Pipeline leakage monitoring method, system and equipment based on Internet of Things platform and medium

Abstract

The embodiment of the invention relates to the field of Internet of Things, and discloses a pipeline leakage monitoring method, system and device based on an Internet of Things platform and a medium. Monitoring signals of all the monitoring devices are collected at the same time from an Internet of Things platform in communication connection with the monitoring devices, and coordinate information of all the monitoring devices is obtained; pre-processing the plurality of monitoring signals, performing cross-correlation analysis, and calculating time delay between the monitoring devices; and calculating the position coordinates of the leakage point according to the time delay, the coordinate information and the preset propagation speed of the leakage signal. According to the method, the leakage position can be positioned more quickly and accurately, the influence of environmental noise on monitoring is reduced, the method is suitable for long-distance and large-scale liquid pipeline monitoring, and the false alarm rate of leakage events can be effectively reduced.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things, and in particular to a pipeline leakage monitoring method, system, equipment and medium based on an Internet of Things platform. Background Art

[0002] Liquid pipelines, especially pipeline transportation systems for natural gas, crude oil, refined oil and chemicals, are one of the key infrastructures of modern industry. As global energy demand continues to grow, pipeline transportation has become the main means of transporting these resources due to its high efficiency, low cost and safety, and the scale and complexity of liquid pipelines are also increasing.

[0003] The safety of pipelines that transport natural gas, crude oil and other hazardous chemicals is of vital importance. Once a leak occurs, it will not only lead to a waste of resources, but may also cause major safety accidents such as fires and explosions, and even cause serious environmental pollution and economic losses. In order to cope with the safety and environmental risks brought about by liquid pipeline leakage, a variety of leak detection technologies have been developed and applied at home and abroad. These technologies can be roughly divided into two categories: physical methods based on detection instruments and computational methods based on data analysis.

[0004] However, physical detection methods often require complex hardware equipment and wiring, and have high installation and maintenance costs; while data analysis methods can improve detection accuracy to a certain extent, they rely on a large amount of historical data and are easily affected by environmental noise and data noise. In addition, existing detection technologies are often difficult to effectively apply in the face of complex pipe network structures and long-distance transmission pipelines, and the detection range is limited. In actual operation, it is easy to produce false positives and missed positives. Summary of the invention

[0005] The purpose of the embodiments of the present invention is to provide a pipeline leakage monitoring method, system, device and medium based on the Internet of Things platform. The present invention analyzes leakage information based on the monitoring data of the pipeline by multiple monitoring devices and the monitoring signals reported simultaneously when the leakage signal is detected, obtains the time delay by performing cross-correlation analysis after performing noise reduction processing such as filtering on the monitoring signal, and then determines the leakage location based on parameters such as the position coordinates of the monitoring device and the propagation speed of the leakage signal. Compared with the prior art, the scheme of the present invention locates the leakage location more quickly and accurately, reduces the impact of environmental noise on monitoring, is suitable for long-distance and large-scale liquid pipeline monitoring, can effectively reduce the false alarm rate, and provides a strong guarantee for the safe operation of the pipeline.

[0006] To solve the above technical problems, an embodiment of the present invention provides a pipeline leakage monitoring method based on an Internet of Things platform, comprising: when multiple monitoring devices used to monitor the pipeline status detect a leakage signal, simultaneously collecting the monitoring signals of each of the monitoring devices from the Internet of Things platform that is communicatively connected to the monitoring devices, and obtaining the coordinate information of each of the monitoring devices; performing cross-correlation analysis on the multiple monitoring signals after pre-processing, and calculating the time delay between each of the monitoring devices; and calculating the coordinates of the leakage point location according to the time delay, the coordinate information and the preset propagation speed of the leakage signal.

[0007] In addition, the simultaneous collection of monitoring signals of each monitoring device as described above includes: separately and simultaneously collecting the monitoring signals of each monitoring device within the leakage detection time; the leakage detection time is from the moment when multiple monitoring devices first detect the leakage signal to the moment when all the monitoring devices detect the leakage signal.

[0008] In addition, the preprocessing of the plurality of monitoring signals as described above at least includes: using a Butterworth filter to filter the plurality of monitoring signals to obtain each of the filtered monitoring signals.

[0009] In addition, as described above, after preprocessing the multiple monitoring signals, cross-correlation analysis is performed to calculate the time delay between each monitoring device, including: dividing every two of the filtered monitoring signals into a group; using the cross-correlation function to calculate in turn the time delay for monitoring the leakage signal between the two monitoring devices corresponding to each group of monitoring signals, to obtain the time delay between each monitoring device.

[0010] In addition, after calculating the leakage point location coordinates as described above, the method further includes: determining a first coordinate system used by each of the monitoring devices and a second coordinate system used by a map platform displaying the pipeline; judging whether the first coordinate system and the second coordinate system are the same coordinate system; if so, directly displaying them on the map platform according to the leakage point location coordinates; if not, converting the leakage point location coordinates into corresponding location coordinates in the second coordinate system, and displaying them on the map platform.

