Intelligent feeding system for Chinese mitten crab pond

Abstract

The invention provides an intelligent feeding system for an eriocheir sinensis pond, and relates to the technical field of intelligent management of aquaculture, the system comprises material tables, a feeding ship and a central control machine, and the working process of the system comprises the steps that the feeding ship uniformly feeds a small amount of bait on the material tables of the pond for food calling; the material platform underwater camera shoots a feeding picture and transmits the feeding picture to the central control machine; the central control machine analyzes the data and identifies a high-density ingestion area; the feeding ship sails to the target area, and the feeding amount and speed are adjusted; the feeding condition is monitored in real time, the feeding strategy is dynamically optimized, the feeding amount is accurately controlled, bait waste is systematically reduced by 40%, ammonia nitrogen generated by rotting of residual bait at the bottom of the pond is effectively reduced from 1.1 mg / L to 0.6 mg / L, nitrite is effectively reduced from 0.18 mg / L to 0.09 mg / L, dissolved oxygen is improved by 38%, and the water quality is comprehensively improved. Experiments show that the water quality standard-reaching rate of the pond applying the system is increased from 65% to 92%, the prevalence rate of the river crabs is reduced by 50%, and the growth environment is remarkably optimized.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent management of aquaculture, and more specifically, particularly relates to an intelligent feeding system for Chinese mitten crabs in ponds. Background Art

[0002] The pond culture of Chinese mitten crabs is an important aquaculture industry in China. However, for a long time, the traditional feeding method has faced significant challenges. Manual feeding relies on the experience of breeders and requires manual throwing of bait according to seasons, weather, and the growth stage of crabs. The labor intensity is high and the efficiency is low, with the average daily time consumption per single pond exceeding 3 hours. This method is difficult to adapt to the complex water environment of the pond, resulting in uneven distribution of bait, overfeeding in some areas while underfeeding in others, causing waste of resources and water pollution. With the progress of technology, simple automated feeding equipment has gradually become popular. It realizes mechanized operation with fixed time intervals and feeding amounts by controlling the motor-driven feeding device through a timer. Although it saves some labor costs, it still cannot sense the feeding dynamics of crabs and adjust strategies in real time, resulting in a high residual rate of bait and the problem of water quality deterioration not being fundamentally solved.

[0003] The core defects of the existing technology include:

[0004] (1) Low feeding accuracy: Under the manual feeding method, it is difficult for breeders to accurately judge the actual distribution density and feeding intensity of Chinese mitten crabs in different areas of the pond. The water environment of the pond is complex and the crabs are unevenly distributed. Simply relying on manual experience, it is impossible to ensure that crabs in every place can obtain an appropriate amount of bait, and it is very easy to have the situation of overfeeding in some areas while underfeeding in others. For simple automated feeding equipment, it can only feed at fixed times and amounts according to a preset program, and it is completely unable to sense the real-time feeding status and distribution dynamics of crabs in the pond, and cannot adjust the feeding strategy according to actual needs, so the feeding accuracy cannot be guaranteed either.

[0005] (2) Bait waste and water pollution: Overfeeding is a common problem in the current Chinese mitten crab farming. Whether it is manual feeding or simple automated feeding, due to the inability to accurately grasp the feeding situation of crabs, overfeeding leads to a large amount of bait not being eaten in time and rotting at the bottom of the pond. This not only causes waste of bait resources and increases the breeding cost, but the rotting bait also consumes dissolved oxygen in the water, produces harmful substances such as ammonia nitrogen and nitrite, seriously pollutes the water quality, destroys the pond ecological environment, and creates conditions for the breeding of crab diseases.

[0006] (3) Adverse effects on the growth and development of Chinese mitten crabs: Insufficient feeding will prevent Eriocheir sinensis from obtaining sufficient nutrition during the growth process, resulting in slow growth and uneven individual sizes, directly affecting the aquaculture yield and quality. Being in a state of long-term nutritional deficiency will also weaken the immunity of Chinese mitten crabs and increase their risk of disease. Moreover, the disease problems caused by water pollution further exacerbate the obstacles to the growth and development of Chinese mitten crabs and reduce the aquaculture efficiency.

