CN212391732U - Workshop monitoring trolley - Google Patents
Workshop monitoring trolley Download PDFInfo
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- CN212391732U CN212391732U CN202021550853.XU CN202021550853U CN212391732U CN 212391732 U CN212391732 U CN 212391732U CN 202021550853 U CN202021550853 U CN 202021550853U CN 212391732 U CN212391732 U CN 212391732U
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- controller
- plate body
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- motor
- control unit
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- 238000012544 monitoring process Methods 0.000 title claims description 16
- 239000000428 dust Substances 0.000 claims abstract description 23
- 230000005540 biological transmission Effects 0.000 claims abstract description 7
- 240000007651 Rubus glaucus Species 0.000 claims description 23
- 235000011034 Rubus glaucus Nutrition 0.000 claims description 23
- 235000009122 Rubus idaeus Nutrition 0.000 claims description 23
- 241000196324 Embryophyta Species 0.000 claims 5
- 238000013527 convolutional neural network Methods 0.000 description 5
- 238000000034 method Methods 0.000 description 5
- 239000000779 smoke Substances 0.000 description 4
- 230000006870 function Effects 0.000 description 3
- 230000000087 stabilizing effect Effects 0.000 description 3
- 239000002245 particle Substances 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 239000000084 colloidal system Substances 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
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Abstract
The utility model relates to a workshop control dolly, include the support and install in camera, main control unit, follow controller, ultrasonic sensor, motor, wheelset, infrared sensor and the dust sensor of support, the motor is connected with the wheelset, connect ultrasonic sensor, motor, infrared sensor and dust sensor from the controller, main control unit connects the camera and follows the controller, main control unit is equipped with wireless transmission module, and battery unit connects main control unit and follows the controller. Compared with the prior art, the system can patrol the blind area of the workshop and can replace manpower to accurately identify, judge and alarm the occurring disaster.
Description
Technical Field
The utility model belongs to the technical field of the workshop control and specifically relates to a workshop control dolly is related to.
Background
In factory workshops, ensuring personnel and equipment safety has always been a very important aspect. Particularly, after work and on holidays, the workshop is in an unmanned state, many potential safety hazards cannot be detected, and the safety of a factory is seriously threatened. Factory fires are sudden and can cause significant losses in a short time, so that in the event of a fire alarm, rescue measures must be taken at an extremely rapid rate. The camera monitoring or the patrol of personnel in the prior art often has a plurality of monitoring dead angles, so that the camera monitoring or the patrol of personnel in the prior art cannot timely deal with the situation that the chemical medicine leakage causes large-area dense smoke, fire or overhigh dust concentration to cause factory explosion.
SUMMERY OF THE UTILITY MODEL
The utility model aims at providing a workshop control dolly in order to overcome the defect that above-mentioned prior art exists.
The purpose of the utility model can be realized through the following technical scheme:
the utility model provides a workshop control dolly, includes the support and installs in camera, main control unit, follow controller, ultrasonic sensor, motor, wheelset, infrared sensor and the dust sensor of support, the motor is connected with the wheelset, connect ultrasonic sensor, motor, infrared sensor and dust sensor from the controller, main control unit connects the camera and follows the controller, main control unit is equipped with wireless transmission module, and main control unit is connected to the battery cell and follows the controller.
The support include first plate body, second plate body and the third plate body that from the top down arranged in proper order, connect through the pillar between first plate body, second plate body and the third plate body, the camera is installed in first plate body top, main control unit, from controller, infrared sensor and dust sensor install in the second plate body, ultrasonic sensor, motor and wheelset are installed in the third plate body.
The camera is connected with the steering engine cloud platform, the steering engine cloud platform is connected with the slave controller, and the steering engine cloud platform is installed on the first plate body.
The main controller is a raspberry pie.
The slave controller is an STM32 single chip microcomputer, and the STM32 single chip microcomputer is provided with a temperature sensing unit.
The infrared sensor is an RPR220 infrared sensor.
The ultrasonic sensor is an HC-SR04 ultrasonic sensor.
The dust sensor is a PD4NS dust sensor.
The motor is connected with an L293D motor control board.
The wireless transmission module is a Wifi module.
Compared with the prior art, the utility model has the advantages of it is following:
(1) the trolley is provided with a main controller and a secondary controller, the main controller collects images shot by the camera, can detect fire images, and can carry a trained CNN network to realize accurate condition judgment as the main controller is only responsible for image processing; collecting information such as dust sensors from a controller, and further judging the field situation; the infrared sensor and the ultrasonic sensor realize tracking and obstacle avoidance, and the portability and the maneuverability of the trolley are matched, so that the blind area of a workshop can be patrolled, and the situation of disasters can be identified, judged and alarmed instead of manual work.
(2) Main control unit is the raspberry group, can make whole dolly small, with low costs, and wireless connection can be realized with the host computer to the raspberry group, independently opens the conflagration image and the main control unit that catch the camera, and is more convenient on equipment layout.
