WO2022120564A1 - 用于温度测量的方法、装置和存储介质 - Google Patents

用于温度测量的方法、装置和存储介质 Download PDF

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WO2022120564A1
WO2022120564A1 PCT/CN2020/134510 CN2020134510W WO2022120564A1 WO 2022120564 A1 WO2022120564 A1 WO 2022120564A1 CN 2020134510 W CN2020134510 W CN 2020134510W WO 2022120564 A1 WO2022120564 A1 WO 2022120564A1
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Prior art keywords
temperature
measurement site
current measurement
preset
temperature prediction
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PCT/CN2020/134510
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English (en)
French (fr)
Inventor
孟梨斌
张宁玲
金星亮
肖科
何先梁
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深圳迈瑞生物医疗电子股份有限公司
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Application filed by 深圳迈瑞生物医疗电子股份有限公司 filed Critical 深圳迈瑞生物医疗电子股份有限公司
Priority to PCT/CN2020/134510 priority Critical patent/WO2022120564A1/zh
Priority to CN202080107740.9A priority patent/CN116583218B/zh
Publication of WO2022120564A1 publication Critical patent/WO2022120564A1/zh

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/01—Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue

Definitions

  • the present application relates to the technical field for temperature measurement, and more particularly to a method, device and storage medium for temperature measurement.
  • the solution for the existing product is: set the measurement part (or select the default measurement part) before each measurement, and the subsequent measurement process and prediction algorithm are based on the set part; in the next measurement, repeat the measurement of the part. Select and confirm, thus guaranteeing the precision and accuracy of each measurement.
  • this processing method has the following shortcomings and deficiencies: 1) the operation is cumbersome, the user easily ignores the setting and confirmation of the measurement site, and the actual measurement site does not match the selected mode; 2) the measurement site cannot be automatically identified in the process, and the Convert and correct the selected prediction mode; 3) When an error occurs in the measurement position, prompts and feedback cannot be given in time on the device display interface.
  • a method for temperature measurement comprising: acquiring temperature data, the temperature data including a period of time output by a temperature measurement device when measuring a current measurement part of an object to be measured temperature data; extracting characteristic parameters for at least part of the temperature data, and identifying the current measurement site based on the characteristic parameters to obtain a recognition result; assisting in realizing temperature prediction based on the recognition result, and outputting the prediction result.
  • an apparatus for temperature measurement includes a memory, a processor and an output device, the memory stores a computer program executed by the processor, the computer program is When being run by the processor, the following steps are performed: acquiring temperature data, the temperature data including temperature data for a period of time output when the temperature measurement device measures the current measurement part of the measured object; for at least one of the temperature data Feature parameters are extracted from part of the data, and the current measurement site is identified based on the feature parameters to obtain a recognition result of the current measurement site; temperature prediction is assisted based on the recognition result, and the output device outputs the prediction result.
  • a storage medium is provided, and a computer program is stored on the storage medium, and the computer program executes the above method for temperature measurement when running.
  • the method, device and storage medium for temperature measurement automatically identify the current measurement position based on the characteristic parameters in the temperature data obtained by measuring the current measurement position of the measured object, and perform temperature prediction according to the identification result, effectively The accuracy of the temperature prediction result is improved, and the operation of selecting and confirming the measurement site before the measurement is eliminated, which brings convenience to the user.
  • FIG. 1 shows a schematic diagram of the temperature change curve when measuring the temperature of the same measured object at different measurement locations.
  • FIG. 2 shows a schematic flowchart of a method for temperature measurement according to an embodiment of the present application.
  • FIG. 3 shows a schematic flowchart of an example of a more specific process of the method for temperature measurement according to an embodiment of the present application.
  • FIG. 4 shows a schematic flowchart of another example of a more specific process of the method for temperature measurement according to an embodiment of the present application.
  • 5 is a graph showing the comparison between the normal prediction result when the measurement part is consistent with the prediction mode and the prediction result when the measurement part is inconsistent with the prediction mode, and the prediction is corrected by adopting the method for temperature measurement according to the embodiment of the present application Schematic diagram of the results.
  • FIG. 6 shows a schematic block diagram of an apparatus for temperature measurement according to an embodiment of the present application.
  • Fig. 1 shows a schematic diagram of the temperature change curve when the temperature is measured at different measurement parts of the same object to be measured.
  • the temperature change curve when the temperature is measured for the oral cavity is 110
  • the temperature change curve when the temperature is measured for the armpit is 120.
  • the curve 110 and the curve 120 do not overlap. , there is a clear difference between the two. Therefore, if the measurement site is inconsistent with the prediction model, for example, the measurement site is the oral cavity but the temperature result is predicted by the axillary prediction mode, or the measurement site is the armpit but the temperature result is predicted by the oral prediction mode, it will be inaccurate or even wrong. forecast result. If each measurement requires the user to set and confirm the measurement site, the operation is cumbersome and prone to errors.
  • FIG. 2 shows a schematic flowchart of a method 200 for temperature measurement according to an embodiment of the present application.
  • the method 200 for temperature measurement may include the following steps:
  • step S210 temperature data is acquired, and the temperature data includes temperature data for a period of time output when the temperature measurement device measures the current measurement part of the measured object.
  • the temperature data acquired in step S210 may be the temperature data at one end of the time when the probe is in contact with the current measurement site (eg, oral cavity, armpit or rectum) of the measured object for measurement, and the temperature during this period of time is
  • the temperature data can be used for the identification of the measurement site and the prediction of the temperature of the measurement site in the steps to be described later.
  • temperature data for a period of time may be acquired, and temperature data that reaches a preset condition may be screened out for subsequent measurement site identification and temperature prediction, or the acquired temperature data for a period of time may be all is the temperature data that reaches the preset condition.
  • the temperature data that reaches the preset condition may refer to: the temperature measurement value after reaching the preset reference time and/or the temperature measurement value after exceeding the preset reference temperature. Performing subsequent measurement site identification and temperature prediction based on the temperature data that meets the preset condition is beneficial to eliminate erroneous data and improve the accuracy of the respective results of the measurement site identification and temperature prediction.
  • step S220 characteristic parameters are extracted for at least part of the temperature data, and the current measurement part is identified based on the characteristic parameters to obtain an identification result of the current measurement part.
  • At least part of the data in the temperature data involved in step S220 may be the aforementioned temperature data that reaches the preset condition, so as to improve the accuracy of the subsequent processing result.
  • the feature parameter extracted for at least part of the temperature data may be at least one of the following: the difference between the temperature measurement values at different times in the at least part of the temperature data; the The average temperature gradient value over a period of time in at least part of the temperature data; the integral area of the curve corresponding to at least part of the temperature data; the energy of the curve corresponding to at least part of the temperature data.
  • the following describes how to identify the current measurement location according to the extracted characteristic parameters by taking the extracted characteristic parameters as the difference of temperature measurement values at different times as an example. Since the temperature rise rate (temperature change rate) of different measurement parts is different, for the temperature data of different measurement parts for a period of time (reaching the preset condition), the temperature measurement between the preset two moments with a certain time interval The difference in values is also different. Based on this, in this example, the temperature measurement values at two times can be extracted from the temperature data of the current measurement site for a period of time, the difference between the temperature measurement values at the two times can be calculated, and the difference value can be compared with each standard measurement value. The reference values of the sites are compared to determine which of the standard measurement sites the current measurement site is.
