change. When |dP / dt|>δ, it indicates severe pressure fluctuations, and the system automatically reduces the time step Δt (e.g., to 1 / 10) to improve computational accuracy and capture rapid dynamics; when pressure changes are gradual, Δt is increased to improve computational speed.

[0054] 3. Early Warning Criteria and Triggering Conditions The early warning mechanism sets up dual criteria based on the model's predicted output (i.e., the predicted rotation angle θ and its rate of change ω): Main criterion (threshold criterion): The system continuously compares the predicted rotation angle θ with the preset safety threshold θ. safe (1.5°). When θ ≥ θ safe If a risk of bouncing is detected, an early warning will be triggered immediately.

[0055] Auxiliary Criterion (Sudden Change Criterion): This criterion is used to detect abnormal opening trends of manhole covers. The system calculates the rate of change of the predicted rotation angle in real time, i.e., angular velocity ω, and monitors its changes. A "sudden change" is represented by calculating angular acceleration α = dω / dt. When the absolute value of the angular acceleration |α| exceeds the set acceleration threshold α... threshold When |α|>α threshold This indicates that the manhole cover's movement has changed drastically. Even if the turning angle θ does not exceed the safety threshold at this time, the system will trigger an early warning.

[0056] When any of the above criteria is met, the system immediately activates the corresponding early warning.

[0057] Furthermore, parameter calibration was performed by applying a counterweight of 0-30 kg, with each condition repeated three times and the average value taken.

[0058] Furthermore, the standard deviation of the Gaussian filter is σ=0.5, which effectively filters out high-frequency noise.

[0059] Furthermore, the Runge-Kutta method employs an adaptive step size, ranging from 0.001 to 0.01 s.

[0060] The initial Δt is 0.01s. When the rate of change of the air pressure signal P(t) dP / dt exceeds the threshold δ (50Pa / s), it indicates that the pressure fluctuation is violent. The system automatically reduces the time step Δt (for example, to 1 / 10, i.e. 0.001s) to improve the calculation accuracy and capture rapid dynamics. When the pressure change is gradual, Δt is increased to improve the calculation speed.

[0061] In addition, the early warning system employs a tiered response mechanism, triggering different levels of alarms based on the size of the turning angle.

[0062] Specifically: Yellow alert: Triggered when the predicted turning angle θ satisfies 1.5°≤θ<3.0°, indicating that there is a risk of the manhole cover being opened. The system sends an alert message to the monitoring center and suggests paying close attention.

[0063] Orange Alert: Triggered when the predicted turning angle θ satisfies 3.0°≤θ<5.0°, indicating a high risk of the manhole cover opening. The system immediately activates the on-site audible and visual alarm and sends an emergency notification to the inspection personnel.

[0064] Red Alert: Triggered when the predicted turning angle θ meets θ≥5.0°, indicating that the manhole cover is about to bounce severely. At the same time as triggering all actions of the orange alert, the system can link with related systems (such as traffic lights) for emergency handling.

[0065] In addition, if the auxiliary criterion (sudden change in angular acceleration) is triggered first, the system will directly activate an early warning of at least orange level.

[0066] See Figure 4 EXP represents the angle at which manhole covers of different weights bounced under different pressures (horizontal axis) in the experimental test. ODE represents the predicted angle at which manhole covers of different weights bounced under different pressures according to this patent.

[0067] The above method was used to monitor manholes in a city's drainage network. The system successfully issued an early warning 50-200 ms before the manhole cover bounced, with an accuracy rate of 95.3%, effectively preventing several potential safety accidents. As shown in the table below, the model-predicted turning angle and the measured turning angle have good consistency, with a correlation coefficient of 0.974.

