Sea tank automatic feeding method, device, equipment, medium and product

Abstract

The invention discloses a sea tank automatic feeding method, device and equipment, a medium and a product. The method comprises the following steps: when a feeding condition is met, obtaining current state information of a fish school in a sea tank; according to the current state information, feeding parameters of this time are determined, and feeding is conducted; acquiring behavior information and residual feed data of fish schools after last feeding, and determining personalized feeding parameters; when the feeding condition is met again, the current state information of the fish school is obtained, and according to the personalized feeding parameters and the current state information, the optimized feeding parameters of this time are determined and feeding is carried out. By analyzing the behavior of the fish school and the state of the residual feed after feeding, the feeding strategy is dynamically optimized, accurate feeding and automatic feeding management are achieved, the operation burden of a user is reduced, residual feed accumulation is reduced, the water quality maintenance period is prolonged, and then water quality protection is achieved.

Description

Technical Field

[0001] This invention relates to the field of intelligent aquarium technology, and in particular to an automatic feeding method, device, equipment, medium and product for marine aquariums. Background Technology

[0002] For busy office workers, pet owners who travel frequently, or aquarium enthusiasts who pursue meticulous pet care, smart feeders can significantly reduce management costs.

[0003] Currently, smart feeders on the market mainly fall into the following categories: timed and quantitative type: feeds according to preset time and fixed amount; simple remote control type: supports users to remotely and manually trigger feeding; basic sensor type: adjusts the feeding amount simply through single parameters such as water temperature.

[0004] However, the aforementioned smart feeders lack perception and feedback on the actual feeding behavior of fish, have rigid feeding strategies, cannot adapt to the differences in habits of different fish species, and rely too much on user experience, making it difficult for novices to master the appropriate feeding amount. Summary of the Invention

[0005] This invention provides an automatic feeding method, device, equipment, medium, and product for marine aquariums to achieve dynamic optimization of feeding strategies.

[0006] According to a first aspect of the present invention, an automatic feeding method for a marine aquarium is provided, comprising:

[0007] When the feeding conditions are met, obtain the current status information of the fish in the marine aquarium;

[0008] Based on the current status information, determine the feeding parameters for this feeding and proceed with the feeding.

[0009] Obtain information on fish behavior and uneaten food after the last feeding, and determine personalized feeding parameters;

[0010] When the feeding conditions are met again, the current status information of the fish group is obtained. Based on the personalized feeding parameters and the current status information, the optimized feeding parameters are determined and feeding is carried out.

[0011] According to a second aspect of the present invention, an automatic feeding device for marine aquariums is provided, comprising:

[0012] The information acquisition module is used to acquire the current status information of the fish in the marine aquarium when the feeding conditions are met.

[0013] The initial feeding module is used to determine the feeding parameters for this feeding based on the current status information and to perform the feeding.

[0014] The personalized determination module is used to obtain information on fish behavior and uneaten food data after the last feeding, and to determine personalized feeding parameters.

[0015] The optimized feeding module is used to obtain the current status information of the fish group when the feeding conditions are met again, and to determine the optimized feeding parameters and feed the fish based on the personalized feeding parameters and the current status information.

[0016] According to a third aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the automatic feeding method for marine aquariums according to any embodiment of the present invention.

[0020] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the automatic feeding method for marine aquariums according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, embodiments of the present invention also provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the automatic feeding method for marine aquariums according to any embodiment of the present invention.

[0022] The technical solution of this invention involves acquiring the current state information of the fish in the aquarium when feeding conditions are met; determining the feeding parameters based on the current state information and feeding accordingly; acquiring fish behavior information and uneaten food data after the last feeding and determining personalized feeding parameters; and acquiring the current state information of the fish again when feeding conditions are met, determining optimized feeding parameters based on the personalized feeding parameters and the current state information, and feeding accordingly. By analyzing fish behavior and uneaten food status after feeding, the feeding strategy is dynamically optimized, achieving precise feeding, automated feeding management, reduced user workload, reduced uneaten food accumulation, and extended water quality maintenance cycles, thereby protecting water quality.

