Pet physiological parameter calculation method and device, equipment and medium

Abstract

The invention relates to a pet physiological parameter calculation method and device, equipment and a medium, and is applied to the technical field of data processing, and the method comprises the steps: obtaining physiological activity data and a body movement threshold value of a target pet; determining an activity state of the target pet based on the physiological activity data and the body movement threshold; extracting and calculating the physiological activity data based on the activity state, and determining respiration data of the target pet; extracting and calculating the physiological activity data based on the respiration data, and determining heartbeat data of the target pet; and generating physiological parameters of the target pet based on the respiration data and the heartbeat data. The pet health monitoring method has the effect of improving the convenience of pet daily health monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a pet physiological parameter calculation method, device, equipment and medium. BACKGROUND

[0002] With the development of social economy and the continuous improvement of residents' living standards, raising pets has become an important lifestyle for many families. As emotional companions and family members, pets are increasingly prominent in their health status, which naturally attracts the attention of the keepers.

[0003] At present, the monitoring and evaluation of the health status of pets, especially the acquisition of some physiological parameters, mainly rely on professional pet medical institutions. However, the existing technical solutions have significant limitations. Most pets have hair on their bodies. In order to accurately obtain physiological parameters, local hair removal treatment is usually required at the detection site. Repeated or long-term hair removal not only may damage the natural protective barrier of pets and increase the risk of skin infection, but also may have adverse effects on the appearance and psychological state of pets. Conventional physiological parameter detection requires pets to be taken to a pet hospital, where professional personnel use special equipment to complete the detection. This mode has the problems of high time cost, heavy economic burden, and great stress reaction of pets for ordinary families, and it is difficult to realize long-term, high-frequency, and convenient home health monitoring. Moreover, pets cannot actively express discomfort, and some subtle health problems are easily overlooked by keepers, which may lead to delayed illness.

[0004] Therefore, there is an urgent need for a pet physiological parameter acquisition and calculation method suitable for home environment to significantly improve the convenience of pet daily health monitoring. SUMMARY

[0005] In order to improve the convenience of pet daily health monitoring, the present application provides a pet physiological parameter calculation method, device, equipment and medium.

[0006] In a first aspect, the present application provides a pet physiological parameter calculation method, which adopts the following technical solution:

[0007] A pet physiological parameter calculation method comprises:

[0008] obtaining physiological activity data and a body movement threshold value of a target pet;

[0009] determining an activity state of the target pet based on the physiological activity data and the body movement threshold value;

[0010] extracting and calculating the physiological activity data based on the activity state to determine respiratory data of the target pet;

[0011] extracting and calculating the physiological activity data based on the respiratory data to determine heartbeat data of the target pet;

[0012] generate a physiological parameter of the target pet based on the respiration data and the heartbeat data.

[0013] By using the physiological activity data of the target pet collected to determine the activity state, the respiration data is first determined according to the obtained activity state, and then the heartbeat data is further determined according to the respiration data. The respiration data and the heartbeat data are integrated to obtain the physiological parameter. The breeder can understand the physiological state of the pet in real time according to the demand to determine whether the current state of the pet is good, thereby improving the convenience of daily health monitoring of the pet.

[0014] Optionally, the determining the activity state of the target pet based on the physiological activity data and the body movement threshold value comprises:

[0015] extracting a fluctuation value from the physiological activity data to determine a fluctuation peak value;

[0016] determining whether the fluctuation peak value is within the range of the body movement threshold value;

[0017] if the fluctuation peak value is within the range of the body movement threshold value, determining that the activity state of the target pet is a body movement state;

[0018] if the fluctuation peak value is not within the range of the body movement threshold value, determining that the activity state of the target pet is a quiet state.

[0019] Optionally, the extracting and calculating the physiological activity data based on the activity state to determine the respiration data of the target pet comprises:

[0020] obtaining a respiration period and original fluctuation data of the quiet state;

[0021] calculating a target time interval based on the respiration period;

[0022] determining the respiration data of the target pet based on the target time interval, the respiration period, and the original fluctuation data.

