Neural Network Health Condition Analysis Method and System Based on Monitoring Pet Food Intake

Abstract

The present application provides a neural network health condition analysis method based on monitoring the food intake of pets. The method includes: obtaining first eating data within a first preset time period, determining whether the pet intake amount within the first preset time period meets the threshold of the first intake amount. When the pet intake amount meets the threshold of the first intake amount, based on a second preset time period, obtaining the change rate of the pet food intake amount. When the change rate of the pet food intake amount meets the preset threshold, based on a third preset time period, obtaining the total amount information of the pet food intake amount of the pet within the third preset time period as the third eating data; using the first eating data, the second eating data and the third eating data as the input of the neural network model to obtain the health state of the pet. The present application analyzes the health condition of the pet through the neural network model, pays attention to the physical health changes of the pet in time, helps the pet grow healthily, and gives the pet high-quality company.

Description

Technical Field

[0001] This application relates to the technical field of canine, feline and similar pet health monitoring, and specifically relates to a neural network health condition analysis method and system based on monitoring pet food intake. Background Art

[0002] Palatability is one of the basic criteria for measuring the success of pet food selection. The special habits of pets will also affect their palatability selection in eating. The general methods for palatability evaluation are the conditioned reflex method, the single-bowl method and the double-bowl method. In fact, in addition to palatability, the durability of food is also an essential indicator for the success of pet food. In previous pet research, there has been little research on durability. In order to more accurately test the palatability and durability of pet food, based on the habits and characteristics of pets' irregular eating throughout the day, to infer the eating and living habits of pets at different times of the day for 24 hours, and to better understand and care for the pets being raised.

[0003] With the existing technical solutions, it is not easy to meet the monitoring and understanding of pets' eating habits for 24 hours a day, and simply monitoring and analyzing the eating and living habits for 24 hours a day is particularly wasteful of manpower and material resources. Currently, companion pets are a very important part of people's lives. In-depth understanding of them can help the owner pay attention to the pet's food preferences and the information on the pet's physical health changes, help the pet grow healthily in time, and provide high-quality companionship for the pet. Summary of the Invention

[0004] In order to overcome the above problems existing in the prior art, this application provides a neural network health condition analysis method and system based on monitoring pet food intake, and adopts the following technical solutions:

[0005] In the first aspect, this application provides a neural network health condition analysis method based on monitoring pet food intake, including:

[0006] Step S1, obtaining pet food information, obtaining the eating data of pet food based on the weighing sensor in the pet feeder, and collecting the eating data of the pet and storing it in the database;

[0007] Step S2, the control center reads the first eating data in the database, and judges whether the pet intake amount within the first preset time period meets the threshold of the first intake amount. When the pet intake amount meets the threshold of the first intake amount, execute Step S3;

[0008] Step S3, the control center reads the second eating data in the database, obtains the change rate of the pet's food intake amount based on the second preset time period, judges the change rate of the pet's food intake amount. When the change rate of the pet's food intake amount meets the preset threshold, then execute Step S4;

[0009] Step S4, the control center reads the third feeding data in the database. Based on the third preset time period, when the change rate of the pet's food intake meets the preset threshold, the total information of the pet's food intake in the third preset time period is obtained.

[0010] Step S5, using the first feeding data, the second feeding data, and the third feeding data as the input of the neural network model to obtain the health status of the pet.

[0011] Step S6, the control center monitors the first feeding data, the second feeding data, the third feeding data, and the health status of the pet in real time, and transmits the monitoring results to the user terminal in real time. When the monitoring result data is abnormal, the control center promptly sends an alarm message to the user terminal to notify the user.

[0012] Further, the first preset time period in step S2 is the time period when the pet first eats pet food.

[0013] Further, the weighing sensor in step S1 is set on the magnetic base of the pet feeder. Weighing sensors are arranged on all four sides of the magnetic base, and the weighing sensor is a half-bridge weighing sensor.

