Pet feeding control method and device, electronic equipment and storage medium

Abstract

This invention relates to the field of computer technology, providing a pet feeding control method, device, electronic device, and storage medium. The pet feeding control method includes: acquiring the weight of a target pet and obtaining the target pet's resting energy intake based on the weight; acquiring an image of the target pet and obtaining the target pet's body condition type based on the image; obtaining a target adjustment coefficient determined at least based on the target pet's body condition type; obtaining the target energy intake of the target pet based on the target adjustment coefficient and the resting energy intake; and controlling the feeding of the target pet based on the target energy intake. This invention obtains a basic resting energy intake from the pet's weight, obtains a pet body condition type reflecting obesity trends from the pet's image, and then determines a target adjustment coefficient to adjust the pet to a healthy state. It then controls the amount of food fed to the pet based on the resting energy intake and the target adjustment coefficient, achieving dynamic adjustment of the diet according to the pet's own condition and enabling health management of the pet.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically, to a pet feeding control method, apparatus, electronic device, and storage medium. Background Technology

[0002] As living standards improve, people are seeking spiritual and emotional fulfillment beyond their daily lives. Pets are becoming indispensable members of more and more families, leading to a period of rapid development for the pet industry.

[0003] Pets require meticulous care in terms of health, food, shelter, and transportation, which has driven the pet industry towards more refined practices. Currently, pet obesity is the leading cause of death among pets, and the root cause of pet obesity is overfeeding.

[0004] Existing smart pet feeding machines generally feed pets at fixed times and in fixed quantities, and cannot dynamically adjust their diet according to the pet's own needs.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides a pet feeding control method, device, electronic device and storage medium, which can dynamically acquire information such as the pet's physical condition type, dynamically adjust the pet's diet, and realize the health management of the pet.

[0007] One aspect of the present invention provides a pet feeding control method, comprising: acquiring the weight of a target pet and obtaining the resting energy intake of the target pet based on the weight; acquiring an image of the target pet and obtaining the body condition type of the target pet based on the image; obtaining a target adjustment coefficient determined at least based on the body condition type of the target pet, obtaining a target energy intake of the target pet based on the target adjustment coefficient and the resting energy intake; and controlling the feeding of the target pet based on the target energy intake.

[0008] In some embodiments, obtaining the body condition type of the target pet based on the image includes: extracting body condition features from the image to obtain the body condition features of the target pet; traversing a body condition feature library based on at least the body condition features of the target pet, wherein the body condition feature library stores body condition feature sets corresponding to different body condition scores, and obtaining a target body condition feature set with the highest matching degree to the body condition features of the target pet; and obtaining the body condition type of the target pet based on the body condition score corresponding to the target body condition feature set.

[0009] In some embodiments, obtaining the target adjustment coefficient determined at least based on the body condition type of the target pet includes: obtaining a first adjustment coefficient corresponding to the body condition type of the target pet based on a first mapping relationship between the body condition type and the first adjustment coefficient, and using it as the target adjustment coefficient; in the first mapping relationship, the body condition type indicates the degree of obesity, and the first adjustment coefficient is negatively correlated with the degree of obesity indicated by the body condition type.

[0010] In some embodiments, after obtaining the physical condition characteristics of the target pet, the method further includes: obtaining pet touch features input in response to the physical condition characteristics of the target pet, and traversing the physical condition feature library according to the physical condition characteristics of the target pet and the pet touch features; after obtaining the first adjustment coefficient corresponding to the physical condition type of the target pet, the method further includes: obtaining the adjustment range input in response to the first adjustment coefficient, updating the first adjustment coefficient, and using the updated first adjustment coefficient as the target adjustment coefficient.

[0011] In some embodiments, obtaining the target adjustment coefficient determined at least according to the body condition type of the target pet further includes: collecting the movement data of the target pet; obtaining the movement intensity of the target pet based on the movement data; obtaining a second adjustment coefficient corresponding to the movement intensity of the target pet based on the mapping relationship between movement intensity and a second adjustment coefficient, wherein the second adjustment coefficient is positively correlated with the movement intensity; and determining the target adjustment coefficient based on the first adjustment coefficient and the second adjustment coefficient.

[0012] In some embodiments, the following formula is used when determining the target adjustment coefficient: λ C =w1*λ1+w2*λ2; where, λ C The target adjustment coefficient is defined as follows: w1 is the first weight coefficient, λ1 is the first adjustment coefficient, w2 is the second weight coefficient, λ2 is the second adjustment coefficient, and w2 > w1.

[0013] In some embodiments, the target pet's exercise data is the target pet's exercise data from the previous day. After obtaining the target pet's exercise intensity based on the exercise data, the method further includes: determining whether the target pet's body condition type corresponds to the highest degree of obesity and whether the target pet's exercise intensity corresponds to the lowest exercise intensity; if so, providing exercise guidance to the target pet and monitoring the target pet's exercise data for the current day until the target pet's exercise data for the current day reaches a preset value, wherein the preset value is determined based on the target pet's weight.

