An intelligent pipeline network fault detection method and system based on flexible sensing materials

Abstract

The present invention discloses a smart pipe network fault detection method and system based on flexible sensing materials, which belongs to the field of fault detection technology. The key points of its technical solution include: determining multiple charge amplifiers currently transmitting electrical signals, and the time when multiple charge amplifiers transmit each electrical signal; determining the flexible sensor corresponding to each charge amplifier in the multiple charge amplifiers, and the preset delay duration corresponding to the flexible sensor; determining the flexible sensor that generates charge based on the time of collecting each electrical signal and the preset delay duration; determining the fault location of the smart pipe network based on the location of the flexible sensor that generates charge. When there are multiple flexible sensors, the present invention reduces the number of charge amplifiers used, and at the same time, through the setting of the delay module, accurately identifies the flexible sensing material corresponding to each signal transmitted by the charge amplifier, and finally determines the fault location of the smart pipe network through signal analysis.

Description

Technical Field

[0001] The present invention relates to the field of fault detection technology, and more particularly to a smart pipe network fault detection method and system based on flexible sensing materials. Background Art

[0002] In modern pipeline fault detection, piezoelectric flexible sensing materials have become a key tool due to their ability to sensitively detect changes in stress, strain, and vibration within pipelines. When exposed to external stimuli (such as vibrations caused by pipeline leaks or stress changes caused by blockages), piezoelectric flexible sensing materials generate weak charge signals. Because these signals are so weak, they cannot be effectively transmitted or processed directly. Therefore, equipping each flexible sensing material with a charge amplifier has become a common technique in the industry.

[0003] However, pipeline detection requires a large amount of flexible sensing materials, which require the configuration of an equal number of charge amplifiers. For example, in a medium-sized urban water supply network, if thousands of sensing materials are deployed and thousands of charge amplifiers are purchased, the hardware cost will increase significantly. Therefore, the existing technology has shortcomings. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the purpose of the present invention is to provide a smart pipeline fault detection method and system based on flexible sensing materials. By setting the flexible sensor and the charge amplifier, the usage of the charge amplifier is reduced, thereby reducing the hardware cost.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The present invention provides a smart pipe network fault detection method based on flexible sensing materials. A plurality of flexible sensors are arranged on the outer peripheral surface of the smart pipe network. The flexible sensors include flexible sensing materials. Each flexible sensor is connected to two charge amplifiers via two independent connection lines. Each charge amplifier is simultaneously connected to multiple flexible sensors. A delay module is configured on one of the connection lines of each flexible sensor. Each delay module is used to delay the charge amplifier for a preset period of time to collect charge. The smart pipe network fault detection method includes:

[0007] determining a plurality of charge amplifiers currently transmitting electrical signals, and a time at which the plurality of charge amplifiers transmit each electrical signal;

[0008] Determining a flexible sensor corresponding to each charge amplifier of the plurality of charge amplifiers and a preset duration corresponding to the flexible sensor;

[0009] determining the flexible sensor generating the charge based on the time of transmitting each electrical signal and the preset duration;

[0010] The fault location of the smart pipe network is determined according to the location of the flexible sensor that generates the charge.

[0011] As a further improvement of the present invention, determining the flexible sensor generating the charge based on the time of transmitting each electrical signal and the preset duration includes:

[0012] determining a charge acquisition time corresponding to each electrical signal according to a time for transmitting each electrical signal and response times corresponding to the plurality of charge amplifiers;

[0013] For each of the electrical signals, obtaining a charge amplifier corresponding to each of the electrical signals, and determining a charge amplifier adjacent to the charge amplifier;

[0014] Calculating the charge acquisition time corresponding to each electrical signal and the time interval between the charge acquisition times corresponding to each signal transmitted by the adjacent charge amplifiers;

[0015] According to the time interval and the preset duration, the flexible sensor generating the charge is determined.

[0016] As a further improvement of the present invention, determining the flexible sensor that generates charge based on the time interval and the preset duration includes:

[0017] matching the time interval with a preset duration corresponding to the flexible sensor;

[0018] The flexible sensor generating the charge is determined based on the matching result.

