Temperature detection method, temperature detection device and storage medium

Abstract

The invention relates to a temperature detection method, a temperature detection device and a storage medium. The temperature detection method comprises the steps that in response to trigger mode switching, a current temperature detection mode of the wearable device is switched, and temperature detection modes supported by the wearable device comprise a long-term monitoring mode and a real-time detection mode; and performing temperature detection on the to-be-detected object by adopting the switched temperature detection mode. According to the invention, the wearable device can detect the temperatures of different objects, and diversified temperature detection is realized.

Description

Technical Field

[0001] The present disclosure relates to the field of temperature measurement, and particularly to a temperature detection method, a temperature detection device, and a storage medium. Background Art

[0002] Temperature is one of the important parameters affecting the human body's physical functions and is also one of the indicators that people pay attention to in daily life. Temperature measurement of objects is required in many scenarios, such as liquid temperature, ambient temperature, etc. However, there is a lack of a device that can measure at any time without affecting normal life. Wearable devices provide a good solution idea, but in the existing technologies, it is usually the measurement of human body temperature and cannot measure the ambient temperature or specific objects. Summary of the Invention

[0003] To overcome the problems existing in the related technologies, the present disclosure provides a temperature detection method, a temperature detection device, and a storage medium.

[0004] According to a first aspect of an embodiment of the present disclosure, a temperature detection method is provided, including switching the current temperature detection mode of a wearable device in response to a trigger mode switch, where the temperature detection modes supported by the wearable device include a long-term monitoring mode and a real-time detection mode; and performing temperature detection on an object to be measured using the switched temperature detection mode.

[0005] In an implementation, the switching the current temperature detection mode of the wearable device in response to a trigger mode switch includes: when the wearable device is currently in the real-time detection mode, in response to triggering at least one of the following conditions, switching the temperature detection mode of the wearable device to the long-term monitoring mode: the wearable device is in a state of being worn by a user; the wearable device is in a state of being used; a first instruction is detected, where the first instruction is used to indicate switching to the long-term monitoring mode; the user behavior of the user wearing the wearable device conforms to the first user historical behavior for triggering entry into the long-term monitoring mode.

[0006] In an implementation, the switching the current temperature detection mode of the wearable device in response to a trigger mode switch includes: when the wearable device is currently in the long-term monitoring mode, in response to determining that at least one of the following conditions is satisfied, switching the temperature detection mode of the wearable device to the real-time detection mode: the wearable device is in a state of not being worn by a user; a second instruction is detected, where the second instruction is used to indicate switching to the real-time detection mode; the user behavior of the user wearing the wearable device conforms to the second user historical behavior for triggering entry into the real-time detection mode.

[0007] In one implementation, the temperature detection of an object matching the current temperature detection mode includes: calling a temperature prediction model matching the current detection mode, and performing temperature detection on the object matching the current temperature detection mode based on the temperature prediction model.

[0008] In one implementation, calling a temperature prediction model matching the current detection mode, and performing temperature detection on the object matching the current temperature detection mode based on the temperature prediction model includes: if the wearable device is in the long-term monitoring mode, calling the first model, and performing temperature detection on the user wearing the wearable device based on the collected user physiological signals and the first model, where the first model is pre-trained based on the historical data of the user, and the historical data includes physiological signals and temperature data.

[0009] In one implementation, calling a temperature prediction model matching the current detection mode, and performing temperature detection on the object matching the current temperature detection mode based on the temperature prediction model includes: if the wearable device is in the real-time detection mode, calling the second model, identifying the current temperature detection scenario based on the currently detected temperature sensor data in real time and the second model, and performing temperature detection on non-living objects in the current temperature detection scenario, where the second model is pre-trained based on the temperature data collected by the temperature sensor for different non-living objects in different temperature detection scenarios.

[0010] In one implementation, the temperature detection of non-living objects in the current temperature detection scenario includes: determining the current temperature determination method corresponding to the current temperature detection scenario based on the correspondence between the temperature detection scenario and the temperature determination method, and determining the temperature of the currently detected non-living object according to the current temperature determination method and the current temperature sensor data; where different temperature detection scenarios correspond to different temperature determination methods, and the temperature determination method represents the correspondence between the data detected by the temperature sensor and the temperature of the non-living object; the temperature detection scenario includes at least one of the following scenarios: liquid temperature measurement scenario, solid temperature measurement scenario, gas temperature measurement scenario; living body temperature measurement scenario, metal temperature measurement scenario.

[0011] In one implementation, the method further includes: obtaining a detection parameter threshold for temperature detection of the object to be measured, where the detection parameter threshold includes at least one of the following: temperature limit, temperature detection duration threshold; in response to determining that the detected parameter satisfies one or more of the detection parameter thresholds during the temperature detection of the object to be measured, sending a prompt message.

[0012] In one implementation, different types of temperature sensors are configured in the wearable device, and different types of temperature sensors are matched with different temperature detection modes; performing temperature detection on the object to be measured by using the switched temperature detection mode includes: enabling the temperature sensor of the type matched with the switched temperature detection mode to perform temperature detection on the object to be measured.

[0013] In one implementation, the current temperature detection mode is a real-time detection mode, and the temperature sensors of the type matched with the real-time detection mode include temperature sensors of different subtypes. Among them, the time used for temperature detection by different subtypes of temperature sensors is different; performing temperature detection on the object to be measured includes: obtaining the target detection duration selected by the user; calling the temperature prediction model corresponding to the sensor whose processing duration meets the target detection duration to perform temperature detection on the object to be measured.

[0014] According to the second aspect of the embodiments of the present disclosure, a temperature detection device is provided, including a switching unit configured to switch the current temperature detection mode of the wearable device in response to a triggered mode switch, where the temperature detection modes supported by the wearable device include a long-term monitoring mode and a real-time detection mode; and a detection unit configured to perform temperature detection on the object to be measured by using the switched temperature detection mode.

[0015] In one implementation, the switching unit switches the current temperature detection mode of the wearable device in response to a triggered mode switch in the following manner, including: when the wearable device is currently in the real-time detection mode, in response to triggering at least one of the following conditions, switching the temperature detection mode of the wearable device to the long-term monitoring mode: the wearable device is in a state of being worn by the user; the wearable device is in a state of being used; a first instruction is detected, and the first instruction is used to indicate switching to the long-term monitoring mode; the user behavior of the user wearing the wearable device conforms to the first user historical behavior for triggering entry into the long-term monitoring mode.

