Intelligent lifting control method and system for lifting net cage

Abstract

The application relates to the technical field of aquaculture, and provides a lifting type net cage intelligent lifting control method and system.The method comprises the following steps: determining a plurality of lifting control strategies of a net cage according to distribution information of polluted water bodies in a water body; the lifting control strategy comprises a lifting direction and a lifting speed; for each lifting control strategy in the plurality of lifting control strategies, a polluted water body invasion index corresponding to the lifting control strategy is determined based on water flow information, the distribution information of the polluted water bodies in the water body, attribute information of the polluted water bodies and a cage structure of the net cage; a target lifting control strategy in the plurality of lifting control strategies is determined according to the polluted water body invasion indexes of the plurality of lifting control strategies, and the net cage is controlled to move according to the target lifting control strategy. Through the scheme, the efficiency of the lifting control of the net cage can be improved.

Description

Technical Field

[0001] This application relates to the field of aquaculture technology, and in particular to an intelligent lifting control method and system for lifting cages. Background Technology

[0002] In modern marine fisheries, lift-type net cage systems are widely used to ensure the healthy growth of high-value fish species. These systems typically sense changes in the surrounding aquatic environment, such as water temperature, salinity, and dissolved oxygen, and then automatically adjust the vertical position of the net cage to keep the fish in the most suitable environment. However, when encountering special emergencies, such as the presence of light, film-like pollutants floating on the water surface, existing control methods may, during hazard avoidance operations, inadvertently stir up the water due to the movement of the net cage itself, carrying the pollutants to the depth where the fish are located. This can reverse the intended hazard avoidance behavior and even worsen the harm to the fish.

[0003] Existing intelligent control systems for lifting net cages typically rely solely on collected water quality information to determine the evacuation direction (e.g., upward or downward), neglecting the significant disturbance and mixing effects the net cage itself, as a massive physical entity, generates on the water during lifting and lowering. This flow field change caused by the evacuation action itself can disrupt the original stratification of pollutants, forcibly mixing surface pollutants into the target safe water layer, thus rendering the evacuation action ineffective or even exacerbating the harm. For example, when a sudden pollution event occurs on the water surface, such as oil spills with light, layered distribution, the system may determine that it needs to sink to avoid the pollution. However, as the net cage sinks rapidly, its massive net and frame create strong downward currents and eddies in the surrounding water, causing the light oil film that was originally floating on the surface to be drawn into the sinking current and spread to deeper water layers, even entering the inside of the net cage, resulting in more severe direct poisoning of the fish.

[0004] In this situation, the control system becomes problematic because feedback indicates that executing the sinking command actually worsens the water quality at the target depth. The system might attempt to continue sinking or surfacing, but each movement could exacerbate water disturbance and pollution spread, causing stress to the fish and further environmental degradation. The entire intelligent control system fails to anticipate the subsequent environmental impact of its physical actions, leading to a failure in its emergency response. Summary of the Invention

[0005] This application provides an intelligent lifting control method and system for lifting cages, aiming to solve the technical problem that when existing lifting cages encounter water pollution, their avoidance operations may disturb the water body due to their own movement, which may aggravate the spread of pollution, leading to the failure of avoidance or even endangering the health of fish.

[0006] The technical solution of this application is as follows: In a first aspect, this application discloses an intelligent lifting control method for a lifting net cage. The net cage is equipped with a polluted water detection device. The method includes: when the polluted water detection device detects polluted water in the water body where the net cage is located, acquiring water flow information, distribution information of the polluted water in the water body, and polluted water attribute information of the polluted water; determining multiple lifting control strategies for the net cage based on the distribution information of the polluted water in the water body; the lifting control strategy includes lifting direction and lifting speed; for each of the multiple lifting control strategies, determining a polluted water invasion index corresponding to the lifting control strategy based on the water flow information, the distribution information of the polluted water in the water body, the polluted water attribute information, and the cage structure; the polluted water invasion index is used to indicate the degree of invasion of the polluted water on the fish in the net cage; determining a target lifting control strategy among the multiple lifting control strategies based on the polluted water invasion index of the multiple lifting control strategies, and controlling the net cage to move according to the target lifting control strategy.

[0007] Furthermore, based on the aforementioned intelligent lifting control method for the lifting cage, an acoustic Doppler current meter is also installed on the cage to acquire water flow information of the water body where the cage is located. This includes: acquiring the detection signal of the acoustic Doppler current meter; determining whether the signal quality index of the detection signal is less than a preset signal quality index threshold; when the signal quality index of the detection signal is greater than or equal to the preset signal quality index threshold, determining the water flow information of the water body where the cage is located based on the detection signal of the acoustic Doppler current meter; when the signal quality index of the detection signal is less than the preset signal quality index threshold, cleaning the sensor of the acoustic Doppler current meter and the water environment of the sensor, acquiring the detection signal of the acoustic Doppler current meter again, and when the signal quality index of the reacquired detection signal is greater than or equal to the preset signal quality index threshold, determining the water flow information of the water body where the cage is located based on the detection signal of the acoustic Doppler current meter; when the signal quality index of the reacquired detection signal is less than the preset signal quality index threshold, generating and sending target information to the management personnel to indicate abnormal detection environment of the acoustic Doppler current meter.

[0008] More specifically, in some implementations, the acoustic Doppler current meter's sensor is equipped with an oscillator, and the acoustic Doppler current meter is also equipped with a first water jet device and a clean water tank connected to the first water jet device, to clean the acoustic Doppler current meter's sensor and the sensor's aquatic environment, including: controlling the oscillator to vibrate at a preset frequency to remove deposits from the sensor; and controlling the first water jet device to spray water from the clean water tank along the sensor's signal transmission direction to move suspended matter in the sensor's aquatic environment away from the sensor.

[0009] Preferably, in the above-mentioned intelligent lifting control method for lifting cages, acquiring the detection signal of the acoustic Doppler current meter again includes: taking the difference between the preset signal quality index threshold and the signal quality index of the detection signal as the quality index deviation value; acquiring a first correspondence; the first correspondence includes multiple quality index deviation value ranges and multiple sampling frequency adjustment coefficients; the sampling frequency adjustment coefficient is greater than 1; taking the sampling frequency adjustment coefficient corresponding to the quality index deviation value range in the first correspondence as the target sampling frequency adjustment coefficient; taking the product of the current sampling frequency of the acoustic Doppler current meter and the target sampling frequency adjustment coefficient as the adjusted sampling frequency of the acoustic Doppler current meter, and acquiring the detection signal of the acoustic Doppler current meter again based on the adjusted sampling frequency.

[0010] Building upon the above, this application further proposes determining the pollution invasion index corresponding to the lifting and lowering control strategy based on water flow information, the distribution information of polluted water in the water body, the attribute information of the polluted water, and the cage structure. This includes: abstracting the polluted water as virtual particles based on the Lagrange particle tracking method and the distribution information of the polluted water in the water body; constructing a hydrodynamic simulation model of the cage based on the water flow information and the cage structure; using the hydrodynamic simulation model to simulate the additional flow field generated by the cage on the surrounding water body when the cage moves according to the lifting and lowering control strategy, given the water flow information in the water body; and determining the ratio of the number of virtual particles entering the cage to the total number of virtual particles when the cage moves according to the lifting and lowering control strategy, based on the hydrodynamic simulation model, the attribute information of the polluted water, and the virtual particles, and using this ratio as the pollution invasion index corresponding to the lifting and lowering control strategy.

