Municipal intelligent construction integrated collaborative design method

Abstract

The invention discloses a municipal intelligent construction integrated collaborative design method, and relates to the technical field of municipal intelligent construction, and the method comprises the following steps: obtaining initial geological survey data of a municipal project, and monitoring the dynamic change of geological data in a construction process in real time through sensor equipment; through bidirectional dynamic linkage of the BIM model and geological data and the geological change prediction model, a municipal project can master geological changes in real time, design and construction schemes are automatically adjusted, it is ensured that the design is matched with the actual geological condition, and hidden dangers such as subgrade settlement and pipeline dislocation are effectively prevented. In combination with machine learning, an optimization algorithm and an intelligent decision-making system, a construction party can generate adjustment suggestions in real time, the construction sequence, material selection and equipment use strategies are flexibly optimized, the construction efficiency is improved, and the cost is reduced. Multi-dimensional evaluation ensures that the construction decision is scientific and reasonable, risks caused by geological changes are reduced, and an efficient solution is provided for digital and intelligent transformation of municipal engineering.

Description

Technical Field

[0001] The present invention relates to the technical field of municipal intelligent construction, and in particular to an integrated collaborative design method for municipal intelligent construction. Background Art

[0002] "Integrated collaborative design of municipal intelligent construction" refers to the integration of various aspects of municipal engineering planning, design, construction, management, etc. through intelligent means to achieve efficient collaboration among different professions, departments and participants. It takes digital technology as the core, integrates BIM (Building Information Model), GIS (Geographic Information System), Internet of Things, artificial intelligence and other technologies, runs through the project life cycle, breaks the traditional single design model, and improves design accuracy and construction efficiency through data sharing, real-time collaboration and automated decision-making, reduces costs and risks, and thus realizes the digitalization, intelligence and greening of the entire process of municipal infrastructure construction.

[0003] The prior art has the following deficiencies: In the process of integrated collaborative design of municipal intelligent construction, insufficient real-time dynamic linkage between geological data and BIM models may lead to serious consequences. Geological survey data are usually collected in the early stage and statically entered into the BIM system, while geological conditions during the construction stage often change, such as groundwater level fluctuations and reduced soil bearing capacity. If there is a lack of dynamic data updates and linkage mechanisms, the design plan will not be able to be adjusted in time, resulting in a mismatch between the basic structure design and the actual geological conditions. In subsequent construction and operation, this deviation may lead to major hidden dangers such as roadbed settlement, dislocation or rupture of underground pipelines, and even tilting of buildings. Since these problems are usually difficult to detect in the early stages, they often require high repair costs and have long-term effects on the safety, stability and service life of municipal facilities.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The purpose of the present invention is to provide an integrated collaborative design method for municipal intelligent construction. Through the two-way dynamic linkage between the BIM model and geological data and the geological change prediction model, municipal projects can grasp geological changes in real time, automatically adjust the design and construction plan, ensure that the design matches the actual geological conditions, and effectively prevent hidden dangers such as roadbed settlement and pipeline dislocation. Combined with machine learning, optimization algorithms and intelligent decision-making systems, the construction party can generate adjustment suggestions in real time, flexibly optimize the construction sequence, material selection and equipment use strategies, improve construction efficiency and reduce costs. Multi-dimensional evaluation ensures that construction decisions are scientific and reasonable, reduces the risks caused by geological changes, and provides an efficient solution for the digital and intelligent transformation of municipal engineering to solve the problems in the above-mentioned background technology.

[0006] In order to achieve the above object, the present invention provides the following technical solution: a method for integrated collaborative design of municipal intelligent construction, comprising the following steps: Obtain initial geological survey data for municipal projects and use sensor equipment to monitor the dynamic changes of geological data during construction in real time; Input the initial geological survey data and real-time monitoring data into the building information model, and use the data interface to achieve two-way dynamic linkage between the building information model and geological information to ensure real-time update of geological data; Construct a geological change prediction model during the construction phase in the building information model. Through machine learning algorithms, the trend of geological condition changes is predicted based on historical geological data and real-time monitoring data, forming an early prediction of geological changes. Generate construction risk warning data based on the output of the geological change prediction model, and dynamically adjust the infrastructure design parameters in the building information model through deep learning algorithms to ensure that the design plan adapts to geological changes; Through the optimization algorithm, the construction plan is adjusted in real time, and the construction sequence, material selection and mechanical equipment use strategy are dynamically optimized according to the real-time geological changes and construction progress to improve construction efficiency and safety; Apply intelligent decision-making algorithms to conduct multi-dimensional evaluation of construction adjustment plans, generate final construction adjustment suggestions, and synchronize them to the construction management system to guide actual construction.

[0007] Preferably, the specific steps of obtaining the initial geological survey data of the municipal project and monitoring the dynamic changes of the geological data in real time during the construction process through sensor equipment are as follows: Formulate geological survey plans to ensure that the collected data is comprehensive and accurate, laying the foundation for subsequent analysis; Deploy sensor equipment according to the plan to ensure accurate and reliable data collection at key geological feature points; Classify, clean and standardize the collected geological data for upload to ensure data accuracy and facilitate subsequent model application; The Internet of Things can be used to achieve dynamic monitoring and early warning of geological data, and to identify abnormal changes in a timely manner to reduce construction risks.

[0008] Preferably, the initial geological survey data and the real-time monitoring data are input into the building information model, and the two-way dynamic linkage between the building information model and the geological information is realized by means of the data interface to ensure the real-time update of the geological data. The specific steps are as follows: Develop data interfaces to achieve conversion, verification and filtering of geological data formats, ensuring that initial data and real-time monitoring data are accurately received by the building information model; Map geological data to corresponding positions and parameters in the building information model according to spatial coordinates to ensure that geological change information is intuitively presented in the design model; Through a two-way dynamic linkage mechanism, geological data is updated in real time and the design parameters of the building information model are automatically adjusted to cope with changes in geological conditions; Establish a geological data anomaly early warning system, combine building information models and historical data, automatically identify risks and provide construction adjustment suggestions, and improve the safety and intelligence level of construction.

[0009] Preferably, a geological change prediction model for the construction phase is constructed in the building information model, and the specific steps of predicting the geological condition change trend based on historical geological data and real-time monitoring data through machine learning algorithms to form an early prediction of geological changes are as follows: Integrate initial geological data and real-time monitoring data, clean outliers and build high-quality data sets to provide a reliable training basis for geological change prediction models; Select machine learning algorithms based on geological data characteristics, optimize model performance through hyperparameter tuning and validation sets, and improve the accuracy and generalization ability of geological change prediction; The trained geological change prediction model is embedded in the building information model and integrated with the two-way dynamic linkage mechanism of geological data to achieve real-time prediction and dynamic design adjustment; By analyzing the prediction results, the building information model automatically triggers risk warnings and generates construction adjustment suggestions to guide the construction party to respond to geological changes in a timely manner and reduce construction risks.

