Deep Temperature Determination Method, Device and Storage Medium

Abstract

The present application provides a deep temperature determination method, device, and storage medium. The method includes: obtaining temperature measurement information of a target user during a target period; determining the ratio of the AC and DC components of the bioimpedance of the target user during the target period according to the AC component and the DC component of the bioimpedance; comparing the ratio of the AC and DC components of the bioimpedance with a preset steady-state threshold. If the ratio of the AC and DC components of the bioimpedance is greater than the preset steady-state threshold, then determine the deep temperature of the target user during the target period according to the temperature measurement information and a previously obtained deep temperature determination model. The present application can accurately obtain the deep temperature of the target user during the target period, avoid the problem of the decrease in the measurement accuracy of the deep temperature caused by the contact air gap between the sensing detection unit and the skin tissue of the target user, and improve the accuracy of the obtained deep temperature.

Description

Technical Field

[0001] This application relates to the technical field of temperature processing, and more specifically, to a method, device, and storage medium for determining deep body temperature. Background Art

[0002] In the field of deep body temperature measurement, as a passive sensor, the non-invasive single-channel heat flux sensor has an important challenge in achieving good contact with the skin. If the contact is poor, the measurement result of the sensor will have a significant difference from the actual skin temperature due to the large contact thermal resistance. Specifically, different skin types, such as deep wrinkles, dry or flexible skin, will form air gaps between the sensor and the skin, significantly affecting the measurement effectiveness and repeatability. In addition, daily activities or human movements may cause the movement of the sensor position or the change of contact pressure, and these dynamic changes will further increase the contact thermal resistance, resulting in inaccurate measurement results.

[0003] In the prior art, medical double-sided tape is usually used to fix the sensor to reduce the air gap, or a strap is used to apply pressure to prevent the sensor from slipping.

[0004] However, although these measures reduce the influence of contact thermal resistance to a certain extent, there may still be temperature differences between adjacent measurement channels at the same body part, resulting in the problem of inaccurate deep body temperature obtained. Summary of the Invention

[0005] The purpose of this application is to provide a method, device, and storage medium for determining deep body temperature to solve the problem of inaccurate deep body temperature obtained in the prior art in view of the deficiencies in the above prior art.

[0006] To achieve the above purpose, the technical solutions adopted in the embodiments of this application are as follows:

[0007] In a first aspect, an embodiment of this application provides a method for determining deep body temperature, and the method includes:

[0008] Obtain the temperature measurement information of the target user during the target period, where the temperature measurement information includes: the AC component of bioimpedance, the DC component of bioimpedance, the body surface temperature, and the heat flux;

[0009] Determine the AC-DC component ratio of bioimpedance of the target user during the target period according to the AC component of bioimpedance and the DC component of bioimpedance;

[0010] Compare the AC-DC component ratio of bioimpedance with a preset steady-state threshold. If the AC-DC component ratio of bioimpedance is greater than the preset steady-state threshold, determine the deep body temperature of the target user during the target period according to the temperature measurement information and a previously obtained deep body temperature determination model.

[0011] In a possible implementation, the deep temperature determination model includes: a thermal resistance fitting model and a temperature estimation model;

[0012] Determining the deep temperature of the target user during the target period according to the temperature measurement information and the pre-obtained deep temperature determination model includes:

[0013] Determining the contact thermal resistance of the target user during the target period according to the DC component of the bioimpedance and the thermal resistance fitting model;

[0014] Determining the deep temperature of the target user during the target period according to the body surface temperature, the heat flux, the contact thermal resistance and the temperature estimation model.

[0015] In a possible implementation, the thermal resistance fitting model is: , where is the contact thermal resistance, is the DC component of the bioimpedance, , and are scaling factors.

[0016] In a possible implementation, the temperature estimation model is: , where is the deep temperature, is the body surface temperature, is the heat flux, is the contact thermal resistance, , are constant coefficients.

[0017] In a possible implementation, the determination process of the thermal resistance fitting model and the temperature estimation model includes:

[0018] Obtaining the sample temperature information of the sample user during the sample period, where the sample temperature information includes: sample deep temperature, sample DC component of bioimpedance, sample body surface temperature and sample heat flux;

[0019] Obtaining an initial thermal resistance fitting model and an initial temperature estimation model, where the scaling factor in the initial thermal resistance fitting model and the constant coefficient in the initial temperature estimation model are both corresponding initial values;

[0020] Inputting the sample DC component of bioimpedance into the initial thermal resistance fitting model to obtain the actual contact thermal resistance, and inputting the sample body surface temperature, the sample heat flux and the actual contact thermal resistance into the initial temperature estimation model to obtain the actual deep temperature;

[0021] Iteratively adjust the scaling factor of the initial thermal resistance fitting model and the constant coefficient of the initial temperature estimation model according to the difference between the actual deep temperature and the sample deep temperature, and obtain the thermal resistance fitting model and the temperature estimation model after the iteration ends.

[0022] In a possible implementation manner, the method further includes:

[0023] If the ratio of the AC and DC components of the bioimpedance is less than a preset steady-state threshold, determine the fluctuation range of the ratio of the AC and DC components of the bioimpedance, and determine the deep temperature of the target user in the target period according to the fluctuation range of the ratio of the AC and DC components of the bioimpedance.

[0024] In a possible implementation manner, the determining the deep temperature of the target user in the target period according to the fluctuation range of the ratio of the AC and DC components of the bioimpedance includes:

[0025] If the fluctuation range of the ratio of the AC and DC components of the bioimpedance is less than a preset fluctuation range threshold, determine the deep temperature of the target user in the target period according to the deep temperature of the target user in the historical period and the body surface temperature.

[0026] In a possible implementation manner, the determining the deep temperature of the target user in the target period according to the fluctuation range of the ratio of the AC and DC components of the bioimpedance includes:

[0027] If the fluctuation range of the ratio of the AC and DC components of the bioimpedance is greater than a preset fluctuation range threshold, determine the deep temperature of the target user in the target period according to the deep temperature of the target user in the historical period.

