Shellfish catching experimental device and detection method

Abstract

The invention discloses a shellfish catching experimental device and a detection method. The shellfish catching experimental device comprises a water tank, and a substrate layer and a water body layer are sequentially arranged in the water tank from bottom to top; the washing component is mounted outside the water tank and provided with a spray head facing the water tank; the first camera element is horizontally arranged at the top position of the water tank; a second image pickup element vertically arranged at a side position of the water tank; the light emitting component is assembled on one side wall of the water tank and emits light towards the water surface of the water tank; the photoelectric sensor is assembled on the side wall, opposite to the light emitting component, of the water tank and used for receiving the light signal scattered by the water flow in the water tank and converting the light signal into an electric signal; and the control unit communicates with the first camera element, the second camera element and the photoelectric sensor. The shellfish catching experimental device provided by the invention can be used for accurately acquiring the motion trail, the water turbidity and the landform change of the shellfish in the scouring process, and a theoretical basis is provided for actual scouring and shellfish catching.

Description

Technical Field

[0001] The invention belongs to the technical field of shellfish harvesting, and in particular relates to an improvement of a shellfish harvesting experimental device. Background Art

[0002] Among marine aquaculture products, shellfish occupy a pivotal position, accounting for about 70% of the total national aquatic output in recent years. At present, shellfish are harvested mainly through three methods: manual harvesting, fishing by fishing boats, and mechanical suction of shellfish. However, these traditional harvesting methods have exposed some significant problems when facing environmental protection and ecological sustainable development. Manual harvesting is time-consuming and labor-intensive, with low production efficiency and is easily affected by the rise and fall of sea water; although fishing by fishing boats can improve the harvesting efficiency to a certain extent, it depends on specific water environment conditions, such as a flat bottom and small waves, which has high environmental requirements. In addition, flexible fishing nets are easily deformed, affecting the effective harvesting of shellfish.

[0003] The mechanical method of sucking out shellfish poses a more serious ecological problem. This method indiscriminately sucks out large and small shellfish together, destroying the continuity of marine life, while causing a large amount of silt and impurities to be carried out, increasing the turbidity of the water. This method not only affects the transparency of the water body, but also changes the bottom topography, leading to the degradation of the ecological environment. The sucked out shellfish are suspended in the seawater, increasing the breakage rate of shellfish, and requiring additional screening and filtering processes, further increasing costs and labor intensity.

[0004] In view of this, a method of flushing shellfish was proposed, which mainly involves flushing shellfish out from those buried in the sand, suspending them in the water, and then collecting and spreading them. This method causes relatively little damage to shellfish, but the impact of this flushing method on the movement and behavior of shellfish, as well as the impact on the geomorphic environment changes around the ocean and the turbidity of the water body need further exploration and research.

[0005] The above information disclosed in the background technology is only used to increase the understanding of the background technology of the present application, and therefore, it may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0006] In view of the above-mentioned technical problems, the present invention proposes a shellfish harvesting experimental device and a detection method, which can not only effectively measure the method of harvesting shellfish by flushing, but also accurately detect the movement trajectory of shellfish, water turbidity and landform changes during the flushing process, thereby providing a theoretical basis for the actual use of flushing shellfish.

[0007] In order to achieve the above invention / design purpose, the present invention adopts the following technical solutions: A shellfish harvesting experimental device, comprising: A water tank, in which a bottom layer and a water layer are arranged in sequence from bottom to top, the bottom layer includes a soil layer and a sand layer, and shellfish are buried in the sand layer; A flushing component is installed outside the water tank and has a nozzle, the nozzle is directed toward the water tank and is used to flush the shellfish buried in the sand layer; A first camera element is horizontally arranged at the top of the water tank and is used to capture a video image from the top of the water tank downwards; A second camera element is vertically arranged at a side position of the water tank and is used to capture a video image from the side of the water tank; The photoelectric detection assembly includes a light emitting component, which is mounted on a side wall of the water tank and emits light toward the water surface of the water tank; The photoelectric sensor is mounted on the side wall of the water tank opposite to the light emitting component, and is used to receive the light signal scattered by the water flow in the water tank and convert the light signal into an electrical signal; The control unit communicates with the first imaging element, the second imaging element and the photoelectric sensor.

