CN111397744B - Detection early warning system for continuously and remotely monitoring body temperature and correcting body temperature by adopting BP (Back propagation) neural network - Google Patents
Detection early warning system for continuously and remotely monitoring body temperature and correcting body temperature by adopting BP (Back propagation) neural network Download PDFInfo
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Abstract
The invention relates to a detection early warning system for continuously and remotely monitoring body temperature and correcting by adopting a BP (back propagation) neural network, when a person enters a classroom, the person scans a two-dimensional code on a label by using a mobile phone, registers personal information through a mobile phone APP (application), and simultaneously detects the body temperature of the person by using an infrared temperature measurement module at the door of the classroom and displays the temperature through a liquid crystal display screen; after the infrared temperature measurement module detects the temperature information of personnel, a temperature detection system of a classroom sends the personnel information and the temperature information to a background server through Bluetooth, and an alarm system is started when the temperature is abnormal; the temperature detection system of the classroom is only used for primary screening, for online temperature detection of each person, a temperature sensor is worn on the arm of each person, temperature information is transmitted, and the temperature information of each person is transmitted to the background server, so that the temperature of each person can be conveniently monitored and analyzed; in order to eliminate the system error in the temperature detection system, the BP neural network is used for correcting the temperature, and the influence of the system error on the temperature measurement is reduced.
Description
Technical Field
The invention relates to the field of temperature detection, in particular to a detection early warning system for continuously and remotely monitoring body temperature and correcting by adopting a BP neural network.
Background
Under the influence of the novel coronavirus, the temperature of a human body needs to be detected in a series of activities such as daily life, production, learning and the like so as to ensure the physical health and social stability of people.
The traditional body temperature detection method is mainly that a nursing staff uses a mercury glass thermometer to carry out manual measurement and recording. The measuring method has the defects of long measuring time (about 5 min), difficulty in continuously and automatically detecting the body temperature of a human body day and night, increased workload of nursing staff and the like.
With the development of science and technology, the use of mercury thermometers for detecting the temperature of a human body is eliminated, and digital electronic thermometers are used for detecting the body temperature. The electronic thermometer realizes the temperature sensing by utilizing a temperature sensor made of semiconductor materials, and an infrared temperature sensor used can not be in contact with a human body. The temperature measuring method has the advantages of rapidness, accuracy, convenience and the like. But still needs to be measured under the supervision of medical care personnel or adults, and the automatic detection and supervision of the body temperature are not realized.
Meanwhile, there will be a problem of systematic error in using the digital electronic thermometer. The BP neural network has good nonlinear mapping capability, self-adaptive capability and generalization capability, so that systematic errors of temperature detection can be eliminated by combining the BP neural network.
Disclosure of Invention
To solve the above existing problems. The invention provides a detection and early warning system for continuously and remotely monitoring body temperature and correcting by adopting a BP neural network, which solves the problem of body temperature monitoring. To achieve this object:
the invention provides a detection and early warning system for continuously and remotely monitoring body temperature and correcting by adopting a BP neural network, which comprises the following specific steps:
step 1: when a person enters a classroom, the person scans the two-dimensional code on the label by using a mobile phone, registers personal information through a mobile phone APP, and simultaneously an infrared temperature measurement module at the door of the classroom detects the body temperature of the person and displays the temperature through a liquid crystal display screen;
step 2: after the infrared temperature measurement module detects the temperature information of personnel, a temperature detection system of a classroom sends the personnel information and the temperature information to a background server through Bluetooth, and an alarm system is started when the temperature is abnormal;
and step 3: the temperature detection system of the classroom is only used for primary screening, for online temperature detection of each person, a temperature sensor is worn on the arm of each person, temperature information is transmitted, and the temperature information of each person is transmitted to the background server, so that the temperature of each person can be conveniently monitored and analyzed;
and 4, step 4: in order to eliminate the system error in the temperature detection system, the BP neural network is used for correcting the temperature, and the influence of the system error on the temperature measurement is reduced.
