Intelligent feeding system for small-scale shrimp culture and method thereof

Abstract

The present application relates to the technical field of aquaculture, and especially to a small-shed shrimp breeding intelligent feeding system and method, which comprises a main control center, autonomous mobile intelligent feeding devices and positioning equipment; the main control center is used for centralized feeding and parameter setting, obtaining the space information of multiple small sheds in the breeding field and the working state and energy state of each autonomous mobile intelligent feeding device, and generating a distribution scheme and a path scheduling scheme of the feeding task based on the information, and performing information interaction with multiple autonomous mobile intelligent feeding devices through a network, and uniformly managing and controlling the feeding task; wherein the main control center comprises a charging device for supplementing the energy of the autonomous mobile intelligent feeding device; in the present application, the main control center, autonomous mobile intelligent feeding devices and positioning equipment are arranged to realize the automatic scheduling of small-shed shrimp breeding feeding operation, multi-machine collaborative efficient operation, autonomous driving and precise feeding in complex scenes.

Description

Technical Field

[0001] This invention relates to the field of aquaculture technology, specifically to an intelligent feeding system and method for shrimp farming in small sheds. Background Technology

[0002] Shrimp are an important economic aquatic product, and their farming scale is increasing year by year, with domestic demand continuing to grow. Among the existing intelligent feeding technologies in the aquaculture field, the first type uses drones to carry feeding devices. For example, Chinese patent document CN116406641A discloses an intelligent feeding system and method for shrimp farming, which realizes the feeding function through drones. The second type is a guide rail-based intelligent feeding system. For example, Chinese patent CN114706399A discloses an IoT-based intelligent control system and method for automated shrimp farming in small sheds. This solution limits the movement trajectory of the feeding robot by laying guide rails in the shed. The third type is an autonomous navigation-based intelligent feeding system. For example, Chinese patent CN114237238A discloses an autonomous navigation intelligent feeding robot and its feeding method, which uses lidar and algorithms to achieve autonomous navigation and feeding.

[0003] However, the level of automation and intelligence in existing shrimp farming technologies is relatively low, especially in feed feeding, which mainly relies on manual operation. This is not only inefficient and labor-intensive, but also suffers from problems such as uneven feeding, untimely feeding, and inaccurate feeding amount. The first method mentioned above has a small feed carrying capacity, which is difficult to meet the needs of large-scale farming. The second method requires each small shed to be independently laid with guide rails and equipped with corresponding feeding devices, and still requires manual feeding operations, resulting in high overall construction and maintenance costs. The third method is mainly suitable for indoor or flat farming environments, and is poorly adapted to unstructured scenarios such as muddy ground outside the shed and narrow stone slab paths inside the shed, which limits its application scope. Therefore, in view of the above problems, an intelligent feeding system and method for shrimp farming in small sheds are proposed. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent feeding system and method for shrimp farming in small sheds, in order to solve the problems of low efficiency, high labor intensity, uneven feeding and inaccurate feeding caused by manual feeding in existing shrimp farming, as well as the poor adaptability of existing intelligent feeding equipment to unstructured and complex small shed scenarios and high construction and maintenance costs.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A smart feeding system for shrimp farming in small sheds includes: a main control center, an autonomous mobile smart feeding device, and a positioning device; The main control center is used for centralized feeding and parameter setting, acquiring spatial information of multiple sheds in the farm and the operating and energy status of each autonomous mobile intelligent feeding device, and generating a feeding task allocation scheme and path scheduling scheme based on the information. It interacts with multiple autonomous mobile intelligent feeding devices through the network to uniformly manage and control the feeding tasks. The main control center includes a charging device for replenishing the energy of the autonomous mobile intelligent feeding devices. The autonomous mobile intelligent feeding device is used to autonomously drive to the target shed and perform the feeding task according to the feeding task assigned by the main control center. The positioning device is used to acquire the location and orientation information of the entrance to the small shed in the breeding farm, and send the information to the main control center to generate a feeding task; The main control center is configured to predict the overall completion time based on the feeding progress and travel path information of each main mobile intelligent feeding device, and to dynamically adjust or redistribute the feeding tasks across devices when the predicted overall completion time exceeds the preset operation deadline, so as to ensure that all small shed feeding tasks are completed within the preset operation deadline.

[0006] Preferably, the main control center includes a feeding hopper, a weighing sensor assembly, a main control unit, a control board, a feeding mechanism, an input and status display module, an environmental sensor, and a charging device; The feeding bin is in the shape of an inverted cone and is used to load bait. The lower side of the feeding bin is equipped with a first frame support leg, a second frame support leg, a third frame support leg and a fourth frame support leg that support it from all directions. The weighing sensor assembly includes a first weighing sensor, a second weighing sensor, a third weighing sensor, and a fourth weighing sensor, which are respectively disposed at the bottom of the first frame support foot, the second frame support foot, the third frame support foot, and the fourth frame support foot, and are used to measure the weight of the bait stored in the feeding bin and the weight of a single feeding. The main control unit, located inside the control box, is a micro computing platform used to generate a feeding task allocation scheme and path scheduling information based on the received input information. It also establishes a communication connection with the autonomous mobile intelligent feeding device and charging device through the network, obtains the operating status and energy status of the autonomous mobile intelligent feeding device in real time, and performs unified management and control of the feeding task based on the information. The control board is located inside the control box, and the control chip is a 32-bit microprocessor. When the autonomous mobile intelligent feeding device is detected to have reached the loading position corresponding to the feeding bin, it outputs a feeding control command to the feeding mechanism. The control box is located below the feeding hopper; The feeding mechanism is located below the feeding hopper and executes feeding start and feeding stop actions according to the received feeding control command; The input and status display module is installed on the left side of the control box. It is used to input the number of small sheds and the corresponding parameters, and to display the feeding task status and the feeding status of each small shed in real time. The environmental sensor is installed at the bottom of the second frame support leg and is used to detect weather changes. When the weather changes, the main control center sends corresponding control commands to the autonomous mobile intelligent feeding device through the network. The charging device is controlled by a microprocessor and uses a magnetic charging structure to match the charging receiver with the charging device output. When the processor receives a charging request, it activates the charging port to start charging. At this time, the three-color warning light installed on the upper side of the charging device is yellow. After charging is completed, the three-color warning light turns green. When no charging request is received, the charging device is set to disabled mode, and the three-color warning light turns red.

