Multi-parameter wireless sensing monitoring and early warning method for buried water supply pipe network

Abstract

The invention discloses a multi-parameter wireless sensing, monitoring and early warning method for an underground water supply network. The method comprises the following steps: arranging wireless data acquisition sensors for acquiring core parameter data representing the safe operation state of the underground water supply network in real time at key nodes of the underground water supply network; calculating real-time comprehensive stress of the section of the pipeline by combining material characteristics, structural characteristics and parameter data of the pipeline; judging whether the inclination angle of the pipeline, the pore water pressure, the soil pressure and the real-time comprehensive stress of the section of the pipeline exceed the corresponding early warning values or not according to the monitored pipeline, if the parameters do not exceed the corresponding early warning values, returning to the previous step, and otherwise, starting early warning and returning to the previous step. The wireless data acquisition sensor is used as a data acquisition and transmission element, is suitable for a buried water supply network, is convenient to install and arrange, and is not limited by the environment; the monitoring sensitivity is high, the reliability is good, and early warning parameters are reasonably set; safety comprehensive early warning of a water supply pipe network is achieved, safe operation of the water supply pipe is guaranteed, leakage is reduced, and resource waste is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless monitoring and early warning of buried water supply pipe networks, and in particular to a method for real-time monitoring of multi-parameter data such as operating status, operating environment and pipeline structure of buried water supply pipe networks and providing early warning. Background Art

[0002] With the acceleration of urbanization, the safe and stable operation of urban water supply systems has become crucial to maintaining the normal operation of urban infrastructure. As a key component of the urban water supply system, the operating efficiency and safety of the water supply network directly affect the daily life and public health of urban residents. However, the traditional water supply network monitoring method has many limitations. For example, it only monitors factors related to pipeline operation, such as flow and pressure, and lacks monitoring of critical parameter information such as pipeline structure and operating environment that affect the safety of pipeline network operation, such as pipeline structure inclination, temperature at the top and bottom of the pipeline, soil pressure and pore water pressure, etc. These pipeline structure and operating environment factors are crucial for evaluating the comprehensive safety performance of the pipeline network. At present, due to the lack of monitoring of these key factors, it is difficult to effectively evaluate the safety performance of the pipeline network, and it leads to the failure to give effective warnings before some serious pipe burst accidents occur, resulting in serious economic losses and even casualties.

[0003] In addition, traditional water supply network monitoring methods mostly rely on wired sensing technology, which must be installed and laid out through pipe wells or pipe corridors, making it difficult to monitor key nodes of buried water supply networks. Moreover, the installation and maintenance costs of such wired systems are high, and they have limitations in terms of wiring and adaptability to complex terrain.

[0004] In addition, real-time monitoring is essential for timely detection and resolution of problems in water supply networks. However, traditional monitoring methods often have difficulty in real-time data collection and processing, resulting in an inability to respond in an emergency. And due to the ambiguity of early warning indicators and thresholds, early monitoring methods often lack effective early warning mechanisms and are unable to provide effective early warnings before accidents occur. Therefore, existing technologies have obvious deficiencies in real-time monitoring, multi-parameter analysis, and early warning mechanisms of water supply networks.

[0005] In view of this, it is particularly urgent and important to develop a method that can comprehensively monitor the buried water supply network with multiple parameters and in real time and provide effective early warning. Summary of the invention

[0006] Due to the above-mentioned defects in the prior art, the present invention provides a method that can comprehensively monitor the buried water supply network with multiple parameters and in real time and provide effective early warning, which overcomes the limitations of the prior art - few monitoring parameters affecting accuracy and inconvenient installation and wiring, improves the efficiency and accuracy of water supply network monitoring, thereby ensuring the safety and reliability of the urban water supply system.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] A multi-parameter wireless sensing monitoring and early warning method for a buried water supply network comprises the following steps:

[0009] (1) Wireless data acquisition sensors are deployed at key nodes of the buried water supply network to collect core parameter data that characterize the safe operation status of the buried water supply network in real time. The core parameter data include water pressure, water flow, pipeline inclination, soil temperature, water temperature in the pipeline, soil pressure, and pore water pressure. There are many types of wireless data acquisition sensors, including water pressure, water temperature, structural inclination, ambient temperature, soil pressure and pore water pressure sensors, which are used to monitor the operation status of the water supply network, pipeline structure and network environment. These sensors can operate without external power supply and complex wiring, and are easy to install and maintain in various parts of the network. Through buried wireless communication technology, the collected data is transmitted to the data platform in real time;

