Urban waterlogging monitoring method and device integrating monitoring data with electronic water gauge data

Abstract

This application proposes a method and device for monitoring urban waterlogging by fusing monitoring data with electronic water gauge data. The method comprises the following steps: obtaining the current reading of the electronic water gauge, using a monitoring camera to acquire a sequence of multiple images of the area to be monitored, including a continuous image of the area where the current electronic water gauge is located; inputting the image sequence into a pre-trained waterlogging segmentation model to obtain the waterlogging area; obtaining a water surface fluctuation factor based on the image sequence, the water surface fluctuation factor representing the water surface fluctuation in the waterlogging area; and using the water surface fluctuation factor to correct the current reading of the electronic water gauge to obtain the waterlogging depth. This solution accurately identifies the waterlogging area through the waterlogging segmentation model, and calculates the water surface fluctuation factor based on the changes in adjacent image pixels and the standard deviation of the water gauge reading to correct the water gauge reading. This method enhances the real-time and stability of monitoring, reduces the impact of environmental interference and equipment errors, and achieves rapid and accurate urban waterlogging monitoring.

Description

Technical Field

[0001] The present application relates to the field of waterlogging monitoring, and in particular to a method and device for urban waterlogging monitoring that integrates monitoring data with electronic water gauge data. Background Art

[0002] With the intensification of global climate change, extreme precipitation events have occurred frequently, and urban waterlogging has become increasingly serious, posing huge challenges to urban transportation, public safety and infrastructure. Therefore, waterlogging monitoring technology has received widespread attention.

[0003] Among the common urban waterlogging monitoring methods currently available, the traditional manual inspection method relies on on-site visual observation and recording of waterlogging conditions by management personnel. Although intuitive, it has problems such as poor real-time performance, inability to provide continuous monitoring data, lack of precise quantitative indicators due to subjective judgment, high labor costs, and difficulty in implementation in bad weather. The electronic water gauge monitoring method is based on the installation of water level sensors at flood-prone points to measure the water level in real time and judge the waterlogging situation. However, it is easily affected by factors such as sludge deposition, obstruction by floating objects, and sensor aging, resulting in unstable readings. In addition, the fixed installation position cannot cover the entire waterlogged area and cannot provide spatial distribution information of the waterlogging range. Video monitoring The waterlogging identification method installs a video surveillance system at flood-prone points and uses image processing or deep learning algorithms to identify water areas from the video. However, it can only identify the existence and scope of the water body and cannot directly measure the water depth. The recognition accuracy is uncertain due to environmental factors such as lighting, reflection, and occlusion. It is difficult to distinguish subtle changes in the depth of waterlogging by visual information alone. The remote sensing and drone monitoring method uses multispectral or radar remote sensing technology to detect surface waterlogging. However, the time resolution of remote sensing satellites is limited, making it difficult to monitor at high frequencies. Drones are restricted by meteorological conditions, difficult to operate in heavy rainfall environments, and are costly, making it difficult to conduct long-term continuous monitoring over a large area.

[0004] In summary, how to quickly and accurately monitor urban waterlogging is a problem that needs to be solved urgently by existing technologies. Summary of the Invention

[0005] The embodiments of the present application provide a method and device for monitoring urban waterlogging by fusing monitoring data with electronic water gauge data. The method accurately identifies waterlogging areas through a waterlogging segmentation model, calculates water surface fluctuation factors based on changes in adjacent image pixels, standard deviation of water gauge readings, and corrects water gauge readings, thereby enhancing the real-time and stability of monitoring, reducing the impact of environmental interference and equipment errors, and achieving fast and accurate urban waterlogging monitoring.

