CN111595486A - Abnormal object detection method, system, machine readable medium and equipment - Google Patents

Abnormal object detection method, system, machine readable medium and equipment Download PDF

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Publication number
CN111595486A
CN111595486A CN202010410279.6A CN202010410279A CN111595486A CN 111595486 A CN111595486 A CN 111595486A CN 202010410279 A CN202010410279 A CN 202010410279A CN 111595486 A CN111595486 A CN 111595486A
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Prior art keywords
temperature
value
abnormal object
detection
abnormal
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周曦
姚志强
王忠林
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Chongqing Zhongke Yuncong Technology Co ltd
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Chongqing Zhongke Yuncong Technology Co ltd
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Priority to CN202010410279.6A priority Critical patent/CN111595486A/en
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    • 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
    • G01K13/223—Infrared clinical thermometers, e.g. tympanic
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
    • G01J5/0022—Radiation pyrometry, e.g. infrared or optical thermometry for sensing the radiation of moving bodies
    • G01J5/0025—Living bodies
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
    • G01J5/48—Thermography; Techniques using wholly visual means
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
    • G01K1/00—Details of thermometers not specially adapted for particular types of thermometer
    • G01K1/02—Means for indicating or recording specially adapted for thermometers
    • G01K1/024—Means for indicating or recording specially adapted for thermometers for remote indication
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
    • G01K3/00—Thermometers giving results other than momentary value of temperature
    • G01K3/08—Thermometers giving results other than momentary value of temperature giving differences of values; giving differentiated values
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
    • G01J2005/0077—Imaging

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  • General Physics & Mathematics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Geophysics And Detection Of Objects (AREA)

Abstract

An abnormal object detection method, system, machine readable medium and device. The invention provides an abnormal object detection method, which comprises the following steps: acquiring a temperature measurement value and temperature measurement time of a detection object; determining the temperature reference value based on a preset average temperature-time curve and the temperature measurement time; and judging whether the detection object is an abnormal object or not based on the difference value between the temperature reference value and the temperature measured value. The invention improves the precision of temperature detection by comparing the difference value of different temperature reference values and temperature measurement values with the set threshold value at different time.

Description

Abnormal object detection method, system, machine readable medium and equipment
Technical Field
The invention belongs to the field of abnormal detection, and particularly relates to an abnormal object detection method, an abnormal object detection system, a machine readable medium and equipment.
Background
Body temperature is used as an important index for reflecting vital signs, is an important basis for judging the vital signs and clinical diseases, and is divided into contact temperature measurement and non-contact temperature measurement, wherein the non-contact temperature measurement comprises infrared temperature measurement, but the existing infrared temperature measurement has the following defects:
1. the infrared temperature measuring equipment is greatly influenced by indoor/outdoor environment, the temperature measurement value is greatly influenced by the environment temperature, and the equipment precision is +/-0.5 ℃; 2. the influence of the environment where the detected object is located is large, for example, outdoor in an environment of minus 10 ℃, the detected face temperature value is low, and the real temperature of the face cannot be fed back objectively; 3. the blackbody constant temperature equipment has high cost, installation and deployment are limited by the environment in partial scenes, the economy is poor, and the blackbody cannot realize the temperature reference function for the thermal sensing equipment for PAD; 4. on the application level, under the condition of non-medical detection, the relative value of the human body temperature detected by temperature measurement is higher than that of the normal body temperature.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, it is an object of the present invention to provide an abnormal object detection method, system, machine-readable medium and device, which are used to solve the problems of the prior art.
To achieve the above and other related objects, the present invention provides an abnormal object detection method, including:
acquiring a temperature measurement value and temperature measurement time of a detection object;
determining the temperature reference value based on a preset average temperature-time curve and the temperature measurement time;
and judging whether the detection object is an abnormal object or not based on the difference value between the temperature reference value and the temperature measured value.
Alternatively, it is determined whether the detection object is an abnormal object based on an absolute value of a difference between the temperature reference value and the temperature measurement value.
Optionally, the preset average temperature-time curve is obtained by: and obtaining a preset average temperature-time curve of the normal body temperature along with the time change according to the human body temperature of the normal samples collected at different times.
Optionally, if the detection object is a normal object, updating the preset average temperature-time curve by using the detection object as a normal sample.
