Waterfall chart processing method and apparatus, and electronic device

Abstract

Embodiments of the present application provide a waterfall diagram processing method, processing device and electronic equipment, belonging to the technical field of pipeline detection. The method comprises: obtaining optical fiber sensing data of a pipeline, and processing the optical sensing data to obtain an initial waterfall diagram comprising event signals; performing enhancement processing on the initial waterfall diagram to obtain an enhanced waterfall diagram; determining the slope of the event signals of the enhanced waterfall diagram; determining the event category of the event signals based on the slope, wherein the event category comprises threat events and non-threat events; and removing the non-threat events in the enhanced waterfall diagram using mask processing to obtain a target waterfall diagram. The process obtains a waterfall diagram by processing the optical fiber transmission data of the pipeline, removes the non-threat events in the waterfall diagram to obtain a target waterfall diagram, and improves the accuracy of waterfall diagram recognition.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pipeline monitoring, in particular to a waterfall diagram processing method and device and electronic equipment. BACKGROUND

[0002] In the prior art, the actual environment of optical fiber laying of a pipeline is diverse. In the case of accompanying high-speed, national roads, railways, etc., the optical fiber signal will have obvious accompanying signals of cars and trains. The accompanying signals in the waterfall diagram mainly appear as diagonal signals. There are many accompanying signals in the waterfall diagram, which leads to low accuracy and low efficiency in identifying threat events when identifying threat events in the waterfall diagram. SUMMARY

[0003] Therefore, the first aspect of the present application provides a waterfall diagram processing method, which improves the accuracy of waterfall diagram identification. The method comprises the following steps:

[0004] Obtaining optical fiber sensing data of a pipeline and processing the optical fiber sensing data to obtain an initial waterfall diagram including event signals;

[0005] Enhancing the initial waterfall diagram to obtain an enhanced waterfall diagram;

[0006] Determining the slope of the event signals of the enhanced waterfall diagram;

[0007] Determining the event category of the event signals based on the slope, wherein the event category includes threat events and non-threat events;

[0008] Removing the non-threat events in the enhanced waterfall diagram by using mask processing to obtain a target waterfall diagram.

[0009] In the embodiment of the present application, the optical fiber sensing data of the pipeline is obtained, and the optical fiber sensing data is processed to obtain an initial waterfall diagram including event signals, which comprises the following steps:

[0010] Determining corresponding two-dimensional matrix data based on the optical fiber sensing data;

[0011] Processing the two-dimensional matrix data to obtain sensing intensity data;

[0012] Generating the initial waterfall diagram based on the sensing intensity data.

[0013] In the embodiment of the present application, the initial waterfall diagram is enhanced to obtain an enhanced waterfall diagram, which comprises the following steps:

[0014] Enlarging the sensing intensity data in the initial waterfall diagram according to a preset ratio to obtain the enhanced waterfall diagram.

[0015] In the embodiment of the present application, the slope of the event signals of the enhanced waterfall diagram is determined, which comprises the following steps:

[0016] Performing inflation processing on the enhanced waterfall diagram to obtain an inflated waterfall diagram;

[0017] Calculating edge information of an event signal in the inflated waterfall diagram based on a preset recognition algorithm;

[0018] Calculating a slope of the event signal based on the edge information.

[0019] In the embodiment of the present application, the slope of the event signal is calculated based on the edge information, including:

[0020] Constructing a polygon according to the edge information of the event signal, and obtaining vertex coordinates of the polygon;

[0021] Determining the slope of the event signal based on the vertex coordinates.

[0022] In the embodiment of the present application, the event category of the event signal is determined based on the slope, including:

[0023] Comparing an absolute value of the slope with a preset threshold value;

[0024] If the absolute value is less than or equal to the preset threshold value, determining that the event category is a non-threat event;

[0025] If the absolute value is greater than the preset threshold value, determining that the event category is a threat event.

[0026] In the embodiment of the present application, the non-threat event in the enhanced waterfall diagram is removed by using mask processing to obtain a target waterfall diagram, including:

[0027] Obtaining a mask image with the same size as the enhanced waterfall diagram;

[0028] Determining a target region of the mask image based on the non-threat event signal, and setting a pixel value of each pixel point in the target region to a first pixel value;

[0029] Overlapping the mask image and the enhanced waterfall diagram based on the first pixel value to remove the non-threat event, and obtaining the target waterfall diagram.

