Leakage processing method, terminal device and readable storage medium

Abstract

The application provides a leakage processing method, a terminal device and a readable storage medium, and is suitable for the technical field of pipe network leakage processing. The leakage processing method comprises the following steps: acquiring acoustic characteristic data of each noise instrument and hydraulic data of each pressure sensor in a predetermined area range within a predetermined time period; determining a leakage state by using a preset leakage identification model based on the acoustic characteristic data and the hydraulic data; and generating leakage alarm information corresponding to the predetermined area range based on the leakage state. The embodiment of the application can fuse the acoustic characteristic data and the hydraulic data, intelligently diagnose the leakage of the pipe network, effectively distinguish the real pipe leakage sound from the environmental noise, improve the accuracy of the leakage alarm, save human resources, and improve the efficiency of the leakage processing.

Description

Technical Field

[0001] This application relates to the field of pipeline leakage treatment technology, and in particular to a leakage treatment method, terminal equipment and readable storage medium. Background Technology

[0002] Currently, leakage control in urban water supply networks mainly relies on noise meters (also known as leak detectors) installed on the pipes. Traditional solutions are usually based on simple threshold judgments, that is, when the volume or vibration frequency detected by a single noise meter exceeds a preset threshold, a leakage alarm is generated.

[0003] However, existing technology cannot effectively distinguish between real pipe leaks and environmental noise (such as passing vehicles or construction vibrations), resulting in a high false alarm rate, generating a large number of invalid alarms, and wasting a lot of manpower on-site investigation. Summary of the Invention

[0004] In view of this, embodiments of this application provide a leakage processing method, a terminal device, and a readable storage medium to solve the problem of high false alarm rate caused by generating leakage alarms solely through a noise meter in the prior art.

[0005] The first aspect of this application provides a leakage handling method for handling leakage in a pipeline network, wherein the pipeline network is equipped with at least two noise meters and at least one pressure sensor; the method includes:

[0006] Acquire acoustic characteristic data of each noise meter and hydraulic data of each pressure sensor within a predetermined time period and area.

[0007] A pre-defined leakage identification model is used to determine the leakage status based on various acoustic feature data and hydraulic data.

[0008] Based on the leakage status, leakage alarm information corresponding to the predetermined area range is generated.

[0009] In one possible implementation, a pre-defined leakage identification model is used to determine the leakage status based on various acoustic feature data and hydraulic data, including:

[0010] The acoustic feature data and hydraulic data are aligned based on the acquisition time and location information to generate a multi-dimensional data snapshot; each acoustic feature data and each hydraulic data includes acquisition time and location information.

[0011] Input a multidimensional data snapshot into the leakage identification model and output the leakage status.

[0012] In one possible implementation, a multidimensional data snapshot is input into the leakage identification model, and the leakage status is output, including:

[0013] Input multidimensional data snapshots into the leakage identification model to identify composite feature patterns, and output the leakage status based on the composite feature patterns;

[0014] Among them, the leakage identification model is trained based on the composite feature pattern formed by acoustic feature sample data and hydraulic sample data and the corresponding sample leakage status. The composite feature pattern is used to indicate whether there is abnormal information in the acoustic feature data and hydraulic data.

[0015] In one possible implementation, after determining the leakage status using a pre-defined leakage identification model based on various acoustic feature data and hydraulic data, and before generating leakage alarm information corresponding to a predetermined area based on the leakage status, the following steps are also included:

[0016] Obtain acoustic feature data that contains anomalies, and obtain at least two anomalous acoustic feature data.

[0017] Using a preset acoustic propagation model, based on preset map information and various abnormal acoustic feature data, the estimated location information of leakage is determined; wherein, the map information includes the location information of each pipe segment of the pipeline network and the equipment setting information of each pipe segment, and the equipment setting information includes the location information of the noise meter and the location information of the pressure sensor.

