Pipeline operating condition diagnosis method and device, electronic equipment and storage medium

Abstract

The present disclosure provides a pipeline operating condition diagnosis method and device, electronic equipment and storage medium, wherein the method comprises: setting multiple monitoring points on the pipeline and acquiring water flow data of each monitoring point, the water flow data at least comprising water flow depth; determining the fullness of each monitoring point according to the water flow depth of each monitoring point; constructing a flow rate correction model based on the deposition depth, the central angle corresponding to the water flow depth and the central angle corresponding to the deposition depth; determining the predicted flow rate of each monitoring point based on the flow rate correction model; establishing a flow rate and fullness curve based on the predicted flow rate and fullness of each monitoring point; and determining the operating condition of each monitoring point based on the curve.

Description

Technical Field

[0001] This disclosure relates to the field of pipeline inspection technology, and in particular to a method, apparatus, electronic device and storage medium for diagnosing pipeline operating conditions. Background Technology

[0002] As a core component of urban infrastructure, the operational status of drainage pipe systems plays a crucial role in ensuring smooth urban functioning and improving residents' quality of life. Pipeline siltation is particularly prevalent, directly leading to decreased drainage network efficiency, potentially causing water pollution, and even threatening urban water security. In the initial stages of drainage network management, the assessment of siltation at the bottom of pipes mainly relies on manual inspections and experience-based estimations. This method is not only highly subjective but also inefficient. Summary of the Invention

[0003] This disclosure provides a method, apparatus, electronic device, and storage medium for diagnosing pipeline operating conditions, in order to at least solve the above-mentioned technical problems existing in the prior art.

[0004] According to a first aspect of this disclosure, a method for diagnosing pipeline operating conditions is provided, the method comprising: Multiple monitoring points are set up on the pipeline, and water flow data is acquired at each monitoring point, wherein the water flow data includes at least the water flow depth; The degree of filling at each monitoring point is determined based on the water flow depth at each monitoring point. A flow velocity correction model is constructed based on the central angles corresponding to siltation depth and flow depth. Based on the aforementioned flow velocity correction model, the predicted flow velocity at each monitoring point is determined; Based on the predicted flow velocity and filling degree of each monitoring point, a curve of flow velocity versus filling degree is established. Based on the graph, the operating conditions of each monitoring point are determined.

[0005] In one possible implementation, the construction of the flow velocity correction model based on the central angles corresponding to the siltation depth, the flow depth, and the siltation depth includes: Obtain the slope and pipe diameter for each monitoring point; Establish a first correlation between the cross-sectional area of ​​the water flow and the pipe diameter, the central angle corresponding to the water flow depth, and the central angle corresponding to the siltation depth; Establish a second correlation between the wetted perimeter of the water flow and the pipe diameter, the central angle corresponding to the water flow depth, the central angle corresponding to the siltation depth, and the siltation depth of the pipe; Establish a flow rate correction model between the predicted flow rate and the first correlation, the second correlation, the slope, and the roughness of the pipe.

[0006] In one possible implementation, constructing the flow rate correction model includes: The velocity correction model is constructed based on the following formula:

[0007] in, To predict flow rate, For slope, For the roughness of the pipe, For pipe diameter, The central angle corresponding to the depth of the water flow. The central angle corresponding to the depth of siltation. The depth of siltation. Here is the roughness uncertainty coefficient. The uncertainty coefficient for the central angle is... The value represents the uncertainty coefficient for siltation depth.

[0008] In one possible implementation, the water flow data further includes: the actual flow velocity at each monitoring point; After constructing the flow rate correction model, the method further includes: The roughness, siltation depth, and various uncertainty coefficients in the velocity correction model are adjusted, and the adjusted predicted velocity is obtained. The adjusted predicted flow velocity is compared with the actual flow velocity to obtain the comparison result; Based on the comparison results, it is determined whether the adjusted predicted flow rate meets the first preset requirement; If the first preset requirement is not met, the roughness, siltation depth, and various uncertainty coefficients in the flow rate correction model are readjusted until the predicted flow rate meets the first preset requirement.

[0009] In one possible implementation, after the predicted flow rate meets a first preset requirement, the method further includes: Determine the objective function between the actual flow velocity and the predicted flow velocity. This objective function is used to evaluate whether the roughness, sedimentation depth, and various uncertainty coefficients in the flow velocity correction model meet the second preset requirements. Determine whether the objective function is within the confidence interval; If it is within the confidence interval, it means that the roughness, siltation depth and various uncertainty coefficients meet the second preset requirements; If the objective function is not within the confidence interval, the roughness, siltation depth, and various uncertainty coefficients are readjusted until the objective function is within the confidence interval.

