Leakage positioning method and system based on pipeline vibration signal and application

Abstract

This invention discloses a leak location method, system, and application based on pipeline vibration signals. The ingenious solution involves deploying vibration acquisition units on the pipeline to collect pipeline vibration signals. When a leak occurs, the vibration signal generated at the leak point can be monitored and collected by the vibration acquisition units. After feature data localization, marking, and extraction, the collected pipeline vibration monitoring data is used for detection and judgment, allowing pipeline vibration monitoring data pointing to suspected leaks to be filtered out. Furthermore, the leak location is calculated by combining two sets of pipeline vibration monitoring data to obtain the relative positional relationship between the leak point and the vibration acquisition unit. This solution also ingeniously fits the pipeline leak location information corresponding to one or more sets of leak investigation data to form a leak location result for points and / or intervals for maintenance personnel to reference, thereby improving maintenance efficiency.

Description

Technical Field

[0001] This invention relates to the fields of pipeline leak detection and monitoring technology, and in particular to a leak location method, system and application based on pipeline vibration signals. Background Technology

[0002] Pipelines, as indispensable carriers for supplying media such as water, gas, and oil, are prone to leakage due to corrosion and damage from external forces, especially since they transport pressurized media. Currently, most pipeline leak detection relies on manual inspection using sound-collecting equipment, which is inefficient and makes it difficult to quickly pinpoint the location of small leaks. Furthermore, manual inspection is challenging for overhead pipelines or those near sources of noise, such as drainage pipes, due to environmental or external noise interference. While some literature suggests using fiber optics or actively generated ultrasonic devices, these methods are susceptible to external interference and often fail to pinpoint the exact location of leaks. Since pipeline leaks generate vibrations, and the leak opening remains relatively stable for a period, utilizing the vibration signals to quickly locate the leak within a small area would significantly improve pipeline maintenance efficiency. Summary of the Invention

[0003] In view of this, the purpose of this invention is to propose a reliable, efficient and accurate leak location method, system and application based on pipeline vibration signals.

[0004] To achieve the above-mentioned technical objectives, the technical solution adopted by this invention is as follows:

[0005] A leak location method based on pipeline vibration signals, wherein multiple vibration acquisition units for sensing pipeline vibration signals are spaced apart on the pipeline, and the leak location method includes:

[0006] S01. Real-time acquisition of pipeline vibration signals to generate pipeline vibration monitoring data, wherein multiple vibration acquisition units generate corresponding pipeline vibration monitoring data.

[0007] S02. Acquire pipeline vibration monitoring data, locate, mark and extract its characteristic signals, obtain characteristic data and associate it with pipeline vibration detection data and corresponding vibration acquisition units;

[0008] S03. According to preset conditions, obtain at least two pipeline vibration monitoring data that point to suspected pipeline leakage from multiple pipeline vibration monitoring data with characteristic signals, wherein the pipeline vibration monitoring data has characteristic data pointing to suspected pipeline leakage.

[0009] S04. Obtain two feature data with a correlation that meets the preset requirements from at least two pipeline vibration monitoring data that point to suspected pipeline leaks, set them as a set of leak investigation data, and simultaneously obtain the location information of the vibration acquisition unit corresponding to the pipeline vibration monitoring data to obtain at least one set of leak investigation data.

[0010] S05. Perform feature data correlation association on two corresponding pipeline vibration monitoring data in a set of leak investigation data, and then obtain the time difference ΔT of the feature signal pointing to the suspected pipeline leak in the two pipeline vibration monitoring data. At the same time, also obtain the spacing information D of the two vibration acquisition units corresponding to the two pipeline vibration monitoring data, as well as the vibration signal propagation speed V between the two vibration acquisition units. Then, determine the pipeline leak location information based on the time difference ΔT, the spacing information D and the vibration signal propagation speed V.

[0011] S06. Based on the pipeline leak location information corresponding to at least one set of leak investigation data, perform data fitting according to preset conditions to obtain the leak location positioning result.

[0012] As one possible implementation, the pipeline described in this scheme is further provided with a vibration acquisition unit every 5 to 20 meters along its straight pipe section.

[0013] As a preferred implementation option, the vibration acquisition units on the pipeline described in this solution are also identified with a unique ID and deployment information. The deployment information includes at least the location information of the vibration acquisition unit, the pipeline information, and the ID of the adjacent vibration acquisition units.

[0014] As a preferred implementation option, the location information in this solution preferably includes at least the GPS positioning information of the vibration acquisition unit and its relative position information on the pipeline. When there are branching branches between adjacent vibration acquisition units, there are multiple adjacent vibration acquisition units.

[0015] As a preferred implementation option, the pipeline information described in this solution preferably includes: the material information and specification information of the pipeline corresponding to the vibration acquisition unit.

[0016] As a preferred implementation option, this solution S02 preferably includes:

[0017] S021. Obtain pipeline vibration monitoring data and perform noise reduction processing according to preset conditions;

[0018] S022. Vibration data with amplitude greater than a preset value are set as feature signals, and vibration monitoring data within a preset time interval before and after the feature signal are located, marked and extracted as feature data.

[0019] S023. Associate the feature data with its corresponding pipeline vibration detection data and vibration acquisition unit.

