Meter data acquisition method and system based on image transmission
By setting marks on the meter and using image recognition technology to automatically identify and store the meter readings, the problem of low efficiency in data collection of meter instruments is solved, and efficient and low-cost data collection is achieved.
Patent Information
- Application Number
- CN202510709887.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-29
- Publication Date
- 2025-09-19
Smart Images
Figure CN120673433A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of data acquisition, and in particular relates to a meter data acquisition method and system based on image transmission. Background Art
[0002] Meters are devices used to measure, monitor, or record specific physical quantities. These quantities can include current, voltage, water flow, air flow, temperature, pressure, and more. Meters are widely used in various fields, such as electricity, water, meteorology, and industry.
[0003] With the development of metering instruments, intelligent meters have gradually become popular. However, there are still a large number of meters that do not have intelligent functions, such as gas meters and water meters, which require manual meter reading for data collection. This data collection method is inefficient and prone to data reporting errors. Summary of the Invention
[0004] The purpose of the present invention is to provide a meter data collection method based on image transmission, aiming to solve the problem that data collection by manual meter reading is inefficient and prone to data reporting errors.
[0005] The present invention is implemented as follows: a meter data collection method based on image transmission, the method comprising:
[0006] Constructing a meter statistics database, wherein the meter statistics database stores type data and tag data corresponding to each meter, and each meter is provided with at least three tags;
[0007] Collect and transmit meter image data, pre-process the meter image data, and identify meter types and markings;
[0008] Extract data from the mark, extract color information and position information, and construct a unique code for the meter based on the color information and position information;
[0009] The meter readings are extracted from the meter image data according to the meter type, the meter statistics database is queried according to the meter unique code, and the meter readings are stored.
[0010] Preferably, the steps of collecting and transmitting meter image data, preprocessing the meter image data, and identifying the meter type and marking specifically include:
[0011] Receive meter image data transmitted from the data acquisition terminal and record the upload time of the meter image data;
[0012] Extract the instrument image from the meter image data, perform grayscale processing and image transformation on the instrument image, and convert it into a line image;
[0013] The meter frame is compared based on the line image to determine the meter type, and the color information in the instrument image is recognized based on the line image to identify the mark.
[0014] Preferably, the step of extracting data from the mark, extracting color information and position information, and constructing a unique code for the meter based on the color information and position information specifically includes:
[0015] Construct a pixel reference coordinate system based on the instrument data, mark each pixel in the pixel reference coordinate system, and obtain pixel coordinates;
[0016] Determine the image center coordinates of each mark and the color information corresponding to each mark based on the pixel group corresponding to the mark;
[0017] The distance between the marks is extracted to obtain position information, and a character string is constructed based on the position information and the color information to obtain a unique code for the meter, wherein the character string includes at least a distance character and a color character.
[0018] Preferably, the steps of extracting meter readings from meter image data according to meter type, querying a meter statistics database according to a unique meter code, and storing the meter readings specifically include:
[0019] Determine the instrument data display mode according to the meter type, and query to obtain the corresponding data reading solution;
[0020] Extract the corresponding meter reading from the instrument image based on the data reading solution;
[0021] The meter statistics database is queried based on the unique code of the meter, the meter number is determined, and the meter reading is stored in the meter statistics database.
[0022] Preferably, in the step of identifying the mark, the contour corresponding to the mark is determined by image processing, a contour coordinate set and an accumulator array are constructed, and the alternative center coordinates and mark radius are determined by the following formulas: a = xr*cos(θ), b = yr*sin(θ), r = sqrt((xa)^2+(yb)^2), where θ is radians and (a, b) are the center coordinates. For each set of calculated a, b and r, they are accumulated in the accumulator array, and the center coordinates and mark radius are determined based on the peak points in the accumulator array.
[0023] Another object of the present invention is to provide a meter data acquisition system based on image transmission, the system comprising:
[0024] A database construction module is used to construct a meter statistics database, wherein the meter statistics database stores type data and tag data corresponding to each meter, and each meter is provided with at least three tags;
[0025] Image preprocessing module, used to collect and transmit meter image data, preprocess the meter image data, and identify the meter type and marking;
[0026] The code recognition module is used to extract data from the mark, extract color information and position information, and construct a unique code for the meter based on the color information and position information;
[0027] The data acquisition module is used to extract meter readings from meter image data according to meter type, query the meter statistics database according to the meter unique code, and store the meter readings.
