Intelligent feeding control method and system for rice and frog co-culture and medium

Abstract

The invention relates to the technical field of rice and frog cultivation, in particular to an intelligent feeding control method and system for rice and frog co-cultivation and a medium. The method comprises the following steps: acquiring preset feeding operation tasks and preset starting time of each feeding operation task; dividing each feeding operation task into a plurality of sub-tasks based on the rice field area; when the preset starting time is reached, generating an execution plan of each subtask in the current feeding operation task by adopting a linear programming method, and executing the execution plan; in the process of executing each sub-task, when the sub-task moves to the feeding starting position of each sub-task, identifying the residual feed area and the ponding area in the corresponding area of the current sub-task in real time to adjust the feeding speed of the current sub-task until the sub-task moves to the feeding ending position of the current sub-task; and after all the sub-tasks are executed, returning to the starting point for standby, and waiting for the next feeding operation task. The precision degree, flexibility and operation efficiency of feeding operation can be improved, and feed waste is effectively reduced.

Description

Technical Field

[0001] The invention relates to the technical field of rice-frog breeding, and in particular to an intelligent feeding control method, system and medium for rice-frog co-breeding. Background Art

[0002] With the development of ecological cycle agricultural model, the "rice-frog" co-cultivation model has gradually become a typical ecological cycle agricultural model in the integrated rice-fish farming system. The "rice-frog" co-cultivation model makes full use of the ecological space of rice fields. On the one hand, the shallow water system of rice fields and rice plants provide a suitable shade place for black-spotted frogs, and black-spotted frogs can grow by preying on pests; on the other hand, rice uses the predation of black-spotted frogs on pests to reduce diseases and insect pests, while playing the role of frog feces in fertility, reducing the application of chemical fertilizers and pesticides, thereby achieving the effect of reducing medicine and weight, dual use of one land, and double harvest of one land. Among them, the feeding link is one of the most important links in the "rice-frog" co-cultivation production. Traditional feeding operations mainly rely on manual labor. Usually, feeding tables are set up around the rice fields, and feed is fed manually according to the growth requirements of black-spotted frogs every day. Not only is the labor intensity high, the labor cost is high, and the operation efficiency is low, but also the randomness of manual feeding is strong, which often causes uneven feed distribution.

[0003] With the development of automation technology and intelligent agricultural equipment, automatic feeding equipment such as track-type automatic feeding systems and small bucket-type fixed-point feeding machines are gradually appearing on the market. For example, Chinese patent CN110731293A, track-type aquaculture tank automatic feeding system and control method thereof, the system includes a large-capacity automatic metering feed bin arranged on the hardened platform of the aquaculture tank shore, and the bin is connected with a reciprocating feeding track; the reciprocating feeding track extends horizontally from the aquaculture tank shore to the top of the aquaculture tank, and at least one track is arranged in parallel; the reciprocating feeding track is provided with a track-type electric walking feeding device, and the track-type aquaculture tank automatic feeding system replaces manual timed and quantitative feeding operations through the track-type electric walking feeding device and the setting of the track, which improves the efficiency of the feeding operation to a certain extent.

[0004] In the rice-frog co-culture system, feed must not only meet the nutritional needs of frogs, but also not have an adverse effect on rice growth, so accurate feed delivery is crucial. However, the existing automated feeding equipment feeds at fixed points and in fixed quantities each time, and cannot dynamically adjust the feeding strategy according to changes in the actual distribution of black-spotted frogs and environmental changes. It is difficult to adapt to the environmental diversity of the "rice-frog" co-culture model, and the low level of intelligence leads to unsatisfactory feeding effects and insufficient precision, resulting in a large amount of feed waste, and even adverse effects such as deterioration of water quality in rice fields. Summary of the invention

[0005] In response to the above technical problems, the present invention proposes an intelligent feeding control method, system and medium for rice-frog co-culture, aiming to design a feeding control method for the environmental diversity of the "rice-frog" co-culture composite agricultural model, which can dynamically adjust the feeding strategy according to the changes in the actual distribution of black-spotted frogs and environmental changes, greatly improve the precision and flexibility of feeding operations, improve the efficiency of feeding operations, and effectively reduce feed waste.

[0006] In a first aspect, the present application provides an intelligent feeding control method for rice-frog co-culture, comprising the following steps: S1, obtaining a feeding operation task table, wherein the feeding operation task table includes a plurality of feeding operation tasks and a scheduled start time for each feeding operation task; S2, dividing each feeding task into several subtasks based on the rice field area, and the parameters of each subtask include a feeding starting position, a feeding ending position and a feeding speed; S3, when the scheduled start time is reached, a linear programming method is used to generate an execution plan for each subtask in the current feeding operation task based on the parameters of each subtask, and each subtask in the current feeding operation task is executed according to the execution plan; S4, in the process of executing each subtask, when moving to the feeding starting position of the subtask, real-time identification of the residual bait area and the accumulated water area in the corresponding area of ​​the current subtask; S5, adjusting the feeding speed of the current subtask based on the residual bait area and the accumulated water area in the area until the feeding end position of the current subtask is moved; S6, repeating steps S4-S5 until all subtasks in the current feeding task are completed, returning to the preset starting point and waiting for the scheduled start time of the next feeding task.

