Inverted frustum structure installation precision control method and installation method

Abstract

The invention provides a precision control method for installation of an inverted frustum structure and an installation method, and belongs to the technical field of structural construction.A design model is created based on the BIM technology, and the design model is stored in a preset collaborative management platform after a corresponding relation is established between the design model and construction data and management data; the digital model is used for recording all construction processes and management data to guide construction; error prediction is carried out on the current construction procedure through the error prediction model according to the obtained real-time installation process data, the error or deviation possibility of the current construction procedure is accurately predicted in time, the error problem in the construction process can be found in time, and if the error possibility exists, the error optimization scheme is obtained, so that the construction efficiency is improved. Performing parameter modification on the current construction process of the digital model to optimize the digital model, and continuing to guide the construction by using the optimized digital model to achieve the effect of timely controlling the precision of the current construction process; and the error is predicted in advance, so that large-scale reworking is not needed.

Description

Technical Field

[0001] The present invention relates to the technical field of structural construction, and more specifically, to a precision control method and an installation method for an inverted frustum structure. Background Art

[0002] During the installation of the aerial collection joint of the independent inverted cone structure, the errors of each installation process will gradually accumulate with the installation steps, and ultimately determine the quality of the aerial installation of the independent inverted cone structure. Therefore, accurate construction data measurement is required at a certain construction stage to ensure that the installation process can proceed as planned.

[0003] In the prior art, traditional measurement methods are generally used to perform installation measurements of inverted frustum structures. It is mainly through manual measurement and calculation, and then the installation accuracy is controlled according to the measurement results. Since this method relies on manual operation, it is prone to errors and has low efficiency. At present, there are also laser scanning and other equipment for measurement to adjust the installation accuracy, but it is expensive and complicated to operate, which is not conducive to large-scale application. However, no matter which of the above methods is used, the measurement is performed after the construction process is carried out. Once a large error is found, it is necessary to rework and readjust the construction data, and it is impossible to achieve real-time monitoring of accuracy.

[0004] In summary, the existing installation accuracy control method of the inverted cone structure cannot provide real-time feedback on errors during the installation process, which can easily lead to the accumulation of installation errors and affect the final installation quality; and after the errors are discovered, a large amount of rework is required, affecting the construction progress. Summary of the invention

[0005] In view of the above problems, the purpose of the present invention is to provide an installation precision control method and installation method for an inverted frustum structure, so as to solve the problems in the prior art that the installation precision control method of the inverted frustum structure cannot provide real-time feedback on errors during the installation process, which easily leads to the accumulation of installation errors and affects the final installation quality; and a large amount of rework is required after errors are discovered, affecting the construction progress.

[0006] The present invention provides a method for controlling the precision of installing an inverted frustum structure, comprising the following steps:

[0007] According to the design data of the inverted frustum structure, the design model of the inverted frustum structure is created using BIM technology;

[0008] A corresponding relationship is established between the design model and the construction data and management data, and the data is imported into a preset collaborative management platform to obtain a digital model of the inverted frustum structure; wherein the preset collaborative management platform is provided with a real-time data interface, an error prediction model connected to the real-time data interface, and an error optimization solution library storing error optimization solutions; the construction data includes installation procedures and installation process data corresponding to the installation procedures;

[0009] Instructing the installation and construction of the inverted frustum structure based on the digital model, and acquiring real-time installation process data of the current installation process in real time through the real-time data interface;

[0010] Inputting the real-time installation process data into the error prediction model, and performing error prediction on the current installation process through the error prediction model;

[0011] When the result of error prediction for the current installation process is that there is a prediction error, obtaining an error optimization solution that matches the prediction error value of the current installation process from the error optimization solution library;

[0012] According to the error optimization scheme, the installation process data of the current installation process is optimized on the digital model to control the accuracy of the current installation process until the installation of the inverted frustum structure is completed.

[0013] The present invention provides an inverted frustum structure installation method, characterized in that during the installation of the inverted frustum structure, the accuracy of the inverted frustum structure is controlled by using the accuracy control method as described above.

