Underwater multi-turbidity self-adaptive visual guidance method

Abstract

The invention relates to an underwater multi-turbidity self-adaptive visual guidance method. An underwater visual marker is composed of a point light source matrix. And the four brightness-adjustable LED light sources are positioned at four vertexes of the point light source matrix. Under low turbidity, the brightness of the LED is increased to cope with the absorption of underwater light, so that the underwater visual guidance range is widened. After the water temperature, the salinity (solute) and the suspended solid concentration are obtained by the sensor, the turbidity is subjected to fusion sensing. By adjusting the brightness of the marker, different enhancement coefficients are adopted for the image to adapt to different underwater turbidities. And finally, the underwater visual guidance is realized through a PnP (Perspective-n-Point) algorithm.

Description

TECHNICAL FIELD

[0001] The present application belongs to a method of fusing perception turbidity and adaptive visual guidance, and relates to a method of underwater multi-turbidity adaptive visual guidance. The present application uses temperature, salinity, and concentration sensors to fuse perception turbidity and realize the technology of multi-turbidity adaptive visual guidance. According to the characteristics of underwater light absorption and scattering under different turbidity, the brightness of LED and image enhancement are dynamically adjusted. The range of visual guidance is increased under low turbidity, and the scattering is reduced under high turbidity to ensure the feasibility of visual guidance. BACKGROUND

[0002] Underwater visual guidance can provide position and attitude information for underwater docking and underwater wireless charging, and is a key technology for ocean exploration. In an indoor pool, the underwater turbidity is small, but in outdoor lake tests and sea tests, the underwater turbidity is usually large, which has a non-negligible impact on visual guidance. The underwater turbidity is determined by water temperature, salinity, and concentration of suspended solids such as particles. The underwater turbidity is different in different water areas, at different times, and at different depths. Underwater visual guidance needs to have the ability to adapt to different turbidity.

[0003] How to obtain turbidity is the first difficulty to be solved. In a laboratory environment, an optical turbidity sensor can be used to directly measure the absorption and scattering of underwater light to obtain underwater turbidity, but it is difficult to measure directly in outdoor. To solve this problem, indirect measurement and parameter calibration are often used to obtain underwater turbidity. The selection of indirect measurement parameters and the fusion of multiple parameters are the focus of research.

[0004] After obtaining the underwater turbidity, the relationship between the brightness of the marker and the underwater turbidity needs to be further determined. Underwater turbidity mainly affects the absorption and scattering of underwater light. In a low turbidity state, the scattering of underwater light is weak, and the absorption of light is mainly considered. The higher the brightness of the marker, the farther the distance can be observed. In a high turbidity state, the scattering of underwater light is strong, and the visual guidance needs to obtain the center point coordinates of the marker. The scattering produced by the high-brightness marker cannot meet the requirements of visual guidance, so the brightness of the marker needs to be reduced. How to determine the brightness range of the marker and the classification of the marker brightness is a key problem.

[0005] After the camera obtains images under different turbidity, secondary adaptive enhancement needs to be performed on the basis of existing image enhancement algorithms. When the turbidity is low and the brightness of the marker is high, the weight of image enhancement needs to be appropriately reduced. When the turbidity is high and the brightness of the marker is low, the weight of image enhancement needs to be appropriately increased. The key to calculating the weight of enhancement using turbidity is to determine the range of the proportion coefficient of the weight.

[0006] The applicant found in the research process that the dissolved matter in water affects the absorption of light, and the solid floating matter in water affects the scattering of light. The visual guidance is mainly affected by the absorption and scattering of underwater light. The amount of dissolved matter in water can be obtained using temperature and salinity sensors, and the amount of solid floating matter in water can be obtained using a concentration meter. The dissolved matter and solid floating matter are fused by weight to obtain the underwater turbidity. The brightness of the marker can be corresponded to different turbidity by using discrete segmentation. The turbidity is used to calculate the image enhancement weight to realize adaptive enhancement under multiple turbidity. SUMMARY

[0007] TECHNICAL PROBLEM

[0008] In order to avoid the shortcomings of the prior art, the present application provides an underwater multi-turbidity adaptive visual guidance method, which solves the influence of turbidity on underwater visual guidance, ensures the feasibility of visual guidance under high turbidity, and increases the visual guidance range under low turbidity.