[0020] As a preferred implementation option, solution S03 preferably includes:

[0021] S031. Input multiple pipeline vibration monitoring data with characteristic signals and the pipeline information corresponding to the pipeline vibration monitoring data as input items into the trained detection neural network for detection, and output the detection result by the detection neural network;

[0022] S032. Based on the detection results, obtain at least two pipeline vibration monitoring data points indicating suspected pipeline leaks, wherein the pipeline vibration monitoring data contains characteristic data indicating suspected pipeline leaks.

[0023] As a preferred implementation option, preferably, in this solution S031, the training method of the trained detection neural network is as follows:

[0024] A01. Construct a pipeline layout model, which includes straight pipe sections and bends. Multiple vibration signal acquisition units are arranged at preset intervals on the straight pipe sections. The bends are the sections where straight pipe sections are connected using bending fittings. At the same time, record the position information of each vibration signal acquisition unit on the pipeline layout model.

[0025] A02. Record the pipe material information, pipe specification information, and the internal fluid transport pressure, as well as the pipe vibration monitoring data when no leakage occurs. Then, create pipe leaks with different leakage rates per unit time in the straight pipe section or the connection between the straight pipe section and the curved pipe fitting in the pipe layout model.

[0026] A03. Record the pipeline vibration monitoring data collected by the vibration signal acquisition unit when a pipeline leak occurs;

[0027] A04. Based on the pipeline vibration monitoring data when no leak occurred, the pipeline vibration monitoring data when a leak occurred is marked with leakage characteristic data to generate marked leakage information. At the same time, the pipeline vibration monitoring data when no leak occurred is also marked with normal information.

[0028] A05. Change the pipe material or specifications, repeat A02 to A04 until the amount of data obtained reaches the preset requirements, and then proceed to A06.

[0029] A06. The pipeline vibration monitoring data marked with normal information and the pipeline vibration monitoring data marked with leakage information are respectively summarized into a set of training data. At the same time, the pipeline material information and pipeline specification information corresponding to the pipeline vibration monitoring data are also associated with the training data. Then the training data are collected to generate a training dataset.

[0030] A07. Extract different amounts of data from the training dataset as training group data and validation group data respectively. Then import the training group data into the neural network for training. In the training group data, pipeline vibration monitoring data is used as input and labeling information is used as result.

[0031] A08. Input the pipeline vibration monitoring data in the validation group into the trained detection neural network. Use the labeled information as the verification item to verify the detection results output by the detection neural network. When the accuracy meets the preset requirements, the model converges. Otherwise, re-import the training group into the trained detection neural network and continue training for the preset number of times until the model converges.

[0032] As a preferred implementation option, this solution S04 preferably includes:

[0033] S041. Obtain waveform data from the characteristic data in the pipeline vibration monitoring data pointing to suspected pipeline leakage, and then locate the characteristic peak pointing to suspected pipeline leakage.

[0034] S042. Based on the characteristic peak, obtain two characteristic data with a correlation that meet the preset requirements from at least two pipeline vibration monitoring data that point to suspected pipeline leaks, set them as a set of leak investigation data, and make the leak point located between the vibration acquisition units corresponding to the set of leak investigation data.

[0035] S043. Obtain the location information of the vibration acquisition unit corresponding to the pipeline vibration monitoring data in the leak investigation data and associate it to obtain at least one set of leak investigation data.

[0036] As a preferred implementation option, this solution S05 preferably includes:

[0037] S051. Perform feature data correlation association on two corresponding pipeline vibration monitoring data in a set of leak investigation data to make the two pipeline vibration monitoring data fall on the same time line.

[0038] S052. Based on the time information of the characteristic peaks pointing to the suspected pipeline leak in the two pipeline vibration monitoring data, the time difference ΔT of the characteristic signal pointing to the suspected pipeline leak in the two pipeline vibration monitoring data is then obtained.

[0039] S053. Obtain the spacing information D of the two vibration acquisition units corresponding to the two pipeline vibration monitoring data, and obtain the vibration signal propagation speed V between the two vibration acquisition units based on the pipeline information.

[0040] S054. Based on the time difference ΔT, the spacing information D, and the vibration signal propagation speed V, the following formula is used to determine the pipeline leak location information:

[0041] D = D1 + D2

[0042] D1=VT1

[0043] D2=VT2

[0044] △T=T1‐T2

[0045] D= VT1+ VT2=V(△T+ T2)+ VT2= V△T+ 2VT2= V△T+2D2

[0046] D2 = (D - V△T) / 2

[0047] Where D represents the distance between the two vibration acquisition units corresponding to the two pipeline vibration monitoring data, D1 represents the distance between one vibration acquisition unit and the leak point, T1 represents the time it takes for the vibration signal from the leak point to reach one vibration acquisition unit, D2 represents the distance between the other vibration acquisition unit and the leak point, T2 represents the time it takes for the vibration signal from the leak point to reach the other vibration acquisition unit; ΔT represents the time difference between the vibration signal from the leak point and the two vibration acquisition units, and V represents the propagation speed of the vibration signal generated at the leak point on the pipeline.

[0048] As a preferred implementation option, solution S06 preferably includes:

[0049] S061. Obtain pipeline leak location information.