[0014] In some embodiments, guiding the movement of the target pet includes: controlling the pet feeder to emit a guiding sound and / or guiding light that is bound to and stored with the pet identity of the target pet; monitoring whether the daily movement data of the target pet reaches the preset value; if not, delaying the feeding time of the pet feeder corresponding to the target pet, or periodically opening and closing the food outlet when the pet feeder feeds the target pet.

[0015] In some embodiments, the target energy intake is the target energy intake for the day; controlling the feeding of the target pet based on the target energy intake includes: acquiring food images, identifying energy tags from the food images, and obtaining the energy per unit of food; obtaining the daily food intake of the target pet based on the target energy intake and the energy per unit of food; obtaining the amount of food fed per meal and the feeding time of the target pet based on the daily food intake, and controlling the pet feeder to feed the target pet.

[0016] In some embodiments, controlling the pet feeder to feed the target pet includes: after each feeding time arrives, acquiring a frontal image of the pet located within the food outlet area of ​​the pet feeder, and extracting the pet's nose print features from the frontal image; performing pet identification based on the pet's nose print features, and determining whether the pet identity corresponding to the pet's nose print features matches the pet identity of the target pet; if so, controlling the pet feeder to feed the target pet.

[0017] In some embodiments, obtaining the resting energy intake of the target pet based on its weight includes: when the weight is within a preset weight range, calculating the resting energy intake of the target pet using the following formula: RER = 30 * weight + 70; when the weight exceeds the preset weight range, calculating the resting energy intake of the target pet using the following formula: RER = 70 * weight 0.75 Where RER is the resting energy intake of the target pet, weight is the body weight in kg, and R is the preset weight range (2 kg). <R<45kg。

[0018] Another aspect of the present invention provides a pet feeding control device, comprising: a resting energy acquisition module for acquiring the weight of a target pet and obtaining the resting energy intake of the target pet based on the weight; a pet body condition acquisition module for acquiring an image of the target pet and obtaining the body condition type of the target pet based on the image; a target energy acquisition module for obtaining a target adjustment coefficient determined at least based on the body condition type of the target pet and obtaining a target energy intake of the target pet based on the target adjustment coefficient and the resting energy intake; and a pet feeding control module for controlling the feeding of the target pet based on the target energy intake.

[0019] In some embodiments, the pet feeding control device is distributed across the pet feeder and the control cloud, and is communicatively connected to the user terminal; or, the pet feeding control device is deployed in the control cloud and is communicatively connected to the user terminal and the pet feeder; or, the pet feeding control device is deployed in the pet feeder and is communicatively connected to the user terminal and the control cloud.

[0020] Another aspect of the present invention provides an electronic device, comprising: a processor; a memory storing executable instructions; wherein, when the executable instructions are executed by the processor, they implement the pet feeding control method as described in any of the above embodiments.

[0021] Another aspect of the present invention provides a computer-readable storage medium for storing a program that, when executed by a processor, implements the pet feeding control method as described in any of the above embodiments.

[0022] The beneficial effects of this invention compared to the prior art include at least the following:

[0023] The system obtains basic resting energy intake by measuring pet weight, obtains pet body condition type reflecting obesity trends by measuring pet images, determines the target adjustment coefficient to adjust the pet to a healthy state, and controls the pet's food intake based on the target energy intake calculated from the target adjustment coefficient and resting energy intake, in conjunction with food energy, to achieve dynamic adjustment of the pet's diet according to the pet's own condition and food type.

[0024] Furthermore, it is possible to collect pet exercise data and calculate the pet's exercise intensity, thereby adjusting the pet's diet in conjunction with the pet's obesity trend and exercise intensity, and achieving comprehensive health management for the pet.

[0025] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0026] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0027] Figure 1 This diagram illustrates an implementation scenario of the pet feeding control method according to an embodiment of the present invention.

[0028] Figure 2 This diagram illustrates the steps of a pet feeding control method according to an embodiment of the present invention.

[0029] Figure 3 This diagram shows a schematic of a module of a pet feeding control device according to an embodiment of the present invention;

[0030] Figure 4 This diagram illustrates the module deployment of a pet feeding control device according to an embodiment of the present invention.

[0031] Figure 5 A schematic diagram of the structure of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation

[0032] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to fully and completely convey the concept of the exemplary embodiments to those skilled in the art.

[0033] The accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0034] Furthermore, the processes shown in the accompanying drawings are merely illustrative and do not necessarily include all steps. For example, some steps can be broken down, some steps can be combined or partially combined, and the actual execution order may change depending on the actual situation. The terms "first," "second," and similar terms used in the specific description do not indicate any order, quantity, or importance, but are only used to distinguish different components. It should be noted that, unless otherwise specified, embodiments of the present invention and features in different embodiments can be combined with each other.

[0035] Figure 1 This illustrates an implementation scenario of a pet feeding control method, referring to... Figure 1 As shown, the pet feeding control method of the present invention can be interactively executed between the pet feeder 110, the user terminal 120, and the control cloud 130.