[0009] The turbidity of underwater environment is closely related to the visual recognition of the marker. The turbidity is related to water temperature, salinity, solid suspended matter, etc. The underwater turbidity of different water areas and different times in the same water area has a large change. The present application obtains the underwater turbidity by using the fusion perception method. For the brightness adjustment of the marker, the variation range of the brightness is first determined and the turbidity and marker brightness variation rule table is established. The weight value of image enhancement is calculated by using the turbidity to realize adaptive enhancement of the image. Finally, the relative position and attitude information is obtained by using the PnP (Perspective-n-Point) algorithm to complete the underwater visual guidance.

[0010] TECHNICAL SCHEME

[0011] An underwater multi-turbidity adaptive visual guidance method, characterized in that the steps are as follows:

[0012] Step 1, calibrate the underwater turbidity perception parameters: calculate the dissolved matter and suspended matter concentration in water according to the temperature, salinity and concentration of the underwater environment;

[0013] Fuse the dissolved matter and suspended matter concentration by weight to obtain the real-time underwater turbidity;

[0014] Calibrate the underwater turbidity perception parameters with the real-time underwater turbidity to obtain the fusion perception underwater turbidity T:

[0015] T = α n × [a × P] + β n × [b × C]

[0016] Wherein: a is the NTU corresponding to unit salinity, b is the NTU corresponding to unit mass concentration, α n is the contribution of dissolved matter, and βn C is the solid suspended concentration per unit volume, in kg / m 3 P is the salinity per unit volume;

[0017] Step 2, according to the corresponding relationship of the underwater turbidity and the LED brightness relationship and the light brightness of the LED point light source, the adaptive adjustment of the LED point light source brightness is carried out:

[0018] The turbidity less than or equal to 5 NTU is low turbidity, the LED point light source brightness adopts the limit brightness, and the corresponding light source adjustment current is T_max;

[0019] The turbidity greater than 5 and less than or equal to 10 NTU is medium turbidity, the LED point light source brightness adopts high brightness, and the corresponding light source adjustment current is T_min+2 / 3(T_max-T_min);

[0020] The turbidity greater than 10 and less than or equal to 15 NTU is high turbidity, the LED point light source brightness adopts medium brightness, and the corresponding light source adjustment current is T_min+1 / 3(T_max-T_min);

[0021] The turbidity greater than 15 and less than 20 NTU is limit turbidity, the LED point light source brightness adopts low brightness, and the corresponding light source adjustment current is T_min;

[0022] Greater than or equal to 20 NTU is invalid turbidity, and the adaptive visual guidance is abandoned;

[0023] Step 3, on the basis of the MSR image enhancement, the guided unmanned vehicle carries out adaptive enhancement on the guided LED light camera image shot by combining the weight calculated by the pixel brightness:

[0024] According to the real-time underwater turbidity, the image enhancement weight w is adjusted as w=μT, and μ is a proportional adjustment parameter, which is used for proportional adjustment between turbidity and weight;

[0025] Step 4: Hough circle detection is carried out on the image enhanced in step 3, four LED point light source center point coordinates are obtained, PnP algorithm is used for solving the four LED point light source center point coordinates, the relative position and attitude of the guided unmanned vehicle are obtained, and underwater visual guidance is completed.

[0026] The salinity per unit volume Where θ0 is the reference temperature on the water surface, θ is the underwater temperature, k is the temperature coefficient, and S is the salinity.

[0027] The solid suspended concentration C per unit volume is obtained by using a concentration meter to obtain the solid suspended concentration per unit volume.