[0007] In some embodiments, in S3, a linear programming method is used to generate an execution plan for each subtask in the current feeding operation task based on the parameters of each subtask, including: Defining decision variables of the subtask, wherein the decision variables of the subtask include the required number of refills and the additional walking mileage for each refill; Based on the parameters of each subtask, determine the constraints of the decision variables; Taking minimizing the sum of the additional walking distance required for the current feeding task as the objective function, a linear programming model is constructed based on the decision variables, objective functions and constraints of the subtasks. Solve the linear programming model based on the parameters of each subtask to obtain a set of optimal decision variable values; Based on the set of optimal decision variable values, an execution plan for each subtask in the current feeding operation task is generated.

[0008] In some embodiments, the parameters of the subtasks also include feeding movement speed, then based on the parameters of each subtask, the constraint conditions of the decision variables are determined, including: Based on the feeding starting position, feeding ending position and feeding moving speed of each subtask, the feeding time of each subtask is calculated; Based on the feeding time and feeding speed of each subtask, the feeding amount of each subtask is calculated; Based on the feeding amount of each subtask, the total feeding amount of the current feeding task is calculated; Based on the total feeding amount of the current feeding task, determine the upper limit of the required feeding times.

[0009] In some embodiments, in S5, adjusting the feeding speed of the current subtask based on the residual bait area and the water accumulation area in the field area includes: Based on the residual bait area, the residual bait amount in the current area is calculated; Based on the amount of residual bait in the current area, a proportional control algorithm is used to adjust the feeding speed of the current subtask to obtain the first feeding speed of the current subtask; Based on the water accumulation area in the current area, a nonlinear control algorithm is used to adjust the first feeding speed of the current subtask to obtain the second feeding speed of the current subtask, and the second feeding speed is used as the final feeding speed of the current subtask.

[0010] In some embodiments, based on the amount of residual bait in the current area, a proportional control algorithm is used to adjust the feeding speed of the current subtask to obtain a first feeding speed of the current subtask, including: Based on the amount of residual bait in the current area, a residual bait correction coefficient is calculated, and the residual bait correction coefficient is inversely proportional to the residual bait amount; Multiply the residual bait correction coefficient by the feeding speed of the current subtask, and the product obtained is the first feeding speed of the current subtask.

[0011] In some embodiments, based on the water accumulation area in the current area, a nonlinear control algorithm is used to adjust the first feeding speed of the current subtask to obtain the second feeding speed of the current subtask, including: Determine whether the water accumulation area in the current area has reached the preset water accumulation threshold. If the water accumulation area in the current area is greater than or equal to the preset water accumulation threshold, adjust the second feeding speed of the current subtask to 0. If the water accumulation area in the current area is less than the preset water accumulation threshold, perform the following steps: Based on the waterlogging area of ​​the current area, a waterlogging correction coefficient is calculated, where the waterlogging correction coefficient is inversely proportional to the waterlogging area; Multiply the water accumulation correction coefficient by the first feeding speed of the current subtask, and the product obtained is the second feeding speed of the current subtask.

[0012] In some embodiments, in S3, each subtask in the current feeding operation task is executed according to the execution plan, including: Before moving to the starting feeding position of each subtask, obtain weather data in real time; Based on the weather data, it is evaluated whether the current weather conditions will have an adverse impact on the feeding operation. If the evaluation result is that it will have an adverse impact on the feeding operation, the feeding speed of the next subtask will be adjusted to 0. If the evaluation result is that it will not have an adverse impact on the feeding operation, the feeding speed of the next subtask will remain unchanged.

[0013] In some embodiments, the following steps are also included: Select several points on the moving path, set an RFID tag for each point and calibrate the position of each RFID tag; In the process of executing each subtask, when moving to the position of each RFID tag, the current positioning data is corrected to the position of the corresponding RFID tag.

[0014] In the second aspect, the present application provides an intelligent feeding control system for rice-frog co-culture, including a feeding robot, a ground track device, a feeding tower, a charging device and a cloud controller. The feeding robot is located on the ground track device and moves along the track of the ground track device. The feeding robot, the feeding tower and the charging device are all communicatively connected with the cloud controller. The feeding robot is used to execute an intelligent feeding control method for rice-frog co-culture as described above.

[0015] In a third aspect, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the aforementioned intelligent feeding control method for rice-frog co-culture.