[0028] In a second aspect, another embodiment of the present application provides a deep temperature determination device, and the device includes:

[0029] An acquisition module, configured to acquire temperature measurement information of a target user in a target period, where the temperature measurement information includes: an AC component of bioimpedance, a DC component of bioimpedance, a body surface temperature, and a heat flux;

[0030] A determination module, configured to determine the ratio of the AC and DC components of the bioimpedance of the target user in the target period according to the AC component of the bioimpedance and the DC component of the bioimpedance;

[0031] A first deep temperature generation module, configured to compare the ratio of the AC and DC components of the bioimpedance with a preset steady-state threshold, and if the ratio of the AC and DC components of the bioimpedance is greater than the preset steady-state threshold, determine the deep temperature of the target user in the target period according to the temperature measurement information and a previously obtained deep temperature determination model.

[0032] In a possible implementation, the deep temperature determination model includes: a thermal resistance fitting model and a temperature estimation model; the first deep temperature generation module is specifically configured to:

[0033] Determine the contact thermal resistance of the target user during the target period according to the DC component of the bio-impedance and the thermal resistance fitting model;

[0034] Determine the deep temperature of the target user during the target period according to the body surface temperature, the heat flux, the contact thermal resistance, and the temperature estimation model.

[0035] In a possible implementation, the thermal resistance fitting model is: , where is the contact thermal resistance, is the DC component of the bio-impedance, , and are scaling factors.

[0036] In a possible implementation, the temperature estimation model is: , where is the deep temperature, is the body surface temperature, is the heat flux, is the contact thermal resistance, , are constant coefficients.

[0037] In a possible implementation, the device further includes a model determination module, and the model determination module is configured to:

[0038] Obtain the sample temperature information of the sample user during the sample period, where the sample temperature information includes: sample deep temperature, sample DC component of bio-impedance, sample body surface temperature, and sample heat flux;

[0039] Obtain an initial thermal resistance fitting model and an initial temperature estimation model, where the scaling factor in the initial thermal resistance fitting model and the constant coefficient in the initial temperature estimation model are both corresponding initial values;

[0040] Input the sample DC component of bio-impedance into the initial thermal resistance fitting model to obtain the actual contact thermal resistance, and input the sample body surface temperature, the sample heat flux, and the actual contact thermal resistance into the initial temperature estimation model to obtain the actual deep temperature;

[0041] Iteratively adjust the scaling factor of the initial thermal resistance fitting model and the constant coefficient of the initial temperature estimation model according to the difference between the actual deep temperature and the sample deep temperature, and obtain the thermal resistance fitting model and the temperature estimation model after the iteration ends.

[0042] In a possible implementation manner, the device further includes: a second deep temperature generation module, configured to:

[0043] If the ratio of the AC and DC components of the bioimpedance is less than a preset steady-state threshold, determine the fluctuation range of the ratio of the AC and DC components of the bioimpedance, and determine the deep temperature of the target user in the target period according to the fluctuation range of the ratio of the AC and DC components of the bioimpedance.

[0044] In a possible implementation manner, the second deep temperature generation module is specifically configured to:

[0045] If the fluctuation range of the ratio of the AC and DC components of the bioimpedance is less than a preset fluctuation range threshold, determine the deep temperature of the target user in the target period according to the deep temperature of the target user in the historical period and the body surface temperature.

[0046] In a possible implementation manner, the second deep temperature generation module is specifically configured to:

[0047] If the fluctuation range of the ratio of the AC and DC components of the bioimpedance is greater than a preset fluctuation range threshold, determine the deep temperature of the target user in the target period according to the deep temperature of the target user in the historical period.

[0048] In a third aspect, another embodiment of the present application provides an electronic device, including: a processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to perform the steps of any method described in the first aspect above.

[0049] In a fourth aspect, another embodiment of the present application provides a storage medium, on which a computer program is stored. When the computer program is run by a processor, it performs the steps of any method described in the first aspect above.

[0050] The beneficial effects of the present application are as follows: By obtaining the alternating current component of bioimpedance, the direct current component of bioimpedance, the body surface temperature, and the heat flux of the target user during the target period, and determining the ratio of the alternating current and direct current components of bioimpedance of the target user during the target period based on the alternating current component of bioimpedance and the direct current component of bioimpedance, the physiological state change of the target user during the target period can be reflected. Comparing the ratio of the alternating current and direct current components of bioimpedance with a preset steady-state threshold can determine whether the contact between the target user and the sensing detection unit is an effective contact. If the ratio of the alternating current and direct current components of bioimpedance is greater than the preset steady-state threshold, the deep temperature of the target user during the target period is determined according to the temperature measurement information and the previously obtained deep temperature determination model. When the target user has an effective contact with the sensing detection unit, the error caused by contact thermal resistance can be compensated based on the deep temperature determination model, and the deep temperature of the target user during the target period can be accurately obtained, avoiding the problem of decreased measurement accuracy of the deep temperature caused by the contact air gap between the sensing detection unit and the skin tissue of the target user, and improving the accuracy of the obtained deep temperature. At the same time, the determination of the deep temperature can be achieved only through the ratio of the alternating current and direct current components of bioimpedance and the previously obtained deep temperature determination model. The calculation is simple and no additional physical equipment needs to be introduced, improving the determination efficiency of the deep temperature, and it can also be applied to any scenario where the deep temperature needs to be determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required for the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0052] Figure 1 A schematic diagram of a scenario of the deep temperature determination method provided by an embodiment of the present application;

[0053] Figure 2 A schematic diagram of a structure of a sensing detection unit in the deep temperature determination method provided by an embodiment of the present application;

[0054] Figure 3 A schematic flowchart of the deep temperature determination method provided by an embodiment of the present application;

[0055] Figure 4 A schematic diagram of a structure of the deep temperature determination model provided by an embodiment of the present application;

[0056] Figure 5 A schematic flowchart of determining the deep temperature of the target user during the target period in the deep temperature determination method provided by an embodiment of the present application;