[0023] Optionally, the determining the respiration data of the target pet based on the target time interval, the respiration period, and the original fluctuation data comprises:

[0024] obtaining a sampling point greater than the respiration period in the original fluctuation data;

[0025] calculating a sampling rate based on the sampling point and the target time interval;

[0026] calculating a heart rate signal average value of the sampling point based on a pre-designed calculation formula, the sampling point, the sampling rate, the breathing cycle and the target time interval;

[0027] filtering the heart rate signal average value in the original fluctuation data to obtain breathing data of the target pet.

[0028] Optionally, the determining the heartbeat data of the target pet based on the breathing data includes:

[0029] obtaining a minimum possible heartbeat frequency of the target pet;

[0030] calculating a decomposition layer number based on the minimum heartbeat frequency and the sampling rate;

[0031] performing wavelet decomposition processing on the original fluctuation data based on the decomposition layer number to generate decomposition result data;

[0032] determining the heartbeat data of the target pet based on the decomposition result data.

[0033] Optionally, the determining the heartbeat data of the target pet based on the decomposition result data includes:

[0034] determining a maximum value and a minimum value based on the decomposition result data;

[0035] calculating a data difference value based on the maximum value and the minimum value;

[0036] obtaining a maximum heartbeat time cycle corresponding to the target pet;

[0037] performing median filtering processing on the maximum heartbeat time cycle based on the maximum heartbeat time cycle to generate a filtering processing result;

[0038] determining the heartbeat data of the target pet based on the filtering processing result.

[0039] Optionally, the generating the physiological parameter of the target pet based on the breathing data and the heartbeat data includes:

[0040] obtaining a labeling manner of the breathing data and the heartbeat data;

[0041] performing data labeling on the original fluctuation data based on the labeling manner to generate a physiological parameter.

[0042] In a second aspect, the present application provides a pet physiological parameter calculation device, which adopts the following technical solution:

[0043] A pet physiological parameter calculation device includes:

[0044] The pet data acquisition module is configured to acquire physiological activity data and a body movement threshold of the target pet.

[0045] The activity state determination module is configured to determine an activity state of the target pet based on the physiological activity data and the body movement threshold.

[0046] The respiration data determination module is configured to determine respiration data of the target pet by performing extraction calculation on the physiological activity data based on the activity state.

[0047] The heartbeat data determination module is configured to determine heartbeat data of the target pet by performing extraction calculation on the physiological activity data based on the respiration data.

[0048] The physiological parameter generation module is configured to generate physiological parameters of the target pet based on the respiration data and the heartbeat data.

[0049] By using the above technical solution, the physiological activity data of the target pet is used to determine the activity state, the respiration data is determined based on the obtained activity state, the heartbeat data is further determined based on the respiration data, the respiration data and the heartbeat data are integrated to obtain the physiological parameters, and the breeder can understand the physiological parameters of the pet in real time according to the needs to determine whether the current state of the pet is good, thereby improving the convenience of daily health monitoring of the pet.

[0050] In a third aspect, the present application provides an electronic device, which adopts the following technical solution:

[0051] An electronic device includes a processor coupled with a memory.

[0052] The processor is configured to execute a computer program stored in the memory, so that the electronic device executes the computer program of the pet physiological parameter calculation method of any one of the first aspect.

[0053] In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical solution:

[0054] A computer readable storage medium stores a computer program capable of being loaded and executed by a processor to execute the pet physiological parameter calculation method of any one of the first aspect.

[0055] In summary, the present application includes at least one of the following beneficial technical effects:

[0056] The physiological activity data of the target pet collected is used to determine the activity state, the respiration data is determined according to the obtained activity state, the heartbeat data is further determined according to the respiration data, the respiration data and the heartbeat data are integrated, the physiological parameters are obtained, and the breeder can understand the physiological state of the pet in real time according to the needs, so as to determine whether the current state of the pet is good, thereby improving the convenience of daily health monitoring of the pet. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 is a flowchart of a pet physiological parameter calculation method provided by an embodiment of the present application.

