Pet health monitoring method and system based on prediction model

Abstract

The invention discloses a pet health monitoring method and system based on a prediction model. The method comprises the following steps: acquiring physiological data and image data of a target pet object; determining a first health prediction result of the target pet object according to the physiological data based on a preset health condition prediction model; determining a second health prediction result of the target pet object according to a trained image recognition algorithm and the image data; according to the first health prediction result and the second health prediction result, performing training optimization on the prediction model to obtain an optimized prediction model; the optimization prediction model is used for further predicting the health condition of the target pet object according to the physiological data. Therefore, the method can achieve the optimization of the prediction model based on the correction training between the prediction relation and image recognition, so as to effectively improve the accuracy and efficiency of pet health monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a pet health monitoring method and system based on a prediction model. Background Art

[0002] As people's love for pets increases, pet health has also become a focus of attention. Without special monitoring technology, people can only judge the health of pets from external characteristics such as the mental state of pets, and cannot understand the health of pets from the physiological data of pets. It is impossible to prevent in advance, which will incur expensive diagnosis and treatment costs. However, most of the existing monitoring technologies still use artificial pre-set data analysis rules to perform health monitoring based on pet data, and do not fully consider the correction between prediction relationship and image recognition to realize the training of prediction model. Therefore, the effect of health monitoring is poor, and it is impossible to achieve more timely intervention treatment of pet diseases. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a pet health monitoring method and system based on a prediction model, which can optimize the prediction model based on correction training between prediction relationship and image recognition, so as to effectively improve the accuracy and efficiency of pet health monitoring and provide a data basis for early intervention in pet disease treatment.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a pet health monitoring method based on a prediction model, the method comprising: Acquiring physiological data and image data of a target pet object; Based on a preset health condition prediction model, and according to the physiological data, determining a first health prediction result of the target pet object; Determine a second health prediction result of the target pet object based on the trained image recognition algorithm and the image data; The prediction model is trained and optimized according to the first health prediction result and the second health prediction result to obtain an optimized prediction model; the optimized prediction model is used to further predict the health condition of the target pet object according to the physiological data.

[0005] As an optional embodiment, in the first aspect of the present invention, the physiological data includes at least one of pet weight, pet food intake, pet gender, pet biological type, pet medical record and pet medication record.

[0006] As an optional embodiment, in the first aspect of the present invention, the prediction model is a polynomial mathematical relationship model, which is obtained by training a training data set including a plurality of training physiological data and corresponding health condition annotations.

[0007] As an optional implementation, in the first aspect of the present invention, the prediction model is trained by the following steps: Acquire pet physiological data and corresponding health condition assessment results of multiple pet objects of the same pet type as the target pet object; Sorting out multiple data indicators in the pet's physiological data; the data indicators are pet weight, pet food intake, pet gender, pet medical treatment type or pet medication type; According to the Lasso-Cox regression model, based on the monitoring data and health status assessment results corresponding to the multiple data indicators, calculate the partial likelihood deviation corresponding to each of the data indicators; According to the partial likelihood deviation, a plurality of target data indicators are selected from all the data indicators; Based on the fitting algorithm, polynomial fitting is performed according to the monitoring data and health status assessment results corresponding to the multiple target data indicators to obtain the prediction model.

[0008] As an optional embodiment, in the first aspect of the present invention, determining the second health prediction result of the target pet object based on the trained image recognition algorithm and the image data includes: Inputting the image data into a preset behavior type classifier to obtain the pet behavior type corresponding to the image data; Determine a health prediction model corresponding to the pet behavior type; the health prediction model is obtained by training a training data set including a plurality of training image data of the same pet behavior type and corresponding health assessment annotations; The image data is input into the health prediction model to obtain a second health prediction result of the target pet object.

[0009] As an optional embodiment, in the first aspect of the present invention, the pet behavior type is eating behavior, resting behavior, attacking behavior, mating behavior, slow walking behavior or running behavior.

[0010] As an optional implementation, in the first aspect of the present invention, the training and optimizing the prediction model according to the first health prediction result and the second health prediction result to obtain an optimized prediction model includes: Calculating a difference between the first health prediction result and the second health prediction result; Determine whether the difference is greater than a preset difference threshold, and obtain a determination result; When the judgment result is yes, calculating an average value of the first health prediction result and the second health prediction result, and using the average value and the physiological data as an optimized training data set; When the judgment result is no, using the second health prediction result and the physiological data as an optimized training data set; The prediction model is trained and optimized according to the optimized training data set to obtain an optimized prediction model.

