A manhole anti-falling net installation construction management system and method

Abstract

This invention provides a construction management system and method for the installation of safety nets for manholes, relating to the field of municipal pipeline construction management technology. This invention monitors manhole construction objects in real time, collecting facility numbers, spatial coordinates, pre-, during-, and post-construction images, and timestamp information to construct a unique identifier for the construction object and generate a standardized information body. Based on this, it calculates the spatial consistency coefficient, construction process integrity coefficient, and construction result reliability coefficient of the construction object, and compares them with corresponding thresholds for evaluation. When the evaluation result is unqualified, it automatically triggers control strategies such as repositioning, re-photographing and re-recording, rework, or manual review, suspending the flow of abnormal data until the evaluation meets the standards and updates and records are completed. This method achieves spatial verification, process constraints, and result quality control during the construction process of safety nets for manholes, improving construction authenticity, data reliability, and management efficiency.

Description

Technical Field

[0001] This invention relates to the field of municipal pipeline construction management technology, specifically to a construction management system and method for installing anti-fall nets for manholes. Background Technology

[0002] Inspection manholes, as crucial infrastructure in municipal water supply, drainage, and stormwater / sewage separation networks, are widely distributed in urban roads, residential areas, and public areas. The interior of these manholes contains a certain depth. Without effective protective measures, personnel, tools, or debris are prone to falling into these manholes, posing a significant safety hazard. To reduce this risk, installing fall protection nets inside inspection manholes has gradually become a common safety measure in recent years.

[0003] In existing technologies, the installation of safety nets for manholes largely relies on manual on-site operations, with management and acceptance of the construction process and results achieved through manual photography, manual reporting, or post-construction random checks. However, due to the large number of manholes, their wide distribution, and the complex construction environment, existing construction management methods generally suffer from the following problems: First, there is a lack of a reliable binding mechanism between the construction object and its spatial location. Existing methods typically rely solely on construction personnel manually selecting manhole numbers or uploading construction photos, without effectively verifying the construction images against the manhole's design coordinates and actual spatial location. This easily leads to problems such as incorrect manhole construction and mismatches between photos and construction objects, resulting in insufficient authenticity and traceability of construction records.

[0004] Secondly, the data correlation during the construction process is weak, and the degree of information structuring is low. Images, time information, and construction status before, during, and after construction are mostly stored in discrete form, lacking a unified data encapsulation and correlation method. This makes it difficult to form a complete and continuous record of the construction process, which is not conducive to subsequent quality assessment and determination of construction responsibility.

[0005] Furthermore, current construction acceptance mainly relies on manual experience and lacks quantifiable quality evaluation standards. Key issues such as the clarity of construction images, the accuracy of shooting angles, and the complete installation of fall protection nets are often determined subjectively by humans, which is easily influenced by personal experience and work attitude, leading to inconsistent judgments, low efficiency, and the risk of missed inspections.

[0006] Furthermore, existing technologies lack automatic identification and handling mechanisms for construction anomalies. When construction images are of insufficient quality, the shooting position is significantly off, or the construction status is abnormal, the system typically fails to automatically detect and issue risk warnings, leading to problems being discovered only during subsequent acceptance or operation phases, increasing management costs and safety hazards. Summary of the Invention

[0007] The purpose of this invention is to provide a construction management system and method for installing anti-fall nets in inspection wells, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A method for managing the installation of fall protection nets for inspection wells, comprising the following steps: Step 1: By real-time monitoring of the construction objects of municipal pipeline manholes, the facility number of the manhole, the spatial coordinate information of the manhole, the images before, during and after construction and the corresponding timestamps, the image acquisition coordinates are collected, and construction status marking information is generated after construction is completed. Step 2: By constructing a unique identifier for the manhole construction object, a complete data association relationship for the manhole construction process is formed, generating a standardized information body for the construction object; Step 3: Calculate the spatial consistency coefficient of the construction object and compare it with the spatial consistency threshold to determine whether the spatial position matching degree of the current manhole construction object is qualified. If it is not qualified, a repositioning and coordinate correction strategy is adopted, the status record is paused, and the standardized information body of the construction object is updated after review. Step 4: Calculate the integrity coefficient of the construction process and compare it with the integrity threshold of the construction process to determine whether the current manhole construction process is complete. If it is not complete, a strategy of reshooting, re-recording, or rework will be adopted, and data export will be prohibited until the integrity of the construction process meets the standard and the standardized information body of the construction object is updated. Step 5: Calculate the credibility coefficient of the construction results and compare it with the credibility threshold of the construction results to determine whether the current installation result of the manhole anti-fall net is credible. If it is not credible, a re-inspection and re-photographing mechanism and manual verification strategy will be implemented, and data generation will be suspended until the credibility reaches the standard. Then, the quality risk will be recorded and the update will be completed.

[0009] Further, step one includes: S11. For inspection wells in municipal water supply and drainage, tap water or rainwater and sewage separation pipe networks that are to be fitted with anti-fall nets, a GIS map system is deployed on the mobile terminal carried by the construction personnel, and the inspection well facility number and its corresponding design coordinate data provided by the pipe network ownership unit are loaded to monitor the identity and spatial location of the construction object of the inspection well in real time, and collect the facility number and spatial coordinate information of the inspection well. S12. Conduct real-time monitoring of the installation and construction phase of the anti-fall net for the inspection well; by calling the visible light camera built into the mobile terminal, collect corresponding pre-construction images, in-construction images, and post-construction images of the same inspection well before construction begins, during construction, and after construction is completed, and automatically record the corresponding timestamp information each time the image is collected, and obtain the timestamp corresponding to the pre-construction image and the timestamp corresponding to the post-construction image. S13. During the construction phase, the satellite positioning component built into the mobile terminal is activated to locate the terminal's geographical location in real time at the moment of image acquisition, thereby obtaining the positioning coordinate information of the image acquisition terminal. S14. After completing the installation of the anti-fall net for the inspection well, the construction personnel use the construction management interface in the mobile terminal to confirm the status of the current construction results of the inspection well and generate construction status marker information.

[0010] Furthermore, step two includes: S21. Based on the manhole facility number, spatial coordinate information, construction image data, construction timestamp information and construction status mark information, an object binding method based on facility identifier and spatial coordinate is adopted to associate the facility number of each manhole with its corresponding spatial coordinate to construct a unique identifier for the manhole construction object. S22. Based on the unique identifier of the manhole construction object, a mapping method based on timestamps and status markers is adopted to map the pre-construction images, in-construction images, and post-construction images corresponding to the same construction object to their acquisition time and construction status, forming a complete data association relationship of the manhole construction process. S23. Based on the complete data association relationship of the inspection well construction process, a structured data encapsulation method is adopted to uniformly encapsulate the verified construction image data, construction time information, construction status information and image quality feature parameters to generate a standardized information body of the construction object.

