Net cage suspension depth information sensing monitoring and adjusting system

Abstract

The present application relates to the technical field of aquaculture net cage control, and particularly relates to a net cage suspension depth fixing information sensing monitoring and adjusting system, which comprises the following steps: collecting multi-source sensor data to provide comprehensive data support for net cage state monitoring; generating a net cage real-time suspension state image through data fusion processing to provide a reliable basis for decision making; generating a multi-dimensional adjusting scheme through judging the suspension state to ensure that the net cage is quickly restored to the set state; executing adjusting instructions through multiple adjusting units to ensure the safe operation of the net cage; and establishing a historical strategy retrieval and optimization mechanism to optimize control parameters and improve the long-term operation efficiency of the system. The present application is used to solve the problems of traditional net cages, such as lagging in suspension state regulation and control under complex sea conditions and poor stability, and can provide a continuous and stable optimal growth water depth and posture for high-value aquaculture organisms sensitive to the environment, effectively improve the survival rate and breeding efficiency of good strains, and has significant industrial application value.

Description

Technical Field

[0001] This invention relates to the field of aquaculture cage control technology, and in particular to a cage suspension depth information sensing, monitoring and adjustment system. Background Technology

[0002] Cage aquaculture, as a key method for modern marine fisheries to efficiently utilize marine resources and increase aquaculture capacity, plays a crucial role in ensuring the yield and quality of aquatic products through stable operation and precise management. Especially in high-value aquaculture stages such as livestock breeding and fish fry and fingerling breeding, which have extremely stringent requirements for the growth environment, even slight deviations in the suspended state of the cage (such as sudden changes in depth or tilting) can lead to abnormal water exchange, uneven distribution of dissolved oxygen, and even stress-induced mortality of fry, seriously affecting the survival rate of improved breeds and the uniformity of growth.

[0003] However, current cage aquaculture management still faces the following problems: In traditional cage aquaculture, monitoring the suspension state of cages typically relies on a single or a few sensors, resulting in incomplete data acquisition. For example, only depth can be monitored, but key parameters such as inclination angle and anchor chain tension cannot be accurately obtained. In complex and variable marine environments, the suspension state of cages is easily affected by external factors such as wind, waves, and currents, deviating from the set value. Traditional adjustment methods often rely on manual judgment and operation, resulting in adjustment lag and an inability to respond promptly to changes in the suspension state of cages, leading to decreased cage stability and potentially causing safety accidents. Under complex sea conditions, anchor chains are easily affected by dynamic loads, resulting in large tension fluctuations. This not only affects the overall stability of the cage but may also damage the anchor chain itself, shortening its service life. Traditional methods are unable to effectively suppress fluctuations in anchor chain tension, leading to insufficient stability of the mooring system. Especially during the fry and fingerling breeding stage, the stable suspension of cages is crucial for the growth environment of fry, and traditional control methods cannot meet the requirements for high-precision and high-response regulation. Therefore, this invention proposes a cage suspension depth-sensing monitoring and adjustment system. Summary of the Invention

[0004] The purpose of this invention is to solve the problems in the background art by proposing a cage suspension depth-sensing monitoring and adjustment system.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] The cage suspension depth-fixed information sensing, monitoring and adjustment system includes: a multi-source data acquisition module, a data fusion and processing module, an adjustment scheme generation module, an execution adjustment module and a feedback optimization module;

[0007] Multi-source data acquisition module: Real-time acquisition of multi-source sensor data, including depth data, tilt data, anchor chain tension data, center mooring cable tension data, and sea state information;

[0008] Data fusion processing module: fuses data from multiple sources to generate a real-time floating status profile of the cage;

[0009] Adjustment scheme generation module: Combining the pre-stored cage dynamics model, it judges whether the cage's suspension state deviates from the set state based on the real-time suspension state profile of the cage, and generates a multi-dimensional suspension state adjustment scheme, including ballast water adjustment amount, target value of center mooring cable tension, and target value of constant tension for anchor chain;

[0010] The execution adjustment module adjusts the buoyancy of the cage, controls the tension of the central mooring cable, and maintains a constant anchor chain tension according to the multi-dimensional suspension state adjustment scheme. The execution adjustment module includes a depth control drive unit, an anchor chain constant tension adjustment unit, and a central mooring cable tension adjustment unit.

[0011] Feedback optimization module: Real-time monitoring of the suspended state of the cage after adjustment, establishment of historical strategy retrieval and optimization mechanism, and generation of recommended control parameters.

[0012] Optional, the multi-source data acquisition module includes:

[0013] Tilt sensors are used to measure the roll and pitch angles of the net cage, and the roll and pitch angles of the net cage constitute the tilt angle data;

[0014] Depth sensors are used to monitor the draft of the net cage in real time, and the draft of the net cage constitutes depth data;

[0015] The tension sensors include anchor chain tension sensors and center mooring cable tension sensors; the anchor chain tension sensors are used to monitor the real-time tension of each anchor chain, and the real-time tension of the anchor chain constitutes the anchor chain tension data; the center mooring cable tension sensors are used to monitor the real-time tension of the center mooring cable, and the real-time tension of the center mooring cable constitutes the center mooring cable tension data.

[0016] The environmental sensor array is used to collect sea state information, including surface current velocity and direction data.

[0017] Optionally, the data fusion processing module fuses multi-source sensor data to generate a real-time floating status profile of the cage, including the following steps:

[0018] Hardware-level time synchronization is performed on the data from multiple sensors, and a coordinate system for the body of the cage is established with the center of the waterline surface of the cage design as the origin and the Z-axis pointing vertically downwards. The data from multiple sensors are then transformed into this coordinate system.

[0019] The converted multi-source sensor data is processed using an adaptive weighted Kalman filter algorithm to output a denoised multidimensional data sequence.

[0020] Based on the acquired multidimensional data sequence, the real-time center of gravity position and tilt angle of the cage under the current sea state are calculated using tilt angle data and depth data; at the same time, the resultant external force vector and resultant moment vector acting on the cage are calculated.

[0021] The calculated real-time center of gravity position, tilt angle, resultant external force vector, and resultant moment vector are fused and analyzed to generate an integrated real-time floating state profile of the cage.

[0022] Optionally, the adjustment scheme generation module, in conjunction with a pre-stored cage dynamics model, determines whether the cage's suspension state deviates from the set state based on a real-time suspension state profile, and generates a multi-dimensional suspension state adjustment scheme. This process includes:

[0023] Continuously receive real-time images of the cage's floating status, extract the depth deviation, abnormal posture area markers, and the ratio coefficient of the net external torque to the restoring torque, and perform comparative analysis.

