Boiler remote operation and maintenance method and system based on identification analysis

Abstract

The invention discloses a boiler remote operation and maintenance method and system based on identification analysis. The method comprises the steps that a user determines a fault boiler assembly and an assembly number of the fault boiler assembly; the user uses the terminal to obtain a first image of the faulted boiler assembly; the user uploads the first image and the component number at the terminal; the remote operation and maintenance platform receives a first image and a component number sent by the terminal; the remote operation and maintenance platform determines whether the first image meets a fault diagnosis requirement; and if the remote operation and maintenance platform determines that the first image does not meet the fault diagnosis requirement, the remote operation and maintenance platform calls a standard image of the fault boiler assembly based on the assembly number. The problem that a user is difficult to correctly shoot a picture of a damaged part due to the fact that an old-version application program cannot provide an effective prompt is effectively solved, so that the repair requesting efficiency of the user and the application program using willingness are remarkably improved.

Description

A remote boiler operation and maintenance method and system based on identifier resolution Technical Field

[0001] This invention relates to the field of boiler technology, and in particular to a method and system for remote operation and maintenance of boilers based on identifier resolution. Background Technology

[0002] In the field of boiler equipment operation and maintenance, traditional methods mainly rely on manual on-site inspections and paper records, which suffer from low information transmission efficiency and difficulties in data traceability. With the development of industrial internet technology, some enterprises have begun to try using identifier resolution systems to manage boiler components through coding, achieving digital association of product information through unique identifiers. In existing technologies, identifier resolution is mostly used for basic product information queries and supply chain collaboration. However, in the scenario of remote boiler operation and maintenance, existing remote operation and maintenance industrial internet platforms still have certain problems.

[0003] Figure 1 shows the after-sales complaint interface of the older version of our company's remote operation and maintenance industrial internet platform. If a user discovers a boiler malfunction, they can report the problem through this interface. For a malfunctioning boiler, the user can first click the "Scan Product" button on the interface in Figure 1. The terminal then enters photo mode, allowing the user to scan the barcode or QR code on the boiler nameplate to automatically obtain the boiler model; alternatively, the user can manually enter the product barcode to obtain the boiler model. Regardless of whether the boiler model is obtained by scanning the barcode or manually entering the product barcode, the terminal will upload the boiler model to the server, and the terminal interface will automatically redirect to the interface shown in Figure 2. In the interface shown in Figure 2, the user needs to fill in necessary information such as customer name and contact person, as well as a description of the problem. Finally, the user also needs to upload necessary pictures and videos to facilitate our engineers' remote assistance in diagnosing and determining the cause of the boiler malfunction and the repair strategy. Finally, the user clicks the "Submit" button, and the terminal will automatically enter the interface shown in Figure 3. Currently, when using this version of the remote operation and maintenance industrial internet platform, our company has found that the application cannot provide effective prompts to users, making it difficult for users to easily provide photos of damaged parts in the boiler. This reduces the efficiency of users reporting repairs through the application and decreases users' willingness to use the application for repairs. Summary of the Invention

[0004] This invention provides a remote boiler operation and maintenance method based on identifier resolution. The method includes the user identifying the faulty boiler component and its number, acquiring and uploading the component image, and the remote operation and maintenance platform receiving the image and checking if it meets the fault diagnosis requirements. If not, a standard image is retrieved for comparison, image defects are identified, and markers are added at corresponding positions on both the user's image and the standard image. Finally, the marked image and defect description are sent to the user's terminal. Through this image comparison and defect marking mechanism, this invention effectively solves the problem that older application versions could not provide effective prompts, making it difficult for users to correctly photograph damaged parts, thereby significantly improving user repair efficiency and application usage willingness.

[0005] This invention provides a remote boiler operation and maintenance method based on identifier resolution. The method includes: a user determining a faulty boiler component and its component number; the user acquiring a first image of the faulty boiler component using a terminal; the user uploading the first image and component number to the terminal; a remote operation and maintenance platform receiving the first image and component number sent by the terminal; the remote operation and maintenance platform determining whether the first image meets the fault diagnosis requirements; if the remote operation and maintenance platform determines that the first image does not meet the fault diagnosis requirements, then the remote operation and maintenance platform retrieves a standard image of the faulty boiler component based on the component number.

[0006] In one implementation, the standard image is an image that meets the requirements for fault diagnosis. The method further includes: comparing a first image and a standard image by a remote operation and maintenance platform; determining at least one defect in the first image by the remote operation and maintenance platform based on the comparison between the first image and the standard image, wherein the at least one defect includes a first defect; marking the first defect on the first image by the remote operation and maintenance platform to generate a first marked first image; and determining by the remote operation and maintenance platform whether marking is required on the standard image based on the first defect.

[0007] In one embodiment, the method further includes: if the remote operation and maintenance platform determines that it is not necessary to mark the standard image, the remote operation and maintenance platform sends the first marked first image, the standard image, and a description of the first defect to the terminal; the terminal displays the first entry to the user, wherein the first entry includes the first marked first image, the standard image, and a description of the first defect.

[0008] In one embodiment, the method further includes: if the remote operation and maintenance platform determines that marking is required on the standard image, the remote operation and maintenance platform marks the standard image at the position corresponding to the position of the first defect in the first image to generate a first marked standard image; the remote operation and maintenance platform sends the first marked first image, the first marked standard image, and a description of the first defect to the terminal; and the terminal displays a second entry to the user, wherein the second entry includes the first marked first image, the first marked standard image, and a description of the first defect.