[0016] In one implementation, the switching unit switches the current temperature detection mode of the wearable device in response to a triggered mode switch in the following manner, including: when the wearable device is currently in the long-term monitoring mode, in response to determining that at least one of the following conditions is met, switching the temperature detection mode of the wearable device to the real-time detection mode: the wearable device is in a state of not being worn by the user; a second instruction is detected, and the second instruction is used to indicate switching to the real-time detection mode; the user behavior of the user wearing the wearable device conforms to the second user historical behavior for triggering entry into the real-time detection mode.

[0017] In one implementation, the detection unit performs temperature detection on an object that matches the current temperature detection mode in the following manner: call a temperature prediction model that matches the current detection mode, and perform temperature detection on the object that matches the current temperature detection mode based on the temperature prediction model.

[0018] In one implementation, the detection unit calls a temperature prediction model that matches the current detection mode in the following manner and performs temperature detection on an object that matches the current temperature detection mode, including: if the wearable device is in the long-term monitoring mode, call the first model, and based on the collected user physiological signals and the first model, perform temperature detection on the user wearing the wearable device, where the first model is pre-trained based on the historical data of the user, and the historical data includes physiological signals and temperature data.

[0019] In one implementation, the detection unit calls a temperature prediction model that matches the current detection mode in the following manner and performs temperature detection on an object that matches the current temperature detection mode, including: if the wearable device is in the real-time detection mode, call the second model, and based on the currently detected temperature sensor data in real time and the second model, identify the current temperature detection scenario, and perform temperature detection on non-living objects in the current temperature detection scenario, where the second model is pre-trained based on the temperature data collected by the temperature sensor for different non-living objects in different temperature detection scenarios.

[0020] In one implementation, the detection unit performs temperature detection on non-living objects in the current temperature detection scenario in the following manner, including: based on the correspondence between the temperature detection scenario and the temperature determination method, determine the current temperature determination method corresponding to the current temperature detection scenario, and according to the current temperature determination method and the current temperature sensor data, determine the temperature of the currently detected non-living object; where different temperature detection scenarios correspond to different temperature determination methods, and the temperature determination method represents the correspondence between the data detected by the temperature sensor and the temperature of the non-living object; the temperature detection scenario includes at least one of the following scenarios: liquid temperature measurement scenario, solid temperature measurement scenario, gas temperature measurement scenario; living body temperature measurement scenario, metal temperature measurement scenario.

[0021] In one implementation, the device is further configured to: obtain a detection parameter threshold for performing temperature detection on a to-be-detected object, where the detection parameter threshold includes at least one of the following: temperature limit value, temperature detection duration threshold; in response to determining that the detected parameter satisfies one or more of the detection parameter thresholds during the process of performing temperature detection on the to-be-detected object, send a prompt message.

[0022] In one implementation, different types of temperature sensors are configured in the wearable device, and different types of temperature sensors are matched with different temperature detection modes; when the switched temperature detection mode is adopted, the detection unit performs temperature detection on the object to be detected in the following manner, including: enabling the temperature sensor of the type matched with the switched temperature detection mode to perform temperature detection on the object to be detected.

[0023] In one implementation, the current temperature detection mode is a real-time detection mode, and the temperature sensors of the type matched with the real-time detection mode include temperature sensors of different subtypes. Among them, the time durations used for temperature detection by different subtypes of temperature sensors are different; the detection unit performs temperature detection on the object to be detected in the following manner, including: obtaining the target detection duration selected by the user; invoking the temperature prediction model corresponding to the sensor whose processing duration meets the target detection duration to perform temperature detection on the object to be detected.

[0024] According to the third aspect of the embodiments of the present disclosure, there is provided a temperature detection device, including: a processor; a memory for storing processor-executable instructions; wherein, the processor is configured to: execute the temperature detection method described in the first aspect or any one of the implementations of the first aspect.

[0025] According to the fourth aspect of the embodiments of the present disclosure, there is provided a storage medium, in which instructions are stored, and when the instructions in the storage medium are executed by a processor, the processor can execute the temperature detection method described in the first aspect or any one of the implementations of the first aspect.

[0026] The technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects: determining the current temperature detection mode of the wearable device, wherein the temperature detection modes supported by the wearable device include a long-term monitoring mode and a real-time detection mode, and performing temperature detection on different objects based on the long-term monitoring mode and the real-time detection mode. Through the present disclosure, the wearable device can perform temperature detection on different objects, realizing diversified temperature detection.

[0027] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure.

[0029] Figure 1 is a flowchart of a temperature detection method shown according to an exemplary embodiment.

[0030] Figure 2It is a schematic diagram of a temperature pattern shown according to an exemplary embodiment.

[0031] Figure 3 It is a flowchart of a temperature detection method based on a temperature prediction model shown according to an exemplary embodiment.

[0032] Figure 4 It is a flowchart of another temperature detection method shown according to an exemplary embodiment.

[0033] Figure 5 It is a schematic diagram of a temperature detection prompt shown according to an exemplary embodiment.

[0034] Figure 6 It is a schematic diagram of another temperature detection prompt shown according to an exemplary embodiment.

[0035] Figure 7 It is a flowchart of a method for detecting temperature in a real-time detection mode shown according to an exemplary embodiment.

[0036] Figure 8 It is a block diagram of a temperature detection device shown according to an exemplary embodiment.

[0037] Figure 9 It is a block diagram of a device for temperature detection shown according to an exemplary embodiment. Detailed implementation

[0038] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present disclosure.

[0039] Temperature is one of the important parameters affecting the human body's physical functions and is also one of the indicators that everyone pays attention to in daily life. Many scenarios require measuring the temperature of objects, such as liquid temperature, environmental temperature, etc. However, there is a lack of a device that can be measured at any time without affecting normal life. Wearable devices provide a good solution idea, but in the prior art, it is usually the measurement of human body temperature, which can only be measured after wearing and cannot be measured after taking it off, and it is also impossible to measure the environmental temperature or the temperature of a specific object, that is, the temperature of non-living organisms.