[0011] As an optional implementation, in the first aspect of the present invention, the calculating the difference between the first health prediction result and the second health prediction result includes: Inputting the physiological data into a trained data error recognition model to obtain a data error degree corresponding to the physiological data; Calculating a first weight proportional to the error degree of the data; Calculating a first product of the first weight and the first health prediction result; Inputting the image data into a trained image definition recognition model to obtain the definition corresponding to the image data; calculating a second weight proportional to the clarity; Calculating a second product of the second weight and the second health prediction result; The difference between the first product and the second product is calculated to obtain the difference between the first health prediction result and the second health prediction result.

[0012] A second aspect of an embodiment of the present invention discloses a pet health monitoring system based on a prediction model, the system comprising: An acquisition module, used to acquire physiological data and image data of a target pet object; A first prediction module, configured to determine a first health prediction result of the target pet object based on a preset health condition prediction model and the physiological data; A second prediction module, used to determine a second health prediction result of the target pet object according to the trained image recognition algorithm and the image data; An optimization module is used to train and optimize the prediction model according to the first health prediction result and the second health prediction result to obtain an optimized prediction model; the optimized prediction model is used to further predict the health condition of the target pet object according to the physiological data.

[0013] As an optional embodiment, in the second aspect of the present invention, the physiological data includes at least one of pet weight, pet food intake, pet gender, pet biological type, pet medical record and pet medication record.

[0014] As an optional embodiment, in the second aspect of the present invention, the prediction model is a polynomial mathematical relationship model, which is obtained by training a training data set including a plurality of training physiological data and corresponding health condition annotations.

[0015] As an optional implementation, in the second aspect of the present invention, the prediction model is trained by the following steps: Acquire pet physiological data and corresponding health condition assessment results of multiple pet objects of the same pet type as the target pet object; Sorting out multiple data indicators in the pet's physiological data; the data indicators are pet weight, pet food intake, pet gender, pet medical treatment type or pet medication type; According to the Lasso-Cox regression model, based on the monitoring data and health status assessment results corresponding to the multiple data indicators, calculate the partial likelihood deviation corresponding to each of the data indicators; According to the partial likelihood deviation, a plurality of target data indicators are selected from all the data indicators; Based on the fitting algorithm, polynomial fitting is performed according to the monitoring data and health status assessment results corresponding to the multiple target data indicators to obtain the prediction model.

[0016] As an optional embodiment, in the second aspect of the present invention, the second prediction module determines the specific manner of the second health prediction result of the target pet object according to the trained image recognition algorithm and the image data, including: Inputting the image data into a preset behavior type classifier to obtain the pet behavior type corresponding to the image data; Determine a health prediction model corresponding to the pet behavior type; the health prediction model is obtained by training a training data set including a plurality of training image data of the same pet behavior type and corresponding health assessment annotations; The image data is input into the health prediction model to obtain a second health prediction result of the target pet object.

[0017] As an optional embodiment, in the second aspect of the present invention, the pet behavior type is eating behavior, resting behavior, attacking behavior, mating behavior, slow walking behavior or running behavior.

[0018] As an optional implementation, in the second aspect of the present invention, the optimization module trains and optimizes the prediction model according to the first health prediction result and the second health prediction result to obtain a specific method for optimizing the prediction model, including: Calculating a difference between the first health prediction result and the second health prediction result; Determine whether the difference is greater than a preset difference threshold, and obtain a determination result; When the judgment result is yes, calculating an average value of the first health prediction result and the second health prediction result, and using the average value and the physiological data as an optimized training data set; When the judgment result is no, using the second health prediction result and the physiological data as an optimized training data set; The prediction model is trained and optimized according to the optimized training data set to obtain an optimized prediction model.