[0036] The pet feeder 110 is equipped with a data acquisition module and a communication module. The data acquisition module is used to collect relevant information about the pet 140 and food 150, and may include an image acquisition module, a weight detection module, etc. The communication module is used to communicate with the user terminal 120, and may be a Bluetooth module. The pet feeder 110 may also be equipped with a processing module to perform partial or complete calculations on the collected pet and food information, which may also be performed by the control cloud 130. The pet feeder 110 also has the common functions of existing smart feeders, such as automatic food dispensing, etc., which will not be described in detail here.

[0037] User terminal 120 can be any smart terminal, such as a smartphone, tablet, etc., to achieve communication connection with pet feeder 110 and control cloud 130. Users can obtain collected pet and food information, as well as related information obtained after each step of calculation and processing, through user terminal 120. Users can also input user commands through user terminal 120 to control the calculation and processing of pet feeder 110 / control cloud 130 and to control the food dispensing from pet feeder 110.

[0038] Figure 2 The main steps of a pet feeding control method in one embodiment are shown, with reference to Figure 2 As shown, the pet feeding control method in this embodiment includes: step S210, collecting the weight of the target pet and obtaining the resting energy intake of the target pet based on the weight; step S220, collecting an image of the target pet and obtaining the body condition type of the target pet based on the image; step S230, obtaining at least a target adjustment coefficient determined based on the body condition type of the target pet, and obtaining the target energy intake of the target pet based on the target adjustment coefficient and the resting energy intake; and step S240, controlling the feeding of the target pet based on the target energy intake.

[0039] The pet feeding control method described above obtains the basic resting energy intake from the pet's weight, obtains the pet's body condition type reflecting obesity trends from pet images, and then determines the target adjustment coefficient to adjust the pet to a healthy state. Based on the target energy intake calculated from the target adjustment coefficient and resting energy intake, the pet's feeding amount is controlled, realizing dynamic adjustment of the pet's diet according to the pet's own situation, and targeted health management for the pet.

[0040] The following is a detailed explanation of each step in the pet feeding control method.

[0041] The target pet's weight can be detected by the pet feeder's weight detection module. When the target pet stands on the feeder's mat, its weight can be automatically measured. Resting energy intake is then calculated based on weight, specifically: when the pet's weight is within a preset weight range, resting energy intake is calculated using the formula "RER = 30 * weight + 70"; when the pet's weight exceeds the preset weight range, resting energy intake is calculated using "RER = 70 * weight". 0.75 The formula is used to calculate resting energy intake; where RER (Resting Energy Requirement) is the resting energy intake, weight is the pet's weight in kg, and R is the preset weight range, 2kg. <R<45kg。

[0042] Images of the target pet can be captured by the image acquisition module of the pet feeder, specifically side and top-view images. The pet's body condition type is determined from the images, including: image processing (using mature image processing techniques such as rotation, translation, cropping, binarization, noise filtering, edge detection, and feature extraction, which will not be elaborated further) and body condition feature extraction (based on the requirements of the Body Condition Score (BCS) table) to obtain the target pet's body condition features; at least based on the target pet's body condition features, the body condition feature database is traversed, storing body condition feature sets corresponding to different body condition scores, to obtain the target body condition feature set with the highest matching degree; based on the body condition score corresponding to the target body condition feature set, the target pet's body condition type is determined.

[0043] Body Condition Score (BCS) is a standardized mechanism for measuring the body shape of small animals. The BCS table contains nine BCS scores, from BCS 1 to BCS 9. Each BCS score corresponds to certain body condition characteristics, and each set of body condition characteristics stored in the body condition characteristic database corresponds to the body condition characteristics of one BCS score. The main areas of examination include: skeleton, including ribs, lumbar vertebrae, and pelvis (hip bones); subcutaneous fat thickness and muscle density (with a focus on the shoulders and back); abdominal circumference, abdominal wall folds, and waist contour. The body condition characteristics corresponding to BCS 1 through BCS 9 are described below.

[0044] BCS 1: From a certain distance, the ribs, lumbar vertebrae, pelvic bones, and all bony prominences are clearly visible; no visible fat is present, and muscle mass is significantly lacking. BCS 2: The ribs, lumbar vertebrae, and pelvic bones are easily visible; no palpable fat; some prominences on other bones. BCS 3: The ribs are easily palpable and visible; no palpable fat; the upper lumbar vertebrae are visible; the pelvic bones are prominent; the lumbar and abdominal folds are obvious. BCS 4: The ribs are easily palpable, with a small amount of fat covering them; the lumbar region is easily visible when viewed from above; the abdominal folds are obvious. BCS 5: The ribs are palpable and not excessively covered with fat; the lumbar region is easily visible when viewed from above; the abdomen is tucked in when viewed from the side. BCS 6: The ribs are palpable, with slightly excessive fat covering them; the lumbar region is discernible from above, but not prominent; the abdominal folds are visible. BCS 7: The ribs are difficult to palpable, with excessive fat covering them; significant fat deposits in the lumbar region and tailbone area; the lumbar region is not visible or barely visible; the abdominal folds may be visible. BCS 8: Ribs are not palpable due to excessive fat coverage, or may be palpable under pressure; excessive fat deposition in the loin and tail root; loin not visible; no abdominal folds; abdomen may be noticeably distended. BCS 9: Excessive fat deposition in the chest, spine, and tail root; absence of loin and abdominal folds; fat deposition in the neck and limbs; abdomen noticeably distended.