[0007] Preferably, the autonomous mobile intelligent feeding device includes a mobile chassis, a box, a vehicle-mounted hopper, a feeding tray, a path detection and recognition module, an emergency stop button, and status indicator lights; The mobile chassis has a chassis width adapted to the narrow passages inside the shed, includes a battery compartment, and adopts a tracked walking structure, as well as a chassis support plate set on the upper side of the tracked walking structure and the battery compartment for supporting the upper equipment, thereby enhancing its adaptability to complex road surfaces. The housing is located on the upper surface of the chassis support plate, and the inner wall of the housing is equipped with a vehicle control center, a vehicle control module, a positioning module, a network connection module, and a remote control receiver module. The vehicle control center is located inside the enclosure and consists of a micro computing platform. It interacts with the main control center via a wireless network. The positioning module is located inside the housing and has a first antenna and a second antenna on the outside, used to acquire the position and attitude information of the autonomous mobile intelligent feeding device in real time. The network connection module is located inside the enclosure and has a router antenna on the outside. It connects to the main control center via a wireless network for information exchange. The remote control receiving module is located inside the housing and has a remote control receiving antenna on the outside for receiving remote control signals; The vehicle-mounted control module is located inside the housing and consists of a 32-bit microprocessor. It is connected to the vehicle-mounted control center via a serial port and is used to receive instructions from the vehicle-mounted control center and execute the control of the throwing motor of the throwing disc and the track motor of the tracked walking structure. The vehicle-mounted hopper has an inverted trapezoidal structure and a capacity to meet the single-time feeding requirements of a single shed, and is used to load bait; The feeding tray is located on the upper side of the chassis support plate, and the feeding motor delivers the bait quantitatively and evenly. The path detection and recognition module consists of a forward path module and a second camera. The forward path module includes a lidar and a first camera. The lidar is located on the front of the upper surface of the chassis plate, the first camera is located on the upper part of the front front surface of the box, and the second camera is located on the upper part of the rear rear surface of the box. The lidar, the first camera, and the second camera are all connected to the vehicle control center via USB to obtain site information in real time. The emergency stop button is located on the outside of the housing and is used for emergency braking. The status indicator light is located on the outside of the housing and is used to display the battery power status.

[0008] A preferred method for intelligent feeding of shrimp in small-scale greenhouse farming includes the following steps: S1. Task Generation: The main control center obtains the spatial information of the small sheds in the farm, the number of small sheds, and the operating status and energy status of each of the autonomous mobile intelligent feeding devices; based on the preset collaborative scheduling control rules of multiple autonomous mobile intelligent feeding devices, the feeding tasks of the small sheds are generated and scheduled. The scheduling control rules are used to determine the execution order and allocation relationship of each feeding task among different autonomous mobile intelligent feeding devices under the premise of meeting the constraints of silo capacity, feeding time, device energy, multiple feeding trips, and overall operation deadline. S2. Task Issuance: The main control center sends the feeding task generated in step S1 to the corresponding autonomous mobile intelligent feeding device through the wireless network, so as to instruct each autonomous mobile intelligent feeding device to perform the corresponding feeding operation according to the scheduling result. S3. Task execution: After the loading task is completed, each autonomous mobile intelligent feeding device drives to the target shed, identifies the film covering position, enters the shed to perform the feeding task, and after completing the feeding of a single shed, the vehicle-mounted hopper is loaded according to the theoretical demand of a single shed, so that the vehicle-mounted hopper is almost empty after the operation of a single shed. After completion, the vehicle reverses out of the shed under the guidance of the second camera. S4. Cyclic Operation and Dynamic Adjustment: After completing the single-shed feeding operation, the autonomous mobile intelligent feeding device executes subsequent feeding-related operations under the control commands generated by the main control center. When the operating status or energy status of the autonomous mobile intelligent feeding device is detected to be unsatisfactory for the execution of subsequent feeding tasks, the main control center adjusts the operation process of the autonomous mobile intelligent feeding device until all assigned feeding operations are completed.

[0009] Preferably, in step S1, after a single shed feeding task is completed, the main control center automatically inserts a replenishment event according to a preset replenishment rule, controlling the autonomous mobile intelligent feeding device to return to the feed bin for one bait loading, and the amount of bait loaded each time meets the feeding needs of a single shed; after completing one replenishment, the autonomous mobile intelligent feeding device only performs a feeding task for one shed, and after completing the feeding task for that shed, the bait in its vehicle-mounted feed bin is just exhausted, and it returns to the feed bin for replenishment before performing the next feeding task; when the remaining power of the autonomous mobile intelligent feeding device is insufficient to support subsequent single-shed feeding tasks, the main control center automatically inserts a charging event according to a preset charging rule, controlling the autonomous mobile feeding device to go to its corresponding dedicated charging device for charging, and continuing to perform subsequent feeding tasks after the power is restored to a set threshold.

