CN111783727A - Automatic meter reading method and system based on machine vision and edge computing technology - Google Patents
Automatic meter reading method and system based on machine vision and edge computing technology Download PDFInfo
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Abstract
The invention provides an automatic meter reading method based on machine vision and edge computing technology, which comprises the following steps: s1, starting edge computing equipment; s2, shooting the pointer instrument through the camera module; s3, scene judgment is carried out through the image processing module, and whether the images or video streams returned by the camera module contain pointer instruments or not is judged; s4, positioning the instrument through the image processing module; s5, carrying out numerical value inference through the image processing module and outputting an inference result to a data processing module on the edge computing equipment; s6, performing mean value filtering processing on the inferred result of the image processing module through the data processing module to obtain a meter reading value; and S7, transmitting the filtered meter reading value to a remote server through a communication module. The invention has the beneficial effects that: the construction difficulty is small, and the production is not influenced; no damage and no invasion; and the server pressure is small by edge calculation.
Description
Technical Field
The invention relates to a meter reading method, in particular to an automatic meter reading method and system based on machine vision and edge computing technology.
Background
Today, communication technology is developed, many scenes needing manual meter reading, such as resident water consumption, electricity consumption, gas and the like, are changed into remote meter reading modes. The main technical scheme of remote meter reading represents narrow-band Internet of things technologies such as NB-IoT, LoRa and the like, the application of the physical network technology reduces the manual participation degree to a great extent, and all things are interconnected to really land. However, the technology needs to replace the whole collection equipment such as an electricity meter, a water meter, a gas meter and the like, and after the collection equipment is replaced, the measurement module and the remote communication module (2G/3G) which are used for establishing the meter are matched, so that the meter reading and data synchronization is realized, and the details are as follows:
a metering module: the meter core is installed in the meter core position, the usage amount is calculated through the rotation angle of the meter core and a formula, and the output value is converted into binary digits through an internal chip and an internal memory and is transmitted to a remote communication module.
A remote communication module: the communication module is generally arranged at the top of the meter, a 2G/3G SIM flow card is generally used by the communication module, key data such as equipment ID, usage amount, battery power and the like in a memory of the equipment are transmitted to the Internet of things remote gateway through the SIM card, and the gateway forwards the key data to a corresponding analysis module for analysis and finally enters a database.
In the aspect of the power supply of thing networking equipment, 500 milliampere batteries are used conventionally, need change when the electric quantity exhausts, and when the battery can not dismantle or the integration level is higher, then need change whole equipment. Of course, there are also devices that use long-term power such as DC5V, 12V, depending on the particular use environment.
Therefore, in the implementation process of the internet of things project, operations such as power failure, water cut, gas cut and the like are often involved from the safety perspective, multi-party negotiation is needed, the construction time is gradually determined, and the normal life of residents is seriously influenced. Meanwhile, professional technicians with relevant licenses are required to conduct the construction process, the construction time for transforming the whole Internet of things is long, the labor investment is high, the whole Internet of things equipment needs to be replaced when technical faults (such as normal use of the equipment and failure in reporting of data) occur, and the whole process is quite time-consuming and labor-consuming.
In the places where large-scale equipment such as factories, subways, high-speed rails and the like are collected by clouds, a plurality of professional equipment not only have replaceable internet of things equipment, but also have a data reporting function (such as a barometer for controlling a gas bottle), and if large-area internet of things construction is carried out, the operation must be stopped, the shutdown is carried out, and a large amount of economic loss is caused. And the data items which need to be paid attention simultaneously in the industry are thousands of items, however, the number of the replaced Internet of things equipment can be few, so manual meter reading and recording are still carried out in many places, and the abnormal condition cannot be linked with a professional system in time, so that the default exists in the plan, and the good control opportunity is missed.
Aiming at the situation, the pressure pointer is recognized by an image recognition technology in Dahua Zhejiang, the principle is that a piece of lightproof cup-shaped equipment is designed, a camera, an LED spotlight and a remote communication module are placed at the bottom of the equipment, the equipment is integrally buckled on an instrument in an inverted mode, light is supplemented and shot through the buckled inside, the shot image is conveyed to a remote server for recognition, and then data uploading is carried out through the remote server. The whole scheme is economical and practical, the data reading can be realized without modifying original equipment, but the terminal does not have the data processing capacity, and the processing pressure of the server is large when the terminal is deployed in a large scale.
