Unmanned precise feeding method, device and system for farm, electronic equipment and storage medium
By using unmanned precision feeding methods, combined with robotic systems and reinforcement learning algorithms, the feed formulation and feeding process are optimized, solving the problems of feed waste and high carbon emissions in traditional farming. This achieves precise and automated feeding management, improving farming efficiency and economic benefits.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- INTELLIGENT EQUIPMENT RESEARCH CENTER BEIJING ACADEMY OF AGRICULTURE AND FORESTRY SCIENCES
- Filing Date
- 2025-12-26
- Publication Date
- 2026-05-12
AI Technical Summary
Traditional livestock farming suffers from problems such as feed waste, nutritional imbalance, and high carbon emissions, and existing technologies struggle to achieve precise and automated feeding management.
By employing unmanned precision feeding methods, and combining feed-spreading, feed-pushing, feed-replenishing, and feed-collecting robots with reinforcement learning algorithms, feed formulations are optimized based on the current farm status and historical feeding information, thereby achieving automated precision feeding, reducing carbon emissions and nutritional deviations.
It improved feed utilization, reduced feeding costs, ensured livestock health and production performance, reduced human intervention, and promoted sustainable development.
Smart Images
Figure CN122004168A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of livestock feeding technology, and in particular to a method, apparatus, system, electronic device and storage medium for unmanned precision feeding in livestock farms. Background Technology
[0002] Traditional livestock farming relies on manual experience for feeding management, leading to problems such as feed waste, nutritional imbalances, and high carbon emissions (e.g., methane emissions). For example, the theoretical feeding formula designed by a nutritionist may deviate from the theoretical formula when processed and mixed using a Total Mixed Ration (TMR) system due to issues such as feed addition accuracy and mixing uniformity. Furthermore, the portion ultimately consumed by the livestock may deviate from the supplied ration due to individual animal picky eating and competition for food, further exacerbating nutritional imbalances and resource waste. Extensive feeding results in low resource utilization, overfeeding exacerbates environmental stress (e.g., nitrogen and phosphorus pollution), while underfeeding negatively impacts animal health and production performance. Summary of the Invention
[0003] This invention provides a method, device, system, electronic device, and storage medium for unmanned precision feeding in livestock farms, in order to overcome the deficiencies existing in related technologies.
[0004] This invention provides a method for unmanned precision feeding in livestock farms, comprising: Obtain the current status of the farm; the current farm status includes the current feed formula, current grouping index, and individual carbon emission data of the livestock; Based on the current feed formulation, the current grouping index, and the individual carbon emission data, the current feed is produced by mixing the feed and, based on the historical feeding information collected during the previous feeding of the livestock, the current feed is delivered to the feeding area of the livestock by the feeding operation system to obtain the current feeding information during the current feeding process. Based on the current feeding information and the current feeding needs of the livestock, a reinforcement learning algorithm is used to update the current feed formula with the goal of minimizing the carbon emission state value function under the current farm condition, thereby generating the latest feed formula for the livestock. Update the current farm status based on the latest feed formulation.
[0005] According to the present invention, an unmanned precision feeding method for a farm is provided, wherein the feeding system includes a feed-spreading robot; The method of using a feeding system to deliver the current feed to the livestock's feeding area based on historical feeding information collected during the previous feeding process includes: Obtain the information about the livestock pens where the livestock are located; Based on the pen information and the historical feeding information, the feeding robot is controlled to spread the current feed onto the feeding area.
[0006] According to the present invention, an unmanned precision feeding method for a farm is provided, wherein the feeding operation system further includes a feeding robot, and the feeding robot is equipped with a visual sensing unit. Based on the pen information and the historical feeding information, the feeding robot is controlled to spread the current feed to the feeding area, and then the process further includes: The feeding robot is controlled to push feed at regular intervals so that the feed is concentrated in the feeding area; The step of obtaining current feeding information during the current feeding process includes: During the feeding process of the feeding robot, the vision sensing unit is controlled to collect images of the feeding area; The image of the feeding area is received and identified to obtain the current feeding information.
[0007] According to the present invention, an unmanned precision feeding method for a farm is provided, wherein the feeding operation system further includes a feeding robot, and the current feeding information includes the feeding location of the livestock and the amount of feed remaining in the feeding area; The method of controlling the feeding robot to push feed at regular intervals to concentrate the feed in the feeding area further includes: Based on the feeding location and the amount of feed remaining, the feeding robot is controlled to replenish the feed.
