Control method, system and equipment of pond trawl fishing device and medium

Abstract

The invention relates to the technical field of aquaculture, in particular to a control method, system and equipment of a pond trawl fishing device and a medium. The method comprises the following steps: determining whether to trigger a highest priority interrupt signal by adopting a threshold judgment method based on an actually measured tension value obtained in real time; when the highest priority interrupt signal is not triggered, position information of a pond obstacle and a net floating line is identified and obtained based on image data, obtained in real time, of the far end of the trawl, the collision risk is pre-judged, and whether a dynamic obstacle avoidance strategy is executed or not is determined based on the pre-judgment result; the actual trawl speed and the actual driving tension are calculated based on trawl power data obtained in real time, and the optimized trawl speed is determined by adopting a fuzzy control strategy and executed. By designing feedback control logic of dynamic resistance in the trawl process, the adaptability to complex pond fishing working conditions is improved, and dynamic balance of equipment safety and fishing efficiency and intelligent upgrading of pond trawl fishing operation are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of aquaculture, and specifically relates to a control method, system, device and medium for a pond trawl fishing device. Background Art

[0002] Aquatic products are a source of high-quality and safe animal protein, and pond culture is the most common and main form of aquaculture. However, the mechanical equipment level of traditional pond culture is low, the intelligent level is not high, and the development of different links is unbalanced. Mechanical operations are mainly concentrated in the oxygenation and feeding links, but fishing and other links still rely on manual operations, resulting in low work efficiency.

[0003] Therefore, the prior art has proposed a trawl fishing scheme for pond culture. For example, Chinese Patent CN102027892A, a simple trawl fishing system for aquaculture ponds, realizes the mechanization of pond fishing trawl operations by setting ground piles and wire guide ropes on both sides of the pond and using a trawl winch and tractor power to pull the net bag for fishing. However, although the trawl operation in this patent is completed by a winch driven by a tractor, the sizes of pond areas vary, and the operator is located at one end of the pond, making it difficult to directly observe the actual state of the trawl at the other end (such as whether the net gear gets stuck in the silt or touches an obstacle, etc.). When the bottom wire of the net gear gets stuck in the silt, the sudden increase in resistance is not discovered in time, resulting in net clothing damage or motor overload. Or when the net gear approaches an obstacle at the edge of the pond, the operator cannot intervene in time, resulting in a reduction in fishing efficiency or equipment damage, affecting normal trawl fishing operations.

[0004] Although Chinese Patent CN103626069A, a constant tension winch control system for trawls, proposes a technical solution to control the winch by using a PLC, frequency converter, tension sensor and rotary encoder to maintain the tension balance of the towing line, however, this patent is for the operation scenario of an ocean trawler, applied to an open ocean environment mainly disturbed by wind and waves, and the core control object is the tension balance of the left and right towing lines, which is difficult to be applied to a closed pond environment mainly disturbed by silt and fixed obstacles. Moreover, this patent only relies on the tension sensor to monitor the pulling force of the towing line and cannot predict the collision risk between the net gear and the obstacle. When the bottom wire of the net gear gets stuck in the silt and causes a sudden change in resistance, adjusting the winch through tension feedback will result in a response delay, still causing motor overload or net gear tearing, resulting in a reduction in fishing efficiency. Summary of the Invention

[0005] In view of the above technical problems, the present invention proposes a control method, system, device and medium for a pond trawl fishing device, aiming to improve the adaptability to complex pond fishing conditions by designing a feedback control logic for dynamic resistance during trawling, realize the dynamic balance between equipment safety and fishing efficiency, and the intelligent upgrade of pond trawl fishing operations.

[0006] In a first aspect, the present application provides a control method for a pond trawl fishing device, including the following steps:

[0007] Obtain the measured tension value of the trawl rope, the image data of the far end of the trawl, and the trawl power data in real time;

[0008] Based on the measured tension value, use the threshold judgment method to determine whether to trigger the highest-priority interrupt signal;

[0009] When the highest-priority interrupt signal is not triggered, based on the image data of the far end of the trawl, identify the position information of the pond obstacles and the floating line of the fishing gear;

[0010] Based on the position information of the pond obstacles and the floating line of the fishing gear, predict the collision risk, and determine whether to execute the dynamic obstacle avoidance strategy based on the prediction result;

[0011] When the highest-priority interrupt signal is not triggered, or after interruption and restart, or the dynamic obstacle avoidance strategy is not executed, based on the trawl power data, calculate the actual trawl speed and the actual driving tension;

[0012] Based on the actual trawl speed and the actual driving tension, use the fuzzy control strategy to determine the optimized trawl speed and execute it.

[0013] In some embodiments, based on the measured tension value, using the threshold judgment method to determine whether to trigger the highest-priority interrupt signal includes:

[0014] Compare the measured tension value with the preset tension threshold. When the measured tension value is greater than or equal to the preset tension threshold, trigger the highest-priority interrupt signal to control the interruption of the trawl power and issue an overload alarm prompt.

[0015] In some embodiments, based on the position information of the pond obstacles and the floating line of the fishing gear, predicting the collision risk and determining whether to execute the dynamic obstacle avoidance strategy based on the prediction result includes:

[0016] Based on the position information of the pond obstacles and the floating line of the fishing gear, calculate the actual distance between the floating line of the fishing gear and the obstacles;

[0017] Compare the actual distance with the preset safety distance threshold. When the actual distance is less than or equal to the preset safety distance threshold, obtain the prediction result with a collision risk, and execute the dynamic obstacle avoidance strategy according to the prediction result with a collision risk.