  • the reference value may be a temperature difference between two moments obtained based on historical data, the two moments and the two moments corresponding to the previously extracted temperature measurement value are the same moment, or the two moments are the same as the previously extracted temperature measurement value.
  • the two time instants corresponding to the temperature measurements have at least the same time interval.
  • the aforementioned two moments may be two moments that meet certain conditions.
  • the two moments are respectively referred to as the first moment and the second moment, and the second moment is later than the first moment, then the first moment
  • the first moment may be a moment not earlier than a preset moment
  • the second moment may be a moment with a preset time interval from the first moment. That is to say, the first moment of the two moments selected when extracting the feature parameters cannot be too early, so as to avoid sampling interference data; the latter moment needs to be separated by a certain period of time, so as to avoid the two moments being too close to accurately reflect the temperature data changes.
  • the average temperature gradient value within a period of time can be used as a characteristic parameter for the temperature data within the period, and it is compared with the reference value of each standard measurement position to determine that the current measurement position is the standard value Which of the measurement sites.
  • the reference value may be an average temperature gradient value of temperature data for a period of time obtained based on historical data, and the period of time may have at least the same time interval as the period of time corresponding to the aforementioned feature parameter extraction.
  • the average temperature gradient value GradT can be calculated as shown in the following formula:
  • fs is the sampling frequency
  • T is the temperature measurement value corresponding to the sampling point
  • i is the number of sampling points.
  • the calculation is calculated by sampling every 20 data points and sampling 3 data points as an example. It should be understood that other suitable sampling points and interval data points can also be selected as required.
  • using the average temperature gradient value over a period of time as the characteristic parameter for the identification of the measurement part can avoid the influence of the accidental error of a single sampling point and improve the accuracy of the identification result.
  • the difference between the temperature measurement values at different times and the average temperature gradient value within a period of time is used as an example to describe how to identify the current measurement site according to the extracted characteristic parameters.
  • other characteristic parameters may also be used.
  • the integral area of the curve corresponding to the temperature data for a period of time, the energy of the corresponding curve, etc. are generally based on the extracted characteristic parameters and the preset reference values (reference characteristic parameters) obtained based on historical data of each standard measurement site. ) are compared to identify the measurement site.
  • a variety of characteristic parameters can also be used to identify the measurement site.
  • the difference between the temperature measurement values at different times and the average temperature gradient over a period of time can be combined to identify the measurement site. It is beneficial to improve the accuracy of recognition results.
  • step S230 based on the recognition result, the temperature prediction is assisted to realize, and the prediction result is output.
  • temperature prediction may be performed on the current measurement site of the measured object.
  • the identification result obtained in step S220 may include the identification result of identifying which measurement site the current measurement site is, or may include the identification result of not identifying which measurement site the current measurement site is.
  • the recognition result can be a prompt message, which can indicate that the current measurement position cannot be identified.
  • the auxiliary temperature prediction realized may include: determining that there is interference in the current measurement process, performing anti-interference processing and outputting the result.
  • the anti-interference processing may include, for example, prompting the user to re-adjust the probe and re-measure in the aforementioned prompt information.
  • the output result may be the temperature prediction result obtained after the anti-jamming processing and after obtaining the real identification result of the current measurement position.
  • the anti-interference processing may include determining the degree of interference, and when the degree of interference is large, switching to a monitoring mode, where the monitoring mode includes: using a temperature measurement device to directly measure the temperature of the current measurement site, obtain a temperature measurement result, and no longer perform Temperature prediction; when the degree of interference is small, the user is prompted to reduce or eliminate the interference factors and re-execute the identification of the aforementioned measurement site and the temperature prediction based on the identification results.
  • the magnitude of the interference degree may be determined according to the characteristic parameter extracted in step S220.
  • the implemented auxiliary temperature prediction may include: based on the recognition result, perform consistency judgment on the preset measurement site, and perform a consistency judgment according to the judgment result. temperature prediction, obtain and output the temperature prediction result of the current measurement site; or perform temperature prediction on the current measurement site based on the identification result, and obtain and output the temperature prediction result of the current measurement site.
  • the two cases are respectively for two scenarios of preset measurement part and non-preset measurement part. These two scenarios are described below.
  • the measurement site may be determined whether the current measurement site is consistent with the preset measurement site based on the recognition result: when the current measurement site is consistent with the preset measurement site, it may be determined based on the The preset temperature prediction model (ie the prediction algorithm) corresponding to the measurement part performs temperature prediction on the current measurement part, and obtains and outputs the temperature prediction result; when the current measurement part is inconsistent with the preset measurement part, it can The preset temperature prediction model is switched to the temperature prediction model corresponding to the current measurement part, and the temperature prediction is performed on the current measurement part based on the switched temperature prediction model, and a temperature prediction result is obtained and output.
  • the temperature prediction result is calculated by using a preset temperature prediction model corresponding to the current measurement site, so the obtained temperature prediction result of the current measurement site is accurate.
  • the method 200 may further include at least one of the following (not shown): outputting a prompt that the current measurement site is inconsistent with the preset measurement site set the preset measurement site for the next measurement as the current measurement site; set the preset temperature prediction model for the next measurement as the temperature prediction model corresponding to the current measurement site.
  • prompt information may also be output to prompt the user to preset the measurement site If it is inconsistent with the current measurement position, the user should modify the preset measurement position on the display interface to the current measurement position, so as to avoid that although the final output is the correct temperature prediction result of the current measurement position, the result displayed on the display interface is the preset measurement. location predictions.
  • the prompting method may be, for example, a visual prompting method and/or an auditory prompting method. For example, if the preset measurement site is the armpit and the actual measurement site is the oral cavity, the word or icon of "mouth” may flash on the display screen, and/ Or remind the user by means of a prompt tone.
  • the preset measurement position on the display interface can also be automatically modified to the current measurement position, thereby reducing user operations and improving user experience.
  • the current measurement site may be the temperature measurement site preferred by the measured object. Based on this, the user can also be automatically or prompted to set the preset measurement site for the next measurement as the current measurement site, so as to avoid the possibility of setting the site from appearing next time. Inconsistency with the measurement site.
  • the preset temperature prediction model for the next measurement can also be set as the temperature prediction model corresponding to the current measurement site.
  • the temperature prediction model to be used for temperature prediction may be determined based on the identification result obtained in step S220, that is, the temperature prediction model corresponding to the identification result, and Based on the determined temperature prediction model, the temperature of the current measurement site is predicted.
  • a corresponding temperature prediction model can be used to automatically output the temperature prediction result of the current measurement site according to the identification result of the current measurement site, without the need to perform temperature prediction on the measurement site before measurement, and output the temperature prediction result, reducing user operations and To ensure the accuracy of prediction results.
  • FIG. 3 shows a schematic flowchart of an example of a more specific process 300 of the method for temperature measurement according to an embodiment of the present application.
  • the process 300 starts at step S310 , and at step S310 , enters an initial measurement preparation stage, in which a preset measurement site and a preset temperature prediction model can be set.
  • a quick thermometer (such as a probe) is put into the measurement site to collect temperature data.
  • characteristic parameters of the temperature rise process are extracted according to the collected temperature data.
  • the current actual measurement part is identified according to the extracted characteristic parameters.
  • step S350 it is determined whether the current actual measurement site is consistent with the preset measurement site, if so, proceed to step S360, if not, proceed to step S370.
  • step S360 a temperature prediction result is calculated and output according to a preset temperature prediction model.
