WO2021189560A1 - Procédé et appareil de correction pour la détection d'une température corporelle, dispositif informatique et support de stockage - Google Patents

Procédé et appareil de correction pour la détection d'une température corporelle, dispositif informatique et support de stockage Download PDF

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WO2021189560A1
WO2021189560A1 PCT/CN2020/084394 CN2020084394W WO2021189560A1 WO 2021189560 A1 WO2021189560 A1 WO 2021189560A1 CN 2020084394 W CN2020084394 W CN 2020084394W WO 2021189560 A1 WO2021189560 A1 WO 2021189560A1
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body temperature
parameter
temperature data
fitting
target
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PCT/CN2020/084394
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Chinese (zh)
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黄维学
赵奇
田晶
刘鹏
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黄维学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
    • G01K15/00—Testing or calibrating of thermometers
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
    • G01K13/00—Thermometers specially adapted for specific purposes
    • G01K13/20—Clinical contact thermometers for use with humans or animals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2503/00—Evaluating a particular growth phase or type of persons or animals
    • A61B2503/40—Animals

Definitions

  • This application relates to the technical field of temperature detection, and more specifically, to a correction method, device, computer equipment, and storage medium for body temperature detection.
  • the present application discloses a correction method, device, computer equipment, and storage medium for body temperature detection, so as to correct errors in body temperature measurement by body temperature detection equipment in different detection environments.
  • a correction method for body temperature detection comprising:
  • the body temperature data is corrected according to the target reference fitting parameter and the target actual measured fitting parameter, and the corrected body temperature data is obtained as the true reference body temperature data of the subject.
  • the target reference fitting parameter includes a first reference parameter
  • the target actual measurement fitting parameter includes a first actual measurement parameter
  • the target reference fitting parameter and the target actual measurement fitting parameter are used, Correct the body temperature data to obtain the corrected body temperature data, including:
  • the sum of the body temperature data and the correction compensation is used as the corrected body temperature data.
  • the target reference fitting parameters include a first reference parameter and a second reference parameter
  • the target measured fitting parameters include a first measured parameter and a second measured parameter
  • the fitting is performed according to the target reference Parameters and the target actual measured fitting parameters to correct the body temperature data to obtain the corrected body temperature data, including:
  • the method further includes:
  • second detection environment is an environment where there are factors influencing body temperature detection
  • the sample object is a detected object with normal body temperature characteristics
  • the reference simulation corresponding to the moment is obtained.
  • the combined parameters are obtained according to the second body temperature data corresponding to each sample object at the time and the preset fitting algorithm to obtain the actual measured fitting parameters corresponding to the time.
  • the reference fitting parameters include a first reference parameter and a second reference parameter
  • the actual measurement fitting parameters include a first actual measurement parameter and a second actual measurement parameter.
  • the corresponding measured fitting parameters include:
  • the first body temperature data corresponding to each sample object, the number of first sample objects, and the preset fitting algorithm at the time, the first reference parameter corresponding to the time is obtained;
  • the second measured parameter corresponding to the moment is obtained.
  • the preset fitting algorithm is a normal distribution fitting algorithm.
  • a correction device for body temperature detection comprising:
  • the first acquisition module is used to acquire the body temperature data of the subject and the target temperature measurement time
  • the query module is used for querying the target reference fitting parameter and target actual measurement fitting parameter corresponding to the target temperature measurement time in the correspondence relationship between the pre-stored temperature measurement time and the reference fitting parameter and the actual measurement fitting parameter;
  • the correction module is used to correct the body temperature data according to the target reference fitting parameters and the target actual measured fitting parameters to obtain corrected body temperature data, which is used as the true reference body temperature data of the subject.
  • the target reference fitting parameters include a first reference parameter
  • the target actual measurement fitting parameters include a first actual measurement parameter
  • the correction module is specifically configured to compare the first reference parameter with the first reference parameter. The difference of the measured parameters is used as correction compensation;
  • the sum of the body temperature data and the correction compensation is used as the corrected body temperature data.
  • the target reference fitting parameters include a first reference parameter and a second reference parameter
  • the target measured fitting parameters include a first measured parameter and a second measured parameter
  • the correction module is specifically configured to The ratio of the second reference parameter to the second measured parameter is used as the correction weight
  • the device further includes:
  • the second acquisition module is configured to acquire the first body temperature data of a plurality of sample objects at each time in the first detection environment and the second body temperature data of the plurality of sample objects at each time in the second detection environment.
