Dynamic monitoring and early warning method, system and terminal for sedimentation of sewage and rain pipe network

Abstract

The invention relates to a dynamic monitoring and early warning method, system and terminal for sedimentation of a sewage and rain pipe network, and relates to the technical field of urban drainage pipe network monitoring, and the method comprises the steps: actively generating a pressure transient wave at a monitoring point of the pipe network, and enabling the pressure transient wave to be transmitted in the pipe network; echo signals generated when the pressure transient waves are propagated in the pipe network are collected, and a pressure time sequence containing geometric structure information in the pipe network is formed; performing reverse reconstruction calculation on the pressure time sequence based on a preset pipe network hydraulic physical model to obtain a continuous deposition profile of a downstream pipe section of the monitoring point; and according to the continuous siltation profile, the siltation state of the pipe network is evaluated, and an early warning signal is generated when the siltation state meets a preset condition. According to the application, the problem that in the prior art, on the premise that normal operation of the pipe network is not interrupted, the effects of remote, automatic and accurate positioning, quantitative evaluation and dynamic early warning on pipe network siltation are difficult to achieve are solved.

Description

Technical Field

[0001] This invention relates to the field of urban drainage network monitoring technology, and in particular to a method, system and terminal for dynamic monitoring and early warning of siltation in sewage and rainwater pipe networks. Background Technology

[0002] As a critical infrastructure ensuring the normal operation of a city, the health of urban sewage and stormwater drainage networks directly affects the city's water environment safety and flood control and drainage capabilities. During long-term operation, due to various factors such as pipeline design, hydraulic conditions, and the nature of inflow sewage, solid particles such as silt and debris accumulate within the pipes, gradually forming sediment. This sedimentation not only encroaches on the effective cross-sectional area of ​​the pipeline, reducing its water conveyance capacity, but in severe cases, it can lead to pipe blockage, causing sewage overflows, urban flooding, and other problems, posing a serious threat to public life and the ecological environment. Therefore, timely and accurate monitoring of the sedimentation status of the pipeline network, and based on this, scientific early warning and maintenance, are crucial for ensuring the safe and efficient operation of the network.

[0003] Currently, the detection of siltation within pipe networks generally relies on techniques that involve direct entry into the pipes for inspection, with closed-circuit television (CCTV) and sonar detection being the most representative methods. However, these methods have significant limitations. Their implementation typically requires interrupting or significantly reducing the normal water transport function of the pipe network and often necessitates pre-flushing of the pipes to ensure the normal operation and effective observation of the detection equipment (such as cameras or sonar probes). This not only interferes with the daily operation of the pipe network but also increases additional operating costs and time. Furthermore, these methods require specialized technicians to carry expensive and bulky equipment to the site, resulting in high labor intensity and implementation costs, making it difficult to achieve routine, high-frequency coverage monitoring of the widespread urban pipe network. More importantly, this type of detection is essentially a low-frequency, "snapshot" survey, unable to continuously capture the dynamic evolution of siltation, especially failing to reflect the rapid formation and scouring processes of siltation during hydrological events such as rainfall, and lacking the ability to predict siltation development trends.

[0004] To overcome the shortcomings of the aforementioned methods, the industry has also attempted indirect monitoring methods based on fixed-point sensors, such as installing level gauges and flow meters in inspection wells to infer the water flow status of the pipeline by analyzing changes in hydraulic parameters. Although this type of method can achieve online monitoring, it still provides information on the hydraulic parameters of the monitoring point itself, rather than information on the internal geometry of the pipeline. When siltation occurs at any location between two monitoring points, these sensors can only vaguely reflect the decrease in the water transport capacity of the pipeline section, and cannot accurately locate the specific location of the siltation, reveal its spatial distribution pattern, or quantify its severity. Only when the siltation develops to a considerable degree, significantly obstructing or impeding the water flow, can its impact be detected by the sensors, by which time the optimal time for preventative dredging has often been missed.

[0005] In summary, existing technologies still have significant gaps in achieving efficient, accurate, low-cost, all-weather monitoring of sewage and stormwater pipe networks with dynamic early warning capabilities. Therefore, there is an urgent need to develop a new method for dynamic monitoring of siltation that can achieve remote, automated, precise positioning, and quantification without interfering with the normal operation of the pipe network, in order to meet the needs of modern smart cities for refined management of drainage systems. Summary of the Invention

[0006] The purpose of this application is to provide a method, system, and terminal for dynamic monitoring and early warning of siltation in sewage and rainwater pipe networks. This aims to address the significant technological gaps in the existing technology regarding efficient, accurate, low-cost, all-weather monitoring of sewage and rainwater pipe networks with dynamic early warning capabilities.

