CN118564841A - Pipeline leakage detection method based on acceleration sensor - Google Patents

Abstract

The present application discloses a pipeline leakage detection method based on an acceleration sensor. The pipeline leakage detection method based on an acceleration sensor comprises the following steps: collecting an original sampling signal through an acceleration sensor; sampling the original sampling signal through a pipeline leakage detection device; processing the original sampling signal through a data processor in the pipeline leakage detection device to obtain a preliminary analysis result; and judging whether the pipeline is leaking based on the preliminary analysis result obtained by the data processor in the pipeline leakage detection device and a preset statistical model. The pipeline leakage detection method based on an acceleration sensor uses an acceleration sensor to detect and locate leakage points in a water supply network, which can improve detection efficiency and reduce false alarm rate.

Description

Pipeline leakage detection method based on acceleration sensor

Technical Field

The present application relates to the field of water supply network leakage detection, and specifically to a pipeline leakage detection method based on an acceleration sensor.

Background Art

The production and sales gap of water supply enterprises has always been one of the bottlenecks restricting the development of water supply enterprises, and it is also a hot spot and difficulty that the water supply industry generally pays attention to. Leakage of water supply network is one of the important reasons for the production and sales gap of water supply enterprises. Daily detection of water supply network leakage is an important link to reduce water waste and improve the production and sales of water supply enterprises. By detecting water supply network leakage, the leakage location can be found, and then the leakage location can be remedied in time.

Traditional technologies for detecting water supply network leaks often have high requirements for environmental sound. Specifically, for traditional technologies for detecting water supply network leaks, sounds other than the sound of water flow are noises. When detecting water supply network leaks, the noise level must be kept as low as possible. Accordingly, traditional technologies for detecting water supply network leaks usually need to be done manually using tools such as listening poles in the dead of night, which is inefficient, has a high false alarm rate, and is high risk.

Summary of the invention

An advantage of the present application is that it provides a pipeline leakage detection method based on an acceleration sensor, wherein the pipeline leakage detection method based on an acceleration sensor utilizes the acceleration sensor to detect and locate leakage points in a water supply network, thereby improving detection efficiency and reducing false alarm rate.

One advantage of the present application is that it provides a pipeline leakage detection method based on an acceleration sensor, wherein the pipeline leakage detection method based on the acceleration sensor uses a data processor in the pipeline leakage detection device to convert and analyze the original sampling signal collected by the acceleration sensor. Compared with sending the original sampling signal collected by the acceleration sensor to a background server through the Internet of Things, and converting and analyzing the collected original sampling signal through the background server, the present application uses the data processor in the pipeline leakage detection device to convert and analyze the original sampling signal collected by the acceleration sensor. It can simplify the signal processing process to a certain extent, improve the algorithm efficiency, and reduce the dependence on the network in the application scenario.

One advantage of the present application is that it provides a pipeline leakage detection method based on an acceleration sensor, wherein the pipeline leakage detection method based on an acceleration sensor uses a preset statistical model to obtain the level of pipeline leakage, and the preset statistical model can be optimized and adjusted according to empirical values, thereby improving recognition capabilities and reducing the probability of false alarms.

According to one aspect of the present application, a pipeline leakage detection method based on an acceleration sensor is provided, which includes the steps of: collecting an original sampling signal through an acceleration sensor; sampling the original sampling signal through a pipeline leakage detection device; processing the original sampling signal through a data processor in the pipeline leakage detection device to obtain a preliminary analysis result; and judging whether a pipeline is leaking based on the preliminary analysis result obtained by the data processor in the pipeline leakage detection device and a preset statistical model.

In one embodiment of the pipeline leakage detection method based on an acceleration sensor described in the present application, the original sampling signal is processed by a data processor in the pipeline leakage detection device to obtain a preliminary analysis result, including the steps of: converting the original sampling signal from the time domain to the frequency domain by the data processor in the pipeline leakage detection device to obtain a frequency domain sampling signal converted to the frequency domain; comparing the frequency domain sampling signal with a preset amplitude value to obtain a comparison result expression value, wherein the comparison result expression value is used to express the comparison result between the frequency domain sampling signal and the preset amplitude value.