  • step S370 the preset temperature prediction model is switched to the temperature prediction model corresponding to the current actual measurement site.
  • step S380 the temperature prediction result is calculated and output according to the switched temperature prediction model.
  • FIG. 4 shows a schematic flowchart of another example of a more specific process 400 of the method for temperature measurement according to an embodiment of the present application.
  • the process 400 starts at step S410, where a quick thermometer (such as a probe) is placed into the measurement site to collect temperature data.
  • a quick thermometer such as a probe
  • characteristic parameters of the temperature rise process are extracted according to the collected temperature data.
  • the current actual measurement part is identified according to the extracted characteristic parameters.
  • a corresponding temperature prediction model is determined according to the identification result.
  • the temperature prediction result is calculated and output according to the determined temperature prediction model.
  • Measurement status Prediction result/°C normal insertion into the oral cavity 37.33 Measured underarms and predicted with oral model 36.84 Convert to underarm pattern prediction after automatic recognition 37.38
  • a quick thermometer is placed in the subject's mouth for measurement.
  • the corresponding prediction algorithm is the oral mode, and the normal output prediction result is 37.33°C, as shown in the curve 510.
  • the oral mode is still set, but the probe is placed under the subject's armpit for measurement.
  • the output predicted temperature is 36.84°C, as shown in the curve 520, the deviation reaches 0.49°C, indicating that the measurement position does not match.
  • the prediction results have large errors and low accuracy.
  • the temperature rise curve 510 in the oral mode and the temperature rise curve 520 in the armpit mode are also different.
  • the method for temperature measurement according to the embodiment of the present application automatically identifies the current measurement position based on the characteristic parameters in the temperature data obtained by measuring the current measurement position of the measured object, and performs temperature prediction according to the identification result, effectively The accuracy of the temperature prediction result is improved, and the operation of selecting and confirming the measurement site before the measurement is eliminated, which brings convenience to the user.
  • FIG. 6 shows a schematic block diagram of an apparatus 600 for temperature measurement according to an embodiment of the present application.
  • the apparatus 600 for temperature measurement may include a memory 610 , a processor 620 and an output device 630 .
  • the memory 610 stores a computer program executed by the processor 620 , and the computer program is executed by the processor 620 .
  • the method for temperature measurement according to the embodiment of the present application is executed at the time.
  • the computer program executes the following steps when executed by the processor 620: acquiring temperature data, where the temperature data includes a segment output when the temperature measuring device measures the current measurement part of the measured object temperature data over time; extract characteristic parameters for at least part of the temperature data, identify the current measurement site based on the characteristic parameters, and obtain a recognition result; perform temperature prediction on the current measurement site based on the recognition result , to assist in realizing temperature prediction, and output the prediction result by the output device 630 .
  • the identification result includes the identification result of the current measurement site
  • the processor 620 is further configured to: perform temperature prediction on the current measurement site based on the identification result, obtain and use the The output device outputs the temperature prediction result of the current measurement site; or performs consistency judgment on the preset measurement site based on the recognition result, and performs temperature prediction according to the judgment result, and obtains and outputs the current measurement site by the output device temperature prediction results.
  • the processor 620 performs temperature prediction on the current measurement site based on the identification result, which may include: determining a temperature prediction model to be used for temperature prediction based on the identification result; The temperature prediction model of the current measurement site performs temperature prediction.
  • the processor 620 performs consistency judgment on the preset measurement site based on the recognition result, and performs temperature prediction according to the judgment result, which may include: determining the current measurement site and the preset measurement site based on the recognition result. Whether the measurement site is consistent; when the current measurement site is consistent with the preset measurement site, perform temperature prediction on the current measurement site based on a preset temperature prediction model corresponding to the preset measurement site; When the measurement part is inconsistent with the preset measurement part, switch from the preset temperature prediction model corresponding to the preset measurement part to the temperature prediction model corresponding to the current measurement part, and based on the switched temperature prediction model. The current measurement site performs temperature prediction.
  • the processor 620 when the current measurement site is inconsistent with the preset measurement site, the processor 620 further performs at least one of the following steps: outputting a message indicating that the current measurement site is inconsistent with the preset measurement site prompt information; set the preset measurement site for the next measurement as the current measurement site; set the preset temperature prediction model for the next measurement as the temperature prediction model corresponding to the current measurement site.
  • the prompt information further includes information for prompting the user to modify the preset measurement site to the current measurement site.
  • the identification result includes prompt information, and the prompt information indicates that the current measurement part cannot be identified.
  • the processor 620 is further configured to: determine that there is interference in the current measurement process, and perform anti-interference. Process and output the result by the output device.
  • the characteristic parameter includes at least one of the following: a difference between temperature measurement values at different times in at least part of the temperature data; a segment in at least part of the temperature data The average temperature gradient value over time; the integral area of the curve corresponding to at least part of the temperature data; the energy of the curve corresponding to at least part of the temperature data.
  • the at least part of the data in the temperature data includes data in the temperature data that reaches a preset condition.
  • the data that reaches the preset condition includes: a temperature measurement value after reaching a preset reference time and/or a temperature measurement value after exceeding the preset reference temperature.
  • the different moments include a first moment and a second moment, the second moment is later than the first moment, the first moment is not earlier than a preset moment, and the first moment
  • the time interval between the second moment and the first moment is a preset time interval.
  • the processor 620 identifies the current measurement part based on the characteristic parameter, which may include: comparing the characteristic parameter with the reference characteristic parameters of each standard measurement part, and extracting the data from the measured part based on the comparison result.
  • the identification result of the current measurement site is obtained from the standard measurement site.
  • the temperature measurement device may be a probe.
  • a storage medium is also provided, and program instructions are stored on the storage medium, and when the program instructions are run by a computer or a processor, the program instructions are used to execute the temperature control method of the embodiments of the present application. Corresponding steps of the method of measurement.
  • the storage medium may include, for example, a memory card of a smartphone, a storage component of a tablet computer, a hard disk of a personal computer, read only memory (ROM), erasable programmable read only memory (EPROM), portable compact disk read only memory (CD-ROM), USB memory, or any combination of the above storage media.
  • the computer-readable storage medium can be any combination of one or more computer-readable storage media.
  • a computer program is also provided, and the computer program can be stored in the cloud or on a local storage medium.
  • the computer program is run by a computer or a processor, it is used to execute the corresponding steps of the method for temperature measurement of the embodiments of the present application.
  • the method, device and storage medium for temperature measurement automatically identify the current measurement site based on the characteristic parameters in the temperature data obtained by measuring the current measurement site of the measured object, and according to the recognition result Performing temperature prediction effectively improves the accuracy of the temperature prediction results, and at the same time eliminates the selection and confirmation of the measurement site before measurement, which brings convenience to users.
  • the disclosed apparatus and method may be implemented in other manners.
  • the apparatus embodiments described above are only illustrative.
  • the division of the units is only a logical function division. In actual implementation, there may be other division methods.
  • multiple units or components may be combined or May be integrated into another device, or some features may be omitted, or not implemented.
  • Various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof.
  • a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all functions of some modules according to the embodiments of the present application.
  • DSP digital signal processor
  • the present application can also be implemented as a program of apparatus (eg, computer programs and computer program products) for performing part or all of the methods described herein.
  • Such a program implementing the present application may be stored on a computer-readable medium, or may be in the form of one or more signals. Such signals may be downloaded from Internet sites, or provided on carrier signals, or in any other form.