[0018] In some embodiments, the execution of the dynamic obstacle avoidance strategy includes:

[0019] Trigger the secondary-priority interrupt signal, and use the S-shaped speed curve to control the trawl power to decelerate to the first speed threshold and then control the interruption of the trawl power;

[0020] Reverse the control of the trawling power to release the trawl rope at a second speed threshold until a preset trawl rope slack length is reached, then control to interrupt the trawling power and issue an obstacle alarm prompt.

[0021] In some embodiments, based on the actual trawling speed and the actual driving pull force, a fuzzy control strategy is adopted to determine the optimized trawling speed, including:

[0022] Calculate the third speed threshold v l,s and the difference from the actual trawling speed v k to obtain the speed deviation e v ;

[0023] Calculate the pulling force safety margin ΔL, and the expression is:

[0024] ΔL = L k - P r,k - R·n

[0025] where L k represents the actual driving pull force, P r,k represents the measured pull force value, R represents the trawl rope resistance, and n represents the preset safety factor;

[0026] Taking the speed deviation e v and the pulling force safety margin ΔL as input variables, and taking the condition that the actual driving pull force is less than the preset pull force threshold and ΔL ≤ 0 as constraints, a fuzzy control strategy is used to output the speed adjustment coefficient k v ;

[0027] Multiply the speed adjustment coefficient by the third speed threshold as the optimized trawling speed.

[0028] In some embodiments, the fuzzy set of the input speed deviation e v is {Negative Big (NB), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Big (PB)};

[0029] The fuzzy set of the input pulling force safety margin ΔL is {Safe (SAFE), Warning (WARN), Danger (DANGER)};

[0030] The fuzzy set of the output speed adjustment coefficient k v is {Stop (STP), Decelerate (DEC), Hold (HOLD), Accelerate (INC), Greatly Accelerate (INC-L)}, then the fuzzy control strategy includes:

[0031] When e v = PB and ΔL = SAFE, output k v = INC-L;

[0032] When e v=PS and when ΔL = SAFE, output k v =INC;

[0033] When e v =ZO and when ΔL = SAFE, output k v =HOLD;

[0034] When e v =NS and when ΔL = WARN, output k v =DEC;

[0035] When e v =NB and when ΔL = DANGER, output k v =STP.

[0036] In some embodiments, the following steps are further included:

[0037] Based on the image data of the far end of the trawl net obtained in real time, identify the visual form of the floating line of the fishing gear;

[0038] Based on the visual form of the floating line of the fishing gear, determine the distance between the centers of adjacent floats and the curvature of the floating line;

[0039] Based on the distance between the centers of adjacent floats and the curvature of the floating line, calculate the fishing gear form anomaly index;

[0040] Record the optimized trawling speed as the first target trawling speed. When the fishing gear form anomaly index is within the first deviation threshold range, reduce the first target trawling speed to the second target trawling speed;

[0041] When the fishing gear form anomaly index exceeds the first deviation threshold range, control to interrupt the trawling power and issue an alarm prompt for serious deviation of the floating line.

[0042] In a second aspect, the present application provides a control system for a pond trawling fishing device, including a trawling motor for providing trawling power, a tension sensor, an image monitoring module, a wheel encoder, and a control module. The trawling motor, the tension sensor, the image monitoring module, and the wheel encoder are all connected to the control module, wherein:

[0043] The tension sensor is used to obtain the measured tension value of the trawling rope in real time;

[0044] The image monitoring module is used to obtain the image data of the far end of the trawling net in real time;

[0045] The wheel encoder is used to obtain the trawling power data in real time;

[0046] The control module is used to execute the following steps:

[0047] Based on the measured pulling force value, the threshold judgment method is used to determine whether to trigger the highest-priority interrupt signal;

[0048] When the highest-priority interrupt signal is not triggered, based on the image data at the far end of the trawl net, the position information of the pond obstacles and the floating line of the net gear is identified;

[0049] Based on the position information of the pond obstacles and the floating line of the net gear, the collision risk is predicted, and whether to execute the dynamic obstacle avoidance strategy is determined based on the prediction result;

[0050] When the highest-priority interrupt signal is not triggered, or after interruption and restart, or when the dynamic obstacle avoidance strategy is not executed, based on the trawl power data, the actual trawl speed and the actual driving pulling force are calculated;

[0051] Based on the actual trawl speed and the actual driving pulling force, the fuzzy control strategy is used to determine the optimized trawl speed and execute it.

[0052] In a third aspect, an electronic device includes a processor and a memory;

[0053] The processor is connected to the memory;

[0054] The memory is used to store executable program codes;

[0055] The processor runs the program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the control method of a pond trawl fishing device as described above.

[0056] In a fourth aspect, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the control method of a pond trawl fishing device as described above is implemented.

[0057] The beneficial technical effects of the present invention at least include:

[0058] 1. A control method, system, equipment and medium for pond trawling fishing device are used to judge overload risk through mechanical signals (measured tension value), avoid obstacles through visual signals (actual distance between the net float line and obstacles), and calibrate actual speed through motion signals (optimized trawling speed). Combined with the hierarchical control logic of "overload shutdown (hardware-level interruption) > obstacle avoidance (software interruption) > adaptive speed adjustment (efficiency optimization)", the three are integrated to form a closed-loop optimization of the control method to ensure that the safety response is not delayed in an emergency, and maintain the best trawling efficiency within the safety range through speed adaptive adjustment, thereby achieving a dynamic balance between equipment safety and fishing efficiency, and at the same time realizing sensor cross-validation to effectively distinguish the cause of overload. Therefore, it avoids misjudgment of obstacle resistance or silt resistance, thereby executing overload shutdown or dynamic obstacle avoidance strategy in a targeted manner, improving the reliability of dynamic resistance feedback control during trawling, improving adaptability to complex pond fishing conditions, and realizing intelligent upgrading of pond trawling operations. The synergy of its technical units not only realizes the enhancement of single functions (such as tension monitoring), but more importantly, through the deep integration of data and dynamic resistance, the system has the full-chain intelligent capability of "perception-analysis-decision-execution", and ultimately achieves the comprehensive effect of improving trawling efficiency, reducing equipment loss, and reducing manual dependence, creatively solving the three major problems existing in the existing technology: invisible remote status, lack of resistance adaptive capability, and manual dependence;