[0028] The alpha nFor the contribution of dissolved matter, the fusion ratio was determined based on typical oceanographic studies: suspended matter dominated in nearshore or estuarine areas, with dissolved matter contributing α1: 30–50%, while dissolved matter had a greater impact in the open ocean, with dissolved matter contributing α2: 50–70%.

[0029] The β n For the contribution of suspended solids, the fusion ratio is determined based on typical oceanographic studies: suspended matter dominates nearshore or estuarine areas, with suspended solids contributing β1:50–70%; dissolved matter in the open ocean has a greater impact, with suspended solids contributing β2:30–50%.

[0030] The current value T_min is defined as the minimum brightness of the LED that meets the camera imaging requirements when the underwater turbidity NTU is 5 and the relative distance is 15m. The current value recorded in this state is expressed as the current value T_min of the minimum brightness of the LED point light source.

[0031] The maximum current T_max is the current value T_max at which the brightness of the LED point light source at the maximum current under the rated voltage is the maximum brightness of the LED point light source.

[0032] The proportional adjustment parameter μ has a value range of 0.5-1.5.

[0033] An electronic device, characterized in that it includes a processor and a memory, wherein the processor is used to implement the steps of the underwater multi-turbidity adaptive visual guidance method as described in any one of claims 1 to 8 when executing the computer program stored in the memory.

[0034] A computer program product, characterized in that it includes computer-executable instructions, which, when executed, are used to implement the method of underwater multi-turbidity adaptive visual guidance described in any one of claims 1 to 8.

[0035] Beneficial effects

[0036] This invention proposes an underwater multi-turbidity adaptive visual guidance method. The underwater visual markers consist of a matrix of point light sources. Four LED light sources with adjustable brightness are located at the four vertices of the matrix. In low turbidity conditions, the LED brightness is increased to cope with underwater light absorption, thereby extending the range of underwater visual guidance. Sensors are used to obtain water temperature, salinity (dissolved matter), and suspended solids concentration, and then fusion perception of turbidity is performed. By adjusting the brightness of the markers, different enhancement coefficients are applied to the image to adapt to different turbidities underwater. Finally, the PnP (Perspective-n-Point) algorithm is used to achieve underwater visual guidance.

[0037] The application is applied to visual guidance in underwater multi-turbidity environment, combines multi-sensor fusion to realize turbidity perception, and adaptively adjusts the brightness of markers and the enhancement of images in different turbidity. The increase of visual guidance range in low turbidity and the feasibility of visual guidance in high turbidity are realized. The beneficial effects are as follows:

[0038] (1) The application solves the problem that the underwater visual guidance system is difficult to obtain environmental turbidity information in real time and accurately to perform adaptive adjustment by indirectly measuring water temperature, salinity and concentration and fusing to obtain underwater turbidity information. Since the physical quantities (temperature, salinity and concentration) highly related to turbidity are measured and data fusion technology is used for processing, the mechanism is to reliably infer the turbidity state through these easily obtained parameters, so that the key environmental parameters can be obtained in real time without deploying a dedicated turbidity sensor, thereby overcoming the defects of the prior art that the underwater environment cannot be perceived conveniently and accurately, and the robustness of the guidance system is limited.

[0039] (2) The application solves the problem that the existing underwater visual guidance system has a limited guidance range in low turbidity by increasing the brightness of LED markers and enhancing the weight of small targets in the image. Since the light source intensity (LED brightness) is increased to enhance the propagation distance of the target signal, and the target that appears as a small size in image processing at a long distance is given a higher attention (enhanced weight), the mechanism enables the system to more effectively detect and identify the marker at a long distance in a relatively clear water body, thereby realizing the effect of significantly expanding the guidance range compared with the prior art.