[0024] Based on the comparative analysis results, determine whether to generate a preliminary adjustment strategy;

[0025] If the initial adjustment strategy is triggered, the pre-stored cage dynamics model is invoked; the simulation results of the cage dynamics model are obtained, and an adjustment scheme including the ballast water adjustment amount and the target value of the center mooring cable tension is generated simultaneously;

[0026] While generating the adjustment scheme, the fluctuation characteristics of the tension of each anchor chain are analyzed in real time; based on the fluctuation characteristic analysis results, a constant tension adjustment command for the anchor chain is embedded in the comprehensive adjustment scheme; finally, a multi-dimensional suspension state adjustment scheme is obtained, including ballast water adjustment amount, target value of center mooring cable tension, and target value of constant tension for the anchor chain.

[0027] Optionally, the process by which the adjustment module adjusts the buoyancy of the cage, the tension of the mooring cable at the control center, and maintains the anchor chain tension constant according to the multi-dimensional suspension state adjustment scheme includes:

[0028] The depth control drive unit drives the distributed pump valve system according to the received ballast water adjustment amount to transfer the ballast water between multiple compartments of the cage, so as to change the buoyancy distribution and center of gravity of the cage.

[0029] The center mooring cable tension adjustment unit adjusts the cable tension by raising and lowering the center mooring cable based on the received target value of the center mooring cable tension and in combination with real-time sea state information and the vertical movement trend of the cage.

[0030] The anchor chain constant tension adjustment unit drives the corresponding anchor chain actuator to dynamically adjust the effective length of the anchor chain based on the received constant tension target value of the anchor chain.

[0031] Optionally, the depth control drive unit further includes:

[0032] The received ballast water adjustment includes the total buoyancy adjustment of the entire cage required to restore the target depth and set the suspension state, which is characterized by the total volume of ballast water added or removed.

[0033] Read the current liquid level status data collected by the liquid level sensors deployed in each ballast water tank;

[0034] The calculated real-time center of gravity position and tilt angle are obtained as the current tilt attitude of the cage;

[0035] Based on the current liquid level data of each ballast tank and the current tilting attitude of the cage, the total buoyancy adjustment is decomposed into the individual adjustment of each tank. The decomposition principle is to prioritize the combination of tanks that can correct the current tilting attitude of the cage to the greatest extent for water allocation.

[0036] Based on the individual adjustment values ​​of each water tank after decomposition, a pump-valve coordinated operation sequence table is generated. This sequence table includes: the start-up sequence, running time and speed range of each water pump; the opening and closing times of the inlet and outlet valves linked to the water pumps; and the time interval settings between key operation nodes.

[0037] Drive the corresponding water pumps and valves according to the timing table to perform water inlet and outlet operations, and complete the coordinated adjustment of the buoyancy of the cage.

[0038] Optionally, the center mooring cable tension adjustment unit further includes:

[0039] Acquire real-time tension data of the central mooring cable, real-time vertical acceleration data of the cage, and surface flow velocity data collected by environmental sensors;

[0040] The real-time tension data of the center mooring cable is compared with the target tension value of the center mooring cable to obtain the first tension deviation;

[0041] Meanwhile, the surface flow velocity data is used as input to query the preset flow velocity-tension disturbance mapping table to obtain the feedforward compensation amount;

[0042] The first tension deviation is superimposed with the feedforward compensation to obtain the composite control target value;

[0043] Based on the magnitude and direction of the composite control target value, combined with the vertical acceleration data, the vertical movement trend of the cage is determined, and the cable winding and unwinding speed commands are dynamically adjusted.

[0044] By dynamically adjusting, the tension of the center mooring cable is ensured to remain stable within the target range under dynamic sea conditions.

[0045] Optionally, the anchor chain constant tension adjustment unit further includes:

[0046] For each anchor chain under constant tension control, the real-time tension data of the anchor chain is continuously monitored, and its average rate of change of tension within a short time window is calculated.

[0047] The real-time tension data of the anchor chain is compared with the constant tension target value of the anchor chain to obtain the second tension deviation;

[0048] Set a dynamic action trigger threshold, which is positively correlated with the absolute value of the average rate of change of tension;

[0049] When the absolute value of the second tension deviation exceeds the dynamic action trigger threshold, an anchor chain length adjustment command is immediately generated;

[0050] The adjustment command is sent to the actuator corresponding to the anchor chain, driving it to make a compensatory adjustment to the length of the anchor chain.

[0051] Optionally, the feedback optimization module establishes a historical strategy retrieval and optimization mechanism, and the process of generating recommended control parameters includes:

[0052] After each comprehensive adjustment reaches the set state, a policy instance data package is created and stored. The policy instance data package encapsulates three types of information: scene feature signature, control policy fingerprint, and performance vector.

[0053] A dynamic policy graph is constructed based on the scene feature signatures in all stored policy instance data packets, where policy instances are associated through the similarity between their scene feature signatures;

[0054] When the adjustment scheme generation module generates a new scheme, the feedback optimization module retrieves the R historical policy instances that are most similar to the current real-time scene feature signature in the dynamic policy graph, and uses a confidence-weighted policy fusion algorithm to fuse the control policy fingerprints of the historical policy instances to generate recommended policy parameters and provide them to the adjustment scheme generation module.

[0055] At the same time, the strategy ecosystem is optimized regularly. The utility of multiple strategy instances describing similar scenarios in the dynamic strategy graph is evaluated and ranked, and the strategy instances with the highest utility ranking are retained.

[0056] Optionally, the feedback optimization module also includes:

[0057] Based on the real-time floating state profile of the cage at the adjustment trigger time and sea condition information, a scene feature signature is generated.

[0058] The core parameters of the adjustment scheme actually executed by the adjustment module constitute the control strategy fingerprint;

[0059] The performance vector is calculated based on the data collected during the evaluation window.

