A method, system, and terminal for monitoring the installation of water meters.

Abstract

This invention discloses a method, system, and terminal for monitoring the installation of water meters, relating to the field of water meter technology. The method includes: acquiring water meter installation information uploaded by construction personnel based on a water meter installation plan; the water meter installation information includes actual installation images; acquiring standard installation images; and performing several types of feature comparison operations based on the actual installation images and the standard installation images; the several types of feature comparison operations include at least one of the following: dial orientation feature comparison, interface position feature comparison, buckle feature comparison, and reading window feature comparison; and determining whether the water meter is installed correctly based on the comparison results of the several types of feature comparison operations. This invention replaces traditional manual inspection with automated image feature comparison, fundamentally solving the problems of high labor costs, low detection efficiency, and inconsistent standards in water meter installation monitoring.

Description

Technical Field

[0001] This invention relates to the field of water meter technology, and in particular to a water meter installation and monitoring method, system, and terminal. Background Technology

[0002] As a key component in the water supply system for measuring water consumption, the installation quality of water meters directly determines the metering accuracy, equipment lifespan, and subsequent pipeline network operation and maintenance efficiency.

[0003] Water meter installation projects typically employ a multi-point, decentralized operation model. Currently, the main method for monitoring the quality of water meter installation is manual on-site inspection: professional quality inspectors are dispatched to each construction site to visually assess the installation status of the water meters and manually record the results. This is the traditional mainstream method, relying heavily on the experience and judgment of the quality inspectors.

[0004] However, manual on-site inspections require covering a large number of scattered locations, resulting in high labor costs and long inspection cycles, making them unsuitable for the time-sensitive needs of large-scale construction scenarios. Furthermore, the judgment standards of different quality inspectors may vary, making it difficult to guarantee the objectivity of monitoring results.

[0005] Therefore, existing technologies still need improvement and development. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a water meter installation monitoring method, system and terminal to address the above-mentioned defects of the prior art. The aim is to solve the problems of high labor costs, low detection efficiency and inconsistent standards caused by the existing method of monitoring water meter installation quality through manual on-site inspection.

[0007] The technical solution adopted by this invention to solve the problem is as follows:

[0008] In a first aspect, embodiments of the present invention provide a method for monitoring the installation of a water meter, the method comprising:

[0009] Obtain water meter installation information uploaded by construction personnel based on the water meter installation plan; the water meter installation information includes: actual installation photos;

[0010] Obtain a standard installation image, and perform several types of feature comparison operations based on the actual installation image and the standard installation image; the several types of feature comparison operations include at least one of the following operations: dial orientation feature comparison operation, interface position feature comparison operation, buckle feature comparison operation, and reading window feature comparison operation.

[0011] Based on the comparison results of the aforementioned feature comparison operations, it is determined whether the water meter is installed correctly.

[0012] In one implementation, the step of obtaining a standard installation image is followed by:

[0013] The actual installation image and the standard installation image are preprocessed; the preprocessing includes at least one of the following: unified resolution, angle correction, illumination equalization, and noise filtering.

[0014] In one embodiment, the step of performing a comparison of several types of features based on the actual installation image and the standard installation image further includes:

[0015] The feature point sets corresponding to the actual installation image and the standard installation image are extracted using a preset feature point detection algorithm.

[0016] Calculate the total number of feature point pairs between the two feature point sets, and calculate the number of feature point matching pairs between the two feature point sets using a preset feature point matching algorithm;

[0017] The effective matching rate is calculated based on the total number of feature point pairs and the number of matching feature point pairs.

[0018] If the effective matching rate reaches a preset matching rate threshold, then several feature comparison operations are performed based on the actual installation image and the standard installation image.

[0019] In one embodiment, the dial orientation feature comparison operation includes:

[0020] For each image, a preset text identifier is located within the image using a preset text detection algorithm, and the rotation angle of the minimum bounding rectangle of the preset text identifier is calculated to obtain the first rotation angle corresponding to the actual installation image and the second rotation angle corresponding to the standard installation image.

[0021] The angle deviation value is calculated based on the first rotation angle and the second rotation angle, and the angle deviation value is used as the comparison result of the dial orientation feature comparison operation.

[0022] In one implementation, the interface location feature comparison operation includes:

[0023] For each image, the interface identifier within the image is located using a preset template matching algorithm, and the center point coordinates of the interface identifier are calculated to obtain the first coordinate data corresponding to the actual installation image and the second coordinate data corresponding to the standard installation image.

[0024] Based on the first coordinate data and the second coordinate data, the interface position deviation value is calculated, and the interface position deviation value is used as the comparison result of the interface position feature comparison operation.

[0025] In one embodiment, the buckle feature comparison operation includes:

[0026] For each image, the buckles are identified using a preset edge detection algorithm and contour filtering algorithm, and the number of buckles and center point coordinates are calculated to obtain the first number of buckles and the third coordinate data corresponding to the actual installation image, as well as the second number of buckles and the fourth coordinate data corresponding to the standard installation image.

