Pipeline leakage detection system

Abstract

The invention relates to the technical field of pipeline leakage detection, and discloses a pipeline leakage detection system, which comprises an acquisition module for determining leakage detection factors of a pipeline monitoring area according to historical monitoring area characteristic data; the judgment module judges whether to adjust the leakage detection factor or not according to the leakage influence coefficient; the processing module is used for adjusting the leakage detection factor according to the adjustment coefficient to obtain a leakage detection adjustment factor; and the first prediction module is used for comparing the real-time leakage detection factor with the leakage detection adjustment factor, and predicting whether leakage occurs in the pipeline monitoring area or not according to a comparison result. The method has the remarkable advantages in the aspects of improving the detection efficiency and accuracy, reducing the cost and enhancing the environmental adaptability, and powerful technical support is provided for safe operation of the pipeline.

Description

Technical Field

[0001] This application relates to the technical field of pipeline leakage detection, and more particularly, to a pipeline leakage detection system. Background Art

[0002] Pipeline leakage detection scenarios widely exist in multiple fields such as oil, natural gas, water supply, and chemical industry. By detecting pipeline leakage in advance, the transmission safety of the pipeline can be ensured, and the normal production and operation of the pipeline can be guaranteed.

[0003] When detecting leakage in existing pipelines, many challenges emerge, including slow detection speed, insufficient accuracy, high maintenance cost, and poor environmental adaptability. Current detection methods usually rely on regular manual inspections, which not only consume a large amount of time and labor but are also difficult to perform in harsh environments. In addition, although some detection systems based on active sensors can provide real-time monitoring, they need to replace batteries regularly, which not only increases the maintenance cost but also imposes an additional burden on the environment. Summary of the Invention

[0004] This application proposes a pipeline leakage detection system, aiming to solve the problems of slow detection speed, insufficient accuracy, high maintenance cost, and poor environmental adaptability in current technology for pipeline leakage detection.

[0005] In a first aspect, this application proposes a pipeline leakage detection system, including:

[0006] An acquisition module, configured to collect historical monitoring area feature data of each pipeline monitoring area based on a wireless passive sensor, and determine a leakage detection factor for the pipeline monitoring area according to the historical monitoring area feature data; the pipeline monitoring area is a pipeline monitoring area among several pipeline monitoring areas into which the pipeline is divided.

[0007] A judgment module, configured to control the acquisition module to collect pipeline feature data of each pipeline monitoring area based on a wireless passive sensor, calculate a leakage influence coefficient of the pipeline based on the pipeline feature data, and determine whether to adjust the leakage detection factor according to the leakage influence coefficient.

[0008] A processing module, configured to, when it is determined to adjust the leakage detection factor, control the acquisition module to collect environmental feature data of the pipeline monitoring area based on a wireless passive sensor, determine an adjustment coefficient of the leakage detection factor according to the environmental feature data, and adjust the leakage detection factor according to the adjustment coefficient to obtain a leakage detection adjustment factor.

[0009] The first prediction module is configured to control the acquisition module to collect real-time pipeline monitoring feature data of the pipeline monitoring area based on the wireless passive sensor, and determine the real-time leakage detection factor of the pipeline monitoring area according to the real-time pipeline monitoring feature data; compare the real-time leakage detection factor with the leakage detection adjustment factor, and predict whether leakage will occur in the pipeline monitoring area according to the comparison result.

[0010] Optionally, the pipeline leakage detection system further includes:

[0011] The second prediction module is configured to, when it is predicted that leakage will occur in the pipeline monitoring area, control the acquisition module to collect signal strength data of each leakage monitoring sub-area based on the wireless passive sensor, and predict the leakage occurrence location according to the signal strength data; the leakage monitoring sub-area is the leakage monitoring sub-area in several leakage monitoring sub-areas divided from the pipeline monitoring area where leakage will occur.

[0012] Optionally, the historical monitoring area feature data includes: historical pipeline internal pressure feature data, historical pipeline external pressure feature data, historical pipeline vibration feature data, historical pipeline flow feature data; determining the leakage detection factor of the pipeline monitoring area according to the historical monitoring area feature data includes:

[0013]

[0014] Wherein, LDF represents the leakage detection factor; Pi represents the historical pipeline internal pressure feature value determined by the historical pipeline internal pressure feature data; Po represents the historical pipeline external pressure feature value determined by the historical pipeline external pressure feature data; V represents the historical pipeline vibration feature value determined by the historical pipeline vibration feature data; F represents the historical pipeline flow feature value determined by the historical pipeline flow feature data; T represents the time feature value of the historical monitoring area; ω1 represents the first weight coefficient; ω2 represents the second weight coefficient; ω3 represents the third weight coefficient; ω4 represents the fourth weight coefficient; ω5 represents the fifth weight coefficient; T represents the historical monitoring duration.

[0015] Optionally, the pipeline feature data includes: pipeline material type data, pipeline service life data, pipeline transmission medium data; calculating the leakage influence coefficient of the pipeline based on the pipeline feature data includes:

[0016]

[0017] Among them, LIC represents the leakage impact coefficient; M represents the risk factor of the pipeline material type determined by the pipeline material type data; Y represents the pipeline service life determined by the pipeline service life data; β represents the weighting index; C represents the risk factor of the pipeline transmission medium determined by the pipeline transmission medium data; γ represents the interaction impact coefficient among the pipeline material, service life, and transmission medium; δ represents the first correction index; θ represents the second correction index; α1 represents the first impact coefficient; α2 represents the second impact coefficient; α3 represents the third impact coefficient.

[0018] Optionally, judging whether to adjust the leakage detection factor according to the leakage impact coefficient includes:

[0019] Calculating the coefficient ratio of the leakage impact coefficient to the leakage impact coefficient threshold.

[0020] When the coefficient ratio is greater than or equal to the coefficient ratio threshold, it is determined to adjust the leakage detection factor; when the coefficient ratio is less than the coefficient ratio threshold, it is determined not to adjust the leakage detection factor.

[0021] Optionally, the environmental characteristic data includes: pipeline internal temperature data, pipeline internal humidity data, pipeline external temperature data, pipeline external humidity data; determining the adjustment coefficient of the leakage detection factor according to the environmental characteristic data includes:

[0022] Determining the environmental impact index based on the pipeline internal temperature data, pipeline internal humidity data, pipeline external temperature data, and pipeline external humidity data.

[0023] Comparing the environmental impact index with the environmental impact index threshold, and determining the adjustment coefficient of the leakage detection factor according to the comparison result.

[0024] Optionally, determining the adjustment coefficient of the leakage detection factor according to the comparison result includes:

[0025] Comparing the environmental impact index with the first environmental impact index threshold and the second environmental impact index threshold, and determining the adjustment coefficient of the leakage detection factor according to the comparison result; among them, the first environmental impact index threshold is less than the second environmental impact index threshold.

[0026] When the environmental impact index is less than or equal to the first environmental impact index threshold, determining the adjustment coefficient of the leakage detection factor as the first adjustment coefficient; when the environmental impact index is greater than the first environmental impact index threshold and less than or equal to the second environmental impact index threshold, determining the adjustment coefficient of the leakage detection factor as the second adjustment coefficient; the first adjustment coefficient is less than the second adjustment coefficient; when the environmental impact index is greater than the second environmental impact index threshold, determining the adjustment coefficient of the leakage detection factor as the third adjustment coefficient; the second adjustment coefficient is less than the third adjustment coefficient.