[0009] In one embodiment, at least one defect further includes a second defect, wherein the method further includes: a remote operation and maintenance platform marking the second defect on a first image to generate a second marked first image; the remote operation and maintenance platform determining, based on the second defect, whether marking on a standard image is necessary; if the remote operation and maintenance platform determines that marking on the standard image is necessary, the remote operation and maintenance platform marking the standard image at a position in the standard image corresponding to the position of the second defect in the first image to generate a second marked standard image; the remote operation and maintenance platform sending the second marked first image, the second marked standard image, and a description of the second defect to a terminal; and the terminal displaying a second entry and a third entry to a user, wherein the third entry includes the second marked first image, the second marked standard image, and a description of the second defect.

[0010] This invention provides a remote boiler operation and maintenance system based on identifier resolution. The system includes modules for performing the following operations: a user identifies a faulty boiler component and its component number; the user obtains a first image of the faulty boiler component using a terminal; the user uploads the first image and component number to the terminal; a remote operation and maintenance platform receives the first image and component number sent by the terminal; the remote operation and maintenance platform determines whether the first image meets the fault diagnosis requirements; if the remote operation and maintenance platform determines that the first image does not meet the fault diagnosis requirements, then the remote operation and maintenance platform retrieves a standard image of the faulty boiler component based on the component number.

[0011] In one implementation, the standard image is an image that meets the requirements for fault diagnosis. The system further includes a module for performing the following operations: comparing a first image and a standard image by a remote operation and maintenance platform; determining at least one defect in the first image by the remote operation and maintenance platform based on the comparison between the first image and the standard image, wherein the at least one defect includes a first defect; marking the first defect on the first image by the remote operation and maintenance platform to generate a first marked first image; and determining by the remote operation and maintenance platform whether marking is required on the standard image based on the first defect.

[0012] In one embodiment, the system further includes a module for performing the following operations: if the remote operation and maintenance platform determines that it is not necessary to mark the standard image, the remote operation and maintenance platform sends the first marked first image, the standard image, and a description of the first defect to the terminal; the terminal displays a first entry to the user, wherein the first entry includes the first marked first image, the standard image, and a description of the first defect.

[0013] In one embodiment, the system further includes a module for performing the following operations: if the remote operation and maintenance platform determines that marking is required on a standard image, the remote operation and maintenance platform marks the standard image at a position in the standard image corresponding to the position of the first defect in the first image to generate a first marked standard image; the remote operation and maintenance platform sends the first marked first image, the first marked standard image, and a description of the first defect to the terminal; and the terminal displays a second entry to the user, wherein the second entry includes the first marked first image, the first marked standard image, and a description of the first defect.

[0014] In one embodiment, at least one defect further includes a second defect, wherein the system further includes a module for performing the following operations: the remote operation and maintenance platform identifies the second defect on a first image to generate a second identified first image; the remote operation and maintenance platform determines, based on the second defect, whether identification on a standard image is required; if the remote operation and maintenance platform determines that identification on the standard image is required, the remote operation and maintenance platform identifies the standard image at a position in the standard image corresponding to the position of the second defect in the first image to generate a second identified standard image; the remote operation and maintenance platform sends the second identified first image, the second identified standard image, and a description of the second defect to a terminal; the terminal displays a second entry and a third entry to the user, wherein the third entry includes the second identified first image, the second identified standard image, and a description of the second defect.

[0015] This invention offers the following technical advantages: The method involves the user identifying the faulty boiler component and its number, acquiring and uploading a component image, and the remote maintenance platform receiving the image and checking if it meets the fault diagnosis requirements. If not, a standard image is retrieved for comparison, image defects are identified, and markings are made at corresponding positions on both the user's image and the standard image. Finally, the marked image and defect description are sent to the user's terminal. Through this image comparison and defect marking mechanism, this invention effectively solves the problem that older versions of the application could not provide effective prompts, making it difficult for users to correctly photograph damaged parts, thereby significantly improving user repair efficiency and application usage willingness. Attached Figure Description

[0016] Figure 1 shows an interface of a remote operation and maintenance industrial internet platform in the prior art.

[0017] Figure 2 shows another interface of the existing remote operation and maintenance industrial internet platform.

[0018] Figure 3 shows another interface of the existing remote operation and maintenance industrial internet platform.

[0019] Figure 4 is a schematic diagram of the system architecture of an embodiment of the present invention.

[0020] Figure 5 is a flowchart of a method according to an embodiment of the present invention.

[0021] Figure 6 is a schematic diagram of the interface for uploading images and component numbers according to an embodiment of the present invention.

[0022] Figure 7 is a schematic diagram of the terminal displaying image defects according to an embodiment of the present invention.

[0023] Figure 8 is a schematic diagram of the terminal displaying image defects according to another embodiment of the present invention. Detailed Implementation

[0024] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings, but it should be understood that the scope of protection of the present invention is not limited to the specific embodiments.

