CN111486956A - Abnormal body temperature screening method and device, computer equipment and storage medium - Google Patents
Abnormal body temperature screening method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to an abnormal body temperature screening method, an abnormal body temperature screening device, computer equipment and a storage medium. The method comprises the following steps: the body temperature detection equipment acquires body temperature data of a tested person and target temperature measurement time; then, the body temperature detection device inquires a target normal body temperature range corresponding to the target temperature measurement time in a pre-stored corresponding relation between the temperature measurement time and the normal body temperature range, wherein the target normal body temperature range is determined according to body temperature fitting parameters corresponding to the target temperature measurement time in an actual temperature measurement environment, and the body temperature fitting parameters corresponding to the target temperature measurement time are obtained according to body temperature data of the sample object at the target temperature measurement time in the actual temperature measurement environment and a preset fitting algorithm; and if the body temperature data is out of the target normal body temperature range, judging that the body temperature data of the tested person is the abnormal body temperature. By adopting the method, the influence of the actual measurement environment on the body temperature detection equipment can be avoided, and the screening of the abnormal body temperature of the human body can be realized.
Description
Technical Field
The present application relates to the field of temperature detection technologies, and in particular, to a method and an apparatus for screening abnormal body temperature, a computer device, and a storage medium.
Background
With the development of medical health care, higher requirements are placed on body temperature detection, and particularly in the disease epidemic prevention and control period, rapid and non-contact body temperature detection needs to be carried out on a crowd in a public place, particularly under variable temperature measurement conditions, so that abnormal body temperature personnel are screened out, and the purpose of controlling epidemic spread is achieved. The traditional non-contact body temperature detection equipment comprises a body temperature gun, a thermal imaging temperature measuring instrument and the like.
However, the existing non-contact body temperature detecting device can cause the measured body temperature value to change due to the changes of temperature, humidity, wind direction, illumination, time interval, temperature measuring part selection and the like in the measuring environment. For example, when the non-contact temperature measuring device is used to measure the temperature of the subject in the evening, the temperature is increased by about 0.8 ℃ compared with the temperature measured at the lowest temperature of 2-5 am, and at this time, it is very inaccurate to determine whether the body temperature of the subject is normal according to the unique body temperature standard, so that an abnormal body temperature screening method suitable for different temperature measuring conditions is urgently needed.
Disclosure of Invention
In view of the above, it is necessary to provide an abnormal body temperature screening method, an abnormal body temperature screening device, a computer device and a storage medium.
In a first aspect, the present application provides a method for screening abnormal body temperature, the method comprising:
acquiring body temperature data and target temperature measurement time of a tested person;
inquiring a target normal body temperature range corresponding to the target temperature measurement time in a pre-stored corresponding relation between the temperature measurement time and the normal body temperature range, wherein the target normal body temperature range is determined according to body temperature fitting parameters corresponding to the target temperature measurement time in an actual temperature measurement environment, and the body temperature fitting parameters corresponding to the target temperature measurement time are obtained according to body temperature data of a sample object at the target temperature measurement time in the actual temperature measurement environment and a preset fitting algorithm;
and if the body temperature data is positioned outside the target normal body temperature range, judging that the body temperature data of the tested person is the abnormal body temperature.
As an optional implementation, the method further comprises:
and if the body temperature data is within the target normal body temperature range, judging that the body temperature data of the tested person is the normal body temperature.
As an optional implementation, the method further comprises:
acquiring body temperature data of a plurality of sample objects at each moment in the actual temperature measuring environment, wherein the sample objects are detected objects with normal body temperature characteristics;
fitting the body temperature data corresponding to each sample object at each moment in the actual temperature measurement environment according to a preset fitting algorithm to obtain a body temperature fitting curve corresponding to the moment, and determining body temperature fitting parameters corresponding to the moment according to the body temperature fitting curve corresponding to the moment;
and determining a normal body temperature range corresponding to the moment according to the body temperature fitting parameter corresponding to the moment in the actual temperature measuring environment and the preset offset.
As an optional implementation manner, the fitting parameters of the body temperature include a first fitting parameter and a second fitting parameter, and the fitting of the body temperature data corresponding to each sample object at each time in the actual temperature measurement environment according to a preset fitting algorithm to obtain a body temperature fitting curve corresponding to the time, and determining the body temperature fitting parameters corresponding to the time according to the body temperature fitting curve corresponding to the time includes:
carrying out probability statistics on the body temperature data corresponding to each sample object at each moment in the actual temperature measurement environment according to a preset temperature interval, and fitting the probability values corresponding to the counted temperature intervals into a body temperature fitting curve;
obtaining a first fitting parameter corresponding to the moment according to the body temperature data corresponding to each sample object at the moment, the number of each sample object at the moment and a preset fitting algorithm;
and obtaining a second fitting parameter corresponding to the moment according to the first fitting parameter corresponding to the moment, the number of the sample objects at the moment and the ratio of the number of the preset interval sample objects.