[0008] Compared with the prior art, the advantages and positive effects of the present invention are: The shellfish collecting experimental device proposed in the present invention achieves a high degree of simulation by setting up a water tank and arranging a bottom layer and a water layer inside it. It can simulate the scouring effect in a real tidal flat environment and accurately restore the behavior patterns of shellfish under natural conditions.

[0009] By combining the control unit with the image acquisition device and the edge detection algorithm dedicated to the shellfish image, the position, movement trajectory and behavior of the shellfish during the flushing process can be monitored and acquired in real time; The turbidity detection method based on optical scattering characteristics is introduced. By combining the light scattering intensity and the gray value comparison method of image processing, the fine detection of water turbidity in high turbidity environment is achieved. The system can capture and analyze the turbidity changes of water in the experiment in real time, and provide feedback to record experimental parameters.

[0010] The bottom sediment change monitoring function uses a high-speed camera and a watershed segmentation algorithm for the bottom sediment to capture the changes in the bottom sediment morphology after scouring in real time, providing reliable data support for the study of tidal flat bottom morphology.

[0011] After reading the specific embodiments of the present invention in conjunction with the accompanying drawings, other features and advantages of the present invention will become more clear. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0013] Figure 1 It is a structural schematic diagram of an embodiment of the shellfish harvesting experimental device proposed by the present invention; Figure 2 A flow chart of obtaining the movement trajectory of shellfishes by the shellfish harvesting experimental device proposed by the present invention; Figure 3 Flow chart of the shellfish harvesting experimental device proposed in the present invention for obtaining water turbidity through frame image grayscale; Figure 4 The shellfish harvesting experimental device proposed by the present invention obtains the turbidity of water through the cooperation of the light detection component; Figure 5 A flow chart of the shellfish harvesting experimental device proposed by the present invention acquiring the bottom landform changes through the second camera element; Figure 6 A flow chart of the shellfish harvesting experimental device proposed in the present invention acquiring the bottom landform changes through the first camera element; Figure 7 A schematic structural diagram of one implementation mode of a water tank of the shellfish harvesting experimental device proposed in the present invention.

[0014] In the figure, 100, water tank; 110, bottom layer; 111, soil layer; 112, sand layer; 120, water layer; 200, flushing component; 210, nozzle; 220, screw; 230, fixing frame; 240, water pipe; 300, first camera element; 400, second camera element; 510, light emitting component; 520, photoelectric sensor; 600, partition; 610, first tank body; 620, second tank body. DETAILED DESCRIPTION

[0015] 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.

[0016] In the description of the present invention, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore, should not be understood as a limitation on the present invention.

[0017] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. In the description of the implementation method, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0018] In some embodiments of the present application, a shellfish harvesting experimental device is proposed. The flushing experimental device can be used to simulate the flushing of shellfish in the sand layer 112 of seawater. It provides a rapid way to harvest shellfish, that is, flushing water through the flushing nozzle 210 to flush out the shellfish buried in the bottom of the water, and suspend the shellfish in the water to facilitate direct harvesting of shellfish.

[0019] By flushing the shellfish out of the bottom layer 110 before harvesting them, damage to the shellfish and destruction to the ocean bottom topography can be reduced.

[0020] The flushing experiment device is also equipped with a camera element, which uses a high-speed camera element to take pictures and record the flushing time period, and record and analyze the turbidity of the water, the movement trajectory of shellfish and the changes in the bottom soil, so as to detect and analyze the impact of flushing on shellfish, the impact of water turbidity and the impact of bottom soil, and provide theoretical guidance for the application of the flushing method of collecting shellfish and actual production.

[0021] In some embodiments of the present application, the flushing test device includes: The water tank 100 has a bottom layer 110 and a water layer 120 arranged in sequence from bottom to top in the water tank 100 . The bottom layer 110 includes a soil layer 111 and a sand layer 112 . Shellfish are buried in the sand layer 112 .

[0022] The soil layer 111 and the sand layer 112 are laid in sequence from bottom to top along the height direction of the water tank 100 .

[0023] The water layer 120 is located above the sand layer 112 . The water layer 120 is seawater, and the shellfish are mainly buried in the sand layer 112 .

[0024] The water tank 100 may be a water tank 100 with a length-to-width ratio of 8:1.

[0025] The bottom layer 110 and the water layer 120 laid inside the water tank 100 can achieve the effect of truly simulating the natural beach environment.