[0014] From the above technical scheme, it can be seen that the precision control method and installation method of the inverted cone structure installation provided by the present invention, by creating a design model based on BIM technology, and establishing a corresponding relationship between the design model and the construction data and management data and storing it in a preset collaborative management platform, so as to obtain a digital model that records all the construction processes and management data to guide the construction; the real-time installation process data of the current construction process is obtained in real time through the real-time data interface on the preset collaborative management platform, and then the error prediction model predicts the error of the current construction process according to the real-time installation process data, so as to timely and accurately predict the possibility of error or deviation in the current construction process, and timely discover the error problem in the construction process. Once the possibility of error is found, the current construction process on the digital model is modified by obtaining a preset error optimization scheme to optimize the digital model, and then the optimized digital model is used to continue to guide the construction, so as to achieve the effect of timely controlling the accuracy of the current construction process; and because the error is predicted in advance, there is no need for large-scale rework. Compared with the existing precision control method, the present invention realizes the precise installation of the independent inverted cone structure aerial collection joint by combining BIM technology and machine learning algorithm, which not only improves the accuracy and efficiency of installation, but also reduces the cost of error measurement, which is conducive to large-scale application.

[0015] In order to achieve the above and related purposes, one or more aspects of the present invention include the features that will be described in detail later. The following description and the accompanying drawings describe some exemplary aspects of the present invention in detail. However, these aspects indicate only some of the various ways in which the principles of the present invention can be used. In addition, the present invention is intended to include all these aspects and their equivalents. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] By referring to the following description in conjunction with the accompanying drawings, and with a more comprehensive understanding of the present invention, other objects and results of the present invention will become more clear and easy to understand. In the accompanying drawings:

[0017] Figure 1 The figure is a flow chart of a method for precision control of installation of an inverted frustum structure according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more embodiments. However, it is apparent that these embodiments may also be implemented without these specific details.

[0019] In view of the problems of the installation precision control method of the inverted frustum structure in the above-mentioned prior art, which cannot provide real-time feedback on errors during the installation process, and the untimely feedback may easily lead to the accumulation of installation errors, thus affecting the final installation quality; and the need to rework after errors are discovered, thus affecting the construction progress, a precision control method and installation method for the installation of an inverted frustum structure are proposed.

[0020] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] In order to illustrate the precision control method and installation method of the inverted frustum structure provided by the present invention, Figure 1 The flowchart of the precision control method for installing an inverted frustum structure according to an embodiment of the present invention is shown.

[0022] like Figure 1 As shown, the precision control method for installing the inverted frustum structure provided by the present invention comprises the following steps:

[0023] Step S1: Create a design model of the inverted frustum structure using BIM technology according to the design data of the inverted frustum structure.

[0024] Specifically, a design model of the inverted frustum structure can be created based on the design data of the inverted frustum structure using tools such as Revit software, wherein the design data includes but is not limited to size, shape, and material.

[0025] As a preferred solution of the present invention, based on the design data of the inverted frustum structure, a design model of the inverted frustum structure is created using BIM technology, including:

[0026] According to the design data of the inverted frustum structure, the size, shape and material of each component of the inverted frustum structure are determined; wherein the design data includes the name of the component of each part of the inverted frustum structure and the size, shape and material corresponding to the name of the component;

[0027] Based on the size, shape and material of each component of the inverted frustum structure, the BIM technology is used to create the component models of the inverted frustum structure respectively;

[0028] The component models of the inverted frustum structure are assembled to obtain a design model of the inverted frustum structure; wherein the name, size and material of each component are marked on the design model.

[0029] Specifically, the component models of the inverted frustum structure are respectively constructed by using tools such as Revit software, and the component models are assembled and spliced ​​to obtain the design model of the frustum structure.

[0030] Step S2, establish a corresponding relationship between the design model and the construction data and management data, and import them into a preset collaborative management platform to obtain a digital model of the inverted cone structure; wherein the preset collaborative management platform creates a real-time data interface, an error prediction model connected to the real-time data interface, and an error optimization solution library storing error optimization solutions; the construction data includes installation procedures and installation process data corresponding to the installation procedures.

[0031] Specifically, the design model is imported into the preset collaborative management platform, such as ProjectWise, after establishing a corresponding relationship with the construction data and management data. The digital modeling of the inverted frustum structure is completed, that is, the digital model of the inverted frustum structure is obtained. The digital model provides guidance for subsequent construction and data support for the precision control of installation and construction.