[0010] Preferably, the specific steps of generating construction risk warning data based on the output results of the geological change prediction model and dynamically adjusting the infrastructure design parameters in the building information model through the deep learning algorithm to ensure that the design scheme adapts to geological changes are as follows: Key output data are extracted from the geological change prediction model, including groundwater level change trend, soil bearing capacity change rate and foundation settlement rate. The construction risk warning value is calculated comprehensively based on the acquired data. The calculation formula of the construction risk warning value is as follows: , where is the construction risk warning value, is the trend of groundwater level change. is the soil bearing capacity change rate, is the ground settlement rate, , as well as are weight coefficients, representing the change trend of groundwater level. , soil bearing capacity change rate and foundation settlement rate The weight of influence on the construction risk warning value; According to the construction risk warning value , calculate the adjustment coefficient of the basic structure, which is used to dynamically adjust the structural design parameters in the BIM model. The calculation expression is as follows: , where is the basic structure adjustment coefficient, is the nonlinear mapping function generated by the deep learning model, is the natural base, is the weight parameter, is the bias parameter; Using the Infrastructure Adjustment Factor Dynamically update the basic structural design parameters in the BIM model, and the adjustment formula is as follows: , , , where is the adjusted foundation width, is the initial foundation width, is the adjusted concrete strength grade, is the initial concrete strength grade, is the adjusted steel bar diameter, is the initial bar diameter.

[0011] Preferably, the construction plan is adjusted in real time through the optimization algorithm, and the construction sequence, material selection and mechanical equipment use strategy are dynamically optimized according to the real-time geological changes and construction progress to improve the construction efficiency and safety. The specific steps are as follows: First, the real-time geological data and construction progress data collected by the sensors are normalized so that data from different sources and units can be calculated in the same model. The construction environment parameters are generated according to the weather impact coefficient of the progress deviation in the current construction stage. The generation formula is as follows: , where is the trend of groundwater level change. is the soil bearing capacity change rate, is the change in soil moisture, is the weather influence coefficient, It is the construction progress deviation. It is a comprehensive parameter of the construction environment; Obtaining comprehensive parameters of the construction environment Finally, the optimization algorithm is used to dynamically adjust the construction sequence to ensure the safety and efficiency of the construction process. The construction sequence weight coefficient is introduced to dynamically adjust the priority of each task according to the sensitivity of each construction task to geological changes and the construction difficulty. The resource utilization coefficient is introduced to calculate the adjustment priority of each construction task. The calculation expression is as follows: , where It is a construction task Adjustment priority, It is a construction task The order weight coefficient of It is a construction task Utilization of required resources; After adjusting the construction sequence, according to the current construction environment parameters and adjust priorities of construction tasks , further optimize the use strategy of mechanical equipment and material selection scheme, introduce mechanical equipment efficiency coefficient and material adaptability coefficient, calculate the total cost-effectiveness ratio of each construction task, and select the optimal equipment and material scheme. The calculation expression is as follows: , where It is a construction task The overall cost-effectiveness ratio, is the material adaptability coefficient, It is the efficiency coefficient of mechanical equipment.

[0012] Preferably, an intelligent decision-making algorithm is applied to conduct a multi-dimensional evaluation of the construction adjustment plan, generate the final construction adjustment suggestions, and synchronize them to the construction management system to guide the actual construction. The specific steps are as follows: First, based on the output results of the geological change prediction model and real-time monitoring data, the comprehensive risk index of the construction adjustment plan is calculated. The comprehensive risk index is used to quantify the potential risks of different construction adjustment plans and evaluate them from three aspects: safety, economy and environmental impact. The calculation formula of the risk index is as follows: , where It is The weight coefficient of geological risk is It is The monitoring value of geological risk, It is The emergency response cost of each geological risk is It is The safety impact level of each geological risk is is the total number of geological risk types, It is The weight coefficient of the construction adjustment strategy, It is The implementation difficulty score of the construction adjustment strategy is It is The time cost of the construction adjustment strategy, It is Environmental friendliness scores of construction adjustment strategies, is the total number of construction adjustment strategies, is the comprehensive risk index; Obtaining a comprehensive risk index After that, the benefit trade-off value of the construction adjustment plan is further calculated to evaluate the overall benefit of the adjustment plan. The calculation expression of the benefit trade-off value is as follows: , where It is The direct economic benefits of the construction adjustment strategy are: It is The construction feasibility scores of the construction adjustment strategies are: It is Social benefit scores of construction adjustment strategies, is the total number of construction adjustment strategies, is the time cost weight coefficient, is the labor cost weight coefficient, is the environmental cost weight coefficient, is the total expected time cost of the construction adjustment plan, is the total expected labor cost of the construction adjustment scenario, is the total environmental cost of the construction adjustment plan, is the benefit trade-off value.

[0013] In the above technical solution, the technical effects and advantages provided by the present invention are: The present invention uses the two-way dynamic linkage between the BIM model and geological data and the application of the geological change prediction model, so that the municipal project can grasp the changes in geological conditions in real time during the construction process, and automatically adjust the design and construction plan accordingly. This dynamic data-driven approach makes the design plan no longer rely on static geological survey data, but is continuously updated according to real-time monitoring results to ensure that the design always matches the actual geological conditions. For example, when it is monitored that the groundwater level rises or the soil bearing capacity decreases, the system can automatically update the drainage system and foundation reinforcement plan, thereby effectively preventing hidden dangers such as roadbed settlement, underground pipeline dislocation, and building tilt. This beneficial effect significantly improves the overall safety of municipal projects, reduces rework, accidents and economic losses caused by geological changes, thereby ensuring the stability and controllability of the construction process.

[0014] The present invention combines the application of machine learning algorithms, optimization algorithms and intelligent decision-making systems, and can automatically identify geological change trends during the construction of municipal projects and generate targeted construction adjustment suggestions. This intelligent construction plan adjustment process enables the construction party to flexibly adjust the construction sequence, material selection and mechanical equipment usage strategy according to the real-time optimization suggestions provided by the system, thereby maximizing construction efficiency and reducing construction costs. At the same time, the system conducts a comprehensive evaluation of the construction adjustment plan in multiple dimensions, including safety, economy and sustainability, to ensure that construction decisions are more scientific and reasonable. This intelligent optimization and decision-making mechanism not only improves the execution efficiency of the project, but also significantly reduces the uncontrollable factors caused by geological changes, providing an effective solution for the digital and intelligent transformation of municipal engineering construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0016] Figure 1 This is a method flow chart of the integrated collaborative design method for municipal intelligent construction of the present invention. DETAILED DESCRIPTION