[0007] This application provides a method for dynamic monitoring and early warning of siltation in sewage and stormwater pipe networks, comprising the following steps: S1. At the monitoring point of the pipeline network, a pressure transient wave is actively generated and propagated within the pipeline network. S2. Collect the echo signal generated by the pressure transient wave propagating in the pipeline network to form a pressure time series containing the internal geometric structure information of the pipeline network; S3. Based on the preset pipeline hydraulic physical model, the pressure time series is reconstructed in reverse to obtain the continuous siltation profile of the downstream pipe section of the monitoring point. S4. Assess the siltation status of the pipeline network based on the continuous siltation profile, and generate an early warning signal when the siltation status meets preset conditions.

[0008] Preferably, prior to the step of actively generating the pressure transient wave, the method further includes: The background hydraulic noise signal of the monitoring point is passively acquired without external excitation; The characteristics of the background hydraulic noise signal are analyzed to assess the current hydraulic environment state of the pipe network; wherein, the analysis includes calculating the root mean square value P of the background hydraulic noise signal. rms : In the formula: P rms The root mean square value of the background hydraulic noise signal; P noise (t i ) represents the time at sampling time t i Instantaneous pressure values ​​of the acquired background hydraulic noise signal; N is the total number of sampling points for the background hydraulic noise signal; The step of actively generating pressure transient waves involves adaptively adjusting the waveform parameters of the pressure transient waves based on the hydraulic environment conditions.

[0009] Preferably, the step of adaptively adjusting the waveform parameters of the pressure transient wave includes: When the hydraulic environment is assessed as low-interference, a single-pulse waveform with a narrow time width and concentrated energy is generated for high-resolution detection. When the hydraulic environment is assessed as high interference, a long-duration, high-energy encoded pulse waveform is generated for high signal-to-noise ratio detection.

[0010] Preferably, the encoded pulse waveform is a linear frequency modulated signal; and before the step of performing the reverse reconstruction calculation, the method further includes a step of performing pulse compression processing on the acquired echo signal, which is achieved by the following formula: In the formula: P comp (t) represents the signal obtained after pulse compression processing; P total (t) represents the original echo signal acquired; The time-reversed conjugate of the linear frequency modulated signal is used as a matched filter. * represents the convolution operator.

[0011] Preferably, the step of performing the reverse reconstruction calculation includes: Discretize the pipe segment along its length and establish the relationship between its effective cross-sectional area and hydraulic resistance. An optimization problem is constructed, using the effective cross-sectional area of ​​the pipe segment as the optimization variable and the error between the simulated pressure response calculated forward based on the physical model and the acquired echo signal as the objective function. The objective function is J(A). w (x) is as follows: In the formula: J(A w (x) is the objective function; Indicator for optimization variable A w Find the minimum value of (x); A w (x) is the effective cross-sectional area of ​​the pipe segment at position x, which is used as the optimization variable; P sim (t,A w (x) is based on the physical model and the given profile A w (x) The simulated pressure response calculated in the forward direction; P meas (t) represents the actual acquired and preprocessed echo signal; T obs This represents the total observation time. By iteratively solving the optimization problem, the optimal solution of the effective cross-sectional area profile is obtained, and the continuous sedimentation profile is determined accordingly.

[0012] Preferably, after the step of assessing the siltation status of the pipeline network, the method further includes: All steps of the method are described periodically to obtain a series of continuous siltation profiles that evolve over time; The deposition rate distribution of the pipe section is calculated by comparing the continuous deposition profiles at different times.

[0013] Preferably, the step of generating the warning signal includes: The sediment thickness, sediment volume, or sediment rate distribution represented by the continuous sediment profile are compared with preset multi-level alarm thresholds. When any indicator meets its corresponding threshold condition, the corresponding level of warning is triggered.

[0014] Preferably, the echo signal also includes echoes from known structural feature points in the pipeline network; the reverse reconstruction calculation step further utilizes the echo information from the known structural feature points to calibrate the effective propagation speed of the pressure transient wave in the pipe in real time.

[0015] A dynamic monitoring and early warning system for sewage and rainwater pipe network siltation includes: The waveform generation module is used to actively generate a pressure transient wave at the monitoring point of the pipeline network and allow it to propagate within the pipeline network; the signal acquisition module is used to acquire the echo signal generated by the pressure transient wave propagating in the pipeline network, forming a pressure time series containing information on the internal geometric structure of the pipeline network. The profile reconstruction module is used to perform reverse reconstruction calculations on the pressure time series based on a preset pipeline hydraulic physical model, so as to obtain a continuous siltation profile of the downstream pipe section of the monitoring point. The assessment and early warning module is used to assess the siltation status of the pipeline network based on the continuous siltation profile, and generate an early warning signal when the siltation status meets preset conditions.