[0027] The buckle quantity deviation value is calculated based on the first buckle quantity and the second buckle quantity, and the buckle position deviation value is calculated based on the third coordinate data and the fourth coordinate data. The buckle quantity deviation value and the buckle position deviation value are used as the comparison results of the buckle feature comparison operation.

[0028] In one implementation, the reading window feature comparison operation includes:

[0029] For each image, a pre-defined edge detection algorithm is used to identify the reading window region. The total area of ​​the reading window and the area of ​​the occluded region are calculated. Based on the total area and the area of ​​the occluded region, the occlusion area ratio is calculated to obtain the first occlusion area ratio corresponding to the actual installation image and the second occlusion area ratio corresponding to the standard installation image.

[0030] The deviation value of the occlusion area ratio is calculated based on the first occlusion area ratio and the second occlusion area ratio, and the deviation value of the occlusion area ratio is used as the comparison result of the reading window feature comparison operation.

[0031] In one implementation, determining whether the water meter is installed correctly based on the comparison results of the aforementioned feature comparison operations includes:

[0032] Each comparison result is scored individually.

[0033] The total score is obtained by weighted summing of the scores corresponding to all the comparison results.

[0034] If the total score reaches the preset score threshold, the water meter installation is deemed qualified.

[0035] Secondly, embodiments of the present invention also provide a water meter installation monitoring system, the system comprising:

[0036] The acquisition module is used to acquire water meter installation information uploaded by construction personnel based on the water meter installation plan; the water meter installation information includes: actual installation pictures;

[0037] The comparison module is used to acquire a standard installation image and perform several types of feature comparison operations based on the actual installation image and the standard installation image; the several types of feature comparison operations include at least one of the following operations: dial orientation feature comparison operation, interface position feature comparison operation, buckle feature comparison operation, and reading window feature comparison operation.

[0038] The judgment module is used to determine whether the water meter is installed correctly based on the comparison results of the comparison operations of the aforementioned features.

[0039] Thirdly, embodiments of the present invention also provide a terminal, the terminal including a memory and one or more processors; the memory stores one or more programs; the programs include instructions for executing the water meter installation monitoring method as described above; the processor is used to execute the programs.

[0040] The beneficial effects of this invention are as follows: This invention acquires water meter installation information uploaded by construction personnel based on the water meter installation plan. This installation information includes actual installation photos. A standard installation photo is then acquired, and several types of feature comparison operations are performed between the actual installation photo and the standard installation photo. These feature comparison operations include at least one of the following: dial orientation feature comparison, interface position feature comparison, buckle feature comparison, and reading window feature comparison. Based on the comparison results of these feature comparison operations, it is determined whether the water meter is installed correctly. This invention replaces traditional manual inspection with automated image feature comparison, fundamentally solving the problems of high labor costs, low detection efficiency, and inconsistent standards in water meter installation monitoring. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating the water meter installation and monitoring method provided in an embodiment of the present invention.

[0043] Figure 2 This is a schematic diagram of the installation monitoring system for water meters provided in an embodiment of the present invention.

[0044] Figure 3 This is a schematic diagram of the terminal provided in the embodiment of the present invention. Detailed Implementation

[0045] This invention discloses a method, system, and terminal for monitoring the installation of water meters. To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention is 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 for explaining the invention and are not intended to limit the invention.

[0046] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0047] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0048] As a key component in the water supply system for measuring water consumption, the installation quality of water meters directly determines the metering accuracy, equipment lifespan, and subsequent pipeline network operation and maintenance efficiency.

[0049] Water meter installation projects typically employ a multi-point, decentralized operation model. Currently, the main method for monitoring the quality of water meter installation is manual on-site inspection: professional quality inspectors are dispatched to each construction site to visually assess the installation status of the water meters and manually record the results. This is the traditional mainstream method, relying heavily on the experience and judgment of the quality inspectors.

[0050] However, manual on-site inspections require covering a large number of scattered locations, resulting in high labor costs and long inspection cycles, making them unsuitable for the time-sensitive needs of large-scale construction scenarios. Furthermore, the judgment standards of different quality inspectors may vary, affecting the accuracy of installation monitoring.

[0051] To address the aforementioned deficiencies in existing technologies, this invention provides a method for monitoring the installation of water meters. The method includes: acquiring water meter installation information uploaded by construction personnel based on a water meter installation plan; the water meter installation information includes actual installation images; acquiring standard installation images; and performing several types of feature comparison operations based on the actual installation images and the standard installation images; the several types of feature comparison operations include at least one of the following: dial orientation feature comparison, interface position feature comparison, buckle feature comparison, and reading window feature comparison; and determining whether the water meter is installed correctly based on the comparison results of the several types of feature comparison operations. This invention replaces traditional manual inspection with automated image feature comparison, fundamentally solving the problems of high labor costs, low detection efficiency, and inconsistent standards in water meter installation monitoring.