[0011] Furthermore, step three includes: S31. Based on the standardized information body of the construction object, call the design spatial coordinates corresponding to the bound manhole facility number and the positioning coordinate information of the image acquisition terminal. Using the geospatial distance calculation method, calculate the spatial offset relationship between the design spatial coordinates and the positioning coordinate information of the image acquisition terminal to obtain the coordinate deviation distance between the manhole design position and the on-site photo position.

[0012] Furthermore, step three also includes: S32. Using the coordinate deviation distance and the normalized deviation mapping method, after dimensionless processing, the spatial consistency coefficient of the construction object is calculated and obtained. S33. By setting a spatial consistency threshold and comparing the spatial consistency coefficient of the construction object with the spatial consistency threshold, the first evaluation result is obtained, including: When the spatial consistency coefficient of the construction object is greater than or equal to the spatial consistency threshold, it indicates that the spatial location matching degree of the current manhole construction object is qualified, and continuous monitoring is required. When the spatial consistency coefficient of the construction object is less than the spatial consistency threshold, it indicates that the spatial location matching degree of the current inspection well construction object is unqualified, and there is a deviation between the construction image and the designed well location. This leads to management risks such as mis-well construction, wrong well recording, or repeated construction, triggering the first warning instruction and generating the first strategy: send a relocation prompt message to the mobile terminal of the corresponding construction personnel, requiring the construction personnel to relocate and acquire images near the target well location; suspend the writing of the construction completion status mark of the current construction object to prevent abnormal data from entering the subsequent management process; initiate the coordinate correction application process, mark the current construction object as pending verification, and submit it to the back-end management terminal for manual or technical review; recalculate until the spatial consistency coefficient of the construction object is greater than or equal to the spatial consistency threshold, and write it back to the corresponding standardized information body of the construction object for update.

[0013] Furthermore, step four includes: S41. Based on the updated standardized information body of the construction object, call the mapped construction stage image data and corresponding construction timestamp information in it, and count the number of construction stage images actually collected by the current inspection well; based on the timestamps corresponding to the images before construction and the timestamps corresponding to the images after construction, use the time difference calculation method to calculate the time interval between adjacent construction stages.

[0014] Furthermore, step four also includes: S42. After dimensionless processing, the integrity coefficient of the construction process is calculated by acquiring the number of images of the construction stage and the time interval between adjacent construction stages. S43. By setting a predefined construction process integrity threshold and comparing the construction process integrity coefficient with the construction process integrity threshold, the second evaluation result is obtained, including: When the construction process integrity coefficient is greater than or equal to the construction process integrity threshold, it indicates that the current manhole construction process is complete and will be continuously monitored. When the construction process integrity coefficient is less than the construction process integrity threshold, it indicates that the current manhole construction process is incomplete, with omissions in construction stages, time anomalies, or missing process records. This poses a risk of insufficient construction authenticity or failure to pass data review, triggering a second warning instruction and generating a second strategy: sending a re-shooting or re-recording instruction to the mobile terminal of the corresponding construction personnel, requiring the re-collection of images of the missing construction stages; issuing a rework prompt for construction objects with time anomalies, requiring the re-execution of the corresponding construction steps and the synchronous collection of complete construction records; prohibiting the export or submission of construction data for the current construction object to prevent incomplete construction data from entering the acceptance or archiving process; after adjustment, recalculating until the construction process integrity coefficient is greater than or equal to the construction process integrity threshold, and writing it back to the corresponding standardized information body of the construction object for updating.

[0015] Furthermore, step five includes: S51. Based on the updated standardized information body of the construction object, extract the images before construction, during construction, and after construction. On the mobile terminal, use image processing and computer vision analysis methods to perform quality analysis on the construction images. By calculating the gradient changes, edge information, and target area features of the images, obtain the image clarity index. By analyzing the geometric features of the fall prevention net and wellhead structure, obtain the image shooting angle deviation index. By detecting the occlusion of the target area, obtain the image occlusion rate index.

[0016] Furthermore, step five also includes: S52. By acquiring the image clarity index, image shooting angle deviation index, and image occlusion rate index, and combining them with the construction status marking information, after dimensionless processing, a multi-index weighted fusion algorithm is used to calculate and obtain the credibility coefficient of the construction result. S53. By setting a preset construction result credibility threshold and comparing the construction result credibility coefficient with the construction result credibility threshold, the third evaluation result is obtained, including: When the credibility coefficient of the construction result is greater than or equal to the credibility threshold of the construction result, it indicates that the current installation result of the anti-fall net for the manhole is credible and should be continuously monitored. When the credibility coefficient of the construction result is less than the credibility threshold of the construction result, it indicates that the current installation result of the anti-fall net for the manhole is unreliable. There is a risk of quality judgment due to insufficient image clarity, shooting angle deviation, target area obstruction, or inconsistent construction status. This triggers a third warning instruction and generates a third strategy: it prompts construction personnel to conduct on-site re-inspection or re-take construction images of the corresponding manhole; it requires the re-collection of images of key nodes after construction to reduce the risk of obstruction or angle deviation; it marks the current construction object as a manually reviewed object and suspends the automatic generation of construction acceptance data; after adjustment, it recalculates until the credibility coefficient of the construction result is greater than or equal to the credibility threshold of the construction result; and it records abnormal construction data and risk types in the construction quality management log for subsequent operation and maintenance optimization.

[0017] Furthermore, a construction management system for the installation of safety nets for manholes includes: The data acquisition module is used to monitor the construction objects of municipal pipeline manholes in real time, collect the manhole facility number, manhole spatial coordinate information, images before, during and after construction and corresponding timestamps, image acquisition coordinates, and generate construction status marking information after construction is completed. The standardized module for identifying construction objects is used to construct a unique identifier for manhole construction objects, form a complete data association relationship in the manhole construction process, and generate a standardized information body for construction objects. The spatial consistency assessment and correction module is used to calculate the spatial consistency coefficient of the construction object and compare it with the spatial consistency threshold to determine whether the spatial position matching degree of the current manhole construction object is qualified. If it is not qualified, a repositioning and coordinate correction strategy is given, the status record is paused, and the standardized information body of the construction object is updated after review. The construction process integrity assessment module is used to calculate the construction process integrity coefficient and compare it with the construction process integrity threshold to determine whether the current manhole construction process is complete. If it is not complete, a strategy of reshooting, re-recording, or rework will be given, and data export will be prohibited until the construction process integrity meets the standard and the standardized information body of the construction object is updated. The construction result quality control module is used to calculate the credibility coefficient of the construction result and compare it with the credibility threshold of the construction result to determine whether the current installation result of the manhole anti-fall net is credible. If it is not credible, a re-inspection and re-photographing and manual review mechanism strategy is given, and data generation is suspended until the credibility reaches the standard, then the quality risk is recorded and the update is completed.

