Urban drainage pipeline sediment thickness dynamic monitoring method and system based on temperature monitoring

Abstract

The invention relates to the technical field of intelligent water affairs, and discloses a dynamic monitoring method and system for the thickness of urban drainage pipeline sediment based on temperature monitoring, and the method comprises the steps: building a simulation device of an urban drainage pipeline sediment distribution scene; acquiring actual temperature time sequence data of sewage and sediments in the urban drainage pipeline; establishing a one-dimensional heat transfer model according to the actual temperature time sequence data, and calibrating model parameters in the one-dimensional heat transfer model; performing numerical simulation in the one-dimensional heat transfer model to obtain a simulation temperature time sequence of the sewage and the sediment; a local maximum / minimum value method and a dynamic tuning and harmonic regression method are adopted to analyze the periodic characteristics of the simulated temperature time sequence, and temperature time sequence characteristics are obtained; and developing a data-driven model, and associating the sediment thickness with the temperature time sequence characteristics. Through temperature monitoring and model analysis, the accuracy and efficiency of sediment monitoring are improved.

Description

Technical Field

[0001] The present invention relates to the field of smart water technology, and in particular to a method and system for dynamically monitoring sediment thickness in urban drainage pipes based on temperature monitoring. Background Art

[0002] Sediment accumulation in urban drainage systems is a key factor leading to increased maintenance costs and reduced hydraulic performance. Current monitoring methods mainly rely on periodic cleaning operations and on-site manual measurements, but these methods are insufficient in terms of real-time performance and are difficult to dynamically predict and control sediment accumulation. In addition, urban drainage system sediment detection technologies also include sonar detection, but online real-time monitoring is not possible. In contrast, temperature monitoring is easy to implement online and facilitates the maintenance of online equipment.

[0003] Therefore, it is necessary to design a method and system for dynamic monitoring of sediment thickness in urban drainage pipes based on temperature monitoring to solve the problems existing in current technology. Summary of the invention

[0004] In view of this, the present invention proposes a method and system for dynamic monitoring of sediment thickness in urban drainage pipes based on temperature monitoring, aiming to solve the problem that current technologies mainly rely on periodic cleaning operations and on-site manual measurements, which are insufficient in real time and difficult to dynamically monitor sediment accumulation.

[0005] In one aspect, the present invention provides a method for dynamically monitoring the thickness of sediment in urban drainage pipes based on temperature monitoring, comprising:

[0006] S100: constructing a simulation device for a sediment distribution scenario in an urban drainage pipeline, wherein the simulation device includes a sediment layer and a sewage layer;

[0007] S200: Collecting actual temperature time series data of sewage and sediment in urban drainage pipes;

[0008] S300: establishing a one-dimensional heat transfer model according to the actual temperature time series data, and calibrating model parameters in the one-dimensional heat transfer model;

[0009] S400: performing numerical simulation in the one-dimensional heat transfer model to obtain a simulated temperature time series of sewage and sediment;

[0010] S500: Analyzing the periodic characteristics of the simulated temperature time series by using a local maximum / minimum method and a dynamic harmonic regression method to obtain temperature time series characteristics;

[0011] S600: Develop a data-driven model to relate sediment thickness to the temperature time series characteristics.

[0012] Furthermore, the sediment layer in the simulation device is the sediment of an actual urban drainage pipe;

[0013] The simulation device is provided with temperature sensors inside the sediment layer and inside the sewage layer, and the temperature sensors obtain actual temperature data inside the sediment layer and inside the sewage layer.

[0014] Furthermore, the one-dimensional heat transfer model is used to describe the heat transfer of sewage and sediment in the urban drainage pipe. When the convection and diffusion process of the sediment layer are ignored and the uniformity of the simulation device is considered, the one-dimensional heat transfer model adopts Formula I:

[0015]

[0016] Where T represents temperature, t represents time dimension, x represents space dimension, ρ represents volume density, Cp represents specific heat capacity, and k represents thermal conductivity;

[0017] Assuming that the thermal conductivity k is uniformly distributed in space, the one-dimensional heat transfer model adopts formula II:

[0018]

[0019] in, k e represents the thermal diffusivity.

[0020] Furthermore, when performing numerical simulation on the one-dimensional heat transfer model, it includes:

[0021] The formula I and the formula II are discretely and implicitly solved by Matlab program to obtain the predicted temperature data of the sediment layer.

