ETFE hyperbolic negative Gaussian cable membrane digital construction method and system based on big data

Abstract

The invention discloses an ETFE hyperbolic negative Gaussian cable membrane digital construction method and system based on big data, and relates to the technical field of digital constructions.The method comprises the steps that mechanical property data and historical data of a cable membrane are obtained, the data are preprocessed, and characteristics related to cable membrane structure modeling are extracted according to the mechanical property data; calculating according to the three-dimensional geometric model of the ETFE hyperbolic negative Gaussian cable membrane to obtain a mechanical property evaluation coefficient; cable and membrane data are monitored in real time, the cable and membrane data monitored in real time and a pre-constructed three-dimensional geometric model are compared and analyzed, and the tension force of the cable is adjusted according to the deviation; and establishing a performance prediction model of the ETFE hyperbolic negative Gaussian cable-membrane structure, predicting the material aging speed of the cable-membrane structure, and selecting an ETFE hyperbolic negative Gaussian cable-membrane modeling structure in combination with the adjusted tension force. According to the method, the problems of insufficient data utilization and missing material aging prediction are solved, and the model precision and reliability are improved.

Description

Technical Field

[0001] The present invention relates to the field of digital construction technology. More specifically, the present invention relates to a digital construction method and system for ETFE hyperbolic negative Gaussian cable membranes based on big data. Background Art

[0002] Membrane structures, as a new type of building structure form, utilize high-strength thin film materials and strengthening members (such as steel frames, steel columns or steel cables) to form a stable spatial structure through a specific tensioning method to bear external loads. ETFE (ethylene-tetrafluoroethylene copolymer) cable membranes, as an excellent membrane material in membrane structures, are widely used in modern architecture due to their excellent physical and chemical properties (light weight, good light transmittance, strong durability). ETFE membranes can form a certain shape through air inflation or can be part of a cable membrane system. The hyperbolic negative Gaussian surface is a common form of building surface with a curved structure that can be designed through certain geometric methods. The negative Gaussian surface usually refers to a surface with negative Gaussian curvature. Such surfaces are very beautiful and structurally stable in architectural design and are suitable for the design of membrane structures and other modern buildings. The cable membrane structure is a building method with cables as the main supporting structure. The tension of the membrane material is borne by steel cables or other tensile elements to form a stable building form. This structural form is very suitable for large-span and buildings with special geometric shapes. In digital construction, big data can be used in multiple aspects, including: Design optimization: By analyzing a large amount of design and construction data, designers can optimize the shape of the membrane structure, material usage, and construction methods, reduce resource waste, and improve the structural performance. Construction monitoring: Through sensors and real-time data collection technologies, the construction process can be accurately monitored and controlled to ensure construction quality and progress. Performance prediction and analysis: Big data technology can help predict the performance of the membrane structure under different climatic conditions, including factors such as durability, light transmittance, and wind load. Digital construction: Digital construction refers to the precise management and control of the entire life cycle of building design, construction, management, etc. through information technologies (such as BIM, CAD, 3D modeling, laser scanning, robotic construction, etc.). Digital construction can make all aspects of building design, construction, operation, etc. more efficient, reduce human errors, and improve construction accuracy. In the construction of ETFE hyperbolic negative Gaussian cable membranes, digital construction technology combines advanced geometric calculations, simulations, simulation tools, and real-time data analysis. It can fully consider the shape of the membrane material, load, and the impact of the external environment on the structure during the design stage, and guide the construction team to implement an accurate construction process through digital tools to ensure the stability and aesthetics of the structure.

[0003] For example, the digital construction method and system of ETFE hyperbolic negative Gaussian cable membrane based on big data disclosed in the invention patent with the publication number of CN114329746B includes the following steps: designing a structural model based on the ETFE hyperbolic negative Gaussian cable membrane; constructing a tension change prediction model; obtaining first environmental data based on the coordinate position information of the construction site; predicting the tension change values of cable membranes of different materials through the tension change prediction model based on the first environmental data; judging whether the tension change values exceed the corresponding tension change limit values. If not, adding the corresponding material cable membrane to the first candidate queue; obtaining the light transmittance requirement, and judging whether the light transmittance of each material cable membrane in the first candidate queue meets the light transmittance requirement. If it meets, adding the corresponding material cable membrane to the second candidate queue; selecting the target material cable membrane based on the anti-tension change ability and light transmittance of the cable membrane, and carrying out physical construction. The construction method of the present invention can improve the stability of the building structure and enhance the user experience.