[0030] In the embodiment of the present application, after the non-threat event in the enhanced waterfall diagram is removed by using mask processing to obtain the target waterfall diagram, further including:

[0031] Inputting the target waterfall diagram into a preset neural network to obtain an event type result;

[0032] In the case that the event type result includes a threat event, determining a position of the threat event by using the target waterfall diagram.

[0033] The second aspect of the present application provides a processing device of a waterfall diagram, including:

[0034] A data acquisition module is configured to acquire optical fiber sensing data of the pipeline and process the optical fiber sensing data to obtain an initial waterfall diagram including event signals.

[0035] A first image processing module is configured to perform enhancement processing on the initial waterfall diagram to obtain an enhanced waterfall diagram.

[0036] A determination module is configured to determine a slope of the event signals of the enhanced waterfall diagram.

[0037] An event category determination module is configured to determine an event category of the event signals based on the slope, wherein the event category includes a threat event and a non-threat event.

[0038] A second image processing module is configured to remove the non-threat event in the enhanced waterfall diagram by using mask processing to obtain a target waterfall diagram.

[0039] In a third aspect, an electronic device is provided, which includes a processor and a memory, the memory storing computer executable instructions capable of being executed by the processor, and the processor executes the computer executable instructions to implement the waterfall diagram processing method of any one of the above aspects.

[0040] In a fourth aspect, a machine readable storage medium is provided, which stores instructions, and the instructions, when executed by a processor, implement the waterfall diagram processing method of any one of the above aspects.

[0041] According to the above technical solution, the optical fiber sensing data of the pipeline is acquired, and the optical fiber sensing data is processed to obtain an initial waterfall diagram including event signals; the initial waterfall diagram is enhanced to obtain an enhanced waterfall diagram; the slope of the event signals of the enhanced waterfall diagram is determined; the event category of the event signals is determined based on the slope, wherein the event category includes a threat event and a non-threat event; and the non-threat event in the enhanced waterfall diagram is removed by using mask processing to obtain a target waterfall diagram. The process is performed on the optical fiber transmission data of the pipeline to obtain a waterfall diagram, and the non-threat event in the waterfall diagram is removed to obtain a target waterfall diagram, thereby improving the accuracy of waterfall diagram identification.

[0042] Other features and advantages of the embodiments of the present application will be described in detail in the following specific implementation part. BRIEF DESCRIPTION OF DRAWINGS

[0043] The accompanying drawings are included to provide a further understanding of the embodiments of the present application, and constitute a part of the specification, and are used together with the following specific implementation part to explain the embodiments of the present application, but do not constitute a limitation on the embodiments of the present application. In the drawings:

[0044] Figure 1 is a flowchart of a waterfall diagram processing method provided by the embodiments of the present application;

[0045] Figure 2 is a structural schematic diagram of a waterfall diagram processing device provided by an embodiment of the present application;

[0046] Figure 3 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0047] The specific embodiments of the embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the embodiments of the present application, and are not used to limit the embodiments of the present application.

[0048] Based on this, the present application provides a waterfall diagram processing method, Figure 1 is a flowchart of a waterfall diagram processing method provided by an embodiment of the present application, as Figure 1 shown, the method comprises:

[0049] Step S101: Obtain the optical fiber sensing data of the pipeline, and process the optical sensing data to obtain an initial waterfall diagram including event signals.

[0050] In actual application, optical fibers are arranged around the pipeline, the optical fiber sensing data of the pipeline is obtained, and the optical fiber sensing data is processed to convert the optical fiber sensing data into an initial waterfall diagram. Specifically, the horizontal axis of the initial waterfall diagram is the distance of the optical fiber arrangement of the pipeline, and the vertical axis of the initial waterfall diagram is time, usually in seconds, indicating the detection duration of the pipeline, which can be set to 10 seconds or 20 seconds.

[0051] In actual application, the waterfall diagram is transformed from blue to red, wherein the red color is a high value event signal, and the high value event signal is a large vibration detected in the pipeline. The event signal value of blue is 0, which is a small vibration or no vibration detected in the pipeline.

[0052] Step S102: Perform enhancement processing on the initial waterfall diagram to obtain an enhanced waterfall diagram.

[0053] In actual application, the initial waterfall diagram can be subjected to point operation, template processing, high-pass filtering, low-pass filtering and other enhancement processing, and the event signals in the enhanced waterfall diagram are enhanced to obtain an enhanced waterfall diagram.

[0054] Step S103: Determine the slope of the event signal of the enhanced waterfall diagram.