[0011] Another embodiment of the present invention provides a pipeline leakage monitoring system based on an Internet of Things platform, including: multiple monitoring devices communicatively connected to the Internet of Things platform, used to monitor the pipeline status, and when multiple monitoring devices detect leakage signals, collect the monitoring signals of each monitoring device and upload them to the Internet of Things platform; a data acquisition module, used to receive each monitoring signal from the Internet of Things platform and obtain the coordinate information of each monitoring device; a data processing module, used to perform cross-correlation analysis on the multiple monitoring signals after pre-processing, and calculate the time delay between each monitoring device; a data analysis module, used to calculate the leakage point location coordinates based on the time delay, the coordinate information and the preset propagation speed of the leakage signal.

[0012] In addition, the system as described above also includes: a data visualization module connected to the map platform, used to determine whether the first coordinate system used by each of the monitoring devices and the second coordinate system used by the map platform are the same coordinate system; if so, the leakage point is directly displayed in the map platform according to the leakage point location coordinates; if not, the leakage point location coordinates are converted into corresponding location coordinates under the second coordinate system and displayed in the map platform.

[0013] An embodiment of the present invention also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps in the pipeline leakage monitoring method based on the Internet of Things platform as described above.

[0014] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the pipeline leakage monitoring method based on the Internet of Things platform as described above.

[0015] An embodiment of the present invention further provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the steps in the pipeline leakage monitoring method based on the Internet of Things platform as described above.

[0016] Compared with the related art, the embodiment of the present invention realizes leakage detection according to the monitoring data of multiple monitoring devices arranged on the pipeline. When the multiple monitoring devices used to monitor the pipeline status detect the leakage signal, the monitoring signals of each monitoring device are collected from the Internet of Things platform connected to the monitoring device, and the coordinate information of each monitoring device is obtained; after pre-processing the multiple monitoring signals, the cross-correlation analysis is performed to calculate the time delay between the monitoring devices; the leakage point position coordinates are calculated according to the time delay, coordinate information and the preset propagation speed of the leakage signal. The embodiment of the present invention analyzes the leakage information according to the monitoring data of the pipeline by multiple monitoring devices, and the monitoring signals reported at the same time when the leakage signal is detected. The time delay is obtained by performing cross-correlation analysis after filtering and other noise reduction processing on the monitoring signal, and then the leakage position is determined according to the monitoring device position coordinates and the propagation speed of the leakage signal and other parameters. Compared with the prior art, the scheme of the present invention locates the leakage position more quickly and accurately, reduces the influence of environmental noise on monitoring, is suitable for long-distance and large-scale liquid pipeline monitoring, can effectively reduce the false alarm rate of leakage events, and provides a strong guarantee for the safe operation of the pipeline. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] One or more embodiments are exemplarily described by the pictures in the corresponding drawings, and these exemplary descriptions do not constitute limitations on the embodiments.

[0018] Figure 1 It is a flow chart of a pipeline leakage monitoring method based on an Internet of Things platform provided by an embodiment of the present invention;

[0019] Figure 2 is a flow chart of a pipeline leakage monitoring method based on an Internet of Things platform provided by another embodiment of the present invention;

[0020] Figure 3 is an example diagram of pipeline leakage provided by another embodiment of the present invention;

[0021] Figure 4 is a schematic structural diagram of a pipeline leakage monitoring system based on an Internet of Things platform provided by another embodiment of the present invention;

[0022] Figure 5 It is a schematic diagram of the structure of an electronic device provided by another embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical scheme and advantages of the present invention clearer, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, it can be understood by those skilled in the art that in the embodiments of the present invention, many technical details are provided to enable readers to better understand the present invention. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present invention can be implemented.

[0024] Before describing the scheme of the present invention in detail, the leakage detection schemes in the prior art are generally described. These technologies can be roughly divided into two categories: physical methods based on detection instruments and calculation methods based on data analysis. Each method generally includes the following methods.

[0025] 1. Leakage monitoring technology based on physical methods

[0026] Traditional physical methods include acoustic wave monitoring, pressure wave monitoring, temperature monitoring, mass balance method, etc. These methods monitor and locate leaks by measuring physical parameters inside the pipeline or the surrounding environment.

[0027] Acoustic wave detection: The flow of fluid caused by leakage will generate acoustic wave signals, and the leakage location can be determined by monitoring the acoustic wave signals. Acoustic wave detection technology is relatively mature, but it is greatly affected by environmental noise and the detection accuracy is limited.

[0028] Pressure wave detection: Leaks cause pressure fluctuations in the pipeline. By monitoring the pressure fluctuations, the occurrence of leaks can be detected. This method has low sensitivity to small-scale leaks and requires high installation costs and complex signal processing technology.