[0028] Preferably, the image preprocessing module includes:
[0029] A data receiving unit is used to receive meter image data transmitted from a data acquisition terminal and record the upload time of the meter image data;
[0030] An image conversion unit is used to extract the instrument image from the meter image data, perform grayscale processing and image transformation on the instrument image, and convert it into a line image;
[0031] The mark recognition unit is used to compare the meter frame based on the line image, determine the meter type, and recognize the color information in the instrument image based on the line image to identify the mark.
[0032] Preferably, the coding recognition module includes:
[0033] A pixel coordinate recognition unit is used to construct a pixel reference coordinate system based on the instrument data, mark each pixel in the pixel reference coordinate system, and obtain pixel coordinates;
[0034] an information extraction unit, configured to determine the image center coordinates of each mark and the color information corresponding to each mark based on the pixel group corresponding to the mark;
[0035] The code synthesis unit is used to extract the distance between the marks, obtain the position information, construct a character string based on the position information and the color information, and obtain a unique code for the meter, wherein the character string includes at least a distance character and a color character.
[0036] Preferably, the data acquisition module includes:
[0037] The reading mode identification unit is used to determine the meter data display mode according to the meter type and query to obtain the corresponding data reading solution;
[0038] An instrument reading unit, configured to extract corresponding instrument readings from an instrument image based on a data reading scheme;
[0039] The data recording unit is used to query the meter statistics database according to the unique code of the meter, determine the meter number, and store the meter reading in the meter statistics database.
[0040] Preferably, in the step of identifying the mark, the contour corresponding to the mark is determined by image processing, a contour coordinate set and an accumulator array are constructed, and the alternative center coordinates and mark radius are determined by the following formulas: a = xr*cos(θ), b = yr*sin(θ), r = sqrt((xa)^2+(yb)^2), where θ is radians and (a, b) are the center coordinates. For each set of calculated a, b and r, they are accumulated in the accumulator array, and the center coordinates and mark radius are determined based on the peak points in the accumulator array.
[0041] The present invention provides a meter data collection method based on image transmission. By setting a mark on a non-intelligent meter, the mark is used to calibrate the non-intelligent meter, and the mark is used to determine the unique identity of the meter. Subsequently, image recognition is used to identify the meter reading, and data collection and storage are automatically completed, which greatly improves the degree of automation and has extremely low construction costs. BRIEF DESCRIPTION OF THE DRAWINGS
[0042] Figure 1 A flowchart of a meter data acquisition method based on image transmission provided by an embodiment of the present invention;
[0043] Figure 2 A flowchart of the steps of collecting and transmitting meter image data, preprocessing the meter image data, and identifying the meter type and marking provided in an embodiment of the present invention;
[0044] Figure 3 A flowchart of the steps of extracting data from a mark, extracting color information and position information, and constructing a unique code for the meter based on the color information and position information provided by an embodiment of the present invention;
[0045] Figure 4 A flowchart of the steps of extracting meter readings from meter image data according to meter type, querying a meter statistics database according to a unique meter code, and storing the meter readings, provided in an embodiment of the present invention;
[0046] Figure 5 An architectural diagram of a meter data acquisition system based on image transmission provided by an embodiment of the present invention;
[0047] Figure 6 An architectural diagram of an image preprocessing module provided by an embodiment of the present invention;
[0048] Figure 7 An architectural diagram of a coding recognition module provided in an embodiment of the present invention;
[0049] Figure 8 This is an architectural diagram of a data acquisition module provided in an embodiment of the present invention. DETAILED DESCRIPTION
[0050] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.
[0051] It is understood that the terms "first," "second," etc., used herein may be used to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script without departing from the scope of this application.
[0052] like Figure 1 FIG. 1 is a flow chart of a meter data collection method based on image transmission provided by an embodiment of the present invention, the method comprising:
[0053] S100: Build a meter statistics database. The meter statistics database stores type data and tag data corresponding to each meter. Each meter is set with at least three tags.