[0010] (2) The processing unit calculates the real-time comprehensive stress of the pipeline cross section by combining the material properties and structural properties of the pipeline, as well as monitoring data such as water pressure, water flow, soil temperature, water temperature in the pipeline, and soil pressure;

[0011] (3) The processing unit determines whether the pipeline inclination exceeds the pipeline inclination warning value, whether the pore water pressure exceeds the pore water pressure warning value, whether the soil pressure exceeds the soil pressure warning value above the pipeline, and whether the real-time comprehensive stress of the pipeline cross section exceeds the pipeline cross section stress warning value for the monitored pipeline. If none of the above parameters are exceeded, the process returns to step (2); otherwise, the process initiates the warning and returns to step (2).

[0012] The change in the structural inclination of the water supply network is a quantitative response to the structural anomalies of the network. The relative inclination of the pipeline includes the relative inclination at both ends of the pipeline interface and the relative inclination of two points within a certain distance at the non-interface. The pore water pressure warning value of the soil around the pipeline is set to assist in the identification of pipeline leakage points. The soil pressure warning value above the pipeline is used to avoid damage to the pipeline network caused by sudden changes in the load above the pipeline. The comprehensive stress of the pipeline section is compared with the bearing capacity of the pipeline section to determine the operation status of the pipeline network.

[0013] The multi-parameter wireless sensing monitoring and early warning method for buried water supply pipe network of the present invention uses wireless data acquisition sensors as data acquisition and transmission elements. Compared with wired sensors, its installation and layout are convenient and not restricted by complex environments. The parameters of pipeline inclination, pore water pressure, soil pressure and real-time comprehensive stress of pipeline cross section are used as early warning parameters, among which the pipeline inclination, pore water pressure and soil pressure are single indicator early warning parameters, and the real-time comprehensive stress of pipeline cross section is a comprehensive indicator early warning parameter. Compared with traditional early warning methods, the early warning parameters are reasonably set, the method has high monitoring sensitivity and good reliability, can realize real-time monitoring and early warning of buried water supply pipe network, ensure the safe operation of water supply pipe, reduce leakage, and reduce waste of resources, and has great application prospects.

[0014] Specifically, the multi-parameter wireless sensing monitoring and early warning method of the buried water supply network of the present invention uses wireless sensing technology, Internet of Things technology, theoretical analysis, and early warning mechanism to monitor the multi-parameter key factors that affect the operation of the water supply network in real time, establish multiple indicator early warning thresholds, and conduct real-time analysis of the monitoring data based on this, and give real-time early warnings for abnormal conditions in the network (based on the pipeline structure inclination angle early warning value, pore water pressure early warning value, soil pressure early warning value above the pipeline, and pipeline cross-sectional stress early warning value established by experiments, software simulations, and theoretical analysis, the abnormal operation state of the network is determined on the data platform. When the monitoring value exceeds the early warning value, the early warning is immediately initiated. The early warning information is promptly communicated to relevant personnel through multiple channels to ensure a rapid and effective response), thereby realizing comprehensive monitoring and management of the water supply network. The application of the method of the present invention can significantly improve the safety and efficiency of urban water supply networks. The innovation of the present invention lies in that it integrates multi-parameter sensing monitoring, wireless transmission technology and data analysis, and proposes multiple indicator warning thresholds based on experiments, simulations and theoretical analysis, providing a more efficient and intelligent water supply network monitoring solution, which can not only timely discover and solve water supply network operation safety problems, but also effectively reduce maintenance costs and improve the reliability and safety of the water supply system.

[0015] As the preferred technical solution:

[0016] In the above-mentioned method for multi-parameter wireless sensing monitoring and early warning of buried water supply pipe network, the processing unit can be arranged in a local end or a remote end;

[0017] The local end arrangement is to directly integrate the processing unit into the wireless data acquisition sensor;

[0018] The remote end arrangement method is to arrange the processing unit in the remote server data platform, and send the data acquired by the wireless data acquisition sensor to the processing unit through a network connection.