Optionally, when an absolute value of a difference between the temperature reference value and the temperature measurement value exceeds a set threshold, the detection object is an abnormal object.
Optionally, adjusting an absolute value of a difference value between the temperature reference value and the temperature measurement value according to environmental factors to obtain a temperature adjustment parameter; and judging whether the detection object is an abnormal object or not based on the temperature adjusting parameter.
Optionally, the temperature adjustment parameter is an absolute value of a difference between the temperature reference value and the temperature measurement value plus a temperature compensation parameter.
Optionally, the environmental factors include at least one of: ambient temperature, lighting conditions.
Optionally, when the temperature adjustment parameter exceeds a set threshold, the detection object is an abnormal object.
To achieve the above and other related objects, the present invention provides an abnormal object detection system, including:
the parameter acquisition module is used for acquiring the temperature measurement value and the temperature measurement time of the detection object;
the temperature reference value acquisition module is used for determining the temperature reference value based on a preset average temperature-time curve and the temperature measurement time;
and the abnormity judging module is used for judging whether the detection object is an abnormal object or not based on the difference value between the temperature reference value and the temperature measured value.
Optionally, it is determined whether the detection object is an abnormal object based on a difference between the temperature reference value and the temperature measurement value.
Optionally, the preset average temperature-time curve is obtained by: and obtaining a preset average temperature-time curve of the normal body temperature along with the time change according to the human body temperature of the normal samples collected at different times.
Optionally, the method further includes updating the preset average temperature-time curve by using the detection object as a normal sample when the detection object is a normal object.
Optionally, when an absolute value of a difference between the temperature reference value and the temperature measurement value exceeds a set threshold, the detection object is an abnormal object.
Optionally, the temperature control device further comprises a temperature adjusting module, configured to adjust an absolute value of a difference between the temperature reference value and the temperature measurement value according to an environmental factor, so as to obtain a temperature adjustment parameter; and judging whether the detection object is an abnormal object or not based on the temperature adjusting parameter.
Optionally, the temperature adjustment parameter is an absolute value of a difference between the temperature reference value and the temperature measurement value plus a temperature compensation parameter.
Optionally, the environmental factors include at least one of: ambient temperature, lighting conditions.
Optionally, when the temperature adjustment parameter exceeds a set threshold, the detection object is an abnormal object.
To achieve the above and other related objects, the present invention provides an apparatus comprising:
one or more processors; and
one or more machine-readable media having instructions stored thereon that, when executed by the one or more processors, cause the apparatus to perform one or more of the methods described previously.
To achieve the foregoing and other related objectives, the present invention provides one or more machine-readable media having instructions stored thereon, which when executed by one or more processors, cause an apparatus to perform one or more of the methods described above.
As described above, the abnormal object detection method, system, machine-readable medium and device provided by the present invention have the following beneficial effects:
the invention provides an abnormal object detection method, which comprises the following steps: acquiring a temperature measurement value and temperature measurement time of a detection object; determining the temperature reference value based on a preset average temperature-time curve and the temperature measurement time; and judging whether the detection object is an abnormal object or not based on the absolute value of the difference value between the temperature reference value and the temperature measured value. The invention improves the precision of temperature detection by comparing the difference value of different temperature reference values and temperature measurement values with the set threshold value at different time.
Drawings
FIG. 1 is a flowchart illustrating a method for detecting an abnormal object according to an embodiment of the present invention;
FIG. 2 is a temperature-time graph according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an abnormal object detection system according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a hardware structure of a terminal device according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a hardware structure of a terminal device according to another embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
As shown in fig. 1, an abnormal object detection method includes:
s11, acquiring a temperature measurement value and temperature measurement time of the detection object;
s12, determining the temperature reference value based on a preset average temperature-time curve and the temperature measurement time;
s13 determines whether the detection object is an abnormal object based on the difference between the temperature reference value and the temperature measurement value.