[0068] Table 1: 25kg Pressure Measured angle Calculated angle 675.05645 0.00717 0.00717 677.72111 0.00867 0.00828 680.03818 0.00977 0.00971 682.08217 0.01084 0.01146 683.58002 0.01393 0.01356 684.14384 0.01639 0.01599 684.14396 0.01912 0.01873 679.80278 0.02167 0.02174 674.52559 0.02458 0.02499 668.63902 0.02795 0.02841 662.46962 0.03141 0.03195 656.08059 0.03459 0.03553 649.22 0.03787 0.03911 641.47063 0.04096 0.0426 632.45764 0.04396 0.04594 621.96753 0.04668 0.04906 609.93646 0.04896 0.05191 596.41622 0.05105 0.0544 581.61222 0.05295 0.05648 565.90034 0.0545 0.05809 549.72102 0.05559 0.05918 533.4271 0.05627 0.05969 517.26263 0.05654 0.05955 501.47226 0.05663 0.05862 486.41788 0.05645 0.05687 472.58594 0.05572 0.05429 460.47756 0.05491 0.05091 450.42157 0.054 0.04679 442.44546 0.05259 0.04203 436.32863 0.05086 0.0368 431.79378 0.04887 0.03128 428.61728 0.04641 0.02571 426.58589 0.04287 0.02036 425.26589 0.02156 0.01555 423.74289 0.00926 0.01159 Table 2: 30kg Pressure Measured angle Calculated angle 750.6956 0.00959 0.00959 753.19455 0.01018 0.01009 755.60808 0.01069 0.01064 758.0228 0.01131 0.01126 760.41355 0.01284 0.01196 762.70557 0.01401 0.01277 764.87122 0.01517 0.01369 766.95632 0.01652 0.01474 769.01415 0.01805 0.01592 770.99686 0.01966 0.01726 772.73079 0.02128 0.01875 774.0232 0.02289 0.0204 774.7969 0.02478 0.02223 775.10358 0.02666 0.02421 775.04424 0.02837 0.02636 774.69975 0.03025 0.02866 774.11288 0.03258 0.03108 773.29977 0.03492 0.03362 772.25407 0.03716 0.03625 770.90356 0.03931 0.03894 769.0548 0.04128 0.04165 766.44108 0.04352 0.04436 762.87828 0.04549 0.04702 758.41814 0.04693 0.04959 753.37937 0.04836 0.05203 748.1972 0.05015 0.05428 743.16325 0.05168 0.0563 738.2774 0.0532 0.05803 733.32913 0.05445 0.05944 728.0672 0.05562 0.06048 722.31925 0.05651 0.0611 716.09125 0.05696 0.06129 709.60468 0.05678 0.06091 703.15642 0.05642 0.05991 696.90282 0.05508 0.05828 690.8076 0.05356 0.05606 684.77288 0.05159 0.05328 678.77753 0.0497 0.04998 672.88915 0.04738 0.04624 667.177 0.04496 0.04213 661.63775 0.04164 0.03773 656.24419 0.0385 0.03316 651.08201 0.03509 0.02852 646.39694 0.03124 0.02391 642.43539 0.02639 0.01946 639.22565 0.02128 0.01529 636.5894 0.01589 0.01149 634.40934 0.01341 0.00819 632.83899 0.01094 0.00548 632.18559 0.00786 0.00344 Table 3: 35kg Pressure Measured angle Calculated angle 898.03833 0.01124 0.01124 900.63549 0.01163 0.01161 903.51574 0.0121 0.01208 906.51138 0.01253 0.01264 909.3229 0.01351 0.01332 911.74228 0.01468 0.01412 913.69918 0.01567 0.01506 915.16457 0.01697 0.01613 916.11559 0.01832 0.01735 916.60337 0.01975 0.01871 916.78766 0.02137 0.02021 916.85295 0.02298 0.02184 916.88012 0.02469 0.02361 916.79746 0.02639 0.0255 916.42255 0.02783 0.02749 915.54181 0.02989 0.02958 914.00336 0.03196 0.03174 911.79854 0.03393 0.03395 909.07043 0.03599 0.03619 906.01714 0.03805 0.03843 902.75504 0.04003 0.04065 899.21642 0.04209 0.0428 895.1854 0.04361 0.04485 890.47402 0.04523 0.04679 885.11241 0.04675 0.04857 879.34264 0.04809 0.05018 873.41917 0.04926 0.05159 867.40602 0.05069 0.05277 861.198 0.05203 0.0537 854.71289 0.05338 0.05436 848.01169 0.05409 0.05472 841.20154 0.05463 0.05478 834.32238 0.05454 0.05437 827.37504 0.05409 0.05342 820.41338 0.05311 0.05196 813.52325 0.05177 0.05002 806.77441 0.04988 0.04765 800.26144 0.048 0.0449 794.17952 0.04549 0.04185 788.70929 0.04281 0.03858 783.78943 0.03922 0.03517 779.08302 0.03527 0.03171 774.30296 0.03142 0.02829 769.55515 0.02756 0.025 765.31396 0.02325 0.02192 762.04118 0.01858 0.01914 759.87952 0.01634 0.01672 758.6856 0.01471 0.01475 758.26677 0.0132 0.01326 Table 4: 40kg Pressure