[0040] In view of this, the present disclosure proposes a temperature detection method. The following describes the implementation process of the temperature detection method in the embodiments of the present disclosure.

[0041] Figure 1 It is a flowchart of a temperature detection method shown according to an exemplary embodiment. As Figure 1As shown, the method includes the following.

[0042] In step S11, in response to a trigger for mode switching, the current temperature detection mode of the wearable device is switched. Among them, the temperature detection modes supported by the wearable device include a long-term monitoring mode and a real-time detection mode.

[0043] In the embodiments of the present disclosure, the wearable device includes, but is not limited to, a watch, a bracelet, earphones, a ring, a heart rate belt, glasses, etc., all of which can be used for temperature detection.

[0044] Among them, the wearable device supports multiple different temperature detection modes, such as a long-term monitoring mode and a real-time detection mode.

[0045] Among them, the long-term monitoring mode can be understood as a temperature detection method with a temperature detection duration greater than a duration threshold, or can be understood as a working mode for temperature monitoring. For example, in the process of a user wearing the wearable device, the wearable device always performs temperature monitoring. Among them, the long-term monitoring mode can also be understood as a monitoring mode without user perception, such as being called a non-sensing monitoring mode.

[0046] Among them, the real-time detection mode can be understood as a temperature detection method with a temperature detection duration less than the duration threshold, or can be understood as a working mode for performing a one-time temperature detection. For example, the working mode used for a one-time temperature detection of a to-be-detected object by the wearable device is the real-time detection mode.

[0047] In the embodiments of the present disclosure, the long-term monitoring mode and the real-time detection mode can also be understood as whether to perform temperature detection based on historical data information. Among them, the long-term monitoring mode performs temperature detection based on historical data. The real-time detection mode does not rely on historical data for temperature detection.

[0048] Among them, switching between different temperature detection modes is supported. Among them, the switching between different detection modes can be performed based on mode selection.

[0049] As Figure 2 shown, Figure 2 is a schematic diagram of a temperature mode shown according to an exemplary embodiment. Among them, 100 is mode selection, which can be understood as the wearable device can select the temperature detection mode to switch to different temperature detection modes for temperature detection. 101 is the long-term monitoring mode, and 102 is the real-time detection mode. It can be understood that the wearable device supports two temperature detection modes, namely the long-term monitoring mode and the real-time detection mode.

[0050] In step S12, the temperature of the to-be-detected object is detected using the switched temperature detection mode.

[0051] In the embodiments of the present disclosure, through two temperature detection modes of the wearable device, by switching the temperature detection modes, the temperature detection of different objects to be measured can be realized, solving the problem that the wearable device cannot detect the temperature of other objects except the human body.

[0052] The embodiments of the present disclosure will hereinafter describe the method for determining the long-term monitoring mode.

[0053] In the embodiments of the present disclosure, when the wearable device is currently in the real-time detection mode, in response to triggering at least one of the following conditions, the temperature monitoring mode of the wearable device is switched to the long-term monitoring mode. The triggering conditions may be at least one of the following: the wearable device is in a state of being worn by the user, the wearable device is in a state of being used, a first instruction is detected, and the user behavior of the user wearing the wearable device conforms to the first user historical behavior for triggering entry into the long-term monitoring mode. Among them, the wearable device being in a state of being worn by the user can be understood as when the user wears the wearable device, the wearable device switches from the real-time detection mode to the long-term monitoring mode and detects the temperature of the user. The wearable device being in a state of being used can be understood as when the wearable device is in the powered-on state and the user is using it normally, the temperature of the user is detected.

[0054] In the embodiments of the present disclosure, a first instruction is detected. Among them, the first instruction can be understood as an instruction indicating that the wearable device switches to the long-term monitoring mode. The first instruction can be an explicit indication message or an implicit indication message. In one example, the first instruction can be a touch button. When the user triggers the touch button, the wearable device can switch from the real-time detection mode to the long-term detection mode, or it can also be to receive an instruction sent by other devices for mode switching. It is detected that the user behavior of the user wearing the wearable device conforms to the first user behavior for triggering entry into the long-term monitoring mode. Among them, the first user historical behavior can be understood as the user's historical habits, or it can also be that the wearable device pre-learns the user's behavior and then switches according to the currently detected user behavior. In one example, if the user is accustomed to wearing the wearable device and switching to the long-term monitoring mode between 1:30 pm and 2:00 pm every day, the wearable device can default to switching from the real-time detection mode to the long-term monitoring mode within the above time.

[0055] In the embodiments of the present disclosure, based on determining that the wearable device is in the long-term monitoring mode, the body temperature of the user wearing the wearable device is detected, realizing accurate selection of the temperature measurement object for temperature detection and reducing the possibility of measuring the temperature of other objects except the user.

[0056] The embodiments of the present disclosure will hereinafter describe the method for determining the real-time detection mode.

[0057] In an embodiment of the present disclosure, when the wearable device is currently in the real-time detection mode, in response to triggering at least one of the following conditions, the temperature monitoring mode of the wearable device is switched to the long-term monitoring mode. The triggering conditions may be at least one of the following: the wearable device is in a state where it is not worn by the user, a second instruction is detected. In one example, the instruction triggering methods include, but are not limited to, clicking on a page, tapping, voice, specific actions, etc. for mode switching. Among them, the wearable device being in a state where it is not worn by the user can be understood as being based on the pre-setting of the device. When the wearable device is in the worn state, it is in the long-term monitoring mode, and when the wearable device is in the state where it is not worn by the user, it is in the real-time detection mode. When the second instruction is detected, it can be switched to the real-time detection mode. Among them, the second instruction is used to indicate switching to the real-time detection mode. The second instruction may be to manually switch to the real-time detection mode based on the user triggering a certain touch button of the wearable device, or an instruction sent by other devices such as a terminal to cause the wearable device to switch to the real-time detection mode.

[0058] In an embodiment of the present disclosure, the triggering condition may also be that the user behavior of the user wearing the wearable device conforms to the second user historical behavior that triggers entering the real-time detection mode. In one example, if the user enters the changing room and removes the wearable device every morning from 7:00 to 8:00, at this time, the wearable device will detect the temperature of the environment in the changing room. Based on the fact that this behavior occurs every morning for the user, the wearable device enters the real-time temperature measurement mode during the period from 7:00 to 8:00 every day based on this habit.