[0011] In some preferred embodiments, the net cage is further equipped with an acoustic Doppler current meter, a second water jet device, and an image acquisition device to acquire polluted water body attribute information, including: controlling the second water jet device to spray water into the polluted water body, and acquiring an image set acquired by the image acquisition device, first information acquired by the acoustic Doppler current meter, and second information acquired by the acoustic Doppler current meter; the image set includes a first image and a second image, the first image including the pixel positions of the edge of the polluted water body in the image before the second water jet device sprays water into the polluted water body, and the second image including the pixel positions of the edge of the polluted water body in the image after the second water jet device sprays water into the polluted water body, the first information being the exponential decay coefficient of the velocity of the water jet sprayed by the second water jet device with distance, and the second information being the intensity and duration of the eddies generated by the water jet sprayed by the second water jet device; determining the polluted water body attribute information based on the image set, the first information, and the second information; the polluted water body attribute information includes the effective density, viscosity, and surface tension of the polluted water body.

[0012] Based on the above, this application further proposes that the net cage is also equipped with a third water jet device, which determines the target lifting control strategy among multiple lifting control strategies based on the pollution water invasion index of multiple lifting control strategies, and controls the net cage to move according to the target lifting control strategy, including: determining whether the smallest pollution water invasion index among multiple pollution water invasion indices is less than the target invasion index threshold; when the smallest pollution water invasion index is less than the target invasion index threshold, taking the lifting control strategy corresponding to the smallest pollution water invasion index as the target lifting control strategy, and controlling the net cage to move according to the target lifting control strategy; when the smallest pollution water invasion index is greater than or equal to the target invasion index threshold, controlling the third water jet device to spray water to drive away the pollution water around the net cage, taking the smallest pollution water invasion index as the target lifting control strategy, and controlling the net cage to move according to the target lifting control strategy.

[0013] More specifically, in some implementation schemes, determining whether the smallest pollution water body invasion index among multiple pollution water body invasion indices is less than a target invasion index threshold includes: obtaining the value index of fish in the net cage; multiplying the fish value index by a preset value adjustment coefficient as a target preset adjustment coefficient; multiplying the initial invasion index threshold by the target preset adjustment coefficient as a target invasion index threshold; and determining whether the smallest pollution water body invasion index among multiple pollution water body invasion indices is less than the target index threshold.

[0014] Preferably, in the above-mentioned intelligent lifting control method for lifting cages, controlling the third water jet device to spray water includes: taking the difference between the viscosity value of the polluted water and the preset viscosity value as the viscosity value deviation value; obtaining a second correspondence; the second correspondence includes a one-to-one correspondence between multiple viscosity value deviation value ranges and multiple water flow pressure adjustment coefficients; taking the water flow pressure adjustment coefficient corresponding to the viscosity value deviation value range in the second correspondence as the target water flow pressure adjustment coefficient; taking the product of the initial water flow pressure of the third water jet device and the target water flow pressure adjustment coefficient as the adjusted water flow pressure; and controlling the third water jet device to spray water onto the polluted water body at the adjusted water flow pressure.

[0015] Secondly, this application also discloses an intelligent lifting control system for a lifting cage, wherein the cage is equipped with a polluted water detection device, including: an acquisition device and a processing device; the acquisition device is used to acquire water flow information, distribution information of polluted water in the water body, and polluted water attribute information of the water body where the cage is located when the polluted water detection device detects the presence of polluted water in the water body where the cage is located; the processing device is used to input the distribution information of polluted water in the water body into a preset lifting control strategy model to obtain multiple lifting control strategies for the cage; the preset lifting control strategy model is used to determine the distribution information of polluted water in the water body. The distribution information in the water body determines multiple lifting and lowering control strategies for the fish cage; a processing device is used to determine the pollution water invasion index corresponding to each of the multiple lifting and lowering control strategies based on water flow information, the distribution information of polluted water in the water body, the attribute information of polluted water, and the cage structure; the pollution water invasion index is used to indicate the degree of invasion of polluted water on fish in the cage; the processing device is used to determine the target lifting and lowering control strategy among the multiple lifting and lowering control strategies based on the pollution water invasion index of the multiple lifting and lowering control strategies, and control the cage to move according to the target lifting and lowering control strategy.

[0016] Beneficial effects The intelligent lifting and lowering control method for lifting net cages disclosed in this application, when a polluted water body is detected by a polluted water detection device, not only acquires information on water flow, distribution, and attributes of the polluted water body, but more importantly, it can predict the degree of invasiveness of the polluted water body on the fish in the net cage under different lifting and lowering control strategies based on this information and the cage structure, and select the optimal target lifting and lowering control strategy accordingly. This technical solution overcomes the limitations of existing technologies that rely solely on water quality information for unidirectional risk avoidance decisions, effectively solving the problem that the movement of the net cage itself may disturb the water body and exacerbate the spread of pollution.

[0017] By introducing the polluted water invasion index as a quantitative indicator, this application can accurately assess the potential environmental impact of cage movements, thus avoiding the counterproductive effects of traditional risk avoidance behaviors. For example, when light oil pollution appears on the water surface, this application will not blindly sink, but will comprehensively assess the impact of various strategies such as sinking, floating, or remaining stationary on the spread of oil pollution, selecting the strategy with the lowest invasion index, and even combining it with active repellency measures when necessary to ensure that the fish are always in the safest environment. Therefore, this application can significantly improve the response capability of lift-type cages in sudden pollution events, protect the healthy growth of high-value fish, and has significant and superior technical effects. Attached Figure Description

[0018] Figure 1 A flowchart illustrating an intelligent lifting control method for a lifting cage provided in this application; Figure 2A flowchart illustrating another intelligent lifting control method for a lifting cage provided in this application; Figure 3 This application provides a schematic diagram of the architecture of an intelligent lifting control system for a lifting cage. Detailed Implementation

[0019] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] Traditional, existing lifting net cage systems, when dealing with water pollution, typically adjust the cage position solely based on water quality parameters, neglecting the potential disturbance to the water body caused by the cage's movement. This can lead to the spread of pollutants and exacerbate the harm to fish. For example, when light oil slicks appear on the water surface, lowering the cage to avoid the pollution may actually wash the oil into deeper water, making the avoidance operation counterproductive.

[0022] In this regard, such as Figure 1 As shown, this application proposes an intelligent lifting control method for a lifting cage, wherein the cage is equipped with a polluted water detection device, and the method includes: S101. When the polluted water body detection device detects the presence of polluted water in the water body where the cage is located, it acquires the water flow information, the distribution information of the polluted water in the water body, and the polluted water body attribute information of the polluted water.

[0023] S102. Determine multiple lifting and lowering control strategies for the cages based on the distribution information of polluted water bodies in the water bodies; the lifting and lowering control strategies include lifting direction and lifting speed.

[0024] S103. For each of the multiple lifting control strategies, based on water flow information, the distribution information of polluted water in the water body, the attribute information of polluted water, and the cage structure, determine the polluted water invasion index corresponding to the lifting control strategy; the polluted water invasion index is used to indicate the degree of invasion of polluted water on fish in the cage.

[0025] S104. Determine the target lifting control strategy among the multiple lifting control strategies based on the polluted water invasion index of multiple lifting control strategies, and control the cage to move according to the target lifting control strategy.

[0026] This application, by comprehensively considering the impact of water flow, pollution distribution, pollution properties, and cage structure on pollution diffusion, can predict the risk of pollution invasion under different lifting and lowering strategies, thereby selecting the optimal risk avoidance strategy. This effectively avoids the drawbacks of traditional methods where cage movement exacerbates pollution, and significantly improves the safety and intelligence level of cage aquaculture.

[0027] To better understand the technical solution proposed in this application, some key terms involved will be explained first.

[0028] "Pollution water detection device" refers to a device that can sense and identify the presence of pollutants in water. It may include, but is not limited to, optical sensors, chemical sensors or biological sensors, and is used to monitor the pollution status of the water body around the cage in real time.