[0022] Furthermore, when calibrating the model parameters in the one-dimensional heat transfer model, it includes:

[0023] Determine the model parameters by minimizing the root mean square error between the predicted temperature data of the sediment layer and the actual temperature data of the sediment layer measured by the temperature sensor;

[0024] The cross-validation method is used to group the acquired data series, and the root mean square error sum of all cross-validation combinations is used to determine the minimized error and the corresponding optimal model parameters.

[0025] Furthermore, when the local maximum / minimum method is used to analyze the periodic characteristics of the simulated temperature time series, it includes:

[0026] Normalizing the actual temperature data measured daily;

[0027] Using signal processing technology to accurately locate the local maximum point and the local minimum point in the simulated temperature time series;

[0028] Comparing the temperature peaks of the sewage layer and the sediment layer in the same time period, and obtaining the temperature ratio and the time lag difference by calculation;

[0029] The temperature ratio and the time lag difference are used to estimate the sediment thickness.

[0030] Furthermore, when the dynamic tuning harmonic regression method is used to analyze the periodic characteristics of the simulated temperature time series to obtain the temperature time series characteristics, it includes:

[0031] Synchronizing and formatting the simulated temperature time series to ensure that wastewater and sediment are compared over the same time period with a uniform sampling frequency;

[0032] extracting amplitude and phase information of the sewage and sediment fundamental frequencies in the simulated temperature time series;

[0033] Amplitude ratios and temporal phase differences were calculated to quantify the attenuation and temporal phase lag in the simulated temperature time series caused by sediment accumulation.

[0034] Furthermore, the data-driven model establishes a thickness relationship with the numerical simulation based on the temperature time series characteristics obtained by the local maximum / minimum method and the dynamic tuning harmonic regression method to estimate the sediment thickness.

[0035] In another aspect, the present invention also proposes a system for dynamic monitoring of sediment thickness in urban drainage pipes based on temperature monitoring, comprising:

[0036] A sensor group, comprising a plurality of temperature sensors, wherein the sensors are evenly arranged in the sediment layer and the sewage layer;

[0037] A controller connected to the sensor group, the controller is used to collect temperature data measured by the temperature sensor and output the temperature data;

[0038] A data storage and analysis module is connected to the controller, and is used to receive and store the temperature data; and is also used to process and analyze the temperature data to obtain an estimated value of the sediment thickness.

[0039] Furthermore, the data storage and analysis module includes:

[0040] A data preprocessing unit, used to collect actual temperature time series data of sewage and sediment in the urban drainage pipe, and to clean and preprocess the actual temperature time series data;

[0041] A model calibration unit, used to establish a one-dimensional heat transfer model according to the actual temperature time series data, and calibrate model parameters in the one-dimensional heat transfer model;

[0042] A thickness estimation unit is used to perform numerical simulation in the one-dimensional heat transfer model to obtain a simulated temperature time series of sewage and sediment; it is also used to analyze the periodic characteristics of the simulated temperature time series using a local maximum / minimum method and a dynamic harmonic regression method to obtain temperature time series characteristics; it is also used to establish a thickness relationship between the temperature time series characteristics obtained by the local maximum / minimum method and the dynamic harmonic regression method and the numerical simulation to estimate the sediment thickness;

[0043] The result output unit is used to organize and display the estimated values ​​of sediment thickness.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a non-invasive, high-precision sediment thickness monitoring solution, which is suitable for dynamic monitoring under different weather conditions. The method avoids on-site manual measurements in traditional methods, reducing operating costs and complexity. At the same time, the accuracy and efficiency of sediment monitoring are improved through temperature monitoring and model analysis. The invention helps to optimize the maintenance and cleaning strategies of urban drainage systems, reduce environmental risks and operating costs. In addition, the method and system of the present invention are particularly suitable for monitoring sediment thickness between 5 and 20 cm, and can provide an innovative sediment management tool for urban drainage systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0046] Figure 1 A flow chart of a method for dynamically monitoring sediment thickness in urban drainage pipes based on temperature monitoring provided by an embodiment of the present invention;

[0047] Figure 2 A structural block diagram of a system for dynamically monitoring the thickness of sediment in urban drainage pipes based on temperature monitoring provided by an embodiment of the present invention;

[0048] Figure 3A structural block diagram of a data storage and analysis module in a dynamic monitoring system for sediment thickness in urban drainage pipes based on temperature monitoring provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0050] See also Figure 1 As shown, in some embodiments of the present application, this embodiment provides a method for dynamically monitoring the thickness of sediment in urban drainage pipes based on temperature monitoring, comprising:

[0051] S100: constructing a simulation device for a sediment distribution scenario in an urban drainage pipeline, wherein the simulation device includes a sediment layer and a sewage layer;

[0052] S200: Collecting actual temperature time series data of sewage and sediment in urban drainage pipes;

[0053] S300: establishing a one-dimensional heat transfer model according to the actual temperature time series data, and calibrating model parameters in the one-dimensional heat transfer model;

[0054] S400: performing numerical simulation in the one-dimensional heat transfer model to obtain a simulated temperature time series of sewage and sediment;

[0055] S500: Analyze the periodic characteristics of the simulated temperature time series by using a local maximum / minimum method and a dynamic tuning harmonic regression method to obtain temperature time series characteristics;

[0056] S600: Develop a data-driven model to relate sediment thickness to the temperature time series characteristics.

[0057] In some embodiments of the present application, two 10-day time series of an urban drainage pipe with a diameter of 0.7 m are selected. The first time series is a weak daily temperature pattern, and the absolute temperature oscillation does not exceed 1°C. The maximum absolute daily temperature difference corresponding to the second time series is greater than that of the first time series, which is greater than 4°C.

[0058] In some embodiments of the present application, the simulation device uses 4 inner dimensions of 15×15×15 cm 3Expanded polystyrene (EPS) insulated boxes were designed and filled with 2 cm, 4 cm, 6 cm and 8 cm of sediment collected from the drainage system, and a 2 cm overlying water layer.

[0059] In some embodiments of the present application, the sediment distribution scenarios in urban drainage pipes are scenarios of drastic temperature changes that may be caused by pumping or rainwater inflow, and scenarios of normal temperature fluctuations during actual daily drainage.

[0060] In some embodiments of the present application, the one-dimensional heat transfer model is calibrated by using three simulation data sets to calibrate the model and one simulation data set to verify the model.

[0061] It can be seen that the method for dynamic monitoring of sediment thickness in urban drainage pipes based on temperature monitoring provided in this embodiment avoids on-site manual measurement in traditional methods, reducing operating costs and complexity; at the same time, through temperature monitoring and model analysis, it helps to optimize the maintenance of urban drainage systems and reduce environmental risks and operating costs.

[0062] Specifically, the sediment layer in the simulation device is the sediment of an actual urban drainage pipe;

[0063] The simulation device is provided with temperature sensors inside the sediment layer and inside the sewage layer, and the temperature sensors obtain actual temperature data inside the sediment layer and inside the sewage layer.

[0064] Specifically, the one-dimensional heat transfer model is used to describe the heat transfer of sewage and sediment in the urban drainage pipe. When the convection and diffusion process of the sediment layer are ignored and the uniformity of the simulation device is considered, the one-dimensional heat transfer model adopts Formula I:

[0065]

[0066] Where T represents temperature, t represents time dimension, x represents space dimension, ρ represents volume density, Cp represents specific heat capacity, and k represents thermal conductivity;

[0067] Assuming that the thermal conductivity k is uniformly distributed in space, the one-dimensional heat transfer model adopts formula II:

[0068]

[0069] in, k e represents the thermal diffusivity.

[0070] It can be seen that both Formula I and Formula II describe the heat transfer process in which the temperature changes with time and space. Using Formula I and Formula II in the one-dimensional heat transfer model can effectively simulate the temperature distribution of sewage and sediment in the urban drainage pipe. By calibrating the model parameters, the accuracy of the model can be ensured, thus providing a reliable basis for subsequent numerical simulations.

[0071] Specifically, when performing numerical simulation on the one-dimensional heat transfer model, it includes:

[0072] The formula I and the formula II are discretely and implicitly solved by using the Matlab subroutine to obtain the predicted temperature data of the sediment layer.

[0073] It can be seen that the use of Matlab subroutine can effectively handle complex numerical calculation problems, and convert continuous partial differential equations into solvable algebraic equations through discretization methods. In this embodiment, the finite difference method is used to discretize Formula I and Formula II, and the continuous temperature field is divided into a finite number of nodes, each node representing a temperature value at a specific position and time point.