[0059] In an embodiment of the present disclosure, based on determining that the wearable device is in the real-time detection mode, the temperature of non-living organisms is detected, realizing accurate selection of the temperature measurement object for temperature detection, and reducing the possibility of incorrect temperature measurement.

[0060] In an embodiment of the present disclosure, the following method for detecting the temperature of an object matching the current temperature detection mode is described.

[0061] Figure 3 It is a flowchart of a temperature detection method based on a temperature prediction model shown according to an exemplary embodiment. As Figure 3 shown, the method includes the following steps.

[0062] In step S21, a temperature prediction model that matches the current temperature detection mode is called.

[0063] In an embodiment of the present disclosure, when it is determined that the current temperature detection mode of the wearable device is the long-term monitoring mode, a prediction model is called, and the body temperature of the user is detected based on the prediction model. When it is determined that the current temperature detection mode of the wearable device is the real-time detection mode, a fast temperature measurement model is called, and the temperature of non-living organisms is detected based on the fast temperature measurement model.

[0064] In step S22, temperature detection is performed on an object that matches the current temperature detection mode based on a temperature prediction model.

[0065] In the embodiments of the present disclosure, the body temperature of a user is detected based on a prediction model. In one example, the skin temperature and ambient temperature obtained by a skin temperature sensor and an ambient temperature sensor can be used to predict the skin temperature or core temperature of the user based on the prediction model. Among them, the prediction model can be predefined and relationships can be established between other temperatures such as oral temperature or axillary temperature collected at the same moment and the temperatures obtained by the temperature sensors to train the prediction model to detect the body temperature of the user. In one example, the prediction model can also introduce richer physiological signals such as heart rate and sleep state. Among them, the prediction model includes but is not limited to linear models, multiple regression models, non-linear models, tree models, machine learning models, deep learning models, etc. The models mentioned above are only illustrative and no specific model is limited.

[0066] In the embodiments of the present disclosure, the temperature of non-living objects is detected based on a fast temperature measurement model. In the real-time detection mode, the temperature sensor of the wearable device is used to approach the object to be measured to achieve fast temperature measurement. It can be understood that in the real-time detection mode, it does not rely on information such as the user's physiological signals or historical data. Further, the fast temperature measurement model can be divided into detailed temperature measurement scenarios. In one example, the temperature measurement scenarios can be liquid temperature measurement scenarios, solid temperature measurement scenarios, gas temperature measurement scenarios, living body temperature measurement scenarios, metal temperature measurement scenarios, etc. The scenarios mentioned above are only illustrative and no specific scenario is limited. It can be understood that based on the above scenarios, the diffraction relationship between the sensor parameters and the true temperature is established in different scenarios and the fast temperature measurement model is trained to analyze the temperature sensor data based on the fast temperature measurement model to measure the temperature of non-living objects.

[0067] In the embodiments of the present disclosure, temperature detection is performed on an object in the current temperature detection mode based on different temperature prediction models, realizing the relative relationship between the temperature detection object and the temperature detection model, and improving the accuracy of the temperature detection object.

[0068] The embodiments of the present disclosure will hereinafter describe the method for temperature detection of the first model.

[0069] In an embodiment of the present disclosure, if the wearable device is in the long-term monitoring mode, the first model is called, where the first model is pre-trained based on the historical data of the user, and the historical data may be the physiological signals, temperature data, etc. of the user. In one example, the historical data may be data such as the oral temperature and axillary temperature of the user. Based on the physiological signals of the user collected and the first model, the temperature of the user currently wearing the wearable device is detected. In one example, the wearable device records the oral temperature of the user collected multiple times as historical data, and based on the physiological signals collected currently, the first model and the historical data are called to detect the temperature of the user.

[0070] In an embodiment of the present disclosure, the temperature of the user wearing the wearable device is detected based on the first model, which improves the accuracy of temperature measurement.

[0071] In the following, the temperature detection method of the second model in the embodiment of the present disclosure will be described.

[0072] In an embodiment of the present disclosure, if the wearable device is in the real-time detection mode, the second model is called. Among them, the second model is pre-trained based on the temperature data collected by the temperature sensor for different non-biological objects in different temperature detection scenarios. It can be understood that there is a diffraction relationship between the temperature sensor in different temperature detection scenarios and the temperature data collected by different non-biological objects. Based on the current temperature sensor data detected in real time and the second model, the current temperature detection scenario is identified, and the temperature of the non-biological object in the current scenario is detected.

[0073] In an embodiment of the present disclosure, based on the data detected by the sensor and the second model, the temperature detection of non-biological objects is realized, which increases the diversity of the use of the wearable device and improves the satisfaction of users.

[0074] In the following, the method for detecting the temperature of non-biological objects in the embodiment of the present disclosure will be described.

[0075] In an embodiment of the present disclosure, there is a corresponding relationship between the temperature detection scenario and the temperature determination method. Based on the corresponding relationship between the temperature detection scenario and the temperature determination method, the current temperature determination method corresponding to the current temperature detection scenario is determined, where the temperature determination method represents the corresponding relationship between the data detected by the temperature sensor and the temperature of the non-biological object. According to the current temperature determination method and the current temperature sensor data, the temperature of the non-biological object currently detected is determined. Among them, the temperature detection scenarios include but are not limited to liquid temperature measurement scenarios, solid temperature measurement scenarios, gas temperature measurement scenarios; living body temperature measurement scenarios, metal temperature measurement scenarios.

[0076] In one example, if the temperature of the currently used electronic device is too high, the wearable device can be placed on the electronic device. The wearable device determines that the electronic device is in a solid temperature measurement scenario, obtains the corresponding relationship between the solid temperature measurement scenario and the non-biological body temperature, and determines the current temperature of the electronic device based on the data of the electronic device detected by the current temperature sensor. It can be understood that different temperature detection scenarios correspond to different corresponding relationships, where the corresponding relationship is the corresponding relationship between the detection data of the temperature sensor and the non-biological body temperature.