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Abstract

一种用于温度测量的方法、装置和存储介质，该方法包括：获取温度数据，所述温度数据包括温度测量设备对被测对象的当前测量部位进行测量时输出的一段时间的温度数据（S210）；针对所述温度数据中的至少部分数据提取特征参数，并基于所述特征参数识别所述当前测量部位，得到识别结果（S220）；基于所述识别结果，辅助实现温度预测，并输出预测结果（S230）。基于对被测对象的当前测量部位测量得到的温度数据中的特征参数自动识别当前测量部位，并根据识别结果进行温度预测，有效提高了温度预测结果的准确性，同时免除了测量前进行测量部位的选择和确认操作，为使用者带来了便利。

Description

用于温度测量的方法、装置和存储介质
说明书
技术领域
本申请涉及用于温度测量的技术领域，更具体地涉及一种用于温度测量的方法、装置和存储介质。
背景技术
在快速体温测量领域，利用探头预热后接触测量的一组早期温度变化值，通过预测算法推断最终的平衡温度是改善响应时间、提高测量速度的常用技术方法。但是，正常情况下不同测量部位(例如口腔、腋下或直肠)的温度范围并不相同，导致探头接触后的温升过程也存在差异，所以工作过程中，必须要求预测算法模型与实测部位相一致，才能得到准确的预测结果。
现有产品的解决方法为：在每次测量前先设定好测量部位(或选择默认测量部位)，后续测量过程和预测算法都基于该设定部位进行；下一次测量时，重复测量部位的选择和确认，从而保证每次测量结果的精度和准确性。然而，该处理方法存在以下缺点与不足：1)操作繁琐，使用者容易忽略测量部位的设置和确认，发生实测部位与所选模式不符的情况；2)无法在过程中自动识别测量部位，不能对所选预测模式进行转换修正；3)当发生测量部位错误时，在装置显示界面上不能及时给出提示和反馈。上述问题的存在，导致现有产品经常因测量部位与预测模型不一致而出现预测错误，显示结果与真实值出现明显偏差，无法准确反映病人的体温状况，造成误判和误诊。
发明内容
为了解决上述问题中的至少一个而提出了本申请。根据本申请一方面，提供了一种用于温度测量的方法，所述方法包括：获取温度数据，所述温度数据包括温度测量设备对被测对象的当前测量部位进行测量时输出的一段时间的温度数据；针对所述温度数据中的至少部分数据提取特征参数， 并基于所述特征参数识别所述当前测量部位，得到识别结果；基于所述识别结果，辅助实现温度预测，并输出预测结果。
本申请另一方面，提供了一种用于温度测量的装置，所述装置包括存储器、处理器和输出设备，所述存储器上存储有由所述处理器运行的计算机程序，所述计算机程序在被所述处理器运行时执行如下步骤：获取温度数据，所述温度数据包括温度测量设备对被测对象的当前测量部位进行测量时输出的一段时间的温度数据；针对所述温度数据中的至少部分数据提取特征参数，并基于所述特征参数识别所述当前测量部位，得到所述当前测量部位的识别结果；基于所述识别结果，辅助实现温度预测，并由所述输出设备输出预测结果。
本申请再一方面，提供了一种存储介质，所述存储介质上存储有计算机程序，所述计算机程序在运行时执行上述用于温度测量的方法。
根据本申请实施例的用于温度测量的方法、装置和存储介质基于对被测对象的当前测量部位测量得到的温度数据中的特征参数自动识别当前测量部位，并根据识别结果进行温度预测，有效提高了温度预测结果的准确性，同时免除了测量前进行测量部位的选择和确认操作，为使用者带来了便利。
附图说明
图1示出对同一被测对象不同测量部位测量温度时的温度变化曲线的示意图。
图2示出根据本申请实施例的用于温度测量的方法的示意性流程图。
图3示出根据本申请实施例的用于温度测量的方法的更具体过程的一个示例的示意性流程图。
图4示出根据本申请实施例的用于温度测量的方法的更具体过程的另一个示例的示意性流程图。
图5示出测量部位与预测模式一致时的正常预测结果以及测量部位与预测模式不一致时的预测结果这两者的对比曲线图，以及采用根据本申请实施例的用于温度测量的方法修正预测结果的示意图。
图6示出根据本申请实施例的用于温度测量的装置的示意性框图。
具体实施方式
为了使得本申请的目的、技术方案和优点更为明显，下面将参照附图详细描述根据本申请的示例实施例。显然，所描述的实施例仅仅是本申请的一部分实施例，而不是本申请的全部实施例，应理解，本申请不受这里描述的示例实施例的限制。基于本申请中描述的本申请实施例，本领域技术人员在没有付出创造性劳动的情况下所得到的所有其它实施例都应落入本申请的保护范围之内。
在下文的描述中，给出了大量具体的细节以便提供对本申请更为彻底的理解。然而，对于本领域技术人员而言显而易见的是，本申请可以无需一个或多个这些细节而得以实施。在其他的例子中，为了避免与本申请发生混淆，对于本领域公知的一些技术特征未进行描述。
应当理解的是，本申请能够以不同形式实施，而不应当解释为局限于这里提出的实施例。相反地，提供这些实施例将使公开彻底和完全，并且将本申请的范围完全地传递给本领域技术人员。
在此使用的术语的目的仅在于描述具体实施例并且不作为本申请的限制。在此使用时，单数形式的“一”、“一个”和“所述/该”也意图包括复数形式，除非上下文清楚指出另外的方式。还应明白术语“组成”和/或“包括”，当在该说明书中使用时，确定所述特征、整数、步骤、操作、元件和/或部件的存在，但不排除一个或更多其它的特征、整数、步骤、操作、元件、部件和/或组的存在或添加。在此使用时，术语“和/或”包括相关所列项目的任何及所有组合。
为了彻底理解本申请，将在下列的描述中提出详细的步骤以及详细的结构，以便阐释本申请提出的技术方案。本申请的较佳实施例详细描述如下，然而除了这些详细描述外，本申请还可以具有其他实施方式。
图1示出了对同一被测对象不同测量部位测量温度时的温度变化曲线的示意图。如图1所示，对于同一被测对象，对口腔进行测量温度时的温度变化曲线为110，对腋下进行测量温度时的温度变化曲线为120，很明显，曲线110和曲线120并不重合，两者有很明显的差异。因此，如果测量部位与预测模式不一致，例如测量部位为口腔但采用腋下预测模式预测温度结果，或者测量部位为腋下但采用口腔预测模式预测温度结果，都将得到不准确的甚至是错误的预测结果。如果每次测量都需要使用者设置和确认 测量部位，又操作繁琐，且容易出错。
基于此，本申请提供一种用于温度测量的方案，下面结合图2到图6来描述。图2示出了根据本申请实施例的用于温度测量的方法200的示意性流程图。如图2所示，用于温度测量的方法200可以包括如下步骤：
在步骤S210，获取温度数据，所述温度数据包括温度测量设备对被测对象的当前测量部位进行测量时输出的一段时间的温度数据。
在本申请的实施例中，在步骤S210所获取的温度数据可以是探头接触被测对象的当前测量部位(例如口腔、腋下或直肠)进行测量的一端时间的温度数据，该段时间内的温度数据可以用于后续将描述的步骤中测量部位的识别以及该测量部位的温度预测。