[0057] Figure 6 It is a schematic flowchart for determining a thermal resistance fitting model and a temperature estimation model in the deep temperature determination method provided by the embodiments of the present application;

[0058] Figure 7 It is a schematic diagram of a deep temperature determination device provided by the embodiments of the present application;

[0059] Figure 8 It is a schematic diagram of the structure of an electronic device provided by the embodiments of the present application. Detailed implementation manners

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It should be understood that the accompanying drawings in the present application are only for the purposes of illustration and description, and are not used to limit the protection scope of the present application. In addition, it should be understood that the schematic drawings are not drawn to actual scale. The flowcharts used in the present application illustrate the operations implemented according to some embodiments of the present application. It should be understood that the operations in the flowchart may not be implemented in sequence, and the steps without logical context relationships may be reversed or implemented simultaneously. In addition, those skilled in the art may add one or more other operations to the flowchart or remove one or more operations from the flowchart under the guidance of the content of the present application.

[0061] In addition, the described embodiments are only some embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present application provided in the accompanying drawings below is not intended to limit the scope of the present application claimed, but only represents the selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts belong to the protection scope of the present application.

[0062] It should be noted that the term "including" will be used in the embodiments of the present application to indicate the existence of the features stated thereafter, but does not exclude adding other features.

[0063] In the prior art, medical double-sided tape is usually used to fix the sensor to reduce the air gap, or a strap is used to apply pressure to prevent the sensor from slipping.

[0064] However, although these measures reduce the influence of contact thermal resistance to a certain extent, there may still be temperature differences between adjacent measurement channels at the same body part, resulting in the problem that the obtained deep temperature is still inaccurate.

[0065] Based on the above problems, embodiments of the present application propose a method for determining deep body temperature. By obtaining the alternating current component of bioimpedance, the direct current component of bioimpedance, the skin surface temperature, and the heat flux of a target user during a target period, and determining the ratio of the alternating current and direct current components of bioimpedance of the target user during the target period according to the alternating current component of bioimpedance and the direct current component of bioimpedance, it is possible to measure the electrical characteristic differences of the target user during the target period. Then, by comparing the ratio of the alternating current and direct current components of bioimpedance with a preset steady-state threshold, it is possible to determine whether the contact between the target user and the sensing detection unit is an effective contact. If the ratio of the alternating current and direct current components of bioimpedance is greater than the preset steady-state threshold, then according to the temperature measurement information and the previously obtained deep body temperature determination model, the deep body temperature of the target user during the target period is determined. When the target user has an effective contact with the sensing detection unit, the error caused by contact thermal resistance can be compensated based on the deep body temperature determination model, so as to accurately obtain the deep body temperature of the target user during the target period.

[0066] First, the application scenarios involved in the deep body temperature determination method provided by embodiments of the present application are described.

[0067] Figure 1 FIG. is a schematic diagram of a scenario for the deep body temperature determination method provided by embodiments of the present application. Refer to Figure 1 As shown, during the deep body temperature measurement process, the heat flow channel serves as a medium for heat transfer. The sensor detection unit is located on one side of the heat flow channel. After the sensing detection unit makes contact with the skin tissue of the target user via the contact interface, it executes the steps of the deep body temperature determination method provided by embodiments of the present application to measure the deep body temperature of the target user and obtain the deep body temperature of the target user.

[0068] Specifically, Figure 2 FIG. is a schematic diagram of the structure of the sensing detection unit in the deep body temperature determination method provided by embodiments of the present application. Refer to Figure 2 As shown, the sensing detection unit includes a bioimpedance measurement sensor, a thermosensor, a heat flux sensor, and a processing module. Among them, the bioimpedance measurement sensor is used to collect the bioimpedance of the target user to obtain the alternating current component of bioimpedance and the direct current component of bioimpedance of the target user. The thermosensor is used to collect the skin surface temperature of the target user. The heat flux sensor is used to collect the heat flux of the target user. The processing module is used to interact with the bioimpedance measurement sensor, the thermosensor, and the heat flux sensor, and execute the steps of the deep body temperature determination method provided by embodiments of the present application to obtain the deep body temperature of the target user.

[0069] The following describes the deep body temperature determination method provided by embodiments of the present application in detail with reference to multiple embodiments.

[0070] Figure 3A schematic flowchart of the deep temperature determination method provided by the embodiment of this application. Refer to Figure 3 As shown, the execution subject of this method can be any electronic device with processing capabilities, such as the processing module in the above-mentioned sensing detection unit. This method includes:

[0071] S301. Obtain the temperature measurement information of the target user during the target period.

[0072] Optionally, the processing module in the sensing detection unit obtains the temperature measurement information of the target user during the target period. Among them, the target user can be the user wearing the sensing detection unit, and the target period can be the current measurement period of the target user, for example: 1 minute or 5 minutes. Among them, the temperature measurement information includes: bioimpedance alternating current component, bioimpedance direct current component, body surface temperature, and heat flux.

[0073] Specifically, the bioimpedance alternating current component refers to the part of the impedance change generated by the response of biological tissues to alternating current in bioelectrical impedance measurement, which is related to the dynamic electrical characteristics such as capacitance and inductance of biological tissues. The bioimpedance direct current component refers to the part of the constant impedance of biological tissues that does not change with time in bioelectrical impedance measurement, which is related to the static resistance characteristics of biological tissues. Both the bioimpedance alternating current component and the bioimpedance direct current component are used to indicate information on the physiological and pathological states of biological tissues.

[0074] Specifically, the body surface temperature refers to the surface temperature of the target user, including the temperature of the skin, subcutaneous tissue, and muscles. The heat flux refers to the thermal energy that reaches the sensing detection unit through the skin tissue of the target user during the target period.

[0075] S302. Determine the bioimpedance AC-DC component ratio of the target user during the target period according to the bioimpedance alternating current component and the bioimpedance direct current component.