[0052] like Figure 1 As shown, the method includes:

[0053] Step S100: Obtain water meter installation information uploaded by construction personnel based on the water meter installation plan; the water meter installation information includes: actual installation pictures.

[0054] Specifically, "construction personnel" refers to those who perform water meter installation work. A water meter installation plan refers to the installation work guidelines issued to the construction personnel, typically including instructions such as the installation area, water meter model, and installation deadline. Water meter installation information refers to relevant information generated during the work process, including but not limited to:

[0055] Actual installation photos are used to reflect the actual installation status of the water meter, such as the connection between the water meter and the pipe, whether the water meter installation location meets the specifications, and whether the water meter number is clear and identifiable.

[0056] Installation location information is used to reflect the actual installation location of the water meter;

[0057] Installation status information is used to reflect the actual operating status of the water meter.

[0058] Construction workers must operate according to the water meter installation plan and upload the corresponding water meter installation information. It should be noted that the method for obtaining water meter installation information is not limited to a single method; it can be achieved through real-time reception, such as retrieving the information immediately after the construction worker uploads it; or through timed retrieval, such as retrieving it from the storage node at a preset period.

[0059] Step S200: Obtain a standard installation image, and perform several types of feature comparison operations based on the actual installation image and the standard installation image; the several types of feature comparison operations include at least one of the following operations: dial orientation feature comparison operation, interface position feature comparison operation, buckle feature comparison operation, and reading window feature comparison operation.

[0060] Specifically, standard installation images refer to photographs of water meters installed according to objective and uniform compliance standards. Using standard installation images can avoid verification biases caused by the subjectivity of human judgment. Through image recognition technology, information corresponding to the same type of features in the actual installation image and the standard installation image is extracted, and then the consistency between the two is compared to determine whether the actually installed water meter complies with the specifications. The types of features compared include, but are not limited to, one or more of the following:

[0061] Dial orientation feature comparison: Extract the spatial orientation of the dial markings or markings in the actual installation image, such as the rotation angle of the dial text or markings, and compare it with the standard orientation of the dial in the standard installation image. This avoids misreading during subsequent manual or smart meter readings due to the dial being upside down or tilted, ensuring the accuracy of the metering data.

[0062] Interface location feature comparison: Extract the relative positional relationship between the water meter interface (inlet or outlet) and the connecting pipe or fitting in the actual installation image, and compare it with the standard connection position of the interface in the standard installation image. This avoids problems such as leakage and water pressure loss caused by interface misalignment, ensuring the sealing and functionality of the installation.

[0063] Buckle feature comparison: Extract the status information of the water meter fixing buckles from the actual installation images, such as whether the buckles are fully engaged, the fit between the buckles and the water meter slots, and whether the buckles are loose or broken. Compare this information with the standard closed state of the buckles in the standard installation images. This prevents the water meter from shifting or vibrating during use due to unengaged buckles, which could affect metering accuracy or interface sealing.

[0064] Reading window feature comparison: Extract the visibility status of the reading window from the actual installation image, such as whether the reading window is obstructed by pipes, insulation layers, or other accessories, and whether the reading window glass is clean and free of stains. Compare this with the standard visible range of the reading window in the standard installation image. This ensures that the metering data can be clearly read during subsequent meter readings, avoiding difficulties in meter reading or data loss due to reading window obstruction.

[0065] These features are all closely related to the functionality of the water meter after installation, such as reading accuracy, safety of use, and ease of subsequent maintenance. The key features to be verified may differ depending on the installation scenario. For example, indoor surface-mounted water meters require verification of all four features together, while water meters in confined spaces may only require priority verification of the interface location and the visibility of the reading window. In practical applications, those skilled in the art can select the types of features to compare based on actual needs.

[0066] Furthermore, before installing the water meter on-site, construction personnel can test the meter's signal strength to determine if the signal strength at the installation location is suitable and if the signal information is correct, thereby ensuring that the equipment can report data normally. If the signal is too weak or there is no signal, it is not suitable for installation, and the location needs to be adjusted or the environment changed. For example, in some remote areas, poor signal may prevent the equipment from working properly, so it is necessary to test in advance.

[0067] Furthermore, when construction workers upload actual installation photos taken on-site, they can include geographical location information to determine whether the installation is in the correct location, i.e., whether the positioning is correct and reasonable.

[0068] Furthermore, feature comparison can be implemented through software systems, specifically via network systems, applications, or mini-programs. This allows for the comparison of standard installation images with actual installation photos taken on-site by construction workers, automatically prompting for adjustments to specific parts and locations. If the installation is not up to standard (e.g., reversed installation), the system can reject the submission or request supplementary explanations.

[0069] In one implementation, the step of obtaining the standard installation image is followed by:

[0070] The actual installation image and the standard installation image are preprocessed; the preprocessing includes at least one of the following: unified resolution, angle correction, illumination equalization, and noise filtering.