[0027] Optionally, the real-time pipeline monitoring feature data includes real-time pipeline internal pressure feature data, real-time pipeline external pressure feature data, real-time pipeline vibration feature data, and real-time pipeline flow feature data; determining the real-time leakage detection factor of the pipeline monitoring area according to the real-time pipeline monitoring feature data includes:

[0028]

[0029] wherein, LDFN represents the real-time leakage detection factor; μi represents the weighting coefficient corresponding to the i-th real-time pipeline monitoring feature value determined by the real-time pipeline monitoring feature data; Qi represents the i-th real-time pipeline monitoring feature value determined by the real-time pipeline monitoring feature data; σi represents the attenuation factor; ti represents the time stamp corresponding to the i-th real-time pipeline monitoring feature value determined by the real-time pipeline monitoring feature data, and i = 1, 2,..., n.

[0030] Optionally, comparing the real-time leakage detection factor with the leakage detection adjustment factor, and predicting whether leakage will occur in the pipeline monitoring area according to the comparison result, including:

[0031] Normalize the real-time leakage detection factor and the leakage detection adjustment factor respectively to obtain the normalized real-time leakage detection factor and the normalized leakage detection adjustment factor; calculate the difference value of the leakage detection factor according to the normalized real-time leakage detection factor and the normalized leakage detection adjustment factor.

[0032] When the difference value of the leakage detection factor is greater than or equal to the leakage detection factor difference threshold, it is predicted that leakage will occur in the pipeline monitoring area; when the difference value of the leakage detection factor is less than the leakage detection factor difference threshold, it is predicted that leakage will not occur in the pipeline monitoring area.

[0033] Optionally, controlling the acquisition module to collect the signal strength data of each leakage monitoring sub-area based on the wireless passive sensor, and predicting the leakage occurrence location according to the signal strength data, including:

[0034] Controlling the acquisition module to collect the signal strength data of each leakage monitoring sub-area based on the wireless passive sensor at the first time interval, and determining the signal strength fluctuation value of each leakage monitoring sub-area within the first time interval according to the signal strength data.

[0035] Establish a signal strength fluctuation data set according to the signal strength fluctuation value, and determine the average value of the signal strength fluctuation according to the signal strength fluctuation data set; mark the leakage monitoring sub-area corresponding to the signal strength fluctuation value greater than the average value of the signal strength fluctuation as the key monitoring sub-area; control the acquisition module to collect the maximum signal strength of each key monitoring sub-area based on the wireless passive sensor at the second time interval, and predict the leakage occurrence location according to the maximum signal strength.

[0036] Optionally, predicting the leakage occurrence location based on the maximum signal strength includes:

[0037] When the maximum signal strength is greater than or equal to the maximum signal strength threshold, it is predicted that the current critical monitoring sub-region is not the leakage occurrence location; when the maximum signal strength is less than the maximum signal strength threshold, it is predicted that the current critical monitoring sub-region is the leakage occurrence location.

[0038] In a second aspect, a pipeline leakage detection method is provided, including:

[0039] Collecting historical monitoring region feature data of each pipeline monitoring region based on a wireless passive sensor, and determining a leakage detection factor for the pipeline monitoring region according to the historical monitoring region feature data; the pipeline monitoring region is a pipeline monitoring region among several pipeline monitoring regions into which the pipeline is divided.

[0040] Collecting pipeline feature data of each pipeline monitoring region based on a wireless passive sensor, calculating a leakage influence coefficient of the pipeline based on the pipeline feature data, and determining whether to adjust the leakage detection factor according to the leakage influence coefficient.

[0041] When it is determined to adjust the leakage detection factor, based on the environmental feature data of the pipeline monitoring region of the wireless passive sensor, determining an adjustment coefficient of the leakage detection factor according to the environmental feature data, and adjusting the leakage detection factor according to the adjustment coefficient to obtain a leakage detection adjustment factor.

[0042] Collecting real-time pipeline monitoring feature data of the pipeline monitoring region based on a wireless passive sensor, and determining a real-time leakage detection factor for the pipeline monitoring region according to the real-time pipeline monitoring feature data; comparing the real-time leakage detection factor with the leakage detection adjustment factor, and predicting whether leakage will occur in the pipeline monitoring region according to the comparison result.

[0043] In a third aspect, a pipeline leakage detection device is provided, including a memory and a processor; the memory is used to store computer execution instructions, and the processor is connected to the memory through a bus; when the pipeline leakage detection device runs, the processor executes the computer execution instructions stored in the memory, so that the pipeline leakage detection device executes the pipeline leakage detection method in the second aspect.

[0044] The pipeline leakage detection device can be an electronic device or a part of a device in an electronic device, such as a chip system in an electronic device. The chip system is used to support the electronic device to implement the functions involved in the first aspect and any possible implementation manner thereof, for example, obtaining, determining, and sending the data and / or information involved in the above pipeline leakage detection method. The chip system includes a chip and may also include other discrete devices or circuit structures.

[0045] Fourthly, a computer-readable storage medium is provided. The computer-readable storage medium includes computer-executable instructions. When the computer-executable instructions run on a computer, the computer is caused to execute the pipeline leakage detection method according to the second aspect.

[0046] Fifthly, a computer program product is further provided. The computer program product includes computer instructions. When the computer instructions run on a pipeline leakage detection device, the pipeline leakage detection device is caused to execute the pipeline leakage detection method according to the second aspect as described above.

[0047] It should be noted that the above computer instructions may be stored in whole or in part on the computer-readable storage medium. Among them, the computer-readable storage medium may be packaged together with the processor of the pipeline leakage detection device, or may be packaged separately from the processor of the pipeline leakage detection device. The embodiments of the present application do not make any limitation in this regard.

[0048] The descriptions of the second aspect, the third aspect, the fourth aspect, and the fifth aspect in the present application may refer to the detailed description of the first aspect.

[0049] In the embodiments of the present application, the name of the above pipeline leakage detection device does not constitute a limitation to the device or functional module itself. In actual implementation, these devices or functional modules may appear under other names. For example, the receiving unit may also be referred to as a receiving module, a receiver, etc. As long as the functions of each device or functional module are similar to those of the present application and fall within the scope of the claims of the present application and their equivalent technologies.

[0050] The pipeline leakage detection system provided by the present application can quickly respond to pipeline leakage events through real-time data collection and analysis of wireless passive sensors, shortening the detection time, so as to take timely measures to prevent the leakage from expanding; by collecting and analyzing various characteristic data, including the pressure, vibration, flow rate, etc. inside and outside the pipeline, combined with historical data and environmental factors, it can more accurately judge the leakage situation; the wireless passive sensors do not require external power supply, reducing the maintenance workload and cost, while extending the service life of the system; it can adapt to different environmental conditions, such as temperature and humidity changes, to ensure stable operation in various environments; through the collection and analysis of signal strength data, the system can accurately predict the specific location where the leakage occurs, providing important information for rapid repair.

[0051] The pipeline leakage detection system provided by the present application has significant advantages in improving detection efficiency, accuracy, reducing costs, and enhancing environmental adaptability, providing strong technical support for the safe operation of pipelines. Description of the Drawings

[0052] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the following detailed description of the preferred embodiments. The drawings are only for the purpose of illustrating the preferred embodiments and are not considered to be a limitation of the present application. Also, throughout the drawings, the same reference numerals are used to denote the same components. In the drawings:

[0053] Figure 1 is a structural block diagram of a pipeline leakage detection system provided by an embodiment of the present application;

[0054] Figure 2 is a flowchart of a pipeline leakage detection method provided by an embodiment of the present application;

[0055] Figure 3 is a structural block diagram of a pipeline leakage detection device provided by an embodiment of the present application. Detailed Embodiments

[0056] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present application.

[0057] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", "front", "rear", "inner", "outer", etc. is based on the orientation or relative positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation of the present application. Without special instructions, in the case of satisfying the relative positional relationship shown in the drawings, the above orientation descriptions can be flexibly set during the actual application process.