[0004] Deficiencies of the prior art: In traditional construction technologies, the mining and utilization of mechanical property data and historical data of cable membrane structures are insufficient, resulting in incomplete extraction of modeling features and deviation between the structural model and the actual working conditions. This solution constructs a more realistic three-dimensional geometric model through systematic data collection, preprocessing, and accurate feature extraction, solving the problem of low model accuracy. In the previous construction process, the monitoring and control means for cable membrane structures were limited, and it was difficult to adjust construction parameters in real time according to the actual situation. This solution compares and analyzes the real-time monitoring data with the model, and adjusts the tension of the cable in time when deviation occurs, realizing the dynamic control of the construction process and avoiding potential structural safety hazards or rework caused by construction deviation. The prior art lacks effective prediction means for the aging of cable membrane structure materials, making maintenance work often lack pertinence and planning, resulting in waste of resources or untimely maintenance. This solution uses machine learning algorithms to establish a performance prediction model to predict the material aging speed in advance, providing a basis for scientific and reasonable maintenance decisions. It is difficult for traditional construction methods to comprehensively consider various factors to optimize the construction plan, resulting in the cable membrane structure constructed being difficult to achieve the optimal balance in terms of safety, economy, and functionality. This solution combines the adjusted tension and material aging prediction, selects the optimal modeling structure for physical construction, and realizes the optimization of the construction plan.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a digital construction method and system of ETFE hyperbolic negative Gaussian cable membrane based on big data, and solve the problems raised in the above-mentioned background technology through the digital construction method and system of ETFE hyperbolic negative Gaussian cable membrane based on big data.

[0007] To achieve the above object, the present invention provides the following technical solutions: A digital construction method and system for ETFE hyperbolic negative Gaussian cable-membrane based on big data, comprising the following steps: Obtain the mechanical property data and historical data of the cable-membrane, and preprocess the data. Extract the features related to the cable-membrane structure modeling from the mechanical property data, including elastic modulus, Poisson's ratio, and the initial tension of the cable; Using finite element analysis software, combined with the extracted features, construct a three-dimensional geometric model of the ETFE hyperbolic negative Gaussian cable-membrane, and calculate the mechanical property evaluation coefficient according to the three-dimensional geometric model of the ETFE hyperbolic negative Gaussian cable-membrane; Real-time monitor the cable-membrane data, and compare and analyze the real-time monitored cable-membrane data with the pre-constructed three-dimensional geometric model. If the deviation between the real-time monitored cable-membrane data and the mechanical property evaluation coefficient exceeds the preset threshold, adjust the tension force of the cable according to the size of the deviation; Use machine learning algorithms to analyze the historical data and the real-time monitored cable-membrane data, establish a performance prediction model for the ETFE hyperbolic negative Gaussian cable-membrane structure, predict the material aging speed of the cable-membrane structure, select the modeling structure of the ETFE hyperbolic negative Gaussian cable-membrane in combination with the adjusted tension force, and perform physical construction according to the structural model.

[0008] In a preferred embodiment, the process of preprocessing the data is as follows: Check whether there are missing values in the data. For a small number of missing values, use the method of mean filling; For samples with a large number of missing values, if it does not affect the integrity of the overall data, delete the sample; Use the box plot method to identify outliers. For the data points identified by the box plot, if it is less than the lower quartile minus 1.5 times the interquartile range IQR or greater than plus 1.5 times the IQR, it is regarded as an outlier, and the outlier is deleted; and use the Z-score normalization method to normalize the data.

[0009] In a preferred embodiment, the process of obtaining the material elastic modulus is as follows: Cut standard specimens from the ETFE membrane and cable materials, and obtain the cross-sectional area A and the original length of the specimens , install the specimens on a tensile testing machine, apply a tensile force at a constant rate, and record the tensile force F and the corresponding elongation ΔL; Divide the tensile force F by the cross-sectional area A of the specimen to obtain the stress, and divide the elongation ΔL by the original length to obtain the strain; According to Hooke's law, within the elastic range, the stress σ and the strain ϵ It is directly proportional. The elastic modulus is calculated through the linear part of the stress-strain curve. The specific calculation formula is as follows: ; In the formula, E is the elastic modulus, F is the tensile force, A is the cross-sectional area of the specimen, is the original length of the specimen, and ΔL is the elongation.