[0029] "Water flow information" refers to the dynamic characteristics of the water body where the net cage is located, such as the flow velocity and direction. This information is crucial for predicting the diffusion path of pollutants.

[0030] "Distribution information of polluted water bodies in water bodies" refers to the static or dynamic distribution characteristics of polluted water bodies in three-dimensional space, such as the location, shape, and concentration gradient, for example, the layered or clumped distribution of pollutants.

[0031] "Polluted water body attribute information" refers to the physicochemical properties of pollutants, such as density, viscosity, and surface tension. These properties directly affect the migration and diffusion behavior of pollutants in water bodies.

[0032] "Lifting and lowering control strategy" refers to the vertical movement scheme of the cage, including the specific lifting and lowering direction (floating or sinking) and lifting and lowering speed, which aims to guide the cage to avoid polluted areas.

[0033] The "Pollution Water Invasion Index" is a quantitative indicator used to assess the potential for pollution water to invade fish in net cages under specific rise and fall control strategies. The higher the index, the greater the risk of invasion.

[0034] "The cage structure" refers to the physical characteristics of the cage, such as its geometry, size, and mesh size. These structural parameters affect the disturbance to the surrounding water flow when the cage moves in the water.

[0035] The core of the intelligent lifting control method for lifting cages proposed in this application lies in achieving intelligent risk avoidance of the cages through refined environmental perception and prediction models.

[0036] Specifically, when a polluted water detection device detects polluted water in the water body where the net cage is located, it is necessary to obtain information on water flow, the distribution of polluted water, and the properties of the polluted water. For example, the detection device can use an optical sensor array to determine the presence of pollution by analyzing changes in the water's transmittance. Water flow information can be obtained using an acoustic Doppler current meter (ADCP) installed on the net cage. This device emits sound waves and receives echoes, calculating the flow velocity and direction through the Doppler effect. The distribution of polluted water can be obtained through a multi-point water quality sensor network. These sensors are distributed at different depths and locations around the net cage, monitoring pollutant concentrations in real time to construct a three-dimensional distribution map of the pollutants. The properties of the polluted water can be obtained through water sample collection and laboratory analysis, or through real-time detection using a spectrometer integrated into the detection device. For example, the density, viscosity, and other properties of pollutants can be inferred by analyzing their spectral characteristics.

[0037] Based on the distribution information of polluted water in the water body, multiple lifting and lowering control strategies for the net cages can be determined. These strategies include the lifting direction and lifting speed. The generation of these strategies can be based on a preset rule base. For example, when the polluted layer is above the net cage, the lifting direction in multiple lifting and lowering control strategies is determined to be downward, and the lifting speeds are determined to be 0.1 m / s, 0.2 m / s, etc.; when the polluted layer is below the net cage, the lifting direction in multiple lifting and lowering control strategies is determined to be upward, and the lifting speeds are determined to be 0.1 m / s, 0.2 m / s, etc.

[0038] For each of the multiple lifting and lowering control strategies, a pollution invasion index needs to be determined based on water flow information, the distribution of polluted water within the water body, the properties of the polluted water, and the structure of the fish cage. The pollution invasion index indicates the degree of invasion of polluted water onto the fish in the fish cage. For example, a fluid dynamics model can be constructed to simulate the disturbance to the surrounding water body caused by the fish cage moving according to a certain lifting and lowering strategy under specific water flow conditions. Then, by combining the distribution and property information of the polluted water, the diffusion path and velocity of pollutants in the disturbed flow field, as well as the amount of pollutants that may enter the fish cage, can be predicted. For example, if the fish cage sinks too quickly, it may generate a strong downward current, entraining surface pollutants to the depth where the fish cage is located. The pollution invasion index can be quantified by calculating the concentration or quantity of pollutants entering the fish cage. This index can be calculated using numerical simulation methods, such as computational fluid dynamics (CFD) simulation, or by rapid estimation based on a simplified model.

[0039] Finally, based on the pollution water invasion index of multiple lifting control strategies, a target lifting control strategy is determined, and the cage is controlled to move according to the target lifting control strategy. For example, the lifting control strategy with the lowest pollution water invasion index can be simply selected as the target lifting control strategy. If multiple strategies have the same minimum invasion index, selection can be based on other auxiliary indicators, such as the strategy with the lowest energy consumption or the strategy with the shortest movement distance. Once the target lifting control strategy is determined, the control system sends instructions to the lifting actuator of the cage, such as controlling the hydraulic system or electric winch, to move the cage according to the specified lifting direction and speed, thereby achieving intelligent risk avoidance.

[0040] The intelligent lifting and lowering control method for lifting cages proposed in this application works by constructing a closed-loop intelligent decision-making system to address the threat of water pollution to cage aquaculture. When the pollution detection device detects pollution in the water, the system immediately activates the information acquisition module to comprehensively collect information on water flow, the distribution of polluted water, and the properties of the polluted water in the cage. This information forms the basis for subsequent decision-making, ensuring a comprehensive and accurate understanding of the pollution situation.

[0041] After acquiring this crucial information, the system intelligently generates multiple potential lifting and lowering control strategies based on the distribution of polluted water within the water body. These strategies not only include the direction of the cage's movement (floating or sinking) but also specify the exact lifting and lowering speeds to address different types of pollution distribution. For example, if the pollution is concentrated on the surface, the system tends to generate a sinking strategy; if the pollution is in deeper layers, it may generate a floating strategy.

[0042] Next, for each generated lifting and lowering control strategy, the system conducts a refined risk assessment. This includes determining the pollution invasion index corresponding to the strategy through complex model calculations based on water flow information, the distribution of polluted water in the water body, the properties of the polluted water, and the structure of the net cage itself. This index is one of the core innovations of this application; it quantifies the potential degree of pollution invasion on fish in the net cage when the strategy is implemented. For example, the system simulates how the disturbance of the net cage to the water body affects the diffusion of pollutants when it moves according to a certain strategy under specific water flow and pollution conditions, and how much pollutant may be drawn into the net cage. This predictive capability allows the system to anticipate the potential negative impacts of its actions, thereby avoiding situations where good intentions lead to bad outcomes.

[0043] Ultimately, the system compares the pollution invasion index of all candidate lifting and lowering control strategies and selects the strategy with the lowest invasion index as the target lifting and lowering control strategy. This means the system chooses the avoidance scheme with the least risk to the fish in the cage. Once the target strategy is determined, the system controls the cage to move precisely according to the strategy, thereby effectively avoiding polluted areas and maximizing the protection of the fish's health. The entire process forms an intelligent closed loop from perception, prediction, decision-making to execution, ensuring that the cage can make the optimal response when facing pollution threats.

[0044] The intelligent lifting and lowering control method for lifting cages proposed in this application demonstrates significant technological progress and innovation in addressing the problem that cage avoidance operations may exacerbate pollution spread in existing technologies. Traditional intelligent control systems for lifting cages typically rely solely on collected water quality information to determine the avoidance direction, neglecting the fact that the cage itself, as a large physical entity, generates strong disturbances and mixing effects on the water body during lifting and lowering. These changes in the flow field caused by the avoidance action itself may disrupt the original stratification of pollutants, forcibly mixing surface pollutants into the target safe water layer, thereby rendering the avoidance action ineffective or even exacerbating the harm.