[0059] 2. The dynamic obstacle avoidance strategy in this application, through the coordination of technical units of visual-mechanical fusion perception, hardware accelerated interruption and adaptive release control, systematically solves the three major pain points of the traditional trawling system in obstacle response, namely, hysteresis, rough shutdown and low recovery efficiency. Specifically, compared with the step-by-step interruption of trawling power with hysteresis in the traditional trawling system, this application controls the trawling power to enter the slow stop mode according to the collision risk predicted by image recognition, and controls the interruption of trawling power after the trawling power is controlled by an S-shaped speed curve to slow down to the first speed threshold, and further combines the trawling rope relaxation program to reduce the tension of the net by releasing the trawling rope in the reverse direction, which can reduce the probability of collision between the net and the obstacle, and greatly reduce the impact force of the floating line, prevent the floating line from breaking, and realize safe, efficient and intelligent trawling obstacle avoidance operation in complex pond environments;

[0060] 3. In the prior art, when the bottom wire of the fishing gear sinks into the silt and causes a sudden change in resistance, simply using a traditional PID to adjust the winch through tension feedback easily leads to response delay, resulting in motor overload or fishing gear tearing. Therefore, in this application, through the synergistic effect of fuzzy-PID composite control, dynamic resistance feedback, and multi-level safety protection, the contradiction between efficiency and safety in existing trawling operations is solved, and reliable intelligent optimization of trawling speed in complex pond environments is achieved. Specifically, in this application, a fuzzy control strategy that combines speed deviation and tensile safety margin quickly predicts the direction of speed adjustment, realizes fuzzy feedforward compensation, effectively solves the technical problem of lag in traditional PID response, and at the same time realizes load adaptive speed regulation. It dynamically increases speed in low-resistance areas to improve trawling operation efficiency, reduces speed in high-resistance areas to reduce the average power consumption of the motor, and triggers shutdown protection in case of sudden resistance to avoid equipment damage.

[0061] 4. The prior art only ensures tension balance and cannot predict the fish escape situation caused by the out-of-control form of the fishing gear, resulting in a decrease in fishing efficiency. Therefore, in this application, the form of the floating wire is visually output through image recognition, and the abnormal index of the fishing gear form is calculated. When the abnormal index of the fishing gear form deviates from the threshold, the trawling speed is dynamically reduced to stabilize the form of the fishing gear to achieve closed-loop repair or the shutdown triggers an alarm for serious deviation of the floating wire. The coupling of visual form monitoring and dynamic speed regulation enables the system to have the ability to monitor the form of the fishing gear, avoids the fish escape phenomenon caused by excessive bending of the floating wire, and effectively improves the overall trawling fishing effect.

[0062] Other features and advantages of the present invention will be disclosed in detail in the following specific embodiments and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The following further describes the present invention with reference to the drawings:

[0064] Figure 1 It is a flowchart of the control method of the pond trawling fishing device according to an embodiment of the present invention.

[0065] Figure 2 It is a schematic structural diagram of the control system of the pond trawling fishing device according to an embodiment of the present invention.

[0066] Figure 3 It is a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] The following explains and illustrates the technical solutions of the embodiments of the present invention with reference to the drawings of the embodiments of the present invention. However, the following embodiments are only the preferred embodiments of the present invention and not all of them. Based on the embodiments in the embodiments, other embodiments obtained by those skilled in the art without creative efforts all fall within the protection scope of the present invention.

[0068] In the following description, terms such as "inner", "outer", "upper", "lower", "left", "right", etc., which indicate orientation or positional relationship, are only for the convenience of describing embodiments and simplifying the description, rather than indicating or implying that the device or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0069] Please refer to the attached Figure 1 , Figure 1 which shows a schematic flowchart of the control method of the pond trawl fishing device provided by an embodiment of this specification.

[0070] As Figure 1 shown, the control method of the pond trawl fishing device may at least include the following steps:

[0071] Step 101, obtain the measured pulling force value of the trawl rope, the image data of the far end of the trawl, and the trawl power data in real time.

[0072] It can be understood that after the operator completes the preparatory work for trawl fishing, such as installing the wire guiding rope, guiding pulley, trawl rope, tension sensor, net, image monitoring module, and wheel encoder, the control system is turned on. The control system controls the speed and torque of the trawl motor according to the preset corresponding relationship between the appropriate traction speed and the trawl motor speed, so as to drive the winch drum to wind up the trawl rope. After the trawl rope is tightened, it pulls the guiding pulley, image monitoring module, and net at the far end to move synchronously. At the same time, the control module starts to obtain in real time the measured pulling force value collected by the tension sensor, the image data collected by the image monitoring module, and the trawl power data collected by the wheel encoder, and sends the above data to the control module of the control system for data processing.

[0073] Furthermore, for the measured pulling force values collected at high frequency, preprocessing operations can also be performed first, such as removing missing values and outliers, and using the recursive average filtering method to eliminate noise interference, and then using the preprocessed measured pulling force values for subsequent control logic.

[0074] Step 102, based on the measured pulling force value, use the threshold judgment method to determine whether to trigger the highest-priority interrupt signal;

[0075] Specifically, in this embodiment, based on the measured pulling force value, using the threshold judgment method to determine whether to trigger the highest-priority interrupt signal includes:

[0076] Compare the measured pulling force value with the preset pulling force threshold. When the measured pulling force value is greater than or equal to the preset pulling force threshold, trigger the highest-priority interrupt signal to control the interruption of the trawl power and issue an overload alarm prompt.