[0077] In the embodiments of the present disclosure, through temperature detection in different scenarios, multi-scenario temperature detection is achieved, meeting the user's usage requirements for measuring the temperature of non-biological bodies in different scenarios.

[0078] The embodiments of the present disclosure will hereinafter describe another temperature detection method.

[0079] Figure 4 It is a flowchart of another temperature detection method shown according to an exemplary embodiment. As Figure 4 shown, the method includes the following.

[0080] In step S31, detection parameter thresholds for temperature detection of a target object are obtained, and the detection parameter thresholds include at least one of the following: temperature limit value and temperature detection duration threshold.

[0081] In the embodiments of the present disclosure, a temperature detection method for reminding users by combining with other devices or other APPs is proposed. That is, the real-time detection mode can provide a care mode in combination with the detection duration threshold and can be used for liquid heating detection. By setting the detection parameter thresholds for target object detection and obtaining the detection parameter thresholds, further judgment is then made. Among them, the detection parameter thresholds include at least one of the following: temperature limit value and temperature detection duration threshold.

[0082] In step S32, in response to the process parameters for temperature detection of an object matching the current temperature detection mode satisfying one or more of the detection parameter thresholds, a prompt message is issued.

[0083] In the embodiments of the present disclosure, the current temperature detection mode can be a real-time detection mode. In response to the process parameters for temperature detection of an object matching the real-time detection mode satisfying one or more of the detection parameter thresholds, a proposed message is issued to prompt the user to receive and view relevant information.

[0084] In one example, as Figure 5 shown, Figure 5It is a schematic diagram of a temperature detection prompt shown according to an exemplary embodiment. Among them, 200 is a smart terminal or a wearable device, 201 is data transmission, 202 is a wearable device, 203 is the object to be measured, 204 is data transmission, and the difference from 201 is that the transmitted data is different, and 205 is a smart terminal. As Figure 5 shown, set the detection parameter threshold through 200. Among them, the detection parameter threshold includes at least one of the following: temperature limit value and temperature detection duration threshold. For example, the upper temperature limit: 55 °C, the lower temperature limit. Relevant operations such as reminders and shutdowns can also be set. After the parameters are set, they reach the wearable device end 202 through data transmission 201. Among them, the data transmission methods include but are not limited to Bluetooth, wireless local area network, etc. In addition, if 200 is a wearable device, the data transmission 201 can be a function call, etc. Place the wearable device 202 outside the object to be measured 203. For example, if the wearable device 202 is a watch and the object to be measured 203 is a water cup, the watch strap can be tied outside the water cup. Then heat the water cup. When the water cup temperature reaches the set detection parameter threshold, send a prompt message to the smart terminal 205 through data transmission 204. The user receives and views the prompt message on the smart terminal 205. Among them, the data transmission methods include but are not limited to Bluetooth, wireless local area network, etc. The prompt message can be the heating time, temperature reminder, etc.

[0085] In another example, Figure 6 It is a schematic diagram of another temperature detection prompt shown according to an exemplary embodiment. As Figure 6 shown, temperature control can be further achieved in combination with a smart setting terminal. Among them, 300 is a smart terminal or a wearable device, 301 is data transmission, 302 is a wearable device, 303 is the object to be measured, 304 is data transmission, and the difference between 304 and 301 is that the transmitted data is different, and 305 is a smart terminal. As Figure 6As shown, the detection parameter thresholds are set by 300, where the detection parameter thresholds include at least one of the following: temperature limit value and temperature detection duration threshold. For example, the upper temperature limit: 55 °C, the lower temperature limit: 40 °C, the maximum measurement time: 1000 s. Among them, the maximum measurement time can be implemented by calling a timer or other devices, which is not limited in this disclosure. Relevant operations such as reminders and shutdowns can also be set. After the parameters are set, they reach the wearable device end 302 through data transmission 301. In addition, if 300 is a wearable device, the data transmission 301 can be a function call or the like. The wearable device 302 is placed outside the object to be measured 303. For example, if the wearable device 302 is a watch and the object to be measured 303 is a water cup, the watch strap can be tied outside the water cup. Then, the water cup is heated. When the temperature of the water cup reaches the set detection parameter threshold, a prompt message is sent to other devices through data transmission 304. Among them, the data transmission methods include but are not limited to Bluetooth, wireless local area network, etc. The prompt message can be the heating time, temperature reminder, etc. The other devices can be a heater or a smart switch 305, a user smart terminal 306, a smart speaker 307, etc. The data involved above is only for illustrative purposes and is not limited to specific data.

[0086] In the embodiments of the present disclosure, based on setting the detection parameter thresholds and sending prompt messages, temperature monitoring can be effectively achieved, the user experience can be improved, and the user satisfaction can be enhanced.

[0087] In the embodiments of the present disclosure, different types of temperature sensors are configured in the wearable device, and different types of temperature sensors match different temperature monitoring modes. For example, a skin temperature sensor for detecting skin temperature and / or an environmental temperature sensor for detecting environmental temperature can match the long-term monitoring mode to monitor the user's body temperature. For another example, an electronic sensor and an infrared sensor can match the real-time detection mode to monitor the user's body temperature.

[0088] In the embodiments of the present disclosure, the long-term monitoring mode and the real-time detection mode detect different detection objects based on the presence or absence of historical data information. Among them, when the wearable device switches to the long-term monitoring mode in the long-term monitoring mode, historical data information is stored. Based on the historical data information, as well as the skin temperature sensor and the environmental temperature sensor, the user's skin temperature and core temperature are predicted. This mode usually corresponds to a dual-sensor device. When the wearable device switches to the real-time detection mode, there is no historical data information in the real-time detection mode, that is, it is mainly used for temperature detection of the environment or an object. In one example, in the real-time detection mode, when the electronic sensor and the infrared sensor are close to the object to be measured, the current temperature of the object to be measured will be output.

[0089] In the embodiments of the present disclosure, temperature is detected based on different temperature sensors, which improves the accuracy of temperature detection.

[0090] In the embodiments of the present disclosure, the same type of temperature sensors under the same temperature detection mode can have different subtypes.

[0091] Figure 7 It is a flowchart of a method for detecting temperature in a real-time detection mode shown according to an exemplary embodiment. As Figure 7 shown, the method includes the following.