[0060] Compared with existing technologies, the beneficial effects of this invention are as follows: By comprehensively and in real-time acquiring key status information of the cages, including depth, inclination angle, tension, and sea state data, a data foundation is provided for precise monitoring; through time synchronization and coordinate transformation, effective integration of multi-source data is achieved; an adaptive weighted Kalman filter algorithm is used to reduce noise and improve data accuracy, generating a real-time suspension status profile of the cages, providing a reliable basis for subsequent adjustments; by combining the cage dynamics model with the real-time status profile, the deviation of the cage status is intelligently judged, and a multi-dimensional adjustment scheme including ballast water adjustment, mooring cable tension adjustment, and anchor chain constant tension control is generated, achieving precise regulation; according to the adjustment scheme, through depth control, center mooring cable tension adjustment, and anchor chain constant tension adjustment... The modular design, through synergistic action, effectively regulates the buoyancy of the net cage, controls the tension of the mooring cables, and maintains a constant anchor chain, ensuring stable suspension of the net cage. Through historical strategy retrieval and optimization mechanisms, recommended control parameters are generated, continuously optimizing the adjustment strategy, improving the system's adaptability and adjustment efficiency, forming a closed-loop control system, and guaranteeing the long-term stable operation of the net cage. Furthermore, by achieving precise, rapid, and adaptive stable control of the net cage's suspension state, this invention can provide continuous and optimal growth suspension depth and stable posture for high-value organisms sensitive to environmental fluctuations, such as livestock breeds, fish fry, and fingerlings. This effectively reduces mortality and growth differences caused by environmental stress, improves the success rate and uniformity of breeding, and thus directly serves the revitalization of the aquaculture seed industry and the development of strategic emerging industries. Attached Figure Description

[0061] Figure 1 This is a block diagram of the cage suspension depth-sensing monitoring and adjustment system proposed in this invention. Detailed Implementation

[0062] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] Reference Figure 1 The cage suspension depth information sensing monitoring and adjustment system includes a multi-source data acquisition module, a data fusion processing module, an adjustment scheme generation module, an execution adjustment module, and a feedback optimization module.

[0064] Multi-source data acquisition module: Real-time acquisition of multi-source sensor data, including depth data, tilt data, anchor chain tension data, center mooring cable tension data, and sea state information;

[0065] Data fusion processing module: fuses data from multiple sources to generate a real-time floating status profile of the cage;

[0066] Adjustment scheme generation module: Combining the pre-stored cage dynamics model, it judges whether the cage's suspension state deviates from the set state based on the real-time suspension state profile of the cage, and generates a multi-dimensional suspension state adjustment scheme, including ballast water adjustment amount, target value of center mooring cable tension, and target value of constant tension for anchor chain;

[0067] The execution adjustment module adjusts the buoyancy of the cage, controls the tension of the central mooring cable, and maintains a constant anchor chain tension according to the multi-dimensional suspension state adjustment scheme. The execution adjustment module includes a depth control drive unit, an anchor chain constant tension adjustment unit, and a central mooring cable tension adjustment unit.

[0068] Feedback optimization module: Real-time monitoring of the suspended state of the cage after adjustment, establishment of historical strategy retrieval and optimization mechanism, and generation of recommended control parameters.

[0069] It should be further explained that, in the specific implementation process, the multi-source data acquisition module includes:

[0070] The tilt sensors are installed in at least three locations on the main frame of the cage in a non-collinear manner to measure the roll and pitch angles of the cage. The measurement data is transmitted via an RS-485 bus within a waterproof enclosure. The roll and pitch angles of the cage constitute the tilt angle data.

[0071] The depth sensor adopts the hydrostatic or ultrasonic ranging principle and is arranged in the center of the bottom of the cage and inside the floating structure at the four corners. It is used to monitor the draft of the cage at different positions in real time; the draft of the cage constitutes the depth data.

[0072] The tension sensors include anchor chain tension sensors and center mooring cable tension sensors. The anchor chain tension sensors are installed in the universal joints at the connection points between each lateral anchor chain and the gabion, and are used to monitor the real-time tension of each anchor chain, which constitutes the anchor chain tension data. The center mooring cable tension sensors are integrated into the vertical winch of the center mooring cable tension adjustment unit, and are used to monitor the real-time tension of the center mooring cable, which constitutes the center mooring cable tension data.

[0073] The environmental sensor array includes an ultrasonic anemometer and wind vane mounted on the mast at the top of the cage, and an acoustic Doppler current profiler fixed to the upstream side of the cage below, used to collect sea state information, including surface current velocity and direction data.

[0074] It should be further explained that, in the specific implementation process, the data fusion processing module fuses multi-source sensor data to generate a real-time floating status profile of the cage, including:

[0075] The IEEE 1588 precision clock protocol is used to perform hardware-level time synchronization of multi-source sensor data, and a coordinate system of the cage body with the center of the waterline surface of the cage design as the origin and the Z-axis pointing vertically downward is established. The multi-source sensor data is uniformly transformed to this coordinate system to provide a consistent spatiotemporal reference for data fusion.

[0076] Under a unified spatiotemporal reference, the transformed multi-source sensor data is processed using an adaptive weighted Kalman filter algorithm. This algorithm dynamically allocates fusion weights based on the variance of the real-time data of each sensor, suppresses high-noise data, enhances high-confidence data, and outputs a denoised multidimensional data sequence.

[0077] Based on the acquired multidimensional data sequence, the real-time center of gravity position (three-dimensional coordinates) and tilt angle of the net cage under the current sea state are calculated using tilt angle and depth data. Noise-reduced depth sensor data is used as the observed value of the net cage's overall vertical displacement. Combined with the net cage's rotation angle represented by denoised tilt angle sensor data, a coordinate transformation based on rigid body kinematics and a weighted least squares fitting algorithm are used to iteratively calculate the three-dimensional coordinates of the net cage's center of gravity in the net cage's body coordinate system that best matches all current observation data. The tilt angle sensor data is then used to calculate the coordinates of the net cage's center of gravity in the net cage's body coordinate system. The data is directly used as the current inclination angle of the cage; at the same time, the resultant external force vector and resultant moment vector of the cage are calculated: the anchor chain tension data of each anchor chain is decomposed into three components along the X, Y, and Z axes in the coordinate system of the cage body according to the spatial coordinates and direction cosine of its connection point; the decomposed forces of all anchor chains and the center mooring cable are vector summed to obtain the resultant external force vector of the cage; for each anchor chain and the center mooring cable, the moment of its tension relative to the center of gravity of the cage is calculated and vector summed to obtain the resultant moment vector of the cage.