[0019] As a further improvement of the present invention, determining the flexible sensor generating the charge according to the matching result includes:

[0020] determining a signal group corresponding to the flexible sensor according to the matching result;

[0021] The flexible sensor generating the charge is determined according to the charge collection time corresponding to each signal in the signal group.

[0022] As a further improvement of the present invention, determining the fault location of the smart pipe network according to the location of the flexible sensor generating the charge includes:

[0023] determining a distance between the flexible sensors that generate charge according to positions of the flexible sensors that generate charge;

[0024] establishing a hyperbolic model according to the charge collection time and the distance between the flexible sensor generating the charge;

[0025] The fault location of the smart pipe network is determined by solving the hyperbolic model.

[0026] As a further improvement of the present invention, the smart pipe network fault detection method further includes determining the fault type of the smart pipe network based on the current transmission signal.

[0027] As a further improvement of the present invention, determining the fault type of the smart pipe network according to the current transmission signal includes:

[0028] Acquiring characteristic parameters of the current transmission signal, wherein the characteristic parameters include amplitude, frequency, and phase;

[0029] determining a fault signal according to the characteristic parameters and a preset standard;

[0030] Determine the fault type of the smart pipe network according to the fault signal.

[0031] As a further improvement of the present invention, determining the fault type of the smart pipe network according to the fault signal includes:

[0032] Acquiring characteristic parameters of the fault signal;

[0033] The fault type of the smart pipe network is determined according to the characteristic parameters of the fault signal, where the fault type includes leakage fault, blockage fault and corrosion fault.

[0034] As a further improvement of the present invention, determining the fault type of the smart pipe network according to the characteristic parameters of the fault signal includes:

[0035] Determining whether there is a leakage fault in the smart pipe network according to the amplitude and high frequency band frequency of the fault signal;

[0036] Determining whether the smart pipe network has a blockage fault based on the low-frequency amplitude and frequency offset of the fault signal;

[0037] Determine whether the smart pipe network has a corrosion fault based on the high-frequency component and amplitude standard deviation of the fault signal.

[0038] The present invention provides a smart pipe network fault detection system based on flexible sensing materials, the system comprising a flexible sensor, a charge amplifier, a delay module and a server;

[0039] The flexible sensor is used to input the collected charge into two corresponding charge amplifiers through two independent connection lines. A delay module is configured on one of the connection lines of the flexible sensor, and the delay module is used to delay the charge amplifier for a preset time to collect the charge;

[0040] The charge amplifier is used to convert the collected charge into a voltage signal, amplify the voltage signal, and transmit the amplified signal to the server;

[0041] The server determines the fault location and fault type of the smart pipe network based on the amplified signal.

[0042] The present invention uses a charge amplifier shared by multiple flexible sensors, and the charge amplifier is connected to multiple flexible sensors. Through the connection setting of the present invention, the number of charge amplifiers used can be reduced when multiple flexible sensors exist at the same time. At the same time, when the number of flexible sensors is increased, there is no need to add the same number of charge amplifiers, so as to further achieve the purpose of reducing the use of charge amplifiers. In addition, the present invention accurately identifies the flexible sensor corresponding to each signal transmitted by the charge amplifier through the setting of the delay module, and finally determines the fault location and fault type of the smart pipe network through signal analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Schematic diagram of the connection between the flexible sensor and the charge amplifier in the example technology;

[0044] Figure 2 This is a flowchart of the steps of a smart pipe network fault detection method based on flexible sensing materials of the present invention;

[0045] Figure 3 Schematic diagram of the corresponding relationship between the flexible sensor and the charge amplifier of the present invention;

[0046] Figure 4 It is a scene schematic diagram of the present invention;

[0047] Figure 5 Schematic diagram of the scenario after adding a flexible sensor and a charge amplifier to the present invention;

[0048] Figure 6 A schematic diagram of the scenario after adding a flexible sensor and charge amplifier to the example technology;

[0049] Figure 7 This is a schematic diagram of locating a pipeline fault according to the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations of the technical solution of the present invention.