[0010] Preferably, in the S1 task generation, the main control center predicts the overall completion time based on the feeding progress and path execution status of each main mobile intelligent feeding device; when the predicted overall completion time exceeds the set operation deadline, the main control center triggers dynamic adjustment of the feeding task, transferring some delayed feeding tasks from the heavily loaded autonomous mobile intelligent feeding devices to other autonomous mobile intelligent feeding devices with lighter loads, so as to ensure that the overall task is completed within the preset operation deadline, and the task allocation process takes into account the operation capacity and current status of each device, so as to avoid the overall operation progress being affected by the task delay of a single device.

[0011] Preferably, during the feeding process, the autonomous mobile intelligent feeding device dynamically adjusts the driving speed of the mobile chassis, the rotation speed of the feeding disc, and the feeding parameters based on the preset total feeding volume requirement of the shed and the feeding requirement per unit area through the vehicle control module, so as to achieve quantitative and uniform feeding of bait in the shed.

[0012] Preferably, the autonomous mobile intelligent feeding device includes a forward path module, which is used to detect obstacles on the driving path. When the forward path module detects an obstacle, the vehicle control center replans the driving path and controls the autonomous mobile intelligent feeding device to drive to the target shed according to the replanned path.

[0013] Preferably, the autonomous mobile intelligent feeding device is configured to enter the shrimp shed through a magnetically attached film door after reaching the entrance of the shed to perform feeding operations. The magnetically attached film door ensures a sealed fit between the door and the device through magnetic attraction. After feeding is completed, the device drives out of the shrimp shed in reverse based on the path information obtained by the second camera. The system also includes an environmental sensor for detecting weather changes. When the rainfall exceeds a set threshold, the main control center is configured to send control commands to each autonomous mobile intelligent feeding device to control the autonomous mobile intelligent feeding device to enter the shed, return to the main control center, or remain in standby mode according to its location.

[0014] Compared with the prior art, the beneficial effects of the present invention are: In this invention, a main control center, autonomous mobile intelligent feeding devices, and positioning equipment are used. The positioning equipment acquires the location and attitude information of the entrances to each small shed within the farm and sends it to the main control center. Based on this information and the operating and energy status of each autonomous mobile intelligent feeding device, the main control center generates a feeding task allocation scheme and path scheduling scheme, achieving unified scheduling and management of multiple devices, reducing manual intervention, and improving feeding efficiency and consistency. Through a mobile chassis and path detection and recognition module, the autonomous mobile intelligent feeding device can identify obstacles and replan its path in complex environments such as muddy ground outside the shed and narrow passages inside, autonomously driving to the target shed and performing the feeding task, improving the device's adaptability to complex scenarios. The main control center, based on the feeding data of each device... The system predicts the overall completion time based on feeding progress and travel path information. When the prediction exceeds the limit, it dynamically adjusts or redistributes feeding tasks across devices, reducing the probability of path conflicts in multi-device collaborative operations, improving overall operational efficiency, and reducing energy consumption. The main control center automatically inserts feeding or charging events based on the energy status of the devices and adaptively adjusts task allocation based on the scale of aquaculture and operational progress, enabling the system to meet the feeding needs of farms of different sizes. This achieves automated scheduling of feeding operations in small-scale shrimp farming, efficient multi-machine collaborative operations, and autonomous passage and precise feeding in complex scenarios. It solves the problems of low efficiency, high labor intensity, uneven feeding, and inaccurate feeding in existing shrimp farming due to reliance on manual labor, as well as the poor adaptability of existing intelligent feeding equipment to unstructured and complex scenarios in small-scale farms and the high construction and maintenance costs. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the system architecture of the present invention; Figure 2 This is a schematic diagram of the feeding method of the system of the present invention; Figure 3 This is a schematic diagram of the structure of the material supply hopper in the main control center of this invention; Figure 4 This is a schematic diagram of the charging device at the main control center of the present invention. Figure 5 This is a schematic diagram of the structure of the autonomous mobile intelligent feeding device of the present invention.

[0016] In the diagram: 1. Feeding bin; 2. Discharging mechanism; 3. First frame support leg; 4. First weighing sensor; 5. Second frame support leg; 6. Second weighing sensor; 7. Third frame support leg; 8. Third weighing sensor; 9. Fourth frame support leg; 10. Fourth weighing sensor; 11. Control box; 12. Input and status display module; 13. Environmental sensor; 14. Charging device; 15. Warning light; 16. First camera; 17. Second camera; 18. Vehicle-mounted hopper; 19. Box body; 20. Discharge tray; 21. LiDAR; 221. Battery compartment; 222. Tracked walking structure; 223. Chassis support plate; 23. Emergency stop button; 24. Status indicator light; 251. First antenna; 252. Second antenna; 26. Router antenna; 27. Remote control receiving antenna. Detailed Implementation

