Deep temperature determination method, device and storage medium

Abstract

The present application provides a method, device and storage medium for determining deep temperature, wherein the method includes: obtaining temperature measurement information of a target user in a target period; determining the ratio of the bioimpedance AC / DC components of the target user in the target period according to the bioimpedance AC component and the bioimpedance DC component; comparing the bioimpedance AC / DC component ratio with a preset steady-state threshold, and 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 present application can accurately obtain the deep temperature of the target user in the target period, avoid the problem of decreased deep temperature measurement accuracy caused by the contact air gap between the sensing detection unit and the target user's skin tissue, and improve the accuracy of the obtained deep temperature.

Description

Technical Field

[0001] The present application relates to the field of temperature processing technology, and in particular to a deep temperature determination method, device and storage medium. Background Art

[0002] In the field of deep temperature measurement, as a passive sensor, good contact between the non-invasive single-channel heat flux sensor and the skin is an important challenge. If the contact is poor, the sensor measurement results will be significantly different 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 an air gap between the sensor and the skin, significantly affecting the effectiveness and repeatability of the measurement. In addition, daily activities or human movement may cause the sensor position to move or the contact pressure to change. These dynamic changes will further increase the contact thermal resistance and lead to 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 can reduce the impact of contact thermal resistance to a certain extent, there may still be temperature differences between adjacent measurement channels in the same body part, resulting in inaccurate deep temperature. Summary of the invention

[0005] The purpose of the present application is to provide a deep temperature determination method, device and storage medium to address the deficiencies in the above-mentioned prior art, so as to solve the problem of inaccurate deep temperature obtained in the prior art.

[0006] To achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows: In a first aspect, an embodiment of the present application provides a method for determining deep temperature, the method comprising: 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; The bioimpedance AC / DC component ratio is compared with a preset steady-state threshold. If the bioimpedance AC / DC component ratio is greater than the preset steady-state threshold, the deep temperature of the target user in the target time period is determined based on the temperature measurement information and a pre-obtained deep temperature determination model.

[0007] In a possible implementation, 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.

[0008] In a possible implementation, 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.

[0009] In a possible implementation, 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.

[0010] In a possible implementation, the process of determining 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 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.

[0011] In a possible implementation, the method further includes: 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.

[0012] In a possible implementation, 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.

[0013] In a possible implementation, 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.

[0014] In a second aspect, another embodiment of the present application provides a deep temperature determination device, the device comprising: An acquisition module, used to acquire temperature measurement information of a target user during a target period of time, wherein the temperature measurement information includes: an AC component of bioimpedance, a DC component of bioimpedance, a body surface temperature, and a heat flux; A determination module, configured to determine a ratio of the bio-impedance AC and DC components of the target user in a target period of time according to the bio-impedance AC component and the bio-impedance DC component; The first deep temperature generating module is used to compare the ratio of the AC and DC components of the bio-impedance with a preset steady-state threshold value. If the ratio of the AC and DC components of the bio-impedance is greater than the preset steady-state threshold value, the deep temperature of the target user in the target time period is determined according to the temperature measurement information and a pre-obtained deep temperature determination model.

[0015] 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 used to: 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.

[0016] In a possible implementation, 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.

[0017] In a possible implementation, 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.

[0018] In a possible implementation manner, the device further includes a model determination module, and the model determination module is used to: 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 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.

[0019] In a possible implementation, the device further includes: a second deep temperature generating module, configured to: 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.

[0020] In a possible implementation, the second deep temperature generating module is specifically configured to: 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.

[0021] In a possible implementation, the second deep temperature generating module is specifically configured to: 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.

[0022] In the third aspect, another embodiment of the present application provides an electronic device, comprising: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, 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.

[0023] In a fourth aspect, another embodiment of the present application provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any method described in the first aspect are executed.