[0077] It can be understood that in this embodiment, when the measured pulling force value is less than the preset pulling force threshold, the highest-priority interrupt signal is not triggered.

[0078] Among them, the preset pulling force threshold is the maximum pulling force threshold allowed by the motor. Trigger the highest-priority interrupt signal to immediately cut off the power supply of the motor, and control the rotational speed and torque output of the motor to 0, so as to avoid adverse situations such as motor overload burnout or net tearing caused by response lag in the prior art.

[0079] Step 103, when the highest-priority interrupt signal is not triggered, based on the image data at the far end of the trawl net, identify the position information of the pond obstacles and the floating line of the net.

[0080] Among them, the Yolo algorithm can be used to identify the position information of the floating line of the net and the obstacles in real time. Specifically, a pre-trained YOLOv8-seg model can be used. Input the image at the far end of the trawl net, and output the coordinate information of the segmentation mask of the floating line of the net and the detection frames of the obstacles (such as aerators, vertical poles, etc.). The operation method of the pre-trained YOLOv8-seg model is similar to the operation method of the pre-trained YOLO model in the prior art, and will not be elaborated in this embodiment.

[0081] Step 104, based on the position information of the pond obstacles and the floating line of the net, predict the collision risk, and determine whether to execute the dynamic obstacle avoidance strategy based on the prediction result;

[0082] Specifically, in this embodiment, based on the position information of the pond obstacles and the floating line of the net, predict the collision risk, and determine whether to execute the dynamic obstacle avoidance strategy, including:

[0083] Step 201, based on the position information of the pond obstacles and the floating line of the net, calculate the actual distance between the floating line of the net and the obstacles.

[0084] Specifically, first calculate the Euclidean distance between the pixel coordinates at the center of the obstacle detection frame and the edge of the segmentation mask of the floating line of the net, and then convert it according to the actual distance corresponding to the image width to obtain the actual distance d (the unit can be meters) between the floating line of the net and the obstacles.

[0085] Step 202, compare the actual distance with the preset safety distance threshold D min When the actual distance d is less than or equal to the preset safety distance threshold (i.e., d ≤ D min ), obtain a prediction result with a collision risk, and execute the dynamic obstacle avoidance strategy according to the prediction result with a collision risk.

[0086] It can be understood that in this embodiment, when d > D min , obtain a prediction result without a collision risk, and do not execute the dynamic obstacle avoidance strategy.

[0087] Furthermore, in this embodiment, executing the dynamic obstacle avoidance strategy includes:

[0088] In step 2021, trigger a secondary priority interrupt signal, and use an S-shaped speed curve to control the trawling power to decelerate to the first speed threshold and then control the interruption of the trawling power.

[0089] It can be understood that according to the prediction result of the collision risk, the secondary priority interrupt signal is triggered first so that the motor receives a slow stop instruction.

[0090] Specifically, the S-shaped speed curve v(t) for the trawling power to decelerate to the first speed threshold can be expressed as:

[0091]

[0092] where v k represents the current actual trawling speed, k represents the preset deceleration slope, and t0 represents the deceleration start time. Based on the trawling power data collected by the wheel encoder, the speed v is monitored in real time k , and when v k decelerates to the first speed threshold, control the interruption of the trawling power.

[0093] In step 2022, control the trawling power in reverse to release the trawl rope at the second speed threshold until the preset trawl rope slack length Δs is reached, control the interruption of the trawling power and issue an obstacle alarm prompt.

[0094] It can be understood that the second speed threshold is the preset low speed. After the slow stop is completed, the motor switches to the reverse mode, releases the trawl rope by Δs meters at the preset low speed, and then controls the interruption of the trawling power and issues an obstacle alarm prompt. This embodiment also includes an obstacle avoidance recovery mechanism, that is, after the image data recognition result at the far end of the trawl confirms that the obstacle has been removed, automatically restart the trawling operation and restore the speed of the trawling power to the preset suitable trawling speed.

[0095] Furthermore, this embodiment can also include a tensile force feedback termination mechanism. Specifically: If during the process of releasing the trawl rope in reverse, the measured tensile force value has dropped to the tensile force safety threshold, stop the reverse release in advance, control the interruption of the trawling power and issue an obstacle alarm prompt, so as to avoid excessive slack leading to entanglement of the fishing gear and reduce the secondary risk through adaptive reverse release.

[0096] It can be understood that different types of pond fishing gears, such as deep water nets, surround nets, etc., can be adapted by adjusting the D min and Δs parameters.

[0097] The dynamic obstacle avoidance strategy in this embodiment, through the collaboration of technical units including visual-mechanical fusion perception, hardware-accelerated interruption and adaptive release control, systematically solves the three major pain points of traditional trawling systems in obstacle response, namely, hysteresis, rough shutdown and low recovery efficiency. Specifically, compared with the step-type interruption of trawling power with hysteresis in traditional trawling systems, this embodiment controls the trawling power to enter a slow stop mode based on the collision risk predicted by image recognition, and uses an S-shaped speed curve to control the trawling power to slow down to a first speed threshold and then control the interruption of the trawling power. It is further combined with the trawling rope relaxation program to reduce the tension of the net by releasing the trawling rope in reverse, which can reduce the probability of collision between the net and obstacles while greatly reducing the impact force of the floating line and preventing the floating line from breaking, thereby realizing safe, efficient and intelligent trawling obstacle avoidance operations in complex pond environments.

[0098] Step 105, when the highest priority interrupt signal is not triggered or the system is restarted after an interruption or the dynamic obstacle avoidance strategy is not executed, the actual trawl speed and the actual driving force are calculated based on the trawl power data.