In one embodiment of the pipeline leakage detection method based on an acceleration sensor described in the present application, the original sampling signal is converted from the time domain to the frequency domain by the data processor in the pipeline leakage detection device to obtain a frequency domain sampling signal converted to the frequency domain, including the steps of: segmenting the original sampling signal according to the time domain so that the original sampling signal is divided into at least two segments of segmented time domain sampling signals, and the sampling time of each segment of the segmented time domain sampling signal is in a different time domain; and converting at least two segments of the segmented time domain sampling signals from the time domain to the frequency domain to obtain at least two segments of segmented frequency sampling signals; comparing the frequency domain sampling signal with a preset amplitude value to obtain a comparison result expression value, including the steps of: comparing at least two segments of the segmented frequency domain sampling signals with corresponding at least two preset amplitude values to obtain at least two comparison preliminary result values.

In one embodiment of the pipeline leakage detection method based on an acceleration sensor described in the present application, the preliminary comparison result value is the frequency of the frequency domain sampling signal in each segment of the segmented frequency domain sampling signal whose amplitude is greater than the preset amplitude value corresponding to it, and the number of frequency domain sampling signals of each frequency in each segment of the segmented frequency domain sampling signal whose amplitude is greater than the preset amplitude value corresponding to it; the comparison result expression value includes the frequency of the frequency domain sampling signal in each segment of the segmented frequency domain sampling signal whose amplitude is greater than the preset amplitude value corresponding to it, and the sum of the number of frequency domain sampling signals of the same frequency in each segment of the segmented frequency domain sampling signal whose amplitude is greater than the preset amplitude value corresponding to it.

In one embodiment of the pipeline leakage detection method based on an acceleration sensor described in the present application, based on the preliminary analysis result obtained by the data processor in the pipeline leakage detection device and a preset statistical model, judging whether the pipeline is leaking includes the steps of: comparing the preliminary analysis result obtained by the data processor in the pipeline leakage detection device with a preset statistical quantity corresponding to a preset statistical frequency of a preset statistical model to determine whether the pipeline is leaking; wherein, in response to the sum of the number of frequency domain sampling signals of the preset statistical frequency in the preliminary analysis result obtained by the data processor in the pipeline leakage detection device being greater than the preset statistical quantity corresponding to the preset statistical frequency in the preset statistical model, determining that the pipeline is leaking.

In one embodiment of the pipeline leakage detection method based on an acceleration sensor described in the present application, in the process of performing domain conversion on the original sampled signal by a data processor in the pipeline leakage detection device, the data processor in the pipeline leakage detection device uses a discrete Fourier algorithm to perform domain conversion on the original sampled signal.

In one embodiment of the pipeline leakage detection method based on an acceleration sensor described in the present application, the original sampling signal is sampled by a pipeline leakage detection device, including the steps of: receiving the original sampling signal from an adaptive gain signal conditioning circuit by a sampling unit in the pipeline leakage detection device.

In one embodiment of the pipeline leakage detection method based on an acceleration sensor described in the present application, the original sampling signal is sampled by a pipeline leakage detection device, comprising the steps of: acquiring the original sampling data collected by the acceleration sensor at a preset sampling frequency within a preset duration through a sampling unit in the pipeline leakage detection device.

In one embodiment of the pipeline leakage detection method based on an acceleration sensor described in the present application, the raw sampling data collected by the acceleration sensor is obtained at a preset sampling frequency within a preset duration by a sampling unit in the pipeline leakage detection device, including the steps of: saving the raw sampling signal to a flash memory according to the preset sampling frequency by the sampling unit in the pipeline leakage detection device.

In one implementation of the pipeline leakage detection method based on an acceleration sensor according to the present application, the data processor in the pipeline leakage detection device is a single chip microcomputer.

Further objectives and advantages of the present application will be fully reflected through understanding of the following description and drawings.

These and other objects, features and advantages of the present application are fully reflected in the following detailed description, drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or.

FIG1 illustrates a schematic flow chart of a pipeline leakage detection method based on an acceleration sensor according to an embodiment of the present application.