[0019] As an optional implementation, in the second aspect of the present invention, the specific manner in which the optimization module calculates the difference between the first health prediction result and the second health prediction result includes: Inputting the physiological data into a trained data error recognition model to obtain a data error degree corresponding to the physiological data; Calculating a first weight proportional to the error degree of the data; Calculating a first product of the first weight and the first health prediction result; Inputting the image data into a trained image definition recognition model to obtain the definition corresponding to the image data; calculating a second weight proportional to the clarity; Calculating a second product of the second weight and the second health prediction result; The difference between the first product and the second product is calculated to obtain the difference between the first health prediction result and the second health prediction result.

[0020] The third aspect of the present invention discloses another pet health monitoring system based on a prediction model, the system comprising: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the pet health monitoring method based on the prediction model disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the pet health monitoring method based on the prediction model disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention can determine a first health prediction result of a pet object based on a preset physiological data and health condition prediction model, and then determine a second health prediction result of the pet object based on an image recognition algorithm and image data, so as to comprehensively train and optimize the prediction model to obtain an optimized prediction model with more accurate prediction effect, thereby being able to optimize the prediction model based on correction training between prediction relationship and image recognition, so as to effectively improve the accuracy and efficiency of pet health monitoring, and provide a data basis for early intervention in pet disease treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a flowchart of a pet health monitoring method based on a prediction model disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural schematic diagram of a pet health monitoring system based on a prediction model disclosed in an embodiment of the present invention.

[0026] Figure 3 It is a structural schematic diagram of another pet health monitoring system based on a prediction model disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] The present invention discloses a pet health monitoring method and system based on a prediction model, which can determine a first health prediction result of a pet object based on a preset prediction model of physiological data and health conditions, and then determine a second health prediction result of the pet object based on an image recognition algorithm and image data, so as to comprehensively train and optimize the prediction model to obtain an optimized prediction model with a more accurate prediction effect, thereby being able to optimize the prediction model based on correction training between the prediction relationship and image recognition, so as to effectively improve the accuracy and efficiency of pet health monitoring, and provide a data basis for early intervention in pet disease treatment. The following are detailed descriptions.

[0031] Embodiment 1 See also Figure 1 , Figure 1 is a flow chart of a pet health monitoring method based on a prediction model disclosed in an embodiment of the present invention. Figure 1 The described pet health monitoring method based on the prediction model can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the pet health monitoring method based on the prediction model may include the following operations: 101. Acquire physiological data and image data of a target pet object.

[0032] 102. Based on a preset health condition prediction model and according to physiological data, determine a first health prediction result of the target pet object. 103. Determine a second health prediction result of the target pet object based on the trained image recognition algorithm and the image data. 104. According to the first health prediction result and the second health prediction result, the prediction model is trained and optimized to obtain an optimized prediction model.

[0033] Optionally, the optimized prediction model is used to make further predictions about the health condition of the target pet subject based on the physiological data.

[0034] It can be seen that the above-mentioned embodiments of the invention can determine the first health prediction result of the pet object based on the preset physiological data and health condition prediction model, and then determine the second health prediction result of the pet object based on the image recognition algorithm and the image data, so as to comprehensively train and optimize the prediction model to obtain an optimized prediction model with more accurate prediction effect, thereby being able to optimize the prediction model based on the correction training between the prediction relationship and image recognition, so as to effectively improve the accuracy and efficiency of pet health monitoring, and provide a data basis for early intervention in the treatment of pet diseases.

[0035] As an optional embodiment, in the above steps, the physiological data includes at least one of pet weight, pet food intake, pet gender, pet biological type, pet medical record and pet medication record.

[0036] It can be seen that through the above optional embodiments, the content of the physiological data is clarified, which can more accurately and comprehensively characterize the overall physiological condition of the pet, so as to facilitate subsequent health predictions, and assist in realizing the optimization of the prediction model based on correction training between the prediction relationship and image recognition, so as to effectively improve the accuracy and efficiency of pet health monitoring, and provide a data basis for early intervention in the treatment of pet diseases.

[0037] As an optional embodiment, in the above steps, the prediction model is a polynomial mathematical relationship model, which is obtained by training a training data set including a plurality of training physiological data and corresponding health condition annotations.

[0038] It can be seen that through the above optional embodiments, the model details and training details of the prediction model are clarified to facilitate subsequent more accurate health predictions, and to assist in the optimization of the prediction model based on correction training between prediction relationships and image recognition, so as to effectively improve the accuracy and efficiency of pet health monitoring, and provide a data basis for early intervention in pet disease treatment.