[0019] As described above, in a multi-parameter wireless sensing monitoring and early warning method for a buried water supply network, the wireless data acquisition sensor is equipped with a high-strength casing, adopts a protection level of IP68 or above, can be directly buried, and has a built-in power supply module, a microprocessor module, a wireless antenna module, and a MEMS (Micro-Electro-Mechanical Systems) sensor module, which can provide ultra-long-life multi-parameter real-time monitoring.

[0020] In the above-mentioned method for monitoring and early warning of multi-parameter wireless sensing of buried water supply pipe network, the early warning value of the core parameters includes three levels, the first level is 70% of the benchmark early warning value, the second level is 85% of the benchmark early warning value, and the third level is 100% of the benchmark early warning value. The core parameters include pipe inclination, pore water pressure, soil pressure, and real-time comprehensive stress of pipe cross section. The multi-level early warning method can provide a more detailed solution for monitoring water supply pipe network.

[0021] In the above-mentioned multi-parameter wireless sensing monitoring and early warning method for buried water supply pipe networks, the benchmark early warning value of the pipe inclination is determined by a combination of mechanical tests on the water supply pipe and finite element simulation analysis using ABAQUS software. Generally, the early warning values ​​of the pipe inclination of pipes of different materials and diameters are different.

[0022] In the above-mentioned multi-parameter wireless sensing monitoring and early warning method for a buried water supply network, the reference early warning value of the pipeline inclination includes a reference early warning value of the relative inclination at the pipeline interface and a reference early warning value of the relative inclination at the pipeline non-interface;

[0023] The reference warning value of the relative inclination angle at the pipeline interface is set to the limit value of the elastic deformation of the pipeline when the interface is bent or the minimum value of the relative inclination angle corresponding to the leakage of the pipeline interface, which is calculated based on the pipeline parameters using the numerical simulation software Abaqus;

[0024] The reference warning value of the relative inclination angle at the non-interface of the pipeline is set to the relative inclination angle value between the two monitoring points corresponding to the maximum elastic flexural deformation limit value of the pipeline when the pipeline is bent, and is calculated based on the pipeline parameters using the numerical simulation software Abaqus.

[0025] In the above-mentioned multi-parameter wireless sensing monitoring and early warning method for buried water supply network, the baseline early warning value of pore water pressure is: ω×γ w ×h, where the buried depth of the pore water pressure sensor is ℎ, γ w is the unit weight of water, ω is the mutation coefficient given in combination with meteorological precipitation information, and its value is related to the precipitation intensity. Under no precipitation conditions, ω=1.2, and under precipitation conditions, ω>1.2.

[0026] In the above-mentioned multi-parameter wireless sensing monitoring and early warning method for buried water supply pipe network, the reference early warning value of the soil pressure above the pipe is based on the maximum soil pressure value γ that the pipe can withstand. S H S Determined, considering a certain safety reserve, 0.7γ S H S As an early warning value of soil pressure;

[0027] The maximum earth pressure value γ that the pipeline can withstand S H S It is determined according to the following formula:

[0028]

[0029] Where φ is the moment reduction coefficient, which is taken as 1.0, and γ G,sv is the vertical earth pressure partial coefficient, which is taken as 1.27; S H S k is the maximum vertical soil pressure that the pipeline can withstand; vm is the maximum bending moment coefficient of the pipe wall section under the vertical earth pressure, which is taken as 0.157; r m Calculate the radius of the pipe; b m Calculate the width for the pipe; t m Calculate the thickness for the pipe; E p E is the deformation modulus of the backfill soil on the pipe side, which is 5MPa; p is the elastic modulus of the pipe; D1 is the outer wall diameter of the pipe.