[0054] In this step, a meter statistics database is constructed. For non-intelligent meters, they do not have the ability to communicate wirelessly, so data collection can only be completed manually. Manual collection is through manual reading and manual data entry. Therefore, whether it is data reading or data transcription, problems are prone to occur, and the operation process is relatively cumbersome. In order to solve this problem, a mark is set on each non-intelligent meter (hereinafter referred to as a meter). The mark can be a pattern of special shape and different colors, such as circular patches of different colors. The number of marks can be three or more. In this embodiment, the number of marks is three. Each meter is determined according to the relative position relationship between the marks and the color of the marks, and is recorded in the meter statistics database, which is the mark data. In other words, there is a correspondence between the mark data and the meter; the type data recorded in the meter statistics database is the specific type of the meter, such as a gas meter, flow meter or pressure gauge, etc., which may also include the installation location of the meter and the household owner information.
[0055] S200 , collecting and transmitting meter image data, pre-processing the meter image data, and identifying the meter type and mark.
[0056] In this step, the meter image data is collected and transmitted. When collecting data, the staff uses a handheld terminal to collect images. The handheld terminal can be a mobile phone or other mobile device with image collection function. After taking the photo, the meter image data is obtained. The meter image data contains the instrument image. In this process, through preprocessing, the instrument outline in the instrument image is revealed, and then it is compared with the preset frame using image recognition to determine the meter type of the instrument, and the mark is identified according to color and shape to determine the position of each mark in the instrument image.
[0057] S300: extract data from the tag, extract color information and position information, and construct a unique code for the meter based on the color information and position information.
[0058] In this step, data is extracted from the mark. The mark is fixed on the meter. When installing the mark, a random setting method is adopted. Three marks of different colors are randomly selected and the three marks are randomly fixed in the same plane on the meter. However, during installation, the mark is not allowed to block the reading part of the meter. Then, when extracting the mark, the center position of each mark is determined by image recognition, and the mutual distance between the three marks is further determined to obtain the position information, and the color information is identified. The position information and color information are used to construct a unique code for the meter. Since the colors of the three marks are different and the position relationship is random, the final unique code for the meter is also random. In order to further improve the non-repeatability of the code, the type information of the meter can be added to the unique code of the meter.
[0059] S400: extracting meter readings from meter image data according to meter type, searching a meter statistics database according to the meter unique code, and storing the meter readings.
[0060] In this step, the meter reading is extracted from the meter image data according to the meter type. After determining the meter type, the reading method of this type of meter can be queried. The instrument image is identified using image recognition technology, and the reading recorded therein is extracted, which is the meter reading. At this time, the meter unique code of the meter has been obtained based on the position information and color information, so the meter statistics database can be queried based on this. After the mark installation is completed, the meter unique code of the meter is determined and recorded in the meter statistics database. Therefore, after extracting the meter reading, it can be bound to the corresponding meter to complete the storage of the meter reading.
[0061] like Figure 2 As shown, as a preferred embodiment of the present invention, the steps of collecting and transmitting meter image data, preprocessing the meter image data, and identifying the meter type and marking specifically include:
[0062] S201, receiving meter image data transmitted from a data acquisition terminal, and recording the upload time of the meter image data.
[0063] In this step, the meter image data transmitted from the data acquisition terminal is received. The data acquisition staff uses the handheld terminal to collect the image of the meter to obtain the meter image, and records the generation time of the meter image, and transmits it as meter image data to the background for processing. The background can be a cloud server or a local server.
[0064] S202 , extracting the instrument image from the meter image data, performing grayscale processing and image transformation on the instrument image, and converting it into a line image.
[0065] In this step, the instrument image is extracted from the meter image data. In order to facilitate meter identification, the instrument image is directly grayscaled to reduce the amount of information in the instrument image. Hough transform is further performed. Specifically, the instrument image is copied multiple times. After grayscale processing, the instrument image is subjected to Hough linear transform respectively on the two groups of grayscale-processed instrument images to identify the lines in the instrument image, that is, to convert it into a line image.
[0066] S203 , performing meter frame comparison based on the line image to determine the meter type, and recognizing color information in the meter image based on the line image to identify the mark.
