Apparatus for shellfish harvesting experiments and detection method

Abstract

The application discloses a kind of shellfish harvesting experimental device and detection method, including: water tank, bottom layer and water layer are sequentially arranged from bottom to top in water tank;Scouring component, installed outside water tank, with spray head, spray head is towards water tank;First camera element, horizontally arranged at the top position of water tank;Second camera element, vertically arranged at the side position of water tank;Light emitting component, assembled on one side wall of water tank, its emission light is towards the water surface of water tank;Photoelectric sensor, assembled on the side wall of water tank opposite to the position of light emitting component, for receiving the light signal after water flow scattering of water tank, and light signal is converted into electrical signal;Control unit, with first camera element, second camera element and photoelectric sensor communication.The shellfish harvesting experimental device provided in the application can accurately obtain the movement trajectory of shellfish, water turbidity and topographic change during scouring process, and provide theoretical basis for practical scouring shellfish.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of shellfish harvesting, and in particular relates to an improved shellfish harvesting experimental device. BACKGROUND

[0002] Among marine aquaculture products, shellfish occupies a pivotal position. Currently, shellfish harvesting is mainly carried out through manual harvesting, fishing boat fishing, and mechanical shell suction. However, these traditional harvesting methods have exposed some significant problems in the face of environmental protection and ecological sustainable development. Manual harvesting is time-consuming and labor-intensive, with low production efficiency and is easily affected by sea level fluctuations; fishing boat fishing can improve harvesting efficiency to a certain extent, but it depends on specific water environmental conditions, such as flat seabed and small waves, which has high environmental requirements. In addition, flexible fishing nets are prone to deformation, affecting the effective harvesting of shellfish.

[0003] The mechanical shell suction method has more serious ecological problems. This method indiscriminately sucks out shellfish of all sizes, destroying the continuity of marine life and causing a large amount of silt and impurities to be brought out, increasing the turbidity of the water body. This method not only affects the transparency of the water body, but also changes the bottom topography, leading to degradation of the ecological environment. The sucked shellfish is suspended in seawater, increasing the breakage rate of shellfish, and requiring additional screening and filtering processes, further increasing the cost and labor intensity.

[0004] In view of this, a flushing shellfish harvesting method is proposed, which mainly flushes shellfish from shellfish buried in sand by flushing, suspends them in water, and then harvests them. This method causes relatively less damage to shellfish, but the impact of this flushing method on the movement and behavior of shellfish, the change of the topography of the marine environment, and the turbidity of the water body need to be further studied.

[0005] The above information disclosed in the background section is only intended to increase the understanding of the background of the present application, and therefore, it can include prior art that is not known to those of ordinary skill in the art. SUMMARY

[0006] The present application aims to solve the above technical problems, and proposes a shellfish harvesting experimental device and detection method, which can not only effectively determine the flushing shellfish harvesting method, but also accurately detect the movement trajectory of shellfish, water turbidity, and topography change during the flushing process, providing a theoretical basis for the practical application of flushing shellfish harvesting.

[0007] To achieve the above-mentioned purposes, the present application adopts the following technical solutions:

[0008] A shellfish harvesting experimental device, comprising:

[0009] a water tank, a bottom layer and a water layer being arranged in the water tank from bottom to top, the bottom layer comprising a soil layer and a sand layer, and the sand layer being provided with shellfish;

[0010] a flushing component installed outside the water tank and provided with a nozzle, the nozzle being directed towards the water tank and used for flushing the shellfish in the sand layer;

[0011] a first camera element horizontally arranged at a top position of the water tank and used for shooting a video image from the top of the water tank downwards;

[0012] a second camera element vertically arranged at a side position of the water tank and used for shooting a video image from the side of the water tank;

[0013] a photoelectric detection assembly comprising a light emitting component and assembled on a side wall of the water tank, the light emitting component emitting light towards the water surface of the water tank;

[0014] a photoelectric sensor assembled on a side wall of the water tank opposite to the light emitting component and used for receiving a light signal scattered by the water flow in the water tank and converting the light signal into an electric signal;

[0015] a control unit in communication with the first camera element, the second camera element and the photoelectric sensor.

