CN212133714U - Instrument state monitoring system based on neural network - Google Patents
Instrument state monitoring system based on neural network Download PDFInfo
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- CN212133714U CN212133714U CN202021391520.7U CN202021391520U CN212133714U CN 212133714 U CN212133714 U CN 212133714U CN 202021391520 U CN202021391520 U CN 202021391520U CN 212133714 U CN212133714 U CN 212133714U
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Abstract
The utility model discloses an instrument state monitoring system based on neural network, including instrument body and instrument state monitoring devices, the fixed hoop of two relative settings of instrument body front side fixedly connected with, instrument state monitoring devices top fixedly connected with connecting rod, connecting rod top fixedly connected with camera base, camera base bottom fixedly connected with camera, instrument state monitoring devices back is provided with warning loudspeaker, inside data acquisition module, image acquisition module, the signal emission module and the signal reception module of being provided with of instrument state monitoring devices. The utility model discloses in, through the fixed hoop of two relative settings of fixed connection in instrument body front side, instrument state monitoring devices is through with fixed hoop around connecing in instrument body surface fixation in instrument body back, fixed hoop tip sets up the external screw thread, and through external screw thread connecting bolt, is convenient for install convenient to detach.
Description
Technical Field
The utility model belongs to the technical field of monitoring system, specifically be an instrument state monitoring system based on neural network.
Background
The instrument, the general term of the instrument for displaying the numerical value, includes a pressure instrument, a flow instrument, various analytical instruments and the like, the performance index of the instrument is generally described by accuracy (also called precision), variation and sensitivity in a process, and the calibration instrument for the instrument is generally used for calibrating the accuracy, the variation and the sensitivity. The variation is the maximum difference between the indicated values of the meter when the measured variable (which can be understood as an input signal) of the meter reaches the same value from different directions for a plurality of times, or the degree of inconsistency between the small-to-large variation (forward characteristic) and the large-to-small variation (reverse characteristic) of the measured parameter of the meter under the condition that the external condition is not changed, and the difference between the two is the variation of the meter. The size of the variation is a percentage of the ratio of the maximum absolute error to the range of the scale of the meter, and the main causes of the variation are the clearance of the meter actuator, the friction of the moving parts, the hysteresis of the elastic element, and the like. The continuous improvement of the manufacturing technology of instruments is overcome, particularly the introduction of the microelectronic technology, many instruments are fully electronic, no movable part is arranged, the analog instrument is changed into a digital instrument, and the like, so the index of the deterioration is not important and is highlighted in the intelligent instrument;
the instruments in the prior art are generally observed manually, and people cannot observe the instruments for 24 hours all day without rest, so that the instrument state is abnormal sometimes, and abnormal signals cannot be received at the first time, so that the maintenance time is delayed, and loss is easily caused.
SUMMERY OF THE UTILITY MODEL
The utility model aims to provide a: the instrument state monitoring system based on the neural network is provided for solving the problems that in the prior art, instruments are generally observed manually, people cannot observe the instruments for 24 hours all day long, the instrument state is abnormal sometimes, abnormal signals cannot be received at the first time, maintenance time is delayed, and loss is easily caused.
The utility model adopts the technical scheme as follows: the utility model provides an instrument state monitoring system based on neural network, includes instrument body and instrument state monitoring devices, the fixed hoop of two relative settings of instrument body front side fixedly connected with, instrument state monitoring devices top fixedly connected with connecting rod, connecting rod top fixedly connected with camera base, camera base bottom fixedly connected with camera, instrument state monitoring devices back is provided with warning speaker, inside data acquisition module, image acquisition module, the signal transmission module and the signal reception module of being provided with of instrument state monitoring devices.
In a preferred embodiment, the meter condition monitoring device is secured to the back of the meter body by attaching a retaining band around the outer surface of the meter body.
In a preferred embodiment, the input end of the data acquisition module is connected with the output end of the meter body through a data line, and directly receives data of the meter body.
In a preferred embodiment, the input end of the image acquisition module is connected with the output end of the camera through a data line, and the shot image information enters the image acquisition module.
In a preferred embodiment, the signal transmitting module and the signal receiving module are in signal connection with the cloud server through GPRS, the acquired data and images are uploaded to the cloud server through the signal transmitting module for storage and detection, and the abnormal signal is received through the signal receiving module.
In a preferred embodiment, the end of the fixing hoop is provided with an external thread, and a bolt is connected through the external thread, so that the fixing hoop is convenient to mount and dismount.
To sum up, owing to adopted above-mentioned technical scheme, the beneficial effects of the utility model are that:
1. the utility model discloses in, through the fixed hoop of two relative settings of fixed connection in instrument body front side, instrument state monitoring devices is through with fixed hoop around connecing in instrument body surface fixation in instrument body back, fixed hoop tip sets up the external screw thread, and through external screw thread connecting bolt, is convenient for install convenient to detach.
2. In the utility model, a data acquisition module, an image acquisition module, a signal transmitting module and a signal receiving module are arranged in a meter state monitoring device, an alarm loudspeaker is arranged at the back of the meter state monitoring device, the input end of the data acquisition module () is connected with the output end of a meter body () through a data line to directly receive the data of the meter body, meanwhile, the meter body is shot through a camera, the input end of the image acquisition module () is connected with the output end of the camera () through a data line, the shot image information enters the image acquisition module, the acquired data and image are uploaded to a cloud server through the signal transmitting module to be stored and detected, when abnormal data are found, the cloud server transmits abnormal signals, the abnormal signals are received through the signal receiving module, and finally, an alarm is sent through the alarm loudspeaker, the abnormity can be found as early as possible, the maintenance can be carried out as early as possible, and the loss is reduced.