[0016] The beneficial technical effects of the present invention may at least include: 1. Compared with the existing track-type automatic feeding system, which can usually only feed at a fixed point and quantitatively according to a preset path and feeding amount, and cannot adjust the feeding amount according to the actual situation, resulting in a large amount of feed waste, the present invention adopts an intelligent feeding control method, system and medium for rice-frog co-cultivation, divides the feeding task into multiple subtasks based on the distribution of the rice field area, uses a linear programming method to generate an execution plan for each subtask, optimizes the feeding path and speed, thereby improving the efficiency of the overall feeding operation, and dynamically adjusts the feeding strategy based on the real-time identified residual bait area and water accumulation area, which can more flexibly respond to the specific conditions of different feeding sections in the rice field area, thereby better adapting to the environmental diversity of the composite agricultural model of rice-frog co-cultivation, and at the same time realizes accurate delivery of feed, effectively reduces feed waste, improves feeding effect, greatly improves the intelligence of the feeding system, and helps to achieve the sustainable development of the rice-frog co-cultivation system; 2. Combined with the characteristics of the feeding robot based on track operation, the constraints of the decision variables are determined by the parameters of each subtask, and a linear programming model is constructed with the objective function of minimizing the sum of the increased walking mileage required for the number of feeding times in the current feeding task. Based on the linear programming model, the autonomous planning of feeding and feeding operations in the entire feeding task process is realized. While ensuring the continuity of the feeding operation, the movement path and feeding strategy are optimized, which greatly improves the efficiency and reliability of the overall feeding operation; 3. Change the existing fixed-point and quantitative feeding method of automatic feeding equipment, and adopt the method of feeding at a certain feeding moving speed and feeding speed in the feeding section between the feeding start / end position, that is, feeding while moving. This can not only improve the overall operation efficiency, but also reduce the waste of feed caused by fixed-point centralized feeding because the feed is evenly distributed along the moving path. At the same time, by monitoring the residual bait area of ​​the feeding section and converting it into residual bait amount, the feeding speed is adjusted in real time according to the actual feeding situation of the black-spotted frog in the current area, and the feeding speed in the area with a large amount of residual bait is reduced to avoid over-feeding, ensure the accuracy of the feeding amount, and avoid adverse effects such as water pollution caused by excessive feed. The impact of waterlogging on the feeding section should be further monitored, and the feeding speed should be adjusted in real time according to the actual waterlogging situation in the current area. Feeding should be stopped or reduced proportionally in sections with larger waterlogging areas to prevent the feed from dissolving in water and being unable to be ingested, thereby reducing feed waste. The initial feeding amount should be adjusted in real time according to the on-site visual feedback of the feeding section, and accurate feeding decisions can be made. Therefore, the feeding strategy can be dynamically adjusted according to the actual feeding situation of the frogs and environmental changes, ensuring that the black-spotted frogs obtain adequate nutrition. At the same time, feed waste can be reduced, feeding can be intelligentized, and manual monitoring and operation can be reduced, which will help to achieve the sustainable development of the rice-frog co-culture system and promote the advancement of ecological agricultural technology.

[0017] 4. By further linking with weather data, according to the current weather conditions, the feeding speed of the current subtask is appropriately adjusted to adjust the feeding amount, thereby optimizing the feeding effect, ensuring the healthy growth of black-spotted frogs and the effective use of feed, reducing feed waste and environmental problems caused by weather reasons, and further improving the intelligence of feeding strategies; 5. Combined with the scene characteristics of the ground track, a positioning correction method based on the fusion of the mileage encoder and RFID tag is proposed. While effectively eliminating the cumulative error of the mileage calculation of the mileage encoder, it realizes low-cost precise positioning and navigation, providing a good positioning data foundation for the accuracy and efficiency of feeding.

[0018] Other features and advantages of the present invention will be disclosed in detail in the following specific embodiments and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described below in conjunction with the accompanying drawings: Figure 1 The present invention is a flowchart of an intelligent feeding control method for rice-frog co-culture according to an embodiment of the present invention.

[0020] Figure 2 The figure is a schematic diagram of the structure of an intelligent feeding control system for rice-frog co-culture according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The technical solutions of the embodiments of the present invention are explained and described below in conjunction with the drawings of the embodiments of the present invention, but the following embodiments are only preferred embodiments of the present invention, not all. Based on the embodiments in the implementation mode, other embodiments obtained by those skilled in the art without creative work are all within the protection scope of the present invention.

[0022] In the following description, terms such as "inside", "outside", "up", "down", "left", "right", etc. that indicate directions or positional relationships are only used to facilitate the description of the embodiments and simplify the description, and do not indicate or imply that the referred device or element must have a specific direction, be constructed and operate in a specific direction. Therefore, it should not be understood as a limitation of the present invention.

[0023] Please refer to the attached Figure 1 , Figure 1 A flow chart of an intelligent feeding control method for rice-frog co-culture provided by an embodiment of the present specification is shown.

[0024] like Figure 1 As shown, the intelligent feeding control method for rice-frog co-culture may at least include the following steps: S1, obtaining a feeding operation task table, which includes a plurality of feeding operation tasks and a scheduled start time for each feeding operation task.

[0025] Among them, the feeding operation task table in this embodiment is entered into the control module of the feeding robot by the farmer through the control program in advance. The feeding operation task table can be a daily feeding operation task or a weekly feeding operation task. The specific corresponding cycle can be set according to the growth needs of the black-spotted frog, and this embodiment does not limit this.

[0026] S2, dividing each feeding operation task into a plurality of subtasks based on the rice field area, and the parameters of each subtask include a feeding starting position, a feeding ending position and a feeding speed.

[0027] It can be understood that the ground track device is paved along the ridge of the rice field area. Each time the feeding robot performs a feeding operation task, it moves on the ground track with a fixed track to feed the entire rice field area. Based on the rice field area, each feeding operation task is divided into several subtasks, so that each subtask corresponds to a different field area and a different feeding strategy.

[0028] Among them, the parameters of the subtask include the feeding starting position and the feeding ending position, which indicate that some sections of the road in the rice field area are preset in this embodiment as the feeding table of the black-spotted frog, that is, the feeding section of the feeding robot, so as to train the black-spotted frog to eat bait in a fixed area, and change the existing fixed-point and quantitative feeding method of the automated feeding equipment. A method of feeding the feed at a certain feeding moving speed and feeding speed is adopted in the feeding section between the feeding starting / ending positions, that is, feeding while moving. This can not only improve the overall work efficiency, but also reduce the waste of feed stacking caused by fixed-point concentrated feeding because the feed is evenly delivered along the moving path. At the same time, by adjusting the feeding moving speed and feeding speed, the feeding amount of the feed can be more accurately controlled according to the actual distribution of the black-spotted frog.

[0029] In this embodiment, a certain track position is set as the positioning origin, such as the corresponding position of a certain feeding tower on the track, which is recorded as D0=0, and the direction of the head of the feeding robot along the track is positive, and the direction of the tail of the feeding robot is negative, then all positions Di on the track can be expressed as the distance from the positioning origin along the track trajectory.