[0010] In a preferred embodiment, the process for obtaining the Poisson's ratio is as follows: While measuring the elastic modulus during the tensile test, use an extensometer to measure the deformation of the specimen in the transverse and longitudinal directions, record the longitudinal elongation and the transverse contraction, and obtain the original transverse dimension of the specimen; According to the definition of Poisson's ratio, calculate the ratio of the transverse strain to the longitudinal strain. The specific calculation formula is as follows: ; In the formula, B is Poisson's ratio, is the original length of the specimen, ΔL is the elongation, is the transverse contraction, is the original transverse dimension of the specimen.

[0011] In a preferred embodiment, the process for obtaining the initial tension of the cable is as follows: For the installed cable structure, use the vibration frequency method and a vibration sensor to measure the vibration frequency of the cable; Obtain the length and mass of the cable, and calculate the mass per unit length of the cable; According to the relationship between the vibration frequency and the tension of the cable, combined with the mass per unit length and the length of the cable, calculate the initial tension of the cable. The specific calculation formula is as follows: ; In the formula, T is the initial tension of the cable, L is the length of the cable, m is the mass of the cable, is the measured first-order vibration frequency; For newly produced cables, directly measure their initial tension using a tensile testing machine before installation.

[0012] In a preferred embodiment, the specific calculation formula for obtaining the mechanical property evaluation coefficient according to the three-dimensional geometric model of the ETFE hyperbolic negative Gaussian cable-membrane is as follows: ; In the formula, P is the mechanical property evaluation coefficient, E is the elastic modulus, B is Poisson's ratio, T is the initial tension of the cable, are the weight coefficients of the elastic modulus, Poisson's ratio, and the initial tension of the cable, respectively, which are obtained through calculation and analysis of historical data.

[0013] In a preferred embodiment, the process of adjusting the tension force of the cable according to the deviation size is as follows: Real-time monitor the mechanical property data of the cable-membrane, and compare and analyze the cable-membrane data with the mechanical property evaluation coefficient, and calculate the deviation value between the mechanical property data of the cable-membrane and the mechanical property evaluation coefficient; Compare and analyze the deviation value between the mechanical property data of the cable-membrane and the mechanical property evaluation coefficient with a preset threshold. If the deviation value between the mechanical property data of the cable-membrane and the mechanical property evaluation coefficient is less than the preset threshold, there is no need to adjust the tension force of the cable; If the deviation value between the mechanical property data of the cable-membrane and the mechanical property evaluation coefficient is greater than the preset threshold, it is necessary to adjust the tension force of the cable according to the deviation size.

[0014] In a preferred embodiment, adjusting the tension force of the cable according to the deviation size, the specific calculation formula is as follows: ; In the formula, is the current tension force of the cable, is the adjusted tension force of the cable, k is the proportional coefficient, which can be determined by experiments or finite element analysis, P is the mechanical property evaluation coefficient, is the real-time monitored mechanical property data of the cable-membrane.

[0015] In a preferred embodiment, the process of selecting the ETFE hyperbolic negative Gaussian cable-membrane modeling structure is as follows: Perform fuzzy inference based on the material aging rate and the adjusted tension force to determine whether to select this model structure as the ETFE hyperbolic negative Gaussian cable-membrane modeling structure; Define the material aging rate and the adjusted tension force as input variables, and divide them into different fuzzy sets respectively; Define whether to select this model structure as the ETFE hyperbolic negative Gaussian cable-membrane modeling structure as the output variable, and divide it into a fuzzy set; Formulate fuzzy rules to describe the influence of the material aging rate and the adjusted tension force on whether to select this model structure as the ETFE hyperbolic negative Gaussian cable-membrane modeling structure; Perform fuzzy inference according to the fuzzy rules to determine whether to select this model structure as the ETFE hyperbolic negative Gaussian cable-membrane modeling structure.

[0016] In a preferred embodiment, it includes an acquisition module, an evaluation module, an adjustment module, and a selection module, and there are connections between the modules: The acquisition module is used to acquire the mechanical property data and historical data of the cable-membrane, and preprocess the data, and extract the features related to the cable-membrane structure modeling according to the mechanical property data, including elastic modulus, Poisson's ratio, and the initial tension of the cable; An evaluation module, which is used to utilize finite element analysis software, combine with the extracted features, construct a three-dimensional geometric model of the ETFE hyperbolic negative Gaussian cable-membrane, and calculate a mechanical property evaluation coefficient based on the three-dimensional geometric model of the ETFE hyperbolic negative Gaussian cable-membrane; An adjustment module, which is used to monitor the cable-membrane data in real time, compare and analyze the real-time monitored cable-membrane data with the pre-constructed three-dimensional geometric model. If the deviation between the real-time monitored cable-membrane data and the mechanical property evaluation coefficient exceeds a preset threshold, adjust the tension force of the cable according to the size of the deviation; A selection module, which is used to analyze the historical data and the real-time monitored cable-membrane data by using machine learning algorithms, establish a performance prediction model of the ETFE hyperbolic negative Gaussian cable-membrane structure, predict the material aging speed of the cable-membrane structure, select the ETFE hyperbolic negative Gaussian cable-membrane modeling structure in combination with the adjusted tension force, and carry out entity construction according to the structural model.