[0099] The trawl power data include but are not limited to the number of pulses m of the wheel encoder within the collection interval Δt, the wheel radius r, the number of encoder lines N, and the motor torque T k The actual trawling speed v k And the actual driving force L k It can be expressed as:

[0100]

[0101] Wherein, i represents the preset reduction ratio.

[0102] Step 106, based on the actual trawl speed and the actual driving force, the fuzzy control strategy is used to determine the optimized trawl speed v k ' and execute.

[0103] The optimized trawling speed can be realized by: the motor receives the output of the fuzzy control strategy v k ′, stepless speed regulation is achieved by adjusting the PWM duty cycle, and the wheel encoder feeds back the actual trawl speed for calibration.

[0104] Furthermore, in this embodiment, based on the actual trawl speed and the actual driving force, a fuzzy control strategy is used to determine the optimized trawl speed, including:

[0105] Step 301, calculate the third speed threshold v l,s The actual trawl speed v k The difference between the speed deviation and the v .

[0106] Among them, the third speed threshold is the preset appropriate trawling speed.

[0107] Step 302, calculate the pulling force safety margin ΔL, and the expression is:

[0108] ΔL = L k - P r,k - R·n

[0109] Among them, L k represents the actual driving pulling force, P r,k represents the measured pulling force value, n represents the empirical safety factor, which is used to provide a safety margin for the control system to prevent overload caused by pulling force safety margin error or sudden resistance, and R represents the resistance of the trawl rope. Among them, S represents the length of the towing rope, and g represents the gravity per unit length of the towing rope. represents the friction resistance coefficient, which can be obtained by experimental measurement or updated according to the actual situation, such as adjusted according to seasons or different pond bottom sediments (silt, sand, etc.). to adapt to the changes in different pond environments.

[0110] Step 303, using the speed deviation e v and the pulling force safety margin ΔL as input variables, and using the actual driving pulling force L k less than the preset pulling force threshold and ΔL ≤ 0 as constraint conditions, and adopting a fuzzy control strategy to output the speed adjustment coefficient k v .

[0111] It can be understood that the preset pulling force threshold is the maximum pulling force threshold allowed by the motor. When the pulling force exceeds the limit, that is, when L k is greater than or equal to the preset pulling force threshold, trigger the highest priority interrupt signal to immediately control the interruption of the trawling power and issue an overload alarm prompt.

[0112] Among them, the fuzzy control strategy in this embodiment is used to accelerate to improve efficiency when the actual trawling speed is lower than the preset appropriate trawling speed and the pulling force is safe (that is, ΔL < 0), and to decelerate or stop to avoid overload when the pulling force is close to the safety threshold (that is, ΔL is close to 0 or ΔL = 0).

[0113] Step 304, take the product of the speed adjustment coefficient k v and the third speed threshold v l,s as the optimized trawling speed v k '.

[0114] Furthermore, in this embodiment, the fuzzy set of the input speed deviation e v is {Negative Big (NB), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Big (PB)}, for example, the range is [-0.5, 0.5] m / s;

[0115] The fuzzy set of the input pulling force safety margin ΔL is {SAFE, WARN, DANGER}, for example, the range is [-200, 0] N;

[0116] The output speed adjustment coefficient k v The fuzzy set of is {STP, DEC, HOLD, INC, INC-L}, for example, the range is [0, 1.5], then the fuzzy control strategy includes:

[0117] When e v = PB and ΔL = SAFE, the output k v = INC-L, for example, k v = 1.5;

[0118] When e v = PS and ΔL = SAFE, the output k v = INC, for example, k v = 1.2;

[0119] When e v = ZO and ΔL = SAFE, the output k v = HOLD, for example, k v = 1.0;

[0120] When e v = NS and ΔL = WARN, the output k v = DEC, for example, k v = 0.5;

[0121] When e v = NB and ΔL = DANGER, the output k v = STP, for example, k v = 0.

[0122] In the prior art, when the bottom wire of the fishing gear gets stuck in the silt, resulting in a sudden change in resistance, only the traditional PID is used to adjust the winch through tension feedback, which is likely to cause response delay, resulting in motor overload or fishing gear tearing. Therefore, in this embodiment, through the synergistic effect of fuzzy-PID composite control, dynamic resistance feedback, and multi-level safety protection, the contradiction between efficiency and safety in the existing trawling operation is solved, and reliable intelligent optimization of the trawling speed in a complex pond environment is achieved. Specifically, in this embodiment, the speed deviation e is combined vThe fuzzy control strategy with the tension safety margin ΔL can quickly predict the speed adjustment direction and realize fuzzy feedforward compensation, which effectively solves the technical problem of traditional PID response lag and realizes load adaptive speed regulation. It dynamically increases the speed in the low resistance area to improve the trawling operation efficiency, reduces the speed in the high resistance area to reduce the average power consumption of the motor, and triggers shutdown protection in the event of sudden resistance to avoid equipment damage.

[0123] In summary, this embodiment uses mechanical signals (measured tension values) to judge overload risks, visual signals (actual distance between the net float line and obstacles) to avoid obstacles, and motion signals (optimized trawl speed) to calibrate the actual speed. Combined with the hierarchical control logic of "overload shutdown (hardware-level interruption) > obstacle avoidance (software interruption) > adaptive speed adjustment (efficiency optimization)", the three are integrated to form a closed-loop optimization of the control method to ensure that safety response is not delayed in emergency situations, and maintain the best trawl efficiency through speed adaptive adjustment within a safe range, thereby achieving a dynamic balance between equipment safety and fishing efficiency, and at the same time realizing sensor cross-validation to effectively distinguish the cause of overload, avoid obstacle resistance or The misjudgment of silt resistance can be prevented, and overload shutdown or dynamic obstacle avoidance strategies can be implemented in a targeted manner, thereby improving the reliability of dynamic resistance feedback control during trawling, improving the adaptability to complex pond fishing conditions, and realizing the intelligent upgrade of pond trawling operations. The synergy of its technical units not only realizes the enhancement of single functions (such as tension monitoring), but more importantly, through the deep integration of data and dynamic resistance, the system has the full-chain intelligent capability of "perception-analysis-decision-execution", and ultimately achieves the comprehensive effect of improving trawling efficiency, reducing equipment loss, and reducing manual dependence, creatively solving the three major problems of invisible remote status, lack of resistance adaptive capability, and manual dependence in the existing technology.