[0076] Optionally, after obtaining the bioimpedance alternating current component and the bioimpedance direct current component of the target user during the target period, the bioimpedance AC-DC component ratio of the target user during the target period can be calculated according to the bioimpedance alternating current component and the bioimpedance direct current component of the target user during the target period.

[0077] Among them, the bioimpedance AC-DC component ratio refers to the ratio of the change in the bioimpedance AC-DC component, which can be obtained by the ratio of the bioimpedance alternating current component to the bioimpedance direct current component, and reflects the difference in the impedance characteristics of the skin tissue of the target user under the action of alternating current and direct current.

[0078] By using the ratio of the AC and DC components of bio-impedance, it is possible to measure the electrical property differences of the target user within the target time period, and to characterize the rate of change of the physiological state of the target user within the target time period, so as to reflect the change of the physiological state of the target user within the target time period.

[0079] S303. Compare the ratio of the AC and DC components of bio-impedance with a preset steady-state threshold. If the ratio of the AC and DC components of bio-impedance is greater than the preset steady-state threshold, then determine the deep temperature of the target user within the target time period according to the temperature measurement information and the previously obtained deep temperature determination model.

[0080] Optionally, after obtaining the ratio of the AC and DC components of bio-impedance, the ratio of the AC and DC components of bio-impedance can be compared with a preset steady-state threshold. If the ratio of the AC and DC components of bio-impedance is greater than the preset steady-state threshold, it can be determined that the contact between the target user and the sensing and detection unit is an effective contact. Then, the deep temperature of the target user within the target time period can be determined according to the temperature measurement information and the previously obtained deep temperature determination model.

[0081] Exemplarily, if the ratio of the AC and DC components of bio-impedance is greater than the preset steady-state threshold, input the temperature measurement information into the previously obtained deep temperature determination model, so as to obtain the deep temperature of the target user within the target time period.

[0082] Among them, the preset steady-state threshold can be obtained by pre-statistical data. The previously obtained deep temperature determination model is used to compensate for the error caused by the contact thermal resistance based on the temperature measurement information, so as to accurately obtain the deep temperature.

[0083] Exemplarily, the previously obtained deep temperature determination model can be obtained through the bio-heat transfer equation.

[0084] In this embodiment, by obtaining the alternating current component of bioimpedance, the direct current component of bioimpedance, the body surface temperature, and the heat flux of the target user during the target period, and determining the ratio of the alternating current and direct current components of bioimpedance of the target user during the target period according to the alternating current component of bioimpedance and the direct current component of bioimpedance, the change in the physiological state of the target user during the target period can be reflected. By comparing the ratio of the alternating current and direct current components of bioimpedance with a preset steady-state threshold, it is possible to determine whether the contact between the target user and the sensing detection unit is an effective contact. If the ratio of the alternating current and direct current components of bioimpedance is greater than the preset steady-state threshold, the deep body temperature of the target user during the target period is determined according to the temperature measurement information and the previously obtained deep body temperature determination model. When the target user has an effective contact with the sensing detection unit, the error caused by the contact thermal resistance can be compensated based on the deep body temperature determination model, and the deep body temperature of the target user during the target period can be accurately obtained, avoiding the problem of the decrease in the measurement accuracy of the deep body temperature caused by the contact air gap between the sensing detection unit and the skin tissue of the target user, and improving the accuracy of the obtained deep body temperature. At the same time, the deep body temperature can be determined only by the ratio of the alternating current and direct current components of bioimpedance and the previously obtained deep body temperature determination model. The calculation is simple and no additional physical equipment needs to be introduced, improving the determination efficiency of the deep body temperature, and it can also be applied to any scenario where the deep body temperature needs to be determined.

[0085] In a possible implementation, Figure 4 FIG. is a schematic structural diagram of a deep body temperature determination model provided by an embodiment of the present application. Figure 5 FIG. is a schematic flowchart of determining the deep body temperature of a target user during a target period in the deep body temperature determination method provided by an embodiment of the present application. Referring to Figure 4 and Figure 5 As shown, the deep body temperature determination model includes: a thermal resistance fitting model and a temperature estimation model. In step S303, according to the temperature measurement information and the previously obtained deep body temperature determination model, determining the deep body temperature of the target user during the target period includes:

[0086] S501. Determine the contact thermal resistance of the target user during the target period according to the direct current component of bioimpedance and the thermal resistance fitting model.

[0087] It can be understood that within the pressure range of 0.73 - 10.98 kPa, the change in skin temperature is significantly affected by the pressure. Therefore, during the measurement of the wearable deep body temperature sensor, the factor that has the greatest impact on the contact thermal resistance is the change in the contact pressure. When the contact pressure between the sensing detection unit and the skin is known, the thermal resistance fitting model can be obtained based on the semi-empirical model, and the contact thermal resistance can be determined through the thermal resistance fitting model.

[0088] Optionally, the DC component of bioimpedance can be input into the thermal resistance fitting model in the deep temperature determination model, so as to fit the contact thermal resistance based on the DC component of bioimpedance, and obtain the contact thermal resistance of the target user during the target period.

[0089] S502. Determine the deep temperature of the target user during the target period according to the body surface temperature, heat flux, contact thermal resistance, and temperature estimation model.

[0090] It can be understood that after determining the contact thermal resistance through the thermal resistance fitting model, the contact thermal resistance in the deep temperature determination process can be compensated based on the bioheat transfer equation and in combination with the contact thermal resistance, so as to obtain the temperature estimation model, and thus the deep temperature of the target user during the target period can be determined according to the body surface temperature, heat flux, contact thermal resistance, and temperature estimation model.

[0091] Optionally, input the body surface temperature, heat flux, and contact thermal resistance into the temperature estimation model to obtain the deep temperature of the target user during the target period.

[0092] By using the DC component of bioimpedance and the thermal resistance fitting model to determine the contact thermal resistance of the target user during the target period, the contact thermal resistance of the target user during the target period can be accurately calculated, and the error caused by the contact thermal resistance can be compensated through the body surface temperature, heat flux, contact thermal resistance, and temperature estimation model, and the deep temperature of the target user during the target period can be accurately determined.