[0014] Further, the control center in step S2 reads the first feeding data in the database and determines whether the pet's intake during the first preset time period meets the threshold of the first intake. Specifically, it is as follows:

[0015] Assume that the change value of the pet cat's food intake over time is x(t), 0 ≤ t ≤ 24h, and the sampling interval t s = 1s, set the initial weight x(0) = x 0 , set X F = 0.5g;

[0016] Select 0 ≤ t ≤ 60s, calculate Δx = x(60s) - x(0). If Δx ≤ X F , then output Flg1 = 1. If Δx ≤ X is not satisfied F then output Flg1 = 0;

[0017] where X F represents the threshold of a pet's first intake. When the threshold of the first intake reaches 0.5g or more, that is, Flg1 = 0. If the threshold of the first intake does not reach 0.5g, that is, Flg1 = 1.

[0018] Further, the step of obtaining the change rate of the amount of food ingested by the pet based on the second preset time period in step S3 and judging the change rate of the amount of food ingested by the pet is specifically manifested as follows:

[0019] Based on the data within any Δt = 60s in the second preset time period, judge. If the preset threshold is satisfied, output Flg2 = 1, and at the same time obtain If the preset threshold is not satisfied, output Flg2 = 0;

[0020] where represents the change rate of the amount of food ingested by a pet within any 60s, that is, the speed of eating. When the change rate of the amount of food ingested by the pet reaches and above, that is, Flg2 = 1. If the change rate of the amount of food ingested by the pet does not reach , that is, Flg2 = 0, where dxMax represents the maximum threshold of the pet's eating speed.

[0021] Further, the step of taking the first eating data, the second eating data and the total amount information of the food ingested by the pet in the third preset time period as the input of the neural network model to obtain the health status of the pet is specifically manifested as follows:

[0022] Based on the initial food intake x of the pet 0 , obtain the total change in food intake of the pet within 24 hours, that is, dxend = x(24h) - x(0), and take the total change in food intake of the pet within 24 hours as the third eating data.

[0023] Further, the step of taking the first eating data, the second eating data and the total amount information of the food ingested by the pet in the third preset time period as the input of the neural network model to obtain the health status of the pet is specifically manifested as follows:

[0024] Take x(t), Flg1, Flg2, dx end = x(24h) - x 0 as X 1 ~X 5 and input them into the neural network model respectively. M 1 , N 1 , J 1 are used as the output;

[0025] Expert training is carried out on the neural network model. Input 100 groups of X 1 ~X 5 data, and give expert judgments for each group of input data; M 1 , N 1, J 1 is divided into four grades, M 1 and N 1 The four grades are excellent, good, medium, and poor respectively. J 1 The four grades of J are healthy, good, average, and poor;

[0026] Complete the training of the neural network to obtain the neural network health status analysis algorithm Q = G(x(t)); G represents the neural network analysis algorithm, and Q represents the output health status, including M 1 , N 1 , J 1 , where M 1 is the digestion and absorption ability, N 1 is the emotional stability, and J 1 is the physical health status.

[0027] In the second aspect, the present application also provides a neural network health status analysis system based on monitoring the food intake of pets, including:

[0028] The first food intake data acquisition module is used to obtain pet food information, acquire the food intake data of the pet based on the weighing sensor in the pet feeder, and store the acquired food intake data of the pet in the database; where the food intake data includes the first food intake data, the second food intake data, and the third food intake data;

[0029] The first intake amount judgment module is used for the control center to read the first food intake data in the database, and judge whether the pet intake amount within the first preset time period meets the threshold of the first intake amount. When the pet intake amount meets the threshold of the first intake amount, execute step S3;

[0030] The intake food amount change rate judgment module is used for the control center to read the second food intake data in the database, obtain the change rate of the pet intake food amount based on the second preset time period, judge the change rate of the pet intake food amount, and when the change rate of the pet intake food amount meets the preset threshold, execute step S4;

[0031] The third food intake data acquisition module is used to use the total amount information of the first food intake data, the second food intake data, and the pet intake food amount in the third preset time period as the input of the neural network model to obtain the health status of the pet;

[0032] The pet health status acquisition module is used to use the total amount information of the first food intake data, the second food intake data, and the pet intake food amount in the third preset time period as the input of the neural network model to obtain the health status of the pet.

[0033] A real-time monitoring module is used to monitor in real time at the control center the first feeding data, the second feeding data, the total information on the amount of food ingested by the pet in the third preset time period, and the health status of the pet, and transmit the monitoring results to the user terminal in real time. When the monitoring result data is abnormal, the control center timely sends an alarm message to the user terminal to notify the user.