[0032] The construction data includes the installation process and the installation process data corresponding to the installation process. The installation process data includes but is not limited to the following:

[0033] Installation progress data: record the installation start time, estimated completion time, actual completion time, etc. of each part. For example, the installation of steel structure A starts on May 1, 2024, is expected to be completed on May 10, and is actually completed on May 8.

[0034] Installation condition data: including environmental conditions at the construction site, such as wind speed, temperature, humidity, etc. These data have an important impact on installation accuracy and safety. For example, during a certain installation, the wind speed was level 3 northeast wind, the temperature was 25℃, and the humidity was 60%.

[0035] Measurement data: precise measurements of spatial position, angle, size, etc. collected in real time during the installation process. For example, the actual position of steel structure B after installation is basically consistent with the theoretical position.

[0036] Installation step record data: Detailed record of each step of the installation operation, including the methods, tools, personnel, etc. For example, the bottom support structure is installed first, and then the main structure is hoisted by a crane, and then accurately aligned and fixed.

[0037] Error record data: The error value measured after each installation, such as horizontality, verticality deviation, etc. For example, after installing steel structure C, the verticality deviation is measured to be ±2mm, which is within the allowable range.

[0038] During the construction process, real-time construction data (such as installation conditions, measurement data, error records, etc.) are input into the error prediction model to predict errors and deviations during the installation process and optimize the installation plan based on the prediction results. The real-time data interface receives real-time construction data in real time during the actual construction and interacts with the error prediction model. It can be used to display the installation progress, predict errors, and recommend adjustment plans in the future, so as to guide on-site personnel to adjust the installation strategy in a timely manner.

[0039] Management data mainly involves various information in the process of project management and coordination, including but not limited to the following:

[0040] Project planning data: overall installation construction schedule, milestones, critical paths, etc. For example, the entire installation construction plan is completed within 6 months and is divided into four stages: design, procurement, prefabrication, and installation, each with a specific start and end time.

[0041] Resource allocation data: the allocation of human resources, material resources, and equipment resources. For example, during the installation phase, 50 workers are needed, including 10 senior welders and 40 ordinary workers; 5 cranes and 20 sets of welding equipment are needed.

[0042] Quality control record data: regular inspection and evaluation records of installation quality, including inspection time, inspectors, inspection results, etc. For example, a quality inspection is conducted once a week and slight scratches are found on the surface of the steel structure, and on-site repairs have been required.

[0043] Safety management record data: safety management situation at the construction site, including safety accidents, safety hazards, safety training, etc. For example, two safety training sessions were conducted this month, and a safety hazard of exposed wires was found and rectified.

[0044] Communication and coordination records: records of the process and results of communication and coordination with the design unit, supervision unit, supplier, etc. For example, after communicating with the design unit, it was decided to optimize the design of steel structure C to reduce the difficulty of installation.

[0045] When building a digital model of an inverted frustum structure, management data such as project planning and resource allocation can be integrated into the ProjectWise platform to achieve information management and collaborative design throughout the entire life cycle. Management data plays a management and coordination role in the installation and construction process and can be used throughout the entire installation process. For example, by viewing project planning data and resource allocation data, the installation schedule and resource allocation can be adjusted in a timely manner; by reviewing quality control and safety management records, it can be ensured that the installation process meets specifications and safety requirements.

[0046] As a preferred solution of the present invention, a corresponding relationship is established between the design model, the construction data and the management data, and the data is imported into a preset collaborative management platform to obtain a digital model of the inverted frustum structure, including:

[0047] On the design model, the corresponding installation processes are divided according to the construction data, and the installation process data is recorded corresponding to the installation process and the corresponding management data is recorded based on the design model, so that the design model has the construction data and the management data recorded;

[0048] The design model that records the construction data and management data is imported into the preset collaborative management platform to obtain the digital model of the inverted frustum structure.

[0049] Specifically, the design model can reflect the installation process and the installation process data corresponding to the installation process, and record the corresponding management data, so that the digital model can realize information management and collaborative design of the entire life cycle of installation construction, and provide guidance for actual construction.