[0030] In the above-mentioned multi-parameter wireless sensing monitoring and early warning method for buried water supply pipe network, the calculation formula of the real-time comprehensive stress of the pipe cross section is as follows:

[0031] ;

[0032] ;

[0033] ;

[0034] ;

[0035] ;

[0036] ;

[0037] ;

[0038] Among them, E p is the elastic modulus of the pipe, P i is the internal pressure of the pipeline, α is the linear expansion coefficient of the pipeline material, ∆T is the temperature change value, , , is the interaction coefficient between pipeline and soil, γ G∆ is the partial coefficient of uneven settlement, taken as 1.27, χ is the axial curvature of the pipeline, R is the radius of the pipeline, P e is the external pressure of the pipeline, t is the wall thickness of the pipeline, ∆T′ is the temperature difference between the inner and outer surfaces of the pipeline, v P is the Poisson's ratio of the pipe, r0 is the inner radius of the pipe, r i is the outer radius of the pipe, r is the distance from the center of the pipe to any point, k gm is the structural self-weight bending moment coefficient, k wm is the water weight bending moment coefficient in the pipe, k vm is the vertical earth pressure moment coefficient, G 1k G is the standard value of the deadweight of cast iron pipe structure. wk is the standard value of water weight in the pipeline, q ik is the standard value of vertical pressure per unit area of ​​traffic load, D is the pipe diameter, F sv,k is the standard value of vertical earth pressure on the top of the pipeline per unit length, σ θ a is the pipeline annular tensile stress, σ θ r is the pipeline annular bending stress, σ x a is the longitudinal tensile stress of the pipeline, σ x Δ is the longitudinal bending stress of the pipeline, σ θ is the pipeline hoop stress, σ x is the longitudinal stress of the pipeline, σ is the real-time comprehensive stress of the pipeline section, and γ0 is the importance coefficient of the pipeline structure.

[0039] Specifically, the material properties and monitoring data of the pipeline are brought into the corresponding formula to calculate the comprehensive stress of the pipeline section, including the following steps:

[0040] (1) Substitute the pipeline elastic modulus, pipeline internal pressure, pipeline material linear expansion coefficient and temperature change value into the formula to calculate the longitudinal tensile stress of the pipeline;

[0041]

[0042] Where E p is the elastic modulus of the pipe, P i is the internal pressure of the pipeline, α is the linear expansion coefficient of the pipeline material, ∆T is the temperature change value, , , is the interaction coefficient between pipe and soil.

[0043] (2) Substitute the elastic modulus of the soil pipe, the partial coefficient of uneven settlement, and the axial curvature of the pipe into the formula to calculate the longitudinal bending stress;

[0044]

[0045] where γ G∆ is the partial coefficient of uneven settlement, which is taken as 1.27; χ is the axial curvature of the pipe, and R is the radius of the pipe.

[0046] (3) Substitute the pipeline internal pressure, external pressure, pipeline diameter, pipeline wall thickness, pipeline elastic modulus and linear expansion coefficient into the formula to calculate the pipeline annular tensile stress;

[0047]

[0048] Where P i is the internal pressure of the pipeline, P e is the external pressure of the pipeline, t is the wall thickness of the pipeline, ∆T′ is the temperature difference between the inner and outer surfaces of the pipeline, E p is the elastic modulus of the pipe, α is the linear expansion coefficient, v P is the Poisson's ratio of the pipe, r0 is the inner radius of the pipe, r i is the outer radius of the pipe, and r is the distance from the center of the pipe to any point.

[0049] (4) Substitute the structural self-weight bending moment coefficient, the water weight bending moment coefficient in the pipe, and the vertical earth pressure bending moment coefficient into the formula to calculate the hoop bending stress;

[0050]

[0051] Where k gm is the structural self-weight bending moment coefficient, k wm is the water weight bending moment coefficient in the pipe, k vm is the vertical earth pressure moment coefficient, G 1k G is the standard value of the deadweight of cast iron pipe structure. wk is the standard value of water weight in the pipeline, q ik is the standard value of vertical pressure per unit area of ​​traffic load, D is the pipe diameter, F sv,k is the standard value of the vertical earth pressure on the top of the pipe per unit length, γ0 is the importance coefficient of the pipeline structure, and t is the pipe wall thickness.

[0052] (5) Combine the component stresses to obtain the comprehensive stress of the cross section and compare it with the allowable stress [σ] of the pipe cross section.

[0053] ;

[0054] ;

[0055] ;

[0056] where σ θ is the hoop stress, σ x is the longitudinal stress.

[0057] In the above-mentioned multi-parameter wireless sensing monitoring and early warning method for buried water supply pipe networks, the reference early warning value of pipe cross-sectional stress is specifically the allowable stress [σ] of the pipe.