[0008] According to the present invention, an unmanned precision feeding method for a farm is provided, wherein the feeding system further includes a receiving robot; Based on the pen information and the historical feeding information, the feeding robot is controlled to spread the current feed to the feeding area, and then the process further includes: After a preset time period, the receiving robot is controlled to collect the remaining feed in the feeding area and collect information on the remaining feed. Receive the remaining feed information, and determine the current feeding information and the next feeding amount based on the remaining feed information.
[0009] According to the present invention, an unmanned precision feeding method for a livestock farm is provided, wherein the step of mixing feed to produce the current feed based on the current feed formula, the current grouping index, and the individual carbon emission data includes: Based on the current feed formulation, the current grouping index, and the individual carbon emission data, determine the demand for each feed component and the water demand. Based on the demand, the feed bins in the mixing system are controlled to supply the corresponding weights of each feed component to the mixing equipment, and the water valves are controlled to supply water to the mixing equipment based on the water demand. Based on the detection equipment installed on the mixing equipment, the uniformity, particle size and freshness of the mixture in the mixing equipment are detected and evaluated online in real time. If the uniformity, particle size, and freshness all reach the preset values, the mixing equipment is controlled to stop, and the current feed is obtained.
[0010] The unmanned precision feeding method for farms provided by the present invention further includes: Receive the first operation information from the mixing operation system and the second operation information fed back by the feeding operation system; Based on the first operation information, the mixing operation of the mixing system in the next mixing process is optimized, and based on the second operation information, the feeding operation of the feeding system in the next feeding process is optimized.
[0011] The present invention also provides an unmanned precision feeding device for farms, comprising: The status acquisition module is used to acquire the current status of the farm; the current farm status includes the current feed formula, current grouping index, and individual carbon emission data of the livestock. The feeding operation module is used to mix and produce the current feed based on the current feed formula, the current grouping index and the individual carbon emission data, and to use the feeding operation system to deliver the current feed to the feeding area where the livestock are located based on the historical feeding information collected during the previous feeding process of the livestock, and to obtain the current feeding information during the current feeding process. The formula update module is used to update the current feed formula based on the current feeding information and the current feeding requirements of the livestock, using a reinforcement learning algorithm with the goal of minimizing the carbon emission state value function under the current farm conditions, and to generate the latest feed formula for the livestock. The status update module is used to update the current farm status based on the latest feed formula.
[0012] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the unmanned precision feeding method for farms as described above.
[0013] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the unmanned precision feeding method for farms as described above.
[0014] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the unmanned precision feeding method for livestock farms as described above.
[0015] This invention provides a method, apparatus, system, electronic equipment, and storage medium for unmanned precision feeding in livestock farms. This method incorporates historical feeding information, reducing the discrepancy between the current feed formulation and actual livestock intake, improving feed utilization, reducing carbon emissions, and lowering feeding costs. The method also considers the current feeding needs of livestock, enabling precise and personalized feeding management. By continuously optimizing the current feed formulation, it further ensures a high degree of consistency between the latest feed formulation and actual livestock intake, improving livestock production performance, avoiding feed waste, maximizing farming profits, and protecting livestock health and productivity. Furthermore, the method utilizes a feeding system to achieve automated feeding, significantly reducing manual intervention and improving the efficiency and accuracy of feeding management, providing strong support for large-scale livestock farming. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in this invention or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 This is a flowchart illustrating the unmanned precision feeding method for livestock farms provided by the present invention.
[0018] Figure 2 This is a schematic diagram of the unmanned precision feeding device for farms provided by the present invention.
[0019] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation
[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.
[0021] Figure 1This is a flowchart illustrating an unmanned precision feeding method for livestock farms provided in an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes: S1, Obtain the current status of the farm; the current status of the farm includes the current feed formula, current grouping index, and individual carbon emission data of the livestock; S2, based on the current feed formula, the current grouping index and the individual carbon emission data, mix the feed to produce the current feed, and based on the historical feeding information collected during the previous feeding process of the livestock, apply the feeding operation system to deliver the current feed to the feeding area where the livestock are located for feeding, and obtain the current feeding information during the current feeding process; S3. Based on the current feeding information and the current feeding needs of the livestock, a reinforcement learning algorithm is used to update the current feed formula with the goal of minimizing the carbon emission state value function under the current farm condition, and to generate the latest feed formula for the livestock. S4. Update the current farm status based on the latest feed formula.
[0022] Specifically, the unmanned precision feeding method for farms provided in this embodiment of the invention is executed by an unmanned precision feeding device for farms. This device can be configured within a central control artificial intelligence (AI) system. The central control AI system can run on a computer, which can be a local computer or a cloud computer. The local computer can be a computer, tablet, etc., without specific limitations here.