[0006] In a first aspect, an embodiment of the present application provides a method for monitoring urban waterlogging by fusing monitoring data with electronic water gauge data, the method comprising:

[0007] Obtain the current reading of the electronic water gauge, and use a surveillance camera to obtain a plurality of image sequences including continuous images of the area to be monitored where the current electronic water gauge is located, and input the image sequences into a pre-trained water accumulation segmentation model to obtain the water accumulation area;

[0008] Obtain the pixel grayscale change of the same pixel position in the waterlogged area of two adjacent images to be monitored as a water surface change parameter, obtain the standard deviation of the historical readings of the electronic water gauge as a water gauge reading fluctuation intensity parameter, obtain the pixel binary change of the same pixel position in the waterlogged area of two adjacent images to be monitored as a texture change parameter, and perform weighted summation of the water surface change parameter, the water gauge reading fluctuation intensity parameter, and the texture change parameter to obtain a water surface fluctuation factor, which represents the water surface fluctuation in the waterlogged area;

[0009] The water depth is obtained by correcting the current reading of the electronic water gauge using the water surface fluctuation factor.

[0010] In a second aspect, an embodiment of the present application provides an urban waterlogging monitoring device that integrates monitoring data with electronic water gauge data, including:

[0011] An acquisition module is used to obtain the current reading of the electronic water gauge and use a monitoring camera to obtain a plurality of image sequences including continuous images of the area to be monitored where the current electronic water gauge is located, and input the image sequences into a pre-trained water accumulation segmentation model to obtain the water accumulation area;

[0012] The fluctuation calculation module obtains the pixel grayscale change of the same pixel position in the waterlogged area of two adjacent images to be monitored as a water surface change parameter, obtains the standard deviation of the historical readings of the electronic water gauge as a water gauge reading fluctuation intensity parameter, obtains the pixel binary change of the same pixel position in the waterlogged area of two adjacent images to be monitored as a texture change parameter, and obtains the water surface fluctuation factor by weighted summation of the water surface change parameter, the water gauge reading fluctuation intensity parameter, and the texture change parameter. The water surface fluctuation factor represents the water surface fluctuation in the waterlogged area;

[0013] The correction module uses the water surface fluctuation factor to correct the current reading of the electronic water gauge to obtain the water depth.

[0014] In a third aspect, an embodiment of the present application provides an electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute a method for monitoring urban waterlogging by fusing monitoring data with electronic water gauge data.

[0015] In a fourth aspect, an embodiment of the present application provides a readable storage medium, in which a computer program is stored. The computer program includes a program code for controlling a process to execute a process. When the program code is executed by a processor, a method for monitoring urban waterlogging by integrating monitoring data with electronic water gauge data is implemented.

[0016] The main contributions and innovations of the present invention are as follows:

[0017] The embodiment of the present application integrates the current reading of the electronic water gauge with the continuous image sequence captured by the surveillance camera, uses the water accumulation segmentation model to accurately identify the water accumulation area, combines the grayscale and binary changes of adjacent image pixels, the standard deviation of the historical water gauge readings, etc. to calculate the water surface fluctuation factor to correct the water gauge reading, and adopts Kalman filtering to reduce noise, which effectively makes up for the shortcomings of a single monitoring method in real-time performance, range coverage, accuracy and environmental adaptability. It can quickly and accurately realize the identification and depth measurement of urban waterlogging areas, enhance monitoring stability, reduce the impact of environmental interference and equipment errors, and provide reliable data support for urban waterlogging prevention and control.

[0018] The details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 This is a flow chart of a method for monitoring urban waterlogging by fusing monitoring data with electronic water gauge data according to an embodiment of the present application;

[0021] Figure 2 This is a diagram showing a successful pairing of a surveillance camera and an electronic water gauge according to an embodiment of the present application;

[0022] Figure 3 is a structural diagram of a water accumulation segmentation model according to an embodiment of the present application;

[0023] Figure 4 This is an identification effect diagram for identifying a waterlogged area according to an embodiment of the present application;

[0024] Figure 5 This is a structural block diagram of an urban waterlogging monitoring device that integrates monitoring data and electronic water gauge data according to an embodiment of the present application;

[0025] Figure 6 Schematic diagram of the hardware structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The implementations described in the following exemplary embodiments are not intended to represent all implementations consistent with one or more embodiments of this specification. Rather, they are merely examples of apparatuses and methods consistent with certain aspects of one or more embodiments of this specification, as detailed in the appended claims.