[0030] Among them, the feeding speed in the parameters of the subtask can indirectly reflect the feeding amount of the feeding robot. Specifically, the feeding robot is usually equipped with a roller for loading feed and a feeding motor. According to the feeding amount of the roller with different specifications and quantities per rotation (for example, in g) and the speed of the feeding motor (for example, in g / min), the actual feeding amount of the feeding robot within the moving time can be calculated.

[0031] S3, when the scheduled start time is reached, a linear programming method is used to generate an execution plan for each subtask in the current feeding operation task based on the parameters of each subtask, and each subtask in the current feeding operation task is executed according to the execution plan.

[0032] The linear programming method is a mathematical method for optimizing (maximizing or minimizing) a linear objective function under given linear constraints. In the feeding task for rice-frog co-culture in this embodiment, the linear programming method is used to find the optimal feeding strategy to maximize the operating efficiency.

[0033] Further, in this embodiment, in S3, a linear programming method is used to generate an execution plan for each subtask in the current feeding operation task based on the parameters of each subtask, including: S301, defining the decision variables of the subtask, where the decision variables of the subtask include the required number of refills and the additional walking mileage for each refill.

[0034] It is understandable that the drum of the feeding robot can only load a certain amount of feed, which may not be enough to support the execution of each subtask in the entire feeding task. When the feeding robot is executing the feeding process according to the subtask execution plan of the feeding task or at the preset starting point, once the feed remaining in the drum of the feeding robot is lower than the set threshold, the feeding robot needs to enter the automatic feeding mode and move to the feeding tower for feed replenishment. Therefore, when to refill and which feeding tower to move to for refilling in the process of executing each subtask in the current feeding task can maximize the operating efficiency of the entire feeding task, which is to be considered in this embodiment.

[0035] Specifically, in this embodiment, for each subtask, the decision variables include the number of refills j, the number y of the target refill tower corresponding to each refill, and the additional walking mileage for the jth refill. The target feeding tower is the nearest feeding tower, and the additional mileage for the jth feeding is It can be expressed as: in, Indicates that the feeding robot has reached the feeding end position of the Fth subtask. represents the position of the feeding tower (i.e., the target feeding tower) y closest to the Fth subtask, That is, after completing a subtask, the distance traveled to the target feeding tower for feeding and returning to the feeding end position of the subtask.

[0036] S302: Determine the constraint conditions of the decision variables based on the parameters of each subtask.

[0037] Specifically, in this embodiment, the parameters of the subtask also include the feeding movement speed. Based on the parameters of each subtask, the constraint conditions of the decision variables are determined, including: Based on the feeding starting position, feeding ending position and feeding moving speed of each subtask, the feeding time of each subtask is calculated; Based on the feeding time and feeding speed of each subtask, the feeding amount of each subtask is calculated; Based on the feeding amount of each subtask, the total feeding amount of the current feeding task is calculated; Based on the total feeding amount of the current feeding task, determine the upper limit of the required feeding times.

[0038] It can be understood that the implementation method of "determining the constraint conditions of the decision variables based on the parameters of each subtask" in this embodiment is: First, since the present embodiment adopts a feeding method of feeding the feed at a certain feeding moving speed and feeding speed in the feeding section between the feeding starting position and the feeding ending position, the quotient obtained by dividing the distance of the feeding section between the feeding starting position and the feeding ending position by the feeding moving speed is the feeding time; Secondly, the feeding robot is usually equipped with a feed-loading drum and a feed-feeding motor. The feeding speed can be converted into the speed of the feed-feeding motor (for example, in g) by dividing the feeding speed by the feed-feeding amount q (for example, in g) per rotation of the drum of different specifications and quantities. Then, the feeding time is multiplied by the speed of the feed-feeding motor, and the product obtained is the actual feeding amount of the feeding robot in the current subtask. Next, add up the feeding amounts of each subtask in the current feeding task to get the total feeding amount required for the current feeding task. , where n represents the total number of subtasks divided from the current feeding task. Indicates the feeding amount for the nth subtask.

[0039] Finally, the constraints of the decision variables are determined as: in, Indicates the amount of full feed in the drum of the feeding robot. represents the ceiling function, represents the number of subtasks completed after the jth feeding, then the sequence number of the feeding subtask completed after the jth feeding is , F represents the number of subtasks that have been completed before the jth refill. , Represents the sum of the feeding amounts of the subtasks completed after the jth feeding.

[0040] S303, taking minimizing the sum of the increased walking mileage due to the number of feeding times required for the current feeding task as the objective function, a linear programming model is constructed based on the decision variables, objective functions and constraints of the subtasks.

[0041] Among them, the objective function of the linear programming model can be expressed as: , where x is a positive integer, which is the same as the number of times the feeding is required for the current feeding task.

[0042] S304, solving the linear programming model based on the parameters of each subtask to obtain a set of optimal decision variable values.

[0043] Use a linear programming algorithm (such as the simplex method or the interior point method, etc.) to solve the objective function of the linear programming model under constraints. The solution will give the optimal number of refills j for the current feeding task, the target refill tower y corresponding to each refill, and the number of subtasks completed after each refill. , as a set of optimal decision variable values.

[0044] S305: Generate an execution plan for each subtask in the current feeding task based on the set of optimal decision variable values.