[0024] The beneficial effects of the present application are as follows: by acquiring the bioimpedance AC component, the bioimpedance DC component, the body surface temperature and the heat flux of the target user in the target time period, and determining the bioimpedance AC / DC component ratio of the target user in the target time period according to the bioimpedance AC component and the bioimpedance DC component, the physiological state changes of the target user in the target time period can be reflected, and the bioimpedance AC / DC component ratio is compared with a preset steady-state threshold, so as to judge whether the contact between the target user and the sensing detection unit is effective contact; if the bioimpedance AC / DC component ratio is greater than the preset steady-state threshold, the deep temperature of the target user in the target time period is determined according to the temperature measurement information and the deep temperature determination model obtained in advance; when the target user is in effective contact with the sensing detection unit, the error caused by the contact thermal resistance can be compensated based on the deep temperature determination model, and the deep temperature of the target user in the target time period can be accurately obtained, thereby avoiding the problem of decreased accuracy of deep temperature measurement due to 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 deep temperature can be determined only through the ratio of the AC and DC components of the bioimpedance and the pre-obtained deep temperature determination model. The calculation is simple and does not require the introduction of additional physical equipment, which improves the efficiency of deep temperature determination and can also be applied to any scenario where deep temperature needs to be determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 A schematic diagram of a scenario of a method for determining deep temperature provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a sensing detection unit in the deep temperature determination method provided in an embodiment of the present application; Figure 3 A schematic diagram of a process flow of a method for determining deep temperature provided in an embodiment of the present application; Figure 4 A schematic diagram of a structure of a deep temperature determination model provided in an embodiment of the present application; Figure 5 A schematic diagram of a process for determining the deep temperature of a target user in a target period of time in the deep temperature determination method provided in an embodiment of the present application; Figure 6 A schematic diagram of a process for determining a thermal resistance fitting model and a temperature estimation model in a deep temperature determination method provided in an embodiment of the present application; Figure 7 A schematic diagram of a deep temperature determination device provided in an embodiment of the present application; Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of explanation and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in this application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can be implemented out of sequence, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart under the guidance of the content of the present application, or remove one or more operations from the flowchart.

[0028] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0029] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0030] 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.

[0031] However, although these measures can reduce the impact of contact thermal resistance to a certain extent, there may still be temperature differences between adjacent measurement channels in the same body part, resulting in inaccurate deep temperature.

[0032] Based on the above problems, an embodiment of the present application proposes a deep temperature determination method, which obtains the bioimpedance AC component, bioimpedance DC component, body surface temperature and heat flux of the target user in the target time period, and determines the bioimpedance AC / DC component ratio of the target user in the target time period based on the bioimpedance AC component and the bioimpedance DC component. The electrical characteristic difference of the target user in the target time period can be measured, and the bioimpedance AC / DC component ratio is compared with a preset steady-state threshold to judge whether the contact between the target user and the sensor detection unit is effective contact. If the bioimpedance AC / DC component ratio is greater than the preset steady-state threshold, the deep temperature of the target user in the target time period is determined based on the temperature measurement information and the deep temperature determination model obtained in advance. When the target user is in effective contact with the sensor detection unit, the error caused by the contact thermal resistance can be compensated based on the deep temperature determination model, thereby accurately obtaining the deep temperature of the target user in the target time period.

[0033] First, the application scenarios involved in the deep temperature determination method provided in the embodiment of the present application are explained.

[0034] Figure 1 A schematic diagram of a scenario of a method for determining deep temperature provided in an embodiment of the present application, referring to Figure 1As shown, during the deep temperature measurement process, the heat flow channel serves as a medium for heat transfer, and the sensor detection unit is located on one side of the heat flow channel. The sensor detection unit is used to contact the skin tissue of the target user via a contact interface, and then execute the steps of the deep temperature determination method provided in the embodiment of the present application to measure the deep temperature of the target user and obtain the deep temperature of the target user.

[0035] Specifically, Figure 2 A schematic diagram of the structure of the sensing detection unit in the deep temperature determination method provided in the embodiment of the present application, referring to Figure 2 As shown, the sensing detection unit includes a bioimpedance measurement sensor, a thermistor, a heat flux sensor, and a processing module. The bioimpedance measurement sensor is used to collect the bioimpedance of the target user, and obtain the target user's bioimpedance AC component and bioimpedance DC component, the thermistor is used to collect the target user's body surface temperature, the heat flux sensor is used to collect the target user's heat flux, and the processing module is used to interact with the bioimpedance measurement sensor, the thermistor, and the heat flux sensor, and execute the steps of the deep temperature determination method provided in the embodiment of the present application to obtain the deep temperature of the target user.

[0036] The deep temperature determination method provided in the embodiments of the present application is described in detail below in combination with multiple embodiments.