[0016] This application also provides a dynamic monitoring and early warning terminal for sewage and rainwater pipe network siltation, including: a processor and a memory, the memory storing a computer program executable by the processor, and the computer program executing the above method when executed by the processor.

[0017] In summary, this application includes at least one of the following beneficial technical effects: 1. This invention achieves continuous and quantitative profile monitoring of siltation status in downstream pipe sections by actively generating pressure transient waves and performing inverse reconstruction calculations based on physical models of the echo signals. Compared to traditional detection methods that rely on single-point level or flow velocity sensors or require interruption of pipeline operation, this invention can obtain a detailed siltation profile distributed along the pipeline length in one go without affecting the normal operation of the pipeline network. It accurately reveals the location, extent, and severity of siltation, providing unprecedented spatial resolution and information dimensions for refined assessment and targeted dredging of the pipeline network. 2. This invention passively senses the hydraulic environment within the pipe before excitation and adaptively adjusts the waveform parameters of the pressure transient wave, enabling the monitoring method to intelligently adapt to different hydraulic conditions. When the water flow is stable and interference is low, the system employs high-resolution single-pulse detection; however, during rainfall or periods of rapid flow increase and high interference, it automatically switches to transmitting high-energy coded pulses combined with subsequent pulse compression processing. This intelligent environmental adaptability ensures the acquisition of high-quality echo signals in various complex and variable practical application scenarios, thereby significantly improving the reliability of the monitoring results and the versatility of the system. 3. This invention employs an inverse reconstruction method by constructing an optimization problem with the objective function of the simulated pressure response versus the error of the measured signal. Furthermore, it uses echoes from known structural feature points to calibrate wave velocities in real time, ensuring a solid physical foundation and data constraints for the sediment profile reconstruction process. This inverse reconstruction method, based on one-dimensional transient flow theory, eliminates reliance on empirical models and assumed parameters. Simultaneously, the real-time calibration of wave velocities eliminates uncertainties caused by medium variations, resulting in higher accuracy and physical realism in the final quantitative results for sediment thickness, volume, etc. Attached Figure Description

[0018] Figure 1 This is a flowchart of a dynamic monitoring and early warning method for siltation in sewage and rainwater pipe networks according to an embodiment of this application; Figure 2 This is an architecture diagram of a dynamic monitoring and early warning system for sewage and rainwater pipe network siltation according to an embodiment of this application. Detailed Implementation

[0019] The following is in conjunction with the appendix Figure 1 This application will be described in further detail below.

[0020] Example: Reference Figure 1 This invention provides a method for dynamic monitoring and early warning of siltation in sewage and rainwater pipe networks, comprising the following steps: S1. At the monitoring point of the pipeline network, actively generate a pressure transient wave and allow it to propagate within the pipeline network; In this embodiment, a pressure transient wave is actively generated at the monitoring point of the pipeline network and propagated within the network. Specifically, this is implemented by injecting a well-defined and controlled detection signal into the hydraulic waveguide system of the pipe section under test, providing a high-quality signal source for subsequent echo signal acquisition (step S2) and profile reverse reconstruction (step S3).

[0021] Specifically, to ensure the effectiveness and reliability of the method under various complex real-world conditions, this step does not employ a fixed excitation method, but rather introduces an adaptive excitation mechanism with environmental awareness. This mechanism enables the invention to intelligently adapt to dynamically changing hydraulic conditions within the pipeline network.

[0022] Specifically, before actively generating pressure transient waves, the method of the present invention preferably includes a preliminary passive sensing and assessment step of the pipe network hydraulic environment. The monitoring terminal deployed at the monitoring point first enters a "passive listening" mode. In this mode, its built-in high-sensitivity, wideband dynamic pressure sensor continuously samples the natural pressure fluctuations of the water in the pipe for a preset time window without applying any external excitation, in order to obtain the original background hydraulic noise signal P. noise (t).

[0023] After acquiring the background hydraulic noise signal, the edge computing unit built into the monitoring terminal or a remote data processing center will analyze its key characteristics to objectively assess the current hydraulic environment status of the pipe network. A core quantitative indicator is the root mean square value P of the background hydraulic noise signal. rms This is used to assess the overall energy intensity of water flow disturbance within the pipe. The calculation formula is as follows: In the formula: P rms The root mean square value of the background hydraulic noise signal directly reflects the strength of the background noise energy. P noise (t i ) represents the time at sampling time t i Instantaneous pressure values ​​of the acquired background hydraulic noise signal; N represents the total number of sampling points for the background hydraulic noise signal within a preset time window.

[0024] In addition to the root mean square value, the analysis may also include spectral analysis of the background hydraulic noise signal to identify the frequency bands where the main interference energy is located and its distribution characteristics. Based on one or more of the above analysis indicators, the system automatically and objectively classifies the current hydraulic environment state into several preset categories, such as "low interference" state and "high interference" state.