[0093] In a possible implementation, the thermal resistance fitting model is: , where is the contact thermal resistance, is the DC component of bioimpedance, , and are scaling factors.

[0094] Optionally, can be the baseline signal of the DC component of bioimpedance. The thermal resistance fitting model can be obtained by fitting the contact thermal resistance using the bioimpedance / photoplethysmogram signal. Specifically, the DC component of bioimpedance can be processed to obtain the baseline signal of the DC component of bioimpedance, and the contact thermal resistance can be fitted through the baseline signal of the DC component of bioimpedance.

[0095] In a possible implementation, the temperature estimation model is: , where is the deep temperature, is the body surface temperature, is the heat flux, is the contact thermal resistance, , are constant coefficients.

[0096] Optionally, the temperature estimation model can be obtained from the bioheat transfer equation. Exemplarily, the bioheat transfer equation is simplified under steady-state boundary conditions, and the bioheat transfer equation is solved to obtain the following formula (1):

[0097]

[0098] where is the deep temperature, is the surface temperature measured by the sensing and detecting unit, is the blood perfusion rate, is the above-mentioned contact thermal resistance, is the thermal diffusivity of the skin tissue, expressed as (k is the thermal conductivity, representing the ability of the material to conduct heat, C b is the specific heat capacity, is the density).

[0099] Exemplarily, according to Fourier's law, the heat flux density in the heat conduction mode is inversely proportional to the thickness x and directly proportional to the temperature gradient T, and there is the following formula (2):

[0100] (2)

[0101] where is the heat flux density, x is the thickness, T is the temperature gradient, is the thermal conductivity.

[0102] Exemplarily, the heat flux at the corresponding skin has the following formula (3):

[0103]

[0104] where is the heat flux, is the blood perfusion rate, is the surface temperature measured by the sensing and detecting unit, is the deep temperature, is the above-mentioned contact thermal resistance, is the thermal diffusivity of the skin tissue, is the thermal conductivity.

[0105] Exemplarily, through the above formulas (1)-(3), the relationship between the deep temperature , the contact thermal resistance and the surface temperature can be determined and simplified. By placing the parameters obtained from the sensing and detecting unit on the right side and the deep temperature on the left side, the following formula (4) can be obtained, which is the above-mentioned temperature estimation model:

[0106] (4)

[0107] Wherein, is the deep temperature, is the body surface temperature measured by the sensing detection unit, is the heat flux, is the blood perfusion rate, is the above-mentioned contact thermal resistance, is the thermal diffusivity of the skin tissue, expressed as (k is the thermal conductivity, representing the ability of the material to conduct heat, C b is the specific heat capacity, is the density), and are both constant coefficients.

[0108] It can be understood that in the actual application process, the scaling factor in the thermal resistance fitting model and the constant coefficients in the temperature estimation model can both be determined in advance. The processes of determining the thermal resistance fitting model and the temperature estimation model are exemplarily described below.

[0109] In a possible implementation manner, Figure 6 is a schematic flowchart of a process for determining the thermal resistance fitting model and the temperature estimation model in the deep temperature determination method provided by the embodiments of the present application. Referring to Figure 6 as shown, the processes of determining the thermal resistance fitting model and the temperature estimation model include:

[0110] S601. Obtain the sample temperature information of the sample user during the sample period.

[0111] Optionally, when determining the thermal resistance fitting model and the temperature estimation model, the sample temperature information of the sample user during the sample period can be obtained. The sample period can be a period with the same physiological state as the target period, and the sample user can be a user with the same physiological state as the target user.

[0112] Among them, the sample temperature information includes: the sample deep temperature, the sample bioimpedance DC component, the sample body surface temperature, and the sample heat flux.

[0113] S602. Obtain the initial thermal resistance fitting model and the initial temperature estimation model.

[0114] Optionally, obtain the initial thermal resistance fitting model and the initial temperature estimation model.

[0115] Exemplarily, the initial thermal resistance fitting model is , wherein, is the sample contact thermal resistance, is the sample bioimpedance DC component, and and are both initial scaling factors.

[0116] Exemplarily, the initial temperature estimation model is: , where is the deep temperature of the sample, is the surface temperature of the sample, is the heat flux of the sample, is the contact thermal resistance of the sample, and are both initial constant coefficients.

[0117] Among them, the scaling factor in the initial thermal resistance fitting model and the constant coefficients in the initial temperature estimation model are both the corresponding initial values.

[0118] S603. Input the DC component of the sample bio-impedance into the initial thermal resistance fitting model to obtain the actual contact thermal resistance, and input the surface temperature of the sample, the heat flux of the sample, and the actual contact thermal resistance into the initial temperature estimation model to obtain the actual deep temperature.

[0119] Optionally, input the DC component of the sample bio-impedance of the sample user during the sample period into the initial thermal resistance fitting model in S602 above, calculate to obtain the actual contact thermal resistance, and input the surface temperature of the sample, the heat flux of the sample, and the actual contact thermal resistance into the initial temperature estimation model in S602 above, and calculate to obtain the actual deep temperature.

[0120] S604. According to the difference between the actual deep temperature and the deep temperature of the sample, iteratively adjust the scaling factor of the initial thermal resistance fitting model and the constant coefficients of the initial temperature estimation model, and obtain the thermal resistance fitting model and the temperature estimation model after the iteration ends.

[0121] Optionally, the difference between the actual deep temperature and the deep temperature of the sample can be calculated, and through the search for optimal parameters, the scaling factor in the initial thermal resistance fitting model and the constant coefficients in the initial temperature estimation model are iteratively adjusted, and the thermal resistance fitting model and the temperature estimation model are obtained after the iteration ends.