[0037] Figure 3 A schematic diagram of a deep temperature determination method provided in an embodiment of the present application, referring to Figure 3 As shown, the method execution subject can be any electronic device with processing capabilities, such as the processing module in the above-mentioned sensor detection unit, and the method includes: S301: Obtain temperature measurement information of a target user during a target period of time.

[0038] Optionally, the processing module in the sensing detection unit obtains the temperature measurement information of the target user in the target time period. The target user may be a user wearing the sensing detection unit, and the target time period may be the current measurement time period of the target user, for example, 1 minute or 5 minutes. The temperature measurement information includes: bioimpedance AC component, bioimpedance DC component, body surface temperature, and heat flux.

[0039] Specifically, the bioimpedance AC component refers to the impedance change part of the biological tissue in response to alternating current in the bioimpedance measurement, which is related to the dynamic electrical characteristics of the biological tissue, such as capacitance and inductance. The bioimpedance DC component refers to the constant impedance part of the biological tissue that does not change over time in the bioimpedance measurement, which is related to the static resistance characteristics of the biological tissue. Both the bioimpedance AC component and the bioimpedance DC component are used to indicate information about the physiological and pathological states of biological tissues.

[0040] Specifically, the body surface temperature refers to the surface temperature of the target user, including the temperature of the skin, subcutaneous tissue, muscle, etc. The heat flux refers to the heat energy that passes through the skin tissue of the target user to reach the sensing detection unit within the target period.

[0041] S302: Determine the ratio of the bio-impedance AC and DC components of the target user in the target time period according to the bio-impedance AC component and the bio-impedance DC component.

[0042] Optionally, after obtaining the target user's bioimpedance AC component and bioimpedance DC component in the target time period, the bioimpedance AC / DC component ratio of the target user in the target time period can be calculated based on the target user's bioimpedance AC component and bioimpedance DC component in the target time period.

[0043] Among them, the bioimpedance AC / DC component ratio refers to the ratio of the changes in the bioimpedance AC / DC components, which can be obtained by the ratio of the bioimpedance AC component to the bioimpedance DC component, reflecting the difference in impedance characteristics of the target user's skin tissue under the action of AC and DC.

[0044] The ratio of the AC and DC components of bioimpedance can be used to measure the difference in electrical characteristics of the target user during the target period, and to characterize the rate of change of the target user's physiological state during the target period, thereby reflecting the change in the target user's physiological state during the target period.

[0045] S303, 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, determining the deep temperature of the target user in the target period based on the temperature measurement information and a pre-obtained deep temperature determination model.

[0046] Optionally, after obtaining the bio-impedance AC / DC component ratio, the bio-impedance AC / DC component ratio can be compared with a preset steady-state threshold. If the bio-impedance AC / DC component ratio is greater than the preset steady-state threshold, it can be determined that the contact between the target user and the sensing detection unit is effective contact. Then, the deep temperature of the target user in the target time period can be determined based on the temperature measurement information and the deep temperature determination model obtained in advance.

[0047] Exemplarily, if the ratio of the AC and DC components of the bioimpedance is greater than a preset steady-state threshold, the temperature measurement information is input into a pre-obtained deep temperature determination model to obtain the deep temperature of the target user in the target period.

[0048] Among them, the preset steady-state threshold can be obtained by performing data statistics in advance, and the pre-obtained deep temperature determination model is used to compensate for the error caused by contact thermal resistance based on temperature measurement information, so as to accurately obtain the deep temperature.

[0049] Exemplarily, the pre-obtained deep temperature determination model can be obtained through the biological heat transfer equation.

[0050] In this embodiment, by acquiring the bioimpedance AC component, bioimpedance DC component, body surface temperature and heat flux of the target user in the target time period, and determining the bioimpedance AC / DC component ratio of the target user in the target time period according to the bioimpedance AC component and the bioimpedance DC component, the physiological state change of the target user in the target time period can be reflected, and the bioimpedance AC / DC component ratio is compared with a preset steady-state threshold, so as to judge whether the contact between the target user and the sensing detection unit is effective contact. If the bioimpedance AC / DC component ratio is greater than the preset steady-state threshold, the deep temperature of the target user in the target time period is determined according to the temperature measurement information and the deep temperature determination model obtained in advance. When the target user is in effective contact with the sensing detection unit, the error caused by the contact thermal resistance can be compensated based on the deep temperature determination model, so as to accurately obtain the deep temperature of the target user in the target time period, avoid the problem of decreased accuracy of deep temperature measurement due to 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. At the same time, the deep temperature can be determined only through the ratio of the AC and DC components of the bioimpedance and the pre-obtained deep temperature determination model. The calculation is simple and does not require the introduction of additional physical equipment, which improves the efficiency of deep temperature determination and can also be applied to any scenario where deep temperature needs to be determined.