[0023] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of an automatic feeding method for a marine aquarium provided according to Embodiment 1 of the present invention;

[0026] Figure 2 This is a flowchart of an automatic feeding method for a marine aquarium according to Embodiment 2 of the present invention;

[0027] Figure 3 This is an example flowchart of an automatic feeding method for a marine aquarium provided in Embodiment 2 of the present invention;

[0028] Figure 4 This is a schematic diagram of the structure of an automatic feeding device for a marine aquarium provided in Embodiment 3 of the present invention;

[0029] Figure 5 This is a schematic diagram of the structure of an electronic device that implements an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] Example 1

[0033] Figure 1This is a flowchart illustrating an automatic feeding method for marine aquariums according to Embodiment 1 of the present invention. This embodiment is applicable to the automatic feeding of marine aquariums. The method can be executed by an automatic feeding device for marine aquariums, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0034] S110. When the feeding conditions are met, obtain the current status information of the fish in the marine aquarium.

[0035] In this embodiment, feeding conditions can be understood as the conditions for automatic feeding, such as user-defined feeding preset times or triggering by remote or click methods to start a feeding cycle. A marine aquarium can be understood as an aquarium system used to raise aquatic organisms. A fish school can be understood as a group of fish raised in the marine aquarium. Current status information can be understood as information used to characterize the activity status of the fish and the population characteristics of the fish.

[0036] Specifically, the processor can monitor time. When the user-set feeding time is reached or a feeding command is received manually by the user, the feeding conditions are met. The processor can capture images of the fish in real time through a high-definition camera set in the aquarium and analyze the images through a set detection algorithm to determine the current status information of the fish in real time. This information may include the number of fish in the current fish group, the size of each fish, and its species. The processor can also set the fish species through user input.

[0037] S120. Determine the feeding parameters based on the current status information and feed the animal.

[0038] In this embodiment, the feeding parameters can be understood as the feeding time and amount of food during this feeding.

[0039] Specifically, the processor can determine the recommended initial feeding amount as the feeding parameter for this feeding based on the current status information and a pre-defined fish species database. If it receives a user-defined feeding amount, it will use that amount as the feeding parameter and feed the fish accordingly. The fish species database can be generated by AI or large models, containing optimal feeding amounts for different fish species and sizes as defined by professionals. The processor can query the optimal feeding amount based on the current status information.

[0040] S130. Obtain information on fish behavior and uneaten food after the last feeding, and determine personalized feeding parameters.

[0041] In this embodiment, the fish behavior information after the last feeding can be understood as characterizing the fish's feeding-related behaviors, such as fish density, swimming trajectory, and feeding behavior during feeding. Uneaten food data can be understood as data representing the amount of food remaining a period of time after feeding. Personalized feeding parameters can be understood as feeding parameters optimized based on research into the habits of fish in a marine aquarium, such as the optimal basic feeding amount and optimal feeding time.

[0042] Specifically, the processor can monitor the feeding behavior of the fish in real time through the camera after the last feeding, forming fish behavior information, and detect the remaining bait in real time to generate uneaten bait data. The processor can obtain the fish behavior information and uneaten bait data generated after the last feeding. The fish behavior information and uneaten bait data can be determined by identifying and detecting the fish and bait based on the captured images of the marine aquarium.

[0043] S140. When the feeding conditions are met again, obtain the current status information of the fish group, determine the optimized feeding parameters based on the personalized feeding parameters and the current status information, and feed the fish.

[0044] In this embodiment, the optimized feeding parameters can be understood as an optimized feeding strategy generated based on the current fish population situation and the fish population behavior after the last feeding.

[0045] The feeding conditions are: reaching the preset feeding time, manually starting the feeding by the user, or reaching the predicted feeding time in the personalized feeding parameters.

[0046] Specifically, when the preset feeding time is reached again, or when a user's feeding instruction or the feeding time predicted in the personalized feeding parameters is received, the feeding conditions are met again. The processor can obtain the current status information of the fish group, determine the current number and size of the fish group, and combine the personalized feeding parameters as the basic feeding information with the current status information to determine the optimized feeding parameters for this time and feed the fish.