[0017] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0018] The present invention provides Figure 1 The integrated collaborative design method for municipal intelligent construction shown includes the following steps: Obtain initial geological survey data for municipal projects, including groundwater level, soil type, soil bearing capacity and other parameters, and use sensor equipment to monitor the dynamic changes of geological data during construction in real time; The specific steps to obtain the initial geological survey data of the municipal project, including parameters such as groundwater level, soil type, soil bearing capacity, and monitor the dynamic changes of geological data in real time during the construction process through sensor equipment are as follows: Formulate geological survey plans to ensure that the collected data is comprehensive and accurate, laying the foundation for subsequent analysis; First, a detailed geological survey plan is developed based on the scale, location and construction requirements of the municipal project. The planning content includes determining the boundaries of the survey area, the types of geological data to be collected (such as groundwater level, soil type, soil bearing capacity, etc.), the survey depth, and the distribution density of sampling points. In this process, factors such as historical geological data and climatic conditions in the area where the municipal project is located must also be considered to ensure the comprehensiveness and scientific nature of the survey plan. At the same time, according to the different geological data collection requirements, appropriate survey tools and equipment, such as geological drilling equipment, static penetration instruments, etc., are selected to lay the foundation for subsequent data collection.

[0019] Planning the scope of geological survey is a key step in initial data collection. Reasonable planning can effectively avoid data blind spots and ensure that the collected geological data is comprehensive and representative. In addition, considering historical geological data can improve the accuracy of data collection, thereby providing more accurate basic data for model prediction.

[0020] Deploy sensor equipment according to the plan to ensure accurate and reliable data collection at key geological feature points; According to the planned survey plan, geological survey equipment is deployed at the construction site, and field sampling is carried out at different survey points. During the deployment process, special attention should be paid to the installation location of the equipment and the distribution density of the data collection points to ensure that the collected data covers the key geological feature points in the construction area. For the collection of parameters such as groundwater level, soil type, and soil density, professional sensor equipment is required, such as groundwater monitoring wells, soil moisture sensors, soil pressure sensors, etc. After the equipment is deployed, it is also necessary to debug and calibrate to ensure that the equipment can accurately and stably collect geological data during operation.

[0021] The reasonable layout of on-site equipment directly affects the quality and reliability of subsequent geological data. In the construction area, geological features usually have large spatial differences, so the reasonable layout of sensors and sampling points can more accurately reflect the geological changes in the construction area. At the same time, the debugging and calibration of equipment can effectively reduce data errors and ensure the accuracy of subsequent model analysis.

[0022] Classify, clean and standardize the collected geological data for upload to ensure data accuracy and facilitate subsequent model application; After the equipment is deployed, the geological data collection work begins. Use professional equipment to collect data such as groundwater level, soil type, soil bearing capacity, etc. in real time, and classify, summarize and digitize the collected data. In order to facilitate the subsequent BIM model linkage, the collected data must be stored in a standardized format and uploaded to the cloud or local database through the data interface. Before uploading the data, it is also necessary to clean and verify the data to eliminate abnormal or erroneous data to ensure the accuracy and consistency of the data. At the same time, the initial geological survey data, as an important input data before construction, also needs to generate a survey report and archive it.

[0023] Accurate collection and uploading of geological data is a key step in ensuring that the BIM model has a reliable data foundation. The data standardization and cleaning process can effectively avoid model analysis deviations caused by inconsistent formats or data errors, thereby improving the accuracy and practicality of model predictions. In addition, cloud storage and sharing of data can improve team collaboration efficiency and facilitate subsequent data updates and management.

[0024] Through the Internet of Things, dynamic monitoring and early warning of geological data can be achieved, and abnormal changes can be identified in time to reduce construction risks; During the construction process, the deployed sensor equipment is used to conduct real-time dynamic monitoring of geological data, including changes in groundwater level, soil moisture, soil bearing capacity, etc. Through the Internet of Things technology, the real-time monitored geological data is automatically transmitted to the BIM system, forming a dynamic update and two-way linkage of geological data. The monitoring system must also have an automatic early warning function. Once abnormal changes in geological data are detected (such as a rapid rise in groundwater levels or a sharp drop in soil bearing capacity), the system will automatically issue an early warning signal to remind the construction party to take preventive measures to avoid construction risks caused by geological changes.

[0025] Real-time monitoring of geological changes can effectively deal with the risks of unforeseen geological changes during construction. Through the Internet of Things technology, the collection, transmission and analysis of geological data are automated, reducing the errors that may be caused by human intervention. At the same time, the automatic early warning function can help the construction party take timely measures before geological changes bring safety hazards, thereby reducing construction risks and improving the safety and stability of the project.

[0026] Input the initial geological survey data and real-time monitoring data into the building information model, and use the data interface to achieve two-way dynamic linkage between the building information model and geological information to ensure real-time update of geological data; The specific steps of inputting the initial geological survey data and real-time monitoring data into the building information model and realizing the two-way dynamic linkage between the building information model and geological information with the help of the data interface to ensure the real-time update of geological data are as follows: Develop data interfaces to achieve conversion, verification and filtering of geological data formats, ensuring that initial data and real-time monitoring data are accurately received by the building information model; Before inputting geological data into the building information model (BIM), it is necessary to first develop or deploy a data interface to ensure that the initial geological survey data and real-time sensor monitoring data can be recognized and received by the BIM system. The initial geological data contains parameters such as groundwater level, soil type, soil bearing capacity, etc., which are usually stored in a static format, while the real-time data collected by sensor equipment is continuously and dynamically changing. Therefore, the data interface needs to have format conversion, data verification and anomaly filtering functions to ensure that the data input into the BIM system remains consistent and accurate. For example, the data interface can convert geological data from different sources into JSON, XML or other standardized formats, and eliminate noise data and invalid data to improve data quality.

[0027] The data interface is the key bridge for the two-way dynamic linkage between the BIM model and geological data. Due to the diverse sources of geological data and complex data types, the data interface must not only ensure format compatibility, but also verify and filter data in real time to avoid data errors affecting the accuracy of model analysis and ensure that the BIM system always receives high-quality geological data.

[0028] Map geological data to corresponding positions and parameters in the building information model according to spatial coordinates to ensure that geological change information is intuitively presented in the design model; After the geological data enters the BIM system through the data interface, it is necessary to map these data to the corresponding positions and parameters in the BIM model. For example, the groundwater level data is mapped to the hydrological model of the foundation part, the soil type data is mapped to the geological layer model of the foundation structure, and the soil bearing capacity data is mapped to the load analysis module of the design structure. This mapping process requires the monitoring data to be accurately matched with the building parts in the BIM model based on the spatial coordinate information of the geological data. In addition, in order to achieve a more intuitive display effect, the geological data can be intuitively presented in the form of color labels or dynamic charts through visualization technology to help designers quickly identify geological changes in different areas.

[0029] Data mapping is an important step in the deep integration of geological information and BIM models. By accurately mapping geological data to various parts of the BIM model, it is possible to achieve a dynamic association between architectural design and geological conditions, allowing designers to intuitively view the impact of geological changes on structural design, and improve design accuracy and construction safety.