[0029] Temperature monitoring: Liquid leakage will affect the temperature of the surrounding environment. By monitoring temperature changes through temperature sensors, leakage can be detected indirectly. Temperature monitoring is suitable for certain specific scenarios, but its response time is long and is greatly affected by environmental factors.

[0030] Mass balance method: By monitoring the flow difference between the inlet and outlet of the pipeline, it is determined whether there is a leak. This method is simple and intuitive, but it is not sensitive to small leaks and is prone to errors in complex pipe networks.

[0031] 2. Leak detection technology based on data analysis

[0032] With the development of computer technology, data analysis methods have gradually become an important means of pipeline leakage detection. These methods detect and predict leakage events by performing real-time analysis and modeling of pipeline operation data.

[0033] Statistical analysis method: Through statistical analysis of historical pipeline operation data, a normal operation mode is established. Once the data deviates from the normal mode, it can be judged that there may be a leak. This method relies on the integrity and accuracy of historical data and is not sensitive to new leakage patterns.

[0034] Artificial neural network: Artificial neural network is used to perform nonlinear analysis on pipeline operation data. By training the neural network model, leakage events can be detected. Artificial neural network has strong self-learning ability, but the training and adjustment of the model requires a large amount of data and is prone to overfitting problems.

[0035] Machine learning and data mining: In recent years, machine learning and data mining technologies have been widely used in pipeline leak detection. These technologies can automatically identify abnormal patterns and detect leaks by learning and mining large amounts of data. Machine learning methods have significant advantages in detection accuracy and efficiency, but their application still faces challenges such as algorithm complexity and large data volume requirements.

[0036] Although existing leak detection technologies have solved the problem of pipeline leakage to a certain extent, there are still some shortcomings in practical applications. First, traditional physical detection methods often require complex hardware equipment and wiring, and have high installation and maintenance costs. Secondly, although data analysis methods can improve detection accuracy to a certain extent, they rely on a large amount of historical data and are easily affected by environmental noise and data noise. In addition, existing detection technologies are often difficult to apply effectively in the face of complex pipeline structures, long-distance transmission pipelines, etc., the detection range is limited, and false alarms and missed alarms are prone to occur in actual operations.

[0037] To solve the above problems, the embodiments of the present invention provide a pipeline leakage monitoring method, system, device and medium based on the Internet of Things platform. This solution relies on the Internet of Things platform to connect multiple monitoring devices distributed along the pipeline to work together to achieve rapid and accurate positioning of pipeline leakage, which can improve the accuracy and efficiency of pipeline leakage detection, and is particularly suitable for real-time monitoring and leakage positioning of liquid pipelines such as natural gas and crude oil. The specific description is as follows.

[0038] An embodiment of the present invention relates to a pipeline leakage monitoring method based on an Internet of Things platform, which can at least be applied to a pipeline leakage monitoring system based on an Internet of Things platform in the following embodiments, such as Figure 1 As shown, the method of this embodiment includes the following steps:

[0039] Step 101: When multiple monitoring devices for monitoring pipeline status detect leakage signals, the monitoring signals of each monitoring device are simultaneously collected from an Internet of Things platform that is communicatively connected to the monitoring devices, and the coordinate information of each monitoring device is obtained.

[0040] Specifically, before the specific execution of step 101, it is necessary to deploy multiple monitoring devices at each key node of the pipeline. This embodiment does not impose specific restrictions on the selection of key nodes. It can be to select multiple nodes equidistantly on the pipeline as key nodes to deploy monitoring devices. The monitoring device can be a sensor integrated device, and each monitoring device is equipped with an acoustic sensor, a pressure sensor, etc., for real-time monitoring of the pipeline state (the physical state inside and outside the pipeline). Each monitoring device can use a high-frequency sampling rate of 16kHz to capture subtle changes in leakage events, and connect to the Internet of Things platform through a built-in communication module (such as LoRa, NB-IoT, 2G / 3G / 4G / 5G, etc.), and perform parameter configuration, including sampling rate, data transmission frequency, and signal threshold, etc. The Internet of Things platform in this embodiment can be the Alibaba Cloud Internet of Things platform.

