Abnormality detection method, device and storage medium

Abstract

The application provides an anomaly detection method and device and a storage medium, relates to the field of oil and gas pipelines, and can determine pipeline network anomalies in a timely manner. The method comprises the following steps: acquiring a first electromagnetic wave signal generated according to the pressure of a pipeline network and sent by a first sensor to a second sensor in a first preset time period, a time when the first sensor sends the first electromagnetic wave signal, and a time when the second sensor receives the first electromagnetic wave signal; converting the first electromagnetic wave signal into a first waveform based on the first electromagnetic wave signal, the time when the first sensor sends the first electromagnetic wave signal, and the time when the second sensor receives the first electromagnetic wave signal; inputting the first waveform into a preset waveform detection model to detect whether the first waveform is abnormal; and determining whether the pipeline network is abnormal based on the detection result of the first waveform.

Description

Technical Field

[0001] This application relates to the field of oil and gas pipelines, and in particular to an anomaly detection method, device and storage medium. Background Technology

[0002] In the fuel oil and chemical industries, oil and gas monitoring is necessary to prevent accidents caused by undetected or unaddressed pipeline leaks. Currently, combustible gas sensors are used for detection, and alarms are triggered when the detected gas concentration exceeds a preset threshold.

[0003] However, the sensitivity of the aforementioned combustible gas sensor may decrease due to prolonged use, resulting in a lower detected gas concentration than the actual concentration, making it difficult to issue an alarm in a timely manner and delaying the processing time. Summary of the Invention

[0004] This application provides an anomaly detection method, device, and storage medium that can promptly identify pipeline anomalies.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] In a first aspect, this application provides an anomaly detection method applied to an anomaly detection device. The anomaly detection device is connected to a first sensor and a second sensor, which are deployed on a pipeline network to detect the pressure of the pipeline network. The method includes: acquiring a first electromagnetic wave signal generated based on the pressure of the pipeline network and sent by the first sensor to the second sensor within a first preset time period, the time when the first sensor sends the first electromagnetic wave signal, and the time when the second sensor receives the first electromagnetic wave signal; converting the first electromagnetic wave signal into a first waveform based on the first electromagnetic wave signal, the time when the first sensor sends the first electromagnetic wave signal, and the time when the second sensor receives the first electromagnetic wave signal; inputting the first waveform into a preset waveform detection model to detect whether the first waveform is abnormal; the preset waveform detection model is used to compare the input waveform with a preset waveform to determine whether the input waveform is abnormal; and determining whether there is an anomaly in the pipeline network based on the detection result of the first waveform.

[0007] In one possible implementation, the following steps are taken: acquiring the second electromagnetic wave signal generated by the first sensor based on the pressure of the pipeline network, which is sent from the first sensor to the second sensor within a second preset time period; the time when the first sensor sends the second electromagnetic wave signal; and the time when the second sensor receives the second electromagnetic wave signal; the second preset time period is the time period before the first preset time period; generating a preset waveform based on the second electromagnetic wave signal, the time when the first sensor sends the second electromagnetic wave signal, and the time when the second sensor receives the second electromagnetic wave signal; and determining a preset waveform detection model based on the preset waveform.

[0008] In one possible implementation, the anomaly detection device is also connected to a third sensor and a fourth sensor, which are deployed on the pipeline to detect the pressure of the pipeline. The preset waveform detection model includes a first waveform detection model and a second waveform detection model. The first waveform detection model is determined based on the transmission of a second electromagnetic wave signal between the first sensor and the second sensor. The second waveform detection model is determined based on the transmission of a third electromagnetic wave signal between the third sensor and the fourth sensor.

[0009] In one possible implementation, the first waveform is input into a preset waveform detection model to detect whether the first waveform has any abnormalities, including: inputting the first waveform into a first waveform detection model to detect whether the first waveform has any abnormalities; or inputting the first waveform into a second waveform detection model to detect whether the first waveform has any abnormalities.

[0010] Secondly, this application provides an anomaly detection system, comprising: an anomaly detection device, a first sensor, and a second sensor; the anomaly detection device is connected to the first sensor and the second sensor; the first sensor and the second sensor are deployed on a pipeline network for detecting the pressure of the pipeline network; the first sensor is used to generate a first electromagnetic wave signal based on the pressure of the pipeline network and send the first electromagnetic wave signal to the second sensor; the second sensor is used to receive the first electromagnetic wave signal; the anomaly detection device is used to acquire the first electromagnetic wave signal generated based on the pressure of the pipeline network sent by the first sensor to the second sensor within a first preset time period, the time when the first sensor sends the first electromagnetic wave signal, and the time when the second sensor receives the first electromagnetic wave signal; based on the first electromagnetic wave signal, the time when the first sensor sends the first electromagnetic wave signal, and the time when the second sensor receives the first electromagnetic wave signal, the first electromagnetic wave signal is converted into a first waveform; the first waveform is input into a preset waveform detection model to detect whether the first waveform is abnormal; and based on the detection result of the first waveform, it is determined whether there is an anomaly in the pipeline network; the preset waveform detection model is used to compare the input waveform with a preset waveform to determine whether the input waveform is abnormal.

[0011] In one possible implementation, the anomaly detection device is further configured to acquire, within a second preset time period, a second electromagnetic wave signal generated by the first sensor based on the pressure of the pipeline network, the time when the first sensor sends the second electromagnetic wave signal, and the time when the second sensor receives the second electromagnetic wave signal; generate a preset waveform based on the second electromagnetic wave signal, the time when the first sensor sends the second electromagnetic wave signal, and the time when the second sensor receives the second electromagnetic wave signal; and determine a preset waveform detection model based on the preset waveform; the second preset time period is the time period prior to the first preset time period.