As an optional implementation manner, the determining the normal body temperature range corresponding to the time according to the body temperature fitting parameter corresponding to the time in the actual temperature measurement environment and the preset offset includes:
taking the product of the second fitting parameter corresponding to the moment in the actual temperature measuring environment and the preset offset as the temperature offset;
and taking the sum of the first fitting parameter corresponding to the moment and the temperature offset as the upper limit body temperature threshold of the normal body temperature range corresponding to the moment, and taking the difference between the first fitting parameter corresponding to the moment and the temperature offset as the lower limit body temperature threshold of the normal body temperature range corresponding to the moment.
As an optional implementation, the preset fitting algorithm is a normal distribution fitting algorithm.
In a second aspect, the present application provides an abnormal body temperature screening device, the device comprising:
the first acquisition module is used for acquiring body temperature data and target temperature measurement time of a tested person;
the query module is used for querying a target normal body temperature range corresponding to the target temperature measurement time in a pre-stored corresponding relation between the temperature measurement time and the normal body temperature range, the target normal body temperature range is determined according to body temperature fitting parameters corresponding to the target temperature measurement time in an actual temperature measurement environment, and the body temperature fitting parameters corresponding to the target temperature measurement time are obtained according to body temperature data of a sample object at the target temperature measurement time in the actual temperature measurement environment and a preset fitting algorithm;
and the judging module is used for judging that the body temperature data of the tested person is the abnormal body temperature if the body temperature data is positioned outside the target normal body temperature range.
As an optional implementation manner, the determination module is further configured to determine that the body temperature data of the subject is a normal body temperature if the body temperature data is within the target normal body temperature range.
As an optional implementation, the apparatus further comprises:
the first acquisition module is used for acquiring body temperature data of a plurality of sample objects at each moment in the actual temperature measurement environment, wherein the sample objects are detected objects with normal body temperature characteristics;
the first determining module is used for fitting the body temperature data corresponding to each sample object at each moment in the actual temperature measuring environment according to a preset fitting algorithm to obtain a body temperature fitting curve corresponding to the moment, and determining body temperature fitting parameters corresponding to the moment according to the body temperature fitting curve corresponding to the moment;
and the second determination module is used for determining a normal body temperature range corresponding to the moment according to the body temperature fitting parameter corresponding to the moment in the actual temperature measurement environment and the preset offset.
As an optional implementation manner, the first determining module is specifically configured to perform, for body temperature data corresponding to each sample object at each time in the actual temperature measurement environment, probability statistics on the body temperature data corresponding to each sample object at the time according to a preset temperature interval, and fit the probability values corresponding to the counted temperature intervals into a body temperature fitting curve;
obtaining a first fitting parameter corresponding to the moment according to the body temperature data corresponding to each sample object at the moment, the number of each sample object at the moment and a preset fitting algorithm;
and obtaining a second fitting parameter corresponding to the moment according to the first fitting parameter corresponding to the moment, the number of the sample objects at the moment and the ratio of the number of the preset interval sample objects.
As an optional implementation manner, the body temperature fitting parameter includes a first fitting parameter and a second fitting parameter, and the second determining module is specifically configured to take a product of the second fitting parameter corresponding to the time in the actual temperature measurement environment and the preset offset as a temperature offset;
and taking the sum of the first fitting parameter corresponding to the moment and the temperature offset as the upper limit body temperature threshold of the normal body temperature range corresponding to the moment, and taking the difference between the first fitting parameter corresponding to the moment and the temperature offset as the lower limit body temperature threshold of the normal body temperature range corresponding to the moment.
As an optional implementation, the preset fitting algorithm is a normal distribution fitting algorithm.
In a third aspect, the present application provides a computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring body temperature data and target temperature measurement time of a tested person;
inquiring a target normal body temperature range corresponding to the target temperature measurement time in a pre-stored corresponding relation between the temperature measurement time and the normal body temperature range, wherein the target normal body temperature range is determined according to body temperature fitting parameters corresponding to the target temperature measurement time in an actual temperature measurement environment, and the body temperature fitting parameters corresponding to the target temperature measurement time are obtained according to body temperature data of a sample object at the target temperature measurement time in the actual temperature measurement environment and a preset fitting algorithm;
and if the body temperature data is positioned outside the target normal body temperature range, judging that the body temperature data of the tested person is the abnormal body temperature.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring body temperature data and target temperature measurement time of a tested person;
inquiring a target normal body temperature range corresponding to the target temperature measurement time in a pre-stored corresponding relation between the temperature measurement time and the normal body temperature range, wherein the target normal body temperature range is determined according to body temperature fitting parameters corresponding to the target temperature measurement time in an actual temperature measurement environment, and the body temperature fitting parameters corresponding to the target temperature measurement time are obtained according to body temperature data of a sample object at the target temperature measurement time in the actual temperature measurement environment and a preset fitting algorithm;
and if the body temperature data is positioned outside the target normal body temperature range, judging that the body temperature data of the tested person is the abnormal body temperature.