[0041] Multiple monitoring devices monitor pipeline status signals in real time, such as sound wave signals, pressure signals, or temperature signals in the pipeline. In practical applications, the detected signals can be set to meet the conditions of leakage signals. For example, when the monitored signal is higher or lower than a threshold, it is considered that a leakage signal is monitored; or, when the monitored signal changes more than a threshold within a unit time, it is considered that a leakage signal is monitored. The present invention does not limit the specific identification method of the leakage signal. It should be noted that the situation in which multiple monitoring devices in this embodiment monitor leakage signals refers to the first time that any one or more monitoring devices among the multiple (all) monitoring devices arranged on the pipeline monitor leakage signals, that is, there are monitoring devices in the multiple monitoring devices that monitor leakage signals. After multiple monitoring devices monitor leakage signals, all leakage devices synchronously start signal acquisition, that is, synchronously collect the current monitoring signals and transmit them to the Internet of Things platform through the specified communication protocol. At this time, the leakage signal exists in the collected monitoring signals. The pipeline leakage monitoring system receives the monitoring signals of each monitoring device from the Internet of Things platform through an API (Application Programming Interface) interface or a message subscription mechanism, and obtains the coordinate information of each monitoring device. The coordinate information of each monitoring device can be pre-stored in the pipeline leakage monitoring system, or can be uploaded synchronously when each monitoring device uploads the monitoring signal. In addition, in this embodiment, the coordinate information of each monitoring device has a unique corresponding relationship with the monitoring signal uploaded by it, and the corresponding relationship can be established by means of a device identification code, etc., and the present invention does not make specific restrictions on this.

[0042] Step 102: Perform cross-correlation analysis on the multiple monitoring signals after pre-processing, and calculate the time delay between the monitoring devices.

[0043] Specifically, after obtaining the monitoring signals uploaded by each monitoring device, each monitoring signal can be preprocessed. The preprocessing operation can be format conversion, cleaning, filtering, etc. for the monitoring signal. The present invention does not impose specific restrictions on this and can be set and adjusted according to actual needs.

[0044] After preprocessing, the cross-correlation algorithm is used to perform cross-correlation analysis on the preprocessed monitoring signals. The cross-correlation algorithm is used to calculate the time delay between the leakage signals received by two or more monitoring devices, so as to infer the location of the leakage point.

[0045] Step 103: Calculate the coordinates of the leakage point according to the time delay, the coordinate information and the preset propagation speed of the leakage signal.

[0046] Specifically, after obtaining the time delay between each monitoring device detecting the leakage signal, the coordinates of the leakage point position can be calculated according to the time delay, coordinate information and the preset propagation speed of the leakage signal. When a leak occurs in the pipeline, the leakage point will generate pressure waves or sound waves (usually sound signals), and these waves will propagate along the pipeline. Monitoring devices installed at different positions of the pipeline can detect these leakage signals. However, due to the different distances between sensors, there are differences in the time it takes for the leakage signal to reach each sensor. This time difference (time delay) is closely related to the sensor spacing and the leakage position. Therefore, this embodiment uses a cross-correlation function to calculate the coordinates of the leakage point position according to the time delay.

[0047] Compared with the related art, the above embodiment of the present invention realizes leakage detection according to the monitoring data of multiple monitoring devices arranged on the pipeline. When the multiple monitoring devices used to monitor the pipeline status detect the leakage signal, the monitoring signals of each monitoring device are collected from the Internet of Things platform connected to the monitoring device, and the coordinate information of each monitoring device is obtained; after pre-processing the multiple monitoring signals, the cross-correlation analysis is performed to calculate the time delay between the monitoring devices; the leakage point position coordinates are calculated according to the time delay, coordinate information and the preset propagation speed of the leakage signal. The embodiment of the present invention analyzes the leakage information according to the monitoring data of the pipeline by multiple monitoring devices, and the monitoring signals reported at the same time when the leakage signal is detected, and the time delay is obtained by performing cross-correlation analysis after filtering and other noise reduction processing on the monitoring signal, and then the leakage position is determined according to the monitoring device position coordinates and the propagation speed of the leakage signal and other parameters. Compared with the prior art, the scheme of the present invention locates the leakage position more quickly and accurately, reduces the influence of environmental noise on monitoring, is suitable for long-distance and large-scale liquid pipeline monitoring, can effectively reduce the false alarm rate, and provides a strong guarantee for the safe operation of the pipeline.

[0048] Another embodiment of the present invention relates to a pipeline leakage monitoring method based on an Internet of Things platform. This embodiment is a supplement to the above-mentioned embodiment, and specifically includes the following contents:

[0049] In one example, the monitoring signals of each monitoring device are collected simultaneously in step 101, including: collecting the monitoring signals of each monitoring device during the leakage detection time; the leakage detection time is from the moment when multiple monitoring devices first detect the leakage signal to the moment when all the monitoring devices detect the leakage signal. Specifically, since a leakage event occurs in a pipeline, generally a leakage occurs at a certain point in the pipeline, that is, the leakage signal is generated at a certain point, and since the monitoring devices are arranged at different positions on the pipeline, the distances between different monitoring devices and the leakage point are different, so different monitoring devices will inevitably produce differences in the time of monitoring the leakage signal. That is, the location of the leakage point can be inferred based on the time when the monitoring devices at different positions detect the leakage signal, so this embodiment needs to determine the time when each monitoring device detects the leakage signal, so as to facilitate the subsequent calculation of the location of the leakage point. In this embodiment, the monitoring signals of each monitoring device during the leakage detection time are collected simultaneously, and the synchronization mechanism can ensure that the monitoring signals collected by each monitoring device at the same time have temporal consistency, providing an accurate basis for subsequent data analysis.