[0124] In yet another embodiment of the present specification, the control method of the pond trawling fishing device may further include the following steps:

[0125] Step 107, based on the image data of the far end of the trawl net acquired in real time, the visualized shape of the floating line of the net is identified.

[0126] The operation method of obtaining the visualization form of the net float line based on the image data recognition at the far end of the trawl in this embodiment is similar to the operation method of performing image recognition processing with the pre-trained YOLO model in the prior art, and will not be described in detail in this embodiment.

[0127] Step 108, based on the visualized form of the net buoy line, determine the distance between the centers of adjacent floats and the curvature of the buoy line.

[0128] It can be understood that based on the visual form of the floating line of the fishing gear, the coordinate information of key points such as the center of the float and the intersection point of the connecting ropes can be obtained, so as to determine the distance between the centers of adjacent floats, and the curvature of the floating line can be determined by fitting the cubic spline curve of the center points of the floats.

[0129] Step 109, based on the distance between the centers of adjacent floats and the curvature of the floating line, calculate the fishing gear shape anomaly index. The fishing gear shape anomaly index E can be expressed as:

[0130]

[0131] where d i represents the distance between the centers of the i-th adjacent floats, d avg represents the average value of the distances between the centers of all adjacent floats, M represents the total number of floats, C max represents the maximum value of the curvature of the floating line, C th represents the preset threshold of the curvature of the floating line, α represents the preset weight of the float spacing, and β represents the preset curvature weight.

[0132] Step 110, record the optimized trawling speed v k ′ as the first target trawling speed. When the fishing gear shape anomaly index E is within the first deviation threshold range, reduce the first target trawling speed to the second target trawling speed;

[0133] When the fishing gear shape anomaly index exceeds the first deviation threshold range, control to interrupt the trawling power and issue an alarm prompt for serious deviation of the floating steel line.

[0134] Exemplarily, taking the first deviation threshold range as [0.5, 1) as an example, the implementation method of this embodiment is as follows: if the fishing gear shape anomaly index E < 0.5, then maintain the first target towing speed v k ′; if 0.5 ≤ E < 1, then reduce the first target towing speed v k ′ to the second target trawling speed v k ″ to stabilize the shape of the floating line; if E ≥ 1, then forcibly cut off the power supply of the motor to control the interruption of the trawling power and issue an alarm prompt for serious deviation of the floating steel line.

[0135] The prior art only ensures tension balance and cannot predict the fish escape situation caused by the out-of-control shape of the fishing gear, resulting in a decline in fishing efficiency. For this reason, in this embodiment, the shape of the floating line is visually output through image recognition, and the fishing gear shape anomaly index is calculated. When the fishing gear shape anomaly index deviates from the threshold, the trawling speed is dynamically reduced to stabilize the shape of the fishing gear to achieve closed-loop repair or stop to trigger an alarm for serious deviation of the floating steel line. The visual shape monitoring and dynamic speed regulation are coupled, enabling the system to have the ability to monitor the shape of the fishing gear, avoiding the fish escape phenomenon caused by excessive bending of the floating line, and effectively improving the overall trawling fishing effect.

[0136] Please refer to the appendix Figure 2 , Figure 2 which is a schematic structural diagram of a control system for a pond trawl fishing device provided by another embodiment of this specification.

[0137] Before describing a control system for a pond trawl fishing device provided by this embodiment, first describe the application scenario, that is, the structure of the pond trawl fishing device in the prior art:

[0138] The pond trawl fishing device in the prior art generally includes a steel wire guide rope, a guiding pulley, a trawl motor for providing trawl power ( Figure 2 not shown in the figure), a trawl rope and a net. The two ends of the steel wire guide rope are respectively connected to the vertical poles at both ends of the pond. The trawl motor is connected to the proximal end of the trawl rope. The guiding pulley is slidably connected to the steel wire guide rope. A snap hook can be arranged at the lower end of the guiding pulley. One end of the snap hook is connected to the distal end of the trawl rope, and the other end of the snap hook is connected to the net through a tension sensor. Among them, the net generally includes a floating steel wire and a plurality of floats located on the floating steel wire (such as the upper gill line and floats shown in Chinese Patent CN210869483U).

[0139] As Figure 2 shown, the control system of this pond trawl fishing device can at least include a trawl motor for providing trawl power, a tension sensor, an image monitoring module, a wheel encoder and a control module ( Figure 2 not shown in the figure). The trawl motor, the tension sensor, the image monitoring module and the wheel encoder are all connected to the control module, among which:[[]]

[0140] The tension sensor is used to obtain the measured tension value of the trawl rope in real time;

[0141] The image monitoring module is used to obtain the image data at the distal end of the trawl in real time;

[0142] The wheel encoder is used to obtain the trawl power data in real time;

[0143] The control module is used to perform the following steps:

[0144] Based on the measured tension value, use the threshold judgment method to determine whether to trigger the highest priority interrupt signal;

[0145] When the highest priority interrupt signal is not triggered, based on the image data at the distal end of the trawl, identify the position information of the pond obstacles and the floating gill line of the net;

[0146] Based on the position information of the pond obstacles and the floating gill line of the net, predict the collision risk, and determine whether to execute the dynamic obstacle avoidance strategy based on the prediction result;

[0147] When the highest-priority interrupt signal is not triggered, or after an interruption and restart, or when the dynamic obstacle avoidance strategy is not executed, based on the trawling power data, the actual trawling speed and the actual driving tension are calculated;

[0148] Based on the actual trawling speed and the actual driving tension, a fuzzy control strategy is adopted to determine the optimized trawling speed and execute it.