[0058] Figure 2 is a line graph provided by an embodiment of the present application for showing body movement.

[0059] Figure 3 is a line graph provided by an embodiment of the present application for showing a quiet state.

[0060] Figure 4 is a line graph provided by an embodiment of the present application for showing respiration data.

[0061] Figure 5 is a line graph provided by an embodiment of the present application for showing non-enhanced heartbeat data.

[0062] Figure 6 is a line graph provided by an embodiment of the present application for showing enhanced heartbeat data.

[0063] Figure 7 is a line graph provided by an embodiment of the present application for showing final heartbeat data.

[0064] Figure 8 is a structural block diagram of a pet physiological parameter calculation device provided by an embodiment of the present application.

[0065] Figure 9 is a structural block diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0066] The present application will be further described in detail below with reference to the accompanying drawings.

[0067] The present application provides a pet physiological parameter calculation method, which can be executed by an electronic device. The electronic device can be a server or a terminal device. The server can be a physical server, a server cluster composed of multiple physical servers, a distributed system, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a desktop computer, or the like, but is not limited thereto.

[0068] Figure 1 A flowchart of a pet physiological parameter calculation method provided for an embodiment of the present application is shown.

[0069] As shown in the figure, the main process of the method is described as follows (steps S101-S105): Figure 1

[0070] In step S101, physiological activity data of a target pet and a body movement threshold value are obtained.

[0071] In this embodiment, the physiological activity data is fluctuation data detected by a sensor in the smart pet pad when the smart pet pad is used, and the data is in the form of a broken line data graph similar to an electrocardiogram. The body movement threshold value is a fluctuation range value set according to the actual type of the pet. Different types and different age groups of pets have different body movement threshold values. For example, the threshold value of a 10Kg large dog is ±200 points of the normal value, and the threshold value of a 1Kg small dog is ±100 points of the normal value, which is used to determine the current activity state of the pet.

[0072] In step S102, the activity state of the target pet is determined based on the physiological activity data and the body movement threshold value.

[0073] For step S102, the fluctuation value of the physiological activity data is extracted to determine the fluctuation peak value. It is determined whether the fluctuation peak value is within the range of the body movement threshold value. If the fluctuation peak value is within the range of the body movement threshold value, it is determined that the activity state of the target pet is a body movement state. If the fluctuation peak value is not within the range of the body movement threshold value, it is determined that the activity state of the target pet is a quiet state.

[0074] In this embodiment, an old dog is taken as an example for data calculation and display. The heart rate of an old dog is generally 70-130 times / minute, and the breathing rate is 10-35 times / minute. When testing, the data range is widened. The heart rate measurement range of the dog is set to 60-180 times / minute, each heartbeat period is 1-3 seconds, the breathing measurement range is 10-40 times / minute, each breathing period is 1.5-6 seconds, and the left ventricular systolic force is the largest, and the systolic time Ts is within 0.1s. Referring to Figure 2 When determining the activity state of the target pet, the fluctuation peak value is compared with the body movement threshold value to determine whether the fluctuation peak value exceeds the body movement threshold value. If the fluctuation peak value exceeds the body movement threshold value, it is determined that the target pet is in an active state, such as walking, jumping, licking, etc. If the fluctuation peak value does not exceed the body movement threshold value, it is determined that the target pet is in a quiet state, and the heart rate and breathing can be detected. The subsequent data processing is based on the quiet state.

[0075] In step S103, the physiological activity data is extracted and calculated based on the activity state to determine the breathing data of the target pet.

[0076] ​For step S103, the original fluctuation data of the breathing cycle and the quiet state are acquired; the target time interval is calculated based on the breathing cycle; and the breathing data of the target pet is determined based on the target time interval, the breathing cycle and the original fluctuation data.

