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
- Publication number
- CN111397744B CN111397744B CN202010403888.9A CN202010403888A CN111397744B CN 111397744 B CN111397744 B CN 111397744B CN 202010403888 A CN202010403888 A CN 202010403888A CN 111397744 B CN111397744 B CN 111397744B
- Authority
- CN
- China
- Prior art keywords
- temperature
- person
- information
- neural network
- infrared
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 61
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 36
- 230000036760 body temperature Effects 0.000 title claims abstract description 33
- 238000012544 monitoring process Methods 0.000 title claims abstract description 24
- 238000009529 body temperature measurement Methods 0.000 claims abstract description 19
- 230000002159 abnormal effect Effects 0.000 claims abstract description 4
- 239000004973 liquid crystal related substance Substances 0.000 claims abstract description 4
- 238000012216 screening Methods 0.000 claims abstract description 4
- 238000000034 method Methods 0.000 claims description 9
- 210000002569 neuron Anatomy 0.000 claims description 9
- 230000009897 systematic effect Effects 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000004891 communication Methods 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 claims description 3
- 230000006870 function Effects 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 2
- 230000001537 neural effect Effects 0.000 claims 1
- 238000013139 quantization Methods 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 5
- 230000006872 improvement Effects 0.000 description 4
- 238000013461 design Methods 0.000 description 2
- QSHDDOUJBYECFT-UHFFFAOYSA-N mercury Chemical compound [Hg] QSHDDOUJBYECFT-UHFFFAOYSA-N 0.000 description 2
- 229910052753 mercury Inorganic materials 0.000 description 2
- 230000000474 nursing effect Effects 0.000 description 2
- 241000711573 Coronaviridae Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000004861 thermometry Methods 0.000 description 1
Images
Classifications
-
- 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
- G01J5/80—Calibration
-
- 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
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Arrangements For Transmission Of Measured Signals (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
- Alarm Systems (AREA)
- Radiation Pyrometers (AREA)
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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010403888.9A CN111397744B (en) | 2020-05-13 | 2020-05-13 | Detection early warning system for continuously and remotely monitoring body temperature and correcting body temperature by adopting BP (Back propagation) neural network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010403888.9A CN111397744B (en) | 2020-05-13 | 2020-05-13 | Detection early warning system for continuously and remotely monitoring body temperature and correcting body temperature by adopting BP (Back propagation) neural network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111397744A CN111397744A (en) | 2020-07-10 |
| CN111397744B true CN111397744B (en) | 2022-10-18 |
Family
ID=71433796
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010403888.9A Active CN111397744B (en) | 2020-05-13 | 2020-05-13 | Detection early warning system for continuously and remotely monitoring body temperature and correcting body temperature by adopting BP (Back propagation) neural network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111397744B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112070206A (en) * | 2020-07-31 | 2020-12-11 | 广州中大数字家庭工程技术研究中心有限公司 | Body temperature measuring method based on neural network and computer readable storage medium |
| CN112304435A (en) * | 2020-10-10 | 2021-02-02 | 广州中大数字家庭工程技术研究中心有限公司 | Human body thermal imaging temperature measurement method combining face recognition |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102147835A (en) * | 2010-11-26 | 2011-08-10 | 中华人民共和国深圳出入境检验检疫局 | Driver body temperature automatic measurement system applied to port lanes and implementation method thereof |
| WO2015158198A1 (en) * | 2014-04-17 | 2015-10-22 | 北京泰乐德信息技术有限公司 | Fault recognition method and system based on neural network self-learning |