[0018] In one possible implementation, a pre-defined acoustic propagation model is used, based on pre-defined map information and various abnormal acoustic feature data, to determine the estimated location information of leakage, including:

[0019] The preset map information and various abnormal acoustic feature data are input into the acoustic propagation model, so that the acoustic propagation model can determine the abnormal time point when each abnormal acoustic feature data captures the abnormality. Based on the time difference and map information corresponding to the abnormal time point of each abnormal acoustic feature data, the leakage prediction location information is determined.

[0020] In one possible implementation, based on the leakage status, leakage alarm information corresponding to a predetermined area range is generated, including:

[0021] Based on the leakage status and the preset correspondence between leakage status and alarm level, determine the alarm level corresponding to the leakage status;

[0022] Based on the estimated location of leakage, leakage status, and alarm level, leakage alarm information corresponding to the predetermined area is generated.

[0023] In one possible implementation, a pre-defined leakage identification model is used to determine the leakage status based on various acoustic feature data and hydraulic data, including:

[0024] Using a pre-defined leakage identification model, based on various acoustic feature data and hydraulic data, the leakage state and the corresponding confidence level are determined;

[0025] After generating leakage alarm information corresponding to the predetermined area based on the estimated leakage location information, leakage status, and alarm level, the process also includes:

[0026] Based on the confidence level and / or alarm level corresponding to each leakage status, the leakage alarm information is prioritized.

[0027] The leakage alarm information is pushed out sequentially according to priority.

[0028] In one possible implementation, within a predetermined time period, acoustic characteristic data of each noise meter and hydraulic data of each pressure sensor within a predetermined area are acquired, including:

[0029] In response to the detection of an anomaly in the acoustic characteristic data of at least one noise meter within a predetermined area, the acoustic characteristic data of each noise meter and the hydraulic data of each pressure sensor within the predetermined area are acquired within a predetermined time period.

[0030] A second aspect of this application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method of the first aspect.

[0031] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method of the first aspect.

[0032] Compared with the prior art, the embodiments of this application have at least the following technical effects:

[0033] The leakage handling method of the first aspect of this application is used for leakage handling in pipeline networks. It can acquire acoustic characteristic data from various noise meters and hydraulic data from various pressure sensors within a predetermined area within a predetermined time period. Then, using a preset leakage identification model, based on the acoustic characteristic data and hydraulic data, the leakage state is determined, and leakage alarm information corresponding to the predetermined area is generated based on the leakage state. Therefore, this application embodiment can integrate acoustic characteristic data and hydraulic data to intelligently diagnose leakage in pipeline networks, thereby effectively distinguishing between real pipeline leaks and environmental noise, improving the accuracy of leakage alarms, saving manpower, and increasing the efficiency of leakage handling.

[0034] It is understood that the beneficial effects of the second and third aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application, 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart of a leakage handling method provided in an embodiment of this application;

[0037] Figure 2 This is a flowchart of another leakage handling method provided in the embodiments of this application;

[0038] Figure 3 This is a schematic diagram of the structure of a leakage treatment device provided in an embodiment of this application;

[0039] Figure 4 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0040] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0041] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0042] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0043] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0044] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0045] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0046] Research has revealed the following significant drawbacks in existing leakage handling methods:

[0047] (1) High false alarm rate: It is impossible to effectively distinguish between real pipe leakage sound and environmental noise (such as vehicle passing by, construction vibration), resulting in a large number of invalid alarms and wasting a lot of manpower for on-site investigation.

[0048] (2) Poor positioning accuracy: It is difficult to accurately locate the leak point based on the data of a single noise meter. Usually, only a vague area can be given. Subsequently, experienced leak finders still need to manually locate the leak by listening to the sound, which is inefficient.

[0049] (3) Lack of diagnostic capabilities: Inability to effectively classify the severity of leaks. Whether it is a minor leak or a serious pipe burst, similar alarms may be generated, and managers cannot reasonably allocate field maintenance resources according to the urgency of the alarm.

[0050] Therefore, there is an urgent need for a new technical solution that can improve the accuracy of leakage diagnosis and positioning, and enable intelligent alarm classification.