[0051] Identical components are denoted by the same reference numerals. It should be noted that the terms "front," "rear," "left," "right," "upper," and "lower" used in the following description refer to directions in the accompanying drawings, and the terms "bottom," "top," "inner," and "outer" refer to directions toward or away from the geometric center of a particular component, respectively.

[0052] The term "and / or" in the following text simply describes an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Additionally, the character " / " generally indicates an "or" relationship between the related objects.

[0053] See also Figure 1 In the example technology, flexible sensors are connected to charge amplifiers to detect pipeline faults. Flexible sensors a, b, c, d, e, f are connected to charge amplifiers A, B, F, C, E, D through wires in a one-to-one correspondence. At this time, the flexible sensing material and the charge amplifier are configured in a one-to-one ratio, thereby increasing the number burden of the charge amplifiers.

[0054] In order to solve the above problems, Figure 2 As shown, the embodiment of the present application provides a smart pipe network fault detection method based on flexible sensing materials, including:

[0055] determining a plurality of charge amplifiers currently transmitting electrical signals and a time at which the plurality of charge amplifiers transmit each electrical signal;

[0056] Determining a flexible sensor corresponding to each charge amplifier in the plurality of charge amplifiers and a preset duration corresponding to the flexible sensor;

[0057] Determining the flexible sensor that generates the charge based on the time and preset duration of each electrical signal transmission;

[0058] The fault location of the smart pipe network is determined based on the location of the flexible sensor that generates charge.

[0059] Among them, multiple flexible sensors are arranged on the peripheral surface of the smart pipe network. The flexible sensors include flexible sensing materials. Each flexible sensor is connected to two charge amplifiers through two independent connecting lines, and each charge amplifier is connected to multiple flexible sensors at the same time. A delay module is configured on one of the connecting lines of each flexible sensor. Each delay module is used to delay the charge amplifier for a preset period of time to collect charge.

[0060] Specifically, this embodiment deploys multiple flexible sensors at key locations on the smart pipe network, such as pipe joints and bends. These flexible sensors are all piezoelectric. Their operating principle is that when a pipe network fault occurs, stress waves and acoustic waves caused by the fault propagate in the surrounding area. Upon reaching the flexible sensors, the stress waves deform the flexible sensing material, while the acoustic waves cause tiny vibrations in the material. This in turn deforms the material's internal crystal structure, leading to relative displacement of the positive and negative charge centers, thus generating polarized charges. Electrodes on the flexible sensing material gather the charges dispersed on the sensing material's surface, forming a relatively concentrated charge source. However, because the generated charges are very weak, they require conversion and amplification by a specialized charge amplifier. The charge amplifier has an input capacitor. When a charge is input, due to the characteristics of the capacitor, the charge generates a voltage change across the amplifier's input capacitor. The charge amplifier amplifies this voltage change, converting the weak charge into a voltage signal with a certain amplitude. This signal is then transmitted to a server for subsequent signal analysis.

[0061] For example, Figure 3 As shown, each flexible sensor corresponds to two charge amplifiers, and the charge amplifiers corresponding to different flexible sensors can be the same, for example Figure 3 The flexible sensors a and b in the figure correspond to charge amplifiers A and B. A delay module is configured on one of the connection lines of each flexible sensor. The delay module includes a resistor and a capacitor. The charge generated by the flexible sensing material charges the capacitor through the resistor. This charging process delays the rising edge of the signal, thereby delaying the time it takes for the charge to reach the charge amplifier. Figure 3 The thicker connecting lines in the middle represent the connection lines configured with the delay module.

[0062] In this embodiment, a plurality of flexible sensors are connected to charge amplifiers through wires, wherein each flexible sensor is connected to two charge amplifiers, and each charge amplifier is connected to at least two flexible sensors. Through the connection setting of the present invention, when the number of flexible sensors is increased, there is no need to add the same number of charge amplifiers, thereby achieving the purpose of reducing the usage of charge amplifiers. At the same time, through the setting of the delay circuit, the flexible sensor corresponding to each signal transmitted by the charge amplifier is accurately identified, and finally the fault location and fault type of the smart pipe network are determined through signal analysis.