[0058] The terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0059] In the description of this application, it should be noted that unless otherwise clearly specified and defined, the terms "installed", "connected", "linked", and "communicated" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection. It can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0060] In the embodiments of this application, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, article or device including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such process, article or device. Without more limitations, the element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, article or device including that element.

[0061] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or having more advantages than other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner.

[0062] In the description of this specification, specific features, structures, materials or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0063] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0064] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be completely conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0065] Refer to Figure 1As shown, in some embodiments of the present application, this embodiment provides a pipeline leakage detection system, including:

[0066] An acquisition module, configured to acquire historical monitoring area feature data of each pipeline monitoring area based on a wireless passive sensor, and determine a leakage detection factor of the pipeline monitoring area according to the historical monitoring area feature data; the pipeline monitoring area is a pipeline monitoring area among several pipeline monitoring areas divided from the pipeline.

[0067] A judgment module, configured to control the acquisition module to acquire pipeline feature data of each pipeline monitoring area based on the wireless passive sensor, calculate a leakage influence coefficient of the pipeline based on the pipeline feature data, and judge whether to adjust the leakage detection factor according to the leakage influence coefficient.

[0068] A processing module, configured to, when it is determined to adjust the leakage detection factor, control the acquisition module to acquire environmental feature data of the pipeline monitoring area based on the wireless passive sensor, determine an adjustment coefficient of the leakage detection factor according to the environmental feature data, and adjust the leakage detection factor according to the adjustment coefficient to obtain a leakage detection adjustment factor.

[0069] A first prediction module, configured to control the acquisition module to acquire real-time pipeline monitoring feature data of the pipeline monitoring area based on the wireless passive sensor, and determine a real-time leakage detection factor of the pipeline monitoring area according to the real-time pipeline monitoring feature data; compare the real-time leakage detection factor with the leakage detection adjustment factor, and predict whether leakage will occur in the pipeline monitoring area according to the comparison result.

[0070] Optionally, the pipeline leakage detection system further includes:

[0071] A second prediction module, configured to, when it is predicted that leakage will occur in the pipeline monitoring area, control the acquisition module to acquire signal strength data of each leakage monitoring sub-area based on the wireless passive sensor, and predict the leakage occurrence position according to the signal strength data; the leakage monitoring sub-area is a leakage monitoring sub-area among several leakage monitoring sub-areas divided from the pipeline monitoring area where leakage occurs.

[0072] It can be seen that the pipeline leakage detection system provided in this embodiment can quickly respond to pipeline leakage events through real-time data collection and analysis of wireless passive sensors, shortening the detection time, and thus taking timely measures to prevent the leakage from expanding; by collecting and analyzing various characteristic data, including internal and external pipeline pressures, vibrations, flows, etc., and combining historical data and environmental factors, it can more accurately judge the leakage situation; wireless passive sensors do not require external power supply, reducing the maintenance workload and cost, and at the same time extending the service life of the system; it can adapt to different environmental conditions, such as temperature and humidity changes, ensuring stable operation in various environments; through the collection and analysis of signal strength data, the system can accurately predict the specific location where the leakage occurs, providing important information for rapid repair.

[0073] It can be understood that the pipeline leakage detection system provided in this embodiment has significant advantages in improving detection efficiency, accuracy, reducing costs, and enhancing environmental adaptability, providing strong technical support for the safe operation of pipelines.

[0074] That is to say, a wireless passive sensor is a sensor that can operate without relying on an external power supply. This sensor is activated by receiving a wireless radio frequency signal of a specific frequency and transmits the detected information back to the reading device through reflection modulation technology. Such sensors are particularly suitable for occasions where wiring is difficult or long-term monitoring is required, such as underground pipelines, bridge structures, etc. The passive characteristics of wireless passive sensors give them significant advantages in installation and maintenance, being able to reduce maintenance costs and improve system reliability.

[0075] Optionally, the historical monitoring area characteristic data includes: historical pipeline internal pressure characteristic data, historical pipeline external pressure characteristic data, historical pipeline vibration characteristic data, historical pipeline flow characteristic data; determining the leakage detection factor of the pipeline monitoring area according to the historical monitoring area characteristic data includes:

[0076] The leakage detection factor is obtained through the following formula:

[0077]

[0078] Wherein, LDF represents the leakage detection factor; Pi represents the historical internal pressure characteristic value of the pipeline determined from the historical internal pressure characteristic data of the pipeline; Po represents the historical external pressure characteristic value of the pipeline determined from the historical external pressure characteristic data of the pipeline; V represents the historical vibration characteristic value of the pipeline determined from the historical pipeline vibration characteristic data; F represents the historical flow characteristic value of the pipeline determined from the historical pipeline flow characteristic data; T represents the time characteristic value of the historical monitoring area; ω1 represents the first weight coefficient; ω2 represents the second weight coefficient; ω3 represents the third weight coefficient; ω4 represents the fourth weight coefficient; ω5 represents the fifth weight coefficient; T represents the historical monitoring duration.

[0079] In this embodiment, the preferred values of the weight coefficients are ω1 = 0.3, ω2 = 0.2, ω3 = 0.25, ω4 = 0.15, ω5 = 0.1. The setting of these weight coefficients is based on the sensitivity evaluation of the changes in each characteristic data when a pipeline leakage event occurs and the statistical analysis of historical data. Through such weight allocation, it can be ensured that in practical applications, the system can pay more attention to the characteristic data that has a greater impact on the leakage event, thereby improving the accuracy and efficiency of leakage detection. For example, the internal pressure and vibration characteristic data of the pipeline usually change significantly when a leakage occurs, so they are given higher weight coefficients. Although the time characteristic value has an impact on leakage detection, it has a relatively smaller impact compared to other characteristic data, so its weight coefficient is lower. In this way, the system can more accurately reflect the real-time state of the pipeline and timely detect potential leakage risks.

[0080] In this embodiment, the historical internal pressure characteristic value of the pipeline refers to the maximum or average value of the pressure data collected by the pressure sensors installed inside the pipeline within a certain time range; the historical external pressure characteristic value of the pipeline refers to the maximum or average value of the pressure data collected by the pressure sensors installed outside the pipeline within the same time range. The historical vibration characteristic value of the pipeline refers to the maximum or average value of the vibration intensity data detected by the vibration sensors installed on the pipeline. The historical flow characteristic value of the pipeline refers to the maximum or average value of the flow data measured by the flow meters installed on the pipeline within a certain time range. Through the comprehensive analysis of these historical monitoring area characteristic data, the health status of the pipeline can be more comprehensively evaluated, providing a more accurate basis for determining the leakage detection factor.

[0081] Optionally, the pipeline characteristic data includes: pipeline material type data, pipeline service life data, pipeline transmission medium data; calculating the leakage impact coefficient of the pipeline based on the pipeline characteristic data, including:

[0082] The leakage impact coefficient is obtained by the following formula:

[0083]

[0084] Wherein, LIC represents the leakage impact coefficient; M represents the risk factor of the pipeline material type determined from the pipeline material type data; Y represents the pipeline service life determined from the pipeline service life data; β represents the weighting index; C represents the risk factor of the pipeline transmission medium determined from the pipeline transmission medium data; γ represents the interaction impact coefficient among the pipeline material, service life, and transmission medium; δ represents the first correction index; θ represents the second correction index; α1 represents the first impact coefficient; α2 represents the second impact coefficient; α3 represents the third impact coefficient.