  • the first detection environment is an environment without factors affecting body temperature detection
  • the second detection environment is an environment where factors affecting body temperature detection exist
  • the sample object is a detected object with normal body temperature characteristics
  • the processing module is used to obtain the first body temperature data and the second body temperature data corresponding to each sample object at each time according to the first body temperature data corresponding to each sample object at that time and a preset fitting algorithm.
  • the reference fitting parameter corresponding to the time is obtained according to the second body temperature data corresponding to each sample object at the time and the preset fitting algorithm to obtain the actually measured fitting parameter corresponding to the time.
  • the reference fitting parameters include a first reference parameter and a second reference parameter
  • the actual measurement fitting parameters include a first actual measurement parameter and a second actual measurement parameter
  • the processing module is specifically configured to download according to the moment.
  • the second measured parameter corresponding to the moment is obtained.
  • the preset fitting algorithm is a normal distribution fitting algorithm.
  • a computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when the processor executes the computer program:
  • the body temperature data is corrected according to the target reference fitting parameter and the target actual measured fitting parameter, and the corrected body temperature data is obtained as the true reference body temperature data of the subject.
  • a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:
  • the body temperature data is corrected according to the target reference fitting parameter and the target actual measured fitting parameter, and the corrected body temperature data is obtained as the true reference body temperature data of the subject.
  • this application discloses a correction method, device, computer equipment and storage medium for body temperature detection.
  • the body temperature detection device obtains the body temperature data of the subject and the target temperature measurement time; In the corresponding relationship between the stored temperature measurement time and the reference fitting parameter and the actual measurement fitting parameter, the target reference fitting parameter and the target actual measurement fitting parameter corresponding to the target temperature measurement time are queried; The reference fitting parameters and the target actual measurement fitting parameters are corrected, and the corrected body temperature data is obtained as the true reference body temperature data of the subject.
  • FIG. 1 is a schematic flowchart of a method for correcting body temperature detection disclosed in an embodiment of the application
  • FIG. 2 is a schematic structural diagram of a correction device for body temperature detection disclosed in an embodiment of the application.
  • FIG. 3 is a schematic flow chart of a method for determining fitting parameters disclosed in an embodiment of the application.
  • Fig. 4 is an internal structure diagram of a computer device disclosed in an embodiment of the application.
  • the embodiment of the application provides a method for correcting body temperature detection.
  • the method can be directly applied to body temperature detection equipment.
  • the method can also be applied to a body temperature detection and correction system including a body temperature detection device and a server.
  • the server's interaction is realized.
  • This embodiment uses the application of the method in a body temperature detection device as an example for description.
  • the body temperature detection device obtains the body temperature data of the subject and the target temperature measurement time; In the corresponding relationship of the combined parameters, query the target benchmark fitting parameters and the target measured fitting parameters corresponding to the target temperature measurement time; finally, the body temperature detection device corrects the body temperature data according to the target benchmark fitting parameters and the target measured fitting parameters.
  • the corrected body temperature data is obtained as the true reference body temperature data of the subject.
  • a method for correcting body temperature detection includes the following steps:
  • Step 101 Obtain body temperature data and target temperature measurement time of the subject.
  • the body temperature detection device may be an infrared thermometer or a thermal imaging thermometer or other temperature measurement devices.
  • the embodiment of the present application does not limit it.
  • the body temperature detection device may, but is not limited to, obtain the subject through a temperature sensor Body temperature data and the target temperature measurement time corresponding to the body temperature data.
  • Step 102 Query the target reference fitting parameter and the target actual measurement fitting parameter corresponding to the target temperature measurement time in the correspondence relationship between the pre-stored temperature measurement time and the reference fitting parameter and the actual measurement fitting parameter.
  • the body temperature detection device queries the target reference fitting corresponding to the target temperature measurement time in the pre-stored correspondence between each temperature measurement time and the reference fitting parameter and the actual measurement fitting parameter according to the acquired target temperature measurement time. Parameters and target measured fitting parameters.
  • the method for determining the target reference fitting parameters corresponding to each temperature measurement time and the target actual measurement fitting parameters stored in the body temperature detection device in advance is shown in the following steps 301-302.
  • Step 103 Correct the body temperature data according to the target benchmark fitting parameters and the target actual measured fitting parameters to obtain the corrected body temperature data, which serves as the testee's true benchmark body temperature data.