[0067] In this step, the instrument frame is compared based on the line image. When the shooting angle is different, the picture may be deformed. For example, when a circular object is photographed from the side with a camera, the circle will be deformed into an ellipse. In order to correct the above deformation, the instrument image is processed by perspective transformation so that the mark in the instrument image is restored to a circle. In this step, the grayscale processed instrument image is subjected to Hough circle transformation to obtain a circular image. The circular image and the line image are superimposed, and circles larger than the mark size are screened out to obtain a meter frame diagram. The appearance of each meter is recorded in the meter statistical database, so that the meter type can be determined by comparison. According to the circular area in the circular image, the original instrument image is framed to obtain multiple solid color circular areas, which are the areas corresponding to the mark. In this process, the wheel corresponding to the mark is determined by image processing. Contour, construct the contour coordinate set and accumulator array, and determine the alternative circle center coordinates and marker radius by the following formula: a=xr*cos(θ), b=yr*sin(θ), r=sqrt((xa)^2+(yb)^2), where θ is radians and (a, b) are the circle center coordinates. For each set of calculated a, b and r, they are accumulated in the accumulator array, and the circle center coordinates and marker radius are determined according to the peak points in the accumulator array. After determining the circular area corresponding to the mark, the color value is extracted to obtain the color information, thereby determining the color of each mark. Specifically, in order to facilitate identification, the number of mark colors needs to be limited to avoid the situation where the colors of two marks are too close and cannot be distinguished, such as distinguishing according to red, orange, green, blue, indigo, purple, pink, brown, gray, black and white.
[0068] like Figure 3 As shown, as a preferred embodiment of the present invention, the steps of extracting data from the mark, extracting color information and position information, and constructing a unique code for the meter based on the color information and position information specifically include:
[0069] S301: construct a pixel reference coordinate system based on the instrument data, mark each pixel in the pixel reference coordinate system, and obtain pixel coordinates.
[0070] In this step, a pixel reference coordinate system is constructed based on the instrument data, a two-dimensional coordinate system is constructed, and the distance between pixels in the instrument image is defined as a unit. Then, a pixel in the instrument image is randomly determined and used as the origin to determine the coordinates of all remaining pixels and obtain the pixel coordinates of each pixel.
[0071] S302 , determining the image center coordinates of each mark based on the pixel group corresponding to the mark, and determining the color information corresponding to each mark.
[0072] In this step, based on the pixel group corresponding to the mark, each mark is a circular area, and a mark contains multiple pixels. Then the average coordinate value of each pixel coordinate is calculated, which is the image center coordinate of the mark, and the color of the mark is identified.
[0073] S303, extracting the distance between the marks to obtain position information, constructing a character string based on the position information and the color information to obtain a unique code for the meter, wherein the character string includes at least a distance character and a color character.
[0074] In this step, the distance between the markers is extracted. The image center coordinates of each marker have been determined. Based on the distance formula between two points, the distance value between any two markers can be calculated, which is the position information. In the previous step, the type of meter has been determined. In order to facilitate classification, the meter code is determined according to the type of meter. For example, the gas meter is identified by the character A and the flow meter is identified by the character B. The markers are set in multiple colors and can be defined as identified by different characters. For example, ABC is used to indicate the type of meter. There are 10 colors of markers, so DEFGHIJ is used. KLM represents ten colors respectively, and the distance value between the marks is L. When there are three groups of marks, there are three groups of distance values, namely L1, L2 and L3. Then, based on the above characters, the unique code of the meter can be obtained. The unique code of the meter includes the meter type character, color character and distance character. For example, the unique code of the meter is A-FHJ-L1L2L3, where L1 is the distance between the marks with colors F and H, L2 is the distance between the marks with colors J and H, and L3 is the distance between the marks with colors F and J. When arranging, L1, L2 and L3 are arranged in ascending order according to size.
[0075] like Figure 4 As shown, as a preferred embodiment of the present invention, the steps of extracting meter readings from meter image data according to meter type, querying a meter statistics database according to a unique meter code, and storing the meter readings specifically include:
[0076] S401, determining the meter data display mode according to the meter type, and querying to obtain the corresponding data reading solution.
[0077] In this step, the meter data display method is determined according to the meter type. After obtaining the unique code of the meter, the display method of the meter can be determined according to the meter type recorded therein. Some meters use digital direct display, such as gas meters, while some meters use pointer display, such as flow meters, pressure gauges, etc. Different data extraction methods are used for different display methods to obtain the meter readings displayed by the corresponding instrument.