[0047] For example, the calculation formula for the optimized feeding parameters can be the base amount in the personalized feeding parameters multiplied by the quantity coefficient, size coefficient, and activity coefficient in the current status information. For example, the more the quantity, the larger the size of the fish, and the more active the fish, the more food is being fed.

[0048] The technical solution of this invention involves acquiring the current state information of the fish in the aquarium when feeding conditions are met; determining the feeding parameters based on the current state information and feeding accordingly; acquiring fish behavior information and uneaten food data after the last feeding and determining personalized feeding parameters; and acquiring the current state information of the fish again when feeding conditions are met, determining optimized feeding parameters based on the personalized feeding parameters and the current state information, and feeding accordingly. By analyzing fish behavior and uneaten food status after feeding, the feeding strategy is dynamically optimized, achieving precise feeding, automated feeding management, reduced user workload, reduced uneaten food accumulation, and extended water quality maintenance cycles, thereby protecting water quality.

[0049] Example 2

[0050] Figure 2 This is a flowchart of an automatic feeding method for a marine aquarium provided in Embodiment 2 of the present invention. This embodiment is a further refinement of the above embodiment. Figure 2 As shown, the method includes:

[0051] S201. When the feeding conditions are met, obtain the current status information of the fish in the marine aquarium.

[0052] S202. Based on the current status information, determine the number and size of the fish.

[0053] Specifically, the processor can distinguish the number and size of surviving fish in the school based on the current status information.

[0054] S203. Determine the initial recommended feeding parameters based on the number and size of the fish population and the fish species database.

[0055] In this embodiment, the fish species database can be understood as a pre-built database containing relevant data such as the optimal feeding amount, feeding time, and growth data for different fish species of different sizes. The initial recommended feeding parameters can be understood as unoptimized feeding parameters.

[0056] Specifically, the processor can use AI or large models to combine with a fish species database, query the current fish population, species, and size to determine the optimal feeding amount as the initial recommended feeding parameters.

[0057] S204. Adjust the initial recommended feeding parameters based on the environmental factors provided by the marine aquarium, determine the current feeding parameters, and then feed the animals.

[0058] In this embodiment, environmental factors can be understood as the parameters of the living environment provided by the marine aquarium for the fish, such as water temperature, light, and season, which affect the feeding of the fish.

[0059] Specifically, the processor can acquire environmental factors provided by the marine aquarium through sensors such as temperature and light sensors. It can then use models, algorithms, or mapping relationships to determine correction values ​​for these environmental factors, adjust the initial recommended feeding parameters, and obtain the current feeding parameters for feeding.

[0060] For example, a specific example can demonstrate how the correction value is determined. This can be achieved through a correction value compensation model constructed using AI algorithm models and real-time collected data from the actual aquaculture environment: Dissolved oxygen, water temperature, weather, ambient temperature, and feed digestibility are used as aquaculture environment data. Based on the fish species and their growth stage, the required protein, fat, and carbohydrate content are determined. Simultaneously, the feed digestibility is calculated based on the protein content, protein digestibility, fat content, fat digestibility, carbohydrate content, and carbohydrate digestibility of the feed. The correlation between each parameter and fish feeding is confirmed using the Pearson correlation coefficient formula. Based on the correlation and historical data of each parameter, combined with the AI ​​algorithm model, a feed feeding prediction deviation compensation model is constructed for each aquaculture area, outputting a correction value. The corrected value is then added to the initial recommended feeding parameters to obtain the current feeding parameters.

[0061] S205. Based on the fish behavior information, uneaten feed data and time threshold information after the last feeding, determine the feeding effect evaluation results of the last feeding.

[0062] In this embodiment, the time threshold information can be understood as a time-based judgment criterion set to determine the feeding effect. The feeding effect evaluation result is used to reflect whether the feeding parameters of the previous feeding were accurate.

[0063] Specifically, the processor can acquire images of the fish and uneaten food captured by the camera in real time after the last feeding. It can analyze the fish density, swimming trajectory and feeding behavior in real time through target detection algorithms to determine the fish behavior information. It can also perform quantitative analysis of fish behavior and uneaten food data through computer vision technology and combine it with time threshold information to determine the feeding effect evaluation results of the last feeding.