[0007] (4) Lack of real-time monitoring and feedback control: In the existing technology, whether it is manual feeding or simple automated feeding equipment, there is a lack of real-time monitoring means for the feeding situation of Chinese mitten crabs in the pond. It is impossible to timely understand key information such as the feeding behavior of Chinese mitten crabs and the remaining bait, and thus it is impossible to dynamically adjust the feeding process according to the actual situation. This lagging feeding management mode is difficult to meet the requirements of the refined management of Eriocheir sinensis aquaculture and restricts the modernization development process of the aquaculture industry.

[0008] Therefore, it is urgent to develop an intelligent feeding system that can real-time monitor the feeding status of Chinese mitten crabs in the pond, accurately control the feeding amount and feeding area, which is exactly the core problem that this patent is committed to solving. Summary of the Invention

[0009] To solve the above technical problems, the present invention provides an intelligent feeding system for Eriocheir sinensis ponds to solve the above problems.

[0010] An intelligent feeding system for Eriocheir sinensis ponds includes:

[0011] Feeding platforms: Made of corrosion-resistant materials, distributed throughout the pond and sunk to the bottom. The feeding platforms are equipped with underwater cameras and wireless transmission modules. The cameras are fixed above the center of the feeding platforms through waterproof sealing devices, and are used to real-time monitor the feeding intensity of Eriocheir sinensis and the remaining bait;

[0012] Feeding boats: Provided with bait storage bins, stirring devices, feeding devices, power systems and navigation equipment. The stirring devices are located inside the storage bins and the rotating shafts are driven by motors to drive the stirring blades to work. The feeding devices are connected to the bottom of the storage bins. The power systems drive the feeding boats to travel. The navigation equipment receives signals from the central control machine to guide the feeding boats to designated positions;

[0013] Central control machine: Receives the image data transmitted by the feeding platforms, and is built-in with image recognition algorithms and data analysis models, used to judge the density and feeding intensity of Chinese mitten crabs, and generate control instructions to be sent to the feeding boats through the wireless communication module to control their travel paths and feeding operations.

[0014] Preferably, the underwater camera and the wireless transmission module are integrated, with a waterproof design, and the image data is real-time transmitted to the central control machine through the wireless transmission module.

[0015] Preferably, the feeding device of the feeding boat can adjust the feeding amount and feeding speed according to the instructions of the central control machine, and the navigation device receives the coordinate signals sent by the central control machine through the GPS or Beidou positioning system.

[0016] Preferably, the image recognition algorithm of the central control machine identifies the number of Chinese mitten crabs and the remaining amount of bait in the feeding area through a deep learning model, and the data analysis model generates a dynamic feeding strategy based on the density and feeding intensity of Chinese mitten crabs.

[0017] Preferably, the feeding platforms are evenly distributed in the pond, the distance between adjacent feeding platforms is 5-15 meters, and the size of the feeding platform is 50cm×50cm×10cm.

[0018] Preferably, the stirring blades of the stirring device adopt a spiral structure, and the rotation speed is 50-150 revolutions per minute, which can prevent the bait from caking and keep it fluid.

[0019] Preferably, the working process of the system includes:

[0020] The feeding boat evenly puts a small amount of bait on the feeding platforms everywhere in the pond for attracting food;

[0021] The underwater camera on the feeding platform takes the feeding pictures and transmits them to the central control machine;

[0022] The central control machine analyzes the data to identify the high-density feeding area;

[0023] The feeding boat sails to the target area and adjusts the feeding amount and speed;

[0024] Monitor the feeding situation in real time and dynamically optimize the feeding strategy.

[0025] Preferably, the central control machine and the feeding boat perform data interaction through ZigBee or LoRa wireless communication modules, the communication distance is ≥500 meters, and the anti-interference ability meets the GB / T18268.1-2010 standard.

[0026] Preferably, the power system includes a lithium battery pack and a twin-screw propeller, the endurance time is ≥8 hours, and the maximum traveling speed is 3 knots, which can adapt to the complex water environment of the pond.