[0077] In the embodiment, the breathing cycle is the maximum cycle, that is, the longest time of one breath, for example, the dog breathes 60-180 times per minute, which is equivalent to one breath of 0.33-1 second, and the expiration cycle is 1 second. The target time interval is calculated according to the obtained breathing cycle and a pre-designed calculation formula Δt1=Δt0 / 10, wherein Δt0 is the breathing cycle. After the target time interval is calculated, the breathing data of the target pet is calculated based on the target time interval, the breathing cycle and the original fluctuation data. Figure 3 The breathing data.

[0078] Further, the breathing data of the target pet is determined based on the target time interval, the breathing cycle and the original fluctuation data, which includes: acquiring the sampling points greater than the breathing cycle in the original fluctuation data; calculating the sampling rate based on the sampling points and the target time interval; calculating the average heart rate signal value of the sampling points based on a pre-designed calculation formula, the sampling points, the sampling rate, the breathing cycle and the target time interval; and filtering the average heart rate signal value in the original fluctuation data to obtain the breathing data of the target pet.

[0079] In the determination of the target respiratory data, the original fluctuation data is down-sampled, that is, the signal in the quiet state is windowed, and then a sliding mean filter is performed. If the window is 1 second, the interference of the heart rate period will be averaged out, and then the average value of 1 second is calculated every 0.1 second. In this way, the heartbeat signal is averaged to be very small, but the respiratory period is large and almost not affected. The average value is calculated by using a pre-designed calculation formula Xm = mean(X(i:i+Δt0*fs)), wherein mean is the average, fs is the sampling rate, and Δt0 is the respiratory period. The sampling rate is calculated by using the sampling point ratio and the target time interval. Then, the obtained sampling point, sampling rate, respiratory period and target time interval are brought into the pre-designed calculation formula to calculate the average value of the heart rate signal. After filtering, the final respiratory data is obtained. Assuming that the cat heart rate signal H is [0, 2, 0, 2, 0, 2, 0, 2, 0, 2, 0], the respiratory period is long, the signal R is [0, 1, 2, 3, 4, 3, 2, 1, 0, 1, 2], and the two are collected by the sensor and the A signal is [0, 3, 2, 5, 4, 5, 2, 3, 0, 3, 2]. At this time, it is assumed that the maximum period of the heart rate is 2, and the step is 1 to calculate the sliding average. A' = [1.5, 2.5, 3.5, 4.5, 4.5, 3.5, 2.5, 1.5, 1.5, 2.5], step 2, A'' = [1.5, 3.5, 4.5, 2.5, 1.5, 2], the respiratory rule is clearer. The time experienced by the two maximum values is the time of one respiration.

[0080] In step S104, the physiological activity data is extracted and calculated based on the respiratory data to determine the heart rate data of the target pet.

[0081] For step S104, the minimum possible heart rate of the target pet is obtained; the decomposition layer number is calculated based on the minimum heart rate and the sampling rate; the original fluctuation data is wavelet decomposed based on the decomposition result data to generate the decomposition result data; and the heart rate data of the target pet is determined based on the decomposition result data.

[0082] In this embodiment, the extraction of the respiratory data also depends on the quiet state. The minimum heart rate that may occur in the pet is collected to obtain the minimum heart rate. The decomposition layer number is calculated by using the minimum heart rate and the sampling rate. The formula lv = ceil(fl / (fs / 2)) is used, wherein ceil is the upward rounding, fl is the minimum heart rate, and fs is the sampling rate. Because the heart shock is relatively small compared with the respiratory signal, and the heart shock oscillates back and forth, the numerical value is required to be higher. In this case, the original data in the quiet state is sym8 wavelet decomposed, the direct current component of the last layer of decomposition is removed, and the direct current signal and the respiratory signal are contained. After reconstruction, the decomposition result data as shown in FIG. 8 is obtained. The heart rate data is determined according to the decomposition result data. Figure 4 ​

[0083] Further, determining the heartbeat data of the target pet based on the decomposition result data comprises: determining a maximum value and a minimum value based on the decomposition result data; calculating a data difference value based on the maximum value and the minimum value; obtaining a maximum heartbeat time period corresponding to the target pet; performing median filtering processing on the maximum heartbeat time period based on the maximum heartbeat time period to generate a filtering processing result; and determining the heartbeat data of the target pet based on the filtering processing result.