[0074] In the discretization process, the time dimension and space dimension are approximated by forward difference and central difference respectively, thus obtaining a set of linear equations. By solving the linear equations, the temperature prediction value of each node at different time points can be obtained. In order to ensure the stability and accuracy of the numerical simulation, it is necessary to select appropriate time step and space step to meet the stability conditions of the numerical calculation.

[0075] Specifically, when calibrating the model parameters in the one-dimensional heat transfer model, it includes:

[0076] Calculating the root mean square error between the predicted temperature data of the sediment layer and the actual temperature data of the sediment layer measured by the temperature sensor using a minimization model;

[0077] The thermal diffusion coefficient Ke is averaged during the cross-validation process to obtain standard model parameters, and the standard model parameters are applied to the one-dimensional heat transfer model to obtain the simulated temperature time series.

[0078] It can be seen that in some embodiments of the present application, by minimizing the root mean square error (RMSE) to calibrate the model parameters, it is possible to ensure that the difference between the model prediction value and the actual measurement value is minimized. In the specific operation, an initial thermal diffusion coefficient Ke is first set, and then the Matlab subroutine is used to perform simulation calculations to obtain the predicted temperature data of the sediment layer. By comparing the predicted temperature data with the actual temperature data, the root mean square error is calculated. In order to further improve the accuracy of the model, the cross-validation method is used to optimize the model parameters. In the cross-validation process, the experimental data are divided into multiple subsets, and one of the subsets is taken as the verification data in turn, and the remaining subsets are taken as the training data. In this way, the generalization ability of the model on different data sets can be evaluated, and the thermal diffusion coefficient Ke is averaged to obtain a set of standard model parameters. Finally, these standard model parameters are applied to the one-dimensional heat transfer model, and a more accurate simulation temperature time series can be obtained. These simulation results not only reflect the temperature distribution of sewage and sediment in the urban drainage pipe, but also can be used to predict the future temperature change trend, providing a scientific basis for the maintenance and cleaning of the urban drainage system.

[0079] That is, this embodiment provides a method for dynamic monitoring of sediment thickness in urban drainage pipes based on temperature monitoring. By analyzing the actual temperature data inside the sediment layer and the sewage layer obtained by the temperature sensor and combining it with a one-dimensional heat transfer model, the thickness of the sediment can be calculated. This method avoids the tediousness and cost of traditional on-site manual measurement and improves monitoring efficiency and accuracy.

[0080] Specifically, when the local maximum / minimum method is used to analyze the periodic characteristics of the simulated temperature time series, it includes:

[0081] Normalizing the actual temperature data measured daily;

[0082] Using signal processing technology to accurately locate the local maximum point and the local minimum point in the simulated temperature time series;

[0083] Comparing the temperature peaks of the sewage layer and the sediment layer in the same time period, and obtaining the temperature ratio and the time lag difference by calculation;

[0084] The temperature ratio and the time lag difference are used to estimate the sediment thickness.

[0085] It can be seen that in this embodiment, the temperature change of the sediment layer is analyzed by the local maximum / minimum method, which can reveal the relationship between the sediment thickness and the temperature change. First, the actual temperature data measured daily are normalized to eliminate the influence of different measurement conditions and ensure the comparability of the data. Then, signal processing techniques such as Fourier transform or wavelet transform are used to determine the local maximum and local minimum points in the simulated temperature time series. These local extreme points reflect the periodic characteristics of the temperature changes of the sediment layer and the sewage layer. By comparing the temperature peaks of the sewage layer and the sediment layer in the same time period, the temperature ratio can be calculated. The temperature ratio refers to the ratio between the temperature peak of the sediment layer and the temperature peak of the sewage layer, which is proportional to the thickness of the sediment. In addition, the time lag difference refers to the lag time of the temperature peak of the sediment layer relative to the temperature peak of the sewage layer, which is inversely proportional to the thickness of the sediment.

[0086] It is understood that when estimating sediment thickness using temperature ratio and time lag difference, an empirical formula or regression model can be established to relate these two parameters to sediment thickness. Through a large amount of experimental data, the parameters of the empirical formula or regression model can be determined, thereby achieving a rapid estimation of sediment thickness.

[0087] It can be understood that this method not only improves the efficiency of sediment thickness monitoring, but also has high accuracy. Compared with the traditional manual measurement method, this method avoids frequent excavation and maintenance of drainage pipes, and reduces the impact on urban traffic and the environment. In addition, this method can also monitor the changes in sediment thickness in real time, providing timely scientific basis for the maintenance and cleaning of urban drainage systems.