[0027] Preferably, the system can further integrate water quality monitoring sensors, and the central control machine adjusts the feeding strategy according to water quality parameters such as dissolved oxygen and ammonia nitrogen to achieve environment-feeding collaborative control.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] In the present invention, through the underwater camera on the feeding platform and the image recognition and data analysis functions of the central control machine, the feeding situation and distribution information of Chinese mitten crabs can be accurately obtained in real time, solving the problem in the prior art that the feeding requirements cannot be accurately grasped, realizing precise feeding, improving the bait utilization rate, and reducing the breeding cost. The feeding boat conducts precise feeding according to the instructions of the central control machine, avoiding the waste of bait, reducing the pollution of the pond water quality by the remaining bait, improving the growth environment of the crabs, being beneficial to the healthy growth of the crabs, and increasing the breeding output and quality. The whole system realizes automated and intelligent feeding management, reduces the manual labor intensity, improves the breeding management efficiency, and promotes the modern development of the Chinese mitten crab breeding industry. In the present invention, the feeding behavior of Chinese mitten crabs is monitored in real time through an underwater camera, combined with the image recognition algorithm (mAP@0.5 = 0.92) and the dynamic feeding strategy model of the central control machine, to achieve a bait delivery error of ≤5%, reducing the remaining bait rate compared with traditional manual feeding. The system dynamically adjusts the feeding amount according to the crab density (number / m 2 ), feeding intensity (remaining bait rate), and water quality parameters (dissolved oxygen, ammonia nitrogen), reducing the bait coefficient from 1.8 to 1.5 and saving bait costs.

[0030] In the present invention, by precisely controlling the feeding amount, the system reduces the bait waste by 40%, effectively reducing the ammonia nitrogen (from 1.1 mg / L to 0.6 mg / L) and nitrite (from 0.18 mg / L to 0.09 mg / L) generated by the decay of the remaining bait at the bottom of the pond, and increasing the dissolved oxygen by 38% (from 4.2 mg / L to 5.8 mg / L), comprehensively improving the water quality. Experiments show that the water quality compliance rate of the ponds applying this system increases from 65% to 92%, and the prevalence of crabs decreases by 50%, significantly optimizing the growth environment.

[0031] In the present invention, through the dynamic regulation of bait supply, the average weight of the crabs increases by 28 g (from 128 g to 156 g), the survival rate increases by 15.4% (from 78% to 90%), and the size uniformity increases by 31.8% (the standard deviation decreases from 22 g to 15 g). In large-scale breeding, the single-pond output increases by 21.9%, and the proportion of high-quality crabs (≥150 g) increases by 35%, significantly improving the breeding economic benefits.

[0032] In the present invention, the system realizes unmanned feeding, reducing the number of single-pond management personnel from 3 to 1, and increasing the labor efficiency by 90%. Through 5G communication and the cloud management platform, more than 100,000 mu of breeding areas can be monitored in real time, and the fault response time is ≤30 seconds. The present invention promotes the formulation of the "Technical Specification for Intelligent Breeding of Chinese Mitten Crabs", puts forward core indicators such as "dynamic bait coefficient" and "feeding intensity index", promotes the domestic substitution of aquaculture equipment, and reduces the cost by 45% compared with imported equipment.

[0033] In the present invention, the system reduces 40% of bait waste through precise feeding, reduces the usage of chemical fertilizers by 20%, and reduces carbon emissions by 40%, meeting the green farming standard (NY / T391-2021). In extreme working condition tests (such as heavy rain and low temperature), the system still operates stably, with a navigation accuracy of ±0.5 meters and a battery life extended to 8 hours, providing reliable technical support for large-scale ecological farming. Brief Description of the Drawings

[0034] Figure 1 is a schematic diagram of the system flow of the present invention;

[0035] Figure 2 is a schematic diagram of the composition of the system in the present invention;

[0036] Figure 3 is a schematic diagram of the relationship between the central control machine and the feeding boat in the present invention;

[0037] Figure 4 is a schematic diagram of the size of the feeding platform in the present invention;

[0038] Figure 5 is a schematic diagram of the content of the feeding platform in the present invention;

[0039] Figure 6 is a schematic diagram of the content of the feeding boat in the present invention;

[0040] Figure 7 is a schematic diagram of the connection between the central control machine and the wireless transmission module in the present invention;

[0041] Figure 8 is a schematic diagram of the connection between the feeding device and the central control machine in the present invention;

[0042] Figure 9 is a schematic diagram of the connection between the navigation device and the central control machine in the present invention;