[0012] In one possible implementation, the anomaly detection system further includes a third sensor and a fourth sensor; the anomaly detection device is also connected to the third sensor and the fourth sensor; the third sensor and the fourth sensor are deployed on the pipeline network to detect the pressure of the pipeline network; the preset waveform detection model includes a first waveform detection model and a second waveform detection model; the first waveform detection model is determined based on the transmission of a second electromagnetic wave signal between the first sensor and the second sensor; the second waveform detection model is determined based on the transmission of a third electromagnetic wave signal between the third sensor and the fourth sensor; the third sensor is used to generate a third electromagnetic wave signal according to the pressure of the pipeline network and send the third electromagnetic wave signal to the fourth sensor; the fourth sensor is used to receive the third electromagnetic wave signal.

[0013] In one possible implementation, the anomaly detection device is further configured to input the first waveform into a first waveform detection model to detect whether the first waveform has an anomaly; or the anomaly detection device is further configured to input the first waveform into a second waveform detection model to detect whether the first waveform has an anomaly.

[0014] Thirdly, this application provides an anomaly detection device applied to an anomaly detection equipment. The anomaly detection equipment is connected to a first sensor and a second sensor, which are deployed on a pipeline network to detect the pressure of the pipeline network. The device includes: a communication unit and a processing unit; the communication unit is used to acquire a first electromagnetic wave signal generated based on the pressure of the pipeline network and sent by the first sensor to the second sensor within a first preset time period, the time when the first sensor sends the first electromagnetic wave signal, and the time when the second sensor receives the first electromagnetic wave signal; the processing unit is used to convert the first electromagnetic wave signal into a first waveform based on the first electromagnetic wave signal, the time when the first sensor sends the first electromagnetic wave signal, and the time when the second sensor receives the first electromagnetic wave signal; the processing unit is also used to input the first waveform into a preset waveform detection model to detect whether the first waveform is abnormal; the preset waveform detection model is used to compare the input waveform with a preset waveform to determine whether the input waveform is abnormal; the processing unit is also used to determine whether there is an anomaly in the pipeline network based on the detection result of the first waveform.

[0015] In one possible implementation, the communication unit is further configured to acquire the second electromagnetic wave signal generated by the first sensor based on the pressure of the pipeline network, which is sent from the first sensor to the second sensor within a second preset time period; the time when the first sensor sends the second electromagnetic wave signal; and the time when the second sensor receives the second electromagnetic wave signal; the second preset time period is the time period prior to the first preset time period; the processing unit is further configured to generate a preset waveform based on the second electromagnetic wave signal, the time when the first sensor sends the second electromagnetic wave signal, and the time when the second sensor receives the second electromagnetic wave signal; the processing unit is further configured to determine a preset waveform detection model based on the preset waveform.

[0016] In one possible implementation, the anomaly detection device is also connected to a third sensor and a fourth sensor, which are deployed on the pipeline to detect the pressure of the pipeline. The preset waveform detection model includes a first waveform detection model and a second waveform detection model. The first waveform detection model is determined based on the transmission of a second electromagnetic wave signal between the first sensor and the second sensor. The second waveform detection model is determined based on the transmission of a third electromagnetic wave signal between the third sensor and the fourth sensor.

[0017] In one possible implementation, the processing unit is further configured to input the first waveform into a first waveform detection model to detect whether the first waveform has any abnormalities; or the processing unit is further configured to input the first waveform into a second waveform detection model to detect whether the first waveform has any abnormalities.

[0018] Fourthly, this application provides an anomaly detection device, which includes: a processor and a communication interface; the communication interface and the processor are coupled, and the processor is used to run computer programs or instructions to implement the anomaly detection method as described in the first aspect and any possible implementation thereof.

[0019] Fifthly, this application provides a computer-readable storage medium storing instructions that, when executed on a terminal, cause the terminal to perform the anomaly detection method as described in the first aspect and any possible implementation thereof.

[0020] Sixthly, this application provides a computer program product containing instructions that, when run on an anomaly detection device, cause the anomaly detection device to execute the anomaly detection method as described in the first aspect and any possible implementation thereof.

[0021] In a seventh aspect, this application provides a chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run computer programs or instructions to implement the anomaly detection method as described in the first aspect and any possible implementation thereof.

[0022] Specifically, the chip provided in this application also includes a memory for storing computer programs or instructions.

[0023] In the anomaly detection method provided in this application embodiment, the anomaly detection device converts the first electromagnetic wave signal into a first waveform based on the first electromagnetic wave signal generated by the first sensor according to the pressure of the pipeline network, the time when the first sensor sends the first electromagnetic wave signal, and the time when the second sensor receives the first electromagnetic wave signal within a first preset time period. The first waveform is then input into a preset waveform detection model. In this way, the anomaly detection device can determine whether there is an anomaly in the first waveform. Since pipeline leakage may change the electric or magnetic field distribution within the pipeline network, thereby causing changes in the waveform corresponding to the electromagnetic wave signal, the anomaly detection device can determine whether there is an anomaly in the pipeline network based on the detection result of the first waveform. Furthermore, since the waveform converted from the electromagnetic wave signal is less affected by sensor loss, the anomaly detection device can determine whether there is an anomaly in the pipeline network by detecting the first waveform. This avoids the situation where sensor loss prevents timely detection of pipeline network anomalies, and thus allows for timely handling of pipeline network anomalies to reduce the risk of accidents. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the structure of an anomaly detection system provided in an embodiment of this application;

[0025] Figure 2 This is a schematic diagram of the structure of an anomaly detection device provided in an embodiment of this application;

[0026] Figure 3 A flowchart of an anomaly detection method provided in an embodiment of this application;

[0027] Figure 4 An example diagram illustrating the acquisition of a first electromagnetic wave signal by an anomaly detection device provided in this application embodiment;

[0028] Figure 5 An example diagram illustrating the offline monitoring of an anomaly detection device provided in this application embodiment;

[0029] Figure 6 A schematic diagram illustrating an anomaly detection device for determining whether an anomaly exists in a pipeline network, provided in an embodiment of this application;

[0030] Figure 7 A flowchart of another anomaly detection method provided in the embodiments of this application;

[0031] Figure 8 This is a schematic diagram of another anomaly detection device provided in an embodiment of this application. Detailed Implementation

[0032] The anomaly detection method, apparatus, and storage medium provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0033] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0034] The terms "first" and "second," etc., used in the specification and drawings of this application are used to distinguish different objects or to distinguish different treatments of the same object, rather than to describe a specific order of objects.

