Steel structure engineering full life cycle management method and system based on digital twin

Abstract

The present invention relates to the field of engineering management technology, and in particular to a full life cycle management method and system for steel structure projects based on digital twins. The present invention can comprehensively and accurately grasp the status of steel structures throughout their life cycle. By real-time monitoring of steel structure connections, shape changes, and the dynamic evolution of design standards, potential safety hazards can be discovered in a timely manner and early warnings can be issued. In aging analysis, full consideration is given to dynamic human-structure interactions and surface damage caused by equipment use. Combined with a quantitative model of environmental factors, the aging process of steel structures can be more accurately predicted. At the same time, it can dynamically adjust the data collection cycle according to the safety risk status at different stages, avoid waste of resources, and achieve flexibility and pertinence in data collection. This scientific, comprehensive, and efficient management method has greatly improved the quality and efficiency of steel structure project management, and effectively guaranteed the safe and stable operation of steel structures throughout their life cycle.

Description

Technical Field

[0001] The present invention relates to the field of engineering management technology, and in particular to a method and system for full life cycle management of steel structure projects based on digital twins. Background Art

[0002] Due to their high strength, light weight, and rapid construction, steel structures are widely used in a wide range of engineering fields, including modern buildings, bridges, and industrial plants. However, steel structures face numerous complex factors throughout their lifecycles, making their safe and stable operation crucial. Effective lifecycle management is therefore crucial.

[0003] Traditional steel structure project management methods rely primarily on regular manual inspections and empirical judgment. Assessments of steel structure connections often rely solely on basic installation parameters and simple visual inspections, making it difficult to fully and accurately assess changes in installation stress and shape during long-term use. The lack of real-time, accurate monitoring of steel structure shape changes prevents the timely detection of potential safety hazards. Furthermore, design standards for steel structures typically focus solely on initial design parameters, ignoring how these standards change over time and environmental factors during actual use. Traditional methods present significant shortcomings in analyzing the aging of steel structures. For one thing, traditional assessments often overlook dynamic human-structure interactions. Climbing and equipment use are common occurrences in actual projects, but traditional methods fail to fully consider the impact of human loads and equipment on steel structures of varying degrees of aging, making it impossible to accurately assess the potential safety risks associated with such activities. On the other hand, traditional methods lack systematic analysis and prediction of surface damage to steel structures caused by equipment use, such as scratches, making it difficult to assess the impact of equipment use on the aging of steel structures in advance. When considering the impact of environmental factors on the aging of steel structures, traditional methods only conduct simple qualitative analysis and fail to establish a scientific and accurate quantitative model. Factors such as ambient temperature and humidity can cause corrosion and damage to steel structures, accelerating their aging process, but traditional methods cannot accurately quantify the specific impact of these environmental factors on steel structures at different times and under different aging conditions.

[0004] In addition, in terms of data collection and monitoring, traditional methods usually adopt a fixed data collection cycle, which lacks flexibility and pertinence. During the entire life cycle of the steel structure, the safety risk conditions at different stages are different. The fixed data collection cycle cannot be adjusted according to the actual risk situation, which may lead to untimely data collection during high-risk periods and unnecessary data collection during low-risk periods, resulting in a waste of resources. Summary of the Invention

[0005] In order to overcome the defects and shortcomings of the existing technology, the present invention provides a full life cycle management method and system for steel structure projects based on digital twins.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for managing the entire life cycle of a steel structure project based on digital twins, comprising the following steps:

[0008] Step S1: Obtain the steel structure connection status, usage status, and environmental conditions of the corresponding location of the project, and build a digital twin model of the steel structure project;

[0009] Step S2: performing an aging anomaly analysis of the corresponding steel structure location based on the connection condition of the steel structure at the corresponding location, the stress and scratch conditions during use;

[0010] Step S3: Predicting steel structure aging based on aging anomaly analysis of the steel structure location and the environmental conditions at the corresponding location;

[0011] Step S4: perform a hazard analysis of the corresponding location based on the steel structure aging prediction results and the personnel usage of the corresponding location, and set a future model data collection cycle based on the safety analysis results of the corresponding location.