As a further improvement of the invention, in the step 1, the infrared temperature measurement module is as follows:
the infrared temperature measurement module includes: an infrared temperature sensor, an amplifier, an A/D converter, a singlechip and the like; the infrared temperature sensor detects the temperature of a human body by detecting an infrared signal of the human body, after the infrared signal is detected, the detected signal is amplified and filtered by the amplifier, then the signal is converted by the A/D converter, and finally the temperature of the human body is calculated by the singlechip.
As a further improvement of the present invention, the bluetooth device in step 2 is as follows:
install bluetooth detection equipment on the roof in classroom, infrared temperature measurement module passes through bluetooth equipment and sends temperature information, uses bluetooth communication protocol to acquire the information of sending, if the temperature after handling is greater than preset normal body temperature, then drives alarm system and reports to the police.
As a further improvement of the invention, the online temperature detection in the step 3 is as follows:
the online temperature detection has the functions of real-time body temperature monitoring, body temperature display and body temperature reporting, the online temperature monitoring system is bound in a mobile phone APP and is connected with the mobile phone through Bluetooth, and the mobile phone transmits information to a background server through a network, so that monitoring personnel can monitor, analyze and process the information.
As a further improvement of the present invention, the temperature correction in step 4 is as follows:
there will be systematic errors, environmental errors, etc. in the temperature detection process, and the systematic errors include: some errors can be eliminated through cold end compensation and an optimization circuit, and some errors existing in the system can not be eliminated, so that the error in the BP neural network calculation temperature detection system is introduced, and the system error is reduced;
before correcting temperature system error, the temperature detection system is used for monitoring the temperature of the constant temperature box to obtain a group of temperature detection results (x) i ,y i ),x i Is the temperature detected by the temperature detection system for the ith time, y i Is the true temperature of the ith testDegree:
x is to be i As input to the neural network, y i Training a neural network as an output of the neural network, b j J = (0, 1.. Multidot., l-1) is the output of the jth neuron of the hidden layer, l is the number of nodes of the hidden layer, v ij And w jk Respectively representing the weight from the ith neuron node of the input layer to the jth node of the hidden layer and the weight from the jth neuron node of the hidden layer to the kth node of the output layer, and obtaining:
wherein eta is a constant and is the learning rate of the BP neural network, and is usually 0< eta <1; the weight value adjusting formula of the hidden layer and the output layer after error back propagation can be calculated through the formula 1 and the formula 2:
the threshold adjustment formulas of the hidden layer and the output layer can be obtained in the same way:
the weight and the threshold value are calculated and updated through a formula and then enter a new round of forward propagation process, and a trained BP neural network is obtained after conditions are met;
after the temperature detection system detects the temperature, the temperature value is input into the neural network to obtain a corrected temperature value, so that the accuracy of the temperature detection system is improved, and the false alarm rate of the alarm system is reduced.
The invention discloses a detection and early warning system for continuously and remotely monitoring body temperature and correcting by adopting a BP neural network, which has the beneficial effects that:
1. the invention corrects the error of the temperature monitoring system by using the BP neural network, reduces the error of the temperature monitoring system, improves the detection precision and reduces the false alarm rate of an alarm system;
2. the invention has simple structure, better stability and simple maintenance;
3. the invention provides an important technical means for continuous remote monitoring and management of body temperature.
Drawings
FIG. 1 is a block diagram of an infrared thermometry module design;
FIG. 2 is a block diagram of a temperature measurement system;
fig. 3 is a diagram of a BP neural network topology.