(3) The support includes first plate body, second plate body and the third plate body that from the top down arranges in proper order, and the layered structure is favorable to the device to arrange.
(4) The combination of a master controller and a slave controller is adopted, the STM32 single chip microcomputer is used for analyzing and processing the information of each sensor module of the trolley, the driving of the trolley is driven, the operand of the raspberry group is reduced, and the image recognition rate is improved.
Drawings
Fig. 1 is a top view of the present invention;
FIG. 2 is a front view of the present invention;
reference numerals:
1 is a battery cell; 2 is a motor control panel; 3 is a slave controller; 4 is a motor; 6 is an infrared sensor; 7 is a steering engine pan-tilt; 8 is a camera; 9 is a third plate body; 10 is a main controller; 11 is a dust sensor; 12 is an ultrasonic sensor; 13 is a universal wheel; 14 is a fixed pulley; and 15 is a second plate body.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
The embodiment provides a workshop monitoring trolley, include the support and install in the camera 8 of support, main control unit 10, from controller 3, ultrasonic sensor 12, motor 4, the wheelset, infrared sensor 6 and dust sensor 11, motor 4 and wheelset are connected, from controller 3 connection ultrasonic sensor 12, motor 4, infrared sensor 6 and dust sensor 11, main control unit 10 connects camera 8 and from controller 3, main control unit 10 is equipped with wireless transmission module, battery unit 1 connects main control unit 10 and from controller 3, camera 8 connects steering wheel cloud platform 7, steering wheel cloud platform 7 connects from controller 3, steering wheel cloud platform 7 installs in the support.
Specifically, the method comprises the following steps:
the support includes first plate body, second plate body 15 and the third plate body 9 that from the top down arranges in proper order, connects through the pillar between first plate body, second plate body 15 and the third plate body 9, and camera 8 is installed in first plate body top, and main control unit 10, from installing in second plate body 15 in controller 3, infrared sensor 6 and dust sensor 11, ultrasonic sensor 12, motor 4 and wheelset are installed in third plate body 9.
The camera 8 shoots continuous images in a factory workshop, the images are transmitted to the raspberry group, the raspberry group preprocesses the shot images, the trained convolutional neural network model is input, the images (fire and smoke) in the case of fire are identified, and the images are finally transmitted to a server platform (upper computer) for alarming and subsequent processing. The auxiliary controller STM32 single chip microcomputer is matched, the infrared sensor 6 tracks the trolley on black tracks, the ultrasonic sensor 12 avoids obstacles, the battery unit 1 comprises a 12V dry battery and an LM7805 voltage stabilizing module, and the LM7805 voltage stabilizing module reduces the direct-current voltage of the 12V dry battery and outputs the reduced voltage into 5V to supply power to the main controller 10 and the auxiliary controller 3; the wheel set comprises two universal wheels 13 and a fixed pulley 14, and the two universal wheels 13 are connected with the motor 4.
The STM32 singlechip communicates with the raspberry pi through a serial port. A computer vision library Opencv, a deep learning library Tensorflow and a Python development environment need to be configured inside the raspberry pie, and a trained CNN model is built in the Tensorflow. The raspberry group uses a Linux derived operating system raspban, has a multi-thread and multi-process advanced management function, and is assisted with an STM32 single chip microcomputer to process sensor information separately and simultaneously so as to improve the image processing speed and improve the operation efficiency.
The motor control board 2 is an L293D motor control board, drives the motors 4 of the left and right universal wheels 13 to rotate positively and negatively, controls the trolley to run forwards, backwards, leftwards and rightwards, and has a double H bridge circuit in the internal structure of the L293D motor control board. The infrared sensor 6 is an RPR220 infrared sensor, has four pins, and is packaged with a transmitting device and a receiving device by black colloid for tracking according to the reflection intensity of light. The ultrasonic sensor 12 uses an HC-SR04 ultrasonic sensor. The dust sensor 11 uses a PD4NS dust sensor, has the main characteristics of PWM output, compact structure, single power supply and low price, and can detect dust particles with the particle size of more than 1 micron by using an optical principle.
The support is a metal support which is connected with the first plate body, the second plate body 15 and the third plate body 9 through bolts.
The wireless transmission module is a Wifi module, and the main controller 10 can selectively upload the successfully identified fire situation images by using the Wifi module, periodically transmit the current dust concentration and workshop temperature information, and provide the information for the server platform to alarm and prevent.
The slave controller 3 specifically adopts an STM32F101C8 model single chip microcomputer and a 32-bit CPU. 32-512KB Flash memory and 6-64KB SRAM memory are integrated on the chip. 3 US-stage a/D converters (16 channels) of 12 bits. A temperature sensing unit is integrated on a chip and comprises 112 fast I/O ports, 4 16-bit timers and a 2-channel 12-bit D/A converter. The main control unit raspberry group selects 3 generations of b +, and compared with a raspberry group, the raspberry group is used as a carrier of image recognition with a common computer, and the raspberry group has the characteristics of small volume, complete functions and convenience in installation.