[0025] In the practical application and user feedback collection of our identifier-based remote boiler operation and maintenance system, our R&D team discovered that while existing technologies have achieved the basic function of remote fault diagnosis through user-uploaded images, significant bottlenecks remain in operation and maintenance efficiency and user experience. In-depth analysis revealed the root cause: when users (usually on-site operators rather than professional maintenance personnel) attempt to photograph faulty components, due to a lack of professional photography knowledge and clear shooting guidance, the uploaded images often suffer from a series of quality problems, including incomplete component composition, improper shooting angles, blurred focus, insufficient or overexposed lighting, and failure to clearly capture key defect features. Our engineers or the automated diagnostic system on the remote operation and maintenance platform are unable to effectively analyze such low-quality images. According to the existing regulations of the boiler remote operation and maintenance system, our engineers will reject the user's appeal request, stating the defects of the uploaded photos in the rejection reason section. After receiving a returned complaint, users can retake photos according to the reasons given in the return section. However, our R&D team found through a questionnaire survey that many users reported that photos retaken based solely on the return reason often still did not meet the requirements of our engineers, leading to repeated submissions and returns of repair requests. To address this issue, our engineers attempted to manually mark defective areas on the user-submitted images with accompanying text prompts, and then communicate more directly with users via chat software such as WeChat or QQ. However, some users did not wish to communicate through unencrypted commercial software, and even with this method, users still could not quickly and accurately understand the defects in their images. In short, this iterative process not only significantly prolonged the fault diagnosis cycle and caused repair delays, but also severely dampened users' enthusiasm for using the application for self-service repairs, greatly reducing the effectiveness of the convenient remote maintenance mode. Based on this, our R&D team recognized the need to provide a method that automatically assesses the usability of user-submitted images and provides clear guidance. This method can resolve the shortcomings of insufficient prompts in the old version of the application. Therefore, the present invention aims to provide users with specific feedback by introducing an image comparison and identification mechanism, thereby improving the success rate of repair requests and the efficiency of the entire operation and maintenance process.

[0026] Figure 4 is a schematic diagram of the system architecture of an embodiment of the present invention. As shown in the figure, in the system of the present invention, the user can obtain boiler information by taking a picture of the QR code or barcode on the boiler's nameplate using the terminal's camera. The user can then operate the application on the terminal to complete the repair request form and send it to the remote operation and maintenance platform. In the schematic diagram of Figure 1, the remote operation and maintenance platform is a server. Our engineers can remotely assist the user in diagnosing boiler faults and providing corresponding repair suggestions on the server side. Example 1

[0027] Figure 5 is a flowchart of a method according to an embodiment of the present invention. As shown in the figure, the method of the present invention includes the following steps: Step 1: The user determines the faulty boiler component and the component number of the faulty boiler component; In one example, the internal feedwater distribution pipe of the boiler drum, which is relatively easy to be damaged in a single-drum longitudinal chain grate boiler, is used as an example to illustrate a specific embodiment of the present invention. The internal feedwater distribution pipe is installed inside the boiler drum to deliver feedwater evenly and stably into the boiler drum, avoiding the direct impact of low-temperature feedwater on the high-temperature boiler drum wall and downcomer, thereby reducing thermal stress and ensuring the stability of water circulation. This component is prone to damage due to feedwater quality problems (such as high oxygen content) or improper shutdown maintenance. Oxygen corrosion and erosion are prone to occur inside the distribution pipe and in the small holes, resulting in thinning of the pipe wall and perforation. It is understood that since boilers are generally high-value heavy assets, buyers usually have a group of technicians to perform basic maintenance and basic fault diagnosis of the boiler. Therefore, after selling the boiler, our company will provide users with basic troubleshooting and maintenance courses. During the training, our company will pay special attention to teaching common faults and damaged parts. Therefore, the user's technicians are able to determine which parts are damaged due to the boiler malfunction. In one example, component numbers can be marked on the outer surface of the feedwater distribution pipe inside the boiler drum. When the user's technicians disassemble the boiler to expose the feedwater distribution pipe inside the boiler drum, they can obtain the component numbers by observing the outer surface of the feedwater distribution pipe. The component numbers are only used by the remote operation and maintenance platform to identify specific information about the faulty boiler components. Therefore, only one component number is required to correspond one-to-one with one component in the boiler, and the component numbers inside a boiler cannot be repeated. In a specific example, the component number of the feedwater distribution pipe inside the boiler drum can be "1A$B". Step 2: The user uses a terminal to obtain the first image of the faulty boiler component. In one example, the first image of the faulty boiler component can be a photograph of the feedwater distribution pipe inside the boiler drum. Step 3: The user uploads the first image and the component number on the terminal. In one example, an example of the user uploading the first image and the component number can be seen in Figure 6. In Figure 6, the design of the basic information section, user information, and basic fault information can still refer to the existing version of the application. For example, the design of this section can refer to Figure 2, and will not be repeated here. The improvement to the existing application is that it requires the user to select a photo of the faulty boiler component stored in the terminal in the red box, and at the same time manually fill in the component number in the yellow box. In this invention, the manual filling can be done by the user typing the component number using a virtual keyboard, or by the user uploading a photo of the component number in the yellow box (which is also taken by the user).