[0017] The technical effects and advantages of the digital construction method and system of the ETFE hyperbolic negative Gaussian cable-membrane based on big data of the present invention: 1. Through comprehensive data processing and feature extraction, and combined with the three-dimensional geometric model constructed by finite element analysis, the present invention can more accurately simulate the mechanical behavior of the cable-membrane structure under different working conditions, improve the accuracy and reliability of the model, and provide a solid foundation for subsequent construction and performance prediction. The real-time monitoring and dynamic adjustment mechanism makes the construction process more accurate, can correct deviations in time, ensure the construction quality of the cable-membrane structure, make the completed structure more in line with the design requirements, and improve the safety and stability of the structure. The application of machine learning algorithms realizes the accurate prediction of the material aging speed of the cable-membrane structure, changes the maintenance work from passive response to active prevention, and prolongs the service life of the cable-membrane structure. The modeling structure selected after comprehensively considering the construction parameter adjustment and material aging factors can achieve the balance of the cable-membrane structure in terms of safety, durability, economy, etc., optimize the construction plan, and improve the overall benefit of the project.

[0018] 2. The present invention is based on big data, running through the whole process of data collection, processing, analysis and application, enabling the construction process to be based on scientific data support, reducing the limitations and uncertainties of human experience judgment. By integrating a variety of advanced technologies such as finite element analysis, real-time monitoring technology, and machine learning algorithms, a complete digital construction system has been formed, representing the application of cutting-edge technologies in the field of building construction and promoting the technological upgrading of the industry. From the model construction before construction, to the control during the construction process, and then to the performance prediction and maintenance after completion, the full life cycle management of the ETFE double-curved negative Gaussian cable-membrane structure has been realized, improving the efficiency and level of project management. Through precise construction and optimized construction plans, construction errors and resource waste have been reduced, and construction costs have been lowered; accurate prediction of material aging and maintenance decisions have extended the service life of the structure and reduced the later maintenance costs, with good economic benefits. At the same time, the structural safety has been ensured, providing a more reliable space environment for users, with significant social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic structural diagram of the digital construction method and system of the ETFE double-curved negative Gaussian cable-membrane based on big data of the present invention.

[0020] Figure 2 It is a schematic structural diagram of the digital construction method and system of the ETFE double-curved negative Gaussian cable-membrane based on big data of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] Embodiment 1, Figure 1 A digital construction method of the ETFE double-curved negative Gaussian cable-membrane based on big data of the present invention is given.

[0023] Obtain the mechanical property data and historical data of the cable-membrane, and preprocess the data. Extract the features related to the cable-membrane structure modeling according to the mechanical property data, including elastic modulus, Poisson's ratio, and initial tension of the cable; Obtain the mechanical property data and historical data of the cable-membrane, and preprocess the data. Check whether there are missing values in the dataset. For a small number of missing values, the method of mean filling can be used; for samples with a large number of missing values, if they do not affect the integrity of the overall data, the sample can be considered for deletion; Identify outliers using the box plot method. For the data points identified by the box plot, if they are less than the lower quartile subtracted by 1.5 times the interquartile range IQR or greater than the upper quartile plus 1.5 times the IQR, they are considered outliers. For outliers, they can be deleted according to the actual situation; To eliminate the influence of the dimension of different data indicators, the Z-score standardization method is adopted.