[0035] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0036] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0037] In high-risk industries such as fuel oil and chemicals, pipeline leaks that are not detected and addressed in a timely manner can lead to accidents such as fires, causing significant losses to people's lives and property, business operations, and the ecological environment. Therefore, oil and gas monitoring is necessary in the fuel oil and chemical industries to prevent accidents caused by undetected or unaddressed pipeline leaks. Currently, combustible gas sensors are used for detection, triggering alarms when the detected gas concentration exceeds a preset threshold, thus prompting maintenance personnel to address the pipeline issue.

[0038] However, if the detected gas concentration is greater than the preset threshold, the leaked gas in the pipeline may be easily ignited, potentially leading to an accident. Furthermore, the sensitivity of the aforementioned combustible gas sensor may decrease due to prolonged use, resulting in a lower detected gas concentration than the actual concentration, making it difficult to issue an alarm in a timely manner and delaying the response.

[0039] In view of this, this application provides an anomaly detection method. The anomaly detection device converts the first electromagnetic wave signal, generated based on the pressure of the pipeline network, sent by the first sensor to the second sensor within a first preset time period, the time when the first sensor sends the first electromagnetic wave signal, and the time when the second sensor receives the first electromagnetic wave signal, into a first waveform. The first waveform is then input into a preset waveform detection model. In this way, the anomaly detection device can determine whether there is an anomaly in the first waveform. Since pipeline leakage may change the electric or magnetic field distribution within the pipeline, thereby causing changes in the waveform corresponding to the electromagnetic wave signal, the anomaly detection device can determine whether there is an anomaly in the pipeline network based on the detection result of the first waveform. Furthermore, since the waveform converted from the electromagnetic wave signal is less affected by sensor wear, the anomaly detection device can determine whether there is an anomaly in the pipeline network by detecting the first waveform, avoiding the situation where sensor wear prevents timely detection of pipeline network anomalies. This allows for timely handling of pipeline network anomalies, thereby reducing the risk of accidents.

[0040] For example, such as Figure 1 As shown, Figure 1 This diagram illustrates the structure of an anomaly detection system according to an embodiment of this application. The anomaly detection system includes an anomaly detection device 101, a first sensor 102, and a second sensor 103. The anomaly detection device 101 is connected to the first sensor 102 and the second sensor 103. The first sensor 102 and the second sensor 103 are deployed on a pipeline network to detect the pressure of the pipeline network. Figure 1 The following description uses an anomaly detection system as an example, which includes an anomaly detection device 101, a first sensor 102, and a second sensor 103.

[0041] The anomaly detection device 101 is used to acquire, within a first preset time period, a first electromagnetic wave signal generated based on the pipeline pressure and transmitted from the first sensor 102 to the second sensor 103, the time when the first sensor 102 transmits the first electromagnetic wave signal, and the time when the second sensor 103 receives the first electromagnetic wave signal. Based on the first electromagnetic wave signal, the time when the first sensor 102 transmits the first electromagnetic wave signal, and the time when the second sensor 103 receives the first electromagnetic wave signal, the device converts the first electromagnetic wave signal into a first waveform. The anomaly detection device 101 is also used to input the first waveform into a preset waveform detection model, detect whether there is an anomaly in the first waveform, and determine whether there is an anomaly in the pipeline network based on the detection result of the first waveform.

[0042] The first sensor 102 is used to generate a first electromagnetic wave signal based on the pressure of the pipeline network and send the first electromagnetic wave signal to the second sensor 103.

[0043] The second sensor 103 is used to receive the first electromagnetic wave signal.

[0044] The preset waveform detection model is used to compare the input waveform with the preset waveform to determine whether the input waveform is abnormal.

[0045] Optionally, during the deployment of the first sensor 102 and the second sensor 103 on the pipeline, a fixing device can be added between the pipeline and the first sensor 102 and the second sensor 103, so that the first sensor 102 and the second sensor 103 are located on the same side of the pipeline, thereby reducing the error of the transmitted and received first electromagnetic wave signal.

[0046] It should be added that since electronic devices (e.g., anomaly detection device 101, first sensor 102, and second sensor 103) are affected by temperature, a temperature sensor can be installed inside the electronic device to compensate the algorithm based on the temperature collected by the temperature sensor, thereby reducing false alarms.

[0047] In one example, the anomaly detection device 101 can be a server. The server can be a single server, or a server cluster consisting of multiple servers. In some implementations, the server cluster can also be a distributed cluster.

[0048] In another example, the anomaly detection device 101 can be terminal equipment, user equipment (UE), mobile station (MS), or mobile terminal (MT), etc. Specifically, the anomaly detection device 101 can be a mobile phone, tablet computer, or computer with wireless transceiver capabilities. It can also be a virtual reality (VR) terminal, an augmented reality (AR) terminal, a wireless terminal in industrial control, a wireless terminal in autonomous driving, a wireless terminal in telemedicine, a wireless terminal in a smart grid, a wireless terminal in a smart city, a smart home, or an in-vehicle terminal, etc. In this embodiment, the device used to implement the function of the anomaly detection device 101 can be the anomaly detection device 101 itself, or it can be a device capable of supporting the anomaly detection device 101 in implementing this function, such as a chip or chip system.

[0049] In one example, the first sensor 102 and the second sensor 103 can be pressure sensors.

[0050] Optionally, the anomaly detection system may also include a third sensor 104 and a fourth sensor 105. The anomaly detection device is also connected to the third sensor 104 and the fourth sensor 105. The third sensor 104 and the fourth sensor 105 are deployed on the pipeline network to detect the pressure of the pipeline network.

[0051] The third sensor 104 is used to generate a third electromagnetic wave signal based on the pressure of the pipeline network and send the third electromagnetic wave signal to the fourth sensor 105.

[0052] The fourth sensor 105 is used to receive the third electromagnetic wave signal.

[0053] In one example, the third sensor 104 and the fourth sensor 105 can be pressure sensors.