[0063] Furthermore, this embodiment provides a flexible sensor for determining a charge generation based on the time of transmitting each electrical signal and a preset duration, including:

[0064] Determining a charge acquisition time corresponding to each electrical signal based on a transmission time of each electrical signal and a response time corresponding to a plurality of charge amplifiers;

[0065] For each electrical signal, obtaining a charge amplifier corresponding to each electrical signal, and determining a charge amplifier adjacent to the charge amplifier;

[0066] Calculating the charge acquisition time corresponding to each electrical signal and the time interval between the charge acquisition times corresponding to each signal transmitted by adjacent charge amplifiers;

[0067] According to the time interval and the preset duration, the flexible sensor generating the charge is determined.

[0068] If the flexible sensors corresponding to two charge amplifiers overlap, the two charge amplifiers are considered adjacent, for example, charge amplifiers A and B are adjacent. The charge amplifier corresponding to an electrical signal is the charge amplifier that collected that electrical signal. Since each charge amplifier collects multiple signals, these signals need to be separated to determine the flexible sensor corresponding to each signal. These flexible sensors are the flexible sensors that generate charge. The response time of a charge amplifier refers to the time it takes for the charge amplifier to respond to the input charge and output the corresponding electrical signal. Therefore, if the transmission time and response time are known, the charge collection time corresponding to each electrical signal can be determined, that is, the time it takes for the charge to reach the charge amplifier.

[0069] In addition to obtaining the response time, the charge collection time can also be obtained based on the charge amplifier's timing circuit. Specifically, the charge amplifier includes a signal processing circuit for amplification and a timing circuit for determining the time when the charge reaches the charge amplifier, i.e., the charge collection time. The charge amplifier then packages each signal and its corresponding charge collection time and transmits them to the server for subsequent signal analysis.

[0070] For example, Figure 4 As shown, assuming that the charge amplifier A collects four electrical signals, these four electrical signals should be the electrical signals corresponding to the flexible sensors a, b, e and f. If it is necessary to further determine the flexible sensor corresponding to each electrical signal, first determine the charge amplifiers D and B adjacent to the charge amplifier A. The electrical signal transmitted by the charge amplifier B includes four electrical signals (signals corresponding to the flexible sensors a, b, d and c), and the electrical signal transmitted by the charge amplifier D includes two electrical signals (electrical signals corresponding to the flexible sensors e and f). A total of six electrical signals are obtained. For each electrical signal collected by the charge amplifier A, it is necessary to obtain the time interval between it and the charge collection time of these six electrical signals.

[0071] Furthermore, this embodiment provides a method for determining a flexible sensor that generates charge based on a time interval and a preset duration, including:

[0072] Matching the time interval with the preset duration corresponding to the flexible sensor;

[0073] The flexible sensor generating the charge is determined based on the matching result.

[0074] Specifically, the delay module includes resistors and capacitors. By controlling the resistance value of the resistor and the capacitance value of the capacitor in the delay circuit, the time for the capacitor to charge can be controlled, thereby delaying the time for the charge to reach the charge amplifier. The time for the capacitor to charge is the preset time. If the delay circuit is not set, such as Figure 4 As shown in the figure, since the distance between the flexible sensors and the charge amplifiers arranged in the same key area is relatively close, and the charge transfer speed is relatively fast, while the accuracy of the charge amplifier is limited, there may be a situation where the identified acquisition time is the same, making it difficult to determine the correspondence between the signal and the flexible sensor. In addition, in this embodiment, if only one of the two charge amplifiers corresponding to the two flexible sensors is the same, for example Figure 4 In the flexible sensor a and the flexible sensor c, or the two charge amplifiers corresponding to the two flexible sensors are different, but there are adjacent charge amplifiers, such as Figure 4 In the flexible sensor f and the flexible sensor c, the preset time lengths corresponding to the two flexible sensors should be different. If the two charge amplifiers corresponding to the two flexible sensors are the same, the preset time lengths corresponding to the two flexible sensors can be the same or different.