[0040] (3) The application solves the problem that the existing underwater visual guidance system is difficult to work normally and has poor feasibility in high turbidity by reducing the brightness of LED markers and enhancing the weight of large targets in the image. Since the light source intensity (LED brightness) is reduced to effectively suppress the scattering and echo at a short distance, and the target that appears as a large size in image processing at a short distance is given a higher attention (enhanced weight), the mechanism enables the system to filter out most of the interference caused by turbidity and focus on the short-distance target with strong signal, thereby maintaining effective visual perception and guidance ability in the high-challenge turbidity environment, overcoming the defect that the prior art completely fails in such an environment. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 : Flowchart of underwater multi-turbidity adaptive visual guidance DETAILED DESCRIPTION

[0042] The application will be further described in combination with embodiments and drawings:

[0043] The underwater visual marker is composed of a matrix of point light sources. Four luminance-adjustable LED light sources are located at the four vertices of the matrix of point light sources. At low turbidity, the LED luminance is increased to cope with the absorption of underwater light, thereby increasing the range of underwater visual guidance. However, as the turbidity increases, the scattering of underwater light increases, and too high LED luminance will result in the inability to identify the center point of the LED point light source, thereby making it impossible to use the PnP algorithm to achieve underwater visual guidance. Therefore, the LED luminance needs to be dynamically adjusted according to different turbidities.

[0044] The degree of scattering of underwater light is different under different turbidity, and the images obtained by the camera have large differences. It is difficult to meet the needs of visual guidance by fixing the image enhancement algorithm. According to the turbidity of the fusion perception, the enhancement weight is calculated, and the existing MSR (Multi-Scale Retinex) image enhancement algorithm is combined to realize adaptive enhancement of images under different turbidity.

[0045] The application is applied to visual guidance in underwater multi-turbidity environment, and combines multi-sensor fusion to realize perception of turbidity, adaptive adjustment of marker luminance and image enhancement under different turbidity. The increase of visual guidance range under low turbidity and the feasibility of visual guidance under high turbidity are realized.

[0046] The underwater multi-turbidity adaptive visual guidance method comprises the following steps:

[0047] Step 1: The temperature sensor, salinity sensor and density sensor are used to measure the temperature, salinity and concentration of the underwater environment to calculate the concentration of dissolved and suspended substances in water. The concentrations of dissolved and suspended substances are weighted and fused to perceive the underwater turbidity. At the same time, the calibration of underwater turbidity perception parameters is completed.

[0048] Step 2: After obtaining the minimum value T_min and the maximum value T_max of the current of the LED point light source, the segmented discretization is performed. According to the corresponding table of turbidity and LED luminance, the adaptive adjustment of the luminance of the LED point light source is performed.

[0049] Step 3: On the basis of the MSR image enhancement, the adaptive enhancement of the camera image is completed by combining the weight calculation of the pixel luminance.

[0050] Step 4: After obtaining the key points of the LED in the enhanced image, the PnP solution is performed to complete the underwater visual guidance.

[0051] The specific identification process of the underwater turbidity in step 1 by using the temperature, salinity sensor and concentration meter fusion is as follows:

[0052] Step 1.1: Temperature and salinity sensor calculates the dissolved matter in water. The turbidity of dissolved matter is usually positively correlated with salinity (the higher the salinity, the more dissolved matter), while the relationship with temperature is nonlinear (high temperature may promote dissolution, but extremely high temperature may change water dynamics). Using P to represent the salinity per unit volume, the calculation formula is where θ0 is the reference temperature at the water surface, θ is the temperature under water, k is the temperature coefficient, and S is the salinity.

[0053] Step 1.2: Use a concentration meter to obtain the solid suspended concentration per unit volume, using C to represent the mass concentration, with the unit of kg / m 3 .

[0054] Step 1.3: Determine the fusion ratio according to typical oceanographic research: in coastal or estuarine areas, suspended matter dominates, dissolved matter contributes α1: 30-50%, and suspended solids contribute β1: 50-70%; in the open ocean, dissolved matter has a greater impact, dissolved matter contributes α2: 50-70%, and suspended solids contribute β2: 30-50%.