In one embodiment, it is determined whether the detection object is an abnormal object based on an absolute value of a difference between the temperature reference value and the temperature measurement value. Specifically, when the absolute value of the difference between the temperature reference value and the temperature measurement value exceeds a set threshold, it is determined that the detection object is an abnormal object. For example, if the set threshold is 0.5 ℃, and the obtained temperature measurement value of the detection object is 36.4 ℃, if the adopted temperature reference value is 37 ℃ and the difference value between the temperature reference value and the temperature measurement value is 0.6, which is greater than the set threshold, the detection object can be considered as an abnormal object, and an alarm prompt can be given when the abnormal object is detected. For another example, if the set threshold is 0.5 ℃, and the obtained temperature measurement value of the detection object is 37 ℃, and if the temperature reference value adopted at this time is 36.4 ℃, the difference between the temperature reference value and the temperature measurement value is-0.6 ℃, an absolute value should be adopted for comparison and judgment, and since the absolute value of-0.6 ℃ is 0.6 ℃, the detection object can be considered as an abnormal object when exceeding the set threshold. The temperature can be measured by a thermal imager.
Of course, in another embodiment, the detection object is an abnormal object when the temperature measurement value exceeds the temperature reference value. Wherein the temperature reference value may be 37.5 ℃. When the temperature measurement value of the detection object exceeds 37.6 ℃, the detection object can be defined as an abnormal object, and an alarm prompt can be given when the abnormal object is detected.
It is understood that there are differences in body temperature in the morning and evening of the day. The normal body temperature of a human body has a stable range, but is not constant. During the day, it appeared high in the day and low at night. Therefore, in an embodiment, the comparison may be performed by using different temperature reference values at different times, so as to determine whether the detection object is an abnormal object. Wherein the temperature reference is an average value, for example, in the range of 9: measuring the temperature of 20 normal objects at 00 hours, and taking the average value of the 20 temperatures as a reference value at the moment; as another example, in 9: the temperatures of 30 normal subjects were measured at 02 deg.f, and the average of these 30 temperatures was used as a reference value at this time. Therefore, the preset average temperature-time curve of the normal body temperature changing along with the time can be obtained according to the human body temperature of the normal samples collected at different times. The preset average temperature-time curve is shown in fig. 2. Further, the method of determining the temperature reference value comprises:
acquiring temperature measurement time for carrying out temperature detection on a detection object;
and determining the temperature reference value based on a preset average temperature-time curve and the temperature measurement time.
For example, when the temperature measurement time for detecting the temperature of the detection target is 12: 00, determining 12: 00, for example 36.5 c, in which case 36.5 c may be used as the temperature reference.
The temperature measurement value of the detection object may be defined as tnThe temperature reference value may be defined as F1The temperature measuring time can be defined as tau, the difference between the temperature reference value and the temperature measured value can be defined as delta T, the set threshold value can be defined as delta T, and the absolute value of the difference between the n +1 th detection object temperature reference value and the temperature measured value is | delta Tn+1|＝|F1(t1,t2,t3,...,tn,τ)-tn+1L. When | Δ tn+1|>At Δ T, the detection target is an abnormal target.
In an embodiment, if the detected object is a normal object, the detected object is used as a normal sample to update the preset average temperature-time curve. The specific updating method is that the detected temperature value of the normal object is added into a normal sample library, and then the average value of all the temperatures in the normal sample library is calculated. By means of the method, the calculation sample parameters of the average value are enlarged, errors can be reduced, and accuracy is improved.
It can be understood that when infrared temperature measurement is performed, environmental factors (such as ambient temperature, lighting conditions, etc.) can affect the accuracy of temperature measurement. For example, during a day, the temperature in the morning and at night is low, and the temperature in the noon is high, so that the absolute value of the difference between the temperature reference value and the temperature measured value needs to be adjusted to obtain a temperature adjustment parameter; and judging whether the detection object is an abnormal object or not based on the temperature adjusting parameter. And when the temperature adjusting parameter exceeds a set threshold value, the detection object is an abnormal object.
The following description will be made in detail by taking environmental factors as an example of the ambient temperature.
The temperature adjustment parameter is equal to the absolute value of the difference between the temperature reference value and the temperature measurement value plus a temperature compensation parameter, which may be defined as F2(T). The method for acquiring the temperature compensation parameters comprises the following steps: according to the temperature-temperature compensation curve F of the temperature compensation parameters obtained at different temperatures along with the temperature change2And obtaining temperature adjusting parameters corresponding to the specific temperature.