[0078] S402: extracting corresponding meter readings from the meter image based on a data reading solution.
[0079] S403: query the meter statistics database according to the unique code of the meter, determine the meter number, and store the meter reading in the meter statistics database.
[0080] In this step, the meter statistics database is queried based on the meter unique code. After the meter installation is completed, the meter unique code of each meter can be determined, and the meter statistics database can be constructed. Then, in the subsequent data collection process, the identity of the meter can be determined by extracting the corresponding meter unique code based on the returned meter image. However, it is taken into account here that due to the different angles of collection of different devices, there is a certain error in the distance value L finally collected. When comparing the unique codes of the meters, the error is taken into account. Specifically, based on The unique meter code generated from the meter image is A-FHJ-L1L2L3, and the unique meter code recorded in the meter statistics database is A-FHJ-L4L5L6. After completing the meter type character comparison and mark color comparison, it is calculated that P1=L1 / L4, P2=L2 / L5, and P3=L3 / L6. P1, P2, and P3 are compared with the preset value P. If the difference is greater than the preset value, it means that the two do not match, otherwise they match. When it is determined to be a match, the identity of the meter corresponding to the unique meter code is determined.
[0081] like Figure 5 As shown, a meter data acquisition system based on image transmission is provided in an embodiment of the present invention, and the system includes:
[0082] The database construction module 100 is used to construct a meter statistics database. The meter statistics database stores type data and tag data corresponding to each meter. Each meter is provided with at least three tags.
[0083] In this system, the database construction module 100 constructs a meter statistics database. For non-intelligent meters, they do not have the ability to communicate wirelessly, so data collection can only be completed manually. Manual collection is through manual reading and manual data entry. Therefore, whether it is data reading or data transcription, problems are prone to occur, and the operation process is relatively cumbersome. To solve this problem, a mark is set on each non-intelligent meter (hereinafter referred to as a meter). The mark can be a pattern of a special shape and different colors, such as circular patches of different colors. The number of marks can be three or more. In this embodiment, the number of marks is three. Each meter is identified according to the relative position relationship between the marks and the color of the marks, and recorded in the meter statistics database, which is the mark data. In other words, there is a correspondence between the mark data and the meter. The type data recorded in the meter statistics database is the specific type of the meter, such as a gas meter, flow meter or pressure meter, which may also include the installation location of the meter and the household owner information.
[0084] The image pre-processing module 200 is used to collect and transmit meter image data, pre-process the meter image data, and identify the meter type and mark.
[0085] In this system, the image preprocessing module 200 collects and transmits meter image data. When collecting data, the staff uses a handheld terminal to collect images. The handheld terminal can be a mobile phone or other mobile device with image collection function. After taking the photo, the meter image data is obtained. The meter image data contains the instrument image. In this process, through preprocessing, the instrument outline in the instrument image is revealed, and then it is compared with the preset frame by using image recognition to determine the meter type of the instrument, and the mark is identified according to color and shape to determine the position of each mark in the instrument image.
[0086] The code recognition module 300 is used to extract data from the mark, extract color information and position information, and construct a unique code for the meter based on the color information and position information.
[0087] In this system, the coding recognition module 300 extracts data from the mark, which is fixed on the meter. When installing the mark, a random setting method is adopted, and three marks of different colors are randomly selected and randomly fixed in the same plane on the meter. However, during installation, the mark is not allowed to block the reading part of the meter. Then, when extracting the mark, the center position of each mark is determined by image recognition, and the mutual distance between the three marks is further determined to obtain the position information, and the color information is identified. The position information and color information are used to construct a unique code for the meter. Since the colors of the three marks are different and the position relationship is random, the final unique code for the meter is also random. In order to further improve the non-repeatability of the code, the type information of the meter can be added to the unique code of the meter.
[0088] The data acquisition module 400 is used to extract meter readings from meter image data according to meter type, query a meter statistics database according to the meter unique code, and store the meter readings.
[0089] In this system, the data acquisition module 400 extracts the meter reading from the meter image data according to the meter type. After determining the meter type, the reading method of this type of meter can be queried and the instrument image is identified using image recognition technology. The reading recorded therein is extracted, which is the meter reading. At this time, the meter unique code of the meter has been obtained based on the position information and color information, so the meter statistics database can be queried based on this. After the tag installation is completed, the meter unique code of the meter is determined and recorded in the meter statistics database. Therefore, after extracting the meter reading, it can be bound to the corresponding meter to complete the storage of the meter reading.