[0051] In one possible implementation, Figure 4 A schematic diagram of a deep temperature determination model provided in an embodiment of the present application, Figure 5 A schematic diagram of a process for determining the deep temperature of a target user during a target period of time in a deep temperature determination method provided in an embodiment of the present application, referring to Figure 4 as well as Figure 5 As shown, the deep temperature determination model includes: a thermal resistance fitting model and a temperature estimation model. In the above S303, the deep temperature of the target user in the target period is determined according to the temperature measurement information and the pre-obtained deep temperature determination model, including: S501 : Determine the contact thermal resistance of the target user in the target period according to the DC component of the bioimpedance and the thermal resistance fitting model.

[0052] It can be understood that under the pressure range of 0.73-10.98 kPa, the change of skin temperature is significantly affected by pressure. Therefore, in the measurement process of wearable deep temperature sensor, the factor that has the greatest impact on contact thermal resistance is the change of 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 by the thermal resistance fitting model.

[0053] Optionally, the DC component of the bioimpedance may be input into a thermal resistance fitting model in the deep temperature determination model, so that the contact thermal resistance is fitted based on the DC component of the bioimpedance to obtain the contact thermal resistance of the target user in the target period.

[0054] S502: Determine the deep temperature of the target user in the target period according to the body surface temperature, heat flux, contact thermal resistance and temperature estimation model.

[0055] It can be understood that after the contact thermal resistance is determined through the thermal resistance fitting model, the contact thermal resistance in the deep temperature determination process can be compensated based on the biological heat transfer equation and combined with the contact thermal resistance, thereby obtaining a temperature estimation model, thereby being able to determine the deep temperature of the target user in the target period based on the body surface temperature, heat flux, contact thermal resistance and temperature estimation model.

[0056] Optionally, the body surface temperature, heat flux, and contact thermal resistance are input into a temperature estimation model to obtain the deep temperature of the target user during the target period.

[0057] The contact thermal resistance of the target user in the target period is determined by the DC component of bioimpedance and the thermal resistance fitting model. The contact thermal resistance of the target user in the target period can be accurately calculated. 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 in the target period can be accurately determined.

[0058] In a possible implementation, the thermal resistance fitting model is: ,in, is the contact thermal resistance, is the DC component of bioimpedance, , as well as is the scaling factor.

[0059] Optionally, It can be a 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 / photocurrent volume wave 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.

[0060] In a possible implementation, 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.

[0061] Optionally, the temperature estimation model can be obtained by a bioheat transfer equation. For example, the bioheat transfer equation is simplified under steady-state boundary conditions, and the bioheat transfer equation is solved to obtain the following formula (1):

[0062] in, is the deep temperature, is the body surface temperature measured by the sensing unit, is the blood perfusion rate, is the above contact thermal resistance, is the thermal diffusivity of skin tissue, expressed as (k is thermal conductivity, which indicates the ability of a material to conduct heat, C b is the specific heat capacity, is the density).

[0063] For example, according to Fourier's law, the heat flux density in the heat conduction mode is It is inversely proportional to the thickness x and directly proportional to the temperature gradient T, as shown in the following formula (2): (2) in, is the heat flux, x is the thickness, T is the temperature gradient, is the thermal conductivity.

[0064] For example, the corresponding heat flow at the skin is given by the following formula (3):

[0065] in, is the heat flux, is the blood perfusion rate, is the body surface temperature measured by the sensing unit, is the deep temperature, is the above contact thermal resistance, is the thermal diffusivity of skin tissue, is the thermal conductivity.

