CN111654550A - NB-IOT (NB-IOT) -based Internet of things deviation alarm system - Google Patents
NB-IOT (NB-IOT) -based Internet of things deviation alarm system Download PDFInfo
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- H—ELECTRICITY
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- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
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- G01C9/00—Measuring inclination, e.g. by clinometers, by levels
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Abstract
The invention discloses an NB-IOT (NB-IOT) -based Internet of things deviation alarm system, which comprises front-end equipment, an Internet of things platform and a client device, wherein the front-end equipment comprises an ARM (advanced RISC machine) microprocessor, a sensor for monitoring an inclination angle, an NB-IOT communication module and a charging device for providing a power supply; the charging device comprises a charger, wherein the charger provides power for the ARM microprocessor; the Internet of things platform is arranged on the LINUX server, and the front-end equipment transmits monitoring information to the LINUX server through the NB-IOT communication module; the server sends information to the client device. The invention aims to provide an Internet of things offset alarm system based on NB-IOT, which can intelligently monitor the inclination angle of a vertical rod, can early warn in time, has strong safety and reliability and is convenient to use.
Description
Technical Field
The invention relates to the field of monitoring of pole setting deviation, in particular to an Internet of things deviation alarm system based on NB-IOT.
Background
With the rapid development of urban and rural construction in China in recent years, the upright posts are distributed in streets, villages and residential districts. However, according to incomplete statistics, accidents that passersby are injured by accidentally toppling and smashing of vertical rods caused by severe weather and the like in various parts of China are rare in recent years, and the accidents are seriously harmful to life and property safety of people.
At present, because the control pole setting is complicated because of pole setting height, xarm length, pole setting environment, the member probably has certain hidden danger of empting, urgently needs a slope degree that can monitor the pole setting to can carry out the device or the system that the early warning was indicateed according to setting for the threshold value.
Chinese patent application No. 201410141621.1, application date: day 10, 04 month 2014, publication day: year 07, 02/2014, with patent names: the invention discloses a danger early warning device for a high-risk street tree in an inclined state, and discloses a danger early warning device for a high-risk street tree in an inclined state. The device is light and handy in size and can be conveniently fixed on a trunk of a tree to be detected, a built-in MEMS triaxial acceleration sensor is used for acquiring real-time acceleration data of the trunk of the tree, the data is converted by a microprocessor and then is compared with a preset angle threshold value, whether dangerous alarm of the inclined state of the tree to be detected is triggered or not is judged, and the alarm mode is divided into GSM directional short message alarm and on-site acousto-optic alarm. The invention monitors the inclination state of the trunk of the detected high-risk street tree in real time, obtains the danger signal of the inclination state of the trunk in advance and informs municipal garden maintenance departments and on-site pedestrians and vehicles in time, thereby effectively preventing the accidents of hurting passersby and vehicles caused by the accidental toppling of the street tree. The method is particularly suitable for places such as urban highways, parks, residential quarters and the like where street trees are widely planted, and has the advantages of low cost, simple and convenient installation and high reliability.
Although the patent literature discloses a dangerous early warning device for the inclined state of a high-risk street tree, the system is not accurate enough in monitoring the inclined angle, and the early warning system does not influence the safety in time, so that the invention cannot meet the requirements of social development.
Disclosure of Invention
In view of the above, the invention provides an internet of things deviation alarm system based on NB-IOT, which can intelligently monitor the inclination angle of a vertical rod, can give an early warning in time, has strong safety and reliability, and is convenient to use.
In order to realize the purpose of the invention, the following technical scheme can be adopted:
an NB-IOT (NB-IOT) -based Internet of things deviation alarm system comprises front-end equipment, an Internet of things platform and a client device, wherein the front-end equipment comprises an ARM (advanced RISC machine) microprocessor, a sensor for monitoring an inclination angle, an NB-IOT communication module for communication and a charging device for providing a power supply; the charging device comprises a charger, wherein the charger provides power for the ARM microprocessor;
the Internet of things platform is arranged on the LINUX server, and the front-end equipment transmits monitoring information to the LINUX server through the NB-IOT communication module;
the sensor comprises a gyroscope or accelerometer or magnetometer;
and the LINUX server sends the monitoring information to the client device.