[0023] First, execute step S1 to obtain the current farm status. The current farm status includes the current feed formula, current grouping indicators, and individual carbon emission data for the livestock. The current farm status refers to the farm status at the current moment, representing the sum of all key conditions of the farm at that moment. This includes not only the current feed formula, current grouping indicators, and individual carbon emission data for each livestock pen, but also the pen status and livestock numbers. The current feed formula refers to the feed formula used at the current moment, which may include the various feed components and their proportions. Each feed component may include roughage, concentrate, and may also include medications.
[0024] Current grouping indicators refer to the current status of each animal in terms of its grouping characteristics. These indicators can be recorded by intelligent animal grouping measurement equipment during the grouping process. Current grouping indicators may include basic individual indicators such as weight, physical signs, and physiological parameters, as well as health and reproductive status. Health status may include disease incidence and immunity, while reproductive status may include pregnancy rate and estrus cycle.
[0025] Individual carbon emission data refers to the carbon emissions of each animal at the current moment, which can be determined by livestock individual carbon emission measuring equipment installed on the farm.
[0026] The status of livestock pens can include available pen area or capacity. The livestock status and quantity can include the number of animals at each growth stage and of each sex. In addition, the current farm status can also include price information such as the selling price per animal.
[0027] Then, step S2 is executed, utilizing the current feed formulation, current grouping indicators, and individual carbon emission data, to produce the current feed using a mixing system. Here, the mixing system may include silos for each feed component, conveyor belts, a reservoir, water pipes, water pumps, and mixing equipment. Silos may include roughage silos and concentrate silos. Valves may be installed at the outlets of each silo and the reservoir. By controlling the opening and closing status of the valves at the silo outlets, the feed components in each silo can be conveyed to the mixing silo of the mixing equipment via the conveyor belt. By controlling the opening and closing status of the valves at the reservoir outlets, water can be drawn from the reservoir by the pump and conveyed to the mixing silo via the water pipes. The mixing equipment may be a Total Mixed Ration (TMR) system. The current feed may be a TMR feed.
[0028] The mixing silo can be equipped with a weight sensor. By collecting the current weight value of the mixing silo by the weight sensor, the supply of each feed component and water can be strictly controlled.
[0029] Since feeding information can be collected during each feeding process, including the animal's physiological condition during feeding, feeding location, amount of feed remaining in the feeding area, and the weight and composition of leftover feed during collection, a feeding system can be used to deliver the current feed to the feeding area of the animal's pen. This ensures that animals in the same pen consume consistent feed and provides current feeding information for the current feeding process.
[0030] Here, the feeding system can include a spreading robot, a pushing robot, a replenishing robot, and a collecting robot. The spreading robot receives the current feed from the mixing bin, transports it to the livestock's feeding area, and spreads it onto the feeding area. Since livestock may touch feed in inaccessible areas during feeding, the pushing robot can frequently push feed outside the feeding area to the feeding area. The replenishing robot can replenish feed for the livestock in a timely manner during the feeding period. The collecting robot can collect any remaining feed after the feeding period ends.
[0031] Then, step S3 is executed, which uses the current feeding information and the current feeding needs of the livestock to update the current feed formula by using a reinforcement learning algorithm with the goal of minimizing the carbon emission state value function under the current farm conditions, and generates the latest feed formula for the livestock.
[0032] Here, the current feeding needs of livestock can be divided according to factors such as growth stage, feed composition, feeding method, classification, and management. Growth stage can include pregnancy, lactation, and fattening; feed composition can include concentrate, total mixed ration (TMR), and single feed; feeding method can include pen feeding and free feeding; group classification can include weight, signs, and body condition; and management can include immunization, hoof trimming, and reproduction.
[0033] The state-value function for carbon emissions can be expressed as: ; Where 's' represents the current state of the farm, and 'a' represents the actions the farmer can take given this state, including selling a specified number of livestock that meet certain conditions; feeding different groups with different feed formulas; adjusting feed amounts; increasing / decreasing nutritional supplements; treating or administering preventative medication to which groups; isolating livestock; purchasing sufficient feed / raw materials; when to deploy robots; and the power level used by the robots. Under 's', 'a' might include: selling 20 head of livestock weighing 500 kg or more; providing additional water to all lactating livestock; purchasing 30 tons of straw; or maintaining the status quo.