[0055] In actual application, the points of the event signal on the enhanced waterfall diagram are usually discontinuous, and the enhanced waterfall diagram is processed to make the event signal in the enhanced waterfall diagram continuous, so as to determine the slope of the event signal in the enhanced waterfall diagram.

[0056] Step S104: determining an event category of the event signal based on the slope, wherein the event category includes a threat event and a non-threat event.

[0057] In practical applications, the event category of the event signal can include a threat event and a non-threat event. Specifically, the event category of the event signal of the vibration caused by a car or a train passing by the pipeline is a non-threat event, and the vibration caused by manual excavation or excavation by an excavator can threaten the pipeline, and the event category of such event signal is a threat event.

[0058] In practical applications, the event category of the event signal is determined according to the slope of the event signal. For example, when the slope of the event signal is positive or negative infinity, the event signal is parallel to the vertical axis of the enhanced waterfall chart, and the event signal is a threat event.

[0059] Step S105: removing the non-threat event in the enhanced waterfall chart by using mask processing to obtain a target waterfall chart.

[0060] In practical applications, after determining the event type of the event signal in the enhanced waterfall chart, the target region of the mask image is determined according to the non-threat event, the non-threat event in the enhanced waterfall chart is removed according to the pixel value of the target region, and the target waterfall chart is obtained.

[0061] Through the above embodiment, the optical fiber sensing data of the pipeline is obtained, and the optical fiber sensing data is processed to obtain an initial waterfall chart including an event signal; the initial waterfall chart is enhanced to obtain an enhanced waterfall chart; the slope of the event signal of the enhanced waterfall chart is determined; the event category of the event signal is determined based on the slope, wherein the event category includes a threat event and a non-threat event; and the non-threat event in the enhanced waterfall chart is removed by using mask processing to obtain a target waterfall chart. The process is processed by the optical fiber transmission data of the pipeline to obtain a waterfall chart, and the target waterfall chart is obtained by removing the non-threat event in the waterfall chart, which improves the accuracy of the waterfall chart recognition.

[0062] In an embodiment, step S101 includes:

[0063] determining corresponding two-dimensional matrix data based on the optical fiber sensing data;

[0064] processing the two-dimensional matrix data to obtain sensing intensity data;

[0065] generating an initial waterfall chart based on the sensing intensity data.

[0066] In practical applications, two-dimensional matrix data with horizontal coordinates as distances, vertical coordinates as detection time lengths, and numerical values as event signal intensities are determined by using optical fiber sensing data. The two-dimensional matrix data is subjected to intensity enhancement processing to obtain sensing intensity data, and an initial waterfall chart corresponding to the pipeline is generated according to the sensing intensity data.

[0067] Through the above embodiment, the corresponding two-dimensional matrix data is determined based on the optical fiber sensing data; the sensing intensity data is obtained by processing the two-dimensional matrix data; and the initial waterfall diagram is generated based on the sensing intensity data. The optical fiber sensing data is processed to obtain the initial waterfall diagram, so that the optical fiber sensing data is converted into image data, and the event signal of the pipeline can be determined by identifying the image data.

[0068] In an embodiment, step S102 comprises:

[0069] The sensing intensity data in the initial waterfall diagram is enlarged according to a preset ratio to obtain an enhanced waterfall diagram.

[0070] In actual application, the sensing intensity data in the initial waterfall diagram is enlarged according to a preset ratio, so as to enhance the event signal in the initial waterfall diagram and obtain an enhanced waterfall diagram. The preset ratio can be set according to needs.

[0071] Through the above embodiment, the sensing intensity data in the initial waterfall diagram is enlarged according to a preset ratio to obtain an enhanced waterfall diagram. The event signal is enhanced by performing enhancement processing on the initial waterfall diagram to obtain an enhanced waterfall diagram, so that the event signal in the enhanced waterfall diagram is more obvious.

[0072] In an embodiment, step S103 comprises:

[0073] The enhanced waterfall diagram is dilated to obtain a dilated waterfall diagram.

[0074] Edge information of the event signal in the dilated waterfall diagram is calculated based on a preset recognition algorithm.

[0075] The slope of the event signal is calculated based on the edge information.

[0076] In actual application, the enhanced waterfall diagram is eroded and dilated, so that the event signal in the enhanced waterfall diagram is continuous, and the processed enhanced waterfall diagram is converted into a black-and-white waterfall diagram to obtain a dilated waterfall diagram.

[0077] In actual application, the preset recognition algorithm is a Sobel edge recognition algorithm, according to which the edge signal of the event signal in the dilated waterfall diagram is highlighted. The edge information of the event signal is obtained by deriving the horizontal axis and the vertical axis of the edge signal and calculating the sum of the horizontal axis derivation and the vertical axis derivation.