[0045] In contrast, the core innovation of this application lies in the introduction of the concept of a "polluted water invasion index." By comprehensively considering water flow information, the distribution of polluted water in the water body, the properties of the polluted water, and the structure of the cage, it predicts and quantifies the risk of pollution invasion under different lifting and lowering control strategies. This predictive capability allows this application to anticipate the potential impact of the cage's own movement on the aquatic environment, thus avoiding the drawbacks of traditional methods that exacerbate pollution due to blind risk avoidance. For example, when a sudden pollution event such as oil spills occurs on the water surface, a traditional system might determine that it needs to sink to avoid the pollution. However, the downward current and eddies generated when the cage sinks rapidly may cause the light oil film that was originally floating on the surface to be drawn into the sinking current and spread to deeper water layers, or even into the interior of the cage. This application can identify such potential risks by calculating the pollution invasion index at different sinking speeds, and select an optimized strategy that can avoid pollution without exacerbating its spread, such as choosing a slower sinking speed or a completely different surfacing strategy (if the surfacing risk is lower).

[0046] Furthermore, this application provides more flexible and intelligent risk avoidance options for fish cages by generating multiple lifting control strategies and conducting risk assessments on them. This multi-strategy assessment mechanism enables the system to dynamically adjust its risk avoidance plan based on real-time environmental data, thereby consistently selecting the optimal course of action in complex and ever-changing aquatic pollution scenarios. This ability to predict and optimize the impact of its own behavior is not available in existing technologies, significantly enhancing the intelligence level of the lifting fish cage system and its effectiveness in fish conservation.

[0047] This application further proposes a method for obtaining water flow information of the water body where the net cage is located.

[0048] The net cage is also equipped with an acoustic Doppler current meter to obtain water flow information of the water body in which the net cage is located, including: The system acquires the detection signal from the acoustic Doppler current meter; determines whether the signal quality index of the detection signal is less than a preset signal quality index threshold; when the signal quality index of the detection signal is greater than or equal to the preset signal quality index threshold, it determines the water flow information of the water body where the cage is located based on the detection signal from the acoustic Doppler current meter; when the signal quality index of the detection signal is less than the preset signal quality index threshold, it cleans the sensor of the acoustic Doppler current meter and the water environment of the sensor, acquires the detection signal from the acoustic Doppler current meter again, and when the signal quality index of the reacquired detection signal is greater than or equal to the preset signal quality index threshold, it determines the water flow information of the water body where the cage is located based on the detection signal from the acoustic Doppler current meter; when the signal quality index of the reacquired detection signal is less than the preset signal quality index threshold, it generates and sends target information to the management personnel to indicate abnormal detection environment of the acoustic Doppler current meter.

[0049] Specifically, an acoustic Doppler current meter is a device that uses the Doppler effect of sound waves to measure the velocity and direction of water flow. It acquires information about particle motion in the water by emitting and receiving sound signals, thereby deducing the flow information. Its detection signal carries detailed data about the water flow, but its quality directly affects the accuracy of the measurement results. The signal quality index is an indicator used to quantify the reliability of the acoustic Doppler current meter's detection signal. It can be calculated based on parameters such as signal strength, signal-to-noise ratio, and correlation coefficient. A higher value generally indicates better signal quality and more reliable data. The preset signal quality index threshold is a benchmark value set based on actual application needs and experience, used to distinguish whether the signal quality meets the requirements of subsequent data processing and application. When the signal quality index of the detected signal is lower than the preset threshold, it indicates that the acoustic Doppler current meter's sensor may be contaminated or that there is interference from the surrounding water environment, leading to unreliable data.

[0050] At this point, the sensor surface of the acoustic Doppler current meter needs to be cleaned to remove any adhering substances, and the surrounding water environment needs to be treated to remove suspended matter or other sources of interference, thereby restoring the sensor to normal operating condition. After cleaning, the detection signal from the acoustic Doppler current meter is acquired again, and its signal quality is reassessed. If the signal quality still does not reach the preset threshold after cleaning, it indicates a possible deeper fault or environmental anomaly. In this case, the system will generate target information and send it to management personnel for timely manual inspection and maintenance to ensure the continuous and stable operation of the system.

[0051] This application's solution effectively addresses the accuracy issues that may arise during water flow information acquisition by introducing a mechanism to assess the signal quality of the acoustic Doppler current meter and combining it with automatic cleaning and anomaly alarm procedures. Specifically, when the acoustic Doppler current meter's signal quality is poor, the system does not directly use this potentially inaccurate data. Instead, it first attempts to restore the sensor and its surrounding water environment to normal operation. This proactive maintenance measure promptly removes physical obstacles affecting sensor performance, thereby improving the quality of the reacquired signal. If the signal quality still fails to meet requirements after cleaning, an anomaly message is sent to management personnel, prompting timely human intervention and preventing the system from making erroneous decisions based on unreliable data. This ensures that the water flow information provided for subsequent lifting and lowering control strategies is accurate and reliable, providing a solid data foundation for the intelligent lifting and lowering control of the cage.

[0052] like Figure 2 As shown, this application further proposes the following steps for cleaning the sensor of the acoustic Doppler current meter and the aquatic environment of the sensor: S201. Control the oscillator to oscillate at a preset frequency so that the attachment on the sensor is removed from the sensor.

[0053] S202. Control the first water jet device to spray water in the water tank along the signal transmission direction of the sensor, so as to keep suspended matter in the water environment of the sensor away from the sensor.

[0054] Specifically, the oscillator can be understood as a device capable of generating mechanical vibration, which is installed on the sensor of the acoustic Doppler current meter. When the oscillator vibrates at a preset frequency, the generated mechanical vibration can effectively act on the sensor surface, causing biological dirt, silt, or other impurities attached to the sensor to loosen and detach from the sensor surface due to the vibration. The preset frequency can be adjusted according to factors such as the sensor material, the type of attachment, and the adhesion strength to achieve the best cleaning effect. The first water jet device is configured to spray water outwards and is connected to a clean water tank. The clean water tank is used to store clean water. When the first water jet device is controlled to spray water, the clean water in the tank is sprayed out along the signal transmission direction of the sensor. This aims to use the impact force of the water flow to disperse and carry away suspended matter (such as silt, algae fragments, etc.) in the surrounding aquatic environment from the sensor area, thereby providing a clean detection environment for the sensor and avoiding scattering and attenuation of the acoustic signal by suspended matter.

[0055] This application's solution achieves coordinated and efficient cleaning of the acoustic Doppler current meter sensor and its surrounding aquatic environment by introducing an oscillator and a first water jet device. Specifically, the oscillator, through mechanical vibration, physically removes deposits from the sensor surface, solving the problem of incomplete cleaning that may exist with traditional methods. Simultaneously, the first water jet device actively removes suspended matter from the aquatic environment surrounding the sensor by spraying clean water, preventing interference from suspended matter on acoustic signal transmission. It is precisely this combination of two cleaning mechanisms that allows the sensor to acquire signals in a cleaner surface and environment, thereby significantly improving the quality of the detection signal and the accuracy of water flow information.

[0056] The above technical solution effectively addresses the issue of signal quality degradation in acoustic Doppler current meter sensors caused by adhering substances and suspended matter in the water. The application of an oscillator ensures the cleanliness of the sensor surface, while the first water jet device guarantees the purity of the surrounding water environment. The synergistic effect of these two components significantly improves the accuracy and reliability of water flow information acquired by the acoustic Doppler current meter, providing more precise basic data for subsequent calculations of polluted water invasion indices and cage lifting control, thereby enhancing the effectiveness of the entire intelligent lifting control method.

[0057] This application further proposes steps for re-acquiring the detection signal from the acoustic Doppler current meter, including: The difference between the preset signal quality index threshold and the signal quality index of the detected signal is taken as the quality index deviation value; a first correspondence is obtained; the first correspondence includes multiple quality index deviation value ranges and multiple sampling frequency adjustment coefficients; the sampling frequency adjustment coefficient is greater than 1; the sampling frequency adjustment coefficient corresponding to the quality index deviation value range in the first correspondence is taken as the target sampling frequency adjustment coefficient; the product of the current sampling frequency of the acoustic Doppler current meter and the target sampling frequency adjustment coefficient is taken as the adjusted sampling frequency of the acoustic Doppler current meter, and the detection signal of the acoustic Doppler current meter is collected again based on the adjusted sampling frequency.