[0012] In one implementation of the present invention, the steel structure connection condition includes the installation stress condition of the corresponding structure of the corresponding steel structure, the shape change condition of the corresponding steel structure and the design standard condition of the corresponding steel structure, wherein the shape change condition of the corresponding steel structure is obtained through an image acquisition terminal, and the design standard condition of the corresponding steel structure includes the standard image and standard force magnitude of the steel structure. The usage condition is the usage planning condition of the corresponding steel structure in the future period, wherein the usage planning condition of the steel structure includes the personnel climbing planning condition and the equipment usage condition. The equipment usage condition includes the number and size of scratches on the surface of the steel structure caused by the equipment during historical climbing, and the scratch size is the area of ​​the scratch. The environmental condition of the corresponding position includes the future environmental temperature and humidity changes of the corresponding steel structure position, and the corresponding steel structure model is generated on the data twin generation software.

[0013] In one implementation of the present invention, the aging anomaly analysis in step S2 includes the following specific steps:

[0014] S21. Obtaining the installation stress condition of the corresponding steel structure, the shape change condition of the corresponding steel structure, and the design standard condition of the corresponding steel structure; analyzing the aging degree of the current steel structure based on the installation stress condition of the corresponding steel structure, the shape change condition of the corresponding steel structure, and the design standard condition of the corresponding steel structure; the aging degree can be obtained by a deep learning neural network;

[0015] S22. Obtain the personnel climbing plan for the next stage of use, and perform climbing aging anomaly analysis based on the personnel climbing plan and the degree of steel structure aging. The climbing aging anomaly analysis process is as follows: obtain the ratio of the climber's weight to the safe climbing weight to obtain the weight hazard situation, and then multiply the weight hazard situation by the coefficient of steel structure aging to obtain the personnel climbing anomaly. The coefficient of steel structure aging is the steel structure aging multiplied by the aging impact coefficient, that is, the amplified effect of aging on personnel climbing safety. By quantitatively coupling personnel loads and equipment aging, the defect of "ignoring dynamic human-structure interaction" in traditional assessments is resolved;

[0016] S23. Obtain the number and size of scratches on the steel structure surface caused by the equipment during historical climbing, and simultaneously obtain the equipment usage plan for future climbing. Obtain the number and size of scratches on the steel structure surface caused by the corresponding equipment during climbing, and set the ratio of the sum of the sizes of all scratches to the safe scratch size as the aging abnormality of the equipment on the steel structure.

[0017] S24. Obtain the weighted sum of the equipment's responses to the aging anomaly of the steel structure and the weight hazard situation to obtain the aging anomaly coefficient for the corresponding steel structure position, and multiply the aging anomaly coefficient by the corresponding degree of aging of the steel structure to obtain the aging anomaly caused by the future operation scenario. This step obtains the weighted sum of the equipment's responses to the aging anomaly of the steel structure and the weight hazard situation to obtain the aging anomaly coefficient, and multiplies it by the degree of aging of the steel structure to obtain the aging anomaly caused by the future operation scenario.

[0018] In one implementation of the present invention, the steel structure aging prediction in step S3 includes the following specific steps:

[0019] S31. Obtain the aging anomalies caused by the future operating scenarios at the corresponding locations and the environmental conditions of the future scenarios, and compare the environmental conditions of the future scenarios with the suitable environment of the corresponding steel structures to obtain the environmental impact value, wherein the environmental impact value is analyzed in the following manner: integrate the deviations between the weather forecast values ​​of various environments in the future operating scenarios and the suitable environment of the corresponding steel structures over time and divide them by the standard time, and weight them according to the impact weights of different environments to obtain the impact of the environment on the aging of the steel structure. The aging anomalies caused by the future environment are obtained by multiplying the impact of the environment on the aging of the steel structure by the corresponding degree of aging of the steel structure. In the aging assessment of steel structures, the impact of environmental factors on their performance is crucial. This step obtains the aging anomalies caused by the future operating scenarios at the corresponding locations and the environmental conditions of the future scenarios, fully considering the role of environmental factors in the aging process of steel structures. Different environmental conditions, such as temperature, humidity, pH, salt spray, etc., will cause varying degrees of corrosion and damage to steel structures, thereby accelerating their aging process. By comparing the environmental conditions of the future scenarios with the suitable environment of the corresponding steel structures, the degree of influence of environmental factors on the aging of steel structures can be accurately quantified. By multiplying the environmental impact on the aging of steel structures by the corresponding degree of aging of the steel structures, the aging anomalies caused by the future environment are obtained, fully considering the sensitivity of the current aging state of the steel structures to environmental influences.