[0085] In this embodiment, the preferred values of the impact coefficients α1, α2, and α3 are 0.4, 0.3, and 0.3 respectively. The determination of these coefficients is based on a comprehensive assessment of the impacts of different pipeline materials, service lives, and transmission media on the leakage risk. For example, certain material types may be more prone to corrosion under specific conditions due to their poor chemical stability, thus increasing the leakage risk, and therefore are given higher risk factors. For pipelines with a longer service life, their structural integrity may decline, and the leakage risk increases accordingly, so the service life is also an important consideration factor. The properties of the transmission medium, such as corrosiveness or flammability, also have a significant impact on the leakage risk. Through such weight allocation, the system can more accurately evaluate the leakage risk of the pipeline and provide a scientific basis for adjusting the leakage detection factor.

[0086] In this embodiment, the interaction impacts among the pipeline material, service life, and transmission medium are also considered. For example, certain material types may exhibit a higher risk within a specific service life, or the leakage risk may increase significantly when certain transmission media are combined with specific material types. Therefore, the interaction impact coefficient γ is introduced to reflect the combined effects of these factors. The correction indices δ and θ are used to adjust the model to adapt to specific pipeline environments and conditions to ensure the accuracy of the prediction. Through these comprehensive considerations, this system can provide a more accurate and reliable leakage prediction, thereby effectively preventing and reducing the occurrence of leakage incidents.

[0087] Optionally, determining whether to adjust the leakage detection factor according to the leakage impact coefficient includes:

[0088] Calculating the coefficient ratio of the leakage impact coefficient to the leakage impact coefficient threshold; comparing the coefficient ratio with the coefficient ratio threshold, and determining whether to adjust the leakage detection factor according to the comparison result; when the coefficient ratio is greater than or equal to the coefficient ratio threshold, it is determined to adjust the leakage detection factor; when the coefficient ratio is less than the coefficient ratio threshold, it is determined not to adjust the leakage detection factor.

[0089] It can be understood that when the coefficient ratio of the leakage impact coefficient to the leakage impact coefficient threshold is greater than or equal to the coefficient ratio threshold, it indicates that the leakage risk of the current pipeline has reached a level that requires attention. Therefore, it is necessary to adjust the leakage detection factor to improve the system's alertness. On the contrary, if the coefficient ratio is less than the coefficient ratio threshold, it means that the leakage risk is still within the controllable range, and at this time, there is no need to adjust the leakage detection factor. The leakage impact coefficient threshold and the coefficient ratio threshold are set based on historical data and practical experience, and they provide a judgment criterion for the system to ensure reasonable decisions can be made in different situations. In this way, the system can dynamically adjust the detection strategy according to the actual situation of the pipeline, thereby avoiding unnecessary false alarms while ensuring the detection sensitivity and ensuring the efficient operation of the pipeline leakage detection system.

[0090] Optionally, the environmental characteristic data includes: pipeline internal temperature data, pipeline internal humidity data, pipeline external temperature data, pipeline external humidity data; determining the adjustment coefficient of the leakage detection factor according to the environmental characteristic data includes:

[0091] Calculating an environmental impact index based on the pipeline internal temperature data, pipeline internal humidity data, pipeline external temperature data, and pipeline external humidity data; comparing the environmental impact index with an environmental impact index threshold, and determining the adjustment coefficient of the leakage detection factor according to the comparison result.

[0092] In this embodiment, when calculating the environmental impact index, EII = (Tin × Hin) + (Tout × Hout) is used for calculation. Wherein, Tin and Tout respectively represent the temperature data inside and outside the pipeline, and Hin and Hout respectively represent the humidity data inside and outside the pipeline. Through such calculation, an index value that comprehensively reflects the changes in the internal and external environments of the pipeline can be obtained.

[0093] Optionally, determining the adjustment coefficient of the leakage detection factor according to the comparison result includes:

[0094] Set an adjustment coefficient range, which includes a first adjustment coefficient, a second adjustment coefficient, and a third adjustment coefficient; compare the environmental impact index with a first environmental impact index threshold and a second environmental impact index threshold, and determine the adjustment coefficient of the leakage detection factor according to the comparison result; wherein, the first environmental impact index threshold is less than the second environmental impact index threshold; when the environmental impact index is less than or equal to the first environmental impact index threshold, determine that the adjustment coefficient of the leakage detection factor is the first adjustment coefficient; when the environmental impact index is greater than the first environmental impact index threshold and less than or equal to the second environmental impact index threshold, determine that the adjustment coefficient of the leakage detection factor is the second adjustment coefficient; when the environmental impact index is greater than the second environmental impact index threshold, determine that the adjustment coefficient of the leakage detection factor is the third adjustment coefficient.

[0095] It can be understood that the first adjustment coefficient < the second adjustment coefficient < the third adjustment coefficient. By setting different adjustment coefficient ranges, the system can dynamically adjust the leakage detection factor according to environmental changes. For example, when the temperature and humidity inside and outside the pipeline change greatly, it may affect the sensitivity and accuracy of the sensor. At this time, by increasing the adjustment coefficient, the alertness of the system can be enhanced to ensure that leaks can be detected in a timely manner even in harsh environments. On the contrary, if the environmental change is small, the adjustment coefficient can be appropriately reduced to reduce the possibility of false alarms. In this way, the system can flexibly adjust the detection strategy according to the actual environmental conditions, thereby improving the accuracy and reliability of detection.

[0096] Optionally, the real-time pipeline monitoring characteristic data includes real-time pipeline internal pressure characteristic data, real-time pipeline external pressure characteristic data, real-time pipeline vibration characteristic data, and real-time pipeline flow characteristic data; determining the real-time leakage detection factor of the pipeline monitoring area according to the real-time pipeline monitoring characteristic data includes:

[0097] The real-time leakage detection factor is obtained by the following formula:

[0098]

[0099] Wherein, LDFN represents the real-time leakage detection factor; μi represents the weighting coefficient corresponding to the i-th real-time pipeline monitoring characteristic value determined by the real-time pipeline monitoring characteristic data; Qi represents the i-th real-time pipeline monitoring characteristic value determined by the real-time pipeline monitoring characteristic data; σi represents the attenuation factor; ti represents the time stamp corresponding to the i-th real-time pipeline monitoring characteristic value determined by the real-time pipeline monitoring characteristic data, and i = 1, 2,..., n.

[0100] In this embodiment, the real-time pipeline monitoring eigenvalue refers to the internal pressure, external pressure, vibration intensity, and flow rate data of the pipeline collected in real time. These data reflect the current operating state of the pipeline and are the key basis for judging whether there is a leak. The collection of real-time monitoring eigenvalues is completed by various sensors installed on the pipeline, and these sensors can continuously send data to the processing module. The processing module can timely detect abnormal fluctuations through the comparative analysis of real-time data and historical data, thereby judging the possibility of a leak occurring. The weighting coefficient μi and attenuation factor σi of the real-time pipeline monitoring eigenvalue are determined based on the dynamic changes of real-time data and the statistical analysis of historical data. The time stamp ti corresponding to the real-time pipeline monitoring eigenvalue is used to reflect the freshness of the data, ensuring that the system can give priority to the latest monitoring data. Through such a calculation method, the system can evaluate the current state of the pipeline in real time and timely detect signs of leakage.

[0101] Optionally, comparing the real-time leakage detection factor with the leakage detection adjustment factor, and predicting whether there will be a leak in the pipeline monitoring area according to the comparison result, including:

[0102] Normalize the real-time leakage detection factor and the leakage detection adjustment factor respectively to obtain a normalized real-time leakage detection factor and a normalized leakage detection adjustment factor.

[0103] Calculate the difference between the leakage detection factors according to the normalized real-time leakage detection factor and the normalized leakage detection adjustment factor; compare the difference between the leakage detection factors with the leakage detection factor difference threshold, and predict whether there will be a leak in the pipeline monitoring area according to the comparison result; when the difference between the leakage detection factors is greater than or equal to the leakage detection factor difference threshold, predict that there will be a leak in the pipeline monitoring area; when the difference between the leakage detection factors is less than the leakage detection factor difference threshold, predict that there will be no leak in the pipeline monitoring area.

