A smart design, construction, operation and maintenance system, method and electronic equipment for wind-resistant roofs

Abstract

This application relates to the field of electronic digital data processing technology, and in particular to a wind-resistant intelligent design, construction, operation, and maintenance system, method, and electronic equipment for roofs. The system includes: a data management platform for storing standard roof construction data and typical construction cases; a modeling and optimization module for generating and iteratively optimizing an initial roof model, outputting an optimized roof model; an automatic layout module for using a genetic algorithm to divide the roof model into sections, outputting layout results that meet the genetic optimization objectives; an automatic material cutting module for generating material processing data based on production data, standard roof construction data, and typical construction cases; and an operation and maintenance monitoring module for monitoring the physical state of the roof panels and their surrounding environmental data, predicting roof panel damage risks, and generating corresponding early warning signals. This application can significantly improve the wind resistance safety, design and construction efficiency, and full life-cycle management level of metal roofs in large public buildings.

Description

Technical Field

[0001] This application relates to the field of electronic digital data processing technology, and in particular to a roof wind-resistant intelligent design, construction, operation and maintenance system, method and electronic equipment. Background Technology

[0002] In the field of modern architecture, large public buildings (such as stadiums and airport terminals) widely adopt complex curved metal roofs to meet aesthetic and functional requirements. However, due to the structural characteristics of high curvature and multi-panel combination, these roofs have strict requirements on wind resistance, design and construction accuracy and operation and maintenance safety.

[0003] In related technologies, wind-resistant design mainly relies on empirical formulas and static wind load calculations, which cannot dynamically reflect the actual characteristics of complex wind fields and the impact of construction errors and material aging on the wind resistance performance of roofs. At the same time, conventional monitoring systems are mostly passive monitoring systems, with only a few anemometers deployed, lacking real-time feedback on the roof structure's response and the ability to predict damage risks. As a result, with the trend of increasingly complex building forms, wind-induced roof uplift accidents occur frequently, making it difficult to ensure the long-term safe and stable operation of roofs of large public buildings. Therefore, it is urgent to design a roof wind-resistant intelligent system that covers the entire process of design, construction, and operation and maintenance. Summary of the Invention

[0004] To address the shortcomings of existing technologies in optimizing wind-resistant design for metal roofs, this application provides an intelligent design, construction, operation, and maintenance system, method, and electronic device for wind-resistant roofs.

[0005] Firstly, this application provides a roof wind-resistant intelligent design, construction, operation and maintenance system, which adopts the following technical solution:

[0006] A smart roof wind-resistant design, construction, operation and maintenance system includes:

[0007] The data management platform is used to store standard roof construction data and typical construction cases;

[0008] The modeling and optimization module is used to generate an initial roof model and iteratively optimize the initial roof model until the roof wind pressure coefficient obtained from the wind tunnel test in the current iteration meets the wind pressure target or the number of iterations reaches the upper limit of the first iteration. The optimized roof model is then output. Each iteration includes surface fitting, radius merging, and wind tunnel testing.

[0009] An automatic layout module is used to receive the roof model transmitted by the modeling and optimization module, divide the roof model into sections using a genetic algorithm, and output a layout result that meets the genetic optimization objective.

[0010] An automatic material feeding module is used to collect production data in real time during the production stage and generate material processing data based on the production data, the roof standard structure data, and the typical construction cases.

[0011] The operation and maintenance monitoring module is used to monitor the physical condition of the roof panel and the environmental data in real time after the roof panel is put into use, predict the risk of damage to the roof panel based on the physical condition and the environmental data, and generate corresponding early warning signals.

[0012] By adopting the above technical solutions, multiple modules work together to cover the entire lifecycle of roof design, construction, and operation and maintenance. The data management platform provides standardized data support for each stage, the modeling and optimization module improves the wind resistance performance of the roof, the automatic layout module ensures the rationality of the segment division, the automatic material cutting module adapts to actual production, and the operation and maintenance monitoring module enables early warning of risks. Overall, it solves the problems of fragmentation of various stages in traditional roofing, insufficient wind resistance design, high production losses, and lagging operation and maintenance, significantly improving the wind resistance safety, design and construction efficiency, and full life cycle management level of metal roofs of large public buildings.

[0013] In a preferred embodiment, this application can be further configured as follows: the modeling optimization module is used to conduct wind tunnel tests on the initial roof model to obtain the initial roof wind pressure coefficient; if the initial roof wind pressure coefficient does not meet the wind pressure target, then global constraints are defined; wherein, the global constraints include: the boundary contour and key control points of the initial roof model remain unchanged;

[0014] The initial roof model is iteratively optimized based on the global constraints until the roof wind pressure coefficient obtained from the wind tunnel test in any iteration meets the wind pressure target or the number of iterations reaches the upper limit of the first iteration number, and the optimized roof model is output.

[0015] By adopting the above technical solution, the wind resistance performance of the initial model is first verified, and then the insurmountable global constraints are clarified. This allows for targeted optimization of the roof shape to improve wind resistance, while ensuring that the optimized roof does not deviate from the original functional and aesthetic requirements of the building design. It avoids damaging the overall roof shape due to over-optimization, thus balancing the improvement of wind resistance performance with the original design intention. At the same time, the setting of the iterative termination condition takes into account both optimization accuracy and engineering efficiency.

[0016] In a preferred embodiment, this application can be further configured such that the modeling optimization module, when working with the initial roof model and performing any iteration, is specifically used for:

[0017] Obtain the previous roof model obtained from the previous iteration and the previous wind pressure distribution cloud map obtained from the wind tunnel test;

[0018] Based on the previous wind pressure distribution cloud map and the preset wind pressure coefficient level, the previous roof model is divided into multiple roof areas, and the variation probability of each roof area is determined. Based on the variation probability, the previous roof model is subjected to curve fitting, radius merging and wind tunnel test to obtain the current wind pressure distribution cloud map.

[0019] Calculate the current roof wind pressure coefficient based on the current wind pressure distribution cloud map, and determine whether the current roof wind pressure coefficient meets the wind pressure target;

[0020] If the current roof wind pressure coefficient does not meet the wind pressure target, then continue to execute the next iteration.

[0021] By adopting the above technical solutions, risk areas are accurately divided based on wind pressure distribution cloud maps, and higher variation probabilities are allocated to high-risk areas, so that optimization resources are concentrated on the parts with excessive wind pressure, avoiding indiscriminate adjustments that lead to wasted efficiency. Each iteration is optimized and verified based on the results of the previous iteration, ensuring continuous improvement in wind resistance performance. At the same time, through the iteration termination mechanism, the optimization effect and time cost are effectively balanced, greatly improving the targeting and efficiency of wind resistance optimization for complex roofs.

[0022] In a preferred embodiment, this application can be further configured such that: when the modeling optimization module performs the calculation of the current roof wind pressure coefficient based on the current wind pressure distribution cloud map, it is specifically used for:

[0023] The average wind pressure coefficient of the roof is calculated based on the current wind pressure distribution cloud map, and areas with excessive wind pressure are identified from the multiple roof areas.

[0024] And determine the local wind pressure coefficient of the area where the local wind pressure exceeds the standard;

[0025] The current roof wind pressure coefficient is calculated based on the average roof wind pressure coefficient and the local wind pressure coefficient.