[0050] As a preferred embodiment of the present invention, the training method of the error prediction model includes:

[0051] Collecting historical data of the installation of the inverted frustum structure; wherein the historical data includes historical installation process data corresponding to different installation procedures and historical error result data corresponding to the historical installation process data based on the same installation procedure;

[0052] Extracting feature data related to the prediction error from the historical installation process data, thereby obtaining a training data set including the feature data and historical error result data corresponding to the feature data;

[0053] Divide the training data set into training set, validation set and test set;

[0054] Taking the feature data in the training set as input and the historical error result data corresponding to the feature data as output, the preset basic model is trained for error prediction to obtain a primary prediction model;

[0055] Taking the feature data in the validation set as input and the historical error result data corresponding to the feature data as output, the parameters of the primary prediction model are adjusted to obtain a validation prediction model;

[0056] Based on the same installation process, the feature data in the test set is input into the verification prediction model, and the error prediction accuracy of the verification prediction model is calculated according to the historical error result data output by the verification prediction model and the historical error result data corresponding to the feature data in the test set;

[0057] When the error prediction accuracy is greater than or equal to the preset accuracy threshold, the verification prediction model is used as the error prediction model.

[0058] Specifically, the historical data of the installation of the inverted cone structure is trained and learned through a machine learning algorithm to identify the rules and characteristics of the installation process of the aerial collection joint of the inverted cone structure, and then predict possible errors and deviations. During the construction process, these prediction results are used to optimize and adjust the installation plan to ensure the smooth progress of the construction process. For example, a machine learning algorithm is written using the TensorFlow framework through Python (online programming). The main function of this algorithm is to identify the rules and characteristics of the installation process of the aerial collection joint of the inverted cone structure through training and learning of historical data, and then predict possible errors and deviations. In practical applications, a large amount of historical data is first input into the algorithm to allow the algorithm to learn and train. Then, the real-time construction data is input into the algorithm, and the algorithm will output the predicted results, and the installation plan can be optimized and adjusted based on these results.

[0059] One of the preferred solutions is to use TensorFlow's high-level API (such as Keras) to simplify the model building and training process.

[0060] Collect data: Collect historical data on the installation of the inverted cone structure, including installation conditions (such as wind speed, temperature, humidity, installer experience, etc.), installation steps, measurement data during the installation process (such as angle, position, size, etc.), and final error and deviation records.

[0061] Feature extraction: Extract features from historical data that are useful for predicting errors and deviations. These features may include physical parameters of the installation environment, specific steps and parameters of the installation process, etc.

[0062] Data partitioning: Divide the data into training set, validation set and test set. Typically, 70% of the data can be used as a training set, 15% as a validation set, and the remaining 15% as a test set.

[0063] Data Standardization / Normalization: Standardize or normalize features to speed up the training process and improve model performance.

[0064] Choose the model type: Since this is a regression problem (predicting numerical errors and biases), you can choose models such as linear regression, decision tree regression, random forest regression, or neural networks (such as fully connected neural networks, convolutional neural networks if the features are suitable). Considering the complexity, neural networks are preferred.

[0065] For example, using TensorFlow / Keras to build a neural network model, i.e., a basic model, you can use several fully connected layers (Dense layers) to build a simple neural network model. The basic model is trained, verified, and tested using the above data. Finally, when the error prediction accuracy is greater than or equal to the preset accuracy threshold, the verification prediction model is used as the error prediction model.

[0066] As a preferred solution of the present invention, collecting historical data of the installation of the inverted frustum structure includes:

[0067] Collect primary historical data of inverted cone structure installation;

[0068] The primary historical data is cleaned to remove the data that does not meet the preset standards in the primary historical data to obtain historical data.

[0069] Specifically, since the collected primary historical data may contain defective data, such as missing values, abnormal values, and inconsistent data, it is necessary to clean the primary historical data to obtain usable historical data.

[0070] Step S3: guiding the installation construction of the inverted frustum structure based on the digital model, and obtaining the real-time installation process data of the current installation process in real time through the real-time data interface.

[0071] Specifically, the digital model is used to guide the installation and construction of the inverted cone structure. During the actual construction process, the real-time installation process data of the current installation process is obtained in real time through the real-time data interface.

[0072] As a preferred solution of the present invention, the installation construction of the inverted frustum structure is guided based on the digital model, and the real-time installation process data of the current installation process is obtained in real time through the real-time data interface, including:

[0073] Determine the current installation process based on the digital model;

[0074] Through the real-time data interface, real-time installation process data is obtained from the data measurement parts installed in the actual construction corresponding to the current installation process.