[0018] Compared with the prior art, the beneficial effects of the present invention are: This invention uses a unique identifier for the construction object of the inspection well and introduces a spatial consistency coefficient for the construction object to quantitatively compare the design coordinates with the coordinates of the field image acquisition. When the spatial matching is not qualified, a repositioning and coordinate correction strategy is automatically triggered. This mechanism prevents problems such as misconduct construction, wrong well recording, or repeated construction, and solves the technical defects of traditional well finding that rely on manual experience, which makes it difficult to detect positioning deviations in a timely manner.

[0019] This invention also analyzes the number of images and construction time intervals during the construction phase to construct a construction process integrity coefficient, which is then compared with a preset threshold. This invention can automatically identify omissions, time anomalies, or missing process records during the construction phase. When the data is found to be unqualified, it prohibits data export and forces re-shooting, re-recording, or rework, thereby ensuring that the construction process data is authentic, complete, and traceable, and avoiding management risks caused by centralized re-recording later.

[0020] This invention also uses a weighted fusion algorithm to calculate the credibility coefficient of the construction results based on multi-dimensional indicators such as image clarity, shooting angle deviation, occlusion rate, and construction status marking, thereby achieving an objective quantitative evaluation of the installation results of the fall prevention net. When the credibility is insufficient, a review and manual verification mechanism is automatically triggered, and the generation of acceptance data is suspended, thereby reducing the risk of quality misjudgment caused by insufficient image quality or inconsistent status, and improving the overall construction quality management level. Attached Figure Description

[0021] Figure 1 This is a schematic diagram illustrating the execution of the core logic flow nodes of the overall system of the present invention; Figure 2 This is a schematic diagram of the overall method steps of the present invention; Figure 3 This is a schematic diagram of the overall system flow and technical route of the present invention. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0023] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0024] Example 1 Please see Figures 1 to 3 This invention provides a technical solution: a method for the installation and construction management of anti-fall nets for inspection wells, the specific steps of which include: Step 1: By real-time monitoring of the construction objects of municipal pipeline manholes, the facility number of the manhole, the spatial coordinate information of the manhole, the images before, during and after construction and the corresponding timestamps, the image acquisition coordinates are collected, and construction status marking information is generated after construction is completed. Step 2: By constructing a unique identifier for the manhole construction object, a complete data association relationship for the manhole construction process is formed, generating a standardized information body for the construction object; Step 3: Calculate the spatial consistency coefficient of the construction object and compare it with the spatial consistency threshold to determine whether the spatial position matching degree of the current manhole construction object is qualified. If it is not qualified, a repositioning and coordinate correction strategy is adopted, the status record is paused, and the standardized information body of the construction object is updated after review. Step 4: Calculate the integrity coefficient of the construction process and compare it with the integrity threshold of the construction process to determine whether the current manhole construction process is complete. If it is not complete, a strategy of reshooting, re-recording, or rework will be adopted, and data export will be prohibited until the integrity of the construction process meets the standard and the standardized information body of the construction object is updated. Step 5: Calculate the credibility coefficient of the construction results and compare it with the credibility threshold of the construction results to determine whether the current installation result of the manhole anti-fall net is credible. If it is not credible, a re-inspection and re-photographing mechanism and manual verification strategy will be implemented, and data generation will be suspended until the credibility reaches the standard. Then, the quality risk will be recorded and the update will be completed.

[0025] Figure 1The core content is divided into two parts: physical entities and logical processes, which fully correspond to the technical solution of this invention. The isometric view of the manhole profile on the left intuitively shows the physical object managed by this method, namely the municipal manhole equipped with anti-fall nets, and the scene monitored in real time through the data acquisition terminal. This corresponds to the process of collecting information such as manhole facility number, spatial coordinates, image, and timestamp in step one. The technical roadmap on the right, guided by arrows, elaborates on the data processing and control process. Among them, the "Construction Data Acquisition and Standardization" flowchart corresponds to the data acquisition in step one and the process of constructing a unique identifier for the construction object and generating a standardized information body in step two; the "Spatial Consistency Verification" flowchart corresponds to the process of calculating the spatial consistency coefficient and making a qualification judgment and strategy response in step three; the "Construction Process Integrity Verification" flowchart corresponds to the process of calculating the construction process integrity coefficient and making a judgment and control in step four; and the "Construction Result Credibility Assessment" flowchart corresponds to the process of calculating the construction result credibility coefficient in step five and making a final quality control and record update based on it. The entire diagram fully demonstrates the complete technical path from on-site data collection to multi-dimensional, phased automated verification and closed-loop management.

[0026] In this embodiment, by implementing object-level data standardization management for the entire process of installing anti-fall nets for inspection wells, and based on a multi-dimensional quantitative evaluation mechanism of spatial consistency, process integrity, and result credibility, the construction process can be traced, the construction status can be verified, and the construction results can be controlled. This effectively avoids problems such as false construction, missed inspections, and misjudgments caused by positioning deviations, process omissions, or image distortion, thereby improving the accuracy of construction quality supervision and the reliability of construction acceptance.

[0027] Example 2 Please see Figures 1 to 3 In this embodiment, as explained in Embodiment 1, specifically, step one includes: S11. For inspection wells in municipal water supply and drainage, tap water or rainwater and sewage separation pipe networks where anti-fall nets are to be installed, a GIS map system is deployed on the mobile terminal carried by the construction personnel. The inspection well facility number and its corresponding design coordinate data provided by the pipe network ownership unit are loaded to monitor the identity and spatial location of the construction object of the inspection well in real time. The facility number of the inspection well is collected and recorded as ID and the spatial coordinate information of the inspection well is recorded as G. S12. Conduct real-time monitoring of the installation and construction phase of the anti-fall net for the inspection well; by calling the visible light camera built into the mobile terminal, before the start of construction, during the construction process, and after the completion of construction of the same inspection well, respectively, collect the corresponding pre-construction image, recorded as Ppre, the in-construction image, recorded as Pmid, and the post-construction image, recorded as Ppost. Automatically record the corresponding timestamp information when each image is collected, obtain the timestamp corresponding to the pre-construction image, recorded as Tpre, and the timestamp corresponding to the post-construction image, recorded as Tpost. S13. During the construction phase, the satellite positioning component built into the mobile terminal is enabled to locate the terminal's geographical location in real time at the moment of image acquisition, and the positioning coordinate information of the image acquisition terminal is obtained, denoted as Gphoto. S14. After completing the installation of the anti-fall net for the inspection well, the construction personnel use the construction management interface in the mobile terminal to confirm the status of the current construction result of the inspection well and generate construction status mark information, which is recorded as S.

