A water condition image processing method and system based on Beidou short message

Abstract

The present application discloses a method for processing water condition images based on Beidou short messages, and a data processing technology, including: obtaining multiple reference images of the target water area under specified environmental conditions in advance, identifying and storing the water area reference boundaries in the reference images; obtaining the current environmental conditions, collecting the water condition images of the target water area; extracting the water condition boundaries from the water condition images; extracting the multi-channel features of the boundary data, and mapping the current environmental conditions to weight values ​​according to the configured mapping table; weighted splicing of the extracted multi-channel features based on the weight values ​​to obtain a local water condition compressed image; performing image encoding processing on the compressed image, and transmitting the encoding processing results using the Beidou spectrum step transmission method. The present application proposes a method for processing water condition data of Beidou short message data in emergency scenarios to improve the efficiency of water condition image processing and transmission.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for processing water condition images based on Beidou short messages. Background Art

[0002] With the application research and technological innovation of new technologies such as the Internet of Things, Beidou, 5G communication and pattern recognition in the field of water telemetry, more functions and monitoring methods can be added to the water telemetry system. In order to cope with the risk that all or part of the water data of the control station cannot be delivered to the dispatching station in time when sudden extreme events such as geological disasters and network failures occur, it is necessary to study the multi-site emergency synchronous transmission system based on Beidou technology.

[0003] In order to meet the needs of real-time data services in existing technologies, it is generally necessary to compress and transmit remote sensing image data. Typical image compression algorithms include JPEG and JPEG2000, which result in high compression losses. In recent years, in pursuit of higher image compression ratios and lower compression losses, artificial intelligence methods such as deep convolutional neural networks, residual networks, and adversarial networks have been introduced into image compression algorithms. However, they rely on a large amount of training sample data and GPU inference environments, and are not suitable for Beidou short message data transmission in emergency scenarios such as power outages and network outages. Summary of the invention

[0004] The embodiments of the present application provide a water condition image processing method and system based on Beidou short messages, which are used to propose a water condition data processing method for Beidou short message data in emergency scenarios. On the one hand, it improves the efficiency of image processing. On the other hand, in some specific examples, it can focus on presenting water condition changes after remote data is restored, thereby improving user recognition and understanding efficiency.

[0005] The present application embodiment proposes a water condition image processing method based on Beidou short message, comprising the following steps:

[0006] Acquire multiple reference images of the target water area under specified environmental conditions in advance, identify the water area reference boundary in the reference images, and store the acquired multiple reference images and the water area reference boundary locally and remotely;

[0007] Acquire current environmental conditions and collect water condition images of the target water area;

[0008] Extracting a water regime boundary from the water regime image, the water regime boundary being a boundary contained within a specified pixel distance outside the identified water area boundary of the water regime image;

[0009] Extracting multi-channel features of the boundary data, and mapping the current environmental condition to a weight value according to a configured mapping table, wherein the mapping table contains a weight mapping relationship between the specified environmental condition and possible environmental conditions;

[0010] Based on the weight values, weighted splicing is performed on the extracted multi-channel features to obtain a local water condition compressed image;

[0011] Performing image coding processing on the compressed image;

[0012] The result of the encoding processing is transmitted using the Beidou spectrum step transmission method, so that the obtained data can be decoded at a remote location and the water situation data can be restored based on the stored multiple reference images and water reference boundaries.

[0013] Optionally, identifying a water reference boundary in the reference image includes:

[0014] Determining the image range where the water area representative pixel is located according to the pixel histogram of the reference image, and converting the reference image into a grayscale image;

[0015] De-noising the grayscale image, and calculating the gradient of the pre-processed grayscale image using a Sobel operator;

[0016] The water area reference boundary is determined according to the gradient calculation result and the image range of the water area representative pixels.

[0017] Optionally, extracting a water regime boundary from the water regime image includes:

[0018] Placing the reference image and the water condition image in the same coordinate system;

[0019] Determine a local area within the water area reference boundary in the water condition image as a water condition representative area, and determine a representative water condition pixel range of the water condition representative area, so as to use a Sobel operator to determine the water area boundary in the water condition image again;

[0020] Expanding a specified pixel distance outside the water area boundary as the water condition boundary; and

[0021] Change the transparency of the sub-area between the water area boundary and the water regime boundary.