[0020] S32. Obtain aging anomalies caused by future operating scenarios and aging anomalies caused by future environments, and sum them up to obtain steel structure aging anomalies. This step obtains aging anomalies caused by future operating scenarios and aging anomalies caused by future environments, and sums them up to obtain steel structure aging anomalies. This comprehensive evaluation method fully considers the combined impact of operational factors such as personnel and equipment, as well as environmental factors, on steel structure aging.

[0021] In one implementation of the present invention, step S4 includes the following specific contents:

[0022] S41. Obtain the steel structure aging anomaly and the corresponding personnel climbing anomaly for the next operation cycle, perform weighted summation, and obtain the corresponding position data hazard level for the next operation cycle. In the operation management of steel structures, steel structure aging anomaly and personnel climbing anomaly are two important risk factors. Steel structure aging can reduce its structural strength and stability, increasing the possibility of accidents.

[0023] S42. Compare the data risk of the next operating cycle with the set risk threshold to obtain a risk ratio, and divide the corresponding set data collection period by the corresponding risk ratio to obtain the model data collection period of the next operating cycle. Compare the data risk of the next operating cycle with the set risk threshold to obtain a risk ratio, and divide the corresponding set data collection period by the risk ratio to obtain the model data collection period of the next operating cycle, thereby realizing dynamic adjustment of the data collection period.

[0024] In a second aspect, the present invention also provides a steel structure engineering life cycle management system based on digital twins, including:

[0025] The data acquisition module obtains the steel structure connection status, usage status and environmental conditions of the corresponding location of the project, and builds a digital twin model of the steel structure project;

[0026] Aging anomaly analysis module, which performs aging anomaly analysis of corresponding steel structure locations based on the steel structure connection conditions, stress and scratch conditions during use;

[0027] Steel structure aging prediction module, which predicts steel structure aging based on aging anomaly analysis of steel structure locations and the environmental conditions of the corresponding locations;

[0028] The collection cycle output module conducts a hazard analysis of the corresponding location based on the steel structure aging prediction results and the personnel usage of the corresponding location, and sets the future model data collection cycle based on the safety analysis results of the corresponding location.

[0029] In a third aspect, the present invention provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a full life cycle management method for steel structure projects based on digital twins by calling the computer program stored in the memory.

[0030] In a fourth aspect, the present invention provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute a full life cycle management method for steel structure projects based on digital twins.

[0031] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0032] The solution can comprehensively and accurately grasp the condition of steel structures throughout their entire life cycle. By real-time monitoring of steel structure connections, shape changes, and the dynamic evolution of design standards, it can promptly identify potential safety hazards and provide early warnings. In aging analysis, it fully considers dynamic human-structure interactions and surface damage caused by equipment use. Combined with quantitative models of environmental factors, it can more accurately predict the aging process of steel structures. At the same time, it can dynamically adjust the data collection cycle according to the safety risk status at different stages, avoiding resource waste and achieving flexible and targeted data collection. This scientific, comprehensive, and efficient management method has greatly improved the quality and efficiency of steel structure project management and effectively guaranteed the safe and stable operation of steel structures throughout their entire life cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0034] Figure 1 This is a schematic diagram of the overall process of Example 1 of the method of the present invention;

[0035] Figure 2 This is a schematic flow chart of step S2 of Example 1 of the method of the present invention;

[0036] Figure 3 This is a structural diagram of embodiment 2 of the system of the present invention. DETAILED DESCRIPTION

[0037] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0038] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0039] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive with other embodiments.

[0040] Example 1

[0041] like Figures 1 to 2 As shown, this embodiment provides a steel structure engineering life cycle management method based on digital twins, which specifically includes the following steps:

[0042] Step S1: Obtain the steel structure connection status, usage status, and environmental conditions of the corresponding location of the project, and build a digital twin model of the steel structure project;

[0043] In this embodiment, the connection condition of the steel structure includes the installation stress condition of the corresponding structure of the corresponding steel structure, the shape change condition of the corresponding steel structure, and the design standard condition of the corresponding steel structure, wherein the shape change condition of the corresponding steel structure is obtained through an image acquisition terminal, the design standard condition of the corresponding steel structure includes the standard image of the steel structure and the standard stress magnitude condition, and the usage condition is the usage plan of the corresponding steel structure in the future period, wherein the usage plan of the steel structure includes the personnel climbing plan and the equipment usage condition, and the equipment usage condition includes the number and size of scratches on the surface of the steel structure caused by the equipment during historical climbing. In steel structure engineering, scratch damage to the anti-rust coating caused by equipment climbing (such as hanging baskets, scaffolding, maintenance machinery, etc.) is a common maintenance challenge. The scratch size is the area of ​​the scratch. The environmental condition of the corresponding location includes the future environmental temperature and humidity changes of the corresponding steel structure location, which are obtained through weather forecasts and the corresponding steel structure model is generated on the data twin generation software;