[0026] By adopting the above technical solution, the current wind pressure coefficient is calculated by combining the average wind pressure coefficient of the roof (reflecting the overall wind resistance performance) and the local wind pressure coefficient (reflecting the situation in areas of concentrated risk). This avoids focusing only on the overall situation while ignoring the problem of local high risks, making the wind pressure coefficient calculation more comprehensive and accurate. This provides a more scientific basis for iterative optimization, ensuring that the optimized roof can not only meet the overall wind resistance requirements, but also eliminate the hidden danger of local wind pressure exceeding the standard, further improving the wind resistance safety of the roof.

[0027] In a preferred embodiment, this application can be further configured as follows: the automatic typesetting module is used to perform iterative optimization based on a genetic algorithm until the adaptive function value of any typesetting result individual obtained in the current iteration satisfies the genetic optimization objective or the number of iterations reaches the upper limit of the second iteration number, and outputs a typesetting result that satisfies the genetic optimization objective;

[0028] Each iteration includes: taking the typesetting result population obtained from the previous iteration as the parent population, and performing selection, crossover, and mutation operations on the parent population based on the adaptive function value of the parent population to obtain the current typesetting result population;

[0029] Calculate the adaptive function value of each individual layout result in the current layout result population, and determine whether the adaptive function value of each individual layout result satisfies the genetic optimization objective;

[0030] If multiple layout result individuals have adaptive function values ​​that satisfy the genetic optimization objective, the layout result individual with the highest adaptive function value is selected as the layout result output by the automatic layout module.

[0031] By adopting the above technical solutions and utilizing the selection, crossover, and mutation operations of genetic algorithms, the advantages of high-quality layout schemes can be preserved, while new schemes can be explored through crossover fusion and mutation, avoiding getting trapped in local optima. At the same time, through adaptive value judgment and the rule of selecting the best among multiple standard individuals, the output layout results are ensured to be optimal in terms of standard board ratio, material utilization rate, and other dimensions, which greatly improves the rationality and economy of roof panel segmentation and reduces the difficulty of subsequent processing and construction.

[0032] In a preferred embodiment, this application can be further configured such that: taking any typesetting result individual as the target individual, the automatic typesetting module, when calculating the adaptive function value of the target individual in the current iteration, is specifically used for:

[0033] The layout results of the target individual are compared with the roof standard construction database to determine the proportion of standard panels in the layout results of the target individual;

[0034] The layout results of the target individual are virtually spliced ​​together to determine the minimum area occupied as the raw material input area. The sum of the actual areas of all boards in the layout results of the target individual is calculated as the actual used area. The material utilization rate is calculated based on the raw material input area and the actual used area.

[0035] Determine the total length of continuous seams and the total length of all seams in the layout result of the target individual, and determine the seam quality value based on the total length of continuous seams and the total length of all seams;

[0036] The cosine of the angle between each board and the corresponding main purlin direction in the layout result of the target individual is determined as the single board direction consistency coefficient. The structural rationality value of the target individual is determined based on the average value of the single board direction consistency coefficients of all boards.

[0037] Based on the standard plate ratio, the material utilization rate, the joint quality value, and the structural rationality value, the adaptive function value of the target individual is calculated.

[0038] By adopting the above technical solutions, adaptive function values ​​are calculated based on multi-dimensional indicators. The proportion of standard panels improves processing efficiency and reduces costs, the degree of material utilization reduces waste, the joint quality value ensures wind resistance, and the structural rationality value ensures roof stability. Multi-dimensional evaluation makes the judgment of the layout scheme more comprehensive and objective, avoiding other dimension defects caused by the optimization of a single indicator. The final output layout scheme can take into account economy, construction and structural safety, and adapt to the multi-objective needs of complex roofs.

[0039] In a preferred embodiment, this application can be further configured as follows: the operation and maintenance monitoring module is used to acquire the attribute data and historical environmental data of the roof panel, and to construct a finite element simulation model based on the attribute data and the historical environmental data;

[0040] The finite element simulation model is adjusted based on the physical state, the predicted environmental data is determined based on the environmental data, the finite element simulation model is retrieved to simulate the predicted environmental data to obtain the predicted response data of the roof panel, and the damage risk of the roof panel is determined based on the predicted response data.

[0041] By adopting the above technical solutions, a finite element model is constructed and adjusted based on actual data to ensure that the model is consistent with the actual state of the roof panel, thereby improving the accuracy of the simulation. Simulation is performed based on the predicted environmental data and the adjusted model to achieve early prediction of roof panel damage risks, changing the lag of traditional passive monitoring, enabling maintenance personnel to formulate maintenance plans in advance, effectively avoiding accidents such as wind-induced damage, extending the service life of the roof, and reducing maintenance costs.

[0042] In a preferred embodiment, this application can be further configured as follows: the automatic feeding module is used to obtain the roof panel processing data list required for the current production batch, and perform initial nesting and layout of the roof panel processing data list based on the raw material specifications in the roof standard construction data;

[0043] The production data and the initial nesting layout are compared, an error correction coefficient is calculated, the board size parameters in the roof panel processing data list are adjusted based on the error correction coefficient, and subsequent production is carried out based on the updated roof panel processing data list.

[0044] By adopting the above technical solution, the initial nesting and layout are used to plan the use of raw materials. Then, the error correction coefficient is calculated based on the production data to adjust the size of the board. This can not only avoid waste of raw materials, but also compensate for errors in production and installation, ensuring that the processed board accurately matches the actual needs on site, reducing rework and material loss caused by dimensional deviations, improving production efficiency and processing accuracy, and reducing construction costs and the risk of project delays.

[0045] Secondly, this application provides a method for intelligent design, construction, operation and maintenance of wind-resistant roofs, which adopts the following technical solution:

[0046] A method for intelligent design, construction, operation and maintenance of wind-resistant roofs, including:

[0047] Acquire and store standard roof construction data and typical construction cases;

[0048] An initial roof model is generated, and the initial roof model is iteratively optimized until the roof wind pressure coefficient obtained by the wind tunnel test in the current iteration meets the wind pressure target or the number of iterations reaches the upper limit of the first iteration. The optimized roof model is then output. Each iteration includes surface fitting, radius merging, and wind tunnel testing.

[0049] The roof model is divided into sections using a genetic algorithm, and the output is a layout result that meets the genetic optimization objective.

[0050] During the production phase, production data is collected in real time, and material processing data is generated based on the production data, the standard roof structure data, and the typical construction cases.

[0051] After the roof panels are put into use, their physical condition and environmental data are monitored in real time. Based on the physical condition and environmental data, the risk of damage to the roof panels is predicted, and corresponding early warning signals are generated.

[0052] Thirdly, this application provides an electronic device that adopts the following technical solution:

[0053] At least one processor;

[0054] Memory;

[0055] At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: execute the roof wind-resistant intelligent design, construction, operation and maintenance method as described in the second aspect.

[0056] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0057] A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the intelligent design, construction, operation and maintenance method for wind-resistant roofs as described in the second aspect.

[0058] Fifthly, this application provides a computer program product, which adopts the following technical solution:

[0059] A computer program product includes a computer program that, when executed by a processor, implements the intelligent design, construction, operation, and maintenance method for wind-resistant roofs as described in the second aspect.

[0060] In summary, this application includes the following beneficial technical effects:

[0061] This application comprehensively addresses the entire lifecycle of roof design, construction, and operation and maintenance through multiple collaborative modules. The data management platform provides standardized data support for each stage, the modeling and optimization module improves the roof's wind resistance performance, the automatic layout module ensures the rationality of the segment division, the automatic material cutting module adapts to actual production, and the operation and maintenance monitoring module enables early warning of risks. It comprehensively solves the problems of fragmentation in traditional roofing processes, insufficient wind resistance design, high production losses, and lagging operation and maintenance, significantly improving the wind resistance safety, design and construction efficiency, and full lifecycle management level of metal roofs for large public buildings. Attached Figure Description

[0062] Figure 1 This is a structural schematic diagram of a roof wind-resistant intelligent design, construction, operation and maintenance system provided in an embodiment of this application;

[0063] Figure 2 This is a flowchart illustrating a method for intelligent design, construction, operation, and maintenance of wind-resistant roofs provided in an embodiment of this application.