[0088] That is, this embodiment provides an efficient method for monitoring the thickness of sediment in urban drainage pipes. By combining the temperature sensor and the one-dimensional heat transfer model, dynamic monitoring of sediment thickness can be achieved, providing strong support for the optimization management of urban drainage systems.

[0089] Specifically, when the dynamic tuning harmonic regression method is used to analyze the periodic characteristics of the simulated temperature time series to obtain the temperature time series characteristics, it includes:

[0090] Synchronizing and formatting the simulated temperature time series to ensure that wastewater and sediment are compared over the same time period with a uniform sampling frequency;

[0091] extracting amplitude and phase information of the sewage and sediment fundamental frequencies in the simulated temperature time series;

[0092] Amplitude ratios and temporal phase differences were calculated to quantify the attenuation and temporal phase lag in the simulated temperature time series caused by sediment accumulation.

[0093] It can be seen that in this embodiment, the temperature change of the sediment layer is analyzed by the dynamic tuning harmonic regression method, which can more accurately reveal the relationship between temperature change and sediment thickness. First, synchronize and format the simulated temperature time series to ensure that sewage and sediment are compared at a uniform sampling frequency in the same time period. This step is crucial because it ensures the accuracy of subsequent analysis. Next, extract the amplitude and phase information of the basic frequency of sewage and sediment in the simulated temperature time series. The amplitude ratio refers to the ratio between the amplitude of the sediment layer and the amplitude of the sewage layer, which is proportional to the thickness of the sediment. The time phase difference refers to the lag time of the temperature change of the sediment layer relative to the temperature change of the sewage layer, which is inversely proportional to the thickness of the sediment. In order to further improve the accuracy of the analysis, the harmonic regression model can be used to model the amplitude ratio and the time phase difference. Through a large amount of experimental data, the parameters of the harmonic regression model can be determined, thereby achieving an accurate estimation of the sediment thickness. The harmonic regression model can effectively separate the periodic components in the temperature time series, thereby more accurately reflecting the influence of sediment thickness on temperature change.

[0094] It can be understood that this method not only improves the accuracy of sediment thickness monitoring, but also has high real-time performance. Compared with the traditional manual measurement method, this method avoids frequent excavation and maintenance of drainage pipes, and reduces the impact on urban traffic and the environment. In addition, this method can also monitor the changes in sediment thickness in real time, providing timely scientific basis for the maintenance and cleaning of urban drainage systems.

[0095] That is, this embodiment provides an efficient method for monitoring the thickness of sediment in urban drainage pipes. By combining temperature sensors, one-dimensional heat transfer models, and dynamic tuning harmonic regression methods, dynamic monitoring of sediment thickness can be achieved, providing strong support for the optimization management of urban drainage systems. This method has high real-time performance and provides a scientific basis for the maintenance and cleaning of urban drainage systems.

[0096] Specifically, the data-driven model establishes a thickness relationship with the numerical simulation based on the temperature time series characteristics obtained by the local maximum / minimum method and the dynamic tuning harmonic regression method to estimate the sediment thickness.

[0097] It can be seen that, in this embodiment, the introduction of the data-driven model further enhances the intelligence and automation level of sediment thickness monitoring. By combining the temperature time series characteristics obtained by the local maximum / minimum value method and the dynamic tuning harmonic regression method, the data-driven model can more accurately establish the relationship between temperature change and sediment thickness. The model uses machine learning algorithms, such as support vector machines (SVM), random forests (RF) or neural networks (NN), to train a large amount of historical data, so as to learn the complex mapping relationship between temperature change and sediment thickness. In practical applications, the data-driven model first receives real-time data from a temperature sensor and removes noise and outliers through a preprocessing step. Then, the model uses the local maximum / minimum value method and the dynamic tuning harmonic regression method to extract the characteristics of the temperature time series, such as temperature ratio, time lag difference, amplitude ratio and time phase difference. These features are input into the trained data-driven model, and the model outputs an estimated value of sediment thickness according to these features.

[0098] See also Figure 2 As shown, in some embodiments of the present application, this embodiment provides a system for dynamic monitoring of sediment thickness in urban drainage pipes based on temperature monitoring, comprising:

[0099] A sensor group, comprising a plurality of temperature sensors, wherein the sensors are evenly arranged in the sediment layer and the sewage layer;

[0100] A controller connected to the sensor group, the controller is used to collect temperature data measured by the temperature sensor and output the temperature data;

[0101] A data storage and analysis module is connected to the controller, and is used to receive and store the temperature data; and is also used to process and analyze the temperature data to obtain an estimated value of the sediment thickness.