[0016] Compared with the prior art, the present application has the following advantages and positive effects:

[0017] The shellfish experimental device provided by the present application realizes high simulation by arranging a water tank and a bottom layer and a water layer in the water tank, and can simulate the flushing effect in a real beach environment and accurately restore the behavior mode of shellfish under natural conditions.

[0018] The combination of the control unit, the image acquisition device and the edge detection algorithm dedicated to shellfish images enables real-time monitoring and acquisition of the position, motion trajectory and behavior of shellfish in the flushing process.

[0019] The turbidity detection method based on optical scattering characteristics is introduced, and the light scattering intensity and the gray value extraction contrast method of image processing are combined to realize fine detection of water turbidity in a high turbidity environment. The system can capture and analyze the turbidity change of the water body in the experimental process in real time and provide feedback to record the experimental parameters.

[0020] The bottom condition change monitoring function uses a high-speed camera and a watershed segmentation algorithm of bottom condition topography to capture the morphological change of the bottom condition topography after flushing in real time, and provides reliable data support for the study of beach bottom condition topography.

[0021] Other features and advantages of the present application will become more apparent after reading the detailed description of the present application in conjunction with the accompanying drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of one embodiment of the shellfish harvesting experimental device proposed in this invention;

[0024] Figure 2 A flowchart illustrating the acquisition of shellfish movement trajectories using the shellfish harvesting experimental device proposed in this invention;

[0025] Figure 3 The flowchart of the shellfish harvesting experimental device proposed in this invention for obtaining water turbidity through frame image grayscale;

[0026] Figure 4 The flowchart of the shellfish harvesting experimental device proposed in this invention for obtaining water turbidity through a light detection component;

[0027] Figure 5 The experimental device for shellfish harvesting proposed in this invention provides a flowchart of the changes in bottom morphology obtained through a second camera element.

[0028] Figure 6 The shellfish harvesting experimental device proposed in this invention obtains a flowchart of the changes in bottom morphology through a first imaging element;

[0029] Figure 7 A schematic diagram of one embodiment of the water tank of the shellfish harvesting experimental device proposed in this invention.

[0030] In the diagram, 100 is the water tank; 110 is the substrate layer; 111 is the soil layer; 112 is the sand layer; 120 is the water layer; 200 is the flushing component; 210 is the nozzle; 220 is the screw; 230 is the mounting bracket; 240 is the water pipe; 300 is the first camera element; 400 is the second camera element; 510 is the light emitting component; and 520 is the photoelectric sensor.

[0031] 600, partition; 610, first tank; 620, second tank. Detailed Implementation

[0032] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below, obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the scope of protection of the present application.

[0033] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, therefore, it cannot be understood as a limitation to the present application.

[0034] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connection" should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integrally connected. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances. In the description of the embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0035] In some embodiments of the present application, a shell catching experimental device is provided, and the flushing experimental device can be used to simulate the experimental device for flushing the shell in the sand layer 112 in seawater, which provides a quick way to catch the shell, that is, the shell buried in the bottom material under water is flushed out by the water jet 210 of the flushing device, so that the shell is suspended in water, so as to directly catch the shell.

[0036] The way of flushing the shell out of the bottom layer 110 and then catching it can reduce the damage to the shell and the damage to the marine bottom topography.

[0037] The flushing experimental device is also provided with a camera element, which records the flushing period by high-speed camera element, records and analyzes the turbidity of the water body, the motion trajectory of the shell and the change of the bottom material, so as to detect and analyze the influence of flushing the shell on the shell, the influence of the turbidity of the water body and the influence of the bottom material, and provide theoretical guidance for the application and actual production of the flushing shell collecting method.

[0038] In some embodiments of the present application, the flushing experimental device comprises:

[0039] A water tank 100, a bottom layer 110 and a water layer 120 are arranged in the water tank 100 from bottom to top.

[0040] The mud layer 111 and the sand layer 112 are sequentially laid from bottom to top along the height direction of the water tank 100.

[0041] The water layer 120 is above the sand layer 112, the water layer 120 is seawater, and the shellfish is mainly buried in the sand layer 112.

[0042] The water tank 100 can be a water tank 100 with a length-width ratio of 8:1.

[0043] The bottom layer 110 and the water layer 120 laid inside the water tank 100 can realize the effect of truly simulating the natural beach environment.