The application provides an abnormal body temperature screening method, an abnormal body temperature screening device, computer equipment and a storage medium, wherein body temperature detection equipment acquires body temperature data of a tested person and target temperature measurement time; then, the body temperature detection device inquires a target normal body temperature range corresponding to the target temperature measurement time in a pre-stored corresponding relation between the temperature measurement time and the normal body temperature range, wherein the target normal body temperature range is determined according to body temperature fitting parameters corresponding to the target temperature measurement time in an actual temperature measurement environment, and the body temperature fitting parameters corresponding to the target temperature measurement time are obtained according to body temperature data of a sample object at the target temperature measurement time in the actual temperature measurement environment and a preset fitting algorithm; and if the body temperature data is positioned outside the target normal body temperature range, the body temperature detection equipment judges that the body temperature data of the tested person is the abnormal body temperature. The body temperature detection equipment adopting the method can not be influenced by the actual measurement environment, and the screening of the abnormal body temperature of the human body is realized.
Drawings
Fig. 1 is a schematic flowchart of an abnormal body temperature screening method according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a method for determining a target normal body temperature range according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an abnormal body temperature screening device according to an embodiment of the present application;
fig. 4 is an internal structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The embodiment of the application provides an abnormal body temperature screening method which can be directly applied to body temperature detection equipment, can also be applied to an abnormal body temperature screening system comprising the body temperature detection equipment and a server, and is realized through interaction of the body temperature detection equipment and the server. In this embodiment, the method is applied to a body temperature detection device for example, and the body temperature detection device obtains body temperature data of a subject and a target temperature measurement time. Then, the body temperature detection equipment inquires a target normal body temperature range corresponding to the target temperature measurement time in a pre-stored corresponding relation between the temperature measurement time and the normal body temperature range, wherein the target normal body temperature range is determined according to body temperature fitting parameters corresponding to the target temperature measurement time in an actual temperature measurement environment; the body temperature fitting parameters corresponding to the target temperature measuring time are obtained according to body temperature data of the sample object in the actual temperature measuring environment at the target temperature measuring time and a preset fitting algorithm. And finally, if the body temperature data is out of the target normal body temperature range, the body temperature detection equipment judges that the body temperature data of the tested person is the abnormal body temperature.
Specifically, in one embodiment, as shown in fig. 1, a method for screening abnormal body temperature is provided, which comprises the following steps:
step 101, obtaining body temperature data of a tested person and target temperature measurement time.
In implementation, the body temperature detection device acquires body temperature data of a tested person and corresponding target detection time. The body temperature detection device can be a temperature measurement gun or a thermal imaging temperature measurement instrument, and the embodiment of the application is not limited.
And 102, inquiring a target normal body temperature range corresponding to the target temperature measurement time in a pre-stored corresponding relation between the temperature measurement time and the normal body temperature range, wherein the target normal body temperature range is determined according to body temperature fitting parameters corresponding to the target temperature measurement time in an actual temperature measurement environment, and the body temperature fitting parameters corresponding to the target temperature measurement time are obtained according to body temperature data of the sample object at the target temperature measurement time in the actual temperature measurement environment and a preset fitting algorithm.
In implementation, the body temperature detection device queries a target normal body temperature range corresponding to a target temperature measurement time of a tested person in a pre-stored corresponding relationship between the temperature measurement time and the normal body temperature range, the target normal body temperature range is determined according to body temperature fitting parameters corresponding to the target temperature measurement time in an actual temperature measurement environment, and the body temperature fitting parameters corresponding to the target temperature measurement time are obtained according to body temperature data of each sample object at the target temperature measurement time in the actual temperature measurement environment and a preset fitting algorithm. Therefore, the method for determining the normal body temperature range of the target by the body temperature detection device according to the body temperature fitting parameters corresponding to the target temperature measurement time will be further described in detail later in this application.
And 103, if the body temperature data is out of the target normal body temperature range, judging that the body temperature data of the tested person is the abnormal body temperature.
In implementation, if the body temperature data measured by the tested person is out of the inquired target normal body temperature range, the body temperature detection device judges that the body temperature data of the tested person is the abnormal body temperature.
As an optional implementation manner, if the body temperature data is within the target normal body temperature range, the body temperature detection device determines that the body temperature data of the tested person is the normal body temperature.