[0058] Specifically, the quality index deviation value refers to the difference between the preset signal quality index threshold and the signal quality index of the currently detected signal. This difference quantifies the degree of deviation between the current signal quality and the acceptable threshold. The first correspondence can be understood as a pre-established mapping table or functional relationship, the purpose of which is to provide corresponding sampling frequency adjustment coefficients based on different quality index deviation value ranges. The sampling frequency adjustment coefficient is a value greater than 1, used to indicate how to increase the sampling frequency of the acoustic Doppler current meter when the signal quality is poor. The target sampling frequency adjustment coefficient is the adjustment coefficient most suitable for the current signal quality condition, found from the first correspondence based on the calculated quality index deviation value. By multiplying the current sampling frequency of the acoustic Doppler current meter by this target sampling frequency adjustment coefficient, the adjusted sampling frequency can be obtained, thereby guiding the acoustic Doppler current meter to acquire signals with more optimized parameters.

[0059] This application's solution addresses the problem of inefficiency or poor results from simple, repeated sampling when signal quality is poor by dynamically adjusting the sampling frequency of the acoustic Doppler current meter. Specifically, when the signal quality index of the detected signal is lower than a preset threshold, the system first calculates the degree of signal quality deviation. Based on this deviation, a suitable sampling frequency adjustment coefficient can be determined through a preset first correspondence. Since the sampling frequency adjustment coefficient is greater than 1, this means that the sampling frequency of the acoustic Doppler current meter will be increased when the signal quality is poor. Increasing the sampling frequency allows the acoustic Doppler current meter to collect more acoustic echo data points per unit time. These additional data points help to perform more effective noise suppression and signal enhancement in subsequent signal processing, such as reducing the impact of random noise through averaging, thereby improving the accuracy and reliability of the final acquired water flow information. This adaptive sampling frequency adjustment mechanism allows the system to flexibly optimize the data acquisition process according to the actual water environment and signal quality conditions, avoiding unnecessary physical cleaning and improving the system's response speed and operating efficiency.

[0060] Specifically, in the above-mentioned intelligent lifting control method for lifting cages, the step of determining the polluted water invasion index corresponding to each of the multiple lifting control strategies can be further refined based on water flow information, the distribution information of polluted water in the water body, the attribute information of polluted water, and the cage structure.

[0061] Based on water flow information, the distribution information of polluted water in the water body, the attribute information of polluted water, and the cage structure, the polluted water invasion index corresponding to the lifting and lowering control strategy is determined, including: Based on the Lagrange particle tracking method, polluted water is abstracted into virtual particles according to the distribution information of polluted water in the water body. A hydrodynamic simulation model of the net cage is constructed based on the water flow information and the cage structure. The hydrodynamic simulation model is used to simulate the additional flow field generated by the net cage on the surrounding water body when the net cage moves according to the lifting control strategy, given the water flow information. Based on the hydrodynamic simulation model, the polluted water attribute information, and the virtual particles, the ratio of the number of virtual particles entering the net cage to the total number of virtual particles when the net cage moves according to the lifting control strategy is determined, and this ratio is used as the polluted water invasion index corresponding to the lifting control strategy.

[0062] The Lagrange particle tracking method is a widely used numerical simulation technique in fluid mechanics. Its core idea is to abstract substances in a fluid (such as polluted water) as a series of discrete virtual particles and track the trajectories of these virtual particles in the flow field. This method can effectively simulate the diffusion, migration, and distribution of polluted water in a water body. Virtual particles can be understood as abstract entities representing tiny units of polluted water, and their trajectories and behaviors are influenced by factors such as water flow information, polluted water properties, and the structure of the net cage.

[0063] The hydrodynamic simulation model aims to accurately describe the impact of a net cage moving in a body of water on the surrounding water flow field. This model can be constructed based on computational fluid dynamics (CFD) methods, such as solving the Navier-Stokes equations using the finite element method, finite volume method, or finite difference method. By inputting water flow information and the net cage's structural parameters, the model can simulate the additional flow field generated by the net cage on the surrounding water when it moves under different lifting control strategies. The additional flow field refers to the local water flow disturbance caused by the net cage's own movement, in addition to the original water flow information, during the net cage's movement.

[0064] The polluted water invasion index is determined by calculating the ratio of the number of virtual particles entering the net cage to the total number of virtual particles. In a hydrodynamic simulation model, the movement of virtual particles under the combined influence of the additional flow field and the original water flow information can be simulated when the net cage moves according to a specific lifting and lowering control strategy. This ratio is obtained by statistically analyzing the number of virtual particles that cross the net cage boundary and enter the net cage within the simulation time and comparing it to the total number of virtual particles in the simulation area. This ratio directly reflects the potential invasion degree of polluted water on fish in the net cage under a specific lifting and lowering control strategy.

[0065] This application's scheme abstracts polluted water bodies into virtual particles and combines this with a hydrodynamic simulation model to precisely simulate the movement trajectory of polluted water bodies around fish cages and the probability of them entering the cages. Specifically, the Lagrange particle tracking method quantifies the diffusion and migration process of polluted water bodies, while the hydrodynamic simulation model accurately captures the impact of cage movement on local water flow, i.e., the additional flow field. It is precisely because of these refined simulations that the calculation of the polluted water invasion index is more accurate and reliable. By calculating the ratio of the number of virtual particles entering the cage to the total number of virtual particles, the invasion risk of polluted water bodies to fish within the cages can be directly quantified, thus providing a scientific basis for determining subsequent target elevation and depression control strategies.

[0066] The above technical solution provides a more accurate and quantitative method to assess the invasiveness of polluted water to fish in net cages under different lifting and lowering control strategies. This method utilizes Lagrange particle tracking and hydrodynamic simulation to overcome potential errors in traditional empirical judgments or simplified models, significantly improving the calculation accuracy of the polluted water invasion index. Therefore, it can more effectively identify lifting and lowering control strategies that minimize pollution risk, providing more reliable decision support for intelligent lifting and lowering control of net cages and ensuring the health and safety of farmed fish within the cages.

[0067] This application further proposes a method for obtaining polluted water body attribute information, wherein the net cage is also equipped with an acoustic Doppler current meter, a second water jet device, and an image acquisition device. The method for obtaining polluted water body attribute information specifically includes the following steps: The system controls a second water jet device to spray water into the polluted water body and acquires an image set acquired by an image acquisition device, first information acquired by an acoustic Doppler current meter, and second information acquired by an acoustic Doppler current meter. The image set includes a first image and a second image. The first image includes the pixel positions of the edge of the polluted water body in the image before the second water jet device sprays water into the polluted water body, and the second image includes the pixel positions of the edge of the polluted water body in the image after the second water jet device sprays water into the polluted water body. The first information is the exponential decay coefficient of the velocity of the water jet sprayed by the second water jet device with distance, and the second information is the intensity and duration of the eddies generated by the water jet sprayed by the second water jet device. Based on the image set, the first information, and the second information, the system determines the polluted water body attribute information, including the effective density, viscosity, and surface tension of the polluted water body.