[0044] The flushing component 200 is installed outside the water tank 100 and has a spray head 210 facing the water tank 100 for flushing the shellfish buried in the sand layer 112.

[0045] In some embodiments of the present application, the flushing component 200 includes a water pipe 240, the spray head 210 is connected to the end of the water pipe 240, a fixed frame 230 is arranged above the spray head 210, and a screw rod 220 is screwed into the fixed frame 230, and the end of the screw rod 220 is connected with the water pipe 240.

[0046] When connected, the end of the screw rod 220 can be clamped on the water pipe 240 by a clamp.

[0047] By adjusting the height of the screw rod 220, the position of the water pipe 240 can be adjusted, and the spray angle of the spray head 210 can be adjusted.

[0048] When performing a flushing experiment, the flushing effect can be observed by adjusting the position of the spray head 210.

[0049] The angle of the spray head 210 can be flexibly adjusted, which can effectively increase the flushing radius and improve the flushing efficiency when flushing the shellfish.

[0050] In some embodiments, a flow control valve is arranged on the water pipe 240, and the flow control valve is adjusted to change the water flow speed of the spray head 210, so as to observe the flushing effect of different water flow speeds on the shellfish, and provide a basis for actual shellfish flushing and laying.

[0051] The first camera element 300 is horizontally arranged at the top of the water tank 100, and is used to shoot a video image downward from the top of the water tank 100.

[0052] The first camera element 300 is arranged at the top of the sink 100 for high-precision real-time recording of image information from the top of the sink 100.

[0053] The second camera element 400 is arranged vertically at the side of the sink 100 for recording video images from the side of the sink 100.

[0054] The second camera element 400 is arranged at the side of the sink 100 for high-precision real-time recording of image information from the side of the sink 100.

[0055] The photoelectric detection assembly includes a light emitting component 510 arranged on a side wall of the sink 100, which emits light towards the water surface of the sink 100.

[0056] The photoelectric sensor 520 is arranged on the side wall of the sink 100 opposite to the light emitting component 510, which receives light signals scattered by the water layer of the sink 100 and converts the light signals into electrical signals.

[0057] The photoelectric detection assembly is mainly used for detecting the turbidity of the water layer.

[0058] The light emitting component 510 can be a component capable of emitting light, such as a lamp bead.

[0059] The light emitting component 510 emits light towards the water layer, and the light is scattered in the water layer after entering the water layer. The scattered light is received by the photoelectric sensor 520.

[0060] The intensity of the light signal received by the photoelectric sensor 520, i.e. the light intensity signal, is different when the turbidity of the water layer is different. The value of the light intensity signal can be reflected by the size of the converted electrical signal value.

[0061] When the turbidity of the water layer is large, the light emitted by the light emitting component 510 is scattered more strongly when passing through the water layer, and the value of the light intensity signal received by the photoelectric sensor 520 is small.

[0062] When the turbidity of the water layer is small, the light emitted by the light emitting component 510 is scattered more weakly when passing through the water layer, and the value of the light intensity signal received by the photoelectric sensor 520 is large.

[0063] According to the change of the light intensity signal received by the photoelectric sensor 520, the change of the turbidity of the water layer can be determined.

[0064] The control unit is in communication with the first camera element 300, the second camera element 400 and the photoelectric sensor 520.

[0065] 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.

[0066] The movement path and behavior pattern of the shellfish during the flushing process can be intuitively observed, which helps researchers analyze the ecological response of the shellfish during the flushing process.

[0067] The rough change of the turbidity of the water body and the influence on the water body during the flushing process of the shellfish can also be intuitively observed through the video.

[0068] The rough change of the bottom topography of the bottom layer 110 and the influence on the bottom layer 110 can also be intuitively observed through the video.

[0069] 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 accurate movement trajectory of the shellfish, the accurate change of the turbidity of the water body, and the accurate change of the bottom layer 110 by processing and analyzing the video images obtained from the first camera element 300 and the second camera element 400, to provide more accurate analysis results for the influence of the shellfish on the shellfish, the water layer 120 and the bottom layer 110.

[0070] In some embodiments of the present application, a detachable longitudinal partition plate 600 is arranged in the water tank, which divides the water tank into a first tank body 610 and a second tank body 620, and the first tank body 610 and the second tank body 620 have different widths.