[0071] Specifically, an image preprocessing module is pre-configured to handle the preprocessing of the actual installation image and the standard installation image, including but not limited to one or more of the following: size and angle alignment, illumination equalization, and noise filtering. The size and angle alignment processing includes: uniform resolution and angle correction.

[0072] Unified resolution: The actual installation image is scaled to the same resolution as the standard installation image, such as 1920×1080, ensuring consistent image size. Specific steps include: obtaining the resolutions of the actual installation image and the standard installation image; calculating the resolution ratio between the two images; scaling the actual installation image using bilinear interpolation; and saving the processed installation image.

[0073] Angle Correction: The center of the water meter dial is located using Hough circle detection. The rotation angle of the dial in the actual installation image is calculated, and an affine transformation is used to rotate it to the same direction as the standard installation image. Specific steps include: using the Hough circle detection algorithm to locate the center of the dial in both the actual and standard installation images; calculating the rotation angle of the actual installation image relative to the standard installation image; performing angle correction on the actual installation image using an affine transformation algorithm; and setting the target angle according to the water meter type, for example, for a horizontal meter. The vertical table is .

[0074] Lighting equalization: The image is converted to the HSV color space, and the CLAHE algorithm (contrast-limited adaptive histogram equalization algorithm) with the same parameters is applied to the V channel (luminance channel) to eliminate brightness differences caused by backlighting. Specific implementation steps include: converting the actual installation image and the standard installation image to the HSV color space; extracting the V channel; applying the CLAHE algorithm to the V channel; merging the processed V channel with the H channel (hue channel) and S channel (saturation channel), and converting it back to the RGB (red, green, blue) color space.

[0075] Noise filtering: High-frequency noise is removed using a 3×3 Gaussian blur to avoid interference from detail differences in feature comparison. Specific implementation steps include: loading the processed actual installation image and the standard installation image; applying a 3×3 Gaussian blur kernel for convolution; and saving the processed image.

[0076] In one implementation, the step of performing a comparison of several types of features based on the actual installation image and the standard installation image further includes:

[0077] The feature point sets corresponding to the actual installation image and the standard installation image are extracted using a preset feature point detection algorithm.

[0078] Calculate the total number of feature point pairs between the two feature point sets, and calculate the number of feature point matching pairs between the two feature point sets using a preset feature point matching algorithm;

[0079] The effective matching rate is calculated based on the total number of feature point pairs and the number of matching feature point pairs.

[0080] If the effective matching rate reaches a preset matching rate threshold, then several feature comparison operations are performed based on the actual installation image and the standard installation image.

[0081] Specifically, to conserve computing power, a two-level comparison mechanism is used between actual installation images and standard installation images: the first-level comparison operation, or basic comparison operation, is a coarse comparison process with relatively low computing power; the second-level comparison operation, or several types of feature comparison operations, is a fine comparison process requiring significant computing power. The first-level comparison operation is performed first, and only after passing the first-level comparison operation can the second-level comparison operation be performed, to avoid wasting computing power on invalid data. The first-level comparison operation is implemented by using a preset feature point detection algorithm to extract feature point sets from both the actual installation images and the standard installation images. Feature points refer to representative points in the image that reflect the key positions of objects, such as the edge points of screws, the corner points of equipment, and the interface points of pipelines. The feature point set refers to the set of points containing coordinates and attribute information formed by all the feature points extracted from each image. The total number of feature point pairs is calculated first, which is the maximum number of feature point pairs that can be matched, usually depending on the smaller feature point set. Next, a preset feature point matching algorithm is used to determine the number of point pairs corresponding to the same position or the same component in the actual installation image and the standard installation image, thus obtaining the feature point matching pair count. Finally, the effective matching rate of the two images can be determined by the ratio of the feature point matching pair count to the total number of feature point pairs. If the effective matching rate reaches the preset matching rate threshold, it means that the two images meet the basic similarity requirements, and subsequent feature comparison operations can be performed. If the effective matching rate does not reach the preset matching rate threshold, it means that the two images do not meet the basic similarity requirements, and subsequent comparisons are invalid, directly terminating the process to avoid wasting computing power or drawing incorrect conclusions. The two-level comparison mechanism in this embodiment can first quickly screen out obviously unqualified images, and then perform fine deviation calculations on qualified images, avoiding wasting computing power and further ensuring the accuracy of the analysis results.

[0082] For example, the first-level comparison operation (i.e., rapid error screening) is implemented using ORB (Oriented Fast and Rotated BRIEF, an algorithm for fast feature point extraction and description) feature matching. The specific steps include: First, using the ORB algorithm to extract ORB keypoints (≥500 keypoints) from the actual installation image and the standard installation image; keypoints can also be called feature points. Then, a brute-force matching algorithm is used to calculate the number of matching feature points, and then the effective matching rate R is calculated as: number of matching feature points ÷ total number of keypoint pairs. Finally, the matching rate is judged. If R is less than the matching rate threshold, it indicates that there may be problems such as incorrect model, incorrect type, or severe skewness. In this case, it is directly judged as obviously unqualified, and the processing is terminated, without further feature comparisons. In practical applications, the matching rate threshold can be set to 70%.