[0058] The above technical solution is only a feasible technical solution of the present invention, and the protection scope of the present invention is not limited thereto. Those skilled in the art can reasonably adjust the specific design according to actual needs.

[0059] The above invention has the following advantages or beneficial effects:

[0060] (1) The multi-parameter wireless sensing monitoring and early warning method for buried water supply pipe networks of the present invention uses wireless data acquisition sensors as data acquisition and transmission elements. Compared with wired sensors, it is easy to install and arrange, and is not restricted by complex environments;

[0061] (2) The multi-parameter wireless sensing monitoring and early warning method of the buried water supply network of the present invention uses the parameters of pipeline inclination, pore water pressure, soil pressure and real-time comprehensive stress of the pipeline cross section as early warning parameters, among which the pipeline inclination, pore water pressure and soil pressure are single indicator early warning parameters, and the real-time comprehensive stress of the pipeline cross section is a comprehensive indicator early warning parameter. Compared with the traditional early warning method, the early warning parameter setting is reasonable, the method has high monitoring sensitivity and good reliability, and can realize real-time monitoring and early warning of the buried water supply network;

[0062] (3) The multi-parameter wireless sensing monitoring and early warning method for the buried water supply network of the present invention solves the problems of single monitoring indicators for the operation information of the water supply network and unclear early warning indicators and thresholds, improves the accuracy of monitoring, reduces maintenance costs, reduces leakage in the water supply network, reduces resource waste, and at the same time enhances the ability to respond to emergencies, ensuring the safe operation of the water supply pipeline, and has good application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The present invention and its features, appearance and advantages will become more apparent by reading the detailed description of non-limiting embodiments with reference to the following drawings. The same reference numerals indicate the same parts in all the drawings. The drawings are not drawn to scale, and emphasis is placed on illustrating the subject matter of the present invention.

[0064] Figure 1 It is an application schematic diagram of the multi-parameter wireless sensing monitoring and early warning method for buried water supply pipe network of the present invention;

[0065] Among them, 1 represents important nodes of the pipeline network such as pipeline interfaces, 2 is a pore water pressure sensor, 3 is a soil temperature sensor, 4 and 5 are inclination sensors, 6 is a soil pressure sensor, 7 is a water pressure sensor, and 8 is a water temperature sensor, 9 is a relay, 10 is a professional gateway, 11 is a 4G / 5G network, and 12 is a data platform. DETAILED DESCRIPTION

[0066] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0067] Example 1

[0068] A multi-parameter wireless sensing monitoring and early warning method for a buried water supply network comprises the following steps:

[0069] (1) Wireless data acquisition sensors are deployed at key nodes of the buried water supply network to collect real-time core parameter data that characterize the safe operation status of the buried water supply network. The core parameter data include water pressure, water flow, pipeline inclination, soil temperature, water temperature in the pipeline, soil pressure, and pore water pressure. Specifically, Figure 1 As shown, at important nodes of the underground water supply network that extends in all directions, such as the pipeline interface 1, a variety of sensors such as pore water pressure sensor 2, soil temperature sensor 3, tilt sensor 4 / 5, soil pressure sensor 6, water pressure sensor 7 and water temperature sensor 8 are arranged by using sensor installation special equipment to monitor the operation status of the network, the structure of the network and the operation environment and other multi-parameter information. The monitored network data is transmitted to the gateway 10 in real time through the wireless Mesh network by using the relay 9. The gateway 10 transmits it to the data platform 12 through the 4G / 5G network 11. The data platform receives, stores, calculates and analyzes the data, and generates warning information for the results exceeding the warning threshold, and warns the relevant personnel. Of course, the processing unit can also be directly integrated into the wireless data acquisition sensor, and the data processing is completed in the wireless data acquisition sensor;

[0070] (2) The processing unit of the data platform combines the material properties of the pipeline as well as the water pressure, water flow, temperature and soil pressure to obtain the real-time comprehensive stress of the pipeline cross section. The calculation formula is as follows:

[0071] ;

[0072] ;

[0073] ;

[0074] ;

[0075] ;

[0076] ;

[0077] ;