[0122] Exemplarily, the difference between the actual deep temperature and the deep temperature of the sample can be calculated, the scaling factor in the initial thermal resistance fitting model is fixed, and the constant coefficients in the initial temperature estimation model are optimized through iteration to search for parameters, and the equivalent contact resistance is calculated through the deep temperature of the sample, the scaling factor in the initial thermal resistance fitting model is optimized to search for parameters, and combined with the preset termination tolerance, the optimal scaling factor and the optimal constant coefficients are obtained, the iteration ends, and the thermal resistance fitting model and the temperature estimation model are obtained based on the optimal scaling factor and the optimal constant coefficients.

[0123] Optionally, the scaling factor in the initial thermal resistance fitting model and the constant coefficient in the initial temperature estimation model may be searched through grid search to find the optimal solution, thereby obtaining the thermal resistance fitting model and the temperature estimation model.

[0124] By obtaining the sample temperature information of the sample user in the sample period, and by obtaining the initial thermal resistance fitting model and the initial temperature estimation model, and inputting the DC component of the sample bioimpedance into the initial thermal resistance fitting model, the actual contact thermal resistance is obtained, and the sample surface temperature, the sample heat flux and the actual contact thermal resistance are input into the initial temperature estimation model to obtain the actual deep temperature, so that according to the difference between the actual deep temperature and the sample deep temperature, the scaling factor of the initial thermal resistance fitting model and the constant coefficient of the initial temperature estimation model are iteratively adjusted, and the thermal resistance fitting model and the temperature estimation model are obtained after the iteration, which can continuously optimize the thermal resistance fitting model and the temperature estimation model, ensure the accuracy of the obtained thermal resistance fitting model and the temperature estimation model, and improve the generalization ability of the obtained thermal resistance fitting model and the temperature estimation model, so as to accurately determine the deep temperature of the target user in the target period.

[0125] The above describes in detail the deep temperature determination process when the ratio of the AC and DC components of bioimpedance is greater than the preset steady-state threshold. The following describes an exemplary description of the deep temperature determination process when the ratio of the AC and DC components of bioimpedance is less than or equal to the preset steady-state threshold.

[0126] In a possible implementation, the deep temperature determination method provided in the embodiment of the present application further includes:

[0127] If the bioimpedance AC / DC component ratio is less than a preset steady-state threshold, the fluctuation range of the bioimpedance AC / DC component ratio is determined, and the deep temperature of the target user in the target period is determined based on the fluctuation range of the bioimpedance AC / DC component ratio.

[0128] It should be understood that when the ratio of the AC and DC components of bio-impedance is less than or equal to a preset steady-state threshold, it can be determined that the contact between the target user and the sensing detection unit is ineffective contact, that is, the target user is in motion or in a state of drastic emotional fluctuations. At this time, the determination of the deep temperature is affected by motion artifacts, and the temperature measurement information obtained by the sensing detection unit is non-steady-state information. The deep temperature of the target user in the target period can be determined by determining the fluctuation range of the ratio of the AC and DC components of bio-impedance and based on the fluctuation range of the ratio of the AC and DC components of bio-impedance.

[0129] Optionally, if the ratio of the AC and DC components of the bioimpedance is less than a preset steady-state threshold, the fluctuation range of the ratio of the AC and DC components of the bioimpedance can be determined by means of statistical analysis according to the ratio of the AC and DC components of the bioimpedance. The fluctuation range of the ratio of the AC and DC components of the bioimpedance is used to indicate the distribution and dispersion degree of the ratio of the AC and DC components of the bioimpedance.

[0130] Exemplarily, after obtaining the fluctuation range of the ratio of the AC and DC components of the bioimpedance, the ratio of the AC and DC components of the bioimpedance at the target position can be determined from the fluctuation range of the ratio of the AC and DC components of the bioimpedance, and based on the ratio of the AC and DC components of the bioimpedance at the target position, the deep temperature of the target user during the target period can be determined.

[0131] Exemplarily, after obtaining the fluctuation range of the ratio of the AC and DC components of the bioimpedance, the fluctuation range of the ratio of the AC and DC components of the bioimpedance can be combined with a preset fluctuation range threshold for judgment, and based on the judgment result, the deep temperature of the target user during the target period can be determined.

[0132] By determining the fluctuation range of the ratio of the AC and DC components of the bioimpedance when the ratio of the AC and DC components of the bioimpedance is less than the preset steady-state threshold, the physiological state of the target user during the target period can be judged, and based on the fluctuation range of the ratio of the AC and DC components of the bioimpedance, the deep temperature of the target user during the target period can be determined, and the deep temperature of the target user during the target period can be accurately obtained through the change of the physiological state of the target user.

[0133] In a possible implementation manner, the above step of determining the deep temperature of the target user during the target period according to the fluctuation range of the ratio of the AC and DC components of the bioimpedance includes:

[0134] If the fluctuation range of the ratio of the AC and DC components of the bioimpedance is less than a preset fluctuation range threshold, the deep temperature of the target user during the target period is determined according to the deep temperature and body surface temperature of the target user in the historical period.

[0135] Optionally, if the fluctuation range of the ratio of the AC and DC components of the bioimpedance is less than a preset fluctuation range threshold, it can be determined that the physiological state of the target user during the target period is in a small fluctuation range, then the deep temperature of the target user during the target period can be determined according to the deep temperature and body surface temperature of the target user in the historical period.

[0136] Wherein, the preset fluctuation range threshold can be 0.7 times of the above steady-state threshold.

[0137] Exemplarily, if the fluctuation range of the ratio of the AC and DC components of the bioimpedance is less than a preset fluctuation range threshold, the deep temperature of the target user in the historical period and the surface temperature of the target user in the target period can be adaptively filtered to eliminate motion artifacts and compensate for the influence of skin sweating, so as to obtain the deep temperature of the target user in the target period.

[0138] Exemplarily, if the fluctuation range of the ratio of the AC and DC components of the bioimpedance is less than a preset fluctuation range threshold, the deep temperature of the target user in the historical period and the surface temperature of the target user in the historical period can be adaptively filtered to eliminate motion artifacts and compensate for the influence of skin sweating, so as to obtain the deep temperature of the target user in the target period.