  • the body temperature detection device corrects the body temperature data of the subject according to the queried target benchmark fitting parameters and target actual measured fitting parameters, and obtains the corrected body temperature data as the subject under the test at this moment Real reference body temperature data in the environment. Since the types of target benchmark fitting parameters and target measured fitting parameters can be varied, based on different target benchmark fitting parameters and target measured fitting parameters, the body temperature detection device is based on the target benchmark fitting parameters and target measured fitting parameters. The process of correcting body temperature data is also different if the parameters are combined.
  • the embodiments of the present application provide two feasible implementation manners, which are specifically as follows.
  • the target reference fitting parameter includes the first reference parameter
  • the target measured fitting parameter includes the first measured parameter
  • the difference between the first reference parameter and the first measured parameter is used as the correction compensation.
  • the sum of the body temperature data and the correction compensation is used as the corrected body temperature data, that is, the true reference body temperature data of the subject.
  • the temperature detecting apparatus according to the difference between the first reference target reference parameter fitting parameters (mean ⁇ 0) and a first target Found fitting parameters (mean ⁇ t) parameter to obtain a corrected compensation ( ⁇ 0 - ⁇ t ), and use the correction compensation to correct the body temperature data of the testee.
  • the body temperature data of the testee at time t is t i
  • the corrected body temperature data is T i
  • T i t i +( ⁇ 0 - ⁇ t ).
  • the body temperature detection device adopts the correction algorithm of the first embodiment, which can reduce the calculation consumption of the body temperature detection device, and obtain the corrected true reference body temperature data more quickly.
  • the target reference fitting parameters include the first reference parameter and the second reference parameter
  • the target measured fitting parameters include the first measured parameter and the second measured parameter
  • the second reference parameter is compared with the second measured parameter.
  • the ratio is used as the correction weight; the difference between the body temperature data and the first measured parameter is calculated; the product of the difference and the correction weight is calculated, and the sum of the first reference parameter and the product is used as the corrected body temperature data.
  • the body temperature detection device uses the ratio of the second reference parameter (standard deviation ⁇ 0 ) in the target reference fitting parameter to the second measured parameter (standard deviation ⁇ t ) in the target measured fitting parameter as the correction weight Then calculates the difference between the temperature data of the test subject and the first measured parameter t i (mean ⁇ t), and calculates the weight difference between the product of the correction weight, the final product of the value and the first reference parameter, as the correction
  • the body temperature data T i is The body temperature detection device adopts the correction algorithm of the second embodiment to consider the proportion of the standard deviation and the mean deviation of the body temperature in the reference environment and the actual measurement environment, and the body temperature data can be corrected more accurately.
  • the embodiment of the present application also provides a method for determining fitting parameters, wherein the fitting parameters include reference fitting parameters and measured fitting parameters. As shown in Fig. 3, the specific processing process of the method includes the following steps.
  • Step 301 Acquire first body temperature data of multiple sample objects at each time in a first detection environment and second body temperature data of multiple sample objects at each time in a second detection environment, where the first detection environment is no-body temperature detection
  • the second detection environment is an environment where there are factors affecting body temperature detection
  • the sample object is a detected object with normal body temperature characteristics.
  • the body temperature detection device obtains in advance the first body temperature data of multiple sample objects at each time in the first detection environment and the second body temperature data of multiple sample objects at each time in the second detection environment.
  • the first detection environment is an environment without factors affecting body temperature detection, that is, the reference environment
  • the second detection environment is an environment with factors affecting body temperature detection, that is, the actual body temperature environment represented by general public places, and the sample object is a detected object with normal body temperature characteristics.
  • a number of subjects such as 500 whose body temperature has been confirmed to be normal through clinical testing are selected as the sample objects to be tested, and then the day (24 hours) is divided into different time periods according to the self-change of the body temperature data of the human body in a day , Such as 1-4 o'clock, 4-6 o'clock, 6-8 o'clock, 8-10 o'clock, 10-13 o'clock, 13-17 o'clock, 17-21 o'clock, 21-24 o'clock.
  • the body temperature detection equipment is for each divided time period, within the time period according to the preset time window d (for example, 5 minutes, the smaller d is, the more the environmental conditions of the sample object can be guaranteed to be the same, but sufficient samples need to be guaranteed Number) to obtain the body temperature data of the sample subject.