[0083] In one implementation, the dial orientation feature comparison operation includes:

[0084] For each image, a preset text identifier is located within the image using a preset text detection algorithm, and the rotation angle of the minimum bounding rectangle of the preset text identifier is calculated to obtain the first rotation angle corresponding to the actual installation image and the second rotation angle corresponding to the standard installation image.

[0085] The angle deviation value is calculated based on the first rotation angle and the second rotation angle, and the angle deviation value is used as the comparison result of the dial orientation feature comparison operation.

[0086] The orientation of the meter dial is a crucial indicator in water meter installation standards. The dial orientation feature comparison operation primarily utilizes angular deviation values ​​to objectively measure the degree of deviation in rotational direction between the actual installed dial and the standard installed dial. Specifically, a preset text detection algorithm is used to locate preset text identifiers within the images of both the actual and standard installations. These preset text identifiers refer to text on the dial with a fixed position and whose font orientation is tied to the overall dial orientation, such as the brand name or unit numbers next to the scale. The rotation angle of the minimum bounding rectangle of the preset text identifiers located in both images is calculated, resulting in two angles: a first rotation angle for the text in the actual installation image and a second rotation angle for the text in the standard installation image. The rotation angle can be understood as the tilt angle of the minimum bounding rectangle relative to a specified direction (e.g., horizontal), thus converting the text orientation into a specific numerical angle. The difference between the first and second rotation angles quantifies the orientation difference between the actual and standard installed dials, yielding the angular deviation value.

[0087] For example, the EAST text detection algorithm (Efficient and Accurate Scene TextDetector) is used to scan both the actual installation image and the standard installation image to locate the cubic meter markers in both images and calculate the rotation angle of their minimum bounding rectangle. This yields the first rotation angle θ1 for the actual installation image and the angle θ0 for the standard installation image. The difference between θ1 and θ0 is then calculated, resulting in the angle deviation value Δθ. Furthermore, the water meter type can be automatically determined based on the dial orientation: the dial orientation angle θ1 of the actual installation image is calculated; if θ1 is close to 0°, it is a horizontal meter; if θ1 is close to 90°, it is a vertical meter.

[0088] In one implementation, the interface location feature comparison operation includes:

[0089] For each image, the interface identifier within the image is located using a preset template matching algorithm, and the center point coordinates of the interface identifier are calculated to obtain the first coordinate data corresponding to the actual installation image and the second coordinate data corresponding to the standard installation image.

[0090] Based on the first coordinate data and the second coordinate data, the interface position deviation value is calculated, and the interface position deviation value is used as the comparison result of the interface position feature comparison operation.

[0091] Interface location is also a crucial indicator in water meter installation standards. Interface location feature comparison primarily utilizes interface location deviation values ​​to objectively measure the coordinate difference between the actual installed interface location and the standard installed interface location. Specifically, a preset template matching algorithm is used to locate the interface identifier in both the actual installation image and the standard installation image. The template matching algorithm uses a known standard interface template as a sample, scans the image, and finds the region most similar to the template, thus accurately locating the interface identifier. The interface identifier reflects the fluid flow direction: the fluid inlet corresponds to the "IN" interface identifier, and the fluid outlet corresponds to the "OUT" interface identifier. Using the geometric center of the interface identifier as the center point coordinate, two sets of coordinates are obtained: the center point coordinates of the interface identifier in the actual installation image (first coordinate data); and the center point coordinates of the interface identifier in the standard installation image (second coordinate data). The difference between the first and second coordinate data quantifies the coordinate difference between the actual installed interface location and the standard installed interface location, thus obtaining the interface location deviation value.

[0092] For example, a template matching algorithm is used to scan both the actual installation image and the standard installation image to locate the "IN" and "OUT" interface identifiers in the two images, and to calculate the center point coordinates of the interface identifiers. The first coordinate data corresponding to the actual installation image is recorded: the "IN" interface identifier (…). , ), "OUT" interface identifier ( , ), and the second coordinate data corresponding to the standard installation image: "IN" interface identifier ( , ), "OUT" interface identifier ( , The interface position deviation value is calculated using both the first and second coordinate data.

[0093] For example, if the interface position deviation value is calculated using the "IN" interface identifier, then the interface position deviation value... The calculation formula is: .

[0094] In one implementation, the buckle feature comparison operation includes:

[0095] For each image, the buckles are identified using a preset edge detection algorithm and contour filtering algorithm, and the number of buckles and center point coordinates are calculated to obtain the first number of buckles and the third coordinate data corresponding to the actual installation image, as well as the second number of buckles and the fourth coordinate data corresponding to the standard installation image.