[0075] If the preset durations corresponding to the two flexible sensors are different, the preset durations are matched with the calculated multiple time intervals, and the two signals corresponding to the time intervals with the same preset duration are used as the signals corresponding to the flexible sensors.

[0076] Furthermore, this embodiment provides a flexible sensor that determines, based on the matching result, that generates a charge, including:

[0077] Determine the signal group corresponding to the flexible sensor according to the matching result;

[0078] According to the charge collection time corresponding to each signal in the signal group, the flexible sensor that generates the charge is determined.

[0079] Specifically, if the preset time lengths corresponding to the two flexible sensors are the same, matching the preset time lengths with the multiple time intervals obtained by calculation will result in a signal group, which includes two groups of signals. The time intervals between the two groups of signals are the same as the delay time. At this time, the signal corresponding to each flexible sensor in the signal group should be determined based on the acquisition time of each signal in the signal group, so that the flexible sensor that generates charge can be determined.

[0080] In summary, in order to further illustrate the execution steps of this embodiment, the charge amplifiers A and B are taken as an example for overall description.

[0081] For example, Figure 4 As shown, for each signal transmitted by the charge amplifier A, it is first necessary to determine the interval between the charge collection time corresponding to each signal transmitted by the charge amplifiers B and D, and obtain multiple time intervals.

[0082] Since the distance between the flexible sensor and the charge amplifier is relatively close, and the speed of charge transfer is relatively fast, while the accuracy of the charge amplifier is limited, if the delay circuit is not set, the charge collection time corresponding to each signal is equal, that is, the time interval is zero. For flexible sensor a, when the delay module is set, the time interval between the two signals corresponding to flexible sensor a should be the same as the preset time length corresponding to flexible sensor a. Since the charge amplifiers corresponding to flexible sensors a and b are the same, if the preset time lengths corresponding to the two flexible sensing materials are different at this time, then the preset time lengths corresponding to any two flexible sensors corresponding to charge amplifiers A, B and D are different. At this time, the two signals corresponding to the time interval with the same preset time length corresponding to flexible sensor a among the multiple time intervals directly obtained are used as the signals corresponding to flexible sensor a. These two signals are Figure 4 The signals corresponding to ① and ② in the figure are as follows. If there are no such two signals, it means that the flexible sensor a is not a flexible sensor that generates charge.

[0083] If the preset durations for the two flexible sensors are the same, and both are charge-generating flexible sensors (this remains undetermined), it's difficult to determine the signals corresponding to each flexible sensor solely based on the time intervals. For example, if the preset duration for flexible sensor a is matched with the multiple calculated time intervals, a signal group corresponding to flexible sensor a is obtained. This signal group includes two groups of signals: ① and ②, and ③ and ④. The time intervals between these two groups of signals are the same as the preset duration for flexible sensor a. Further determination is then required based on the charge collection time of each signal in the signal group. Since the route of the delay module is known, for flexible sensor a, charge amplifier A should collect charge later than charge amplifier B. Based on this, it can be determined that the signals corresponding to flexible sensor a are those corresponding to ① and ②.

[0084] This embodiment can effectively reduce the number of charge amplifiers used through the connection configuration between the flexible sensor and the charge amplifier, and if the number of flexible sensors is increased on this basis, there is no need to increase the number of charge amplifiers, for example Figure 5 As shown, if Figure 4 To add flexible sensors h and j on the basis of Figure 6As shown, adding flexible sensors h and j to the exemplary technology requires two additional charge amplifiers H and J. Therefore, the approach provided by this embodiment reduces hardware costs. Furthermore, since each flexible sensor in this embodiment corresponds to two signals, if one signal deforms during transmission, the other signal can be used as a supplement to ensure efficient data collection.