[0034] Given a value 's', the probability distribution of choosing action 'a' determines the tendencies to make a move under certain circumstances. The goal is to learn and select the probability distribution of actions that, in the long run, yield the highest expected total return. For example, under 's' (beef price 20 yuan / kg, beef cattle 500kg), the optimized strategy might be allocated as follows: That is, the probability of immediately selling a small portion. This refers to the probability of choosing to raise the animal to a larger weight or to wait for a higher price.
[0035] The action-value function means that under condition s, 'a' is executed first, and then the policy is followed. The expected cumulative return that can be obtained by continuing down this path is used to measure the long-term value of executing option 'a' under condition 's'. For example, under condition 's', if option 'a' is executed, such as reducing the concentrate ratio, and thereafter all decisions are made according to a predetermined optimal decision rule... To manage a livestock farm, we project the expected total profit from now until the future. We consider the immediate benefits of 'a' (e.g., income from selling cattle) and all subsequent benefits / costs resulting from future state changes (e.g., reduced carbon emissions, decreased meat / milk production, increased capital).
[0036] Let be the carbon emission state value function, representing the farm's state under condition s, according to a predetermined optimal decision rule. To operate, it is expected to generate the total expected value of individual carbon emissions from all livestock in the farm over the period from now until the future.
[0037] By evaluating the long-term value of each possible action 'a' under each condition 's', the optimal course of action under condition 's' is determined. Through continuous learning and optimization, the system can consistently select actions that bring the minimum total expected value in the long run under various complex and dynamic farming environments, thereby minimizing carbon emissions from livestock farming under low-carbon conditions. This leads to the current feed formulation.
[0038] Finally, step S4 is executed, which uses the latest feed formula to update the current farm status. Steps S1-S3 are executed iteratively, which not only uses the latest feed formula to generate the current feed and feeds the livestock with the current feed, but also continues to update the latest feed formula to ensure that the latest feed formula can always achieve the farm's highest expected total revenue.
[0039] The unmanned precision feeding method for livestock farms provided in this embodiment of the invention first obtains the current farm status, which includes the current feed formula, current grouping indicators, and individual carbon emission data. Then, based on the current feed formula, current grouping indicators, and individual carbon emission data, the current feed is mixed and produced. Using historical feeding information collected from the previous feeding process, a feeding system is applied to deliver the current feed to the livestock's feeding area, obtaining current feeding information during the current feeding process. Subsequently, based on the current feeding information and the livestock's current feeding needs, a reinforcement learning algorithm is used to update the current feed formula with the objective of minimizing the carbon emission state value function under the current farm status, generating a new feed formula for the livestock. Finally, the current farm status is updated based on the new feed formula. This method, combined with historical feeding information, can reduce the deviation between the current feed formula and the actual intake by the livestock, improve feed utilization, reduce carbon emissions, and lower feeding costs. This method also considers the current feeding needs of livestock, enabling precise and personalized feeding management. By continuously optimizing the current feed formula, it further ensures a high degree of consistency between the latest feed formula and the actual intake of livestock, improving livestock production performance, avoiding feed waste, maximizing breeding profits, and protecting livestock health and productivity. Moreover, the method utilizes a feeding system to achieve automated feeding, significantly reducing manual intervention and improving the efficiency and accuracy of feeding management, providing strong support for large-scale livestock farming.
[0040] Based on the above embodiments, the feeding operation system includes a spreading robot; The method of using a feeding system to deliver the current feed to the livestock's feeding area based on historical feeding information collected during the previous feeding process includes: Obtain the information about the livestock pens where the livestock are located; Based on the pen information and the historical feeding information, the feeding robot is controlled to spread the current feed onto the feeding area.
[0041] Specifically, the feeding operation system may include unmanned, new energy-powered feeding robots. In the process of feeding the livestock by distributing the current feed to the feeding area, the system can first obtain the information of the livestock pen, which may include the location information of the pen and the location information of each feeding area in the pen.
[0042] Subsequently, by using the pen information and historical feeding information, the walking route of the feeding robot can be planned, and the walking speed and feeding amount can be determined. Then, based on the walking route, walking speed and feeding amount, the feeding robot can be controlled to spread the current feed onto the feeding area.
[0043] In this embodiment of the invention, by combining pen information and historical feeding information, the feeding robot can be controlled to perform variable feeding to precisely control the amount of feed and reduce feeding costs.
[0044] Based on the above embodiments, the feeding operation system further includes a pushing robot, which is equipped with a vision sensing unit; Based on the pen information and the historical feeding information, the feeding robot is controlled to spread the current feed to the feeding area, and then the process further includes: The feeding robot is controlled to push the feed at regular intervals after the feeding robot spreads the feed, so that the feed is concentrated on the feeding area; The step of obtaining current feeding information during the current feeding process includes: During the feeding process of the feeding robot, the vision sensing unit is controlled to collect images of the feeding area; The system receives images of the feeding area and identifies the images of the livestock to obtain the current feeding information.