[0078] The calculated real-time center of gravity position (3D coordinates), tilt angle, resultant external force vector, and resultant moment vector are fused and analyzed to generate an integrated real-time suspension status profile of the net cage. This profile includes at least the depth deviation (characterizing depth stability), the attitude anomaly region marker (characterizing attitude stability), and the ratio coefficient of resultant external moment to restoring moment (characterizing the stability of the mooring system). The depth deviation is obtained by calculating the algebraic difference between the average value of the current depth sensor data and the user-defined target depth. The absolute values ​​of the current roll and pitch angles are compared with preset tilt angle safety thresholds; if either angle exceeds the threshold, the direction corresponding to that angle is marked as an attitude anomaly region. The still water restoring moment is calculated based on the net cage's current draft, float shape, and center of gravity position. The ratio coefficient of resultant external moment to restoring moment is obtained by comparing the magnitude of the calculated resultant moment vector with the magnitude of the still water restoring moment.

[0079] It should be further explained that, in the specific implementation process, the adjustment scheme generation module, in conjunction with the pre-stored cage dynamics model, determines whether the cage's suspension state deviates from the set state based on the cage's real-time suspension state profile, and generates a multi-dimensional suspension state adjustment scheme. This process includes:

[0080] Continuously receive real-time images of the cage's floating status and extract the depth deviation, abnormal posture area markers, and the ratio coefficient of the net external torque to the restoring torque.

[0081] Dynamic judgment thresholds are set, including dynamic depth tolerance thresholds and dynamic instability thresholds. The dynamic judgment threshold is determined based on real-time sea state information and the current load status of the cage, and on the correlation between the cage dynamics model and sea state and load parameters. The dynamic depth tolerance threshold is the maximum allowable dynamic deviation between the actual depth of the cage and the set target depth. When the depth deviation exceeds this threshold, an adjustment mechanism is triggered to prevent suspension instability. The dynamic instability threshold is the critical value of the ratio of the net external torque on the cage to the hydrostatic restoring torque. When the ratio exceeds this threshold, it indicates that the anchorage cannot maintain stability by its own restoring force, and immediate intervention is required.

[0082] The extracted depth deviation is compared with the dynamic depth tolerance threshold, and the abnormal attitude region marker is checked to see if it is non-empty (i.e., whether there is an abnormal region). The ratio coefficient of the resultant external torque to the restoring torque is compared with the dynamic instability threshold. When the depth deviation exceeds the corresponding dynamic judgment threshold, the abnormal attitude region marker is non-empty, or the ratio coefficient exceeds the corresponding dynamic judgment threshold, the generation of the comprehensive adjustment strategy is triggered.

[0083] If the integrated adjustment strategy is triggered, a pre-stored cage dynamics model is invoked. This model stores the inherent property parameters of the cage, including its mass distribution and main dimensions. Based on these inherent property parameters, a real-time image of the cage's suspension state, and real-time sea state information, the model numerically solves coupled equations containing hydrodynamic, hydrostatic restoring force, and mooring force terms to simulate and predict the dynamic response of ballast water distribution and center mooring cable tension adjustment to the cage's suspension state. The expression for the coupled equations is as follows: In the formula, The mass matrix includes the additional mass; The damping coefficient; The restoring force matrix in still water; These are the acceleration, velocity, and displacement vectors of the net cage, respectively. This is an environmental load vector calculated based on real-time sea state information; This is the mooring force vector calculated based on the current and set mooring tensions; The buoyancy change vector is calculated based on the ballast water regulation amount. The fourth-order Runge-Kutta method is used to numerically integrate the coupled equations to solve for the depth, tilt angle, and trajectory of the cage over a future period. By evaluating simulation results under different combinations of [ballast water regulation amount and target tension value of the center mooring cable], the optimal integrated regulation scheme is selected as the combination that allows the cage to recover to the set state fastest and minimizes overshoot. Based on the simulation results, a regulation scheme incorporating ballast water regulation amount and target tension value of the center mooring cable is generated simultaneously.

[0084] While generating the adjustment scheme, the fluctuation characteristics of the tension of each anchor chain are analyzed in real time: online time-series analysis is performed on the real-time tension data of each anchor chain to identify the adjacent tension peaks and valleys within an analysis time window, and the average value of all (peak-valley) differences within the window is calculated and defined as the current extreme fluctuation intensity of the anchor chain; if the extreme fluctuation intensity of the anchor chain is found to continuously exceed the preset intensity threshold, a constant tension adjustment command for the anchor chain is embedded in the integrated adjustment scheme, wherein the constant tension adjustment command includes the constant tension target value of the anchor chain; the intensity threshold is the tension fluctuation intensity threshold value, which defines the maximum average amplitude of dynamic tension fluctuations caused by periodic environmental loads (such as waves) that a single anchor chain can tolerate under specific sea conditions; finally, a multi-dimensional suspension state adjustment scheme is obtained, including ballast water adjustment amount, center mooring cable tension target value, and constant tension target value for the anchor chain.

[0085] It should be further explained that, in the specific implementation process, the process by which the adjustment module adjusts the buoyancy of the cage, the tension of the mooring cable at the control center, and maintains the anchor chain tension constant according to the multi-dimensional suspension state adjustment scheme includes:

[0086] The constant depth control drive unit drives the distributed pump valve system according to the received ballast water adjustment amount to transfer ballast water between multiple compartments of the cage, so as to change the buoyancy distribution and center of gravity position of the cage.

[0087] Specifically, the ballast water adjustment includes the total buoyancy adjustment of the entire cage required to restore the target depth and set the suspension state, which is characterized by the total volume of ballast water that is added or removed.

[0088] Read the current liquid level status data collected by the liquid level sensors deployed in each ballast water tank, that is, obtain the current actual water volume of each water tank;

[0089] Obtain the calculated real-time center of gravity position (3D coordinates) and tilt angle as the current tilt attitude of the cage;

[0090] Based on the current liquid level data of each ballast tank and the current tilting attitude of the cage, the total buoyancy adjustment is decomposed into the individual adjustment of each tank. The decomposition principle is to prioritize the combination of tanks that can correct the current tilting attitude of the cage to the greatest extent for water allocation.

[0091] Based on the individual adjustment values ​​of each water tank after decomposition, a pump and valve coordinated operation sequence table is generated. This sequence table includes: the start-up sequence, running time, and speed range of each water pump; the opening and closing times of the inlet and outlet valves linked to the water pumps; and the time interval settings between key operation nodes. The key operation nodes include the delay between the end of drainage of the previous water tank and the start of water intake of the next water tank, as well as the buffer period between the start and stop of the high-flow pump, to ensure smooth water transfer between water tanks and avoid violent oscillations in the cage posture caused by sudden changes in water flow.