[0027] It should be noted that in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the method may include more or fewer steps than those described in this specification. In addition, a single step described in this specification may be broken down into multiple steps for description in other embodiments, and multiple steps described in this specification may be combined into a single step for description in other embodiments.

[0028] Example 1

[0029] The embodiment of the present application provides a method for monitoring urban waterlogging by integrating monitoring data with electronic water gauge data. The method accurately identifies waterlogging areas through a waterlogging segmentation model, calculates water surface fluctuation factors based on adjacent image pixel changes, water gauge reading standard deviations, and corrects water gauge readings, thereby enhancing real-time monitoring and stability, reducing the impact of environmental interference and equipment errors, and achieving rapid and accurate urban waterlogging monitoring. Specifically, reference Figure 1 , the method comprising:

[0030] Obtain the current reading of the electronic water gauge, and use a surveillance camera to obtain a plurality of image sequences including continuous images of the area to be monitored where the current electronic water gauge is located, and input the image sequences into a pre-trained water accumulation segmentation model to obtain the water accumulation area;

[0031] Obtain the pixel grayscale change of the same pixel position in the waterlogged area of two adjacent images to be monitored as a water surface change parameter, obtain the standard deviation of the historical readings of the electronic water gauge as a water gauge reading fluctuation intensity parameter, obtain the pixel binary change of the same pixel position in the waterlogged area of two adjacent images to be monitored as a texture change parameter, and perform weighted summation of the water surface change parameter, the water gauge reading fluctuation intensity parameter, and the texture change parameter to obtain a water surface fluctuation factor, which represents the water surface fluctuation in the waterlogged area;

[0032] The water depth is obtained by correcting the current reading of the electronic water gauge using the water surface fluctuation factor.

[0033] In some embodiments, in order to ensure that the monitoring camera can clearly capture the electronic water gauge, a monitoring camera must be matched with the electronic water gauge first. Specifically, when the horizontal distance between the monitoring camera and the electronic water gauge is less than a distance threshold, and the angle between the line of sight direction vector of the monitoring camera and the direction vector of the electronic water gauge relative to the monitoring camera is less than an angle threshold, the monitoring camera is used to obtain multiple continuous images to be monitored including the area where the current electronic water gauge is located.

[0034] Specifically, the monitoring camera acquires continuous images to be monitored at the same location and the same shooting angle.

[0035] Specifically, let the installation coordinates of the surveillance camera be , the installation coordinates of the electronic water gauge are , the horizontal distance between the surveillance camera and the electronic water gauge is calculated as follows:

[0036]

[0037] Where D is the horizontal distance between the monitoring camera and the electronic water gauge. The distance threshold set by this scheme is , distance threshold Usually 3~10 meters.

[0038] Specifically, the calculation formula of the surveillance camera's sight direction vector is as follows:

[0039]

[0040] in, is the sight direction vector of the surveillance camera, is the horizontal rotation angle of the surveillance camera, is the camera's pitch angle.

[0041] Specifically, the calculation formula for the direction vector of the electronic water gauge relative to the surveillance camera is as follows:

[0042]

[0043] in, is the direction vector of the electronic water gauge relative to the surveillance camera, is the installation coordinate of the surveillance camera, The installation coordinates of the electronic water gauge.

[0044] Specifically, the angle between the surveillance camera's sight line direction vector and the direction vector of the electronic water gauge relative to the surveillance camera is calculated using the following formula:

[0045]

[0046] in, is the angle between the surveillance camera’s sight line vector and the direction vector of the electronic water gauge relative to the surveillance camera, is the direction vector of the electronic water gauge relative to the surveillance camera, To monitor the camera’s sight direction vector, the angle threshold set in this solution is , angle threshold Typically 10° to 15°.