[0044] Step S2: performing an aging anomaly analysis of the corresponding steel structure location based on the steel structure connection condition, stress and scratch conditions during use;

[0045] In this embodiment, the aging abnormality analysis in step S2 includes the following specific steps:

[0046] First, obtain the installation stress conditions of the corresponding structure of the corresponding steel structure, the shape change conditions of the corresponding steel structure and the design standard conditions of the corresponding steel structure, and analyze the aging degree of the current steel structure through the installation stress conditions of the corresponding structure of the corresponding steel structure, the shape change conditions of the corresponding steel structure and the design standard conditions of the corresponding steel structure. The aging degree can be obtained by means of a deep learning neural network, wherein the specific steps of the aging degree analysis are: divide the steel structure into several aging stages according to the average life of the steel structure, for example, divide it into ten aging stages, 1 is a brand new steel structure, 10 is a steel structure that has used up its life, and each aging stage corresponds to an aging degree, obtain the installation stress conditions of the corresponding structure of the historical steel structure, the shape change conditions of the corresponding steel structure, the design standard conditions of the corresponding steel structure and the future cycle aging degree conditions of the corresponding steel structure, construct the installation stress conditions of the corresponding structure of the steel structure, the shape change conditions of the corresponding steel structure and the design standard conditions of the corresponding steel structure as the input, and output the future cycle aging degree conditions of the corresponding steel structure. The deep learning neural network model of the situation is used to divide the obtained historical data into 75% weight and bias training sets and 25% weight and bias test sets; the 75% weight and bias training sets are input into the deep learning neural network model for training to obtain the initial deep learning neural network model; the initial deep learning neural network model is tested with the 25% weight and bias test sets, and the output of the initial deep learning neural network model that meets the maximum accuracy of judging the future cycle aging degree of the corresponding steel structure is used as the deep learning neural network model to obtain the installation stress condition of the corresponding structure of the current steel structure, the shape change condition of the corresponding steel structure and the design standard condition of the corresponding steel structure, and import them into the constructed prediction model to obtain the future cycle aging degree. This step uses deep learning neural networks to analyze the aging degree of steel structures, which has significant advantages. First, the life of the steel structure is evenly divided into several aging stages. This quantitative method makes the assessment of the aging degree more intuitive and accurate, and provides a clear reference standard for subsequent abnormal analysis. By collecting multifaceted data from historical steel structures to construct a deep learning neural network model, and using a large amount of historical information for training and testing, we can fully learn the inherent laws of steel structure aging. Using historical data in a ratio of 75% for the training set and 25% for the test set ensures that the model has sufficient data for learning and effectively verifies its accuracy, resulting in a model that accurately reflects the relationship between the degree of steel structure aging and installation stress, shape changes, and design standards. Using this model to predict the degree of aging of the current steel structure can provide basic data for subsequent anomaly analysis, helping to identify potential aging problems in the steel structure in advance.

[0047] Secondly, the personnel climbing planning situation in the next stage of use is obtained, and the climbing aging anomaly analysis is performed based on the personnel climbing planning situation and the aging degree of the steel structure. The climbing aging anomaly analysis process is as follows: obtain the ratio of the climber's weight to the safe climbing weight to obtain the weight hazard situation, and then multiply the weight hazard situation by the aging degree of the steel structure after the coefficient to obtain the personnel climbing anomaly. The aging degree of the steel structure after the coefficient is the aging degree of the steel structure multiplied by the aging influence coefficient, that is, the amplified effect of the aging degree on the safety of personnel climbing. By quantitatively coupling personnel loads and equipment aging, the defect of "ignoring dynamic human-structure interaction" in traditional evaluation is solved, and the aging influence coefficient is introduced to reflect the nonlinear effect of material degradation. The step is to analyze climbing anomalies based on the personnel climbing planning and the aging degree of the steel structure, which solves the defect of "ignoring dynamic human-structure interaction" in traditional assessments. The weight risk situation is obtained by calculating the ratio of the climber's weight to the safe climbing weight, and the ratio is multiplied by the aging degree of the steel structure after the coefficient to obtain the personnel climbing anomaly. This quantitative coupling of personnel load and equipment aging fully considers the impact of personnel climbing behavior on steel structures with different aging degrees. The introduction of the aging influence coefficient reflects the nonlinear effect of material degradation, making the assessment of personnel climbing safety more scientific and reasonable. It can more accurately judge whether there is an abnormal risk when personnel climb on steel structures at different aging stages, providing a strong basis for ensuring the safety of personnel climbing on steel structures.