[0030] Through a two-way dynamic linkage mechanism, geological data is updated in real time and the design parameters of the building information model are automatically adjusted to cope with changes in geological conditions; After completing the input and mapping of geological data, it is necessary to establish a two-way dynamic data linkage mechanism so that the BIM model can update geological data in real time and, in turn, provide feedback and adjustments to changes in geological data. Through this mechanism, when the sensor equipment detects changes in parameters such as groundwater level and soil bearing capacity, the BIM system will automatically update the corresponding geological model and adjust the basic structure design parameters according to the changes. For example, when the groundwater level rises beyond the set threshold, the BIM model will automatically add drainage system design or adjust the foundation reinforcement plan. Two-way linkage also includes feedback from the BIM system to the construction site, such as synchronizing optimized construction adjustment suggestions to the sensor system to further guide the work of the monitoring equipment.

[0031] The two-way dynamic linkage mechanism ensures that the BIM system is synchronized with geological data in real time and automatically adjusts the design plan according to changes. Through this linkage, not only real-time data updates are achieved during the construction process, but also the BIM system has the ability to automatically feedback and make decisions on geological changes, thereby improving the safety and construction efficiency of the project.

[0032] Establish a geological data anomaly early warning system, combine building information models and historical data, automatically identify risks and provide construction adjustment suggestions, and improve the safety and intelligence level of construction; On the basis of two-way dynamic linkage, a geological data abnormality warning mechanism is further set up. When the sensor equipment detects abnormal changes such as rapid rise in groundwater level and sharp drop in soil bearing capacity, the BIM system will automatically trigger a warning signal and provide corresponding construction adjustment suggestions. For example, when it is detected that the soil bearing capacity of a certain area is reduced, the system will automatically prompt the designer to adjust the basic structure design, increase foundation reinforcement measures or change the construction sequence. In order to avoid false alarms or missed alarms, it is also necessary to combine historical geological data and deep learning algorithms to dynamically optimize the warning threshold to ensure the accuracy and timeliness of the warning signal.

[0033] Abnormal warning and linkage response mechanism are important links in achieving intelligent decision-making. Through real-time monitoring and intelligent warning, the BIM system can promptly identify potential construction risks and provide optimization suggestions to help construction parties take measures in advance to avoid safety accidents and construction delays caused by geological changes. This mechanism effectively improves the intelligence level and resilience of municipal project construction.

[0034] Construct a geological change prediction model during the construction phase in the building information model. Through machine learning algorithms, the trend of geological condition changes is predicted based on historical geological data and real-time monitoring data, forming an early prediction of geological changes. The geological change prediction model during the construction phase is constructed in the building information model. The specific steps for predicting the geological condition change trend based on historical geological data and real-time monitoring data through machine learning algorithms and forming an early prediction of geological changes are as follows: Integrate initial geological data and real-time monitoring data, clean outliers and build high-quality data sets to provide a reliable training basis for geological change prediction models; Before building a geological change prediction model for the construction phase in the building information model (BIM), it is first necessary to collect and integrate initial geological survey data and real-time monitoring data to form a comprehensive data set. In order to improve the accuracy and stability of the model, these data need to be cleaned and preprocessed, including removing outliers, filling missing data, and unifying data formats. By combining historical geological data (such as groundwater level fluctuation records, soil bearing capacity change trends) and real-time monitoring data (dynamic data provided by sensor equipment), a geological data set covering multiple dimensions and long time spans can be constructed to provide high-quality input data for the subsequent training of machine learning algorithms.

[0035] Data collection and cleaning are the basic links in the construction of geological change prediction models, which directly affect the accuracy and reliability of the models. By integrating multi-source geological data and removing noise data, we can effectively avoid the deviation of the prediction model due to data quality problems and ensure that the model training process is scientific and reasonable.

[0036] Select machine learning algorithms based on geological data characteristics, optimize model performance through hyperparameter tuning and validation sets, and improve the accuracy and generalization ability of geological change prediction; After preparing the training data set, select a machine learning algorithm suitable for geological change prediction, such as time series analysis, random forest, support vector machine, etc. According to the characteristics of the geological data and the prediction target, determine the input variables (such as groundwater level, soil moisture content, air temperature, etc.) and output variables (such as soil bearing capacity change trend, foundation settlement probability, etc.) of the model. Subsequently, divide the data set into a training set and a validation set, and use the training set to train the model so that it can identify the changing patterns of geological data. During the training process, through hyperparameter tuning and cross-validation, the prediction accuracy and generalization ability of the model are continuously improved to ensure that the model can effectively cope with complex and changing geological conditions.

[0037] The selection of machine learning algorithms and the training process of the models are crucial. Different algorithms perform differently when processing nonlinear data, time series data, and high-dimensional data. By selecting appropriate algorithms and continuously optimizing model parameters, the accuracy of geological change prediction models can be improved, enabling them to more accurately predict geological risks.

[0038] The trained geological change prediction model is embedded in the building information model and integrated with the two-way dynamic linkage mechanism of geological data to achieve real-time prediction and dynamic design adjustment; After training a prediction model with good performance, it is embedded in the BIM model and integrated with the two-way dynamic linkage mechanism of geological data. The model can obtain monitoring data from sensor equipment in real time, input new data into the prediction model, and generate prediction results of geological changes during the construction phase. For example, the model can predict the trend of groundwater levels in the next week, the changes in soil bearing capacity in a specific area, etc. These prediction results will be automatically updated to the BIM model to help designers and construction parties identify potential risks in advance and take corresponding countermeasures in the design and construction plan.

[0039] Embedding the geological change prediction model into the BIM model and realizing dynamic linkage can enable the BIM model to have real-time prediction capabilities. Through this linkage mechanism, the construction party can dynamically adjust the construction plan according to the latest geological change prediction results, thereby effectively reducing construction risks.

[0040] By analyzing the prediction results, the building information model automatically triggers risk warnings and generates construction adjustment suggestions to guide the construction party to respond to geological changes in a timely manner and reduce construction risks; Based on the prediction results generated by the geological change prediction model, the BIM model automatically analyzes potential risks and generates construction adjustment suggestions. When the model predicts that the groundwater level may rise to a dangerous level or the soil bearing capacity may decrease, the system will automatically trigger a risk warning and prompt the construction personnel to take emergency measures. At the same time, the system combines the prediction results and the construction progress to automatically optimize the construction adjustment plan, such as adjusting the construction sequence, adding foundation reinforcement measures, or changing the material selection to cope with the upcoming geological changes. These adjustment suggestions will be synchronized to the construction management system to guide the actual operation of the construction party.