[0028] In this embodiment, by integrating GIS positioning, image acquisition and status marking mechanisms into the mobile terminals of construction personnel, the spatial location, construction stage and construction results of the inspection well anti-fall net installation are collected and bound in real time, so that the identity of the construction object, the construction timeline and image evidence form a one-to-one correspondence, thereby improving the authenticity and spatial verifiability of construction data from the source and reducing the management risks caused by misconduct in well construction and process omissions.

[0029] Example 3 Please see Figures 1 to 3 In the explanation of Example 2, this embodiment specifically includes the following steps: S21. Based on the manhole facility number, spatial coordinate information, construction image data, construction timestamp information and construction status mark information, adopt the object binding method based on facility identifier and spatial coordinate to associate the facility number ID of each manhole with its corresponding spatial coordinate G, and construct a unique identifier for the manhole construction object. S22. Based on the unique identifier of the manhole construction object, a mapping method based on timestamps and status markers is adopted to map the pre-construction images, in-construction images, and post-construction images corresponding to the same construction object to their acquisition time and construction status, forming a complete data association relationship of the manhole construction process. S23. Based on the complete data association relationship of the inspection well construction process, a structured data encapsulation method is adopted to uniformly encapsulate the verified construction image data, construction time information, construction status information and image quality feature parameters to generate a standardized information body D of the construction object.

[0030] In this embodiment, by uniformly binding and structurally encapsulating the manhole facility identification, spatial coordinates, construction images, timestamps, and construction status, a unique identifier for the construction object is constructed and a standardized information body is generated. This ensures that the data throughout the construction process has a consistent data structure and a clear object attribution, providing a stable and reusable data foundation for subsequent spatial consistency verification, process integrity assessment, and construction quality credibility determination, thereby reducing the management and auditing difficulties caused by the dispersion of multi-source construction data.

[0031] Example 4 Please see Figures 1 to 3 In the explanation of Example 3, this embodiment specifically includes the following steps: S31. Based on the standardized information body D of the construction object, call the design spatial coordinates G corresponding to the bound manhole facility number, and the positioning coordinate information Gphoto of the image acquisition terminal. Using the geospatial distance calculation method, calculate the spatial offset relationship between the design spatial coordinates G and the positioning coordinate information Gphoto of the image acquisition terminal, and obtain the coordinate deviation distance between the manhole design position and the on-site photo position, denoted as d.

[0032] In this embodiment, by quantitatively calculating the offset between the design spatial coordinates of the inspection well and the coordinates of the construction site image acquisition, the spatial consistency between the actual location of the construction object and the design well location can be reflected intuitively and objectively. This provides a basis for timely identification of misconduct during construction, misaligned photography, or positioning deviations, thereby improving the accuracy of positioning verification during the construction process and the authenticity of construction data.

[0033] Example 5 Please see Figures 1 to 3 In the explanation of Example 4, specifically, step three further includes: S32. Using the coordinate deviation distance d, and employing the normalized deviation mapping method, after dimensionless processing, the spatial consistency coefficient of the construction object is calculated and obtained, denoted as Cloc, as follows:

[0034] In the formula, Dmax represents the maximum allowable positioning deviation threshold, which is obtained by: statistically analyzing the spatial deviation between the construction image acquisition coordinates and the design coordinates through the positioning data of historical inspection well construction projects, and using interval distribution analysis and tolerance constraint determination methods to determine the maximum allowable positioning deviation range that can meet the requirements of construction well finding accuracy and management risk control, and obtaining the maximum allowable positioning deviation threshold. The physical principle of the formula: The formula is based on the monotonic inverse correlation between spatial deviation and the matching degree of the construction object's location. The coordinate deviation distance d represents the actual spatial offset between the on-site construction image acquisition location and the designed location of the inspection well. The larger the value, the lower the spatial matching degree between the construction object and the target well location. The maximum allowable positioning deviation threshold Dmax is used to limit the maximum spatial offset scale within the acceptable range of construction management and engineering safety. By normalizing the coordinate deviation distance d relative to Dmax, the spatial offset at different scales can be uniformly mapped to the interval [0, 1], thereby eliminating the influence of dimensions. In this mapping relationship, when d is close to 0, it indicates that the construction location is consistent with the design well location height, and the spatial consistency coefficient Cloc of the construction object is close to 1; when d is close to Dmax, it indicates that the construction location has reached the upper limit of the allowable deviation, the spatial consistency is significantly reduced, and the spatial consistency coefficient Cloc of the construction object is close to 0. This linear mapping method intuitively reflects the degree of influence of spatial deviation on the reliability of the construction object location, so that the spatial consistency assessment results have clear physical meaning and interpretability, which facilitates subsequent threshold determination and the triggering of construction risk control strategies. S33. By setting a spatial consistency threshold, denoted as Cth, and comparing the spatial consistency coefficient Cloc of the construction object with the spatial consistency threshold Cth, the first evaluation results are obtained, including: When the spatial consistency coefficient Cloc of the construction object is greater than or equal to the spatial consistency threshold Cth, it indicates that the spatial location matching degree of the current manhole construction object is qualified, and continuous monitoring is required. When the spatial consistency coefficient Cloc of the construction object is less than the spatial consistency threshold Cth, it indicates that the spatial location matching degree of the current inspection well construction object is unqualified, and there is a deviation between the construction image and the designed well location. This leads to management risks such as mis-well construction, wrong well recording, or repeated construction, triggering the first warning instruction and generating the first strategy: send a relocation prompt message to the mobile terminal of the corresponding construction personnel, requiring the construction personnel to relocate and acquire images near the target well location; suspend the writing of the construction completion status mark of the current construction object to prevent abnormal data from entering the subsequent management process; initiate the coordinate correction application process, mark the current construction object as pending verification, and submit it to the back-end management terminal for manual or technical review; recalculate until the spatial consistency coefficient Cloc of the construction object is greater than or equal to the spatial consistency threshold Cth, and write it back to the corresponding standardized information body D of the construction object for update.