[0078] Among them, E p is the elastic modulus of the pipe, P i is the internal pressure of the pipeline, α is the linear expansion coefficient of the pipeline material, ∆T is the temperature change value, , , is the interaction coefficient between pipeline and soil, γ G∆ is the partial coefficient of uneven settlement, taken as 1.27, χ is the axial curvature of the pipeline, R is the radius of the pipeline, P e is the external pressure of the pipeline, t is the wall thickness of the pipeline, ∆T′ is the temperature difference between the inner and outer surfaces of the pipeline, v P is the Poisson's ratio of the pipe, r0 is the inner radius of the pipe, r i is the outer radius of the pipe, r is the distance from the center of the pipe to any point, k gm is the structural self-weight bending moment coefficient, k wm is the water weight bending moment coefficient in the pipe, k vm is the vertical earth pressure moment coefficient, G 1k G is the standard value of the deadweight of cast iron pipe structure. wk is the standard value of water weight in the pipeline, q ik is the standard value of vertical pressure per unit area of ​​traffic load, D is the pipe diameter, F sv,k is the standard value of vertical earth pressure on the top of the pipeline per unit length, σ θ a is the pipeline annular tensile stress, σ θ r is the pipeline annular bending stress, σ x a is the longitudinal tensile stress of the pipeline, σ x Δ is the longitudinal bending stress of the pipeline, σ θ is the pipeline hoop stress, σ x is the longitudinal stress of the pipeline, σ is the real-time comprehensive stress of the pipeline cross section;

[0079] (3) The processing unit of the data platform determines for each pipeline whether the pipeline inclination exceeds the pipeline inclination warning value (determining whether the pipeline structure is abnormal), whether the pore water pressure exceeds the pore water pressure warning value (determining whether the pipeline network is leaking), whether the soil pressure exceeds the soil pressure warning value above the pipeline (determining whether the soil pressure above the pipeline is too large), and whether the real-time comprehensive stress of the pipeline cross section exceeds the pipeline cross section stress warning value (determining whether the pipeline cross section stress is abnormal). If none of the above parameters exceed the warning value, the process returns to step (2); otherwise, the warning is activated (after the automatic warning mechanism is activated, the data platform notifies the relevant maintenance team and decision makers through SMS, email or application) and returns to step (2);

[0080] The warning values ​​of core parameters include three levels: level one is 70% of the baseline warning value, level two is 85% of the baseline warning value, and level three is 100% of the baseline warning value. The core parameters include pipeline inclination, pore water pressure, soil pressure, and real-time comprehensive stress of pipeline cross section.

[0081] The benchmark warning value of pipeline inclination is determined by mechanical testing of water supply pipelines and finite element simulation analysis using ABAQUS software. The benchmark warning value of pipeline inclination includes the benchmark warning value of relative inclination at pipeline interfaces and the benchmark warning value of relative inclination at pipeline non-interfaces. The benchmark warning value of relative inclination at pipeline interfaces is set to the minimum value of relative inclination corresponding to the limit value of elastic deformation of pipeline when the interface is bent or the minimum value of relative inclination corresponding to water leakage at pipeline interface. The benchmark warning value of relative inclination at pipeline non-interfaces is set to the relative inclination value of two points corresponding to the maximum elastic flexural deformation limit value of pipeline when the pipeline is bent between two monitoring points.

[0082] The baseline warning value of pore water pressure is: ω×γ w ×h, where the buried depth of the pore water pressure sensor is ℎ, γ w is the unit weight of water, ω is the mutation coefficient given in combination with meteorological precipitation information, and its value is related to the precipitation intensity. Under no precipitation conditions, ω=1.2, and under precipitation conditions, ω>1.2;

[0083] The baseline warning value of the soil pressure above the pipeline is based on the maximum soil pressure value γ that the pipeline can withstand. S H S Determined, considering a certain safety reserve, 0.7γ S H S As the soil pressure warning value, the maximum soil pressure value γ that the pipeline can withstand S H S It is determined according to the following formula:

[0084]

[0085] Where φ is the moment reduction coefficient, which is taken as 1.0, and γ G,svis the vertical earth pressure partial coefficient, which is taken as 1.27; S H S k is the maximum vertical soil pressure that the pipeline can withstand; vm is the maximum bending moment coefficient of the pipe wall section under the vertical earth pressure, which is taken as 0.157; r m Calculate the radius of the pipe; b m Calculate the width for the pipe; t m Calculate the thickness for the pipe; E p E is the deformation modulus of the backfill soil on the pipe side, which is 5MPa; p is the elastic modulus of the pipe; D1 is the outer wall diameter of the pipe;

[0086] The benchmark warning value of pipeline cross-sectional stress is specifically the allowable stress [σ] of the pipeline.