[0096] The buckle quantity deviation value is calculated based on the first buckle quantity and the second buckle quantity, and the buckle position deviation value is calculated based on the third coordinate data and the fourth coordinate data. The buckle quantity deviation value and the buckle position deviation value are used as the comparison results of the buckle feature comparison operation.

[0097] Clips are a key structural component used for fixing parts in water meter installation, and their quantity and position are important indicators of water meter installation specifications. Clip feature comparison mainly uses clip quantity deviation and clip position deviation values ​​to objectively measure the difference in quantity and coordinates between the actual installed clips and the standard installed clips. Specifically, a preset edge detection algorithm scans both the actual installation image and the standard installation image to capture the edge contours of the clips. Then, a contour filtering algorithm filters the identified edge contours, retaining only those conforming to preset clip shape characteristics to identify the true clip contours. The number of clip contours retained after filtering is calculated, yielding the clip quantity. The coordinates of the center point are recorded, using the geometric center of each clip contour as the center point. Finally, two sets of clip quantity and coordinate data are obtained: the first clip quantity and third coordinate data corresponding to the actual installation image, and the second clip quantity and fourth coordinate data corresponding to the standard installation image. The difference between the first and second clip quantities yields the clip quantity deviation value. The difference between the third and fourth coordinate data yields the clip position deviation value.

[0098] For example, a template matching algorithm is used to scan both the actual installation image and the standard installation image to extract the image edges from both images. Then, a contour filtering algorithm is used to identify the buckle features based on the extracted image edges, and the number of buckles and the coordinates of their center points are calculated. The number of buckles N1 and the third coordinate data of the actual installation image are recorded. , ), ( , ), and the number of clips N0 and fourth coordinate data in the standard installation image ( , ), ( , Calculate the difference between N1 and N0 to obtain the buckle quantity deviation value ΔN. Calculate the difference between the third coordinate data and the fourth coordinate data to obtain the buckle position deviation value.

[0099] In one implementation, the reading window feature comparison operation includes:

[0100] For each image, a pre-defined edge detection algorithm is used to identify the reading window region. The total area of ​​the reading window and the area of ​​the occluded region are calculated. Based on the total area and the area of ​​the occluded region, the occlusion area ratio is calculated to obtain the first occlusion area ratio corresponding to the actual installation image and the second occlusion area ratio corresponding to the standard installation image.

[0101] The deviation value of the occlusion area ratio is calculated based on the first occlusion area ratio and the second occlusion area ratio, and the deviation value of the occlusion area ratio is used as the comparison result of the reading window feature comparison operation.

[0102] The reading window is a crucial area on a water meter used to read values, and its visibility is an important indicator of water meter installation standards. Specifically, a preset edge detection algorithm scans both actual installation images and standard installation images to accurately select the reading window area. For each image, the complete area of ​​the reading window area is calculated, yielding the total area. The area obscured by other objects within the reading window area is calculated using pixel grayscale differences or texture variations, yielding the obscured area. Dividing the obscured area by the total area gives the percentage of obscured area in that image. Two percentages of obscured area are obtained: the first percentage corresponding to the actual installation image and the second percentage corresponding to the standard installation image. These percentages quantify the visibility of the reading window area. The difference between the first and second percentages determines the deviation. This deviation allows for a quick assessment of whether the water meter installation meets the reading window visibility requirements.

[0103] For example, an edge detection algorithm is used to scan both the actual installation image and the standard installation image to identify the reading window region in both images. The total area of ​​the reading window and the area of ​​the occluded region are calculated, and then the occlusion area ratio is calculated. The occlusion area ratio S1 of the actual installation image and S0 of the standard installation image are recorded. The difference between S1 and S0 is calculated, which is the deviation value ΔS of the occlusion area ratio.

[0104] Table 1. Different types of feature comparison operations

[0105]

[0106] Step S300: Based on the comparison results of the aforementioned feature comparison operations, determine whether the water meter is installed correctly.

[0107] Specifically, different types of feature comparison operations are used to verify different key dimensions of water meter installation. All comparison results are integrated through a preset strategy to ultimately determine whether the overall water meter installation meets the standards. This avoids the one-sidedness of judging from a single dimension, ensuring that the water meter installation not only functions normally but also meets the requirements of safety, reliability, and ease of maintenance.

[0108] In one implementation, determining whether the water meter is installed correctly based on the comparison results of the aforementioned feature comparison operations includes:

[0109] Each comparison result is scored individually.

[0110] The total score is obtained by weighted summing of the scores corresponding to all the comparison results.

[0111] If the total score reaches the preset score threshold, the water meter installation is deemed qualified.

[0112] Specifically, the score for each individual comparison result needs to be confirmed first, and then the scores of all comparison results are summed through a weighted calculation. The overall installation of the water meter is judged to meet the overall requirements for qualified installation by whether the total score reaches a preset threshold, rather than relying solely on a single characteristic. The threshold score can be set by those skilled in the art based on actual needs and professional experience. A total score below the threshold indicates unqualified installation; a total score reaching the threshold indicates qualified installation.