The above prior art has the following disadvantages:
1. the implementation cost is high: the automatic meter reading in a narrow-band Internet of things mode needs to spend a great deal of time and energy on coordination of construction time, and because the construction involves water cut, power cut and production stop, the loss of enterprises, especially production type enterprises, is huge;
2. the maintenance cost is high: the narrow-band Internet of things equipment is highly integrated equipment, if any fine module is damaged, the problem that data cannot be uploaded and the like occurs, the equipment needs to be replaced by operations such as water cut-off, power cut and the like, and the maintenance cost is gradually increased;
3. poor replaceability: the number of data items which need to be paid attention to simultaneously in the industry is up to thousands, however, a few of replaced internet of things devices can be provided, the related products of the narrow-band internet of things devices in the scene are not many, the related working conditions of a plurality of modules in the narrow-band internet of things are not in line with use, and the related qualification cannot reach the standard;
4. the personnel overhaul is inconvenient: although the dahua equipment solves the problems to a great extent, when the equipment is overhauled, workers need to manually read the meter and troubleshoot the instrument and meter, and the dahua equipment adopts a direct covering mode, so that the workers cannot directly operate the instrument and meter during overhauling, inconvenience is brought to related work, and the risk of damaging the equipment is increased;
5. the remote calculation pressure is large: due to the characteristics of Dahua equipment, a terminal is only responsible for collecting and transmitting images, and a calculation task is carried out at a server end, so that the server pressure is huge when a plurality of pieces of equipment need to be analyzed simultaneously, and the downtime is easy to threaten other software running on the machine;
6. poor information security: due to the fact that a public network (an operator network) is used for data transmission, Dahua equipment is difficult to use in a scene needing data confidentiality, a narrow-band Internet of things mode can be achieved through the LoRa, the LoRa is paved through a private gateway, data are transmitted from the private network, safety can be effectively guaranteed, and the implementation, maintenance, replaceability and the like can be achieved through the mode.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides an automatic meter reading method and system based on machine vision and edge calculation technology.
The invention provides an automatic meter reading method based on machine vision and edge computing technology, which comprises the following steps:
s1, starting edge computing equipment, wherein the edge computing equipment is terminal equipment and comprises a camera module, an image processing module, a data processing module and a communication module, the edge computing equipment is used as a computing edge to be registered, and the camera module on the edge computing equipment is started after permission is obtained;
s2, shooting the pointer instrument through the camera module, and outputting the shot image or video stream to the image processing module on the edge computing device;
s3, scene judgment is carried out through the image processing module, whether the images or video streams returned by the camera module contain pointer instruments or not is judged, if not, the step S2 is returned, and if yes, the next step is carried out;
s4, positioning the instrument through the image processing module;
s5, carrying out numerical value inference through the image processing module and outputting an inference result to a data processing module on the edge computing equipment;
s6, performing mean value filtering processing on the inferred result of the image processing module through the data processing module to obtain a meter reading value;
and S7, transmitting the filtered meter reading value to a remote server through a communication module.
As a further improvement of the present invention, in step S3, it is retrieved whether the image or video stream contains pointer instruments according to the pre-trained neural network model.
As a further improvement of the invention, the pre-trained neural network model is a LeNet neural network model.
As a further improvement of the present invention, in step S4, the retrieved coordinates of the pointer instrument are preprocessed, including scaling, noise reduction, gray scale and binarization, to obtain an image of the pointer instrument;
as a further improvement of the invention, in step S5, the center coordinates of the instrument in the pointer instrument image are determined by machine vision technology, four image boundaries are drawn by taking the coordinates as (0, 0), the coordinates of the pointer are determined, the position of the image boundary where the pointer is located is judged, the numerical value, the initial position, the minimum value, the maximum value and the step length of the instrument are determined, and finally, the inferred numerical value is obtained by calculation according to a trigonometric function formula.
As a further improvement of the present invention, in step S6, a data filter is used to perform an average filtering process, the data filter is responsible for filtering generated values, when the values exceed a maximum value and are less than a minimum value, and sudden increase and decrease trigger fusing, i.e. re-operation, and continuous recursive processing, and finally outputting a meter reading value conforming to the filtering, and the data entering the data filter is locally cached for the basis of the self-learning and parameter adjustment of the data filter.
As a further improvement of the invention, in step S7, group verification is performed on the calculation result through the communication module, and then the meter reading value is pushed to a remote server by using a communication interface on the edge computing device, so as to complete data archiving.