[0024] Extract the features related to the cable-membrane structure modeling from the mechanical property data, including the material elastic modulus, Poisson's ratio, and the initial tension of the cable as follows: Directly extract the material elastic modulus, Poisson's ratio, and the initial tension of the cable from the preprocessed mechanical property data as the features related to the cable-membrane structure modeling. These features will be used as important input parameters for the subsequent cable-membrane structure modeling; The process of obtaining the material elastic modulus is as follows: Cut standard specimens from the ETFE membrane and cable materials, and obtain the cross-sectional area A and the original length of the specimens , install the specimens on a tensile testing machine, apply a tensile force at a constant rate, and record the tensile force F and the corresponding elongation ΔL at the same time; Divide the tensile force F by the cross-sectional area A of the specimen to obtain the stress, and divide the elongation ΔL by the original length to obtain the strain; According to Hooke's law, within the elastic range, the stress σ is proportional to the strain ϵ , and calculate the elastic modulus through the linear part of the stress-strain curve. The specific calculation formula is as follows: ; In the formula, E is the elastic modulus, F is the tensile force, A is the cross-sectional area of the specimen, is the original length of the specimen, and ΔL is the elongation; The process of obtaining Poisson's ratio is as follows: While measuring the elastic modulus during the tensile test, use an extensometer to measure the deformation of the specimen in the transverse and longitudinal directions, record the longitudinal elongation and the transverse contraction, and obtain the original transverse dimension of the specimen; According to the definition of Poisson's ratio, calculate the ratio of the transverse strain to the longitudinal strain. The specific calculation formula is as follows: ; In the formula, B is Poisson's ratio, is the original length of the specimen, ΔL is the elongation, is the transverse contraction, is the original transverse dimension of the specimen; The process of obtaining the initial tension of the cable is as follows: For the installed cable structure, the vibration frequency method is adopted, and a vibration sensor is used to measure the vibration frequency of the cable; Obtain the length and mass of the cable, and calculate the mass per unit length of the cable; According to the relationship between the vibration frequency and the tension of the cable, combined with the mass per unit length and the length of the cable, calculate the initial tension of the cable. The specific calculation formula is as follows: ; In the formula, T is the initial tension of the cable, L is the length of the cable, m is the mass of the cable, is the measured first-order vibration frequency; For newly produced cables, use a tensile testing machine to directly measure their initial tension before installation.

[0025] Using finite element analysis software, combined with the extracted features, construct a three-dimensional geometric model of the ETFE hyperbolic negative Gaussian cable-membrane, and calculate the mechanical property evaluation coefficient based on the three-dimensional geometric model of the ETFE hyperbolic negative Gaussian cable-membrane; Based on the design drawings and actual dimensions, use the geometric modeling tool of finite element analysis software to construct a three-dimensional geometric model of the ETFE hyperbolic negative Gaussian cable-membrane; Assign the previously extracted features related to the cable-membrane structure modeling, namely the material elastic modulus, Poisson's ratio, and the initial tension of the cable, to the corresponding cable and membrane elements; Perform mesh division on the geometric model, discretize it into a finite number of elements, and select the element type and mesh density according to the complexity of the model and the requirements of calculation accuracy; for the cable structure, use rod elements or cable elements; for the membrane structure, shell elements can be selected; Define the different working conditions that need to be considered for the cable-membrane structure, such as self-weight, wind load, and snow load. According to the requirements of different working conditions, apply the corresponding loads to the finite element model to obtain the three-dimensional geometric model of the ETFE hyperbolic negative Gaussian cable-membrane; The specific calculation formula for calculating the mechanical property evaluation coefficient based on the three-dimensional geometric model of the ETFE hyperbolic negative Gaussian cable-membrane is as follows: ; In the formula, P is the mechanical property evaluation coefficient, E is the elastic modulus, B is Poisson's ratio, T is the initial tension of the cable, are the weight coefficients of the elastic modulus, Poisson's ratio, and the initial tension of the cable, respectively, which are obtained by calculating and analyzing historical data.

[0026] Real-time monitor the cable-membrane data, and compare and analyze the real-time monitored cable-membrane data with the pre-constructed three-dimensional geometric model. If the deviation between the real-time monitored cable-membrane data and the mechanical property evaluation coefficient exceeds the preset threshold, adjust the tensioning force of the cable according to the deviation size; Monitor the mechanical property data of the cable-membrane structure in real time, compare and analyze the cable-membrane data with the mechanical property evaluation coefficient, and calculate the deviation value between the mechanical property data of the cable-membrane and the mechanical property evaluation coefficient; Compare and analyze the deviation value between the mechanical property data of the cable-membrane and the mechanical property evaluation coefficient with a preset threshold. If the deviation value between the mechanical property data of the cable-membrane and the mechanical property evaluation coefficient is less than the preset threshold, there is no need to adjust the tension force of the cable; if the deviation value between the mechanical property data of the cable-membrane and the mechanical property evaluation coefficient is greater than the preset threshold, it is necessary to adjust the tension force of the cable according to the size of the deviation. The specific calculation formula is as follows: ; In the formula, is the current tension force of the cable, is the adjusted tension force of the cable, k is the proportionality coefficient, which can be determined by experiments or finite element analysis, P is the mechanical property evaluation coefficient, is the mechanical property data of the cable-membrane structure monitored in real time.