[0092] In step S41, the current temperature detection mode is the real-time detection mode.

[0093] In the embodiments of the present disclosure, it is determined that the current temperature detection mode is the real-time detection mode, wherein the temperature sensors matching the real-time detection mode include temperature sensors of different subtypes. In one example, the temperature sensor can be an infrared temperature sensor or an electronic temperature sensor. The time durations used for temperature detection by different sensors are different.

[0094] In step S42, the target detection duration selected by the user is obtained.

[0095] In the embodiments of the present disclosure, the wearable device pre-sets multiple detection durations for selecting the target detection duration based on the multiple detection durations to achieve rapid temperature measurement.

[0096] In step S43, the temperature prediction model corresponding to the sensor whose processing duration meets the target detection duration is called to detect the temperature of the object to be measured.

[0097] In the embodiments of the present disclosure, the temperature prediction models vary to some extent according to different sensor types. In one example, if the sensor is an infrared temperature sensor, the temperature prediction model can directly perform prediction using a correction model or a calibration model. If the sensor is an electronic temperature sensor, the temperature rise process of the sensor needs to be considered, and the temperature prediction model includes, but is not limited to, a temperature rise prediction mode, a temperature drop prediction mode, etc. In addition, based on a predefined duration, rapid temperature measurement can be achieved.

[0098] In the embodiments of the present disclosure, diverse temperature detections are achieved based on using different sensors for temperature detection, and the speed of temperature detection is improved based on the selection of the predefined duration, enhancing the user experience.

[0099] In the embodiments of the present disclosure, the method for temperature detection is described below.

[0100] In the embodiments of the present disclosure, temperature detection is performed based on a wearable device, where the wearable device includes, but is not limited to, a watch, a bracelet, earphones, a ring, a heart rate belt, glasses, etc. The wearable device includes a skin temperature sensor and an ambient temperature sensor, and the sensors include, but are not limited to, electronic sensors, infrared sensors, etc. There are two temperature detection modes in the wearable device, and different objects can be detected by switching different temperature detection modes. Among them, the mode switching function can be located in the wearable device or a control terminal such as a terminal, a tablet, a computer, etc. The switching modes include, but are not limited to, clicking on a page, tapping, voice, specific actions, etc.

[0101] In the embodiments of the present disclosure, the temperature detection modes include a long-term monitoring mode and a real-time detection mode to achieve temperature detection of different objects to be measured.

[0102] Among them, the long-term monitoring mode is used to monitor the skin temperature or core temperature of the user. The skin temperature or core temperature of the user is predicted based on the skin temperature and ambient temperature obtained by the sensor. A prediction model is used in the long-term monitoring mode, and the prediction model includes, but is not limited to, a linear model, a multiple regression model, a non-linear model, a tree model, a machine learning model, a deep learning model, etc. In addition, the prediction model can also introduce richer physiological signal information such as heart rate, sleep status, etc.

[0103] In the embodiments of the present disclosure, in the real-time detection mode, the temperature sensor of the wearable device is used to approach the object to be measured to achieve rapid temperature measurement. In the real-time detection mode, it does not rely on information such as the user's physiological signals or historical data. In the real-time detection mode, a rapid temperature measurement model is used, and the temperature of the object is measured based on the analysis of the temperature sensor data by the rapid temperature measurement model. Further, the rapid temperature measurement can also divide the temperature measurement scenarios, and the temperature measurement scenarios such as liquid temperature measurement scenarios, solid temperature measurement scenarios, gas temperature measurement scenarios, living body temperature measurement scenarios, metal temperature measurement scenarios, etc. In the real-time detection mode, it can also be combined with other APPs or other intelligent devices to expand the application scenarios.

[0104] The embodiments of the present disclosure solve the problem that wearable devices cannot be used for ambient temperature or object temperature measurement, achieve temperature detection of different objects, and improve user satisfaction.

[0105] Based on the same concept, the embodiments of the present disclosure also provide a temperature detection device 400.

[0106] It can be understood that in order to implement the above functions, the temperature detection device 400 provided in the embodiments of the present disclosure includes the corresponding hardware structures and / or software modules for performing each function. Combining the units and algorithm steps of the examples disclosed in the embodiments of the present disclosure, the embodiments of the present disclosure can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the technical solution of the embodiments of the present disclosure.

[0107] Figure 8 is a block diagram of a temperature detection device 400 shown according to an exemplary embodiment. Referring to Figure 8 , the device includes a switching unit 401 and a detection unit 402.

[0108] The switching unit 401 is configured to switch the current temperature detection mode of the wearable device in response to a triggered mode switch, where the temperature detection modes supported by the wearable device include a long-term monitoring mode and a real-time detection mode.

[0109] The detection unit 402 is configured to perform temperature detection on the object to be measured by using the switched temperature detection mode.

[0110] In one implementation manner, the switching unit 401 switches the current temperature detection mode of the wearable device in response to a triggered mode switch in the following manner, including: when the wearable device is currently in the real-time detection mode, in response to triggering at least one of the following conditions, switching the temperature detection mode of the wearable device to the long-term monitoring mode: the wearable device is in a state of being worn by the user; the wearable device is in a state of being used; a first instruction is detected, and the first instruction is used to indicate switching to the long-term monitoring mode; the user behavior of the user wearing the wearable device conforms to the first user historical behavior for triggering entry into the long-term monitoring mode.

[0111] In one implementation manner, the switching unit 401 switches the current temperature detection mode of the wearable device in response to a triggered mode switch in the following manner, including: when the wearable device is currently in the long-term monitoring mode, in response to determining that at least one of the following conditions is satisfied, switching the temperature detection mode of the wearable device to the real-time detection mode: the wearable device is in a state of not being worn by the user; a second instruction is detected, and the second instruction is used to indicate switching to the real-time detection mode; the user behavior of the user wearing the wearable device conforms to the second user historical behavior for triggering entry into the real-time detection mode.

[0112] In one implementation, the detection unit 402 performs temperature detection on an object that matches the current temperature detection mode in the following manner: invoking a temperature prediction model that matches the current detection mode, and performing temperature detection on the object that matches the current temperature detection mode based on the temperature prediction model.