[0010] In one possible implementation, the operating conditions include at least: ideal operating conditions, high liquid level operating conditions, flow control operating conditions, overload operating conditions, and backflow operating conditions.

[0011] According to a second aspect of this disclosure, a diagnostic device for pipeline operating conditions is provided, the device comprising: The acquisition module is used to set up multiple monitoring points on the pipeline and acquire water flow data at each monitoring point, wherein the water flow data includes at least the water flow depth; The first determining module is used to determine the fullness of each monitoring point based on the water flow depth at each monitoring point; The module is used to build a flow velocity correction model based on the central angles corresponding to siltation depth and water flow depth. The second determining module is used to determine the predicted flow velocity at each monitoring point based on the flow velocity correction model. A module is built to generate a curve of flow velocity versus fullness based on the predicted flow velocity and fullness at each monitoring point. The third determining module is used to determine the operating conditions of each monitoring point based on the curve graph.

[0012] In one possible implementation, the building module includes: The first sub-acquisition module is used to acquire the slope and pipe diameter of each monitoring point; The first sub-module is used to establish a first correlation between the cross-sectional area of ​​the water flow and the pipe diameter, the central angle corresponding to the water flow depth, and the central angle corresponding to the siltation depth; The second sub-module is used to establish a second correlation between the wetted perimeter of the water flow and the pipe diameter, the central angle corresponding to the water flow depth, the central angle corresponding to the siltation depth, and the siltation depth of the pipe. The third sub-module is used to establish a flow rate correction model between the predicted flow rate and the first correlation, the second correlation, the slope, and the roughness of the pipe.

[0013] According to a third aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and a memory communicatively connected to said at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the methods described in this disclosure.

[0014] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions for causing the computer to perform the methods described in this disclosure.

[0015] The pipeline operation condition diagnosis method, device, electronic equipment, and storage medium disclosed herein collect water flow data such as liquid level, water flow depth, and central angle corresponding to siltation depth by setting up multiple monitoring points in the pipeline. It introduces fullness as a key parameter and, combined with the central angle corresponding to water flow depth and siltation depth, constructs a flow velocity correction model suitable for pipelines with siltation. This model accurately derives and predicts flow velocity and plots flow velocity versus fullness curves. It can systematically identify and classify pipeline operation conditions, thereby significantly improving the accuracy, comprehensiveness, and applicability of pipeline condition diagnosis, effectively reducing the manpower and time costs of traditional diagnosis, and providing a reliable basis for the intelligent operation and scientific management of drainage pipe network systems.

[0016] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0017] The above and other objects, features, and advantages of this disclosure will become readily apparent from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings. Several embodiments of this disclosure are illustrated in the drawings by way of example and not limitation, in which: In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts.

[0018] Figure 1 A flowchart of a method for diagnosing pipeline operating conditions provided in an embodiment of this disclosure; Figure 2 A detailed flowchart of the pipeline operating condition diagnosis method provided in the embodiments of this disclosure; Figures 3a to 3k The graphs show the flow velocity versus the degree of filling from monitoring point A to monitoring point K, respectively. Figure 4 A graph showing the flow velocity versus the degree of filling at monitoring point L; Figure 5 A graph showing the flow velocity versus the degree of filling at monitoring point M; Figure 6 A graph showing the flow velocity versus the degree of filling at monitoring point N; Figure 7 A schematic diagram of the structure of the diagnostic device for pipeline operating conditions provided in this embodiment of the present disclosure; Figure 8 A schematic diagram of the composition structure of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0019] To make the objectives, features, and advantages of this disclosure more apparent and understandable, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0020] This disclosure provides a method for diagnosing pipeline operating conditions. Figure 1 This is a flowchart of a method for diagnosing pipeline operating conditions provided in an embodiment of this disclosure. Figure 2 A detailed flowchart of the pipeline operating condition diagnosis method provided in the embodiments of this disclosure is shown below. Figure 1 and Figure 2 As shown, the method includes: Step 101: Set up multiple monitoring points on the pipeline and acquire water flow data for each monitoring point. The water flow data shall include at least the water flow depth.

[0021] The layout of monitoring points in the drainage system should consider the principles of uniformity and representativeness. The selected monitoring points should be able to accurately reflect the overall condition of the drainage system and should cover both stormwater and sewage pipe networks to achieve comprehensive analysis. For sewage pipe networks, representative nodes under various operating conditions should be considered, including but not limited to upstream nodes that are significantly affected by residents' daily routines, downstream nodes that are not significantly affected by residents' daily routines, and nodes that are under the active control of drainage facilities. For stormwater pipe networks, the impact of rainfall events should be considered in particular.