[0034] In a third aspect, the present application provides an electronic device, including:

[0035] One or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the device, cause the device to execute the method described in the first aspect.

[0036] In a fourth aspect, the present application provides a computer-readable storage medium in which a computer program is stored, and when it runs on a computer, it causes the computer to execute the method described in the first aspect.

[0037] In a fifth aspect, the present application provides a computer program that, when executed by a computer, is used to execute the method described in the first aspect.

[0038] In a possible design, the program in the fifth aspect can be stored in whole or in part on a storage medium packaged together with the processor, or can be stored in whole or in part on a memory not packaged together with the processor.

[0039] The present application has the following beneficial effects:

[0040] 1. By using the weighing sensor in the pet feeder to obtain the feeding information in the first preset time period, the present application can provide data for the owner in terms of pet diet management and health monitoring, helping the owner better pay attention to and take care of the healthy growth of the pet.

[0041] 2. By evaluating the palatability of the pet when the pet first contacts the pet food, and by judging whether the pet intake amount in the first preset time period meets the threshold of the first intake amount, the present application can help the owner timely understand the palatability of the pet food for the pet. When the selected pet food has poor palatability, it is convenient for the owner to timely change the pet food for the pet.

[0042] 3. When the pet intake amount meets the threshold of the first intake amount, the present application judges the change rate of the pet food intake amount, which can help the owner evaluate the palatability of the pet food. If the pet has poor palatability for the current food, the owner can consider changing the food for the pet.

[0043] 4. Based on the food with good palatability and mouthfeel for pets, this application can analyze the health of pets through a neural network model, enabling in-depth understanding of the pets' health, helping the owners pay attention to the physical health changes of the pets, timely assisting the healthy growth of the pets, and providing high-quality companionship to the pets.

[0044] 5. When the monitored result data is abnormal, the control center promptly sends an alarm message to the user terminal to notify the user, enabling the owners of the pets to pay attention to the physical health change information of the pets in a timely manner when they are not around the pets, promptly detecting the abnormal situations of the pets, and taking timely measures for the abnormal situations of the pets to ensure the health and safety of the pets. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is an exemplary system architecture diagram to which the embodiments of this application can be applied;

[0046] Figure 2 It is a flowchart of the method of the embodiments of this application;

[0047] Figure 3 It is a schematic diagram of the neural network model of the embodiments of this application;

[0048] Figure 4 It is a system flowchart of the embodiments of this application;

[0049] Figure 5 It is a schematic diagram of the computer device of the embodiments of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs; the terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application or the above drawings are intended to cover non-exclusive inclusion. The terms "first", "second", etc. in the specification and claims of this application or the above drawings are used to distinguish different objects and not to describe a specific order.

[0051] Referring to "embodiments" herein means that specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of this application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0052] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0053] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0054] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0055] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, Moving Picture Experts Group Audio Layer 3), MP4 (Moving Picture Experts Group Audio Layer IV, Moving Picture Experts Group Audio Layer 4) players, laptop computers and desktop computers, etc.

[0056] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0057] It should be noted that the neural network health status analysis method based on monitoring the pet's food intake provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the neural network health status analysis system based on monitoring the pet's food intake is generally set in the server / terminal device.

[0058] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0059] Continue to refer Figure 2 , the figure shows a flow chart of a neural network health status analysis method based on monitoring pet food intake in the present application, the method comprising the following steps:

[0060] Step S1: Obtain pet food information, acquire the feeding data of the pet food based on the weighing sensor in the pet feeder, and collect and store the feeding data of the pet in a database; wherein the feeding data includes first feeding data, second feeding data, and third feeding data.

[0061] Obtaining pet food information includes obtaining the name information of the pet food. When the database records the pet food for the first time, a unique identifier is generated for the pet food. In this application, by collecting the name information of the pet food, it is ensured that the recorded pet food has not changed. When the pet food changes, the name information of the pet food needs to be updated, and a unique identifier is generated for the new pet food to distinguish it from the recorded information of other pet foods and avoid inaccurate data collection.

[0062] This application obtains pet food information, acquires the photo information of the pet food based on the camera set in the pet feeder, and obtains the name information of the pet food by extracting the features of the photo information of the pet food.