[0039] As an optional embodiment, in the above steps, the prediction model is trained by the following steps: Acquire pet physiological data and corresponding health condition assessment results of multiple pet objects of the same pet type as the target pet object; Sort out multiple data indicators in the pet's physiological data; optionally, the data indicators are pet weight, pet food intake, pet gender, pet medical treatment type, or pet medication type; According to the Lasso-Cox regression model, the partial likelihood deviation corresponding to each data indicator is calculated based on the monitoring data and health status assessment results corresponding to multiple data indicators; According to the partial likelihood deviation, multiple target data indicators are selected from all data indicators; Based on the fitting algorithm, polynomial fitting is performed according to the monitoring data and health status assessment results corresponding to multiple target data indicators to obtain a prediction model.

[0040] It can be seen that through the above optional embodiments, it is possible to screen out data indicators that are more relevant to health conditions from pet physiological data based on the Lasso-cox regression model, and obtain a prediction model based on data polynomial fitting to facilitate subsequent health predictions, and assist in optimizing the prediction model based on correction training between prediction relationships and image recognition, so as to effectively improve the accuracy and efficiency of pet health monitoring and provide a data basis for early intervention in pet disease treatment.

[0041] As an optional embodiment, in the above steps, determining the second health prediction result of the target pet object according to the trained image recognition algorithm and the image data includes: Input the image data into a preset behavior type classifier to obtain the pet behavior type corresponding to the image data; Determine a health prediction model corresponding to the pet behavior type; optionally, the health prediction model is obtained by training a training data set including a plurality of training image data of the same pet behavior type and corresponding health assessment annotations; The image data is input into the health prediction model to obtain a second health prediction result of the target pet object.

[0042] It can be seen that through the above-mentioned optional embodiments, the corresponding health prediction model can be screened out based on the pet behavior type to obtain accurate health prediction results, so as to facilitate subsequent model optimization, and assist in realizing the optimization of the prediction model based on the correction training between the prediction relationship and image recognition, so as to effectively improve the accuracy and efficiency of pet health monitoring, and provide a data basis for the early intervention of pet disease treatment.

[0043] As an optional embodiment, in the above steps, the pet behavior type is eating behavior, resting behavior, attacking behavior, mating behavior, slow walking behavior or running behavior.

[0044] It can be seen that through the above optional embodiments, the types of pet behaviors are clarified, which can accurately reflect the behavioral characteristics of pets, so as to facilitate subsequent health predictions, and assist in realizing the optimization of the prediction model based on correction training between prediction relationships and image recognition, so as to effectively improve the accuracy and efficiency of pet health monitoring, and provide a data basis for early intervention in pet disease treatment.

[0045] As an optional embodiment, in the above steps, training and optimizing the prediction model according to the first health prediction result and the second health prediction result to obtain an optimized prediction model includes: Calculate the difference between the first health prediction result and the second health prediction result; Determine whether the difference is greater than a preset difference threshold and obtain a determination result; When the judgment result is yes, calculating the average of the first health prediction result and the second health prediction result, and using the average and the physiological data as an optimized training data set; When the judgment result is no, the second health prediction result and the physiological data are used as an optimized training data set; The prediction model is trained and optimized according to the optimized training data set to obtain an optimized prediction model.

[0046] It can be seen that through the above-mentioned optional embodiments, the difference between the two health prediction results can be calculated and judged to determine the content of the optimized training data set, and the correction training based on the prediction relationship and image recognition can be implemented to achieve the optimization of the prediction model, so as to effectively improve the accuracy and efficiency of pet health monitoring and provide a data basis for early intervention in pet disease treatment.

[0047] As an optional embodiment, in the above step, calculating the difference between the first health prediction result and the second health prediction result includes: Inputting the physiological data into the trained data error recognition model to obtain the data error degree corresponding to the physiological data; Calculate the first weight proportional to the degree of data error; Calculating a first product of a first weight and a first health prediction result; Input the image data into the trained image clarity recognition model to obtain the clarity corresponding to the image data; calculating a second weight proportional to the clarity; Calculate a second product of the second weight and the second health prediction result; The difference between the first product and the second product is calculated to obtain the difference between the first health prediction result and the second health prediction result.

