A method, device and electronic device for predicting blue algae bloom

Abstract

The present invention provides a method, device and electronic device for predicting cyanobacteria blooms. The scheme first acquires a captured image of a target water surface, and then determines whether a peak value of a green light band of the captured image is greater than a first preset value. If the peak value is greater than the first preset value, it indicates that there is a possibility of an outbreak of cyanobacteria blooms on the target water surface, thereby directly analyzing whether there is a possibility of an outbreak of cyanobacteria blooms in the target water area based on the image of the water surface.

Description

Technical Field

[0001] The invention relates to the technical field of water quality detection, and in particular to a method, a device and an electronic device for predicting cyanobacteria bloom. Background Art

[0002] In the context of the growing development of citizen science, digital cameras and mobile phones have been successfully used in surface water environment monitoring. The outbreak of cyanobacteria is one of the important contents of water environment monitoring. When cyanobacteria break out, a series of physical properties such as water temperature, chromaticity and transparency change, which in turn causes changes in the water reflection spectrum characteristics. Its high chlorophyll a content makes the water reflection spectrum have low reflectivity in the visible light band, but significantly increases reflectivity in the near-infrared band. However, digital photos only contain digital values ​​in the visible light band, and it is impossible to use the near-infrared band to identify cyanobacteria blooms. Therefore, a new cyanobacteria bloom identification method suitable for digital photos is of great significance. Summary of the invention

[0003] In view of this, embodiments of the present invention provide a method, device and electronic device for predicting a cyanobacteria bloom, so as to predict whether a cyanobacteria bloom may occur in a target water area through image photos.

[0004] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0005] A method for predicting cyanobacteria blooms, comprising:

[0006] Acquire a collection image of the target water surface;

[0007] It is determined whether the peak value of the green light band of the collected image is greater than a first preset value. If it is greater than the first preset value, it indicates that there is a possibility of a blue algae bloom on the target water surface.

[0008] Optionally, in the above-mentioned cyanobacteria bloom prediction method, judging whether the peak value of the green light band of the collected image is greater than a first preset value includes:

[0009] The green light baseline value of the collected image is calculated to determine whether the green light baseline value is greater than a second preset value. If so, it indicates whether the peak value of the green light band of the collected image is greater than a first preset value.

[0010] Optionally, in the above-mentioned cyanobacteria bloom prediction method, the green light baseline value of the collected image is calculated as follows:

[0011] Calculate the green light baseline value of the acquired image based on the formula NGBL=(G-α*(R+B)) / (G+α*(R+B));

[0012] Among them, the NGBL is the green light baseline value, the G is the digital value of the green light band of the captured image, the R is the digital value of the red light band of the captured image, the B is the digital value of the blue light band of the captured image, and α is a preset coefficient.

[0013] Optionally, in the above-mentioned cyanobacteria bloom prediction method, before acquiring the collected image of the target water surface, the method further includes:

[0014] Setting exposure parameters and sensitivity of image acquisition equipment based on environmental data;

[0015] An image of the target water surface is captured based on the exposure parameter and the sensitivity.

[0016] Optionally, in the above-mentioned cyanobacteria bloom prediction method, before collecting images of the target water surface based on the exposure parameters and the sensitivity, the method further includes:

[0017] The photographing angle of the image acquisition device is controlled to be adjusted to a preset photographing angle.

[0018] Optionally, in the above-mentioned cyanobacteria bloom prediction method, the preset shooting angle includes:

[0019] The observation azimuth angle is within a first preset range, and the observation zenith angle is within a second preset range.

[0020] Optionally, in the above-mentioned cyanobacteria bloom prediction method, the first preset range is [130°, 140°], and the second preset range is [30°, 40°].

[0021] Optionally, in the above-mentioned cyanobacteria bloom prediction method, judging whether the peak value of the green light band of the collected image is greater than a first preset value includes:

[0022] Identify the collected image and determine a target area in the collected image, where the target area is an area with the lowest interference in the collected image;

[0023] It is determined whether the peak value of the green light band of the target area is greater than a first preset value.

[0024] A cyanobacteria bloom prediction device, comprising:

[0025] An image acquisition unit, used to acquire an image of a target water surface;

[0026] The data processing unit is used to determine whether the peak value of the green light band of the collected image is greater than a first preset value. If it is greater than the first preset value, it indicates that there is a possibility of a blue algae bloom on the target water surface.

[0027] An electronic device, comprising:

[0028] A memory and a processor, wherein the memory stores a plurality of instructions, and the instructions are suitable for the processor to load to execute the steps in any one of the above-mentioned cyanobacteria bloom prediction methods.