[0078] In actual application, the slope of the event signal is determined according to the edge information of the event signal.

[0079] The edge information of the event signal in the expanded waterfall graph is calculated based on a preset recognition algorithm; and the slope of the event signal is calculated based on the edge information. The process makes the event signal continuous by processing the enhanced waterfall graph, and calculates the edge information of the event signal through the Sobel edge recognition algorithm, so as to calculate the slope of the event signal, thereby improving the accuracy of event signal recognition.

[0080] In an embodiment, the slope of the event signal is calculated based on the edge information, including:

[0081] A polygon is constructed according to the edge information of the event signal, and the vertex coordinates of the polygon are obtained;

[0082] The slope of the event signal is determined based on the vertex coordinates.

[0083] In actual application, the event signal in the expanded waterfall graph can be approximately regarded as a slope line with a slope, and the slope line is selected by the polygon, that is, the slope line is arranged inside the polygon, and the slope line is compared with the vertex of the polygon. The slope of the event signal is calculated by obtaining the coordinates of the vertex of the polygon in the expanded waterfall graph.

[0084] In an embodiment, step S104 includes:

[0085] The absolute value of the slope is compared with a preset threshold value;

[0086] If the absolute value is less than or equal to the preset threshold value, it is determined that the event category is a non-threat event;

[0087] If the absolute value is greater than the preset threshold value, it is determined that the event category is a threat event.

[0088] In actual application, the absolute value of the slope of the event signal is compared with the preset threshold value. It can be understood that the preset threshold value can be set as needed.

[0089] In actual application, in the case that the absolute value of the slope is less than or equal to the preset threshold value, it is determined that the event category of the event signal is a non-threat event. In the case that the absolute value of the slope is greater than the preset threshold value, it is determined that the event category of the event signal is a threat event.

[0090] Through the above embodiment, the absolute value of the slope is compared with the preset threshold value; if the absolute value is less than or equal to the preset threshold value, it is determined that the event category is a non-threat event; and if the absolute value is greater than the preset threshold value, it is determined that the event category is a threat event. The process compares the slope with the preset threshold value to determine the event category of the event signal, thereby improving the accuracy of determining the event category.

[0091] In an embodiment, step S105 includes:

[0092] obtain a mask image with the same size as the enhanced waterfall image;

[0093] determine a target region of the mask image based on the non-threat event signal, and set a pixel value of each pixel point in the target region to a first pixel value;

[0094] overlap the mask image and the enhanced waterfall image based on the first pixel value to remove the non-threat event, and obtain a target waterfall image.

[0095] In practical applications, a mask image with the same size as the enhanced waterfall image is obtained. A target region in the mask image is determined based on the non-threat event in the enhanced waterfall image, and a pixel value of each pixel point in the target region is set to a first pixel value, i.e., 0.

[0096] In practical applications, the mask image including the target region and the enhanced waterfall image are overlapped to calculate and remove the non-threat event in the enhanced waterfall image, and obtain the target waterfall image.

[0097] Through the above embodiments, a mask image with the same size as the enhanced waterfall image is obtained. A target region of the mask image is determined based on the non-threat event signal, and a pixel value of each pixel point in the target region is set to a first pixel value. The mask image and the enhanced waterfall image are overlapped based on the first pixel value to remove the non-threat event, and obtain a target waterfall image. This process removes the non-threat event by masking the enhanced waterfall image to obtain the target waterfall image.

[0098] In an embodiment, the method further comprises:

[0099] inputting the target waterfall image into a preset neural network to obtain an event type result;

[0100] in a case where the event type result includes a threat event, determining a position of the threat event using the target waterfall image.

[0101] In practical applications, the preset neural network can be set to identify the threat event in the target waterfall image. The target waterfall image is input into the preset neural network, and an event category result of the event signal in the target waterfall image is output. Specifically, the event type result includes a threat event and does not include a threat event.

[0102] In practical applications, in a case where the event type result includes a threat event, the position of the threat event is determined using the abscissa of the threat event in the target waterfall image. The position of the threat event is sent to a remote background server, so that a staff member determines that the pipeline has a threat event.

[0103] By the above embodiment, the target waterfall diagram is input to the preset neural network to obtain an event type result; in a case where the event type result includes a threat event, a position of the threat event is determined by using the target waterfall diagram. This process improves the accuracy of the preset neural network in identifying the waterfall diagram and improves the accuracy of identifying the threat event of the pipeline.