[0017] Please see Figure 1-5 The present invention provides a technical solution: A smart feeding system and method for shrimp farming in small sheds includes: a main control center, an autonomous mobile smart feeding device, and a positioning device; The main control center is used for centralized feeding and parameter setting, to acquire spatial information of multiple sheds in the farm and the operating status and energy status of each autonomous mobile intelligent feeding device, and to generate feeding task allocation schemes and path scheduling schemes based on the information. It interacts with multiple autonomous mobile intelligent feeding devices through the network to uniformly manage and control the feeding tasks. The main control center includes a charging device 14 for replenishing the energy of the autonomous mobile intelligent feeding devices. In this embodiment, the main control center includes: The feeding bin 1 is in the shape of an inverted cone and is used to load bait. The lower side of the feeding bin 1 is equipped with a first frame support leg 3, a second frame support leg 5, a third frame support leg 7 and a fourth frame support leg 9 that support it from all directions. The weighing sensor assembly includes a first weighing sensor 4, a second weighing sensor 6, a third weighing sensor 8, and a fourth weighing sensor 10, which are respectively disposed at the bottom of the first frame support foot 3, the second frame support foot 5, the third frame support foot 7, and the fourth frame support foot 9, and are used to measure the weight of the bait stored in the feeding bin 1 and the weight of a single feeding. The main control unit, located inside the control box 11, is a micro computing platform used to generate feeding task allocation schemes and path scheduling information based on the received input information. It also establishes a communication connection with the autonomous mobile intelligent feeding device and charging device 14 through the network, obtains the operating status and energy status of the autonomous mobile intelligent feeding device in real time, and performs unified management and control of feeding tasks based on the information. The control board is located inside the control box 11. The control chip is a 32-bit microprocessor. When the autonomous mobile intelligent feeding device is detected to have reached the loading position corresponding to the feeding bin 1, it outputs a feeding control command to the feeding mechanism 2. The control box 11 is located below the feeding hopper 1; The feeding mechanism 2 is located below the feeding bin 1 and executes the feeding start and feeding stop actions according to the received feeding control command; The input and status display module 12 is installed on the left side of the control box 11. It is used to input the number of small sheds and the corresponding parameters, and to display the feeding task status and feeding status of each small shed in real time. An environmental sensor 13 is installed at the bottom of the second frame support leg 5 to detect weather changes. When the weather changes, the main control center sends corresponding control commands to the autonomous mobile intelligent feeding device via the network. The charging device 14 is controlled by a microprocessor and uses a magnetic charging structure to match the charging receiver with the output of the charging device 14. When the processor receives a charging request, it activates the charging port to start charging. At this time, the three-color warning light 15 installed on the upper side of the charging device 14 is yellow. After charging is completed, the three-color warning light 15 turns green. When no charging request is received, the charging device 14 is set to disabled mode and the three-color warning light 15 turns red.

[0018] The autonomous mobile intelligent feeding device is used to autonomously drive to the target shed and perform the feeding task according to the feeding task assigned by the main control center. In this embodiment, the autonomous mobile intelligent feeding device includes: The mobile chassis has a chassis width adapted to the narrow passages inside the shed, includes a battery compartment 221, and adopts a tracked walking structure 222, as well as a chassis support plate 223 set on the upper side of the tracked walking structure 222 and the battery compartment 221 for carrying the upper equipment, thereby enhancing the adaptability to complex road surfaces. The housing 19 is located on the upper surface of the chassis tray 223. The inner wall of the housing 19 is equipped with a vehicle control center, a vehicle control module, a positioning module, a network connection module, and a remote control receiver module. The vehicle control center, located inside the enclosure 19, consists of a micro computing platform and interacts with the main control center via a wireless network. The positioning module is located inside the housing 19, and is equipped with a first antenna 251 and a second antenna 252 on the outside, which are used to acquire the position and attitude information of the autonomous mobile intelligent feeding device in real time. The network connection module is located inside the enclosure 19 and has a router antenna 26 on the outside. It connects to the main control center via a wireless network for information exchange. The remote control receiver module is located inside the housing 19, and has a remote control receiving antenna 27 on the outside for receiving remote control signals; The vehicle-mounted control module, located inside the housing 19, is composed of a 32-bit microprocessor and is connected to the vehicle-mounted control center via a serial port. It is used to receive instructions from the vehicle-mounted control center and execute the control of the throwing motor of the throwing disc 20 and the control of the track motor of the tracked walking structure 222. The vehicle-mounted feed bin 18 has an inverted trapezoidal structure and a capacity to meet the single-time feeding requirements of a single shed, and is used to load bait. The feeding tray 20 is set on the upper side of the chassis support plate 223, and feeds the bait quantitatively and evenly through the feeding motor; The path detection and recognition module consists of a forward path module and a second camera 17. The forward path module includes a lidar 21 and a first camera 16. The lidar 21 is located on the front of the upper surface of the chassis support plate 223, the first camera 16 is located on the upper part of the front front surface of the housing 19, and the second camera 17 is located on the upper part of the rear surface of the housing 19. The lidar 21, the first camera 16, and the second camera 17 are all connected to the vehicle control center via USB to obtain site information in real time. Furthermore, the lidar 21 has strong light resistance and is used for outdoor ground condition and obstacle detection; Furthermore, the second camera 17 uses a micro-distortion or distortion-free camera, and the images it captures are provided to the vehicle control center for identification of paths within the shed. Emergency stop button 23 is located on the outside of the housing 19 and is used for emergency braking; Status indicator light 24 is located on the outside of the housing 19 and is used to display the battery power status.