[0104] It can be understood that the normalization process is to eliminate the influence of the dimension of different eigenvalues, so that each eigenvalue can be compared and calculated under the same standard. Through normalization, eigenvalues of different magnitudes can be converted to a unified scale, thereby ensuring the accuracy and reliability of the calculation results. In this system, the normalized real-time leakage detection factor and leakage detection adjustment factor can more fairly reflect the leakage risk of the pipeline, avoiding misjudgment caused by different dimensions of eigenvalues. In addition, the normalization process helps to improve the convergence speed and stability of the algorithm, enabling the system to respond to leakage events faster and take corresponding preventive measures in a timely manner.

[0105] Optionally, control the acquisition module to collect the signal strength data of each leakage monitoring sub-region based on the wireless passive sensor, and predict the leakage occurrence location according to the signal strength data, that is, when controlling the acquisition module to collect the signal strength data of each leakage monitoring sub-region and predict the leakage occurrence location according to the signal strength data, it includes:

[0106] Control the acquisition module to collect the signal strength data of each leakage monitoring sub-region based on the wireless passive sensor at a first time interval, and determine the signal strength fluctuation value of each leakage monitoring sub-region within the first time interval according to the signal strength data.

[0107] Establish a signal strength fluctuation data set according to the signal strength fluctuation value, and determine the average signal strength fluctuation value according to the signal strength fluctuation data set; mark the leakage monitoring sub-region corresponding to the signal strength fluctuation value greater than the average signal strength fluctuation value as the key monitoring sub-region; control the acquisition module to collect the maximum signal strength of each key monitoring sub-region based on the wireless passive sensor at a second time interval, and predict the leakage occurrence location according to the maximum signal strength.

[0108] In this embodiment, the selection of the first time interval and the second time interval is based on the sensitivity evaluation of the signal strength change during the pipeline leakage event and the statistical analysis of historical data. By setting such time intervals, it can be ensured that in practical applications, the system can pay more attention to the signal strength data that has a greater impact on the leakage event, thereby improving the accuracy and efficiency of leakage detection. For example, when a leakage occurs, the signal strength usually changes significantly. Therefore, collecting the signal strength data within the first time interval can capture these changes in a timely manner. And within the second time interval, by collecting the maximum signal strength of the key monitoring sub-region, the leakage occurrence location can be predicted more accurately. In this way, the system can more accurately reflect the real-time state of the pipeline and detect potential leakage risks in a timely manner.

[0109] Optionally, predicting the leakage occurrence location according to the maximum signal strength includes:

[0110] Compare the maximum signal strength with the maximum signal strength threshold, and predict the leakage occurrence location according to the comparison result; when the maximum signal strength is greater than or equal to the maximum signal strength threshold, predict that the current key monitoring sub-region is not the leakage occurrence location; when the maximum signal strength is less than the maximum signal strength threshold, predict that the current key monitoring sub-region is the leakage occurrence location.

[0111] It is understandable that by setting a maximum signal strength threshold, the system can distinguish the signal fluctuations during normal operation from the abnormal signals caused by leakage events. When the signal strength exceeds the threshold, it indicates that the signal fluctuation in this area is abnormal and may be caused by leakage. On the contrary, if the signal strength is lower than the threshold, it is considered that the signal fluctuation in this area is within the normal range and the possibility of leakage is relatively low. Through this threshold-based judgment method, the system can effectively identify the area where leakage occurs, thereby quickly locating the leakage point and taking repair measures in a timely manner to reduce the losses and impacts caused by leakage.

[0112] In some embodiments, as Figure 2 shown, the embodiment of the present application further provides a pipeline leakage detection method, which is applied to the above-mentioned Figure 1 pipeline leakage detection system, including:

[0113] S201. Collect historical monitoring area feature data of each pipeline monitoring area based on the wireless passive sensor, and determine the leakage detection factor of the pipeline monitoring area according to the historical monitoring area feature data.

[0114] Among them, the pipeline monitoring area is the pipeline monitoring area in several pipeline monitoring areas divided by the pipeline.

[0115] S202. Collect pipeline feature data of each pipeline monitoring area based on the wireless passive sensor, calculate the leakage influence coefficient of the pipeline based on the pipeline feature data, and determine whether to adjust the leakage detection factor according to the leakage influence coefficient.

[0116] S203. When it is determined to adjust the leakage detection factor, collect environmental feature data of the pipeline monitoring area based on the wireless passive sensor, determine the adjustment coefficient of the leakage detection factor according to the environmental feature data, and adjust the leakage detection factor according to the adjustment coefficient to obtain the leakage detection adjustment factor.

[0117] S204. Collect real-time pipeline monitoring feature data of the pipeline monitoring area based on the wireless passive sensor, and determine the real-time leakage detection factor of the pipeline monitoring area according to the real-time pipeline monitoring feature data; compare the real-time leakage detection factor with the leakage detection adjustment factor, and predict whether leakage will occur in the pipeline monitoring area according to the comparison result.

[0118] Optionally, the pipeline leakage detection method further includes:

[0119] When it is predicted that leakage will occur in the pipeline monitoring area, collect signal strength data of each leakage monitoring sub-area based on the wireless passive sensor, and predict the leakage occurrence location according to the signal strength data.

[0120] Among them, the leakage monitoring sub-region is the leakage monitoring sub-region among several leakage monitoring sub-regions obtained by dividing the pipeline monitoring region where leakage occurs.

[0121] Optionally, the historical monitoring region characteristic data includes: historical pipeline internal pressure characteristic data, historical pipeline external pressure characteristic data, historical pipeline vibration characteristic data, historical pipeline flow characteristic data; determining the leakage detection factor of the pipeline monitoring region according to the historical monitoring region characteristic data includes:

[0122]

[0123] Among them, LDF represents the leakage detection factor; P i represents the historical pipeline internal pressure characteristic value determined by the historical pipeline internal pressure characteristic data; Po represents the historical pipeline external pressure characteristic value determined by the historical pipeline external pressure characteristic data; V represents the historical pipeline vibration characteristic value determined by the historical pipeline vibration characteristic data; F represents the historical pipeline flow characteristic value determined by the historical pipeline flow characteristic data; T represents the time characteristic value of the historical monitoring region; ω1 represents the first weight coefficient; ω2 represents the second weight coefficient; ω3 represents the third weight coefficient; ω4 represents the fourth weight coefficient; ω5 represents the fifth weight coefficient; T represents the historical monitoring duration.

[0124] Optionally, the pipeline characteristic data includes: pipeline material type data, pipeline service life data, pipeline transmission medium data; calculating the leakage influence coefficient of the pipeline based on the pipeline characteristic data includes:

[0125]

[0126] Among them, LIC represents the leakage influence coefficient; M represents the risk factor of the pipeline material type determined by the pipeline material type data; Y represents the pipeline service life determined by the pipeline service life data; β represents the weighting index; C represents the risk factor of the pipeline transmission medium determined by the pipeline transmission medium data; γ represents the interaction influence coefficient among the pipeline material, service life and transmission medium; δ represents the first correction index; θ represents the second correction index; α1 represents the first influence coefficient; α2 represents the second influence coefficient; α3 represents the third influence coefficient.

[0127] Optionally, judging whether to adjust the leakage detection factor according to the leakage influence coefficient includes:

[0128] Calculating the coefficient ratio of the leakage influence coefficient to the leakage influence coefficient threshold.

[0129] When the coefficient ratio is greater than or equal to the coefficient ratio threshold, it is determined to adjust the leakage detection factor; when the coefficient ratio is less than the coefficient ratio threshold, it is determined not to adjust the leakage detection factor.