[0068] Specifically, the acoustic Doppler current meter is a device that measures water flow velocity using the Doppler effect of sound waves. It provides high-precision water flow velocity data, including the exponential decay coefficient of the water flow velocity with distance and the intensity and duration of the eddies generated by the water flow. A second water jet device is configured to jet a water flow with specific pressure and flow rate into the polluted water body to generate observable disturbances within it. An image acquisition device is used to capture images of the polluted water body before and after the water jet jet to record changes in the position of the polluted water body's edge. The first and second images in the image set record the pixel positions of the polluted water body's edge in the images before and after the water jet jet, respectively. This positional information is key data for analyzing the physical properties of the polluted water body. The first and second information are provided by the acoustic Doppler current meter and used to quantify the dynamic characteristics of the interaction between the jet water flow and the polluted water body. Polluted water body properties, namely effective density, viscosity, and surface tension, are important parameters describing the physical properties of the polluted water body. They directly affect the diffusion and mixing of the polluted water in the water body and its ability to invade fish in net cages.

[0069] This application's solution achieves precise acquisition of polluted water body attribute information through active water jet injection combined with multi-source sensor data. Specifically, when the second water jet injection device injects water into the polluted water body, the water jet interacts with the polluted water body, altering its shape and motion. The image acquisition device captures a set of images before and after the water jet injection. By comparing the changes in the pixel positions of the polluted water body's edges in the first and second images, the deformation and displacement of the polluted water body under the impact of the water jet can be quantified. This is closely related to the effective density and surface tension of the polluted water body. Simultaneously, the first information (exponential decay coefficient of water flow velocity with distance) and the second information (eddy current intensity and duration) collected by the acoustic Doppler current meter provide the dynamic characteristics of water flow propagation and dissipation in the polluted water body. These characteristics directly reflect the viscosity of the polluted water body. By comprehensively analyzing this multi-source data, including the deformation of the polluted water body's edges, the decay of water flow velocity, and the generation and dissipation of eddies, a physical model can be established or machine learning algorithms can be used to accurately calculate key attribute information such as the effective density, viscosity, and surface tension of the polluted water body. This active detection and multi-sensor fusion approach overcomes the limitations of traditional passive observation in obtaining the deep physical properties of polluted water bodies.

[0070] This application further proposes an intelligent lifting control method for a lifting cage, which includes a third water jet device installed on the cage. Based on the polluted water invasion index of multiple lifting control strategies, a target lifting control strategy is determined among the multiple lifting control strategies, and the cage is controlled to move according to the target lifting control strategy. Specifically, this includes: Determine whether the smallest pollution water body invasion index among multiple pollution water body invasion indices is less than the target invasion index threshold; if the smallest pollution water body invasion index is less than the target invasion index threshold, use the lifting and lowering control strategy corresponding to the smallest pollution water body invasion index as the target lifting and lowering control strategy, and control the cage to move according to the target lifting and lowering control strategy; if the smallest pollution water body invasion index is greater than or equal to the target invasion index threshold, control the third water jet device to spray water to drive away the pollution water body around the cage, and use the smallest pollution water body invasion index as the target lifting and lowering control strategy, and control the cage to move according to the target lifting and lowering control strategy.

[0071] Specifically, a third-stage water jet device can be understood as a device capable of spraying water into a body of water. Its purpose is to use the impact or disturbance of the water flow to drive away or dilute polluted water around the net cage, thereby reducing the concentration of pollutants inside the net cage. This device can be configured on the outside of the net cage, such as by installing it on the side wall or bottom, to effectively spray water towards the polluted water. In practical applications, the third-stage water jet device can take various forms, such as nozzles connected to a high-pressure water pump or propeller-type propellers. The intensity, direction, and duration of the sprayed water flow can all be adjusted according to actual needs.

[0072] A key decision point in this scheme is determining whether the smallest pollution invasion index among multiple pollution invasion indices is less than the target invasion index threshold. The target invasion index threshold is a preset, acceptable upper limit for pollution invasion levels, used to measure whether the risk faced by fish in the net cages is within a safe range. When the smallest pollution invasion index is less than this threshold, it indicates that the pollution invasion risk to the fish can be controlled to an acceptable level simply by moving the net cages. In this case, no additional pollution intervention measures are needed, and the corresponding lifting and lowering control strategy can be directly implemented. However, when the smallest pollution invasion index is greater than or equal to the target invasion index threshold, it means that relying solely on net cage movement is insufficient to effectively protect the fish, and an active intervention mechanism needs to be activated.

[0073] Based on this, when the minimum pollution water invasion index is greater than or equal to the target invasion index threshold, the scheme of this application controls the third water jet device to spray water. This aims to actively intervene in the aquatic environment around the cage through physical means, such as by generating local water flow or eddies, to push away, dilute, or accelerate the diffusion of polluted water near the cage, thereby further reducing the pollution concentration around the cage before or during its movement. After the water jetting is completed to drive away the polluted water, the cage will still move according to the previously determined lifting and lowering control strategy with the minimum pollution water invasion index. This combined control strategy ensures that more proactive and effective measures can be taken to protect the fish in the cage when the pollution risk is high.

[0074] This application's solution effectively addresses the technical limitations of relying solely on cage movement when high pollution risks cannot be adequately mitigated. Specifically, the third water jet device is activated when the system determines that even with the optimal movement strategy, the pollution's impact on fish in the cage will exceed a preset safety threshold. This device actively intervenes physically in the polluted water surrounding the cage by jetting water, such as pushing it away from the cage area or accelerating its dilution. This reduces the actual pollution concentration in the cage's environment before or simultaneously with cage movement. This proactive intervention mechanism, combined with the cage's movement strategy, forms a more comprehensive and flexible pollution response system, significantly enhancing the cage's ability to protect fish in complex or highly polluted water environments.

[0075] Through the aforementioned technical solution, this application provides a higher level of intelligent protection for lift-type net cages. Especially when the pollution intrusion index of the water body is high, and simply moving the net cage is insufficient to ensure fish safety, the active intervention capability of the third water jet device can significantly reduce the pollution concentration around the net cage, thereby effectively reducing the risk of fish exposure to polluted water. This strategy, combining active repulsion and passive avoidance, enables the net cage to provide more reliable and efficient protection when facing severe pollution challenges, further safeguarding the health and economic benefits of farmed fish.

[0076] This application further proposes the following steps for determining whether the smallest pollution water body invasion index among multiple pollution water body invasion indices is less than the target invasion index threshold: Obtain the value index of fish in the net cage; multiply the value index of fish by a preset value adjustment coefficient as the target preset adjustment coefficient; multiply the initial invasion index threshold by the target preset adjustment coefficient as the target invasion index threshold; determine whether the smallest pollution water body invasion index among multiple pollution water body invasion indices is less than the target index threshold.

[0077] Specifically, obtaining the value index of fish in a net cage refers to a quantitative indicator of the economic or ecological value of the fish currently being farmed in the net cage. For example, for fish with high economic value, their value index will be set to a higher value; conversely, for fish with low economic value, their value index will be set to a lower value.

[0078] The preset value adjustment coefficient can be understood as a proportional factor used to convert the value index of fish into an impact on the invasion index threshold. This coefficient can be optimized based on empirical data, expert knowledge, risk assessment models, or historical operational data to ensure that changes in fish value are reasonably reflected in the adjustment of the target invasion index threshold.

[0079] The target preset adjustment coefficient is an intermediate result obtained by multiplying the fish's value index by a preset value adjustment coefficient. Its function is to transform the fish's value information into a factor that can be used to adjust the threshold. In some implementations, this target preset adjustment coefficient can be used directly as the target preset adjustment coefficient.

[0080] The initial invasion index threshold is a pre-set baseline invasion index threshold without considering fish value factors. This initial threshold can be determined based on factors such as general pollution risk standards, water quality requirements, or cage structure characteristics.

[0081] The target invasion index threshold is the final threshold obtained by multiplying the initial invasion index threshold by the target preset adjustment coefficient. This target invasion index threshold is dynamically adjusted and can be personalized according to the actual value of the fish in the cage, thereby achieving more refined pollution avoidance decisions.