[0019] The positioning device is used to obtain the location and orientation information of the entrance to the small shed in the farm and send the information to the main control center to generate feeding tasks; Furthermore, the position and orientation information is obtained through pre-calibration, including the global coordinates and orientation angle of each shed entrance.

[0020] The main control center is configured to predict the overall completion time based on the feeding progress and travel path information of each main mobile intelligent feeding device, and to dynamically adjust or redistribute the feeding tasks across devices when the predicted overall completion time exceeds the preset work deadline, so as to ensure that all small shed feeding tasks are completed within the preset work deadline.

[0021] A smart feeding method for shrimp farming in small greenhouses is provided for use in a smart feeding system for shrimp farming in small greenhouses. The method is executed collaboratively by a main control center and multiple autonomous mobile smart feeding devices, and its process includes the following steps: S1. Task Generation: The main control center obtains the spatial information of the small sheds in the farm, the number of small sheds, and the operating status and energy status of each autonomous mobile intelligent feeding device; based on the preset collaborative scheduling control rules of multiple autonomous mobile intelligent feeding devices, the feeding tasks of the small sheds are generated and scheduled. The scheduling control rules are used to determine the execution order and allocation relationship of each feeding task among different autonomous mobile intelligent feeding devices under the premise of meeting the constraints of silo capacity, feeding time, device energy, multiple feeding trips, and overall operation deadline. S2. Task Issuance: The main control center sends the feeding task generated in step S1 to the corresponding autonomous mobile intelligent feeding device through the wireless network, so as to instruct each autonomous mobile intelligent feeding device to perform the corresponding feeding operation according to the scheduling result. S3. Task execution: After the loading task is completed, each autonomous mobile intelligent feeding device drives to the target shed, identifies the film covering position, enters the shed to perform the feeding task, and after completing the feeding of a single shed, the load of the vehicle-mounted material bin 18 is loaded according to the theoretical demand of a single shed, so that the vehicle-mounted material bin 18 is almost empty after the operation of a single shed. After completion, the vehicle reverses out of the shed under the guidance of the second camera 17. S4. Cyclic Operation and Dynamic Adjustment: After completing the single-shed feeding operation, the autonomous mobile intelligent feeding device executes subsequent feeding-related operations under the control commands generated by the main control center. When the operating status or energy status of the autonomous mobile intelligent feeding device is detected to be unsatisfactory for the execution of subsequent feeding tasks, the main control center adjusts the operation process of the autonomous mobile intelligent feeding device until all assigned feeding operations are completed.

[0022] In one implementation, in step S1, after a single shed feeding task is completed, the main control center automatically inserts a feeding event according to a preset feeding rule, controls the autonomous mobile intelligent feeding device to return to the feeding bin 1 to load bait once, and the amount of bait loaded each time meets the feeding needs of a single shed once; after completing a feeding, the autonomous mobile intelligent feeding device only performs a feeding task for one shed, and after completing the feeding task for that shed, the bait in its vehicle-mounted bin 18 is just exhausted, and it returns to the feeding bin 1 to refill before performing the next feeding task; When the remaining power of the autonomous mobile intelligent feeding device is insufficient to support subsequent single-shed feeding tasks, the main control center automatically inserts a charging event according to the preset charging rules, controls the autonomous mobile feeding device to go to its corresponding dedicated charging device 14 for charging, and continues to execute subsequent feeding tasks after the power is restored to the set threshold, thereby realizing the charging constraint of one device and one charging pile.

[0023] In one implementation, during task generation in S1, the main control center predicts the overall completion time based on the feeding progress and path execution status of each main mobile intelligent feeding device. When the predicted overall completion time exceeds the set work deadline, the main control center triggers dynamic adjustment of the feeding tasks, transferring some delayed feeding tasks from the heavily loaded autonomous mobile intelligent feeding devices to other autonomous mobile intelligent feeding devices with lighter loads, so as to ensure that the overall task is completed within the preset work deadline. Moreover, the task allocation process takes into account the operating capacity and current status of each device, avoiding the impact of the overall work progress on the task delay of a single device.

[0024] In a preferred embodiment, during the feeding process, the autonomous mobile intelligent feeding device dynamically adjusts the driving speed of the mobile chassis, the rotation speed of the throwing disc 20, and the feeding parameters based on the total feeding volume requirement preset in the shed and the feeding requirement per unit area through the vehicle control module, so as to achieve quantitative and uniform feeding of bait in the shed.

[0025] Specifically, the main control center or vehicle-mounted control center calculates the target feeding amount per unit time based on the total amount of feed required for a single shed and the spatial dimensions of the shed, and sends the corresponding feeding control parameters to the vehicle-mounted control module. Based on the feeding control parameters, the vehicle-mounted control module adjusts the driving speed of the autonomous mobile intelligent feeding device and the rotation speed of the feeding disc 20 in real time during the feeding process, so that the feed is evenly scattered along the driving path in the shed. In this way, the quantitative and uniform feeding of feed in a single shed is achieved, improving the feeding accuracy and the consistency of aquaculture.

[0026] In one embodiment, the autonomous mobile intelligent feeding device includes a forward path module, which is used to detect obstacles on the driving path. When the forward path module detects an obstacle, the vehicle control center replans the driving path and controls the autonomous mobile intelligent feeding device to drive to the target shed according to the replanned path.