[0050] After synchronous collection begins, the monitoring signals of each monitoring device within the leakage detection time are collected. The leakage detection time is from the moment when multiple monitoring devices first detect the leakage signal to the moment when all monitoring devices detect the leakage signal. The moment when multiple monitoring devices first detect the leakage signal is the moment when any one or more monitoring devices among the multiple monitoring devices first detect the leakage signal, until the moment when all monitoring devices detect the leakage signal. In some cases, the number of monitoring devices that detect the leakage signal can also be preset as a condition for stopping collection. As long as a specified number of monitoring devices among the multiple monitoring devices detect the leakage signal, the collection can be stopped. This embodiment does not impose any specific restrictions on the specific duration of the leakage detection time.

[0051] In another example, the preprocessing of the plurality of monitoring signals in step 102 at least includes:

[0052] A bandpass filter of a Butterworth filter is used to filter the multiple monitoring signals to obtain filtered monitoring signals.

[0053] Specifically, in order to improve the detection accuracy of the leakage signal, it is first necessary to filter the collected original monitoring signal to remove unnecessary noise and interference signals. The main methods of filtering and noise reduction include time domain filtering, frequency domain filtering, adaptive filtering, wavelet transform, Kalman filtering, etc., which can be selected according to actual needs, and this embodiment does not make specific restrictions. Since the ambient noise around the pipeline leakage is generally periodic or white noise, it is necessary to filter it with a filter. In this embodiment, a Butterworth filter is used for filtering and noise reduction. The Butterworth filter is a classic analog filter, which is famous for its very smooth frequency response curve. It is an ideal filter designed to smooth the in-band signal to the maximum extent while effectively attenuating the out-of-band signal (noise). A main feature of the Butterworth filter is that its frequency response curve is completely flat in the passband without ripples (i.e., maximum flat response). This makes it relatively smooth without introducing any unnecessary signals in the passband, rather than a sharp transition. The order of the Butterworth filter determines the sharpness of its frequency response. The higher the order, the steeper the transition band of the filter, but it also means that the complexity of the filter increases.

[0054] The frequency response H(ω) of the Butterworth filter is given by:

[0055]

[0056] Where ω is the angular frequency, ω c is the cutoff frequency of the filter and n is the order of the filter.

[0057] The types of Butterworth filters are divided into low-pass filters, high-pass filters, band-pass filters and band-stop filters. The band-pass filter used in this embodiment can filter the low-frequency noise and high-frequency noise of the pipeline surrounding environment according to the preset low cut-off frequency and high cut-off frequency to obtain the filtered monitoring signals.

[0058] After preprocessing, the cross-correlation algorithm is used to perform cross-correlation analysis on the preprocessed monitoring signals. The cross-correlation algorithm is used to calculate the time delay between the leakage signals received by two or more monitoring devices, so as to infer the location of the leakage point.

[0059] In another example, Figure 2 As shown, step 102 performs cross-correlation analysis on the pre-processed monitoring signals to calculate the time delay between the monitoring devices, including the following steps:

[0060] Step 1021: grouping every two filtered monitoring signals into one group;

[0061] Step 1022: using the cross-correlation function, the time delay between the two monitoring devices corresponding to each group of monitoring signals detecting the leakage signal is calculated in turn, and the time delay between each monitoring device is obtained.

[0062] Specifically, every two adjacent or non-adjacent monitoring signals after filtering are grouped into one group, and the monitoring signals in each group may or may not be repeated with the remaining groups. In the case of repetition, at least one monitoring signal in one group of monitoring signals also exists in another group of monitoring signals. The present invention does not limit the specific grouping method. After grouping, the time delay of the leakage signal detected between the two monitoring devices corresponding to each group of monitoring signals is calculated in turn using the cross-correlation function to obtain the time delay between each monitoring device.

[0063] The basic principle of calculating time delay using the cross-correlation function is as follows:

[0064] Given two signals x(t) and y(t), their cross-correlation function R xy (τ) is defined as:

[0065]

[0066] Where τ is the time lag, and the cross-correlation function R xy (τ) measures the similarity between the signal x(t) and the signal y(t) at a time lag of τ.

[0067] In actual situations, the monitoring signals are usually discrete. The two monitoring signals are denoted as x[n] and y[n]. The cross-correlation function R of the discrete monitoring signals is xy [k] is defined as:

[0068]

[0069] Where k is the discrete time lag, usually rounded to an integer. The calculated R xy [k] varies with k, usually when k = k max The maximum value is reached at this point, and k max The corresponding time delay Δt can be expressed as: Δt = k max ·ΔT; where ΔT is the sampling period.