[0045] When extracting body condition features from an image of a target pet, regions such as ribs, waist, and abdomen can be detected separately to extract the pet's body condition features. After obtaining these features, the pet feeder / control cloud platform can also feed back the extracted features to the user terminal and prompt the user terminal to output the pet's tactile characteristics. For example, it can send a text / voice message to the user terminal: "Please touch your pet's ribs and input your tactile sensation." Thus, the pet feeder / control cloud platform receives the pet's tactile characteristics in response to the target pet's body condition features. Based on the target pet's body condition features and the pet's tactile characteristics, it traverses the body condition feature database to obtain the most matching target body condition feature set, thereby improving the accuracy of determining the target pet's body condition type.

[0046] Of the nine BCS scores from BCS 1 to BCS 9, BCS 1 to BCS 3 correspond to a pet's body condition type of being underweight, BCS 4 and BCS 5 correspond to a pet's body condition type of being in ideal shape, and BCS 6 to BCS 9 correspond to a pet's body condition type of being overweight. Of course, in other embodiments, the pet's body condition type corresponding to different BCS scores can be adjusted as needed, and is not limited to the examples listed here.

[0047] After obtaining the target pet's body condition type, a target adjustment coefficient can be determined accordingly. Obtaining a target adjustment coefficient determined at least based on the body condition type specifically includes: obtaining a first adjustment coefficient corresponding to the target pet's body condition type based on a first mapping relationship between body condition type and first adjustment coefficient, which serves as the target adjustment coefficient; in the first mapping relationship, the body condition type indicates the degree of obesity, and the first adjustment coefficient is negatively correlated with the degree of obesity indicated by the body condition type.

[0048] Taking the body condition types of underweight, ideal body shape, and overweight as an example, the corresponding first adjustment coefficients are 1.4, 1.2, and 1.0, respectively. Of course, in other embodiments, the first adjustment coefficients corresponding to different body condition types can be adjusted as needed, and are not limited to those listed here.

[0049] Furthermore, after obtaining the first adjustment coefficient corresponding to the target pet's body condition type, the process may further include: the pet feeder / control cloud platform feeding back the first adjustment coefficient corresponding to the target pet's body condition type to the user terminal. The user can then adjust the first adjustment coefficient within a preset range (e.g., ±10%) based on their preference for the target pet's body shape via the user terminal. Thus, the pet feeder / control cloud platform receives the adjustment range within the preset range in response to the first adjustment coefficient, updates the first adjustment coefficient, and uses the updated first adjustment coefficient as the target adjustment coefficient.

[0050] After obtaining the target adjustment factor, multiplying it by the resting energy intake yields the target energy intake. In this embodiment, the target energy intake is the target energy intake for the day. The pet feeder / control cloud can calculate the target energy intake for the target pet each morning based on information such as the target pet's weight and image detected the previous day. Of course, in other embodiments, the target energy intake can also be calculated every half day, before each meal, or at other suitable times, and is not limited to the examples listed here.

[0051] The feeding of the target pet is controlled based on the target energy intake. Specifically, this includes: acquiring food images, identifying energy labels from the food images, and obtaining the energy per unit of food; obtaining the target pet's daily food intake based on the target energy intake and the energy per unit of food; obtaining the target pet's food amount and feeding time per meal based on the daily food intake, and controlling the pet feeder to feed the target pet, that is, controlling the pet feeder to provide the target pet with the corresponding amount of food at the corresponding time.

[0052] Food images specifically refer to images of food packaging. OCR (Optical Character Recognition) technology can be used to identify energy labels from food packaging images. The process of identifying energy labels using OCR technology is as follows: Identifying the text region in the food image (this can be done by using a sliding window algorithm to traverse the entire food image, judging the features of supervised labeled training samples, finding the target image, and extracting it into a rectangle); segmenting the text region into rectangles, breaking it down into different characters (this can be done by moving a one-dimensional sliding window within the rectangle to determine the spacing between characters and segment them); character classification (this can be done using a supervised algorithm to predict the segmented characters); recognizing the text (ultimately recognizing the entire character); and post-processing and correction, where the recognized text is further processed and corrected. For example, if the recognition model identifies the word "Because" as "8ecause," a grammar detector can be used to correct this spelling error, replacing "8" with "B" to complete the correction. This completes the entire OCR process.

[0053] In a specific example, the energy label identified from the food image is 5500 kcal / kg. Based on this, the energy per unit of food, for example, the energy per gram of food, is 5.5 kcal / g. Furthermore, the target pet in this example is a young Border Collie puppy weighing 12.6 kg. The calculated Resting Energy Requirement (RER) is 30 * 12.6 + 70 = 448 kcal. The target pet's BCS score is BCS 2, corresponding to an underweight condition. The target adjustment factor is 1.4, so the target Daily Energy Requirement (DER) is 1.4 * 448 = 627.2 kcal. Therefore, based on the target energy intake and the energy per unit of food, the target pet's daily food intake is 627.2 / 5.5 = 114 g.