[0139] When the fluctuation range of the ratio of the AC and DC components of the bioimpedance is less than a preset fluctuation range threshold, by using the deep temperature and the surface temperature of the target user in the historical period to determine the deep temperature of the target user in the target period, it is possible to eliminate motion artifacts and compensate for the influence of skin sweating. Thus, when the contact between the target user and the sensing and detecting unit is ineffective contact, the deep temperature of the target user in the target period can be accurately obtained, reducing the influence of the fluctuation of the ratio of the AC and DC components of the bioimpedance on the deep temperature, and thereby improving the robustness in determining the deep temperature in complex living scenarios.

[0140] In a possible implementation manner, the above step of determining the deep temperature of the target user in the target period according to the fluctuation range of the ratio of the AC and DC components of the bioimpedance includes:

[0141] If the fluctuation range of the ratio of the AC and DC components of the bioimpedance is greater than a preset fluctuation range threshold, then determine the deep temperature of the target user in the target period according to the deep temperature of the target user in the historical period.

[0142] Optionally, if the fluctuation range of the ratio of the AC and DC components of the bioimpedance is greater than a preset fluctuation range threshold, it can be determined that the physiological state of the target user in the target period is in a large fluctuation range. Then, the deep temperature of the target user in the target period can be determined according to the deep temperature of the target user in the historical period. Among them, the preset fluctuation range threshold can be 0.7 times the above-mentioned steady state threshold.

[0143] Optionally, if the fluctuation range of the ratio of the AC and DC components of the bioimpedance is less than a preset fluctuation range threshold, the deep temperature of the target user in the historical period can be interpolated using a statistical method to obtain the deep temperature of the target user in the target period.

[0144] Optionally, when the DC component of the bioimpedance exceeds a preset baseline threshold, it is determined that the DC component of the bioimpedance is sensor displacement, that is, there is an error in the DC component of the bioimpedance. Then, interpolation can be performed on the displacement period through the data of a specified duration before and after the sensor displacement period, so as to obtain the deep temperature of the target user in the target period.

[0145] Exemplarily, when the DC component of the bioimpedance at a target moment in the target period exceeds a preset baseline threshold, polynomial interpolation can be performed on the deep temperature of the target user at the first moment in the historical period, so as to obtain the deep temperature of the target user at a certain moment in the target period, and thus obtain the deep temperature of the target user in the target period. Wherein, the first moment and the target moment in the target period can be separated by a specified duration.

[0146] When the fluctuation range of the AC / DC component ratio of the bioimpedance is greater than a preset fluctuation range threshold, the deep temperature of the target user in the target period is determined through the deep temperature of the target user in the historical period, which can accurately determine the deep temperature of the target user in the target period when the fluctuation is large, reduce the influence of the fluctuation of the AC / DC component ratio of the bioimpedance on the deep temperature, and thus improve the robustness in determining the deep temperature in complex living scenarios.

[0147] Based on the same inventive concept, an apparatus for determining deep temperature corresponding to the method for determining deep temperature is further provided in the embodiments of the present application. Since the principle of solving problems by the apparatus in the embodiments of the present application is similar to the above-mentioned method for determining deep temperature in the embodiments of the present application, the implementation of the apparatus can refer to the implementation of the method, and the repeated parts will not be described again.

[0148] Figure 7 is a schematic diagram of an apparatus for determining deep temperature provided by an embodiment of the present application. Referring to Figure 7 as shown, the apparatus includes: an acquisition module 701, a determination module 702, and a first deep temperature generation module 703;

[0149] The acquisition module 701 is configured to acquire temperature measurement information of a target user in a target period, where the temperature measurement information includes: an AC component of the bioimpedance, a DC component of the bioimpedance, a body surface temperature, and a heat flux;

[0150] The determination module 702 is configured to determine the AC / DC component ratio of the bioimpedance of the target user in the target period according to the AC component of the bioimpedance and the DC component of the bioimpedance;

[0151] The first deep temperature generation module 703 is configured to compare the AC / DC component ratio with a preset steady-state threshold. If the AC / DC component ratio is greater than the preset steady-state threshold, the deep temperature of the target user in the target period is determined according to the temperature measurement information and a previously obtained deep temperature determination model.

[0152] In a possible implementation, the deep temperature determination model includes: a thermal resistance fitting model and a temperature estimation model; the first deep temperature generation module 703 is specifically configured to:

[0153] Determine the contact thermal resistance of the target user during the target period according to the DC component of the bio-impedance and the thermal resistance fitting model;

[0154] Determine the deep temperature of the target user during the target period according to the body surface temperature, the heat flux, the contact thermal resistance, and the temperature estimation model.

[0155] In a possible implementation, the thermal resistance fitting model is: , where is the contact thermal resistance, is the DC component of the bio-impedance, , and are scaling factors.

[0156] In a possible implementation, the temperature estimation model is: , where is the deep temperature, is the body surface temperature, is the heat flux, is the contact thermal resistance, , are constant coefficients.

[0157] In a possible implementation, the device further includes a model determination module, and the model determination module is configured to:

[0158] Obtain the sample temperature information of the sample user during the sample period, where the sample temperature information includes: the sample deep temperature, the sample DC component of the bio-impedance, the sample body surface temperature, and the sample heat flux;

[0159] Obtain the initial thermal resistance fitting model and the initial temperature estimation model, where the scaling factor in the initial thermal resistance fitting model and the constant coefficient in the initial temperature estimation model are both corresponding initial values;

[0160] Input the sample DC component of the bio-impedance into the initial thermal resistance fitting model to obtain the actual contact thermal resistance, and input the sample body surface temperature, the sample heat flux, and the actual contact thermal resistance into the initial temperature estimation model to obtain the actual deep temperature;

[0161] Iteratively adjust the scaling factor of the initial thermal resistance fitting model and the constant coefficient of the initial temperature estimation model according to the difference between the actual deep temperature and the sample deep temperature, and obtain the thermal resistance fitting model and the temperature estimation model after the iteration ends.