Measured angle Calculated angle 1055.23985 0.0055 0.0055 1057.42885 0.00641 0.00615 1059.323 0.00723 0.00694 1060.95282 0.00817 0.00788 1062.23617 0.0097 0.00896 1063.09394 0.01131 0.01017 1063.58433 0.01302 0.01152 1063.88317 0.01455 0.01299 1064.14552 0.01616 0.01458 1064.39374 0.01778 0.01628 1064.49549 0.01921 0.01809 1064.24015 0.02065 0.01998 1063.46937 0.022 0.02195 1062.15432 0.02352 0.02396 1060.39385 0.02496 0.026 1058.38082 0.02621 0.02805 1056.33231 0.02756 0.03008 1054.33862 0.02891 0.03208 1052.23479 0.03043 0.034 1049.65035 0.03223 0.03584 1046.22801 0.03402 0.03757 1041.82783 0.03563 0.03916 1036.58945 0.03725 0.04059 1030.85493 0.03886 0.04183 1024.9847 0.04039 0.04288 1019.16933 0.04173 0.04371 1013.36865 0.04276 0.04431 1007.43257 0.04348 0.04467 1001.24976 0.0442 0.04478 994.78349 0.04491 0.04454 988.04535 0.04509 0.0439 981.10733 0.04509 0.04286 974.10197 0.04482 0.04143 967.16064 0.04424 0.03961 960.36591 0.04352 0.03744 953.77549 0.04263 0.03494 947.41699 0.04137 0.03216 941.24692 0.03949 0.02915 935.20245 0.03743 0.02598 929.33753 0.03518 0.02273 923.82895 0.03294 0.01947 918.84194 0.02881 0.0163 914.44731 0.02379 0.0133 910.66734 0.01885 0.01057 907.52355 0.01463 0.00819 905.03317 0.01041 0.00627 903.21256 0.00765 0.00488 902.11425 0.00415 0.0041 901.84679 0.00236 0.00399 Table 5: 45kg Pressure Measured angle Calculated angle 1199.65005 0.00927 0.00927 1201.01107 0.01006 0.00985 1202.27131 0.01079 0.0105 1202.95421 0.01149 0.01124 1202.79927 0.01311 0.01208 1201.96957 0.01437 0.01302 1200.88412 0.01562 0.01408 1199.84432 0.01697 0.01526 1198.81218 0.01859 0.01656 1197.5233 0.02011 0.01797 1195.76151 0.02164 0.0195 1193.46867 0.02307 0.02114 1190.61561 0.02451 0.02287 1187.12009 0.02586 0.02468 1182.97164 0.02729 0.02654 1178.3446 0.02837 0.02843 1173.48039 0.02953 0.03031 1168.49679 0.03079 0.03216 1163.36732 0.0324 0.03395 1158.02849 0.03402 0.03562 1152.44491 0.03554 0.03717 1146.61058 0.0368 0.03854 1140.55816 0.03805 0.03971 1134.35279 0.03922 0.04065 1128.03115 0.0403 0.04136 1121.56944 0.04083 0.0418 1114.95578 0.04083 0.04197 1108.25295 0.04066 0.04181 1101.51899 0.04012 0.04121 1094.70959 0.03877 0.04016 1087.72505 0.03716 0.03871 1080.52392 0.03527 0.03688 1073.15717 0.0333 0.03473 1065.74237 0.03124 0.03229 1058.44242 0.029 0.02963 1051.4416 0.02639 0.02681 1044.91133 0.02415 0.02389 1039.01745 0.02146 0.02093 1033.92141 0.01858 0.01802 1029.72992 0.01526 0.0152 1026.39268 0.01203 0.01254 1023.68549 0.00889 0.0101 1021.39348 0.00733 0.00794 1019.56857 0.00566 0.0061 1018.53588 0.00424 0.00462 In summary, this patent achieves accurate prediction of manhole cover bounce risk by establishing a dynamic model based on the principle of torque balance and fusing it with real-time monitoring data. The model includes multiple parameters such as rotational inertia, nonlinear friction, and structural stiffness, and uses the Runge-Kutta method to solve the control equations in real time. Gaussian filtering of sensor data effectively suppresses on-site noise interference. Based on the comparison between the predicted rotation angle and a safety threshold, tiered early warning is achieved. This method accurately describes the dynamic response of manhole covers while ensuring computational efficiency. It can be integrated into urban flood monitoring platforms or smart municipal management systems, providing highly reliable and efficient early warning of bounce risk for scenarios such as articulated manhole covers of different specifications and complex pipe network nodes, effectively supporting the safe operation of urban drainage systems and public safety.