[0075] Specifically, in actual construction, measuring parts are provided on the components, for example, the measurement of installation conditions (wind speed, temperature, etc.) and the measurement of the constructed installation structure (such as position, angle, size, etc.). According to the digital model, the current installation process is determined, and then the real-time installation process data is obtained from the above-mentioned data measuring parts in real time through the real-time data interface.

[0076] Step S4: input the real-time installation process data into the error prediction model, and use the error prediction model to perform error prediction on the current installation process.

[0077] Specifically, the real-time installation process data is input into the error prediction model, and the error prediction model performs error analysis on the real-time installation process data to obtain an error prediction result.

[0078] As a preferred solution of the present invention, after inputting the real-time installation process data into the error prediction model and performing error prediction on the current installation process through the error prediction model, the method further includes:

[0079] When the result of error prediction for the current installation process is that there is no prediction error, the current installation process is continued according to the digital model.

[0080] Specifically, when the result of error prediction for the current installation process is that there is no prediction error, it means that the process is carried out according to plan, there is no error or deviation risk, and the current installation process can continue according to the digital model.

[0081] Step S5: When the result of error prediction for the current installation process is that a prediction error exists, an error optimization solution matching the prediction error value of the current installation process is obtained from the error optimization solution library.

[0082] Specifically, when the result of the error prediction for the current installation process shows that there is a prediction error, it means that there is a risk of error or deviation in the current construction process, so the digital model needs to be optimized and adjusted to avoid subsequent error accumulation. Based on these prediction results, on-site construction personnel can adjust and optimize the specific construction parts with problems, such as adjusting installation parameters, changing installation steps, or taking other corrective measures to ensure the smooth progress of the subsequent construction process and the final installation quality. This adjustment is usually local and targeted, aiming to quickly respond to prediction results, reduce error accumulation, and improve installation accuracy and efficiency.

[0083] As a preferred solution of the present invention, when the result of error prediction for the current installation process is that there is a prediction error, an error optimization solution matching the prediction error value of the current installation process is obtained from the error optimization solution library, including:

[0084] When the result of error prediction for the current installation process is that there is a prediction error, the prediction error value is compared with the preset error threshold based on the current process;

[0085] When the prediction error value is less than or equal to the preset error threshold, an automatic optimization adjustment plan is obtained from the error optimization plan library; when the prediction error value is greater than the preset error threshold, an auxiliary selection adjustment plan is obtained from the error optimization plan library;

[0086] Among them, the automatic optimization and adjustment scheme is to automatically modify the installation process data of the current installation process of the digital model into real-time installation process data;

[0087] The auxiliary selection of the adjustment plan is to generate a forecast report and a recommended adjustment plan according to the forecast error value, and display them on the display page of the preset collaborative management platform;

[0088] According to the selection result of the recommended adjustment plan, the installation process data of the current installation process of the digital model is modified.

[0089] Specifically, when the error prediction model predicts an error or deviation, the predicted error value is compared with a preset error threshold, and the preset error threshold or rule is used to judge the severity and urgency of the prediction result. If the predicted error value is small and belongs to the range that can be automatically adjusted, that is, when the predicted error value is less than or equal to the preset error threshold, the model parameters are directly modified according to the built-in algorithm or rule to correct the predicted error. For example, if a deviation is predicted in the position of a component, the position parameters of the component in the digital model may be automatically adjusted. For more complex errors or deviations, it may not be possible to make automatic adjustments directly, but information to assist in decision-making can be provided. That is, when the predicted error value is greater than the preset error threshold, an auxiliary selection adjustment scheme is obtained from the error optimization scheme library, and a prediction report can be generated based on the auxiliary selection adjustment scheme, the error can be visualized, and an adjustment scheme can be recommended. This information can also be clearly presented to the user through the user interface (UI) to help the user quickly understand the problem and make reasonable adjustment decisions. Based on the prediction results and the user's adjustment decision, a new construction guidance document can be further generated or an existing construction plan can be updated. These construction guidance documents will contain revised installation steps, precautions, new model parameters and other information to ensure that the subsequent installation process can be carried out according to the revised plan.

[0090] Step S6: According to the error optimization scheme, the installation process data of the current installation process is optimized on the digital model to control the accuracy of the current installation process until the installation of the inverted frustum structure is completed.

[0091] Specifically, according to the error optimization scheme, the installation process data of the current installation process is optimized on the digital model, and then the optimized digital model is used as a construction guide until the installation of the inverted frustum structure is completed.