Optionally, the body temperature detected by the non-contact body temperature detection device is the body surface temperature of the sample object, and is easily affected by temperature measurement conditions such as an external temperature measurement environment, and the like, so that the numerical value of the measured body temperature has a large deviation along with the difference of the temperature measurement conditions, for example, in early 3 months, beijing takes a company entrance (outdoor), a hall or a subway security inspection port as an actual temperature measurement environment, measures the temperature of the tested person, and the measured body temperature data is generally lower than 32 ℃, so that the body temperature is still higher than 37.3 ℃ as the human body abnormal body temperature screening standard (i.e. the daily average normal body temperature range is less than or equal to 37.3 ℃ under the indoor normal temperature standard condition), the meaning of screening the abnormal body temperature of the tested person is lost, and the purpose of controlling the disease propagation is. The body temperature of the human body is continuously changed in one day, the body temperature of the human body is the lowest at 2:00-5:00 in the morning and the body temperature of the human body is the highest at 13:00-18:00 in the morning, and the difference between the body temperature and the temperature is changed by 0.6-1 ℃ approximately. According to the statistics of clinical medicine on the human body temperature data of the large sample, the distribution rule that the human body temperature data basically meet the normal distribution probability density function can be obtained. Based on the situation, the embodiment of the application also provides the following method for determining the target normal body temperature range.
The embodiment of the application also provides a method for determining the target normal body temperature range, as shown in fig. 2, the method specifically comprises the following operation processes:
step 201, obtaining body temperature data of a plurality of sample objects at each moment in an actual temperature measurement environment, wherein the sample objects are detected objects with normal body temperature characteristics.
In implementation, the body temperature detection device acquires body temperature data of a plurality of sample objects in the actual measurement environment at each moment in the selected actual temperature measurement environment, wherein the selected actual temperature measurement environment refers to the actual body temperature detection environment of the same temperature measurement place (with specific temperature, humidity, wind power, wind direction and illumination), the same or similar body temperature detection device at the same time interval and the same temperature measurement process; the sample object is a detected object which is clinically detected and has normal body temperature characteristics. Specifically, one day (24 hours) is divided into different time periods, such as 1-4 hours, 4-6 hours, 6-8 hours, 8-10 hours, 10-13 hours, 13-17 hours, 17-21 hours and 21-24 hours, according to the self-change of the body temperature of a human body in one day. The body temperature detection device acquires body temperature data of each sample object (for example, 500 sample objects) in the actual measurement environment according to a preset time window d (for example, 5 minutes, the smaller d can ensure that the environmental conditions of the sample objects are more similar, but a sufficient number of samples need to be ensured). The set of body temperature data may be a set of initial sample data acquired by the body temperature detection device.
Preferably, because the actual temperature measuring environment (also referred to as the actual measurement environment) for measuring the body temperature is various, the body temperature data of each sample object at each time in the actual measurement environment is needed for each different actual measurement environment, and in consideration of the sampling cost, each time in the actual measurement environment may also be a time window (e.g. 5 minutes) including the time of the temperature measuring time of the tested person and before the time, if the temperature measuring time of the tested person in a specific actual temperature measuring environment is 9:00 am, the time period including 8:55-9:00 am may be correspondingly selected for the temperature measuring time in the actual temperature measuring environment, and the body temperature detecting device only needs to obtain the body temperature data corresponding to each sample object (part of sample objects, e.g. 100 sample objects closest to the target temperature measuring time of the tested person) in the time period (e.g. 8:55-9:00) in the actual measurement environment, the initial body temperature data in the body temperature detection device is updated in real time (the updating time interval is small and can be in the order of seconds). By adopting the sampling method, the sampling cost for pre-sampling a plurality of different actual measurement environments can be saved.
Step 202, fitting the body temperature data corresponding to each sample object at each moment in the actual temperature measurement environment according to a preset fitting algorithm to obtain a body temperature fitting curve corresponding to the moment, and determining body temperature fitting parameters corresponding to the moment according to the body temperature fitting curve corresponding to the moment.
In implementation, for body temperature data corresponding to each sample object at each moment in a selected actual temperature measurement environment, body temperature detection equipment fits the body temperature data according to a preset fitting algorithm to obtain a body temperature fitting curve corresponding to the moment, and then the body temperature detection equipment determines body temperature fitting parameters corresponding to the moment according to the body temperature fitting curve corresponding to the moment in the actual temperature measurement environment.