[0113] For example, the total score for each item, such as dial orientation, interface position, buckle features, and reading window obstruction, is set to 40 points, 30 points, 20 points, and 10 points respectively, for a total of 100 points.

[0114] The score for single-feature alignment is calculated as follows:

[0115] (1) Interface location score: The score is calculated based on the interface location deviation value Δd_IN.

[0116] If Δd_IN ≤ 5 pixels, score 40 points;

[0117] If 5 pixels < Δd_IN ≤ 10 pixels, the score = 40 × (10 - Δd_IN) / 5;

[0118] If Δd_IN > 10 pixels, score 0.

[0119] (2) Dial orientation score: The score is calculated based on the angle deviation value Δθ.

[0120] If |Δθ|≤2°, you get 30 points;

[0121] 2°<|Δθ|≤5°, score = 30×(5-|Δθ|) / 3

[0122] |Δθ|>5°, 0 points.

[0123] (3) Buckle feature scoring: The score is calculated based on the number and location of the buckles.

[0124] ΔN=0 and all buckle position deviations ≤5 pixels, score 20 points;

[0125] If ΔN≠0 or any buckle position deviation value >5 pixels, 0 points are awarded.

[0126] (4) Reading window occlusion score: The score is calculated based on the deviation value ΔS of the occlusion area ratio.

[0127] If ΔS≤0%, 10 points are awarded.

[0128] 0%<ΔS≤10%, score = 10×(10-ΔS) / 10;

[0129] If ΔS > 10%, you get 0 points.

[0130] The total score for the comprehensive assessment is calculated as follows:

[0131] The total score is calculated by summing the scores of each individual item. The installation is then judged as qualified based on the total score.

[0132] If the total score is ≥80 points, the installation is considered qualified, and the deviation value of each individual feature will be output.

[0133] If the total score is less than 80 points, the installation is considered unqualified. The single feature with the largest output deviation and rectification suggestions are provided.

[0134] The methods for generating rectification suggestions include:

[0135] The score for each individual feature is compared to its corresponding passing score. If the score is lower than the passing score, the feature is considered unqualified. Rectification suggestions can be generated for the feature with the largest deviation, or for all unqualified features. When generating rectification suggestions, information such as the direction of deviation (too high or too low) and the magnitude of the deviation value can be considered. Finally, the specific rectification suggestions are output in text format.

[0136] Based on the above embodiments, the present invention also provides a water meter installation monitoring system, such as... Figure 2 As shown, the system includes:

[0137] Module 01 is used to acquire water meter installation information uploaded by construction personnel based on the water meter installation plan; the water meter installation information includes: actual installation pictures;

[0138] The comparison module 02 is used to acquire a standard installation image and perform several types of feature comparison operations based on the actual installation image and the standard installation image. The several types of feature comparison operations include at least one of the following: dial orientation feature comparison operation, interface position feature comparison operation, buckle feature comparison operation, and reading window feature comparison operation.

[0139] The judgment module 03 is used to determine whether the water meter is installed correctly based on the comparison results of the comparison operations of the aforementioned features.

[0140] Based on the above embodiments, the present invention also provides a terminal, the principle block diagram of which can be as follows: Figure 3 As shown, the terminal includes a processor, memory, network interface, and display screen connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for monitoring the installation of the water meter. The display screen can be an LCD screen or an e-ink screen.

[0141] Those skilled in the art will understand that Figure 3 The schematic diagram shown is merely a partial structural diagram related to the present invention and does not constitute a limitation on the terminal to which the present invention is applied. A specific terminal may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0142] In one implementation, the terminal's memory stores one or more programs, and these programs are configured to be executed by one or more processors, and the programs contain instructions for performing a water meter installation monitoring method.

[0143] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention 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), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0144] In summary, this invention discloses a method, system, and terminal for monitoring the installation of water meters, relating to the field of water meter technology. The method includes: acquiring water meter installation information uploaded by construction personnel based on a water meter installation plan; the water meter installation information includes actual installation images; acquiring standard installation images; and performing several types of feature comparison operations based on the actual installation images and the standard installation images; the several types of feature comparison operations include at least one of the following: dial orientation feature comparison, interface position feature comparison, buckle feature comparison, and reading window feature comparison; and determining whether the water meter is installed correctly based on the comparison results of the several types of feature comparison operations. This invention replaces traditional manual inspection with automated image feature comparison, fundamentally solving the problems of high labor costs, low detection efficiency, and inconsistent standards in water meter installation monitoring.