[0064] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0065] The following is in conjunction with the appendix Figure 1 To be continued Figure 3 This application will be described in further detail.

[0066] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0067] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0068] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0069] It should be noted that, in the optional embodiments of this application, the data related to object information, when applied to specific products or technologies, requires the permission or consent of the object. Furthermore, the collection, use, and processing of this data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. In other words, if the embodiments of this application involve data related to an object, it must be obtained with the object's authorization and consent, the authorization and consent of relevant departments, and in accordance with the relevant laws, regulations, and standards of the country and region. If the embodiments involve personal information, the acquisition of all personal information requires the individual's consent. If sensitive information is involved, the separate consent of the information subject is required. The embodiments also need to be implemented with the object's authorization and consent.

[0070] This application provides an intelligent design, construction, operation, and maintenance system for wind-resistant roofs, such as... Figure 1 As shown, the system includes: a data management platform 101, a modeling optimization module 102, an automatic layout module 103, an automatic material cutting module 104, and an operation and maintenance monitoring module 105.

[0071] Data Management Platform 101 stores standard roof construction data and typical construction cases. Specifically, it collects standard roof construction data and typical construction cases by connecting with building industry standards, metal roofing component manufacturers, and past project teams. Standard roof construction data includes dimensions, materials, and connection methods for various standard metal roof constructions, while typical construction cases include project type, roof structure, construction challenges, and wind resistance performance. A data standard interface has been established to fully support the integration and data push functions for on-site inspection data.

[0072] A structured database is used to categorize and store standard roof construction data and typical construction case data. Search tags such as material and specifications are set for standard roof construction data, and indexes such as project scenarios are set for typical construction cases to facilitate quick retrieval. Newly released industry construction standards and the latest construction cases are regularly added, and data accuracy is ensured through manual review and system verification.

[0073] The modeling and optimization module 102 is used to generate an initial roof model and iteratively optimize the initial roof model until the roof wind pressure coefficient obtained by the wind tunnel test in the current iteration meets the wind pressure target or the number of iterations reaches the upper limit of the first iteration. The optimized roof model is then output. Each iteration includes surface fitting, radius merging and wind tunnel testing.

[0074] Specifically, based on the roof outline and dimensional parameters of the architectural design drawings, an initial roof model is built using BIM software. This initial roof model is a 3D model, and basic attributes such as roof material and thickness are defined. A wind tunnel test is performed on the initial roof model to obtain the initial roof wind pressure coefficient. The wind pressure target for qualified roof panels is retrieved from the data management platform to determine whether the initial roof wind pressure coefficient meets the target.

[0075] Wind tunnel testing refers to numerical wind tunnel simulation testing, which uses fluid dynamics simulation software to simulate a real wind field environment and calculate the wind pressure distribution on the roof surface. Wind pressure values ​​are divided into positive and negative pressure. Negative pressure represents wind suction, directed upwards; the larger the absolute value, the more dangerous the situation. Positive pressure represents wind pressure, directed downwards; the larger the value, the more dangerous the situation. The wind pressure target represents a threshold value for the wind pressure coefficient determined according to the building wind resistance code and the roof's importance level for the project location. This includes positive and negative threshold values. In this embodiment, calculating the roof wind pressure coefficient includes calculating both the positive and negative wind pressure coefficients. The calculation methods are the same. The calculated positive wind pressure coefficient is compared with the positive threshold, and the negative wind pressure coefficient is compared with the negative threshold. Meeting the wind pressure target means that the positive wind pressure coefficient does not exceed the positive threshold, and the negative wind pressure coefficient is not less than the negative threshold. The roof wind pressure coefficient mentioned in the following embodiments refers to either the positive or negative wind pressure coefficient.

[0076] If the initial roof wind pressure coefficient meets the wind pressure target, the initial roof model is radius-merged to obtain the optimized roof model. If the initial roof wind pressure coefficient does not meet the wind pressure target, an iterative optimization process is executed. In each iteration, the roof model is surface-fitted and radius-merged using parametric design tools, and the wind tunnel test is re-executed to calculate the roof wind pressure coefficient for the current round. This iteration continues until the roof wind pressure coefficient meets the wind pressure target. The optimized roof model is then exported in a general format and transferred to the automatic layout module. If no situation meets the wind pressure target even after reaching the upper limit of the first iteration, technical personnel can be notified to intervene and correct the initial roof model. Surface fitting involves adjusting the curvature of the roof curve, and radius merging means merging similar curved radii to a standard radius, which is stored in the data management platform.

[0077] The automatic layout module 103 is used to receive the roof model transmitted by the modeling and optimization module, use a genetic algorithm to divide the roof model into sections, and output the layout result that meets the genetic optimization objective.

[0078] Specifically, a staggered overlapping scheme can be prioritized to create a turbulence structure at the panel seams, reducing wind-induced noise. The automatic layout module aims to divide the roof into multiple discrete panels for discrete production and final assembly into roof panels. Key information such as the surface morphology and supporting structure (e.g., purlin positions) of the roof model is extracted to determine the boundary range of the panel division. Multiple panel division schemes are initialized as an initial population. In each iteration, selection, crossover, and mutation operations are performed on the population based on an adaptive function to generate a new population and calculate the adaptive function value of each individual, with each individual representing a panel division scheme. The iteration stops when the adaptive function value of an individual in a given iteration satisfies the genetic optimization result. The number of individuals satisfying the genetic optimization result is determined. If the number is one, the panel division scheme corresponding to that individual is used as the layout result; if the number is greater than one, the scheme with the highest adaptive function value is selected as the layout result. The adaptive function value for each individual is calculated based on the proportion of standard plates, material utilization, joint quality, and structural rationality. The genetic optimization objective can be set by technical personnel based on practical experience; this implementation does not impose any restrictions. If the genetic optimization objective is not satisfied even after reaching the upper limit of the second iteration, technical personnel can be notified to intervene and make corrections.

[0079] After the automatic layout is completed, run a wind vibration simulation test to ensure that the wind suction at the connection point of each board does not exceed 80% of the design value.

[0080] The automatic feeding module 104 is used to collect production data in real time during the production stage and generate material processing data based on the production data, roof standard construction data and typical construction cases.

[0081] Specifically, production data, including raw material specifications, equipment processing precision, and on-site installation errors, is collected in real time through workshop sensors and production equipment control systems. Standard roof construction data (such as raw material dimensions) and typical construction cases (such as raw material loss reference values) are retrieved from the data management platform. Combined with the collected production data, the processing requirements and potential errors of the current production batch are analyzed. Based on the analysis results, nesting software (such as FastCAM) is used to optimize the board cutting path, determine the processing dimensions, quantity, and cutting sequence of each board, generate a material processing list and equipment operation instructions, and transmit them to the production equipment.

[0082] For purlin production and processing, technicians import existing line models and skins, and classify the line models into layers according to the drawings, while assigning corresponding roof purlin specifications to the models. Based on the "operation rules" of purlin layout, the platform automatically extracts the curve segments of purlins and purlin brackets, and generates a classification number for each purlin and purlin bracket. Users can select component processing nodes, input parameters such as opening spacing and quantity, and the system will automatically generate corresponding component processing drawings, thereby achieving an efficient and accurate design and processing flow.