[0025] After assessing the hydraulic environment, this step adaptively selects and executes the optimal pressure transient wave generation strategy based on the assessment results. When the hydraulic environment is assessed as "low interference," it indicates low background noise and a high signal-to-noise ratio within the pipe. In this case, the system aims to achieve the highest spatial resolution by driving the exciter to generate a single-pulse waveform with a narrow time width and concentrated energy. Preferably, this waveform can be an approximate Gaussian pulse or a narrow square wave pulse. Its good impact characteristics in the time domain are beneficial for accurately distinguishing multiple spatially adjacent reflecting interfaces in the subsequent echo signal, thereby achieving high-precision sedimentation profile reconstruction.

[0026] Conversely, when the hydraulic environment is assessed as "high interference," such as during rainfall or upstream pumping station activity, the background noise energy is high and the signal-to-noise ratio is low. In this case, if a single pulse with limited energy is still used, its echo signal is easily overwhelmed by noise. Therefore, the system automatically switches to a high signal-to-noise ratio detection mode, driving the exciter to generate a long-duration, high-energy coded pulse waveform. In this embodiment, a preferred coded pulse waveform is a linear frequency modulated (Chirp) signal, whose mathematical expression can be summarized as: In the formula: s chirp (t) represents the waveform of the transmitted linear frequency modulated signal; A p The peak amplitude of the signal; rect(·) is a rectangular function that defines the rectangular coordinates of a signal over a time period of T. p Valid within the window; T p The duration of the encoded pulse; fc is the center frequency of the signal; μ is the frequency modulation rate, i.e., the frequency modulation rate.

[0027] The core advantage of this coded pulse waveform lies in its expansion of the transmitted energy over time, thereby significantly increasing the total energy of the transmitted signal without substantially increasing the instantaneous peak power of the exciter. This energy dispersion effectively combats short-duration, intense noise interference, ensuring a identifiable echo signal is obtained in subsequent signal acquisition (step S2). Simultaneously, this coding characteristic lays the foundation for subsequent pulse compression processing to restore temporal resolution and improve the signal-to-noise ratio.

[0028] After determining the waveform parameters of the pressure transient wave, the pressure transient wave actuator integrated within the monitoring terminal is driven to generate the waveform. In a preferred embodiment, the actuator can be an electrically controlled miniature piston that generates the desired pressure wave by precisely controlling its reciprocating motion in the water. In another embodiment, it can be a high-speed switching valve that creates pressure disturbances by rapidly opening and closing a bypass connected to the main pipeline. Regardless of the specific form, the actuator, under the command of the control system, generates a controlled pressure transient wave in the water within the pipeline network that matches the selected strategy. This wave then propagates downstream along the pipeline, thus completing all operations in step S1.

[0029] S2. Collect the echo signal generated when the pressure transient wave propagates in the pipeline network to form a pressure time series containing information on the internal geometric structure of the pipeline network; In this embodiment, the echo signal generated by the pressure transient wave propagating in the pipeline network is collected to form a pressure time series containing information on the internal geometric structure of the pipeline network. The purpose is to capture and purify all reflected signals carrying information on the internal structure of the pipeline with high fidelity, so as to provide a reliable data foundation for the accurate physical inversion in the subsequent step S3.

[0030] After the pressure transient wave actively generated in step S1 propagates along the pipeline, the signal acquisition and preprocessing function of the monitoring terminal is activated. Specifically, this step may include the following steps: First, the entire sequence of echo signals is acquired. A dynamic pressure sensor, physically located or adjacent to the pressure transient wave exciter in step S1, is triggered simultaneously with or shortly after the exciter emits the pressure transient wave, initiating continuous data acquisition. In a preferred embodiment, the sampling rate of the dynamic pressure sensor is set to be significantly higher than twice the highest frequency component of the pressure transient wave to fully satisfy the Nyquist sampling theorem, thereby recording the complete pressure response process without distortion. The signal captured by this sensor is the original pressure time series P. comp(t). This sequence is a complex superposition of signals, containing not only the initial pressure transient waveform but, more importantly, a series of reflected echoes generated when this waveform encounters any hydraulic impedance discontinuities during its downstream propagation. These discontinuities can be caused by various factors, such as the start and end points of sediment deposits, changes in sediment morphology, cracks or damage in the pipe itself, and variations in pipe diameter or branch pipe connections. Therefore, P comp (t) contains rich information about the internal geometric structure of the pipe section to be tested.