The ARM microprocessor is of a model STM32L412K8T 6.
The sensor is a nine-axis sensor with the model number of MPU 9250.
The sensor comprises a gyroscope or an accelerometer or magnetometer.
The sensor monitors the attitude angle value of the inclination angle by acquiring gyroscope data.
The attitude angle value is obtained by Euler algorithm, and comprises angular velocity value which is obtained by integrating angle [ phi ]am,θam,ψam]。
The sensor monitors the attitude angle value of the inclination angle by collecting the acceleration value of the accelerometer and the magnetic force value of the magnetometer.
The attitude angle value of the monitoring inclination angle is obtained by the following formula:
elevation angle:
transverse roll angle:
φg＝tan-1(-ayb,-azb),φg∈(-π,π)；
yaw angle:
ψg＝-tan-1(my,mx),ψg∈(-π,π)
wherein:
mx＝mxbcosθ+mybsinθsinφ+mzbsinθcosφ
my＝mybconφ-mxbsinθ
And correcting the attitude angle obtained by collecting the acceleration value and the magnetic force value through an angle value obtained by integrating the gyroscope.
The correction is given by the following formula:
φ＝φg+k(φam-φg)
θ＝θg+k(θam-θg)
ψ＝ψg+k(ψam-ψg)
the first attitude angle phi, theta, psi is obtained.
And obtaining a second attitude angle by the obtained attitude angle through an extended Kalman filtering algorithm.
The extended kalman filter algorithm formula is as follows:
the sensor obtains an acceleration value by adopting a time domain and frequency domain mixed integration algorithm on the acquired acceleration value; and carrying out primary integration on the acquired acceleration value by adopting a low-frequency attenuation integration algorithm to obtain a speed value.
And the velocity value obtained by integration is subjected to primary integration by adopting a polynomial fitting integration algorithm and is fitted with a linear error term to obtain a displacement signal.
And the velocity value obtained by integration is subjected to primary integration by adopting a polynomial fitting integration algorithm and is fitted with a linear error term to obtain a displacement signal.
The frequency domain integral control function formula is as follows:
the equation for obtaining the velocity value is as follows:
the derived displacement signal is obtained by the following formula:
s(t)＝∫v(t)dt-p1t-p0；
the coefficients in the formula are as follows:
the charger includes a solar panel or a lithium battery.
The LINUX server comprises an MQTT server.
The LINUX server comprises a database, and the database comprises an in-memory database or an SQL database.
The LINUX server comprises an equipment monitoring module, an alarm service module or a remote upgrading module.
The client device includes a WEB framework that includes a vue.
The client device includes a D3.js3D display interface.
The client device comprises a WeChat applet or a nailed mobile terminal.
The invention has the beneficial effects that: 1) the inclination alarm device based on NB-IOT solar power supply can be arranged at the top end of the vertical rod, the horizontal arm of the vertical rod and the vertical rod to monitor the inclination degree and displacement of the vertical rod, and has high monitoring accuracy, safety and reliability; 2) the inclination sensor detects inclination information in real time and uploads the information to the Internet of things platform, an alarm threshold value is set, and once a detected inclination value exceeds a set value, early warning is carried out on the platform immediately, hidden dangers are found and further processing is carried out; the client can set WeChat, nail, short message and telephone to inform operation and maintenance personnel to deal with potential safety hazard in time, display early warning points on a large monitoring screen, and can check a 3D model diagram and real-time angle or horizontal information of a monitored object by double-click; 3) the invention has the advantages of high sensitivity, no wiring, simple installation, environmental protection, energy saving, remote alarm and the like.