[0022] Multiple monitoring points are selected on the pipeline, and online monitoring equipment is configured at each monitoring point as a sensing unit to acquire minute-level water flow data, including water flow depth (liquid level), actual flow velocity, and flow rate.

[0023] Furthermore, information such as the diameter and slope of the pipeline at each monitoring point is obtained.

[0024] In this disclosure, 11 pipeline monitoring points can be selected. The monitoring point information is confirmed based on pipeline mapping information and on-site verification, as shown in Table 1.

[0025] Table 1: Summary Table of Monitoring Point Information

[0026] Furthermore, in this embodiment, the water flow data at each monitoring point covers a monitoring period of seven consecutive effective dry days unaffected by rainfall. To ensure the quality of the water flow data, the minute-level data acquisition rate for each dry day is greater than 80% (greater than 1152 data points). The water flow data is statistically analyzed, and the flow velocity and liquid level data for different days are averaged at the corresponding time (minute level). For example, the average liquid level at 8:00 AM each day over these seven days is used to obtain the liquid level corresponding to 8:00 AM in a given day. After obtaining the average values ​​of liquid level and flow velocity for each day, a dry-day characteristic curve is formed, and the daily flow velocity and liquid level data are simultaneously plotted.

[0027] Step 102: Determine the filling degree of each monitoring point based on the water flow depth at each monitoring point.

[0028] In one embodiment, the degree of filling is equal to the ratio of the water depth (liquid level) to the pipe diameter. Therefore, the degree of filling can be obtained after measuring the liquid level at each monitoring point.

[0029] In this disclosure, a velocity correction model is obtained by modifying the Manning formula. The Chezy formula describes the relationship between pipe characteristics, liquid level, and flow velocity under uniform flow conditions in an open channel. The Manning formula is an improved form of the Chezy formula, characterized by its simplicity and wide applicability, and plays an indispensable role in the field of hydraulics.

[0030] Manning's formula is shown below:

[0031] in, To predict flow rate, For slope, R is the roughness of the pipe, and R is the hydraulic radius.

[0032] In this disclosure, after obtaining the degree of filling, to facilitate calculation and ensure the consistency of the parameters in the Manning formula with the measured data of the circular pipe, the pipe diameter is... and fullness By incorporating the Manning formula, a variant of the Manning formula applicable to conventional circular pipes is derived.

[0033] The Manning formula for a conventional circular pipe is shown below:

[0034] in, Here is the roughness uncertainty coefficient. The degree of uncertainty is the degree of fullness.

[0035] The Manning formula for conventional circular pipes is applicable to conditions where there is no sediment buildup inside the pipe. However, drainage pipes are unique in that they operate in harsh environments, and sediment buildup at the bottom is very common. Therefore, it is necessary to further derive the Manning formula for circular pipes that is applicable to conditions with bottom sediment buildup.

[0036] Step 103: Construct a flow velocity correction model based on the central angles corresponding to the siltation depth and the flow depth.

[0037] Considering the impact of pipe siltation on the core parameters of the Manning formula (flow cross-sectional area and wetted perimeter), the siltation depth is substituted into the formula, and the central angle (liquid level wrap angle) corresponding to the flow depth and the central angle (siltation wrap angle) corresponding to the siltation depth are used to replace the degree of filling. This leads to the derivation of the Manning formula applicable to circular tubes with siltation, also known as the velocity correction model. Among these, the degree of filling... , The central angle corresponds to the depth of the water flow.

[0038] In one embodiment, a flow velocity correction model is constructed based on the central angle corresponding to the siltation depth, the flow depth, and the central angle corresponding to the siltation depth, including: Obtain the slope and pipe diameter for each monitoring point; Establish the first correlation between the cross-sectional area of ​​the water flow and the central angles corresponding to the pipe diameter, water flow depth, and sedimentation depth; Establish a second correlation between the wetted perimeter of the water flow and the pipe diameter, the central angle corresponding to the water flow depth, the central angle corresponding to the sedimentation depth, and the sedimentation depth of the pipe; Establish a flow velocity correction model between the predicted flow velocity and the first correlation, the second correlation, the slope, and the roughness of the pipe.

[0039] Specifically, the hydraulic radius R in Manning's formula is equal to the ratio of the cross-sectional area to the wetted perimeter, so the cross-sectional area and wetted perimeter need to be determined first.