[0104] Based on the above processing method of the waterfall diagram, an embodiment of the present application further provides a processing device 200 of a waterfall diagram, Figure 2 A structural schematic diagram of a processing device of a waterfall diagram provided by an embodiment of the present application is shown in the figure, and the processing device 200 of the waterfall diagram comprises:

[0105] A data acquisition module 201 is configured to acquire optical fiber sensing data of a pipeline and process the optical sensing data to obtain an initial waterfall diagram including event signals;

[0106] A first image processing module 202 is configured to perform enhancement processing on the initial waterfall diagram to obtain an enhanced waterfall diagram;

[0107] A determination module 203 is configured to determine a slope of the event signals of the enhanced waterfall diagram;

[0108] An event category determination module 204 is configured to determine an event category of the event signals based on the slope, wherein the event category includes a threat event and a non-threat event;

[0109] A second image processing module 205 is configured to remove the non-threat event in the enhanced waterfall diagram by using mask processing to obtain a target waterfall diagram.

[0110] The processing device of the waterfall diagram provided by the embodiment of the present application can implement each process of the processing method of the waterfall diagram in the method embodiment, and can achieve the same technical effect. To avoid repetition, details are not described here.

[0111] An embodiment of the present application further provides an electronic device, as shown in the figure, Figure 3 The electronic device comprises a processor 130 and a memory 131, the memory 131 stores machine executable instructions capable of being executed by the processor 130, and the processor 130 executes the machine executable instructions to implement the above processing method of the waterfall diagram.

[0112] Further, Figure 3 The electronic device shown in the figure further comprises a bus 132 and a communication interface 133, and the processor 130, the communication interface 133 and the memory 131 are connected through the bus 132.

[0113] The memory 131 can include a high-speed random access memory (RAM) and can also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 133 (which can be wired or wireless), and the Internet, a wide area network, a local area network, a metropolitan area network, etc. can be used. The bus 132 can be an ISA bus, a PCI bus, or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one bidirectional arrow is used to represent the system in the figure, but it does not mean that there is only one bus or one type of bus.

[0114] The processor 130 can be an integrated circuit chip with signal processing capability. In the implementation process, each step of the above method can be completed by the integrated logic circuit of hardware in the processor 130 or the instructions in the form of software. The processor 130 described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. Each method, step and logic block disclosed in the embodiment of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the embodiment of the present application can be directly embodied as a hardware code processor for execution, or a combination of hardware and software modules in the code processor for execution. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, etc. The storage medium in the art. The storage medium is located in the memory 131, and the processor 130 reads the information in the memory 131, and combines the hardware to complete the steps of the method of the above embodiment.

[0115] The embodiment of the present application also provides a machine readable storage medium, which stores machine executable instructions, when the machine executable instructions are called and executed by a processor, the machine executable instructions cause the processor to implement the processing method of the waterfall chart. For details, please refer to the method embodiment, which will not be repeated here.

[0116] The embodiment of the present application provides a waterfall diagram processing method, device and electronic equipment, including a computer readable storage medium storing program codes, instructions included in the program codes are used for executing the method in the foregoing method embodiment, and specific implementation can be referred to the method embodiment, and details are not described herein again.

[0117] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and device described above can refer to the corresponding process in the foregoing method embodiment, and details are not described herein again.

[0118] In addition, in the description of the embodiment of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connecting" should be understood in a broad sense, for example, can be fixedly connected, or can be detachably connected, or integrally connected, can be mechanically connected, or can be electrically connected, can be directly connected, or indirectly connected through an intermediate medium, can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to specific circumstances.

[0119] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or the part of the present application which essentially contributes to the prior art or the part of the technical solutions can be embodied in the form of software products. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, an electronic device or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk and various storage medium capable of storing program codes.

[0120] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, therefore it cannot be understood as a limitation on the present application. In addition, the terms "first", "second", "third" are only for the purpose of description, and cannot be understood as indicating or implying relative importance.