[0070] On this basis, the specific principle of the cross-correlation function to calculate the leakage point location coordinates according to the time delay is as follows: Figure 3 As shown in the figure, the signals of monitoring device A and monitoring device B are analyzed as a group of monitoring signals. Assuming that the distance between the leakage point and monitoring device A is d, the distance from the leakage point to monitoring device B is Dd. When a leakage occurs somewhere in the pipeline, the generated leakage signal propagates along the pipeline at a preset propagation speed v. Therefore, the time t when the leakage signal reaches monitoring device A is Aand the time t when the leakage signal reaches the monitoring device B B They are:

[0071] Then the time delay Δt between monitoring device A and monitoring device B to detect the leakage signal is:

[0072]

[0073] Then the expression of the leakage point position d can be derived as:

[0074]

[0075] Since the time delay Δt has been measured in step 102, the location of the leakage point can be obtained according to the coordinate information of the monitoring device and the preset propagation speed v of the leakage signal.

[0076] In some cases, the preset propagation velocity v has different values, and the specific value is related to the pipe material and specifications. For example, when the pipe thickness is greater than the inner diameter of the pipe and the pipe material is lead, the preset propagation velocity v of the leakage signal is calculated as follows:

[0077]

[0078] Among them, E v is the volume elastic coefficient of water, E is the elastic volume coefficient of the pipe material, ρ is the density of water, r1 is the inner radius of the pipe, and r2 is the outer radius of the pipe.

[0079] When the pipe wall thickness is smaller than the inner diameter of the pipe and the pipe material is cast iron or steel, the preset propagation velocity v of the leakage signal is calculated as follows:

[0080]

[0081] Among them, E v is the volume elastic coefficient of water, E is the elastic volume coefficient of the pipeline material, ρ is the density of water, R is the inner diameter of the pipeline, and h is the wall thickness of the pipeline.

[0082] The preset propagation velocity in this embodiment can be calculated according to the formula when the actual condition of the pipeline meets the above conditions. However, in the actual pipeline environment, there will be a propagation velocity error Δv due to factors such as seasonal water temperature and pipeline water rust. This error will affect the leakage point positioning accuracy of the final leakage detection. Therefore, the preset propagation velocity v in this embodiment should be preset by experimental measurement, and this embodiment does not limit the specific method for determining the preset propagation velocity.

[0083] In another example, after calculating the leak point location coordinates, the method of this embodiment further includes:

[0084] Determine the first coordinate system used by each monitoring device and the second coordinate system used by the map platform displaying the pipeline; determine whether the first coordinate system and the second coordinate system are the same coordinate system; if so, directly display them on the map platform according to the coordinates of the leakage point location; if not, convert the coordinates of the leakage point location into the corresponding position coordinates in the second coordinate system and display them on the map platform.

[0085] Specifically, different coordinate systems are used, and the longitude and latitude data provided for the same location are also different. Therefore, if you want to accurately display the determined leakage point location on the map platform, you need to ensure that the leakage point location coordinates and the map platform use a unified coordinate system. Determine the first coordinate system used by each monitoring device and the second coordinate system used by the map platform that displays the pipeline. Generally speaking, each monitoring device uses a unified coordinate system, and the coordinate system information can be pre-stored in the Internet of Things platform or the pipeline leakage monitoring system or uploaded to the Internet of Things platform synchronously with the monitoring signal. The pipeline leakage monitoring system obtains the coordinate system information of the map platform that displays the pipeline, and determines whether the first coordinate system and the second coordinate system are the same coordinate system; if so, it is directly displayed on the map platform according to the leakage point location coordinates; if not, the leakage point location coordinates are converted to the corresponding location coordinates in the second coordinate system and displayed on the map platform.

[0086] For example, it is assumed that the coordinate system used by the monitoring equipment is generally the earth coordinate system WGS-84. WGS-84 is the most widely used geographic coordinate system in the world and is widely used in GPS devices, navigation systems and global positioning services. The map platform provided by domestic map service providers generally uses the Mars coordinate system, which is a coordinate system based on WGS-84 after encryption transformation. Therefore, in order to correctly display the pipeline leakage location on the map platform, it is necessary to convert the WGS-84 coordinates provided by the monitoring equipment side into the GCJ-02 coordinate system. When the pipeline leakage monitoring system obtains the leakage location coordinates, it automatically calls the conversion algorithm to convert the leakage location coordinates of the WGS-84 coordinate system into the GCJ-02 coordinate system to ensure that the leakage location and other geographic information are accurately displayed on the Amap. Compared with the relevant technology, this embodiment reduces interference by preprocessing the monitoring signal, calculates the time delay, derives the leakage point location, accurately locates the leakage point, and graphically displays the pipeline leakage location, thereby improving the safety and efficiency of pipeline operation and maintenance.