[0035] The spatial consistency threshold Cth is obtained by statistically analyzing a large amount of historical spatial positioning and image acquisition data from manhole safety net installation projects. This involves extracting the spatial deviation distribution range between the designed manhole coordinates and the construction image acquisition coordinates under normal construction conditions, as well as the spatial deviation characteristic intervals under abnormal conditions such as misaligned manhole construction or incorrect manhole records. Combining the accuracy requirements of municipal pipeline construction, the tolerance range of on-site manhole location operations, and management risk control experience, a reasonable spatial consistency judgment threshold that can distinguish between normal construction and spatial location mismatch is determined. This threshold is used to accurately identify the spatial location deviation risk of the construction object, preventing misaligned manhole construction and construction data mismatch problems.

[0036] In this embodiment, by introducing a spatial consistency coefficient based on coordinate deviation and combining it with threshold judgment and hierarchical early warning strategies, quantitative assessment and closed-loop management of the deviation of the inspection well construction position can be achieved. This enables timely detection and correction of positioning anomalies during the construction phase, avoiding management risks such as misplaced wells, incorrect wells, and repeated construction, ensuring the consistency between construction data and design information, and improving the controllability of the construction process and the credibility of the construction results.

[0037] Example 6 Please see Figures 1 to 3 In the explanation of Example 5, specifically, step four includes: S41. Based on the updated standardized information body D of the construction object, call the mapped construction stage image data and corresponding construction timestamp information in it, and count the number of construction stage images actually collected by the current inspection well, denoted as N; based on the timestamp Tpre corresponding to the image before construction and the timestamp Tpost corresponding to the image after construction, use the time difference calculation method to calculate the time interval between adjacent construction stages, denoted as ΔT.

[0038] In this embodiment, by performing quantitative statistical analysis on the number of images in the construction phase and the time interval between adjacent construction phases, an objective judgment on the integrity and timing of the installation process of the manhole safety net can be achieved. This can effectively identify problems such as missing construction phases, time anomalies, or skipped steps in the process, providing a reliable data foundation for subsequent integrity assessment and control of the construction process.

[0039] Example 7 Please see Figures 1 to 3 In the explanation of Example Six, specifically, step four further includes: S42. After dimensionless processing using the number of construction stage images N and the time interval ΔT between adjacent construction stages, the construction process integrity coefficient, denoted as PROC, is calculated. The formula is as follows:

[0040] In the formula, w1 and w2 represent weighting coefficients; Nstd represents the standard number of construction images collected, which is obtained by: statistically analyzing the construction record data of a large number of manhole anti-fall net installation projects, extracting the distribution range of the number of construction images collected at each construction stage under normal construction conditions where the construction process is standardized, the data is complete, and the acceptance is passed; combining the construction process requirements of municipal pipeline anti-fall net installation, the construction process record management specifications, and the engineering quality acceptance standards, determining the standard number of images collected that can completely represent the entire construction process. This standard number of construction images collected is used as a benchmark reference for assessing the integrity of the construction process, so as to accurately determine whether there are omissions or missing records in the construction stage. The reference construction time interval is obtained by statistically analyzing a large amount of timestamp data from the installation of manhole safety nets, extracting the range of time intervals between adjacent construction stages under normal construction rhythm and process conditions; combining the standard operating procedures for safety net installation, on-site construction organization requirements, and experience in judging the rationality of the construction process, a reference construction time interval that can reflect the continuity of normal construction is determined. This reference construction time interval is used as a benchmark for evaluating the rationality of the construction process time, in order to identify situations such as construction being too fast, too slow, or abnormally interrupted. The number of images during the construction phase has a dominant weight in the assessment of the integrity of the construction process; the number of construction images directly reflects whether key construction nodes have been fully recorded, and is the core basis for judging whether the construction process is authentic and traceable, so it is given a high weight. The weight of the reasonableness of the time interval between construction phases on the integrity of the construction process is characterized; the construction time interval reflects whether the construction rhythm conforms to the process specifications and can be used to identify skipped construction, abnormal reshoots or false records. Its importance is less than the number of images, but it has an important constraint on the determination of authenticity. This formula is based on the engineering understanding that "the integrity of the construction process is jointly determined by the coverage of process nodes and the rationality of time." It quantifies the coverage of construction stages by normalizing the actual number of acquired images N with the standard number of acquired images Nstd. At the same time, it normalizes and maps the deviation of the actual construction time interval ΔT relative to the reference time interval ΔTref to reflect the rationality of the construction rhythm. After weighted fusion, the two can comprehensively characterize whether the construction process record is complete, continuous and reliable under a unified dimension, providing a quantitative basis for the identification and control strategy triggering of construction process anomalies.

[0041] S43. By setting a preset construction process integrity threshold, denoted as Pth, and comparing the construction process integrity coefficient PROC with the construction process integrity threshold Pth, the second evaluation results are obtained, including: When the construction process integrity coefficient PROC ≥ the construction process integrity threshold Pth, it indicates that the current manhole construction process is complete and will be continuously monitored. When the construction process integrity coefficient PROC < the construction process integrity threshold Pth, it indicates that the current manhole construction process is incomplete, with omissions in construction stages, time anomalies, or missing process records, leading to insufficient construction authenticity or failure of data review. This triggers a second warning instruction and generates a second strategy: sending a re-shooting or re-recording instruction to the mobile terminal of the corresponding construction personnel, requiring the re-collection of images of the missing construction stages; issuing a rework prompt for construction objects with time anomalies, requiring the re-execution of the corresponding construction steps and the synchronous collection of complete construction records; prohibiting the export or submission of construction data for the current construction object to prevent incomplete construction data from entering the acceptance or archiving process; after adjustment, recalculating until the construction process integrity coefficient PROC ≥ the construction process integrity threshold Pth, and writing it back to the corresponding construction object's standardized information body D for updating.

[0042] The method for obtaining the construction process integrity threshold Pth is as follows: Based on a large amount of data recorded during the installation of manhole safety nets, statistical analysis is performed on the number of images, time intervals, and combinations thereof during each construction stage under the standard construction process. This extracts the distribution range of integrity coefficients for complete and chronologically ordered construction processes, as well as the characteristic range of integrity coefficients in cases of missing stages, time anomalies, or missing process records. Combining construction process specifications, requirements for construction process record management, and engineering quality management experience, a reasonable critical value for determining construction process integrity is determined. This threshold is used to accurately judge the completeness and authenticity of construction process records, ensuring that construction process data meets acceptance and traceability requirements.