[0064] Furthermore, based on the above embodiments, the steps for determining the feeding effect evaluation result of the previous feeding based on the fish behavior information, uneaten feed data, and time threshold information after the previous feeding can be refined as follows:

[0065] If the time of the uneaten feed data falls within the first time threshold in the time threshold information and the fish behavior information meets the condition of insufficient feeding, then the feeding effect evaluation result of the previous feeding is determined to be insufficient feeding; if the time of the uneaten feed falls within the second time threshold in the time threshold information, then the feeding effect evaluation result is determined to be overfeeding; wherein, the first time threshold is less than the second time threshold; if the amount of uneaten feed and the time of the uneaten feed data meet the condition of adequate feeding, then the feeding effect evaluation result is determined to be adequate feeding.

[0066] In this embodiment, the residual bait time can be understood as the time during which residual bait remains, such as residual bait remaining at 5 minutes or 10 minutes. The residual bait amount is used to characterize the degree of bait remaining. The first time threshold can be understood as the duration for determining whether feeding is insufficient, such as 5 minutes after feeding. The insufficient feeding condition can be understood as the set fish behavior conditions for determining whether feeding is insufficient, such as fish activity. The second time threshold can be understood as the duration for determining whether feeding is excessive, such as 10 minutes after feeding.

[0067] Specifically, the processor can compare the quantified uneaten food data with time threshold information. If the uneaten food time falls within the first time threshold and the fish behavior information meets the underfeeding condition, the feeding effect assessment result of the previous feeding is determined to be underfeeding. If the uneaten food time falls within the second time threshold, the feeding effect assessment result is determined to be overfeeding. The first time threshold is less than the second time threshold. If the uneaten food quantity and uneaten food time meet the appropriate amount condition, the feeding effect assessment result is determined to be appropriate feeding. The processor can determine feeding recommendations based on the feeding effect assessment results. For example, when the result is underfeeding, the recommended increase in feeding amount can be determined based on the time taken for the fish food to be eaten in the uneaten food data; when the result is overfeeding, the recommended decrease in feeding amount can be determined based on the time taken for the fish food to stop decreasing in the uneaten food data.

[0068] For example, if the comparison of the remaining bait time with the first time threshold indicates that the bait was eaten within 5 minutes and the fish behavior information shows that the fish are active and meets the first time threshold, then the feeding effect assessment result is insufficient feeding. If the comparison of the remaining bait time with the second time threshold indicates that there is still bait left after 10 minutes, then the feeding effect assessment result is overfeeding.

[0069] S206. Based on the feeding effect evaluation results, the previous feeding parameters, and the personalized feeding model, determine the personalized feeding parameters.

[0070] In this embodiment, the parameters of the previous feeding can be understood as including parameters such as the amount of food fed in the previous feeding and the number of fish in the school. The personalized feeding model can be understood as a model obtained after training through machine learning and other methods.

[0071] This involves recording user adjustment habits and fish growth data, establishing personalized feeding models through machine learning algorithms, and predicting the optimal feeding time and amount based on historical data.

[0072] Specifically, the processor can input the feeding effect evaluation results and the previous feeding parameters into the personalized feeding model, and obtain the model output results as the predicted personalized feeding parameters.

[0073] S207. When the feeding conditions are met again, determine the fish group attribute coefficient based on the current status information.

[0074] In this embodiment, the fish school attribute coefficient can be understood as a coefficient used to represent the amount of change in feeding, such as the fish school quantity coefficient, the fish school size coefficient, and the fish school activity coefficient.

[0075] Specifically, the processor can determine the fish attribute coefficients corresponding to different states of the fish population based on the current state information and methods such as mapping tables.

[0076] For example, if the number of fish in the current status information is 'a', the fish quantity coefficient corresponding to the number of fish a is determined by looking up a table as a1; if the size of the fish in the current status information is 'b', the fish quantity coefficient corresponding to the size of the fish b is determined by looking up a table as b2; if the activity level of the fish in the current status information is 'c', the fish activity coefficient corresponding to the activity level of the fish c is determined by looking up a table as c1.