[0026] The flushing component 200 is installed outside the water tank 100 and has a nozzle 210. The nozzle 210 is directed toward the water tank 100 and is used to flush the shellfish buried in the sand layer 112.

[0027] In some embodiments of the present application, the flushing component 200 includes a water pipe 240, the nozzle 210 is connected to the end of the water pipe 240, a fixing frame 230 and a screw 220 spirally assembled into the fixing frame 230 are arranged above the nozzle 210, and the end of the screw 220 is connected to the water pipe 240.

[0028] During connection, the end of the screw rod 220 can be clamped on the water pipe 240 through a clamp.

[0029] By adjusting the height of the screw rod 220 up and down, the position of the water pipe 240 can be adjusted, and then the spray angle of the nozzle 210 can be adjusted.

[0030] When performing the flushing experiment, the flushing effect can be observed by adjusting the positions of the nozzle 210 .

[0031] The nozzle 210 angle can be flexibly adjusted, which can effectively increase the flushing radius and improve the flushing efficiency when flushing shellfish.

[0032] In some embodiments, a flow control valve is provided on the water pipe 240. By adjusting the flow control valve to change the water flow rate of the nozzle 210, the flushing effect of different water flow jet speeds on shellfish can be observed, so as to provide a basis for the actual flushing and collection of shellfish.

[0033] The first camera element 300 is horizontally arranged at the top of the water tank 100 , and is used to capture video images from the top of the water tank 100 downwards.

[0034] The first imaging element 300 is a first camera, which is arranged at the top of the water tank 100 and is used for capturing and recording image information viewed from the top of the water tank 100 in real time with high precision.

[0035] The second camera element 400 is vertically arranged at a side position of the water tank 100 , and is used to capture a video image from the side of the water tank 100 .

[0036] The second imaging element 400 is a second camera, which is arranged at the outer side of the water tank 100 and is used to capture and record the image information in the water tank 100 corresponding to the side in real time with high precision.

[0037] The photoelectric detection assembly includes a light emitting component 510, which is mounted on a side wall of the water tank 100 and emits light toward the water surface of the water tank 100; The photoelectric sensor 520 is mounted on the side wall of the water tank 100 opposite to the light emitting component 510, and is used to receive the light signal scattered by the water layer of the water tank 100, and convert the light signal into an electrical signal for output.

[0038] The photoelectric detection component is mainly used to detect the turbidity of the water layer.

[0039] The light emitting component 510 can be a component that can emit light, such as a lamp bead.

[0040] The emitted light is directed toward the water layer. After the light enters the water layer, it is scattered in the water layer. The scattered light is received by the photoelectric sensor 520 .

[0041] The intensity of the light signal received by the photoelectric sensor 520, that is, the light intensity signal, has different corresponding values ​​when the turbidity of the water body is different. The value of the light intensity signal can be reflected by the size of the electrical signal value converted from it.

[0042] When the turbidity of the water layer is relatively high, the light emitted by the light emitting component 510 is scattered more strongly when passing through the water layer, and the light intensity signal value received by the photoelectric sensor 520 is smaller.

[0043] When the turbidity of the water layer is relatively low, the light emitted by the light emitting component 510 is scattered weaker when passing through the water layer, and the light intensity signal received by the photoelectric sensor 520 is larger.

[0044] The change in turbidity of the water body can be determined based on the change in the light intensity signal received by the photoelectric sensor 520.

[0045] The control unit communicates with the first imaging element 300 , the second imaging element 400 and the photoelectric sensor 520 .

[0046] The control unit communicates with the first camera element 300 and the second camera element 400 and can be used to acquire video images of the first camera element 300 and the second camera element 400 in real time.

[0047] On the one hand, the movement paths and behavior patterns of shellfish during the flushing process can be intuitively observed through video, helping researchers analyze the ecological responses of shellfish during the flushing process.

[0048] The video also allows for intuitive observation of the rough changes in water turbidity and the impact on the water body during the shellfish flushing process; The video also provides a rough and intuitive understanding of the changes in the subgrade topography of the subgrade layer 110 and its impact on the subgrade layer 110 .