[0028] Step 4: The remote maintenance platform receives the first image and component number sent by the terminal. In one example, after the user submits a maintenance request, the terminal sends the first image and component number to the remote maintenance platform. Step 5: The remote maintenance platform determines whether the first image meets the fault diagnosis requirements. In one example, our engineers can view the first image sent by the user on the display device of the remote maintenance platform to determine whether the first image meets the fault diagnosis requirements. For example, if our engineers believe that the faulty component in the first image is too small to be clearly displayed, they can determine that the first image does not meet the fault diagnosis requirements. Step 6: If the remote maintenance platform determines that the first image does not meet the fault diagnosis requirements, it retrieves the standard image of the faulty boiler component based on the component number. In one example, after receiving the component number "1A$B", the remote maintenance platform can determine that the component corresponding to this component number is the "boiler drum internal feedwater distribution pipe". Subsequently, the remote maintenance platform can retrieve the standard image of the boiler drum internal feedwater distribution pipe stored within the remote maintenance platform. It is understood that the standard image is an image of the undamaged water supply distribution pipe inside the boiler drum. This image is free from human-caused image problems, such as size issues, lighting problems, geometric distortion, blurring, occlusion / background interference, etc. In one example, the standard image can be pre-taken by our engineers and pre-stored on the remote operation and maintenance platform. Example 2