[0077] S208. Based on the fish population attribute coefficients and personalized feeding parameters, determine the optimized feeding parameters and feed the fish.

[0078] Specifically, the processor can compensate for personalized feeding parameters based on fish attribute coefficients, determine the optimized feeding parameters for this feeding, and then feed the fish.

[0079] For example, the fish population attribute coefficients can be multiplied by the personalized feeding parameters to obtain the optimized feeding parameters for this operation.

[0080] S209. Generate feeding suggestions based on the optimized feeding parameters and personalized feeding parameters, and provide feedback.

[0081] In this embodiment, the feeding recommendation can be understood as a way to characterize the results of the judgment on the feeding of the fish.

[0082] Specifically, the processor can jointly determine feeding reports for multiple feeding scenarios based on the feeding effect evaluation results from the optimized feeding parameters and personalized feeding parameters. Based on the feeding adjustment strategies in the reports, it can generate feeding suggestions and provide feedback to the user, which can be done through an app or similar means. Furthermore, the effect and data of each feeding are recorded. The processor can use this data to revise and optimize the personalized feeding model. The optimized model will then guide the next feeding decision, thus achieving a dynamic optimization loop that becomes more accurate with each use.

[0083] The technical solution of this invention monitors the actual situation of the fish population in real time, determines the fish behavior information during feeding, and combines it with uneaten feed data to dynamically adjust the feeding amount, avoiding waste and water pollution. Through continuous learning, a personalized feeding model is established, making the personalized prediction model increasingly accurate and improving the accuracy of feeding. Real-time monitoring and reminder mechanisms effectively prevent overfeeding. This reduces the accumulation of uneaten feed in the water, extends the water quality maintenance cycle, and lowers maintenance costs. Automated feeding management enhances the user experience and reduces the user's operational burden.

[0084] For example, based on the above embodiments, the present invention can be illustrated by a specific example. Figure 3 This is an example flowchart of an automatic feeding method for a marine aquarium provided in Embodiment 2 of the present invention, as shown below. Figure 3 As shown, the steps may include:

[0085] S301. Monitor the status of fish in the marine aquarium in real time via camera;

[0086] S302. Determine whether the preset feeding time has been reached or whether the user has manually started feeding; if yes, proceed to step S303; otherwise, proceed to step S301.

[0087] S303. Obtain the current status information of the fish school and identify the number and size of the fish school;

[0088] S304. Based on the identification results and personalized feeding parameters, determine the optimized feeding parameters for this feeding and then feed the animals.

[0089] S305. Continuous monitoring and analysis after feeding to determine fish behavior information (including tracking feeding activity and swimming speed) and uneaten food data (including analyzing the sinking trajectory of food over time and detecting the accumulation of uneaten food over time).

[0090] S306. If the fish are eaten within the first time threshold and the fish are active, the feeding effect assessment result is determined to be insufficient feeding. It is recommended to increase the feeding amount next time.

[0091] S307. After reaching the second time threshold, the residual feed data still showed that there was still feed left. The feeding effect assessment result was determined to be overfeeding. It is recommended to reduce the amount of feed next time.

[0092] S308. The amount and time of residual feed in the residual feed data meet the conditions of appropriate amount with no residue, and the feeding effect evaluation result is determined to be appropriate amount of feeding.

[0093] S309. Record the feeding parameters and feeding effect evaluation results for this feeding, and determine the personalized feeding parameters according to the personalized feeding model. End the feeding cycle and return to step S301.

[0094] Example 3

[0095] Figure 4 This is a schematic diagram of an automatic feeding device for a marine aquarium provided in Embodiment 3 of the present invention. Figure 4 As shown, the device includes:

[0096] The information acquisition module 41 is used to acquire the current status information of the fish in the marine aquarium when the feeding conditions are met.

[0097] The initial feeding module 42 is used to determine the feeding parameters for this feeding based on the current status information and to perform the feeding.

[0098] The personalized determination module 43 is used to obtain information on fish behavior and uneaten food data after the last feeding, and to determine personalized feeding parameters.