[0027] The autonomous mobile intelligent feeding device is configured to enter the shrimp shed through a magnetic film-covered door after arriving at the entrance of the shed to perform feeding operations. The magnetic film-covered door ensures a sealed fit between the door and the device through magnetic attraction. After feeding is completed, the device drives out of the shrimp shed in reverse based on the path information obtained by the second camera 17. In another embodiment, the system also includes an environmental sensor 13, which is used to detect weather change information. When the rainfall exceeds a set threshold, the main control center is configured to send control commands to each autonomous mobile intelligent feeding device to control the autonomous mobile intelligent feeding device to enter the shed, return to the main control center, or remain in standby mode according to its own location.

[0028] To facilitate understanding of the process by which the main control center of this invention generates feeding tasks under the constraints of silo capacity, feeding time, device energy, and replenishment and charging events, the following example, in conjunction with exemplary parameters, provides a round-by-round allocation embodiment of feeding tasks for five small sheds by three autonomous mobile intelligent feeding devices.

[0029] It should be understood that the number of devices, the number of sheds, the spatial coordinates, and the numerical parameters in this embodiment are only used to illustrate the scheduling and control process of the present invention and do not constitute a limitation on the scope of protection of the present invention; in other embodiments, adjustments can be made according to the actual breeding scale and operational needs.

[0030] 1. Parameter settings: 1.1 The coordinates of the feeding point d are set to (0, 0), and the travel speed of the autonomous mobile intelligent feeding device is set to v. In this embodiment, v = 1 (distance unit / minute). Then, the travel time between any two points u and v satisfies:

[0031] The above distance and time calculation methods are only examples; equivalent path length estimation, grid map cost, or odometer measurement methods can also be used.

[0032] 1.2. Let the collection of small sheds awaiting material feeding be... The parameters for each greenhouse are as follows:

[0033] 1.3 The autonomous mobile intelligent feeding device is assembled as follows: Its energy and charging parameters are as follows: Maximum power capacity Safe power threshold Energy consumption per unit distance Charging rate Target charging capacity .

[0034] Each device is initially located at the feeding point d, and its initial operating time and power consumption are as follows: 2. Calculation rules: 2.1. Energy consumption calculation for round trip: The energy consumption of the autonomous mobile intelligent feeding device traveling from the feeding point to a certain small shed i is:

[0035] 2.2 Feasibility of Electricity Supply When the device's current power level satisfy: When the time is right, the feeding task of small shed i can be executed directly; otherwise, the main control center will automatically insert a charging event, and the control device will go to its corresponding charging device to charge.

[0036] Charging time is calculated as follows:

[0037] 2.3 Task Completion Time and Time Window Constraints The autonomous mobile intelligent feeding device k performs the feeding task for shed i, and its completion time is:

[0038] The completion time must meet the following requirements:

[0039] 2.4 Incremental Time Criterion To select the appropriate device, the main control center calculates the time increment required for each of the main mobile intelligent feeding devices to execute the current task in the small shed:

[0040] Among the devices that meet the time window and energy constraints, the device with the smallest incremental time is selected to perform the task.

[0041] 3. Task allocation process in each round: The main control center sets the latest completion time for each small shed. Sort the sheds from smallest to largest to obtain the allocation order:

[0042] For each small shed i in the sequence, the main control center performs a virtual trial allocation for each device k, eliminating schemes that do not meet the time window or energy constraints, and selecting incremental time schemes from the feasible schemes. The smallest device performs the feeding task for the small shed and updates the operating time and power status of the selected device.

[0043] (1) In the first round of allocation of shed 5: Device A and Device B are feasible and have the same increment, so select Device A to execute; update the status of Device A.

[0044] (2) Second round of allocation of shed 1: Device A does not meet the time window, device B is feasible and has the smallest increment, so device B is selected for execution; update the status of device B.

[0045] (3) In the third round of allocation of shed 3: Device A is feasible and has the smallest increment, so device A is selected for execution; update the status of device A.

[0046] (4) In the fourth round of allocation of shed 2: Device C is feasible after charging and has the smallest increment, so device C is selected to execute; update the status of device C.

[0047] (5) In the fifth round of allocation of shed 4: All three devices are feasible after being charged. The main control center selects device B to execute according to the preset selection rules (e.g., the earliest completion time takes priority); and updates the status of device B.

[0048] In this embodiment, the task allocation result is as follows: Device A executes sheds 5 and shed 3, Device B executes sheds 1 and shed 4, and Device C executes shed 2. During the scheduling process, the main control center performs feasibility screening based on the above-mentioned time window constraints and energy constraints, and inserts charging events when the power is insufficient and material replenishment events after the completion of a single shed task, thereby satisfying the "single shed depletion" material replenishment constraint and the "one device, one charging pile" charging constraint. At the same time, the main control center can predict the overall completion time based on the device progress. When the prediction timeout or the load is unbalanced, the task reassignment is triggered to ensure that the overall material feeding task is completed within the preset time.

[0049] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only for the purpose of helping to understand the method and core ideas of the present invention. The above descriptions are only preferred embodiments of the present invention. It should be noted that due to the limitations of textual expression, while there are objectively infinite specific structures, those skilled in the art can make several improvements, modifications, or changes without departing from the principles of the present invention, and can also combine the above technical features in an appropriate manner. These improvements, modifications, changes, or combinations, or the direct application of the inventive concept and technical solution to other situations without modification, should all be considered within the scope of protection of the present invention.