[0022] Optionally, weighted splicing of the extracted multi-channel features based on the weight values ​​to obtain a local water condition compressed image includes:

[0023] According to the configured mapping table, determine the weight value of the mapping under the current environmental conditions;

[0024] The extracted multi-channel features are weighted according to the weight values ​​to satisfy:

[0025]

[0026] Among them, H represents the weighted image feature, S represents the pixel area of ​​the water boundary, represents the pixel area within the water boundary, Represent the weight values ​​of red, green and blue channel features respectively, Represents the transparency weight value, R, G, B, A represent the characteristic values ​​of red, green, blue and transparent channels respectively, and C is the compensation amount.

[0027] Optionally, decoding based on the acquired data at a remote location and restoring water condition data based on the stored multiple reference images and water reference boundaries includes:

[0028] Decoding and dequantizing the acquired data at a remote location to obtain a mapped local image of the water situation;

[0029] Determining a target reference image based on the multiple reference images and the water reference boundary;

[0030] The mapped local water condition image is filled into the target reference image to restore the water condition data.

[0031] Optionally, determining the target reference image based on the multiple reference images and the water reference boundary includes:

[0032] Determine a sub-region between a water regime boundary and a water area boundary in the water regime local image according to transparency;

[0033] Determine sub-regions of the same size from outside the water reference boundary of the plurality of reference images according to a set step size, and calculate the similarity between the sub-regions;

[0034] The reference image containing the highest sub-region similarity is used as the target reference image.

[0035] Optionally, filling the mapped local water condition image into the target reference image to restore the water condition data includes:

[0036] Based on the area in the target reference image, the sub-area in the water situation local image is superimposed and the image clarity is calculated;

[0037] When the clarity is lower than the clarity threshold, local adjustment is performed on the sub-region so that the clarity of the image calculated after adjustment meets the clarity threshold, and the mapped water condition local image is filled into the target reference image at the current position to restore the water condition data.

[0038] Optionally, also include:

[0039] For the restored water condition data, based on the target reference image, the image area between the water area reference boundary and the water area boundary of the water condition partial image is flashed or highlighted.

[0040] An embodiment of the present application also proposes a water condition image processing system based on Beidou short messages, including a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the water condition image processing method based on Beidou short messages as described above are implemented.

[0041] The embodiment of the present application proposes a method for processing water condition data of Beidou short message data in an emergency scenario. On the one hand, it improves the efficiency of image processing. On the other hand, in some specific examples, it can focus on presenting water condition changes after remote data is restored, thereby improving user identification and understanding efficiency.

[0042] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0044] Figure 1 The following is a schematic diagram of the basic process of the water condition image processing method based on Beidou short messages in this embodiment. DETAILED DESCRIPTION

[0045] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0046] The present application embodiment proposes a water condition image processing method based on Beidou short message, comprising the following steps:

[0047] In step S101, multiple reference images of the target water area are acquired in advance under specified environmental conditions, the water area reference boundary in the reference image is identified, and the acquired multiple reference images and the water area reference boundary are stored locally and remotely. In some specific examples, both the local and remote centralized control stores multiple reference images of the target water area acquired under specified environmental conditions and their corresponding boundaries, wherein the specified environmental conditions may be conditions with good lighting conditions, and the multiple reference images may be reference images of the vicinity of the water area acquired by the monitoring equipment at specified multiple angles, such as the monitoring equipment of a power station or a control station.

[0048] In step S102, the current environmental conditions are acquired, and the water condition image of the target water area is collected. Accordingly, the same monitoring device can be used to collect images of the monitored water area according to the set image collection parameters.

[0049] In step S103, a water condition boundary is extracted from the water condition image, wherein the water condition boundary is a boundary contained within a specified pixel distance outside the identified water area boundary of the water condition image. Specifically, in the embodiment of the present application, by identifying the water area boundary and the water condition boundary, image processing and synthesis can be performed at a distance based on the two boundaries in subsequent examples.