[0045] It can be understood that the execution plan of each subtask in the current feeding operation task includes the total number of subtasks n, the optimal number of feeding times, which subtasks need to be fed before executing, the target feeding tower number y corresponding to each feeding, and the moving path planning of the overall feeding operation task, so as to ensure the optimization of the efficiency of the overall feeding operation.

[0046] Combined with the characteristics of the feeding robot based on track operation, the constraints of the decision variables are determined by the parameters of each subtask, and a linear programming model is constructed with the objective function of minimizing the sum of the increased walking mileage required for the number of refills in the current feeding task. Based on the linear programming model, autonomous planning of feeding and refilling operations in the entire feeding operation process is realized. While ensuring the continuity of the feeding operation, the moving path and refilling strategy are optimized, which greatly improves the efficiency and reliability of the overall feeding operation.

[0047] S4, in the process of executing each subtask, when moving to the feeding starting position of the subtask, real-time identification of the residual bait area and the accumulated water area in the corresponding area of ​​the current subtask.

[0048] Among them, "residual bait" refers to the feed that is not eaten by black-spotted frogs during the feeding process and is left on the feeding route. It is understandable that the purpose of identifying the residual bait area is to evaluate the animal's feeding situation, because when obtaining the initial feeding amount set in the feeding task, the animal's feeding amount may be overestimated, resulting in excessive feed, and the appetite of black-spotted frogs is easily affected by many factors, such as environmental changes, health status, mood swings, etc., which may result in it not eating all the feed, resulting in residual bait.

[0049] It is understandable that identifying the area of ​​accumulated water is to assess the edibility of the feed, because the feed is easily soluble in water, and the increase in the area of ​​accumulated water will cause more feed to dissolve in the water, which will directly affect the edibility of the feed, because the dissolved feed will not be ingested by the black-spotted frog, resulting in feed waste.

[0050] S5, adjusting the feeding speed of the current subtask based on the residual bait area and the accumulated water area in the area until the feeding ends at the current subtask.

[0051] Further, in this embodiment, in S5, adjusting the feeding speed of the current subtask based on the residual bait area and the water accumulation area in the region includes: S501, based on the residual bait area, calculate the residual bait amount in the current area.

[0052] It can be understood that, since the present embodiment adopts a feeding method of feeding the feed at a certain feeding moving speed and feeding speed in the feeding section between the feeding start / end positions, rather than adopting the fixed-point quantitative feeding method of the prior art, the feed will not be superimposed at a fixed point, but will be dispersed in the feeding section in the form of particles, and the single volume of the feed is very small, so the identified residual bait area can be converted into residual bait weight.

[0053] For example, based on the residual bait area, the residual bait amount in the current area can be calculated by: 1. Calculate the volume of a single feed particle: Usually the particles of black-spotted frog feed are regular in shape, so the volume can be calculated using geometric formulas. For example, for spherical particles, the volume V = (4 / 3)πr³, where r is the radius of a single feed particle.

[0054] 2. Estimate the number of particles per unit area: Count the number of particles N on a known area A', then the number of particles per unit area n = N / A'.

[0055] 3. Convert the identified residual bait area into residual bait weight: residual bait weight W = residual bait area × number of particles per unit area n × volume of a single particle V × density of feed particles ρ.

[0056] It should be noted that this is only an example of calculating the amount of residual bait in the current area based on the residual bait area. The calculation method in this example relies on the estimation of the number of particles, volume and density. There may be some errors in actual operation. More complex measurement methods can also be used to improve the accuracy of the estimation. This embodiment does not limit this.

[0057] S502, based on the amount of residual bait in the current area, a proportional control algorithm is used to adjust the feeding speed of the current subtask to obtain a first feeding speed of the current subtask.

[0058] Specifically, in this embodiment, based on the amount of residual bait in the current area, a proportional control algorithm is used to adjust the feeding speed of the current subtask to obtain the first feeding speed of the current subtask, including: Based on the amount of residual bait in the current area, the residual bait correction coefficient is calculated, and the residual bait correction coefficient is inversely proportional to the residual bait amount; Multiply the residual bait correction coefficient by the feeding speed of the current subtask, and the product obtained is the first feeding speed of the current subtask.

[0059] It can be understood that the residual bait correction coefficient is inversely proportional to the residual bait amount, which means that the more residual bait there is, the smaller the residual bait correction coefficient is, and the slower the first feeding speed of the current subtask is, thereby reducing the actual feeding amount of the current subtask.

[0060] For example, the residual bait correction factor The expression can be: in, Indicates the amount of residual bait in the current area, k indicates the field of view parameter of the feeding robot, and when the amount of residual bait is 0, the residual bait correction coefficient is 1. The field of view parameter (FOV) is a parameter that describes the range of scenes that can be observed by an imaging system (such as a camera, telescope, microscope, radar, etc.) or the human eye. It defines the maximum monitoring area that can be observed by the imaging system or the range of scenes that can be captured.

[0061] S503, based on the water accumulation area in the current area, a nonlinear control algorithm is used to adjust the first feeding speed of the current subtask to obtain a second feeding speed of the current subtask, and the second feeding speed is used as the final feeding speed of the current subtask.

[0062] Among them, the nonlinear control algorithm in this embodiment can be expressed as a proportional control algorithm with a threshold cutoff, that is, this adjustment strategy combines the characteristics of proportional control and threshold control, and therefore belongs to a type of nonlinear control algorithm. In industrial control systems, this type of control algorithm is sometimes called "two-position control" or "bang-bang control" because it has only two states: fully open (in this case, normal proportional reduction) and fully closed (feeding speed is 0). The principle of the nonlinear control algorithm is that when a certain condition is reached (that is, the water accumulation area reaches the threshold), the control action (that is, the feeding speed) will change discontinuously.