[0043] In this embodiment, by constructing a construction process integrity coefficient and combining it with the number of construction images and construction time intervals for comprehensive quantitative evaluation, the system can automatically determine the integrity and timing of the entire process of installing the manhole safety net. When anomalies are detected, timely triggering of reshooting, rework, and control strategies can effectively prevent incomplete or abnormal construction data from entering the acceptance and archiving process, thereby improving the standardization, reliability, and traceability of construction process management.

[0044] Example 8 Please see Figures 1 to 3 In the explanation of Example 7, specifically, step five includes: S51. Based on the updated standardized information body D of the construction object, extract the pre-construction image Ppre, the in-construction image Pmid, and the post-construction image Ppost. On the mobile terminal, use image processing and computer vision analysis methods to perform quality analysis on the construction images. By calculating the gradient change, edge information, and target area features of the image, obtain the image clarity index, denoted as Qclar. By analyzing the geometric features of the fall prevention net and wellhead structure, obtain the image shooting angle deviation index, denoted as Qangle. By detecting the occlusion of the target area, obtain the image occlusion rate index, denoted as Qocc.

[0045] In this embodiment, by quantitatively analyzing the clarity, shooting angle deviation, and target area occlusion of images before, during, and after construction, an objective assessment of the image quality of the anti-fall net installation is achieved. This effectively reduces the risk of misjudging construction results due to insufficient image quality and provides a reliable data foundation for subsequent credibility determination and quality control of construction results.

[0046] Example 9 Please see Figures 1 to 3 In the explanation of Embodiment Eight, specifically, step five further includes: S52. By acquiring the image clarity index Qclar, image shooting angle deviation index Qangle, and image occlusion rate index Qocc, and combining them with the construction status label information S, after dimensionless processing, a multi-index weighted fusion algorithm is used to calculate the construction result reliability coefficient, denoted as RES, as follows:

[0047] In the formula, a1, a2, a3, and a4 represent weighting coefficients; Image clarity is a key indicator that represents the impact of image clarity on the credibility of construction results. Image clarity directly determines whether the structural details, fixing status, and installation quality of the fall protection net can be effectively identified, and therefore has a high weight. : Characterizes the weight of the influence of image shooting angle deviation on credibility; the shooting angle determines the spatial relationship between the fall prevention net and the wellhead structure and the visibility, which has an important impact on quality judgment and accounts for a medium weight. The weight of the image occlusion rate on the credibility of the construction results is represented by the following: occlusion of the target area reduces the readability of the image and increases the risk of misjudgment. Its importance is comparable to that of the shooting angle, and it accounts for a medium weight. The auxiliary influence weight of construction status marking information on the credibility of construction results is used to reflect the subjective confirmation of the construction completion status by construction personnel and plays a supplementary constraint role on the credibility of the results. This formula is based on the engineering principle that "the credibility of construction results depends on the consistency between the image's interpretability and the construction status." By normalizing objective quality indicators such as image clarity, shooting angle deviation, and occlusion degree, and combining them with construction status marker information for weighted fusion, the verifiability of construction results can be comprehensively quantified under a unified evaluation scale. When the credibility coefficient RES value of the construction results is high, it indicates that the construction image quality is sufficient, the perspective is reasonable, and the occlusion is small, and the construction results have high credibility. Conversely, it indicates that there is a risk of quality judgment, and a review or reshoot mechanism needs to be triggered.

[0048] S53. By setting a preset construction result credibility threshold, denoted as Rth, and comparing the construction result credibility coefficient RES with the construction result credibility threshold Rth, the third evaluation results are obtained, including: When the reliability coefficient RES of the construction result is greater than or equal to the reliability threshold Rth of the construction result, it indicates that the current installation result of the anti-fall net of the inspection well is reliable and should be continuously monitored. When the reliability coefficient RES of the construction result is less than the reliability threshold Rth of the construction result, it indicates that the current installation result of the anti-fall net for the manhole is unreliable. There is a risk of quality judgment due to insufficient image clarity, shooting angle deviation, target area obstruction, or inconsistent construction status. This triggers a third warning instruction and generates a third strategy: it prompts construction personnel to conduct on-site re-inspection or re-take construction images of the corresponding manhole; it requires the re-collection of images of key nodes after construction to reduce the risk of obstruction or angle deviation; it marks the current construction object as a manually reviewed object and suspends the automatic generation of construction acceptance data; after adjustment, it recalculates until the reliability coefficient RES of the construction result is greater than or equal to the reliability threshold Rth of the construction result; and it records abnormal construction data and risk types in the construction quality management log for subsequent operation and maintenance optimization.

[0049] The reliability threshold Rth for construction results is obtained as follows: Based on a large amount of construction images and acceptance results data from completed manhole safety net installation projects, statistical analysis is performed on indicators such as image clarity, shooting angle deviation, target area obstruction, and consistency of construction status. The distribution range of the reliability coefficient when the construction results are truly reliable is extracted, as well as the characteristic range of the reliability coefficient under conditions of insufficient image quality or disputed construction results. Combining municipal engineering acceptance standards, image data evidence requirements, and construction quality management experience, a reasonable critical value for determining the reliability of construction results is determined. This threshold is used to accurately identify quality risks in construction results and reduce misjudgments or acceptance disputes caused by insufficient image evidence.

[0050] In this embodiment, by weighting and fusing multiple indicators such as image clarity, shooting angle deviation, occlusion rate and construction status markers, a construction result credibility coefficient is formed. Based on a threshold-triggered targeted review and manual verification mechanism, a quantitative and reliable judgment and risk closed-loop management of the anti-fall net installation results are achieved, preventing unreliable construction results from entering the acceptance and archiving process, and significantly improving the accuracy and traceability of construction quality management.

[0051] Example 10 Please refer to the installation and management system for manhole safety nets. Figures 1 to 3 ,include: The data acquisition module is used to monitor the construction objects of municipal pipeline manholes in real time, collect the manhole facility number, manhole spatial coordinate information, images before, during and after construction and corresponding timestamps, image acquisition coordinates, and generate construction status marking information after construction is completed. The standardized module for identifying construction objects is used to construct a unique identifier for manhole construction objects, form a complete data association relationship in the manhole construction process, and generate a standardized information body for construction objects. The spatial consistency assessment and correction module is used to calculate the spatial consistency coefficient of the construction object and compare it with the spatial consistency threshold to determine whether the spatial position matching degree of the current manhole construction object is qualified. If it is not qualified, a repositioning and coordinate correction strategy is given, the status record is paused, and the standardized information body of the construction object is updated after review. The construction process integrity assessment module is used to calculate the construction process integrity coefficient and compare it with the construction process integrity threshold to determine whether the current manhole construction process is complete. If it is not complete, a strategy of reshooting, re-recording, or rework will be given, and data export will be prohibited until the construction process integrity meets the standard and the standardized information body of the construction object is updated. The construction result quality control module is used to calculate the credibility coefficient of the construction result and compare it with the credibility threshold of the construction result to determine whether the current installation result of the manhole anti-fall net is credible. If it is not credible, a re-inspection and re-photographing and manual review mechanism strategy is given, and data generation is suspended until the credibility reaches the standard, then the quality risk is recorded and the update is completed.