[0084] In the determination of the heartbeat data of the target pet based on the decomposition result, the heartbeat is completed by the left ventricle, the right ventricle, the left ventricle, and the right ventricle in coordination, so there will be multiple impact waves of different sizes in the same heartbeat period, which cannot be simply separated. Considering that the strongest and fastest compression of the heartbeat is a clear marker. In Δt2, the difference between the maximum value and the minimum value is calculated, where Δt2 is determined by different pets, y = faststp(data, 6), where the faststp function is to calculate the maximum difference of data in data every n points, and n is taken as 6 in this application, that is, the maximum value minus the minimum value in six points, and the data shown in Figure 6 is obtained, and the above y is subjected to median filtering processing, and the window length is the maximum heartbeat time period of the target pet, y1 = mdfilt(y3, fs, Δt0), and the filtering processing result shown in Figure 7 is obtained, and the characteristics are more obvious again, where the thick curve is y1, and the thin curve is y. In the interval t3, that is, the minimum heart rate period of the target pet, the maximum value in this time window is the possible strongest heart impact point. The impact point is at least 2 / 3 of the average value of the two strongest heart impact points, and 2 / 3 is the threshold value obtained by experiment. When the two conditions of time and impact strength are met, the real strongest heart impact point is obtained by repeating the calculation twice, and the interval of the two strongest heart impact points is the heartbeat period.

[0085] In step S105, the physiological parameters of the target pet are generated based on the respiratory data and the heartbeat data.

[0086] For step S105, the labeling method of obtaining the respiratory data and the heartbeat data is obtained; and the physiological parameters are generated by labeling the original fluctuation data based on the labeling method.

[0087] In this embodiment, after the respiratory data and the heartbeat data are obtained by processing, the corresponding labeling method is obtained, and the final physiological parameters are obtained by distinguishing and labeling the original fluctuation data using the labeling method. The obtained respiratory data and heartbeat data can also be directly used as physiological parameters, so that the breeder can understand the physiological parameters.

[0088] Figure 8 A structure block diagram of a pet physiological parameter calculation device 200 provided for the application embodiment is shown.

[0089] As shown in Figure 8 The pet physiological parameter calculation device 200 mainly comprises:

[0090] A pet data acquisition module 201 is configured to acquire physiological activity data and a body movement threshold of a target pet.

[0091] An activity state determination module 202 is configured to determine an activity state of the target pet based on the physiological activity data and the body movement threshold.

[0092] A respiration data determination module 203 is configured to determine respiration data of the target pet by performing extraction calculation on the physiological activity data based on the activity state.

[0093] A heartbeat data determination module 204 is configured to determine heartbeat data of the target pet by performing extraction calculation on the physiological activity data based on the respiration data.

[0094] A physiological parameter generation module 205 is configured to generate physiological parameters of the target pet based on the respiration data and the heartbeat data.

[0095] As an optional implementation of the present embodiment, the activity state determination module 202 is specifically configured to extract a fluctuation value from the physiological activity data, determine a fluctuation peak value, judge whether the fluctuation peak value is within the range of the body movement threshold, determine that the activity state of the target pet is a body movement state if the fluctuation peak value is within the range of the body movement threshold, and determine that the activity state of the target pet is a quiet state if the fluctuation peak value is not within the range of the body movement threshold.

[0096] As an optional implementation of the present embodiment, the respiration data determination module 203 comprises:

[0097] An original data acquisition module is configured to acquire original fluctuation data of a respiration cycle and a quiet state.

[0098] A time interval calculation module is configured to calculate a target time interval based on the respiration cycle.

[0099] A pet respiration determination module is configured to determine respiration data of the target pet based on the target time interval, the respiration cycle and the original fluctuation data.