[0031] After obtaining the original pressure time series P comp Following step (t), in order to extract useful echo information and suppress interference, the method further includes a signal preprocessing step. The first step in preprocessing is typically digital bandpass filtering. By applying a bandpass filter whose center frequency and bandwidth match the signal transmitted in step S1, noise components unrelated to the frequency band of the probed signal can be effectively filtered out, such as low-frequency water flow fluctuations caused by a water pump or turbulence, as well as high-frequency electronic noise introduced by the sensor and circuitry itself.

[0032] Subsequently, a crucial preprocessing step is pulse compression. It's important to note that this step is not performed in all cases, but rather as a conditional process linked to the adaptive excitation strategy in step S1. Specifically, this pulse compression process is performed only when a linear frequency modulation (Chirp) coded pulse waveform is transmitted in step S1 due to an environmental assessment of "high interference." The core of this process involves convolving the acquired and filtered echo signal with the matched filter of the transmitted Chirp signal. The calculation formula is as follows: In the formula: P comp (t) represents the high signal-to-noise ratio and high resolution signal obtained after pulse compression processing; P total (t) represents the echo signal after bandpass filtering; For linear frequency modulated signal s chirp The time-reversed conjugate of (t) constitutes the basis of s in signal processing theory. chirp The optimal matched filter for (t); * represents the convolution operator.

[0033] The fundamental purpose of pulse compression processing is to refocus the energy of the temporally broadened coded pulse signal into a single pulse peak with an extremely narrow time width through coherent superposition. This is equivalent to transmitting an ideal single pulse with extremely high power but a very short duration, thereby significantly improving the effective signal-to-noise ratio and temporal resolution of the signal without increasing the peak power load on the transmitter exciter. This processing is a key technique to ensure that weak echo signals can be reliably detected from strong noise backgrounds in high-interference environments.

[0034] After one or more of the above preprocessing steps, a clear and effective measurement signal that accurately reflects the impulse response of the pipeline system is finally obtained, denoted as P in this invention. meas (t). The P meas (t) The time series has been largely purified of noise and the signal-to-noise ratio has been improved through pulse compression where necessary. It faithfully displays the echo sequence from different downstream reflection points on the time axis. This signal will serve as the direct and sole data input for the reverse reconstruction calculation in the core step S3 of the method of this invention, and its quality directly determines the accuracy and reliability of the final sedimentation profile result.

[0035] S3. Based on the preset hydraulic physical model of the pipeline network, the pressure time series is reconstructed in reverse to obtain the continuous sedimentation profile of the downstream pipe section of the monitoring point. In this embodiment, the core technology of the invention lies in the reverse reconstruction calculation of the pressure time series based on a preset pipeline hydraulic physical model to obtain a continuous sedimentation profile of the downstream pipe section of the monitoring point. This step aims to achieve the key transformation from a one-dimensional time-domain signal to a one-dimensional spatial-domain physical profile, which converts the effective measurement signal P collected and processed in step S2 into a one-dimensional spatial-domain physical profile. meas (t) is decoded into an intuitive and quantitative spatial description of the siltation state inside the pipeline network.

[0036] The physical basis for this step lies in the fact that the pipe section can be considered a one-dimensional hydraulic waveguide. When a pressure transient wave propagates within it, its behavior is controlled by the hydraulic impedance at various points along the pipe. Any effective cross-sectional area A of the pipe caused by siltation... w Changes in (x) will lead to corresponding changes in the hydraulic impedance at that point, thus generating characteristic echoes at the discontinuous interface. The fundamental task of this step is to deduce the unknown pipe geometry that causes these echoes by analyzing their arrival time and shape.

[0037] Specifically, this reverse reconstruction calculation is methodologically structured as a mathematical-physical inversion problem and solved through an optimization problem. Its goal is to find a pipe cross-sectional area profile that best interprets actual measurement data.

[0038] First, this step requires establishing a forward physical model capable of describing the propagation behavior of pressure transient waves in the pipe. In a preferred embodiment, this model is a set of equations based on one-dimensional transient flow theory, such as the one-dimensional Saint-Venant equations. This model can be determined based on a given pipe geometry profile (i.e., an assumed A). w Given (x) and the initial pressure excitation conditions, the simulated pressure response P that should be observable at the monitoring point is calculated numerically (e.g., using the method of characteristics). sim (t,A w (x)).

[0039] Subsequently, a profile A based on the effective cross-sectional area of ​​the pipe segment is constructed. w (x) is the optimization variable, and the objective function is the error between the simulated pressure response and the actual measured signal obtained in step S2. The objective function is J(A) w (x) can be expressed as follows: In the formula: J(A w (x) is the objective function, and its physical meaning is the total energy error between the simulated signal and the measured signal throughout the entire observation time; The objective of this problem is to find the optimal variable A that minimizes the objective function J. w (x); A w (x) is the effective cross-sectional area of ​​the pipe segment at position x along its axis, which is the unknown function to be solved in this optimization problem, i.e. the optimization variable; P sim (t,A w (x) represents a physical model and a given set of hypothetical profiles A. w (x), the simulated pressure time series obtained through forward calculation; P meas (t) represents the effective echo signal actually acquired and preprocessed in step S2; T obs The preset total observation time must be long enough to ensure that all relevant echoes from the furthest detection distance are received.