[0085] Furthermore, this embodiment provides a step of determining a fault location and fault type of the smart pipe network based on the signal, including:

[0086] Acquire characteristic parameters of the signal, including amplitude, frequency and phase;

[0087] Determine fault signals based on characteristic parameters and preset standards;

[0088] Determine the fault type and location of the smart pipe network based on the fault signal.

[0089] Specifically, the preset standard can be determined based on the parameter data of the pipeline network when no fault occurs. For example, according to the 3 Sigma principle, the parameter data of the pipeline network over a period of time when no fault occurs is first obtained, and the mean value corresponding to each parameter is calculated. and standard deviation , any parameter value exceeds The signal of the range is used as a fault signal.

[0090] Furthermore, this embodiment provides a step of determining the fault type and location of the smart pipe network based on the fault signal, including:

[0091] Obtain characteristic parameters and acquisition time of fault signals;

[0092] Determine the fault type of the smart pipe network based on the characteristic parameters of the fault signal. Fault types include leakage, blockage, and corrosion.

[0093] Determine the fault location of the smart pipe network based on the collection time.

[0094] Furthermore, this embodiment provides a step of determining the fault type of the smart pipe network based on characteristic parameters of the fault signal, including:

[0095] Determine whether there is a leakage fault in the smart pipe network based on the amplitude and high-frequency band frequency of the fault signal;

[0096] Determine whether there is a blockage fault in the smart pipe network based on the low-frequency amplitude and frequency offset of the fault signal;

[0097] Based on the high-frequency component and amplitude standard deviation of the fault signal, it is determined whether there is a corrosion fault in the smart pipe network.

[0098] Specifically, this embodiment selects specific signatures for identification of different fault types. When a leakage occurs, fluid loss within the pipeline generates additional vibration. The greater the change in fluid velocity and flow rate, the stronger the impact on the pipeline, resulting in a larger signal amplitude. When fluid ejected through the leak hole creates turbulence, the pipeline generates complex vibrations, which in turn excite new frequency components in the high-frequency band (500Hz-2kHz). Therefore, high-frequency signatures are a significant indicator of leakage faults, distinguishing them from other faults and facilitating accurate leak identification. When a blockage occurs, the blockage alters the fluid flow, causing abnormal pressure distribution and generating low-frequency stress fluctuations (10Hz-100Hz) on the pipeline wall. A larger blockage area ratio results in greater fluid obstruction, more severe low-frequency stress fluctuations, and a larger signal amplitude. Therefore, the low-frequency amplitude effectively indicates the degree of blockage. Blockage also alters the natural frequency of the fluid within the pipeline, shifting the signal's frequency distribution. Therefore, the frequency shift is closely related to the blockage area ratio, and analyzing the frequency shift can be used to determine the severity of the blockage. When a corrosion failure occurs, as the corrosion deepens, the local stiffness of the pipeline decreases. Under vibration excitation, the high-frequency response is enhanced. The more severe the corrosion, the higher the energy of the high-frequency component. Therefore, by calculating the energy of the high-frequency component, the degree of corrosion can be effectively quantified. In addition, corrosion causes the pipeline wall to be uneven, making the vibration state of the pipeline during operation more complex and the signal amplitude fluctuation intensified. The amplitude standard deviation is used to measure the degree of signal amplitude dispersion. It can well reflect the amplitude fluctuation changes caused by corrosion and can be used as an important parameter for assessing the severity of corrosion failures.

[0099] After determining the specific parameters, different fault models can be determined through simulation experiments. For example, for a blockage fault, blockages with different blockage area ratios (10%, 20%, 30%, 40%, and 50%) can be fabricated to ensure they fit securely within the pipeline without causing additional damage. Flexible sensors and charge amplifiers are then installed on the pipeline to acquire signals over a period of time. Signal processing then obtains the signal's characteristic parameters, and an amplitude-blockage area ratio model and a frequency offset-blockage area ratio model are established. These models can be built using methods such as linear regression and support vector machine regression. Finally, the characteristic parameter values of the fault signal are input into the model. The blockage area ratio can be determined by the low-frequency signal amplitude, and the severity of the blockage fault can be determined by the frequency offset. Other faults can also be analyzed using the dual number method, which is not described in detail in this embodiment.