[0048] Then, obtain the number and size of scratches on the steel structure surface caused by the equipment during historical climbing, and obtain the equipment usage plan for future climbing, obtain the number and size of scratches on the steel structure surface caused by the corresponding equipment climbing, and set the ratio of the sum of all scratch sizes to the safe scratch size as the aging anomaly of the equipment on the steel structure. In this step, the equipment usage plan is combined to predict future damage trends. By obtaining the scratches on the steel structure surface caused by the equipment during historical climbing and the equipment usage plan for future climbing, the ratio of the sum of all scratch sizes to the safe scratch size is calculated to determine the aging anomaly of the equipment on the steel structure. This method can fully consider the damage caused to the steel structure surface by the use of the equipment, and make predictions based on the future equipment usage plan, and evaluate the aging risks that may be caused by the use of the equipment in advance;

[0049] Finally, the aging anomaly coefficient of the corresponding steel structure position is obtained by weighted summing the aging anomaly coefficient of the equipment and the weight hazard situation of the steel structure. The aging anomaly coefficient is multiplied by the corresponding aging degree of the steel structure to obtain the aging anomaly caused by the future operation scenario. This step obtains the aging anomaly coefficient by weighted summing the aging anomaly coefficient of the equipment and the weight hazard situation of the steel structure, and multiplies it by the aging degree of the steel structure to obtain the aging anomaly caused by the future operation scenario. This method of comprehensively considering the impact of personnel and equipment factors on the aging of the steel structure comprehensively evaluates the aging of the steel structure under the future operation scenario. Through the weighted summation method, the influence of personnel and equipment factors can be reasonably weighted according to the actual situation, so that the evaluation result is more in line with the actual situation.

[0050] Step S3: Predicting steel structure aging based on aging anomaly analysis of the steel structure location and the environmental conditions at the corresponding location;

[0051] In this embodiment, the steel structure aging prediction in step S3 includes the following specific steps:

[0052] First, the aging anomalies caused by the future operating scenarios at the corresponding locations and the environmental conditions of the future scenarios are obtained. The environmental impact values ​​are compared based on the environmental conditions of the future scenarios and the suitable environments of the corresponding steel structures. The environmental impact values ​​are analyzed as follows: the deviations between the weather forecast values ​​of various environments in the future operating scenarios and the suitable environments of the corresponding steel structures are integrated over time and divided by the standard time. The impact of the environment on the aging of the steel structure is weighted according to the impact weights of different environments. The aging anomalies caused by the future environment are obtained by multiplying the impact of the environment on the aging of the steel structure with the corresponding degree of aging of the steel structure. In the aging assessment of steel structures, the impact of environmental factors on their performance is crucial. This step obtains the aging anomalies caused by the future operating scenarios at the corresponding locations and the environmental conditions of the future scenarios, and fully considers the role of environmental factors in the aging process of steel structures. Different environmental conditions, such as temperature, humidity, pH, salt spray, etc., will cause different degrees of corrosion and damage to steel structures, thereby accelerating their aging process. By comparing the environmental conditions of the future scenarios with the suitable environment of the corresponding steel structures, the degree of influence of environmental factors on the aging of steel structures can be accurately quantified. By multiplying the impact of the environment on the aging of steel structures with the corresponding degree of aging of the steel structures, the aging anomalies caused by the future environment are obtained, and the sensitivity of the current aging state of the steel structure to the impact of the environment is fully considered. Steel structures with different degrees of aging have different tolerance to environmental factors. Steel structures with higher degrees of aging may be more sensitive to environmental changes and more susceptible to environmental factors. Therefore, calculations combined with the degree of aging of the steel structures can more accurately assess the impact of future environmental factors on the aging of steel structures, providing a more targeted reference for the maintenance and management of steel structures.