  • the body temperature detection device in the first detection environment obtains the body temperature data of each sample object.
  • the first detection environment is the reference environment, because the external environment's wind (wind speed and flow angle), temperature, humidity, light, etc. will cause disturbances.
  • the first test environment is selected as the clinically applicable reference environment, such as an environment with a temperature of 25-28°C, a humidity of 50-60%, no wind, and no obvious radiation as the reference environment .
  • the body temperature detection device measures the temperature of each sample object in the reference environment, and obtains the average body temperature data of the multiple temperature measurements of each sample object.
  • the body temperature detection device obtains the average body temperature data of multiple temperature measurements of each sample object from the same temperature measurement part (such as the forehead) of the same batch of sample objects.
  • the second detection environment is the actual measurement environment.
  • the company The actual temperature measurement environment such as halls, subway security checkpoints, factory and school gates, etc.
  • the body temperature detection device measures the temperature of each sample object in the actual measurement environment, and obtains the body temperature data of each sample object.
  • the body temperature detection equipment needs to obtain in advance the body temperature data corresponding to each sample object in a set of reference environments (first detection environment) to obtain reference fitting parameters. It is also necessary to obtain multiple sets of measured body temperature data of the same batch of sample subjects in multiple different measured environments to obtain corresponding multiple sets of measured fitting parameters. However, obtaining the body temperature data of the same batch of sample subjects in multiple actual measurement environments will increase the implementation cost of this method. For economic performance considerations, it is preferable to obtain the actual body temperature in a variety of different actual measurement environments (the second detection environment) Data can also be obtained in real time according to the actual measurement environment where the testee is in the actual measurement process, and fitting calculations are performed.
  • the temperature measurement time corresponding to the testee is calculated
  • the body temperature data corresponding to some sample objects in the actual measurement environment obtained in the previous time window (for example, 5 minutes) is used as the sample data in the actual measurement environment to perform the actual measurement body temperature data fitting. This method saves the acquisition of the same The cost of sampling the body temperature data of a batch of sample subjects in multiple actual measurement environments.
  • the human body temperature data is also affected by one's own age, gender, race, etc.
  • further restrictions can be made when selecting the sample object, such as dividing the sample object into a female sample object and a male sample object; or dividing the sample
  • the objects are divided into adult sample objects, elderly sample objects, and children sample objects; influencing factors such as gender, age, and race can also be combined to classify the sample objects.
  • the body temperature detection device measures the temperature of the divided sample objects in the first detection environment and the second detection environment respectively, and obtains body temperature data that considers more factors affecting body temperature.
  • the sample object is not limited to the population. Any warm-blooded animal can be selected as the sample object, and the warm-blooded animals of the same species are classified according to different body temperature influencing factors to obtain different types of sample objects, and obtain the body temperature of this type of sample object data.
  • Step 302 For the first body temperature data and the second body temperature data corresponding to each sample object at each moment, according to the first body temperature data corresponding to each sample object at the moment and the preset fitting algorithm, obtain the benchmark corresponding to the moment Fitting parameters, according to the second body temperature data corresponding to each sample object at the moment and the preset fitting algorithm, obtain the actual measured fitting parameters corresponding to the moment.
  • the body temperature detection device first obtains the reference fitting parameters corresponding to the first body temperature corresponding to each sample object at each moment and the preset fitting algorithm. Then, the body temperature detection device obtains the actually measured fitting parameters corresponding to the moment according to the second body temperature data corresponding to each sample object at the moment and the preset fitting algorithm.
  • the preset fitting algorithm is a normal distribution fitting algorithm. For the same body temperature normal population, the same temperature measurement site is measured, and the probability distribution fitting is performed according to the big data statistical principle. It can be obtained that the body temperature distribution law conforms to the normal distribution principle. Therefore, the normal distribution fitting algorithm is selected to compare the first The body temperature data and the second body temperature data perform a fitting operation.
  • the specific body temperature detection device obtains the benchmark fitting parameters corresponding to the moment according to the first body temperature data and the normal distribution fitting algorithm; the body temperature detection device obtains the actual measurement corresponding to the moment according to the second body temperature data and the normal distribution fitting algorithm Fitting parameters.