[0100] Furthermore, this embodiment provides a step of determining a fault location of the smart pipe network based on the acquisition time, including:

[0101] A hyperbolic model is established based on the charge collection time and the corresponding distance between flexible sensors;

[0102] The fault location of the smart pipe network is determined by solving the hyperbolic model.

[0103] Specifically, for circuits without a delay module, the time it takes for the flexible sensor to output charge can be determined based on the charge amplifier's charge acquisition time and the charge's transmission time along the circuit. For circuits with a delay module, the time it takes for the flexible sensor to output charge can be determined based on the charge amplifier's charge acquisition time, a preset duration, and the charge's transmission time along the circuit. Based on the characteristics of the flexible sensing material in the flexible sensor, the time from charge generation to charge output can be determined, ultimately yielding the charge generation time of the flexible sensor corresponding to the fault signal.

[0104] For example, Figure 7 As shown in Figure 2, assuming that the flexible sensors corresponding to the fault signal are a, e, and f, the distance difference between the fault point and each flexible sensor can be obtained based on the time difference of charge generation of each flexible sensor. Figure 7 The dashed line represents the distance between the two flexible sensors. A hyperbola is then constructed with the flexible sensor positions as the focal points and the distance difference as the major axis. The intersection of the hyperbolas is the fault location. The figures provided in this embodiment are all plan views, but the pipeline surface is curved. In practical applications, software such as CATIA can be used to fit the plan view to the pipeline surface.

[0105] This embodiment achieves accurate identification of different fault types through real-time analysis and judgment of signal characteristic parameters. At the same time, it combines the hyperbolic model and the charge generation time and position relationship of the sensor to determine the fault location, making full use of the signal propagation characteristics and effectively improving the accuracy of fault location.

[0106] Furthermore, an embodiment of the present application provides a smart pipe network fault detection system based on flexible sensing materials, including a flexible sensor, a charge amplifier, a delay module and a server;

[0107] The flexible sensor is used to input the collected charge into two corresponding charge amplifiers through two independent connection lines. A delay module is configured on one of the connection lines of the flexible sensor, which is used to delay the charge amplifier to collect charge for a preset time.

[0108] The charge amplifier is used to convert the collected charge into a voltage signal, amplify the voltage signal, and finally transmit the amplified signal to the server;

[0109] The server determines the fault location and type of the smart pipe network based on the amplified signal.

[0110] The embodiments of the present application provide a smart pipe network fault detection method and system based on flexible sensing materials. Compared with the prior art method of configuring a charge amplifier for each flexible sensor, this embodiment uses wires to connect multiple flexible sensors to the charge amplifier, reducing the use of charge amplifiers. At the same time, through the setting of a delay circuit, the flexible sensor corresponding to each signal collected by the charge amplifier is accurately identified. Finally, through signal analysis, the fault location and fault type of the smart pipe network are determined.

[0111] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0113] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A smart pipe network fault detection method based on flexible sensing materials, characterized in that: The outer peripheral surface of the smart pipe network is provided with a plurality of flexible sensors, each of which comprises a flexible sensing material. Each flexible sensor is connected to two charge amplifiers via two independent connection lines, and each charge amplifier is simultaneously connected to a plurality of flexible sensors. A delay module is configured on one of the connection lines of each flexible sensor, and each delay module is configured to cause the charge amplifier to delay collecting charge for a preset period of time. The smart pipe network fault detection method comprises: determining a plurality of charge amplifiers currently transmitting electrical signals, and a time at which the plurality of charge amplifiers transmit each electrical signal; Determining a flexible sensor corresponding to each charge amplifier of the plurality of charge amplifiers and a preset duration corresponding to the flexible sensor; determining the flexible sensor generating the charge based on the time of transmitting each electrical signal and the preset duration; The fault location of the smart pipe network is determined according to the location of the flexible sensor that generates the charge.