[0041] Risk warnings and construction adjustment suggestions based on geological change predictions can help construction parties prevent potential geological risks in advance. Through automated early warning and adjustment plan generation, the BIM model not only improves the safety and intelligence level of construction, but also significantly reduces rework and economic losses caused by geological changes.

[0042] Based on the output of the geological change prediction model, construction risk warning data is generated, and the infrastructure design parameters in the building information model are dynamically adjusted through deep learning algorithms to ensure that the design plan adapts to geological changes; According to the output results of the geological change prediction model, the construction risk warning data is generated, and the infrastructure design parameters in the building information model are dynamically adjusted through the deep learning algorithm to ensure that the design plan adapts to geological changes. The specific steps are as follows: Key output data are extracted from the geological change prediction model, including groundwater level change trend, soil bearing capacity change rate and foundation settlement rate. The construction risk warning value is calculated comprehensively based on the acquired data. The calculation formula of the construction risk warning value is as follows: , where It is the construction risk warning value, ranging from 0 to 1. The larger the value, the higher the risk. is the trend of groundwater level change, indicating the rate of change of groundwater level in the future. is the soil bearing capacity change rate, which indicates the change of soil bearing capacity over time. is the foundation settlement rate, which indicates the speed of foundation settlement. , as well as are weight coefficients, representing the change trend of groundwater level. , soil bearing capacity change rate and foundation settlement rate The weight of influence on the construction risk warning value, Used to regulate groundwater level trends Contribution to risk value, Used to adjust the soil bearing capacity change rate Contribution to risk value, Used to adjust the ground settlement rate Contribution to VaR; This step comprehensively considers the impact of changes in groundwater level, soil bearing capacity and foundation settlement on construction risks. By assigning weights to different factors and adjusting the calculation method according to the geological characteristics of the specific project, the risk level can be more accurately reflected. The generated risk warning value will serve as an important basis for subsequent adjustments to the foundation structure design.

[0043] According to the construction risk warning value , calculate the adjustment coefficient of the foundation structure, which is used to dynamically adjust the structural design parameters in the BIM model. The adjustment coefficient is generated by a deep learning algorithm, taking into account the foundation type (such as shallow foundation, pile foundation) and material properties (such as concrete strength, steel density) of the construction area. The calculation expression is as follows: , where is the foundation structure adjustment coefficient, ranging from 0 to 1, which is used to indicate the adjustment range of the foundation structure design parameters. is the nonlinear mapping function generated by the deep learning model, is the natural base, is a weight parameter used to control the construction risk warning value Adjustment factor for infrastructure The intensity of the impact, It is a bias parameter, which is used to adjust the output range and curve shape of the function to ensure the adjustment of the basic structure of the model output. Can more reasonably match the actual construction needs; Infrastructure adjustment factor It is used to dynamically adjust the basic structural design parameters in the BIM model, such as foundation size, concrete grade, reinforcement configuration, etc. By introducing nonlinear function mapping, the change of adjustment coefficient is smoother and more reasonable, which can effectively avoid excessive or insufficient adjustment of structural design caused by geological changes.

[0044] Using the Infrastructure Adjustment Factor Dynamically update the basic structural design parameters in the BIM model, and the adjustment formula is as follows: , , , where is the adjusted foundation width, is the initial foundation width, is the adjusted concrete strength grade, is the initial concrete strength grade, is the adjusted steel bar diameter, is the initial bar diameter.

[0045] Through the above steps, the basic structure adjustment coefficient Dynamic association with key structural parameters enables automatic optimization of foundation structure design. The adjustment range of foundation width and concrete strength is large to cope with more significant geological changes; the adjustment range of steel bar diameter is small to avoid material waste and increased construction complexity caused by excessive adjustment. These adjustment parameters will be synchronized to the BIM model in real time to ensure that the design can continue to adapt to geological changes and improve the safety and economy of construction.

[0046] Through the optimization algorithm, the construction plan is adjusted in real time, and the construction sequence, material selection and mechanical equipment use strategy are dynamically optimized according to the real-time geological changes and construction progress to improve construction efficiency and safety; Through the optimization algorithm, the construction plan is adjusted in real time. The construction sequence, material selection and mechanical equipment use strategy are dynamically optimized according to the real-time geological changes and construction progress. The specific steps to improve construction efficiency and safety are as follows: First, the real-time geological data and construction progress data collected by the sensors are normalized so that data from different sources and units can be calculated in the same model. The construction environment parameters are generated according to the weather impact coefficient of the progress deviation in the current construction stage. The generation formula is as follows: , where is the trend of groundwater level change, indicating the rate of change of groundwater level in the future. is the soil bearing capacity change rate, which indicates the change of soil bearing capacity over time. It is the soil moisture change, the real-time monitoring soil moisture change value, is the weather influence coefficient, a parameter value set according to the real-time weather conditions. It is the construction progress deviation, the difference between the actual progress and the planned progress. It is a comprehensive parameter of the construction environment, reflecting the comprehensive impact of current geological conditions and construction progress; Generate comprehensive construction environment parameters through comprehensive analysis of geological changes and construction progress , which is used in the subsequent steps for dynamic optimization calculation of construction sequence and equipment strategy.

[0047] Obtaining comprehensive parameters of the construction environment Finally, the optimization algorithm is used to dynamically adjust the construction sequence to ensure the safety and efficiency of the construction process. The construction sequence weight coefficient is introduced to dynamically adjust the priority of each task according to the sensitivity of each construction task to geological changes and the construction difficulty. The resource utilization coefficient is introduced to calculate the adjustment priority of each construction task. The calculation expression is as follows: , where It is a construction task The adjustment priority, the larger the value, the higher the priority. It is a construction task The order weight coefficient represents the comprehensive weight of the task’s sensitivity to geological changes and construction difficulty. It is a construction task The utilization rate of required resources (manpower, equipment, materials, etc.). The larger the value, the more resources the task consumes and the more difficult it is to execute. Calculate the adjustment priority of each construction task through the construction sequence dynamic adjustment model , the construction party can optimize the construction plan in real time according to the priority level to ensure the construction safety and resource utilization efficiency under the risk of geological changes.

[0048] After adjusting the construction sequence, according to the current construction environment parameters and adjust priorities of construction tasks , further optimize the use strategy of mechanical equipment and material selection scheme, introduce mechanical equipment efficiency coefficient and material adaptability coefficient, calculate the total cost-effectiveness ratio of each construction task, and select the optimal equipment and material scheme. The calculation expression is as follows: , where It is a construction task The total cost-effectiveness ratio is as follows: the larger the value, the higher the cost-effectiveness of the current equipment and material selection options. It is the material adaptability coefficient, which reflects the applicability of the material under the current geological conditions, such as water resistance, compressive strength, etc. It is the efficiency coefficient of mechanical equipment, which reflects the actual working efficiency of mechanical equipment under current geological conditions.