[0069] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for early warning of the bounce risk of articulated manhole covers used in urban drainage shafts, characterized in that, Includes the following steps: Data acquisition and processing steps: Real-time acquisition of the gas pressure signal P(t) in the vertical shaft below the hinged manhole cover, and filtering of the acquired pressure signal; Model solving and state prediction steps: Based on a preset dynamic control equation describing the rotation of the articulated manhole cover about its hinge axis, the filtered pressure signal P(t) is used as input to solve the equation in real time to predict the rotation angle θ(t) of the articulated manhole cover in the future. Risk assessment and early warning steps: Compare the predicted rotation angle θ(t) with a preset safety angle threshold θ safe The comparison is made when the predicted rotation angle θ(t) reaches or exceeds the safety angle threshold θ. safe When this happens, a warning signal is triggered; The dynamic control equations include at least a pneumatic excitation torque term generated by gas pressure, a gravity restoring torque term generated by the weight of the manhole cover itself, and a rotational inertia torque term characterizing the rotational inertia of the manhole cover.

2. The method for early warning of bouncing risk of articulated manhole covers for urban drainage shafts according to claim 1, characterized in that, The governing equations for the dynamics are: Where J is the moment of inertia of the manhole cover about the hinge axis, C is the damping coefficient, F is the equivalent frictional torque, K is the stiffness coefficient, A is the effective area, m is the total mass of the manhole cover, and R is the radius of gravity.

3. The method for early warning of bouncing risk of articulated manhole covers for urban drainage shafts according to claim 2, characterized in that: The standard deviation σ during filtering is set to 0.

5.

4. The method for early warning of bouncing risk of articulated manhole covers for urban drainage shafts according to claim 3, characterized in that: When setting the dynamic control equations, the parameters were calibrated by applying a counterweight of 0-30 kg, and each working condition was repeated 3 times and the average value was taken.

5. The method for early warning of bouncing risk of articulated manhole covers for urban drainage shafts according to claim 4, characterized in that: The rotation angle θ(t) is solved in real time using the fourth-order Runge-Kutta method with an adaptive step size ranging from 0.001 to 0.01 s.

6. The method for early warning of bouncing risk of articulated manhole covers for urban drainage shafts according to claim 5, characterized in that: The safety angle threshold θ safe Set to 1.5°.

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