在本申请的实施例中，可以获取一段时间的温度数据，并从中筛选出达到预设条件的温度数据用于后续的测量部位识别和温度预测，或者，所获取的一段时间的温度数据可以均是达到该预设条件的温度数据。其中，达到预设条件的温度数据可以是指：到达预设基准时刻后的温度测量值和/或超过预设基准温度后的温度测量值。基于达到该预设条件的温度数据进行后续的测量部位识别和温度预测有利于剔除错误数据，提高测量部位识别和温度预测各自结果的准确性。
在步骤S220，针对所述温度数据中的至少部分数据提取特征参数，并基于所述特征参数识别所述当前测量部位，得到所述当前测量部位的识别结果。
在本申请的实施例中，在步骤S220中涉及的温度数据中的至少部分数据可以为前述的达到预设条件的温度数据，以提高后续处理结果的准确性。在本申请的实施例中，针对温度数据中的至少部分数据提取的特征参数可以是以下中的至少一项：所述温度数据的至少部分数据中不同时刻的温度测量值的差值；所述温度数据的至少部分数据中一段时间内的平均温度梯度值；所述温度数据的至少部分数据对应的曲线的积分面积；所述温度数据的至少部分数据对应的曲线的能量。基于所提取的特征参数，可以将其与各标准测量部位的参考特征参数进行比较，并基于比较结果从标准测量部位中得到当前测量部位的识别结果。
下面以提取的特征参数是不同时刻的温度测量值的差值为例来描述如何根据提取的特征参数对当前测量部位进行识别。由于不同测量部位的 温升速率(温度变化速率)不同，因此，对于不同测量部位的一段时间的(达到预设条件的)温度数据，具有一定时间间隔的预设两个时刻之间的温度测量值的差值也是不同的。基于此，在该示例中，可以针对当前测量部位的一段时间的温度数据提取两个时刻的温度测量值，计算这两个时刻的温度测量值的差值，并将该差值与各标准测量部位的参考值进行比较，以确定当前测量部位是标准测量部位中的哪一个。其中，该参考值可以是基于历史数据得到的两个时刻的温度差值，该两个时刻与前述提取的温度测量值对应的两个时刻是相同的时刻，或者该两个时刻与前述提取的温度测量值对应的两个时刻至少具有相同的时间间隔。
例如，将达到预设条件的第一个采样点作为起始点，结合采样频率，分别提取t＝t1(如0.5s)和t＝t2(如2s)两个不同时刻的实测温度值T1与T2，计算得到温度差值△T＝T2-T1，根据该温度差值与不同标准测量部位参考值(如历史数据中t1和t2这两个时刻的温度值的差值)相比较，从而确定当前实测部位。
在本申请的实施例中，前述的两个时刻可以是符合一定条件的两个时刻，例如将两个时刻分别称为第一时刻和第二时刻，第二时刻晚于第一时刻，则第一时刻可以是不早于预设时刻的时刻，第二时刻可以是与第一时刻之间具有预设时间间隔的时刻。也就是说，提取特征参数时选择的两个时刻中的前一时刻不能过早，这样可以避免采样到干扰数据；后一时刻需要间隔一定时长，避免两个时刻过近而无法准确反映温度数据的变化情况。
下面再以提取的特征参数是一段时间内的平均温度梯度值为例来描述如何根据提取的特征参数对当前测量部位进行识别。在该实施例中，可以针对一段时间内的温度数据进行该段时间内的平均温度梯度值以作为特征参数，并将其与各标准测量部位的参考值进行比较，以确定当前测量部位是标准测量部位中的哪一个。其中，该参考值可以是基于历史数据得到的一段时间的温度数据的平均温度梯度值，该段时间与前述提取特征参数所对应的一段时间可以至少具有相同的时间间隔。
例如，假定在一段时间内采样3个数据点进行计算，每隔20个数据点采样一次，则平均温度梯度值GradT的计算可以如下面的公式所示的：
Figure PCTCN2020134510-appb-000001
在上述公式中，fs为采样频率，T为对应采样点的温度测量值，i表示采样点的数目。在该公式中，是以每隔20个数据点采样一次，共采样3个数据点为例来计算的，应该理解，还可以根据需要选择其他合适的采样点数以及间隔数据点的个数。总体上，以一段时间内的平均温度梯度值作为特征参数用于测量部位的识别可以避免单一采样点偶然误差的影响，提高识别结果的准确性。
以上以不同时刻的温度测量值的差值和一段时间内的平均温度梯度值为例描述了如何根据提取的特征参数对当前测量部位进行识别，在其他实施例中，还可以基于其他特征参数，诸如一段时间的温度数据对应的曲线的积分面积、对应的曲线的能量等等，总体上，均是基于提取的特征参数与各标准测量部位的基于历史数据得到的预设参考值(参考特征参数)进行比较来进行测量部位的识别。当然，还可以结合多种特征参数进行测量部位的识别，例如综合前述的不同时刻的温度测量值的差值和一段时间内的平均温度梯度这两种特征参数来进行测量部位的识别，这更有利于提高识别结果的准确性。
下面继续参考图2，描述根据本申请实施例的用于温度测量的方法200的后续步骤。
在步骤S230，基于所述识别结果，辅助实现温度预测，并输出预测结果。
在本申请的实施例中，基于在步骤S220得到的对当前测量部位的识别，可以对被测对象的当前测量部位进行温度预测。在本申请的实施例中，步骤S220得到的识别结果可以包括识别出当前测量部位是哪个测量部位的识别结果，也可以包括未识别出当前测量部位是哪个测量部位的识别结果。对于未识别出当前测量部位是哪个测量部位的场景，可能是存在探头移动太大等干扰情况，因此，对于该场景，识别结果可以是提示信息，该提示信息可以提示无法识别出当前测量部位是哪个测量部位，所实现的辅助温度预测可以包括：确定当前测量过程存在干扰，进行抗干扰处理并输出结果。其中，抗干扰处理可以包括例如在前述的提示信息中提示用户重新调整探头重新测量等等。相应地，输出的结果可以是抗干扰处理后、获得真正的当前测量部位的识别结果后得到的温度预测结果。或者，抗干扰处理可以包括确定干扰程度，当干扰程度较大时，转到监护模式，所述监 护模式包括：采用温度测量设备直接对当前测量部位进行温度测量，得到温度测量结果，不再进行温度预测；当干扰程度较小时，提示用户减少或消除干扰因素后重新执行前述测量部位的识别和根据识别结果的温度预测。其中，可以根据步骤S220所提取的特征参数确定干扰程度的大小。
对于识别出当前测量部位是哪个测量部位的场景，在本申请的实施例中，所实现的辅助温度预测可以包括：基于所述识别结果对预设测量部位进行一致性判断，并根据判断结果进行温度预测，得到并输出所述当前测量部位的温度预测结果；或者基于所述识别结果对所述当前测量部位进行温度预测，得到并输出所述当前测量部位的温度预测结果。这两种情况分别针对预设了测量部位和未预设测量部位这两种场景。下面分别描述这两种场景。
在一个示例中，如果在测量前已预设过测量部位，则可以基于识别结果确定当前测量部位与预设测量部位是否一致：在当前测量部位与预设测量部位一致时，可以基于与预设测量部位对应的预设温度预测模型(即预测算法)对当前测量部位进行温度预测，得到并输出温度预测结果；在当前测量部位与预设测量部位不一致时，可以从与预设测量部位对应的预设温度预测模型切换到与当前测量部位对应的温度预测模型，并基于切换后的温度预测模型对当前测量部位进行温度预测，得到并输出温度预测结果。该温度预测结果是采用与当前测量部位相对应的预设温度预测模型计算得到的，因此得到的当前测量部位的温度预测结果是准确的。