Drawings
Fig. 1 is a circuit block diagram of a front-end device of an internet of things offset alarm system based on NB-IOT in an embodiment of the present invention;
fig. 2 is a system block diagram of an internet of things offset alarm system based on NB-IOT in an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and embodiments thereof.
Example 1
Referring to fig. 1 and 2, the shift alarm system of the internet of things based on NB-IOT comprises a front-end device 6 and a platform 7 of the internet of things, wherein the front-end device 6 comprises an ARM microprocessor 1, a sensor 2 for monitoring an inclination angle, an NB-IOT communication module 4 for communication, and a charging device for providing a power supply; the charging device comprises a charger 5, wherein the charger 5 provides power for the ARM microprocessor 1, and the ARM microprocessor 1 respectively controls a sensor 2 for monitoring an inclination angle and an NB-IOT communication module 4;
the internet of things platform 7 is arranged on the LINUX server, and the front-end equipment sends monitoring information to the LINUX server through the NB-IOT communication module 4.
Preferably, the ARM microprocessor 1 is model STM32L412K8T 6.
Preferably, the sensor 2 is a nine-axis sensor with model number of MPU 9250.
Further, preferably, the sensor includes two sensors, one of which is the sensor 2 and the other of which is the sensor 3, and the sensor 3 is also a nine-axis sensor of the type MPU 9250.
In this embodiment, the charger 5 preferably includes a solar panel 51 or a lithium battery 52.
In the embodiment, the front-end equipment 6 acquisition device adopts a 32-bit ARM microprocessor 1; the sensor comprises two MPU9250 nine-axis sensors 2, an MPU9250 nine-axis sensor 3, an NB-IOT communication module 4 and a Flash memory chip; the solar energy charging management system comprises a W25Q16 solar charging management chip, a solar cell panel and a lithium battery.
The ARM microprocessor 1 adopts a microprocessor with FPU
Bit
CPU, the CPU frequency is up to 80MHz, support FPU (floating point operation) and DSP order of 100DMIPS, the size of the program memory is 64kB, the size of the data RAM is 40 kB; interface types I2C, LPUART, SPI, QSPI, USART; the sensor chip is connected through the SPI interface, the communication speed reaches 1MHz, and an interrupt port is arranged to respond to an interrupt signal of the sensor in real time; the USART interface is connected with the NB-IOT communication module 4, and the AT instruction sent by the ARM microprocessor 1 controls the NB-IOT communication module 4 to communicate with the Internet of things platform 4 and send alarm information.
In this embodiment, the sensor 2 comprises a gyroscope 21 or an accelerometer 22 or a magnetometer 23.
The sensor 2 monitors the attitude angle value of the inclination angle by acquiring the data of the gyroscope 21.
In the embodiment, the sensor adopts two MPU9250 nine-axis sensors 2 and an MPU9250 nine-axis sensor 3, the ARM microprocessor 1 adopts an SPI interface to communicate with the sensor, the communication speed can reach 1MHz, and the MPU9250 sensor chip can configure different measuring ranges through a register, so that the obtained data is more accurate;
the MPU9250 nine-axis sensor uses three 16-bit ADCs for the gyroscope 21, the accelerometer 11 and the magnetometer 23 respectively, and converts the measured analog quantity into an outputable digital quantity. In order to accurately track fast and slow movements, the measurement ranges of the MPU9250 nine-axis sensor 2 and MPU9250 nine-axis sensor 3 may be configured by registers, and the measurable range of the gyroscope 21 is ± 250, + -500, + -1000, + -2000 °/second (dps); the measurable range of the accelerometer 22 is +/-2, +/-4, +/-8, +/-16 g; the magnetometer 23 has a maximum measurement range of ± 4800 uT. The resolution of the ADC (digital-to-analog conversion) is 16 bits, and the MPU9250 nine-axis sensor can provide data of different levels of accuracy when the measurement ranges are different.