[0029] In Example 2, the standard image is an image that meets the requirements for fault diagnosis. The method further includes: comparing the first image and the standard image by a remote operation and maintenance platform; determining at least one defect in the first image based on the comparison between the first image and the standard image by the remote operation and maintenance platform, wherein the at least one defect includes the first defect. In one example, defect identification can be achieved based on known machine vision technology. An example program code for determining the defect of the first image based on the comparison between the first image and the standard image is as follows: `import cv2 import numpy as np from skimage.metrics import structural_similarity as ssim from scipy import ndimage import matplotlib.pyplot as plt from matplotlib.patches import Rectangle class BoilerComponentDefectDetector: def __init__(self, reference_image_path): """Initialize detector parameters: reference_image_path: Standard reference image path"""self.reference_img = cv2.imread(reference_image_path) if self.reference_img is None: raise ValueError("Unable to load reference image") self.reference_gray =` cv2.cvtColor(self.reference_img, cv2.COLOR_BGR2GRAY) self.defects_info = {} def detect_all_defects(self, test_image_path): """Detect all types of shooting defects Parameters: test_image_path: Path to the image to be detected Return: Dictionary of defect detection results""" self.test_img = cv2.imread(test_image_path) if self.test_img is None: raise ValueError("Unable to load test image") self.test_gray = cv2.cvtColor(self.test_img, cv2.COLOR_BGR2GRAY) self.defects_info = {} # Perform all defect detection self.defects_info['size_issues'] = self.detect_size_issues() self.defects_info['lighting_issues'] = self.detect_lighting_issues() self.defects_info['geometric_issues'] = self.detect_geometric_issues() self.defects_info['quality_issues'] = self.detect_quality_issues() self.defects_info['alignment_issues'] = self.detect_alignment_issues() return self.defects_info def detect_size_issues(self): """Detect size-related issues""" issues = {} # 1. Detect the size ratio of components ref_contour = self._extract_main_contour(self.reference_img) test_contour = self._extract_main_contour(self.test_img) if ref_contour is not None and test_contour is not None: ref_area = cv2.contourArea(ref_contour) test_area = cv2.contourArea(test_contour) img_area = self.reference_img.shape[0] * self.reference_img.shape[1] ref_ratio = ref_area / img_areatest_ratio = test_area / img_areaissues['size_ratio'] = {'reference_ratio': ref_ratio,'test_ratio': test_ratio,'deviation': abs(ref_ratio - test_ratio) / ref_ratio* 100} # If the size deviation exceeds 30%, it is considered that there is a size problem if abs(ref_ratio - test_ratio) / ref_ratio>0.3:issues['size_warning'] = "Component size abnormal: reference {:.1f}% vs test {:.1f}%".format(ref_ratio*100, test_ratio*100)return issuesdef detect_lighting_issues(self):"""Detect lighting-related issues"""issues = {}# 1. Detect overexposed area over_exposed = self.test_gray>240over_exposed_ratio = np.sum(over_exposed) / (self.test_gray.shape[0]* self.test_gray.shape[1])if over_exposed_ratio>0.05: # More than 5% area is overexposed issues['over_exposure'] = {'ratio': over_exposed_ratio,'regions': np.where(over_exposed)}# 2. Detect underexposed area under_exposed = self.test_gray<15under_exposed_ratio = np.sum(under_exposed) / (self.test_gray.shape[0] * self.test_gray.shape[1])if under_exposed_ratio>0.05: # More than 5% area is underexposed issues['under_exposure'] = {'ratio': under_exposed_ratio,'regions': np.where(under_exposed)}# 3.Detecting uniformity of illumination: brightness_std = np.std(self.test_gray) if brightness_std>60: # Brightness standard deviation is too large issues['uneven_lighting'] = {'brightness_std': brightness_std} return issues def detect_geometric_issues(self): """Detecting geometric deformation issues""" issues = {} # Detecting geometric deformation using SIFT feature points sift = cv2.SIFT_create() kp1, des1 = sift.detectAndCompute(self.reference_gray, None) kp2, des2 = sift.detectAndCompute(self.test_gray, None) if des1 is not None and des2 is not None: # Feature point matching bf = cv2.BFMatcher() matches = bf.knnMatch(des1, des2, k=2) # Applying ratio test good_matches = [] for m, n in matches:if m.distance<0.75 * n.distance:good_matches.append(m)iflen(good_matches)>10:# Calculate the homography matrix and detect perspective transformation src_pts = np.float32([kp1[m.queryIdx].ptfor m in good_matches]).reshape(-1, 1, 2) dst_pts = np.float32([kp2[m.trainIdx].ptfor m in good_matches]).reshape(-1, 1, 2)H, mask = cv2.findHomography(src_pts, dst_pts,cv2.RANSAC, 5.0)if H is not None:# Analyze the deformation parameters of the homography matrix h11, h12, h13 = H[0]h21, h22, h23 = H[1]h31, h32, h33 = H[2]# Calculate scaling, rotation, and shearing parameters: scale_x = np.sqrt(h11**2 + h21**2) scale_y = np.sqrt(h12**2 + h22**2) # If the scaling difference is too large, it is considered that there is geometric distortion if abs(scale_x - scale_y) / min(scale_x, scale_y)>0.2: issues['perspective_distortion'] = {'scale_x': scale_x,'scale_y': scale_y,'distortion_ratio': abs(scale_x - scale_y) / min(scale_x, scale_y)}return issuesdef detect_quality_issues(self):"""Detect image quality issues""" issues = {} # 1. Detect blur blur_value = cv2.Laplacian(self.test_gray, cv2.CV_64F).var() if blur_value<100: # Laplacian variance threshold issues['blur'] = {'blur_value': blur_value,'severity': 'severe' if blur_value<50 else 'Medium'}# 2. Detect noise noise_level = np.std(self.test_gray - cv2.medianBlur(self.test_gray,3))if noise_level>15:issues['noise'] = {'noise_level': noise_level}return issuesdef detect_alignment_issues(self):"""Detect alignment and matching issues"""issues = {}# Adjust the size of the test image to match the reference image test_resized = cv2.resize(self.test_gray,(self.reference_gray.shape[1],self.reference_gray.shape[0]))# Calculate structural similarity similarity, diff = ssim(self.reference_gray, test_resized, full=True)issues['similarity'] = similarity * 100if similarity<0.8: # Similarity below 80% issues['alignment_warning'] = "Low image alignment may affect analysis accuracy" issues['difference_map'] = diffreturn issuesdef _extract_main_contour(self, image):"""Extract the main contour (boiler components) from the image"""gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)# Use adaptive thresholding to handle different lighting conditionsthresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY, 11, 2)# Morphological operations to remove noise kernel = np.ones((5,5), np.uint8)thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel) # Find contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) if contours: # Return the contour with the largest area return max(contours, key=cv2.contourArea) return None def generate_defect_report(self, output_path="defect_report.png"): """Generate defect detection report and marked image""" fig, axes = plt.subplots(2, 3, figsize=(18, 12)) axes = axes.ravel() # 1. Display reference image axes[0].imshow(cv2.cvtColor(self.reference_img, cv2.COLOR_BGR2RGB))axes[0].set_title('Reference Image (Standard Boiler Assembly)')axes[0].axis('off')# 2. Display test imageaxes[1].imshow(cv2.cvtColor(self.test_img, cv2.COLOR_BGR2RGB))axes[1].set_title('Test Image (To be Detected)')axes[1].axis('off')# 3. Mark size issues self._plot_size_issues(axes[2])# 4. Mark lighting issues self._plot_lighting_issues(axes[3])# 5. Mark quality issues self._plot_quality_issues(axes[4])# 6. Display detection report self._plot_detection_report(axes[5])plt.tight_layout()plt.savefig(output_path, dpi=300, bbox_inches='tight')plt.show()returnoutput_pathdef _plot_size_issues(self, ax):"""Draw size issue markers"""ax.imshow(cv2.cvtColor(self.test_img, cv2.COLOR_BGR2RGB))ref_contour = self._extract_main_contour(self.reference_img)test_contour = self._extract_main_contour(self.test_img)ifref_contour is not None and test_contour is not None:# Draw the test image contour x, y, w, h = cv2.boundingRect(test_contour)rect = Rectangle((x, y), w, h, linewidth=2, edgecolor='r', facecolor='none')ax.add_patch(rect)ax.text(x, y-10, f'detection area: {w}x{h}', color='red', fontweight='bold')ax.set_title('size detection')ax.axis('off')def _plot_lighting_issues(self, ax):"""Draw lighting problem markers"""test_rgb = cv2.cvtColor(self.test_img, cv2.COLOR_BGR2RGB)# Mark overexposed areas over_exposed = self.test_gray>240ifnp.any(over_exposed):mask = np.zeros_like(test_rgb)mask[over_exposed] = [255, 0, 0] # Red marks overexposure test_rgb = cv2.addWeighted(test_rgb, 0.7, mask, 0.3, 0) # Mark underexposure area under_exposed = self.test_gray<15ifnp.any(under_exposed):mask = np.zeros_like(test_rgb)mask[under_exposed] = [0, 0, 255] # Blue marks underexposure test_rgb = cv2.addWeighted(test_rgb, 0.7, mask, 0.3, 0)ax.imshow(test_rgb)ax.set_title('Lighting problem detection\n(Red: Overexposure, Blue: Underexposed)')ax.axis('off')def _plot_quality_issues(self, ax):"""Draw quality issue markers"""# Calculate blur map blur_map = cv2.Laplacian(self.test_gray, cv2.CV_64F)blur_map = np.abs(blur_map)ax.imshow(blur_map, cmap='hot')ax.set_title('Image quality detection\n(Hot spot: Blurred area)')ax.axis('off')def _plot_detection_report(self, ax):"""Draw detection report"""ax.axis('off')report_text = "Boiler component image quality detection report\n\n"report_text += "="*50 + "\n"# Summarize all defect information total_issues = 0 warnings = []fordefect_type, issues in self.defects_info.items():if issues: # If there are issues, check if defect_type == 'size_issues' and 'size_warning' in issues: warnings.append(". " + issues['size_warning'])total_issues += 1ifdefect_type == 'lighting_issues':if 'over_exposure' in issues:ratio = issues['over_exposure']['ratio']* 100warnings.append(f" Overexposed area: {ratio:.1f}%")total_issues += 1if 'under_exposure' in issues:ratio = issues['under_exposure']['ratio']* 100warnings.append(f" Underexposed area: {ratio:.1f}%")total_issues += 1ifdefect_type == 'quality_issues':if 'blur' in issues:warnings.append(f" Image blurring: {issues['blur']['severity']}")total_issues += 1if 'noise' in issues:warnings.append(" Excessive image noise")total_issues += 1ifdefect_type == 'alignment_issues' and 'alignment_warning' inissues:similarity = issues['similarity']warnings.append(f" Image alignment: {similarity:.1f}%")total_issues += 1iftotal_issues == 0:report_text += " Image quality is good and can be used for fault analysis. "else:report_text += f" Found {total_issues} image quality issues:\n\n"for warning in warnings:report_text += warning + "\n"report_text += "\nRecommendation: Retake the image for more accurate fault analysis results"ax.text(0.05, 0.95, report_text, transform=ax.transAxes, fontsize=10,verticalalignment='top', fontfamily='monospace')def get_detection_summary(self):"""Get detection summary"""summary = {'total_issues': 0,'issues_details': [],'recommendation': ''}fordefect_type, issues in self.defects_info.items():if issues:ifdefect_type == 'size_issues' and 'size_warning' in issues:summary['issues_details'].append(issues['size_warning'])summary['total_issues'] += 1ifdefect_type == 'lighting_issues':if 'over_exposure' in issues:summary['issues_details'].append("overexposure problem")summary['total_issues'] += 1if 'under_exposure' in issues:summary['issues_details'].append("Underexposure problem")summary['total_issues'] += 1ifdefect_type == 'quality_issues':if 'blur' in issues:summary['issues_details'].append("Image blur")summary['total_issues'] += 1if 'noise' in issues:summary['issues_details'].`append("image noise")` `summary['total_issues'] += 1` `if summary['total_issues'] == 0:` `summary['recommendation'] = "Image quality is acceptable, fault analysis can be performed"` `else:` `summary['recommendation'] = f"{summary['total_issues']} image quality issues were found, reshooting is recommended"` `return summary`