FIG. 2 illustrates a schematic diagram of an original sampling signal, wherein the original sampling signal is a time domain sampling signal.

FIG. 3 is a schematic diagram showing a comparison between the frequency of the first segmented frequency sampling signal and the first preset amplitude value.

FIG. 4 is a schematic diagram showing a comparison between the frequency of the second segmented frequency sampling signal and the second preset amplitude value.

FIG5 is a schematic diagram showing a comparison between the frequency of the S-th segmented frequency sampling signal and the S-th preset amplitude value.

FIG. 6 illustrates the number of frequency sampling signals having frequencies greater than corresponding preset amplitude values in each segmented frequency sampling signal.

DETAILED DESCRIPTION

Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

It is understood that the term "one" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "one" cannot be understood as a limitation on the number. "Multiple" means greater than or equal to two.

Although ordinals such as "first," "second," and the like will be used to describe various components, those components are not limited herein. The term is used only to distinguish one component from another. For example, a first component may be referred to as a second component, and likewise, a second component may be referred to as a first component without departing from the teachings of the present application. The term "and/or" as used herein includes any and all combinations of one or more associated listed items.

The terms used herein are only used for the purpose of describing various embodiments and are not intended to be limiting. As used herein, the singular form is intended to include the plural form, unless the context clearly indicates an exception. It will also be understood that the terms "including" and/or "having" when used in this specification specify the presence of the described features, numbers, operations, components, elements, or combinations thereof, without excluding the presence or addition of one or more other features, numbers, operations, components, elements, or combinations thereof.

Application Overview

As mentioned above, traditional technologies for detecting water supply network leaks often have high requirements for environmental sound. Specifically, for traditional technologies for detecting water supply network leaks, sounds other than the sound of water flow are noises. When detecting water supply network leaks, the noise must be kept as low as possible. Accordingly, traditional technologies for detecting water supply network leaks usually need to be done manually using tools such as listening poles in the dead of night, which is inefficient, has a high false alarm rate, and is high risk.

The present application proposes that water supply network leaks can be detected by using devices that are insensitive to sound, so as to avoid the influence of sounds other than water flow sounds on the detection results. Specifically, the present application proposes that water supply network leaks can be detected by using an acceleration sensor. When a water supply pipeline leaks, the water flow impacts the pipe wall and the surrounding soil to generate vibrations, and the vibration signal is transmitted through the water supply pipeline. The acceleration sensor in the pipeline leakage detection device adsorbed in the pipeline senses the vibration signal. The vibration signal can be analyzed to determine whether the water supply network is leaking.

Compared with judging whether a water supply network is leaking by manually distinguishing the sound of water flow by listening to it, using acceleration sensors to detect water supply network leaks can not only improve efficiency but also reduce the false alarm rate.

It is worth mentioning that, in theory, the signals collected by the accelerometer can be sent to the backend server through the Internet of Things, and then the powerful computing power of the backend server can be used to calculate and analyze the signals collected by the accelerometer. However, this method has the problem of over-reliance on the communication network. When encountering complex on-site conditions that cause network anomalies, detection will not be possible.

Based on this, this application proposes to move the calculation and analysis steps forward and use the pipeline leakage detection equipment's own computing power to calculate and analyze the signals collected by the acceleration sensor, which can simplify the signal processing process to a certain extent, improve the efficiency of the algorithm, and reduce the dependence on the network in the application scenario. And the recognition ability of the pipeline leakage detection equipment can be continuously optimized and adjusted by adjusting the parameters of the preset statistical model.

Schematic diagram of pipeline leakage detection method based on acceleration sensor

As shown in Figures 1 to 6, the pipeline leakage detection method based on the acceleration sensor according to the embodiment of the present application is explained. Compared with judging whether the water supply network is leaking by manually distinguishing the sound of water flow by listening to the sound, the pipeline leakage detection method based on the acceleration sensor described in the embodiment of the present application detects the leakage of the water supply network by the acceleration sensor, which can not only improve the efficiency, but also reduce the false alarm rate. And the pipeline leakage detection method based on the acceleration sensor described in the embodiment of the present application uses the computing power of the pipeline leakage detection equipment itself to calculate and analyze the signal collected by the acceleration sensor, which can simplify the signal processing process to a certain extent, improve the efficiency of the algorithm, and reduce the dependence on the network in the application scenario.