[0040] The formula for calculating the cross-sectional area of ​​the flow path is as follows:

[0041] in, The cross-sectional area of ​​the flow path is... For pipe diameter, The central angle corresponding to the depth of the water flow. The central angle corresponding to the depth of siltation. Let be the uncertainty coefficient of the central angle. The above formula represents the first correlation.

[0042] The formula for calculating wetted perimeter is as follows:

[0043] in, For wet period, The depth of siltation. The above formula represents the second correlation, where is the uncertainty coefficient for siltation depth.

[0044] Then, the cross-sectional area and wetted perimeter are substituted into Manning's formula to obtain the velocity correction model.

[0045] In one embodiment, constructing a flow rate correction model includes: The velocity correction model is constructed based on the following formula:

[0046] in, To predict flow rate, For slope, For the roughness of the pipe, For pipe diameter, The central angle corresponding to the depth of the water flow. The central angle corresponding to the depth of siltation. The depth of siltation. Here is the roughness uncertainty coefficient. The uncertainty coefficient for the central angle is... The value represents the uncertainty coefficient for siltation depth.

[0047] Step 104: Determine the predicted flow velocity at each monitoring point based on the flow velocity correction model.

[0048] In this disclosure, the predicted flow velocity at each monitoring point can be obtained based on the formula corresponding to the above-mentioned flow velocity correction model.

[0049] Step 105: Based on the predicted flow velocity and filling degree of each monitoring point, establish a curve of flow velocity versus filling degree.

[0050] In one embodiment, the water flow data further includes: the actual flow velocity at each monitoring point; After constructing the flow rate correction model, the method also includes: The roughness, sedimentation depth, and various uncertainty coefficients in the velocity correction model are adjusted, and the adjusted predicted velocity is obtained. The adjusted predicted flow velocity is compared with the actual flow velocity to obtain the comparison result; Based on the comparison results, determine whether the adjusted predicted flow rate meets the first preset requirement; If the first preset requirement is not met, the roughness, sedimentation depth, and various uncertainty coefficients in the flow rate correction model are readjusted until the predicted flow rate meets the first preset requirement.

[0051] Specifically, in the aforementioned velocity correction model, the unknown parameters include roughness, sedimentation depth, and various uncertainty coefficients. Therefore, a trial-and-error iterative method can be used to adjust these unknown parameters. Based on the adjusted roughness, sedimentation depth, and various uncertainty coefficients, the adjusted predicted velocity is determined, and the adjusted predicted velocity is compared with the actual velocity. Specifically, by combining the liquid level and the diameter of the monitoring pipe, the pipe filling degree is calculated. The filling degree is used as the independent variable, and the actual velocity and the predicted velocity (calculated value) derived using the velocity correction model are used as dependent variables. This results in a curve reflecting the relationship between the pipe liquid level and the velocity, i.e., a velocity-filling degree curve. These data are then fitted, and the fitting results are used to determine whether the adjusted predicted velocity meets the first preset requirement. Specifically, this is determined by observing whether the curve corresponding to the actual velocity and the curve corresponding to the predicted velocity fit together. If the curve corresponding to the actual velocity and the curve corresponding to the predicted velocity do not fit together, the roughness, sedimentation depth, and various uncertainty coefficients are readjusted until the curve corresponding to the actual velocity and the curve corresponding to the predicted velocity fit together.

[0052] In this embodiment of the disclosure, the roughness and siltation depth obtained under the best fitting effect are shown in Table 2.

[0053] Table 2: Results of Roughness and Accumulation Depth Identification

[0054] Figures 3a to 3k The graphs show the flow velocity versus the degree of filling from monitoring point A to monitoring point K, respectively. Figures 3a to 3k This is the curve showing the best fit.

[0055] from Figures 3a to 3k Based on the data fitting results, the flow velocity correction model in this disclosure can effectively calculate the flow velocity according to the liquid level in the drainage pipe, and can reflect the basic characteristics of water flow in the drainage pipe, especially for drainage pipes with siltation.

[0056] In one embodiment, after the predicted flow rate meets a first preset requirement, the method further includes: Determine the objective function between the actual flow velocity and the predicted flow velocity. This objective function is used to evaluate whether the roughness, sedimentation depth, and various uncertainty coefficients in the flow velocity correction model meet the second preset requirements. Determine whether the objective function is within the confidence interval; If it is within the confidence interval, it means that the roughness, siltation depth and various uncertainty coefficients meet the second preset requirements; If the objective function is not within the confidence interval, the roughness, siltation depth, and various uncertainty coefficients are readjusted until the objective function is within the confidence interval.