[0050] When they are grouped together, based on the positional relationship between the leak point and the vibration acquisition unit in the pipeline leak location information, as well as the GPS positioning information of the vibration acquisition unit and its relative position information on the pipeline, the GPS positioning information of the leak point and its relative position information on the pipeline are determined, and the leak point location estimation information is generated. Then, along the pipeline layout length direction, the error calculation is performed on the GPS positioning information and the relative position information of the leak point on the pipeline to form interval location information, and the leak point location interval information is generated. Then, the leak point location estimation information and the leak point location interval information are output as the leak location positioning result.

[0051] When there are multiple groups, based on the positional relationship between the leak point and the vibration acquisition unit in multiple pipeline leak location information, as well as the GPS positioning information of the vibration acquisition unit and its relative position information on the pipeline, multiple GPS positioning information of the leak point and its relative position information on the pipeline are obtained. Then, these are fitted into interval positions to generate the leak point location interval information as the leak location positioning result output.

[0052] Based on the above, the present invention also provides a leak location system based on pipeline vibration signals, which includes:

[0053] Vibration acquisition units, which are multiple and spaced apart on the pipeline, are used to collect the vibration signals of the pipeline in real time and generate pipeline vibration monitoring data. Each vibration acquisition unit generates corresponding pipeline vibration monitoring data.

[0054] The signal processing unit is used to acquire pipeline vibration monitoring data, locate, mark and extract its characteristic signals, obtain characteristic data and associate it with pipeline vibration detection data and corresponding vibration acquisition units;

[0055] The signal judgment unit is used to obtain at least two pipeline vibration monitoring data that point to suspected pipeline leakage from multiple pipeline vibration monitoring data with characteristic signals according to preset conditions, wherein the pipeline vibration monitoring data has characteristic data pointing to suspected pipeline leakage.

[0056] The signal matching unit is used to obtain two feature data with a correlation that meet preset requirements from at least two pipeline vibration monitoring data pointing to suspected pipeline leaks, set them as a set of leak investigation data, and simultaneously obtain the position information of the vibration acquisition unit corresponding to the pipeline vibration monitoring data to obtain at least one set of leak investigation data.

[0057] The data processing unit is used to perform feature data correlation association on two corresponding pipeline vibration monitoring data in a set of leak investigation data, and then obtain the time difference ΔT of the feature signal pointing to the suspected pipeline leak in the two pipeline vibration monitoring data. At the same time, it also obtains the spacing information D of the two vibration acquisition units corresponding to the two pipeline vibration monitoring data, as well as the vibration signal propagation speed V between the two vibration acquisition units. Then, based on the time difference ΔT, the spacing information D, and the vibration signal propagation speed V, the pipeline leak location information is determined.

[0058] The data fitting unit is used to fit the pipeline leak location information corresponding to at least one set of leak investigation data according to preset conditions and obtain the leak location location result.

[0059] Based on the above, the present invention also provides a method for investigating leaks in pressure pipelines, which includes the leak location method based on pipeline vibration signals described above.

[0060] Based on the above, the present invention also provides a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the above-described leak location method based on pipeline vibration signals.

[0061] By adopting the above technical solution, the present invention has the following advantages compared with the prior art: The present invention ingeniously uses vibration acquisition units deployed on the pipeline to achieve real-time acquisition of pipeline vibration signals. This allows the vibration signals generated at the leak point to be monitored and acquired by the vibration acquisition units. After feature data localization, marking, and extraction, the acquired pipeline vibration monitoring data is used for detection and judgment, enabling the selection of pipeline vibration monitoring data pointing to suspected pipeline leaks. Furthermore, by combining two pipeline vibration monitoring data sets to form leak investigation data, the location of the leak is calculated to obtain the relative positional relationship between the leak point and the vibration acquisition unit. The present invention also ingeniously fits the pipeline leak location information corresponding to one or more sets of leak investigation data to form leak location results for points and / or intervals for maintenance personnel to reference, thus greatly improving maintenance efficiency. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 This is one of the simplified implementation flow diagrams of the present invention;

[0064] Figure 2 This is a schematic diagram of the training process of the detection neural network mentioned in the present invention.

[0065] Figure 3 This is a simplified schematic diagram of the present invention's solution during pipeline leak detection;

[0066] Figure 4 This is one of the simplified unit connection diagrams of the system of the present invention;

[0067] Figure 5 This is the second simplified unit connection diagram of the system of the present invention. Detailed Implementation

[0068] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the invention. Similarly, the following embodiments are only some, not all, embodiments of the present invention, and all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] like Figure 1 As shown in the figure, this embodiment provides a leak location method based on pipeline vibration signals. The pipeline is equipped with multiple vibration acquisition units (which can be directly mounted on the pipeline or have their detection ends in contact with the pipeline surface) spaced apart. The leak location method includes:

[0070] S01. Real-time acquisition of pipeline vibration signals to generate pipeline vibration monitoring data, wherein multiple vibration acquisition units generate corresponding pipeline vibration monitoring data.

[0071] S02. Acquire pipeline vibration monitoring data, locate, mark and extract its characteristic signals, obtain characteristic data and associate it with pipeline vibration detection data and corresponding vibration acquisition units;

[0072] S03. According to preset conditions, obtain at least two pipeline vibration monitoring data that point to suspected pipeline leakage from multiple pipeline vibration monitoring data with characteristic signals, wherein the pipeline vibration monitoring data has characteristic data pointing to suspected pipeline leakage.