As shown in FIG1 , the pipeline leakage detection method based on the acceleration sensor includes the following steps: S110, collecting original sampling signals through the acceleration sensor; S120, sampling the original sampling signals through the pipeline leakage detection device; S130, processing the original sampling signals through the data processor in the pipeline leakage detection device to obtain a preliminary analysis result; and S140, obtaining the level of pipeline leakage based on the preliminary analysis result obtained by the data processor in the pipeline leakage detection device and a preset statistical model.

In step S110, the original sampling signal is collected by the acceleration sensor. Specifically, the pipeline leakage detection device is installed in the pipeline or near the pipeline. When the water flows, the pipe wall and the surrounding soil vibrate, and then the acceleration sensor in the pipeline leakage detection device detects the original sampling signal. Accordingly, the original sampling signal is formed by the vibration signal generated by the water flow. The original sampling signal is a weak signal.

It is worth mentioning that in the present application, the pipeline leakage detection device specifically refers to a pipeline leakage detection device installed on or near a pipeline, and is not a host computer used to control the pipeline leakage detection device, or does not include a host computer used to control the pipeline leakage detection device.

In step S120, the original sampling signal is sampled by the pipeline leakage detection device. Specifically, after the original sampling signal is collected by the acceleration sensor, the original sampling signal is transmitted to the sampling unit in the pipeline leakage detection device through the adaptive gain signal conditioning circuit, so that the sampling unit of the pipeline leakage detection device can sample the original sampling signal.

Correspondingly, sampling the original sampling signal by the pipeline leakage detection device includes the steps of: receiving the original sampling signal from the adaptive gain signal conditioning circuit by a sampling unit in the pipeline leakage detection device.

Specifically, in step S120, the raw sampling data collected by the acceleration sensor is obtained by the sampling unit in the pipeline leakage detection device within a preset duration and at a preset sampling frequency. For example, the preset duration is M seconds, the preset sampling frequency is N Hz/second, and accordingly, the total number of sampling data is L=M*N; L represents the total number of sampling data; M represents the preset duration; N represents the preset sampling frequency, that is, sampling is performed at a sampling frequency of N Hz/second within a sampling duration of M seconds, and the total number of sampling data is M*N; M*N represents the product of M and N.

Optionally, the sampling unit in the pipeline leakage detection device saves the original sampling signal to a flash memory according to a preset sampling frequency, or the sampling unit in the pipeline leakage detection device saves the original sampling signal to other types of memory according to a preset sampling frequency.

The sampling unit in the pipeline leakage detection device may be an analog sampling unit.

In step S130, the raw sampling signal is processed by the data processor in the pipeline leakage detection device to obtain a preliminary analysis result. It is worth mentioning that in the present application, the focus is on obtaining the preliminary analysis result by calculating by the data processor in the pipeline leakage detection device, rather than calculating and obtaining the preliminary analysis result by using the background server that is communicatively connected to the pipeline leakage detection device through the Internet of Things. Therefore, the specific implementation method of processing the raw sampling signal by the data processor in the pipeline leakage detection device to obtain the preliminary analysis result is not limited.

The present application proposes an exemplary implementation method of processing the original sampling signal through a data processor in the pipeline leakage detection device to obtain a preliminary analysis result.

Specifically, in one embodiment of the present application, the original sampling signal is a time domain sampling signal. The original sampling signal is first domain converted, and then the sampling data after domain conversion is analyzed. Accordingly, step S130 includes the steps of: S131, converting the original sampling signal from the time domain to the frequency domain by the data processor in the pipeline leakage detection device to obtain a frequency domain sampling signal converted to the frequency domain; S132, comparing the frequency domain sampling signal with a preset amplitude value to obtain a comparison result expression value, and the comparison result expression value is used to express the comparison result of the frequency domain sampling signal and the preset amplitude value.

In step S131, the data processor in the pipeline leakage detection device may use a discrete Fourier algorithm to perform domain conversion on the original sampling signal.