[0057] Specifically, the concept of an objective function is introduced. Based on the dataset obtained by fitting the curves of pipeline flow velocity and filling degree, the accuracy and applicability of the pipeline network parameters at the monitoring points are verified through the calculation and processing of the objective function. Furthermore, based on the difference analysis of the objective function evaluation results under different working conditions, a specific objective function applicable to this disclosure is determined.

[0058] In this disclosure, relative error, Nash efficiency coefficient, Pearson correlation coefficient and root mean square error are selected as objective functions. The relative error, Nash efficiency coefficient, Pearson correlation coefficient and root mean square error are independent of each other, have no obvious correlation, and do not follow any linear pattern or trend. This enables effective cross-validation of the fitting results and has a wide range of applications and relatively accurate computational capabilities.

[0059] Then, the confidence intervals corresponding to each objective function are determined, where the relative error is less than 0.4, the Nash efficiency coefficient is greater than 0.6, the root mean square error is less than 0.2, and the Pearson correlation coefficient is greater than 0.6 or less than -0.6. These set confidence intervals strictly follow the established usage guidelines, thereby ensuring the credibility of the fitting effect verification.

[0060] Then, based on the predicted flow velocity and the actual flow velocity, the value of the objective function for each monitoring point is determined. The calculation results obtained by the flow velocity correction model for each monitoring point are evaluated based on the value of the objective function, as shown in Table 3.

[0061] Table 3: Values ​​of the objective function for each monitoring point

[0062] As shown in Table 3 above, the relative error and Nash efficiency coefficient fluctuate significantly. Taking monitoring points B and F as examples, both points simultaneously satisfy the conditions of relative error < 40% and Nash efficiency coefficient > 0.6. The relative error value of point B is greater than that of point F, and the Nash efficiency coefficient of point F is closer to 1 than that of point B. However, according to... Figure 3b and Figure 3f In the curve fitting, point B is significantly better than point F.

[0063] The root mean square error (RMSE) showed relatively small fluctuations, with an overall RMSE of 13.64% across the 11 points, less than the required 20% for the sample. Taking monitoring points B and K as examples, the RMSE at point B was smaller than that at point K, but point B performed better than point K in curve fitting. Therefore, relative error, Nash efficiency coefficient, and RMSE are not suitable as objective functions for evaluating the correction formula; thus, the Pearson correlation coefficient is used as the objective function of this disclosure.

[0064] After adopting the Pearson correlation coefficient as the objective function of this disclosure, the value of the objective function for each monitoring point is determined based on the predicted flow velocity and the actual flow velocity obtained by the flow velocity correction model for each monitoring point. It is then determined whether the value is within the confidence interval. If it is within the confidence interval, it indicates that the roughness, sedimentation depth, and various uncertainty coefficients in the flow velocity correction model meet the second preset requirements. If it is not within the confidence interval, it is necessary to go back and readjust the roughness, sedimentation depth, and various uncertainty coefficients, and recalculate the objective function based on the readjusted flow velocity correction model until the objective function is within the confidence interval.

[0065] In this disclosure, the objective function is verified by adding a monitoring point (point L).

[0066] An additional monitoring point (point L) was added within the monitoring area to calculate the applicability of the formula. The pipe diameter at the monitoring point was DN1000, the pipe wall roughness was 0.015, and the pipe slope was 0.6%. The measured Pearson correlation coefficient was 0.87, which is within the confidence interval.

[0067] In this disclosure, such as Figure 2 As shown, after the objective function is within the confidence interval, parameter verification can be performed. This mainly involves comparing the final roughness and sedimentation depth with the actual roughness and sedimentation depth. If the error is within the preset range, it indicates that the result obtained through the flow velocity correction model is reliable. If the error is not within the preset range, it is necessary to review the pipeline parameters, delete abnormally fluctuating data, supplement the missing time period data according to similar working conditions, correct the data, and then readjust the roughness and sedimentation depth in the flow velocity correction model for refitting.

[0068] Specifically, for monitoring point L, different sedimentation depths (0.0m, 0.1m, 0.2m, 0.3m) were set in the flow velocity correction model, and the measured data and calculated values ​​were fitted. Figure 4 As shown in the figure, the fitted curve indicates that the siltation depth at point L is 0.3m. To verify this, on-site verification measurements and QV image analysis were conducted, and the measured siltation depth was 0.32m, with a deviation of 6.7%, indicating that the result is reliable.