[0047] That is to say, in this scheme, when and When the monitoring camera can clearly capture the electronic water gauge, pair the corresponding monitoring camera with the electronic water gauge. The effect of the successful pairing of the monitoring camera and the electronic water gauge is as follows: Figure 2 shown.

[0048] In some embodiments, the structure of the water segmentation model is as follows: Figure 3 As shown, the water accumulation segmentation model includes an encoding module, a decoding module, a timing analysis module and an output module. The encoding module is used to extract the color feature map, the water accumulation surface fluctuation feature map and the water accumulation edge feature map of each image to be monitored, and superimpose the color feature map, the water accumulation edge feature map and the water accumulation texture feature map of each image to be monitored to obtain a comprehensive feature map. The decoding module decodes the comprehensive feature map of each image to be monitored and inputs it into the timing analysis module. The timing analysis module performs a fusion analysis on the timing information of each comprehensive feature map to obtain the water accumulation area and outputs it by the output module.

[0049] For example, by converting the image sequence graph 、 、 Input into the water accumulation segmentation model, and the encoding module outputs the color feature map , Water surface fluctuation characteristics map And the water edge feature map , and then superimpose the color feature map, the water surface fluctuation feature map and the water edge feature map to obtain the comprehensive feature map .

[0050] Furthermore, the encoding module is composed of multiple encoding units connected in series, and each encoding unit includes an encoder, a channel attention mechanism, and a void convolution layer.

[0051] Specifically, the encoder is used to extract feature representations of the image to be monitored; the channel attention mechanism dynamically adjusts each channel so that the model pays more attention to those water accumulation areas with low saturation and low contrast. The channel attention mechanism improves the recognition accuracy of water accumulation areas in complex urban environments by enhancing the expression ability of these water body features; the void convolution layer captures water body features of different scales by expanding the receptive field. Since there are usually water bodies of various scales in urban areas, such as water accumulation on streets and around sewers, void convolution can help the model extract the features of these areas while maintaining resolution, thereby improving the accurate recognition of water accumulation areas, especially in the face of dense cities and complex ground reflections.

[0052] In some specific embodiments, the principle of extracting a color feature map, a water surface fluctuation feature map, and a water edge feature map by the encoding module is described. Before the encoding module performs feature extraction, considering that urban waterlogging usually has a specific color distribution, such as low grayscale and a strong blue channel, the image to be monitored is converted into the HSV color space before being input into the encoding module. The feature extraction formula of the color feature map is expressed as:

[0053]

[0054] Among them, x, y represent the pixel coordinates on the image to be monitored, is the preset color threshold, is the channel pixel value of the blue channel, is the channel pixel value of the red channel, is the feature extraction result of the (x, y) coordinate on the color feature map. That is to say, when extracting the features of the color feature map, when the value of the (x, y) coordinate is assigned to 1, the pixel value of the coordinate point (x, y) is retained; when the value of the (x, y) coordinate is assigned to 0, the pixel value of the coordinate point (x, y) is not retained. This scheme sets the threshold of each channel in the HSV color space to highlight the waterlogged area in the image to be monitored, providing useful information for subsequent waterlogging identification.

[0055] Specifically, the Laplacian variance is used to calculate the influence of each pixel on the water surface of the monitored image, so as to extract the water surface fluctuation feature map. The formula is expressed as follows:

[0056]

[0057] in, is the feature extraction result of the (x, y) coordinate on the water surface fluctuation feature map, is the preset fuzzy threshold, It is the variance of the pixels after Laplacian transformation of the monitored image (x, y). That is to say, when extracting the water surface fluctuation feature map, when the value of the (x, y) coordinate is assigned to 1, the pixel value of the coordinate point (x, y) is retained; when the value of the (x, y) coordinate is assigned to 0, the pixel value of the coordinate point (x, y) is not retained.

[0058] Specifically, the Canny edge detection algorithm is used to extract edge features of the monitored image to obtain a water accumulation edge feature map. The Canny edge detection algorithm is a commonly used edge feature extraction method and will not be described in detail here.