[0050] In step S104, the multi-channel features of the boundary data are extracted, and the current environmental conditions are mapped to weight values ​​according to a configured mapping table, wherein the mapping table contains the weight mapping relationship between the specified environmental conditions and possible environmental conditions. In some embodiments, the mapping table can be configured in advance according to the conditions of the possible acquisition environment, for example, the specified environmental condition is a good lighting condition, and the image channel weight value can be mapped according to the actual acquisition lighting condition, so as to achieve a balance between the water condition image and the reference image after mapping. Wavelet transform and other methods can be used to extract the multi-channel features of the boundary data.

[0051] In step S105, the extracted multi-channel features are weighted spliced ​​based on the weight values ​​to obtain a local water condition compressed image. In some embodiments, the weighted spliced ​​multi-channel features can be further compressed to obtain a local water condition compressed image. In the present application, by only extracting image features within the water condition boundary including the water area boundary, the subsequent encoding amount can be reduced compared to extracting features of the entire image, thereby reducing the amount of data that needs to be transmitted.

[0052] In step S106, the compressed image is coded, and the coding result is transmitted using the Beidou spectrum step transmission method, so that the obtained data can be decoded at a remote location and the water condition data can be restored based on the stored multiple reference images and water reference boundaries.

[0053] The embodiment of the present application proposes a method for processing water condition data of Beidou short message data in an emergency scenario. On the one hand, it improves the efficiency of image processing. On the other hand, in some specific examples, it can focus on presenting water condition changes after remote data is restored, thereby improving user identification and understanding efficiency.

[0054] In some embodiments, identifying a water reference boundary in the reference image comprises:

[0055] The image range where the water area representative pixels are located is determined according to the pixel histogram of the reference image, and the reference image is converted into a grayscale image.

[0056] The grayscale image is denoised, and the gradient of the pre-processed grayscale image is calculated using the Sobel operator. In the specific example, the boundary range of the reference image is generally clear, so there is no need to have a high precision requirement for boundary detection. In the embodiment of the present application, the Sobel operator is used to detect the edge based on the phenomenon that the weighted difference of the grayscale of the upper and lower and left and right neighboring points of the pixel reaches an extreme value at the edge. It has a smoothing effect on noise and provides more accurate edge direction information.

[0057] The water area reference boundary is determined according to the gradient calculation result and the image range of the water area representative pixels.

[0058] In some embodiments, extracting a water regime boundary from the water regime image includes:

[0059] The reference image and the water condition image are placed in the same coordinate system. In some examples, they are placed in the same coordinate system without complete alignment, so as to roughly determine the position of the water area pixels.

[0060] The local area within the water area reference boundary in the water condition image is determined as the water condition representative area, and the representative water condition pixel range of the water condition representative area is determined, so as to use the Sobel operator to determine the water area boundary in the water condition image again. For the water condition image collected later, the aforementioned Sobel operator is still used to determine the water area boundary in the water condition image again.

[0061] A specified pixel distance is expanded outside the water area boundary as the water condition boundary; and, for example, in some examples, a suitable specified pixel distance for expansion can be determined based on the area ratio between the water area boundary and the remaining boundary, so as to extract the water condition boundary as the water condition image to be transmitted.

[0062] The transparency of the sub-region between the water area boundary and the water condition boundary is changed. In some examples, the transparency is changed. For example, the transparency of the sub-region between the water area boundary and the water condition boundary can be adjusted to 50%-70%, so as to perform image restoration in subsequent processing.

[0063] In some embodiments, weighted splicing of the extracted multi-channel features based on the weight values ​​to obtain a local water condition compressed image includes:

[0064] According to the configured mapping table, determine the weight value of the mapping under the current environmental conditions;

[0065] The extracted multi-channel features are weighted according to the weight values ​​to satisfy:

[0066]

[0067] Among them, H represents the weighted image feature, S represents the pixel area of ​​the water boundary, represents the pixel area within the water boundary, Represent the weight values ​​of red, green and blue channel features respectively, Represents the transparency feature weight value, R, G, B, A represent the feature values ​​of red, green, blue, and transparent channels respectively, and C is the compensation amount. The embodiment of the present application adopts RGBA image channel features and completes weight mapping based on a mapping table. In a specific water condition image acquisition environment, due to different environmental conditions, even under the same acquisition parameters, the image may have different brightness and contrast, etc. On the one hand, the image can be compressed through mapping, and on the other hand, the color of the acquired image can be balanced based on the weight value. In some specific examples, the weighted spliced ​​image features can also be compressed again to further reduce the amount of data required to be sent.