[0054] Furthermore, the anomaly detection system described in the embodiments of this application is for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and does not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new anomaly detection systems, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0055] In practical implementation, Figure 1 All the equipment in the middle can be adopted Figure 2 The shown composition structure, or including Figure 2 The components shown. Figure 2 This is a schematic diagram illustrating the composition of an anomaly detection device 200 provided in an embodiment of this application. The anomaly detection device 200 can be an anomaly detection equipment 101 or a chip or system-on-a-chip within the anomaly detection equipment 101. For example... Figure 2 As shown, the anomaly detection device 200 may include a processor 201 and a communication line 202.

[0056] Furthermore, the anomaly detection device 200 may also include a communication interface 203 and a memory 204. The processor 201, memory 204, and communication interface 203 can be connected via a communication line 202.

[0057] The processor 201 can be a CPU, a general-purpose processor, a network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD), or any combination thereof. The processor 201 can also be other devices with processing capabilities, such as circuits, devices, or software modules, without limitation.

[0058] Communication line 202 is used to transmit information between the components included in the anomaly detection device 200.

[0059] Communication interface 203 is used to communicate with other devices or other communication networks. These other communication networks can be Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc. Communication interface 203 can be a module, circuit, communication interface, or any device capable of enabling communication.

[0060] Memory 204 is used to store instructions. These instructions can be computer programs.

[0061] The memory 204 can be a read-only memory (ROM) or other type of static storage device that can store static information and / or instructions; it can also be a random access memory (RAM) or other type of dynamic storage device that can store information and / or instructions; it can also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, etc., without limitation.

[0062] It should be noted that the memory 204 can exist independently of the processor 201 or can be integrated with the processor 201. The memory 204 can be used to store instructions, program code, or some data, etc. The memory 204 can be located inside or outside the anomaly detection device 200, without limitation. The processor 201 is used to execute the instructions stored in the memory 204 to implement the anomaly detection method provided in the following embodiments of this application.

[0063] In one example, processor 201 may include one or more CPUs, such as CPU0 and CPU1.

[0064] As an optional implementation, the anomaly detection device 200 includes multiple processors.

[0065] As an optional implementation, the anomaly detection device 200 also includes output devices and input devices. For example, the output device is a display screen, a speaker, or other similar device, while the input device is a keyboard, mouse, microphone, or joystick, or other similar device.

[0066] It should be noted that the anomaly detection device 200 can be a desktop computer, laptop computer, network server, mobile phone, tablet computer, wireless terminal, embedded device, chip system, or other device. Figure 2 Equipment with a similar structure. Furthermore... Figure 2 The composition shown does not constitute a basis for the interpretation of this invention. Figure 1 as well as Figure 2 The limitations of each device in the process, except Figure 2 In addition to the components shown, Figure 1 as well as Figure 2 The various devices may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0067] In this embodiment of the application, the chip system may be composed of chips or may include chips and other discrete devices.

[0068] Furthermore, the actions, terms, etc., involved in the various embodiments of this application can be referenced interchangeably without limitation. The message names or parameter names in the messages exchanged between the various devices in the embodiments of this application are merely examples, and other names may be used in specific implementations without limitation.

[0069] The following is combined with Figure 1 The anomaly detection system shown describes the anomaly detection method provided in the embodiments of this application. The actions, terminology, etc., involved in the various embodiments of this application can be referenced interchangeably without limitation. The message names or parameter names in the messages exchanged between various devices in the embodiments of this application are merely examples; other names can be used in specific implementations without limitation. The actions involved in the various embodiments of this application are merely examples; other names can be used in specific implementations. For example, "included in" in the embodiments of this application can be replaced with "carried in" or "carried in," etc.

[0070] To address the problems existing in the prior art, this application proposes an anomaly detection method that can promptly identify pipeline anomalies. For example... Figure 3 As shown, the method includes:

[0071] S301, the anomaly detection device acquires the first electromagnetic wave signal generated by the first sensor based on the pressure of the pipeline network, which is sent from the first sensor to the second sensor within a first preset time period, the time when the first sensor sends the first electromagnetic wave signal, and the time when the second sensor receives the first electromagnetic wave signal.

[0072] The anomaly detection device is connected to a first sensor and a second sensor. The first and second sensors are deployed on the pipeline network to detect the pressure within the network.

[0073] As an example, the first preset time period can be any time period. For example, the first preset time period can be from 8:00 on December 26, 2023 to 18:00 on December 26, 2023. The above is only an exemplary description of the first preset time period. The first preset time period can also be other time periods, and this application does not impose any restrictions on it.

[0074] As one possible implementation, prior to S301, the anomaly detection device can send first indication information to the first sensor. Correspondingly, the first sensor can receive the first indication information from the anomaly detection device. The first indication information is used to instruct the first sensor to send a first electromagnetic wave signal to the second sensor.

[0075] Optionally, during the process of the anomaly detection device acquiring the first electromagnetic wave signal transmitted between the first sensor and the second sensor (i.e., S301), the anomaly detection device can acquire the first electromagnetic wave signal in real time and determine the time when the first sensor sends the first electromagnetic wave signal and the time when the second sensor receives the first electromagnetic wave signal.

[0076] Alternatively, during the process of the anomaly detection device acquiring the first electromagnetic wave signal transmitted between the first sensor and the second sensor (i.e., S301), the anomaly detection device can employ low-power processing technology to periodically start and acquire the first electromagnetic wave signal. This reduces the power consumption of the anomaly detection device, allowing it to operate for a longer period even when powered by a battery, thus extending its runtime.

[0077] For example, such as Figure 4 As shown, Figure 4 An example diagram is shown of an anomaly detection device acquiring a first electromagnetic wave signal. The anomaly detection device can acquire the signal every 3 seconds.

[0078] As one possible implementation, the first and second sensors mentioned above can be pressure sensors used to detect the pressure of the pipeline network and thus determine the vibration information of the pipeline network.

[0079] It should be noted that vibration information is generated even when the pipeline is operating normally. However, the vibration information of the pipeline may change when a leak occurs. Therefore, the anomaly detection equipment can determine whether the vibration information of the pipeline is abnormal based on the vibration information under normal conditions, and thus determine whether a leak has occurred in the pipeline.