[0130] Optionally, the environmental characteristic data includes: internal pipeline temperature data, internal pipeline humidity data, external pipeline temperature data, and external pipeline humidity data; determining an adjustment coefficient of the leakage detection factor based on the environmental characteristic data includes:

[0131] Determine an environmental impact index based on the internal pipeline temperature data, internal pipeline humidity data, external pipeline temperature data, and external pipeline humidity data.

[0132] Compare the environmental impact index with an environmental impact index threshold, and determine an adjustment coefficient of the leakage detection factor according to the comparison result.

[0133] Optionally, determining an adjustment coefficient of the leakage detection factor according to the comparison result includes:

[0134] Compare the environmental impact index with a first environmental impact index threshold and a second environmental impact index threshold, and determine an adjustment coefficient of the leakage detection factor according to the comparison result; wherein, the first environmental impact index threshold is less than the second environmental impact index threshold.

[0135] When the environmental impact index is less than or equal to the first environmental impact index threshold, determine that the adjustment coefficient of the leakage detection factor is the first adjustment coefficient; when the environmental impact index is greater than the first environmental impact index threshold and less than or equal to the second environmental impact index threshold, determine that the adjustment coefficient of the leakage detection factor is the second adjustment coefficient; the first adjustment coefficient is less than the second adjustment coefficient; when the environmental impact index is greater than the second environmental impact index threshold, determine that the adjustment coefficient of the leakage detection factor is the third adjustment coefficient; the second adjustment coefficient is less than the third adjustment coefficient.

[0136] Optionally, the real-time pipeline monitoring characteristic data includes real-time internal pipeline pressure characteristic data, real-time external pipeline pressure characteristic data, real-time pipeline vibration characteristic data, and real-time pipeline flow characteristic data; determining a real-time leakage detection factor of the pipeline monitoring area based on the real-time pipeline monitoring characteristic data includes:

[0137]

[0138] wherein, LDFN represents the real-time leakage detection factor; μi represents the weighting coefficient corresponding to the i-th real-time pipeline monitoring characteristic value determined by the real-time pipeline monitoring characteristic data; Qi represents the i-th real-time pipeline monitoring characteristic value determined by the real-time pipeline monitoring characteristic data; σi represents the attenuation factor; ti represents the time stamp corresponding to the i-th real-time pipeline monitoring characteristic value determined by the real-time pipeline monitoring characteristic data, and i = 1, 2,..., n.

[0139] Optionally, compare the real-time leakage detection factor with a leakage detection adjustment factor, and predict whether leakage will occur in the pipeline monitoring area according to the comparison result, including:

[0140] Normalize the real-time leakage detection factor and the leakage detection adjustment factor respectively to obtain the normalized real-time leakage detection factor and the normalized leakage detection adjustment factor; calculate the difference of the leakage detection factors according to the normalized real-time leakage detection factor and the normalized leakage detection adjustment factor.

[0141] When the difference of the leakage detection factors is greater than or equal to the threshold of the difference of the leakage detection factors, it is predicted that leakage will occur in the pipeline monitoring area; when the difference of the leakage detection factors is less than the threshold of the difference of the leakage detection factors, it is predicted that no leakage will occur in the pipeline monitoring area.

[0142] Optionally, control the acquisition module to collect the signal strength data of each leakage monitoring sub-area based on the wireless passive sensor, and predict the leakage occurrence location according to the signal strength data, including:

[0143] Control the acquisition module to collect the signal strength data of each leakage monitoring sub-area based on the wireless passive sensor at the first time interval, and determine the signal strength fluctuation value of each leakage monitoring sub-area within the first time interval according to the signal strength data.

[0144] Establish a signal strength fluctuation data set according to the signal strength fluctuation values, and determine the average value of the signal strength fluctuations according to the signal strength fluctuation data set; mark the leakage monitoring sub-areas corresponding to the signal strength fluctuation values greater than the average value of the signal strength fluctuations as key monitoring sub-areas; control the acquisition module to collect the maximum signal strength of each key monitoring sub-area based on the wireless passive sensor at the second time interval, and predict the leakage occurrence location according to the maximum signal strength.

[0145] Optionally, predicting the leakage occurrence location according to the maximum signal strength includes:

[0146] When the maximum signal strength is greater than or equal to the maximum signal strength threshold, it is predicted that the current key monitoring sub-area is not the leakage occurrence location; when the maximum signal strength is less than the maximum signal strength threshold, it is predicted that the current key monitoring sub-area is the leakage occurrence location.

[0147] The above mainly introduces the solution provided by the embodiments of the present application from the perspective of methods. To implement the above functions, it includes the corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should easily realize that, combining the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0148] In the embodiments of the present application, the pipeline leakage detection device can be divided into functional modules according to the above method examples. For example, each functional module can be corresponding to each function, or two or more functions can be integrated into one processing module. The above integrated modules can be implemented in the form of hardware or in the form of software functional modules. Optionally, the division of modules in the embodiments of the present application is illustrative, and is only a logical function division. There may be other division methods in actual implementation.

[0149] As Figure 3 shown, it is a schematic structural diagram of a pipeline leakage detection device provided by an embodiment of the present application. Figure 3 The pipeline leakage detection device shown includes: a communication unit 301 and a processing unit 302.

[0150] The communication unit 301 is configured to collect historical monitoring area feature data of each pipeline monitoring area based on a wireless passive sensor, and determine a leakage detection factor of the pipeline monitoring area according to the historical monitoring area feature data.

[0151] Wherein, the pipeline monitoring area is a pipeline monitoring area among several pipeline monitoring areas divided from the pipeline.

[0152] The communication unit 301 is further configured to collect pipeline feature data of each pipeline monitoring area based on a wireless passive sensor, calculate a leakage influence coefficient of the pipeline based on the pipeline feature data, and determine whether to adjust the leakage detection factor according to the leakage influence coefficient.

[0153] The processing unit 302 is configured to, when it is determined to adjust the leakage detection factor, collect environmental feature data of the pipeline monitoring area based on a wireless passive sensor, determine an adjustment coefficient of the leakage detection factor according to the environmental feature data, and adjust the leakage detection factor according to the adjustment coefficient to obtain a leakage detection adjustment factor.

[0154] The processing unit 302 is further configured to collect real-time pipeline monitoring feature data of the pipeline monitoring area based on a wireless passive sensor, and determine a real-time leakage detection factor of the pipeline monitoring area according to the real-time pipeline monitoring feature data; compare the real-time leakage detection factor with the leakage detection adjustment factor, and predict whether leakage will occur in the pipeline monitoring area according to the comparison result.

[0155] Optionally, the processing unit 302 is further configured to:

[0156] When it is predicted that leakage will occur in the pipeline monitoring area, collect signal strength data of each leakage monitoring sub-area based on a wireless passive sensor, and predict the leakage occurrence location according to the signal strength data.

[0157] Among them, the leakage monitoring sub-region is the leakage monitoring sub-region among several leakage monitoring sub-regions obtained by dividing the pipeline monitoring region where leakage occurs.

[0158] Optionally, the historical monitoring region characteristic data includes: historical pipeline internal pressure characteristic data, historical pipeline external pressure characteristic data, historical pipeline vibration characteristic data, and historical pipeline flow characteristic data; the processing unit 302 is specifically configured to:

[0159]

[0160] Among them, LDF represents the leakage detection factor; Pi represents the historical pipeline internal pressure characteristic value determined by the historical pipeline internal pressure characteristic data; Po represents the historical pipeline external pressure characteristic value determined by the historical pipeline external pressure characteristic data; V represents the historical pipeline vibration characteristic value determined by the historical pipeline vibration characteristic data; F represents the historical pipeline flow characteristic value determined by the historical pipeline flow characteristic data; T represents the time characteristic value of the historical monitoring region; ω1 represents the first weight coefficient; ω2 represents the second weight coefficient; ω3 represents the third weight coefficient; ω4 represents the fourth weight coefficient; ω5 represents the fifth weight coefficient; T represents the historical monitoring duration.