[0113] In one implementation, the detection unit 402 invokes a temperature prediction model that matches the current detection mode in the following manner and performs temperature detection on an object that matches the current temperature detection mode based on the temperature prediction model, including: if the wearable device is in the long-term monitoring mode, invoking the first model, and performing temperature detection on the user wearing the wearable device based on the collected user physiological signals and the first model, where the first model is pre-trained based on the user's historical data, and the historical data includes physiological signals and temperature data.

[0114] In one implementation, the detection unit 402 invokes a temperature prediction model that matches the current detection mode in the following manner and performs temperature detection on an object that matches the current temperature detection mode based on the temperature prediction model, including: if the wearable device is in the real-time detection mode, invoking the second model, identifying the current temperature detection scenario based on the currently detected temperature sensor data in real time and the second model, and performing temperature detection on non-living objects in the current temperature detection scenario, where the second model is pre-trained based on the temperature data collected by the temperature sensor for different non-living objects under different temperature detection scenarios.

[0115] In one implementation, the detection unit 402 performs temperature detection on non-living objects in the current temperature detection scenario in the following manner, including: determining the current temperature determination method corresponding to the current temperature detection scenario based on the correspondence between the temperature detection scenario and the temperature determination method, and determining the temperature of the currently detected non-living object according to the current temperature determination method and the current temperature sensor data; where different temperature detection scenarios correspond to different temperature determination methods, and the temperature determination method represents the correspondence between the data detected by the temperature sensor and the temperature of the non-living object; the temperature detection scenario includes at least one of the following scenarios: liquid temperature measurement scenario, solid temperature measurement scenario, gas temperature measurement scenario; living body temperature measurement scenario, metal temperature measurement scenario.

[0116] In one implementation, the device is further configured to: obtain a detection parameter threshold for performing temperature detection on an object to be measured, where the detection parameter threshold includes at least one of the following: temperature limit value, temperature detection duration threshold; and in response to determining that the detected parameter satisfies one or more of the detection parameter thresholds during the process of performing temperature detection on the object to be measured, sending a prompt message.

[0117] In one implementation, different types of temperature sensors are configured in the wearable device, and different types of temperature sensors are matched with different temperature detection modes; adopting the switched temperature detection mode, the detection unit 402 performs temperature detection on the object to be measured in the following manner, including: enabling the temperature sensor of the type matched with the switched temperature detection mode to perform temperature detection on the object to be measured.

[0118] In one implementation, the current temperature detection mode is the real-time detection mode, and the temperature sensors of the type matched with the real-time detection mode include temperature sensors of different subtypes. Among them, the time durations used for temperature detection by different subtypes of temperature sensors are different; the detection unit 402 performs temperature detection on the object to be measured in the following manner, including: obtaining the target detection duration selected by the user; calling the temperature prediction model corresponding to the sensor whose processing duration meets the target detection duration to perform temperature detection on the object to be measured.

[0119] Regarding the device in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0120] Figure 9 It is a block diagram of a device 500 for temperature detection shown according to an exemplary embodiment. The device 500 may be provided as a terminal. For example, the device 500 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0121] Referring to Figure 9 , the device 500 may include one or more of the following components: a processing component 502, a memory 504, a power component 506, a multimedia component 508, an audio component 510, an input / output (I / O) interface 512, a sensor component 514, and a communication component 516.

[0122] The processing component 502 generally controls the overall operation of the device 500, such as operations associated with display, telephone call, data communication, camera operation, and recording operation. The processing component 502 may include one or more processors 520 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 502 may include one or more modules to facilitate the interaction between the processing component 502 and other components. For example, the processing component 502 may include a multimedia module to facilitate the interaction between the multimedia component 508 and the processing component 502.

[0123] The memory 504 is configured to store various types of data to support the operation of the device 500. Examples of such data include instructions for any application or method operating on the device 500, contact data, phone book data, messages, pictures, videos, and the like. The memory 504 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disk.

[0124] The power component 506 provides power to the various components of the device 500. The power component 506 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 500.

[0125] The multimedia component 508 includes a screen that provides an output interface between the device 500 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can not only sense the boundaries of the touch or swipe actions but also detect the duration and pressure associated with the touch or swipe operation. In some embodiments, the multimedia component 508 includes a front camera and / or a rear camera. When the device 500 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each of the front camera and the rear camera can be a fixed optical lens system or have a focal length and optical zoom capabilities.

[0126] The audio component 510 is configured to output and / or input audio signals. For example, the audio component 510 includes a microphone (MIC) that is configured to receive external audio signals when the device 500 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the memory 504 or transmitted via the communication component 516. In some embodiments, the audio component 510 further includes a speaker for outputting audio signals.

[0127] The I / O interface 512 provides an interface between the processing component 502 and a peripheral interface module, which can be a keyboard, a click wheel, buttons, etc. These buttons can include, but are not limited to: a home button, a volume button, a power-on button, and a lock button.

[0128] The sensor assembly 514 includes one or more sensors for providing a status assessment of various aspects of the device 500. For example, the sensor assembly 514 can detect the on / off state of the device 500, the relative positioning of components, such as the display and keypad of the device 500. The sensor assembly 514 can also detect a change in the position of the device 500 or a component of the device 500, the presence or absence of user contact with the device 500, the orientation or acceleration / deceleration of the device 500, and the temperature change of the device 500. The sensor assembly 514 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 514 can also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 514 can also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0129] The communication component 516 is configured to facilitate communication between the device 500 and other devices in a wired or wireless manner. The device 500 can access a wireless network based on communication standards, such as WiFi, 5G, or 3G, or a combination thereof. In an exemplary embodiment, the communication component 516 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 516 further includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0130] In an exemplary embodiment, the device 500 can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above method.

[0131] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 504 including instructions that can be executed by a processor 520 of the device 500 to complete the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0132] It can be understood that in this disclosure, "a plurality of" means two or more, and other quantifiers are similar thereto. "And / or" describes the association relationship of associated objects and indicates that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after. The singular forms of "a", "the", and "said" are also intended to include the plural forms unless the context clearly indicates otherwise.