[0054] Furthermore, in one embodiment, when there are two or more pets, to ensure that food is provided to the target pet without being accidentally eaten by other pets, the pet feeder is controlled to feed the target pet. Specifically, this includes: after each feeding time, acquiring a frontal image of the pet located within the food outlet area of ​​the pet feeder, and extracting the pet's nose print features from the frontal image; performing pet identification based on the pet's nose print features, and determining whether the pet identity corresponding to the pet's nose print features matches the pet identity of the target pet; if so, controlling the pet feeder to feed the target pet.

[0055] The extraction and identification of pet nose print features can utilize existing technologies, and this invention does not impose any limitations on this. When the target pet's feeding time arrives, the image acquisition module of the pet feeder monitors the frontal images of pets within the food dispensing area of ​​the feeder, ensuring that no food is dispensed when other pets approach, until the target pet is detected, thus achieving precise food delivery to the target pet without the risk of other pets accidentally ingesting it.

[0056] When there are two or more pets, the pet identity of each pet can be pre-recorded using the pet's nose print feature. When collecting pet information such as pet weight and pet image, the collected pet information is bound and stored with the corresponding pet identity.

[0057] Additionally, if you have more than one pet, you can either set up a separate pet feeder for each pet, or multiple pets can share one pet feeder. If multiple pets share one pet feeder, you can stagger the feeding times for each pet to ensure that each pet is fed accurately.

[0058] Furthermore, in one embodiment, in addition to adjusting the pet's diet based on the obesity trend reflected by the pet's physical condition characteristics, the intensity of the pet's exercise is also taken into account to achieve more comprehensive health management for the pet.

[0059] Specifically, obtaining the target adjustment coefficient, which is determined at least based on the target pet's body condition type, also includes: collecting the target pet's movement data; obtaining the target pet's movement intensity based on the movement data; obtaining the second adjustment coefficient corresponding to the target pet's movement intensity based on the mapping relationship between movement intensity and the second adjustment coefficient, where the second adjustment coefficient is positively correlated with movement intensity; and determining the target adjustment coefficient based on the first adjustment coefficient and the second adjustment coefficient.

[0060] The target pet's movement data can be collected through the image acquisition module of the pet feeder. For example, the image acquisition module monitors the target pet in real time and counts its steps as movement data. When calculating the target pet's target energy intake for the day, the target pet's movement data from the previous day can be used. That is, the pet feeder monitors the pet's movement data and saves the day's movement data for use the following day when calculating the pet's exercise intensity and target energy intake. There is a preset mapping relationship between movement data and exercise intensity, which can be set as needed. The specific exercise intensity can include very low intensity, medium intensity, and very high intensity, with second adjustment coefficients corresponding to the three exercise intensities being 2.0, 3.0, and 4.0, respectively. Of course, in other embodiments, the type of exercise intensity and the value of the second adjustment coefficient can be adjusted as needed, and are not limited to the examples listed here.

[0061] When determining the target adjustment factor based on the first and second adjustment factors, the formula is: λ C=w1*λ1+w2*λ2; where, λ C Let w1 be the target adjustment coefficient, λ1 be the first adjustment coefficient, w2 be the second adjustment coefficient, λ2 be the second adjustment coefficient, and w2 > w1.

[0062] The above formula ensures that the feeding control of the target pet fully considers the pet's physical condition and activity level. By setting w1 and w2, the influence of the pet's activity on its diet is made greater than the influence of its physical condition, thus avoiding situations where excessive consumption leads to insufficient intake and negatively impacting the pet's health. This achieves comprehensive and healthy dietary management for the target pet.

[0063] In a specific example, w1 + w2 > 1 and both w1 and w2 are less than 1, for example, w1 is 0.6 and w2 is 0.8. Following the example above, the first adjustment factor is 1.4, and the second adjustment factor is 3.0. Therefore, the target adjustment factor is: 0.6 * 1.4 + 0.8 * 3.0 = 3.24. The target energy intake DER is: 3.24 * 448 = 1451.52 kcal. In other examples, w1 and w2 can be set to other values ​​as needed.

[0064] Furthermore, after obtaining the target pet's exercise intensity based on the exercise data, the process also includes: determining whether the target pet's body condition type corresponds to the highest level of obesity and whether the target pet's exercise intensity corresponds to the lowest level of exercise; if so, guiding the target pet to exercise and monitoring the target pet's daily exercise data until the target pet's daily exercise data reaches a preset value, which is determined based on the target pet's weight. The preset value is the lower limit of the target pet's daily exercise volume; generally, the heavier the target pet, the higher the corresponding preset value.

[0065] If the target pet's body condition corresponds to the highest level of obesity and its exercise intensity corresponds to the lowest level of exercise intensity, then simply adjusting the diet to reduce the target pet's energy intake to achieve weight loss may affect the target pet's health. Therefore, it is better to guide the target pet to exercise so that it can achieve healthy weight loss by increasing exercise and adjusting its diet.