[0048] It can be seen that through the above-mentioned optional embodiments, the difference between the first health prediction result and the second health prediction result can be accurately calculated based on the weights related to the physiological data and the image data, so as to facilitate the subsequent reasonable determination of the content of the optimized training data set, and to achieve optimization of the prediction model based on correction training between the prediction relationship and image recognition, so as to effectively improve the accuracy and efficiency of pet health monitoring and provide a data basis for early intervention in the treatment of pet diseases.

[0049] Embodiment 2 See also Figure 2 , Figure 2 is a structural diagram of a pet health monitoring system based on a prediction model disclosed in an embodiment of the present invention. Figure 2 The described pet health monitoring system based on the prediction model can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the pet health monitoring system based on the prediction model may include: The acquisition module 201 is used to acquire physiological data and image data of a target pet object.

[0050] The first prediction module 202 is used to determine a first health prediction result of the target pet object based on a preset health condition prediction model and according to physiological data. The second prediction module 203 is used to determine a second health prediction result of the target pet object according to the trained image recognition algorithm and the image data. The optimization module 204 is used to train and optimize the prediction model according to the first health prediction result and the second health prediction result to obtain an optimized prediction model.

[0051] Optionally, the optimized prediction model is used to make further predictions about the health condition of the target pet subject based on the physiological data.

[0052] It can be seen that the above-mentioned embodiments of the invention can determine the first health prediction result of the pet object based on the preset physiological data and health condition prediction model, and then determine the second health prediction result of the pet object based on the image recognition algorithm and the image data, so as to comprehensively train and optimize the prediction model to obtain an optimized prediction model with more accurate prediction effect, thereby being able to optimize the prediction model based on the correction training between the prediction relationship and image recognition, so as to effectively improve the accuracy and efficiency of pet health monitoring, and provide a data basis for early intervention in the treatment of pet diseases.

[0053] As an optional embodiment, the physiological data includes at least one of pet weight, pet food intake, pet gender, pet biological type, pet medical record and pet medication record.

[0054] It can be seen that through the above optional embodiments, the content of the physiological data is clarified, which can more accurately and comprehensively characterize the overall physiological condition of the pet, so as to facilitate subsequent health predictions, and assist in realizing the optimization of the prediction model based on correction training between the prediction relationship and image recognition, so as to effectively improve the accuracy and efficiency of pet health monitoring, and provide a data basis for early intervention in the treatment of pet diseases.

[0055] As an optional embodiment, the prediction model is a polynomial mathematical relationship model, which is obtained by training a training data set including a plurality of training physiological data and corresponding health condition annotations.

[0056] It can be seen that through the above optional embodiments, the model details and training details of the prediction model are clarified to facilitate subsequent more accurate health predictions, and to assist in the optimization of the prediction model based on correction training between prediction relationships and image recognition, so as to effectively improve the accuracy and efficiency of pet health monitoring, and provide a data basis for early intervention in pet disease treatment.

[0057] As an optional embodiment, the prediction model is trained by the following steps: Acquire pet physiological data and corresponding health condition assessment results of multiple pet objects of the same pet type as the target pet object; Sort out multiple data indicators in the pet's physiological data; optionally, the data indicators are pet weight, pet food intake, pet gender, pet medical treatment type, or pet medication type; According to the Lasso-Cox regression model, the partial likelihood deviation corresponding to each data indicator is calculated based on the monitoring data and health status assessment results corresponding to multiple data indicators; According to the partial likelihood deviation, multiple target data indicators are selected from all data indicators; Based on the fitting algorithm, polynomial fitting is performed according to the monitoring data and health status assessment results corresponding to multiple target data indicators to obtain a prediction model.

[0058] It can be seen that through the above optional embodiments, it is possible to screen out data indicators that are more relevant to health conditions from pet physiological data based on the Lasso-cox regression model, and obtain a prediction model based on data polynomial fitting to facilitate subsequent health predictions, and assist in optimizing the prediction model based on correction training between prediction relationships and image recognition, so as to effectively improve the accuracy and efficiency of pet health monitoring and provide a data basis for early intervention in pet disease treatment.