[0059] In some specific embodiments, the coding units in the coding module are gradually increased from 128 channels to 1024 channels to extract the abstract features of the water body layer by layer, while retaining low-level spatial information to adapt to the complex waterlogging scenes in the city. The decoding module is composed of multiple decoding units, the number of decoding units is the same as the number of coding units, and each layer of decoding units is directly connected to the corresponding coding units. The decoding units gradually restore the information of the waterlogged areas in the urban environment through the process of restoring the resolution, and at the same time fuse it with the low-level features extracted by the corresponding coding units to improve the accuracy of water body segmentation. Especially in cities, the decoding units can effectively distinguish the boundaries between waterlogging and the surrounding complex environment, and improve the recognition accuracy of urban waterlogging areas. The recognition effect diagram of the waterlogged areas identified by this scheme is shown in the figure. Figure 4 shown.

[0060] In some embodiments, the timing analysis module is an LSTM structure. The timing analysis module enables the model to transcend time continuity by fusing the timing information of the previous and next frames of adjacent monitored images to reduce the impact of lighting changes, moving objects or reflected light in a short period of time, so as to more stably identify waterlogged areas, especially in urban environments with strong interference from light changes or dynamic objects.

[0061] In some embodiments, the output module outputs the result of the timing analysis module to obtain the water accumulation area. The output module is a decoding unit that decodes the edge of the water accumulation area and then obtains the water accumulation area based on the edge of the water accumulation area. The calculation of the edge of the water accumulation area is expressed by the following formula:

[0062]

[0063] in, The edge of the waterlogged area. is the color feature map, is the characteristic diagram of water surface fluctuation, is the water edge feature map, is the corresponding image to be monitored.

[0064] In some embodiments, the ratio of the number of pixels in the water accumulation area to the total number of pixels in the monitored image is determined. If the ratio is greater than the water accumulation threshold, it is determined that water accumulation exists in the area. If the ratio is not greater than the water accumulation threshold, it is determined that no water accumulation exists in the area. That is to say, this scheme will only perform subsequent calculations of the water accumulation depth when it is determined that water accumulation exists in the area. If there is no water accumulation, there is no need to calculate the water accumulation depth.

[0065] Specifically, the formula for determining whether there is water accumulation is as follows:

[0066]

[0067] in, is the ratio of the number of pixels in the waterlogged area to the total number of pixels in the image to be monitored, is the number of pixels in the waterlogged area.

[0068] In some specific embodiments, the water surface change parameter is calculated as follows:

[0069]

[0070] in, is the water surface change parameter, W is the water accumulation area, x, y are the pixel coordinates in the water accumulation area, is the grayscale value of the image to be monitored at the pixel coordinate (x, y) of the t-th frame, is the grayscale value of the image to be monitored at the pixel coordinate (x, y) of the t-1th frame.

[0071] That is to say, the number of pixels and the positions of the pixels occupied by the waterlogged area in the same set of image sequences are the same, so the changes in the water surface can be represented by the grayscale values of the corresponding pixels in two adjacent frames.

[0072] In some specific embodiments, the water gauge reading fluctuation intensity parameter is calculated as follows:

[0073]

[0074] in, is the water gauge reading fluctuation intensity parameter, N is the time window size, is the average reading within the time window size, It is the historical reading of the electronic water gauge.

[0075] In some specific embodiments, the texture change parameter is calculated as follows:

[0076]

[0077] in, is the texture change parameter, W is the water accumulation area, x, y are the pixel coordinates in the water accumulation area, is the binary pattern of the image to be monitored at the pixel coordinate (x, y) of the t-th frame, is the binary pattern of the pixel coordinate (x, y) of the image to be monitored in the t-1th frame.

[0078] In some specific embodiments, the calculation formula of the water surface fluctuation factor is expressed as:

[0079]

[0080] in, is the water surface fluctuation factor, is the water surface variation parameter, is the water gauge reading fluctuation intensity parameter, is the texture variation parameter, is the weight parameter, Adjust according to experimental data.