[0055] Step 1.4: Calibration of underwater turbidity sensing parameters, according to Step 1.3, calculate the weighted fusion according to the water area. When using temperature, salinity, and concentration to obtain underwater turbidity, parameter calibration is required to correct the correspondence between sensing parameters and turbidity. The typical values of sensing parameters and calibration methods are shown in Table 1. T is the fused underwater turbidity, with the unit of NTU, as shown in the formula T = α n × [a × P] + β n × [b × C]. Where a is the NTU corresponding to unit salinity, and b is the NTU corresponding to unit mass concentration.

[0056] In specific embodiments:

[0057] Temperature calibration, use temperature gradient experiments to obtain the temperature sensitivity of the underwater environment. The typical value range obtained is ±0.01-0.05℃ -1 ;

[0058] Reference temperature, measure the average temperature of the underwater environment area. The range is 15-25℃;

[0059] Unit salinity corresponding NTU sensing parameter calibration, use regression fitting experiments to measure the NTU value corresponding to unit salinity. The salinity sensing parameter range is 0.3-0.6 NTU / P;

[0060] Unit mass concentration corresponding NTU sensing parameter calibration, use laboratory scattering measurement method.

[0061] Table 1 Calibration of underwater turbidity sensing parameters

[0062]

[0063] Step 1.5: When the underwater turbidity exceeds 20 NTU, it is impossible to guide by vision. When the underwater turbidity is less than 5 NTU, there is good underwater light propagation. The fused perception turbidity is discretely classified, and turbidity less than or equal to 5 NTU is classified as low turbidity; turbidity greater than 5 and less than or equal to 10 NTU is classified as medium turbidity; turbidity greater than 10 and less than or equal to 15 NTU is classified as high turbidity; turbidity greater than 15 and less than 20 NTU is classified as extreme turbidity, and greater than or equal to 20 NTU is classified as invalid turbidity.

[0064] The specific process of adjusting the brightness of the marker in step 2 is as follows:

[0065] Step 2.1: Obtain the brightness range of the LED point light source. The brightness of the LED point light source at the maximum current T_max under the rated voltage is the maximum brightness of the LED point light source. The minimum brightness of the LED point light source is obtained through underwater experiments. When the underwater turbidity NTU is 5 and the relative distance is 15 m, the LED brightness meets the minimum brightness required for camera imaging, and the current value T_min at this state is recorded as the minimum brightness of the LED point light source.

[0066] In the embodiment:

[0067] Obtain the minimum LED current value. Under the condition that the underwater turbidity NTU is 5 and the relative distance between the LED and the camera is 15 m, the camera can obtain the imaging of the LED. The current value at this state is recorded as the minimum current value of the LED;

[0068] Obtain the maximum LED current value. Adjust the LED to the rated voltage and increase the current. When the maximum power of the LED is reached, the brightness is the maximum. The current value at this state is recorded as the maximum current value of the LED;

[0069] Step 2.2: Discretize the brightness of the LED point light source. Discretize the brightness of the LED point light source into four segments. The brightness of the LED point light source is positively correlated with the current, and the current is controlled to achieve brightness control. The current corresponding to the discretized LED brightness is: low brightness T_min; medium brightness T_min+1 / 3(T_max-T_min); high brightness T_min+2 / 3(T_max-T_min); and extreme brightness T_max.

[0070] Step 2.3 Adaptive adjustment of LED point light source brightness under multiple turbidity. The relationship between turbidity and brightness is determined. According to the principle of underwater light scattering, high turbidity has strong scattering, and higher LED brightness will lead to the inability to obtain the center point of the marker, and the LED brightness needs to be reduced to ensure the feasibility of underwater visual guidance. Low turbidity has weak underwater light scattering, and according to the principle of underwater light absorption, the higher the brightness, the farther the underwater light propagation distance, and the LED brightness can be increased to expand the range of underwater visual guidance. The adaptive adjustment rule of LED point light source marker brightness is shown in Table 2. When the turbidity reaches the invalid turbidity, underwater visual guidance cannot be performed at this time, and the LED brightness is adjusted to the invalid brightness, i.e. the LED is turned off.