[0051] The leakage handling method, terminal device, and readable storage medium provided in this application are intended to solve the above-mentioned technical problems of the prior art.

[0052] The technical solution of this application and how it solves the above-mentioned technical problems are described in detail below with specific embodiments. It should be noted that the following embodiments can be referenced, borrowed, or combined with each other, and the same terms, similar features, and similar implementation steps in different embodiments will not be described again.

[0053] See Figure 1 As shown, this application provides a flowchart of a leakage handling method. The leakage handling method of this application is used for leakage handling in a pipeline network, which is equipped with at least two noise meters and at least one pressure sensor. Figure 1 As shown, the leakage processing method of this application embodiment includes steps S101 to S103.

[0054] S101. Within a predetermined time period, acquire acoustic characteristic data of each noise meter and hydraulic data of each pressure sensor within a predetermined area.

[0055] The leakage handling method of this application embodiment can be applied to a terminal device, which executes the leakage handling method of this application embodiment as a remote control system.

[0056] Optionally, the pipeline network can be a water supply network, and the acoustic characteristic data includes at least one of sound pressure information and acoustic spectrum, while the hydraulic data includes at least one of pressure value and flow rate information.

[0057] Optionally, the predetermined time period can be set according to the actual application, such as 15 minutes. Within 15 minutes, data can be collected at predetermined intervals, and the acquired acoustic feature data and hydraulic data are all timestamped.

[0058] In some embodiments, acquiring acoustic characteristic data of each noise meter and hydraulic data of each pressure sensor within a predetermined area within a predetermined time period includes:

[0059] In response to the detection of an anomaly in the acoustic characteristic data of at least one noise meter within a predetermined area, the acoustic characteristic data of each noise meter and the hydraulic data of each pressure sensor within the predetermined area are acquired within a predetermined time period.

[0060] In practical applications, the acoustic characteristic data of each noise meter can be detected. When abnormal acoustic characteristic data is found, the predetermined area range where the noise meter is located can be determined based on the location information of the noise meter corresponding to the acoustic characteristic data.

[0061] Optionally, the predetermined area is an area that currently requires leakage treatment. It can be a pre-defined area, or it can be an area centered on the location of the noise meter that detected abnormal acoustic characteristic data, formed at predetermined distances. The predetermined area can be circular or a water supply zone.

[0062] S102. Using a preset leakage identification model, based on various acoustic feature data and hydraulic data, determine the leakage status.

[0063] In some embodiments, a preset leakage identification model is used to determine the leakage status based on various acoustic feature data and various hydraulic data, including:

[0064] The acoustic feature data and hydraulic data are aligned based on the acquisition time and location information to generate a multi-dimensional data snapshot; each acoustic feature data and each hydraulic data includes acquisition time and location information.

[0065] Input a multidimensional data snapshot into the leakage identification model and output the leakage status.

[0066] Optionally, the collection time can be a timestamp, and the location information can be the geographical location information in a preset map.

[0067] This application embodiment can align multi-source data acquisition with spatiotemporal information. Within the same water supply zone (DMA), acoustic characteristic data from multiple noise meters are simultaneously acquired over a preset time period, along with hydraulic data from the water supply zone's pipe network. This application embodiment aligns these data from different sensors based on timestamps and geographic location information to form a multi-dimensional data snapshot containing spatiotemporal information.

[0068] In some embodiments, a multidimensional data snapshot is input into the leakage identification model, and the leakage status is output, including:

[0069] Input multidimensional data snapshots into the leakage identification model to identify composite feature patterns, and output the leakage status based on the composite feature patterns;

[0070] Among them, the leakage identification model is trained based on the composite feature pattern formed by acoustic feature sample data and hydraulic sample data and the corresponding sample leakage status. The composite feature pattern is used to indicate whether there is abnormal information in the acoustic feature data and hydraulic data.

[0071] This application embodiment uses a leakage identification model to intelligently identify composite feature patterns by inputting multidimensional data snapshots into a pre-trained leakage identification model. The leakage identification model is not based on simple threshold judgment, but rather learns and identifies composite feature patterns of acoustic and hydraulic data corresponding to different leakage states (such as normal, minor leakage, general leakage, and pipe burst) through algorithms such as deep learning.