[0030] It should be understood that the aforementioned computer program code is merely an exemplary embodiment, used to assist in illustrating the technical means of the present invention, and is not a limitation on the only implementation of the present invention. Those skilled in the art should understand that, based on the overall disclosure of this specification and in conjunction with well-known programming principles, algorithms, and software development techniques in the field, those skilled in the art are fully capable of designing and implementing other equivalent program code or software modules to execute the technical solutions claimed in this invention. Furthermore, in specific implementation processes, those skilled in the art can, according to actual needs, write and optimize code through conventional software engineering practices or by hiring professional software engineers; this all falls within the scope of ordinary technical capabilities in the field. The remote operation and maintenance platform identifies the first defect on the first image to generate a first identified first image; in one example, the first defect on the first image can be identified by means of boxes, arrows, etc.; the remote operation and maintenance platform determines whether identification on the standard image is necessary based on the first defect. In one example, if a user-provided photo of the internal water supply distribution pipe of the boiler drum shows that the pipe is too large or too small, it generally does not need to be marked on the standard image. However, if the user-provided photo of the internal water supply distribution pipe shows defects such as localized blurring or overexposure, then corresponding markings are required on the standard image. Example 3

[0031] In Embodiment 3, the method further includes: if the remote maintenance platform determines that it does not need to mark the standard image, the remote maintenance platform sends the first marked first image, the standard image, and a description of the first defect to the terminal; the terminal displays the first entry to the user, wherein the first entry includes the first marked first image, the standard image, and a description of the first defect. In one example, after the terminal receives the first marked first image, the standard image, and the description of the first defect, the terminal can notify the user in the terminal notification bar that the remote maintenance platform has provided feedback on the repair request. Subsequently, the user enters the application and clicks on the feedback information to see the first entry. In one example, an example of the terminal displaying the first entry to the user can be seen in Figure 7. Figure 7 shows the status of the first entry displayed by the application when the faulty component in the first image provided by the user is too small. As shown in Figure 7, in the application interface, the APP can provide a text description of "user-provided image" to remind the user which image is the photo taken by the user, the APP can provide a text description of "standard image" to remind the user which image is the standard image of the water supply distribution pipe, and the APP also provides a text description of the defect at the bottom. In addition, while displaying the user-provided image, the app uses arrows on the image to indicate that the user should increase the proportion of the water supply distribution pipe in the photo. This invention enhances the prompting strength of existing systems for users through text prompts, arrow prompts, and comparison prompts with standard images. This helps users provide photos that meet the requirements more quickly and accurately, improving the user experience. Example 4