[0069] Step 106: Based on the curve graph, determine the operating conditions of each monitoring point.

[0070] In this disclosure, the server and its professional functional analysis modules are integrated into a smart brain system. Based on the real-time data collected by the online monitoring equipment of the sensing unit, the system plots the curves of pipeline flow velocity and filling degree, and further subdivides the pipeline operating conditions accordingly. For each operating condition category, clear discrimination criteria are set to assist the smart brain system in data analysis and diagnostic decision-making, thereby achieving a comprehensive perception of the dynamic operation of the drainage pipe network system.

[0071] Based on the common conditions in the actual operation and management of drainage pipe networks, common operating conditions are divided into ideal operating conditions, high liquid level operating conditions, flow control operating conditions, overload operating conditions, backflow operating conditions, and other operating conditions. Ideal operating conditions refer to situations where there is no siltation, overload, or backflow in the pipe; sewage is transported by gravity, and the flow is stable and uniform, unaffected by artificial facilities such as pumping stations or dams. High liquid level operating conditions refer to situations where the flow pattern in the pipe still conforms to Manning's theory, but does not conform to the characteristics of a circular pipe, and instead maintains a continuously high liquid level. Causes of high liquid level conditions include siltation and blockage. Flow control operating conditions refer to situations where the flow rate in the pipe is maintained at a certain level. The operating conditions within the specified range are caused by artificial control or pipeline restriction of water flow, such as flow control by artificial facilities like pumping stations and dams, or overflow near the pipeline, where the flow rate remains basically constant due to pipeline restriction; overload conditions refer to situations where the flow rate in the pipeline exceeds the pipeline's operating capacity, and the filling degree exceeds the full pipe; backflow conditions refer to situations where water flows in the pipeline in the opposite direction, and in drainage systems, backflow is often caused by downstream blockage or backflow at the end; other operating conditions refer to situations where the characteristics of pipeline operation are not obvious and do not meet the characteristics of ideal, high liquid level operation, flow control, overload, backflow, etc.

[0072] According to the pipeline flow velocity versus fill degree operating condition curve, under ideal conditions, as the pipeline fill degree increases, the flow velocity also shows a corresponding increasing trend. When the fill degree approaches 1, the flow velocity reaches its peak. After that, even if the fill degree continues to increase, the flow velocity no longer increases, but instead shows a decreasing trend. Under high liquid level conditions, the relationship between flow velocity and liquid level conforms to the law of the liquid level-flow velocity operating condition curve of silted pipelines, that is, the flow velocity increases with the increase of fill degree. However, the main difference from the ideal operating condition is that the fill degree remains at a certain level when the flow velocity is low. Therefore, the pipeline liquid level-flow velocity operating condition curve does not pass through the origin. Under flow control conditions, since the flow rate remains constant, the fill degree... As the flow rate increases, the flow velocity decreases. When the pipe is fully filled, the flow velocity remains constant. Under overload conditions, as the flow velocity increases, the pipe's filling level gradually increases along its specific performance curve. When the pipe reaches its critical point of full-pipe flow, the flow velocity remains relatively stable even as the flow rate continues to increase. At this point, the filling level will continue to climb until the pipe becomes overloaded. Under backflow conditions, drainage obstruction occurs before backflow occurs, manifested as a decrease in flow velocity and an increase in filling level. When the filling level approaches 1, the flow velocity increases briefly and then decreases as the filling level increases. Subsequently, the upstream and downstream pressures balance, the flow velocity turns negative, and the backflow phenomenon occurs.

[0073] Furthermore, the operating conditions are identified and described by adding monitoring points M and N.

[0074] In this embodiment of the disclosure, sewage monitoring point M encountered a situation of mixed rainwater and sewage flow during the rainy season, which caused pipeline overload problems. The point information is shown in Table 4.

[0075] Table 4: Summary Table of Point M

[0076] Figure 5 This is a graph showing the flow velocity versus the degree of filling at monitoring point M.

[0077] Analysis of the curve graph showed that the Pearson correlation coefficient was 0.86, indicating a reliable fit. Figure 5 As shown, before the rainfall, the pipeline was in ideal operating condition, with its filling degree fluctuating between 0.4 and 0.6, and the flow velocity stable within the range of 0.76 m / s to 1.09 m / s, which is consistent with the pipeline level-flow-velocity operating condition curve. Subsequently, rainfall occurred, with a rainfall amount of 26.12 mm and a duration of 240 minutes. The runoff rainwater continuously flowed into the pipeline, causing the pipeline filling degree to rise continuously from 0.75, while the flow velocity remained almost unchanged, indicating that the pipeline was in a state of severe overload. In this embodiment, wastewater monitoring point N is located on the main inlet pipe of the wastewater treatment plant and is affected by the regulation of the wastewater treatment plant's pumping station. The location information is shown in Table 5.