[0049] On the other hand, the control unit communicates with the first camera element 300 and the second camera element 400 and can also obtain the precise movement trajectory of the shellfish, the precise changes in the turbidity of the water body, and the precise changes in the bottom layer 110 by processing and analyzing the video images obtained from the first camera element 300 and the second camera element 400, thereby providing a more accurate analysis result for the impact of flushing shellfish and catching shellfish on the shellfish and the water layer 120 and the bottom layer 110.

[0050] In some embodiments of the present application, a detachable longitudinal partition 600 is provided in the water tank, and the water tank is separated into a first tank body 610 and a second tank body 620 by the longitudinal partition 600, and the first tank body 610 and the second tank body 620 have different widths.

[0051] When the partition 600 is installed in the water tank, the water tank is divided into a first tank body 610 and a second tank body 620; Divide the original width of the sink into two parts with different widths.

[0052] The partition 600 is detachable. After the partition 600 is removed, the width of the water trough is the widest. By installing or removing the partition 600, the water trough can form three trough bodies with different widths. Each trough body can be used for scouring experiments separately. The setting of the partition 600 can realize shellfish scouring experiments under trough bodies with three different widths. The experimental analysis of the effects of water flow on shellfish scouring height, turbidity changes and bottom topography changes can realize the simulation of a variety of different tidal flat fishing experimental environments.

[0053] In some embodiments of the present application, a method for obtaining the movement trajectory of shellfish using the above shellfish harvesting experimental device is proposed, comprising the following steps: Step 1: Use the second camera element 400 to collect videos of shellfish before, during and after flushing; By real-time recording through the second camera element 400, video footage before, after and during the flushing of shellfish can be obtained.

[0054] Step 2: Based on the captured video, divide the video into multiple continuous frame images at different times.

[0055] Video segmentation can be achieved through frame decomposition technology. The frame extraction function can be realized by using some script commands in the video processing library. Multiple continuous frame images correspond to different moments of the video.

[0056] Step 3: Preprocess multiple consecutive frame images; The preprocessing of the frame image may include removing background noise, image enhancement, etc.

[0057] Step 4: Use the Canny edge algorithm to process the preprocessed multiple frame images to obtain the shellfish outline and mark it.

[0058] The Canny mudflat shellfish edge detection algorithm is used to process multiple frame images corresponding to multiple different moments to obtain and mark the corresponding shellfish edge contours in the multiple frame images.

[0059] The Canny edge algorithm is an existing algorithm structure and will not be described in detail here.

[0060] Step 5: Establish a coordinate system for each frame image according to the same reference point, determine the centroid coordinates of the shellfish outline in each frame image, connect the centroid coordinates of the shellfish outline corresponding to multiple frame images to draw the movement trajectory curve of the shellfish in the height direction, and determine the impact of scouring and harvesting shellfish on the movement form of the shellfish based on the shellfish movement trajectory curve.

[0061] By establishing a coordinate system for multiple frame images according to the same reference point, it can be ensured that the shellfish in the multiple frame images can be located in the same coordinate system.

[0062] When selecting a reference point, the small left corner of each frame image can be selected as the origin coordinate to establish a coordinate system.

[0063] After the coordinate system is established, the center of mass coordinates of the shellfish in the frame image can be obtained.

[0064] The centroid coordinates of the shellfish outline are plotted for multiple sets of video data frames to obtain a motion trajectory curve of the shellfish in the height direction with respect to the change of the flushing time.

[0065] By penetrating the curve of the shellfish's movement trajectory in the height direction, we can observe the movement trajectory before, after and during the flushing process, as well as parameters such as the maximum floating height during the flushing process. We can also observe the impact of the flushing process on the behavior and movement state of the shellfish, helping researchers analyze the ecological response of the shellfish during the flushing process.

[0066] When in use, the flow rate and angle of the nozzle 210 can also be reasonably adjusted to form multiple shellfish movement trajectory curves in the height direction, so as to distinguish the influence of different spray angles and flow rates on the shellfish behavior patterns and movement paths, find the suitable optimal spray angle and flow rate, and provide theoretical guidance for the actual collection and laying of shellfish.

[0067] In some embodiments of the present application, a method for obtaining water turbidity using the shellfish harvesting experimental device is proposed, comprising the following steps: The preset water turbidity levels are N levels, each level corresponds to a turbidity value range; The video before, during and after flushing is acquired through the second camera element 400; The video is divided into a plurality of continuous frame images at different moments; the video segmentation can be achieved through the frame segmentation technology.