The invention provides an automatic meter reading system based on machine vision and edge computing technology, which is used for realizing the method in any one of the aspects.
As a further improvement of the present invention, the system includes a terminal device and a remote server, the terminal device is an edge computing device, the edge computing device includes a camera module, an image processing module, a data processing module and a communication module, the camera module shoots a pointer instrument, an output end of the camera module is connected to the image processing module, an output end of the image processing module is connected to the data processing module, and the data processing module is connected to the remote server through the communication module.
As a further improvement of the invention, the camera module comprises a camera, a horizontal moving mechanism, a vertical moving mechanism and an LED light supplement lamp.
The invention has the beneficial effects that:
1. the construction difficulty is little, does not influence production: the invention uses a visual mode to carry out butt joint of the pointer instrument, does not need to replace the original equipment, has small difficulty in construction and maintenance, and does not need operations such as production halt, industry halt and the like;
2. no destruction, no invasion: when important industrial equipment is butted, the important industrial equipment does not need to be damaged and is not sensed by an accessed industrial instrument;
3. edge calculation, server pressure is small: the terminal equipment completes extraction of key images, instrument positioning and numerical value estimation and completes all calculation processes at the terminal, and therefore information transmission and server data processing pressure is greatly relieved.
Drawings
FIG. 1 is a data flow diagram of an automated meter reading method based on machine vision and edge computing technology.
Fig. 2 is a schematic diagram of a camera module of an automatic meter reading system based on machine vision and edge computing technology.
FIG. 3 is a schematic diagram of an image processing module of an automatic meter reading method based on machine vision and edge computing technology.
FIG. 4 is a schematic diagram of an image processing module of an automatic meter reading method based on machine vision and edge computing technology.
FIG. 5 is a schematic diagram of a data processing module of an automated meter reading method based on machine vision and edge computing technology.
FIG. 6 is a schematic diagram of a communication module of an automated meter reading method based on machine vision and edge computing technology according to the present invention.
Detailed Description
The invention is further described with reference to the following description and embodiments in conjunction with the accompanying drawings.
As shown in fig. 1 to 6, an automatic meter reading system based on machine vision and edge computing technology comprises a terminal device and a remote server, wherein the terminal device is an edge computing device, the edge computing device comprises a camera module, an image processing module, a data processing module and a communication module, the camera module shoots a pointer instrument, the output end of the camera module is connected with the image processing module, the output end of the image processing module is connected with the data processing module, and the data processing module is connected with the remote server through the communication module.
The camera module comprises a camera 3, a horizontal moving mechanism 1, a vertical moving mechanism 2 and an LED light supplementing lamp.
The invention integrates the communication module, the camera module and the memory module by using the circuit board, realizes image recognition on hardware, namely meter reading, and transmits a final result back to the server, and combines the narrow-band Internet of things technology with the image recognition technology.
Based on the system, the following steps are carried out:
step 1, starting edge computing equipment, registering the equipment as a computing edge, and starting a camera module on the equipment after permission is obtained;
step 4, performing data inference according to a trigonometric function through pointer included angle positioning, circle center positioning, instrument minimum value and step length;
step 5, carrying out mean value filtering processing on the inferred numerical values, and outputting final reasonable data;
and 6, the communication module performs group verification on the calculation result and then uses the edge calculation terminal to push the kilomega network port/4G network card/wifi and the like to a remote server to finish data archiving.
The method realizes automatic reading of the instrument data without shutdown and modification, and does not influence the normal industrial production requirements (such as manual inspection and the like), and the detailed technical scheme content is as follows:
hardware part
In combination with the actual use scenario, the main functions of the hardware part are as follows: firstly, a basic operation environment can be provided for software; secondly, data sources can be provided for software; thirdly, the mode of remote transmission of at least two types of data can be supported; fourthly, the system is small enough and can be deployed in any scene; fifthly, the LED can be driven to supplement light sources in the shooting process.
(II) software part
The software is used as the soul of the method, and solves the problems of driving of physical hardware such as a camera, an LED lamp and the like, shooting position adjustment, dynamic image acquisition, image content analysis, data processing, remote server communication and the like. The relationship is illustrated in figure 1.