[0027] Use machine learning algorithms to analyze historical data and real-time monitored cable-membrane data, establish a performance prediction model for the ETFE hyperbolic negative Gaussian cable-membrane structure, predict the material aging rate of the cable-membrane structure, select the ETFE hyperbolic negative Gaussian cable-membrane modeling structure in combination with the adjusted tension force, and carry out physical construction according to the structural model.

[0028] Preprocess the historical data and real-time monitored cable-membrane data, and select machine learning algorithms according to the data characteristics and prediction goals; Extract the features related to the material aging rate from the preprocessed data, such as stress level, temperature change, and cumulative amount of ultraviolet radiation; Divide the historical data and real-time monitored data into training sets and test sets, use the training sets to train the selected machine learning model, and adjust the parameters of the model so that the model can learn the patterns and rules in the data; during the training process, methods such as cross-validation can be used to evaluate the performance of the model to prevent overfitting; Input the current real-time monitored data into the trained model to predict the material aging rate of the ETFE hyperbolic negative Gaussian cable-membrane structure; Conduct fuzzy reasoning based on the material aging rate and the adjusted tension force to determine whether to select this model structure as the ETFE hyperbolic negative Gaussian cable-membrane modeling structure; The specific steps are as follows: Step C1, define the material aging rate and the adjusted tension force as input variables, and divide them into different fuzzy sets respectively.

[0029] For example, "Low", "Medium", "High" for the material aging rate, "Low", "Medium", "High" for the adjusted tension force; Step C2: Define whether to select this model structure as the ETFE hyperbolic negative Gaussian cable membrane modeling structure as the output variable, and divide it into fuzzy sets. For example, "No", "Yes" for whether to select this model structure as the ETFE hyperbolic negative Gaussian cable membrane modeling structure; Step C3: Develop a set of fuzzy rules to describe the influence of different input variables on the output variable. The definition of the rules can be based on professional knowledge or obtained through data analysis and experiments. For example: Mark the material aging rate as H, and mark the adjusted tension force as , and mark whether to select this model structure as the ETFE hyperbolic negative Gaussian cable membrane modeling structure as D. Then, it can be defined as Rule 1: IF (H is High) AND ( is Low) THEN (D is No ) Rule 2: IF (H is Low) AND ( is High) THEN (D is Yes ) ... Step C4: Perform fuzzy reasoning according to the fuzzy rules to determine whether to select this model structure as the ETFE hyperbolic negative Gaussian cable membrane modeling structure.

[0030] It should be noted that the division of the fuzzy sets can be adjusted according to the actual situation. For example, although three fuzzy sets are used as examples in this embodiment, in fact, the material aging rate, the adjusted tension force, and whether to select this model structure as the ETFE hyperbolic negative Gaussian cable membrane modeling structure can be divided into more than three sets to facilitate better precise adjustment.

[0031] Furthermore, for the judgment of whether to select this model structure as the ETFE hyperbolic negative Gaussian cable membrane modeling structure, a threshold can be set for judgment according to the actual situation. For example, when the material aging rate exceeds 0.8, it is calibrated as "High", and when the adjusted tension force is higher than 10, it is calibrated as "High", etc., which will not be elaborated here.

[0032] Embodiment 2 Figure 2 A digital construction system for ETFE hyperbolic negative Gaussian cable membranes based on big data is given.

[0033] The digital construction system of ETFE hyperbolic negative Gaussian cable membrane based on big data includes an acquisition module, an evaluation module, an adjustment module, and a selection module. There are connections between the modules: The acquisition module is used to obtain the mechanical property data and historical data of the cable membrane, preprocess the data, and extract the features related to the cable membrane structure modeling according to the mechanical property data, including elastic modulus, Poisson's ratio, and the initial tension of the cable; The evaluation module is used to use finite element analysis software, combine the extracted features, construct a three-dimensional geometric model of the ETFE hyperbolic negative Gaussian cable membrane, and calculate the mechanical property evaluation coefficient according to the three-dimensional geometric model of the ETFE hyperbolic negative Gaussian cable membrane; The adjustment module is used to monitor the cable membrane data in real time, compare and analyze the real-time monitored cable membrane data with the pre-constructed three-dimensional geometric model. If the deviation between the real-time monitored cable membrane data and the mechanical property evaluation coefficient exceeds the preset threshold, the tension force of the cable is adjusted according to the deviation size; The selection module is used to analyze the historical data and the real-time monitored cable membrane data by using machine learning algorithms, establish a performance prediction model of the ETFE hyperbolic negative Gaussian cable membrane structure, predict the material aging speed of the cable membrane structure, select the ETFE hyperbolic negative Gaussian cable membrane modeling structure in combination with the adjusted tension force, and perform physical construction according to the structural model.