[0071] When the partition plate 600 is installed in the water tank, the water tank is divided into the first tank body 610 and the second tank body 620.

[0072] The original width of the water tank is divided into two tank bodies with different widths.

[0073] The partition plate 600 is detachable, and when the partition plate 600 is detached, the water tank has the widest width. By installing or detaching the partition plate 600, the water tank can form three tank bodies with different widths, and each tank body can be used for flushing experiments. The partition plate 600 is arranged to enable shellfish flushing experiments in three tank bodies with different widths, and experimental analysis of the flushing height of the shellfish, the change of the turbidity, and the change of the bottom topography, to simulate various beach harvesting experimental environments.

[0074] In some embodiments of the present application, a method for obtaining the movement trajectory of the shellfish by using the above-mentioned shellfish harvesting experimental device is provided, which includes the following steps:

[0075] Step 1: collect the video of the shellfish before and after the scouring and during the scouring by using the second camera element 400;

[0076] The video of the shellfish before, after and during the scouring can be obtained by recording in real time by the second camera element 400.

[0077] Step 2: based on the collected video, the video is divided into a plurality of continuous frame images at different time points.

[0078] The division of the video can be realized by frame decomposition technology, and the frame picture extraction function can be realized by using some script commands in the video processing library. A plurality of continuous frame images correspond to different time points of the video.

[0079] Step 3: pre-processing the plurality of continuous frame images;

[0080] The pre-processing of the frame image can include removing background noise, image enhancement, etc.

[0081] Step 4: processing the plurality of frame images after pre-processing by using the Canny edge algorithm to obtain and mark the shellfish contour.

[0082] The Canny shellfish edge detection algorithm is used to process a plurality of frame images corresponding to different time points respectively to obtain and mark the shellfish edge contour in the plurality of frame images.

[0083] The Canny edge algorithm is an existing algorithm structure, which will not be described here.

[0084] Step 5: establishing a coordinate system for each frame image according to the same reference point, determining the centroid coordinates of the shellfish contour in each frame image, connecting the centroid coordinates of the shellfish contour corresponding to a plurality of frame images to draw the motion trajectory curve of the shellfish in the height direction, and determining the influence of the scouring and catching shellfish on the motion form of the shellfish according to the motion trajectory curve of the shellfish.

[0085] The plurality of frame images are established according to the same reference point to ensure that the shellfish in the plurality of frame images can be located in the same coordinate system.

[0086] When selecting the reference point, the left small corner of each frame image can be selected as the origin coordinate to establish the coordinate system.

[0087] After establishing the coordinate system, the centroid coordinates of the shellfish in the frame image can be obtained.

[0088] The centroid coordinates of the shellfish contour of the plurality of frame images of the video data are plotted to obtain the motion trajectory curve of the shellfish in the height direction with the change of the scouring time.

[0089] The movement trajectory of the shellfish in the height direction before, during and after the scouring can be observed through the trajectory curve, and the maximum floating height during the scouring can be observed, so as to observe the influence of the shellfish behavior and movement state during the scouring, and help researchers analyze the ecological response of the shellfish during the scouring.

[0090] In use, the flow rate and angle of the spray head 210 can be reasonably adjusted to form a plurality of trajectory curves of the shellfish in the height direction, so as to distinguish the influence of different spray angles and flow rates on the behavior mode and movement path of the shellfish, and find the optimal spray angle and flow rate, thereby providing theoretical guidance for actual shellfish harvesting.

[0091] In some embodiments of the present application, a method for obtaining water turbidity by using the shellfish harvesting experimental device is provided, which comprises the following steps:

[0092] The preset water turbidity levels are N levels, and each level corresponds to a turbidity value interval.

[0093] The video before, during and after the scouring is obtained by the second camera element 400.

[0094] The video is divided into a plurality of continuous frame images at different time points. The video can be divided by frame division technology.

[0095] The plurality of frame images are preprocessed, and the preprocessing of the frame images includes noise reduction and image enhancement.

[0096] The image is processed by the watershed algorithm to segment the substrate layer 110 and the water layer 120.