| CN105987775A (en) * | 2016-07-20 | 2016-10-05 | 天津理工大学中环信息学院 | Temperature sensor nonlinearity correction method and system based on BP neural network |
| CN207457728U (en) * | 2017-10-18 | 2018-06-05 | 郑州极致科技有限公司 | A kind of wisdom classroom management system based on wireless network |
| CN109855763A (en) * | 2018-12-29 | 2019-06-07 | 宏景科技股份有限公司 | A kind of temperature sensor correction method based on BP neural network and PLC |
| CN111102638A (en) * | 2020-01-17 | 2020-05-05 | 平顶山学院 | Intelligent teaching place and use method thereof |
-
2020
- 2020-05-13 CN CN202010403888.9A patent/CN111397744B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102147835A (en) * | 2010-11-26 | 2011-08-10 | 中华人民共和国深圳出入境检验检疫局 | Driver body temperature automatic measurement system applied to port lanes and implementation method thereof |
| WO2015158198A1 (en) * | 2014-04-17 | 2015-10-22 | 北京泰乐德信息技术有限公司 | Fault recognition method and system based on neural network self-learning |
| CN105987775A (en) * | 2016-07-20 | 2016-10-05 | 天津理工大学中环信息学院 | Temperature sensor nonlinearity correction method and system based on BP neural network |
| CN207457728U (en) * | 2017-10-18 | 2018-06-05 | 郑州极致科技有限公司 | A kind of wisdom classroom management system based on wireless network |
| CN109855763A (en) * | 2018-12-29 | 2019-06-07 | 宏景科技股份有限公司 | A kind of temperature sensor correction method based on BP neural network and PLC |
| CN111102638A (en) * | 2020-01-17 | 2020-05-05 | 平顶山学院 | Intelligent teaching place and use method thereof |
Non-Patent Citations (1)
| Title |
|---|
| 基于物联网的高压带电体温度在线预警系统;何建强等;《自动化与仪表》;20170415(第04期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111397744A (en) | 2020-07-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108717678B (en) | Wisdom endowment system | |
| CN111397744B (en) | Detection early warning system for continuously and remotely monitoring body temperature and correcting body temperature by adopting BP (Back propagation) neural network | |
| CN207515910U (en) | A kind of full-automatic infrared temperature measurer | |
| CN108899084A (en) | A kind of wisdom endowment health monitoring system | |
| CN110464564B (en) | Intelligent bed board system based on automatic detection and remote analysis | |
| WO2018136402A2 (en) | Non intrusive intelligent elderly monitoring system | |
| CN102843646A (en) | Intelligent alarm positioning system of nursing home | |
| CN111473883A (en) | Body surface temperature detection device and detection method | |
| CN108460957A (en) | A kind of the elder's health group monitoring warning system and method | |
| CN101103909A (en) | Blood pressure dynamic measuring and tendency analyzing observation method and system | |
| CN111426392A (en) | Infrared body surface temperature measuring equipment and detection method | |
| CN109541022A (en) | A kind of bridge structure crack health monitoring analysis method | |
| CN110870760A (en) | Infant monitoring system and infant monitoring method | |
| CN205251524U (en) | Intelligence physical signs detection device | |
| CN105468927A (en) | Baby health monitoring method and device | |
| CN111887866B (en) | Cushion type real-time hyperactivity monitoring system and method | |
| CN113053063A (en) | Mobile terminal-based disaster online disposal flow implementation method | |
| CN111583582A (en) | Behavior analysis system of multi-sensor information fusion technology suitable for home-based care of the aged | |
| CN111678617A (en) | Body temperature tracking monitoring method, electronic equipment and body temperature tracking monitoring system | |
| CN203506697U (en) | Wearable monitoring device | |
| CN111134632A (en) | Medical monitoring system | |
| CN106289578A (en) | A kind of temperature sensor intelligent checking system | |
| CN117854746A (en) | Intelligent housekeeper for home care | |
| CN104739383A (en) | Real-time dynamic body temperature monitoring and alarming system | |
| CN116067423A (en) | Clean room and related controlled environment online monitoring system and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| EE01 | Entry into force of recordation of patent licensing contract |
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 |
|
| EE01 | Entry into force of recordation of patent licensing contract |