[0072] For example, the leakage detection model identified a composite feature pattern: "the pressure at point A drops slightly, while the noise meters at points B and C continuously detect faint abnormal noises at a specific frequency at night," and diagnosed it as "minor leakage."

[0073] S103. Based on the leakage status, generate leakage alarm information corresponding to the predetermined area range.

[0074] The leakage handling method of this application embodiment is used for leakage handling in pipeline networks. It can acquire acoustic characteristic data of each noise meter and hydraulic data of each pressure sensor within a predetermined time period. Then, using a preset leakage identification model, the leakage status is determined based on each acoustic characteristic data and each hydraulic data, thereby generating leakage alarm information corresponding to the predetermined area based on the leakage status.

[0075] Therefore, the embodiments of this application can integrate acoustic feature data and hydraulic data to intelligently diagnose pipeline leaks, thereby effectively distinguishing between real pipeline leak sounds and environmental noise, improving the accuracy of leak alarms, saving human resources, and increasing the efficiency of leak handling.

[0076] In some embodiments, after determining the leakage status using a preset leakage identification model based on various acoustic feature data and hydraulic data, and before generating leakage alarm information corresponding to a predetermined area based on the leakage status, the method further includes:

[0077] Obtain acoustic feature data that contains anomalies, and obtain at least two anomalous acoustic feature data.

[0078] Using a preset acoustic propagation model, based on preset map information and various abnormal acoustic feature data, the estimated location information of leakage is determined; wherein, the map information includes the location information of each pipe segment of the pipeline network and the equipment setting information of each pipe segment, and the equipment setting information includes the location information of the noise meter and the location information of the pressure sensor.

[0079] The location information in the embodiments of this application is all geographical location information in a preset map corresponding to preset map information, which can be latitude and longitude coordinates. For example, the leakage prediction location information is the latitude and longitude coordinates in the preset map.

[0080] Optionally, based on the leakage status, leakage alarm information corresponding to a predetermined area is generated, including: determining the alarm level corresponding to the leakage status based on the leakage status and the preset correspondence between leakage status and alarm level, and generating leakage alarm information corresponding to the predetermined area based on the leakage status and alarm level.

[0081] In some embodiments, based on the leakage status, leakage alarm information corresponding to a predetermined area range is generated, including:

[0082] Based on the leakage status and the preset correspondence between leakage status and alarm level, determine the alarm level corresponding to the leakage status;

[0083] Based on the estimated location of leakage, leakage status, and alarm level, leakage alarm information corresponding to the predetermined area is generated.

[0084] Optionally, based on the estimated location information of leakage, the status of leakage, and the alarm level, leakage alarm information corresponding to the predetermined area is generated, including: generating leakage alarm information corresponding to the predetermined area based on the estimated location information of leakage, the status of leakage, the alarm level, and relevant evidence data.

[0085] Optionally, the relevant evidence data includes various acoustic feature data and various hydraulic data. It is conceivable that the relevant evidence data may also include anomalous acoustic feature data and anomalous hydraulic data determined from various acoustic feature data and various hydraulic data.

[0086] See Figure 2 As shown, this application provides a flowchart of another leakage handling method. Figure 2 As shown, the leakage processing method of this application embodiment includes steps S201 to S206.

[0087] S201. Within a predetermined time period, acquire acoustic characteristic data of each noise meter and hydraulic data of each pressure sensor within a predetermined area.

[0088] S202. Using a preset leakage identification model, the leakage status is determined based on various acoustic feature data and hydraulic data.

[0089] Specifically, the principles of steps S201 to S202 in this application embodiment are the same as those of steps S101 to S102 in this application embodiment, and will not be repeated here.

[0090] S203. Obtain abnormal acoustic feature data, and obtain at least two abnormal acoustic feature data.

[0091] Optionally, acquiring abnormal acoustic feature data includes: acquiring abnormal acoustic feature data output by the leakage identification model.