[0092] Drive the corresponding water pumps and valves according to the timing table to perform water inlet and outlet operations, and complete the coordinated adjustment of the buoyancy of the cage.

[0093] The center mooring cable tension adjustment unit controls the variable frequency motor of the vertical winch based on the received target value of the center mooring cable tension and in combination with real-time sea condition information and the vertical movement trend of the cage, thereby adjusting the cable tension by winding and unwinding the center mooring cable.

[0094] Specifically, real-time tension data of the central mooring cable, real-time vertical acceleration data of the cage, and surface flow velocity data collected by environmental sensors are obtained; among them, the real-time vertical acceleration data of the cage is directly measured by an acceleration sensor deployed at the center of gravity of the cage.

[0095] The real-time tension data of the center mooring cable is compared with the target tension value of the center mooring cable to obtain the first tension deviation; the difference between the real-time tension data of the center mooring cable and the target tension value of the center mooring cable is the first tension deviation, and its positive or negative sign indicates the direction of the deviation.

[0096] Simultaneously, surface flow velocity data is used as input to query a preset flow velocity-tension disturbance mapping table to obtain the feedforward compensation amount. The flow velocity-tension disturbance mapping table is a data lookup table preset in the central mooring cable tension adjustment unit. This lookup table records the preset cable winding and unwinding compensation amount for compensating for flow-induced tension disturbances at different flow velocity levels, and this compensation amount is used as the feedforward compensation amount.

[0097] The first tension deviation is superimposed with the feedforward compensation to obtain the composite control target value;

[0098] Based on the magnitude and direction of the composite control target value, and combined with vertical acceleration data, the vertical movement trend of the cage is determined, and the cable release and take-up speed commands of the vertical winch are dynamically adjusted. Specifically, the process is as follows: a reference speed adjustment amount proportional to the absolute value of the composite control target value is set; when the composite control target value is positive, the vertical winch is controlled to perform a cable release operation, with the release speed being the sum of the base speed and the reference speed adjustment amount. The base speed is the default cable release and take-up speed of the vertical winch when there is no additional adjustment requirement (i.e., the composite control target value is zero or close to zero); if the vertical acceleration of the cage is positive at this time (i.e., accelerating upwards), the cable release speed is further increased to actively cope with the further increase in tension caused by the cage rising; when the composite control target value is negative, the vertical winch is controlled to perform a cable take-up operation, with the take-up speed being the sum of the base speed and the reference speed adjustment amount; if the vertical acceleration of the cage is negative at this time (i.e., accelerating downwards), the take-up speed is further increased to actively compensate for the tension loss trend caused by the cage sinking.

[0099] Through dynamic adjustments, the tension of the center mooring cable is ensured to remain stable within the target range under dynamic sea conditions. The target range is within a tolerance range of ±5% above and below the target value of the center mooring cable tension.

[0100] The anchor chain constant tension adjustment unit drives the corresponding anchor chain actuator based on the received constant tension target value of the anchor chain, dynamically adjusting the effective length of the anchor chain and stabilizing it near the constant tension target value specified by the anchor chain constant tension adjustment command. The actuator is either an electric tension float or a hydraulic winch, responsible for dynamically adjusting the effective length of the anchor chain based on the received constant tension target value to maintain constant anchor chain tension. The electric tension float adjusts the length and tension of the connected anchor chain by braking the float's up-and-down movement with an electric motor. The hydraulic winch uses a hydraulic system to drive the winch drum to rotate, adjusting the tension by winding up and unwinding the anchor chain.

[0101] Specifically, for each anchor chain under constant tension control, the real-time tension data of the anchor chain is continuously monitored, and its average tension change rate within a short time window is calculated. The specific process is as follows: real-time tension data of the anchor chain is acquired at a fixed sampling frequency, and all sampled values ​​within a time window (e.g., 10 seconds) are recorded; the time series is fitted using a linear regression algorithm, and the slope of the fitted line is the average tension change rate within the time window.

[0102] The real-time tension data of the anchor chain is compared with the constant tension target value of the anchor chain to obtain the second tension deviation;

[0103] A dynamic action trigger threshold is set, which is positively correlated with the absolute value of the average rate of change of tension; wherein, the dynamic action trigger threshold is given by the formula... The calculation yields the following result: Threshold for triggering dynamic actions; The preset static basic trigger threshold, This is the sensitivity coefficient. This is the absolute value of the average rate of change of tension; this formula allows for earlier intervention with a lower tolerance for deviation when tension changes drastically.

[0104] When the absolute value of the second tension deviation exceeds the dynamic action trigger threshold, an anchor chain length adjustment command is immediately generated. The adjustment direction of the command is used to counteract the current tension change trend, and the adjustment range is determined by the magnitude of the second tension deviation and the average tension change rate.

[0105] The adjustment command is sent to the actuator corresponding to the anchor chain, driving it to make a compensatory adjustment of the anchor chain length in order to actively suppress the continuous deviation of the tension.

[0106] The adjustment actions of the three units—the depth control drive unit, the anchor chain constant tension adjustment unit, and the center mooring cable tension adjustment unit—are scheduled based on a unified timing controller to ensure the continuity of the adjustment actions and avoid mutual interference.

[0107] It should be further explained that, in the specific implementation process, the feedback optimization module's process of formulating historical strategy retrieval and optimization mechanisms and generating recommended control parameters includes:

[0108] After each comprehensive adjustment reaches the set state, a policy instance data package is created and stored. The policy instance data package encapsulates three types of information: scene feature signature, control policy fingerprint, and performance vector.

[0109] Specifically, based on the real-time suspension status profile of the net cage at the trigger moment and sea state information, a scene feature signature is generated: Based on the calculated real-time center of gravity position, the projected coordinates of the real-time center of gravity position on the horizontal plane (XY plane) are extracted as spatial position features; based on the calculated resultant external moment vector, the angle between the projection direction of the resultant external moment vector on the horizontal plane and the X-axis of the net cage's coordinate system (i.e., direction angle) is calculated as a mechanical direction feature; spectral analysis is performed on the time-series data of the net cage's vertical displacement and tilt angle contained in the real-time suspension status profile to identify the characteristic frequencies and amplitudes of the dominant motion modes as dynamic mode features; simultaneously, the amplitude and direction features of the surface current velocity are extracted from the sea state information as environmental disturbance features; all feature parameters are standardized and then concatenated to form a fixed-dimensional scene feature signature vector.