[0081] Specifically, in this solution, when When it is larger, it means that the water surface is in a state of violent fluctuation, and the credibility of the electronic water gauge reading decreases.

[0082] In some specific embodiments, the formula for correcting the current reading of the electronic water gauge using the water surface fluctuation factor is expressed as:

[0083]

[0084] in, is the depth of water accumulation, is the current reading of the electronic water gauge, is the water surface fluctuation factor, is the dynamic adjustment coefficient.

[0085] That is to say, in this scheme, a fluctuation threshold is set ,when When the water surface fluctuates greatly, the coefficient is adjusted dynamically. Take a larger value to reduce the error, and vice versa.

[0086] In some specific embodiments, Kalman filtering is used to reduce the noise of the water depth. Specifically, the state equation of the Kalman filter is first calculated:

[0087]

[0088] in, is the water level change rate, which is estimated based on the historical water depth.

[0089] Calculate the observation equation of the Kalman filter:

[0090]

[0091] in, is the corrected water gauge measurement value, is the observation noise.

[0092] Calculate the Kalman gain and perform noise reduction on the water depth based on the Kalman gain:

[0093]

[0094]

[0095] in, is the forecast error covariance, is the measurement noise variance, is the Kalman gain, which is used to determine the weight during fusion. is the corrected water gauge measurement value, The depth of water accumulation.

[0096] Example 2

[0097] Based on the same concept, refer to Figure 5 This application also proposes an urban waterlogging monitoring device that integrates monitoring data with electronic water gauge data, including:

[0098] An acquisition module is used to obtain the current reading of the electronic water gauge and use a monitoring camera to obtain a plurality of image sequences including continuous images of the area to be monitored where the current electronic water gauge is located, and input the image sequences into a pre-trained water accumulation segmentation model to obtain the water accumulation area;

[0099] The fluctuation calculation module obtains the pixel grayscale change of the same pixel position in the waterlogged area of two adjacent images to be monitored as a water surface change parameter, obtains the standard deviation of the historical readings of the electronic water gauge as a water gauge reading fluctuation intensity parameter, obtains the pixel binary change of the same pixel position in the waterlogged area of two adjacent images to be monitored as a texture change parameter, and obtains the water surface fluctuation factor by weighted summation of the water surface change parameter, the water gauge reading fluctuation intensity parameter, and the texture change parameter. The water surface fluctuation factor represents the water surface fluctuation in the waterlogged area;

[0100] The correction module uses the water surface fluctuation factor to correct the current reading of the electronic water gauge to obtain the water depth.

[0101] Example 3

[0102] This embodiment also provides an electronic device, referring to Figure 6, includes a memory 404 and a processor 402, wherein the memory 404 stores a computer program, and the processor 402 is configured to run the computer program to perform the steps in any of the above method embodiments.

[0103] Specifically, the processor 402 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0104] Memory 404 may include a large-capacity memory 404 for data or instructions. By way of example, and not limitation, memory 404 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 404 may include removable or non-removable (or fixed) media. Where appropriate, memory 404 may be internal or external to the data processing device. In certain embodiments, memory 404 is non-volatile memory. In certain embodiments, memory 404 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. In appropriate circumstances, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM may be a fast page mode dynamic random access memory 404 (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0105] The memory 404 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 402 .

[0106] The processor 402 reads and executes computer program instructions stored in the memory 404 to implement any one of the urban waterlogging monitoring methods for fusing monitoring data with electronic water gauge data in the above embodiments.

[0107] Optionally, the electronic device may further include a transmission device 406 and an input / output device 408 , wherein the transmission device 406 is connected to the processor 402 , and the input / output device 408 is connected to the processor 402 .