Wherein, the temperature compensation parameter changes with the temperature2Can be obtained by the following method: forming a temperature-temperature compensation curve F by using temperature compensation parameters corresponding to different ambient temperatures2. For example, a temperature compensation parameter is obtained at 36.0 ℃; obtaining a temperature compensation parameter at 36.2 ℃; obtaining a temperature compensation parameter at 36.5 ℃; obtaining a temperature compensation parameter at 37.0 deg.C, and forming a temperature-temperature compensation curve F by obtaining a large amount of temperatures and corresponding temperature compensation parameters2. And in the specific application, acquiring the real-time ambient temperature T, and then obtaining the temperature-temperature compensation curve F2Finding the corresponding temperature compensation parameter, the temperature adjustment parameter is | F1(t1,t2,t3,...,tn,τ)-tn+1|+F2(T); when | F1(t1,t2,t3,...,tn,τ)-tn+1|+F2(T) when the value exceeds a set threshold, the detection target is an abnormal target. Generally, the temperature changes over the course of the day, for example, in the morning the temperature is relatively low and in the afternoon the temperature is high, so that the temperature changes can also be reflected by the time.
The method for mapping the normal temperature of the human body to the high-temperature early warning value is characterized in that 37 ℃ can be used as the high-temperature early warning value, and the normal temperature is mapped and displayed between 36 ℃ and 37 ℃, and the mapping relation is as follows: sn+1＝F3(F1(t1,t2,t3,...,tn,τ),Δtn+1)，Sn+1To indicate the temperature, F3Is a mapping relationship.
As shown in fig. 3, the present invention provides an abnormal object detection system, including:
the parameter acquisition module 31 is used for acquiring a temperature measurement value and temperature measurement time of a detection object;
a temperature reference value obtaining module 32, configured to determine the temperature reference value based on a preset average temperature-time curve and the temperature measurement time;
and an abnormality determining module 33, configured to determine whether the detected object is an abnormal object based on a difference between the temperature reference value and the temperature measurement value.
In one embodiment, it is determined whether the detection object is an abnormal object based on an absolute value of a difference between the temperature reference value and the temperature measurement value. Specifically, when the absolute value of the difference between the temperature reference value and the temperature measurement value exceeds a set threshold, it is determined that the detection object is an abnormal object. For example, if the set threshold is 0.5 ℃, and the obtained temperature measurement value of the detection object is 36.4 ℃, if the adopted temperature reference value is 37 ℃ and the difference value between the temperature reference value and the temperature measurement value is 0.6, which is greater than the set threshold, the detection object can be considered as an abnormal object, and an alarm prompt can be given when the abnormal object is detected. For another example, if the set threshold is 0.5 ℃, and the obtained temperature measurement value of the detection object is 37 ℃, and if the temperature reference value adopted at this time is 36.4 ℃, the difference between the temperature reference value and the temperature measurement value is-0.6 ℃, an absolute value should be adopted for comparison and judgment, and since the absolute value of-0.6 ℃ is 0.6 ℃, the detection object can be considered as an abnormal object when exceeding the set threshold. The temperature can be measured by a thermal imager.
Of course, in another embodiment, the detection object is an abnormal object when the temperature measurement value exceeds the temperature reference value. Wherein the temperature reference value may be 37.5 ℃. When the temperature measurement value of the detection object exceeds 37.6 ℃, the detection object can be defined as an abnormal object, and an alarm prompt can be given when the abnormal object is detected.
It is understood that there are differences in body temperature in the morning and evening of the day. The normal body temperature of a human body has a stable range, but is not constant. During the day, it appeared high in the day and low at night. Therefore, in an embodiment, the comparison may be performed by using different temperature reference values at different times, so as to determine whether the detection object is an abnormal object. Wherein the temperature reference is an average value, for example, in the range of 9: measuring the temperature of 20 normal objects at 00 hours, and taking the average value of the 20 temperatures as a reference value at the moment; as another example, in 9: the temperatures of 30 normal subjects were measured at 02 deg.f, and the average of these 30 temperatures was used as a reference value at this time. Therefore, the preset average temperature-time curve of the normal body temperature changing along with the time can be obtained according to the human body temperature of the normal samples collected at different times. The preset average temperature-time curve is shown in fig. 2. Further, the method of determining the temperature reference value comprises:
acquiring temperature measurement time for carrying out temperature detection on a detection object;
and determining the temperature reference value based on a preset average temperature-time curve and the temperature measurement time.
For example, when the temperature measurement time for detecting the temperature of the detection target is 12: 00, determining 12: 00, for example 36.5 c, in which case 36.5 c may be used as the temperature reference.