[0063] The photo information of the pet food is the photo information of the outer packaging of the pet food or the photo information marked on the box containing the pet food. Some owners are used to storing the pet food in a packaging bag after purchasing it; while some owners will repackage the pet food into a box for storage after purchasing it; if the camera cannot obtain the effective information of the pet food, the control center sends a prompt message to the user terminal of the owner to confirm the pet food information. After the pet owner confirms the pet food information, if the pet food has not changed, the pet feeding information will be recorded in the pet food information.

[0064] In a possible implementation manner, the first preset time period in step S2 is the time period when the pet first eats the pet food.

[0065] In a possible implementation manner, the weighing sensor in step S1 is set on the magnetic base of the pet feeder, and weighing sensors are arranged on all four sides of the magnetic base. The weighing sensor is a half-bridge weighing sensor.

[0066] Step S2: The control center reads the first feeding data in the database, determines whether the pet intake amount within the first preset time period meets the threshold of the first intake amount, and executes step S3 when the pet intake amount meets the threshold of the first intake amount.

[0067] The first feeding data includes the weight information of pet food collected at regular intervals based on a preset time interval. The acquisition unit collects the time point of the recording moment and the weight information of the pet food in the weighing sensor, and transmits it to the database for recording.

[0068] In a possible implementation manner, the control center in step S2 reads the first feeding data in the database and determines whether the pet intake within the first preset time period meets the threshold of the first intake amount. Specifically, it is shown as follows:

[0069] Assume that the change value of the food intake of a pet cat with time is x(t), 0 ≤ t ≤ 24h, and the sampling interval t s = 1s, set the initial weight x(0) = x 0 , set X F = 0.5g;

[0070] Select 0 ≤ t ≤ 60s, calculate Δx = x(60s) - x(0). If Δx ≤ X F , then output Flg1 = 1. If Δx ≤ X is not satisfied F then output Flg1 = 0;

[0071] where X F represents the threshold of the first intake amount of a pet. When the threshold of the first intake amount reaches 0.5g or more, that is, Flg1 = 0. If the threshold of the first intake amount does not reach 0.5g, that is, Flg1 = 1.

[0072] In a possible implementation manner, the palatability of the pet is judged by whether the pet intake within the first preset time period meets the threshold of the first intake amount. When the threshold of the first intake amount is met, it indicates that the pet has good palatability for the current food; when the threshold of the first intake amount is not met, it indicates that the pet has poor palatability for the current food. When the pet has poor palatability, the food for the pet can be considered to be replaced.

[0073] Step S3, the control center reads the second feeding data in the database, obtains the change rate of the pet food intake amount based on the second preset time period, and judges the change rate of the pet food intake amount. When the change rate of the pet food intake amount meets the preset threshold, step S4 is executed;

[0074] In a possible implementation manner, in step S3, obtaining the change rate of the pet food intake amount based on the second preset time period and judging the change rate of the pet food intake amount is specifically shown as follows:

[0075] Based on the data within any Δt = 60s within the second preset time period, for Make a judgment. If the preset threshold is met, output Flg2 = 1 and obtain If the preset threshold is not met, output Flg2 = 0;

[0076] Among them represents the change rate of the amount of food ingested by a pet within any 60s, that is, the speed of food intake. When the change rate of the amount of food ingested by the pet reaches and above, that is, Flg2 = 1. If the change rate of the amount of food ingested by the pet does not reach at this time, that is, Flg2 = 0, where dxMax represents the maximum threshold of the pet's food intake speed.

[0077] In a possible implementation manner, when the pet has good palatability for the current food, by judging the change rate of the amount of food ingested by the pet, the palatability of the pet for the current food can be obtained. When the change rate of the amount of food ingested by the pet is greater than or equal to the preset threshold, it indicates that the pet has good palatability for the current food. When the change rate of the amount of food ingested by the pet is less than the preset threshold, it indicates that the pet has poor palatability for the current food. If the pet has poor palatability for the current food, the food for the pet can be considered to be changed.

[0078] Step S4, use the first feeding data, the second feeding data, and the total amount information of the food ingested by the pet in the third preset time period as the input of the neural network model to obtain the health status of the pet;

[0079] In a possible implementation manner, the step of using the first feeding data, the second feeding data, and the total amount information of the food ingested by the pet in the third preset time period as the input of the neural network model to obtain the health status of the pet is specifically manifested as:

[0080] Based on the initial food intake x 0 of the pet, obtain the total change in food intake of the pet within 24 hours, that is, dxend = x(24h) - x(0), and use the total change in food intake of the pet within 24 hours as the third feeding data.