[0049] Through the calculation of the mechanical equipment and material selection optimization model, the construction party can select the equipment and material solutions that best suit the current environment based on real-time geological changes and construction task priorities, thereby reducing construction costs, improving construction efficiency, and effectively avoiding construction risks.

[0050] Apply intelligent decision-making algorithms to conduct multi-dimensional evaluation of construction adjustment plans, conduct comprehensive analysis from the aspects of safety, economy and sustainability, generate final construction adjustment suggestions, and synchronize them to the construction management system to guide actual construction; Apply intelligent decision-making algorithms to conduct multi-dimensional evaluation of construction adjustment plans, conduct comprehensive analysis from the aspects of safety, economy and sustainability, generate final construction adjustment suggestions, and synchronize them to the construction management system to guide actual construction. The specific steps are as follows: First, based on the output results of the geological change prediction model and real-time monitoring data, the comprehensive risk index of the construction adjustment plan is calculated. The comprehensive risk index is used to quantify the potential risks of different construction adjustment plans and evaluate them from three aspects: safety, economy and environmental impact. The calculation formula of the risk index is as follows: , where It is The weight coefficient of geological risk is It is The monitoring value of geological risk, It is The emergency response cost of each geological risk is It is The safety impact level of each geological risk is is the total number of geological risk types, It is The weight coefficient of the construction adjustment strategy, It is The implementation difficulty score of the construction adjustment strategy is It is The time cost of the construction adjustment strategy, It is Environmental friendliness scores of construction adjustment strategies, is the total number of construction adjustment strategies, is the comprehensive risk index; This step quantifies the potential risk of the construction adjustment plan into a comparable index by comprehensively considering multiple geological risk factors and the feasibility of the construction strategy. The calculated comprehensive risk index The higher the value, the greater the risk of the solution, and further optimization is needed.

[0051] Obtaining a comprehensive risk index After that, the benefit trade-off value of the construction adjustment plan is further calculated to evaluate the overall benefit of the adjustment plan, and a comprehensive trade-off is made from the dimensions of economic cost, construction efficiency and sustainable development. The calculation expression of the benefit trade-off value is as follows: , where It is The direct economic benefits of the construction adjustment strategy are: It is The construction feasibility scores of the construction adjustment strategies are: It is Social benefit scores of construction adjustment strategies, is the total number of construction adjustment strategies, is the time cost weight coefficient, which indicates the weight of time cost in the evaluation of construction adjustment plan. is the labor cost weight coefficient, which indicates the weight of labor cost in the evaluation of construction adjustment plan. is the environmental cost weight coefficient, which indicates the weight of environmental cost in the evaluation of construction adjustment plan. , and The project management team will determine the actual situation. is the total expected time cost of the construction adjustment plan, which indicates the total time required to implement the adjustment plan, including the time cost required for construction adjustment, material transportation, etc. is the total expected labor cost of the construction adjustment scenario, is the total environmental cost of the construction adjustment plan, which indicates the environmental cost brought about by the construction adjustment plan, including waste disposal costs, pollution control costs, etc. It is the benefit trade-off value, which means the comprehensive benefit value calculated after multi-dimensional evaluation of the construction adjustment plan. It is used to judge whether the construction adjustment plan is reasonable, optimal and worth implementing.

[0052] The formula maximizes the benefits by weighing direct economic benefits, construction feasibility and social benefits, while taking the comprehensive risk index into account. Take this into consideration to ensure the safety and sustainability of the solution. Benefit trade-off The higher it is, the greater the overall benefit of the scheme is and the more suitable it is for actual construction.

[0053] Implementation method 1: The design and construction process of municipal engineering projects usually rely on the support of geological survey data, but traditional geological data is often static data collected once, lacking a dynamic update mechanism, and unable to reflect geological changes during the construction process, which can easily lead to design deviations and construction risks. To solve this problem, a two-way dynamic linkage method between BIM model and geological data is adopted to seamlessly integrate the initial geological survey data with the real-time monitoring data during the construction phase, ensuring that the geological parameters in the BIM model can be updated in real time according to actual conditions.

[0054] The specific implementation steps are as follows: At the beginning of the project, professional geological survey tools were used to collect initial geological data, including groundwater level, soil type, soil bearing capacity, rock layer structure, etc. After cleaning and preprocessing, these data were entered into the BIM system in a standardized format to form the initial geological basis of the BIM model. The entry of initial data is the first step in dynamic linkage, which provides a benchmark for the design of the BIM model and ensures that the basic structure design can adapt to local geological conditions.

[0055] Various sensor devices are deployed at the construction site, including groundwater level monitors, soil moisture sensors, soil pressure sensors, etc. These sensors are interconnected with the BIM system through the Internet of Things technology, and can collect geological change data during the construction process in real time and automatically upload the data to the cloud or local database. The real-time collection of sensor data provides data support for the dynamic update of the BIM model.

[0056] In order to realize the two-way dynamic linkage between BIM model and geological data, a dedicated data interface must be developed. The core function of the data interface is to convert, verify and clean the format of initial geological data and real-time monitoring data to ensure the accuracy and consistency of the data. The data interface supports the BIM system to receive data input from different sources and realize the automatic entry and update of geological data.

[0057] When real-time monitoring data is input into the BIM system, the model will automatically update geological parameters. For example, when the groundwater level rises to a dangerous threshold, the BIM model will automatically adjust the drainage system design; when the soil bearing capacity decreases, the model will add foundation reinforcement measures. This dynamic update mechanism ensures that the construction plan is consistent with the actual geological conditions, thereby effectively avoiding design deviations and construction risks caused by geological changes.

[0058] The dynamic linkage of the BIM model includes not only the input of geological data, but also feedback and adjustments from the construction site. When geological changes are detected that may affect construction safety, the BIM system will issue an early warning to the construction party and simultaneously send construction adjustment suggestions to the construction management system. For example, when the soil moisture of a certain section of the roadbed is too high, the system will suggest that the construction party adjust the construction sequence and give priority to areas with stable geological conditions.

[0059] This dynamic linkage method can significantly improve the design accuracy and construction safety of municipal projects. During the project implementation, the BIM model is always updated synchronously with the actual geological conditions. Designers can optimize the design plan based on real-time geological data, and the construction party can adjust the construction plan according to the system's suggestions, thereby effectively reducing construction risks, rework and cost waste, and improving the sustainability and service life of the project.

[0060] Implementation method 2: The changes in geological conditions are complex and random, and it is often impossible to fully grasp the geological change trends by relying solely on real-time monitoring data. To this end, a geological change prediction model is constructed using machine learning algorithms. By analyzing historical geological data and real-time monitoring data, future geological change trends can be predicted in advance to provide support for the optimization and adjustment of construction plans.

[0061] The specific implementation steps are as follows: First, the initial geological survey data and real-time monitoring data during the construction phase are collected to form a multi-dimensional, long-span geological data set. These data include groundwater level fluctuations, soil bearing capacity changes, foundation settlement trends, etc. In order to ensure the high quality of the data, the data needs to be cleaned, denoised, and formatted to remove outliers and invalid data to ensure that the data input into the model is representative and consistent.