[0099] The optimized feeding module 44 is used to obtain the current status information of the fish group when the feeding conditions are met again, and to determine the optimized feeding parameters and feed the fish based on the personalized feeding parameters and the current status information.

[0100] The technical solution of this invention involves acquiring the current state information of the fish in the aquarium when feeding conditions are met; determining the feeding parameters based on the current state information and feeding accordingly; acquiring fish behavior information and uneaten food data after the last feeding and determining personalized feeding parameters; and acquiring the current state information of the fish again when feeding conditions are met, determining optimized feeding parameters based on the personalized feeding parameters and the current state information, and feeding accordingly. By analyzing fish behavior and uneaten food status after feeding, the feeding strategy is dynamically optimized, achieving precise feeding, automated feeding management, reduced user workload, reduced uneaten food accumulation, and extended water quality maintenance cycles, thereby protecting water quality.

[0101] The feeding conditions are: reaching a preset feeding time, being manually started by the user, or reaching a predicted feeding time in the personalized feeding parameters.

[0102] Furthermore, the initial feeding module 42 is specifically used for:

[0103] Based on the current status information, determine the number and size of the fish school;

[0104] Based on the number and size of the fish population and a fish species database, the initial recommended feeding parameters are determined.

[0105] The initial recommended feeding parameters are adjusted based on the environmental factors provided by the marine aquarium to determine the current feeding parameters and then feeding is carried out.

[0106] Furthermore, the personalization determination module 43 includes:

[0107] The effect evaluation unit is used to determine the feeding effect evaluation results of the previous feeding based on the fish behavior information, uneaten feed data and time threshold information after the previous feeding.

[0108] The parameter determination unit is used to determine personalized feeding parameters based on the feeding effect evaluation results, the previous feeding parameters, and the personalized feeding model.

[0109] Specifically, the effect evaluation unit is used for:

[0110] If the residual feed time of the residual feed data belongs to the first time threshold in the time threshold information and the fish behavior information meets the condition of insufficient feeding, then the feeding effect evaluation result of the previous feeding is determined to be insufficient feeding.

[0111] If the time of uneaten feed falls within the second time threshold in the time threshold information, then the feeding effect evaluation result is determined to be overfeeding; wherein, the first time threshold is less than the second time threshold;

[0112] If the amount of uneaten feed and the time of uneaten feed meet the appropriate feeding conditions, then the feeding effect evaluation result is determined to be an appropriate feeding amount.

[0113] Furthermore, the optimized feeding module 44 is specifically used for:

[0114] Based on the current state information, determine the fish school attribute coefficients;

[0115] Based on the fish population attribute coefficients and the personalized feeding parameters, the optimized feeding parameters for this operation are determined and feeding is carried out.

[0116] Feeding suggestions are generated and feedback is provided based on the optimized feeding parameters and the personalized feeding parameters.

[0117] The automatic feeding device for marine aquariums provided in this embodiment of the invention can execute the automatic feeding method for marine aquariums provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0118] Example 4

[0119] Figure 5 A schematic diagram of an electronic device 50 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0120] like Figure 5 As shown, the electronic device 50 includes at least one processor 51 and a memory, such as a read-only memory (ROM) 52 and a random access memory (RAM) 53, communicatively connected to the at least one processor 51. The memory stores computer programs executable by the at least one processor. The processor 51 can perform various appropriate actions and processes based on the computer program stored in the ROM 52 or loaded from storage unit 58 into the RAM 53. The RAM 53 can also store various programs and data required for the operation of the electronic device 50. The processor 51, ROM 52, and RAM 53 are interconnected via a bus 54. An input / output (I / O) interface 55 is also connected to the bus 54.

[0121] Multiple components in electronic device 50 are connected to I / O interface 55, including: input unit 56, such as keyboard, mouse, etc.; output unit 57, such as various types of monitors, speakers, etc.; storage unit 58, such as disk, optical disk, etc.; and communication unit 59, such as network card, modem, wireless transceiver, etc. Communication unit 59 allows electronic device 50 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0122] Processor 51 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 51 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 51 performs the various methods and processes described above, such as automatic feeding methods for marine aquariums.