[0087] It has been verified that the multi-parameter wireless sensing monitoring and early warning method for buried water supply pipe network of the present invention uses wireless data acquisition sensors as data acquisition and transmission elements. Compared with wired sensors, its installation and layout are convenient and not restricted by complex environments. The parameters of pipeline inclination, pore water pressure, soil pressure and real-time comprehensive stress of pipeline cross section are used as early warning parameters, among which pipeline inclination, pore water pressure and soil pressure are single indicator early warning parameters, and real-time comprehensive stress of pipeline cross section is comprehensive indicator early warning parameter. Compared with traditional early warning methods, early warning parameters are reasonably set, the method has high monitoring sensitivity and good reliability, and can realize real-time monitoring and early warning of buried water supply pipe network. It solves the problems of single monitoring indicators of water supply pipe network operation information and unclear early warning indicators and thresholds, improves monitoring accuracy, reduces maintenance costs, reduces leakage of water supply pipe network, reduces waste of resources, and at the same time enhances the ability to respond to emergencies, ensures the safe operation of water supply pipes, and has good application prospects.

[0088] Those skilled in the art should understand that those skilled in the art can implement variations by combining the prior art and the above embodiments, which will not be described in detail here. Such variations do not affect the essential content of the present invention, and will not be described in detail here.

[0089] The above describes the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the above-mentioned specific embodiments, and the devices and structures that are not described in detail should be understood to be implemented in a common manner in the art; any technician familiar with the art can use the above-disclosed methods and technical contents to make many possible changes and modifications to the technical solutions of the present invention without departing from the scope of the technical solutions of the present invention, or modify them into equivalent embodiments of equivalent changes, which does not affect the essential content of the present invention. Therefore, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention are still within the scope of protection of the technical solutions of the present invention.

Claims

1. A multi-parameter wireless sensing monitoring and early warning method for buried water supply network, characterized in that: The following steps are involved: (1) Wireless data acquisition sensors are deployed at key nodes of the buried water supply network to collect real-time core parameter data that characterize the safe operation status of the buried water supply network. The core parameter data include water pressure, water flow, pipeline inclination, soil temperature, water temperature in the pipeline, soil pressure, and pore water pressure; (2) The processing unit calculates the real-time comprehensive stress of the pipe section by combining the material properties and structural properties of the pipe, as well as the water pressure, water flow, soil temperature, water temperature in the pipe, and soil pressure; (3) The processing unit determines whether the pipeline inclination exceeds the pipeline inclination warning value, whether the pore water pressure exceeds the pore water pressure warning value, whether the soil pressure exceeds the soil pressure warning value above the pipeline, and whether the real-time comprehensive stress of the pipeline cross section exceeds the pipeline cross section stress warning value for the monitored pipeline. If none of the above parameters are exceeded, the process returns to step (2); otherwise, the process initiates the warning and returns to step (2).

2. According to claim 1, a multi-parameter wireless sensing monitoring and early warning method for buried water supply pipe network is characterized in that: The processing unit can be arranged in a local end or a remote end; The local end arrangement is to directly integrate the processing unit into the wireless data acquisition sensor; The remote end arrangement method is to arrange the processing unit in the remote server data platform, and send the data acquired by the wireless data acquisition sensor to the processing unit through a network connection.

3. According to claim 1, a multi-parameter wireless sensing monitoring and early warning method for buried water supply pipe network is characterized in that: The wireless data acquisition sensor is equipped with a high-strength shell, which has a power supply module, a microprocessor module, a wireless antenna module, and a MEMS sensor module built in it.

4. A method for multi-parameter wireless sensing monitoring and early warning of a buried water supply network according to claim 1, characterized in that: The warning values ​​of core parameters include three levels: level one is 70% of the benchmark warning value, level two is 85% of the benchmark warning value, and level three is 100% of the benchmark warning value. The core parameters include pipeline inclination, pore water pressure, soil pressure, and real-time comprehensive stress of the pipeline cross section.