[0066] When guiding a target pet to exercise, the pet feeder can be controlled to emit guiding sounds and / or guiding lights that are linked to the target pet's identity. Simultaneously, the target pet's daily exercise data is monitored to see if it reaches a preset value. If not, it indicates that the target pet cannot effectively exercise even with the guidance of the sound / light. In this case, the feeding time corresponding to the target pet can be delayed, or the food dispensing nozzle can be periodically opened and closed during feeding to "force" the target pet to exercise.

[0067] The aforementioned process of "forcing" the target pet to exercise can be carried out under the control of the user terminal to ensure that it will not affect the health of the target pet.

[0068] In summary, the pet feeding control method described above obtains the basic resting energy intake based on the pet's weight, obtains the pet's body condition type reflecting obesity trends through pet images, and then determines the target adjustment coefficient to bring the pet to a healthy state. Based on the target adjustment coefficient and the resting energy intake, which is calculated to meet the individual pet's needs, the method combines food energy to control the amount of food fed to the pet, achieving dynamic adjustment of the pet's diet according to the pet's own condition and food type. Furthermore, it can also collect pet exercise data and calculate the pet's exercise intensity, thereby jointly adjusting the pet's diet based on the pet's obesity trend and exercise intensity, achieving comprehensive health management for the pet.

[0069] This invention also provides a pet feeding control device, which can be used to implement the pet feeding control method described in any of the above embodiments. The features and principles of the pet feeding control method described in any of the above embodiments can be applied to the following pet feeding control device embodiments. In the following pet feeding control device embodiments, the features and principles of pet feeding control already explained will not be repeated.

[0070] Figure 3 The main module of a pet feeding control device in one embodiment is shown, with reference to... Figure 3 As shown, the pet feeding control device 300 in this embodiment includes: a resting energy acquisition module 310, used to acquire the weight of the target pet and obtain the resting energy intake of the target pet based on the weight; a pet body condition acquisition module 320, used to acquire an image of the target pet and obtain the body condition type of the target pet based on the image; a target energy acquisition module 330, used to obtain a target adjustment coefficient determined at least based on the body condition type of the target pet, and obtain the target energy intake of the target pet based on the target adjustment coefficient and the resting energy intake; and a pet feeding control module 340, used to control the feeding of the target pet based on the target energy intake.

[0071] Each module in the aforementioned pet feeding control device 300 can be distributed between the pet feeder and the control cloud, and communicate with the user terminal; or, it can be deployed in the control cloud and communicate with the user terminal and the pet feeder; or, it can be deployed in the pet feeder and communicate with the user terminal and the control cloud.

[0072] The interaction between the pet feeder, user terminal, and control cloud can be referenced. Figure 1 As shown. In Figure 1 Based on the illustrated embodiment, Figure 4 The module deployment of a pet feeding control device in one embodiment is shown, with reference to Figure 4 As shown, the resting energy acquisition module may specifically include a weight detection module 411 deployed on the pet feeder 110 and a resting energy calculation module 412 deployed on the control cloud 130. The pet body condition acquisition module may specifically include an image acquisition module 421 and an image processing module 422 deployed on the pet feeder 110, and a body condition feature processing module 423 deployed on the control cloud 130. The target energy acquisition module may specifically include a target energy calculation module 431 deployed on the control cloud 130. The pet feeding control module may specifically include an image acquisition module 421, an energy recognition module 442 deployed on the pet feeder 110, and a food weight calculation module 443 deployed on the control cloud 130. The user terminal 120 is equipped with a Bluetooth module that communicates with the pet feeder 110, as well as an application APP for displaying information and inputting commands, which is not specifically shown in the figure. In addition, the pet feeder 110 may also be equipped with a motion data monitoring module to monitor the motion data of the target pet, which is not specifically shown in the figure. The specific principles of each module can be found in the description of the above-mentioned pet feeding control method embodiment, and will not be repeated here.

[0073] The pet feeding control device of the present invention can obtain the basic resting energy intake through the pet's weight, obtain the pet's body condition type reflecting obesity trends through pet images, and then determine the target adjustment coefficient to adjust the pet to a healthy state. Based on the target energy intake calculated from the target adjustment coefficient and resting energy intake to meet the individual needs of the pet, the device combines food energy to control the amount of food fed to the pet, thereby realizing dynamic adjustment of the pet's diet according to the pet's own condition and food type. Furthermore, it can also collect pet exercise data and calculate the pet's exercise intensity, thereby adjusting the pet's diet in conjunction with the pet's obesity trend and exercise intensity, achieving comprehensive health management for the pet.

[0074] This invention also provides an electronic device, including a processor and a memory, wherein the memory stores executable instructions, and when the executable instructions are executed by the processor, the pet feeding control method described in any of the above embodiments is implemented.