As an alternative embodiment, the body temperature fitting parameters include a first fitting parameter and a second fitting parameter, and the specific processing procedure of step 202 is as follows:
carrying out probability statistics on the body temperature data corresponding to each sample object at each moment in the actual temperature measurement environment according to a preset temperature interval, and fitting the probability values corresponding to the counted temperature intervals into a body temperature fitting curve; obtaining a first fitting parameter corresponding to the moment according to the body temperature data corresponding to each sample object at the moment, the number of each sample object at the moment and a preset fitting algorithm; and obtaining a second fitting parameter corresponding to the moment according to the first fitting parameter corresponding to the moment, the number of each sample object at the moment and the preset number ratio of the interval sample objects. Optionally, the preset fitting algorithm may be a normal distribution fitting algorithm.
Specifically, the body temperature detection device performs probability statistics on body temperature data corresponding to each sample object included at each time under the selected actual temperature measurement condition according to preset temperature intervals (such as [20.0 ℃,20.1 ℃), [20.1 ℃,20.2 ℃), [20.2 ℃,20.3 ℃), … … [36.0 ℃,36.1 ℃), [36.1 ℃,36.2 ℃), [36.2 ℃,36.3 ℃), … … [37.2 ℃,37.3 ℃), [37.3 ℃,37.4 ℃,37.5 ℃) and … …) on the body temperature data corresponding to each sample object at the time (such as the time period of 8:55 to 9:00), counts probability values of samples occupying the total samples in each temperature interval, and fits the counted probability values of each temperature interval into a body temperature curve. Then, the body temperature detection device obtains a first fitting parameter corresponding to the time according to body temperature data corresponding to each sample object (total sample) corresponding to the time (for example, a time period of 8:55 to 9:00) in the actual temperature measurement environment, the number of each sample object (total sample number), and a preset fitting algorithm (for example, a normal distribution fitting algorithm), and then, the body temperature detection device obtains a second fitting parameter corresponding to the time according to the first fitting parameter, the number of each sample object (total sample number), and a preset interval sample object number ratio. If the preset fitting algorithm is a normal distribution fitting algorithm, the first fitting parameter obtained by the body temperature detection equipment is the mean value mutThen, then
Wherein x is1.....xmAnd (3) representing the body temperature data corresponding to each sample object, wherein m is the number of the sample objects of each normal body temperature. Wherein, the ratio of the number of the sample objects in the preset interval is within the interval of normal distribution, and the average value mu is usedtCentered, shifted to the left and right by one standard deviation sigmatSample interval [ mu ] oft-σt,μt+σt]The ratio 68 of the number of samples in (a) to the total number of samples268949%, according to the mean value mutAnd the number m of each sample object is compared with the number of preset interval sample objects (68.268949%), and the obtained second fitting parameter is standard deviation sigmat。
Optionally, when the fitting curve obtained by performing fitting operation on the body temperature data corresponding to the sample object due to different measured body temperature parts (measured wrist temperature) and other reasons is a skewed distribution (that is, the fitted curve is asymmetric in left-right distribution), the body temperature detection device may determine body temperature fitting parameters (including a mode, a mean, and a standard deviation) of the body temperature data corresponding to the batch of sample objects according to the skewed distribution fitting curve. The process of obtaining the body temperature fitting parameters and the body temperature state judgment according to the skewed distribution fitting curve is similar to the normal distribution process, and details are not repeated in the embodiments of the application.
And 203, determining a normal body temperature range corresponding to the moment according to the body temperature fitting parameter corresponding to the moment in the actual temperature measuring environment and the preset offset.
In implementation, the body temperature detection device determines a normal body temperature range corresponding to the moment according to the body temperature fitting parameter corresponding to the moment in the selected actual temperature measurement environment and the preset offset. Specifically, under the fitting algorithm of normal distribution, the body temperature fitting parameter includes the first fitting parameter (mean μ)t) And a second fitting parameter (standard deviation σ)t)。
As an optional implementation manner, the body temperature fitting parameters in step 203 include a first fitting parameter and a second fitting parameter, and the specific processing procedure is as follows:
and taking the product of the second fitting parameter corresponding to the moment and the preset offset as the temperature offset.
And taking the sum of the first fitting parameter and the temperature offset corresponding to the moment as the upper limit body temperature threshold of the normal body temperature range corresponding to the moment, and taking the difference between the first fitting parameter and the temperature offset corresponding to the moment as the lower limit body temperature threshold of the normal body temperature range corresponding to the moment.
In practice, the body temperature detection device is in a selected actual measurement environmentCorresponding the second fitting parameter (standard deviation sigma) at the time (such as the time t)t) Multiplied by a predetermined offset n as a temperature offset (n σ)t) Then, the body temperature detection device applies the first fitting parameter (mean μ)t) Offset from temperature (n σ)t) The sum of (d) is used as the upper threshold value (mu) of the target normal body temperature range corresponding to the time in the selected actual measurement environmentt+nσt). Finally, the body temperature detection device will fit the first fitting parameter (mean μ)t) Offset from temperature (n σ)t) Is used as the lower threshold value (mu) of the target normal body temperature range corresponding to the moment in the selected actual measurement environmentt-nσt). Namely the range of the target normal body temperature corresponding to the target temperature measurement time of the tested person is [ mu ]t-nσt,μt+nσt]. The body temperature data measured by the tested person is tiWhen (t)i-μt)>nσtBody temperature data t of the subjectiIs a high temperature anomaly; when (t)i-μt)<nσtBody temperature data t of the subjectiA low temperature anomaly; when-n sigmat≤(ti-μt)≤nσtAnd then, the body temperature data of the tested person is normal body temperature data.