[0108] Transmission device 406 can be used to receive or transmit data via a network. Specific examples of such networks may include wired or wireless networks provided by the electronic device's communications provider. In one embodiment, the transmission device includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, transmission device 406 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0109] The input and output device 408 is used to input or output information. In this embodiment, the input information may be the reading of the electronic water gauge, image sequence, etc., and the output information may be the water surface fluctuation, water depth, etc.

[0110] Optionally, in this embodiment, the processor 402 may be configured to execute the following steps through a computer program:

[0111] Obtain the current reading of the electronic water gauge, and use a surveillance camera to obtain a plurality of image sequences including continuous images of the area to be monitored where the current electronic water gauge is located, and input the image sequences into a pre-trained water accumulation segmentation model to obtain the water accumulation area;

[0112] Obtain the pixel grayscale change of the same pixel position in the waterlogged area of two adjacent images to be monitored as a water surface change parameter, obtain the standard deviation of the historical readings of the electronic water gauge as a water gauge reading fluctuation intensity parameter, obtain the pixel binary change of the same pixel position in the waterlogged area of two adjacent images to be monitored as a texture change parameter, and perform weighted summation of the water surface change parameter, the water gauge reading fluctuation intensity parameter, and the texture change parameter to obtain a water surface fluctuation factor, which represents the water surface fluctuation in the waterlogged area;

[0113] The water depth is obtained by correcting the current reading of the electronic water gauge using the water surface fluctuation factor.

[0114] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be repeated here.

[0115] In general, various embodiments may be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects of the invention may be implemented in hardware, while other aspects may be implemented in firmware or software executed by a controller, microprocessor, or other computing device, but the invention is not limited thereto. Although various aspects of the invention may be shown and described as block diagrams, flow charts, or using some other graphical representation, it should be understood that, as non-limiting examples, the blocks, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or a controller or other computing device, or some combination thereof.

[0116] The embodiments of the present invention may be implemented by computer software that is executable by a data processor of a mobile device, such as in a processor entity, or by hardware, or by a combination of software and hardware. Computer software or programs (also referred to as program products) including software routines, applets and / or macros may be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. A computer program product may include one or more computer executable components that are configured to perform an embodiment when the program is run. One or more computer executable components may be at least one software code or a portion thereof. In addition, it should be noted at this point that, for example, Figure 6 Any block of the logic flow in the program may represent program steps, or interconnected logic circuits, blocks and functions, or a combination of program steps and logic circuits, blocks and functions. The software may be stored on physical media such as memory chips or memory blocks implemented within the processor, magnetic media such as hard disks or floppy disks, and optical media such as, for example, DVDs and their data variants, CDs, etc. Physical media are non-transitory media.

[0117] Those skilled in the art should understand that the technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0118] The above embodiments merely illustrate several embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for monitoring urban waterlogging by integrating monitoring data with electronic water gauge data, characterized in that: The following steps are involved: Obtain the current reading of the electronic water gauge, and use a surveillance camera to obtain a plurality of image sequences including continuous images of the area to be monitored where the current electronic water gauge is located, and input the image sequences into a pre-trained water accumulation segmentation model to obtain the water accumulation area; Obtain the pixel grayscale change of the same pixel position in the waterlogged area of two adjacent images to be monitored as a water surface change parameter, obtain the standard deviation of the historical readings of the electronic water gauge as a water gauge reading fluctuation intensity parameter, obtain the pixel binary change of the same pixel position in the waterlogged area of two adjacent images to be monitored as a texture change parameter, and perform weighted summation of the water surface change parameter, the water gauge reading fluctuation intensity parameter, and the texture change parameter to obtain a water surface fluctuation factor, which represents the water surface fluctuation in the waterlogged area; The water depth is obtained by correcting the current reading of the electronic water gauge using the water surface fluctuation factor.

2. The urban waterlogging monitoring method according to claim 1, wherein the monitoring data is integrated with the electronic water gauge data, When the horizontal distance between the monitoring camera and the electronic water level gauge is less than the distance threshold, and the angle between the line of sight direction vector of the monitoring camera and the direction vector of the electronic water level gauge relative to the monitoring camera is less than the angle threshold, the monitoring camera is used to obtain multiple continuous images to be monitored including the area where the current electronic water level gauge is located.