[0062] According to the characteristics of geological data and prediction objectives, select the appropriate machine learning algorithm. Commonly used algorithms include time series analysis, random forest, support vector machine and long short-term memory network (LSTM). Time series analysis is suitable for the prediction of groundwater level changes, random forest can identify the factors affecting soil bearing capacity changes, and LSTM has a good effect on processing continuous time series data.

[0063] The geological data set is divided into a training set and a validation set, and the model is trained using the training set to identify the changing patterns of geological data. During the training process, the prediction accuracy and generalization ability of the model are continuously optimized through hyperparameter tuning and cross-validation to ensure that the model can effectively respond to geological changes in different regions and time periods.

[0064] The trained geological change prediction model is embedded in the BIM system and dynamically linked with the real-time monitoring data. When the sensor equipment collects new data, the prediction model will automatically analyze the data and predict the geological change trend in the future. For example, the model can predict the rise of groundwater level in the next week, the speed of soil bearing capacity decline, etc., to provide construction parties with early prediction and decision support.

[0065] Using machine learning to build a geological change prediction model can help construction parties identify potential geological risks in advance and reduce the impact of sudden geological changes on construction safety. At the same time, the automatic update and linkage mechanism of the prediction model enables the BIM system to have a higher level of intelligence and decision-making ability, thereby improving the overall controllability and construction efficiency of the project.

[0066] Implementation method 3: In the actual construction process of municipal projects, even with real-time monitoring and geological change prediction capabilities, it is still necessary to dynamically adjust and optimize the construction plan to cope with the changing geological conditions. To this end, optimization algorithms and intelligent decision-making systems are used to optimize and adjust each link of the construction plan and generate feasible construction adjustment suggestions.

[0067] The specific implementation steps are as follows: According to the actual needs of the construction plan, a suitable optimization algorithm model is constructed. The goal of the optimization algorithm is to improve construction efficiency, reduce construction risks and reduce construction costs. The algorithm model will comprehensively consider multiple factors such as real-time monitoring data, geological change prediction results, construction progress, material cost and safety, and optimize the construction sequence, material selection and mechanical equipment use strategy.

[0068] When the system detects changes in geological conditions, the optimization algorithm automatically calculates the best construction adjustment plan. For example, when the soil bearing capacity decreases, the system may suggest that the construction party replace the foundation reinforcement material or adjust the construction sequence to give priority to completing the construction of stable areas. The generated adjustment suggestions will be synchronized to the construction management system in the form of digital reports to guide the actual operation of the construction party.

[0069] After the construction adjustment proposal is generated, the intelligent decision-making system will conduct a multi-dimensional evaluation of the adjustment plan, including indicators such as safety, economy, and sustainability. The system will recommend the best plan based on the evaluation results and update it to the BIM model in real time to ensure that the construction party can quickly obtain the latest adjustment proposals.

[0070] Through the application of optimization algorithms and intelligent decision-making systems, the uncertainty in the construction process can be effectively reduced, and the safety and efficiency of construction can be improved. In addition, the automated evaluation and adjustment suggestions of the intelligent decision-making system can help the construction party to quickly respond to geological changes, reduce construction delays and rework costs, and maximize the economic benefits of the project.

[0071] The present invention uses the two-way dynamic linkage between the BIM model and geological data and the application of the geological change prediction model, so that the municipal project can grasp the changes in geological conditions in real time during the construction process, and automatically adjust the design and construction plan accordingly. This dynamic data-driven approach makes the design plan no longer rely on static geological survey data, but is continuously updated according to real-time monitoring results to ensure that the design always matches the actual geological conditions. For example, when it is monitored that the groundwater level rises or the soil bearing capacity decreases, the system can automatically update the drainage system and foundation reinforcement plan, thereby effectively preventing hidden dangers such as roadbed settlement, underground pipeline dislocation, and building tilt. This beneficial effect significantly improves the overall safety of municipal projects, reduces rework, accidents and economic losses caused by geological changes, thereby ensuring the stability and controllability of the construction process.

[0072] The present invention combines the application of machine learning algorithms, optimization algorithms and intelligent decision-making systems, and can automatically identify geological change trends during the construction of municipal projects and generate targeted construction adjustment suggestions. This intelligent construction plan adjustment process enables the construction party to flexibly adjust the construction sequence, material selection and mechanical equipment usage strategy according to the real-time optimization suggestions provided by the system, thereby maximizing construction efficiency and reducing construction costs. At the same time, the system conducts a comprehensive evaluation of the construction adjustment plan in multiple dimensions, including safety, economy and sustainability, to ensure that construction decisions are more scientific and reasonable. This intelligent optimization and decision-making mechanism not only improves the execution efficiency of the project, but also significantly reduces the uncontrollable factors caused by geological changes, providing an effective solution for the digital and intelligent transformation of municipal engineering construction.

[0073] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0074] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0075] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0076] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0077] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0078] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0079] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0080] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0081] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0082] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. An integrated collaborative design method for municipal intelligent construction, characterized in that: The following steps are involved: Obtain initial geological survey data for municipal projects and use sensor equipment to monitor the dynamic changes of geological data during construction in real time; Input the initial geological survey data and real-time monitoring data into the building information model, and use the data interface to achieve two-way dynamic linkage between the building information model and geological information to ensure real-time update of geological data; Construct a geological change prediction model during the construction phase in the building information model. Through machine learning algorithms, the trend of geological condition changes is predicted based on historical geological data and real-time monitoring data, forming an early prediction of geological changes. Generate construction risk warning data based on the output of the geological change prediction model, and dynamically adjust the infrastructure design parameters in the building information model through deep learning algorithms to ensure that the design plan adapts to geological changes; Through the optimization algorithm, the construction plan is adjusted in real time, and the construction sequence, material selection and mechanical equipment use strategy are dynamically optimized according to the real-time geological changes and construction progress to improve construction efficiency and safety; Apply intelligent decision-making algorithms to conduct multi-dimensional evaluation of construction adjustment plans, generate final construction adjustment suggestions, and synchronize them to the construction management system to guide actual construction.

2. The integrated collaborative design method for municipal intelligent construction according to claim 1 is characterized in that: The specific steps to obtain the initial geological survey data of a municipal project and monitor the dynamic changes of geological data during construction in real time through sensor equipment are as follows: Formulate geological survey plans to ensure that the collected data is comprehensive and accurate, laying the foundation for subsequent analysis; Deploy sensor equipment according to the plan to ensure accurate and reliable data collection at key geological feature points; Classify, clean and standardize the collected geological data for upload to ensure data accuracy and facilitate subsequent model application; The Internet of Things can be used to achieve dynamic monitoring and early warning of geological data, and to identify abnormal changes in a timely manner to reduce construction risks.