[0097] The watershed algorithm is an existing algorithm, which is used to process a plurality of preprocessed frame images to separate the water layer and the substrate layer 110 in the plurality of frame images, and then distinguish the water area corresponding to the water layer and the substrate area corresponding to the substrate layer 110.

[0098] The gray value of the water layer corresponding to the plurality of frame images is obtained.

[0099] The gray value of the water layer of the frame image is divided by the maximum gray value, and then the turbidity discrimination value is obtained by multiplying the value of the division by the number of turbidity levels. According to the comparison between the turbidity discrimination value and the interval corresponding to the plurality of turbidity levels, the turbidity level corresponding to each frame image is obtained. The turbidity levels of the plurality of frame images are obtained in the above manner, and the first water turbidity change curve is drawn according to the plurality of turbidity levels and the time of the frame image corresponding to the level.

[0100] The water turbidity level is N levels, and N is a natural number greater than or equal to 2.

[0101] For the convenience of description, it is assumed that N=10, i.e. the turbidity of the water body is divided into 10 levels.

[0102] Each level corresponds to a numerical range area.

[0103] Level 1:, Level 2:, Level 3:...

[0104] Before flushing, the turbidity level of the water body is level 1, and the maximum gray value corresponds to the turbidity level of the water body of level 10.

[0105] The turbidity level of the water body between level 1 and level 10 can be obtained by the quotient of the gray value in the frame image and the product of the maximum gray value and the number of turbidity levels.

[0106] For example, it is assumed that the gray value of the water layer in one frame image is A, and the maximum value of the 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 the gray value A is:

[0107] B=A / Cx10, according to the comparison of the size of the B value and the interval corresponding to multiple water turbidity levels, it is determined which interval range the B falls into, and then the corresponding water turbidity level of the frame image with the gray value A is determined.

[0108] After the turbidity levels of the water layers of multiple frame images are calculated in the above manner, the turbidity levels of the water layers corresponding to multiple frame images changing with time can be obtained.

[0109] According to the turbidity levels of the water layers of multiple frame images and the corresponding time, a water turbidity change curve changing with the flushing time is drawn, so that the researchers can intuitively observe the influence of the flushing experiment on the water turbidity.

[0110] In order to ensure accurate detection of the turbidity of the water layer, the flushing experiment device is also provided with a photoelectric detection component to further detect the turbidity of the water body.

[0111] The photoelectric sensor 520 is used to obtain multiple light intensity signals scattered by the water layer at different times;

[0112] The quotient of the minimum light intensity signal in the multiple light intensity signals and any light intensity signal is used to obtain a turbidity discrimination value by multiplying the obtained quotient and the total number of turbidity levels, and the turbidity level corresponding to the light intensity signal is obtained by comparing the turbidity discrimination value with the interval corresponding to multiple turbidity levels, and the turbidity levels corresponding to multiple light intensity signals are calculated in the above manner.

[0113] According to the multiple turbidity levels and the corresponding time, a second water turbidity change curve is drawn.

[0114] Let the minimum light intensity signal be G, wherein one light intensity signal after scattering is E, the turbidity determination value corresponding to the light intensity signal is F, F is:

[0115] F=G / Ex10.

[0116] According to the value calculated according to F and the interval value of the plurality of turbidity levels, the corresponding turbidity level is obtained.

[0117] According to the above manner, the turbidity levels corresponding to the plurality of light intensity signals are sequentially calculated.

[0118] According to the time corresponding to the plurality of light intensity signals and the turbidity level, a second water body turbidity change curve with time is drawn.

[0119] Through the second water body turbidity change curve, the change of the water body turbidity during the scouring experiment can also be clearly observed.

[0120] In order to ensure accurate judgment of the water body turbidity, when judging the water body turbidity, the first water body turbidity curve and the second water body turbidity curve are combined to comprehensively judge the water body turbidity.

[0121] For example, when the first water body turbidity curve and the second water body turbidity curve corresponding to the first time are displayed, the water body turbidity is at a certain level.

[0122] If the turbidity levels of the first water body turbidity curve and the second water body turbidity curve are different at the same time, the gray value corresponding to the time and the turbidity discrimination value corresponding to the gray value are obtained, the light intensity value corresponding to the time and the turbidity determination value corresponding to the light intensity value are obtained, the average value of the two is taken, and the turbidity level is determined according to the size of the average value.