[0073] S04. Obtain two feature data with a correlation that meets the preset requirements from at least two pipeline vibration monitoring data that point to suspected pipeline leaks, set them as a set of leak investigation data, and simultaneously obtain the location information of the vibration acquisition unit corresponding to the pipeline vibration monitoring data to obtain at least one set of leak investigation data.

[0074] S05. Perform feature data correlation association on two corresponding pipeline vibration monitoring data in a set of leak investigation data, and then obtain the time difference ΔT of the feature signal pointing to the suspected pipeline leak in the two pipeline vibration monitoring data. At the same time, also obtain the spacing information D of the two vibration acquisition units corresponding to the two pipeline vibration monitoring data, as well as the vibration signal propagation speed V between the two vibration acquisition units. Then, determine the pipeline leak location information based on the time difference ΔT, the spacing information D and the vibration signal propagation speed V.

[0075] S06. Based on the pipeline leak location information corresponding to at least one set of leak investigation data, perform data fitting according to preset conditions to obtain the leak location positioning result.

[0076] Since pipeline leaks typically begin as small-hole leaks, the resulting vibrations are easily weakened during transmission due to energy loss from the pipeline medium. To improve the reliability of leak location, as a possible implementation method, this solution further includes a vibration acquisition unit installed every 5 to 20 meters along the straight section of the pipeline. Furthermore, to enhance the intuitiveness of the correspondence between the collected pipeline vibration monitoring data and the vibration acquisition unit, and to increase the reference value of the on-site data, preferably, each vibration acquisition unit on the pipeline in this solution is also identified with a unique ID and deployment information. This deployment information includes at least the location information of the vibration acquisition unit, the pipeline information, and the ID of its adjacent vibration acquisition units.

[0077] In addition, the location information described in this solution includes at least the GPS positioning information of the vibration acquisition unit and its relative position information on the pipeline (e.g., its relative distance (pipeline distance) to adjacent vibration acquisition units). When there are branching branches between adjacent vibration acquisition units, there are multiple adjacent vibration acquisition units. The pipeline information described in this solution includes: the material information and specification information of the pipeline corresponding to the vibration acquisition unit.

[0078] The ID and deployment information of the aforementioned vibration acquisition unit can be stored using a passive NFC tag for maintenance personnel to access. Alternatively, the ID can be labeled with a physical tag, allowing maintenance personnel to retrieve the information by accessing a specific database or data table.

[0079] The above methods enable maintenance personnel to form a relatively intuitive impression of the pipeline layout structure based solely on the data information associated with the vibration acquisition unit, even without a pipeline layout blueprint, thereby improving the reliability and focus of on-site work.

[0080] In the investigation of vibration monitoring data, as a preferred implementation option, this solution S02 includes:

[0081] S021. Obtain pipeline vibration monitoring data and perform noise reduction processing according to preset conditions;

[0082] S022. Vibration data with amplitude greater than a preset value are set as feature signals, and vibration monitoring data within a preset time interval before and after the feature signal are located, marked and extracted as feature data.

[0083] S023. Associate the feature data with its corresponding pipeline vibration detection data and vibration acquisition unit.

[0084] The above method can filter out conventional vibrations in pipeline vibration monitoring data, such as those from roadsides or other pipeline perimeters, thereby highlighting abnormal vibration data. However, since abnormal vibration data also has a certain degree of randomness, such as vibrations caused by passing vehicles, to further investigate pipeline vibration monitoring data with characteristic data, as a preferred implementation option, solution S03 preferably includes:

[0085] S031. Input multiple pipeline vibration monitoring data with characteristic signals and the pipeline information corresponding to the pipeline vibration monitoring data as input items into the trained detection neural network for detection, and output the detection result by the detection neural network;

[0086] S032. Based on the detection results, obtain at least two pipeline vibration monitoring data points indicating suspected pipeline leaks, wherein the pipeline vibration monitoring data contains characteristic data indicating suspected pipeline leaks.

[0087] Combination Figure 2 As shown, in this scheme S031, the training method of the trained detection neural network is as follows:

[0088] A01. Construct a pipeline layout model, which includes straight pipe sections and bends. Multiple vibration signal acquisition units are arranged at preset intervals on the straight pipe sections. The bends are the sections where straight pipe sections are connected using bending fittings. At the same time, record the position information of each vibration signal acquisition unit on the pipeline layout model.

[0089] A02. Record the pipe material information, pipe specification information, and the internal fluid transport pressure, as well as the pipe vibration monitoring data when no leakage occurs. Then, create pipe leaks with different leakage rates per unit time in the straight pipe section or the connection between the straight pipe section and the curved pipe fitting in the pipe layout model.

[0090] A03. Record the pipeline vibration monitoring data collected by the vibration signal acquisition unit when a pipeline leak occurs;

[0091] A04. Based on the pipeline vibration monitoring data when no leak occurred, the pipeline vibration monitoring data when a leak occurred is marked with leakage characteristic data to generate marked leakage information. At the same time, the pipeline vibration monitoring data when no leak occurred is also marked with normal information.