[0071] Table 2 Adaptive adjustment rule of LED point light source marker brightness

[0072] Haze LED brightness Low haze Limit brightness Medium haze High brightness High haze Medium brightness Limit haze Low brightness Invalid haze Invalid brightness

[0073] The specific process of image adaptive enhancement in step 3 is as follows:

[0074] Step 3.1: Calculate the image enhancement weight according to the turbidity. In step 2, the turbidity increases, the LED point light source brightness decreases, and the brightness value obtained by image acquisition also decreases, so the image enhancement weight needs to be increased. The image enhancement weight is proportional to the turbidity. The weight is represented as w = μT. w is the image enhancement weight value. μ is the proportional adjustment parameter, which is used for proportional adjustment between turbidity and weight. T is the turbidity. The proportional adjustment parameter value range is 0.5-1.5.

[0075] Step 3.2: MSR image enhancement is only for image pixels, and image enhancement on the image pixel level is completed. The collected image is an RGB three-color image, which is converted to a gray-scale image with a value of 0-255.

[0076] Step 3.3: Adaptive enhancement under multiple turbidity, on the basis of image brightness enhancement, combined with the factor of turbidity, the turbidity weight is further increased to the weight. The enhanced image is enhanced again, and the pixel weight is amplified.

[0077] The specific process of relative position and attitude calculation in step 4 is as follows:

[0078] Step 4.1: Key point extraction is performed on the enhanced image to find the local maximum value in the parameter space, and the corresponding circle center coordinates (u, v) and radius r are extracted. According to the actual situation, the parameters of Hough transform can be adjusted, such as the minimum / maximum radius range, accumulator threshold, etc., to obtain the best detection effect.

[0079] Step 4.2: The Hough circle detection may detect multiple circles, which need to be screened according to the actual situation. Radius range limit: according to the known size of the circular marker on the target object, a reasonable radius range is set to exclude obviously too large or too small circles. Center distance limit: if multiple circular markers are close to each other, the minimum distance between the centers can be set to avoid repeated detection. The detected circles are verified in combination with the color features of the image to improve accuracy.

[0080] Step 4.3: After screening and confirmation, the pixel coordinates of the four key points in the image (u1, v1), (u2, v2), (u3, v3), (u4, v4) are obtained. These coordinates will be used as input for the PnP algorithm.

[0081] Step 4.4: Using the four key point coordinates, the PnP (Perspective-n-Point) algorithm is used to solve the relative position and attitude, and the underwater visual guidance is completed. The output of the PnP algorithm is the rotation matrix r of the camera and the translation vector t. The rotation matrix r describes the rotation relationship of the camera coordinate system relative to the target object coordinate system, and the translation vector t describes the coordinates of the camera coordinate system origin in the target object coordinate system. r and t together constitute the pose of the camera relative to the target object, and the relative position and attitude are obtained, and the underwater visual guidance is completed.

[0082] The present application indirectly measures the temperature, salinity and concentration in water, and then fuses them to obtain the turbidity under water. By increasing the brightness of the LED marker under low turbidity and increasing the weight of the image to enhance the range of underwater visual guidance. Under high turbidity, reduce the brightness of the LED marker, and increase the weight of the image to ensure the feasibility of underwater visual guidance.