[0043] Figure 10 is a diagram of the operation steps of the system in the present invention. Detailed Embodiments

[0044] The following further describes in detail the embodiments of the present invention in conjunction with the drawings and examples. The following examples are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0045] Please refer to Figures 1-10 , the present invention provides an intelligent feeding system for Chinese mitten crabs in ponds, including:

[0046] Feeding platforms: Made of corrosion-resistant materials, distributed throughout the pond and sunk to the bottom. The feeding platforms are equipped with underwater cameras and wireless transmission modules. The cameras are fixed above the center of the feeding platforms through waterproof sealing devices, and are used to monitor the feeding intensity of Chinese mitten crabs and the remaining bait in real time;

[0047] Feeding boat: It is equipped with a bait storage bin, a stirring device, a feeding device, a power system and a navigation device. The stirring device is located inside the storage bin and the rotating shaft driven by the motor drives the stirring blades to work. The feeding device is connected to the bottom of the storage bin. The power system drives the feeding boat to travel, and the navigation device receives the signal from the central control machine to guide the feeding boat to the designated position;

[0048] Central control machine: It receives the image data transmitted by the feeding platform, and is built-in with an image recognition algorithm and a data analysis model, which are used to judge the density and feeding intensity of river crabs, and generate control instructions to be sent to the feeding boat through the wireless communication module to control its travel path and feeding operation.

[0049] The underwater camera and the wireless transmission module are integrated, with a waterproof design. The image data is transmitted to the central control machine in real time through the wireless transmission module. The feeding device of the feeding boat can adjust the feeding amount and feeding speed according to the instructions of the central control machine. The navigation device receives the coordinate signal sent by the central control machine through the GPS or Beidou positioning system. The image recognition algorithm of the central control machine identifies the number of river crabs and the remaining amount of bait in the feeding platform area through the deep learning model. The data analysis model generates a dynamic feeding strategy based on the density and feeding intensity of river crabs. The feeding platforms are evenly distributed in the pond, and the distance between adjacent feeding platforms is 5 - 15 meters. The size of the feeding platform is 50cm×50cm×10cm. The stirring blades of the stirring device adopt a spiral structure, and the rotation speed is 50 - 150 revolutions per minute to prevent the bait from caking and keep it fluid;

[0050] The system working process includes:

[0051] The feeding boat evenly puts a small amount of bait at each feeding platform in the pond for attracting food;

[0052] The underwater camera of the feeding platform takes the feeding pictures and transmits them to the central control machine;

[0053] The central control machine analyzes the data to identify the high-density feeding area;

[0054] The feeding boat sails towards the target area and adjusts the feeding amount and speed;

[0055] Monitor the feeding situation in real time and dynamically optimize the feeding strategy.

[0056] The central control machine and the feeding boat conduct data interaction through the ZigBee or LoRa wireless communication module. The communication distance is ≥500 meters, and the anti-interference ability meets the GB / T18268.1 - 2010 standard. The power system includes a lithium battery pack and a dual propeller thruster. The endurance time is ≥8 hours, and the maximum travel speed is 3 knots. It can adapt to the complex water environment of the pond. The system can further integrate water quality monitoring sensors, and the central control machine adjusts the feeding strategy according to water quality parameters such as dissolved oxygen and ammonia nitrogen to achieve environment-feeding coordinated control.

[0057] Example 1: Construction of the feeding platform system:

[0058] 1.1 Structure parameters of the feeding platform:

[0059]

[0060] 1.2 Wireless transmission module:

[0061] Model: Huawei OceanConnect IoT module;

[0062] Transmission protocol: LoRaWAN, spreading factor 7 - 12 adaptive; Power consumption: sleep mode ≤ 10 μA, transmission mode ≤ 150 mA; Communication distance: line of sight 800 meters, attenuation through water surface ≤ 20 dB.

[0063] Example 2: Design of the feeding boat:

[0064] 2.1 Hull parameters:

[0065]

[0066] 2.2 Feeding device:

[0067] Storage bin capacity: 300 L, conical bottom design (angle 45°); Stirring device: spiral blade (diameter 20 cm), rotation speed 100 rpm; Feeding control: stepper motor driven rotary valve (opening 0 - 100% adjustable); Feeding accuracy: ±2% (real - time calibration through load cell).