Statistically, P (. mu.)t-nσt,μt+nσt) The abnormal body temperature detection device comprises a body temperature detection device, a body temperature detection device and a control unit, wherein the body temperature detection device comprises a plurality of sampling points, p is more than or equal to 1-p, wherein p is the probability of the abnormal body temperature sample data (namely the small probability event occurrence probability), when an offset n is approximately equal to 3, p is 0.3%, which means that when the offset selected by the body temperature detection device is approximately 3, the abnormal body temperature occurrence probability is 0.3%. Similarly, when the offset n is approximately equal to 2.58, p is 1%; when the offset n ≈ 1.96, p ═ 5%. The corresponding relation between the offset n and the abnormal body temperature occurrence probability p can be obtained by normal distribution function integration. The value of the specific offset n can be adjusted according to the strictness degree of body temperature detection, and when the body temperature detection requirement is strict, the normal body temperature range with smaller offset is selected as the screening standard. Otherwise, selecting the normal body temperature range with larger offset as the screening standard.
The embodiment of the application provides an abnormal body temperature screening method, wherein body temperature detection equipment acquires body temperature data of a tested person and target temperature measurement time; then, the body temperature detection device inquires a target normal body temperature range corresponding to the target temperature measurement time in a pre-stored corresponding relation between the temperature measurement time and the normal body temperature range, wherein the target normal body temperature range is determined according to body temperature fitting parameters corresponding to the target temperature measurement time, and the body temperature fitting parameters corresponding to the target temperature measurement time are obtained according to body temperature data of the sample object at the target temperature measurement time and a preset fitting algorithm; and finally, if the body temperature data is out of the target normal body temperature range, the body temperature detection equipment judges that the body temperature data of the tested person is the abnormal body temperature. The body temperature detection equipment adopting the method can not be influenced by the actual measurement environment, and the screening of the abnormal body temperature of the human body is realized.
It should be understood that although the various steps in the flow charts of fig. 1-2 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1-2 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 3, the present application further provides an abnormal body temperature screening device, comprising:
the first obtaining module 310 is configured to obtain body temperature data of a subject and a target temperature measurement time.
The query module 320 is configured to query a target normal body temperature range corresponding to the target temperature measurement time in a pre-stored correspondence relationship between the temperature measurement time and the normal body temperature range, where the target normal body temperature range is determined according to body temperature fitting parameters corresponding to the target temperature measurement time in an actual temperature measurement environment, and the body temperature fitting parameters corresponding to the target temperature measurement time are obtained according to body temperature data of the sample object at the target temperature measurement time in the actual temperature measurement environment and a preset fitting algorithm.
The determination module 330 is configured to determine that the body temperature data of the subject is the abnormal body temperature if the body temperature data is outside the target normal body temperature range.
As an optional implementation manner, the determination module 330 is further configured to determine that the body temperature data of the subject is the normal body temperature if the body temperature data is within the target normal body temperature range.
As an optional implementation, the apparatus further comprises:
the first acquisition module is used for acquiring body temperature data of a plurality of sample objects at each moment in an actual temperature measurement environment, wherein the sample objects are detected objects with normal body temperature characteristics.
The first determining module is used for fitting the body temperature data corresponding to each sample object at each moment in the actual temperature measuring environment according to a preset fitting algorithm to obtain a body temperature fitting curve corresponding to the moment, and determining body temperature fitting parameters corresponding to the moment according to the body temperature fitting curve corresponding to the moment.
And the second determination module is used for determining the normal body temperature range corresponding to the moment according to the body temperature fitting parameter corresponding to the moment in the actual temperature measurement environment and the preset offset.
As an optional implementation manner, the first determining module is specifically configured to perform probability statistics on the body temperature data corresponding to each sample object at each time in the actual temperature measurement environment according to a preset temperature interval, and fit the probability values corresponding to the counted temperature intervals into a body temperature fitting curve.
And obtaining a first fitting parameter corresponding to the moment according to the body temperature data corresponding to each sample object at the moment, the number of each sample object at the moment and a preset fitting algorithm.
And obtaining a second fitting parameter corresponding to the moment according to the first fitting parameter corresponding to the moment, the number of each sample object at the moment and the preset number ratio of the interval sample objects.