[0053] Then, the aging anomalies caused by future operating scenarios and future environments are obtained and summed to obtain the steel structure aging anomalies. This step obtains the aging anomalies caused by future operating scenarios and future environments, and sums them to obtain the steel structure aging anomalies. This comprehensive assessment method fully considers the combined impact of operational factors such as personnel and equipment, as well as environmental factors, on steel structure aging.

[0054] Step S4: Conduct a risk analysis of the corresponding location based on the steel structure aging prediction results and the personnel usage of the corresponding location, and set a future model data collection cycle based on the safety analysis results of the corresponding location;

[0055] In this embodiment, step S4 includes the following specific contents:

[0056] First, the steel structure aging anomaly and the corresponding personnel climbing anomaly in the next operation cycle are obtained, and the corresponding position data hazard of the next operation cycle is obtained after weighted summation. In the operation management of steel structures, steel structure aging anomaly and personnel climbing anomaly are two important risk factors. The aging of steel structures will reduce their structural strength and stability and increase the possibility of accidents; while personnel climbing anomaly may cause safety accidents such as personnel falling, and may also cause additional damage to the steel structure, further aggravating the degree of aging. By obtaining the steel structure aging anomaly and the corresponding personnel climbing anomaly in the next operation cycle and performing weighted summation to obtain the corresponding position data hazard of the next operation cycle, these two important risk factors can be comprehensively considered to comprehensively evaluate the safety risk status of the location in the future operation cycle;

[0057] Then, the data hazard of the next operation cycle is compared with the set hazard threshold to obtain the hazard ratio, and the model data collection cycle of the next operation cycle is obtained by dividing the corresponding set data collection cycle by the corresponding hazard ratio. The data hazard of the next operation cycle is compared with the set hazard threshold to obtain the hazard ratio, and the model data collection cycle of the next operation cycle is obtained by dividing the corresponding set data collection cycle by the hazard ratio, thereby realizing dynamic adjustment of the data collection cycle. The risk status of the steel structure may change in different operation cycles. When the data hazard is high, it means that there is a greater safety risk at this location, and more frequent data collection is required to timely discover potential problems and take measures to deal with them; when the data hazard is low, the data collection cycle can be appropriately extended to reduce the workload and cost of data collection.

[0058] At the same time, in this embodiment, the setting parameters (weights and standard values, etc.) are obtained by fitting through MATLAB software: the steel structure connection conditions, usage conditions and environmental conditions of the corresponding locations of the historical projects are obtained. In order to extract parameter values ​​such as weights and standard values, it is necessary to first comprehensively collect relevant historical experimental data. Regarding the connection situation, it is necessary to obtain information such as the connection node type, connection strength, and stress distribution from laboratory test reports, field monitoring records, and research literature. Shape change data can be collected through measurement records such as total stations and laser scanners to collect component deformation, cross-sectional size changes, etc. Aging index data can be obtained through non-destructive testing results, such as the degree of steel corrosion and fatigue crack propagation. The collected data is then cleaned to remove duplicates, errors, and outliers, unify the data format, and group them according to factors such as structural type and usage environment. The sorted data is then stored as a CSV or Excel file and imported using Matlab's readtable function. When selecting a fitting function, if the data is approximately in a linear relationship, linear fitting can be used. If the relationship is complex, it is necessary to combine professional knowledge and data characteristics and consider nonlinear fitting functions such as polynomials, exponentials, and logarithms to accurately describe the data change pattern and provide a basis for subsequent analysis.

[0059] It should be noted that in this embodiment, this embodiment has the following benefits and advantages: it can comprehensively and accurately grasp the status of the steel structure throughout its life cycle, and through real-time monitoring of the connection status, shape changes and dynamic evolution of design standards of the steel structure, it can timely discover potential safety hazards and provide early warnings. In aging analysis, it fully considers the dynamic human-structure interaction and surface damage caused by equipment use, and combines the quantitative model of environmental factors to more accurately predict the aging process of the steel structure. At the same time, it can dynamically adjust the data collection cycle according to the safety risk status at different stages, avoid waste of resources, and achieve flexibility and pertinence in data collection. This scientific, comprehensive and efficient management method has greatly improved the quality and efficiency of steel structure project management, and effectively guaranteed the safe and stable operation of the steel structure throughout its life cycle.