[0040] Due to its inherent nonlinearity, this optimization problem typically requires an iterative algorithm for solution. In a preferred embodiment, efficient gradient-based optimization algorithms such as the conjugate gradient method or the quasi-Newton method can be employed. The mechanism of this iterative solution process is as follows: The algorithm begins with an initial profile guess (e.g., an ideal pipe profile with design dimensions and no siltation). In each iteration, the algorithm calculates the objective function value under the current profile and its value relative to the profile function A. w The gradient of (x) indicates the profile correction direction that most quickly reduces the objective function value. Subsequently, the algorithm modifies the profile A along the opposite direction of this gradient with an appropriate step size. w (x) is updated. This process repeats until the objective function J(A) is updated. w The iteration terminates when the value of (x) converges to a minimum or when its change is less than a preset minimum threshold.

[0041] When the iterative algorithm converges, its final output optimization variables This is considered the best estimate of the actual effective cross-sectional area of ​​the pipeline. Therefore, using the known design cross-sectional area A of the pipeline... d The difference between this estimated profile and the actual profile can be used to obtain the continuous siltation profile A of the pipe section downstream of the monitoring point. s (x)=A d -A w (x).

[0042] To further improve the accuracy of the reconstruction results, in a preferred embodiment, this step also utilizes echo information contained in the echo signal generated by known structural feature points in the pipeline network (e.g., the next manhole shaft at a fixed distance, a known pipe diameter change, or valve location). Through P meas The echo peak values ​​corresponding to these known feature points are identified in (t), and based on their arrival times, the effective propagation velocity C of the pressure transient wave in the current pipe medium can be calculated in reverse. This real-time calibrated wave velocity C is then substituted into the forward physical model to replace a fixed, theoretical wave velocity value. This effectively eliminates the wave velocity uncertainty caused by changes in actual operating conditions such as pipe liquid level, fluid temperature, and sediment content, thereby significantly improving the accuracy of the final sedimentation profile in spatial positioning and geometric quantification.

[0043] Finally, the continuous siltation profile output in step S3 is a quantified siltation cross-sectional area function distributed along the pipeline length. It will serve as the direct basis for siltation status assessment and dynamic early warning in the subsequent step S4.

[0044] S4. Assess the siltation status of the pipeline network based on the continuous siltation profile, and generate an early warning signal when the siltation status meets the preset conditions.

[0045] In this embodiment, the final value of the method lies in assessing the siltation status of the pipeline network based on continuous siltation profiles and generating an early warning signal when the siltation status meets preset conditions. Its core task is to transform the physically meaningful but still somewhat primitive siltation profile data output in step S3 into quantitative indicators and decision-making information that maintenance personnel can directly understand and use to guide their actual work.

[0046] The execution of this step marks the completion of the entire monitoring process, transitioning from "passive detection" to "proactive early warning." Its specific implementation may include the following interrelated steps: First, after obtaining the continuous sedimentation profile A output in step S3... s After (x), this method performs depth quantization of the siltation state. This is not merely a simple determination of whether siltation exists, but rather a multi-dimensional parameter extraction. In a preferred embodiment, the quantization includes: Calculate the sediment thickness at any point x along the line. This thickness is based on the sediment cross-sectional area A. s (x) and the known pipeline design geometry (e.g., for circular pipelines, the chord height needs to be solved through geometric relationships) are used to calculate the siltation level, providing maintenance personnel with the most intuitive perception of the siltation level.

[0047] Identify and locate the most severely contaminated points. This is achieved by analyzing the entire profile function A. s (x), the system can automatically determine the location of the maximum value of the siltation cross-sectional area and calculate the degree of siltation at that point, for example, by expressing it as a percentage of the siltation area to the total cross-sectional area of ​​the pipeline.

[0048] Calculate the total volume of silt accumulation within a specified pipe section. By integrating the siltation profile function over the specified interval [L1, L2], the total siltation volume within the pipe section can be accurately obtained, providing data support for the assessment of dredging work quantities. The calculation formula is as follows: In the formula: V s This represents the total volume of silt accumulated within the pipe section. A s (x) is the siltation cross-sectional area function obtained from step S3, distributed along the pipeline position x; [L1,L2] represents the start and end positions of the target pipe segment whose volume needs to be calculated.