5. A method for multi-parameter wireless sensing monitoring and early warning of buried water supply network according to claim 4, characterized in that: The benchmark warning value of the pipeline inclination is determined by comprehensive mechanical testing and finite element simulation analysis of the water supply pipeline.

6. A method for multi-parameter wireless sensing monitoring and early warning of buried water supply network according to claim 5, characterized in that: The reference warning value of pipeline inclination includes the reference warning value of relative inclination at pipeline interface and the reference warning value of relative inclination at pipeline non-interface; The reference warning value of the relative inclination angle at the pipeline interface is set to the limit value of elastic deformation of the pipeline when the interface is bent or the minimum value of the relative inclination angle corresponding to the leakage of the pipeline interface; The reference warning value of the relative inclination angle at the non-interface of the pipeline is set as the relative inclination angle value between the two monitoring points corresponding to the maximum elastic flexural deformation limit value of the pipeline when the pipeline is bent.

7. A method for multi-parameter wireless sensing monitoring and early warning of buried water supply network according to claim 4, characterized in that: The baseline warning value of pore water pressure is: ω×γ w ×h, where the buried depth of the pore water pressure sensor is ℎ, γ w is the unit weight of water, ω is the mutation coefficient given in combination with meteorological precipitation information, and its value is related to the precipitation intensity. Under no precipitation conditions, ω=1.2, and under precipitation conditions, ω>1.

2.

8. The method for multi-parameter wireless sensing monitoring and early warning of buried water supply network according to claim 4 is characterized in that: The baseline warning value of the soil pressure above the pipeline is based on the maximum soil pressure value γ that the pipeline can withstand. S H S Determined; The maximum earth pressure value γ that the pipeline can withstand S H S It is determined according to the following formula: Where φ is the moment reduction factor, γ G,sv is the vertical earth pressure partial coefficient; S H S k is the maximum vertical soil pressure that the pipeline can withstand; vm is the maximum bending moment coefficient of the pipe wall section under the vertical earth pressure; r m Calculate the radius of the pipe; b m Calculate the width for the pipe; t m Calculate the thickness for the pipe; E p is the deformation modulus of the backfill soil on the pipe side; E p is the elastic modulus of the pipe; D1 is the outer wall diameter of the pipe.

9. The method for multi-parameter wireless sensing monitoring and early warning of buried water supply network according to claim 1, characterized in that: The calculation formula of the real-time comprehensive stress of the pipeline section is as follows: ； ； ； ； ； ； ； Among them, E p is the elastic modulus of the pipe, P i is the internal pressure of the pipeline, α is the linear expansion coefficient of the pipeline material, ∆T is the temperature change value, χ1, χ2, χ3 are the interaction coefficients between the pipeline and the soil, γ G∆ is the partial coefficient of uneven settlement, taken as 1.27, χ is the axial curvature of the pipeline, R is the radius of the pipeline, P e is the external pressure of the pipeline, t is the wall thickness of the pipeline, ∆T′ is the temperature difference between the inner and outer surfaces of the pipeline, v P is the Poisson's ratio of the pipe, r0 is the inner radius of the pipe, r i is the outer radius of the pipe, r is the distance from the center of the pipe to any point, k gm is the structural self-weight bending moment coefficient, k wm is the water weight bending moment coefficient in the pipe, k vm is the vertical earth pressure moment coefficient, G 1k G is the standard value of the deadweight of cast iron pipe structure. wk is the standard value of water weight in the pipeline, q ik is the standard value of vertical pressure per unit area of ​​traffic load, D is the pipe diameter, F sv,k is the standard value of vertical earth pressure on the top of the pipeline per unit length, σ θ a is the pipeline annular tensile stress, σ θ r is the pipeline annular bending stress, σ x a is the longitudinal tensile stress of the pipeline, σ x Δ is the longitudinal bending stress of the pipeline, σ θ is the pipeline hoop stress, σ x is the longitudinal stress of the pipeline, σ is the real-time comprehensive stress of the pipeline section, and γ0 is the importance coefficient of the pipeline structure.

10. A method for multi-parameter wireless sensing monitoring and early warning of a buried water supply network according to claim 4, characterized in that: The benchmark warning value of pipeline cross-sectional stress is specifically the allowable stress [σ] of the pipeline.

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