In the invention, a fusion algorithm of complementary filtering and extended Kalman filtering is adopted for attitude calculation, and three attitude angles are calculated by a combined settlement method by combining acceleration values and magnetic force values acquired by an accelerometer 22 and a magnetometer 23 from an MPU9250 sensor 2 and an MPU9250 sensor 3;
calculating an attitude angle of the data of the gyroscope 21 by using an Euler angle method, and then correcting the attitude angle settled by the gyroscope by using the attitude angle settled by the accelerometer and the electronic compass to obtain a fused attitude angle; and performing extended Kalman filtering, wherein an attitude angle directly calculated by the accelerometer and the magnetometer is used as an observed quantity, and an attitude angle calculated by complementary filtering is used as an observed variable of the model. The parameters of the extended Kalman filtering algorithm adopt dynamic parameters, different filtering parameters are adopted when the front-end equipment is in a slow speed state and a fast speed state, and the filtering algorithm can effectively avoid noise interference and inhibit drift in a static state; in a dynamic state, the filtering algorithm can effectively filter noise, settle the attitude more quickly, track the attitude quickly when the attitude changes quickly, and achieve the accuracy of 0.1 degree.
The attitude angle value is obtained through an Euler algorithm, the attitude angle value comprises an angular velocity value, in the embodiment, the angular velocity value is obtained through a gyroscope 21, and the angular velocity value is integrated to obtain an angle [ phi ]am,θam,ψam]。
The MPU9250 sensor 2 and the MPU9250 sensor 3 monitor the attitude angle value of the inclination angle by collecting the acceleration value of the accelerometer 21 and the magnetic force value of the magnetometer 23 to be combined.
The attitude angle value for monitoring the tilt angle using the accelerometer 21 and the magnetometer 23 is obtained by the following formula:
elevation angle:
transverse roll angle:
φg＝tan-1(-ayb,-azb),φg∈(-π,π)；
yaw angle:
ψg＝-tan-1(my,mx),ψg∈(-π,π)
wherein:
mx＝mxbcosθ+mybsinθsinφ+mzbsinθcosφ
my＝mybconφ-mxbsinθ
And correcting the attitude angle obtained by collecting the acceleration value and the magnetic force value through an angle value obtained by integrating the gyroscope.
The correction is given by the following formula:
φ＝φg+k(φam-φg)
θ＝θg+k(θam-θg)
ψ＝ψg+k(ψam-ψg)
the first attitude angle phi, theta, psi is obtained.
And obtaining a second attitude angle by the obtained attitude angle through an extended Kalman filtering algorithm.
The extended kalman filter algorithm formula is as follows:
in this embodiment, the MPU9250 sensor 2 and the MPU9250 sensor 3 respectively obtain acceleration values from the collected acceleration values by adopting a time-domain and frequency-domain mixed integration algorithm; and carrying out primary integration on the acquired acceleration value by adopting a low-frequency attenuation integration algorithm to obtain a speed value.
And the velocity value obtained by integration is subjected to primary integration by adopting a polynomial fitting integration algorithm and is fitted with a linear error term to obtain a displacement signal.
And the velocity value obtained by integration is subjected to primary integration by adopting a polynomial fitting integration algorithm and is fitted with a linear error term to obtain a displacement signal.
The frequency domain integral control function formula is as follows:
the equation for obtaining the velocity value is as follows:
the derived displacement signal is obtained by the following formula:
s(t)＝∫v(t)dt-p1t-p0；
the coefficients in the formula are as follows:
the sampling rate of the acceleration is 2Khz, 2000 acceleration data points are collected every second, and the requirement of a hybrid integration algorithm on data volume can be met.
The power consumption of the whole machine is low, the MCU adopts a low-power-consumption microprocessor, the wireless communication module enters a low-power-consumption working mode when no data flow exists, data are collected uninterruptedly, and the power consumption of the whole machine is 5mA when no data flow exists; the average power consumption is about 30mA when the traffic is in the data flow state; the data interaction algorithm is optimized, and the data flow is reduced as much as possible on the premise of ensuring the real-time property of the burst data; the daily average energy consumption is about 125mAH, and the power consumption is low.