[0092] Optionally, in practical applications, if abnormal acoustic feature data is obtained, and an abnormal acoustic feature data is obtained, the leakage prediction location information can be determined based on the abnormal acoustic feature data.

[0093] S204. Using a preset acoustic propagation model, based on preset map information and various abnormal acoustic feature data, determine the estimated location information of leakage; wherein, the map information includes the location information of each pipe segment of the pipeline network and the equipment setting information of each pipe segment, and the equipment setting information includes the location information of the noise meter and the location information of the pressure sensor.

[0094] Optionally, a preset acoustic propagation model is used to determine the estimated location of leakage based on preset map information and various abnormal acoustic feature data. This includes: using a preset acoustic propagation model, based on preset map information, various abnormal acoustic feature data, and the pipe material of the pipe section, to determine the estimated location of leakage.

[0095] In some embodiments, a preset acoustic propagation model is used to determine the estimated location information of leakage based on preset map information and various abnormal acoustic feature data, including:

[0096] The preset map information and various abnormal acoustic feature data are input into the acoustic propagation model, so that the acoustic propagation model can determine the abnormal time point when each abnormal acoustic feature data captures the abnormality. Based on the time difference and map information corresponding to the abnormal time point of each abnormal acoustic feature data, the leakage prediction location information is determined.

[0097] Optionally, when the leakage identification model identifies a composite feature pattern, the estimated location information of the leakage point can be calculated by using triangulation or a more complex acoustic propagation model, utilizing the time difference of arrival (TDOA) of abnormal signals detected by multiple noise meters, and combining the GIS (Geographic Information System) data of the pipeline network (i.e., the preset map information).

[0098] S205. Based on the leakage status and the preset correspondence between leakage status and alarm level, determine the alarm level corresponding to the leakage status.

[0099] Optionally, leakage status includes minor leaks, general leaks, and pipe bursts. Pipe bursts correspond to high-level P1, general leaks correspond to medium-level P2, and minor leaks correspond to low-level P3. Leakage status can also include normal, in which case no alarm is needed.

[0100] S206. Based on the estimated location information of leakage, leakage status and alarm level, generate leakage alarm information corresponding to the predetermined area range.

[0101] Optionally, based on the estimated location information of leakage, the status of leakage, and the alarm level, leakage alarm information corresponding to the predetermined area is generated, including: generating leakage alarm information corresponding to the predetermined area based on the estimated location information of leakage, the status of leakage, the alarm level, and relevant evidence data.

[0102] Optionally, the relevant evidence data includes various acoustic feature data and various hydraulic data. It is conceivable that the relevant evidence data may also include anomalous acoustic feature data and anomalous hydraulic data determined from various acoustic feature data and various hydraulic data.

[0103] According to the embodiment of this application, the alarm can be automatically classified (such as P1-emergency pipe burst, P2-general leakage, P3-minor leakage) based on the diagnostic results of the leakage status output by the leakage identification model, and an intelligent "leak detection" or "inspection" work order containing leakage prediction location information, alarm level, leakage status and related evidence data can be generated and pushed to the field task center.

[0104] Optionally, after generating leakage alarm information corresponding to a predetermined area based on the estimated leakage location information, leakage status, and alarm level, the process further includes: prioritizing each leakage alarm information based on the alarm level corresponding to each leakage status; and pushing each leakage alarm information sequentially according to the priority.

[0105] In some embodiments, a preset leakage identification model is used to determine the leakage status based on various acoustic feature data and various hydraulic data, including:

[0106] Using a pre-defined leakage identification model, based on various acoustic feature data and hydraulic data, the leakage state and the corresponding confidence level are determined;

[0107] After generating leakage alarm information corresponding to the predetermined area based on the estimated leakage location information, leakage status, and alarm level, the process also includes:

[0108] Based on the confidence level and / or alarm level corresponding to each leakage status, the leakage alarm information is prioritized.

[0109] The leakage alarm information is pushed out sequentially according to priority.