[0068] In some embodiments, decoding based on the acquired data at a remote location and recovering water condition data based on the stored multiple reference images and water reference boundaries includes:

[0069] The obtained data is decoded and dequantized at a remote location to obtain a mapped local image of the water situation. For example, at a remote centralized control center, a local image of the water situation including the water situation boundary is obtained after decoding and dequantization according to the mapping table.

[0070] Based on the multiple reference images and the water area reference boundary, a target reference image is determined, that is, a reference image that best matches the local water situation image can be selected from the multiple reference images.

[0071] The mapped local water condition image is filled into the target reference image to restore the water condition data.

[0072] In some embodiments, determining the target reference image based on the multiple reference images and the water reference boundary includes:

[0073] A sub-region between the water condition boundary and the water area boundary in the water condition partial image is determined according to the transparency, that is, a partial region including transparency.

[0074] Sub-regions of the same size are determined from outside the water reference boundary of the multiple reference images according to the set step size, and the similarity between the sub-regions is calculated. For example, in some examples, a partial region containing transparency can be used as a selection area, so that sub-regions of the same size are selected from the multiple reference images, and the similarity between the selected sub-region and the sub-region containing transparency is calculated.

[0075] The reference image containing the highest sub-region similarity is used as the target reference image.

[0076] In some embodiments, filling the mapped water condition partial image into the target reference image to restore the water condition data includes:

[0077] Based on the area in the target reference image and the sub-area in the local water condition image, the image clarity is superimposed and calculated. When the clarity is lower than the clarity threshold, the sub-area is locally adjusted so that the clarity of the image calculated after adjustment meets the clarity threshold. The reference image with the highest sub-area similarity is not necessarily the best image restoration position. In a specific example, local fine-tuning can be performed based on the sub-area position so that the superimposed image maintains a relatively high clarity. The mapped local water condition image is filled into the target reference image at the current position to restore the water condition data. In some embodiments, if the clarity requirement cannot be met, local stretching and scaling can be performed based on the sub-area containing transparency to meet the clarity requirement, and then the mapped local water condition image is filled into the target reference image.

[0078] In some embodiments, the method further includes: for the restored water condition data, based on the target reference image, flashing or highlighting the image area between the water reference boundary and the water boundary of the water condition partial image. By flashing or highlighting, the user can be shown only the relevant water conditions and possible water condition danger areas, thereby improving the user's recognition and understanding efficiency.

[0079] An embodiment of the present application also proposes a water condition image processing system based on Beidou short messages, including a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the water condition image processing method based on Beidou short messages as described above are implemented.

[0080] In addition, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present disclosure with equivalent elements, modifications, omissions, combinations (e.g., various embodiments intersecting schemes), adaptations or changes. It is not limited to the examples described in this specification or during the implementation of this application, and its examples will be interpreted as non-exclusive.

[0081] The above description is intended to be illustrative rather than restrictive. For example, the above examples (or one or more of them) can be used in combination with each other. For example, those of ordinary skill in the art may use other embodiments when reading the above description.

[0082] The above embodiments are merely exemplary embodiments of the present disclosure. Those skilled in the art may make various modifications or equivalent substitutions to the present invention within the essence and protection scope of the present disclosure, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present invention.