[0123] Taking T1 time as an example, at T1 time, the corresponding frame image is the second frame image of a plurality of continuous frame images, at this time, the water layer gray value of T1 time can be extracted and the corresponding turbidity discrimination value is calculated by the above manner;

[0124] At the same time, the light intensity signal corresponding to T1 time can be obtained, and the turbidity determination value corresponding to the light intensity signal is calculated through the light intensity signal.

[0125] The turbidity level is determined according to the average value of the turbidity determination value and the turbidity discrimination value.

[0126] At T1 moment, the acquired turbidity discrimination value is A, the turbidity discrimination value is B, the average value is A+B / 2, according to the result, the value belongs to which interval of turbidity grade, to further determine the turbidity grade.

[0127] In some embodiments of the present application, a method for obtaining the bottom topography of the shellfish catching experimental device is proposed, and the second camera element 400 is used to collect the video before and after the shellfish is washed and during the washing process.

[0128] Based on the collected video, the video is divided into a plurality of continuous frame images at different times;

[0129] The plurality of continuous frame images are preprocessed; image preprocessing includes noise reduction, image enhancement, etc.

[0130] The image is processed by the watershed algorithm, and the bottom layer 110 and the water layer are segmented;

[0131] The image contour of the bottom layer 110 is marked. The plurality of frame images are segmented by the watershed algorithm, and the image contour of the bottom layer 110 in the plurality of frame images is obtained and marked.

[0132] A coordinate system is established according to the same reference point for each frame image, the highest point coordinate value of the image contour of the bottom layer 110 of each frame image is recorded, and the bottom topography change curve is drawn according to the highest point values of the bottom layer 110 of the plurality of frame images.

[0133] The reference point can be the lower left corner origin position of the plurality of frame images.

[0134] By marking the highest point of the image contour of the bottom layer 110 in the plurality of frame images, the height change of the bottom layer 110 during the washing process can be directly observed, and the influence of shellfish washing on the bottom layer 110 can be directly understood.

[0135] The first camera element 300 is used to collect the video before and after the shellfish is washed and during the washing process.

[0136] The first camera element 300 is downwardly photographed, and the video image viewed from top to bottom can be photographed.

[0137] The video is divided into a plurality of continuous frame images corresponding to different times, and the plurality of continuous frame images are preprocessed, which includes noise reduction, image enhancement, etc.

[0138] The OpenCV algorithm is used to detect and identify the closed boundary in the frame image and extract the pit and hole area contour.

[0139] The pit and hole area is calculated by the ratio of the number of pixel points of the pit and hole area contour to the number of pixel points in the entire frame image.

[0140] The OpenCV algorithm is an existing algorithm, and the outline of the pothole area can be directly obtained by processing the frame image.

[0141] The proportion ratio of the number of pixel points of the pothole area outline to the number of pixel points in the entire frame image can also be directly obtained by the OpenCV algorithm.

[0142] The influence of the scouring on the topography of the bottom layer 110 can be obtained through the change of the size of the pothole area.

[0143] In the experimental device in the embodiment, the water tank 100 is arranged, and the bottom layer 110 and the water layer are arranged in the water tank 100, so that high simulation is realized, the scouring effect in the real tidal flat environment can be simulated, and the behavior mode of the shellfish under the natural condition can be accurately restored.

[0144] By combining the control unit, the image acquisition device and the edge detection algorithm special for the shellfish image, the position, the motion trajectory and the behavior of the shellfish in the scouring process can be monitored and obtained in real time.

[0145] The turbidity detection method based on the optical scattering characteristics is introduced, the light scattering intensity and the gray value extraction contrast mode of image processing are combined, and the fine detection of the water turbidity in the high turbidity environment is realized. The system can capture and analyze the turbidity change of the water in the experiment in real time, and provide feedback and record the experimental parameters.

[0146] The bottom change monitoring function uses a high-speed camera and a watershed segmentation algorithm of the bottom topography to capture the change of the bottom topography after scouring in real time, and provides reliable data support for the study of the topography of the tidal flat bottom.

[0147] The terms "first", "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first", "second" can explicitly or implicitly include one or more of the features.

[0148] In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0149] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application has been described in detail with reference to the foregoing embodiments, the technical solutions recorded in the foregoing embodiments can still be modified by those skilled in the art, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions claimed by the present application.