[0075] The electronic device of this invention can obtain basic resting energy intake from a pet's weight, obtain a pet's body condition type reflecting obesity trends from pet images, and then determine a target adjustment coefficient to bring the pet to a healthy state. Based on the target energy intake calculated from the target adjustment coefficient and resting energy intake to meet the individual needs of the pet, the device combines food energy to control the amount of food fed to the pet, thereby achieving dynamic adjustment of the pet's diet according to the pet's own condition and food type. Furthermore, it can also collect pet exercise data and calculate the pet's exercise intensity, thereby adjusting the pet's diet in conjunction with the pet's obesity trend and exercise intensity, achieving comprehensive health management for the pet.

[0076] Figure 5 This is a schematic diagram of the structure of the electronic device in an embodiment of the present invention. It should be understood that... Figure 5 The modules are merely shown schematically. These modules can be virtual software modules or actual hardware modules. The merging, splitting, and addition of other modules are all within the scope of protection of this invention.

[0077] like Figure 5 As shown, the electronic device 600 is presented in the form of a general-purpose computing device. The components of the electronic device 600 include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0078] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps of the pet feeding control method described in any of the above embodiments. For example, the processing unit 610 can perform actions such as... Figure 2 The steps are shown.

[0079] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0080] Storage unit 620 may also include a program / utility 6204 having one or more program modules 6205, such program modules 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0081] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0082] Electronic device 600 can also communicate with one or more external devices 700, which may be one or more of the following: keyboard, pointing device, Bluetooth device, etc. These external devices 700 enable users to interact and communicate with electronic device 600. Electronic device 600 can also communicate with one or more other computing devices, including routers and modems. This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0083] This invention also provides a computer-readable storage medium for storing a program that, when executed, implements the pet feeding control method described in any of the above embodiments. In some possible implementations, various aspects of this invention can also be implemented as a program product comprising program code that, when run on a terminal device, causes the terminal device to execute the pet feeding control method described in any of the above embodiments.

[0084] The computer-readable storage medium of this invention, when executed, can obtain basic resting energy intake from a pet's weight, obtain a pet's body condition type reflecting obesity trends from pet images, and then determine a target adjustment coefficient to bring the pet to a healthy state. Based on the target energy intake calculated from the target adjustment coefficient and resting energy intake to meet the individual needs of the pet, and combined with food energy, it controls the amount of food fed to the pet, realizing dynamic adjustment of the pet's diet according to the pet's own condition and food type. Furthermore, it can also collect pet exercise data, calculate the pet's exercise intensity, and thus adjust the pet's diet in conjunction with the pet's obesity trend and exercise intensity, realizing comprehensive health management of the pet.

[0085] The program product may be a portable compact disc read-only memory (CD-ROM) containing program code and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto; it may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0086] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media include, but are not limited to: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0087] A readable storage medium may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0088] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device, for example, via the Internet using an Internet service provider.

[0089] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A pet feeding control method characterized by, include: Collect the target pet's weight and obtain the target pet's resting energy intake based on the weight; Acquiring an image of the target pet and determining the target pet's physical condition type based on the image includes: extracting physical condition features from the image to obtain the target pet's physical condition features; traversing a physical condition feature database based on the target pet's physical condition features, wherein the physical condition feature database stores physical condition feature sets corresponding to different physical condition scores, and obtaining a target physical condition feature set with the highest matching degree to the target pet's physical condition features; and determining the target pet's physical condition type based on the physical condition score corresponding to the target physical condition feature set. Obtaining a target adjustment coefficient determined at least based on the target pet's body condition type includes: obtaining a first adjustment coefficient corresponding to the target pet's body condition type based on a first mapping relationship between body condition type and a first adjustment coefficient, wherein the body condition type indicates the degree of obesity, and the first adjustment coefficient is negatively correlated with the degree of obesity indicated by the body condition type; collecting the target pet's exercise data, obtaining the target pet's exercise intensity based on the exercise data, and obtaining a second adjustment coefficient corresponding to the target pet's exercise intensity based on a mapping relationship between exercise intensity and a second adjustment coefficient, wherein the second adjustment coefficient is positively correlated with the exercise intensity; and determining the target adjustment coefficient based on the first adjustment coefficient and the second adjustment coefficient. wherein the target adjustment coefficient is determined using the formula λ C = w1 λ1 + w2 λ2; wherein λ C is the target adjustment coefficient, w1 is a first weight coefficient, λ1 is the first adjustment coefficient, w2 is a second weight coefficient, and λ2 is the second adjustment coefficient, and w2 > w1. The target energy intake of the target pet is obtained based on the target adjustment factor and the resting energy intake. The feeding of the target pet is controlled based on the target energy intake.

2. The pet feeding control method as described in claim 1, characterized in that, After obtaining the physical characteristics of the target pet, the process further includes: Obtain pet tactile features input in response to the target pet's physical condition characteristics, and traverse the physical condition feature database based on the target pet's physical condition characteristics and the pet tactile features; After obtaining the first adjustment coefficient corresponding to the body condition type of the target pet, the method further includes: Obtain the adjustment magnitude input in response to the first adjustment coefficient, update the first adjustment coefficient, and use the updated first adjustment coefficient as the target adjustment coefficient.