The camera 8 adopts a raspberry group camera, is matched with a raspberry group development board, is better configured, and can be successfully activated only by inputting a plurality of commands at a terminal. The pixels of the raspberry-type camera can be selected in various ways as common cameras.
The using method comprises the following steps:
and when the power switch is turned on, the LM7805 voltage stabilizing module outputs 5V voltage to supply power to the raspberry pie and the STM32 single chip microcomputer, the trolley is placed in the center of a pre-laid track line, and the camera is driven by the steering engine to carry out all-dimensional image acquisition on the surrounding environment and transmit the image to the raspberry pie. A proper CNN neural network model is trained by utilizing a fire smoke data set in advance, images received by the raspberry are preprocessed in an Opencv library, and then picture data are read into a Tensorflow library to identify and judge whether the images are fire images (fire light and smoke). The raspberry group uploads the disaster situation to the server platform if the disaster situation is identified through the Wifi module. Meanwhile, the I/O port of the single chip microcomputer is defined, so that information of each function sensor is transmitted through the I/O port of the STM32 single chip microcomputer, is transmitted to the single chip microcomputer firstly, is transmitted to the raspberry group through the serial port, and is finally transmitted to the server platform for processing, and the operand of the raspberry group is reduced. The speed regulation of the trolley motor adopts PWM pulse width regulation, the rotating speed of the motor is changed by programming and regulating the duty ratio, the forward running speed of the trolley can be regulated, two-wheel drive is adopted, and the tail of the trolley is supported by a fixed pulley.
The workshop monitoring trolley of the embodiment has the following advantages:
the trolley is provided with a main controller and a secondary controller, the main controller collects images shot by the camera, can detect fire images, and can carry a trained CNN network to realize accurate condition judgment as the main controller is only responsible for image processing; collecting information such as dust sensors from a controller, and further judging the field situation; the infrared sensor and the ultrasonic sensor realize tracking and obstacle avoidance, and the portability and the maneuverability of the trolley are matched, so that the blind area of a workshop can be patrolled, and the situation of disasters can be identified, judged and alarmed instead of manual work.
Claims (10)
1. The utility model provides a workshop control dolly, its characterized in that includes the support and installs in camera (8), main control unit (10) of support, from controller (3), ultrasonic sensor (12), motor (4), wheelset, infrared sensor (6) and dust sensor (11), motor (4) and wheelset connection, connect ultrasonic sensor (12), motor (4), infrared sensor (6) and dust sensor (11) from controller (3), main control unit (10) are connected camera (8) and are followed controller (3), main control unit (10) are equipped with wireless transmission module, and main control unit (10) and follow controller (3) are connected in battery unit (1).
2. The workshop monitoring trolley according to claim 1, wherein the support comprises a first plate body, a second plate body (15) and a third plate body (9) which are sequentially arranged from top to bottom, the first plate body, the second plate body (15) and the third plate body (9) are connected through a support column, the camera (8) is installed above the first plate body, the master controller (10), the slave controller (3), the infrared sensor (6) and the dust sensor (11) are installed on the second plate body (15), and the ultrasonic sensor (12), the motor (4) and the wheel set are installed on the third plate body (9).
3. The workshop monitoring trolley according to claim 1, wherein the camera (8) is connected with a steering engine cradle head (7), the steering engine cradle head (7) is connected with the slave controller (3), and the steering engine cradle head (7) is mounted on the first plate body.
4. The plant monitoring trolley according to claim 1, characterized in that the main controller (10) is a raspberry.
5. The workshop monitoring trolley according to claim 1, wherein the slave controller (3) is an STM32 single chip microcomputer, and an STM32 single chip microcomputer is provided with a temperature sensing unit.
6. The plant monitoring trolley according to claim 1, characterized in that the infrared sensor (6) is an RPR220 infrared sensor.
7. The plant monitoring trolley according to claim 1, wherein the ultrasonic sensor (12) is an HC-SR04 ultrasonic sensor.
8. A plant monitoring trolley as claimed in claim 1, characterized in that the dust sensor (11) is a PD4NS dust sensor.
9. A plant monitoring trolley as claimed in claim 1, characterised in that the motor (4) is connected to the L293D motor control board.
10. The workshop monitoring trolley as claimed in claim 1, wherein the wireless transmission module is a Wifi module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202021550853.XU CN212391732U (en) | 2020-07-30 | 2020-07-30 | Workshop monitoring trolley |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202021550853.XU CN212391732U (en) | 2020-07-30 | 2020-07-30 | Workshop monitoring trolley |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN212391732U true CN212391732U (en) | 2021-01-22 |
Family
ID=74253591
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202021550853.XU Expired - Fee Related CN212391732U (en) | 2020-07-30 | 2020-07-30 | Workshop monitoring trolley |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN212391732U (en) |
-
2020
- 2020-07-30 CN CN202021550853.XU patent/CN212391732U/en not_active Expired - Fee Related
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| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20210122 |