[0063] Specifically, in this embodiment, based on the water accumulation area in the current area, a nonlinear control algorithm is used to adjust the first feeding speed of the current subtask to obtain the second feeding speed of the current subtask, including: Determine whether the water accumulation area in the current area has reached the preset water accumulation threshold. If the water accumulation area in the current area is greater than or equal to the preset water accumulation threshold, adjust the second feeding speed of the current subtask to 0. If the water accumulation area in the current area is less than the preset water accumulation threshold, perform the following steps: Based on the waterlogging area of ​​the current area, the waterlogging correction coefficient is calculated, and the waterlogging correction coefficient is inversely proportional to the waterlogging area; Multiply the water accumulation correction coefficient by the first feeding speed of the current subtask, and the product obtained is the second feeding speed of the current subtask.

[0064] It is understandable that feed is easily dissolved in water, and an increase in the water accumulation area will cause more feed to dissolve in the water, which will directly affect the edibility of the feed, because the dissolved feed will not be ingested by the black-spotted frog, resulting in feed waste. For this reason, a nonlinear control strategy for water accumulation is designed in this embodiment. Once the water accumulation area reaches or exceeds the preset water accumulation threshold, no matter how much the feeding amount initially set for the current subtask is, the feeding speed is directly set to 0, that is, no feeding is performed; and when the water accumulation area does not exceed the threshold, the dissolution rate of the feed may be relatively slow, and the feeding speed of the black-spotted frog is sufficient to ingest most of the undissolved feed. However, in order to reduce the waste caused by feed dissolution, when the water accumulation area increases but does not reach the dangerous level of the preset water accumulation threshold, the feeding speed can be reduced proportionally to reduce the amount of feed put into the water, thereby reducing the impact of feed dissolution on feeding behavior.

[0065] For example, the water correction factor The expression can be: in, represents the water accumulation area in the current area, b represents the preset water accumulation threshold, k represents the field of view parameter of the feeding robot, and when the water accumulation area is 0, the water accumulation correction coefficient is 1.

[0066] The environment in the rice-frog co-culture system is changeable, including the growth of rice, the activity habits of frogs, the depth and fluidity of water, etc. These factors will affect the distribution and feeding efficiency of black-spotted frogs in rice fields. To this end, this embodiment monitors the residual bait area of ​​the feeding section and converts it into the residual bait amount, adjusts the feeding speed in real time according to the actual feeding situation of the black-spotted frog in the current area, reduces the feeding speed in the area with a large amount of residual bait, avoids overfeeding, ensures the accuracy of the feeding amount, and avoids adverse effects such as water pollution caused by excessive feed. The waterlogged area of ​​the feeding section is further monitored, and the feeding speed is adjusted in real time according to the actual waterlogging situation in the current area. The feeding is stopped or the feeding speed is proportionally reduced in sections with larger waterlogging areas to avoid the feed dissolving in water and being unable to be ingested, thereby reducing feed waste, so as to achieve accurate decision-making on the initial feeding amount in real time according to the on-site visual feedback of the feeding section, thereby dynamically adjusting the feeding strategy according to the changes in the actual feeding situation of the frogs and environmental changes, ensuring that the black-spotted frog obtains sufficient nutrition, while reducing feed waste, realizing intelligent feeding, reducing manual monitoring and operation, and contributing to the sustainable development of the rice-frog co-culture system and promoting the progress of ecological agricultural technology.

[0067] S6, repeating steps S4-S5 until all subtasks in the current feeding task are completed, returning to the preset starting point and waiting for the scheduled start time of the next feeding task.

[0068] The existing track-type automatic feeding system can usually only feed at a fixed point and in a fixed quantity according to the preset path and feeding amount, and cannot adjust the feeding amount according to the actual situation, resulting in a large amount of feed waste. However, this embodiment divides the feeding task into multiple subtasks based on the distribution of the rice field area, uses a linear programming method to generate the execution plan of each subtask, optimizes the feeding path and speed, thereby improving the efficiency of the overall feeding operation, and dynamically adjusts the feeding strategy based on the real-time identified residual bait area and water accumulation area, which can more flexibly respond to the specific conditions of different feeding sections in the rice field area, thereby better adapting to the environmental diversity of the composite agricultural model of rice-frog co-culture, while achieving accurate delivery of feed, effectively reducing feed waste, improving feeding effects, and greatly improving the intelligence of the feeding system, which helps to achieve the sustainable development of the rice-frog co-culture system.

[0069] In one embodiment of the present specification, in S3, each subtask in the current feeding operation task is executed according to the execution plan, including: Before moving to the starting feeding position of each subtask, obtain weather data in real time; Based on the weather data, it is evaluated whether the current weather conditions will have an adverse impact on the feeding operation. If the evaluation result is that it will have an adverse impact on the feeding operation, the feeding speed of the next subtask will be adjusted to 0. If the evaluation result is that it will not have an adverse impact on the feeding operation, the feeding speed of the next subtask will remain unchanged.

[0070] Among them, the next subtask is to move to the feeding starting position and start the feeding operation.

[0071] The weather conditions that may have an adverse effect on the feeding operation in this embodiment include but are not limited to the following, which are not limited in this embodiment: 1. Excessive rainfall will cause water accumulation in the feeding area, affecting the activity and feeding behavior of black-spotted frogs. Heavy rainfall may wash away feed, resulting in waste.