[0083] In engineering projects, taking purlins as an example, the lengths of main and secondary purlins vary, and some purlins require bending. Therefore, the system first uses intelligent analysis to accurately provide cutting data for the main purlins, secondary purlins, aluminum panel frame, and glass frame. Fitting is then performed for metal roof purlins with a wave-like shape. As the primary and final shape-supporting structure, the purlins need to conform to the hyperbolic outer skin. Therefore, the aluminum alloy keel must be classified and bent during the design process. Specifically, the sag is used as the criterion for bending: if the sag of the aluminum alloy keel is greater than 15mm, it is considered bending; if the sag is less than or equal to 15mm, it is considered straightening. This classification ensures both the accuracy and aesthetics of the structure.

[0084] For roof panel production and processing: The system automatically generates a panel layout diagram based on the panel type and processing dimensions, enabling effective layout and combination of standard panels and sash panels. Finally, it integrates the bill of materials, generating a detailed document containing the roof panel drawing attributes and processing list, providing accurate data support for the material processing of standard and sash panels. By importing the roof panel model surface and refining its regions, reasonable block areas are set. The system simplifies layout and material cutting by determining the angle between the roof panel and the two nearest lines, and iterates through the nearest roof lines to combine roof panels, numbering them sequentially.

[0085] The operation and maintenance monitoring module 105 is used to monitor the physical condition of the roof panel and the environmental data in real time after the roof panel is put into use, predict the risk of damage to the roof panel based on the physical condition and environmental data, and generate corresponding early warning signals.

[0086] Specifically, strain gauges, wind speed sensors, and temperature and humidity sensors are installed in key areas of the roof panels (such as areas of concentrated wind pressure and joints) to collect real-time physical conditions (strain, deformation) and environmental data (wind speed, temperature). The monitored data is transmitted to the cloud and combined with roof attribute data (such as material strength) from the data management platform. Machine learning models are used to analyze data trends and predict roof panel damage risks (e.g., excessive strain may lead to cracking). When the predicted risk reaches a threshold, the system automatically generates an early warning signal (including risk level and location) and pushes it to administrators via SMS, platform pop-ups, etc. Simultaneously, the system records the early warning and subsequent handling, and updates the data management platform.

[0087] This embodiment uses multiple modules to collaboratively cover the entire lifecycle of roof design, construction, and operation and maintenance. The data management platform provides standardized data support for each stage, the modeling and optimization module improves the roof's wind resistance performance, the automatic layout module ensures the rationality of the segment division, the automatic material cutting module adapts to actual production, and the operation and maintenance monitoring module enables early warning of risks. Overall, it solves the problems of fragmentation in traditional roofing processes, insufficient wind resistance design, high production losses, and lagging operation and maintenance, significantly improving the wind resistance safety, design and construction efficiency, and full lifecycle management level of metal roofs for large public buildings.

[0088] One possible implementation of this application embodiment is a modeling optimization module, which is used to conduct wind tunnel tests on the initial roof model to obtain the initial roof wind pressure coefficient; if the initial roof wind pressure coefficient does not meet the wind pressure target, then global constraints are defined; wherein, the global constraints include: the boundary contour and key control points of the initial roof model remain unchanged;

[0089] The initial roof model is iteratively optimized based on global constraints until the roof wind pressure coefficient obtained from wind tunnel testing in any iteration meets the wind pressure target or the number of iterations reaches the upper limit of the first iteration. The optimized roof model is then output.

[0090] In this embodiment, the global optimization condition is an insurmountable constraint during the iterative optimization process. The unchanged boundary contour means that the edge lines connecting the roof and the main building structure and the overall planar projection range remain unchanged. The unchanged key control points mean that the spatial coordinates of the iconic points in the roof shape (such as the highest point of the curved surface and the corner points of the irregular shape) remain unchanged.

[0091] Import the initial roof model into the numerical wind tunnel simulation software, and set the simulation parameters and global constraints. The simulation parameters include: reference wind speed, wind field type, and turbulence intensity. The simulation parameters can be set with reference to historical wind data of the project location. The global constraints can be set using the boundary locking tool. After the simulation, the software outputs a wind pressure distribution cloud map of the roof surface, which marks the wind pressure at different locations. The initial roof wind pressure coefficient is calculated based on the wind pressure distribution cloud map. The calculation method for the initial roof wind pressure coefficient is the same as that for the roof wind pressure coefficient obtained from the wind tunnel test in each iteration.

[0092] This embodiment first verifies the wind resistance performance of the initial model, and then clarifies the insurmountable global constraints. This allows for targeted optimization of the roof shape to improve wind resistance, while ensuring that the optimized roof does not deviate from the original functional and aesthetic requirements of the building design. It avoids damaging the overall roof shape due to over-optimization, thus balancing the improvement of wind resistance performance with the original design intention. At the same time, the setting of the iteration termination condition takes into account both optimization accuracy and engineering efficiency.

[0093] One possible implementation of this application embodiment is that the modeling optimization module, when working with the initial roof model and performing any round of iterations, is specifically used for:

[0094] Obtain the previous roof model obtained from the previous iteration and the previous wind pressure distribution cloud map obtained from the wind tunnel test;

[0095] Based on the previous wind pressure distribution cloud map and the preset wind pressure coefficient level, the previous roof model is divided into multiple roof areas, and the variation probability of each roof area is determined. Based on the variation probability, curve fitting, radius merging and wind tunnel test are performed on the previous roof model to obtain the current wind pressure distribution cloud map.

[0096] Calculate the current roof wind pressure coefficient based on the current wind pressure distribution cloud map, and determine whether the current roof wind pressure coefficient meets the wind pressure target;

[0097] If the current roof wind pressure coefficient does not meet the wind pressure target, the next iteration will continue.

[0098] In this embodiment, a roof model can be divided into several sub-regions according to a preset grid size (e.g., 1m × 1m). A preset wind pressure coefficient level represents the correspondence between wind pressure value ranges and wind pressure levels. Wind pressure levels can include high, medium, and low levels. Each sub-region is labeled with a wind pressure level based on the preset wind pressure coefficient level. Adjacent sub-regions of the same level are merged to form continuous roof areas, and a roof area division vector map is output. A probability mapping rule is retrieved, which represents the pre-set probability of variation corresponding to different wind pressure levels. The higher the level, the higher the degree of danger, and the greater the corresponding probability of variation. The probability of variation for each roof area is determined according to the probability mapping rule.

[0099] The surface fitting process includes: for each roof area, the system randomly generates a value between 0 and 1. If the value is not greater than the variation probability of that roof area, surface adjustment for that area is initiated. Adjustment methods include: for high-grade roof areas, peak reduction is applied to protruding parts, increasing the local radius of curvature using Bezier curve fitting technology to reduce wind load concentration; valley filling is applied to recessed parts, fitting a smooth transition curve. For low- and medium-grade roof areas, only minor local protrusions are fine-tuned (curvature radius change ≤10%) to avoid over-adjustment affecting the overall shape.

[0100] The radius merging process includes: extracting all the curved radius values ​​of the adjusted surface (such as 5.2m, 5.5m, 8.1m, 8.3m), and merging them to the standard radius according to the principle of merging similar radii (difference ≤ 0.5m is considered similar). For example, 5.2m and 5.5m are merged into 5.3m, and 8.1m and 8.3m are merged into 8.2m.