3. The urban waterlogging monitoring method according to claim 1, wherein the monitoring data is integrated with the electronic water gauge data, The water accumulation segmentation model includes an encoding module, a decoding module and a timing analysis module. The encoding module is used to extract the color feature map, water accumulation surface fluctuation feature map and water accumulation edge feature map of each image to be monitored, and superimpose the color feature map, water accumulation edge feature map and water accumulation texture feature map of each image to be monitored to obtain a comprehensive feature map. The decoding module decodes the comprehensive feature map of each image to be monitored and inputs all decoded comprehensive feature maps into the timing analysis module. The timing analysis module performs a fusion analysis on the timing information of each comprehensive feature map to obtain the water accumulation area.

4. The urban waterlogging monitoring method according to claim 3, wherein: The encoding module is composed of multiple encoding units connected in series, and each encoding unit includes an encoder, a channel attention mechanism and a void convolution layer.

5. The urban waterlogging monitoring method of integrating monitoring data with electronic water gauge data according to claim 1 is characterized in that: The calculation method of the water surface change parameter is as follows: in, is the water surface change parameter, W is the water accumulation area, x, y are the pixel coordinates in the water accumulation area, is the grayscale value of the image to be monitored at the pixel coordinate (x, y) of the t-th frame, is the grayscale value of the image to be monitored at the pixel coordinate (x, y) of the t-1th frame; The calculation method of the water gauge reading fluctuation intensity parameter is as follows: in, is the water gauge reading fluctuation intensity parameter, N is the time window size, is the average reading within the time window size, It is the historical reading of the electronic water gauge; The texture change parameters are calculated as follows: in, is the texture change parameter, W is the water accumulation area, x, y are the pixel coordinates in the water accumulation area, is the binary pattern of the image to be monitored at the pixel coordinate (x, y) of the t-th frame, is the binary pattern of the pixel coordinate (x, y) of the image to be monitored in the t-1th frame.

6. The urban waterlogging monitoring method of integrating monitoring data with electronic water gauge data according to claim 1 is characterized in that: The formula for correcting the current reading of the electronic water gauge using the water surface fluctuation factor is expressed as follows: in, is the depth of water accumulation, is the current reading of the electronic water gauge, is the water surface fluctuation factor, is the dynamic adjustment coefficient.

7. An urban waterlogging monitoring device that integrates monitoring data with electronic water gauge data, characterized in that: include: An acquisition module is used to obtain the current reading of the electronic water gauge and use a monitoring camera to obtain multiple continuous images to be monitored, including the electronic water gauge, to form an image sequence, and input the image sequence into a pre-trained water accumulation segmentation model to obtain the water accumulation area; A fluctuation calculation module, which uses the grayscale change of pixels of two adjacent images to be monitored at relative positions within the waterlogged area as a water surface change parameter, calculates the standard deviation of historical electronic water gauge readings as a water gauge reading fluctuation intensity parameter, and uses the binary value change of pixels of two adjacent images to be monitored at relative positions within the waterlogged area as a texture change parameter. The water surface change parameter, the water gauge reading fluctuation intensity parameter, and the texture change parameter are weightedly summed to form a water surface fluctuation factor, which represents the water surface fluctuation in the waterlogged area. The correction module uses the water surface fluctuation factor to correct the current reading of the electronic water gauge to obtain the water depth.

8. An electronic device comprising a memory and a processor, characterized in that: The memory stores a computer program, and the processor is configured to run the computer program to execute the urban waterlogging monitoring method of fusing monitoring data with electronic water gauge data as described in any one of claims 1 to 6.

9. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which includes a program code for controlling a process to execute a process. When the program code is executed by a processor, an urban waterlogging monitoring method that integrates monitoring data and electronic water gauge data as described in any one of claims 1 to 6 is implemented.

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