[0068] Preprocessing is performed on multiple frame images, and the preprocessing of the frame images includes noise reduction, image enhancement, etc.

[0069] The image is processed by a watershed algorithm to segment the bottom layer 110 and the water layer 120 .

[0070] The watershed algorithm is an existing algorithm, which is used to process multiple preprocessed frame images to separate the water layer and the bottom layer 110 in the multiple frame images, and then distinguish the water area corresponding to the water layer and the bottom area corresponding to the bottom layer 110.

[0071] Obtain the grayscale values ​​of the water layer corresponding to the multiple frame images; The grayscale value of the water layer of the frame image is divided by the maximum grayscale value, and then the turbidity discrimination value is obtained by multiplying the quotient value and the number of turbidity levels. The turbidity discrimination value is compared with the intervals corresponding to the multiple turbidity levels to obtain the turbidity levels corresponding to the frame images. The turbidity levels of the multiple frame images are respectively obtained in the above manner, and the first water body turbidity change curve is drawn according to the multiple turbidity levels and the moments of the frame images corresponding to the levels.

[0072] Assume that the turbidity level of the water body is N, where N is a natural number greater than or equal to 2.

[0073] For the convenience of description, let N=10, that is, the turbidity of water is divided into 10 levels.

[0074] Each level corresponds to a value range area.

[0075] Level 1:, Level 2:, Level 3.... Before flushing, the turbidity level of the water body is level 1, and the turbidity level of the water body corresponding to the maximum gray value is level 10.

[0076] The turbidity level of the water body between level 1 and level 10 can be obtained by multiplying the quotient of the gray value and the maximum gray value in this frame image by the number of turbidity levels.

[0077] For example, if the gray value of the water layer in one frame image is A, and the maximum gray value of the water layer in multiple frame images is C, then the turbidity discrimination value B of the water layer in the frame image with gray value A is: B=A / Cx10. By comparing the size of the B value with the intervals corresponding to multiple water body chaos levels, we can see which level of the interval B falls into, and then we can determine the water body turbidity level corresponding to the frame image with grayscale A.

[0078] After the turbidity levels of the water layers of the multiple frame images are calculated in the above manner, the turbidity levels of the water layers corresponding to the multiple frame images that change over time can be obtained.

[0079] According to the turbidity levels of the water layer in multiple frame images and their corresponding moments, a turbidity change curve of the water body with the change of flushing time is drawn, so that researchers can intuitively observe the impact of the flushing experiment on the turbidity of the water body.

[0080] In order to ensure accurate detection of the turbidity of the water layer, the scouring experiment device is also equipped with a photoelectric detection component to further detect the turbidity of the water.

[0081] Using the photoelectric sensor 520 to obtain multiple light intensity signals after being scattered by the water layer at different times; The turbidity determination value is obtained by multiplying the obtained quotient by the total number of turbidity levels using the minimum light intensity signal among the multiple light intensity signals and any one of the light intensity signals, and the turbidity level corresponding to the light intensity signal is obtained by comparing the turbidity determination value with the intervals corresponding to the multiple turbidity levels, and the turbidity levels corresponding to the multiple light intensity signals are calculated in sequence according to the above method; Draw a turbidity change curve of the second water body according to the multiple turbidity levels and corresponding time points; Assume that the minimum light intensity signal is G, one of the scattered light intensity signals is E, and the turbidity judgment value corresponding to this light intensity signal is F, which is: F=G / Ex10.

[0082] The calculated value of F is compared with the interval values ​​of multiple turbidity levels to obtain the corresponding turbidity level.

[0083] According to the above method, the turbidity levels corresponding to the multiple light intensity signals are calculated in sequence.

[0084] A turbidity variation curve of the second water body that varies with time is drawn according to the time points and turbidity levels corresponding to the multiple light intensity signals.

[0085] The change in water turbidity during the flushing experiment can also be clearly observed through the second water turbidity change curve.

[0086] In order to ensure accurate judgment of water turbidity, the first water turbidity curve and the second water turbidity curve are combined to comprehensively judge the water turbidity.

[0087] For example, when the first water body turbidity curve and the second water body turbidity curve correspondingly displayed at the first moment both show that the water body turbidity is at a certain level, it means that the water body turbidity is at this level.