[0087] Another embodiment of the present invention relates to a pipeline leakage monitoring system based on an Internet of Things platform, which is used to execute the pipeline leakage monitoring method based on the Internet of Things platform in the above embodiment, such as Figure 4 As shown, including:

[0088] Multiple monitoring devices 201 that are connected to the IoT platform 202 for communication are used to monitor the pipeline status, and when multiple monitoring devices detect leakage signals, the monitoring signals of each monitoring device are collected and uploaded to the IoT platform 202;

[0089] The data acquisition module 203 is used to receive each monitoring signal from the Internet of Things platform and obtain the coordinate information of each monitoring device;

[0090] The data processing module 204 is used to perform cross-correlation analysis on the multiple monitoring signals after pre-processing and calculate the time delay between the monitoring devices;

[0091] The data analysis module 205 is used to calculate the coordinates of the leakage point according to the time delay, the coordinate information and the preset propagation speed of the leakage signal.

[0092] Specifically, the monitoring signal reported by the monitoring device 201 will be transmitted to the IoT platform 202 via the MQTT (Message Queuing Telemetry Transport) protocol. The pipeline leakage monitoring system can subscribe to the IoT platform via the AMQP (Advanced Message Queuing Protocol) protocol to obtain the monitoring signal and store the data in the local search engine Elastic Search. During the data storage process, the system will add a timestamp and device identifier to each piece of data to support subsequent data analysis and traceability.

[0093] After receiving each monitoring signal from the Internet of Things platform and obtaining the coordinate information of each monitoring device, the data acquisition module 203 needs to ensure the integrity and timeliness of the data to avoid data loss or delay.

[0094] After the data acquisition module 203 receives each monitoring signal and obtains the coordinate information of each monitoring device, the data processing module 204 will first sort and clean the monitoring signal data before preprocessing multiple monitoring signals. The data sorting steps include data format conversion, unit standardization, data smoothing, etc. to ensure the accuracy and consistency of the data. The cleaning operation is used to eliminate invalid data and abnormal data points, such as sudden abnormal values ​​caused by monitoring equipment failure. In addition, the data processing module 204 can also use feature extraction algorithms to extract feature values ​​meaningful for leak detection from the data source. For example, the spectral characteristics of the signal are extracted by frequency domain analysis, and the statistical characteristics of the signal (such as mean, variance, peak value, etc.) are calculated by time domain analysis. The data after feature extraction will be further converted and normalized to facilitate subsequent cross-correlation algorithm calculations. In an example, the pipeline leakage monitoring system based on the Internet of Things platform of this embodiment also includes:

[0095] A data visualization module (not shown in the figure) connected to the map platform is used to determine whether the first coordinate system used by each monitoring device and the second coordinate system used by the map platform are the same coordinate system;

[0096] If so, the leakage point is directly displayed on the map platform according to the location coordinates of the leakage point; if not, the location coordinates of the leakage point are converted into corresponding location coordinates in the second coordinate system and displayed on the map platform.

[0097] In addition, the data processing module 204 performs cross-correlation analysis on multiple monitoring signals after pre-processing, and calculates the time delay between each monitoring device; and the data analysis module 205 calculates the specific implementation details of the leakage point location coordinates according to the time delay, coordinate information and the preset propagation speed of the leakage signal, which have been recorded in the above-mentioned method embodiments. It is not difficult to find that this embodiment is a system embodiment corresponding to the above-mentioned method embodiments, and therefore, the relevant implementation details recorded in the above-mentioned method embodiments are still applicable in this embodiment.

[0098] Compared with the related art, the pipeline leakage monitoring system based on the Internet of Things platform in this embodiment realizes leakage detection according to the monitoring data of multiple monitoring devices arranged on the pipeline. When multiple monitoring devices used to monitor the pipeline status detect leakage signals, the monitoring signals of each monitoring device are collected from the Internet of Things platform connected to the monitoring device for communication, and the coordinate information of each monitoring device is obtained; after pre-processing multiple monitoring signals, cross-correlation analysis is performed to calculate the time delay between each of the monitoring devices; the leakage point position coordinates are calculated according to the time delay, coordinate information and the preset propagation speed of the leakage signal. The embodiment of the present invention analyzes the leakage information according to the monitoring data of multiple monitoring devices on the pipeline, and the monitoring signals reported simultaneously when the leakage signal is detected, and the time delay is obtained by performing cross-correlation analysis after filtering and other noise reduction processing on the monitoring signal, and then the leakage position is determined according to the parameters such as the position coordinates of the monitoring device and the propagation speed of the leakage signal. Compared with the prior art, the scheme of the present invention locates the leakage position more quickly and accurately, reduces the influence of environmental noise on monitoring, is suitable for long-distance and large-scale liquid pipeline monitoring, can effectively reduce the false alarm rate, and provides a strong guarantee for the safe operation of the pipeline.