[0101] This embodiment accurately delineates risk areas based on wind pressure distribution cloud maps, assigning higher variation probabilities to high-risk areas. This concentrates optimization resources on areas with excessive wind pressure, avoiding indiscriminate adjustments that lead to wasted efficiency. Each iteration optimizes and verifies the results of the previous iteration, ensuring continuous improvement in wind resistance performance. At the same time, the iteration termination mechanism effectively balances optimization results and time costs, significantly improving the targeting and efficiency of wind resistance optimization for complex roofs.

[0102] In one possible implementation of this application embodiment, the modeling optimization module, when performing the calculation of the current roof wind pressure coefficient based on the current wind pressure distribution cloud map, is specifically used for:

[0103] The average wind pressure coefficient of the roof is calculated based on the current wind pressure distribution cloud map, and local areas with excessive wind pressure are identified from multiple roof areas.

[0104] And determine the local wind pressure coefficient in areas where local wind pressure exceeds the standard;

[0105] The current roof wind pressure coefficient is calculated based on the average roof wind pressure coefficient and the local wind pressure coefficient.

[0106] In this embodiment, the current wind pressure distribution cloud map includes the wind pressure coefficient values ​​of all grid points, and the average value of the wind pressure coefficient values ​​of all grid points is calculated as the roof average wind pressure coefficient.

[0107] A local safety threshold is set. If the current calculation is for a positive roof wind pressure coefficient, the local safety threshold is positive, and the current wind pressure distribution cloud map is traversed, marking all grid points exceeding the local safety threshold. If the current calculation is for a negative roof wind pressure coefficient, the local safety threshold is negative, and the current wind pressure distribution cloud map is traversed, marking all grid points below the local safety threshold. The preset wind pressure coefficient levels are also divided into negative pressure level classification rules and positive pressure level classification rules. The negative local safety threshold is the wind pressure boundary value between high and medium levels in the negative pressure level classification rules, and the positive local safety threshold is the wind pressure boundary value between high and medium levels in the positive pressure level classification rules.

[0108] Adjacent grid points exceeding the standard are merged to form continuous local wind pressure exceeding the standard areas. The average wind pressure coefficient of all local wind pressure exceeding the standard areas is calculated as the local wind pressure coefficient. The current roof wind pressure coefficient is obtained by weighted summation of the average wind pressure coefficient and the local wind pressure coefficient based on preset weights.

[0109] This embodiment calculates the current wind pressure coefficient by combining the average wind pressure coefficient of the roof (reflecting the overall wind resistance performance) and the local wind pressure coefficient (reflecting the situation in areas with concentrated risks). This avoids focusing only on the overall situation while ignoring the problem of local high risks, making the wind pressure coefficient calculation more comprehensive and accurate. It provides a more scientific basis for iterative optimization, ensuring that the optimized roof can not only meet the overall wind resistance requirements, but also eliminate the hidden danger of local wind pressure exceeding the standard, further improving the wind resistance safety of the roof.

[0110] One possible implementation of this application embodiment is an automatic layout module, which is used to perform iterative optimization based on a genetic algorithm until the adaptive function value of any layout result individual obtained in the current iteration satisfies the genetic optimization objective or the number of iterations reaches the upper limit of the second iteration number, and outputs a layout result that satisfies the genetic optimization objective.

[0111] Each iteration includes: taking the typesetting result population obtained from the previous iteration as the parent population, performing selection, crossover, and mutation operations on the parent population based on the adaptive function value of the parent population, and obtaining the current typesetting result population.

[0112] Calculate the adaptive function value of each individual typesetting result in the current typesetting result population, and determine whether the adaptive function value of each individual typesetting result satisfies the genetic optimization objective;

[0113] If multiple layout result individuals have adaptive function values ​​that satisfy the genetic optimization objective, the layout result individual with the highest adaptive function value is selected as the layout result output by the automatic layout module.

[0114] In this embodiment, a genetic algorithm, simulating the biological evolution mechanism, abstracts layout schemes into individuals. Multiple layout schemes form a population, and the optimal scheme is iteratively selected through selection, crossover, and mutation operations to meet the multi-objective optimization requirements of roof panel partitioning. Each layout result individual represents a single complete roof panel partitioning scheme, including information such as panel size, arrangement, panel type (standard / customized), and joint location; it is the basic optimization unit of the genetic algorithm. The adaptive function value is a comprehensive index for evaluating the quality of individual layout results, integrating dimensions such as the proportion of standard panels, material utilization, joint quality, and structural rationality; a higher value indicates a better scheme. The genetic optimization objective is a preset adaptive function value threshold to ensure that the output scheme meets engineering requirements.

[0115] Before the iteration process begins, parameters are initialized by retrieving key parameters of the roof model (including roof area, curvature distribution, main purlin position, etc.) and standard board specifications from the data management platform, and determining the preset population size, the upper limit of the second iteration, and the genetic optimization objective.

[0116] Under the premise of meeting the basic partitioning rules, the system randomly generates the initial population. Each individual in the initial population represents a partitioning scheme. The basic partitioning rules are set by technicians according to actual needs and may include: the edge of the partition does not cross the main purlin, and the size of the customized partition is not smaller than the minimum processing specification. The adaptive function value of each parent individual is calculated according to the preset adaptive function formula. After sorting, high-quality individuals (such as the top 20) are marked as the focus of subsequent operations.

[0117] The next iteration is executed, including selection, crossover, and mutation operations, generating the current population, calculating the adaptive function value of the current population, and determining the iteration termination condition. Specifically, the adaptive function value of each individual in the current population is calculated according to the preset adaptive function formula. It is then checked whether there are individuals in the current population with an adaptive value greater than or equal to the genetic optimization target: if so, the adaptive function values ​​of all qualified individuals are recorded; if not, the process proceeds directly to the next iteration. The optimal individual is selected: if there are multiple qualified individuals in the current population, the individual with the highest adaptive function value is selected as the optimal layout result and output; if there is only one qualified individual, that individual is directly selected and output.

[0118] This embodiment utilizes selection, crossover, and mutation operations of a genetic algorithm to retain the advantages of a high-quality layout scheme while exploring new schemes through crossover fusion and mutation, avoiding getting trapped in local optima. At the same time, through adaptive value judgment and the rule of selecting the best among multiple standard individuals, it ensures that the output layout result is optimal in terms of standard board ratio, material utilization rate, and other dimensions, which greatly improves the rationality and economy of roof panel segmentation and reduces the difficulty of subsequent processing and construction.

[0119] In one possible implementation of this application embodiment, any typesetting result individual is taken as the target individual. When the automatic typesetting module calculates the adaptive function value of the target individual in the current iteration, it is specifically used for:

[0120] The layout results of the target individual are compared with the standard roof construction database to determine the proportion of standard panels in the layout results of the target individual.

[0121] The layout results of the target individual are virtually spliced ​​together to determine the minimum area occupied as the raw material input area. The sum of the actual areas of all boards in the layout results of the target individual is calculated as the actual used area. The material utilization rate is calculated based on the raw material input area and the actual used area.

[0122] Determine the total length of continuous seams and the total length of all seams in the layout result of the target individual, and determine the seam quality value based on the total length of continuous seams and the total length of all seams;

[0123] The cosine of the angle between each board and the corresponding main purlin direction in the layout result of the target individual is determined as the single board direction consistency coefficient. The structural rationality value of the target individual is determined based on the average value of the single board direction consistency coefficients of all boards.

[0124] Based on the proportion of standard panels, the degree of material utilization, the value of joint quality, and the value of structural rationality, the adaptive function value of the target individual is calculated.