[0092] As a preferred solution of the present invention, after optimizing the installation process data of the current installation process on the digital model according to the error optimization solution to control the accuracy of the current installation process until the installation of the inverted frustum structure is completed, it also includes:

[0093] The optimized data of the installation process data of the installation process and the error data corresponding to the optimized data are used as the performance optimized data of the error prediction model.

[0094] Specifically, the new real-time data and the adjusted results, i.e., the optimized data of the installation process data of the installation process and the error data corresponding to the optimized data, can be used to further train and optimize the error prediction model to improve its prediction accuracy and generalization ability. In this way, as the construction process progresses and data continues to accumulate, the error prediction model can continue to learn and improve its performance, providing more reliable support for the precise control of the installation process.

[0095] The inverted frustum structure installation method provided by the present invention uses the precision control method provided by the present invention to control the precision of the inverted frustum structure during the installation process of the inverted frustum structure.

[0096] It can be seen from the above specific implementation methods that the precision control method and suspension splicing method for the installation of the inverted cone structure provided by the present invention, by creating a design model based on BIM technology, and establishing a corresponding relationship between the design model and the construction data and management data and storing it in a preset collaborative management platform, so as to obtain a digital model that records the entire construction process and management data for guiding construction; the real-time installation process data of the current construction process is obtained in real time through the real-time data interface on the preset collaborative management platform, and then the error prediction model predicts the error of the current construction process according to the real-time installation process data, so as to timely and accurately predict the possibility of error or deviation in the current construction process, and timely discover the error problem in the construction process. Once the possibility of error is found, the current construction process on the digital model is modified by obtaining a preset error optimization scheme to optimize the digital model, and then the optimized digital model is used to continue to guide the construction, so as to achieve the effect of timely controlling the accuracy of the current construction process; and because the error is predicted in advance, there is no need for large-scale rework. Compared with the existing precision control method, the present invention realizes the precise installation of the aerial collection joint of the independent inverted cone structure by combining BIM technology and machine learning algorithm, which not only improves the accuracy and efficiency of installation, but also reduces the cost of error measurement, which is conducive to large-scale application.

[0097] As described above, the precision control method for installing the inverted frustum structure and the suspended splicing method proposed in the present invention are described by way of example with reference to the accompanying drawings. However, those skilled in the art should understand that various improvements can be made to the precision control method for installing the inverted frustum structure and the suspended splicing method proposed in the present invention without departing from the content of the present invention. Therefore, the protection scope of the present invention should be determined by the content of the attached claims.

Claims

1. A method for controlling the precision of installing an inverted frustum structure, characterized in that: The steps include: According to the design data of the inverted frustum structure, the design model of the inverted frustum structure is created using BIM technology; A corresponding relationship is established between the design model and the construction data and management data, and the data is imported into a preset collaborative management platform to obtain a digital model of the inverted frustum structure; wherein the preset collaborative management platform is provided with a real-time data interface, an error prediction model connected to the real-time data interface, and an error optimization solution library storing error optimization solutions; the construction data includes installation procedures and installation process data corresponding to the installation procedures; Instructing the installation and construction of the inverted frustum structure based on the digital model, and acquiring real-time installation process data of the current installation process in real time through the real-time data interface; Inputting the real-time installation process data into the error prediction model, and performing error prediction on the current installation process through the error prediction model; When the result of error prediction for the current installation process is that a prediction error exists, obtaining an error optimization solution matching the prediction error value of the current installation process from the error optimization solution library; According to the error optimization scheme, the installation process data of the current installation process is optimized on the digital model to control the accuracy of the current installation process until the installation of the inverted frustum structure is completed.

2. The method for controlling the precision of the installation of the inverted frustum structure according to claim 1, characterized in that: The method of creating a design model of the inverted frustum structure using BIM technology based on the design data of the inverted frustum structure includes: Determine the size, shape and material of each component of the inverted frustum structure according to the design data of the inverted frustum structure; wherein the design data includes the name of the component of each part of the inverted frustum structure and the size, shape and material corresponding to the name of the component; Based on the size, shape and material of each component of the inverted frustum structure, using BIM technology, respectively create a model of each component of the inverted frustum structure; The component models of the inverted frustum structure are assembled to obtain a design model of the inverted frustum structure; wherein the name, size and material of each component are marked on the design model.