Drawings
Fig. 1 is a front view of a neural network-based instrument status monitoring system according to the present invention;
fig. 2 is a back view of the instrument status monitoring system based on the neural network of the present invention;
fig. 3 is a block diagram of an instrument status monitoring system based on a neural network.
The labels in the figure are: the system comprises an instrument body 1, an instrument state monitoring device 2, a fixing hoop 3, a connecting rod 4, a camera base 5, a camera 6, an alarm loudspeaker 7, a data acquisition module 8, an image acquisition module 9, a signal transmitting module 10 and a signal receiving module 11.
Detailed Description
To make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the embodiments of the present invention are combined to clearly and completely describe the technical solution in the embodiments of the present invention, and obviously, the described embodiments are some embodiments of the present invention, not all embodiments. Based on the embodiments in the present invention, all other embodiments obtained by a person skilled in the art without creative efforts belong to the protection scope of the present invention.
Referring to fig. 1-3, an instrument status monitoring system based on a neural network comprises an instrument body 1 and an instrument status monitoring device 2, two oppositely arranged fixing hoops 3 are fixedly connected to the front side of the instrument body 1, the instrument status monitoring device 2 is fixed to the back of the instrument body 1 by winding the fixing hoops 3 on the outer surface of the instrument body 1, the end of the fixing hoop 3 is provided with external threads and is connected with a bolt through the external threads, so that the instrument is convenient to mount and dismount, the top of the instrument status monitoring device 2 is fixedly connected with a connecting rod 4, the top of the connecting rod 4 is fixedly connected with a camera base 5, the bottom of the camera base 5 is fixedly connected with a camera 6, the back of the instrument status monitoring device 2 is provided with an alarm speaker 7, a data acquisition module 8, an image acquisition module 9, a signal transmitting module 10 and a, the input of data acquisition module 8 passes through the data line with instrument body 1's output and is connected, the data of direct receipt instrument body 1, the input of image acquisition module 9 passes through the data line with camera 6's output and is connected, image information after the shooting gets into in the image acquisition module 9, signal transmission module 10 and signal reception module 11 are all through GPRS and high in the clouds server signal connection, upload data and the image of gathering to the high in the clouds server through signal transmission module 10 and preserve and detect, pass through signal reception module 11, receive abnormal signal, send out the police dispatch newspaper through warning speaker 7 at last, be convenient for discover unusually as early as possible, maintain as early as possible, reduce the loss.
The working principle is as follows: the method comprises the steps of winding a fixed hoop 3 on the outer wall of an instrument body 1, fixing the end 3 of the fixed hoop through a bolt, fixing an instrument state monitoring device 2 on the outer wall of the instrument body 1, connecting the input end of a data acquisition module (8) with the output end of the instrument body (1) through a data line, directly receiving data of the instrument body 1, shooting the instrument body 1 through a camera 6, connecting the input end of an image acquisition module (9) with the output end of the camera (6) through the data line, enabling shot image information to enter the image acquisition module 9, uploading the acquired data and images to a cloud server through a signal transmission module 10 for storage and detection, transmitting an abnormal signal by the cloud server when abnormal data is found, receiving the abnormal signal through a signal receiving module 11, and finally transmitting an alarm through an alarm loudspeaker 7, the abnormity can be found as early as possible, the maintenance can be carried out as early as possible, and the loss is reduced.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above embodiments are only used to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention in its corresponding aspects.
Claims (6)
1. The utility model provides an instrument state monitoring system based on neural network, includes instrument body (1) and instrument state monitoring device (2), its characterized in that: the utility model discloses an instrument state monitoring device, including instrument body (1), fixed hoop (3) of two relative settings of instrument front side fixedly connected with, instrument state monitoring device (2) top fixedly connected with connecting rod (4), connecting rod (4) top fixedly connected with camera base (5), camera base (5) bottom fixedly connected with camera (6), instrument state monitoring device (2) back is provided with warning speaker (7), instrument state monitoring device (2) inside is provided with data acquisition module (8), image acquisition module (9), signal emission module (10) and signal reception module (11).
2. The neural network-based meter state monitoring system of claim 1, wherein: the instrument state monitoring device (2) is fixed on the back of the instrument body (1) by winding the fixed hoop (3) on the outer surface of the instrument body (1).
3. The neural network-based meter state monitoring system of claim 1, wherein: the input end of the data acquisition module (8) is connected with the output end of the instrument body (1) through a data line.
4. The neural network-based meter state monitoring system of claim 1, wherein: the input end of the image acquisition module (9) is connected with the output end of the camera (6) through a data line.
5. The neural network-based meter state monitoring system of claim 1, wherein: the signal transmitting module (10) and the signal receiving module (11) are in signal connection with the cloud server through GPRS.
6. The neural network-based meter state monitoring system of claim 1, wherein: the end part of the fixed hoop (3) is provided with an external thread, and a bolt is connected through the external thread.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202021391520.7U CN212133714U (en) | 2020-07-15 | 2020-07-15 | Instrument state monitoring system based on neural network |
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| CN202021391520.7U CN212133714U (en) | 2020-07-15 | 2020-07-15 | Instrument state monitoring system based on neural network |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113452877A (en) * | 2021-05-07 | 2021-09-28 | 戌亥检测技术(上海)有限公司 | State monitoring system based on whole plant equipment intelligent management |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113452877A (en) * | 2021-05-07 | 2021-09-28 | 戌亥检测技术(上海)有限公司 | State monitoring system based on whole plant equipment intelligent management |
| CN113452877B (en) * | 2021-05-07 | 2022-08-26 | 上海希麦斯工业仪器技术有限公司 | State monitoring system based on whole plant equipment intelligent management |
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