[0145] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for monitoring the installation of a water meter, characterized in that, The method includes: Obtain water meter installation information uploaded by construction personnel based on the water meter installation plan; the water meter installation information includes: actual installation photos; Obtain a standard installation image, and perform several types of feature comparison operations based on the actual installation image and the standard installation image; the several types of feature comparison operations include at least one of the following operations: dial orientation feature comparison operation, interface position feature comparison operation, buckle feature comparison operation, and reading window feature comparison operation. Based on the comparison results of the aforementioned feature comparison operations, determine whether the water meter is installed correctly. The dial orientation feature comparison operation includes: For each image, a preset text identifier is located within the image using a preset text detection algorithm, and the rotation angle of the minimum bounding rectangle of the preset text identifier is calculated to obtain the first rotation angle corresponding to the actual installation image and the second rotation angle corresponding to the standard installation image. The angle deviation value is calculated based on the first rotation angle and the second rotation angle, and the angle deviation value is used as the comparison result of the dial orientation feature comparison operation.

2. The water meter installation and monitoring method according to claim 1, characterized in that, After obtaining the standard installation image, the steps also include: The actual installation image and the standard installation image are preprocessed; the preprocessing includes at least one of the following: unified resolution, angle correction, illumination equalization, and noise filtering.

3. The water meter installation and monitoring method according to claim 1, characterized in that, Before the step of performing several types of feature comparison operations based on the actual installation image and the standard installation image, the following steps are also included: The feature point sets corresponding to the actual installation image and the standard installation image are extracted using a preset feature point detection algorithm. Calculate the total number of feature point pairs between the two feature point sets, and calculate the number of feature point matching pairs between the two feature point sets using a preset feature point matching algorithm; The effective matching rate is calculated based on the total number of feature point pairs and the number of matching feature point pairs. If the effective matching rate reaches a preset matching rate threshold, then several feature comparison operations are performed based on the actual installation image and the standard installation image.

4. The water meter installation and monitoring method according to claim 1, characterized in that, The interface location feature comparison operation includes: For each image, the interface identifier within the image is located using a preset template matching algorithm, and the center point coordinates of the interface identifier are calculated to obtain the first coordinate data corresponding to the actual installation image and the second coordinate data corresponding to the standard installation image. Based on the first coordinate data and the second coordinate data, the interface position deviation value is calculated, and the interface position deviation value is used as the comparison result of the interface position feature comparison operation.

5. The water meter installation and monitoring method according to claim 1, characterized in that, The buckle feature comparison operation includes: For each image, the buckles are identified using a preset edge detection algorithm and contour filtering algorithm, and the number of buckles and center point coordinates are calculated to obtain the first number of buckles and the third coordinate data corresponding to the actual installation image, as well as the second number of buckles and the fourth coordinate data corresponding to the standard installation image. The buckle quantity deviation value is calculated based on the first buckle quantity and the second buckle quantity, and the buckle position deviation value is calculated based on the third coordinate data and the fourth coordinate data. The buckle quantity deviation value and the buckle position deviation value are used as the comparison results of the buckle feature comparison operation.

6. The water meter installation and monitoring method according to claim 1, characterized in that, The reading window feature comparison operation includes: For each image, a pre-defined edge detection algorithm is used to identify the reading window region. The total area of ​​the reading window and the area of ​​the occluded region are calculated. Based on the total area and the area of ​​the occluded region, the occlusion area ratio is calculated to obtain the first occlusion area ratio corresponding to the actual installation image and the second occlusion area ratio corresponding to the standard installation image. The deviation value of the occlusion area ratio is calculated based on the first occlusion area ratio and the second occlusion area ratio, and the deviation value of the occlusion area ratio is used as the comparison result of the reading window feature comparison operation.

7. The water meter installation and monitoring method according to claim 1, characterized in that, Based on the comparison results of the aforementioned feature comparison operations, it is determined whether the water meter is installed correctly, including: Each comparison result is scored individually. The total score is obtained by weighted summing of the scores corresponding to all the comparison results. If the total score reaches the preset score threshold, the water meter installation is deemed qualified.

8. A water meter installation monitoring system, characterized in that, The system includes: The acquisition module is used to acquire water meter installation information uploaded by construction personnel based on the water meter installation plan; the water meter installation information includes: actual installation pictures; The comparison module is used to acquire a standard installation image and perform several types of feature comparison operations based on the actual installation image and the standard installation image; the several types of feature comparison operations include at least one of the following operations: dial orientation feature comparison operation, interface position feature comparison operation, buckle feature comparison operation, and reading window feature comparison operation. The judgment module is used to determine whether the water meter is installed correctly based on the comparison results of the comparison operations of the aforementioned feature types. The dial orientation feature comparison operation includes: For each image, a preset text identifier is located within the image using a preset text detection algorithm, and the rotation angle of the minimum bounding rectangle of the preset text identifier is calculated to obtain the first rotation angle corresponding to the actual installation image and the second rotation angle corresponding to the standard installation image. The angle deviation value is calculated based on the first rotation angle and the second rotation angle, and the angle deviation value is used as the comparison result of the dial orientation feature comparison operation.

9. A terminal, characterized in that, The terminal includes a memory and one or more processors; the memory stores one or more programs; the programs contain instructions for executing the water meter installation monitoring method as described in any one of claims 1 to 7; the processor is used to execute the programs.

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