[0082] This application's solution addresses the problem of static threshold setting in traditional methods by introducing a value index for fish in the net cages and dynamically adjusting the target invasion index threshold based on this value index. Specifically, after obtaining the value index of the fish in the net cages, this value index is multiplied by a preset value adjustment coefficient to generate a target preset adjustment coefficient. This target preset adjustment coefficient is then used to adjust the initial invasion index threshold, transforming it into a target invasion index threshold closely related to the value of the fish. Thus, when high-value fish are cultured in the net cages, the target invasion index threshold is lowered accordingly, making the system more sensitive to the invasion of polluted water and enabling earlier and more proactive avoidance measures. Conversely, when low-value fish are cultured, the target invasion index threshold is raised, allowing the system to tolerate a higher risk of pollution invasion to some extent, thus avoiding unnecessary resource consumption. This dynamic adjustment mechanism allows pollution avoidance decisions to better adapt to actual aquaculture needs, achieving a balance between economic benefits and environmental protection.

[0083] This application further proposes steps for controlling the water jetting device of the third water jetting device, including: The difference between the viscosity value of the polluted water and the preset viscosity value is taken as the viscosity deviation value; a second correspondence is obtained; the second correspondence includes a one-to-one correspondence between multiple viscosity deviation value ranges and multiple water flow pressure adjustment coefficients; the water flow pressure adjustment coefficient corresponding to the viscosity deviation value range in the second correspondence is taken as the target water flow pressure adjustment coefficient; the product of the initial water flow pressure of the third water flow jet device and the target water flow pressure adjustment coefficient is taken as the adjusted water flow pressure; the third water flow jet device is controlled to spray water into the polluted water body at the adjusted water flow pressure.

[0084] Specifically, the steps for controlling the water jet from the third water jet device described above can be broken down into the following sub-steps: First, the difference between the viscosity value of the polluted water and the preset viscosity value is calculated as the viscosity deviation value. The viscosity value of the polluted water can be obtained through the property information of the polluted water, while the preset viscosity value can be an empirical value or a benchmark value set according to a specific aquatic environment. Its purpose is to quantify the degree of difference between the current viscosity of the polluted water and the standard viscosity.

[0085] Secondly, a second correspondence is obtained. This second correspondence is pre-established and includes a one-to-one correspondence between multiple viscosity value deviation ranges and multiple water flow pressure adjustment coefficients. For example, when the viscosity value deviation is within a certain range, it corresponds to a specific water flow pressure adjustment coefficient. These adjustment coefficients are typically greater than 1 to ensure that the water flow pressure can be increased when needed.

[0086] Next, the calculated viscosity deviation value is matched with the viscosity deviation value range in the second correspondence to determine the water flow pressure adjustment coefficient corresponding to the range of the viscosity deviation value, and this coefficient is used as the target water flow pressure adjustment coefficient.

[0087] Subsequently, the initial water flow pressure of the third water jet device is multiplied by the determined target water flow pressure adjustment coefficient to obtain the adjusted water flow pressure. The initial water flow pressure can be the default injection pressure of the third water jet device under standard operating conditions.

[0088] Finally, the third water jet device is controlled to spray water into the polluted water body at the adjusted water pressure. Thus, the spray pressure of the third water jet device can be dynamically adjusted according to the actual viscosity of the polluted water body.

[0089] This application's solution achieves adaptive adjustment of the jet pressure of the third water jet device by incorporating consideration of the viscosity value of the polluted water. Specifically, when the viscosity value of the polluted water is high, the calculated viscosity deviation will be large. According to a preset second correspondence, a larger water pressure adjustment coefficient will be matched. Therefore, the initial water pressure of the third water jet device will be multiplied by a larger adjustment coefficient, resulting in a higher adjusted water pressure. This higher water pressure provides stronger kinetic energy, effectively overcoming the internal resistance of highly viscous polluted water, making it easier to be driven away from the area around the net cage. Conversely, when the viscosity value of the polluted water is low, the viscosity deviation is small, and the corresponding water pressure adjustment coefficient is also small, keeping the jet pressure at a low level. This effectively drives away the polluted water while avoiding unnecessary energy consumption. It is precisely because of this dynamic adjustment mechanism that the third water jet device can provide the most suitable driving force according to the actual physical characteristics of the polluted water.

[0090] Through the above technical solution, this application can intelligently adjust the jet pressure of the third water jet device according to the actual viscosity value of the polluted water. Compared with the scheme of using a fixed water flow pressure for removal, this application can significantly improve the removal efficiency and effect on polluted water with different viscosity, ensuring that polluted water around the net cage can be more effectively removed or pushed away under various pollution environments, thereby minimizing the degree of invasion of polluted water on fish in the net cage. In addition, by avoiding the use of excessively high jet pressure in low viscosity polluted water, it also helps to save energy consumption, extend the service life of equipment, and improve the economy and sustainability of the entire intelligent lifting control method.

[0091] This application also discloses a lifting and lowering intelligent control system for a lifting net cage. The net cage is equipped with a polluted water detection device, comprising: an acquisition device and a processing device; the acquisition device is used to acquire water flow information, distribution information of the polluted water in the water body, and polluted water attribute information of the water body where the net cage is located when the polluted water detection device detects the presence of polluted water in the water body; the processing device is used to input the distribution information of the polluted water in the water body into a preset lifting and lowering control strategy model to obtain multiple lifting and lowering control strategies for the net cage; the preset lifting and lowering control strategy model is used to determine the distribution information of the polluted water in the water body based on the distribution information of the polluted water in the water body. The distribution information within the fish cage is used to determine multiple lifting and lowering control strategies. A processing device is used to determine the pollution invasion index corresponding to each of the multiple lifting and lowering control strategies, based on water flow information, the distribution information of polluted water within the water body, the attribute information of the polluted water body, and the cage structure. The pollution invasion index indicates the degree of invasion of polluted water into the fish in the cage. The processing device is used to determine the target lifting and lowering control strategy among the multiple lifting and lowering control strategies based on the pollution invasion index, and to control the cage to move according to the target lifting and lowering control strategy.

[0092] The acquisition device can be a hardware module integrating multiple sensors. For example, it may include a flow velocity sensor for measuring water flow speed and direction, a multi-point water quality sensor array for monitoring pollutant concentration and spatial distribution, and a spectrometer or chemical sensor for analyzing the physicochemical properties of pollutants. Data from these sensors is acquired in real time and transmitted to the central processing unit of the acquisition device for initial integration.

[0093] The processing device is configured to receive information from the acquisition device and perform a series of complex decision-making and control tasks. The processing device can be a high-performance embedded processor, an industrial control computer, or a virtual processing unit on a cloud computing platform.

[0094] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for intelligent lifting control of a lifting cage, characterized in that, The net cages are equipped with water pollution detection devices, and the methods include: When the polluted water detection device detects the presence of polluted water in the water body where the cage is located, it acquires the water flow information, the distribution information of the polluted water in the water body, and the polluted water attribute information of the polluted water. Multiple lifting and lowering control strategies for the cages are determined based on the distribution information of polluted water bodies in the water bodies; the lifting and lowering control strategies include lifting direction and lifting speed; For each of the multiple lifting control strategies, based on water flow information, the distribution information of polluted water in the water body, the attribute information of polluted water, and the cage structure, the polluted water invasion index corresponding to the lifting control strategy is determined; the polluted water invasion index is used to indicate the degree of invasion of polluted water on fish in the cage. Based on the polluted water invasion index of multiple lifting control strategies, the target lifting control strategy is determined among multiple lifting control strategies, and the cage is controlled to move according to the target lifting control strategy.