[0088] If at the same moment, the turbidity levels of the first water body turbidity curve and the second water body turbidity curve are different, then obtain the grayscale value corresponding to this moment and the turbidity judgment value corresponding to this grayscale value, obtain the light intensity value corresponding to this moment and the turbidity judgment value corresponding to this light intensity value, take the average of the two and determine the turbidity level according to the size of the average value.

[0089] Taking the time T1 as an example, at the time T1, the corresponding frame image is the second frame image of multiple continuous frame images. At this time, the gray value of the water layer at the time T1 can be extracted and the corresponding turbidity discrimination value can be obtained by calculation in the above manner; At the same time, the light intensity signal corresponding to the time T1 can be obtained, and the turbidity determination value corresponding to the light intensity signal can be calculated accordingly through the light intensity signal.

[0090] The turbidity level is determined based on the average of the turbidity determination value and the turbidity discrimination value.

[0091] Assume that at time T1, the turbidity discrimination value obtained is A, the turbidity judgment value is B, and the average value is A+B / 2. Based on this result, see which range of turbidity levels the value belongs to, and further determine the turbidity level.

[0092] In some embodiments of the present application, a method for obtaining the bottom topography by the shellfish harvesting experimental device is proposed, using the second camera element 400 to collect videos before, during and after the shellfish are scoured; Based on the collected video, the video is divided into a plurality of continuous frame images at different moments; Preprocess multiple consecutive frame images; image preprocessing includes noise reduction, image enhancement, etc.

[0093] The image is processed by a watershed algorithm to segment the bottom layer 110 and the water layer; Mark the image contour of the substrate layer 110. Segment the multiple frame images using the Pink Ridge algorithm to obtain and mark the image contours of the substrate layer 110 in the multiple frame images.

[0094] A coordinate system is established for each frame image according to the same reference point, the coordinate value of the highest point of the image contour of the substrate layer 110 of each frame image is recorded, and a substrate topography change curve is drawn based on the highest point values ​​of the substrate layer 110 of multiple frame images.

[0095] The reference point can be selected from the lower left corner origin positions of multiple frame images.

[0096] By marking the highest point of the bottom layer 110 image contour in multiple frame images, the height change of the bottom layer 110 during the scouring process can be observed intuitively, and the impact of scouring and shellfish collection on the bottom layer 110 can be intuitively understood.

[0097] Using the first camera element 300 to collect videos before, during and after shellfish flushing; The first camera element 300 shoots downward, and can capture video images viewed from top to bottom.

[0098] The video is divided into a plurality of continuous frame images corresponding to different moments, and the plurality of continuous frame images are preprocessed, and the preprocessing includes noise reduction, image enhancement, etc.

[0099] Use OpenCV algorithm to detect and identify closed boundaries in frame images and extract the contours of pothole areas. The area of ​​the pothole region is calculated by the ratio of the number of pixels of the pothole region outline to the number of pixels in the entire frame image.

[0100] The OpenCV algorithm is an existing algorithm, which can directly obtain the contour of the pothole area by processing the frame image.

[0101] The ratio of the number of pixels of the pothole area contour to the number of pixels in the entire frame image can also be directly obtained through the OpenCV algorithm.

[0102] The impact of scouring on the topography of the bottom layer 110 can be obtained through the changes in the size of the potholes.

[0103] The experimental device in this embodiment achieves a high degree of simulation by setting up a water tank 100 and arranging a bottom layer 110 and a water layer therein, and can simulate the scouring effect in a real tidal flat environment and accurately restore the behavior pattern of shellfish under natural conditions.

[0104] By combining the control unit with the image acquisition device and the edge detection algorithm dedicated to the shellfish image, the position, movement trajectory and behavior of the shellfish during the flushing process can be monitored and acquired in real time; The turbidity detection method based on optical scattering characteristics is introduced. By combining the light scattering intensity and the gray value comparison method of image processing, the fine detection of water turbidity in high turbidity environment is achieved. The system can capture and analyze the turbidity changes of water in the experiment in real time, and provide feedback to record experimental parameters.

[0105] The bottom sediment change monitoring function uses a high-speed camera and a watershed segmentation algorithm for the bottom sediment to capture the changes in the bottom sediment morphology after scouring in real time, providing reliable data support for the study of tidal flat bottom morphology.

[0106] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features.

[0107] In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0108] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for a person skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to replace some of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions claimed to be protected by the present invention.