3. The precision control method for installing an inverted frustum structure according to claim 1, characterized in that: The design model is established in correspondence with the construction data and the management data, and is imported into a preset collaborative management platform to obtain a digital model of the inverted frustum structure, including: On the design model, the corresponding installation processes are divided according to the construction data, and installation process data corresponding to the installation process and corresponding management data based on the design model are recorded, so that the design model has construction data and management data recorded; The design model that records the construction data and management data is imported into the preset collaborative management platform to obtain the digital model of the inverted frustum structure.

4. The method for controlling the precision of the installation of the inverted frustum structure according to claim 1, characterized in that: The training method of the error prediction model includes: Collecting historical data of the installation of the inverted frustum structure; wherein the historical data includes historical installation process data corresponding to different installation procedures and historical error result data corresponding to the historical installation process data based on the same installation procedure; Extracting feature data related to the prediction error from the historical installation process data, thereby obtaining a training data set including the feature data and historical error result data corresponding to the feature data; Dividing the training data set into a training set, a validation set and a test set; Taking the feature data in the training set as input and the historical error result data corresponding to the feature data as output, performing error prediction training on the preset basic model to obtain a prediction primary model; Taking the feature data in the validation set as input and the historical error result data corresponding to the feature data as output, adjusting the parameters of the primary prediction model to obtain a validation prediction model; Based on the same installation process, the feature data in the test set is input into the verification prediction model, and the error prediction accuracy of the verification prediction model is calculated according to the historical error result data output by the verification prediction model and the historical error result data in the test set corresponding to the feature data; When the error prediction accuracy is greater than or equal to a preset accuracy threshold, the verification prediction model is used as the error prediction model.

5. The method for controlling the installation precision of an inverted frustum structure according to claim 4, characterized in that: The historical data collected for the installation of the inverted frustum structure includes: Collect primary historical data of inverted cone structure installation; The primary historical data is cleaned to remove data that does not meet preset standards in the primary historical data to obtain historical data.

6. The method for controlling the precision of installing an inverted frustum structure according to claim 1, characterized in that: The method of guiding the installation construction of the inverted frustum structure based on the digital model and obtaining the real-time installation process data of the current installation process in real time through the real-time data interface includes: Determining a current installation process according to the digital model; Through the real-time data interface, real-time installation process data is obtained from the data measurement components installed in the actual construction corresponding to the current installation process.

7. The method for controlling the precision of installing an inverted frustum structure according to claim 1, characterized in that: After inputting the real-time installation process data into the error prediction model and performing error prediction on the current installation process through the error prediction model, the method further includes: When the result of error prediction for the current installation process is that there is no prediction error, the current installation process is continued according to the digital model.

8. The method for controlling the precision of installing an inverted frustum structure according to claim 1, characterized in that: When the result of error prediction for the current installation process is that a prediction error exists, obtaining an error optimization solution matching the prediction error value of the current installation process from the error optimization solution library includes: When the result of error prediction for the current installation process is that a prediction error exists, comparing the prediction error value with a preset error threshold based on the current process; When the prediction error value is less than or equal to the preset error threshold, an automatic optimization adjustment scheme is obtained from the error optimization scheme library; when the prediction error value is greater than the preset error threshold, an auxiliary selection adjustment scheme is obtained from the error optimization scheme library; Wherein, the automatic optimization and adjustment scheme is to automatically modify the installation process data of the current installation process of the digital model into real-time installation process data; The auxiliary selection of the adjustment plan is to generate a forecast report and a recommended adjustment plan according to the forecast error value, and display them on a display page of the preset collaborative management platform; According to the selection result of the recommended adjustment scheme, the installation process data of the current installation process of the digital model is modified.

9. The method for controlling the precision of installing an inverted frustum structure according to claim 1, characterized in that: After optimizing the installation process data of the current installation process on the digital model according to the error optimization scheme to control the accuracy of the current installation process until the installation of the inverted frustum structure is completed, the method further includes: The optimized data of the installation process data of the installation process and the error data corresponding to the optimized data are used as the performance optimized data of the error prediction model.

10. A method for installing an inverted frustum structure, characterized in that: During the installation of the inverted frustum structure, the accuracy of the inverted frustum structure is controlled by using the accuracy control method described in any one of claims 1 to 9.

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