[0110] The core parameters of the adjustment scheme actually executed by the adjustment module constitute the control strategy fingerprint: the individual adjustment amounts of each water tank obtained by the depth control drive unit after performing the decomposition operation are recorded and normalized to the distribution ratio of the water volume of each water tank to the total adjustment amount, constituting the water volume allocation fingerprint; the control parameters actually used by the center mooring cable tension adjustment unit during the dynamic adjustment process (such as the vertical winch winding and unwinding speed command) are recorded, constituting the mooring cable control fingerprint; the anchor chain control parameters set by the anchor chain constant tension adjustment unit are recorded, constituting the anchor chain control fingerprint; the water volume allocation fingerprint, mooring control fingerprint, and anchor chain control fingerprint are encoded to form the control strategy fingerprint;

[0111] Based on the data collected during the evaluation window, a performance vector is calculated: within the set evaluation window, the root mean square of the difference between the mean of the depth data sequence collected by the depth sensor and the target depth is used as the depth steady-state accuracy index; the maximum absolute value of the roll and pitch angle data sequences collected by the tilt sensor is used as the tilt angle steady-state accuracy index; the total energy consumption of the actuator is calculated, and the adjustment process duration is calculated to form the adjustment efficiency index; where the adjustment process duration is the time required for the cage to go from the start of adjustment to the suspension state and enter a stable state; the steady-state accuracy indices of depth and tilt angle are combined with the adjustment efficiency index to form a multi-dimensional performance vector;

[0112] A dynamic policy graph is constructed based on the scene feature signatures in all stored policy instance data packets, where policy instances are associated through the similarity between their scene feature signatures;

[0113] When the adjustment scheme generation module needs to generate a new scheme, the feedback optimization module retrieves the R historical policy instances that are most similar to the current real-time scene feature signature in the dynamic policy graph, and uses a confidence-weighted policy fusion algorithm to fuse the control policy fingerprints of the historical policy instances to generate recommended policy parameters and provide them to the adjustment scheme generation module.

[0114] Specifically, C1, in the dynamic policy graph, calculate the Euclidean distance between the current real-time scene feature signature and the scene feature signature of each historical policy instance; sort them according to the distance value from smallest to largest, and select the R historical policy instances with the smallest distance to form a similar instance set; where R is the number of historical policy instances, and its value is dynamically adjusted according to the total number of instances in the dynamic policy graph, and the range is set between 5 and 10.

[0115] C2. Read the performance vector and control strategy fingerprint of each historical strategy instance in the similar instance set; the performance vector includes the deep steady-state accuracy index, the tilt angle steady-state accuracy index, and the regulation efficiency index; calculate a comprehensive performance score for each historical instance, which is the weighted sum of each index in its performance vector, where the weight of each index is a preset fixed value.

[0116] C3. Based on the comprehensive performance score of each historical instance, calculate its confidence weight. The calculation process is as follows: perform an exponential operation on the comprehensive performance score of each historical instance. The exponent of the exponential function is the product of the score and a negative smoothing factor. Sum the exponential operation results of all historical instances. Divide the exponential operation result of each historical instance by the sum to obtain its normalized confidence weight. Instances with higher comprehensive performance scores are assigned higher confidence weights.

[0117] C4. For each parameter in the control strategy fingerprint, perform weighted fusion. The fusion calculation is as follows: multiply the value of the parameter in the control strategy fingerprint of each historical instance by its corresponding confidence weight, add the weighted results of R historical instances, and obtain the fused recommended value of the parameter. The fused recommended values ​​of all parameters together constitute the recommended strategy parameters.

[0118] At the same time, the strategy ecosystem is optimized regularly. The utility of multiple strategy instances describing similar scenarios in the dynamic strategy graph is evaluated and ranked. The strategy instances with the highest utility ranking are retained, and inefficient strategy instances that have performed poorly in the long term or have been covered are archived or removed, thereby achieving the self-evolution of the strategy graph.

[0119] Specifically, D1, clusters the scene feature signatures of all policy instances in the dynamic policy graph. The clustering algorithm used is DBSCAN, and its neighborhood distance parameter and the minimum number of instances contained in the core object are set according to the distribution density of instances in the dynamic policy graph. Through cluster analysis, instances whose scene feature signatures are close to each other in the feature space are grouped into the same group, forming multiple similar scene policy groups.

[0120] D2. For each similar scenario strategy group, evaluate the utility of all strategy instances within the group. The evaluation is based on the performance vector stored for each instance, which includes the depth steady-state accuracy index, the tilt angle steady-state accuracy index, and the adjustment efficiency index. Calculate a utility score for each instance in the group, which is the sum of the products of the values ​​of each index in its performance vector and the preset performance weight coefficients. The performance weight coefficients are consistent for all instances in the group.

[0121] D3. Based on the calculated utility scores, sort all policy instances within the same policy group in descending order to generate a utility ranking list for that group.

[0122] D4. Perform retention and cleanup operations on strategy instances based on the ranking list: Set a retention threshold, which is a fixed offset of the utility score being higher than the group average score. This threshold is used as the utility score standard to determine whether a strategy instance is worth retaining. Strategy instances whose utility ranking is above the retention threshold are marked as preferred strategy instances and retained in the dynamic strategy graph.

[0123] D5. For strategy instances whose utility ranking does not reach the retention threshold, mark them as pending instances; check pending instances: if the Euclidean distance between its scene feature signature and the scene feature signature of any preferred strategy instance is lower than the preset coverage threshold, and its utility score is lower than that preferred strategy instance, then it is determined to be a covered stale strategy instance; where the coverage threshold is used as a distance standard to judge the similarity between strategy instances; if its utility score is lower than the fixed difference of the average utility score of all instances in the group, then it is determined to be an inefficient strategy instance; for strategy instances determined to be inefficient, remove them from the dynamic strategy graph and transfer them to an independent archive database. Example 1:

[0124] To verify the feasibility of this invention in practice, it was applied to a deep-sea aquaculture farm in a certain sea area, particularly suitable for the stability control of net cages during the breeding stage of fish fry and fingerlings. A practical operation scenario based on the net cage suspension depth-fixed information sensing, monitoring and adjustment system was constructed. In this sea area, the net cages are constantly disturbed by complex sea conditions such as wind, waves and currents, and there is a risk of unstable depth, tilting, and sudden changes in anchor chain tension, which seriously affects the safety of aquaculture and the lifespan of the net cage structure. In the past, the ballast adjustment in this area mainly relied on manual experience, which resulted in slow response and frequent occurrences of net cage suspension instability.