[0110] Optionally, embodiments of this application may prioritize each leakage alarm information based on confidence level or alarm level, or may use data fusion methods such as weighted processing of confidence level and alarm level to prioritize each leakage alarm information.

[0111] Based on the technical solution of the embodiments of this application, this application provides an example of a leakage processing method, including the following steps:

[0112] Step 1: Data Acquisition and Alignment. Deploy multiple noise meters (e.g., N1, N2, N3) and one pressure sensor (M1) within a DMA partition. Acoustic characteristic data from each noise meter and hydraulic data from the pressure sensor are acquired in 15-minute intervals, corresponding to a predetermined period. For example, at 03:00:00 on 2025-07-21, the acoustic spectra of N1, N2, and N3 and the pressure value of M1 are acquired, and these data are correlated to the same spatiotemporal snapshot (i.e., a multidimensional data snapshot).

[0113] Step Two: Feature Recognition. A multi-dimensional data snapshot is input into the leakage detection model. Internally, the leakage detection model may be a convolutional neural network (CNN) or a recurrent neural network (RNN) that has learned from a large amount of historical data. The leakage detection model discovers that the pressure value of M1 is 5% lower than the historical nighttime average for the same period, while the acoustic data of N2 and N3 show weak but persistent energy peaks in the 120Hz-150Hz frequency band. Based on its learned knowledge, the leakage detection model identifies this composite feature pattern as a "minor leak" with a confidence level of 85%.

[0114] Step 3: Location. An anomaly was detected in the acoustic feature data captured by N2 at 03:00:05, while an anomaly was detected by N3 at 03:00:07, a time difference of 2 seconds. Based on the locations of N2 and N3 on the pipeline network GIS map, and the pipe material (which affects the speed of sound propagation), the leak point is calculated to be closer to N2, and an estimated latitude and longitude coordinate (i.e., estimated leak location information) is provided.

[0115] Step 4: Work Order Generation. Because the diagnosis result is "minor leak", the alarm level is automatically set to P3 (low level), and a "leak detection" work order is generated, which includes: "A suspected minor leak has been found near [estimated leak location information]. Please send personnel to verify." The work order is then pushed to the task center of the field personnel's mobile app.

[0116] This application relates to the fields of smart water management and IoT data analysis technology, specifically to a method and system for intelligent identification, accurate location, and alarm classification of water supply network leakage status using multi-source sensor data. This application aims to solve the technical problems of high false alarm rate, inaccurate location, and inability to intelligently classify leakage in existing technologies, providing a more accurate, efficient, and intelligent method for diagnosing and locating network leakage.

[0117] The leakage handling method according to the embodiments of this application can achieve at least the following technical effects:

[0118] (1) Significantly reduce false alarm rate: By integrating hydraulic data for cross-validation, interference from single data sources such as environmental noise is effectively eliminated, greatly improving the accuracy of alarms.

[0119] (2) Significantly improve positioning accuracy: Calculate using the time difference of multi-point acoustic feature data to improve the positioning of leak points from the vague "area" level to the more accurate "segment" level.

[0120] (3) Realize intelligent diagnosis and classification: upgrade from simple "yes / no" alarm to intelligent diagnosis of leakage status (such as seepage, pipe burst), and automatically prioritize alarms to make the scheduling of maintenance resources more scientific and efficient.

[0121] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0122] See Figure 3 As shown, this application provides a schematic diagram of the structure of a leakage treatment device 30. Figure 3 As shown, the leakage handling device 30 is used for leakage handling in the pipeline network, which is equipped with at least two noise meters and at least one pressure sensor. The leakage handling device 30 includes: an acquisition module 301, a first determination module 302, and an alarm module 303.

[0123] The acquisition module 301 is used to acquire acoustic characteristic data of each noise meter and hydraulic data of each pressure sensor within a predetermined area within a predetermined time period.

[0124] The first determining module 302 is used to determine the leakage status based on a preset leakage identification model and various acoustic feature data and hydraulic data.