[0066] For example, the deep temperature can be determined by the above formulas (1)-(3): , thermal contact resistance and body temperature , and simplify it. Put the parameters obtained from the sensing unit on the right and the deep temperature on the left. We can get the following formula (4), which is the temperature estimation model mentioned above: (4) in, is the deep temperature, is the body surface temperature measured by the sensing unit, is the heat flux, is the blood perfusion rate, is the above contact thermal resistance, is the thermal diffusivity of skin tissue, expressed as (k is thermal conductivity, which indicates the ability of a material to conduct heat, C b is the specific heat capacity, is the density), , are all constant coefficients.

[0067] It can be understood that in actual application, the scaling factor in the thermal resistance fitting model and the constant coefficient in the temperature estimation model can be obtained by predetermination. The following is an exemplary description of the process of determining the thermal resistance fitting model and the temperature estimation model.

[0068] In one possible implementation, Figure 6 A schematic diagram of a process for determining a thermal resistance fitting model and a temperature estimation model in a deep temperature determination method provided in an embodiment of the present application, referring to Figure 6 As shown in FIG. 1 , the determination process of the thermal resistance fitting model and the temperature estimation model includes: S601. Obtain sample temperature information of a sample user in a sample period.

[0069] Optionally, when determining the thermal resistance fitting model and the temperature estimation model, sample temperature information of the sample user in the sample period may be obtained. The sample period may be a period with the same physiological state as the target period, and the sample user may be a user with the same physiological state as the target user.

[0070] The sample temperature information includes: sample deep temperature, sample bioimpedance DC component, sample surface temperature and sample heat flux.

[0071] S602: Obtain an initial thermal resistance fitting model and an initial temperature estimation model.

[0072] Optionally, an initial thermal resistance fitting model and an initial temperature estimation model are obtained.

[0073] Exemplarily, the initial thermal resistance fitting model is ,in, is the sample contact thermal resistance, is the DC component of the sample bioimpedance, , as well as are the initial scaling factors.

[0074] Exemplarily, the initial temperature estimation model is: ,in, is the deep temperature of the sample, is the sample surface temperature, is the sample heat flux, is the sample contact thermal resistance, , are all initial constant coefficients.

[0075] Among them, the scaling factor in the initial thermal resistance fitting model and the constant coefficient in the initial temperature estimation model are all corresponding initial values. S603, 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 surface temperature, the sample heat flux and the actual contact thermal resistance into the initial temperature estimation model to obtain the actual deep temperature.

[0076] Optionally, the DC component of the sample bioimpedance of the sample user during the sample period is input into the initial thermal resistance fitting model in S602 to calculate the actual contact thermal resistance, and the sample body surface temperature, sample heat flux and actual contact thermal resistance are input into the initial temperature estimation model in S602 to calculate the actual deep temperature.

[0077] S604, iteratively adjusting 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 obtaining the thermal resistance fitting model and the temperature estimation model after the iteration.

[0078] Optionally, the difference between the actual deep temperature and the deep temperature of the sample can be calculated, and the scaling factor in the initial thermal resistance fitting model and the constant coefficient in the initial temperature estimation model can be iteratively adjusted through optimization parameter search, and the thermal resistance fitting model and the temperature estimation model can be obtained after the iteration.

[0079] 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 can be fixed, and the constant coefficient in the initial temperature estimation model can be iteratively optimized for parameter search, and the equivalent contact resistance can be calculated through the deep temperature of the sample, and the scaling factor in the initial thermal resistance fitting model can be optimized for parameter search, and combined with the preset termination tolerance, the optimal scaling factor and the optimal constant coefficient can be obtained, the iteration is terminated, and the thermal resistance fitting model and the temperature estimation model are obtained based on the optimal scaling factor and the optimal constant coefficient.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] In a possible implementation, the deep temperature determination method provided in the embodiment of the present application further includes: 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.

[0084] 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.

[0085] Optionally, 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 can be determined by statistical analysis based on the bioimpedance AC / DC component ratio. The fluctuation range of the bioimpedance AC / DC component ratio is used to indicate the distribution and discreteness of the bioimpedance AC / DC component ratio.

[0086] Exemplarily, after obtaining the fluctuation range of the bio-impedance AC / DC component ratio, the bio-impedance AC / DC component ratio at the target position can be determined from the fluctuation range of the bio-impedance AC / DC component ratio, and based on the bio-impedance AC / DC component ratio at the target position, the deep temperature of the target user in the target time period can be determined.