[0092] A05. Change the pipe material or specifications, repeat A02 to A04 until the amount of data obtained reaches the preset requirements, and then proceed to A06.

[0093] A06. The pipeline vibration monitoring data marked with normal information and the pipeline vibration monitoring data marked with leakage information are respectively summarized into a set of training data. At the same time, the pipeline material information and pipeline specification information corresponding to the pipeline vibration monitoring data are also associated with the training data. Then the training data are collected to generate a training dataset.

[0094] A07. Extract different amounts of data from the training dataset as training group data and validation group data respectively. Then import the training group data into the neural network for training. In the training group data, pipeline vibration monitoring data is used as input and labeling information is used as result.

[0095] A08. Input the pipeline vibration monitoring data in the validation group into the trained detection neural network. Use the labeled information as the verification item to verify the detection results output by the detection neural network. When the accuracy meets the preset requirements, the model converges. Otherwise, re-import the training group into the trained detection neural network and continue training for the preset number of times until the model converges.

[0096] By utilizing the aforementioned detection neural network, the reliability of leak point signal identification in this solution can be improved. Furthermore, the detection neural network trained with large amounts of data can improve the efficiency of anomaly monitoring data identification and reduce reliance on human experience. At the same time, by constructing leak monitoring data under different conditions as training data, this solution can obtain a more targeted detection neural network for the scenario in which this solution is applied. Since neural network training is already common knowledge, the ingenuity of this solution lies in constructing targeted training data. Therefore, the training mechanism of the neural network will not be elaborated upon here.

[0097] In this solution, the use of neural network-assisted judgment is only to better locate pipeline vibration monitoring data with leakage anomalies. However, how to select pipeline vibration monitoring data for auxiliary calculation to determine the leak location is also crucial. As a preferred implementation option, solution S04 preferably includes:

[0098] S041. Obtain waveform data from the characteristic data in the pipeline vibration monitoring data pointing to suspected pipeline leakage, and then locate the characteristic peak pointing to suspected pipeline leakage.

[0099] S042. Based on the characteristic peak, obtain two characteristic data with a correlation that meet the preset requirements from at least two pipeline vibration monitoring data that point to suspected pipeline leaks, set them as a set of leak investigation data, and make the leak point located between the vibration acquisition units corresponding to the set of leak investigation data.

[0100] S043. Obtain the location information of the vibration acquisition unit corresponding to the pipeline vibration monitoring data in the leak investigation data and associate it to obtain at least one set of leak investigation data.

[0101] In this scheme, since the characteristic peaks generated by pipeline leaks have certain morphological characteristics, the data correspondence between different pipeline vibration monitoring data can be determined by the shape of the characteristic peaks. Because the vibration signal at the leak point experiences some loss as it propagates along the pipeline, the peak value gradually decreases with increasing propagation distance. Under the same medium, the period of the characteristic waveform is often relatively fixed, and the loss is not significant over relatively short distances. Therefore, in this scheme with a deployment spacing of 5–20 meters, the propagation speed can be considered a fixed value. For selecting a set of leak investigation data, the optimal choice is to take pipeline vibration monitoring data collected by one vibration acquisition unit on each side of the leak point as the leak investigation data. This is because the distance between the two vibration acquisition units is the smallest, and the peak drop height is relatively small. Therefore, the peak height can be used as a correlation coefficient for matching, and two characteristic data points with a preset correlation coefficient are selected as a set of leak investigation data.

[0102] In addition, the method of locating the leak point between a set of leak investigation data can be to use the peaks of characteristic peaks for auxiliary determination. That is, as the propagation distance of the vibration generated at the leak point increases, the peak value of the characteristic peak formed by it shows a trend of gradually decreasing. If the two peak values ​​are similar, it is highly likely that the leak point is located between the vibration acquisition units corresponding to the two pipeline vibration detection data. Alternatively, an assumption can be made, and then further investigation can be carried out to determine the leak point (for example, by using other forms of leak monitoring to rule out other leaks).

[0103] Combination Figure 3 As shown, in calculating the relative positions of the leak point and the vibration acquisition unit, as a preferred implementation option, this scheme S05 preferably includes:

[0104] S051. Perform feature data correlation association on two corresponding pipeline vibration monitoring data in a set of leak investigation data to make the two pipeline vibration monitoring data fall on the same time line.

[0105] S052. Based on the time information of the characteristic peaks pointing to the suspected pipeline leak in the two pipeline vibration monitoring data, the time difference ΔT of the characteristic signal pointing to the suspected pipeline leak in the two pipeline vibration monitoring data is then obtained.

[0106] S053. Obtain the spacing information D of the two vibration acquisition units corresponding to the two pipeline vibration monitoring data, and obtain the vibration signal propagation speed V between the two vibration acquisition units based on the pipeline information.