[0068] Example 3: Configuration of the central control unit:

[0069] 3.1 Hardware parameters:

[0070]

[0071]

[0072] 3.2 Software system:

[0073] Image recognition algorithm:

[0074] Improved model based on YOLOv5s, training set contains 100,000 images of crabs feeding; Detection accuracy mAP@0.5 = 0.92, single - frame processing time ≤ 50 ms.

[0075] Feeding strategy model:

[0076] Dynamic programming algorithm, input parameters include:

[0077] Crab density (number / m 2 )

[0078] Feeding intensity (bait remaining rate);

[0079] Water quality parameters (dissolved oxygen, pH value).

[0080] Example 4: Intelligent feeding process:

[0081] 4.1 Initial feeding attraction stage:

[0082] Feeding boat path planning:

[0083] Use the A* algorithm to generate a full-coverage path and traverse all feeding platforms in the pond;

[0084] Feeding amount: 30% of the basic bait amount (50 g / platform) is put on each platform.

[0085] Feeding attraction time control:

[0086] Summer: 18:00 - 19:00 (peak activity period of Chinese mitten crabs);

[0087] Winter: 14:00 - 15:00 (period with relatively high water temperature).

[0088] 4.2 Feeding monitoring stage:

[0089] Data acquisition frequency:

[0090] The camera takes 1 frame of image every 30 seconds and continuously acquires for 30 minutes;

[0091] Synchronously record environmental parameters such as water temperature and dissolved oxygen (provided by the integrated sensor).

[0092] Image analysis process:

[0093] Python:

[0094] def analyze_feeding_state(image):

[0095] # Step 1: Image enhancement (CLAHE algorithm)

[0096] # Step 2: Object detection (YOLOv5s model)

[0097] # Step 3: Behavior recognition (judge feeding actions by optical flow method)

[0098] # Step 4: Calculate the remaining bait amount (image segmentation + color recognition)

[0099] return crab_density,feeding_intensity, residual_ratio4.3 Precise feeding stage:

[0100] Feeding strategy generation:

[0101] When the density of Chinese mitten crabs > 5 individuals / m 2 and the remaining bait rate < 20%, additional feeding is triggered. The calculation formula for the feeding amount: Q = k×(D×S×F) / (T×E).

[0102] Where:

[0103] k = 1.2 (safety factor);

[0104] D = density of Chinese mitten crabs (individuals / m 2 );

[0105] S = area of the target area (m 2 );

[0106] F = average food intake (2 g / individual·day);

[0107] T = feeding interval (hours);

[0108] E = bait coefficient (1.5).

[0109] Path optimization:

[0110] Select the shortest path based on the Dijkstra algorithm and avoid obstacles (such as aerators); the traveling speed of the feeding boat is controlled at 1.5 knots (2.7 km / h).

[0111] Experimental example 1: Feeding accuracy test:

[0112] 1.1 Test method:

[0113] Control group: Traditional manual feeding (empirical method);

[0114] Experimental group: The system of the present invention.

[0115] Indicators:

[0116] Bait remaining rate (measured 2 hours after feeding);

[0117] Feeding uniformity of Chinese mitten crabs (Gini coefficient).

[0118] 1.2 Experimental data:

[0119]

[0120]

[0121] Conclusion: The system reduces the bait remaining rate by 72% and improves the feeding uniformity by 69%.

[0122] Experimental example 2: Water quality improvement effect:

[0123] 2.1 Test parameters:

[0124]

[0125] 2.2 Experimental conditions:

[0126] Pond area: 10 mu, average water depth 1.5 m;

[0127] Cultivation density: 800 per mu;

[0128] Test period: 30 days.

[0129] Conclusion: The system increases dissolved oxygen by 38%, ammonia nitrogen decreases by 45%, and nitrite decreases by 50%.

[0130] Experimental Example 3: Comparison of the growth performance of Chinese mitten crabs:

[0131] 3.1 Test indicators:

[0132]

[0133] 3.2 Cultivation period:

[0134] Release of crab seedlings: May 1, 2023 (specification 10 g per crab);

[0135] Harvesting time: October 15, 2023;

[0136] Feeding frequency: 2 times a day (06:00, 18:00).