3. The pet feeding control method as described in claim 1, characterized in that, The target pet's movement data is the target pet's movement data from the previous day. After obtaining the target pet's movement intensity based on the movement data, the process further includes: Determine whether the target pet's body condition type corresponds to the highest degree of obesity and whether the target pet's exercise intensity corresponds to the lowest exercise intensity; If so, exercise guidance is provided to the target pet, and the target pet's daily exercise data is monitored until the target pet's daily exercise data reaches a preset value, which is determined based on the target pet's weight.

4. The pet feeding control method as described in claim 3, characterized in that, The step of guiding the movement of the target pet includes: The pet feeder is controlled to emit a guiding sound and / or guiding light that is linked to and stored in relation to the pet's identity. Monitor whether the target pet's daily activity data reaches the preset value; If not, postpone the feeding time of the pet feeder corresponding to the target pet, or periodically open and close the food outlet when the pet feeder feeds the target pet.

5. The pet feeding control method as described in claim 1, characterized in that, The target energy intake is the target energy intake for the day. The method of controlling the feeding of the target pet based on the target energy intake includes: Collect food images, identify energy tags from the food images, and obtain the energy per unit of food. Based on the target energy intake and the energy per unit of food, the daily food intake of the target pet is obtained; Based on the daily food intake, the amount of food to be fed per meal and the feeding time of each meal for the target pet are obtained, and the pet feeder is controlled to feed the target pet.

6. The pet feeding control method as described in claim 5, characterized in that, The control of the pet feeder to feed the target pet includes: After each feeding time, a frontal image of the pet located within the food outlet area of ​​the pet feeder is captured, and the pet's nose print features are extracted from the frontal image of the pet. Pet identity is identified based on the pet's nose print features, and it is determined whether the pet identity corresponding to the pet's nose print features matches the pet identity of the target pet. If so, control the pet feeder to feed the target pet.

7. The pet feeding control method as described in claim 1, characterized in that, The step of obtaining the resting energy intake of the target pet based on its weight includes: When the target pet's weight is within a preset weight range, the resting energy intake is calculated using the following formula: RER=30 weight+70; When the target pet's weight exceeds the preset weight range, the resting energy intake is calculated using the following formula: RER=70 weight 0.75 ; Where RER is the resting energy intake of the target pet, weight is the body weight in kg, and R is the preset weight range (2 kg). <R<45kg。 8. A pet feeding control device, characterized in that, include: A resting energy acquisition module is used to collect the weight of a target pet and obtain the resting energy intake of the target pet based on the weight. A pet condition acquisition module is used to acquire images of the target pet and obtain the pet's condition type based on the images. This includes: extracting condition features from the images to obtain the target pet's condition features; traversing a condition feature library based on the target pet's condition features, where the library stores condition feature sets corresponding to different condition scores, and obtaining the target condition feature set with the highest matching degree to the target pet's condition features; and obtaining the target pet's condition type based on the condition score corresponding to the target condition feature set. A target energy acquisition module is used to obtain a target adjustment coefficient determined at least according to the body condition type of the target pet, and to obtain the target intake energy of the target pet according to the target adjustment coefficient and the resting intake energy; The target energy acquisition module obtains a target adjustment coefficient determined at least based on the target pet's body condition type, including: obtaining a first adjustment coefficient corresponding to the target pet's body condition type based on a first mapping relationship between body condition type and a first adjustment coefficient, wherein the body condition type indicates obesity level and the first adjustment coefficient is negatively correlated with the obesity level indicated by the body condition type; collecting the target pet's exercise data, obtaining the target pet's exercise intensity based on the exercise data, and obtaining a second adjustment coefficient corresponding to the target pet's exercise intensity based on a mapping relationship between exercise intensity and a second adjustment coefficient, wherein the second adjustment coefficient is positively correlated with the exercise intensity; and determining the target adjustment coefficient based on the first adjustment coefficient and the second adjustment coefficient. When determining the target adjustment coefficient, formula λ is used. C =w1 λ1+w2 λ2; where λ C Let w1 be the first weighting coefficient, λ1 be the first adjustment coefficient, w2 be the second weighting coefficient, λ2 be the second adjustment coefficient, and w2 > w1. A pet feeding control module is used to control the feeding of the target pet based on the target energy intake.

9. The pet feeding control device as described in claim 8, characterized in that, The pet feeding control device is distributed across the pet feeder and the control cloud, and communicates with the user terminal; or The pet feeding control device is deployed in the control cloud and is communicatively connected to the user terminal and the pet feeder; or The pet feeding control device is deployed on the pet feeder and is communicatively connected to the user terminal and the control cloud.

10. An electronic device, characterized in that, include: One processor; A memory, wherein executable instructions are stored; When the executable instructions are executed by the processor, they implement the pet feeding control method as described in any one of claims 1 to 7.

11. A computer-readable storage medium for storing a program, characterized in that, When the program is executed by the processor, it implements the pet feeding control method as described in any one of claims 1 to 7.

Images