[0125] In this practical application scenario, the system uses key sensors deployed on the cage to collect real-time data on depth, tilt angle, anchor chain and center mooring cable tension, surface flow velocity and direction. After data fusion processing, a real-time floating state profile of the cage is generated. The adjustment scheme generation module determines whether the state deviates from the set value based on this profile. When the adjustment condition is triggered, the cage dynamics model is invoked to perform dynamic simulation, and a multi-dimensional adjustment scheme including ballast water adjustment scheme, target value of center mooring cable tension, and target value of anchor chain constant tension is generated simultaneously. The adjustment execution module then drives the constant depth control, center mooring cable tension adjustment and anchor chain constant tension adjustment units to coordinately perform water volume transfer, cable reeling and deployment, and anchor chain length compensation operations. The feedback optimization module records the adjustment process and constructs a dynamic strategy map to provide parameter recommendations for subsequent optimization.

[0126] In actual operation, the system's performance was particularly evident during a passing storm. Taking a 2-hour period during the storm as an example, the original monitoring data showed that the maximum depth deviation of the cage reached 1.5 meters, the roll angle exceeded 8 degrees, and the extreme fluctuation intensity of the tension of multiple anchor chains exceeded the threshold. After the system triggered the adjustment, the dynamic model calculated that a total of 4.5 cubic meters of ballast water needed to be transferred to a specific water tank, and the target value of the center mooring cable tension was increased by 15%. The execution adjustment module completed the main water volume allocation within 5 minutes, the cage depth deviation recovered to within ±0.2 meters within 15 minutes, the roll angle stabilized below 3 degrees, and the fluctuation amplitude of the anchor chain tension decreased by more than 60%.

[0127] During the 30-day continuous operation test, the system automatically triggered and completed 41 effective adjustments. Compared with historical data from the same period before the system was put into use, the stability of the cage suspension state was significantly improved: the average depth deviation decreased from 0.8 meters to 0.15 meters, the frequency of maximum tilt angle occurrence decreased by 75%, and the number of anchor chain tension over-limit events decreased by 82%. In addition, through historical strategy learning by the feedback optimization module, the system's response speed to recurring sea conditions improved by about 20%, and the total energy consumption of the actuators decreased by about 15%.

[0128] Therefore, this invention improves the suspension stability, safety, and operational economy of the cages in complex marine environments by constructing an intelligent control architecture that integrates perception, decision-making, execution, and optimization, and by introducing a forward-looking simulation based on dynamic models and a strategy self-learning mechanism based on historical data. It has good engineering application and promotion value. Example 2:

[0129] To address the specific needs of fish fry and fingerlings during their breeding season for light, water temperature, and surface flow velocity, the system can be configured accordingly. For example, the adjustment scheme generation module pre-stores multiple sets of setting parameters corresponding to different fry growth stages (such as the initial feeding stage and the juvenile stage). These parameters include, but are not limited to, the optimal suspension depth for light and the stable attitude angle to avoid surface turbulence. When the system identifies the cultured object as fry at a certain stage, it automatically calls the corresponding setting. Simultaneously, the performance evaluation index in the feedback optimization module can be enhanced with environmental stability indicators that are positively correlated with fry growth rate or survival rate (such as depth fluctuation frequency and tilt angle exceeding the limit duration), allowing the system's self-optimization direction to directly serve to improve fry rearing efficiency. Through targeted design, this system not only achieves physical cage stability but also further realizes intelligent environmental maintenance coupled with the growth needs of high-value organisms, significantly enhancing its application value in high-precision aquaculture scenarios such as livestock breeding and fish fry reproduction.

[0130] It should be understood that determining B based on A does not mean determining B solely based on A; it also means determining B based on A and / or other information.

[0131] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0132] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A net cage suspension depth information sensing monitoring adjusting system, characterized in that: The system comprises a multi-source data acquisition module, a data fusion processing module, an adjustment scheme generation module, an execution adjustment module, and a feedback optimization module. The multi-source data acquisition module acquires real-time multi-source sensor data, including depth data, inclination data, anchor chain tension data, central mooring cable tension data, and sea state information. The data fusion processing module fuses and processes the multi-source sensor data to generate a real-time suspension state image of the net cage, including: The multi-source sensor data is subjected to hardware-level time synchronization, and a net cage body coordinate system is established with the center of the designed waterline surface of the net cage as the origin and the Z-axis vertically downward. The multi-source sensor data is converted to the coordinate system. The converted multi-source sensor data is processed using an adaptive weighted Kalman filtering algorithm to output a denoised multi-dimensional data sequence. Based on the obtained multi-dimensional data sequence, the real-time center of gravity position and inclination of the net cage under the current sea state are calculated through the inclination data and the depth data. At the same time, the resultant external force vector and the resultant moment vector acting on the net cage are calculated. The calculated real-time center of gravity position, inclination, resultant external force vector, and resultant moment vector are fused and analyzed to generate an integrated real-time suspension state image of the net cage. The adjustment scheme generation module combines a pre-stored net cage dynamics model, judges whether the net cage suspension state deviates from the set state based on the real-time suspension state image of the net cage, and generates a multi-dimensional suspension state adjustment scheme, including the ballast water adjustment amount, the central mooring cable tension target value, and the constant tension target value for the anchor chain. The execution adjustment module adjusts the net cage buoyancy, controls the central mooring cable tension, and maintains the anchor chain tension constant according to the multi-dimensional suspension state adjustment scheme. The execution adjustment module includes a depth control driving unit, an anchor chain constant tension adjustment unit, and a central mooring cable tension adjustment unit. The feedback optimization module monitors the adjusted net cage suspension state in real time, formulates a historical strategy retrieval and optimization mechanism, and generates recommended control parameters.

2. The net cage suspension depth information sensing monitoring adjusting system according to claim 1, characterized in that: The multi-source data acquisition module includes: An inclination sensor is used to measure the roll angle and pitch angle of the net cage, which constitutes the inclination data. A depth sensor is used to monitor the draft of the net cage in real time, which constitutes the depth data. The tension sensor includes an anchor chain tension sensor and a central mooring cable tension sensor. The anchor chain tension sensor is used to monitor the real-time tension of each anchor chain, which constitutes the anchor chain tension data. The central mooring cable tension sensor is used to monitor the real-time tension of the central mooring cable, which constitutes the central mooring cable tension data. An environmental sensor group is used to collect sea state information, including surface flow velocity and direction data.