[0045] Specifically, the feeding system also includes unmanned, new energy-powered feeding robots equipped with visual sensing units such as cameras. After controlling the spreading robot to spread the current feed into the feeding area, the feeding robot can also be controlled to push the feed at regular intervals to concentrate the feed in the feeding area.
[0046] During the feeding process, the visual sensing unit can be controlled to collect images of the feeding area. The visual sensing unit can then send these images to the execution unit, which receives and identifies the images to obtain current feeding information. This information can include the livestock's physiological condition, feeding location, and the amount of feed remaining in the feeding area during the current feeding process. Based on the livestock's physiological condition, the execution unit can screen for diseases and abnormal animals, facilitating disease prevention. Furthermore, based on the feeding location and the amount of feed remaining in the feeding area, the execution unit can assist in controlling the amount of feed added or distributed during the next feeding cycle. For example, in areas with high feed consumption, the movement speed of the adding or distributing robot can be slowed down to increase the amount of feed distributed.
[0047] Based on the above embodiments, the feeding operation system also includes a feeding robot, and the current feeding information includes the livestock's feeding location and the amount of feed remaining in the feeding area; The method of controlling the feeding robot to push feed at regular intervals to concentrate the feed in the feeding area further includes: Based on the feeding location and the amount of feed remaining, the feeding robot is controlled to replenish the feed.
[0048] Specifically, the feeding system also includes unmanned, new energy-powered feeding robots. Current feeding information includes the livestock's feeding location and the amount of feed remaining in the feeding area. After the pusher robot delivers feed at set times, the feeding robot can be controlled to precisely replenish the remaining feed based on the livestock's feeding location and the amount of feed remaining, allowing for more accurate feeding. It is understandable that the feeding robots can also be used to supplement concentrated feed or carbon emission inhibitors.
[0049] Based on the above embodiments, the feeding operation system also includes a receiving robot; Based on the pen information and the historical feeding information, the feeding robot is controlled to spread the current feed to the feeding area, and then the process further includes: After a preset time period, the receiving robot is controlled to collect the remaining feed in the feeding area and collect information on the remaining feed. Receive the remaining feed information, and determine the current feeding information and the next feeding amount based on the remaining feed information.
[0050] Specifically, the feeding system also includes unmanned, new energy-powered receiving robots. After the spreading robot distributes the feed to the feeding area, the receiving robot can be controlled to collect the remaining feed after a preset time period, collect information on the remaining feed, and feed this information back to the executing entity. The remaining feed information may include the weight and composition of the remaining feed.
[0051] The implementing entity receives information on remaining feed and can use this information to determine the current feeding schedule and the amount to be fed next time. For example, the implementing entity can reduce the amount of feed scattered among livestock during the next feeding to avoid leaving too much feed uneaten. In particular, the feed collection process focuses on precise control between pens and on supplementary feeding, while the detection of the feed pushing process is for precise control within the pens. The two complement each other and jointly affect the feeding process.
[0052] Based on the above embodiments, the step of mixing feed to produce the current feed based on the current feed formulation, the current grouping index, and the individual carbon emission data includes: Based on the current feed formulation, the current grouping index, and the individual carbon emission data, determine the demand for each feed component and the water demand. Based on the demand, the feed bins in the mixing system are controlled to supply the corresponding weights of each feed component to the mixing equipment, and the water valves are controlled to supply water to the mixing equipment based on the water demand. Based on the detection equipment installed on the mixing equipment, the uniformity, particle size and freshness of the mixture in the mixing equipment are detected and evaluated online in real time. If the uniformity, particle size, and freshness all reach the preset values, the mixing equipment is controlled to stop, and the current feed is obtained.
[0053] Specifically, in the process of mixing feed to generate the current feed, the current feed formula, current grouping index and individual carbon emission data can be used to determine the demand for each feed component and the water demand. Then, the demand for each feed component is used to control the supply of the corresponding weight of each feed component from each silo in the mixing system to the mixing silo in the mixing equipment, and the water demand is used to control the water valve of the reservoir to supply water to the mixing silo in the mixing equipment.
[0054] The mixing equipment can be equipped with a detection device, which can be an online detection device for near-infrared / visual / terahertz spectroscopy / images. Using this detection device, the uniformity, particle size and freshness of the mixture in the mixing bin of the mixing equipment can be evaluated.