[0029] Based on the above technical solution, the cyanobacteria bloom prediction solution provided by the embodiment of the present invention first obtains a captured image of the target water surface, and then determines whether the peak value of the green light band of the captured image is greater than a first preset value. If it is greater than the first preset value, it indicates that there is a possibility of an outbreak of cyanobacteria bloom on the target water surface, thereby directly analyzing whether there is a possibility of an outbreak of cyanobacteria bloom in the target water area based on the image of the water surface. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0031] Figure 1 A schematic diagram of a process for predicting a cyanobacterial bloom disclosed in an embodiment of the present application;

[0032] Figure 2 This is a flow chart of a method for predicting cyanobacteria blooms disclosed in another embodiment of the present application;

[0033] Figure 3 This is a flow chart of a method for predicting cyanobacteria blooms disclosed in another embodiment of the present application;

[0034] Figure 4 This is a flow chart of a method for predicting cyanobacteria blooms disclosed in another embodiment of the present application;

[0035] Figure 5 A schematic diagram of an image acquisition scene disclosed in an embodiment of the present application;

[0036] Figure 6 This is a flow chart of a method for predicting cyanobacteria blooms disclosed in another embodiment of the present application;

[0037] Figure 7 This is a schematic diagram of the structure of the cyanobacteria bloom prediction device disclosed in the embodiment of the present application;

[0038] Figure 8 A schematic diagram of the structure of an electronic device disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] In order to be able to directly analyze whether there is a possibility of an outbreak of cyanobacterial bloom in a target water area based on an image of the water surface, the present application provides a cyanobacterial bloom prediction method. The method acquires image data of the water area by acquiring images of the water surface of the target water area, and then determines whether a peak value of a green light band of the acquired image is greater than a first preset value. When it is greater than the first preset value, it indicates that there is a possibility of an outbreak of cyanobacterial bloom in the target water area.

[0041] See also Figure 1 The present application discloses a method for predicting cyanobacterial blooms, which may include:

[0042] Step S101: Acquire a captured image of a target water surface.

[0043] In this solution, the captured image of the target water surface is an image of the target water surface obtained by photographing the target water surface of the target water area using an image acquisition device with image acquisition function such as a digital camera or a smart phone. The target water surface is the water surface of a certain area of ​​the target water area.

[0044] Step S102: determining whether the peak value of the green light band of the collected image is greater than a first preset value; if it is greater than the first preset value, it indicates that there is a possibility of a cyanobacteria bloom on the target water surface.

[0045] In the present solution, the acquired image is processed by a built-in processing module of a device terminal to which the present method is applied, so as to obtain a green light band peak value of the acquired image or a correlation value of the green light band peak value, and whether the green light band peak value is greater than a first preset value is determined by comparing the green light band peak value or the correlation value of the green light band peak value with a preset value. When the green light band peak value is greater than the first preset value, it indicates that there is a possibility of a blue-green algae bloom outbreak in the target water area. At this time, prompt information or related reports for characterizing the possibility of a blue-green algae bloom outbreak in the target water area can be output.

[0046] Since the green light baseline value is positively correlated with the green light band peak value in the image data, in this solution, the green light band peak value of the collected image can be measured by the green light baseline value of the collected image. Figure 2 , judging whether the peak value of the green light band of the collected image is greater than a first preset value, comprising:

[0047] Step S201: Calculate the green light baseline value of the acquired image.

[0048] The green light baseline value refers to the distance from the digital value of the green light band to the line connecting the red and blue bands, that is, NGBL, where NGBL = (G-α*(R+B)) / (G+α*(R+B)). Wherein, the NGBL is the green light baseline value, the G is the digital value of the green light band of the captured image, the R is the digital value of the red light band of the captured image, the B is the digital value of the blue light band of the captured image, and α is a preset coefficient. In this solution, the value of the preset coefficient α can be configured according to user needs. For example, in this solution, the value of α can be set to 0.5, and of course it can also be other values.

[0049] Step S202: determining whether the green light baseline value is greater than a second preset value, and if so, whether the peak value of the green light band of the acquired image is greater than a first preset value.

[0050] In the present embodiment, the green light baseline value associated with the green light band peak value is used to indirectly determine whether the green light band peak value is greater than a first preset value. When the green light baseline value is greater than a second preset value, it indicates that the green light band peak value of the acquired image is greater than the first preset value. The size of the second preset value can be set according to user needs. For example, in the present embodiment, the second preset value can be set to 0.25, that is, when NGBL>0.25, it indicates that the green light band peak value of the acquired image is greater than the first preset value, and there is a possibility of a blue-green algae bloom in the target water area.

[0051] That is, in this solution, the green light baseline value of the acquired image is calculated specifically as follows: the green light baseline value of the acquired image is calculated based on the formula NGBL=(G-α*(R+B)) / (G+α*(R+B)).