[0107] S054. Based on the time difference ΔT, the spacing information D, and the vibration signal propagation speed V, the following formula is used to determine the pipeline leak location information:

[0108] D = D1 + D2

[0109] D1=VT1

[0110] D2=VT2

[0111] △T=T1‐T2

[0112] D= VT1+ VT2=V(△T+ T2)+ VT2= V△T+ 2VT2= V△T+2D2

[0113] D2 = (D - V△T) / 2

[0114] Where D represents the distance between the two vibration acquisition units corresponding to the two pipeline vibration monitoring data, D1 represents the distance between one vibration acquisition unit and the leak point, T1 represents the time it takes for the vibration signal from the leak point to reach one vibration acquisition unit, D2 represents the distance between the other vibration acquisition unit and the leak point, T2 represents the time it takes for the vibration signal from the leak point to reach the other vibration acquisition unit; ΔT represents the time difference between the vibration signal from the leak point and the two vibration acquisition units, and V represents the propagation speed of the vibration signal generated at the leak point on the pipeline.

[0115] In this scheme, calculating the leak point and the relative position of the two vibration acquisition units based on the correlation of pipeline vibration monitoring data to obtain pipeline leak location information has a certain degree of error in practice. To improve the reference value of the location data, as a better implementation option, this scheme S06 preferably includes:

[0116] S061. Obtain pipeline leak location information.

[0117] When they are grouped together, based on the positional relationship between the leak point and the vibration acquisition unit in the pipeline leak location information, as well as the GPS positioning information of the vibration acquisition unit and its relative position information on the pipeline, the GPS positioning information of the leak point and its relative position information on the pipeline are determined, and the leak point location estimation information is generated. Then, along the pipeline layout length direction, the error calculation is performed on the GPS positioning information and the relative position information of the leak point on the pipeline to form interval location information, and the leak point location interval information is generated. Then, the leak point location estimation information and the leak point location interval information are output as the leak location positioning result.

[0118] When there are multiple groups, based on the positional relationship between the leak point and the vibration acquisition unit in multiple pipeline leak location information, as well as the GPS positioning information of the vibration acquisition unit and its relative position information on the pipeline, multiple GPS positioning information of the leak point and its relative position information on the pipeline are obtained. Then, these are fitted into interval positions to generate the leak point location interval information as the leak location positioning result output.

[0119] Using the above method, this solution utilizes a small-range approach to locate leaks. When there is only one set of pipeline leak location information, it can provide data references in both point and range aspects, providing more valuable assistance to maintenance personnel. When there are multiple sets of pipeline leak location information, the solution can be fitted into a range, allowing maintenance personnel to directly investigate leaks in pipeline sections within a small range.

[0120] Based on the above, the leak location method of this embodiment can be used for leak investigation in pressure pipelines.

[0121] Combination Figure 4 As shown, based on the above, this embodiment also provides a leak location system based on pipeline vibration signals, which includes:

[0122] Vibration acquisition units, which are multiple and spaced apart on the pipeline, are used to collect the vibration signals of the pipeline in real time and generate pipeline vibration monitoring data. Each vibration acquisition unit generates corresponding pipeline vibration monitoring data.

[0123] The signal processing unit is used to acquire pipeline vibration monitoring data, locate, mark and extract its characteristic signals, obtain characteristic data and associate it with pipeline vibration detection data and corresponding vibration acquisition units;

[0124] The signal judgment unit is used to obtain at least two pipeline vibration monitoring data that point to suspected pipeline leakage from multiple pipeline vibration monitoring data with characteristic signals according to preset conditions, wherein the pipeline vibration monitoring data has characteristic data pointing to suspected pipeline leakage.

[0125] The signal matching unit is used to obtain two feature data with a correlation that meet preset requirements from at least two pipeline vibration monitoring data pointing to suspected pipeline leaks, set them as a set of leak investigation data, and simultaneously obtain the position information of the vibration acquisition unit corresponding to the pipeline vibration monitoring data to obtain at least one set of leak investigation data.

[0126] The data processing unit is used to perform feature data correlation association on two corresponding pipeline vibration monitoring data in a set of leak investigation data, and then obtain the time difference ΔT of the feature signal pointing to the suspected pipeline leak in the two pipeline vibration monitoring data. At the same time, it also obtains the spacing information D of the two vibration acquisition units corresponding to the two pipeline vibration monitoring data, as well as the vibration signal propagation speed V between the two vibration acquisition units. Then, based on the time difference ΔT, the spacing information D, and the vibration signal propagation speed V, the pipeline leak location information is determined.

[0127] The data fitting unit is used to fit the pipeline leak location information corresponding to at least one set of leak investigation data according to preset conditions and obtain the leak location location result.

[0128] Combination Figure 5 As shown, in order to improve the feasibility of remote monitoring, this solution can provide more efficient assistance to the signal judgment unit by connecting to a cloud server and utilizing the detection neural network loaded on the server.