As can be understood from fig. 1, the present invention is implemented as a physical middleware, which is located between an industrial device and a remote server, and performs non-transformation upgrade on an original industrial device by using a machine vision method to complete interconnection of heterogeneous systems, and each module is described as follows:
1. camera module
The camera module shown in fig. 2 is formed by combining 2 steering engines, 1 camera and 1 LED lamp set, so that specific resolution is used, and image shooting of equipment is performed at different angles and different illumination.
The resolution range of the camera module is larger than or equal to 720P (1280 multiplied by 720), the image shooting of instruments and meters can be realized, the horizontal direction moving angle is 0 to 180 degrees, the left and right movement of the camera can be realized, the vertical direction moving angle is 0 to 180 degrees, the up and down movement of the camera can be realized, the LED light supplement is supported, and the light supplement adjustment can be carried out aiming at the scene illumination problem.
2. Image processing module
As shown in fig. 3, the module is responsible for solving the problem of images taken by the camera module. The method comprises three parts of target scene judgment, instrument positioning and numerical value inference.
As shown in fig. 4, the module is used as the core content of the present invention, and the specific processing method is as follows:
scene judgment: the step is used for determining the use scenes of the model and the equipment, and judging whether the video stream returned by the camera module contains the instrument or not by using the LeNet neural network model, so that the graphic calculation pressure of the terminal can be greatly reduced. When the scene does not contain any instrument, the terminal equipment does not perform graphic calculation, so that the energy consumption is reduced, and the service life of the equipment is prolonged; according to the specification of the artificial intelligent neural network, instrument images (including forward and reverse directions) need to be collected as much as possible, label marking is carried out manually, training is carried out, a hook is used for outputting a model result of an optimal threshold value, and the result is used in the next step;
positioning an instrument: the method comprises the steps that a preprocessing model is used for positioning instruments in an image and intercepting the instruments one by one, normalization processing is needed after intercepting due to the fact that the image has the size problem, namely the image is consistent in size and scaling, and the processed image needs noise reduction processing to improve the accuracy of numerical value inference;
numerical extrapolation: after the instrument is determined, the center coordinates of the instrument are determined by using a machine vision technology, four image boundaries are drawn by taking the coordinates as (0, 0), and then the coordinates of a pointer are determined by using the technology to judge which image boundary the pointer is in. And taking all the calculated key parameters as formulas to be brought in one by one, and finally calculating according to a trigonometric function formula to obtain a final result.
3. Data processing module
As shown in fig. 5, the data processing module is configured to process the result output by the image processing module, where the processing content includes numerical filtering and local caching.
A data filter: because numerical calculation is a complex process, especially multiple key parameters are required to carry out mutual calculation, numerical value inference is carried out in an image processing module, certain numerical value deviation can be generated when the influences of illumination, fluctuation and the like occur, a data filter is responsible for filtering the generated numerical values, fusing is triggered when the influences of illumination, fluctuation and the like exceed the maximum value, are smaller than the minimum value, suddenly increase and decrease, the operation is carried out again, recursion processing is carried out continuously, and finally the numerical value which is in accordance with the filtering is output;
local caching: the data entering the filter is locally cached for key basis of filter self-learning and parameter adjustment, and the method is also suitable for a solution scheme of temporary storage under the condition of network fluctuation or no network.
4. Communication module
As shown in fig. 6, the module for communicating with the remote server supports the cellular mobile network, WIFI, gigabit network, bluetooth, etc. to synchronize the locally cached data according to the agreed format, and also can receive the command issued by the remote server to perform simple operation and maintenance, such as angle adjustment, turning on/off the light, etc.
Data packet packaging/unpacking: assembling or disassembling the data according to the message format of the remote server;
checking and encryption/decryption: the data content is subjected to RSA asymmetric encryption and integrity check by using SHA256, so that no message loss is ensured;
event subscription: and processing and corresponding to the instruction sent by the remote server.
An automatic meter reading method and system based on machine vision and edge computing technology has the following advantages:
1) the method for automatically reading the meter by the pointer instrument has the advantages of no invasion, no change of the current situation and no influence on the current industrial equipment/system;
2) the method comprises the steps of having a distributed edge computing device with unified control management;
3) the method comprises the steps of identifying and positioning an instrument based on a neural network, positioning a pointer and a dial plate based on machine vision, and calculating numerical formulas of four image boundaries;
4) wifi, cellular mobile network, gigabit network, Bluetooth and other wired/wireless data transmission are supported;
5) secondary development and information encryption/decryption of software are supported.