在本申请的进一步的实施例中，在当前测量部位与预设测量部位不一致时，方法200还可以包括以下中的至少一项(未示出)：输出提示当前测量部位与预设测量部位不一致的提示信息；将用于下次测量的预设测量部位设置为当前测量部位；将用于下次测量的预设温度预测模型设置为与当前测量部位对应的温度预测模型。
在该实施例中，在确定当前测量部位与预设测量部位不一致时，除了切换到与当前测量部位相对应的温度预测模型进行温度预测以外，还可以输出提示信息，以提示用户预设测量部位与当前测量部位不一致，以由用户将显示界面上的预设测量部位修改为当前测量部位，避免虽然最终输出的是当前测量部位的正确温度预测结果，但显示界面上显示该结果是预设测量部位的预测结果。其中，提示方式例如可以是视觉提示方式和/或听觉 提示方式，例如如果预设测量部位是腋下，而实测部位为口腔，则可以在显示屏幕上闪烁出现“口腔”字样或图标，和/或通过提示音的方式提醒用户。
当然，在确定当前测量部位与预设测量部位不一致时，也可以自动将显示界面的预设测量部位修改为当前测量部位，减少用户操作，提高用户体验。此外，当前测量部位可能是被测对象偏好的温度测量部位，基于此，还可以自动或提示用户将用于下次测量的预设测量部位设置为当前测量部位，以避免下次可能出现设置部位与测量部位不一致的情况。类似地，还可以将用于下次测量的预设温度预测模型设置为与当前测量部位对应的温度预测模型。
在另一个示例中，如果在测量前未预设过测量部位，则可以基于步骤S220得到的识别结果确定待用于进行温度预测的温度预测模型，即与该识别结果对应的温度预测模型，并基于所确定的温度预测模型对当前测量部位进行温度预测。在该实施例中，可以自动根据当前测量部位的识别结果采用相应的温度预测模型输出当前测量部位的温度预测结果，无需在测量前对测量部位进行温度预测，输出温度预测结果，减少用户操作且能确保预测结果准确性。
下面结合图3和图4描述根据上述两个示例结合前述步骤的更具体过程。
图3示出根据本申请实施例的用于温度测量的方法的更具体过程300的一个示例的示意性流程图。如图3所示，过程300开始于步骤S310，在步骤S310，进入初始化测量准备阶段，在该阶段，可以设置预设测量部位和预设温度预测模型。在步骤S320，将快速体温计(诸如探头)放入测量部位采集温度数据。在步骤S330，根据采集的温度数据提取温升过程的特征参数。在步骤S340，根据提取的特征参数识别当前实际测量部位。在步骤S350，确定当前实际测量部位与预设测量部位是否一致，如果一致，则进行到步骤S360，如果不一致，则进行到步骤S370。在步骤S360，根据预设温度预测模型计算并输出温度预测结果。在步骤S370，将预设温度预测模型切换为与当前实际测量部位对应的温度预测模型。在步骤S380，根据切换后的温度预测模型计算并输出温度预测结果。
图4示出根据本申请实施例的用于温度测量的方法的更具体过程400 的另一个示例的示意性流程图。如图4所示，过程400开始于步骤S410，在步骤S410，将快速体温计(诸如探头)放入测量部位采集温度数据。在步骤S420，根据采集的温度数据提取温升过程的特征参数。在步骤S430，根据提取的特征参数识别当前实际测量部位。在步骤S440，根据识别结果确定对应的温度预测模型。在步骤S450，根据确定的温度预测模型计算并输出温度预测结果。
下面结合图5和表1通过一个示例描述根据本申请实施例的用于温度测量的方法取得的效果。
表1
测量状态 预测结果/℃
正常放入口腔测量 37.33
实测腋下，以口腔模型进行预测 36.84
自动识别后转换为腋下模式预测 37.38
如图5和表1所示，在一个示例中，将快速体温计放入被测者口腔内进行测量，此时对应预测算法为口腔模式，输出正常的预测结果为37.33℃，如曲线510所示的；接着，仍设定为口腔模式，但探头放入被测者腋下进行测量，此时输出预测温度为36.84℃，如曲线520所示的，偏差达0.49℃，表明测量部位不符时，预测结果误差很大，准确性较低。如图5所示的，口腔模式下的温升曲线510和腋下模式下的温升曲线520也是不同的，按照本申请的方法，根据温升过程的特征参数差异，在测量过程中自动识别出实际测量部位为腋下后，对预测算法进行切换，得出按腋下模式预测的结果值为37.38℃，该结果与正常口腔测量所得值非常接近。同时，在显示屏幕上闪烁出现“腋下”字样和图标，及时提示使用者注意切换测量部位，有效地提高预测结果的精准性。
基于上面的描述，根据本申请实施例的用于温度测量的方法基于对被测对象的当前测量部位测量得到的温度数据中的特征参数自动识别当前测量部位，并根据识别结果进行温度预测，有效提高了温度预测结果的准确性，同时免除了测量前进行测量部位的选择和确认操作，为使用者带来了便利。
以上示例性地示出了根据本申请实施例的用于温度测量的方法。下面 结合图6描述根据本申请另一方面提供的用于温度测量的装置。图6示出了根据本申请实施例的用于温度测量的装置600的示意性框图。如图6所示，用于温度测量的装置600可以包括存储器610、处理器620和输出设备630，存储器610上存储有由处理器620运行的计算机程序，所述计算机程序在被处理器620运行时执行根据本申请实施例的用于温度测量的方法。本领域技术人员可以结合前文的描述理解根据本申请实施例的用于温度测量的装置600中各部件的操作，为了简洁，此处仅描述其主要操作，不再描述细节。
在本申请的一个实施例中，所述计算机程序在被处理器620运行时执行如下步骤：获取温度数据，所述温度数据包括温度测量设备对被测对象的当前测量部位进行测量时输出的一段时间的温度数据；针对所述温度数据中的至少部分数据提取特征参数，并基于所述特征参数识别所述当前测量部位，得到识别结果；基于所述识别结果对所述当前测量部位进行温度预测，辅助实现温度预测，并由输出设备630输出预测结果。
在本申请的一个实施例中，所述识别结果包括所述当前测量部位的识别结果，处理器620进一步配置为：基于所述识别结果对所述当前测量部位进行温度预测，得到并由所述输出设备输出所述当前测量部位的温度预测结果；或者基于所述识别结果对预设测量部位进行一致性判断，并根据判断结果进行温度预测，得到并由所述输出设备输出所述当前测量部位的温度预测结果。
在本申请的一个实施例中，处理器620基于所述识别结果对所述当前测量部位进行温度预测，可以包括：基于所述识别结果确定待用于进行温度预测的温度预测模型；基于所确定的温度预测模型对所述当前测量部位进行温度预测。
在本申请的一个实施例中，处理器620基于所述识别结果对预设测量部位进行一致性判断，并根据判断结果进行温度预测，可以包括：基于所述识别结果确定当前测量部位与预设测量部位是否一致；当所述当前测量部位与所述预设测量部位一致时，基于与所述预设测量部位对应的预设温度预测模型对所述当前测量部位进行温度预测；当所述当前测量部位与所述预设测量部位不一致时，从与所述预设测量部位对应的预设温度预测模型切换到与所述当前测量部位对应的温度预测模型，并基于切换后的温度 预测模型对所述当前测量部位进行温度预测。
在本申请的一个实施例中，当所述当前测量部位与所述预设测量部位不一致时，处理器620还执行以下步骤中的至少一项：输出提示当前测量部位与预设测量部位不一致的提示信息；将用于下次测量的预设测量部位设置为所述当前测量部位；将用于下次测量的预设温度预测模型设置为与所述当前测量部位对应的温度预测模型。