Claims

1. An underwater multi-turbidity adaptive visual guidance method, characterized in that Here are the steps: Step 1: Calibrate underwater turbidity sensing parameters: calculate the concentration of dissolved and suspended matter in water based on the temperature, salinity, and concentration of the underwater environment; The concentrations of dissolved matter and suspended matter are weighted and integrated to obtain real-time underwater turbidity; The underwater turbidity sensing parameters are calibrated with the real-time underwater turbidity to obtain the fused sensed underwater turbidity T: T=a n ×[a×P]+β n ×[b×C] Where: a is the NTU corresponding to unit salinity, b is the NTU corresponding to unit mass concentration, α n Contribution to dissolved matter, β n is the suspended solids contribution, C is the suspended solids concentration per unit volume, and the unit is kg / m 3 , P is the salinity per unit volume; Step 2: Determine the required current based on the relationship between underwater turbidity, LED brightness, and the brightness of the LED point light source, and perform adaptive adjustment of the LED point light source brightness: Turbidity is less than or equal to 5NTU, which is low turbidity. The brightness of the LED point light source adopts the extreme brightness, and the corresponding light source adjustment current is T_max. Turbidity greater than 5 and less than or equal to 10 NTU is medium turbidity, the LED point light source brightness uses high brightness, and the corresponding light source adjustment current is T_min+2 / 3(T_max-T_min); Turbidity greater than 10 and less than or equal to 15 NTU is considered high turbidity. The brightness of the LED point light source adopts medium brightness, and the corresponding light source adjustment current is T_min+1 / 3(T_max-T_min); The turbidity is greater than 15 and less than 20 NTU, which is the limit turbidity. The brightness of the LED point light source is low, and the corresponding light source adjustment current is T_min. Turbidity greater than or equal to 20 NTU is invalid and adaptive visual guidance is abandoned; Step 3: Based on the MSR image enhancement and the weight calculated by pixel brightness, the guided UAV performs adaptive enhancement on the image captured by the guidance LED light camera: Adjust the image enhancement weight w = μT according to the real-time underwater turbidity, where μ is a proportional adjustment parameter used to adjust the ratio between turbidity and weight; Step 4: Perform Hough circle detection on the image enhanced in step 3 to obtain the coordinates of the center points of the four LED point light sources. Use the PnP algorithm to solve the coordinates of the center points of the four LED point light sources to obtain the relative position and posture of the guided unmanned vehicle, completing the underwater visual guidance.

2. The underwater multi-turbidity adaptive visual guidance method according to claim 1, characterized in that: The salinity per unit volume Where θ0 is the water surface reference temperature, θ is the underwater temperature, k is the temperature coefficient, and S is the salinity.

3. The underwater multi-turbidity adaptive visual guidance method according to claim 1, characterized in that: The solid suspension concentration C per unit volume is obtained by using a concentration meter.

4. The underwater multi-turbidity adaptive visual guidance method according to claim 1, characterized in that: The α n For the contribution of dissolved matter, the fusion ratio was determined based on typical oceanographic studies: suspended matter dominated in nearshore or estuarine areas, with dissolved matter contributing α1: 30–50%, while dissolved matter had a greater impact in the open ocean, with dissolved matter contributing α2: 50–70%.

5. The underwater multi-turbidity adaptive visual guidance method according to claim 1, characterized in that: The β n For the contribution of suspended solids, the fusion ratio is determined based on typical oceanographic studies: suspended matter dominates nearshore or estuarine areas, with suspended solids contributing β1:50–70%; dissolved matter in the open ocean has a greater impact, with suspended solids contributing β2:30–50%.

6. The underwater multi-turbidity adaptive visual guidance method according to claim 1, characterized in that: The current value T_min is defined as the minimum brightness of the LED that meets the camera imaging requirements when the underwater turbidity NTU is 5 and the relative distance is 15m. The current value recorded in this state is expressed as the current value T_min of the minimum brightness of the LED point light source.

7. The underwater multi-turbidity adaptive visual guidance method according to claim 1, characterized in that: The maximum current T_max is the current value T_max at which the brightness of the LED point light source at the maximum current under the rated voltage is the maximum brightness of the LED point light source.

8. The underwater multi-turbidity adaptive visual guidance method according to claim 1, characterized in that: The proportional adjustment parameter μ has a value range of 0.5-1.

5.

9. An electronic device, characterized in that: The system comprises a processor and a memory, wherein the processor is configured to implement the steps of the underwater multi-turbidity adaptive visual guidance method according to any one of claims 1 to 8 when executing a computer program stored in the memory.

10. A computer program product, characterized in that The invention comprises computer executable instructions, which are used to implement the method of underwater multi-turbidity adaptive visual guidance as claimed in any one of claims 1 to 8 when being executed.

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