[0125] In this embodiment, the specification parameters of all standard panels are extracted from the roof standard construction data of the data management platform. The layout result of the target individual is analyzed, the size of each panel is extracted, and a panel parameter list is generated. The panel parameter list and the standard panel specifications are compared one by one to determine the number of standard panels and the total number of panels in the panel parameter list. Method 1: Standard panel percentage = Number of standard panels / Total number of panels × 100; Method 2: Standard panel percentage = Total area of ​​standard panels / Total area of ​​total panels × 100. Example: The target individual has a total of 100 panels, of which 85 are standard panels. The standard panel percentage = 85 ÷ 100 × 100 = 85%, thus mapping the standard panel percentage to the range of 0-100.

[0126] Virtual splicing simulates the cutting and arrangement of roof panel pieces on raw material boards using 3D modeling software. It does not actually consume materials; it is only used for calculating raw material utilization efficiency. Raw material specifications are retrieved from a data management platform as the base material for virtual splicing. All panel models of the target individual are imported into the 3D modeling software, and each panel is arranged one by one on the virtual raw material board according to a compact arrangement principle (gap between panels no greater than 5mm to avoid waste). The amount of raw material required to accommodate all panels is recorded. Multiple virtual splicing operations can be performed, and the one requiring the smallest amount of raw material is selected. The minimum area occupied, i.e., the raw material input area, is obtained by multiplying the area of ​​a single raw material piece by the minimum amount of raw material. The sum of the areas of all panels in the target individual is the actual usable area. Material utilization rate = actual usable area / raw material input area × 100, thus mapping the material utilization rate within the range of 0-100. The material utilization rate reflects the degree of raw material waste; the higher the value, the better the cost control effect.

[0127] The total length of continuous joints represents the sum of the joint lengths of the panels that are continuously arranged in the same direction (such as laterally) in the target individual (continuous joints have a low risk of wind load concentration); the total length of all joints represents the total length of the joints between all panels in the target individual. The ratio of the two reflects the wind resistance performance of the joints. Continuous joints can disperse wind loads and reduce the risk of panel detachment caused by local wind pressure concentration. It is an important indicator to ensure the wind resistance safety of the roof.

[0128] The layout of the target unit is analyzed to extract the joint information between all panels. Joints running in the same direction without interruption are considered continuous joints. For example, if 10 panels are arranged horizontally in a continuous manner, a horizontal continuous joint is formed. The length of this horizontal continuous joint is calculated as 9 × panel width. The total length of all continuous joints is then calculated. The total length of all joints between all panels is taken as the total length of all joints. The joint quality value is calculated as (total length of continuous joints / total length of all joints) × 100, thus mapping the joint quality value to a range of 0-100. A higher value indicates better wind resistance and a lower risk of wind-induced leakage.

[0129] The single-panel orientation consistency coefficient is the cosine of the angle between the long side of a single roof panel and the long side of the corresponding main purlin (coefficient = 1 when the angle is 0°, coefficient = 0 when the angle is 90°). The average single-panel orientation consistency coefficient of all panels is calculated, and the product of the average value and 100 is used as the structural rationality value of the target individual. This maps the rationality value to the range of 0-100, reflecting the stress matching degree between the panel and the supporting structure. The higher the value, the more rational the stress on the panel and the stronger the overall structural stability of the roof.

[0130] The preset adaptive function formula is a weighted sum of the standard board ratio, material utilization, joint quality, and structural rationality. The weights can be flexibly set according to actual needs. The adaptive function value is the weighted sum calculated by each individual according to the preset adaptive function formula, and it is the core indicator for judging the quality of the layout scheme. The higher the value, the better the scheme.

[0131] This embodiment calculates adaptive function values ​​based on multi-dimensional indicators. The proportion of standard panels improves processing efficiency and reduces costs, the degree of material utilization reduces waste, the joint quality value ensures wind resistance, and the structural rationality value ensures roof stability. Multi-dimensional evaluation makes the judgment of the layout scheme more comprehensive and objective, avoiding other dimension defects caused by the optimization of a single indicator. The final output layout scheme can take into account economy, construction and structural safety, and adapt to the multi-objective needs of complex roofs.

[0132] One possible implementation of this application embodiment is an operation and maintenance monitoring module, which is used to acquire the attribute data and historical environmental data of the roof panel, and construct a finite element simulation model based on the attribute data and historical environmental data;

[0133] Based on the finite element simulation model of physical state adjustment, the predicted environmental data is determined based on the environmental data. The finite element simulation model is then used to simulate the predicted environmental data to obtain the predicted response data of the roof panel. Based on the predicted response data, the damage risk of the roof panel is determined.

[0134] In this embodiment, the roof panel design and construction files are retrieved from the data management platform, and the roof panel attribute data, including material parameters, geometric parameters, and installation information, are extracted. Historical environmental data, covering at least one full year, is extracted from the meteorological station database at the project location and past roof maintenance records, including wind load data (daily maximum wind speed, frequency of wind direction changes), temperature and humidity data, and precipitation data.

[0135] General-purpose finite element software was selected. Shell elements were chosen to simulate the roof panels (shell elements are suitable for thin components, offering high computational efficiency and meeting accuracy requirements), while beam elements were selected to simulate the fasteners and supporting purlins. The roof panel geometry was reconstructed in the software based on attribute data, including: drawing the dimensions of individual panels, assembling them into a complete roof according to the layout results output by the automatic layout module, setting the positions of the fasteners and purlin support nodes, and generating a finite element mesh. Attribute data was assigned to the corresponding elements, and boundary conditions were set: the connection nodes between the purlins and the roof panels were set as hinged (allowing slight rotation to reflect actual stress conditions), and the fastener positions were set as fixed constraints (limiting displacement).

[0136] Historical environmental data is converted into load parameters and applied to the finite element model. The model's response (such as strain and displacement) under historical loads is calculated and compared with the actual response data recorded in past operations and maintenance. If the error is ≤10%, the model is considered valid; if the error exceeds 10%, material parameters or boundary conditions are adjusted, and the model is re-verified until it meets the standard. The environmental data and corresponding valid response data during the simulation process are stored in the wind vibration response spectrum database.

[0137] Due to factors such as material aging and construction errors, the initial finite element model may deviate from the actual condition of the roof panels. Real-time physical state data is used to correct the model, ensuring the accuracy of subsequent simulation predictions. Data is acquired through sensors deployed in key areas of the roof panels: strain gauges collect panel strain, displacement sensors collect relative panel displacement, and acceleration sensors collect vibration frequencies. Sensor data is transmitted in real-time to the operation and maintenance monitoring module via an IoT gateway. The selection and layout of sensors (data detection points) are as follows: First, the structural health status of different areas is analyzed to assess the types of damage and their impact. Detection points are prioritized according to risk level to ensure more detection points are deployed in key areas for timely detection of potential problems. Simultaneously, considering the spacing between sensor deployments, coverage of key parts of the entire roof is ensured to avoid blind spots. After the deployment of data detection points is completed, system testing and debugging are conducted to verify the function and data accuracy of each detection point. Based on initial operational data feedback, the location or number of detection points is adjusted in a timely manner to optimize monitoring effectiveness and the comprehensiveness of data acquisition.