Claims

1. A shellfish harvesting experimental device, characterized in that: Included are: A water tank, in which a bottom layer and a water layer are arranged in sequence from bottom to top, the bottom layer includes a soil layer and a sand layer, and shellfish are buried in the sand layer; A flushing component is installed outside the water tank and has a nozzle, the nozzle is directed toward the water tank and is used to flush the shellfish buried in the sand layer; A first camera element is horizontally arranged at the top of the water tank and is used to capture a video image from the top of the water tank downwards; A second camera element is vertically arranged at a side position of the water tank and is used to capture a video image from the side of the water tank; The photoelectric detection assembly includes a light emitting component, which is mounted on a side wall of the water tank and emits light toward the water layer of the water tank; The photoelectric sensor is mounted on the side wall of the water tank opposite to the light emitting component, and is used to receive the light signal scattered by the water flow in the water tank and convert the light signal into an electrical signal; The control unit communicates with the first imaging element, the second imaging element and the photoelectric sensor.

2. A detection method using the shellfish harvesting experimental device according to claim 1, the detection method comprising a method for detecting the movement trajectory of shellfish, characterized in that: The steps include: Step 1: Using a second camera element to collect videos of shellfish before, during and after scouring; Step 2: Based on the collected video, the video is divided into a plurality of continuous frame images at different times; Step 3: Preprocess multiple consecutive frame images; Step 4: Use the Canny edge algorithm to process the preprocessed multiple frame images to obtain the shellfish outline and mark it; Step 5: Establish a coordinate system for each frame image according to the same reference point, determine the centroid coordinates of the shellfish outline in each frame image, connect the centroid coordinates of the shellfish outline corresponding to multiple frame images to draw the movement trajectory curve of the shellfish in the height direction, and determine the impact of scouring and harvesting shellfish on the movement form of the shellfish based on the shellfish movement trajectory curve.

3. The detection method of the shellfish harvesting experimental device according to claim 2, wherein the detection method also includes a method for detecting water turbidity, characterized in that: The steps include: The water turbidity level is preset to N levels, and each level corresponds to a turbidity value range; The video before, during and after flushing is obtained by a second camera element; Split the video into multiple continuous frame images at different moments; Preprocessing multiple frame images; The image is processed by watershed algorithm to segment the bottom layer and water layer; Obtain the grayscale values ​​of the water layer corresponding to the multiple frame images; The turbidity level of the water layer in each frame image is obtained through the corresponding gray value of each frame image, the maximum gray value among the gray values ​​of multiple frame images, and the turbidity level number; Draw a turbidity change curve of the first water body according to the multiple turbidity levels and the moments of the frame images corresponding to the levels; Photoelectric sensors are used to obtain multiple light intensity signals after being scattered by the water layer at different times; Each light intensity signal obtains the turbidity level corresponding to the light intensity signal through its own light intensity value, the minimum light intensity signal value among the multiple light intensity signals and the turbidity level number; A second water body turbidity change curve is drawn according to the multiple turbidity levels and the corresponding time points, and the turbidity of the water body is determined by combining the first water body turbidity curve and the second water body turbidity curve.

4. The detection method of the shellfish harvesting experimental device according to claim 2, characterized in that: Also included is a method for detecting bottom landforms, comprising the steps of: Using a second camera element to capture video of the shellfish before, during and after scouring; Based on the collected video, the video is divided into a plurality of continuous frame images at different moments; Preprocessing a plurality of continuous frame images; The image is processed by watershed algorithm to segment the bottom layer and water layer; Mark the bottom layer image outline; A coordinate system is established for each frame of image according to the same reference point, the coordinate value of the highest point of the bottom layer image contour of each frame of image is recorded, and the bottom landform change curve is drawn according to the highest point value of the bottom layer of multiple frames of image.

5. The detection method of the shellfish harvesting experimental device according to claim 4, characterized in that: Also includes the following steps: Using the first camera element to collect videos before, during and after shellfish scouring; Split the video into multiple continuous frame images; Preprocessing a plurality of continuous frame images; Use OpenCV algorithm to detect and identify closed boundaries in frame images and extract the contours of pothole areas. The area of ​​the pothole region is calculated by the ratio of the number of pixels of the pothole region outline to the number of pixels in the entire auxiliary frame image.

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