It is worth mentioning that when the time domain width of the original sampling signal is wide, the amount of data is large, the storage level is deep, and serial calculation cannot meet the efficiency requirements. The present application proposes to perform segmented processing on the time domain sampling signal, and then perform domain conversion on the segmented time domain sampling signal, and then analyze the converted sampling signal. Define the number of segmented sampling points P, the number of segments S=L/P, and the frequency resolution=N/P; P represents the number of sampling points of each segmented time domain sampling signal; S represents the number of segments of the segmented time domain sampling signal; L represents the total number of sampled data.

Accordingly, step S131 includes the steps of: S1311, segmenting the original sampling signal according to the time domain, so that the original sampling signal is divided into at least two segments of segmented time domain sampling signals, and the sampling time of each segment of the segmented time domain sampling signal is in a different time domain; and S1312, converting the at least two segments of the segmented time domain sampling signals from the time domain to the frequency domain to obtain at least two segments of segmented frequency sampling signals. Accordingly, the frequency domain sampling signal converted to the frequency domain includes at least two segments of segmented frequency domain sampling signals.

As shown in FIG. 2 , in an example of the present application, in step S1311, the original sampling signal may be segmented according to the time domain, so that the original sampling signal is divided into S segmented time domain sampling signals. The S segmented time domain sampling signals are respectively the first segmented time domain sampling signal Q ₁ , the second segmented time domain sampling signal Q ₂ , the third segmented time domain sampling signal Q ₃ , the fourth segmented time domain sampling signal Q ₄ , ..., the S segmented time domain sampling signal Q _S . The first segmented time domain sampling signal Q ₁ , the second segmented time domain sampling signal Q ₂ , the third segmented time domain sampling signal Q ₃ , the fourth segmented time domain sampling signal Q ₄ , ..., the S segmented time domain sampling signal Q _S are arranged in sequence according to time sequence.

Step S132 includes the step: S1321, comparing at least two segments of the segmented frequency domain sampling signals and corresponding at least two preset amplitude values to obtain at least two preliminary comparison result values.

The preliminary comparison result value is the frequency of the frequency domain sampling signals in each segmented frequency domain sampling signal whose amplitude is greater than the corresponding preset amplitude value, and the number of frequency domain sampling signals of each frequency in each segmented frequency domain sampling signal whose amplitude is greater than the corresponding preset amplitude value; the comparison result expression value includes the frequency of the frequency domain sampling signals in each segmented frequency domain sampling signal whose amplitude is greater than the corresponding preset amplitude value, and the sum of the number of frequency domain sampling signals of the same frequency in each segmented frequency domain sampling signal whose amplitude is greater than the corresponding preset amplitude value.

As shown in Fig. 3, there are three frequency sampling signals with amplitudes greater than the first preset amplitude value in the first segmented time domain sampling signal _Q1 . The frequencies of the three frequency sampling signals with amplitudes greater than the first preset amplitude value in the first segmented time domain sampling signal Q1 are 300, 400, and 800 respectively.

The number of _frequency sampling signals with an amplitude greater than the first preset amplitude value and a frequency of 300 in the first segmented time domain sampling signal _Q1 is 1, the number of frequency sampling signals with an amplitude greater than the first preset amplitude value and a frequency of 400 in the first segmented time domain sampling signal Q1 is 1, and the number of frequency sampling signals with an amplitude greater than the first preset amplitude value and a frequency of 800 in the first segmented time domain sampling signal _Q1 is 1.

Correspondingly, Fx1(300)=1, Fx1(400)=1, Fx1(800)=1; Fx1(f) represents the number of frequency sampling signals with a frequency f in the first segmented time domain sampling signal _Q1 whose amplitude is greater than the first preset amplitude value.

As shown in Fig. 4, there are 5 frequency sampling signals with amplitudes greater than the second preset amplitude value in the second segmented time domain sampling signal _Q2 . The frequencies of the 5 frequency sampling signals with amplitudes greater than the second preset amplitude value in the second segmented time domain sampling signal _Q2 are 200, 400, 500, 800, and 900, respectively.