[0137] Conclusion: The system increases the average weight of Chinese mitten crabs by 28 g and the survival rate by 15.4%. IV. Comparative example (comparison with traditional methods):

[0138] Comparative Example 1: Cost-benefit analysis:

[0139]

[0140] Comparative Example 2: Comparison of labor intensity:

[0141]

[0142]

[0143] V. Mass production example (scale verification):

[0144] 5.1 System deployment parameters:

[0145]

[0146] 5.2 Scale benefits:

[0147] Bait cost: The annual bait cost saved in the thousand-acre base is about ￥1.2 million; Management efficiency: The number of managers per pond is reduced from 3 to 1;

[0148] Emergency response: The automatic switching time for system failures ≤ 30 seconds.

[0149] Through the above embodiments and experiments, the present invention has achieved breakthrough progress in the following dimensions:

[0150] Precise feeding: The image recognition accuracy reaches 92%, the bait remaining rate is reduced by 72%, and the feeding uniformity is increased by 69%;

[0151] Water quality optimization: The dissolved oxygen is increased by 38%, the ammonia nitrogen is decreased by 45%, and the nitrite is reduced by 50%;

[0152] Growth performance: The average weight of Chinese mitten crabs increases by 28 g, and the survival rate is increased by 15.4%;

[0153] Economic benefits: The comprehensive cost is reduced by 32.8%, and the labor efficiency is increased by 90%;

[0154] Environmentally friendly: The carbon emission is reduced by 40%, meeting the green farming standard (NY / T391-2021).

[0155] Through the closed-loop control of "intelligent perception - precise decision - dynamic execution", the present invention has reconstructed the feeding mode of Chinese mitten crab pond farming, breaking through the bottleneck of traditional methods relying on manual experience and low efficiency.

[0156] Multi-dimensional data fusion:

[0157] An underwater camera (resolution 1080P) collaborates with a water quality sensor for monitoring, realizing the synchronous analysis of Chinese mitten crab feeding behavior (recognition accuracy mAP@0.5 = 0.92) and environmental parameters (dissolved oxygen, ammonia nitrogen, etc.).

[0158] The dynamic feeding strategy model (based on Chinese mitten crab density, feeding intensity, and water quality parameters) reduces the bait coefficient from 1.8 to 1.5.

[0159] Intelligent upgrade of equipment:

[0160] The navigation accuracy of the feeding boat is ±0.5 m, enabling unmanned operation and saving 70% of the labor cost;

[0161] The modular feeding platform design (spacing 10 m) enables a monitoring coverage rate of 100%, with a 90% improvement in efficiency compared to manual inspection.

[0162] Ecologically friendly farming:

[0163] The bait waste is reduced by 40%, and the ammonia nitrogen concentration in the pond drops by 35% (from 1.2 mg / L to 0.78 mg / L), meeting the "Discharge Requirements for Aquaculture Water in Freshwater Ponds" (SC / T 9101-2007).

[0164] The system integrates water quality - feeding collaborative control, increasing the survival rate of Chinese mitten crabs by 12% and reaching the leading level of 90% in the industry.

[0165] Through the innovative model of "intelligent perception - precise feeding - ecological coordination", the present invention solves the long - standing pain points in the cultivation of Chinese mitten crabs, such as bait waste, water pollution, and extensive management, providing a benchmark solution for the digital transformation of the aquaculture industry. With the deep integration of 5G and AIoT technologies, this system will be further upgraded in the directions of "predictive feeding" (planning bait 72 hours in advance based on the growth model of Chinese mitten crabs) and "adaptive regulation" (responding to climate change in real - time), and finally achieving the green aquaculture goal of "obtaining the maximum ecological benefits with the least resource input".

[0166] Working principle: After the system is started, the feeding boat first evenly distributes a small amount of bait at the feeding platforms in various parts of the pond to attract the crabs. After a period of time, the underwater camera at the feeding platform starts to work, transmitting the captured feeding images of Chinese mitten crabs and the remaining bait situation to the central control machine. The central control machine analyzes this data to identify the areas with high density and strong feeding intensity of Chinese mitten crabs. Subsequently, the central control machine sends instructions to the feeding boat, controlling the feeding boat to sail to these areas and adjusting the feeding amount and feeding speed for precise feeding. During the feeding process, the camera continuously monitors the feeding situation, and the central control machine continuously adjusts the feeding operation of the feeding boat according to the real - time feedback data until the feeding intensity reaches the expected level.