[0129] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0130] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0131] The above description is only a part of the embodiments of the present invention and does not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made based on the content of the present invention specification and drawings, or direct or indirect application in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A leak location method based on pipeline vibration signals, characterized in that, The pipeline is equipped with multiple vibration acquisition units at intervals for sensing pipeline vibration signals, and the leak location method includes: S01. Real-time acquisition of pipeline vibration signals and generation of pipeline vibration monitoring data, wherein multiple vibration acquisition units generate corresponding pipeline vibration monitoring data. S02. Acquire pipeline vibration monitoring data, set vibration data with amplitude greater than preset value as feature signals, locate, mark and extract feature signals from pipeline vibration monitoring data, obtain feature data and associate them with pipeline vibration monitoring data and corresponding vibration acquisition units; S03. According to preset conditions, obtain at least two pipeline vibration monitoring data that point to suspected pipeline leakage from multiple pipeline vibration monitoring data with characteristic signals, wherein the pipeline vibration monitoring data has characteristic data pointing to suspected pipeline leakage. S04. Obtain two feature data with a correlation that meet the preset requirements from at least two pipeline vibration monitoring data that point to suspected pipeline leaks, set them as a set of leak investigation data, and simultaneously obtain the location information of the vibration acquisition unit corresponding to the pipeline vibration monitoring data to obtain at least one set of leak investigation data. S05. Perform feature data correlation association on two corresponding pipeline vibration monitoring data in a set of leak investigation data, and then obtain the time difference ΔT of the feature signal pointing to the suspected pipeline leak in the two pipeline vibration monitoring data. At the same time, also obtain the spacing information D of the two vibration acquisition units corresponding to the two pipeline vibration monitoring data, as well as the vibration signal propagation speed V between the two vibration acquisition units. Then, determine the pipeline leak location information based on the time difference ΔT, the spacing information D and the vibration signal propagation speed V. S06. Based on the pipeline leak location information corresponding to at least one set of leak investigation data, perform data fitting according to preset conditions to obtain the leak location positioning result; S04 includes: S041. Obtain waveform data from the characteristic data in the pipeline vibration monitoring data pointing to suspected pipeline leakage, and then locate the characteristic peak pointing to suspected pipeline leakage. S042. Based on the characteristic peak, obtain two characteristic data with a correlation that meet the preset requirements from at least two pipeline vibration monitoring data that point to suspected pipeline leaks, set them as a set of leak investigation data, and make the leak point located between the vibration acquisition units corresponding to the set of leak investigation data. S043. Obtain the location information of the vibration acquisition unit corresponding to the pipeline vibration monitoring data in the leak investigation data and associate it to obtain at least one set of leak investigation data.

2. The leak location method based on pipeline vibration signals as described in claim 1, characterized in that, The pipeline is equipped with a vibration acquisition unit every 5 to 20 meters along its straight section; Each vibration acquisition unit on the pipeline is also identified by a unique ID and deployment information. This deployment information includes at least the location information of the vibration acquisition unit, the pipeline information, and the IDs of its adjacent vibration acquisition units. The location information includes at least the GPS positioning information of the vibration acquisition unit and its relative position information on the pipeline. When there are branching branches between adjacent vibration acquisition units, there are multiple adjacent vibration acquisition units. The pipeline information includes: material information and specification information of the pipeline corresponding to the vibration acquisition unit.

3. The leak location method based on pipeline vibration signals as described in claim 2, characterized in that, S02 includes: S021. Obtain pipeline vibration monitoring data and perform noise reduction processing on it according to preset conditions; S022. Vibration data with amplitude greater than a preset value are set as feature signals, and vibration monitoring data within a preset time interval before and after the feature signal are located, marked and extracted as feature data. S023. Associate the feature data with its corresponding pipeline vibration monitoring data and vibration acquisition unit.

4. The leak location method based on pipeline vibration signals as described in claim 3, characterized in that, S03 includes: S031. Input multiple pipeline vibration monitoring data with characteristic signals and the pipeline information corresponding to the pipeline vibration monitoring data as input items into the trained detection neural network for detection, and output the detection result by the detection neural network; S032. Based on the detection results, obtain at least two pipeline vibration monitoring data points indicating suspected pipeline leaks, wherein the pipeline vibration monitoring data contains characteristic data indicating suspected pipeline leaks.

5. The leak location method based on pipeline vibration signals as described in claim 4, characterized in that, In S031, the training method for the trained detection neural network is as follows: A01. Construct a pipeline layout model, which includes straight pipe sections and bends. Multiple vibration signal acquisition units are arranged at preset intervals on the straight pipe sections. The bends are the sections where straight pipe sections are connected using bending fittings. At the same time, record the position information of each vibration signal acquisition unit on the pipeline layout model. A02. Record the pipe material information, pipe specification information, and the internal fluid transport pressure, as well as the pipe vibration monitoring data when no leakage occurs. Then, create pipe leaks with different leakage rates per unit time in the straight pipe section or the connection between the straight pipe section and the curved pipe fitting in the pipe layout model. A03. Record the pipeline vibration monitoring data collected by the vibration signal acquisition unit when a pipeline leak occurs; A04. Based on the pipeline vibration monitoring data when no leak occurred, the pipeline vibration monitoring data when a leak occurred is marked with leakage characteristic data to generate marked leakage information. At the same time, the pipeline vibration monitoring data when no leak occurred is also marked with normal information. A05. Change the pipe material or specifications, repeat A02 to A04 until the amount of data obtained reaches the preset requirements, and then proceed to A06. A06. The pipeline vibration monitoring data marked with normal information and the pipeline vibration monitoring data marked with leakage information are respectively summarized into a set of training data. At the same time, the pipeline material information and pipeline specification information corresponding to the pipeline vibration monitoring data are also associated with the training data. Then the training data are collected to generate a training dataset. A07. Extract different amounts of data from the training dataset as training group data and validation group data respectively. Then import the training group data into the neural network for training. In the training group data, pipeline vibration monitoring data is used as input and labeling information is used as result. A08. Input the pipeline vibration monitoring data in the validation group into the trained detection neural network. Use the labeled information as the verification item to verify the detection results output by the detection neural network. When the accuracy meets the preset requirements, the model converges. Otherwise, re-import the training group into the trained detection neural network and continue training for the preset number of times until the model converges.