[0138] Calculate the theoretical response data of the finite element model under the current real-time environmental load. Compare the theoretical response with the actual physical state. If the difference exceeds a preset deviation threshold, the model is determined to have a deviation, and the finite element simulation model is adjusted. The adjustment of the finite element simulation model can be done manually or by determining the cause of the deviation based on a pre-trained classification model, and adjusting the model parameters according to the cause of the deviation. The correspondence between the cause of the deviation and the adjustment method of the model parameters can be preset and stored in the data management platform for real-time retrieval. For example, if the strain deviation is due to material aging, reduce the elastic modulus of the material in the corresponding region (e.g., from 70 GPa to 65 GPa); if the frequency deviation is due to loose fasteners, increase the constraint stiffness of the fasteners (e.g., change the hinged node to a semi-rigid connection). After correction, recalculate the model's response under the current environment. If the deviation from the real-time physical state data does not exceed the preset deviation threshold, the model adjustment is complete; if the deviation still exceeds the threshold, repeat the correction until the threshold is met.

[0139] A predictive algorithm combining real-time and historical environmental data is used to obtain predicted environmental parameters for a future period, providing input for simulation. Parameters from the predicted environmental data table are converted into loads, such as the predicted maximum wind speed being converted into a standard wind load value, and the predicted temperature being converted into a temperature load. Loads are gradually applied to the adjusted finite element model according to the predicted time series (one load case per hour). The finite element software calculates the response under each load case, including the maximum strain, maximum displacement, and maximum stress at each predicted time, forming predicted response data. Risk assessment thresholds are set based on the ultimate performance and specification requirements of the roof panel material: such as a maximum allowable strain of 280 με, a maximum allowable displacement of 0.8 mm, and a maximum allowable stress of 280 MPa. Any indicator exceeding the corresponding threshold is considered high-risk. The exceeding indicators are used as damage types, and the damage types and risk levels at all predicted times are summarized, marking high-risk times and corresponding areas to provide early warnings for maintenance personnel.

[0140] The system issues alerts to managers through a visual interface (such as color-coding and pop-up notifications) and multi-channel notifications (SMS, email, and mobile push notifications). Combining historical data with industry standards, the system generates targeted maintenance plans (such as suggestions for localized reinforcement, coating repair, or complete replacement). Managers assign maintenance tasks to relevant teams through the platform and track task progress in real time. After maintenance is completed, the system re-collects data and verifies the repair effect, updating the database to optimize subsequent analysis models. All monitoring data and maintenance records are archived and stored, forming a full lifecycle database for metal roofing. Models are periodically trained based on new data to improve defect prediction accuracy and system adaptability.

[0141] This embodiment constructs and adjusts a finite element model based on actual data to ensure that the model is consistent with the actual state of the roof panel, thereby improving the accuracy of the simulation. Simulation is performed based on predicted environmental data and the adjusted model to achieve early prediction of roof panel damage risk, changing the lag of traditional passive monitoring, enabling maintenance personnel to formulate maintenance plans in advance, effectively avoiding accidents such as wind-induced damage, extending the service life of the roof, and reducing maintenance costs.

[0142] One possible implementation of this application embodiment is an automatic material feeding module, which is used to obtain the roof panel processing data list required for the current production batch, and perform initial nesting and layout of the roof panel processing data list based on the raw material specifications in the roof standard construction data;

[0143] The production data is compared with the initial nesting layout, the error correction coefficient is calculated, the board size parameters in the roof panel processing data list are adjusted based on the error correction coefficient, and subsequent production is carried out based on the updated roof panel processing data list.

[0144] In this embodiment, the system receives a list of roof panel processing data transmitted by the automatic layout module. The parsed content includes: panel number, theoretical dimensions, material, quantity, and processing technology. The system then retrieves the raw material specifications for the appropriate material from the roof standard construction database of the data management platform.

[0145] The initial nesting layout represents a preliminary plan for planning the cutting positions of roof panels on the raw material sheets. The goal is to maximize the use of raw materials and minimize waste; it is the initial plan for nesting optimization. Rectangular nesting algorithms and roll-to-roll nesting algorithms are used to set the initial nesting layout. The rectangular nesting algorithm is suitable for flat materials, while the roll-to-roll nesting algorithm is suitable for curved materials, thereby determining the cutting diagram for each raw material.

[0146] Production data is obtained by 3D scanning of the installed section. Equipment processing error records are extracted from this data. The equipment processing error represents the difference between the theoretical and actual processing width. The average equipment processing error of each panel in the installed section is calculated as the error correction coefficient. For the theoretical dimensions in the roof panel processing data list, the sum of the theoretical dimensions and the error correction coefficient is calculated as the adjusted panel size parameters.

[0147] This embodiment utilizes initial nesting and layout to preliminarily plan the use of raw materials, and then combines production data to calculate error correction coefficients to adjust the board size. This not only avoids waste of raw materials, but also compensates for errors in production and installation, ensuring that the processed boards accurately meet the actual needs of the site, reducing rework and material loss caused by dimensional deviations, improving production efficiency and processing accuracy, and reducing construction costs and the risk of project delays.

[0148] This application provides a method for intelligent design, construction, operation and maintenance of wind-resistant roofs, such as... Figure 2 As shown, the method provided in this application embodiment is executed by an electronic device, which can be a server or a terminal device. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication. This application embodiment does not impose any limitations on this connection. The method includes steps S201-S205, wherein:

[0149] S201. Obtain and store standard roof construction data and typical construction cases.

[0150] S202. Generate an initial roof model and iteratively optimize the initial roof model until the roof wind pressure coefficient obtained from the wind tunnel test in the current iteration meets the wind pressure target or the number of iterations reaches the upper limit of the first iteration. Output the optimized roof model. Each iteration includes surface fitting, radius merging and wind tunnel test.

[0151] S203. Use a genetic algorithm to divide the roof model into sections and output a layout result that meets the genetic optimization objective.

[0152] S204. Collect production data in real time during the production phase, and generate material processing data based on production data, roof standard construction data, and typical construction cases.

[0153] S205. After the roof panels are put into use, monitor the physical condition of the roof panels and the environmental data in real time, predict the risk of damage to the roof panels based on the physical condition and environmental data, and generate corresponding early warning signals.

[0154] This application provides an electronic device, such as... Figure 3 As shown, Figure 3 The illustrated electronic device 300 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this electronic device 300 does not constitute a limitation on the embodiments of this application.

[0155] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0156] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0157] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0158] The memory 303 is used to store the application code for executing the solution of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the aforementioned embodiment of the roof wind-resistant intelligent design, construction and operation method.

[0159] Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0160] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the contents shown in the aforementioned embodiment of the intelligent design, construction, operation and maintenance method for wind-resistant roofs.

[0161] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0162] This application provides a computer program product, including a computer program, which, when executed by a processor, implements the content shown in the aforementioned embodiment of the intelligent design, construction, operation and maintenance method for wind-resistant roofs.