[0121] Finally, it should be noted that the above examples are merely specific embodiments of the present application, and are used to illustrate the technical solutions of the present application, but are not intended to limit the present application. The protection scope of the present application is not limited thereto. Although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that any person skilled in the art can still modify or easily think of changes to the technical solutions recorded in the foregoing examples, or make equivalent replacements to some of the technical features, within the technical range disclosed by the present application. These modifications, changes or replacements do not cause the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method of processing a waterfall plot, characterized by, include: Acquire fiber optic sensing data of the pipeline and process the fiber optic sensing data to obtain an initial waterfall plot including event signals. The initial waterfall plot is enhanced to obtain an enhanced waterfall plot; Determine the slope of the event signal in the enhanced waterfall plot; The event category of the event signal is determined based on the slope, wherein the event category includes threatening events and non-threatening events, the threatening events include vibrations generated by manual digging of the pipeline or excavator digging, and the non-threatening events include vibrations generated by cars or trains passing through the pipeline; The non-threat events in the enhanced waterfall plot are removed using masking to obtain the target waterfall plot; The step of determining the slope of the event signal in the enhanced waterfall plot includes: The enhanced waterfall plot is then dilated to obtain an inflated waterfall plot. The edge information of the event signal in the inflated waterfall diagram is calculated based on a preset recognition algorithm; The slope of the event signal is calculated based on the edge information; The process of determining the event category of the event signal based on the slope includes: The absolute value of the slope is compared with a preset threshold. If the absolute value is less than or equal to the preset threshold, the event category is determined to be the non-threat event; If the absolute value is greater than the preset threshold, the event category is determined to be the threat event.

2. The method of claim 1, wherein, The process of acquiring fiber optic sensing data from the pipeline and processing the fiber optic sensing data to obtain an initial waterfall plot including event signals includes: The corresponding two-dimensional matrix data is determined based on the fiber optic sensing data; The two-dimensional matrix data is processed to obtain the sensing intensity data; An initial waterfall plot is generated based on the sensor intensity data.

3. The method of claim 2, wherein, The enhancement process of the initial waterfall plot to obtain the enhanced waterfall plot includes: The enhanced waterfall map is obtained by scaling up the sensor intensity data in the initial waterfall map according to a preset ratio.

4. The method of claim 1, wherein, The calculation of the slope of the event signal based on the edge information includes: A polygon is constructed based on the edge information of the event signal, and the vertex coordinates of the polygon are obtained; The slope of the event signal is determined based on the vertex coordinates.

5. The method of claim 1, wherein, The process of removing non-threat events from the enhanced waterfall plot using masking to obtain the target waterfall plot includes: Obtain a mask image with the same size as the enhanced waterfall plot; The target region of the masked image is determined based on the non-threat event signal, and the pixel value of each pixel in the target region is set to the first pixel value; The mask image and the enhanced waterfall plot are overlaid based on the first pixel value to remove the non-threat events and obtain the target waterfall plot.

6. The method of claim 1, wherein, After removing the non-threat events from the enhanced waterfall plot using masking to obtain the target waterfall plot, the process further includes: The target waterfall plot is input into a preset neural network to obtain the event type results; If the event type result includes a threat event, the location of the threat event is determined using the target waterfall plot.

7. A waterfall graph processing apparatus, characterized by comprising: include: The data acquisition module is used to acquire fiber optic sensing data of the pipeline and process the fiber optic sensing data to obtain an initial waterfall plot including event signals. The first image processing module is configured to perform enhancement processing on the initial waterfall diagram to obtain an enhanced waterfall diagram. The determination module is configured to determine a slope of an event signal of the enhanced waterfall diagram. The event category determination module is configured to determine an event category of the event signal based on the slope, wherein the event category includes a threat event and a non-threat event, the threat event includes vibration caused by manual excavation or excavation by an excavator, and the non-threat event includes vibration caused by a passing car or train. The second image processing module is configured to remove the non-threat event in the enhanced waterfall diagram by mask processing to obtain a target waterfall diagram. The determination of the slope of the event signal of the enhanced waterfall diagram includes: performing dilation processing on the enhanced waterfall diagram to obtain a dilated waterfall diagram; calculating edge information of the event signal in the dilated waterfall diagram based on a preset recognition algorithm; calculating the slope of the event signal based on the edge information; The determination of the event category of the event signal based on the slope includes: comparing an absolute value of the slope with a preset threshold value; if the absolute value is less than or equal to the preset threshold value, determining that the event category is the non-threat event; if the absolute value is greater than the preset threshold value, determining that the event category is the threat event.

8. An electronic device, comprising: The electronic device includes a processor and a memory, the memory stores computer executable instructions capable of being executed by the processor, and the processor executes the computer executable instructions to implement the waterfall diagram processing method in any one of claims 1 to 6.

9. A machine-readable storage medium having stored thereon instructions, the instructions comprising: The instructions, when executed by the processor, implement the waterfall diagram processing method in any one of claims 1 to 6.

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