The solar energy conversion and lithium battery energy storage mode is adopted for power supply, the solar power supply circuit adopts a CN3761 MPPT solar charging management chip, and the CN3761 PWM voltage reduction lithium battery charging management chip has trickle, constant current and constant voltage charging modes, so that the service life of the lithium battery is prolonged; the CN302 over-discharge protection chip is adopted to protect the lithium battery, so that the lithium battery is prevented from being damaged by over-discharge; the battery adopts a 5000mAh lithium battery, so that sufficient electric quantity is ensured, and uninterrupted power supply can be ensured under the condition of continuous no illumination.
In this embodiment, the NB-IOT communication module 4 adopts a middle mobile internet of things 2G/NB-IOT dual-mode module: the model number of the double-module is M5313, and the communication module supports double-mode communication.
In this embodiment, the NB-IOT communication module 4 preferentially uses NB mode communication, registers the GSM network for data communication when an NB cell cannot be connected, and periodically searches for an NB background in the GSM network mode, returns to the NB mode, and ensures network communication; and switching to a GSM network mode at regular time, inquiring the firmware version of the server platform by the front-end equipment, and carrying out remote upgrade through a GPRS network if the firmware version is new.
Example 2
Referring to fig. 1 and 2, the difference between the foregoing embodiments is that the internet of things platform 7 is disposed on a LINUX server, and the LINUX server includes an MQTT server 76.
The LINUX server also includes a database that includes an in-memory database 74 or an SQL database 75.
The LINUX server includes a device monitoring module 71, an alarm service module 72, or a remote upgrade module 73.
In this embodiment, the internet of things platform 7 is deployed on the linux server, and the system stability is high. The method comprises the steps that an asynchronous communication frame Twisted of an Internet of things device access layer adopts a mosquitto open source MQTT server as a server for device access.
In the embodiment, a gradient storage structure of Redis + SQL data is adopted on data storage, Redis is used as a cache database of real-time data, the data disappear at regular time, data points do not need to be deleted at regular time, the data insertion and retrieval speed is remarkable, and the scale is controllable; for data which is stored for a long time and has small quantity, storing the data in an SQL database; redis supports a Pub/Sub mode, and after data receiving is achieved, the data are distributed to a database, a real-time data analysis service and a real-time monitoring service through Redis. The Nginx is used as the proxy server to provide the reverse proxy, the reverse proxy can be set to buffer the request, the request is preprocessed, load balance is set, the request processing efficiency is optimized, the static file processing efficiency is high, and the safety factor is high.
Example 3
Referring to fig. 1 and 2, the difference from the above embodiment is that the present invention further includes a client device 8, and the LINUX server transmits monitoring information to the client device 8.
The client device 8 includes a WEB framework 81, and the WEB framework 81 includes a vue.
The client device 8 also includes a d3.js3d display interface 83.
The client device 8 also includes a wechat applet 84 or a nailer mobile terminal 82.
In this embodiment, the background development language of the present invention adopts Python 3.7, the Web framework 81 adopts flash, the front-end framework adopts a vue.js progressive framework, and the front-back interaction is realized by accessing a RESTful API interface of the flash framework through axios by vue.js. On the front-end interface display, a D3.js visual library is adopted to display a 3D model of a structure monitored by front-end equipment, so that the inclination angle and the displacement condition are displayed more intuitively; a plurality of sensors can be arranged on one structure, and the deformation condition of the whole structure can be dynamically displayed.