3. The integrated collaborative design method for municipal intelligent construction according to claim 1 is characterized in that: The specific steps of inputting the initial geological survey data and real-time monitoring data into the building information model and realizing the two-way dynamic linkage between the building information model and geological information with the help of the data interface to ensure the real-time update of geological data are as follows: Develop data interfaces to achieve conversion, verification and filtering of geological data formats, ensuring that initial data and real-time monitoring data are accurately received by the building information model; Map geological data to corresponding positions and parameters in the building information model according to spatial coordinates to ensure that geological change information is intuitively presented in the design model; Through a two-way dynamic linkage mechanism, geological data is updated in real time and the design parameters of the building information model are automatically adjusted to cope with changes in geological conditions; Establish a geological data anomaly early warning system, combine building information models and historical data, automatically identify risks and provide construction adjustment suggestions, and improve the safety and intelligence level of construction.

4. The integrated collaborative design method for municipal intelligent construction according to claim 1 is characterized in that: The geological change prediction model during the construction phase is constructed in the building information model. The specific steps for predicting the geological condition change trend based on historical geological data and real-time monitoring data through machine learning algorithms and forming an early prediction of geological changes are as follows: Integrate initial geological data and real-time monitoring data, clean outliers and build high-quality data sets to provide a reliable training basis for geological change prediction models; Select machine learning algorithms based on geological data characteristics, optimize model performance through hyperparameter tuning and validation sets, and improve the accuracy and generalization ability of geological change prediction; The trained geological change prediction model is embedded in the building information model and integrated with the two-way dynamic linkage mechanism of geological data to achieve real-time prediction and dynamic design adjustment; By analyzing the prediction results, the building information model automatically triggers risk warnings and generates construction adjustment suggestions to guide the construction party to respond to geological changes in a timely manner and reduce construction risks.

5. The integrated collaborative design method for municipal intelligent construction according to claim 1 is characterized in that: According to the output results of the geological change prediction model, the construction risk warning data is generated, and the infrastructure design parameters in the building information model are dynamically adjusted through the deep learning algorithm to ensure that the design plan adapts to geological changes. The specific steps are as follows: Key output data are extracted from the geological change prediction model, including groundwater level change trend, soil bearing capacity change rate and foundation settlement rate. The construction risk warning value is calculated comprehensively based on the acquired data. The calculation formula of the construction risk warning value is as follows: , where is the construction risk warning value, is the trend of groundwater level change. is the soil bearing capacity change rate, is the ground settlement rate, , as well as are weight coefficients, representing the change trend of groundwater level. , soil bearing capacity change rate and foundation settlement rate The weight of influence on the construction risk warning value; According to the construction risk warning value , calculate the adjustment coefficient of the basic structure, which is used to dynamically adjust the structural design parameters in the BIM model. The calculation expression is as follows: , where is the basic structure adjustment coefficient, is the nonlinear mapping function generated by the deep learning model, is the natural base, is the weight parameter, is the bias parameter; Using the Infrastructure Adjustment Factor Dynamically update the basic structural design parameters in the BIM model, and the adjustment formula is as follows: , , , where is the adjusted foundation width, is the initial foundation width, is the adjusted concrete strength grade, is the initial concrete strength grade, is the adjusted steel bar diameter, is the initial bar diameter.

6. The integrated collaborative design method for municipal intelligent construction according to claim 1 is characterized in that: Through the optimization algorithm, the construction plan is adjusted in real time. The construction sequence, material selection and mechanical equipment use strategy are dynamically optimized according to the real-time geological changes and construction progress. The specific steps to improve construction efficiency and safety are as follows: First, the real-time geological data and construction progress data collected by the sensors are normalized so that data from different sources and units can be calculated in the same model. The construction environment parameters are generated according to the weather impact coefficient of the progress deviation in the current construction stage. The generation formula is as follows: , where is the trend of groundwater level change. is the soil bearing capacity change rate, is the change in soil moisture, is the weather influence coefficient, It is the construction progress deviation. It is a comprehensive parameter of the construction environment; Obtaining comprehensive parameters of the construction environment Finally, the optimization algorithm is used to dynamically adjust the construction sequence to ensure the safety and efficiency of the construction process. The construction sequence weight coefficient is introduced to dynamically adjust the priority of each task according to the sensitivity of each construction task to geological changes and the construction difficulty. The resource utilization coefficient is introduced to calculate the adjustment priority of each construction task. The calculation expression is as follows: , where It is a construction task Adjust the priority of It is a construction task The order weight coefficient of It is a construction task Utilization of required resources; After adjusting the construction sequence, according to the current construction environment parameters and adjust priorities of construction tasks , further optimize the use strategy of mechanical equipment and material selection scheme, introduce mechanical equipment efficiency coefficient and material adaptability coefficient, calculate the total cost-effectiveness ratio of each construction task, and select the optimal equipment and material scheme. The calculation expression is as follows: , where It is a construction task The overall cost-effectiveness ratio, is the material adaptability coefficient, It is the efficiency coefficient of mechanical equipment.

7. The integrated collaborative design method for municipal intelligent construction according to claim 1 is characterized in that: Apply intelligent decision-making algorithms to conduct multi-dimensional evaluation of construction adjustment plans, generate final construction adjustment suggestions, and synchronize them to the construction management system to guide actual construction. The specific steps are as follows: First, based on the output results of the geological change prediction model and real-time monitoring data, the comprehensive risk index of the construction adjustment plan is calculated. The comprehensive risk index is used to quantify the potential risks of different construction adjustment plans and evaluate them from three aspects: safety, economy and environmental impact. The calculation formula of the risk index is as follows: , where It is The weight coefficient of geological risk is It is The monitoring value of geological risk, It is The emergency response cost of each geological risk is It is The safety impact level of each geological risk is is the total number of geological risk types, It is The weight coefficient of the construction adjustment strategy, It is The implementation difficulty score of the construction adjustment strategy is It is The time cost of the construction adjustment strategy, It is Environmental friendliness scores of construction adjustment strategies, is the total number of construction adjustment strategies, is the comprehensive risk index; Obtaining a comprehensive risk index After that, the benefit trade-off value of the construction adjustment plan is further calculated to evaluate the overall benefit of the adjustment plan. The calculation expression of the benefit trade-off value is as follows: , where It is The direct economic benefits of the construction adjustment strategy are: It is The construction feasibility scores of the construction adjustment strategies are: It is Social benefit scores of construction adjustment strategies, is the total number of construction adjustment strategies, is the time cost weight coefficient, is the labor cost weight coefficient, is the environmental cost weight coefficient, is the total expected time cost of the construction adjustment plan, is the total expected labor cost of the construction adjustment scenario, is the total environmental cost of the construction adjustment plan, is the benefit trade-off value.

Images