[0081] Step S5, use the first feeding data, the second feeding data, and the total amount information of the food ingested by the pet in the third preset time period as the input of the neural network model to obtain the health status of the pet.

[0082] In a possible implementation manner, the step of using the first feeding data, the second feeding data, and the total amount information of the food ingested by the pet in the third preset time period as the input of the neural network model to obtain the health status of the pet is specifically manifested as:

[0083] Take x(t), Flg1, Flg2, dx end = x(24h) - x 0 as X 1 ~X 5 and input them into the neural network model respectively. M 1 , N 1 , J 1 are used as the outputs;

[0084] Conduct expert training on the neural network model. Input 100 groups of X 1 ~X 5 data and give expert judgments for each group of input data; M 1 , N 1 , J 1 are divided into four levels. M 1 and N 1 The four levels are excellent, good, medium, and poor respectively. The four levels of J 1 are healthy, good, average, and poor;

[0085] Complete the training of the neural network to obtain the neural network health status analysis algorithm Q = G(x(t)); G represents the neural network analysis algorithm, and Q represents the output health status, including M 1 , N 1 , J 1 , where M 1 is the digestion and absorption ability, N 1 is the emotional stability, and J 1 is the physical health status.

[0086] Step S6, the control center monitors in real time the first feeding data, the second feeding data, the total information of the amount of food ingested by the pet in the third preset time period, and the health status of the pet, and transmits the monitoring results to the user terminal in real time. When the monitoring result data is abnormal, the control center promptly sends an alarm message to the user terminal to notify the user.

[0087] In a possible implementation manner, the user terminal includes a mobile phone, a PC, a PAD, etc.

[0088] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. This computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disc, a read-only memory (ROM), or a random access memory (RAM), etc.

[0089] It should be understood that although the steps in the flowchart of the accompanying drawings are shown sequentially in the direction of the arrows, these steps are not necessarily executed sequentially in the direction of the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order restriction, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0090] Continue to refer to Figure 4 , the neural network health status analysis system based on monitoring the pet's food intake described in this embodiment includes:

[0091] The first food intake data acquisition module 401 is used to obtain pet food information, obtain the food intake data of the pet based on the weighing sensor in the pet feeder, and collect the food intake data of the pet and store it in the database; wherein the food intake data includes first food intake data, second food intake data, and third food intake data;

[0092] The first intake judgment module 402 is used for the control center to read the first food intake data in the database, and judge whether the pet intake within the first preset time period meets the threshold of the first intake. When the pet intake meets the threshold of the first intake, step S3 is executed;

[0093] The food intake change rate judgment module 403 is used for the control center to read the second food intake data in the database, obtain the change rate of the pet's food intake based on the second preset time period, judge the change rate of the pet's food intake, and when the change rate of the pet's food intake meets the preset threshold, step S4 is executed;

[0094] The third food intake data acquisition module 404 is configured to use the first food intake data, the second food intake data, and the total information on the amount of food ingested by the pet in the third preset time period as the input of a neural network model to obtain the health status of the pet;

[0095] The pet health status acquisition module 405 is configured to use the first food intake data, the second food intake data, and the total information on the amount of food ingested by the pet in the third preset time period as the input of a neural network model to obtain the health status of the pet.

[0096] The real-time monitoring module 406 is configured to enable the control center to monitor in real time the first food intake data, the second food intake data, the total information on the amount of food ingested by the pet in the third preset time period, and the health status of the pet, and transmit the monitoring results to the user terminal in real time. When the monitoring result data is abnormal, the control center promptly sends an alarm message to the user terminal to notify the user.

[0097] To solve the above technical problems, the embodiments of the present application further provide a computer device. For details, please refer to Figure 5 , Figure 5 which is the basic structural block diagram of the computer device in this embodiment.