[0034] The above formulas are all dimensionless and take their numerical values for calculation. The formula is a formula obtained by collecting a large amount of data for software simulation to get the closest real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0035] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0036] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0037] In addition, the functional modules in each embodiment of the present application can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0038] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the said claims.

[0039] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A digital construction method of ETFE hyperbolic negative Gaussian cable membrane based on big data, characterized in that: The method comprises the following steps: Obtain the mechanical properties data and historical data of the cable membrane, preprocess the data, and extract the features related to the cable-membrane structure modeling based on the mechanical properties data, including elastic modulus, Poisson's ratio, and initial tension of the cable; Finite element analysis software is used to construct a three-dimensional geometric model of the ETFE hyperbolic negative Gaussian cable membrane in combination with the extracted features, and the mechanical performance evaluation coefficient is calculated based on the three-dimensional geometric model of the ETFE hyperbolic negative Gaussian cable membrane; Real-time monitoring of cable-membrane data, and comparison and analysis of the real-time monitoring cable-membrane data with the pre-built three-dimensional geometric model. If the deviation between the real-time monitoring cable-membrane data and the mechanical performance evaluation coefficient exceeds a preset threshold, the tensioning force of the cable is adjusted according to the deviation. Machine learning algorithms are used to analyze historical data and real-time monitoring cable-membrane data, and a performance prediction model for ETFE hyperbolic negative Gaussian cable-membrane structures is established to predict the material aging rate of the cable-membrane structure. The ETFE hyperbolic negative Gaussian cable-membrane modeling structure is selected based on the adjusted tensioning force, and the physical construction is carried out according to the structural model.

2. The digital construction method of ETFE hyperbolic negative Gaussian cable membrane based on big data according to claim 1, characterized in that: The data preprocessing process is as follows: Check whether there are missing values ​​in the data. For a small number of missing values, use the mean filling method; For samples with a large number of missing values, delete the sample if it does not affect the integrity of the overall data; Use the box plot method to identify outliers. For data points identified by the box plot, if they are less than the lower quartile The values ​​less than 1.5 times the interquartile range (IQR) or greater than 1.5 times the IQR were considered outliers and deleted; the data were standardized using the Z-score standardization method.

3. The digital construction method of ETFE hyperbolic negative Gaussian cable membrane based on big data according to claim 2, characterized in that: The process of obtaining the elastic modulus of the material is as follows: Cut standard specimens from ETFE membrane and cable materials, and obtain the cross-sectional area A and original length of the specimens , install the sample on a tensile testing machine, apply tension at a constant rate, and record the tension F and the corresponding elongation ΔL; The stress is obtained by dividing the tensile force F by the cross-sectional area A of the specimen, and the elongation ΔL is divided by the original length. Divide them to get the strain; According to Hooke's law, within the elastic range, stress σ is related to strain The elastic modulus is directly proportional to the linear part of the stress-strain curve. The specific calculation formula is as follows: ； Where E is the elastic modulus, F is the tensile force, and A is the cross-sectional area of ​​the specimen. is the original length of the specimen and ΔL is the elongation.

4. The digital construction method of ETFE hyperbolic negative Gaussian cable membrane based on big data according to claim 3 is characterized in that: The process of obtaining Poisson's ratio is as follows: While conducting a tensile test to measure the elastic modulus, use an extensometer to measure the transverse and longitudinal deformation of the specimen, record the longitudinal elongation and transverse contraction, and obtain the original transverse dimension of the specimen; According to the definition of Poisson's ratio, the ratio of transverse strain to longitudinal strain is calculated. The specific calculation formula is as follows: ; Where B is Poisson's ratio, is the original length of the specimen, ΔL is the elongation, is the lateral shrinkage, is the original transverse dimension of the specimen.

5. The digital construction method of ETFE hyperbolic negative Gaussian cable membrane based on big data according to claim 4, characterized in that: The process of obtaining the initial tension of the cable is as follows: For installed cable structures, the vibration frequency method is used to measure the vibration frequency of the cable using a vibration sensor; Get the length and mass of the cable, and calculate the mass per unit length of the cable; According to the relationship between the vibration frequency and tension of the cable, the initial tension of the cable is calculated by combining the mass per unit length of the cable and the length of the cable. The specific calculation formula is as follows: ; Where T is the initial tension of the cable, L is the length of the cable, and m is the mass of the cable. is the measured first-order vibration frequency; For newly produced cables, the initial tension is measured directly using a tensile testing machine before installation.