[0090] like Figure 6 As shown, as a preferred embodiment of the present invention, the image preprocessing module 200 includes:
[0091] The data receiving unit 201 is configured to receive meter image data transmitted from a data acquisition terminal and record the upload time of the meter image data.
[0092] In this module, the data receiving unit 201 receives meter image data transmitted from the data acquisition terminal. The data acquisition staff uses the handheld terminal to collect images of the instrument to obtain the instrument image, and records the generation time of the instrument image, and transmits it as meter image data to the background for processing. The background can be a cloud server or a local server.
[0093] The image conversion unit 202 is used to extract the instrument image from the meter image data, perform grayscale processing and image transformation on the instrument image, and convert it into a line image.
[0094] In this module, the image conversion unit 202 extracts the instrument image from the meter image data. To facilitate meter identification, the instrument image is directly grayscaled to reduce the amount of information in the instrument image. The Hough transform is further performed. Specifically, the instrument image is copied multiple times. After the instrument image is grayscaled, the two groups of grayscaled instrument images are respectively subjected to Hough linear transform to identify the lines in the instrument image, that is, to convert it into a line image.
[0095] The mark recognition unit 203 is used to compare the meter frame based on the line image to determine the meter type, and recognize the color information in the meter image based on the line image to identify the mark.
[0096] In this module, the mark recognition unit 203 compares the instrument frame based on the line image. When the shooting angle is different, the picture may be deformed. For example, when a circular object is photographed from the side by a camera, the circle will be deformed into an ellipse. In order to correct the above deformation, the instrument image is processed by perspective transformation so that the mark in the instrument image is restored to a circle. In this step, the grayscale processed instrument image is subjected to Hough circle transformation to obtain a circular image. The circular image and the line image are superimposed, and circles larger than the mark size are screened out to obtain a meter frame diagram. The appearance of each meter is recorded in the meter statistical database, so that the meter type can be determined by comparison. According to the circular area in the circular image, the original instrument image is framed to obtain multiple solid color circular areas, which are the areas corresponding to the mark. In this process, the mark is determined by image processing. Record the corresponding contour, construct the contour coordinate set and accumulator array, and determine the alternative circle center coordinates and marker radius using the following formula: a = xr*cos(θ), b = yr*sin(θ), r = sqrt((xa)^2+(yb)^2), where θ is radians and (a, b) are the circle center coordinates. For each set of calculated a, b, and r, they are accumulated in the accumulator array. The circle center coordinates and marker radius are determined based on the peak points in the accumulator array. After determining the circular area corresponding to the marker, the color value is extracted to obtain the color information, thereby determining the color of each marker. Specifically, for ease of identification, the number of marker colors needs to be limited to avoid the situation where the colors of two markers are too close and cannot be distinguished, such as distinguishing according to red, orange, green, blue, indigo, purple, pink, brown, gray, black, and white.
[0097] like Figure 7 As shown, as a preferred embodiment of the present invention, the code recognition module 300 includes:
[0098] The pixel coordinate identification unit 301 is used to construct a pixel reference coordinate system based on the instrument data, mark each pixel in the pixel reference coordinate system, and obtain pixel coordinates.
[0099] In this module, the pixel coordinate identification unit 301 constructs a pixel reference coordinate system based on the instrument data, constructs a two-dimensional coordinate system, and defines the distance between pixels in the instrument image as a unit. Then, by randomly determining a pixel in the instrument image and using it as the origin, the coordinates of all remaining pixels can be determined to obtain the pixel coordinates of each pixel.
[0100] The information extraction unit 302 is configured to determine the image center coordinates of each mark and the color information corresponding to each mark based on the pixel group corresponding to the mark.
[0101] In this module, the information extraction unit 302 calculates the average coordinate value of each pixel coordinate based on the pixel group corresponding to the mark. Each mark is a circular area, and a mark contains multiple pixels. The average coordinate value is the image center coordinate of the mark, and the color of the mark is identified.