  • the reference fitting parameters include the first reference parameter and the second reference parameter
  • the measured fitting parameters include the first measured parameter and the second measured parameter
  • the first reference parameter corresponding to the time is obtained; according to the first reference parameter, the first sample object
  • the second reference parameter corresponding to the time is obtained according to the second body temperature data corresponding to each sample object at the time, the number of second sample objects and the preset fitting algorithm , Obtain the first measured parameter corresponding to the moment; according to the first measured parameter, the number of second sample objects and the proportion of the number of sample objects in the preset interval, obtain the second measured parameter corresponding to the moment.
  • the reference fitting parameters include the first reference parameter (mean value ⁇ 0 ) and the second reference parameter (standard deviation ⁇ 0 ), and the body temperature detection device according to the first body temperature data and first body temperature data corresponding to each sample object at the moment
  • the number of sample objects and the preset fitting algorithm are used to obtain the first reference parameter (mean value ⁇ 0 ) corresponding to the moment.
  • the second reference parameter (standard deviation ⁇ 0 ) of the object at the time is obtained.
  • the number of first sample objects n is 500, according to the first body temperature data x i corresponding to each sample object i, Obtain the first reference parameter (that is, the mean value ⁇ 0 ) corresponding to the moment.
  • x 1 ?? x n represents the first body temperature data corresponding to each sample object.
  • the sample interval is offset by one standard deviation ⁇ 0 to the left and right [ ⁇ 0 - ⁇ 0 , ⁇ 0 + ⁇
  • the body temperature detection device obtains the first actual measured parameter (mean value ⁇ t ) corresponding to the moment according to the second body temperature data corresponding to each sample object at the moment and the number of second sample objects;
  • the specific actual measurement fitting parameter solution process is similar to the benchmark fitting parameter, and will not be repeated in the embodiment of the present application.
  • different parts of the measured body temperature may cause the fitting operation of the body temperature data corresponding to the sample object, and the obtained fitting curve is a skew distribution (ie fitting When fitting the curve, the body temperature detection device can obtain the mode, median, mean and standard deviation of the batch of samples to establish the temperature corresponding to the skew distribution under the reference conditions and the actual measurement conditions. The relationship is to correct the measured temperature to the reference temperature.
  • the embodiment of the present application provides a correction method for body temperature detection.
  • the body temperature detection device is used to obtain the body temperature data and target temperature measurement time of the subject; then, the body temperature detection device stores the temperature measurement time and benchmark fitting parameters in advance In the correspondence relationship between the measured fitting parameters and the measured fitting parameters, query the target benchmark fitting parameters and the target measured fitting parameters corresponding to the target temperature measurement time; finally, the body temperature detection device compares the body temperature data according to the target benchmark fitting parameters and the target measured fitting parameters After correction, the corrected body temperature data is obtained as the true reference body temperature data of the subject.
  • the error of body temperature measurement by body temperature detection equipment in different detection environments can be corrected.
  • steps in the flowchart of FIG. 1 are displayed in sequence as indicated by the arrows, these steps are not necessarily performed in sequence in the order indicated by the arrows. Unless specifically stated in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least part of the steps in FIG. 1 may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times, and the order of execution of these steps or stages is also It is not necessarily performed sequentially, but may be performed alternately or alternately with other steps or at least a part of the steps or stages in other steps.
  • the present application also provides a correction device for body temperature detection, which includes:
  • the first acquiring module 210 is used to acquire the body temperature data of the subject and the target temperature measurement time.
  • the query module 220 is used for querying the target reference fitting parameter and the target actual measurement fitting parameter corresponding to the target temperature measurement time in the correspondence relationship between the prestored temperature measurement time and the reference fitting parameter and the actual measurement fitting parameter.
  • the correction module 230 is used to correct the body temperature data according to the target benchmark fitting parameters and the target actual measured fitting parameters to obtain the corrected body temperature data, which is used as the true benchmark body temperature data of the subject.
  • the target reference fitting parameters include the first reference parameter
  • the target measured fitting parameters include the first measured parameter.
  • the correction module 230 is specifically configured to calculate the difference between the first reference parameter and the first measured parameter. Value, as a correction compensation.
  • the sum of the body temperature data and the correction compensation is used as the corrected body temperature data.
  • the target reference fitting parameters include the first reference parameter and the second reference parameter
  • the target actual measurement fitting parameters include the first actual measurement parameter and the second actual measurement parameter
  • the correction module 230 is specifically configured to use the first The ratio of the second reference parameter to the second measured parameter is used as the correction weight.