[0072] 2. High temperature may cause the feed to deteriorate faster and become unsuitable for feeding.

[0073] 3. Strong winds may scatter the feed and affect the accuracy of feeding.

[0074] 4. Black-spotted frogs may be unwilling to move under strong light, thus affecting the feeding effect.

[0075] 5. Changes in temperature and humidity caused by seasonal changes may affect the growth cycle and feeding habits of black-spotted frogs.

[0076] This embodiment is further linked with weather data. According to the current weather conditions, the feeding speed of the current subtask is appropriately adjusted to adjust the feeding amount, thereby optimizing the feeding effect, ensuring the healthy growth of black-spotted frogs and the effective use of feed, reducing feed waste and environmental problems caused by weather reasons, and further improving the intelligence of the feeding strategy.

[0077] In the existing track-type automatic feeding system, the positioning data of the feeding robot is crucial to ensure the accuracy and efficiency of feeding. However, complex positioning methods usually require more precise sensors and more complex hardware equipment, which increases the installation and maintenance costs of the feeding system and also increases the complexity of the system, which may reduce its reliability and affect the normal feeding operation of the feeding robot. To this end, in one embodiment of the present specification, the following steps are also included: Select several points on the moving path, set an RFID tag for each point and calibrate the position of each RFID tag; In the process of executing each subtask, when moving to the position of each RFID tag, the current positioning data is corrected to the position of the corresponding RFID tag.

[0078] Among them, RFID (Radio-Frequency Identification) tags, that is, wireless radio frequency identification tags, are a technology that uses radio waves to achieve data communication in order to identify specific targets and read and write related data.

[0079] Specifically, the implementation of this embodiment is as follows: First, the selection of several points on the moving path can be fixed facility points such as a feeding tower, a charging device, or columns arranged at equal intervals along the track, which is not limited in this embodiment; Secondly, the track distance of each RFID tag relative to the positioning origin is measured and calibrated along the track trajectory as the position of each RFID tag; Next, when the feeding robot moves, the central control system obtains the mileage encoder information of the track running wheel at a preset sampling interval, and calculates the rolling mileage of the track running wheel through the mileage encoder information to determine the positioning data of the feeding robot on the track. Among them, the mileage encoder (also called incremental encoder or rotary encoder) is a low-cost sensor used to measure the angle or position of the rotating shaft, and is a commonly used position feedback device; Finally, whenever the feeding robot moves to the position of each RFID tag, the current positioning data of the feeding robot is corrected to the position of the corresponding RFID tag after detection by the RFID reader.

[0080] It can be understood that this embodiment combines the scene characteristics of the ground track and proposes a positioning correction method based on the fusion of the mileage encoder and the RFID tag. While effectively eliminating the cumulative error of the mileage calculation of the mileage encoder, it realizes low-cost precise positioning navigation, providing a good positioning data foundation for the accuracy and efficiency of feeding.

[0081] Please refer to the attached Figure 2 , Figure 2 This is a schematic diagram of the structure of an intelligent feeding control system for rice-frog co-culture provided in one embodiment of this specification.

[0082] like Figure 2 As shown, the intelligent feeding control system for rice-frog co-culture may include at least a feeding robot 1, a ground track device 2, a feeding tower 3, a charging device 4 and a cloud controller ( Figure 2 (not shown), the feeding robot 1 is located on the ground track device 2 and moves along the track of the ground track device 2. The feeding robot 1, the feeding tower 3 and the charging device 4 are all connected to the cloud controller for communication. The feeding robot 1 is used to execute an intelligent feeding control method for rice-frog co-culture as provided in the above-mentioned embodiment.

[0083] In order to realize an intelligent feeding control method for rice-frog co-culture as provided in the aforementioned embodiment, the feeding robot 1 in this embodiment is correspondingly provided with hardware devices such as a visual recognition module and a mileage encoder, which will not be described in detail in this embodiment.

[0084] It can be understood that the technical concept of the feeding robot of the intelligent feeding control system for rice-frog co-culture provided in this embodiment is similar to the technical concept of the intelligent feeding control method for rice-frog co-culture provided in the previous embodiment, and this embodiment will not be repeated here.

[0085] The walking control operation, feeding control operation, feeding control operation between the feeding robot 1 and the feeding tower 3, and charging control operation between the feeding robot 1 and the charging device 4 in the intelligent feeding control system for rice-frog co-culture provided in this embodiment are similar to the control operations of the existing track-type automatic feeding system, and will not be repeated in this embodiment.

[0086] Another embodiment of the present specification provides a computer-readable storage medium, which stores instructions, and when the instructions are executed on a computer or a processor, the computer or the processor executes one or more steps in the above-mentioned intelligent feeding control method for rice-frog co-culture. If the components of the above-mentioned electronic device are implemented in the form of software functional units and predicted or used as independent downstream tasks, they can be stored in the computer-readable storage medium.

[0087] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of this specification is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state drive (SSD)).

[0088] The above description is only the preferred embodiment disclosed in this application and the description of the technical principle used. Those skilled in the art should understand that the scope of protection involved in this disclosure is not limited to the technical solution formed by the specific combination of the above technical features, but also should cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this disclosure (but not limited to) to form a technical solution.