2. The intelligent lifting control method for a lifting cage according to claim 1, characterized in that, The net cage is also equipped with an acoustic Doppler current meter to obtain water flow information of the water body in which the net cage is located, including: Acquire the detection signal from the acoustic Doppler current meter; Determine whether the signal quality index of the detected signal is less than a preset signal quality index threshold. When the signal quality index of the detected signal is greater than or equal to the preset signal quality index threshold, the water flow information of the water body where the cage is located is determined based on the detection signal of the acoustic Doppler current meter. When the signal quality index of the detected signal is less than the preset signal quality index threshold, the sensor of the acoustic Doppler current meter and the water environment of the sensor are cleaned, and the detection signal of the acoustic Doppler current meter is acquired again. When the signal quality index of the acquired detection signal is greater than or equal to the preset signal quality index threshold, the water flow information of the water body where the cage is located is determined based on the detection signal of the acoustic Doppler current meter. When the signal quality index of the reacquired detection signal is less than the preset signal quality index threshold, target information is generated and sent to the management personnel to indicate an abnormal detection environment of the acoustic Doppler current meter.

3. The intelligent lifting control method for a lifting cage according to claim 2, characterized in that, The acoustic Doppler current meter has an oscillator installed on its sensor. It also includes a first water jet device and a clean water tank connected to the first water jet device. This cleans the sensor and the surrounding water environment, including: The oscillator is controlled to oscillate at a preset frequency so that the adhering material on the sensor is removed from the sensor; The first water jet device is controlled to spray water from the water tank along the signal transmission direction of the sensor, so as to keep suspended matter in the water environment away from the sensor.

4. The intelligent lifting control method for a lifting cage according to claim 2, characterized in that, The detection signal from the acoustic Doppler current meter was acquired again, including: The difference between the preset signal quality index threshold and the signal quality index of the detected signal is used as the quality index deviation value. Obtain the first correspondence; the first correspondence includes multiple quality index deviation value ranges and multiple sampling frequency adjustment coefficients; the sampling frequency adjustment coefficient is greater than 1; The sampling frequency adjustment coefficient corresponding to the range of quality index deviation values ​​in the first correspondence is used as the target sampling frequency adjustment coefficient. The product of the current sampling frequency of the acoustic Doppler current meter and the target sampling frequency adjustment coefficient is used as the adjusted sampling frequency of the acoustic Doppler current meter, and the detection signal of the acoustic Doppler current meter is collected again based on the adjusted sampling frequency.

5. The intelligent lifting control method for a lifting cage according to claim 1, characterized in that, Based on water flow information, the distribution information of polluted water in the water body, the attribute information of polluted water, and the cage structure, the polluted water invasion index corresponding to the lifting and lowering control strategy is determined, including: Based on the Lagrange particle tracking method, polluted water bodies are abstracted into virtual particles according to the distribution information of polluted water bodies in the water body; A hydrodynamic simulation model of the net cage is constructed based on water flow information and the cage structure. The hydrodynamic simulation model is used to simulate the additional flow field generated by the net cage on the surrounding water body when the net cage moves according to the lifting control strategy under the condition that the water body has water flow information. Based on the fluid dynamics simulation model, polluted water body attribute information, and virtual particles, the ratio of the number of virtual particles entering the cage to the total number of virtual particles when the cage moves according to the lifting control strategy is determined, and the ratio is used as the polluted water body invasion index corresponding to the lifting control strategy.

6. The intelligent lifting control method for a lifting cage according to claim 1, characterized in that, The net cage is also equipped with an acoustic Doppler current meter, a second water jet device, and an image acquisition device to obtain information on the properties of the polluted water, including: The system controls a second water jet device to spray water into a polluted water body and acquires an image set acquired by an image acquisition device, first information acquired by an acoustic Doppler current meter, and second information acquired by an acoustic Doppler current meter. The image set includes a first image and a second image. The first image includes the pixel positions of the edge of the polluted water body in the image before the second water jet device sprays water into the polluted water body, and the second image includes the pixel positions of the edge of the polluted water body in the image after the second water jet device sprays water into the polluted water body. The first information is the exponential decay coefficient of the velocity of the water jet sprayed by the second water jet device with distance, and the second information is the intensity and duration of the vortex generated by the water jet sprayed by the second water jet device. The polluted water body attribute information is determined based on the image set, the first information, and the second information; the polluted water body attribute information includes the effective density, viscosity value, and surface tension of the polluted water body.

7. The intelligent lifting control method for a lifting cage according to claim 6, characterized in that, The cage is also equipped with a third water jet device. Based on the pollution water invasion index of multiple lifting control strategies, a target lifting control strategy is determined among the multiple lifting control strategies, and the cage is controlled to move according to the target lifting control strategy, including: Determine whether the smallest pollution water body invasion index among multiple pollution water body invasion indices is less than the target invasion index threshold; When the minimum pollution water body invasion index is less than the target invasion index threshold, the lifting control strategy corresponding to the minimum pollution water body invasion index is taken as the target lifting control strategy, and the cage is controlled to move according to the target lifting control strategy. When the minimum pollution water invasion index is greater than or equal to the target invasion index threshold, the third water jet device is controlled to spray water to drive away the polluted water around the cage. The minimum pollution water invasion index is used as the target lifting and lowering control strategy, and the cage is controlled to move according to the target lifting and lowering control strategy.

8. The intelligent lifting control method for a lifting cage according to claim 7, characterized in that, Determining whether the smallest pollution water body invasion index among multiple pollution water body invasion indices is less than the target invasion index threshold includes: Obtain the value index of fish in the net cages; The product of the fish value index and the preset value adjustment coefficient is used as the target preset adjustment coefficient. The product of the initial invasion index threshold and the target preset adjustment coefficient is used as the target invasion index threshold; Determine whether the smallest pollution water body invasion index among multiple pollution water body invasion indices is less than the target invasion index threshold.

9. The intelligent lifting control method for a lifting cage according to claim 7, characterized in that, Controlling the water jetting device of the third water jet includes: The difference between the viscosity value of the polluted water and the preset viscosity value is used as the viscosity deviation value; Obtain the second correspondence; the second correspondence includes a one-to-one correspondence between multiple viscosity value deviation ranges and multiple water flow pressure adjustment coefficients; The water flow pressure adjustment coefficient corresponding to the viscosity deviation value range in the second correspondence is taken as the target water flow pressure adjustment coefficient. The product of the initial water flow pressure of the third water jet device and the target water flow pressure adjustment coefficient is used as the adjusted water flow pressure. The third water jet device is controlled to spray water into the polluted water body at the adjusted water pressure.

10. A lifting intelligent control system for a lifting cage, characterized in that, The cage is equipped with a polluted water detection device, including: a data acquisition device and a treatment device; The acquisition device is used to acquire water flow information, distribution information of polluted water in the water body, and polluted water attribute information of the water body where the cage is located when the polluted water detection device detects the presence of polluted water in the water body where the cage is located. The processing device is used to input the distribution information of polluted water in the water body into a preset lifting control strategy model to obtain multiple lifting control strategies for the cage; the preset lifting control strategy model is used to determine multiple lifting control strategies for the cage based on the distribution information of polluted water in the water body. The processing device is used to determine the pollution water invasion index corresponding to each of the multiple lifting control strategies, based on water flow information, the distribution information of polluted water in the water body, the attribute information of polluted water, and the cage structure; the pollution water invasion index is used to indicate the degree of invasion of polluted water on fish in the cage. The processing device is used to determine the target lifting control strategy among multiple lifting control strategies based on the polluted water invasion index of multiple lifting control strategies, and to control the cage to move according to the target lifting control strategy.

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