[0149] Exemplarily, the trawling motor providing the trawling power may include a motor and a rope winding drum. The output shaft of the motor is connected to the rope winding drum shaft. One end of the trawling rope is fixed to the rope winding drum and wound around the rope winding drum. The other end of the trawling rope passes through a wire guiding device outside the rope winding drum and a wheel encoder, and finally is connected to a snap fastener at the lower end of a guiding pulley hanging on a guiding steel wire rope. Both the wheel encoder and the motor are connected to the control module.

[0150] Exemplarily, the image monitoring module may include a housing, a pulley set, a self-stabilizing anti-shake pan-tilt, a camera, an image data remote transmission unit, a battery, etc. The housing of the image monitoring module is connected to the guiding pulley mechanism through a movable thin rod to achieve the synchronous movement of the image monitoring module and the guiding pulley. In the middle of the upper part of the housing along the direction of the steel wire guiding rope, two pulley sets are provided. Each pulley set consists of 3 pulleys symmetrically installed in a triangular shape. The steel wire guiding rope for mechanical trawling passes through them. When the trawling rope drags, the entire image monitoring module moves along the steel wire guiding rope. One end of the tension sensor is connected to the snap fastener at the lower end of the guiding pulley of the mechanical trawling, and the other end of the tension sensor is connected to the fishing gear. At the same time, the tension sensor is connected to the control module. The self-stabilizing anti-shake pan-tilt includes a connecting seat. The connecting seat is connected to the bracket of the housing through screws and is fixed below the housing. A heading axis with a motor is provided at the lower part of the connecting seat, which is responsible for controlling the horizontal direction. An L-shaped connecting rod is provided below the heading axis. A roll axis is provided at the horizontal end of the L-shaped connecting rod. A U-shaped connecting rod is provided at the other end of the roll axis. Pitching axes are provided on both sides of the opening of the U-shaped connecting rod. The pitching axes are provided with mounting holes to connect the camera. The camera is installed at the mounting hole of the self-stabilizing anti-shake pan-tilt and is connected to the image data remote transmission unit through a transmission line. The image data remote transmission unit is arranged in the housing and is used to obtain the video data of the camera and send it to the receiving end of the control module through the antenna unit.

[0151] It can be understood that the technical concept of the control system of a pond trawling fishing device provided in this embodiment is similar to the technical concept of the control method of a pond trawling fishing device described above, and will not be elaborated herein.

[0152] Please refer to the appendix Figure 3 , Figure 3 which is a schematic structural diagram of an electronic device provided for another embodiment of this specification.

[0153] As Figure 3As shown, the electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0154] Among them, the communication bus 302 can be used to realize the connection and communication of the above-mentioned components.

[0155] Among them, the user interface 303 may include buttons, and the optional user interface may further include a standard wired interface and a wireless interface.

[0156] Among them, the network interface 304 may include, but is not limited to, a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0157] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire electronic device 300 through various interfaces and lines, and by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, as well as calling data stored in the memory 305, it executes various functions of the electronic device 300 and processes data. Optionally, the processor 301 may be implemented in at least one of the hardware forms of DSP, FPGA, and PLA. The processor 301 may integrate one or several combinations of CPU, GPU, and modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 301 and may be implemented separately through a single chip.

[0158] Among them, the memory 305 may include RAM and may also include ROM. Optionally, the memory 305 includes a non-transitory computer-readable medium. The memory 305 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store the data involved in the above-mentioned various method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. As a computer storage medium, the memory 305 may include an operating system, a network communication module, a user interface module, and application programs. The processor 301 can be used to call the application programs stored in the memory 305 and execute the methods in the above-mentioned one or more embodiments.

[0159] Another embodiment of this specification provides a computer-readable storage medium storing instructions that, when run on a computer or a processor, cause the computer or the processor to execute one or more steps of the above embodiments. If each component module of the above electronic device is implemented in the form of a software functional unit and used as an independent downstream task prediction, it can be stored in the computer-readable storage medium.

[0160] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this specification are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or a data center that includes one or more integrated available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a Digital Versatile Disc (DVD)), or a semiconductor medium (for example, a Solid State Disk (SSD)).

[0161] As described above, it is only the preferred embodiments disclosed in this application and the description of the applied technical principles. Those skilled in the art should understand that the protection scope involved in this disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the technical solutions formed by mutually replacing the above features with the (but not limited to) technical features with similar functions disclosed in this disclosure.

[0162] Moreover, although the operations are depicted in a particular order, this should not be construed as requiring that the operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the foregoing discussion, these should not be construed as limitations on the scope of the present disclosure. Certain features that are described in the context of separate embodiments may also be implemented combinatorially in a single embodiment. Conversely, various features that are described in the context of a single embodiment may also be implemented separately or in any suitable sub-combination in multiple embodiments.

Claims

1. A control method for a pond trawl fishing device, characterized in that It includes the following steps: Obtain the measured tensile force value of the trawl rope, the image data of the far end of the trawl, and the trawl power data in real time; Based on the measured tensile force value, use the threshold judgment method to determine whether to trigger the highest-priority interrupt signal; When the highest-priority interrupt signal is not triggered, based on the image data of the far end of the trawl, identify the positions of pond obstacles and the floating line of the net gear; Based on the positions of pond obstacles and the floating line of the net gear, predict the collision risk, and based on the prediction result, determine whether to execute the dynamic obstacle avoidance strategy; When the highest-priority interrupt signal is not triggered, or after interruption and restart, or the dynamic obstacle avoidance strategy is not executed, based on the trawl power data, calculate the actual trawl speed and the actual driving tensile force; Based on the actual trawl speed and the actual driving tensile force, use the fuzzy control strategy to determine the optimized trawl speed and execute it.