Detailed Description
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
the invention provides a detection and early warning system for continuously and remotely monitoring body temperature and correcting by adopting a BP neural network, which comprises the following specific steps:
step 1: when a person enters a classroom, the person scans the two-dimensional code on the label by using a mobile phone, personal information is registered through a mobile phone APP, and meanwhile, the body temperature of the person is detected by an infrared temperature measuring module at the door of the classroom, and the temperature is displayed through a liquid crystal display screen;
the infrared temperature measuring module in the step 1 is specifically described as follows:
the design block diagram of the infrared temperature measurement module is shown in fig. 1, and the infrared temperature measurement module comprises: an infrared temperature sensor, an amplifier, an A/D converter, a singlechip and the like; the infrared temperature sensor detects the temperature of a human body by detecting an infrared signal of the human body, after the infrared signal is detected, the detected signal is amplified and filtered by the amplifier, then the signal is converted by the A/D converter, and finally the temperature of the human body is calculated by the singlechip.
Step 2: after the infrared temperature measurement module detects the temperature information of personnel, a temperature detection system of a classroom sends the personnel information and the temperature information to a background server through Bluetooth, and an alarm system is started when the temperature is abnormal;
the bluetooth equipment in step 2 is specifically described as follows:
the temperature measuring system is characterized in that a Bluetooth detection device is installed on the roof of a classroom, an infrared temperature measuring module sends temperature information through the Bluetooth device, the sent information is obtained through a Bluetooth communication protocol, and if the processed temperature is larger than a preset normal body temperature, an alarm system is driven to give an alarm.
And step 3: the temperature detection system of the classroom is only used for primary screening, for online temperature detection of each person, a temperature sensor is worn on the arm of each person, temperature information is transmitted, and the temperature information of each person is transmitted to the background server, so that the temperature of each person can be conveniently monitored and analyzed;
the on-line temperature detection in step 3 is specifically described as follows:
the online temperature detection has the functions of real-time body temperature monitoring, body temperature display and body temperature reporting, the online temperature monitoring system is bound in a mobile phone APP and is connected with the mobile phone through Bluetooth, and the mobile phone transmits information to a background server through a network, so that monitoring personnel can monitor, analyze and process the information.
And 4, step 4: in order to eliminate system errors in the temperature detection system, the BP neural network is used for correcting the temperature, so that the influence of the system errors on temperature measurement is reduced;
the temperature correction in step 4 is specifically described as follows:
there will be systematic errors, environmental errors, etc. in the process of temperature detection, and the systematic errors include: some errors can be eliminated through cold end compensation and an optimization circuit, and some errors existing in the system can not be eliminated, so that the error in the BP neural network calculation temperature detection system is introduced, and the system error is reduced;
before correcting temperature system error, the temperature detection system is used for monitoring the temperature of the constant temperature box to obtain a group of temperature detection results (x) i ,y i ),x i Is the temperature detected by the temperature detection system for the ith time, y i Is the true temperature of the ith test:
x is to be i As input to the neural network, y i Training a neural network as an output of the neural network, b j J = (0, 1.,. 1) is the output of the jth neuron of the hidden layer, l is the number of nodes of the hidden layer, v-1 ij And w jk Respectively representing the weight from the ith neuron node of the input layer to the jth node of the hidden layer and the weight from the jth neuron node of the hidden layer to the kth node of the output layer, and obtaining:
wherein eta is a constant and is the learning rate of the BP neural network, and is usually 0< eta <1; the weight value adjusting formula of the hidden layer and the output layer after error back propagation can be calculated through the formula 1 and the formula 2:
the threshold adjustment formulas of the hidden layer and the output layer can be obtained in the same way:
the weight and the threshold value are calculated and updated through a formula, then a new round of forward propagation process is carried out, a trained BP neural network is obtained after conditions are met, and a topological structure diagram of the BP neural network is shown in figure 3;
after the temperature detection system detects the temperature, the temperature value is input into the neural network to obtain a corrected temperature value, so that the accuracy of the temperature detection system is improved, and the false alarm rate of the alarm system is reduced.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and any modifications or equivalent variations made in accordance with the technical spirit of the present invention may fall within the scope of the present invention as claimed.