[0102] The code synthesis unit 303 is used to extract the distance between the marks, obtain the position information, construct a character string based on the position information and the color information, and obtain a unique code for the meter. The character string includes at least a distance character and a color character.
[0103] In this module, the code synthesis unit 303 extracts the distance between the markers and has determined the image center coordinates of each marker. Based on the distance formula between two points, the distance value between any two markers can be calculated, which is the position information. In the previous step, the type of meter has been determined. In order to facilitate classification, the code of the meter is determined according to the type of meter. For example, the gas meter is identified by the character A and the flow meter is identified by the character B. The markers are set in multiple colors and can be defined as identified by different characters. For example, ABC is used to indicate the type of meter, and there are 10 colors of markers, so DEF is used. GHIJKLM represent ten colors respectively, and the distance value between the marks is L. When there are three groups of marks, there are three groups of distance values, namely L1, L2 and L3. Then, based on the above characters, the unique code of the meter can be obtained. The unique code of the meter includes the meter type character, color character and distance character. For example, the unique code of the meter is A-FHJ-L1L2L3, where L1 is the distance between the marks with colors F and H, L2 is the distance between the marks with colors J and H, and L3 is the distance between the marks with colors F and J. When arranging, L1, L2 and L3 are arranged in ascending order according to size.
[0104] like Figure 8 As shown, as a preferred embodiment of the present invention, the data acquisition module 400 includes:
[0105] The reading mode identification unit 401 is used to determine the meter data display mode according to the meter type and query to obtain the corresponding data reading solution.
[0106] In this module, the reading mode identification unit 401 determines the meter data display mode according to the meter type. After obtaining the unique code of the meter, the display mode of the meter can be determined according to the meter type recorded therein. Some meters use digital direct display, such as gas meters, while some meters use pointer display, such as flow meters and pressure gauges. Different data extraction methods are used for different display modes to obtain the meter reading displayed by the corresponding instrument.
[0107] The meter reading unit 402 is configured to extract corresponding meter readings from the meter image based on a data reading solution.
[0108] The data recording unit 403 is used to query the meter statistics database according to the unique code of the meter, determine the meter number, and store the meter reading in the meter statistics database.
[0109] In this module, the meter statistics database is queried based on the meter's unique code. After the meter is installed, the meter's unique code can be determined for each meter, and the meter statistics database can be constructed. Then, in the subsequent data collection process, the identity of the meter can be determined by extracting the corresponding meter's unique code based on the returned meter image. However, it is taken into account here that due to the different angles of collection of different devices, there is a certain error in the distance value L finally collected. When comparing the meter's unique code, the error is taken into account. Specifically, based on The unique meter code generated from the meter image is A-FHJ-L1L2L3, and the unique meter code recorded in the meter statistics database is A-FHJ-L4L5L6. After completing the meter type character comparison and mark color comparison, it is calculated that P1=L1 / L4, P2=L2 / L5, and P3=L3 / L6. P1, P2, and P3 are compared with the preset value P. If the difference is greater than the preset value, it means that the two do not match, otherwise they match. When it is determined to be a match, the identity of the meter corresponding to the unique meter code is determined.
[0110] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.
[0111] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
[0112] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0113] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.
[0114] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.
Claims
1. A meter data collection method based on image transmission, characterized in that: The method comprises: Constructing a meter statistics database, wherein the meter statistics database stores type data and tag data corresponding to each meter, and each meter is provided with at least three tags; Collect and transmit meter image data, pre-process the meter image data, and identify meter types and markings; Extract data from the mark, extract color information and position information, and construct a unique code for the meter based on the color information and position information; The meter readings are extracted from the meter image data according to the meter type, the meter statistics database is queried according to the meter unique code, and the meter readings are stored.
2. The meter data collection method based on image transmission according to claim 1, characterized in that: The steps of collecting and transmitting meter image data, preprocessing the meter image data, and identifying the meter type and marking specifically include: Receive meter image data transmitted from the data acquisition terminal and record the upload time of the meter image data; Extract the instrument image from the meter image data, perform grayscale processing and image transformation on the instrument image, and convert it into a line image; The meter frame is compared based on the line image to determine the meter type, and the color information in the instrument image is recognized based on the line image to identify the mark.