3. The net cage suspension depth information sensing monitoring adjusting system according to claim 1, characterized in that: The process of the adjustment scheme generation module combining a pre-stored net cage dynamics model, judging whether the net cage suspension state deviates from the set state based on the real-time suspension state image of the net cage, and generating a multi-dimensional suspension state adjustment scheme includes: Continuously receiving the real-time suspension state image of the net cage, extracting the depth deviation, the attitude abnormal region identifier, and the ratio coefficient of the resultant moment and the restoring moment, and performing comparison analysis; Based on the comparison analysis result, determining whether to generate a preliminary adjustment strategy; If the preliminary adjustment strategy is triggered, a pre-stored net dynamics model is called; the simulation results of the net dynamics model are obtained, and a regulation scheme containing the ballast water adjustment amount and the center mooring cable tension target value is generated synchronously; At the same time of generating the regulation scheme, the fluctuation characteristics of each anchor chain tension are analyzed in real time; according to the analysis results of the fluctuation characteristics, the constant tension adjustment instruction for the anchor chain is embedded in the comprehensive adjustment scheme; finally, the multi-dimensional suspension state adjustment scheme is obtained, including the ballast water adjustment amount, the center mooring cable tension target value and the constant tension target value for the anchor chain.

4. The net cage suspension depth information sensing monitoring adjusting system according to claim 1, characterized in that: The process of adjusting the net buoyancy, controlling the center mooring cable tension and maintaining the constant tension of the anchor chain according to the multi-dimensional suspension state adjustment scheme by the execution adjustment module includes: The depth control driving unit drives the distributed pump-valve system to transport the ballast water between the multiple compartments of the net according to the received ballast water adjustment amount, so as to change the buoyancy distribution and the center of gravity of the net; The center mooring cable tension adjustment unit adjusts the cable tension by winding and unwinding the center mooring cable according to the received center mooring cable tension target value and in combination with the real-time sea state information and the vertical movement trend of the net; The anchor chain constant tension adjustment unit drives the corresponding actuator of the anchor chain according to the received constant tension target value of the anchor chain, and dynamically adjusts the effective length of the anchor chain.

5. The net cage suspension depth information sensing monitoring adjusting system according to claim 4, characterized in that: The depth control driving unit further includes: The ballast water adjustment amount includes the total buoyancy adjustment amount of the net required for restoring the target depth and setting the suspension state, which is represented by the total volume of the ballast water to be adjusted in or out; The current liquid level state data collected by the liquid level sensor arranged in each ballast water tank is read; The calculated real-time center of gravity position and inclination are obtained as the current inclination posture of the net; According to the current liquid level state data of each ballast water tank and the current inclination posture of the net, the total buoyancy adjustment amount is decomposed into individual adjustment amounts of each water tank, wherein the decomposition principle is to preferentially select the water tank combination that can correct the current inclination posture of the net to the greatest extent for water allocation; Based on the decomposed individual adjustment amounts of each water tank, a pump-valve cooperative operation time sequence table is generated, which includes: the starting order, running time and speed gear of each water pump; the opening and closing time of the water inlet and outlet valve linked with the water pump; and the time interval setting between key operation nodes; According to the time sequence table, the corresponding water pump and valve are driven to perform water inlet and outlet operation, and the cooperative adjustment of the buoyancy of the net is completed.

6. The net cage suspension depth information sensing monitoring adjusting system according to claim 4, characterized in that: The center mooring cable tension adjustment unit further includes: The real-time tension data of the center mooring cable, the real-time vertical acceleration data of the net and the surface flow velocity data collected by the environmental sensor are obtained; The real-time tension data of the center mooring cable is compared with the center mooring cable tension target value to obtain a first tension deviation; At the same time, the surface flow velocity data is taken as the input to query the pre-set flow velocity-tension disturbance mapping relationship table to obtain a feedforward compensation amount; The first tension deviation and the feedforward compensation amount are superimposed to obtain a composite control target value; According to the size and direction of the composite control target value, the vertical movement trend of the net is judged in combination with the vertical acceleration data to dynamically adjust the cable winding and unwinding speed instruction; Through dynamic adjustment, the center mooring cable tension is stabilized in the target interval under dynamic sea conditions.

7. The net cage suspension depth information sensing monitoring adjusting system according to claim 4, characterized in that: The anchor chain constant tension adjusting unit further comprises: For each anchor chain under constant tension control, the real-time tension data of the anchor chain is continuously monitored, and the average tension change rate thereof in a short time window is calculated; The real-time tension data of the anchor chain is compared with the constant tension target value of the anchor chain to obtain a second tension deviation; A dynamic action triggering threshold is set, which is positively correlated with the absolute value of the average tension change rate; When the absolute value of the second tension deviation exceeds the dynamic action triggering threshold, an anchor chain length adjusting instruction is immediately generated; The adjusting instruction is sent to the execution mechanism corresponding to the anchor chain to drive the compensatory adjustment of the anchor chain length.

8. The net cage suspension depth information sensing monitoring adjusting system according to claim 1, characterized in that: The feedback optimization module formulates a historical strategy retrieval and optimization mechanism, and the process of generating recommended control parameters includes: After each comprehensive adjustment reaches the set state, a strategy instance data packet is created and stored, wherein the strategy instance data packet encapsulates three types of information: scene feature signature, control strategy fingerprint, and performance vector; Based on the scene feature signatures in all stored strategy instance data packets, a dynamic strategy atlas is constructed, wherein strategy instances are associated through the similarity between their scene feature signatures; When the adjustment scheme generation module generates a new scheme, the feedback optimization module retrieves R historical strategy instances most similar to the current real-time scene feature signature in the dynamic strategy atlas, and uses a confidence-based strategy fusion algorithm to fuse the control strategy fingerprints of the historical strategy instances, to generate recommended strategy parameters and provide them to the adjustment scheme generation module; At the same time, the strategy ecological optimization is regularly executed to perform utility evaluation and sorting on multiple strategy instances describing similar scenes in the dynamic strategy atlas, and the strategy instances with high utility ranking are retained.

9. The net cage suspension depth information sensing monitoring adjusting system according to claim 8, characterized in that: The feedback optimization module further comprises: Based on the real-time suspension state portrait of the cage at the adjustment triggering time and the sea state information, a scene feature signature is generated; The core parameters of the adjustment scheme actually executed by the execution adjustment module constitute the control strategy fingerprint; Based on the data collected in the evaluation window period, a performance vector is calculated.

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