The number of frequency sampling signals with an amplitude greater than _the second preset amplitude value and a frequency of 200 in the second-segmented time domain sampling signal _Q2 is 1, the number of frequency sampling signals with an amplitude greater than the second preset amplitude value and a frequency of 400 in the second-segmented time domain sampling signal _Q2 is 1, the number of frequency sampling signals with an amplitude greater than the second preset amplitude value and a frequency of 500 in the second-segmented time domain sampling signal Q2 is 1, the number of frequency sampling signals with an amplitude greater than the second preset amplitude value and a frequency of 800 in the second-segmented time domain sampling signal _Q2 is 1, and the number of frequency sampling signals with an amplitude greater than the second preset amplitude value and a frequency of 900 in the second-segmented time domain sampling signal _Q2 is 1.

Correspondingly, Fx2(200)=1, Fx2(400)=1, Fx2(500)=1, Fx2(800)=1, Fx2(900)=1; Fx2(f) represents the number of frequency sampling signals with an amplitude greater than the second preset amplitude value in the second segmented time domain sampling signal _Q2 .

As shown in Fig. 5, there are four frequency sampling signals whose amplitude is greater than the Sth preset amplitude value in the Sth segmented time domain sampling signal Q _S. The frequencies of the four frequency sampling signals whose amplitude is greater than the Sth preset amplitude value in the Sth segmented time domain sampling signal Q _S are 300, 400, 500, and 700 respectively.

The number of frequency sampling signals with an amplitude greater than the Sth preset amplitude value and a frequency of 300 in the S-th segmented time domain sampling signal Q _S is 1, the number of frequency sampling signals with an amplitude greater than the S-th preset amplitude value and a frequency of 400 in the S-th segmented time domain sampling signal Q _S is 1, the number of frequency sampling signals with an amplitude greater than the S-th preset amplitude value and a frequency of 500 in _{the S} -th segmented time domain sampling signal Q S is 1, and the number of frequency sampling signals with an amplitude greater than the S-th preset amplitude value and a frequency of 700 in the S-th segmented time domain sampling signal Q _S is 1.

Correspondingly, FxS(300)=1, FxS(400)=1, FxS(500)=1, FxS(700)=1; FxS(f) represents the number of frequency sampling signals with an amplitude greater than the Sth preset amplitude value and a frequency f in the Sth segmented time domain sampling signal _QS .

As shown in FIG6, in an example of the present application, the sum of the number of frequency domain sampling signals with an amplitude greater than the corresponding preset amplitude value of 100 from the segmented frequency domain sampling signal of the first segment to the segmented frequency domain sampling signal of the S segment is 0; the sum of the number of frequency domain sampling signals with an amplitude greater than the corresponding preset amplitude value of 200 from the segmented frequency domain sampling signal of the first segment to the segmented frequency domain sampling signal of the S segment is 1; the sum of the number of frequency domain sampling signals with an amplitude greater than the corresponding preset amplitude value of 300 from the segmented frequency domain sampling signal of the first segment to the segmented frequency domain sampling signal of the S segment is 2; the sum of the number of frequency domain sampling signals with an amplitude greater than the corresponding preset amplitude value of 400 from the segmented frequency domain sampling signal of the first segment to the segmented frequency domain sampling signal of the S segment is 5; The sum of the number of frequency domain sampling signals with an amplitude of 500 and an amplitude greater than the corresponding preset amplitude value in the sample signal is 6; the sum of the number of frequency domain sampling signals with an amplitude of 600 and an amplitude greater than the corresponding preset amplitude value in the segmented frequency domain sampling signal of the first section to the segmented frequency domain sampling signal of the S section is 4; the sum of the number of frequency domain sampling signals with an amplitude of 700 and an amplitude greater than the corresponding preset amplitude value in the segmented frequency domain sampling signal of the first section to the segmented frequency domain sampling signal of the S section is 3; the sum of the number of frequency domain sampling signals with an amplitude of 800 and an amplitude greater than the corresponding preset amplitude value in the segmented frequency domain sampling signal of the first section to the segmented frequency domain sampling signal of the S section is 4; the sum of the number of frequency domain sampling signals with an amplitude of 1000 and an amplitude greater than the corresponding preset amplitude value in the segmented frequency domain sampling signal of the first section to the segmented frequency domain sampling signal of the S section is 0.