[0163] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A roof wind-resistant intelligent design construction operation and maintenance system, characterized in that, The system comprises: a data management platform for storing roofing standard structure data and typical construction cases; a modeling optimization module for generating an initial roofing model, iteratively optimizing the initial roofing model until the current round of iteration performs a wind tunnel test to obtain a roofing wind pressure coefficient that meets a wind pressure target or the number of iterations reaches a first iteration upper limit, and outputting an optimized roofing model; wherein each round of iteration includes surface fitting, radius merging, and wind tunnel testing; an automatic layout module for receiving the roofing model transmitted by the modeling optimization module, dividing the roofing model into plates using a genetic algorithm, and outputting a layout result that meets a genetic optimization target; an automatic nesting module for collecting production data in real time during the production stage, generating material processing data based on the production data, the roofing standard structure data, and the typical construction cases; an operation and maintenance monitoring module for monitoring the physical state of the roofing panel and the environmental data in which the roofing panel is located in real time after the roofing panel is put into use, predicting the damage risk of the roofing panel based on the physical state and the environmental data, and generating a corresponding early warning signal; the automatic layout module is configured to perform iterative optimization based on a genetic algorithm until the adaptive function value of any layout result individual obtained in the current round of iteration meets the genetic optimization target or the number of iterations reaches a second iteration upper limit, and output a layout result that meets the genetic optimization target; wherein any round of iteration includes: taking the layout result population obtained in the last round of iteration as a parent population, performing selection, crossover, and mutation operations on the parent population based on the adaptive function value of the parent population to obtain a current layout result population; calculating the adaptive function value of each layout result individual in the current layout result population, and determining whether the adaptive function value of each layout result individual meets the genetic optimization target; if there are multiple layout result individuals whose adaptive function values meet the genetic optimization target, selecting the one with the highest adaptive function value as the layout result output by the automatic layout module; the automatic layout module, when calculating the adaptive function value of the target individual in the current round of iteration, is specifically configured to: compare the layout result of the target individual with the roofing standard structure data to determine the proportion of standard plates in the layout result of the target individual; virtually splice the layout result of the target individual to determine the minimum area occupied as the raw material input area, calculate the sum of the actual areas of all plates in the layout result of the target individual as the actual use area, and calculate the material utilization degree based on the raw material input area and the actual use area; determine the total length of continuous joints and the total length of all joints in the layout result of the target individual, and determine the joint quality value based on the total length of continuous joints and the total length of all joints; determine the cosine value of the angle between each plate in the layout result of the target individual and the corresponding main purlin direction as the single plate direction consistency coefficient, and determine the structure rationality value of the target individual based on the average value of the single plate direction consistency coefficients of all plates; An adaptive function value of the target individual is calculated based on the standard plate proportion, the material utilization degree, the joint quality value, and the structure rationality value.

2. The wind-resistant intelligent design construction and operation system for roofing according to claim 1, characterized in that, The modeling optimization module is configured to perform a wind tunnel test on the initial roof model to obtain an initial roof wind pressure coefficient, and define a global constraint condition if the initial roof wind pressure coefficient does not meet the wind pressure target, wherein the global constraint condition includes that a boundary profile and key control points of the initial roof model are unchanged. The modeling optimization module is configured to perform iterative optimization on the initial roof model based on the global constraint condition until the roof wind pressure coefficient obtained by performing the wind tunnel test in any iteration meets the wind pressure target or the number of iterations reaches a first iteration upper limit, and output an optimized roof model.

3. The wind-resistant intelligent design construction and operation system for roofing according to claim 1, characterized in that, In the process of performing the iterative optimization on the initial roof model, the modeling optimization module is specifically configured to: obtain a previous roof model obtained by completing a previous iteration and a previous wind pressure distribution cloud diagram obtained by performing the wind tunnel test; divide the previous roof model into a plurality of roof areas based on the previous wind pressure distribution cloud diagram and a preset wind pressure coefficient level, determine a variation probability of each roof area, and perform curve fitting, radius merging, and the wind tunnel test on the previous roof model based on the variation probability to obtain a current wind pressure distribution cloud diagram; calculate a current roof wind pressure coefficient based on the current wind pressure distribution cloud diagram, and determine whether the current roof wind pressure coefficient meets the wind pressure target; if the current roof wind pressure coefficient does not meet the wind pressure target, continue to perform a next iteration.

4. The wind-resistant intelligent design construction and operation system for roofing according to claim 3, characterized in that, In the process of calculating the current roof wind pressure coefficient based on the current wind pressure distribution cloud diagram, the modeling optimization module is specifically configured to: calculate a roof average wind pressure coefficient based on the current wind pressure distribution cloud diagram, and identify a local wind pressure over-standard area from the plurality of roof areas; determine a local wind pressure coefficient of the local wind pressure over-standard area; calculate the current roof wind pressure coefficient based on the roof average wind pressure coefficient and the local wind pressure coefficient.

5. The wind-resistant intelligent design construction operation and maintenance system for roofing according to claim 1, characterized in that, The operation and monitoring module is configured to obtain attribute data and historical environment data of the roof panel, construct a finite element simulation model based on the attribute data and the historical environment data, adjust the finite element simulation model based on the physical state, determine predicted environment data based on the environment data, simulate the predicted environment data by using the finite element simulation model to obtain predicted response data of the roof panel, and determine a damage risk of the roof panel based on the predicted response data. The automatic unloading module is configured to obtain roof panel processing data list required for a current production batch, perform initial nesting layout on the roof panel processing data list based on raw material specifications in the roof standard construction data, compare the production data with the initial nesting layout, calculate an error correction coefficient, adjust panel size parameters in the roof panel processing data list based on the error correction coefficient, and perform subsequent production based on the updated roof panel processing data list.

6. The wind resistant intelligent design construction operation and maintenance system of claim 1, wherein, The method includes: obtaining and storing roof standard construction data and typical construction cases.

7. A roof wind-resistant intelligent design construction operation and maintenance method, characterized in that, ​ ​ generating an initial roof model, iteratively optimizing the initial roof model until a wind tunnel test of a current round of iteration meets a wind pressure target or a number of iterations reaches a first iteration upper limit, and outputting an optimized roof model; wherein each round of iteration includes surface fitting, radius merging, and wind tunnel test; performing plate division on the roof model by using a genetic algorithm, and outputting a layout result meeting a genetic optimization target; collecting production data in real time in a production stage, generating material processing data based on the production data, the roof standard construction data, and the typical construction case; monitoring a physical state and environmental data of the roof panel in real time after the roof panel is put into use, predicting a roof panel damage risk based on the physical state and the environmental data, and generating a corresponding early warning signal; the plate division on the roof model by using the genetic algorithm and the outputting of the layout result meeting the genetic optimization target include: performing iterative optimization based on a genetic algorithm until an adaptive function value of any layout result individual of a current round of iteration meets the genetic optimization target or a number of iterations reaches a second iteration upper limit, and outputting the layout result meeting the genetic optimization target; wherein any round of iteration includes: taking a layout result population of a previous round of iteration as a parent population, performing selection operation, crossover operation, and mutation operation on the parent population based on adaptive function values of the parent population, and obtaining a current layout result population; calculating the adaptive function value of each layout result individual in the current layout result population, and judging whether the adaptive function value of each layout result individual meets the genetic optimization target; if there are multiple layout result individuals whose adaptive function values meet the genetic optimization target, selecting one layout result with the highest adaptive function value for output; taking any layout result individual as a target individual, and calculating the adaptive function value of the target individual in the current round of iteration includes: comparing the layout result of the target individual with the roof standard construction data, determining a standard plate proportion in the layout result of the target individual; virtually splicing the layout result of the target individual, determining a minimum area occupied as a raw material input area, calculating a sum of actual areas of all plates in the layout result of the target individual as an actual use area, and calculating a material utilization degree based on the raw material input area and the actual use area; determining a total length of continuous joints and a total length of all joints in the layout result of the target individual, and determining a joint quality value based on the total length of continuous joints and the total length of all joints; determining a cosine value of an angle between each plate and a corresponding main purlin direction in the layout result of the target individual as a single plate direction consistency coefficient, and determining a structure rationality value of the target individual based on an average value of single plate direction consistency coefficients of all plates; calculating the adaptive function value of the target individual based on the standard plate proportion, the material utilization degree, the joint quality value, and the structure rationality value.

8. An electronic device, comprising: include: at least one processor; a memory; At least one application, wherein the at least one application is stored in the memory and configured to be executed by the at least one processor, the at least one application being configured to: perform the roof wind-resistant intelligent design construction operation and maintenance method of claim 7.

Images