[0055] If the uniformity, particle size, and freshness of the mixture in the mixing bin of the mixing equipment all reach the preset values, the mixing equipment can be stopped, and the mixture at this point can be used as the current feed for feeding. The preset values can be set as needed; no specific limitations are made here.
[0056] In this embodiment of the invention, by evaluating the uniformity of the mixture in the mixing equipment, the uniformity, particle size, and freshness of the current feed can be guaranteed.
[0057] Based on the above embodiments, it also includes: Receive the first operation information from the mixing operation system and the second operation information fed back by the feeding operation system; Based on the first operation information, the mixing operation of the mixing system in the next mixing process is optimized, and based on the second operation information, the feeding operation of the feeding system in the next feeding process is optimized.
[0058] Specifically, in this embodiment of the invention, the executing entity can also receive the first operation information from the mixing operation system and the second operation information from the feeding operation system in real time.
[0059] The first operational information may include the working status of each device in the mixing system and various operational data collected during the operation. The second operational information may include the working status of each device in the feeding system and various operational data collected during the operation.
[0060] Subsequently, the mixing operation of the mixing system can be optimized in the next mixing process using the first operation information, and the feeding operation of the feeding system can be optimized in the next feeding process using the second operation information.
[0061] In addition, the pens can be equipped with temperature and humidity sensors to detect the temperature and humidity of the livestock's environment in real time, so as to optimize the environment in a timely manner and enable the livestock to grow healthier in the environment.
[0062] In summary, the unmanned precision feeding method for farms provided by this invention includes six stages: feed preparation, feed mixing, feed spreading, feed pushing, feed replenishment, and feed collection. The specific working process of each stage includes: The batching process is jointly controlled by the valves, conveyor belts, water pipes, and water pumps at the on-site coarse material silos, fine material silos, and reservoir outlet. Mixing process: The required feed ingredients and water are transported to the mixing bin of the mixing equipment via conveyor belt and water pipe. The main control unit in the mixing bin mixes and stirs the mixture, and the uniformity of the mixture in the mixing bin is detected in real time by the detection equipment. When the uniformity reaches the preset value, the current feed is obtained. Feeding process: Control the feeding robot to move below the mixing conveyor belt, open the outlet of the mixing bin, start the mixing conveyor belt, and transport the current feed into the feeding robot's bin; after the transport is completed, the feeding robot moves the current feed to the feeding area of the pen to spread the feed. Material pushing process: After the material is sprinkled, it will be pushed remotely at regular intervals; Material replenishment process: Material will be replenished remotely at regular intervals after the material is pushed out; Material collection stage: When the preset time period is reached, the remaining materials are recycled; After completing the day's workflow, the implementing entity will summarize and analyze all kinds of data from the day to optimize and improve every detail of the next feeding process, including updating the current feed formula, the mixing system, and the control strategies of each piece of equipment in the feeding system.
[0063] The unmanned precision feeding method for farms provided in this embodiment of the invention has the following beneficial effects: 1) Improve feed utilization Through precise, up-to-date feed formulations and dynamic adjustment mechanisms, the amount and frequency of feed can be accurately controlled according to the current feeding needs of livestock, reducing feed waste, improving feed utilization, maintaining feeding consistency, and lowering feeding costs.
[0064] 2) Improve livestock health and productivity By employing personalized feeding strategies, we ensure that each herd receives the most suitable nutritional support at different stages of its growth, thereby promoting healthy growth and improving production performance.
[0065] 3) Reduce manual intervention and management costs Through highly automated operations, the need for manual intervention is greatly reduced, labor costs are lowered, management accuracy and efficiency are improved, and errors caused by human operation are reduced.
[0066] 4) Promote the sustainable development of animal husbandry Optimizing feed formulation and reducing feed waste helps reduce the environmental burden of livestock carbon emissions, ensures the rational use of feed resources, and contributes to environmental protection and sustainable development.
[0067] 5) Enhance decision support and optimize management Real-time data collection and analysis provide scientific decision support for feeding management. This enables managers to adjust feeding strategies in a timely manner based on actual conditions, optimize management processes, and improve overall production efficiency.
[0068] 6) Achieve refined management of the entire feeding cycle using AI. This method covers the entire feeding cycle of livestock from growth to production, achieving full-cycle management of livestock through real-time AI monitoring and optimization at each stage. This ensures that each animal receives the most appropriate management at every stage, improving the overall productivity and health of the herd.
[0069] 7) Improve economic efficiency By reducing feed waste, the economic efficiency of livestock farming is ultimately improved, enabling farmers to achieve higher output at lower costs, thereby increasing overall profit margins.