[0100] In the present optional embodiment, the pet respiration determination module is specifically configured to acquire a sampling point greater than the respiration cycle in the original fluctuation data, calculate a sampling rate based on the sampling point and the target time interval, calculate an average value of a heart rate signal of the sampling point based on a pre-designed calculation formula, the sampling point, the sampling rate, the respiration cycle and the target time interval, and filter the average value of the heart rate signal in the original fluctuation data to obtain the respiration data of the target pet.

[0101] As an optional implementation of the embodiment, the heartbeat data determination module 204 comprises:

[0102] a minimum frequency acquisition module configured to acquire a minimum possible heartbeat frequency of the target pet;

[0103] a decomposition layer number calculation module configured to calculate a decomposition layer number based on the minimum heartbeat frequency and the sampling rate;

[0104] a decomposition data generation module configured to perform wavelet decomposition processing on the original fluctuation data based on the decomposition layer number to generate decomposition result data;

[0105] a pet heartbeat determination module configured to determine heartbeat data of the target pet based on the decomposition result data.

[0106] In the optional embodiment, the pet heartbeat determination module is specifically configured to determine a maximum value and a minimum value based on the decomposition result data, calculate a data difference value based on the maximum value and the minimum value, acquire a maximum heartbeat time period corresponding to the target pet, perform median filtering processing on the maximum heartbeat time period based on the maximum heartbeat time period to generate a filtering processing result, and determine the heartbeat data of the target pet based on the filtering processing result.

[0107] As an optional implementation of the embodiment, the physiological parameter generation module 205 is specifically configured to acquire a labeling manner of the respiration data and the heartbeat data, perform data labeling on the original fluctuation data based on the labeling manner to generate the physiological parameter.

[0108] In one example, the modules in any of the above apparatuses can be one or more integrated circuits configured to implement the above methods, for example, one or more application specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0109] For another example, when the modules in the apparatus can be implemented in the form of a processing element scheduler, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can invoke programs. For another example, these modules can be integrated together to be implemented in the form of a system-on-a-chip (SOC).

[0110] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described device and module can refer to the corresponding process in the foregoing method embodiment, and will not be described here.

[0111] Figure 9 The structural block diagram of the electronic device 300 provided in the embodiments of the present application is shown.

[0112] As shown in Figure 9 The electronic device 300 includes a processor 301 and a memory 302, and can further include one or more of an information input / output (I / O) interface 303, a communication component 304, and a communication bus 305.

[0113] The processor 301 is configured to control the overall operation of the electronic device 300 to complete all or part of the steps of the pet physiological parameter calculation method described above; the memory 302 is configured to store various types of data to support the operation of the electronic device 300, which can include, for example, instructions for operating any application or method on the electronic device 300, and application-related data. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as one or more of static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0114] The I / O interface 303 provides an interface between the processor 301 and other interface modules, which can be a keyboard, a mouse, a button, etc. These buttons can be virtual buttons or physical buttons. The communication component 304 is configured to perform wired or wireless communication between the electronic device 300 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, near field communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 304 can include a Wi-Fi component, a Bluetooth component, and an NFC component.

[0115] The electronic device 300 can be implemented with one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for performing the pet physiological parameter calculation method according to the above-described embodiments.

[0116] The communication bus 305 can include a path for transmitting information between the above-described components. The communication bus 305 can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, or the like. The communication bus 305 can be divided into an address bus, a data bus, a control bus, and the like.

[0117] The electronic device 300 can include, but is not limited to, a mobile terminal of a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (Personal Digital Assistant), a PAD (Tablet PC), a PMP (Portable Multimedia Player), a car terminal (for example, a car navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like, and can also be a server or the like.

[0118] The present application also provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the steps of the pet physiological parameter calculation method described above.

[0119] The computer-readable storage medium can include a U disk, a mobile hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like, various media capable of storing program codes.

[0120] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0121] The description above merely illustrates preferred embodiments of the present application and the principles of the technology employed. It should be understood by those skilled in the art that the scope of the application involved in the present application is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by the combinations of the above technical features or their equivalent features without departing from the above application concept. For example, the above technical features can be replaced with the technical features applied in the present application (but not limited to) having similar functions to form technical solutions.