[0099] Another embodiment of the present invention relates to an electronic device, such as Figure 5 As shown, it includes at least one processor 401; and a memory 402 that is communicatively connected to the at least one processor 401; wherein the memory 402 stores instructions that can be executed by the at least one processor 401, and the instructions are executed by the at least one processor 401, so that the at least one processor 401 can execute the pipeline leakage monitoring method based on the Internet of Things platform of any of the above embodiments.

[0100] The memory 402 and the processor 401 are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors 401 and the memory 402 together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor 401 is transmitted on a wireless medium via an antenna, and further, the antenna also receives data and transmits the data to the processor 401.

[0101] The processor 401 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management and other control functions. The memory 402 can be used to store data used by the processor 401 when performing operations.

[0102] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program, which implements the above method embodiment when executed by a processor.

[0103] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including several instructions for making a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0104] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the pipeline leakage monitoring method based on the Internet of Things platform as described in any of the above embodiments.

[0105] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present invention, and that in actual applications, various changes may be made in form and detail without departing from the spirit and scope of the present invention.

Claims

1. A pipeline leakage monitoring method based on the Internet of Things platform, characterized in that: include: When a plurality of monitoring devices for monitoring the pipeline status detect a leakage signal, the monitoring signals of each of the monitoring devices are simultaneously collected from an Internet of Things platform that is communicatively connected to the monitoring devices, and the coordinate information of each of the monitoring devices is obtained; Performing cross-correlation analysis on the plurality of monitoring signals after preprocessing, and calculating the time delay between the monitoring devices; The coordinates of the leakage point are calculated according to the time delay, the coordinate information and the preset propagation speed of the leakage signal.

2. The method according to claim 1, characterized in that The simultaneously collecting the monitoring signals of the monitoring devices comprises: respectively and simultaneously collecting the monitoring signals of each of the monitoring devices during the leakage detection time; The leakage detection time is from the moment when the plurality of monitoring devices first detect the leakage signal to the moment when all the monitoring devices detect the leakage signal.

3. The method according to claim 1, characterized in that The preprocessing of the plurality of monitoring signals at least includes: using a Butterworth filter to filter the plurality of monitoring signals to obtain each of the filtered monitoring signals.

4. The method according to claim 3, characterized in that The pre-processing of the plurality of monitoring signals and performing cross-correlation analysis to calculate the time delay between the monitoring devices includes: Grouping every two of the filtered monitoring signals into one group; The time delay between the two monitoring devices corresponding to each group of the monitoring signals for monitoring the leakage signal is calculated in sequence by using a cross-correlation function to obtain the time delay between the monitoring devices.

5. The method according to claim 1, characterized in that After calculating the leak point location coordinates, the method further includes: Determining a first coordinate system used by each of the monitoring devices and a second coordinate system used by a map platform displaying the pipeline; Determine whether the first coordinate system and the second coordinate system are the same coordinate system; If so, the leakage point location coordinates are directly displayed on the map platform; if not, the leakage point location coordinates are converted into corresponding location coordinates in the second coordinate system and displayed on the map platform.

6. A pipeline leakage monitoring system based on the Internet of Things platform, characterized in that: include: A plurality of monitoring devices connected to the Internet of Things platform for monitoring the pipeline status, and when a plurality of the monitoring devices detect a leakage signal, the monitoring signal of each of the monitoring devices is collected and uploaded to the Internet of Things platform; A data acquisition module, used to receive each of the monitoring signals from the Internet of Things platform and obtain the coordinate information of each of the monitoring devices; A data processing module, used for performing cross-correlation analysis on the plurality of monitoring signals after pre-processing, and calculating the time delay between the monitoring devices; A data analysis module is used to calculate the coordinates of the leakage point according to the time delay, the coordinate information and the preset propagation speed of the leakage signal.

7. The system according to claim 1, characterized in that The system further comprises: A data visualization module connected to the map platform is used to determine whether the first coordinate system used by each monitoring device and the second coordinate system used by the map platform are the same coordinate system; if so, the leakage point is directly displayed in the map platform according to the leakage point location coordinates; if not, the leakage point location coordinates are converted into corresponding location coordinates in the second coordinate system and displayed in the map platform.

8. An electronic device, characterized in that: include: at least one processor; And, a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps in the pipeline leakage monitoring method based on the Internet of Things platform as claimed in any one of claims 1 to 5.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps in the pipeline leakage monitoring method based on the Internet of Things platform as claimed in any one of claims 1 to 5 are implemented.

10. A computer program product, characterized in that The computer program product is stored in a storage medium, and the computer program product is executed by at least one processor to implement the steps in the pipeline leakage monitoring method based on the Internet of Things platform as described in any one of claims 1 to 5.

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