The invention relates to a method for automatically reading a meter by a pointer instrument based on edge calculation (artificial intelligence and machine vision are combined); and adopting a data acquisition strategy of an industrial pointer instrument based on edge computing equipment.
The invention discloses a method for automatic meter reading under the conditions that an industrial scene is not changed, no invasion is caused, and the work of the existing meter is not influenced. The camera is adopted to shoot the instrument image and complete logic operation on the edge computing terminal equipment (namely, a machine vision method is used for replacing human eyes to read the pointer instrument), and finally data transmission is completed through the equipment to realize reporting.
The method described in the invention is based on reading the instrument by machine vision technology, and synchronously collecting data by using distributed intelligent edge computing technology. The intelligent application system is suitable for various application scenes such as smart cities, smart communities, intelligent manufacturing, traditional industries and the like, is simple and easy to use, and is rich in imagination and creativity.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. An automatic meter reading method based on machine vision and edge computing technology is characterized by comprising the following steps:
s1, starting edge computing equipment, wherein the edge computing equipment is terminal equipment and comprises a camera module, an image processing module, a data processing module and a communication module, the edge computing equipment is used as a computing edge to be registered, and the camera module on the edge computing equipment is started after permission is obtained;
s2, shooting the pointer instrument through the camera module, and outputting the shot image or video stream to the image processing module on the edge computing device;
s3, scene judgment is carried out through the image processing module, whether the images or video streams returned by the camera module contain pointer instruments or not is judged, if not, the step S2 is returned, and if yes, the next step is carried out;
s4, positioning the instrument through the image processing module;
s5, carrying out numerical value inference through the image processing module and outputting an inference result to a data processing module on the edge computing equipment;
s6, performing mean value filtering processing on the inferred result of the image processing module through the data processing module to obtain a meter reading value;
and S7, transmitting the filtered meter reading value to a remote server through a communication module.
2. The automatic meter reading method based on the machine vision and the edge computing technology as claimed in claim 1, wherein: in step S3, it is retrieved whether the image or video stream contains a pointer instrument based on the pre-trained neural network model.
3. The automatic meter reading method based on the machine vision and the edge computing technology as claimed in claim 2, wherein: the pre-trained neural network model is a LeNet neural network model.
4. The automatic meter reading method based on the machine vision and the edge computing technology as claimed in claim 2, wherein: in step S4, the pointer instrument coordinates obtained by the search are preprocessed, including scaling, noise reduction, gradation, and binarization, to obtain a pointer instrument image.
5. The automatic meter reading method based on the machine vision and the edge computing technology as claimed in claim 4, wherein: in step S5, the center coordinates of the meter in the pointer-type meter image are determined by machine vision technique, four image boundaries are drawn with the coordinates as (0, 0), the coordinates of the pointer are determined, the position of the image boundary where the pointer is located is determined, the value, the initial position, the minimum value, the maximum value and the step length of the meter are determined, and finally the inferred value is calculated according to the trigonometric function formula.
6. The automatic meter reading method based on the machine vision and the edge computing technology as claimed in claim 1, wherein: in step S6, mean filtering processing is performed by the data filter, the data filter is responsible for filtering generated values, fusing is triggered when the values exceed a maximum value and are less than a minimum value and suddenly increase or decrease, i.e., re-operation and continuous recursive processing are performed, and finally a meter reading value conforming to filtering is output, and data entering the data filter is locally cached for the basis of self-learning and parameter adjustment of the data filter.
7. The automatic meter reading method based on the machine vision and the edge computing technology as claimed in claim 1, wherein: in step S7, group verification is performed on the calculation result through the communication module, and then the meter reading value is pushed to the remote server by using the communication interface on the edge computing device, thereby completing data archiving.
8. An automatic meter reading system based on machine vision and edge computing technology is characterized in that: for implementing the method according to any one of claims 1 to 7.
9. The automated meter reading system based on machine vision and edge computing technology of claim 8, wherein: the system comprises terminal equipment and a remote server, wherein the terminal equipment is edge computing equipment, the edge computing equipment comprises a camera module, an image processing module, a data processing module and a communication module, the camera module shoots a pointer instrument, the output end of the camera module is connected with the image processing module, the output end of the image processing module is connected with the data processing module, and the data processing module is connected with the remote server through the communication module.
10. The automated meter reading system based on machine vision and edge computing technology of claim 9, wherein: the camera module comprises a camera, a horizontal moving mechanism, a vertical moving mechanism and an LED light supplementing lamp.
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