[0032] In embodiment 4, the method further includes: if the remote operation and maintenance platform determines that marking is required on the standard image, the remote operation and maintenance platform marks the standard image at the position corresponding to the position of the first defect in the first image to generate a first marked standard image; the remote operation and maintenance platform sends the first marked first image, the first marked standard image, and a description of the first defect to the terminal; the terminal displays a second entry to the user, wherein the second entry includes the first marked first image, the first marked standard image, and a description of the first defect. In one example, an example of the terminal displaying the second entry to the user can be seen in Figure 8. Figure 8 shows the status of the second entry in the application when the faulty component in the user-provided first image is overexposed (the overexposure is caused, for example, by the light above the plant shining directly on a part of the water supply distribution pipe, causing excessive brightness in that part of the water supply distribution pipe, resulting in overexposure). As shown in Figure 8, in the application interface, the APP can provide text descriptions for "user-provided images" to remind the user which image was taken by the user, and can provide text descriptions for "standard images" to remind the user which image is the standard image of the water supply distribution pipe. At the same time, the APP also provides text descriptions of defects at the bottom.

[0033] In one embodiment, at least one defect further includes a second defect, wherein the method further includes: a remote operation and maintenance platform marking the second defect on a first image to generate a second marked first image; the remote operation and maintenance platform determining, based on the second defect, whether marking on a standard image is necessary; if the remote operation and maintenance platform determines that marking on the standard image is necessary, the remote operation and maintenance platform marking the standard image at a position in the standard image corresponding to the position of the second defect in the first image to generate a second marked standard image; the remote operation and maintenance platform sending the second marked first image, the second marked standard image, and a description of the second defect to a terminal; and the terminal displaying a second entry and a third entry to a user, wherein the third entry includes the second marked first image, the second marked standard image, and a description of the second defect. Example 5

[0034] This invention provides a remote boiler operation and maintenance system based on identifier resolution. The system includes modules for performing the following operations: a user identifies a faulty boiler component and its component number; the user obtains a first image of the faulty boiler component using a terminal; the user uploads the first image and component number to the terminal; a remote operation and maintenance platform receives the first image and component number sent by the terminal; the remote operation and maintenance platform determines whether the first image meets the fault diagnosis requirements; if the remote operation and maintenance platform determines that the first image does not meet the fault diagnosis requirements, then the remote operation and maintenance platform retrieves a standard image of the faulty boiler component based on the component number.

[0035] In one implementation, the standard image is an image that meets the requirements for fault diagnosis. The system further includes a module for performing the following operations: comparing a first image and a standard image by a remote operation and maintenance platform; determining at least one defect in the first image by the remote operation and maintenance platform based on the comparison between the first image and the standard image, wherein the at least one defect includes a first defect; marking the first defect on the first image by the remote operation and maintenance platform to generate a first marked first image; and determining by the remote operation and maintenance platform whether marking is required on the standard image based on the first defect.

[0036] In one embodiment, the system further includes a module for performing the following operations: if the remote operation and maintenance platform determines that it is not necessary to mark the standard image, the remote operation and maintenance platform sends the first marked first image, the standard image, and a description of the first defect to the terminal; the terminal displays a first entry to the user, wherein the first entry includes the first marked first image, the standard image, and a description of the first defect.

[0037] In one embodiment, the system further includes a module for performing the following operations: if the remote operation and maintenance platform determines that marking is required on a standard image, the remote operation and maintenance platform marks the standard image at a position in the standard image corresponding to the position of the first defect in the first image to generate a first marked standard image; the remote operation and maintenance platform sends the first marked first image, the first marked standard image, and a description of the first defect to the terminal; and the terminal displays a second entry to the user, wherein the second entry includes the first marked first image, the first marked standard image, and a description of the first defect.

[0038] In one embodiment, at least one defect further includes a second defect, wherein the system further includes a module for performing the following operations: the remote operation and maintenance platform identifies the second defect on a first image to generate a second identified first image; the remote operation and maintenance platform determines, based on the second defect, whether identification on a standard image is required; if the remote operation and maintenance platform determines that identification on the standard image is required, the remote operation and maintenance platform identifies the standard image at a position in the standard image corresponding to the position of the second defect in the first image to generate a second identified standard image; the remote operation and maintenance platform sends the second identified first image, the second identified standard image, and a description of the second defect to a terminal; the terminal displays a second entry and a third entry to the user, wherein the third entry includes the second identified first image, the second identified standard image, and a description of the second defect.

[0039] It should be noted that the above description of specific embodiments of the present invention is only intended to clearly illustrate the technical solutions and implementation methods of the present invention, and should not be construed as any limitation on the scope of protection of the present invention. Those skilled in the art should understand that within the core concept disclosed in the present invention and the scope defined by the claims, any easily conceivable changes or modifications based on the present invention, such as equivalent substitutions of specific technical features, minor adjustments to some structures, or parameter modifications made to adapt to specific application scenarios, should be considered to fall within the scope of protection of the present invention. The specific process parameters, materials, and structural details mentioned in the specification are only illustrative and are not intended to limit the claims to the specific embodiments described. The scope of protection of the present invention should be determined by the wording of the claims and covers all equivalent technical solutions that those skilled in the art can reasonably foresee after reading the claims and combining them with all the content disclosed in the present invention.