[0102] Specifically, the data storage and analysis module includes:

[0103] A data preprocessing unit, used to collect actual temperature time series data of sewage and sediment in the urban drainage pipe, and to clean and preprocess the actual temperature time series data;

[0104] A model calibration unit, used to establish a one-dimensional heat transfer model according to the actual temperature time series data, and calibrate model parameters in the one-dimensional heat transfer model;

[0105] A thickness estimation unit is used to perform numerical simulation in the one-dimensional heat transfer model to obtain a simulated temperature time series of sewage and sediment; it is also used to analyze the periodic characteristics of the simulated temperature time series using a local maximum / minimum method and a dynamic harmonic regression method to obtain temperature time series characteristics; it is also used to establish a thickness relationship between the temperature time series characteristics obtained by the local maximum / minimum method and the dynamic harmonic regression method and the numerical simulation to estimate the sediment thickness;

[0106] The result output unit is used to organize and display the estimated values ​​of sediment thickness.

[0107] It can be seen that the sensor group is used to monitor the temperature changes in the urban drainage pipe in real time. These temperature sensors are evenly arranged in the sediment layer and sewage layer to ensure that the details of the temperature changes can be accurately captured; the controller is responsible for collecting the temperature data from the sensor group and outputting this data to the data storage and analysis module. The controller is also responsible for ensuring the stability and real-time performance of data transmission to facilitate subsequent analysis. The data storage and analysis module is the core of the entire system, which is responsible for receiving, storing and processing temperature data. The data preprocessing unit first cleans and preprocesses the collected temperature time series data to ensure data quality. The model calibration unit establishes a one-dimensional heat transfer model based on the actual temperature time series data, and calibrates the model parameters to ensure the accuracy of the model. The thickness estimation unit performs numerical simulation in the calibrated one-dimensional heat transfer model to obtain the simulated temperature time series of sewage and sediment. The periodic characteristics of the simulated temperature time series are analyzed by using the local maximum / minimum method and the dynamic tuning harmonic regression method to obtain the temperature time series characteristics. These characteristics establish a thickness relationship with the numerical simulation to estimate the sediment thickness. The result output unit is responsible for organizing and displaying the estimated values ​​of the sediment thickness to facilitate relevant personnel to make decisions and take corresponding maintenance measures.

[0108] In summary, this embodiment provides a dynamic monitoring system for sediment thickness in urban drainage pipes based on temperature monitoring. The system has the characteristics of high precision, high real-time performance and intelligence, and provides strong technical support for the maintenance and management of urban drainage systems.

[0109] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0110] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for dynamic monitoring of sediment thickness in urban drainage pipes based on temperature monitoring, characterized in that: include: Building a simulation device for the sediment distribution scenario of an urban drainage pipe, wherein the simulation device includes a sediment layer and a sewage layer; Collect actual temperature time series data of sewage and sediment in urban drainage pipes; Establishing a one-dimensional heat transfer model according to the actual temperature time series data, and calibrating model parameters in the one-dimensional heat transfer model; Performing numerical simulation in the one-dimensional heat transfer model to obtain simulated temperature time series of sewage and sediment; The local maximum / minimum method and the dynamic tuning harmonic regression method are used to analyze the periodic characteristics of the simulated temperature time series to obtain the temperature time series characteristics; A data-driven model was developed to relate sediment thickness to the temperature time series characteristics.

2. The method for dynamic monitoring of sediment thickness in urban drainage pipes based on temperature monitoring according to claim 1 is characterized in that: The sediment layer in the simulation device is the sediment of an actual urban drainage pipe; The simulation device is provided with temperature sensors inside the sediment layer and inside the sewage layer, and the temperature sensors obtain actual temperature data inside the sediment layer and inside the sewage layer.

3. The method for dynamic monitoring of sediment thickness in urban drainage pipes based on temperature monitoring according to claim 2 is characterized in that: The one-dimensional heat transfer model is used to describe the heat transfer of sewage and sediment in the urban drainage pipe. When the convection and diffusion process of the sediment layer are ignored and the uniformity of the simulation device is considered, the one-dimensional heat transfer model adopts Formula I: Where T represents temperature, t represents time dimension, x represents space dimension, ρ represents volume density, Cp represents specific heat capacity, and k represents thermal conductivity; Assuming that the thermal conductivity k is uniformly distributed in space, the one-dimensional heat transfer model adopts formula II: in, k e represents the thermal diffusivity.