[0078] Table 5: Summary of N Monitoring Points

[0079] Figure 6 This is a graph showing the flow velocity versus the degree of filling at monitoring point N.

[0080] Analysis of the curve graph showed that the Pearson correlation coefficient was -0.91, indicating a reliable fit. Figure 6 As shown, before the sewage treatment plant's pumping station started its pumping operation, the liquid level in the pipeline was high, with a filling degree as high as 1.5, and the flow velocity was too low, only 0.5 m / s, indicating a severe overload. At this time, the downstream pumping station started its pumping operation. In the initial stage of pumping, affected by the pumping effect, the flow rate increased rapidly for a short period, reaching a peak of 827.03 L / s. At the same time, the filling degree dropped sharply to 0.6, and the silt in the pipeline was removed by the huge suction force. As the pumping station gradually entered the stable operation stage, the flow rate also tended to stabilize, entering a flow mode with a constant flow rate of 620.90 L / s. At this time, the liquid level in the pipeline dropped to 0.65 m, and the flow velocity steadily increased to 1.5 m / s.

[0081] This disclosure deeply considers the impact of sedimentation on the flow characteristics of circular pipes, innovatively proposes the Manning formula applicable to sediment-accumulated circular pipes, and constructs an evaluation system for pipeline operating condition diagnosis based on parameter verification. In practical application, the effectiveness and wide applicability of this diagnostic method have been fully verified through comprehensive collection and analysis of field data, providing new ideas and approaches for optimized operation strategies and maintenance management schemes for pipeline systems.

[0082] This disclosure also provides a diagnostic device for pipeline operating conditions. Figure 7 This is a schematic diagram of the structure of the pipeline operation condition diagnostic device provided in the embodiments of this disclosure, as shown below. Figure 7 As shown, the device includes: The acquisition module 701 is used to set up multiple monitoring points on the pipeline and acquire water flow data at each monitoring point, the water flow data including at least the water flow depth; The first determining module 702 is used to determine the degree of filling of each monitoring point based on the water flow depth at each monitoring point; Module 703 is used to construct a flow velocity correction model based on the central angles corresponding to siltation depth and flow depth, and the central angles corresponding to siltation depth. The second determining module 704 is used to determine the predicted flow velocity at each monitoring point based on the flow velocity correction model; Module 705 is established to generate a curve of flow velocity versus fullness based on the predicted flow velocity and fullness at each monitoring point. The third determination module 706 is used to determine the operating conditions of each monitoring point based on the curve graph.

[0083] In one embodiment, the construction module 703 includes: The first sub-acquisition module is used to acquire the slope and pipe diameter of each monitoring point; The first sub-module is used to establish the first correlation between the cross-sectional area of ​​the water flow and the pipe diameter, the central angle corresponding to the water flow depth and the central angle corresponding to the siltation depth; The second sub-module is used to establish a second correlation between the wetted perimeter of the water flow and the pipe diameter, the central angle corresponding to the water flow depth, the central angle corresponding to the siltation depth, and the siltation depth of the pipe. The third sub-module is used to establish a flow rate correction model between the predicted flow rate and the first correlation, the second correlation, the slope, and the roughness of the pipe.

[0084] The specific details of each part of the above-mentioned device have been described in detail in the method section of the implementation plan. For any undisclosed details, please refer to the implementation plan of the method section, and therefore will not be repeated here.

[0085] According to embodiments of this disclosure, this disclosure also provides an electronic device and a readable storage medium.

[0086] Figure 8A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0087] like Figure 8 As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 802 or a computer program loaded from storage unit 808 into random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0088] Multiple components in device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of monitors, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0089] The computing unit 801 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as methods for diagnosing pipeline operating conditions. For example, in some embodiments, the methods for diagnosing pipeline operating conditions may be implemented as computer software programs tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the methods for diagnosing pipeline operating conditions described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform diagnostic methods for pipeline operating conditions by any other suitable means (e.g., by means of firmware).

[0090] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0091] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0092] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0093] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0094] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0095] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0096] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this disclosure can be achieved, and this is not limited herein.

[0097] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means two or more, unless otherwise explicitly specified.