As an optional implementation manner, the body temperature fitting parameter includes a first fitting parameter and a second fitting parameter, and the second determining module is specifically configured to use a product of the second fitting parameter corresponding to the time in the actual temperature measurement environment and a preset offset as the temperature offset.
And taking the sum of the first fitting parameter and the temperature offset corresponding to the moment as the upper limit body temperature threshold of the normal body temperature range corresponding to the moment, and taking the difference between the first fitting parameter and the temperature offset corresponding to the moment as the lower limit body temperature threshold of the normal body temperature range corresponding to the moment.
As an alternative embodiment, the preset fitting algorithm is a normal distribution fitting algorithm.
The embodiment of the application provides an abnormal body temperature screening device, wherein body temperature detection equipment acquires body temperature data of a tested person and target temperature measurement time; then, the body temperature detection device inquires a target normal body temperature range corresponding to the target temperature measurement time in a pre-stored corresponding relation between the temperature measurement time and the normal body temperature range, wherein the target normal body temperature range is determined according to body temperature fitting parameters corresponding to the target temperature measurement time, and the body temperature fitting parameters corresponding to the target temperature measurement time are obtained according to body temperature data of the sample object at the target temperature measurement time and a preset fitting algorithm; and finally, if the body temperature data is out of the target normal body temperature range, the body temperature detection equipment judges that the body temperature data of the tested person is the abnormal body temperature. By adopting the method, the influence of the actual measurement environment on the body temperature detection equipment can be avoided, and the screening of the abnormal body temperature of the human body can be realized.
For specific definition of the abnormal body temperature screening device, reference may be made to the above definition of the abnormal body temperature screening method, which is not described herein again. The modules in the abnormal body temperature screening device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile 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 an operating system and computer programs in the non-volatile storage medium. The database of the computer equipment is used for storing temperature measurement time and target normal body temperature range data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of abnormal body temperature screening.
Those skilled in the art will appreciate that the architecture shown in fig. 4 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring body temperature data and target temperature measurement time of a tested person;
inquiring a target normal body temperature range corresponding to the target temperature measurement time in a pre-stored corresponding relationship between the temperature measurement time and the normal body temperature range, wherein the target normal body temperature range is determined according to body temperature fitting parameters corresponding to the target temperature measurement time, and the body temperature fitting parameters corresponding to the target temperature measurement time are obtained according to body temperature data of a sample object at the target temperature measurement time and a preset fitting algorithm;
and if the body temperature data is out of the target normal body temperature range, judging that the body temperature data of the tested person is the abnormal body temperature.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring body temperature data and target temperature measurement time of a tested person;
inquiring a target normal body temperature range corresponding to the target temperature measurement time in a pre-stored corresponding relationship between the temperature measurement time and the normal body temperature range, wherein the target normal body temperature range is determined according to body temperature fitting parameters corresponding to the target temperature measurement time, and the body temperature fitting parameters corresponding to the target temperature measurement time are obtained according to body temperature data of a sample object at the target temperature measurement time and a preset fitting algorithm;
and if the body temperature data is out of the target normal body temperature range, judging that the body temperature data of the tested person is the abnormal body temperature.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (14)
1. A method of abnormal body temperature screening, the method comprising:
acquiring body temperature data and target temperature measurement time of a tested person;
inquiring a target normal body temperature range corresponding to the target temperature measurement time in a pre-stored corresponding relation between the temperature measurement time and the normal body temperature range, wherein the target normal body temperature range is determined according to body temperature fitting parameters corresponding to the target temperature measurement time in an actual temperature measurement environment, and the body temperature fitting parameters corresponding to the target temperature measurement time are obtained according to body temperature data of a sample object at the target temperature measurement time in the actual temperature measurement environment and a preset fitting algorithm;
and if the body temperature data is positioned outside the target normal body temperature range, judging that the body temperature data of the tested person is the abnormal body temperature.
2. The method of claim 1, further comprising:
and if the body temperature data is within the target normal body temperature range, judging that the body temperature data of the tested person is the normal body temperature.
3. The method of claim 1, further comprising:
acquiring body temperature data of a plurality of sample objects at each moment in the actual temperature measuring environment, wherein the sample objects are detected objects with normal body temperature characteristics;
fitting the body temperature data corresponding to each sample object at each moment in the actual temperature measurement environment according to a preset fitting algorithm to obtain a body temperature fitting curve corresponding to the moment, and determining body temperature fitting parameters corresponding to the moment according to the body temperature fitting curve corresponding to the moment;
and determining a normal body temperature range corresponding to the moment according to the body temperature fitting parameter corresponding to the moment in the actual temperature measuring environment and the preset offset.