在本申请的一个实施例中，所述提示信息还包括用于提示用户将所述预设测量部位修改为所述当前测量部位的信息。
在本申请的一个实施例中，所述识别结果包括提示信息，所述提示信息提示无法识别出当前测量部位是哪个测量部位，处理器620进一步配置为：确定当前测量过程存在干扰，进行抗干扰处理并由所述输出设备输出结果。
在本申请的一个实施例中，所述特征参数包括以下中的至少一项：所述温度数据的至少部分数据中不同时刻的温度测量值的差值；所述温度数据的至少部分数据中一段时间内的平均温度梯度值；所述温度数据的至少部分数据对应的曲线的积分面积；所述温度数据的至少部分数据对应的曲线的能量。
在本申请的一个实施例中，所述温度数据中的所述至少部分数据包括所述温度数据中达到预设条件的数据。
在本申请的一个实施例中，所述达到预设条件的数据包括：到达预设基准时刻后的温度测量值和/或超过预设基准温度后的温度测量值。
在本申请的一个实施例中，所述不同时刻包括第一时刻和第二时刻，所述第二时刻晚于所述第一时刻，所述第一时刻不早于预设时刻，所述第二时刻与所述第一时刻之间的时间间隔为预设时间间隔。
在本申请的一个实施例中，处理器620基于所述特征参数识别所述当前测量部位，可以包括：将所述特征参数与各标准测量部位的参考特征参数进行比较，并基于比较结果从所述标准测量部位中得到所述当前测量部位的识别结果。
在本申请的一个实施例中，所述温度测量设备可以为探头。
此外，根据本申请实施例，还提供了一种存储介质，在所述存储介质上存储了程序指令，在所述程序指令被计算机或处理器运行时用于执行本 申请实施例的用于温度测量的方法的相应步骤。所述存储介质例如可以包括智能电话的存储卡、平板电脑的存储部件、个人计算机的硬盘、只读存储器(ROM)、可擦除可编程只读存储器(EPROM)、便携式紧致盘只读存储器(CD-ROM)、USB存储器、或者上述存储介质的任意组合。所述计算机可读存储介质可以是一个或多个计算机可读存储介质的任意组合。
此外，根据本申请实施例，还提供了一种计算机程序，该计算机程序可以存储在云端或本地的存储介质上。在该计算机程序被计算机或处理器运行时用于执行本申请实施例的用于温度测量的方法的相应步骤。
基于上面的描述，根据本申请实施例的用于温度测量的方法、装置和存储介质基于对被测对象的当前测量部位测量得到的温度数据中的特征参数自动识别当前测量部位，并根据识别结果进行温度预测，有效提高了温度预测结果的准确性，同时免除了测量前进行测量部位的选择和确认操作，为使用者带来了便利。
尽管这里已经参考附图描述了示例实施例，应理解上述示例实施例仅仅是示例性的，并且不意图将本申请的范围限制于此。本领域普通技术人员可以在其中进行各种改变和修改，而不偏离本申请的范围和精神。所有这些改变和修改意在被包括在所附权利要求所要求的本申请的范围之内。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个装置，或一些特征可以忽略，或不执行。
在此处所提供的说明书中，说明了大量具体细节。然而，能够理解，本申请的实施例可以在没有这些具体细节的情况下实践。在一些实例中，并未详细示出公知的方法、结构和技术，以便不模糊对本说明书的理解。
类似地，应当理解，为了精简本申请并帮助理解各个发明方面中的一个或多个，在对本申请的示例性实施例的描述中，本申请的各个特征有时被一起分组到单个实施例、图、或者对其的描述中。然而，并不应将该本申请的方法解释成反映如下意图：即所要求保护的本申请要求比在每个权利要求中所明确记载的特征更多的特征。更确切地说，如相应的权利要求书所反映的那样，其发明点在于可以用少于某个公开的单个实施例的所有特征的特征来解决相应的技术问题。因此，遵循具体实施方式的权利要求书由此明确地并入该具体实施方式，其中每个权利要求本身都作为本申请的单独实施例。
本领域的技术人员可以理解，除了特征之间相互排斥之外，可以采用任何组合对本说明书(包括伴随的权利要求、摘要和附图)中公开的所有特征以及如此公开的任何方法或者装置的所有过程或单元进行组合。除非另外明确陈述，本说明书(包括伴随的权利要求、摘要和附图)中公开的每个特征可以由提供相同、等同或相似目的的替代特征来代替。
此外，本领域的技术人员能够理解，尽管在此所述的一些实施例包括其它实施例中所包括的某些特征而不是其它特征，但是不同实施例的特征的组合意味着处于本申请的范围之内并且形成不同的实施例。例如，在权利要求书中，所要求保护的实施例的任意之一都可以以任意的组合方式来使用。
本申请的各个部件实施例可以以硬件实现，或者以在一个或者多个处理器上运行的软件模块实现，或者以它们的组合实现。本领域的技术人员应当理解，可以在实践中使用微处理器或者数字信号处理器(DSP)来实现根据本申请实施例的一些模块的一些或者全部功能。本申请还可以实现为用于执行这里所描述的方法的一部分或者全部的装置程序(例如，计算机程序和计算机程序产品)。这样的实现本申请的程序可以存储在计算机可读介质上，或者可以具有一个或者多个信号的形式。这样的信号可以从因特网网站上下载得到，或者在载体信号上提供，或者以任何其他形式提供。
应该注意的是上述实施例对本申请进行说明而不是对本申请进行限制，并且本领域技术人员在不脱离所附权利要求的范围的情况下可设计出替换实施例。在权利要求中，不应将位于括号之间的任何参考符号构造成对权利要求的限制。本申请可以借助于包括有若干不同元件的硬件以及借 助于适当编程的计算机来实现。在列举了若干装置的单元权利要求中，这些装置中的若干个可以是通过同一个硬件项来具体体现。单词第一、第二、以及第三等的使用不表示任何顺序。可将这些单词解释为名称。
以上所述，仅为本申请的具体实施方式或对具体实施方式的说明，本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。本申请的保护范围应以权利要求的保护范围为准。

Claims (26)

  1. 一种用于温度测量的方法，其特征在于，所述方法包括：
    获取温度数据，所述温度数据包括温度测量设备对被测对象的当前测量部位进行测量时输出的一段时间的温度数据；
    针对所述温度数据中的至少部分数据提取特征参数，并基于所述特征参数识别所述当前测量部位，得到识别结果；
    基于所述识别结果，辅助实现温度预测，并输出预测结果。
  2. 根据权利要求1所述的方法，其特征在于，所述识别结果包括所述当前测量部位的识别结果，所述辅助实现温度预测包括：
    基于所述识别结果对所述当前测量部位进行温度预测，得到并输出所述当前测量部位的温度预测结果；或者
    基于所述识别结果对预设测量部位进行一致性判断，并根据判断结果进行温度预测，得到并输出所述当前测量部位的温度预测结果。
  3. 根据权利要求2所述的方法，其特征在于，所述基于所述识别结果对所述当前测量部位进行温度预测，包括：
    基于所述识别结果确定待用于进行温度预测的温度预测模型；