[0167] The embodiments of the present invention are given for purposes of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed form. Many modifications and variations are obvious to those of ordinary skill in the art. The embodiments are chosen and described in order to better explain the principles of the invention and its practical application, and to enable those of ordinary skill in the art to understand the invention and design various embodiments with various modifications suitable for specific purposes.

Claims

1. An intelligent feeding system for Chinese mitten crabs in ponds, characterized in that, Including: Feeding platform: Made of corrosion-resistant materials, distributed throughout the pond and sunk to the bottom. An underwater camera and a wireless transmission module are installed on the feeding platform. The underwater camera is fixed above the center of the feeding platform through a waterproof sealing device; Feeding boat: Equipped with a bait storage bin, a stirring device, a feeding device, a power system and a navigation device. The stirring device is located inside the bait storage bin and the stirring blades are driven by a rotating shaft driven by an electric motor. The feeding device is connected to the bottom of the storage bin. The power system drives the feeding boat to travel, and the navigation device receives signals from the central control machine to guide the feeding boat to the designated position; Central control machine: Receives the image data transmitted by the feeding platform, and has an image recognition algorithm and a data analysis model built-in, used to judge the density and feeding intensity of river crabs, and generates control instructions to be sent to the feeding boat through a wireless communication module to control its travel path and feeding operation.

2. The intelligent feeding system for Chinese mitten crabs in a pond according to claim 1, characterized in that, The underwater camera and the wireless transmission module are integrated, with a waterproof design. The image data is transmitted to the central control machine in real time through the wireless transmission module.

3. The intelligent feeding system for Chinese mitten crabs in a pond according to claim 2, characterized in that, The feeding device of the feeding boat can adjust the feeding amount and feeding speed according to the instructions of the central control machine. The navigation device receives the coordinate signals sent by the central control machine through the GPS or Beidou positioning system.

4. The intelligent feeding system for Chinese mitten crabs in a pond according to claim 3, wherein, The image recognition algorithm of the central control machine recognizes the number of river crabs and the remaining amount of bait in the feeding platform area through a deep learning model. The data analysis model generates a dynamic feeding strategy based on the density and feeding intensity of river crabs.

5. The intelligent feeding system for Chinese mitten crabs in a pond according to claim 4, wherein The feeding platforms are evenly distributed in the pond, the distance between adjacent feeding platforms is 5 - 15 meters, and the size of the feeding platform is 50cm×50cm×10cm.

6. The intelligent feeding system for Chinese mitten crabs in a pond according to claim 5, characterized in that, The stirring blades of the stirring device adopt a spiral structure, with a rotation speed of 50 - 150 revolutions per minute, to prevent the bait from caking and keep it fluid.

7. The intelligent feeding system for Chinese mitten crabs in a pond according to claim 6, characterized in that, The working process of the system includes: S1: The feeding boat evenly puts a small amount of bait at each feeding platform in the pond for attracting food; S2: The underwater camera on the feeding platform takes the feeding pictures and transmits them to the central control machine; S3: The central control machine analyzes the data to identify the high-density feeding area; S4: The feeding boat sails to the target area and adjusts the feeding amount and speed; S5: Monitor the feeding situation in real time and dynamically optimize the feeding strategy.

8. The intelligent feeding system for Chinese mitten crabs in a pond according to claim 7, characterized in that, The central control machine and the feeding boat conduct data interaction through the ZigBee or LoRa wireless communication module. The communication distance is ≥500 meters, and the anti-interference ability meets the GB / T18268.1-2010 standard.

9. The intelligent feeding system for Chinese mitten crabs in a pond according to claim 7, characterized in that The power system includes a lithium battery pack and a twin-screw propeller. The endurance time is ≥8 hours, and the maximum traveling speed is 3 knots, which can adapt to the complex water environment of the pond.

10. The intelligent feeding system for Chinese mitten crabs in a pond according to claim 7, wherein, This system can be further integrated with water quality monitoring sensors. The central control machine adjusts the feeding strategy according to water quality parameters such as dissolved oxygen and ammonia nitrogen to achieve environment-feeding coordinated control.

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