  • the device further includes:
  • the second acquisition module is used to acquire the first body temperature data of multiple sample objects at each time in the first detection environment and the second body temperature data of multiple sample objects at each time in the second detection environment, the first detection environment It is an environment without factors affecting body temperature detection, the second detection environment is an environment with factors affecting body temperature detection, and the sample object is a detected object with normal body temperature characteristics.
  • the processing module is used to obtain the corresponding first body temperature data and second body temperature data of each sample subject at each moment according to the first body temperature data corresponding to each sample subject at that moment and a preset fitting algorithm According to the second body temperature data corresponding to each sample object at the moment and the preset fitting algorithm, the measured fitting parameters corresponding to the moment are obtained.
  • the reference fitting parameters include the first reference parameter and the second reference parameter
  • the measured fitting parameters include the first measured parameter and the second measured parameter
  • the processing module is specifically configured to perform the The first body temperature data corresponding to the sample object, the number of the first sample object, and the preset fitting algorithm, to obtain the first reference parameter corresponding to the moment;
  • the second reference parameter corresponding to the moment is obtained.
  • the first measured parameter corresponding to the time is obtained.
  • the second measured parameter corresponding to the moment is obtained.
  • the preset fitting algorithm is a normal distribution fitting algorithm.
  • the embodiment of the application provides a correction device for body temperature detection.
  • the body temperature detection device obtains the temperature data of the subject and the target temperature measurement time; then, the body temperature detection device stores the temperature measurement time and benchmark fitting parameters and actual measurement simulations in advance. In the corresponding relationship of the combined parameters, query the target benchmark fitting parameters and the target measured fitting parameters corresponding to the target temperature measurement time; finally, the body temperature detection device corrects the body temperature data according to the target benchmark fitting parameters and the target measured fitting parameters.
  • the corrected body temperature data is obtained as the true reference body temperature data of the subject.
  • each module in the above-mentioned correction device for body temperature detection can be implemented in whole or in part by software, hardware, and a combination thereof.
  • the above-mentioned modules may be embedded in the form of hardware or independent of the processor in the computer equipment, or may be stored in the memory of the computer equipment in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.
  • a computer device is provided.
  • the computer device may be a server, and its internal structure diagram may be as shown in FIG. 4.
  • the computer equipment includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide calculation and control capabilities.
  • the memory of the computer device includes a non-volatile storage medium and an internal memory.
  • the non-volatile storage medium stores an operating system, a computer program, and a database.
  • the internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium.
  • the database of the computer equipment is used to store body temperature data and temperature measurement time data.
  • the network interface of the computer device is used to communicate with an external terminal through a network connection.
  • the computer program is executed by the processor to realize a correction method of body temperature detection.
  • FIG. 4 is only a block diagram of part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied.
  • the specific computer device may Including more or fewer parts than shown in the figure, or combining some parts, or having a different arrangement of parts.
  • a computer device including a memory and a processor, a computer program is stored in the memory, and the processor implements the following steps when the processor executes the computer program:
  • the body temperature data is corrected, and the corrected body temperature data is obtained as the true benchmark body temperature data of the testee.
  • a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:
  • the body temperature data is corrected, and the corrected body temperature data is obtained as the true benchmark body temperature data of the testee.
  • Non-volatile memory may include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, or optical storage.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.

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Abstract

La présente invention concerne un procédé et un appareil pour la détection d'une température corporelle, un dispositif informatique et un support de stockage. Le procédé consiste à : acquérir des données de température corporelle d'un sujet et un temps de mesure de température cible (S101); dans une correspondance pré-stockée entre un temps de mesure de température et des paramètres d'ajustement de référence et des paramètres d'ajustement mesurés, interroger un paramètre d'ajustement de référence cible et un paramètre d'ajustement mesuré cible correspondant au temps de mesure de température cible (S102); et en fonction du paramètre d'ajustement de référence cible et du paramètre d'ajustement mesuré cible, corriger les données de température corporelle pour obtenir des données de température corporelle corrigées afin d'agir en tant que données de température corporelle de référence réelles du sujet (S103). Grâce au procédé de l'invention, des erreurs de mesure de température corporelle par un dispositif de détection de température corporelle dans différents environnements de détection peuvent être corrigées.
PCT/CN2020/084394 2020-03-23 2020-04-13 Procédé et appareil de correction pour la détection d'une température corporelle, dispositif informatique et support de stockage WO2021189560A1 (fr)

Applications Claiming Priority (2)

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