[0162] In a possible implementation, the device further includes: a second deep temperature generation module, configured to:

[0163] If the ratio of the AC and DC components of the bioimpedance is less than a preset steady-state threshold, determine the fluctuation range of the ratio of the AC and DC components of the bioimpedance, and determine the deep temperature of the target user during the target period according to the fluctuation range of the ratio of the AC and DC components of the bioimpedance.

[0164] In a possible implementation, the second deep temperature generation module is specifically configured to:

[0165] If the fluctuation range of the ratio of the AC and DC components of the bioimpedance is less than a preset fluctuation range threshold, determine the deep temperature of the target user during the target period according to the deep temperature and the body surface temperature of the target user during the historical period.

[0166] In a possible implementation, the second deep temperature generation module is specifically configured to:

[0167] If the fluctuation range of the ratio of the AC and DC components of the bioimpedance is greater than a preset fluctuation range threshold, determine the deep temperature of the target user during the target period according to the deep temperature of the target user during the historical period.

[0168] Descriptions of the processing flows of the modules in the device and the interaction flows between the modules can refer to the relevant descriptions in the above method embodiments and will not be elaborated here.

[0169] This application embodiment also provides an electronic device, as Figure 8 shown, Figure 8 is a schematic structural diagram of the electronic device provided by this application embodiment, including: a processor 801, a memory 802, and optionally, a bus 803 may also be included. The memory 802 stores machine-readable instructions executable by the processor 801 (such as Figure 7 the execution instructions corresponding to the acquisition module 701, the determination module 702, and the first deep temperature generation module 703 in the device), and when the electronic device runs, the processor 801 communicates with the memory 802 through the bus 803, and when the machine-readable instructions are executed by the processor 801, the steps of the above deep temperature determination method are executed.

[0170] This application embodiment also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, the steps of the above deep temperature determination method are executed.

[0171] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems and devices described above can refer to the corresponding processes in the method embodiments, and will not be elaborated herein. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation. For another example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces. The indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0172] In addition, in each embodiment of this application, each functional unit can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical disks and other various media that can store program codes.

[0173] The above are only the specific implementation manners of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application.

Claims

1. A method for determining deep temperature, characterized in that: include: Acquiring temperature measurement information of a target user during a target period of time, wherein the temperature measurement information includes: an alternating current component of bioimpedance, a direct current component of bioimpedance, a body surface temperature, and a heat flux; Determining a bioimpedance AC / DC component ratio of a target user in a target period of time according to the bioimpedance AC component and the bioimpedance DC component; Comparing the bioimpedance AC / DC component ratio with a preset steady-state threshold, if the bioimpedance AC / DC component ratio is greater than the preset steady-state threshold, determining the deep temperature of the target user in the target period according to the temperature measurement information and a pre-obtained deep temperature determination model; The deep temperature determination model includes: a thermal resistance fitting model and a temperature estimation model; The determining the deep temperature of the target user in the target time period according to the temperature measurement information and a pre-obtained deep temperature determination model includes: Determining the contact thermal resistance of the target user in a target period of time according to the bioimpedance DC component and the thermal resistance fitting model; The deep temperature of the target user in a target period is determined according to the body surface temperature, the heat flux, the contact thermal resistance, and the temperature estimation model.

2. The deep temperature determination method according to claim 1, characterized in that: The thermal resistance fitting model is: ,in, is the contact thermal resistance, is the DC component of the bioimpedance, , as well as is the scaling factor.

3. The deep temperature determination method according to claim 2, characterized in that: The temperature estimation model is: ,in, is the deep temperature, is the body surface temperature, is the heat flux, is the contact thermal resistance, , is a constant coefficient.

4. The deep temperature determination method according to claim 3, characterized in that: The determination process of the thermal resistance fitting model and the temperature estimation model includes: Acquiring sample temperature information of the sample user during the sample period, wherein the sample temperature information includes: sample deep temperature, sample bioimpedance DC component, sample body surface temperature, and sample heat flux; Obtaining an initial thermal resistance fitting model and an initial temperature estimation model, wherein the scaling factor in the initial thermal resistance fitting model and the constant coefficient in the initial temperature estimation model are both corresponding initial values; Inputting the DC component of the sample bioimpedance into the initial thermal resistance fitting model to obtain the actual contact thermal resistance, and inputting the sample body surface temperature, sample heat flux and the actual contact thermal resistance into the initial temperature estimation model to obtain the actual deep temperature; According to the difference between the actual deep temperature and the sample deep temperature, the scaling factor of the initial thermal resistance fitting model and the constant coefficient of the initial temperature estimation model are iteratively adjusted, and the thermal resistance fitting model and the temperature estimation model are obtained after the iteration.

5. The deep temperature determination method according to claim 1, characterized in that: The method further comprises: If the bio-impedance AC / DC component ratio is less than a preset steady-state threshold, the fluctuation range of the bio-impedance AC / DC component ratio is determined, and based on the fluctuation range of the bio-impedance AC / DC component ratio, the deep temperature of the target user in the target time period is determined.

6. The deep temperature determination method according to claim 5, characterized in that: The determining the deep temperature of the target user in the target period according to the fluctuation range of the ratio of the AC and DC components of the bio-impedance includes: If the fluctuation range of the ratio of the AC and DC components of the bioimpedance is less than a preset fluctuation range threshold, the deep temperature of the target user in the target period is determined according to the deep temperature of the target user in the historical period and the body surface temperature.

7. The deep temperature determination method according to claim 5, characterized in that: The determining the deep temperature of the target user in the target period according to the fluctuation range of the ratio of the AC and DC components of the bio-impedance includes: If the fluctuation range of the ratio of the AC and DC components of the bioimpedance is greater than a preset fluctuation range threshold, the deep temperature of the target user in the target period is determined according to the deep temperature of the target user in the historical period.

8. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor executes the machine-readable instructions to perform the steps of the deep temperature determination method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the deep temperature determination method as described in any one of claims 1 to 7 are executed.

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