[0052] When photographing the target water surface, different exposure parameters and sensitivities used will affect the quality of the collected photos. Therefore, before acquiring the collected image of the target water surface in this solution, it is necessary to adjust the exposure parameters and sensitivity of the image acquisition device. Specifically, the adjustment includes:

[0053] Step S301: setting exposure parameters and sensitivity of an image acquisition device based on environmental data;

[0054] In this solution, when the image acquisition device is an image acquisition device with an automatic adjustment function for exposure parameters and sensitivity, the "setting the exposure parameters and sensitivity of the image acquisition device based on environmental data" may be an automatic configuration process for the exposure parameters and sensitivity of the image acquisition device. When the image acquisition device cannot automatically configure the exposure parameters and sensitivity, the process is a human-involved adjustment process. In this solution, the exposure parameters mainly refer to the exposure time. Different environmental parameters result in different exposure times.

[0055] Step S302: capturing an image of the target water surface based on the exposure parameters and sensitivity.

[0056] After the exposure parameters and sensitivity are configured, the image acquisition device takes a picture of the target water surface based on these parameters to obtain a captured image of the target water surface.

[0057] In addition to the exposure parameters and sensitivity, the shooting angle will also affect the shooting effect. In order to obtain high-quality images of the target water surface, refer to Figure 4 In this solution, before collecting images of the target water surface based on the exposure parameters and the sensitivity, it also includes:

[0058] Step S401: controlling the photographing angle of the image acquisition device to be adjusted to a preset photographing angle.

[0059] In this solution, the shooting angles that need to be adjusted include the observation azimuth angle and the observation zenith angle.

[0060] Regarding the observation azimuth angle, when shooting the target water surface, it is defined that when the camera of the image acquisition device faces the sun, the observation azimuth angle is 0 degrees, and when it faces away from the sun, the observation azimuth angle is 180 degrees. The optimal range of the observation azimuth angle of the image acquisition device is the first preset range, which is [130°, 140°], that is, the sun is at the upper left side or the upper right side of the image acquisition device.

[0061] For observation zenith angle, see Figure 5 , define the observation zenith angle of the image acquisition device (which can be a mobile phone) when the camera is vertically downward as 0 degrees, the observation zenith angle when the camera is horizontally forward as 90 degrees, and the optimal observation zenith angle is within the second preset range, and the second preset range is [30°, 40°]

[0062] In the technical solution disclosed in another embodiment of the present application, considering that flares, shadows, floating objects and other debris on the water surface may interfere with the analysis results, in this solution, in order to make the analysis results more reliable, in this solution, when judging whether the peak value of the green light band of the acquired image is greater than a preset value, only the area with the least interference in the acquired image may be analyzed, and other areas with flares, shadows, floating objects and other debris may not be analyzed. That is, in the above solution, judging whether the peak value of the green light band of the acquired image is greater than a preset value includes:

[0063] Step S601: Identify the collected image and determine a target area in the collected image, where the target area is an area with the lowest interference in the collected image.

[0064] In this solution, the image processing software pre-configured in the system can be used to divide the acquired image into multiple sub-images, and each sub-image can be identified and processed to determine whether the sub-image has interference such as flare, shadow, floating objects, etc., and the sub-images with interference factors such as flare, shadow, floating objects, etc. are filtered out, and the sub-images without interference factors such as flare, shadow, floating objects, etc. or with the smallest interference area are used as target areas. In this solution, the larger the area occupied by flare, shadow, floating objects in the sub-image, the higher the interference degree of the sub-image.

[0065] Step S602: Determine whether the peak value of the green light band of the target area is greater than a preset value.

[0066] In this embodiment, when the green light band peak value of the target area is greater than the preset value, it indicates that the green light band peak value of the acquired image is greater than the first preset value; when the green light band peak value of the target area is less than the preset value, it indicates that the green light band peak value of the acquired image is less than the first preset value.

[0067] In this embodiment, a cyanobacteria bloom prediction device is disclosed. For the specific working contents of each unit in the device, please refer to the contents of the above method embodiment.

[0068] The following is a description of a cyanobacteria bloom prediction device provided by an embodiment of the present invention. The cyanobacteria bloom prediction device described below and the cyanobacteria bloom prediction method described above can be referenced to each other.

[0069] For details, see Figure 7 The blue algae bloom prediction device may include:

[0070] An image acquisition unit A is used to acquire an image of a target water surface;

[0071] The data processing unit B is used to determine whether the peak value of the green light band of the collected image is greater than a preset value. If it is greater than the preset value, it indicates that there is a possibility of a blue algae bloom on the target water surface.

[0072] Corresponding to the above method, the present application discloses an electronic device, which may be a smart phone, a digital camera, or other image acquisition device with a camera function, and of course may also be other smart terminals such as a computer.