    基于所确定的温度预测模型对所述当前测量部位进行温度预测。
  4. 根据权利要求2所述的方法，其特征在于，所述基于所述识别结果对预设测量部位进行一致性判断，并根据判断结果进行温度预测，包括：
    基于所述识别结果确定当前测量部位与预设测量部位是否一致；
    当所述当前测量部位与所述预设测量部位一致时，基于与所述预设测量部位对应的预设温度预测模型对所述当前测量部位进行温度预测；
    当所述当前测量部位与所述预设测量部位不一致时，从与所述预设测量部位对应的预设温度预测模型切换到与所述当前测量部位对应的温度预测模型，并基于切换后的温度预测模型对所述当前测量部位进行温度预测。
  5. 根据权利要求4所述的方法，其特征在于，当所述当前测量部位与所述预设测量部位不一致时，所述方法还包括以下中的至少一项：
    输出提示当前测量部位与预设测量部位不一致的提示信息；
    将用于下次测量的预设测量部位设置为所述当前测量部位；
    将用于下次测量的预设温度预测模型设置为与所述当前测量部位对应的温度预测模型。
  6. 根据权利要求5所述的方法，其特征在于，所述提示信息还包括用于提示用户将所述预设测量部位修改为所述当前测量部位的信息。
  7. 根据权利要求1所述的方法，其特征在于，所述识别结果包括提示信息，所述提示信息提示无法识别出当前测量部位是哪个测量部位，所述辅助实现温度预测包括：
    确定当前测量过程存在干扰，进行抗干扰处理并输出结果。
  8. 根据权利要求1-7中的任一项所述的方法，其特征在于，所述特征参数包括以下中的至少一项：
    所述温度数据的至少部分数据中不同时刻的温度测量值的差值；
    所述温度数据的至少部分数据中一段时间内的平均温度梯度值；
    所述温度数据的至少部分数据对应的曲线的积分面积；
    所述温度数据的至少部分数据对应的曲线的能量。
  9. 根据权利要求1-8中的任一项所述的方法，其特征在于，所述温度数据中的所述至少部分数据包括所述温度数据中达到预设条件的数据。
  10. 根据权利要求9所述的方法，其特征在于，所述达到预设条件的数据包括：到达预设基准时刻后的温度测量值和/或超过预设基准温度后的温度测量值。
  11. 根据权利要求8-10中的任一项所述的方法，其特征在于，所述不同时刻包括第一时刻和第二时刻，所述第二时刻晚于所述第一时刻，所述第一时刻不早于预设时刻，所述第二时刻与所述第一时刻之间的时间间隔为预设时间间隔。
  12. 根据权利要求1-11中的任一项所述的方法，其特征在于，所述基于所述特征参数识别所述当前测量部位，包括：
    将所述特征参数与各标准测量部位的参考特征参数进行比较，并基于比较结果从所述标准测量部位中得到所述识别结果。
  13. 一种用于温度测量的装置，其特征在于，所述装置包括存储器、处理器和输出设备，所述存储器上存储有由所述处理器运行的计算机程序，所述计算机程序在被所述处理器运行时执行如下步骤：
    获取温度数据，所述温度数据包括温度测量设备对被测对象的当前测量部位进行测量时输出的一段时间的温度数据；
    针对所述温度数据中的至少部分数据提取特征参数，并基于所述特征 参数识别所述当前测量部位，得到识别结果；
    基于所述识别结果，辅助实现温度预测，并由所述输出设备输出预测结果。
  14. 根据权利要求13所述的装置，其特征在于，所述识别结果包括所述当前测量部位的识别结果，所述处理器进一步配置为：
    基于所述识别结果对所述当前测量部位进行温度预测，得到并由所述输出设备输出所述当前测量部位的温度预测结果；或者
    基于所述识别结果对预设测量部位进行一致性判断，并根据判断结果进行温度预测，得到并由所述输出设备输出所述当前测量部位的温度预测结果。
  15. 根据权利要求14所述的装置，其特征在于，所述处理器进一步配置为：
    基于所述识别结果确定待用于进行温度预测的温度预测模型；
    基于所确定的温度预测模型对所述当前测量部位进行温度预测。
  16. 根据权利要求14所述的装置，其特征在于，所述处理器进一步配置为：
    基于所述识别结果确定当前测量部位与预设测量部位是否一致；
    当所述当前测量部位与所述预设测量部位一致时，基于与所述预设测量部位对应的预设温度预测模型对所述当前测量部位进行温度预测；
    当所述当前测量部位与所述预设测量部位不一致时，从与所述预设测量部位对应的预设温度预测模型切换到与所述当前测量部位对应的温度预测模型，并基于切换后的温度预测模型对所述当前测量部位进行温度预测。
  17. 根据权利要求16所述的装置，其特征在于，当所述当前测量部位与所述预设测量部位不一致时，所述处理器还执行以下步骤中的至少一项：
    输出提示当前测量部位与预设测量部位不一致的提示信息；
    将用于下次测量的预设测量部位设置为所述当前测量部位；
    将用于下次测量的预设温度预测模型设置为与所述当前测量部位对应的温度预测模型。
  18. 根据权利要求17所述的装置，其特征在于，所述提示信息还包括用于提示用户将所述预设测量部位修改为所述当前测量部位的信息。
  19. 根据权利要求13所述的装置，其特征在于，所述识别结果包括提示信息，所述提示信息提示无法识别出当前测量部位是哪个测量部位，所述处理器进一步配置为：
    确定当前测量过程存在干扰，进行抗干扰处理并由所述输出设备输出结果。
  20. 根据权利要求13-19中的任一项所述的装置，其特征在于，所述特征参数包括以下中的至少一项：
    所述温度数据的至少部分数据中不同时刻的温度测量值的差值；
    所述温度数据的至少部分数据中一段时间内的平均温度梯度值；
    所述温度数据的至少部分数据对应的曲线的积分面积；
    所述温度数据的至少部分数据对应的曲线的能量。
  21. 根据权利要求13-20中的任一项所述的装置，其特征在于，所述温度数据中的所述至少部分数据包括所述温度数据中达到预设条件的数据。
  22. 根据权利要求21所述的装置，其特征在于，所述达到预设条件的数据包括：到达预设基准时刻后的温度测量值和/或超过预设基准温度后的温度测量值。
  23. 根据权利要求20-22中的任一项所述的装置，其特征在于，所述不同时刻包括第一时刻和第二时刻，所述第二时刻晚于所述第一时刻，所述第一时刻不早于预设时刻，所述第二时刻与所述第一时刻之间的时间间隔为预设时间间隔。
  24. 根据权利要求13-23中的任一项所述的装置，其特征在于，所述处理器基于所述特征参数识别所述当前测量部位，包括：
    将所述特征参数与各标准测量部位的参考特征参数进行比较，并基于比较结果从所述标准测量部位中得到所述当前测量部位的识别结果。
  25. 根据权利要求13-24中的任一项所述的装置，其特征在于，所述温度测量设备为探头。
  26. 一种存储介质，其特征在于，所述存储介质上存储有计算机程序，所述计算机程序在运行时执行如权利要求1-12中的任一项所述的用于温度测量的方法。
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