[0125] The alarm module 303 is used to generate leakage alarm information corresponding to a predetermined area range based on the leakage status.

[0126] Optionally, the first determining module 302 is used to align the acoustic feature data and hydraulic data based on the acquisition time and location information to generate a multi-dimensional data snapshot; wherein each acoustic feature data carries acquisition time and location information, and each hydraulic data carries acquisition time and location information; the multi-dimensional data snapshot is input into the leakage identification model to output the leakage status.

[0127] Optionally, the first determining module 302 is used to input a multi-dimensional data snapshot into the leakage identification model, identify the composite feature pattern, and output the leakage status based on the composite feature pattern;

[0128] Among them, the leakage identification model is trained based on the composite feature pattern formed by acoustic feature sample data and hydraulic sample data and the corresponding sample leakage status. The composite feature pattern is used to indicate whether there is abnormal information in the acoustic feature data and hydraulic data.

[0129] Optionally, the leakage handling device 30 further includes a second determining module, which is used to acquire abnormal acoustic feature data and obtain at least two abnormal acoustic feature data; using a preset acoustic propagation model, based on preset map information and each abnormal acoustic feature data, to determine the estimated location information of the leakage; wherein, the map information includes the location information of each pipe segment of the pipeline network and the equipment setting information of each pipe segment, and the equipment setting information includes the location information of the noise meter and the location information of the pressure sensor.

[0130] Optionally, the second determining module is used to input the preset map information and each abnormal acoustic feature data into the acoustic propagation model, so that the acoustic propagation model determines the abnormal time point when each abnormal acoustic feature data captures the abnormality, and determines the leakage prediction location information based on the time difference and map information corresponding to the abnormal time point of each abnormal acoustic feature data.

[0131] Optionally, the alarm module 303 is used to determine the alarm level corresponding to the leakage state based on the leakage state and the preset correspondence between leakage state and alarm level; and to generate leakage alarm information corresponding to a predetermined area based on the leakage estimated location information, leakage state and alarm level.

[0132] Optionally, the first determining module 302 is used to determine the leakage state and the confidence level corresponding to the leakage state by adopting a preset leakage identification model based on each acoustic feature data and each hydraulic data;

[0133] Optionally, the alarm module 303 is used to prioritize each leakage alarm information based on the confidence level and / or alarm level corresponding to each leakage status; and to push each leakage alarm information sequentially according to the priority.

[0134] Optionally, the acquisition module 301 is configured to acquire acoustic characteristic data of each noise meter and hydraulic data of each pressure sensor within a predetermined time period in response to detecting an anomaly in the acoustic characteristic data of at least one noise meter within a predetermined area.

[0135] In applications, the modules in the leakage processing device 30 can be software program modules, or they can be implemented by different logic circuits integrated in the processor, or they can be implemented by multiple distributed processors.

[0136] The leakage processing device 30 of this application embodiment can execute the method provided in this application embodiment. The implementation principle is similar. The actions performed by each module in the leakage processing device 30 of each embodiment of this application correspond to the steps in the method of each embodiment of this application. For detailed functional descriptions of each module of the leakage processing device 30, please refer to the descriptions in the corresponding methods shown above, which will not be repeated here.

[0137] See Figure 4 As shown, this application provides a schematic diagram of the structure of a terminal device 40. Figure 4 As shown, the terminal device 40 of this application embodiment includes: a memory 42, a processor 41, and a computer program 43 stored in the memory 42 and executable on the processor 41. When the processor 41 executes the computer program, it implements the steps of the methods of the various embodiments of this application.

[0138] Terminal device 40 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. Terminal device 40 may include, but is not limited to, a processor 41 and a memory 42. Those skilled in the art will understand that... Figure 4 This is merely an example of terminal device 40 and does not constitute a limitation on terminal device 40. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0139] The processor 41 can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0140] In some embodiments, memory 42 may be an internal storage unit, such as a hard disk or RAM. Memory 42 may be a removable / non-removable, volatile / non-volatile computer system storage medium; for example, memory 42 may be a non-volatile memory used for reading and writing non-volatile magnetic media. In other embodiments, memory 42 may be an external storage device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., provided on terminal device 40. Memory 42 is used to store operating systems, applications, bootloaders, data, and other programs, such as program code for computer programs. Memory 42 may also be used to temporarily store data that has been output or will be output.