[0087] Exemplarily, after obtaining the fluctuation range of the ratio of the AC and DC components of bio-impedance, the fluctuation range of the ratio of the AC and DC components of bio-impedance can be judged in combination with a preset fluctuation range threshold, and based on the judgment result, the deep temperature of the target user in the target time period can be determined.

[0088] By determining the fluctuation range of the bio-impedance AC / DC component ratio when the bio-impedance AC / DC component ratio is less than a preset steady-state threshold, the physiological state of the target user in the target time period can be judged, and the deep temperature of the target user in the target time period can be determined 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 can be accurately obtained through changes in the target user's physiological state.

[0089] In a possible implementation, the above steps 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, including: 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 based on the deep temperature and the surface temperature of the target user in the historical period.

[0090] Optionally, if the fluctuation range of the ratio of the AC and DC components of bioimpedance is smaller than a preset fluctuation range threshold, it can be determined that the physiological state of the target user during the target period is within a small fluctuation range. Then, the deep temperature of the target user during the target period can be determined based on the deep temperature and surface temperature of the target user during the historical period.

[0091] The preset fluctuation range threshold may be 0.7 times the above-mentioned steady-state threshold.

[0092] Exemplarily, if the fluctuation range of the ratio of the AC and DC components of bio-impedance is smaller 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 effects of skin sweating, thereby obtaining the deep temperature of the target user in the target period.

[0093] Exemplarily, if the fluctuation range of the ratio of the AC and DC components of bio-impedance is smaller 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 effects of skin sweating, thereby obtaining the deep temperature of the target user in the target period.

[0094] When the fluctuation range of the ratio of the AC and DC components of bio-impedance is less than a preset fluctuation range threshold, the deep temperature of the target user in the target period is determined by the deep temperature and surface temperature of the target user in the historical period, so that motion artifacts can be eliminated and the influence of skin sweating can be compensated. Therefore, when the contact between the target user and the sensor detection unit is ineffective, the deep temperature of the target user in the target period can be accurately obtained, and the influence of the fluctuation of the ratio of the AC and DC components of bio-impedance on the deep temperature is reduced, thereby improving the robustness of deep temperature determination in complex life scenarios.

[0095] In a possible implementation, the above steps 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, including: 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 based on the deep temperature of the target user in the historical period.

[0096] 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, and the deep temperature of the target user in the target period can be determined based on the deep temperature of the target user in the historical period. The preset fluctuation range threshold can be 0.7 times the above-mentioned steady-state threshold.

[0097] 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 may be interpolated using a statistical method to obtain the deep temperature of the target user in the target period.

[0098] Optionally, when the DC component of bioimpedance exceeds a preset baseline threshold, the DC component of bioimpedance is determined to be sensor displacement, that is, there is an error in the DC component of bioimpedance. Then, the displacement period can be interpolated through data of a specified time length before and after the sensor displacement time period to obtain the deep temperature of the target user in the target time period.

[0099] For example, when the DC component of the bioimpedance at the target moment in the target period exceeds a preset baseline threshold, the deep temperature of the target user at the first moment in the historical period can be polynomially interpolated to obtain the deep temperature of the target user at a certain moment in the target period, thereby obtaining the deep temperature of the target user in the target period. The first moment and the target moment in the target period can be separated by a specified time.

[0100] When the fluctuation range of the ratio of the AC and DC components of bio-impedance is greater than the preset fluctuation range threshold, the deep temperature of the target user in the target period is determined by the deep temperature of the target user in the historical period. When the fluctuation is large, the deep temperature of the target user in the target period can be accurately determined, reducing the impact of the fluctuation of the ratio of the AC and DC components of bio-impedance on the deep temperature, thereby improving the robustness of deep temperature determination in complex life scenarios.