Claims

1. A water condition image processing method based on Beidou short message, characterized in that: The steps include: Acquire multiple reference images of the target water area under specified environmental conditions in advance, identify the water area reference boundary in the reference images, and store the acquired multiple reference images and the water area reference boundary locally and remotely; Acquire current environmental conditions and collect water condition images of the target water area; Extracting a water regime boundary from the water regime image, the water regime boundary being a boundary contained within a specified pixel distance outside the identified water area boundary of the water regime image; Extracting multi-channel features of the water regime boundary, and mapping the current environmental condition to a weight value according to a configured mapping table, wherein the mapping table contains a weight mapping relationship between the specified environmental condition and possible environmental conditions; Based on the weight values, weighted splicing is performed on the extracted multi-channel features to obtain a local water condition compressed image; The compressed image is subjected to image coding processing, and the coding result is transmitted using the Beidou spectrum stepping transmission method, so as to decode the obtained data at a remote location and restore the water condition data based on the stored multiple reference images and water reference boundaries.

2. A water condition image processing method based on Beidou short message as claimed in claim 1, characterized in that: Identifying a water reference boundary in the reference image includes: Determining the image range where the water area representative pixel is located according to the pixel histogram of the reference image, and converting the reference image into a grayscale image; De-noising the grayscale image, and calculating the gradient of the pre-processed grayscale image using a Sobel operator; The water area reference boundary is determined according to the result of gradient calculation and the image range of the representative pixels of the water area.

3. A water condition image processing method based on Beidou short message as claimed in claim 2, characterized in that: Extracting the water regime boundary from the water regime image comprises: Placing the reference image and the water condition image in the same coordinate system; Determine a local area within the water area reference boundary in the water condition image as a water condition representative area, and determine a representative water condition pixel range of the water condition representative area, so as to determine the water area boundary in the water condition image by using a Sobel operator; Expanding a specified pixel distance outside the water area boundary as the water condition boundary; and Change the transparency of the sub-area between the water area boundary and the water regime boundary.

4. A method for processing water condition images based on Beidou short messages as claimed in claim 3, characterized in that: The weighted splicing of the extracted multi-channel features based on the weight value to obtain a local water condition compressed image includes: According to the configured mapping table, determine the weight value of the mapping under the current environmental conditions; The extracted multi-channel features are weighted according to the weight values ​​to satisfy: Among them, H represents the weighted image feature, S represents the pixel area of ​​the water boundary, represents the pixel area within the water boundary, Represent the weight values ​​of red, green and blue channel features respectively, Represents the transparency weight value, R, G, B, A represent the characteristic values ​​of red, green, blue and transparent channels respectively, and C is the compensation amount.

5. A method for processing water condition images based on Beidou short messages as claimed in claim 1, characterized in that: Decoding the acquired data at a remote location and restoring the water situation data based on the stored multiple reference images and water reference boundaries include: Decoding and dequantizing the acquired data at a remote location to obtain a mapped local image of the water situation; Determining a target reference image based on the multiple reference images and the water reference boundary; The mapped local water condition image is filled into the target reference image to restore the water condition data.

6. A method for processing water condition images based on Beidou short messages as claimed in claim 5, characterized in that: Based on the multiple reference images and the water reference boundary, determining the target reference image includes: Determine a sub-region between a water regime boundary and a water area boundary in the water regime local image according to transparency; Determine sub-regions of the same size from outside the water reference boundary of the plurality of reference images according to a set step size, and calculate the similarity between the sub-regions; The reference image containing the highest sub-region similarity is used as the target reference image.

7. A method for processing water condition images based on Beidou short messages as claimed in claim 6, characterized in that: Filling the mapped water condition local image into the target reference image to restore the water condition data includes: Based on the area in the target reference image, the sub-area in the water situation local image is superimposed and the image clarity is calculated; When the clarity is lower than the clarity threshold, local adjustment is performed on the sub-region so that the clarity of the image calculated after adjustment meets the clarity threshold, and the mapped water condition local image is filled into the target reference image at the current position to restore the water condition data.

8. A method for processing water condition images based on Beidou short messages as claimed in claim 6, characterized in that: Also includes: For the restored water condition data, based on the target reference image, the image area between the water area reference boundary and the water area boundary of the water condition partial image is flashed or highlighted.

9. A water condition image processing system based on Beidou short message, characterized in that: It comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the water condition image processing method based on Beidou short message are implemented as described in any one of claims 1 to 8.

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