Claims

1. A smart feeding system for shrimp farming in small sheds, characterized in that, include: Main control center, autonomous mobile intelligent feeding device and positioning equipment; The main control center is used for centralized feeding and parameter setting, acquiring spatial information of multiple sheds in the farm and the operating status and energy status of each autonomous mobile intelligent feeding device, and generating a feeding task allocation scheme and path scheduling scheme based on the information. It interacts with multiple autonomous mobile intelligent feeding devices through the network to uniformly manage and control the feeding tasks. The main control center includes a charging device (14) for replenishing the energy of the autonomous mobile intelligent feeding devices. The autonomous mobile intelligent feeding device is used to autonomously drive to the target shed and perform the feeding task according to the feeding task assigned by the main control center. The positioning device is used to acquire the location and orientation information of the entrance to the small shed in the breeding farm, and send the information to the main control center to generate a feeding task; The main control center is configured to predict the overall completion time based on the feeding progress and travel path information of each main mobile intelligent feeding device, and to dynamically adjust or redistribute the feeding tasks across devices when the predicted overall completion time exceeds the preset operation deadline, so as to ensure that all small shed feeding tasks are completed within the preset operation deadline.

2. The intelligent feeding system for shrimp farming in a small greenhouse according to claim 1, characterized in that: The main control center includes a feeding bin (1), a weighing sensor assembly, a main control unit, a control board, a feeding mechanism (2), an input and status display module (12), an environmental sensor (13), and a charging device (14). The feeding bin (1) is in the shape of an inverted cone and is used to load bait. The lower side of the feeding bin (1) is equipped with a first frame support leg (3), a second frame support leg (5), a third frame support leg (7) and a fourth frame support leg (9) that support it from all directions. The weighing sensor assembly includes a first weighing sensor (4), a second weighing sensor (6), a third weighing sensor (8) and a fourth weighing sensor (10), which are respectively disposed at the bottom of the first frame support foot (3), the second frame support foot (5), the third frame support foot (7) and the fourth frame support foot (9), and are used to measure the weight of the bait stored in the feeding bin (1) and the weight of a single feeding. The main control unit is located inside the control box (11) and is a micro computing platform. It is used to generate a feeding task allocation scheme and path scheduling information based on the received input information, and establish a communication connection with the autonomous mobile intelligent feeding device and charging device (14) through the network. It can obtain the operating status and energy status of the autonomous mobile intelligent feeding device in real time, and manage and control the feeding task in a unified manner based on the information. The control board is located inside the control box (11). The control chip is a 32-bit microprocessor. When the autonomous mobile intelligent feeding device is detected to have reached the loading position corresponding to the feeding bin (1), it outputs a feeding control command to the feeding mechanism (2). The control box (11) is located below the feeding hopper (1); The feeding mechanism (2) is located below the feeding bin (1) and performs feeding start and feeding stop actions according to the received feeding control command; The input and status display module (12) is installed on the left side of the control box (11) and is used to input the number of small sheds and the corresponding parameters, and to display the feeding task status and feeding status of each small shed in real time. The environmental sensor (13) is installed at the bottom of the second frame support leg (5) to detect weather changes. When the weather changes, the main control center sends corresponding control commands to the autonomous mobile intelligent feeding device through the network. The charging device (14) is controlled by a microprocessor and uses a magnetic charging structure to match the charging receiver with the output of the charging device (14). When the processor receives a charging request, it activates the charging port to start charging. At this time, the three-color warning light (15) installed on the upper side of the charging device (14) is yellow. After charging is completed, the three-color warning light (15) is green. When no charging request is received, the charging device (14) is set to disabled mode and the three-color warning light (15) is red.

3. The intelligent feeding system for shrimp farming in a small greenhouse according to claim 1, characterized in that: The autonomous mobile intelligent feeding device includes a mobile chassis, a box (19), a vehicle-mounted hopper (18), a feeding tray (20), a path detection and recognition module, an emergency stop button (23), and a status indicator light (24). The mobile chassis has a chassis width adapted to the narrow passage in the shed, includes a battery compartment (221), and adopts a tracked walking structure (222), as well as a chassis support plate (223) set on the upper side of the tracked walking structure (222) and the battery compartment (221) for carrying the upper equipment, thereby enhancing the applicability to complex road surfaces; The housing (19) is located on the upper surface of the chassis tray (223), and the inner wall of the housing (19) is equipped with a vehicle control center, a vehicle control module, a positioning module, a network connection module and a remote control receiving module. The vehicle control center is located inside the housing (19) and is composed of a micro computing platform. It interacts with the main control center via a wireless network. The positioning module is located inside the housing (19) and has a first antenna (251) and a second antenna (252) on the outside, for real-time acquisition of the position and attitude information of the autonomous mobile intelligent feeding device; The network connection module is located inside the housing (19) and has a router antenna (26) on the outside. It connects to the main control center via a wireless network for information exchange. The remote control receiving module is located inside the housing (19) and has a remote control receiving antenna (27) on the outside for receiving remote control signals; The vehicle control module is located inside the housing (19) and is composed of a 32-bit microprocessor. It is connected to the vehicle control center via a serial port and is used to receive instructions from the vehicle control center and execute the control of the throwing motor of the throwing disc (20) and the control of the track motor of the tracked walking structure (222). The vehicle-mounted hopper (18) has an inverted trapezoidal structure and a capacity to meet the single-shed feeding requirements, and is used to load bait; The feeding disc (20) is set on the upper side of the chassis support plate (223), and the feeding motor delivers the bait quantitatively and evenly. The path detection and recognition module consists of a forward path module and a second camera (17). The forward path module includes a lidar (21) and a first camera (16). The lidar (21) is located at the front of the upper surface of the chassis tray (223). The first camera (16) is located at the upper front surface of the box (19). The second camera (17) is located at the upper rear surface of the box (19). The lidar (21), the first camera (16) and the second camera (17) are all connected to the vehicle control center via USB to obtain site information in real time. The emergency stop button (23) is located on the outside of the housing (19) and is used for emergency braking; The status indicator light (24) is located on the outside of the housing (19) and is used to display the battery power status.