[0070] like Figure 2 As shown, based on the above embodiments, this embodiment of the invention provides an unmanned precision feeding device for livestock farms, comprising: The status acquisition module 21 is used to acquire the current status of the farm; the current farm status includes the current feed formula, current grouping index and individual carbon emission data of the livestock; The feeding operation module 22 is used to mix and produce the current feed based on the current feed formula, the current grouping index and the individual carbon emission data, and to use the feeding operation system to deliver the current feed to the feeding area where the livestock are located based on the historical feeding information collected during the previous feeding process of the livestock, and to obtain the current feeding information during the current feeding process. The formula update module 23 is used to update the current feed formula based on the current feeding information and the current feeding needs of the livestock, using a reinforcement learning algorithm with the goal of minimizing the carbon emission state value function under the current farm conditions, and to generate the latest feed formula for the livestock. The status update module 24 is used to update the current farm status based on the latest feed formula.
[0071] Based on the above embodiments, the unmanned precision feeding device for farms provided in this embodiment of the invention includes a feeding operation system comprising a feed-spreading robot; The feeding module is specifically used for: Obtain the information about the livestock pens where the livestock are located; Based on the pen information and the historical feeding information, the feeding robot is controlled to spread the current feed onto the feeding area.
[0072] Based on the above embodiments, the unmanned precision feeding device for farms provided in this embodiment of the invention further includes a feeding operation system that includes a feeding robot equipped with a visual sensing unit; the feeding operation module is also specifically used for: The feeding robot is controlled to push feed at regular intervals so that the feed is concentrated in the feeding area; The step of obtaining current feeding information during the current feeding process includes: During the feeding process of the feeding robot, the vision sensing unit is controlled to collect images of the feeding area; The image of the feeding area is received and identified to obtain the current feeding information.
[0073] Based on the above embodiments, the unmanned precision feeding device for farms provided in this embodiment of the invention further includes a feeding robot in the feeding operation system, and the current feeding information includes the feeding position of the livestock and the amount of feed remaining in the feeding area; The feeding module is also specifically used for: Based on the feeding location and the amount of feed remaining, the feeding robot is controlled to replenish the feed.
[0074] Based on the above embodiments, the unmanned precision feeding device for farms provided in this embodiment of the invention further includes a feeding operation system that also includes a receiving robot. The feeding module is also specifically used for: After a preset time period, the receiving robot is controlled to collect the remaining feed in the feeding area and collect information on the remaining feed. Receive the remaining feed information, and determine the current feeding information and the next feeding amount based on the remaining feed information.
[0075] Based on the above embodiments, the unmanned precision feeding device for farms provided in this embodiment of the invention further includes a feeding operation module specifically used for: Based on the current feed formulation, the current grouping index, and the individual carbon emission data, determine the demand for each feed component and the water demand. Based on the demand, the feed bins in the mixing system are controlled to supply the corresponding weights of each feed component to the mixing equipment, and the water valves are controlled to supply water to the mixing equipment based on the water demand. Based on the detection equipment installed on the mixing equipment, the uniformity, particle size and freshness of the mixture in the mixing equipment are detected and evaluated online in real time. If the uniformity, particle size, and freshness all reach the preset values, the mixing equipment is controlled to stop, and the current feed is obtained.
[0076] Based on the above embodiments, the unmanned precision feeding device for farms provided in this embodiment of the invention further includes an operation optimization module, used for: Receive the first operation information from the mixing operation system and the second operation information fed back by the feeding operation system; Based on the first operation information, the mixing operation of the mixing system in the next mixing process is optimized, and based on the second operation information, the feeding operation of the feeding system in the next feeding process is optimized.
[0077] Specifically, the functions of each module in the unmanned precision feeding device for farms provided in this embodiment of the invention correspond one-to-one with the operation flow of each step in the above-mentioned method-like embodiments, and the achieved effects are also the same. For details, please refer to the above embodiments, and this will not be repeated in this embodiment of the invention.
[0078] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include a processor 810, a communications interface 820, a memory 830, and a communication bus 840. The processor 810, communications interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions stored in the memory 830 to execute the unmanned precision feeding method for livestock farms provided in the above embodiments.
[0079] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to related technologies, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0080] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the unmanned precision feeding method for farms provided in the above embodiments.
[0081] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the unmanned precision feeding method for livestock farms provided in the above embodiments. This computer-readable storage medium can be either a non-transitory computer-readable storage medium or a transient computer-readable storage medium, and no specific limitation is made herein.