[0049] Furthermore, a significant feature of this invention is its ability to analyze the dynamic evolution trend of sedimentation. To achieve this, steps S1 to S3 of this invention are designed to be repeated periodically and automatically (e.g., daily, weekly, or triggered after a specific event such as a rainstorm). Thus, the system can obtain a series of continuous sedimentation profiles that evolve over time, denoted as A. s (x,t k ), where t k This represents the timestamp of the k-th measurement. By comparing the timestamps at two consecutive measurement moments (t... k With t k-1 Based on the obtained profile data, the system can calculate the sedimentation rate distribution at any location within the pipe section. In a preferred embodiment, this rate can be calculated as: In the formula: R s (x, Δt) represents the time interval Δt = t. k -t k-1 Within, the rate of change of the average siltation cross-sectional area at location x; A s (x,t k ) and A s (x,t k-1 The values ​​() represent the cross-sectional areas of the siltation at the current and previous measurement moments, respectively. Obtaining this siltation rate allows managers to not only grasp the static stock of siltation but also to understand its dynamic development trend. For example, it enables them to determine whether a certain siltation is developing rapidly, stabilizing, or being eroded by water flow, providing a crucial basis for predictive maintenance.

[0050] Finally, based on the comprehensive quantitative assessment and dynamic analysis results described above, this step executes intelligent early warning generation and dissemination. In a preferred embodiment, at a remote data processing center or cloud platform, operations and maintenance managers can pre-set a multi-level alarm threshold system according to the importance level of the pipeline network and management and maintenance requirements. This system can set different alarm levels for the aforementioned key indicators (such as maximum sediment thickness, total sediment volume, maximum sediment rate, etc.), such as "attention level," "early warning level," and "alarm level." The system continuously and automatically compares the various indicators calculated in real time with these preset thresholds. Once any indicator is detected to exceed its corresponding threshold condition, the system will immediately trigger an early warning event of the corresponding level.

[0051] The generated early warning signal is not a simple alarm, but a detailed early warning report. This report preferably includes the precise geospatial location of the problematic pipe section (e.g., highlighted on a GIS map), detailed quantitative data on siltation (such as siltation profiles, siltation volume, and siltation percentage), and dynamic evolution trend analysis (such as siltation rate). This early warning report can be sent instantly to designated maintenance personnel through various channels, such as push notifications from mobile applications, email, or SMS. This provides unprecedented, scientific, and comprehensive data support for their decisions regarding dredging measures, intervention timing, and optimization of dredging resource allocation, truly realizing a shift from a passive response to a proactive prevention model in pipeline network maintenance.

[0052] Reference Appendix Figure 2 Another embodiment of the present invention provides a dynamic monitoring and early warning system for sewage and rainwater pipe network siltation, comprising the following steps: The waveform generation module is used to actively generate a pressure transient wave at the monitoring point of the pipeline network and make it propagate within the pipeline network. The signal acquisition module is used to acquire the echo signal generated by the pressure transient wave propagating in the pipeline network, forming a pressure time series that includes information on the internal geometric structure of the pipeline network. The profile reconstruction module is used to perform reverse reconstruction calculations on the pressure time series based on a preset pipeline hydraulic physical model in order to obtain a continuous sedimentation profile of the downstream pipe section of the monitoring point. The assessment and early warning module is used to assess the siltation status of the pipeline network based on the continuous siltation profile, and generate an early warning signal when the siltation status meets preset conditions.

[0053] The system in this embodiment can be used to execute the above method embodiments, and its principle and technical effect are similar, so they will not be described again here.

[0054] This application also provides a computer device, including: a processor and a memory, the memory storing a computer program executable by the processor, the computer program performing the method described above when executed by the processor.

[0055] The embodiments described in this specific implementation are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Identical components are represented by the same reference numerals. Therefore, all equivalent changes made to the structure, shape, and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for dynamic monitoring and early warning of siltation in sewage and rainwater pipe networks, comprising the following steps: S1. At the monitoring point of the pipeline network, a pressure transient wave is actively generated and propagated within the pipeline network. S2. Collect the echo signal generated by the pressure transient wave propagating in the pipeline network to form a pressure time series containing the internal geometric structure information of the pipeline network; S3. Based on the preset pipeline hydraulic physical model, the pressure time series is reconstructed in reverse to obtain the continuous siltation profile of the downstream pipe section of the monitoring point. S4. Assess the siltation status of the pipeline network based on the continuous siltation profile, and generate an early warning signal when the siltation status meets preset conditions.