[0052] In this embodiment, by integrating functional modules such as construction data collection, object identification standardization, spatial consistency correction, process integrity assessment, and result quality control in a layered manner, the entire process of manhole anti-fall net installation is automated, quantitatively assessed, and closed-loop corrected. This enables timely identification of risks such as positioning deviations, process omissions, and unreliable results during the construction phase, preventing abnormal construction data from entering the acceptance and archiving stages. As a result, the standardization, reliability, and quality control of municipal pipeline manhole construction management are significantly improved.

[0053] It should be noted that all calculation formulas in this application employ regression analysis, including but not limited to machine learning algorithms, to deeply analyze the collected parameters and identify their natural trends and interrelationships. Specialized software, such as Python's Scikit-learn library or the R language, is used to automatically generate mathematical models that match the data. Then, cross-validation and other methods are used to objectively evaluate the model performance, and continuous feedback and optimization are combined to ensure that the created formulas truly reflect the inherent laws of the data, thereby guaranteeing their effectiveness and accuracy. In all calculation formulas in this application, the parameters in each formula undergo dimensionless processing within a consistent range to ensure that different physical quantities are compared on the same scale; dimensionless processing techniques include, but are not limited to, min-max-normalization and Z-score standardization. The algorithm of this invention is implemented as a Python script. Before executing the core logic, the program first executes a data loading module (e.g., using the widely used pandas library in Python) configured to read the aforementioned spreadsheet file and load its contents into the program's working memory (e.g., a DataFrame data structure). Subsequent algorithm steps will directly query and retrieve the required configuration parameters from this in-memory data structure.

[0054] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for the installation and construction management of anti-fall nets for inspection wells, characterized in that, The specific steps include: Step 1: By real-time monitoring of the construction objects of municipal pipeline manholes, the facility number of the manhole, the spatial coordinate information of the manhole, the images before, during and after construction and the corresponding timestamps, the image acquisition coordinates are collected, and construction status marking information is generated after construction is completed. Step 2: By constructing a unique identifier for the manhole construction object, a complete data association relationship for the manhole construction process is formed, generating a standardized information body for the construction object; Step 3: Calculate the spatial consistency coefficient of the construction object and compare it with the spatial consistency threshold to determine whether the spatial position matching degree of the current manhole construction object is qualified. If it is not qualified, a repositioning and coordinate correction strategy is adopted, the status record is paused, and the standardized information body of the construction object is updated after review. Step 4: Calculate the integrity coefficient of the construction process and compare it with the integrity threshold of the construction process to determine whether the current manhole construction process is complete. If it is not complete, a strategy of reshooting, re-recording, or rework will be adopted, and data export will be prohibited until the integrity of the construction process meets the standard and the standardized information body of the construction object is updated. The methods for obtaining the integrity coefficient of the construction process include: S41, based on the updated standardized information body D of the construction object, calling the mapped construction stage image data and corresponding construction timestamp information, and statistically obtaining the number of construction stage images actually collected by the current inspection well, denoted as N; based on the timestamp Tpre corresponding to the image before construction and the timestamp Tpost corresponding to the image after construction, the time difference calculation method is used to calculate the time interval between adjacent construction stages, denoted as ΔT. S42. After dimensionless processing using the number of construction stage images N and the time interval ΔT between adjacent construction stages, the construction process integrity coefficient, denoted as PROC, is calculated. The formula is as follows: In the formula, w1 and w2 represent weighting coefficients; Nstd represents the number of standard construction images collected; Indicates the reference construction time interval; Step 5: Calculate the credibility coefficient of the construction results and compare it with the credibility threshold of the construction results to determine whether the current installation result of the manhole anti-fall net is credible. If it is not credible, a re-inspection and re-photographing and manual review mechanism strategy will be implemented, and data generation will be suspended until the credibility reaches the standard. Then, the quality risk will be recorded and the update will be completed. The methods for obtaining the credibility coefficient of construction results include: S51. Based on the updated standardized information body of the construction object, extract the images before construction, during construction and after construction. In the mobile terminal, use image processing and computer vision analysis methods to perform quality analysis on the construction images. By calculating the gradient change, edge information and target area features of the image, obtain the image clarity index. By analyzing the geometric features of the fall prevention net and wellhead structure, obtain the image shooting angle deviation index. By detecting the occlusion of the target area, obtain the image occlusion rate index. S52. By acquiring image clarity index, image shooting angle deviation index, and image occlusion rate index, and combining them with construction status marking information, after dimensionless processing, a multi-index weighted fusion algorithm is used to calculate and obtain the credibility coefficient of the construction result.

2. The method for construction management of the installation of anti-fall nets for inspection wells according to claim 1, characterized in that: Step one includes: S11. For inspection wells in municipal water supply and drainage, tap water or rainwater and sewage separation pipe networks that are to be fitted with anti-fall nets, a GIS map system is deployed on the mobile terminal carried by the construction personnel, and the inspection well facility number and its corresponding design coordinate data provided by the pipe network ownership unit are loaded to monitor the identity and spatial location of the construction object of the inspection well in real time, and collect the facility number and spatial coordinate information of the inspection well. S12. Conduct real-time monitoring of the installation and construction phase of the anti-fall net for the inspection well; by calling the visible light camera built into the mobile terminal, collect corresponding pre-construction images, in-construction images, and post-construction images of the same inspection well before construction begins, during construction, and after construction is completed, and automatically record the corresponding timestamp information each time the image is collected, and obtain the timestamp corresponding to the pre-construction image and the timestamp corresponding to the post-construction image. S13. During the construction phase, the satellite positioning component built into the mobile terminal is activated to locate the terminal's geographical location in real time at the moment of image acquisition, thereby obtaining the positioning coordinate information of the image acquisition terminal. S14. After completing the installation of the anti-fall net for the inspection well, the construction personnel use the construction management interface in the mobile terminal to confirm the status of the current construction results of the inspection well and generate construction status marker information.