Correspondingly, Fx(100)=0; Fx(200)=1; Fx(300)=2; Fx(400)=5; Fx(500)=6; Fx(600) =4; Fx(700) =3; Fx(800)=4; Fx(900)=1; Fx(1000)=0; Fx(f) represents the sum of the number of frequency sampling signals with an amplitude greater than the corresponding preset amplitude value of the segmented frequency domain sampling signal of each _segment , from the first segmented frequency domain sampling signal _Q1 to the S segmented time domain sampling signal QS.

The data processor in the pipeline leakage detection device may be a single chip microcomputer.

In step S140, based on the preliminary analysis result obtained by the data processor in the pipeline leakage detection device and the preset statistical model, the level of pipeline leakage is obtained. Specifically, in step S140, the preliminary analysis result obtained by the data processor in the pipeline leakage detection device and the preset statistical quantity corresponding to the preset statistical frequency of the preset statistical model are compared to determine whether the pipeline is leaking. More specifically, in response to the sum of the number of frequency domain sampling signals of the preset statistical frequency in the preliminary analysis result obtained by the data processor in the pipeline leakage detection device being greater than the preset statistical quantity corresponding to the preset statistical frequency in the preset statistical model, it is determined that the pipeline is leaking.

In an example of the present application, the preset statistical frequency is 500, and the preset statistical quantity corresponding to the frequency 500 of the preset statistical model is 5; in step S130, it is calculated that the sum of the number of frequency domain sampling signals with an amplitude greater than the corresponding preset amplitude value of 500 from the segmented frequency domain sampling signal of the first segment to the segmented frequency domain sampling signal of the S segment is 6, that is, Fx(500)=6, the sum of the number of frequency domain sampling signals with an amplitude greater than the corresponding preset amplitude value of 500 from the segmented frequency domain sampling signal of the first segment to the segmented frequency domain sampling signal of the S segment (that is, 6) is greater than the preset statistical quantity corresponding to the frequency 500 of the preset statistical model (that is, 5), and it is determined that the pipeline is leaking.

It is worth mentioning that the preset statistical model can be optimized and adjusted according to empirical values, so as to improve the recognition ability and reduce the probability of false alarms.

In summary, the pipeline leakage detection method based on the acceleration sensor according to the embodiment of the present application is explained. The pipeline leakage detection method based on the acceleration sensor detects water supply network leakage through the acceleration sensor, which can not only improve efficiency but also reduce the false alarm rate. And compared with sending the original sampling signal collected by the acceleration sensor to the background server through the Internet of Things, and converting and analyzing the collected original sampling signal through the background server, the pipeline leakage detection method based on the acceleration sensor described in the present application uses the data processor in the pipeline leakage detection device to convert and analyze the original sampling signal collected by the acceleration sensor, which can simplify the signal processing process to a certain extent, improve the efficiency of the algorithm, and reduce the dependence on the network in the application scenario.

The present application and its implementation methods are described above, and such description is not restrictive. The drawings show only one implementation method of the present application, and the actual structure is not limited thereto. In short, if ordinary technicians in this field are inspired by it and design structural methods and embodiments similar to the technical solution without creative design without departing from the inventive purpose of the present application, they should all fall within the protection scope of the present application.

Claims (10)