[0060] Example 2

[0061] like Figure 3As shown, this embodiment provides a full life cycle management system for steel structure projects based on digital twins, which is implemented based on the full life cycle management method for steel structure projects based on digital twins in Example 1, including: a data acquisition module, which acquires the steel structure connection status, usage status and environmental conditions of the corresponding location of the project, and constructs a digital twin model of the steel structure project; an aging abnormality analysis module, which performs aging abnormality analysis of the corresponding steel structure location based on the steel structure connection status, stress and scratch conditions in the usage status; a steel structure aging prediction module, which predicts steel structure aging based on the aging abnormality analysis of the steel structure location and the environmental conditions of the corresponding location; an acquisition cycle output module, which performs a hazard analysis of the corresponding location through the steel structure aging prediction results and the personnel usage status of the corresponding location, and sets the future model data acquisition cycle based on the safety analysis results of the corresponding location. The specific steps of each module in the embodiment of this system are the same as the specific steps of the method embodiment in Example 1, and will not be repeated here.

[0062] Example 3

[0063] An electronic device according to an embodiment of the present invention includes a processor and a memory, wherein the memory stores a computer program that can be called by the processor. The processor executes a method for managing the entire life cycle of a steel structure project based on a digital twin by calling the computer program stored in the memory. It should be noted that all computer programs in the method for managing the entire life cycle of a steel structure project based on a digital twin are implemented in the C language.

[0064] Example 4

[0065] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0066] When the computer program runs on a computer device, the computer device executes the above-mentioned digital twin-based steel structure engineering life cycle management method.

[0067] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0068] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0069] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only one type. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0070] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0071] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0072] Throughout this specification, references to terms such as "one embodiment," "example," and "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0073] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A digital twin-based steel structure engineering life cycle management method, characterized by: The steps include: Step S1: Obtain the steel structure connection status, usage status, and environmental conditions of the corresponding location of the project, and build a digital twin model of the steel structure project; Step S2: performing an aging anomaly analysis of the corresponding steel structure location based on the connection condition of the steel structure at the corresponding location, the stress and scratch conditions during use; The specific steps include: S21. Obtaining the installation stress condition of the corresponding steel structure, the shape change condition of the corresponding steel structure, and the design standard condition of the corresponding steel structure, and analyzing the aging degree of the current steel structure based on the installation stress condition of the corresponding steel structure, the shape change condition of the corresponding steel structure, and the design standard condition of the corresponding steel structure; S22. Obtaining the personnel climbing plan during the next stage of use, and performing a climbing aging anomaly analysis based on the personnel climbing plan and the degree of aging of the steel structure. The climbing aging anomaly analysis process includes: obtaining the ratio of the climber's weight to the safe climbing weight to obtain the weight risk, and then multiplying the weight risk by the coefficient-adjusted degree of aging of the steel structure to obtain the personnel climbing anomaly. The coefficient-adjusted degree of aging of the steel structure is the steel structure aging multiplied by the aging impact coefficient. S23. Obtain the number and size of scratches on the steel structure surface caused by the equipment during historical climbing, and simultaneously obtain the equipment usage plan for future climbing. Obtain the number and size of scratches on the steel structure surface caused by the corresponding equipment during climbing, and set the ratio of the sum of the sizes of all scratches to the safe scratch size as the aging abnormality of the equipment on the steel structure. S24. Obtaining the weighted sum of the aging anomaly and weight risk of the steel structure by the equipment to obtain an aging anomaly coefficient for the corresponding steel structure location, and multiplying the aging anomaly coefficient by the corresponding aging degree of the steel structure to obtain the aging anomaly caused by the future operation scenario; Step S3: Predicting steel structure aging based on aging anomaly analysis of the steel structure location and the environmental conditions at the corresponding location; Step S4: perform a hazard analysis of the corresponding location based on the steel structure aging prediction results and the personnel usage of the corresponding location, and set a future model data collection cycle based on the safety analysis results of the corresponding location.

2. The digital twin-based steel structure engineering life cycle management method according to claim 1 is characterized in that: The steel structure aging prediction includes the following specific steps: S31. Obtaining aging anomalies caused by future operating scenarios at the corresponding location and environmental conditions in the future scenarios, and comparing the environmental conditions in the future scenarios with the suitable environment for the corresponding steel structure to obtain an environmental impact value, wherein the environmental impact value is analyzed by integrating the deviations between weather forecast values ​​for various environments in the future operating scenarios and the suitable environment for the corresponding steel structure over time and dividing the deviations by the standard time, and weighting the deviations according to the impact weights of different environments to obtain the environmental impact on the aging of the steel structure. The aging anomaly caused by the future environment is obtained by multiplying the environmental impact on the aging of the steel structure by the corresponding degree of aging of the steel structure. S32. Obtain aging anomalies caused by future operation scenarios and aging anomalies caused by future environments, and sum them up to obtain steel structure aging anomalies.