[0089] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

Claims

1. An intelligent feeding control method for rice-frog co-culture, characterized in that: The following steps are involved: S1, obtaining a feeding operation task table, wherein the feeding operation task table includes a plurality of feeding operation tasks and a scheduled start time for each feeding operation task; S2, dividing each feeding task into several subtasks based on the rice field area, and the parameters of each subtask include a feeding starting position, a feeding ending position and a feeding speed; S3, when the scheduled start time is reached, a linear programming method is used to generate an execution plan for each subtask in the current feeding operation task based on the parameters of each subtask, and each subtask in the current feeding operation task is executed according to the execution plan; S4, in the process of executing each subtask, when moving to the feeding starting position of the subtask, real-time identification of the residual bait area and the accumulated water area in the corresponding area of ​​the current subtask; S5, adjusting the feeding speed of the current subtask based on the residual bait area and the accumulated water area in the area until the feeding end position of the current subtask is moved; S6, repeating steps S4-S5 until all subtasks in the current feeding task are completed, returning to the preset starting point and waiting for the scheduled start time of the next feeding task.

2. The intelligent feeding control method for rice-frog co-cultivation as claimed in claim 1, characterized in that: In S3, a linear programming method is used to generate an execution plan for each subtask in the current feeding task based on the parameters of each subtask, including: Defining decision variables of the subtask, wherein the decision variables of the subtask include the required number of refills and the additional walking mileage for each refill; Based on the parameters of each subtask, determine the constraints of the decision variables; Taking minimizing the sum of the additional walking distance required for the current feeding task as the objective function, a linear programming model is constructed based on the decision variables, objective functions and constraints of the subtasks. Solve the linear programming model based on the parameters of each subtask to obtain a set of optimal decision variable values; Based on the set of optimal decision variable values, an execution plan for each subtask in the current feeding operation task is generated.

3. The intelligent feeding control method for rice-frog co-cultivation as claimed in claim 2, characterized in that: The parameters of the subtasks also include the feeding movement speed. Based on the parameters of each subtask, the constraint conditions of the decision variables are determined, including: Based on the feeding starting position, feeding ending position and feeding moving speed of each subtask, the feeding time of each subtask is calculated; Based on the feeding time and feeding speed of each subtask, the feeding amount of each subtask is calculated; Based on the feeding amount of each subtask, the total feeding amount of the current feeding task is calculated; Based on the total feeding amount of the current feeding task, determine the upper limit of the required feeding times.

4. The intelligent feeding control method for rice-frog co-cultivation as claimed in claim 1, characterized in that: In S5, the feeding speed of the current subtask is adjusted based on the remaining bait area and the accumulated water area in the area, including: Based on the residual bait area, the residual bait amount in the current area is calculated; Based on the amount of residual bait in the current area, a proportional control algorithm is used to adjust the feeding speed of the current subtask to obtain the first feeding speed of the current subtask; Based on the water accumulation area in the current area, a nonlinear control algorithm is used to adjust the first feeding speed of the current subtask to obtain the second feeding speed of the current subtask, and the second feeding speed is used as the final feeding speed of the current subtask.

5. The intelligent feeding control method for rice-frog co-cultivation as claimed in claim 4, characterized in that: Based on the amount of residual bait in the current area, the proportional control algorithm is used to adjust the feeding speed of the current subtask to obtain the first feeding speed of the current subtask, including: Based on the amount of residual bait in the current area, a residual bait correction coefficient is calculated, and the residual bait correction coefficient is inversely proportional to the residual bait amount; Multiply the residual bait correction coefficient by the feeding speed of the current subtask, and the product obtained is the first feeding speed of the current subtask.

6. The intelligent feeding control method for rice-frog co-culture according to claim 4, characterized in that: Based on the water accumulation area in the current area, a nonlinear control algorithm is used to adjust the first feeding speed of the current subtask to obtain the second feeding speed of the current subtask, including: Determine whether the water accumulation area in the current area has reached the preset water accumulation threshold. If the water accumulation area in the current area is greater than or equal to the preset water accumulation threshold, adjust the second feeding speed of the current subtask to 0. If the water accumulation area in the current area is less than the preset water accumulation threshold, perform the following steps: Based on the waterlogging area of ​​the current area, a waterlogging correction coefficient is calculated, where the waterlogging correction coefficient is inversely proportional to the waterlogging area; Multiply the water accumulation correction coefficient by the first feeding speed of the current subtask, and the product obtained is the second feeding speed of the current subtask.

7. The intelligent feeding control method for rice-frog co-culture according to claim 1, characterized in that: In S3, each subtask in the current feeding task is executed according to the execution plan, including: Before moving to the starting feeding position of each subtask, obtain weather data in real time; Based on the weather data, it is evaluated whether the current weather conditions will have an adverse impact on the feeding operation. If the evaluation result is that it will have an adverse impact on the feeding operation, the feeding speed of the next subtask will be adjusted to 0. If the evaluation result is that it will not have an adverse impact on the feeding operation, the feeding speed of the next subtask will remain unchanged.

8. The intelligent feeding control method for rice-frog co-culture according to claim 1, characterized in that: The following steps are also included: Select several points on the moving path, set an RFID tag for each point and calibrate the position of each RFID tag; In the process of executing each subtask, when moving to the position of each RFID tag, the current positioning data is corrected to the position of the corresponding RFID tag.

9. An intelligent feeding control system for rice-frog co-culture, characterized in that: The invention comprises a feeding robot, a ground track device, a feeding tower, a charging device and a cloud controller, wherein the feeding robot is located on the ground track device and moves along the track of the ground track device, the feeding robot, the feeding tower and the charging device are all communicatively connected with the cloud controller, and the feeding robot is used to execute the intelligent feeding control method for rice-frog co-culture as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, an intelligent feeding control method for rice-frog co-culture as described in any one of claims 1 to 8 is implemented.

Images