The temperature measurement value of the detection object may be defined as tnThe temperature reference value may be defined as F1The temperature measuring time can be defined as tau, the difference between the temperature reference value and the temperature measured value can be defined as delta T, the set threshold value can be defined as delta T, and the absolute value of the difference between the n +1 th detection object temperature reference value and the temperature measured value is | delta Tn+1|＝|F1(t1,t2,t3,...,tn,τ)-tn+1L. When | Δ tn+1|>At Δ T, the detection target is an abnormal target.
In an embodiment, if the detected object is a normal object, the detected object is used as a normal sample to update the preset average temperature-time curve. The specific updating method is that the detected temperature value of the normal object is added into a normal sample library, and then the average value of all the temperatures in the normal sample library is calculated. By means of the method, the calculation sample parameters of the average value are enlarged, errors can be reduced, and accuracy is improved.
It can be understood that when infrared temperature measurement is performed, environmental factors (such as ambient temperature, lighting conditions, etc.) can affect the accuracy of temperature measurement. For example, during a day, the temperature in the morning and at night is low, and the temperature in the noon is high, so the detection system further comprises a temperature adjusting module, which is used for adjusting the absolute value of the difference value between the temperature reference value and the temperature measured value according to environmental factors to obtain a temperature adjusting parameter; and judging whether the detection object is an abnormal object or not based on the temperature adjusting parameter. And when the temperature adjusting parameter exceeds a set threshold value, the detection object is an abnormal object.
The following description will be made in detail by taking environmental factors as an example of the ambient temperature.
The temperature adjustment parameter is equal to the absolute value of the difference between the temperature reference value and the temperature measurement value plus a temperature compensation parameter, which may be defined as F2(T). The method for acquiring the temperature compensation parameters comprises the following steps: according to the temperature-temperature compensation curve F of the temperature compensation parameters obtained at different temperatures along with the temperature change2And obtaining temperature adjusting parameters corresponding to the specific temperature.
Wherein, the temperature compensation parameter changes with the temperature2Can be obtained by the following method: forming a temperature-temperature compensation curve F by using temperature compensation parameters corresponding to different temperatures2. For example, a temperature compensation parameter is obtained at 36.0 ℃; at 36.2 deg.CAcquiring a temperature compensation parameter; obtaining a temperature compensation parameter at 36.5 ℃; obtaining a temperature compensation parameter at 37.0 deg.C, and forming a temperature-temperature compensation curve F by obtaining a large amount of temperatures and corresponding temperature compensation parameters2. And in the specific application, acquiring the real-time ambient temperature T, and then obtaining the temperature-temperature compensation curve F2Finding the corresponding temperature compensation parameter, the temperature adjustment parameter is | F1(t1,t2,t3,...,tn,τ)-tn+1|+F2(T); when | F1(t1,t2,t3,...,tn,τ)-tn+1|+F2(T) when the value exceeds a set threshold, the detection target is an abnormal target. Generally, the temperature changes over the course of the day, for example, in the morning the temperature is relatively low and in the afternoon the temperature is high, so that the temperature changes can also be reflected by the time.
The method for mapping the normal temperature of the human body to the high-temperature early warning value is characterized in that 37 ℃ can be used as the high-temperature early warning value, and the normal temperature is mapped and displayed between 36 ℃ and 37 ℃, and the mapping relation is as follows: sn+1＝F3(F1(t1,t2,t3,...,tn,τ),Δtn+1)，Sn+1To indicate the temperature, F3Is a mapping relationship.
An embodiment of the present application further provides an apparatus, which may include: one or more processors; and one or more machine readable media having instructions stored thereon that, when executed by the one or more processors, cause the apparatus to perform the method of fig. 1. In practical applications, the device may be used as a terminal device, and may also be used as a server, where examples of the terminal device may include: the mobile terminal includes a smart phone, a tablet computer, an electronic book reader, an MP3 (Moving Picture Experts Group Audio Layer III) player, an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop, a vehicle-mounted computer, a desktop computer, a set-top box, an intelligent television, a wearable device, and the like.
The present application further provides a non-transitory readable storage medium, where one or more modules (programs) are stored in the storage medium, and when the one or more modules are applied to a device, the device may be caused to execute instructions (instructions) of steps included in the method in fig. 1 according to the present application.