[0141] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0142] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0143] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0144] If the integrated units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a device / terminal equipment, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0145] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc. The storage medium can also include combinations of the above types of memory.

[0146] This application provides a computer program product that, when run on a processor, enables the processor to execute the steps described in the various method embodiments above.

[0147] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0148] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0149] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0150] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0151] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for handling leakage, characterized in that, For handling leakage in a pipeline network, the pipeline network is equipped with at least two noise meters and at least one pressure sensor; the method includes: Acoustic characteristic data of each noise meter and hydraulic data of each pressure sensor are acquired within a predetermined time period and within a predetermined area. A preset leakage identification model is used to determine the leakage status based on the acoustic feature data and the hydraulic data. Based on the leakage status, a leakage alarm message corresponding to the predetermined area range is generated; The step of using a preset leakage identification model to determine the leakage status based on the acoustic feature data and the hydraulic data includes: The acoustic feature data and the hydraulic data are aligned based on the acquisition time and location information to generate a multi-dimensional data snapshot; wherein each acoustic feature data and each hydraulic data carries acquisition time and location information. The multidimensional data snapshot is input into the leakage identification model to identify the composite feature pattern, and the leakage status is output based on the composite feature pattern. The leakage identification model is trained based on the composite feature pattern formed by acoustic feature sample data and hydraulic sample data and the corresponding sample leakage status. The composite feature pattern is used to indicate whether there is abnormal information in the acoustic feature data and the hydraulic data. After determining the leakage status using a preset leakage identification model based on the acoustic feature data and the hydraulic data, and before generating leakage alarm information corresponding to the predetermined area based on the leakage status, the method further includes: Obtain acoustic feature data that contains anomalies, and obtain at least two anomalous acoustic feature data. The preset map information and the abnormal acoustic feature data are input into the acoustic propagation model, so that the acoustic propagation model determines the abnormal time point when the abnormal acoustic feature data captures the abnormality. Based on the time difference corresponding to the abnormal time point of each abnormal acoustic feature data and the map information, the leakage prediction location information is determined. The map information includes the location information of each pipe segment of the pipeline network and the equipment setting information of each pipe segment. The equipment setting information includes the location information of the noise meter and the location information of the pressure sensor.

2. The leakage treatment method according to claim 1, characterized in that, The step of generating leakage alarm information corresponding to the predetermined area range based on the leakage status includes: Based on the leakage status and the preset correspondence between leakage status and alarm level, determine the alarm level corresponding to the leakage status; Based on the estimated location information of leakage, the leakage status, and the alarm level, leakage alarm information corresponding to the predetermined area range is generated.

3. The leakage treatment method according to claim 2, characterized in that, The method employs a preset leakage identification model to determine the leakage status based on the acoustic feature data and the hydraulic data, including: Using a preset leakage identification model, based on the acoustic feature data and the hydraulic data, the leakage state and the confidence level corresponding to the leakage state are determined; After generating leakage alarm information corresponding to the predetermined area range based on the estimated leakage location information, the leakage status, and the alarm level, the method further includes: Based on the confidence level and / or alarm level corresponding to each of the aforementioned leakage states, the leakage alarm information is prioritized. According to the priority order, the leakage alarm information is pushed out sequentially.

4. The leakage treatment method according to any one of claims 1-3, characterized in that, The step of acquiring acoustic characteristic data of each noise meter and hydraulic data of each pressure sensor within a predetermined area within a predetermined time period includes: In response to the detection of an anomaly in the acoustic characteristic data of at least one of the noise meters within the predetermined area, the acoustic characteristic data of each of the noise meters and the hydraulic data of each of the pressure sensors within the predetermined area are acquired within a predetermined time period.

5. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 4.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 4.

Images