[0059] As an optional embodiment, the second prediction module determines the specific manner of the second health prediction result of the target pet object according to the trained image recognition algorithm and the image data, including: Input the image data into a preset behavior type classifier to obtain the pet behavior type corresponding to the image data; Determine a health prediction model corresponding to the pet behavior type; optionally, the health prediction model is obtained by training a training data set including a plurality of training image data of the same pet behavior type and corresponding health assessment annotations; The image data is input into the health prediction model to obtain a second health prediction result of the target pet object.

[0060] It can be seen that through the above-mentioned optional embodiments, the corresponding health prediction model can be screened out based on the pet behavior type to obtain accurate health prediction results, so as to facilitate subsequent model optimization, and assist in realizing the optimization of the prediction model based on the correction training between the prediction relationship and image recognition, so as to effectively improve the accuracy and efficiency of pet health monitoring, and provide a data basis for the early intervention of pet disease treatment.

[0061] As an optional embodiment, the pet behavior type is eating behavior, resting behavior, attacking behavior, mating behavior, walking slowly behavior or running behavior.

[0062] It can be seen that through the above optional embodiments, the types of pet behaviors are clarified, which can accurately reflect the behavioral characteristics of pets, so as to facilitate subsequent health predictions, and assist in realizing the optimization of the prediction model based on correction training between prediction relationships and image recognition, so as to effectively improve the accuracy and efficiency of pet health monitoring, and provide a data basis for early intervention in pet disease treatment.

[0063] As an optional embodiment, the optimization module trains and optimizes the prediction model according to the first health prediction result and the second health prediction result to obtain a specific method for optimizing the prediction model, including: Calculate the difference between the first health prediction result and the second health prediction result; Determine whether the difference is greater than a preset difference threshold and obtain a determination result; When the judgment result is yes, calculating the average of the first health prediction result and the second health prediction result, and using the average and the physiological data as an optimized training data set; When the judgment result is no, the second health prediction result and the physiological data are used as an optimized training data set; The prediction model is trained and optimized according to the optimized training data set to obtain an optimized prediction model.

[0064] It can be seen that through the above-mentioned optional embodiments, the difference between the two health prediction results can be calculated and judged to determine the content of the optimized training data set, and the correction training based on the prediction relationship and image recognition can be implemented to achieve the optimization of the prediction model, so as to effectively improve the accuracy and efficiency of pet health monitoring and provide a data basis for early intervention in pet disease treatment.

[0065] As an optional embodiment, the specific manner in which the optimization module calculates the difference between the first health prediction result and the second health prediction result includes: Inputting the physiological data into the trained data error recognition model to obtain the data error degree corresponding to the physiological data; Calculate the first weight proportional to the degree of data error; Calculating a first product of a first weight and a first health prediction result; Input the image data into the trained image clarity recognition model to obtain the clarity corresponding to the image data; calculating a second weight proportional to the clarity; Calculate a second product of the second weight and the second health prediction result; The difference between the first product and the second product is calculated to obtain the difference between the first health prediction result and the second health prediction result.

[0066] It can be seen that through the above-mentioned optional embodiments, the difference between the first health prediction result and the second health prediction result can be accurately calculated based on the weights related to the physiological data and the image data, so as to facilitate the subsequent reasonable determination of the content of the optimized training data set, and to achieve optimization of the prediction model based on correction training between the prediction relationship and image recognition, so as to effectively improve the accuracy and efficiency of pet health monitoring and provide a data basis for early intervention in the treatment of pet diseases.

[0067] Embodiment 3 See also Figure 3 , Figure 3 This is another pet health monitoring system based on a prediction model disclosed in an embodiment of the present invention. Figure 3 The described pet health monitoring system based on the prediction model is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the pet health monitoring system based on the prediction model may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the pet health monitoring method based on the prediction model described in the first embodiment.

[0068] Embodiment 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the pet health monitoring method based on a prediction model described in the first embodiment.

[0069] Embodiment 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the pet health monitoring method based on the prediction model described in Example 1.

[0070] The above describes specific embodiments of the present specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily have to be performed in the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0072] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0073] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may be in the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of this specification may be in the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0074] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0075] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0077] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0078] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0079] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0080] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0081] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0082] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0083] Finally, it should be noted that the pet health monitoring method and system based on the predictive model disclosed in the embodiment of the present invention discloses only the preferred embodiment of the present invention, which is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A pet health monitoring method based on a prediction model, characterized in that: The method comprises: Acquiring physiological data and image data of a target pet object; Based on a preset health condition prediction model, and according to the physiological data, determining a first health prediction result of the target pet object; Determine a second health prediction result of the target pet object based on the trained image recognition algorithm and the image data; The prediction model is trained and optimized according to the first health prediction result and the second health prediction result to obtain an optimized prediction model; the optimized prediction model is used to further predict the health condition of the target pet object according to the physiological data.