Fig. 4 is a schematic diagram of a hardware structure of a terminal device according to an embodiment of the present application. As shown, the terminal device may include: an input device 1100, a first processor 1101, an output device 1102, a first memory 1103, and at least one communication bus 1104. The communication bus 1104 is used to implement communication connections between the elements. The first memory 1103 may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk memory, and the first memory 1103 may store various programs for performing various processing functions and implementing the method steps of the present embodiment.
Alternatively, the first processor 1101 may be, for example, a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and the first processor 1101 is coupled to the input device 1100 and the output device 1102 through a wired or wireless connection.
Optionally, the input device 1100 may include a variety of input devices, such as at least one of a user-oriented user interface, a device-oriented device interface, a software programmable interface, a camera, and a sensor. Optionally, the device interface facing the device may be a wired interface for data transmission between devices, or may be a hardware plug-in interface (e.g., a USB interface, a serial port, etc.) for data transmission between devices; optionally, the user-facing user interface may be, for example, a user-facing control key, a voice input device for receiving voice input, and a touch sensing device (e.g., a touch screen with a touch sensing function, a touch pad, etc.) for receiving user touch input; optionally, the programmable interface of the software may be, for example, an entry for a user to edit or modify a program, such as an input pin interface or an input interface of a chip; the output devices 1102 may include output devices such as a display, audio, and the like.
In this embodiment, the processor of the terminal device includes a module for executing functions of each module in each device, and specific functions and technical effects may refer to the foregoing embodiments, which are not described herein again.
Fig. 5 is a schematic hardware structure diagram of a terminal device according to an embodiment of the present application. Fig. 5 is a specific embodiment of the implementation process of fig. 4. As shown, the terminal device of the present embodiment may include a second processor 1201 and a second memory 1202.
The second processor 1201 executes the computer program code stored in the second memory 1202 to implement the method described in fig. 1 in the above embodiment.
The second memory 1202 is configured to store various types of data to support operations at the terminal device. Examples of such data include instructions for any application or method operating on the terminal device, such as messages, pictures, videos, and so forth. The second memory 1202 may include a Random Access Memory (RAM) and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
Optionally, a second processor 1201 is provided in the processing assembly 1200. The terminal device may further include: communication component 1203, power component 1204, multimedia component 1205, speech component 1206, input/output interfaces 1207, and/or sensor component 1208. The specific components included in the terminal device are set according to actual requirements, which is not limited in this embodiment.
The processing component 1200 generally controls the overall operation of the terminal device. The processing assembly 1200 may include one or more second processors 1201 to execute instructions to perform all or part of the steps of the data processing method described above. Further, the processing component 1200 can include one or more modules that facilitate interaction between the processing component 1200 and other components. For example, the processing component 1200 can include a multimedia module to facilitate interaction between the multimedia component 1205 and the processing component 1200.
The power supply component 1204 provides power to the various components of the terminal device. The power components 1204 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the terminal device.
The multimedia components 1205 include a display screen that provides an output interface between the terminal device and the user. In some embodiments, the display screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the display screen includes a touch panel, the display screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.
The voice component 1206 is configured to output and/or input voice signals. For example, the voice component 1206 includes a Microphone (MIC) configured to receive external voice signals when the terminal device is in an operational mode, such as a voice recognition mode. The received speech signal may further be stored in the second memory 1202 or transmitted via the communication component 1203. In some embodiments, the speech component 1206 further comprises a speaker for outputting speech signals.
The input/output interface 1207 provides an interface between the processing component 1200 and peripheral interface modules, which may be click wheels, buttons, etc. These buttons may include, but are not limited to: a volume button, a start button, and a lock button.
The sensor component 1208 includes one or more sensors for providing various aspects of status assessment for the terminal device. For example, the sensor component 1208 may detect an open/closed state of the terminal device, relative positioning of the components, presence or absence of user contact with the terminal device. The sensor assembly 1208 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact, including detecting the distance between the user and the terminal device. In some embodiments, the sensor assembly 1208 may also include a camera or the like.
The communication component 1203 is configured to facilitate communications between the terminal device and other devices in a wired or wireless manner. The terminal device may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In one embodiment, the terminal device may include a SIM card slot therein for inserting a SIM card therein, so that the terminal device may log onto a GPRS network to establish communication with the server via the internet.