[0073] Figure 8 For a hardware structure diagram of an electronic device provided by an embodiment of the present invention, see Figure 8 As shown, it may include: at least one processor 100, at least one communication interface 200, at least one memory 300 and at least one communication bus 400;

[0074] In the embodiment of the present invention, the number of the processor 100, the communication interface 200, the memory 300, and the communication bus 400 is at least one, and the processor 100, the communication interface 200, and the memory 300 communicate with each other through the communication bus 400; obviously, Figure 8 The communication connections shown for the processor 100, the communication interface 200, the memory 300, and the communication bus 400 are merely optional;

[0075] Optionally, the communication interface 200 may be an interface of a communication module, such as an interface of a GSM module;

[0076] The processor 100 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0077] The memory 300 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0078] The processor 100 is specifically configured to:

[0079] Acquire a collection image of the target water surface;

[0080] It is determined whether the peak value of the green light band of the collected image is greater than a first preset value. If it is greater than the first preset value, it indicates that there is a possibility of a blue algae bloom on the target water surface.

[0081] Of course, the processor is also used to execute the various steps of the cyanobacteria bloom prediction method disclosed in the other embodiments of the present application.

[0082] For the convenience of description, the above system is described as being divided into various modules according to their functions. Of course, when implementing the present invention, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0083] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without creative work.

[0084] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0085] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0086] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0087] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting cyanobacterial blooms, characterized in that: include: Acquire a collection image of the target water surface; Determine whether the peak value of the green light band of the collected image is greater than a first preset value, and if it is greater than the first preset value, it indicates that there is a possibility of a blue algae bloom on the target water surface; Determining whether a peak value of a green light band of the collected image is greater than a first preset value includes: Calculating a green light baseline value of the collected image, and determining whether the green light baseline value is greater than a second preset value, and if so, indicating that a peak value of a green light band of the collected image is greater than a first preset value; The green light baseline value of the acquired image is calculated as follows: Calculate the green light baseline value of the acquired image based on the formula NGBL=(G-α*(R+B)) / (G+α*(R+B)); Wherein, the NGBL is a green light baseline value, the G is a digital value of a green light band of a captured image, the R is a digital value of a red light band of a captured image, the B is a digital value of a blue light band of a captured image, and α is a preset coefficient; Before acquiring the acquisition image of the target water surface, it also includes: Setting exposure parameters and sensitivity of image acquisition equipment based on environmental data; Capturing an image of the target water surface based on the exposure parameter and the sensitivity; Before collecting an image of the target water surface based on the exposure parameter and the sensitivity, the method further includes: The photographing angle of the image acquisition device is controlled to be adjusted to a preset photographing angle.

2. The method for predicting cyanobacterial blooms according to claim 1, characterized in that: The preset shooting angles include: The observation azimuth angle is within a first preset range, and the observation zenith angle is within a second preset range.

3. The method for predicting cyanobacterial blooms according to claim 2, characterized in that: The first preset range is [130°, 140°], and the second preset range is [30°, 40°].

4. The method for predicting cyanobacterial blooms according to claim 1, characterized in that: Determining whether a peak value of a green light band of the collected image is greater than a first preset value includes: Identify the collected image and determine a target area in the collected image, where the target area is an area with the lowest interference in the collected image; It is determined whether the peak value of the green light band of the target area is greater than a first preset value.

5. A cyanobacteria bloom prediction device, characterized in that: include: An image acquisition unit, used to acquire an image of a target water surface; A data processing unit, used for determining whether a peak value of a green light band of the collected image is greater than a first preset value, and if it is greater than the first preset value, indicating that there is a possibility of a blue algae bloom on the target water surface; Wherein, judging whether the peak value of the green light band of the collected image is greater than a first preset value includes: Calculating a green light baseline value of the collected image, and determining whether the green light baseline value is greater than a second preset value, and if so, indicating that a peak value of a green light band of the collected image is greater than a first preset value; The green light baseline value of the acquired image is calculated as follows: Calculate the green light baseline value of the acquired image based on the formula NGBL=(G-α*(R+B)) / (G+α*(R+B)); Wherein, the NGBL is a green light baseline value, the G is a digital value of a green light band of a captured image, the R is a digital value of a red light band of a captured image, the B is a digital value of a blue light band of a captured image, and α is a preset coefficient; Before acquiring the collected image of the target water surface, the blue algae bloom prediction device is also used to: Setting exposure parameters and sensitivity of image acquisition equipment based on environmental data; Capturing an image of the target water surface based on the exposure parameter and the sensitivity; Before collecting an image of the target water surface based on the exposure parameter and the sensitivity, the method further includes: The photographing angle of the image acquisition device is controlled to be adjusted to a preset photographing angle.

6. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores a plurality of instructions, and the instructions are suitable for the processor to load to execute the steps in the cyanobacteria bloom prediction method according to any one of claims 1 to 4.

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