3. The method for construction management of the installation of anti-fall nets for inspection wells according to claim 2, characterized in that: Step two includes: S21. Based on the manhole facility number, spatial coordinate information, construction image data, construction timestamp information and construction status mark information, an object binding method based on facility identifier and spatial coordinate is adopted to associate the facility number of each manhole with its corresponding spatial coordinate to construct a unique identifier for the manhole construction object. S22. Based on the unique identifier of the manhole construction object, a mapping method based on timestamps and status markers is adopted to map the pre-construction images, in-construction images, and post-construction images corresponding to the same construction object to their acquisition time and construction status, forming a complete data association relationship of the manhole construction process. S23. Based on the complete data association relationship of the inspection well construction process, a structured data encapsulation method is adopted to uniformly encapsulate the verified construction image data, construction time information, construction status information and image quality feature parameters to generate a standardized information body of the construction object.

4. The method for construction management of the installation of anti-fall nets for inspection wells according to claim 3, characterized in that: Step three includes: S31. Based on the standardized information body of the construction object, call the design spatial coordinates corresponding to the bound manhole facility number and the positioning coordinate information of the image acquisition terminal. Using the geospatial distance calculation method, calculate the spatial offset relationship between the design spatial coordinates and the positioning coordinate information of the image acquisition terminal to obtain the coordinate deviation distance between the manhole design position and the on-site photo position.

5. The method for construction management of the installation of anti-fall nets for inspection wells according to claim 4, characterized in that: Step three also includes: S32. Using the coordinate deviation distance and the normalized deviation mapping method, after dimensionless processing, the spatial consistency coefficient of the construction object is calculated and obtained. S33. By setting a spatial consistency threshold and comparing the spatial consistency coefficient of the construction object with the spatial consistency threshold, the first evaluation result is obtained, including: When the spatial consistency coefficient of the construction object is greater than or equal to the spatial consistency threshold, it indicates that the spatial location matching degree of the current manhole construction object is qualified, and continuous monitoring is required. When the spatial consistency coefficient of the construction object is less than the spatial consistency threshold, it indicates that the spatial location matching degree of the current inspection well construction object is unqualified, and there is a deviation between the construction image and the designed well location. This leads to management risks such as mis-well construction, wrong well recording, or repeated construction, triggering the first warning instruction and generating the first strategy: send a relocation prompt message to the mobile terminal of the corresponding construction personnel, requiring the construction personnel to relocate and acquire images near the target well location; suspend the writing of the construction completion status mark of the current construction object to prevent abnormal data from entering the subsequent management process; initiate the coordinate correction application process, mark the current construction object as pending verification, and submit it to the back-end management terminal for manual or technical review; recalculate until the spatial consistency coefficient of the construction object is greater than or equal to the spatial consistency threshold, and write it back to the corresponding standardized information body of the construction object for update.

6. The method for construction management of the installation of anti-fall nets for inspection wells according to claim 1, characterized in that: Step four also includes: S43. By setting a predefined construction process integrity threshold and comparing the construction process integrity coefficient with the construction process integrity threshold, the second evaluation result is obtained, including: When the construction process integrity coefficient is greater than or equal to the construction process integrity threshold, it indicates that the current manhole construction process is complete and will be continuously monitored. When the construction process integrity coefficient is less than the construction process integrity threshold, it indicates that the current manhole construction process is incomplete, with omissions in construction stages, time anomalies, or missing process records. This poses a risk of insufficient construction authenticity or failure to pass data review, triggering a second warning instruction and generating a second strategy: sending a re-shooting or re-recording instruction to the mobile terminal of the corresponding construction personnel, requiring the re-collection of images of the missing construction stages; issuing a rework prompt for construction objects with time anomalies, requiring the re-execution of the corresponding construction steps and the synchronous collection of complete construction records; prohibiting the export or submission of construction data for the current construction object to prevent incomplete construction data from entering the acceptance or archiving process; after adjustment, recalculating until the construction process integrity coefficient is greater than or equal to the construction process integrity threshold, and writing it back to the corresponding standardized information body of the construction object for updating.

7. The method for construction management of the installation of anti-fall nets for inspection wells according to claim 1, characterized in that: Step five also includes: S53. By setting a preset construction result credibility threshold and comparing the construction result credibility coefficient with the construction result credibility threshold, the third evaluation result is obtained, including: When the credibility coefficient of the construction result is greater than or equal to the credibility threshold of the construction result, it indicates that the current installation result of the anti-fall net for the manhole is credible and should be continuously monitored. When the credibility coefficient of the construction result is less than the credibility threshold of the construction result, it indicates that the current installation result of the anti-fall net for the manhole is unreliable. There is a risk of quality judgment due to insufficient image clarity, shooting angle deviation, target area obstruction, or inconsistent construction status. This triggers a third warning instruction and generates a third strategy: it prompts construction personnel to conduct on-site re-inspection or re-take construction images of the corresponding manhole; it requires the re-collection of images of key nodes after construction to reduce the risk of obstruction or angle deviation; it marks the current construction object as a manually reviewed object and suspends the automatic generation of construction acceptance data; after adjustment, it recalculates until the credibility coefficient of the construction result is greater than or equal to the credibility threshold of the construction result; and it records abnormal construction data and risk types in the construction quality management log for subsequent operation and maintenance optimization.

8. A construction management system for the installation of safety nets for manholes, applied to the construction management method for the installation of safety nets for manholes as described in any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to monitor the construction objects of municipal pipeline manholes in real time, collect the manhole facility number, manhole spatial coordinate information, images before, during and after construction and corresponding timestamps, image acquisition coordinates, and generate construction status marking information after construction is completed. The standardized module for identifying construction objects is used to construct a unique identifier for manhole construction objects, form a complete data association relationship in the manhole construction process, and generate a standardized information body for construction objects. The spatial consistency assessment and correction module is used to calculate the spatial consistency coefficient of the construction object and compare it with the spatial consistency threshold to determine whether the spatial position matching degree of the current manhole construction object is qualified. If it is not qualified, a repositioning and coordinate correction strategy is given, the status record is paused, and the standardized information body of the construction object is updated after review. The construction process integrity assessment module is used to calculate the construction process integrity coefficient and compare it with the construction process integrity threshold to determine whether the current manhole construction process is complete. If it is not complete, a strategy of reshooting, re-recording, or rework will be given, and data export will be prohibited until the construction process integrity meets the standard and the standardized information body of the construction object is updated. The construction result quality control module is used to calculate the credibility coefficient of the construction result and compare it with the credibility threshold of the construction result to determine whether the current installation result of the manhole anti-fall net is credible. If it is not credible, a re-inspection and re-photographing and manual review mechanism strategy is given, and data generation is suspended until the credibility reaches the standard, then the quality risk is recorded and the update is completed.

Images