2. The intelligent pipe network fault detection method based on flexible sensing materials according to claim 1 is characterized in that: The step of determining the flexible sensor generating the charge according to the time of transmitting each electrical signal and the preset duration includes: determining a charge acquisition time corresponding to each electrical signal according to a time for transmitting each electrical signal and response times corresponding to the plurality of charge amplifiers; For each of the electrical signals, obtaining a charge amplifier corresponding to each of the electrical signals, and determining a charge amplifier adjacent to the charge amplifier; Calculating the charge acquisition time corresponding to each electrical signal and the time interval between the charge acquisition times corresponding to each signal transmitted by the adjacent charge amplifiers; According to the time interval and the preset duration, the flexible sensor generating the charge is determined.

3. The intelligent pipe network fault detection method based on flexible sensing materials according to claim 2 is characterized in that: The step of determining the flexible sensor that generates the charge according to the time interval and the preset duration includes: matching the time interval with a preset duration corresponding to the flexible sensor; The flexible sensor generating the charge is determined based on the matching result.

4. The intelligent pipe network fault detection method based on flexible sensing materials according to claim 3 is characterized in that: The method of determining the flexible sensor generating the charge according to the matching result includes: determining a signal group corresponding to the flexible sensor according to the matching result; The flexible sensor generating the charge is determined according to the charge collection time corresponding to each signal in the signal group.

5. The intelligent pipe network fault detection method based on flexible sensing materials according to claim 2 is characterized in that: The method of determining the fault location of the smart pipe network according to the location of the flexible sensor generating the charge includes: determining a distance between the flexible sensors that generate charge according to positions of the flexible sensors that generate charge; establishing a hyperbolic model according to the charge collection time and the distance between the flexible sensor generating the charge; The fault location of the smart pipe network is determined by solving the hyperbolic model.

6. The intelligent pipe network fault detection method based on flexible sensing materials according to claim 5 is characterized in that: The smart pipe network fault detection method further includes determining a fault type of the smart pipe network based on a current transmission signal.

7. The intelligent pipe network fault detection method based on flexible sensing materials according to claim 6 is characterized in that: The determining the fault type of the smart pipe network according to the current transmission signal includes: Acquiring characteristic parameters of the current transmission signal, wherein the characteristic parameters include amplitude, frequency, and phase; determining a fault signal according to the characteristic parameters and a preset standard; Determine the fault type of the smart pipe network according to the fault signal.

8. The intelligent pipe network fault detection method based on flexible sensing materials according to claim 7 is characterized in that: Determining the fault type of the smart pipe network according to the fault signal includes: Acquiring characteristic parameters of the fault signal; The fault type of the smart pipe network is determined according to the characteristic parameters of the fault signal, where the fault type includes leakage fault, blockage fault and corrosion fault.

9. The intelligent pipe network fault detection method based on flexible sensing materials according to claim 8 is characterized in that: The determining the fault type of the smart pipe network according to the characteristic parameters of the fault signal includes: Determining whether there is a leakage fault in the smart pipe network according to the amplitude and high frequency band frequency of the fault signal; Determining whether the smart pipe network has a blockage fault based on the low-frequency amplitude and frequency offset of the fault signal; Determine whether the smart pipe network has a corrosion fault based on the high-frequency component and amplitude standard deviation of the fault signal.

10. A smart pipe network fault detection system based on flexible sensing materials, used to implement a smart pipe network fault detection method based on flexible sensing materials according to any one of claims 1 to 9, characterized in that: The system includes a flexible sensor, a charge amplifier, a delay module and a server; The flexible sensor is used to input the collected charge into two corresponding charge amplifiers through two independent connection lines. A delay module is configured on one of the connection lines of the flexible sensor, and the delay module is used to delay the charge amplifier for a preset time to collect the charge; The charge amplifier is used to convert the collected charge into a voltage signal, amplify the voltage signal, and transmit the amplified signal to the server; The server determines the fault location and fault type of the smart pipe network based on the amplified signal.

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