4. The method for dynamic monitoring of sediment thickness in urban drainage pipes based on temperature monitoring according to claim 3 is characterized in that: When performing numerical simulation on the one-dimensional heat transfer model, it includes: The formula I and the formula II are discretely and implicitly solved by Matlab subroutine to obtain the predicted temperature data of the sediment layer.

5. The method for dynamic monitoring of sediment thickness in urban drainage pipes based on temperature monitoring according to claim 4 is characterized in that: When calibrating the model parameters in the one-dimensional heat transfer model, it includes: Calculating the root mean square error between the predicted temperature data of the sediment layer and the actual temperature data of the sediment layer measured by the temperature sensor using a minimization model; The thermal diffusion coefficient Ke is averaged during the cross-validation process to obtain standard model parameters, and the standard model parameters are applied to the one-dimensional heat transfer model to obtain the simulated temperature time series.

6. The method for dynamic monitoring of sediment thickness in urban drainage pipes based on temperature monitoring according to claim 5 is characterized in that: When the local maximum / minimum method is used to analyze the periodic characteristics of the simulated temperature time series, it includes: Normalizing the actual temperature data measured daily; Using signal processing technology to accurately locate the local maximum point and the local minimum point in the simulated temperature time series; Comparing the temperature peaks of the sewage layer and the sediment layer in the same time period, and obtaining the temperature ratio and the time lag difference by calculation; The temperature ratio and the time lag difference are used to estimate the sediment thickness.

7. The method for dynamic monitoring of sediment thickness in urban drainage pipes based on temperature monitoring according to claim 6 is characterized in that: When the dynamic tuning harmonic regression method is used to analyze the periodic characteristics of the simulated temperature time series to obtain the temperature time series characteristics, it includes: Synchronizing and formatting the simulated temperature time series to ensure that wastewater and sediment are compared over the same time period with a uniform sampling frequency; extracting amplitude and phase information of the sewage and sediment fundamental frequencies in the simulated temperature time series; Amplitude ratios and temporal phase differences were calculated to quantify the attenuation and temporal phase lag in the simulated temperature time series caused by sediment accumulation.

8. The method for dynamic monitoring of sediment thickness in urban drainage pipes based on temperature monitoring according to claim 7 is characterized in that: The data driven model establishes a thickness relationship with the numerical simulation based on the temperature time series characteristics obtained by the local maximum / minimum method and the dynamic tuning harmonic regression method, and estimates the sediment thickness.

9. A system for dynamic monitoring of sediment thickness in urban drainage pipes based on temperature monitoring, applied to the method for dynamic monitoring of sediment thickness in urban drainage pipes based on temperature monitoring as claimed in any one of claims 1 to 8, characterized in that: include: A sensor group, comprising a plurality of temperature sensors, wherein the sensors are evenly arranged in the sediment layer and the sewage layer; A controller connected to the sensor group, the controller is used to collect temperature data measured by the temperature sensor and output the temperature data; A data storage and analysis module is connected to the controller, and is used to receive and store the temperature data; and is also used to process and analyze the temperature data to obtain an estimated value of the sediment thickness.

10. The system for dynamic monitoring of sediment thickness in urban drainage pipes based on temperature monitoring according to claim 9 is characterized in that: The data storage and analysis module includes: A data preprocessing unit, used to collect actual temperature time series data of sewage and sediment in the urban drainage pipe, and to clean and preprocess the actual temperature time series data; A model calibration unit, used to establish a one-dimensional heat transfer model according to the actual temperature time series data, and calibrate model parameters in the one-dimensional heat transfer model; The thickness estimation unit is used to perform numerical simulation in the one-dimensional heat transfer model to obtain a simulated temperature time series of sewage and sediment; and is also used to analyze the periodic characteristics of the simulated temperature time series using a local maximum / minimum method and a dynamic tuning harmonic regression method to obtain temperature time series characteristics; It is also used to establish a thickness relationship with the numerical simulation based on the temperature time series characteristics obtained by the local maximum / minimum method and the dynamic harmonic regression method to estimate the sediment thickness; The result output unit is used to organize and display the estimated values ​​of sediment thickness.

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