3. The meter data acquisition method based on image transmission according to claim 2, characterized in that: The steps of extracting data from the mark, extracting color information and position information, and constructing a unique code for the meter based on the color information and position information specifically include: Construct a pixel reference coordinate system based on the instrument data, mark each pixel in the pixel reference coordinate system, and obtain pixel coordinates; Determine the image center coordinates of each mark and the color information corresponding to each mark based on the pixel group corresponding to the mark; The distance between the marks is extracted to obtain position information, and a character string is constructed based on the position information and the color information to obtain a unique code for the meter, wherein the character string includes at least a distance character and a color character.
4. The meter data acquisition method based on image transmission according to claim 1, characterized in that: The steps of extracting meter readings from meter image data according to meter type, querying a meter statistics database according to a unique meter code, and storing the meter readings specifically include: Determine the instrument data display mode according to the meter type, and query to obtain the corresponding data reading solution; Extract the corresponding meter reading from the instrument image based on the data reading solution; The meter statistics database is queried based on the unique code of the meter, the meter number is determined, and the meter reading is stored in the meter statistics database.
5. The meter data collection method based on image transmission according to claim 2, characterized in that: In the step of identifying the mark, the contour corresponding to the mark is determined by image processing, and a contour coordinate set and an accumulator array are constructed. The alternative center coordinates and the mark radius are determined by the following formulas: a = xr*cos(θ), b = yr*sin(θ), r = sqrt((xa)^2+(yb)^2), where θ is radians and (a, b) are the center coordinates. For each set of calculated a, b, and r, they are accumulated in the accumulator array, and the center coordinates and the mark radius are determined based on the peak points in the accumulator array.
6. A meter data acquisition system based on image transmission, characterized in that: The system comprises: A database construction module is used to construct a meter statistics database, wherein the meter statistics database stores type data and tag data corresponding to each meter, and each meter is provided with at least three tags; Image preprocessing module, used to collect and transmit meter image data, preprocess the meter image data, and identify the meter type and marking; The code recognition module is used to extract data from the mark, extract color information and position information, and construct a unique code for the meter based on the color information and position information; The data acquisition module is used to extract meter readings from meter image data according to meter type, query the meter statistics database according to the meter unique code, and store the meter readings.
7. The meter data acquisition system based on image transmission according to claim 6, characterized in that: The image preprocessing module includes: A data receiving unit is used to receive meter image data transmitted from a data acquisition terminal and record the upload time of the meter image data; An image conversion unit is used to extract the instrument image from the meter image data, perform grayscale processing and image transformation on the instrument image, and convert it into a line image; The mark recognition unit is used to compare the meter frame based on the line image to determine the meter type, and recognize the color information in the instrument image based on the line image to identify the mark.
8. The meter data acquisition system based on image transmission according to claim 7, characterized in that: The coding recognition module includes: A pixel coordinate recognition unit is used to construct a pixel reference coordinate system based on the instrument data, mark each pixel in the pixel reference coordinate system, and obtain pixel coordinates; an information extraction unit, configured to determine the image center coordinates of each mark and the color information corresponding to each mark based on the pixel group corresponding to the mark; The code synthesis unit is used to extract the distance between the marks, obtain the position information, construct a character string based on the position information and the color information, and obtain a unique code for the meter, wherein the character string includes at least a distance character and a color character.
9. The meter data acquisition system based on image transmission according to claim 6, characterized in that: The data acquisition module includes: The reading mode identification unit is used to determine the meter data display mode according to the meter type and query to obtain the corresponding data reading solution; An instrument reading unit, configured to extract corresponding instrument readings from an instrument image based on a data reading scheme; The data recording unit is used to query the meter statistics database according to the unique code of the meter, determine the meter number, and store the meter reading in the meter statistics database.
10. The meter data acquisition system based on image transmission according to claim 7, characterized in that: In the step of identifying the mark, the contour corresponding to the mark is determined by image processing, and a contour coordinate set and an accumulator array are constructed. The alternative center coordinates and the mark radius are determined by the following formulas: a = xr*cos(θ), b = yr*sin(θ), r = sqrt((xa)^2+(yb)^2), where θ is radians and (a, b) are the center coordinates. For each set of calculated a, b, and r, they are accumulated in the accumulator array, and the center coordinates and the mark radius are determined based on the peak points in the accumulator array.