2. The method for dynamic monitoring and early warning of siltation in sewage and rainwater pipe networks according to claim 1, characterized in that, Prior to the step of actively generating the pressure transient wave, the method further includes: The background hydraulic noise signal of the monitoring point is passively acquired without external excitation; The characteristics of the background hydraulic noise signal are analyzed to assess the current hydraulic environment state of the pipe network; wherein, the analysis includes calculating the root mean square value P of the background hydraulic noise signal. rms : In the formula: P rms The root mean square value of the background hydraulic noise signal; P noise (t i ) represents the time at sampling time t i Instantaneous pressure values ​​of the acquired background hydraulic noise signal; N is the total number of sampling points for the background hydraulic noise signal; The step of actively generating pressure transient waves involves adaptively adjusting the waveform parameters of the pressure transient waves based on the hydraulic environment conditions.

3. The method for dynamic monitoring and early warning of siltation in sewage and rainwater pipe networks according to claim 2, characterized in that, The step of adaptively adjusting the waveform parameters of the pressure transient wave includes: When the hydraulic environment is assessed as low-interference, a single-pulse waveform with a narrow time width and concentrated energy is generated for high-resolution detection. When the hydraulic environment is assessed as high interference, a long-duration, high-energy encoded pulse waveform is generated for high signal-to-noise ratio detection.

4. The method for dynamic monitoring and early warning of siltation in sewage and rainwater pipe networks according to claim 3, characterized in that, The encoded pulse waveform is a linear frequency modulated signal; and before the step of performing the reverse reconstruction calculation, the method further includes a step of performing pulse compression processing on the acquired echo signal, which is achieved by the following calculation: In the formula: P comp (t) represents the signal obtained after pulse compression processing; P total (t) represents the original echo signal acquired; The time-reversed conjugate of the linear frequency modulated signal is used as a matched filter. * represents the convolution operator.

5. The method for dynamic monitoring and early warning of siltation in sewage and rainwater pipe networks according to claim 1, characterized in that, The steps for performing the reverse reconstruction calculation include: Discretize the pipe segment along its length and establish the relationship between its effective cross-sectional area and hydraulic resistance. An optimization problem is constructed, using the effective cross-sectional area of ​​the pipe segment as the optimization variable and the error between the simulated pressure response calculated forward based on the physical model and the acquired echo signal as the objective function. The objective function is J(A). w (x) is as follows: In the formula: J(A w (x) is the objective function; Indicator for optimization variable A w Find the minimum value of (x); A w (x) is the effective cross-sectional area of ​​the pipe segment at position x, which is used as the optimization variable; P sim (t,A w (x) is based on the physical model and the given profile A w (x) The simulated pressure response calculated in the forward direction; P meas (t) represents the actual acquired and preprocessed echo signal; T obs This represents the total observation time. By iteratively solving the optimization problem, the optimal solution of the effective cross-sectional area profile is obtained, and the continuous sedimentation profile is determined accordingly.

6. The method for dynamic monitoring and early warning of siltation in sewage and rainwater pipe networks according to claim 1, characterized in that, Following the step of assessing the siltation status of the pipeline network, the method further includes: All steps of the method are repeated periodically to obtain a series of continuous sedimentary profiles that evolve over time; The deposition rate distribution of the pipe section is calculated by comparing the continuous deposition profiles at different times.

7. The method for dynamic monitoring and early warning of siltation in sewage and rainwater pipe networks according to claim 6, characterized in that, The steps for generating the early warning signal include: The sediment thickness, sediment volume, or sediment rate distribution represented by the continuous sediment profile are compared with preset multi-level alarm thresholds. When any indicator meets its corresponding threshold condition, the corresponding level of warning is triggered.

8. The method for dynamic monitoring and early warning of siltation in sewage and rainwater pipe networks according to claim 1, characterized in that, The echo signal also includes echoes from known structural feature points in the pipeline network; the reverse reconstruction calculation step further utilizes the echo information from the known structural feature points to calibrate the effective propagation speed of the pressure transient wave in the pipe in real time.

9. A dynamic monitoring and early warning system for siltation in sewage and rainwater pipe networks, and a method for dynamic monitoring and early warning of siltation in sewage and rainwater pipe networks according to any one of claims 1-8, characterized in that, include: The waveform generation module is used to actively generate a pressure transient wave at the monitoring point of the pipeline network and allow it to propagate within the pipeline network. The signal acquisition module is used to acquire the echo signal generated by the pressure transient wave propagating in the pipeline network, and form a pressure time series containing information on the internal geometric structure of the pipeline network. The profile reconstruction module is used to perform reverse reconstruction calculations on the pressure time series based on a preset pipeline hydraulic physical model, so as to obtain a continuous siltation profile of the downstream pipe section of the monitoring point. The assessment and early warning module is used to assess the siltation status of the pipeline network based on the continuous siltation profile, and generate an early warning signal when the siltation status meets preset conditions.

10. A dynamic monitoring and early warning terminal for sewage and rainwater pipe network siltation, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-8.

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