[0161] Optionally, the pipeline characteristic data includes: pipeline material type data, pipeline service life data, and pipeline transmission medium data; the processing unit 302 is specifically configured to:

[0162]

[0163] Among them, LIC represents the leakage impact coefficient; M represents the risk factor of the pipeline material type determined by the pipeline material type data; Y represents the pipeline service life determined by the pipeline service life data; β represents the weighting exponent; C represents the risk factor of the pipeline transmission medium determined by the pipeline transmission medium data; γ represents the interaction impact coefficient between the pipeline material, service life, and transmission medium; δ represents the first correction exponent; θ represents the second correction exponent; α1 represents the first impact coefficient; α2 represents the second impact coefficient; α3 represents the third impact coefficient.

[0164] Optionally, the processing unit 302 is specifically configured to:

[0165] Calculate the coefficient ratio of the leakage impact coefficient to the leakage impact coefficient threshold.

[0166] When the coefficient ratio is greater than or equal to the coefficient ratio threshold, it is determined that the leakage detection factor is adjusted; when the coefficient ratio is less than the coefficient ratio threshold, it is determined that the leakage detection factor is not adjusted.

[0167] Optionally, the environmental characteristic data includes: internal pipeline temperature data, internal pipeline humidity data, external pipeline temperature data, external pipeline humidity data; the processing unit 302 is specifically configured to:

[0168] Determine an environmental impact index based on the internal pipeline temperature data, internal pipeline humidity data, external pipeline temperature data, and external pipeline humidity data.

[0169] Compare the environmental impact index with an environmental impact index threshold, and determine an adjustment coefficient of the leakage detection factor according to the comparison result.

[0170] Optionally, the processing unit 302 is specifically configured to:

[0171] Compare the environmental impact index with a first environmental impact index threshold and a second environmental impact index threshold, and determine an adjustment coefficient of the leakage detection factor according to the comparison result; wherein, the first environmental impact index threshold is less than the second environmental impact index threshold.

[0172] When the environmental impact index is less than or equal to the first environmental impact index threshold, determine that the adjustment coefficient of the leakage detection factor is a first adjustment coefficient; when the environmental impact index is greater than the first environmental impact index threshold and less than or equal to the second environmental impact index threshold, determine that the adjustment coefficient of the leakage detection factor is a second adjustment coefficient; the first adjustment coefficient is less than the second adjustment coefficient; when the environmental impact index is greater than the second environmental impact index threshold, determine that the adjustment coefficient of the leakage detection factor is a third adjustment coefficient; the second adjustment coefficient is less than the third adjustment coefficient.

[0173] Optionally, the real-time pipeline monitoring characteristic data includes real-time internal pipeline pressure characteristic data, real-time external pipeline pressure characteristic data, real-time pipeline vibration characteristic data, real-time pipeline flow characteristic data; the processing unit 302 is specifically configured to:

[0174]

[0175] Wherein, LDFN represents the real-time leakage detection factor; μi represents the weighting coefficient corresponding to the i-th real-time pipeline monitoring characteristic value determined by the real-time pipeline monitoring characteristic data; Qi represents the i-th real-time pipeline monitoring characteristic value determined by the real-time pipeline monitoring characteristic data; σi represents the attenuation factor; ti represents the time stamp corresponding to the i-th real-time pipeline monitoring characteristic value determined by the real-time pipeline monitoring characteristic data, and i = 1, 2,..., n.

[0176] Optionally, the processing unit 302 is specifically configured to:

[0177] Normalize the real-time leakage detection factor and the leakage detection adjustment factor respectively to obtain the normalized real-time leakage detection factor and the normalized leakage detection adjustment factor; calculate the difference of the leakage detection factors according to the normalized real-time leakage detection factor and the normalized leakage detection adjustment factor.

[0178] When the difference of the leakage detection factors is greater than or equal to the threshold of the difference of the leakage detection factors, it is predicted that leakage will occur in the pipeline monitoring area; when the difference of the leakage detection factors is less than the threshold of the difference of the leakage detection factors, it is predicted that no leakage will occur in the pipeline monitoring area.

[0179] Optionally, the processing unit 302 is specifically configured to:

[0180] Control the acquisition module to collect the signal strength data of each leakage monitoring sub-area based on the wireless passive sensor in the first time interval, and determine the signal strength fluctuation value of each leakage monitoring sub-area in the first time interval according to the signal strength data.

[0181] Establish a signal strength fluctuation data set according to the signal strength fluctuation values, and determine the average value of the signal strength fluctuations according to the signal strength fluctuation data set; record the leakage monitoring sub-areas corresponding to the signal strength fluctuation values greater than the average value of the signal strength fluctuations as key monitoring sub-areas; control the acquisition module to collect the maximum signal strength of each key monitoring sub-area based on the wireless passive sensor in the second time interval, and predict the leakage occurrence location according to the maximum signal strength.

[0182] Optionally, the processing unit 302 is specifically configured to:

[0183] When the maximum signal strength is greater than or equal to the maximum signal strength threshold, it is predicted that the current key monitoring sub-area is not the leakage occurrence location; when the maximum signal strength is less than the maximum signal strength threshold, it is predicted that the current key monitoring sub-area is the leakage occurrence location.

[0184] The embodiment of the present application also provides a computer-readable storage medium, which includes computer-executable instructions. When the computer-executable instructions run on a computer, the computer is enabled to execute the pipeline leakage detection method provided in the above embodiment.

[0185] The embodiment of the present application also provides a computer program product. The computer program product can be directly loaded into a memory and contains software code. After being loaded and executed by a computer, the computer program product can implement the pipeline leakage detection method provided in the above embodiment. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements do not make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

[0186] For the system provided in the above embodiment, only the division of the above functional modules is used as an example for illustration. In practical applications, the above functions can be allocated to different functional modules as needed, that is, the modules or steps in the embodiment of the present invention can be further decomposed or combined. For example, the modules in the above embodiment can be combined into one module, or further split into multiple sub-modules to complete all or part of the functions described above. For the names of the modules and steps involved in the embodiment of the present invention, they are only used to distinguish each module or step and are not regarded as an improper limitation of the present invention.

[0187] Those skilled in the art should be able to realize that the modules and method steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. The programs corresponding to the software modules and method steps can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically erasable programmable ROM, register, hard disk, removable disk, CD-ROM, or any other form of storage medium well-known in the technical field. To clearly illustrate the interchangeability of electronic hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in the form of electronic 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 to exceed the scope of the present invention.

Claims

1. A pipeline leakage detection system, characterized in that, Including: A collection module, configured to collect historical monitoring area characteristic data of each pipeline monitoring area based on a wireless passive sensor, and determine a leakage detection factor of the pipeline monitoring area according to the historical monitoring area characteristic data; the pipeline monitoring area is a pipeline monitoring area in a plurality of pipeline monitoring areas divided from a pipeline; A judgment module, configured to control the collection module to collect pipeline characteristic data of each pipeline monitoring area based on the wireless passive sensor, calculate a leakage influence coefficient of the pipeline based on the pipeline characteristic data, and judge whether to adjust the leakage detection factor according to the leakage influence coefficient; A processing module, configured to, when it is determined to adjust the leakage detection factor, control the collection module to collect environmental characteristic data of the pipeline monitoring area based on the wireless passive sensor, determine an adjustment coefficient of the leakage detection factor according to the environmental characteristic data, and adjust the leakage detection factor according to the adjustment coefficient to obtain a leakage detection adjustment factor; A first prediction module, configured to control the collection module to collect real-time pipeline monitoring characteristic data of the pipeline monitoring area based on the wireless passive sensor, and determine a real-time leakage detection factor of the pipeline monitoring area according to the real-time pipeline monitoring characteristic data; compare the real-time leakage detection factor with the leakage detection adjustment factor, and predict whether leakage will occur in the pipeline monitoring area according to the comparison result.