4. A method for intelligent feeding of shrimp in small-scale greenhouse farming, used in the intelligent feeding system for shrimp in small-scale greenhouse farming as described in claims 1-3, characterized in that, Includes the following steps: S1. Task Generation: The main control center obtains the spatial information of the small sheds in the farm, the number of small sheds, and the operating status and energy status of each of the main mobile intelligent feeding devices; based on the preset collaborative scheduling control rules of multiple autonomous mobile intelligent feeding devices, the feeding tasks of the small sheds are generated and scheduled. The scheduling control rules are used to determine the execution order and allocation relationship of each feeding task among different autonomous mobile intelligent feeding devices under the premise of meeting the constraints of silo capacity, feeding time, device energy, multiple feeding trips, and overall operation deadline. S2. Task Issuance: The main control center sends the feeding task generated in step S1 to the corresponding autonomous mobile intelligent feeding device through the wireless network, so as to instruct each autonomous mobile intelligent feeding device to perform the corresponding feeding operation according to the scheduling result. S3, Task execution: After the loading task is completed, each autonomous mobile intelligent feeding device drives to the target shed, identifies the film covering position and enters the shed to perform the feeding task. After the feeding of a single shed is completed, the load of the vehicle-mounted hopper (18) is loaded according to the theoretical demand of a single shed, so that the vehicle-mounted hopper (18) is almost empty after the operation of a single shed. After completion, the vehicle reverses out of the shed under the guidance of the second camera (17). S4. Cyclic Operation and Dynamic Adjustment: After completing the single-shed feeding operation, the autonomous mobile intelligent feeding device executes subsequent feeding-related operations under the control commands generated by the main control center. When the operating status or energy status of the autonomous mobile intelligent feeding device is detected to be unsatisfactory for the execution of subsequent feeding tasks, the main control center adjusts the operation process of the autonomous mobile intelligent feeding device until all assigned feeding operations are completed.

5. The intelligent feeding method for shrimp farming in small sheds according to claim 4, characterized in that: In step S1, after a single shed feeding task is completed, the main control center automatically inserts a feeding event according to the preset feeding rules, controls the autonomous mobile intelligent feeding device to return to the feeding bin (1) to load bait once, and the amount of bait loaded each time meets the feeding needs of a single shed once; after completing a feeding, the autonomous mobile intelligent feeding device only performs a feeding task for a single shed, and after completing the feeding task for that shed, the bait in its vehicle-mounted bin (18) is just exhausted, and it returns to the feeding bin (1) to refill before performing the next feeding task; when the remaining power of the autonomous mobile intelligent feeding device is insufficient to support subsequent single shed feeding tasks, the main control center automatically inserts a charging event according to the preset charging rules, controls the autonomous mobile feeding device to go to its corresponding dedicated charging device (14) for charging, and continues to perform subsequent feeding tasks after the power is restored to the set threshold.

6. The intelligent feeding method for shrimp farming in small sheds according to claim 4, characterized in that: In the S1 task generation, the main control center predicts the overall completion time based on the feeding progress and path execution status of each main mobile intelligent feeding device. When the predicted overall completion time exceeds the set operation deadline, the main control center triggers dynamic adjustment of the feeding task, transferring some delayed feeding tasks from the heavily loaded autonomous mobile intelligent feeding devices to other autonomous mobile intelligent feeding devices with lighter loads, so as to ensure that the overall task is completed within the preset operation deadline. Moreover, the task allocation process takes into account the operation capacity and current status of each device, so as to avoid the overall operation progress being affected by the task delay of a single device.

7. The intelligent feeding method for shrimp farming in small sheds according to claim 4, characterized in that: During the feeding process, the autonomous mobile intelligent feeding device dynamically adjusts the driving speed of the mobile chassis and the rotation speed and feeding parameters of the throwing plate (20) based on the feeding requirements of the small shed and the unit area, according to the preset total feeding requirements of the small shed, so as to realize the quantitative and uniform feeding of bait in the small shed.

8. The intelligent feeding method for shrimp farming in small sheds according to claim 4, characterized in that: The autonomous mobile intelligent feeding device includes a forward path module, which is used to detect obstacles on the driving path. When an obstacle is detected, the vehicle control center replans the driving path and controls the autonomous mobile intelligent feeding device to drive to the target shed according to the replanned path.

9. The intelligent feeding method for shrimp farming in small sheds according to claim 4, characterized in that: The autonomous mobile intelligent feeding device is configured to enter the shed through a magnetic film-covered door after reaching the shed entrance to perform feeding operations. The magnetic film-covered door ensures a sealed fit between the door and the device through magnetic adsorption. After feeding is completed, the device drives out of the shrimp shed in reverse based on the path information obtained by the second camera (17). The system also includes an environmental sensor (13) for detecting weather change information. When the rainfall exceeds a set threshold, the main control center is configured to send control commands to each autonomous mobile intelligent feeding device to control the autonomous mobile intelligent feeding device to enter the shed, return to the main control center, or remain in standby mode according to its location.

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