3. The digital twin-based steel structure engineering life cycle management method according to claim 2 is characterized in that: The step S4 includes the following specific contents: S41. Obtain the steel structure aging anomaly and the corresponding personnel climbing anomaly in the next operation cycle, perform weighted summation, and obtain the corresponding position data hazard level in the next operation cycle; S42. Compare the data risk of the next operating cycle with the set risk threshold to obtain a risk ratio, and obtain the model data collection period of the next operating cycle by dividing the corresponding set data collection period by the corresponding risk ratio. Compare the data risk of the next operating cycle with the set risk threshold to obtain a risk ratio, and obtain the model data collection period of the next operating cycle by dividing the corresponding set data collection period by the risk ratio.

4. The digital twin-based steel structure engineering life cycle management method according to claim 3 is characterized in that: The aging degree is obtained by means of a deep learning neural network, wherein the specific steps of the aging degree analysis are as follows: obtaining the installation stress conditions of the corresponding structure of the historical steel structure, the shape change conditions of the corresponding steel structure, the design standard conditions of the corresponding steel structure, and the aging degree conditions of the corresponding steel structure, constructing a deep learning neural network model whose input is the installation stress conditions of the corresponding structure of the steel structure, the shape change conditions of the corresponding steel structure, and the design standard conditions of the corresponding steel structure, and outputting the future cycle aging degree conditions of the corresponding steel structure, dividing the obtained historical data into a 75% weight and bias training set and a 25% weight and bias test set; inputting the 75% weight and bias training set into the deep learning neural network model for training to obtain an initial deep learning neural network model; using the 25% weight and bias test set to test the initial deep learning neural network model, outputting the initial deep learning neural network model that meets the maximum accuracy rate for judging the future cycle aging degree of the corresponding steel structure as the deep learning neural network model, obtaining the installation stress conditions of the corresponding structure of the current steel structure, the shape change conditions of the corresponding steel structure, and the design standard conditions of the corresponding steel structure, and importing them into the constructed prediction model to obtain the future cycle aging degree.

5. The steel structure engineering full life cycle management method based on digital twin according to claim 1 is characterized in that: The steel structure connection condition includes the installation stress condition of the corresponding structure of the corresponding steel structure, the shape change condition of the corresponding steel structure and the design standard condition of the corresponding steel structure, wherein the shape change condition of the corresponding steel structure is obtained through the image acquisition terminal, and the design standard condition of the corresponding steel structure includes the standard image and standard force size of the steel structure. The usage condition is the usage plan of the corresponding steel structure in the future period, wherein the usage plan of the steel structure includes the personnel climbing plan and the equipment usage condition, and the equipment usage condition includes the number and size of scratches on the surface of the steel structure caused by the equipment during historical climbing. The environmental condition of the corresponding position includes the future environmental temperature and humidity changes of the corresponding steel structure position, which are obtained through weather forecasts and the corresponding steel structure model is generated on the data twin generation software.

6. A digital twin-based steel structure engineering full life cycle management system, used to implement the digital twin-based steel structure engineering full life cycle management method according to any one of claims 1 to 5, characterized in that: The system comprises: The data acquisition module obtains the steel structure connection status, usage status and environmental conditions of the corresponding location of the project, and builds a digital twin model of the steel structure project; Aging anomaly analysis module, which performs aging anomaly analysis of corresponding steel structure locations based on the steel structure connection conditions, stress and scratch conditions during use; Steel structure aging prediction module, which predicts steel structure aging based on aging anomaly analysis of steel structure locations and the environmental conditions of the corresponding locations; The collection cycle output module conducts a hazard analysis of the corresponding location based on the steel structure aging prediction results and the personnel usage of the corresponding location, and sets the future model data collection cycle based on the safety analysis results of the corresponding location.

7. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the full life cycle management method of steel structure engineering based on digital twins as described in any one of claims 1 to 5 by calling the computer program stored in the memory.

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