6. The leak location method based on pipeline vibration signals as described in claim 4 or 5, characterized in that, S05 includes: S051. Perform feature data correlation association on two corresponding pipeline vibration monitoring data in a set of leak investigation data to make the two pipeline vibration monitoring data fall on the same time line. S052. Based on the time information of the characteristic peaks pointing to the suspected pipeline leak in the two pipeline vibration monitoring data, the time difference ΔT of the characteristic signal pointing to the suspected pipeline leak in the two pipeline vibration monitoring data is then obtained. S053. Obtain the spacing information D of the two vibration acquisition units corresponding to the two pipeline vibration monitoring data, and obtain the vibration signal propagation speed V between the two vibration acquisition units based on the pipeline information. S054. Based on the time difference ΔT, the spacing information D, and the vibration signal propagation speed V, the following formula is used to determine the pipeline leak location information: D = D1 + D2 D1=VT1 D2=VT2 △T=T1‐T2 D= VT1+ VT2=V(△T+ T2)+ VT2= V△T+ 2VT2= V△T+2D2 D2 = (D - V△T) / 2 Where D represents the distance between the two vibration acquisition units corresponding to the two pipeline vibration monitoring data, D1 represents the distance between one vibration acquisition unit and the leak point, T1 represents the time it takes for the vibration signal from the leak point to reach one vibration acquisition unit, D2 represents the distance between the other vibration acquisition unit and the leak point, T2 represents the time it takes for the vibration signal from the leak point to reach the other vibration acquisition unit; ΔT represents the time difference between the vibration signal from the leak point and the two vibration acquisition units, and V represents the propagation speed of the vibration signal generated at the leak point on the pipeline.

7. The leak location method based on pipeline vibration signals as described in claim 6, characterized in that, S06 includes: S061. Obtain pipeline leak location information. When they are grouped together, based on the positional relationship between the leak point and the vibration acquisition unit in the pipeline leak location information, as well as the GPS positioning information of the vibration acquisition unit and its relative position information on the pipeline, the GPS positioning information of the leak point and its relative position information on the pipeline are determined, and the leak point location estimation information is generated. Then, along the pipeline layout length direction, the error calculation is performed on the GPS positioning information and the relative position information of the leak point on the pipeline to form interval location information, and the leak point location interval information is generated. Then, the leak point location estimation information and the leak point location interval information are output as the leak location positioning result. When there are multiple groups, based on the positional relationship between the leak point and the vibration acquisition unit in multiple pipeline leak location information, as well as the GPS positioning information of the vibration acquisition unit and its relative position information on the pipeline, multiple GPS positioning information of the leak point and its relative position information on the pipeline are obtained. Then, they are fitted into interval positions to generate the leak point location interval information as the leak location positioning result output.

8. A leak location system based on pipeline vibration signals, which applies the leak location method based on pipeline vibration signals as described in any one of claims 1 to 7, characterized in that, It includes: Vibration acquisition units, which are multiple and spaced apart on the pipeline, are used to collect the vibration signals of the pipeline in real time and generate pipeline vibration monitoring data. Each vibration acquisition unit generates corresponding pipeline vibration monitoring data. The signal processing unit is used to acquire pipeline vibration monitoring data, locate, mark and extract its characteristic signals, obtain characteristic data and associate it with pipeline vibration monitoring data and the corresponding vibration acquisition unit; The signal judgment unit is used to obtain at least two pipeline vibration monitoring data that point to suspected pipeline leakage from multiple pipeline vibration monitoring data with characteristic signals according to preset conditions, wherein the pipeline vibration monitoring data has characteristic data pointing to suspected pipeline leakage. The signal matching unit is used to obtain two feature data with a correlation that meet preset requirements from at least two pipeline vibration monitoring data pointing to suspected pipeline leaks, set them as a set of leak investigation data, and simultaneously obtain the position information of the vibration acquisition unit corresponding to the pipeline vibration monitoring data to obtain at least one set of leak investigation data. The data processing unit is used to perform feature data correlation association on two corresponding pipeline vibration monitoring data in a set of leak investigation data, and then obtain the time difference ΔT of the feature signal pointing to the suspected pipeline leak in the two pipeline vibration monitoring data. At the same time, it also obtains the spacing information D of the two vibration acquisition units corresponding to the two pipeline vibration monitoring data, as well as the vibration signal propagation speed V between the two vibration acquisition units. Then, based on the time difference ΔT, the spacing information D, and the vibration signal propagation speed V, the pipeline leak location information is determined. The data fitting unit is used to fit the pipeline leak location information corresponding to at least one set of leak investigation data according to preset conditions and obtain the leak location result.

9. A method for diagnosing leaks in pressure pipelines, characterized in that: It includes the leak location method based on pipeline vibration signals as described in any one of claims 1 to 7.

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