2. The control method of a pond trawl fishing device according to claim 1, characterized in that Based on the measured tensile force value, using the threshold judgment method to determine whether to trigger the highest-priority interrupt signal, includes: Compare the measured tensile force value with the preset tensile force threshold. When the measured tensile force value is greater than or equal to the preset tensile force threshold, trigger the highest-priority interrupt signal to control the interruption of the trawl power and issue an overload alarm prompt.

3. The control method of a pond trawl fishing device according to claim 1, characterized in that Based on the positions of pond obstacles and the floating line of the net gear, predict the collision risk, and based on the prediction result, determine whether to execute the dynamic obstacle avoidance strategy, includes: Based on the positions of pond obstacles and the floating line of the net gear, calculate the actual distance between the floating line of the net gear and the obstacle; Compare the actual distance with the preset safety distance threshold. When the actual distance is less than or equal to the preset safety distance threshold, obtain the prediction result with a collision risk, and execute the dynamic obstacle avoidance strategy according to the prediction result with a collision risk.

4. The control method of a pond trawl fishing device according to claim 3, characterized in that The execution of the dynamic obstacle avoidance strategy includes: Trigger the secondary-priority interrupt signal, and use the S-shaped speed curve to control the trawl power to decelerate to the first speed threshold and then control the interruption of the trawl power; Control the trawl power in the reverse direction to release the trawl rope at the second speed threshold until the preset trawl rope slack length is reached, control the interruption of the trawl power and issue an obstacle alarm prompt.

5. The control method of a pond trawl fishing device according to claim 1, characterized in that Based on the actual trawl speed and the actual driving tensile force, using the fuzzy control strategy to determine the optimized trawl speed, includes: Calculate the third speed threshold v l,s and the actual trawling speed v k to obtain the speed deviation e v ; Calculate the tensile force safety margin ΔL, and the expression is: ΔL = L k -P r,k -R·n Among them, L k represents the actual driving pulling force, P r,k represents the measured pulling force value, R represents the trawl rope resistance, and n represents the preset safety factor; With the speed deviation e v and the pulling force safety margin ΔL as input variables, and with the actual driving pulling force being less than the preset pulling force threshold and ΔL ≤ 0 as constraints, a fuzzy control strategy is used to output the speed adjustment coefficient k v ; Take the product of the speed adjustment coefficient and the third speed threshold as the optimized trawl speed.

6. The control method of a pond trawl fishing device according to claim 5, characterized in that Input speed deviation e v has fuzzy sets of {Negative Big (NB), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Big (PB)}; The fuzzy set of the input tensile force safety margin ΔL is {SAFE, WARN, DANGER}; Output speed adjustment coefficient k v The fuzzy set of is {Stop (STP), Deceleration (DEC), Hold (HOLD), Acceleration (INC), Large acceleration (INC-L)}, then the fuzzy control strategy includes: When e v = PB and ΔL = SAFE, output k v = INC-L; When e v = PS and ΔL = SAFE, output k v = INC; When e v = ZO and ΔL = SAFE, output k v = HOLD; When e v = NS and ΔL = WARN, output k v = DEC; When e v = NB and ΔL = DANGER, output k v = STP.

7. The control method of a pond trawl fishing device according to claim 1, characterized in that It also includes the following steps: Based on the image data of the far end of the trawl net obtained in real time, the visual form of the floating line of the fishing gear is identified; Based on the visual form of the floating line of the fishing gear, the distance between the centers of adjacent floats and the curvature of the floating line are determined; Based on the distance between the centers of adjacent floats and the curvature of the floating line, the fishing gear form anomaly index is calculated; The optimized trawling speed is recorded as the first target trawling speed. When the fishing gear form anomaly index is within the first deviation threshold range, the first target trawling speed is reduced to the second target trawling speed; When the fishing gear form anomaly index exceeds the first deviation threshold range, the trawling power is controlled to be interrupted and an alarm prompt for serious deviation of the floating line is issued.

8. A control system for a pond trawl fishing device, characterized in that, It includes a trawling motor for providing trawling power, a tension sensor, an image monitoring module, a wheel encoder, and a control module. The trawling motor, the tension sensor, the image monitoring module, and the wheel encoder are all connected to the control module, where: The tension sensor is used to obtain the measured tension value of the trawling rope in real time; The image monitoring module is used to obtain the image data of the far end of the trawl net in real time; The wheel encoder is used to obtain the trawling power data in real time; The control module is used to execute the following steps: Based on the measured tension value, the threshold judgment method is used to determine whether to trigger the highest priority interrupt signal; When the highest priority interrupt signal is not triggered, based on the image data of the far end of the trawl net, the position information of the pond obstacles and the floating line of the fishing gear is identified; Based on the position information of the pond obstacles and the floating line of the fishing gear, the collision risk is predicted, and based on the prediction result, it is determined whether to execute the dynamic obstacle avoidance strategy; When the highest priority interrupt signal is not triggered, or after interruption and restart, or the dynamic obstacle avoidance strategy is not executed, based on the trawling power data, the actual trawling speed and the actual driving tension are calculated; Based on the actual trawling speed and the actual driving tension, the fuzzy control strategy is used to determine the optimized trawling speed and execute it.

9. An electronic device, characterized in that, It includes a processor and a memory; The processor is connected to the memory; The memory is used to store the executable program code; The processor runs the program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the control method of a pond trawling fishing device according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it realizes the control method of a pond trawling fishing device according to any one of claims 1 to 7.

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