Claims

1. A pet physiological parameter calculation method characterized by, The method comprises the following steps: acquiring physiological activity data and a body movement threshold value of a target pet; determining an activity state of the target pet based on the physiological activity data and the body movement threshold value; extracting and calculating the physiological activity data based on the activity state to determine breathing data of the target pet; extracting and calculating the physiological activity data based on the breathing data to determine heartbeat data of the target pet; generating physiological parameters of the target pet based on the breathing data and the heartbeat data.

2. The method of claim 1, wherein, The step of determining the activity state of the target pet based on the physiological activity data and the body movement threshold value comprises the following steps: extracting a fluctuation value from the physiological activity data to determine a fluctuation peak value; judging whether the fluctuation peak value is within the range of the body movement threshold value; if the fluctuation peak value is within the range of the body movement threshold value, determining that the activity state of the target pet is a body movement state; if the fluctuation peak value is not within the range of the body movement threshold value, determining that the activity state of the target pet is a quiet state.

3. The method of claim 2, wherein, The step of extracting and calculating the physiological activity data based on the activity state to determine the breathing data of the target pet comprises the following steps: acquiring a breathing cycle and original fluctuation data of the quiet state; calculating a target time interval based on the breathing cycle; determining the breathing data of the target pet based on the target time interval, the breathing cycle and the original fluctuation data.

4. The method of claim 3, wherein, The step of determining the breathing data of the target pet based on the target time interval, the breathing cycle and the original fluctuation data comprises the following steps: acquiring a sampling point greater than the breathing cycle in the original fluctuation data; calculating a sampling rate based on the sampling point and the target time interval; calculating a heart rate signal average value of the sampling point based on a pre-designed calculation formula, the sampling point, the sampling rate, the breathing cycle and the target time interval; filtering the heart rate signal average value in the original fluctuation data to obtain the breathing data of the target pet.

5. The method of claim 4, wherein, The step of extracting and calculating the physiological activity data based on the breathing data to determine the heartbeat data of the target pet comprises the following steps: acquiring a minimum possible heartbeat frequency of the target pet; calculating a decomposition layer number based on the minimum heartbeat frequency and the sampling rate; generating decomposition result data by performing wavelet decomposition processing on the original fluctuation data based on the decomposition layer number; determining the heartbeat data of the target pet based on the decomposition result data.

6. The method of claim 5, wherein, The step of determining the heartbeat data of the target pet based on the decomposition result data comprises the following steps: determining a maximum value and a minimum value based on the decomposition result data; calculating a data difference value based on the maximum value and the minimum value; acquiring a maximum heartbeat time cycle corresponding to the target pet; generating a filtering processing result by performing median filtering processing on the maximum heartbeat time cycle based on the maximum heartbeat time cycle; determining the heartbeat data of the target pet based on the filtering processing result.

7. The method of claim 3, wherein, The step of generating physiological parameters of the target pet based on the breathing data and the heartbeat data comprises the following steps: acquiring a labeling mode of the breathing data and the heartbeat data; generating physiological parameters by performing data labeling on the original fluctuation data based on the labeling mode.

8. A pet physiological parameter calculating apparatus characterized by comprising: The method comprises: a pet data acquisition module configured to acquire physiological activity data and a body movement threshold value of a target pet; an activity state determination module configured to determine an activity state of the target pet based on the physiological activity data and the body movement threshold value; a respiration data determination module configured to determine respiration data of the target pet by performing extraction calculation on the physiological activity data based on the activity state; a heartbeat data determination module configured to determine heartbeat data of the target pet by performing extraction calculation on the physiological activity data based on the respiration data; a physiological parameter generation module configured to generate a physiological parameter of the target pet based on the respiration data and the heartbeat data.

9. An electronic device, comprising: The electronic device comprises a processor coupled with a memory; the processor is configured to execute a computer program stored in the memory, so that the electronic device performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer program or instructions, when executed on a computer, cause the computer to perform the method according to any one of claims 1 to 7.

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