[0098] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A method for diagnosing pipeline operating conditions, characterized in that, The method includes: Multiple monitoring points are set up on the pipeline, and water flow data is acquired at each monitoring point, wherein the water flow data includes at least the water flow depth; The degree of filling at each monitoring point is determined based on the water flow depth at each monitoring point. A flow velocity correction model is constructed based on the central angles corresponding to siltation depth and flow depth. Based on the aforementioned flow velocity correction model, the predicted flow velocity at each monitoring point is determined; Based on the predicted flow velocity and filling degree of each monitoring point, a curve of flow velocity versus filling degree is established. Based on the graph, the operating conditions of each monitoring point are determined.

2. The method according to claim 1, characterized in that, The flow velocity correction model is constructed based on the central angles corresponding to siltation depth and flow depth, including: Obtain the slope and pipe diameter for each monitoring point; Establish a first correlation between the cross-sectional area of ​​the water flow and the pipe diameter, the central angle corresponding to the water flow depth, and the central angle corresponding to the siltation depth; Establish a second correlation between the wetted perimeter of the water flow and the pipe diameter, the central angle corresponding to the water flow depth, the central angle corresponding to the siltation depth, and the siltation depth of the pipe; Establish a flow rate correction model between the predicted flow rate and the first correlation, the second correlation, the slope, and the roughness of the pipe.

3. The method according to claim 2, characterized in that, The construction of the flow rate correction model includes: The velocity correction model is constructed based on the following formula: in, To predict flow rate, For slope, For the roughness of the pipe, For pipe diameter, The central angle corresponding to the depth of the water flow. The central angle corresponding to the depth of siltation. The depth of siltation. Here is the roughness uncertainty coefficient. The uncertainty coefficient for the central angle is... The value represents the uncertainty coefficient for siltation depth.

4. The method according to claim 2, characterized in that, The water flow data also includes: the actual flow velocity at each monitoring point; After constructing the flow rate correction model, the method further includes: The roughness, siltation depth, and various uncertainty coefficients in the velocity correction model are adjusted, and the adjusted predicted velocity is obtained. The adjusted predicted flow velocity is compared with the actual flow velocity to obtain the comparison result; Based on the comparison results, it is determined whether the adjusted predicted flow rate meets the first preset requirement; If the first preset requirement is not met, the roughness, siltation depth, and various uncertainty coefficients in the flow rate correction model are readjusted until the predicted flow rate meets the first preset requirement.

5. The method according to claim 4, characterized in that, After the predicted flow rate meets the first preset requirement, the method further includes: Determine the objective function between the actual flow velocity and the predicted flow velocity. This objective function is used to evaluate whether the roughness, sedimentation depth, and various uncertainty coefficients in the flow velocity correction model meet the second preset requirements. Determine whether the objective function is within the confidence interval; If it is within the confidence interval, it means that the roughness, siltation depth and various uncertainty coefficients meet the second preset requirements; If the objective function is not within the confidence interval, the roughness, siltation depth, and various uncertainty coefficients are readjusted until the objective function is within the confidence interval.

6. The method according to claim 1, characterized in that, The operating conditions include at least: ideal operating condition, high liquid level operating condition, flow control operating condition, overload operating condition, and backflow operating condition.

7. A diagnostic device for pipeline operating conditions, characterized in that, The device includes: The acquisition module is used to set up multiple monitoring points on the pipeline and acquire water flow data at each monitoring point, wherein the water flow data includes at least the water flow depth; The first determining module is used to determine the fullness of each monitoring point based on the water flow depth at each monitoring point; The module is used to build a flow velocity correction model based on the central angles corresponding to siltation depth and water flow depth. The second determining module is used to determine the predicted flow velocity at each monitoring point based on the flow velocity correction model. A module is built to generate a curve of flow velocity versus fullness based on the predicted flow velocity and fullness at each monitoring point. The third determining module is used to determine the operating conditions of each monitoring point based on the curve graph.

8. The apparatus according to claim 7, characterized in that, The building module includes: The first sub-acquisition module is used to acquire the slope and pipe diameter of each monitoring point; The first sub-module is used to establish a first correlation between the cross-sectional area of ​​the water flow and the pipe diameter, the central angle corresponding to the water flow depth, and the central angle corresponding to the siltation depth; The second sub-module is used to establish a second correlation between the wetted perimeter of the water flow and the pipe diameter, the central angle corresponding to the water flow depth, the central angle corresponding to the siltation depth, and the siltation depth of the pipe. The third sub-module is used to establish a flow rate correction model between the predicted flow rate and the first correlation, the second correlation, the slope, and the roughness of the pipe.

9. An electronic device, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

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