[0133] It can be further understood that the terms "first", "second", etc. are used to describe various information, but this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other and do not represent a specific order or degree of importance. In fact, the expressions such as "first" and "second" can be used interchangeably. For example, without departing from the scope of this disclosure, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information.

[0134] It can be further understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "front", "rear", "upper", "lower", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing this embodiment and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation.

[0135] It can be further understood that unless otherwise specified, "connection" includes direct connection between two without other components therebetween, and also includes indirect connection between two with other elements therebetween.

[0136] It can be further understood that although the operations are described in a specific order in the drawings in the embodiments of this disclosure, it should not be understood as requiring these operations to be performed in the specific order shown or in a serial order, or requiring all the operations shown to obtain the desired result. In a specific environment, multitasking and parallel processing may be advantageous.

[0137] Those skilled in the art will readily think of other embodiments of this disclosure after considering the specification and practicing the invention disclosed herein. This disclosure aims to cover any variations, uses, or adaptive changes of this solution, and these variations, uses, or adaptive changes follow the general principles of this disclosure and include the common general knowledge or conventional technical means in the technical field not disclosed in this disclosure.

[0138] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A temperature detection method, characterized in that, it includes: In response to a trigger for mode switching, switching the current temperature detection mode of the wearable device, wherein the temperature detection modes supported by the wearable device include a long-term monitoring mode and a real-time detection mode; Using the switched temperature detection mode to perform temperature detection on the object to be measured.

2. The method according to claim 1, characterized in that, the step of in response to a trigger for mode switching, switching the current temperature detection mode of the wearable device, includes: When the wearable device is currently in the real-time detection mode, in response to triggering at least one of the following conditions, switching the temperature detection mode of the wearable device to the long-term monitoring mode: The wearable device is in a state of being worn by the user; The wearable device is in a state of being used; Detecting a first instruction for instructing switching to the long-term monitoring mode; Detecting that the user behavior of the user wearing the wearable device conforms to the first user historical behavior for triggering entry into the long-term monitoring mode.

3. The method according to claim 1, characterized in that, the step of in response to a trigger for mode switching, switching the current temperature detection mode of the wearable device, includes: When the wearable device is currently in the long-term monitoring mode, in response to determining that at least one of the following conditions is met, switching the temperature detection mode of the wearable device to the real-time detection mode: The wearable device is in a state of not being worn by the user; Detecting a second instruction for instructing switching to the real-time detection mode; Detecting that the user behavior of the user wearing the wearable device conforms to the second user historical behavior for triggering entry into the real-time detection mode.

4. The method according to any one of claims 1 to 3, characterized in that, the step of using the switched temperature detection mode to perform temperature detection on the object to be measured, includes: Invoking a temperature prediction model that matches the current temperature detection mode, and performing temperature detection on an object that matches the current temperature detection mode based on the temperature prediction model.

5. The method according to claim 4, characterized in that, invoking a temperature prediction model that matches the current detection mode, and performing temperature detection on an object that matches the current temperature detection mode based on the temperature prediction model, includes: If the wearable device is in the long-term monitoring mode, then invoking a first model, and performing temperature detection on the user wearing the wearable device based on the collected user physiological signals and the first model, wherein the first model is pre-trained based on the historical data of the user, and the historical data includes physiological signals and temperature data.

6. The method according to claim 4, characterized in that, the step of invoking a temperature prediction model that matches the current detection mode, and performing temperature detection on an object that matches the current temperature detection mode based on the temperature prediction model, includes: If the wearable device is in the real-time detection mode, the second model is called, and based on the currently detected temperature sensor data in real time and the second model, the current temperature detection scenario is identified, and the temperature of non-living organisms in the current temperature detection scenario is detected. The second model is pre-trained based on the temperature data collected by the temperature sensor for different non-living organisms under different temperature detection scenarios.

7. The method according to claim 6, wherein, the detecting the temperature of non-living organisms in the current temperature detection scenario includes: determining the current temperature determination method corresponding to the current temperature detection scenario based on the correspondence between the temperature detection scenario and the temperature determination method, and determining the temperature of the currently detected non-living organism according to the current temperature determination method and the current temperature sensor data; wherein, different temperature detection scenarios correspond to different temperature determination methods, and the temperature determination method represents the correspondence between the data detected by the temperature sensor and the temperature of the non-living organism; the temperature detection scenario includes at least one of the following scenarios: liquid temperature measurement scenario, solid temperature measurement scenario, gas temperature measurement scenario; living body temperature measurement scenario, metal temperature measurement scenario.

8. The method according to claim 1, wherein, the method further includes: acquiring a detection parameter threshold for temperature detection of the object to be measured, the detection parameter threshold including at least one of the following: temperature limit value, temperature detection duration threshold; responding to determining that the detected parameter satisfies one or more of the detection parameter thresholds during the temperature detection of the object to be measured, and sending a prompt message.

9. The method according to claim 1, wherein, different types of temperature sensors are configured in the wearable device, and different types of temperature sensors are matched with different temperature detection modes; the temperature detection of the object to be measured using the switched temperature detection mode includes: enabling the temperature sensor of the type matched with the switched temperature detection mode to detect the temperature of the object to be measured.

10. The method according to claim 9, wherein, the current temperature detection mode is the real-time detection mode, and the temperature sensors of the type matched with the real-time detection mode include temperature sensors of different subtypes. Among them, the duration of temperature detection used by different subtypes of temperature sensors is different; the temperature detection of the object to be measured includes: acquiring the target detection duration selected by the user; invoking the temperature prediction model corresponding to the sensor whose processing duration meets the target detection duration to detect the temperature of the object to be measured.

11. A temperature detection device, wherein, it includes: a switching unit, which responds to the triggered mode switching and switches the current temperature detection mode of the wearable device, wherein the temperature detection modes supported by the wearable device include a long-term monitoring mode and a real-time detection mode; a detection unit, which uses the switched temperature detection mode to detect the temperature of the object to be measured.

12. A temperature detection device, wherein, it includes: a processor: a memory for storing instructions executable by the processor; Wherein, the processor is configured to execute the degree detection method according to any one of claims 1 to 10.

13. A storage medium, characterized in that the storage medium stores instructions, which, when executed by a processor, enable the processor to execute the degree detection method according to any one of claims 1 to 10.

Images