[0082] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0083] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of software products. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for unmanned precision feeding in a livestock farm, characterized in that, include: Obtain the current status of the farm; the current farm status includes the current feed formula, current grouping index, and individual carbon emission data of the livestock; Based on the current feed formulation, the current grouping index, and the individual carbon emission data, the current feed is produced by mixing the feed and, based on the historical feeding information collected during the previous feeding of the livestock, the current feed is delivered to the feeding area of the livestock by the feeding operation system to obtain the current feeding information during the current feeding process. Based on the current feeding information and the current feeding needs of the livestock, a reinforcement learning algorithm is used to update the current feed formula with the goal of minimizing the carbon emission state value function under the current farm condition, thereby generating the latest feed formula for the livestock. Update the current farm status based on the latest feed formulation.
2. The unmanned precision feeding method for livestock farms according to claim 1, characterized in that, The feeding system includes a feeding robot; The method of using a feeding system to deliver the current feed to the livestock's feeding area based on historical feeding information collected during the previous feeding process includes: Obtain the information about the livestock pens where the livestock are located; Based on the pen information and the historical feeding information, the feeding robot is controlled to spread the current feed onto the feeding area.
3. The unmanned precision feeding method for livestock farms according to claim 2, characterized in that, The feeding system also includes a feeding robot equipped with a vision sensing unit; based on the pen information and historical feeding information, the system controls the feeding robot to spread the current feed to the feeding area, and then further includes: The feeding robot is controlled to push feed at regular intervals so that the feed is concentrated in the feeding area; The step of obtaining current feeding information during the current feeding process includes: During the feeding process of the feeding robot, the vision sensing unit is controlled to collect images of the feeding area; The image of the feeding area is received and identified to obtain the current feeding information.
4. The unmanned precision feeding method for livestock farms according to claim 3, characterized in that, The feeding system also includes a feeding robot, and the current feeding information includes the livestock's feeding location and the amount of feed remaining in the feeding area; The method of controlling the feeding robot to push feed at regular intervals to concentrate the feed in the feeding area further includes: Based on the feeding location and the amount of feed remaining, the feeding robot is controlled to replenish the feed.
5. The unmanned precision feeding method for livestock farms according to claim 2, characterized in that, The feeding system also includes a receiving robot; Based on the pen information and the historical feeding information, the feeding robot is controlled to spread the current feed to the feeding area, and then the process further includes: After a preset time period, the receiving robot is controlled to collect the remaining feed in the feeding area and collect information on the remaining feed. Receive the remaining feed information, and determine the current feeding information and the next feeding amount based on the remaining feed information.
6. The unmanned precision feeding method for livestock farms according to any one of claims 1-5, characterized in that, The process of mixing feed to produce the current feed based on the current feed formulation, the current grouping index, and the individual carbon emission data includes: Based on the current feed formulation, the current grouping index, and the individual carbon emission data, determine the demand for each feed component and the water demand. Based on the demand, the feed bins in the mixing system are controlled to supply the corresponding weights of each feed component to the mixing equipment, and the water valves are controlled to supply water to the mixing equipment based on the water demand. Based on the detection equipment installed on the mixing equipment, the uniformity, particle size and freshness of the mixture in the mixing equipment are detected and evaluated online in real time. If the uniformity, particle size, and freshness all reach the preset values, the mixing equipment is controlled to stop, and the current feed is obtained.
7. The unmanned precision feeding method for livestock farms according to claim 6, characterized in that, Also includes: Receive the first operation information from the mixing operation system and the second operation information fed back by the feeding operation system; Based on the first operation information, the mixing operation of the mixing system in the next mixing process is optimized, and based on the second operation information, the feeding operation of the feeding system in the next feeding process is optimized.
8. A precision feeding device for livestock farms, characterized in that, include: The status acquisition module is used to acquire the current status of the farm; the current farm status includes the current feed formula, current grouping index, and individual carbon emission data of the livestock. The feeding operation module is used to mix and produce the current feed based on the current feed formula, the current grouping index and the individual carbon emission data, and to use the feeding operation system to deliver the current feed to the feeding area where the livestock are located based on the historical feeding information collected during the previous feeding process of the livestock, and to obtain the current feeding information during the current feeding process. The formula update module is used to update the current feed formula based on the current feeding information and the current feeding requirements of the livestock, using a reinforcement learning algorithm with the goal of minimizing the carbon emission state value function under the current farm conditions, and to generate the latest feed formula for the livestock. The status update module is used to update the current farm status based on the latest feed formula.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the unmanned precision feeding method for farms as described in any one of claims 1-7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the unmanned precision feeding method for farms as described in any one of claims 1-7.