[0098] The computer device 5 includes a memory 5a, a processor 5b, and a network interface 5c that are communicatively connected to each other through a system bus. It should be noted that only the computer device 5 with components 5a - 5c is shown in the figure, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be alternatively implemented. Among them, those skilled in the art of the present technology can understand that a computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0099] The computer device can be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The computer device can perform human-computer interaction with the user through a keyboard, a mouse, a remote control, a touchpad, a voice control device, or other means.

[0100] The memory 5a includes at least one type of readable storage medium, which includes flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disc, etc. In some embodiments, the memory 5a may be an internal storage unit of the computer device 5, such as the hard disk or memory of the computer device 5. In other embodiments, the memory 5a may also be an external storage device of the computer device 5, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, FlashCard, etc. equipped on the computer device 5. Of course, the memory 5a may also include both the internal storage unit and the external storage device of the computer device 5. In this embodiment, the memory 5a is generally used to store the operating system and various application software installed on the computer device 5, such as the program code of the neural network health status analysis method based on monitoring the pet's food intake. In addition, the memory 5a can also be used to temporarily store various data that have been output or will be output.

[0101] In some embodiments, the processor 5b may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 5b is generally used to control the overall operation of the computer device 5. In this embodiment, the processor 5b is used to run the program code stored in the memory 5a or process data, such as running the program code of the neural network health status analysis method based on monitoring the pet's food intake.

[0102] The network interface 5c may include a wireless network interface or a wired network interface, and this network interface 5c is generally used to establish a communication connection between the computer device 5 and other electronic devices.

[0103] This application also provides another implementation manner, that is, to provide a non-volatile computer-readable storage medium, which stores a program of a neural network health status analysis method based on monitoring the pet's food intake, and the neural network health status analysis based on monitoring the pet's food intake can be executed by at least one processor, so that the at least one processor executes the steps of the neural network health status analysis method based on monitoring the pet's food intake as described above.

[0104] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0105] Obviously, the above-described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The accompanying drawings show the preferred embodiments of the present application, but do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosed content of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions described in the foregoing specific embodiments, or perform equivalent replacements on some of the technical features. Any equivalent structure directly or indirectly using the content of the specification and drawings of the present application in other related technical fields is similarly within the scope of the patent protection of the present application.

Claims

1. A neural network health status analysis method based on monitoring pet food intake, characterized in that: include: Step S1, obtaining pet food information, obtaining pet food eating data based on a weighing sensor in a pet feeder, collecting the eating data of the pet and storing it in a database; wherein the eating data includes first eating data, second eating data and third eating data; Step S2, the control center reads the first eating data in the database, determines whether the pet's intake in the first preset time period meets the threshold of the first intake, and executes step S3 when the pet's intake meets the threshold of the first intake; Step S3, the control center reads the second eating data from the database, obtains the change rate of the pet's food intake based on the second preset time period, and judges the change rate of the pet's food intake. When the change rate of the pet's food intake meets the preset threshold, step S4 is executed; Step S4, the control center reads the third eating data in the database, and based on the third preset time period, when the change rate of the amount of food ingested by the pet meets the preset threshold, obtains the total amount of food ingested by the pet in the third preset time period; Step S5, using the first eating data, the second eating data and the total amount of food intake of the pet in the third preset time period as inputs of a neural network model to obtain the health status of the pet; Step S6, the control center monitors the first eating data, the second eating data, the total amount of food intake of the pet in the third preset time period and the health status of the pet in real time, and transmits the monitoring result data to the user terminal in real time. When the monitoring result data is abnormal, the control center promptly sends an alarm message to the user terminal to notify the user.

2. The neural network health status analysis method based on monitoring pet food intake according to claim 1, characterized in that: The weighing sensor in step S1 is arranged on the magnetic base in the pet feeder, and weighing sensors are arranged on four sides of the magnetic base, and the weighing sensors are half-bridge type weighing sensors.

3. The neural network health status analysis method based on monitoring pet food intake according to claim 1, characterized in that: The first preset time period in step S2 is the time period when the pet eats pet food for the first time.

4. The neural network health status analysis method based on monitoring pet food intake according to claim 1, characterized in that: The control center in step S2 reads the first eating data in the database and determines whether the pet's intake in the first preset time period meets the threshold of the first intake, which is specifically manifested as follows: Assume that the value of the pet cat's food intake over time is x(t), 0≤t≤24h, and the sampling interval is t s = 1s, set the initial weight x(0) = x0, set X F =0.5g; Select 0≤t≤60s, calculate Δx=x(60s)-x(0), if Δx≤X F , then the output Flg1=1, if Δx≤X is not satisfied F Then the output Flg1=0; Where X F It indicates the threshold of the first intake of a pet. When the threshold of the first intake reaches 0.5g or above, Flg1=0. If the threshold of the first intake does not reach 0.5g, Flg1=1.