[0080] S302, the anomaly detection device converts the first electromagnetic wave signal into a first waveform based on the first electromagnetic wave signal, the time when the first sensor sends the first electromagnetic wave signal, and the time when the second sensor receives the first electromagnetic wave signal.

[0081] In one possible implementation, the above-mentioned S302 process can be as follows: The anomaly detection device determines the time required for the first electromagnetic wave signal to propagate from the first sensor to the second sensor by the difference between the time when the second sensor receives the first electromagnetic wave signal and the time when the first sensor sends the first electromagnetic wave signal. The anomaly detection device can determine the propagation distance of the first electromagnetic wave signal by multiplying the time required for the first electromagnetic wave signal to propagate from the first sensor to the second sensor by the speed of electromagnetic waves (usually the speed of light). The anomaly detection device can determine the first waveform based on the time required for the first electromagnetic wave signal to propagate from the first sensor to the second sensor and the propagation distance of the first electromagnetic wave signal.

[0082] Optionally, the anomaly detection device can adjust the shape and characteristics of the first waveform based on the relative positions of the first and second sensors. For example, when the first and second sensors are located on the same straight line and the time required for the first electromagnetic wave signal to propagate from the first sensor to the second sensor is known, the anomaly detection device can use a mathematical formula (e.g., a sine function or a cosine function) to describe the obtained first waveform.

[0083] S303. The anomaly detection device inputs the first waveform into a preset waveform detection model to detect whether there is an anomaly in the first waveform.

[0084] The preset waveform detection model is used to compare the input waveform with the preset waveform to determine whether the input waveform is abnormal.

[0085] In one possible implementation, the anomaly detection device can generate a preset waveform based on the second electromagnetic wave signal acquired within a time period prior to the first preset time period, the time when the first sensor sends the second electromagnetic wave signal, and the time when the second sensor receives the second electromagnetic wave signal, and determine a preset waveform detection model based on the preset waveform.

[0086] In one possible embodiment, the anomaly detection device is also connected to a third sensor and a fourth sensor. The third and fourth sensors are deployed on the pipeline network to detect the network pressure. The preset waveform detection model includes a first waveform detection model and a second waveform detection model. The first waveform detection model is determined based on the transmission of a second electromagnetic wave signal between the first and second sensors. The second waveform detection model is determined based on the transmission of a third electromagnetic wave signal between the third and fourth sensors.

[0087] It is understandable that the anomaly detection device determines the first waveform detection model based on the transmission of the second electromagnetic wave signal between the first and second sensors, and determines the second waveform detection model based on the transmission of the third electromagnetic wave signal between the third and fourth sensors. Since the first, second, third, and fourth sensors are all deployed on the pipeline to detect the pressure of the pipeline, the first and second waveform detection models obtained by the anomaly detection device can both detect the pressure waveform in the pipeline, so as to realize the detection of pressure waveforms on multiple sides of the pipeline, and thus can detect even minor leaks in the pipeline.

[0088] In one possible embodiment, the above-mentioned S303 implementation process can be: the anomaly detection device inputs the first waveform into the first waveform detection model to detect whether the first waveform is abnormal, or the anomaly detection device inputs the first waveform into the second waveform detection model to detect whether the first waveform is abnormal.

[0089] It is understandable that the anomaly detection device inputs the first waveform into a first waveform detection model or a second waveform detection model to detect whether the first waveform is abnormal. In this way, by inputting the first waveform into multiple waveform detection models, the anomaly detection device can improve the accuracy of detecting the first waveform and thus determine whether the first waveform is abnormal.

[0090] Optionally, the anomaly detection device can acquire the fourth electromagnetic wave signal generated by the third sensor based on the pressure of the pipeline network, which is sent from the third sensor to the fourth sensor within a first preset time period, the time when the third sensor sends the fourth electromagnetic wave signal, and the time when the fourth sensor receives the fourth electromagnetic wave signal, and convert the fourth electromagnetic wave signal into a second waveform based on the fourth electromagnetic wave signal, the time when the third sensor sends the fourth electromagnetic wave signal, and the time when the fourth sensor receives the fourth electromagnetic wave signal.

[0091] As one possible implementation, the anomaly detection device can input the second waveform into the first waveform detection model to detect whether the second waveform has any anomalies, or the anomaly detection device can input the second waveform into the second waveform detection model to detect whether the second waveform has any anomalies.

[0092] In other words, the first waveform corresponding to the first electromagnetic wave signal transmitted between the first and second sensors, and the second waveform corresponding to the fourth electromagnetic wave signal transmitted between the third and fourth sensors, can both be verified by the first waveform detection model and the second waveform detection model. This mutual verification further improves the accuracy of waveform detection.

[0093] Optionally, if the anomaly detection device determines that there is a deviation between the first waveform and the preset waveform, the anomaly detection device may determine that the first waveform may be abnormal. If the anomaly detection device determines that there is no deviation between the first waveform and the preset waveform, the anomaly detection device may determine that the first waveform is not abnormal.

[0094] As one possible implementation, if the anomaly detection device determines that there is an anomaly in the first waveform, the anomaly detection device can generate a first alarm message so that maintenance personnel can take corresponding measures to maintain the pipeline network.

[0095] S304. The anomaly detection device determines whether there is an anomaly in the pipeline network based on the detection result of the first waveform.

[0096] As one possible implementation, the above-mentioned S304 process can be as follows: if the detection result of the first waveform is abnormal, the anomaly detection device can determine that there is an anomaly in the pipeline network. If the detection result of the first waveform is not abnormal, the anomaly detection device can determine that there is no anomaly in the pipeline network.

[0097] Optionally, if the anomaly detection device determines that there is an anomaly in the pipeline network, the anomaly detection device can generate a second alarm message so that maintenance personnel can perform maintenance on the pipeline network to avoid accidents.

[0098] As one possible implementation, such as Figure 5 As shown, Figure 5 An example diagram is shown for monitoring the disconnection of an anomaly detection device. The anomaly detection device can be connected to the sensor via a sensor mounting ring and mounting screws. The wireless acquisition and alarm device can monitor the disconnection of the anomaly detection device via wireless transmission to determine whether the device has gone offline.