[0123] In some embodiments, the automatic feeding method for marine aquariums may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 58. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 50 via ROM 52 and / or communication unit 59. When the computer program is loaded into RAM 53 and executed by processor 51, one or more steps of the automatic feeding method for marine aquariums described above may be performed. Alternatively, in other embodiments, processor 51 may be configured to perform the automatic feeding method for marine aquariums by any other suitable means (e.g., by means of firmware).

[0124] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0125] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0126] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0127] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0128] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0129] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0130] In one embodiment, the present invention further includes a computer program product, which includes a computer program that, when executed by a processor, implements the automatic feeding method for marine aquariums according to any embodiment of the present invention.

[0131] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0132] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0133] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An automatic feeding method for a marine aquarium, characterized in that, include: When the feeding conditions are met, obtain the current status information of the fish in the marine aquarium; Based on the current status information, determine the feeding parameters for this feeding and proceed with the feeding. Obtain information on fish behavior and uneaten food after the last feeding, and determine personalized feeding parameters; When the feeding conditions are met again, the current status information of the fish group is obtained. Based on the personalized feeding parameters and the current status information, the optimized feeding parameters are determined and feeding is carried out.

2. The method according to claim 1, characterized in that, The step of determining the feeding parameters and feeding based on the current status information includes: Based on the current status information, determine the number and size of the fish school; Based on the number and size of the fish population and a fish species database, the initial recommended feeding parameters are determined. The initial recommended feeding parameters are adjusted based on the environmental factors provided by the marine aquarium to determine the current feeding parameters and then feeding is carried out.

3. The method according to claim 1, characterized in that, The process of obtaining fish behavior information and uneaten food data after the last feeding, and determining personalized feeding parameters, includes: Based on the fish behavior information, uneaten food data, and time threshold information after the last feeding, determine the feeding effect evaluation results of the last feeding; Based on the feeding effect evaluation results, the previous feeding parameters, and the personalized feeding model, the personalized feeding parameters are determined.

4. The method according to claim 3, characterized in that, The evaluation result of the feeding effect of the previous feeding is determined based on the fish behavior information, uneaten feed data, and time threshold information after the previous feeding, including: If the residual feed time of the residual feed data belongs to the first time threshold in the time threshold information and the fish behavior information meets the condition of insufficient feeding, then the feeding effect evaluation result of the previous feeding is determined to be insufficient feeding. If the time of uneaten feed falls within the second time threshold in the time threshold information, then the feeding effect evaluation result is determined to be overfeeding; wherein, the first time threshold is less than the second time threshold; If the amount of uneaten feed and the time of uneaten feed meet the appropriate feeding conditions, then the feeding effect evaluation result is determined to be an appropriate feeding amount.

5. The method according to claim 1, characterized in that, The step of determining the optimized feeding parameters and feeding based on the personalized feeding parameters and the current status information includes: Based on the current state information, determine the fish school attribute coefficients; Based on the fish population attribute coefficients and the personalized feeding parameters, the optimized feeding parameters for this operation are determined and feeding is carried out. Feeding suggestions are generated and feedback is provided based on the optimized feeding parameters and the personalized feeding parameters.

6. The method according to claim 1, characterized in that, The feeding conditions are: reaching a preset feeding time, being manually started by the user, or reaching a predicted feeding time in the personalized feeding parameters.

7. An automatic feeding device for marine aquariums, characterized in that, include: The information acquisition module is used to acquire the current status information of the fish in the marine aquarium when the feeding conditions are met. The initial feeding module is used to determine the feeding parameters for this feeding based on the current status information and to perform the feeding. The personalized determination module is used to obtain information on fish behavior and uneaten food data after the last feeding, and to determine personalized feeding parameters. The optimized feeding module is used to obtain the current status information of the fish group when the feeding conditions are met again, and to determine the optimized feeding parameters and feed the fish based on the personalized feeding parameters and the current status information.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the automatic feeding method for marine aquariums according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the automatic feeding method for marine aquariums as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the automatic feeding method for marine aquariums according to any one of claims 1-6.

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