The invention can be arranged at the top end of the vertical rod, the horizontal arm of the vertical rod and the vertical rod to monitor the inclination degree and displacement of the vertical rod; the system can set WeChat, nail, short message and telephone to inform operation and maintenance personnel to deal with potential safety hazard in time, display early warning point positions on a large monitoring screen, and can check a 3D model diagram and real-time angle or horizontal information of a monitored object by double-click. The invention has the advantages of high sensitivity, no wiring, simple installation, environmental protection, energy saving, remote alarm, lead-free tin spraying manufacture of the PCB and the like.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
Claims (14)
1. The utility model provides a thing networking skew alarm system based on NB-IOT which characterized in that: the system comprises front-end equipment, an Internet of things platform and a client device, wherein the front-end equipment comprises an ARM microprocessor, a sensor for monitoring an inclination angle, an NB-IOT communication module for communication and a charging device for providing a power supply; the charging device comprises a charger, wherein the charger provides power for the ARM microprocessor;
the Internet of things platform is arranged on the LINUX server, and the front-end equipment transmits monitoring information to the LINUX server through the NB-IOT communication module;
the sensor comprises a gyroscope or accelerometer or magnetometer;
and the LINUX server sends the monitoring information to the client device.
2. The NB-IOT based internet of things offset warning system of claim 1, wherein: the sensor monitors the attitude angle value of the inclination angle by acquiring gyroscope data.
3. The NB-IOT based internet of things offset warning system of claim 2, wherein: the attitude angle value is obtained by Euler algorithm, and comprises angular velocity value which is obtained by integrating angle [ phi ]am,θam,ψam]。
4. The NB-IOT based internet of things offset warning system of claim 1, wherein: the sensor monitors the attitude angle value of the inclination angle by collecting the acceleration value of the accelerometer and the magnetic force value of the magnetometer.
5. The NB-IOT based IOT deviation alarm system of claim 4, wherein: the attitude angle value of the monitoring inclination angle is obtained by the following formula:
elevation angle:
transverse roll angle:
φg＝tan-1(-ayb,-azb),φg∈(-π,π)；
yaw angle:
ψg＝-tan-1(my,mx),ψg∈(-π,π)
wherein:
mx＝mxbcosθ+mybsinθsinφ+mzbsinθcosφ
my＝mybconφ-mxbsinθ
6. The NB-IOT based IOT deviation alarm system of claim 5, wherein: and correcting the attitude angle obtained by collecting the acceleration value and the magnetic force value through an angle value obtained by integrating the gyroscope.
7. The NB-IOT based IOT deviation alarm system of claim 6, wherein: the correction is given by the following formula:
φ＝φg+k(φam-φg)
θ＝θg+k(θam-θg)
ψ＝ψg+k(ψam-ψg)
the first attitude angle phi, theta, psi is obtained.
8. The NB-IOT based internet of things offset warning system of claim 7, wherein: and obtaining a second attitude angle by the obtained attitude angle through an extended Kalman filtering algorithm.
9. The NB-IOT based internet of things offset warning system of claim 8, wherein: the extended kalman filter algorithm formula is as follows:
10. the NB-IOT based IOT deviation alarm system of claim 4, wherein: the sensor obtains an acceleration value by adopting a time domain and frequency domain mixed integration algorithm on the acquired acceleration value; and carrying out primary integration on the acquired acceleration value by adopting a low-frequency attenuation integration algorithm to obtain a speed value.
11. The NB-IOT based internet of things offset warning system of claim 10, wherein: and the velocity value obtained by integration is subjected to primary integration by adopting a polynomial fitting integration algorithm and is fitted with a linear error term to obtain a displacement signal.
12. The NB-IOT based internet of things offset warning system of claim 10, wherein: the frequency domain integral control function formula is as follows:
13. the NB-IOT based internet of things offset warning system of claim 10, wherein: the equation for obtaining the velocity value is as follows:
14. the NB-IOT based internet of things offset warning system of claim 11, wherein: the derived displacement signal is obtained by the following formula:
s(t)＝∫v(t)dt-p1t-p0；
the coefficients in the formula are as follows:
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| CN116055777A (en) * | 2023-01-03 | 2023-05-02 | 北京流金岁月传媒科技股份有限公司 | Remote control system of multi-channel set top box |
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