[0101] Based on the same inventive concept, a deep temperature determination device corresponding to the deep temperature determination method is also provided in the embodiment of the present application. Since the principle of solving the problem by the device in the embodiment of the present application is similar to the above-mentioned deep temperature determination method in the embodiment of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0102] Figure 7 A schematic diagram of a deep temperature determination device provided in an embodiment of the present application, referring to Figure 7 As shown, the device includes: an acquisition module 701, a determination module 702, and a first deep temperature generation module 703; An acquisition module 701 is used to acquire temperature measurement information of a target user during a target period of time, wherein the temperature measurement information includes: an AC component of bioimpedance, a DC component of bioimpedance, a body surface temperature, and a heat flux; A determination module 702 is used to determine the ratio of the bio-impedance AC and DC components of the target user in the target period of time according to the bio-impedance AC component and the bio-impedance DC component; The first deep temperature generating module 703 is used to compare the ratio of the AC and DC components of the bio-impedance with a preset steady-state threshold. If the ratio of the AC and DC components of the bio-impedance is greater than the preset steady-state threshold, the deep temperature of the target user in the target period is determined based on the temperature measurement information and a pre-obtained deep temperature determination model.

[0103] In a possible implementation, the deep temperature determination model includes: a thermal resistance fitting model and a temperature estimation model; a first deep temperature generation module 703, specifically configured to: Determine the contact thermal resistance of the target user in the target period according to the DC component of the bioimpedance and the thermal resistance fitting model; The deep temperature of the target user during the target period is determined based on the body surface temperature, heat flux, contact thermal resistance, and the temperature estimation model.

[0104] In a possible implementation, the thermal resistance fitting model is: ,in, is the contact thermal resistance, is the DC component of bioimpedance, , as well as is the scaling factor.

[0105] In a possible implementation, 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.

[0106] In a possible implementation manner, the device further includes a model determination module, and the model determination module is used to: Obtaining sample temperature information of the sample user during the sample period, the sample temperature information including: sample deep temperature, sample bioimpedance DC component, sample surface temperature and sample heat flux; An initial thermal resistance fitting model and an initial temperature estimation model are obtained, wherein the scaling factor in the initial thermal resistance fitting model and the constant coefficient in the initial temperature estimation model are corresponding initial values; The DC component of the sample bioimpedance is input into the initial thermal resistance fitting model to obtain the actual contact thermal resistance, and the sample surface temperature, sample heat flux and actual contact thermal resistance are input into the initial temperature estimation model to obtain the actual deep temperature; According to the difference between the actual deep temperature and the deep temperature of the sample, 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.

[0107] In a possible implementation, the device further includes: a second deep temperature generating module, configured to: 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.

[0108] In a possible implementation, the second deep temperature generating module is specifically configured to: 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 based on the deep temperature and the surface temperature of the target user in the historical period.

[0109] In a possible implementation, the second deep temperature generating module is specifically configured to: 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 based on the deep temperature of the target user in the historical period.

[0110] For descriptions of the processing flow of each module in the device and the interaction flow between each module, reference may be made to the relevant descriptions in the above method embodiment, which will not be described in detail here.

[0111] The present application also provides an electronic device, such as Figure 8 As shown, Figure 8 The electronic device structure diagram provided in the embodiment of the present application includes: a processor 801, a memory 802, and optionally, a bus 803. The memory 802 stores machine-readable instructions executable by the processor 801 (for example, Figure 7 In the device, the acquisition module 701, the determination module 702, the execution instructions corresponding to the first deep temperature generation module 703, etc.), when the electronic device is running, 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-mentioned deep temperature determination method are performed.

[0112] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned deep temperature determination method are executed.

[0113] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the system and device described above can refer to the corresponding process in the method embodiment, and will not be repeated in this application. In the several embodiments provided in this application, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation. For 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0114] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into 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 this understanding, the technical solution of the present invention is essentially or part of the technical solution that contributes to the prior art or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), disk or optical disk and other media that can store program code.

[0115] The above are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present 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; The bioimpedance AC / DC component ratio is compared with a preset steady-state threshold. If the bioimpedance AC / DC component ratio is greater than the preset steady-state threshold, the deep temperature of the target user in the target time period is determined based on the temperature measurement information and a pre-obtained deep temperature determination model.

2. The deep temperature determination method according to claim 1, characterized in that: 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.

3. The deep temperature determination method according to claim 2, 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.

4. The deep temperature determination method according to claim 3, 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.

5. The deep temperature determination method according to claim 4, 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.

6. 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.

7. The method for determining deep temperature according to claim 6, 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.

8. The method for determining deep temperature according to claim 6, 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.

9. 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 8.

10. 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 claimed in any one of claims 1 to 8 are executed.

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