6. The digital construction method of ETFE hyperbolic negative Gaussian cable membrane based on big data according to claim 5, characterized in that: The specific calculation formula of the mechanical performance evaluation coefficient calculated based on the three-dimensional geometric model of ETFE hyperbolic negative Gaussian cable membrane is as follows: ； In the formula, P is the mechanical property evaluation coefficient, E is the elastic modulus, B is the Poisson's ratio, T is the initial tension of the cable, They are the elastic modulus, Poisson's ratio, and weight coefficient of the initial tension of the cable, which are calculated and analyzed from historical data.

7. The digital construction method of ETFE hyperbolic negative Gaussian cable membrane based on big data according to claim 6, characterized in that: The process of adjusting the tension of the cable according to the deviation is as follows: Monitor the cable-membrane mechanical performance data in real time, compare and analyze the cable-membrane data with the mechanical performance evaluation coefficient, and calculate the deviation value between the cable-membrane mechanical performance data and the mechanical performance evaluation coefficient; Compare and analyze the deviation value between the cable-membrane mechanical performance data and the mechanical performance evaluation coefficient with the preset threshold value. If the deviation value between the cable-membrane mechanical performance data and the mechanical performance evaluation coefficient is less than the preset threshold value, there is no need to adjust the tension of the cable; If the deviation between the cable-membrane mechanical property data and the mechanical property evaluation coefficient is greater than the preset threshold, the tensioning force of the cable needs to be adjusted according to the deviation.

8. The digital construction method of ETFE hyperbolic negative Gaussian cable membrane based on big data according to claim 7, characterized in that: The tensioning force of the cable is adjusted according to the deviation. The specific calculation formula is as follows: ； In the formula, is the current tension of the cable, is the tension strength of the adjusted cable, k is the proportionality coefficient, which can be determined by experiment or finite element analysis, and P is the mechanical property evaluation coefficient. It is the real-time monitoring data of cable-membrane mechanical properties.

9. The digital construction method of ETFE hyperbolic negative Gaussian cable membrane based on big data according to claim 8, characterized in that: The process of selecting the ETFE hyperbolic negative Gaussian cable membrane modeling structure is as follows: Fuzzy reasoning is performed based on the material aging rate and the adjusted tensioning strength to determine whether to select the model structure as the ETFE hyperbolic negative Gaussian cable membrane modeling structure; The material aging rate and adjusted tension strength are defined as input variables, and they are divided into different fuzzy sets respectively; Whether to select the model structure as the ETFE hyperbolic negative Gaussian cable membrane modeling structure is defined as an output variable and divided into fuzzy sets; Formulate fuzzy rules to describe the influence of material aging rate and adjusted tension strength on whether to select the model structure as the ETFE hyperbolic negative Gaussian cable membrane modeling structure; Fuzzy reasoning is performed according to fuzzy rules to determine whether to select the model structure as the ETFE hyperbolic negative Gaussian cable membrane modeling structure.

10. The ETFE hyperbolic negative Gaussian cable membrane digital construction system based on big data is characterized by: It includes acquisition module, evaluation module, adjustment module and selection module. There are connections between the modules: The acquisition module is used to obtain the mechanical performance data and historical data of the cable membrane, and pre-process the data. According to the mechanical performance data, the features related to the cable-membrane structure modeling are extracted, including elastic modulus, Poisson's ratio, and initial tension of the cable; An evaluation module is used to construct a three-dimensional geometric model of the ETFE hyperbolic negative Gaussian cable membrane by using finite element analysis software and combining the extracted features, and to calculate a mechanical property evaluation coefficient based on the three-dimensional geometric model of the ETFE hyperbolic negative Gaussian cable membrane; The adjustment module is used to monitor the cable-membrane data in real time and compare and analyze the real-time monitored cable-membrane data with the pre-built three-dimensional geometric model. If the deviation between the real-time monitored cable-membrane data and the mechanical performance evaluation coefficient exceeds a preset threshold, the tensioning force of the cable is adjusted according to the deviation. A selection module is used to analyze historical data and real-time monitoring cable-membrane data using machine learning algorithms, establish a performance prediction model for ETFE hyperbolic negative Gaussian cable-membrane structures, predict the material aging rate of cable-membrane structures, select the ETFE hyperbolic negative Gaussian cable-membrane modeling structure based on the adjusted tensioning force, and perform physical construction according to the structural model.

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