[0099] For example, the wireless data acquisition and alarm device can perform a disconnection detection every 20 seconds and generate a second indication message when the abnormal detection device disconnects. This allows for the monitoring of abnormal detection device disconnections while maintaining a relatively low average power consumption. The second indication message is used to instruct management personnel to investigate the abnormal detection device.

[0100] It should be noted that variations in gas flow velocity within the pipeline network lead to variations in network pressure. This pressure affects the waveform of the electromagnetic signals transmitted between sensors within the network. Therefore, changes in gas flow velocity will also alter the waveform of the electromagnetic signals. Furthermore, pipeline leaks affect gas flow velocity. Consequently, leaks will also cause changes in the waveform of the electromagnetic signals transmitted between sensors, allowing for the identification of waveform anomalies and the potential presence of a leak in the pipeline network.

[0101] As one possible implementation, the method for anomaly detection devices to determine whether there is an anomaly in the pipeline network also includes: the anomaly detection device acquiring the input flow rate of the pipeline network and at least one output flow rate, and determining the sum of the at least one output flow rate. If the input flow rate is greater than the sum of the at least one output flow rate, a leak is determined to have occurred in the pipeline network.

[0102] For example, such as Figure 6 As shown, Figure 6 This diagram illustrates an anomaly detection device for determining the presence of anomalies in a pipeline network. Flow meter 1 can be used for input flow statistics, while flow meters 2 and 3 can be used for output flow statistics. If the flow rate recorded by flow meter 1 equals the sum of the flow rates recorded by flow meter 2 and flow meter 3, the anomaly detection device can determine that the pipeline network is free of anomalies. If the flow rate recorded by flow meter 1 is greater than the sum of the flow rates recorded by flow meter 2 and flow meter 3, the anomaly detection device can determine that the pipeline network contains anomalies.

[0103] It should be noted that in the method used by anomaly detection equipment to determine whether there are anomalies in the pipeline network based on the input and output of flow, the anomaly detection equipment relies heavily on the accuracy of the aforementioned flow-detecting sensors (i.e., flow meters). If the accuracy of these sensors is poor, the flow rate detected by the sensors may be inaccurate, leading to false alarms. Furthermore, when there are multiple branches in the output flow, the errors will accumulate, causing a contradiction between the error generated by the anomaly detection equipment based on the above method and the timeliness of the alarm.

[0104] In the anomaly detection method provided in this application embodiment, the anomaly detection device converts the first electromagnetic wave signal into a first waveform based on the first electromagnetic wave signal generated by the first sensor according to the pressure of the pipeline network, the time when the first sensor sends the first electromagnetic wave signal, and the time when the second sensor receives the first electromagnetic wave signal within a first preset time period. The first waveform is then input into a preset waveform detection model. In this way, the anomaly detection device can determine whether there is an anomaly in the first waveform. Since pipeline leakage may change the electric or magnetic field distribution within the pipeline network, thereby causing changes in the waveform corresponding to the electromagnetic wave signal, the anomaly detection device can determine whether there is an anomaly in the pipeline network based on the detection result of the first waveform. Furthermore, since the waveform converted from the electromagnetic wave signal is less affected by sensor loss, the anomaly detection device can determine whether there is an anomaly in the pipeline network by detecting the first waveform. This avoids the situation where sensor loss prevents timely detection of pipeline network anomalies, and thus allows for timely handling of pipeline network anomalies to reduce the risk of accidents.

[0105] In one possible embodiment, before the anomaly detection device determines whether the first waveform is abnormal, the anomaly detection device can determine a preset waveform detection model, enabling the anomaly detection device to detect the first waveform based on the preset waveform detection model, thereby determining whether the first waveform is abnormal. Figure 3 Based on the illustrated method embodiments, this embodiment provides a possible implementation method, combined with Figure 3 ,like Figure 7 As shown, the process of determining the preset waveform detection model by the anomaly detection device can be determined by the following steps S701 to S703.

[0106] S701, the anomaly detection device acquires the second electromagnetic wave signal generated by the first sensor based on the pressure of the pipeline network, which is sent from the first sensor to the second sensor within a second preset time period, the time when the first sensor sends the second electromagnetic wave signal, and the time when the second sensor receives the second electromagnetic wave signal.

[0107] The second preset time period is the time period preceding the first preset time period.

[0108] Optionally, the aforementioned second preset time period can be the initial stage of the connection between the first and second sensors and the pipeline network. Alternatively, the aforementioned second preset time period can also be the time period during which the anomaly detection device determines that there are no anomalies in the pipeline network. The above is merely an exemplary description of the second preset time period, and the aforementioned second preset time period can also be other time periods, which this application does not limit in any way.

[0109] S702, the anomaly detection device generates a preset waveform based on the second electromagnetic wave signal, the time when the first sensor sends the second electromagnetic wave signal, and the time when the second sensor receives the second electromagnetic wave signal.

[0110] As one possible implementation, the process by which the anomaly detection device generates a preset waveform based on the second electromagnetic wave signal, the time when the first sensor sends the second electromagnetic wave signal, and the time when the second sensor receives the second electromagnetic wave signal can be understood by referring to the description in the corresponding position above (i.e., the implementation process of S302), and will not be repeated here.

[0111] As another possible implementation, the above-mentioned S702 implementation process can also be as follows: the anomaly detection device can generate a preset waveform based on the third electromagnetic wave signal generated by the third sensor according to the pressure of the pipeline network sent by the third sensor to the fourth sensor within the second preset time period, the time when the third sensor sends the third electromagnetic wave signal, and the time when the fourth sensor receives the third electromagnetic wave signal.

[0112] S703, The anomaly detection device determines the preset waveform detection model based on the preset waveform.