5. The neural network health status analysis method based on monitoring pet food intake according to claim 4 is characterized in that: The control center in step S3 reads the second eating data in the database, obtains the change rate of the pet's food intake based on the second preset time period, and judges the change rate of the pet's food intake, which is specifically manifested as: Based on the data within any Δt=60s in the second preset time period, If the preset threshold is met, the output is Flg2 = 1, and the If the preset threshold is not met, the output is Flg2=0; in It indicates the rate of change of the amount of food a pet consumes within any 60 seconds, that is, the speed of eating. When Flg2=1, if the change rate of the pet's food intake does not reach When Flg2=0, dx MAX Indicates the maximum threshold of the pet's food intake speed.

6. The neural network health status analysis method based on monitoring pet food intake according to claim 5, characterized in that: The control center in step S4 reads the third eating data in the database, and based on the third preset time period, when the change rate of the amount of food ingested by the pet meets the preset threshold, obtains the total amount of food ingested by the pet in the third preset time period, which is specifically manifested as: Based on the initial weight x0 of the pet, the total food intake change of the pet within 24 hours is obtained, that is, dx end =x(24h)-x0, and the change in the total food intake of the pet within 24 hours is taken as the third food intake data.

7. The neural network health status analysis method based on monitoring pet food intake according to claim 6, characterized in that: The first eating data, the second eating data and the total amount of food intake of the pet in the third preset time period are used as inputs of the neural network model to obtain the health status of the pet, which is specifically manifested as follows: x(t), Flg1, Flg2, dx end =x(24h)-x0 are respectively input into the neural network model as X1~X5, and M1, N1, J1 are output; The neural network model is trained by experts, 100 groups of X1-X5 data are input, and expert judgment is given for each group of input data; after the neural network training is completed, a neural network health status analysis algorithm Q=G(x(t)) is obtained; G represents the neural network analysis algorithm, and Q represents the output health status, including M1, N1, and J1, where M1 represents digestion and absorption capacity, N1 represents emotional stability, and J1 represents physical health status.

8. A neural network health status analysis system based on monitoring pet food intake, used to implement the neural network health status analysis method based on monitoring pet food intake of claims 1-7, characterized in that: include: A first eating data collection module is used to obtain pet food information, obtain pet food eating data based on a weighing sensor in a pet feeder, and collect the eating data of the pet and store it in a database; wherein the eating data includes first eating data, second eating data and third eating data; The first intake judgment module is used for the control center to read the first eating data in the database, judge whether the pet's intake in the first preset time period meets the threshold of the first intake, and execute step S3 when the pet's intake meets the threshold of the first intake; The food intake change rate judgment module is used for the control center to read the second eating data in the database, obtain the change rate of the pet's food intake based on the second preset time period, and judge the change rate of the pet's food intake. When the change rate of the pet's food intake meets the preset threshold, step S4 is executed; A third eating data acquisition module, used to use the first eating data, the second eating data and the total amount of food intake of the pet in the third preset time period as inputs of a neural network model to acquire the health status of the pet; A pet health status acquisition module, used to use the first eating data, the second eating data and the total amount of food intake of the pet in the third preset time period as inputs of a neural network model to acquire the health status of the pet; The real-time monitoring module is used for the control center to monitor the first eating data, the second eating data, the total amount of food intake of the pet in the third preset time period and the health status of the pet in real time, and transmit the monitoring results to the user terminal in real time. When the monitoring result data is abnormal, the control center promptly sends an alarm message to the user terminal to notify the user.

9. An electronic device, characterized in that: include: one or more processors; Memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions, which, when executed by the device, enable the device to perform the steps of the neural network health status analysis method based on monitoring the pet's food intake as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer readable storage medium stores a computer program, which, when executed on a computer, enables the computer to execute the steps of the neural network health status analysis method based on monitoring the food intake of a pet as described in any one of claims 1 to 7.

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