As can be seen from the above, the communication component 1203, the voice component 1206, the input/output interface 1207 and the sensor component 1208 involved in the embodiment of fig. 5 can be implemented as the input device in the embodiment of fig. 4.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.

Claims (20)

1. An abnormal object detection method, comprising:
acquiring a temperature measurement value and temperature measurement time of a detection object;
determining the temperature reference value based on a preset average temperature-time curve and the temperature measurement time;
and judging whether the detection object is an abnormal object or not based on the difference value between the temperature reference value and the temperature measured value.
2. The abnormal object detection method according to claim 1, wherein it is determined whether the detection object is an abnormal object based on an absolute value of a difference between the temperature reference value and the temperature measurement value.
3. The abnormal object detection method according to claim 1, wherein the preset average temperature-time curve is obtained by: and obtaining a preset average temperature-time curve of the normal body temperature along with the time change according to the human body temperature of the normal samples collected at different times.
4. The abnormal object detection method according to claim 3, wherein if the detection object is a normal object, the detection object is used as a normal sample to update the preset average temperature-time curve.
5. The abnormal object detection method according to claim 2, wherein the detection object is an abnormal object when an absolute value of a difference between the temperature reference value and the temperature measurement value exceeds a set threshold.
6. The abnormal object detection method according to claim 2, wherein an absolute value of a difference between the temperature reference value and the temperature measurement value is adjusted according to an environmental factor to obtain a temperature adjustment parameter; and judging whether the detection object is an abnormal object or not based on the temperature adjusting parameter.
7. The abnormal object detection method according to claim 6, wherein the temperature adjustment parameter is an absolute value of a difference between the temperature reference value and the temperature measurement value plus a temperature compensation parameter.
8. The abnormal object detection method according to claim 6, wherein the environmental factor includes at least one of: ambient temperature, lighting conditions.
9. The abnormal object detection method according to claim 6, wherein the detection object is an abnormal object when the temperature adjustment parameter exceeds a set threshold.
10. An abnormal object detection system, comprising:
the parameter acquisition module is used for acquiring the temperature measurement value and the temperature measurement time of the detection object;
the temperature reference value acquisition module is used for determining the temperature reference value based on a preset average temperature-time curve and the temperature measurement time;
and the abnormity judging module is used for judging whether the detection object is an abnormal object or not based on the difference value between the temperature reference value and the temperature measured value.
11. The abnormal object detection system according to claim 10, wherein it is determined whether the detection object is an abnormal object based on an absolute value of a difference between the temperature reference value and the temperature measurement value.
12. The abnormal object detection system of claim 10, wherein the preset average temperature-time curve is obtained by: and obtaining a preset average temperature-time curve of the normal body temperature along with the time change according to the human body temperature of the normal samples collected at different times.
13. The abnormal object detection system according to claim 12, further comprising an updating module configured to update the preset average temperature-time curve with the detection object as a normal sample if the detection object is a normal object.
14. The abnormal object detection system according to claim 11, wherein the detection object is an abnormal object when an absolute value of a difference between the temperature reference value and the temperature measurement value exceeds a set threshold.
15. The abnormal object detection system of claim 11, further comprising a temperature adjustment module for adjusting an absolute value of a difference between the temperature reference value and the temperature measurement value according to an environmental factor to obtain a temperature adjustment parameter; and judging whether the detection object is an abnormal object or not based on the temperature adjusting parameter.
16. The abnormal object detection system of claim 15, wherein the temperature adjustment parameter is an absolute value of a difference between the temperature reference value and the temperature measurement value plus a temperature compensation parameter.
17. The abnormal object detection system of claim 15, wherein the environmental factors include at least one of: ambient temperature, lighting conditions.
18. The abnormal object detection system according to claim 15, wherein the detection object is an abnormal object when the temperature adjustment parameter exceeds a set threshold.
19. An apparatus, comprising:
one or more processors; and
one or more machine-readable media having instructions stored thereon that, when executed by the one or more processors, cause the apparatus to perform the method of one or more of claims 1-9.
20. One or more machine-readable media having instructions stored thereon, which when executed by one or more processors, cause an apparatus to perform one or more of the methods recited in claims 1-9.
CN202010410279.6A 2020-05-15 2020-05-15 Abnormal object detection method, system, machine readable medium and equipment Pending CN111595486A (en)

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Application publication date: 20200828