Claims

1. A detection method based on a shellfish harvesting experiment device, the shellfish harvesting experiment device comprising: a water tank, a substrate layer and a water layer being arranged in the water tank from bottom to top, the substrate layer comprising a soil layer and a sand layer, and shellfish being buried in the sand layer; a flushing component installed outside the water tank and having a nozzle facing the water tank for flushing the shellfish buried in the sand layer; a first camera element horizontally arranged at a top position of the water tank for shooting video images downward from the top of the water tank; a second camera element vertically arranged at a side position of the water tank for shooting video images from the side of the water tank; a photoelectric detection assembly comprising a light emitting component mounted on a side wall of the water tank, the light emitting component emitting light towards the water layer of the water tank; a photoelectric sensor mounted on a side wall of the water tank opposite to the light emitting component for receiving light signals scattered by the water flow in the water tank and converting the light signals into electrical signals; a control unit in communication with the first camera element, the second camera element and the photoelectric sensor, characterized in that the detection method comprises a method for detecting the movement trajectory of the shellfish, comprising the following steps: Step 1: collecting the video before, during and after the flushing of the shellfish by using the second camera element; Step 2: dividing the collected video into a plurality of continuous frame images at different time instants; Step 3: pre-processing the plurality of continuous frame images; Step 4: processing the pre-processed frame images by using the Canny edge algorithm to obtain and mark the shellfish contour; Step 5: establishing a coordinate system for each frame image according to a same reference point, determining the centroid coordinates of the shellfish contour in each frame image, connecting the centroid coordinates of the shellfish contour corresponding to the plurality of frame images to draw a movement trajectory curve of the shellfish in the height direction, and determining the influence of the flushed and harvested shellfish on the movement pattern of the shellfish according to the movement trajectory curve of the shellfish; the detection method further comprises a method for detecting the turbidity of the water body, comprising the following steps: presetting N levels of turbidity of the water body, each level corresponding to a turbidity value interval; obtaining the video before, during and after the flushing by using the second camera element; dividing the video into a plurality of continuous frame images at different time instants; pre-processing the plurality of frame images; processing the frame images by using the watershed algorithm to segment the substrate layer and the water layer; obtaining the gray value of the water layer corresponding to the plurality of frame images; obtaining the turbidity level of the water layer of each frame image by using the corresponding gray value, the maximum gray value among the gray values of the plurality of frame images and the number of turbidity levels; drawing a first water body turbidity change curve according to the plurality of turbidity levels and the time instants of the frame images corresponding to the levels; obtaining a plurality of light intensity signals scattered by the water layer at different time instants by using the photoelectric sensor; obtaining the turbidity level corresponding to each light intensity signal by using the light intensity value of the light intensity signal, the minimum light intensity signal value among the plurality of light intensity signals and the number of turbidity levels; drawing a second water body turbidity change curve according to the plurality of turbidity levels and the corresponding time instants, and determining the turbidity condition of the water body by combining the first water body turbidity curve and the second water body turbidity curve.

2. The method of detecting the shellfish catching test device according to claim 1, characterized by, The method for detecting the bottom landform comprises the following steps: collecting videos before and after the shell scouring and during the scouring by using a second camera element; segmenting the videos into a plurality of continuous frame images at different time points based on the collected videos; preprocessing the plurality of continuous frame images; processing the images by using a watershed algorithm to segment the bottom layer and the water layer; labeling the bottom layer image contour; establishing a coordinate system for each frame image according to the same reference point, recording the highest point coordinate value of the bottom layer image contour of each frame image, and drawing a bottom landform change curve according to the highest point values of the bottom layers of the plurality of frame images.

3. The method of detecting the shellfish catching test device according to claim 2, characterized by, The method further comprises the following steps: collecting videos before and after the shell scouring and during the scouring by using a first camera element; segmenting the videos into a plurality of continuous frame images; preprocessing the plurality of continuous frame images; detecting and recognizing closed boundaries in the frame images by using an OpenCV algorithm and extracting a pit and hollow region contour, calculating the pit and hollow region area by using the proportion ratio of the pixel point number of the pit and hollow region contour in the pixel point number of the whole frame image.

Images