[0113] As one possible implementation, when the preset waveform is generated by the anomaly detection device based on the second electromagnetic wave signal, the time when the first sensor sends the second electromagnetic wave signal, and the time when the second sensor receives the second electromagnetic wave signal, the anomaly detection device can determine that the preset waveform detection model is the first waveform detection model. When the preset waveform can be generated by the anomaly detection device based on the third electromagnetic wave signal generated by the third sensor according to the pipeline pressure sent to the fourth sensor within a second preset time period, the time when the third sensor sends the third electromagnetic wave signal, and the time when the fourth sensor receives the third electromagnetic wave signal, the anomaly detection device can determine that the preset waveform detection model is the second waveform detection model.

[0114] In the anomaly detection method provided in this application embodiment, the anomaly detection device generates a preset waveform based on the second electromagnetic wave signal generated by the first sensor according to the pressure of the pipeline network, the time when the first sensor sends the second electromagnetic wave signal, and the time when the second sensor receives the second electromagnetic wave signal within a second preset time period. Based on the preset waveform, a preset waveform detection model is determined. This makes it convenient for the anomaly detection device to directly input the first waveform into the preset waveform detection model when it determines the first waveform, so as to determine whether the first waveform is abnormal, and thus be able to determine whether there is an anomaly in the pipeline network in a timely manner.

[0115] It is understood that the above-described anomaly detection method can be implemented by an anomaly detection device. To achieve the above functions, the anomaly detection device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, the embodiments disclosed in this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware 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 the embodiments disclosed in this application.

[0116] The embodiments disclosed in this application can divide the anomaly detection device generated according to the above method examples into functional modules. For example, each function can be divided into its own functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in the embodiments disclosed in this application is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0117] Figure 8 This is a schematic diagram of an anomaly detection device provided in an embodiment of the present invention. Figure 8 As shown, the anomaly detection device 80 can be used to perform... Figures 3-7 The anomaly detection method shown is described. The anomaly detection device 80 includes a communication unit 801 and a processing unit 802.

[0118] The communication unit 801 is used to acquire, within a first preset time period, a first electromagnetic wave signal generated based on the pressure of the pipeline network and transmitted from the first sensor to the second sensor, the time when the first sensor transmits the first electromagnetic wave signal, and the time when the second sensor receives the first electromagnetic wave signal; the processing unit 802 is used to convert the first electromagnetic wave signal into a first waveform based on the first electromagnetic wave signal, the time when the first sensor transmits the first electromagnetic wave signal, and the time when the second sensor receives the first electromagnetic wave signal; the processing unit 802 is also used to input the first waveform into a preset waveform detection model to detect whether the first waveform is abnormal; the preset waveform detection model is used to compare the input waveform with a preset waveform to determine whether the input waveform is abnormal; the processing unit 802 is also used to determine whether there is an abnormality in the pipeline network based on the detection result of the first waveform.

[0119] In one possible implementation, the communication unit 801 is further configured to acquire the second electromagnetic wave signal generated by the first sensor based on the pressure of the pipeline network, which is sent from the first sensor to the second sensor within a second preset time period; the time when the first sensor sends the second electromagnetic wave signal; and the time when the second sensor receives the second electromagnetic wave signal; the second preset time period is the time period prior to the first preset time period; the processing unit 802 is further configured to generate a preset waveform based on the second electromagnetic wave signal, the time when the first sensor sends the second electromagnetic wave signal, and the time when the second sensor receives the second electromagnetic wave signal; the processing unit 802 is further configured to determine a preset waveform detection model based on the preset waveform.

[0120] In one possible implementation, the anomaly detection device is also connected to a third sensor and a fourth sensor, which are deployed on the pipeline to detect the pressure of the pipeline. The preset waveform detection model includes a first waveform detection model and a second waveform detection model. The first waveform detection model is determined based on the transmission of a second electromagnetic wave signal between the first sensor and the second sensor. The second waveform detection model is determined based on the transmission of a third electromagnetic wave signal between the third sensor and the fourth sensor.

[0121] In one possible implementation, the processing unit 802 is further configured to input the first waveform into the first waveform detection model to detect whether the first waveform has any abnormality; or the processing unit 802 is further configured to input the first waveform into the second waveform detection model to detect whether the first waveform has any abnormality.

[0122] Through the above description of the embodiments, those skilled in the art will clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0123] This disclosure also provides a computer-readable storage medium storing instructions that, when executed by a processor of an electronic device, enable the electronic device to perform the anomaly detection method provided in the embodiments of this disclosure described above.

[0124] This disclosure also provides a computer program product containing instructions that, when run on an electronic device, cause the electronic device to execute the anomaly detection method provided in the above-described embodiments of this disclosure.

[0125] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires; portable computer disks; hard disks; random access memory (RAM); read-only memory (ROM); erasable programmable read-only memory (EPROM); registers; hard disks; optical fibers; portable compact disc read-only memory (CD-ROM); optical storage devices; magnetic storage devices; or any suitable combination thereof; or any other form of computer-readable storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium may also be a component of the processor. The processor and the storage medium may reside in an application-specific integrated circuit (ASIC). In the embodiments of this application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0126] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An anomaly detection method, characterized in that, An anomaly detection device is applied to a pipeline network, the anomaly detection device being connected to a first sensor and a second sensor, the first sensor and the second sensor being deployed on a pipeline network for detecting the pressure of the pipeline network, the method comprising: The system acquires the first electromagnetic wave signal generated by the first sensor based on the pressure of the pipeline network, which is sent from the first sensor to the second sensor within a first preset time period; the time when the first sensor sends the first electromagnetic wave signal; and the time when the second sensor receives the first electromagnetic wave signal. Based on the first electromagnetic wave signal, the time when the first sensor sends the first electromagnetic wave signal, and the time when the second sensor receives the first electromagnetic wave signal, the first electromagnetic wave signal is converted into a first waveform. The first waveform is input into a preset waveform detection model to detect whether the first waveform is abnormal; the preset waveform detection model is used to compare the input waveform with a preset waveform to determine whether the input waveform is abnormal. Based on the detection results of the first waveform, it is determined whether there is an abnormality in the pipeline network; The system acquires the second electromagnetic wave signal generated by the pressure of the pipeline network and sent from the first sensor to the second sensor within a second preset time period, the time when the first sensor sends the second electromagnetic wave signal, and the time when the second sensor receives the second electromagnetic wave signal; the second preset time period is the time period before the first preset time period. The preset waveform is generated based on the second electromagnetic wave signal, the time when the first sensor sends the second electromagnetic wave signal, and the time when the second sensor receives the second electromagnetic wave signal. Based on the preset waveform, the preset waveform detection model is determined.