1. A pipeline leakage detection method based on an acceleration sensor, characterized in that it comprises the following steps: Collecting original sampling signals through acceleration sensors; Sampling the original sampling signal by a pipeline leakage detection device; Processing the original sampling signal by a data processor in the pipeline leakage detection device to obtain a preliminary analysis result; and Based on the preliminary analysis result obtained by the data processor in the pipeline leakage detection device and the preset statistical model, it is determined whether the pipeline is leaking. 2. The pipeline leakage detection method based on an acceleration sensor according to claim 1, wherein the raw sampling signal is processed by a data processor in the pipeline leakage detection device to obtain a preliminary analysis result, comprising the steps of: The data processor in the pipeline leakage detection device converts the original sampling signal from the time domain to the frequency domain to obtain a frequency domain sampling signal converted to the frequency domain; The frequency domain sampling signal and the preset amplitude value are compared to obtain a comparison result expression value, wherein the comparison result expression value is used to express the comparison result between the frequency domain sampling signal and the preset amplitude value. 3. The pipeline leakage detection method based on an acceleration sensor according to claim 2, wherein the data processor in the pipeline leakage detection device converts the original sampling signal from the time domain to the frequency domain to obtain a frequency domain sampling signal converted to the frequency domain, comprising the steps of: Segmenting the original sampling signal according to the time domain, so that the original sampling signal is divided into at least two segments of segmented time domain sampling signals, and the sampling time of each segment of the segmented time domain sampling signal is in a different time domain; and Converting at least two of the segmented time domain sampling signals from the time domain to the frequency domain to obtain at least two segmented frequency sampling signals; Comparing the frequency domain sampling signal with a preset amplitude value to obtain a comparison result expression value comprises the steps of: Compare at least two segments of the segmented frequency domain sampling signals with corresponding at least two preset amplitude values to obtain at least two preliminary comparison result values. 4. The pipeline leakage detection method based on acceleration sensor according to claim 3, wherein the preliminary comparison result value is the frequency of the frequency domain sampling signal in each segment of the segmented frequency domain sampling signal whose amplitude is greater than the preset amplitude value corresponding to it, and the number of frequency domain sampling signals of each frequency in each segment of the segmented frequency domain sampling signal whose amplitude is greater than the preset amplitude value corresponding to it; the comparison result expression value includes the frequency of the frequency domain sampling signal in each segment of the segmented frequency domain sampling signal whose amplitude is greater than the preset amplitude value corresponding to it, and the sum of the number of frequency domain sampling signals of the same frequency in each segment of the segmented frequency domain sampling signal whose amplitude is greater than the preset amplitude value corresponding to it. 5. The pipeline leakage detection method based on an acceleration sensor according to claim 4, wherein judging whether the pipeline is leaking based on the preliminary analysis result obtained by the data processor in the pipeline leakage detection device and a preset statistical model comprises the steps of: Comparing the preliminary analysis result obtained by the data processor in the pipeline leakage detection device with a preset statistical quantity corresponding to a preset statistical frequency of a preset statistical model to determine whether the pipeline is leaking; Among them, in response to the fact that the sum of the number of frequency domain sampling signals of the preset statistical frequency in the preliminary analysis results obtained by the data processor in the pipeline leakage detection equipment is greater than the preset statistical quantity corresponding to the preset statistical frequency in the preset statistical model, pipeline leakage is determined. 6. The pipeline leakage detection method based on an acceleration sensor according to claim 1, wherein, in the process of performing domain conversion on the original sampled signal by the data processor in the pipeline leakage detection device, the data processor in the pipeline leakage detection device uses a discrete Fourier algorithm to perform domain conversion on the original sampled signal. 7. The pipeline leakage detection method based on an acceleration sensor according to claim 1, wherein the original sampling signal is sampled by a pipeline leakage detection device, comprising the steps of: The raw sampling signal from the adaptive gain signal conditioning circuit is received by a sampling unit in the pipeline leakage detection device. 8. The pipeline leakage detection method based on an acceleration sensor according to claim 1, wherein the original sampling signal is sampled by a pipeline leakage detection device, comprising the steps of: The raw sampling data collected by the acceleration sensor is acquired by a sampling unit in the pipeline leakage detection device at a preset sampling frequency within a preset duration. 9. The pipeline leakage detection method based on an acceleration sensor according to claim 8, wherein the raw sampling data collected by the acceleration sensor is obtained by a sampling unit in the pipeline leakage detection device at a preset sampling frequency within a preset duration, comprising the steps of: The original sampling signal is saved to a flash memory according to a preset sampling frequency by the sampling unit in the pipeline leakage detection device. 10 . The pipeline leakage detection method based on acceleration sensor according to claim 1 , wherein the data processor in the pipeline leakage detection device is a single chip microcomputer.