2. The pet health monitoring method based on the prediction model according to claim 1, characterized in that: The physiological data includes at least one of pet weight, pet food intake, pet gender, pet biological type, pet medical records, and pet medication records.

3. The pet health monitoring method based on the prediction model according to claim 1, characterized in that: The prediction model is a polynomial mathematical relationship model, which is obtained by training a training data set including a plurality of training physiological data and corresponding health condition annotations.

4. The pet health monitoring method based on the prediction model according to claim 3 is characterized in that: The prediction model is trained by the following steps: Acquire pet physiological data and corresponding health condition assessment results of multiple pet objects of the same pet type as the target pet object; Sorting out multiple data indicators in the pet's physiological data; the data indicators are pet weight, pet food intake, pet gender, pet medical treatment type or pet medication type; According to the Lasso-Cox regression model, based on the monitoring data and health status assessment results corresponding to the multiple data indicators, calculate the partial likelihood deviation corresponding to each of the data indicators; According to the partial likelihood deviation, a plurality of target data indicators are selected from all the data indicators; Based on the fitting algorithm, polynomial fitting is performed according to the monitoring data and health status assessment results corresponding to the multiple target data indicators to obtain the prediction model.

5. The pet health monitoring method based on the prediction model according to claim 1, characterized in that: Determining the second health prediction result of the target pet object based on the trained image recognition algorithm and the image data includes: Inputting the image data into a preset behavior type classifier to obtain the pet behavior type corresponding to the image data; Determine a health prediction model corresponding to the pet behavior type; the health prediction model is obtained by training a training data set including a plurality of training image data of the same pet behavior type and corresponding health assessment annotations; The image data is input into the health prediction model to obtain a second health prediction result of the target pet object.

6. The pet health monitoring method based on the prediction model according to claim 5, characterized in that: The pet behavior type is eating behavior, resting behavior, attacking behavior, mating behavior, slow walking behavior or running behavior.

7. The pet health monitoring method based on the prediction model according to claim 1, characterized in that: The step of training and optimizing the prediction model according to the first health prediction result and the second health prediction result to obtain an optimized prediction model includes: Calculating a difference between the first health prediction result and the second health prediction result; Determine whether the difference is greater than a preset difference threshold, and obtain a determination result; When the judgment result is yes, calculating an average value of the first health prediction result and the second health prediction result, and using the average value and the physiological data as an optimized training data set; When the judgment result is no, using the second health prediction result and the physiological data as an optimized training data set; The prediction model is trained and optimized according to the optimized training data set to obtain an optimized prediction model.

8. The pet health monitoring method based on the prediction model according to claim 7, characterized in that: The calculating the difference between the first health prediction result and the second health prediction result includes: Inputting the physiological data into a trained data error recognition model to obtain a data error degree corresponding to the physiological data; Calculating a first weight proportional to the error degree of the data; Calculating a first product of the first weight and the first health prediction result; Inputting the image data into a trained image definition recognition model to obtain the definition corresponding to the image data; calculating a second weight proportional to the clarity; Calculating a second product of the second weight and the second health prediction result; The difference between the first product and the second product is calculated to obtain the difference between the first health prediction result and the second health prediction result.

9. A pet health monitoring system based on a prediction model, characterized in that: The system comprises: An acquisition module, used to acquire physiological data and image data of a target pet object; A first prediction module, configured to determine a first health prediction result of the target pet object based on a preset health condition prediction model and the physiological data; A second prediction module, used to determine a second health prediction result of the target pet object according to the trained image recognition algorithm and the image data; An optimization module is used to train and optimize the prediction model according to the first health prediction result and the second health prediction result to obtain an optimized prediction model; the optimized prediction model is used to further predict the health condition of the target pet object according to the physiological data.

10. A pet health monitoring system based on a prediction model, characterized in that: The system comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the pet health monitoring method based on the prediction model as described in any one of claims 1-8.

Images