Claims (1)
1. The detection and early warning system for continuously and remotely monitoring body temperature and correcting by adopting the BP neural network comprises the following specific steps,
step 1: when a person enters a classroom, the person scans the two-dimensional code on the label by using a mobile phone, registers personal information through a mobile phone APP, and simultaneously detects the body temperature of the person by using an infrared temperature measurement module at the door of the classroom and displays the temperature through a liquid crystal display screen;
the infrared temperature measurement module in the step 1 is as follows:
the infrared temperature measurement module includes: the infrared temperature sensor, the amplifier, the A/D converter and the singlechip are arranged on the single chip; the infrared temperature sensor detects the temperature of a human body by detecting an infrared signal of the human body, after the infrared signal is detected, the detected signal is amplified and filtered by the amplifier, then the signal is converted by the A/D converter, and finally the temperature of the human body is calculated by the singlechip;
step 2: after the infrared temperature measurement module detects the temperature information of personnel, a temperature detection system of a classroom sends the personnel information and the temperature information to a background server through Bluetooth, and an alarm system is started when the temperature is abnormal;
the bluetooth equipment in step 2 is as follows:
installing Bluetooth detection equipment on the roof of a classroom, sending temperature information by an infrared temperature measurement module through the Bluetooth equipment, acquiring the sent information by using a Bluetooth communication protocol, and driving an alarm system to give an alarm if the processed temperature is higher than a preset normal body temperature;
and step 3: the temperature detection system of the classroom is only used for primary screening, for online temperature detection of each person, a temperature sensor is worn on the arm of each person, temperature information is transmitted at the same time, and the temperature information of each person is transmitted to the background server, so that the temperature of each person can be conveniently monitored and analyzed;
the online temperature detection in step 3 is as follows:
the online temperature detection system has the functions of real-time body temperature monitoring, body temperature display and body temperature reporting, is bound with a mobile phone APP, is connected with the mobile phone by using Bluetooth, and transmits information to a background server through a network so as to be conveniently monitored, analyzed and processed by monitoring personnel;
and 4, step 4: in order to eliminate system errors in the temperature detection system, the BP neural network is used for correcting the temperature, so that the influence of the system errors on the temperature detection is reduced;
the temperature correction in step 4 is as follows:
there will be systematic error, environmental error in the process of temperature detection, and systematic error includes: quantization error, bias error and linear error, some errors can be eliminated by cold end compensation and an optimization circuit, and some errors existing in the system can not be eliminated, so that error in the BP neural network calculation temperature detection system is introduced, and the system error is reduced;
before correcting temperature system error, the temperature detection system is used for monitoring the temperature of the constant temperature box to obtain a group of temperature detection results (x) i ,y i ),x i Is the temperature detected by the temperature detection system at the ith time, y i Is the true temperature of the ith test:
x is to be i As a neural networkInput of (a) y i Training a neural network as an output of the neural network, b j J = (0, 1.,. 1) is the output of the jth neuron of the hidden layer, l is the number of nodes of the hidden layer, v-1 ij And w jk Respectively representing the weight from the ith neuron node of the input layer to the jth node of the hidden layer and the weight from the jth neuron node of the hidden layer to the kth node of the output layer, and obtaining:
wherein theta is j And phi i Thresholds for the hidden layer and the output layer, respectively;
after the temperature detection system detects the temperature, the temperature value is input into the neural network to obtain a corrected temperature value, so that the accuracy of the temperature detection system is improved, and the false alarm rate of the alarm system is reduced.
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Application publication date: 20200710 Assignee: JIANGSU D-SHORE INFORMATION TECHNOLOGY CO.,LTD. Assignor: JINLING INSTITUTE OF TECHNOLOGY Contract record no.: X2023980053496 Denomination of invention: A detection and early warning system for continuous remote monitoring of body temperature and correction using BP neural network Granted publication date: 20221018 License type: Common License Record date: 20231222 |
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