2. The method according to claim 1, characterized in that, The anomaly detection device is also connected to a third sensor and a fourth sensor, which are deployed on the pipeline to detect the pressure of the pipeline. The preset waveform detection model includes a first waveform detection model and a second waveform detection model; the first waveform detection model is determined based on the transmission of a second electromagnetic wave signal between the first sensor and the second sensor; the second waveform detection model is determined based on the transmission of a third electromagnetic wave signal between the third sensor and the fourth sensor.

3. The method according to claim 2, characterized in that, The step of inputting the first waveform into a preset waveform detection model to detect whether the first waveform has any abnormalities includes: The first waveform is input into the first waveform detection model to detect whether there is an anomaly in the first waveform; Alternatively, the first waveform can be input into the second waveform detection model to detect whether there is an anomaly in the first waveform.

4. An anomaly detection system, characterized in that, The anomaly detection system includes: anomaly detection equipment, a first sensor, and a second sensor; The anomaly detection device is connected to the first sensor and the second sensor; the first sensor and the second sensor are deployed on the pipeline network to detect the pressure of the pipeline network. The first sensor is used to generate a first electromagnetic wave signal based on the pressure of the pipeline network and send the first electromagnetic wave signal to the second sensor. The second sensor is used to receive the first electromagnetic wave signal; The anomaly detection device is used to acquire, within a first preset time period, a first electromagnetic wave signal generated by the first sensor based on the pressure of the pipeline network, which is sent from the first sensor to the second sensor; the time when the first sensor sends the first electromagnetic wave signal; and the time when the second sensor receives the first electromagnetic wave signal. Based on the first electromagnetic wave signal, the time when the first sensor sends the first electromagnetic wave signal, and the time when the second sensor receives the first electromagnetic wave signal, the device converts the first electromagnetic wave signal into a first waveform. The device inputs the first waveform into a preset waveform detection model to detect whether the first waveform is abnormal. Based on the detection result of the first waveform, the device determines whether the pipeline network is abnormal. The preset waveform detection model is used to compare the input waveform with a preset waveform to determine whether the input waveform is abnormal. The anomaly detection device is further configured to acquire, within a second preset time period, a second electromagnetic wave signal generated by the first sensor based on the pressure of the pipeline network, the time when the first sensor sends the second electromagnetic wave signal, and the time when the second sensor receives the second electromagnetic wave signal; generate the preset waveform based on the second electromagnetic wave signal, the time when the first sensor sends the second electromagnetic wave signal, and the time when the second sensor receives the second electromagnetic wave signal; and determine the preset waveform detection model based on the preset waveform; the second preset time period is the time period before the first preset time period.

5. The system according to claim 4, characterized in that, The anomaly detection system further includes a third sensor and a fourth sensor; the anomaly detection device is also connected to the third sensor and the fourth sensor; the third sensor and the fourth sensor are deployed on the pipeline network to detect the pressure of the pipeline network; the preset waveform detection model includes a first waveform detection model and a second waveform detection model; the first waveform detection model is determined based on the transmission of a second electromagnetic wave signal between the first sensor and the second sensor; the second waveform detection model is determined based on the transmission of a third electromagnetic wave signal between the third sensor and the fourth sensor; The third sensor is used to generate a third electromagnetic wave signal based on the pressure of the pipeline network and send the third electromagnetic wave signal to the fourth sensor. The fourth sensor is used to receive the third electromagnetic wave signal.

6. The system according to claim 5, characterized in that, The anomaly detection device is further configured to input the first waveform into the first waveform detection model to detect whether the first waveform is abnormal; or the anomaly detection device is further configured to input the first waveform into the second waveform detection model to detect whether the first waveform is abnormal.

7. An anomaly detection device, characterized in that, An anomaly detection device is used, which is connected to a first sensor and a second sensor. The first sensor and the second sensor are deployed on a pipeline to detect the pressure of the pipeline. The device includes a communication unit and a processing unit. The communication unit is used to acquire, within a first preset time period, a first electromagnetic wave signal generated by the first sensor based on the pressure of the pipeline network and sent from the first sensor to the second sensor, the time when the first sensor sends the first electromagnetic wave signal, and the time when the second sensor receives the first electromagnetic wave signal. The processing unit is configured to convert the first electromagnetic wave signal into a first waveform based on the first electromagnetic wave signal, the time when the first sensor sends the first electromagnetic wave signal, and the time when the second sensor receives the first electromagnetic wave signal. The processing unit is further configured to input the first waveform into a preset waveform detection model to detect whether the first waveform is abnormal; the preset waveform detection model is configured to compare the input waveform with a preset waveform to determine whether the input waveform is abnormal. The processing unit is also used to determine whether there is an abnormality in the pipeline network based on the detection result of the first waveform; The communication unit is further configured to acquire the second electromagnetic wave signal generated by the pressure of the pipeline network and sent by the first sensor to the second sensor within a second preset time period, the time when the first sensor sends the second electromagnetic wave signal, and the time when the second sensor receives the second electromagnetic wave signal; the second preset time period is the time period before the first preset time period. The processing unit is further configured to generate the preset waveform based on the second electromagnetic wave signal, the time when the first sensor sends the second electromagnetic wave signal, and the time when the second sensor receives the second electromagnetic wave signal; The processing unit is further configured to determine the preset waveform detection model based on the preset waveform.

8. An anomaly detection device, characterized in that, include: A processor and a communication interface; the communication interface is coupled to the processor, the processor being used to run computer programs or instructions to implement the anomaly detection method as described in any one of claims 1-3.

9. A computer-readable storage medium storing instructions, characterized in that, When the computer executes the instruction, the computer performs the anomaly detection method as described in any one of claims 1-3.

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