Claims

1. A remote boiler operation and maintenance method based on identifier resolution, characterized in that, The method includes: a user determining a faulty boiler component and its component number; the user acquiring a first image of the faulty boiler component using a terminal; the user uploading the first image and the component number to the terminal; a remote maintenance platform receiving the first image and the component number sent by the terminal; the remote maintenance platform determining whether the first image meets the fault diagnosis requirements; if the remote maintenance platform determines that the first image does not meet the fault diagnosis requirements, then the remote maintenance platform retrieving a standard image of the faulty boiler component based on the component number.

2. The method according to claim 1, wherein, The standard image is an image that meets the fault diagnosis requirements. The method further includes: comparing the first image and the standard image by a remote operation and maintenance platform; determining at least one defect in the first image by the remote operation and maintenance platform based on the comparison between the first image and the standard image, wherein the at least one defect includes a first defect; marking the first defect on the first image by the remote operation and maintenance platform to generate a first marked first image; and determining whether marking is required on the standard image based on the first defect by the remote operation and maintenance platform.

3. The method according to claim 2, wherein, The method further includes: if the remote operation and maintenance platform determines that it is not necessary to mark the standard image, the remote operation and maintenance platform sends the first marked first image, the standard image, and a description of the first defect to the terminal; the terminal displays a first entry to the user, wherein the first entry includes the first marked first image, the standard image, and a description of the first defect.

4. The method according to claim 2, wherein, The method further includes: if the remote operation and maintenance platform determines that it needs to mark the standard image, the remote operation and maintenance platform marks the standard image at the position in the standard image corresponding to the position of the first defect in the first image to generate a first marked standard image; the remote operation and maintenance platform sends the first marked first image, the first marked standard image, and a description of the first defect to the terminal; the terminal displays a second entry to the user, wherein the second entry includes the first marked first image, the first marked standard image, and a description of the first defect.

5. The method according to claim 4, wherein, The at least one defect further includes a second defect, wherein the method further includes: marking the second defect on the first image by a remote operation and maintenance platform to generate a second marked first image; determining, based on the second defect, whether marking on the standard image is required; if the remote operation and maintenance platform determines that marking on the standard image is required, marking the standard image at the position in the standard image corresponding to the position of the second defect in the first image to generate a second marked standard image; sending the second marked first image, the second marked standard image, and a description of the second defect to the terminal by the remote operation and maintenance platform; and displaying the second entry and the third entry to the user, wherein the third entry includes the second marked first image, the second marked standard image, and a description of the second defect.

6. A remote boiler operation and maintenance system based on identifier resolution, characterized in that, The system includes modules for performing the following operations: a user determines a faulty boiler component and its component number; a user acquires a first image of the faulty boiler component using a terminal; and a user uploads the first image and the component number to the terminal. The remote operation and maintenance platform receives the first image and the component number sent by the terminal; The remote operation and maintenance platform determines whether the first image meets the fault diagnosis requirements; if the remote operation and maintenance platform determines that the first image does not meet the fault diagnosis requirements, the remote operation and maintenance platform retrieves the standard image of the faulty boiler component based on the component number.

7. The system according to claim 6, wherein, The standard image is an image that meets the fault diagnosis requirements. The system further includes a module for performing the following operations: comparing the first image and the standard image by a remote operation and maintenance platform; determining at least one defect in the first image based on the comparison between the first image and the standard image by the remote operation and maintenance platform, wherein the at least one defect includes a first defect; marking the first defect on the first image by the remote operation and maintenance platform to generate a first marked first image; and determining whether marking is required on the standard image based on the first defect by the remote operation and maintenance platform.

8. The system according to claim 7, wherein, The system also includes a module for performing the following operations: if the remote operation and maintenance platform determines that it is not necessary to mark the standard image, the remote operation and maintenance platform sends the first marked first image, the standard image, and a description of the first defect to the terminal. The terminal displays a first entry to the user, wherein the first entry includes the first identified first image, the standard image, and a description of the first defect.

9. The system according to claim 7, wherein, The system also includes a module for performing the following operations: if the remote operation and maintenance platform determines that it is necessary to mark the standard image, the remote operation and maintenance platform marks the standard image at the position in the standard image corresponding to the position of the first defect in the first image, so as to generate a first marked standard image. The remote operation and maintenance platform sends the first identified first image, the first identified standard image, and a description of the first defect to the terminal. The terminal displays a second entry to the user, wherein the second entry includes the first identified first image, the first identified standard image, and a description of the first defect.

10. The system according to claim 9, wherein, The at least one defect further includes a second defect, wherein the system further includes a module for performing the following operations: the remote operation and maintenance platform identifies the second defect on the first image to generate a second identified first image; the remote operation and maintenance platform determines whether identification on the standard image is required based on the second defect; if the remote operation and maintenance platform determines that identification on the standard image is required, the remote operation and maintenance platform identifies the standard image at the position in the standard image corresponding to the position of the second defect in the first image to generate a second identified standard image; the remote operation and maintenance platform sends the second identified first image, the second identified standard image, and a description of the second defect to the terminal; the terminal displays the second entry and the third entry to the user, wherein the third entry includes the second identified first image, the second identified standard image, and a description of the second defect.

Images