4. The method according to claim 3, wherein the body temperature fitting parameters include a first fitting parameter and a second fitting parameter, and the fitting the body temperature data corresponding to each sample object at each time in the actual temperature measurement environment according to a preset fitting algorithm to obtain a body temperature fitting curve corresponding to the time, and determining the body temperature fitting parameters corresponding to the time according to the body temperature fitting curve corresponding to the time comprises:
carrying out probability statistics on the body temperature data corresponding to each sample object at each moment in the actual temperature measurement environment according to a preset temperature interval, and fitting the probability values corresponding to the counted temperature intervals into a body temperature fitting curve;
obtaining a first fitting parameter corresponding to the moment according to the body temperature data corresponding to each sample object at the moment, the number of each sample object at the moment and a preset fitting algorithm;
and obtaining a second fitting parameter corresponding to the moment according to the first fitting parameter corresponding to the moment, the number of the sample objects at the moment and the ratio of the number of the preset interval sample objects.
5. The method according to claim 3, wherein the body temperature fitting parameters include a first fitting parameter and a second fitting parameter, and the determining the normal body temperature range corresponding to the time according to the body temperature fitting parameter corresponding to the time in the actual temperature measurement environment and the preset offset includes:
taking the product of the second fitting parameter corresponding to the moment in the actual temperature measuring environment and the preset offset as the temperature offset;
and taking the sum of the first fitting parameter corresponding to the moment and the temperature offset as the upper limit body temperature threshold of the normal body temperature range corresponding to the moment, and taking the difference between the first fitting parameter corresponding to the moment and the temperature offset as the lower limit body temperature threshold of the normal body temperature range corresponding to the moment.
6. The method according to any one of claims 1 to 5, wherein the predetermined fitting algorithm is a normal distribution fitting algorithm.
7. An abnormal body temperature screening device, characterized in that the device comprises:
the first acquisition module is used for acquiring body temperature data and target temperature measurement time of a tested person;
the query module is used for querying a target normal body temperature range corresponding to the target temperature measurement time in a pre-stored corresponding relation between the temperature measurement time and the normal body temperature range, the target normal body temperature range is determined according to body temperature fitting parameters corresponding to the target temperature measurement time in an actual temperature measurement environment, and the body temperature fitting parameters corresponding to the target temperature measurement time are obtained according to body temperature data of a sample object at the target temperature measurement time in the actual temperature measurement environment and a preset fitting algorithm;
and the judging module is used for judging that the body temperature data of the tested person is the abnormal body temperature if the body temperature data is positioned outside the target normal body temperature range.
8. The device of claim 7, wherein the determination module is further configured to determine that the body temperature data of the subject is normal body temperature if the body temperature data is within the target normal body temperature range.
9. The apparatus of claim 7, further comprising:
the first acquisition module is used for acquiring body temperature data of a plurality of sample objects at each moment in the actual temperature measurement environment, wherein the sample objects are detected objects with normal body temperature characteristics;
the first determining module is used for fitting the body temperature data corresponding to each sample object at each moment in the actual temperature measuring environment according to a preset fitting algorithm to obtain a body temperature fitting curve corresponding to the moment, and determining body temperature fitting parameters corresponding to the moment according to the body temperature fitting curve corresponding to the moment;
and the second determination module is used for determining a normal body temperature range corresponding to the moment according to the body temperature fitting parameter corresponding to the moment in the actual temperature measurement environment and the preset offset.
10. The apparatus according to claim 9, wherein the first determining module is specifically configured to perform, for the body temperature data corresponding to each sample subject at each time in the actual temperature measurement environment, probability statistics on the body temperature data corresponding to each sample subject at the time according to a preset temperature interval, and fit probability values corresponding to the counted temperature intervals into a body temperature fitting curve;
obtaining a first fitting parameter corresponding to the moment according to the body temperature data corresponding to each sample object at the moment, the number of each sample object at the moment and a preset fitting algorithm;
and obtaining a second fitting parameter corresponding to the moment according to the first fitting parameter corresponding to the moment, the number of the sample objects at the moment and the ratio of the number of the preset interval sample objects.
11. The device according to claim 9, wherein the body temperature fitting parameters include a first fitting parameter and a second fitting parameter, and the second determining module is specifically configured to take a product of the second fitting parameter corresponding to the time in the actual temperature measurement environment and the preset offset as a temperature offset;
and taking the sum of the first fitting parameter corresponding to the moment and the temperature offset as the upper limit body temperature threshold of the normal body temperature range corresponding to the moment, and taking the difference between the first fitting parameter corresponding to the moment and the temperature offset as the lower limit body temperature threshold of the normal body temperature range corresponding to the moment.
12. The apparatus according to any one of claims 7 to 11, wherein the predetermined fitting algorithm is a normal distribution fitting algorithm.
13. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.
14. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
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