2. The pipeline leakage detection system according to claim 1, wherein Further including: A second prediction module, configured to, when it is predicted that leakage will occur in the pipeline monitoring area, control the collection module to collect signal strength data of each leakage monitoring sub-area based on the wireless passive sensor, and predict the leakage occurrence position according to the signal strength data; the leakage monitoring sub-area is a leakage monitoring sub-area in a plurality of leakage monitoring sub-areas divided from the pipeline monitoring area where leakage occurs.

3. The pipeline leakage detection system according to claim 1, wherein, The historical monitoring area characteristic data includes: historical pipeline internal pressure characteristic data, historical pipeline external pressure characteristic data, historical pipeline vibration characteristic data, historical pipeline flow characteristic data; The determining the leakage detection factor of the pipeline monitoring area according to the historical monitoring area characteristic data includes: Wherein, LDF represents the leakage detection factor; Pi represents a historical pipeline internal pressure characteristic value determined through the historical pipeline internal pressure characteristic data; Po represents a historical pipeline external pressure characteristic value determined through the historical pipeline external pressure characteristic data; V represents a historical pipeline vibration characteristic value determined through the historical pipeline vibration characteristic data; F represents a historical pipeline flow characteristic value determined through the historical pipeline flow characteristic data; T represents a time characteristic value of the historical monitoring area; ω1 represents a first weight coefficient; ω2 represents a second weight coefficient; ω3 represents a third weight coefficient; ω4 represents a fourth weight coefficient; ω5 represents a fifth weight coefficient; T represents the historical monitoring duration.

4. The pipeline leakage detection system according to claim 1, characterized in that, The pipeline characteristic data includes: pipeline material type data, pipeline service life data, pipeline transmission medium data; Calculating the leakage influence coefficient of the pipeline based on the pipeline characteristic data includes: Where LIC represents the leakage influence coefficient; M represents the risk factor of the pipeline material type determined by the pipeline material type data; Y represents the pipeline service life determined by the pipeline service life data; β represents the weighting exponent; C represents the risk factor of the pipeline transmission medium determined by the pipeline transmission medium data; γ represents the interaction influence coefficient among the pipeline material, service life, and transmission medium; δ represents the first correction exponent; θ represents the second correction exponent; α1 represents the first influence coefficient; α2 represents the second influence coefficient; α3 represents the third influence coefficient.

5. The pipeline leakage detection system according to claim 1, characterized in that, Judging whether to adjust the leakage detection factor according to the leakage influence coefficient includes: Calculating the coefficient ratio of the leakage influence coefficient to the leakage influence coefficient threshold; When the coefficient ratio is greater than or equal to the coefficient ratio threshold, it is determined to adjust the leakage detection factor; When the coefficient ratio is less than the coefficient ratio threshold, it is determined not to adjust the leakage detection factor.

6. The pipeline leakage detection system according to claim 1, wherein The environmental characteristic data includes: pipeline internal temperature data, pipeline internal humidity data, pipeline external temperature data, pipeline external humidity data; Determining the adjustment coefficient of the leakage detection factor according to the environmental characteristic data includes: Determining the environmental influence index based on the pipeline internal temperature data, the pipeline internal humidity data, the pipeline external temperature data, and the pipeline external humidity data; Comparing the environmental influence index with the environmental influence index threshold, and determining the adjustment coefficient of the leakage detection factor according to the comparison result.

7. The pipeline leakage detection system according to claim 6, characterized in that, Determining the adjustment coefficient of the leakage detection factor according to the comparison result includes: Comparing the environmental influence index with the first environmental influence index threshold and the second environmental influence index threshold, and determining the adjustment coefficient of the leakage detection factor according to the comparison result; where the first environmental influence index threshold is less than the second environmental influence index threshold; When the environmental influence index is less than or equal to the first environmental influence index threshold, determining the adjustment coefficient of the leakage detection factor as the first adjustment coefficient; When the environmental influence index is greater than the first environmental influence index threshold and less than or equal to the second environmental influence index threshold, determining the adjustment coefficient of the leakage detection factor as the second adjustment coefficient; the first adjustment coefficient is less than the second adjustment coefficient; When the environmental influence index is greater than the second environmental influence index threshold, determining the adjustment coefficient of the leakage detection factor as the third adjustment coefficient; the second adjustment coefficient is less than the third adjustment coefficient.

8. The pipeline leakage detection system according to claim 1, wherein The real-time pipeline monitoring characteristic data includes real-time pipeline internal pressure characteristic data, real-time pipeline external pressure characteristic data, real-time pipeline vibration characteristic data, real-time pipeline flow characteristic data; Determining the real-time leakage detection factor of the pipeline monitoring area according to the real-time pipeline monitoring characteristic data includes: Wherein, LDFN represents the real-time leakage detection factor; μi represents the weighting coefficient corresponding to the i-th real-time pipeline monitoring feature value determined by the real-time pipeline monitoring feature data; Qi represents the i-th real-time pipeline monitoring feature value determined by the real-time pipeline monitoring feature data; σi represents the attenuation factor; ti represents the time stamp corresponding to the i-th real-time pipeline monitoring feature value determined by the real-time pipeline monitoring feature data, and i = 1, 2, …, n.

9. The pipeline leakage detection system according to claim 1, wherein, The comparison between the real-time leakage detection factor and the leakage detection adjustment factor, and predicting whether leakage will occur in the pipeline monitoring area according to the comparison result, includes: Normalize the real-time leakage detection factor and the leakage detection adjustment factor respectively to obtain a normalized real-time leakage detection factor and a normalized leakage detection adjustment factor; Calculate the leakage detection factor difference according to the normalized real-time leakage detection factor and the normalized leakage detection adjustment factor; When the leakage detection factor difference is greater than or equal to the leakage detection factor difference threshold, predict that leakage will occur in the pipeline monitoring area; When the leakage detection factor difference is less than the leakage detection factor difference threshold, predict that leakage will not occur in the pipeline monitoring area.

10. The pipeline leakage detection system according to claim 2, characterized in that, The control of the acquisition module to collect the signal strength data of each leakage monitoring sub-area based on the wireless passive sensor, and predict the leakage occurrence location according to the signal strength data, includes: Control the acquisition module to collect the signal strength data of each leakage monitoring sub-area based on the wireless passive sensor at the first time interval, and determine the signal strength fluctuation value of each leakage monitoring sub-area within the first time interval according to the signal strength data; Establish a signal strength fluctuation data set according to the signal strength fluctuation value, and determine the average signal strength fluctuation value according to the signal strength fluctuation data set; Mark the leakage monitoring sub-area corresponding to the signal strength fluctuation value greater than the average signal strength fluctuation value as the key monitoring sub-area; Control the acquisition module to collect the maximum signal strength of each key monitoring sub-area based on the wireless passive sensor at the second time interval, and predict the leakage occurrence location according to the maximum signal strength.

11. The pipeline leakage detection system according to claim 10, characterized in that, The prediction of the leakage occurrence location according to the maximum signal strength includes: When the maximum signal strength is greater than or equal to the maximum signal strength threshold, predict that the current key monitoring sub-area is not the leakage occurrence location; When the maximum signal strength is less than the maximum signal strength threshold, predict that the current key monitoring sub-area is the leakage occurrence location.

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