Water conservancy project earthwork filling control system based on BIM + GIS

Abstract

The invention provides a hydraulic engineering earthwork filling control system based on BIM + GIS, and relates to the technical field of earthwork filling control. The hydraulic engineering earthwork filling control system comprises a BIM and GIS-based control system, the BIM and GIS-based control system is connected with a system assembly and a system function, the system assembly and the system function are connected with an environment influence early warning system, and the environment influence early warning system comprises a data acquisition and integration module, a data processing module, a data processing module and a data processing module. The data acquisition and integration module is connected with an established machine learning model, the established machine learning model is connected with a scene simulation and analysis module, the scene simulation and analysis module is connected with optimized earthwork filling, and the optimized earthwork filling module is connected with adaptive measure formulation. By integrating BIM and GIS technologies into the hydraulic engineering earthwork filling control system, the engineering quality and safety can be improved, and by establishing a machine learning model, the influence of climate change on the hydraulic engineering earthwork filling control system can be accurately predicted.

Description

Technical Field

[0001] The present invention relates to the technical field of earth filling control, in particular to a water conservancy project earth filling control system based on BIM+GIS. Background Art

[0002] Earthwork filling control in water conservancy projects refers to monitoring, testing and management of the earthwork filling process in water conservancy projects to ensure that the quality of the filling project meets the design requirements and achieves the expected results. This includes monitoring and testing the materials, construction technology, filling layer thickness, slope, compaction, etc. of the earthwork filling to ensure that the filled project can withstand the effects of hydraulic and other external forces, thereby ensuring the safety and stability of the project. Filling control is an important part of water conservancy project construction and has a significant impact on project quality and long-term operation results.

[0003] Traditional earth filling control systems often only consider the technical parameters and construction process of earth filling, while ignoring the environmental factors and ecological impacts related to climate change. In addition, the design of its control system is often based on historical climate data and statistical analysis, without considering the impact of future climate change on filling projects. Climate change may cause changes in rainfall patterns, temperature, surface water levels, etc., which will directly affect the design and construction of earth filling. Most of the existing technologies are based on fixed design standards and filling parameters for control, and cannot flexibly adapt to new situations brought about by climate change. Once the climate changes abnormally, the existing system is often difficult to adjust in time, resulting in greater risks for filling projects. At the same time, there is a certain degree of uncertainty in the prediction of future climate change. Existing technologies often cannot effectively deal with the impact of uncertain factors on filling control systems. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies in the prior art, the present invention provides a water conservancy project earth filling control system based on BIM+GIS, which solves the problem that environmental changes can easily directly affect earth filling design and construction.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a water conservancy project earthwork filling control system based on BIM+GIS, including a control system based on BIM and GIS, wherein the control system based on BIM and GIS is connected with system components and system functions, and the system components and system functions are connected with an environmental impact early warning system;

[0008] The environmental impact warning system includes data collection and integration, which is connected to the establishment of a machine learning model, which is connected to scenario simulation and analysis, which is connected to the optimization of earth filling, and which is connected to the formulation of adaptive measures.

[0009] Preferably, the system functions include a design and planning module, a construction simulation module, a real-time monitoring module, a quality management module, an environmental management module, a safety management module and an analysis report module.

[0010] Preferably, the environmental management module includes an integrated management system, which includes rainy season construction adaptability and adjustment strategies, drought climate adaptability and adjustment strategies, cold climate adaptability and adjustment strategies, high temperature climate adaptability and adjustment strategies, and windy and sandy climate adaptability and adjustment strategies.

[0011] Preferably, the environmental impact early warning system specifically includes:

[0012] Data collection and integration

[0013] Collect meteorological data including temperature T, precipitation P and wind speed W, soil moisture H, groundwater level G and soil type S, vegetation coverage V, terrain slope R and land use LU;

[0014] Building a machine learning model

[0015] Input features: X = [H, P, T, S, G, W, V, R, LU]

[0016] Target variable: Y represents the strength and stability of earth fill, specifically the compression strength C or settlement D;

[0017] Select algorithm: Artificial neural network ANN

[0018] Model Training

[0019] Loss function: Define the loss function L suitable for the target, including the mean square error (MSE):

[0020]

[0021] Among them, $Y_i$ is the true value, $\hat{Y}_i$ is the predicted value, and $n$ is the number of samples;

[0022] Optimization algorithm: Use gradient descent to update model parameters θ:

[0023]

[0024] Among them, $\alpha$ is the learning rate, $\nablaL(\theta)$ is the gradient of the loss function with respect to the parameters;

[0025] Scenario simulation and analysis

[0026] Using specific climate model RCPs to predict future climate scenarios C f

[0027] Climate change data C f Input into machine learning model to obtain prediction of earthfill strength

[0028]

[0029] pass Assess the risk of earthfill under different climate scenarios, where R is the risk value and g is the risk assessment function;

[0030] Use gradients or other methods to estimate the impact of different features on the model output, such as the rate of change of the output after changing an input feature.

[0031] Preferably, the environmental impact warning system also includes an optimized earth filling control system

[0032] Real-time collection of meteorological and soil data t , which is fed back into the model;

[0033] Using feedback data (D t) Update models to make earthwork decisions more accurately.

[0034] Working principle: This system first plans and designs the project, uses GIS technology to perform terrain analysis, and provides accurate terrain data for the BIM model. Then, GIS data is integrated into the BIM model to achieve collaborative design by multiple professional teams, ensuring that the design plan takes geographical and environmental factors into consideration. After the planning is completed, preparations are made before construction, and construction simulation is performed through BIM software to predict problems that may be encountered during the construction process. In the process of predicting construction, an effective machine learning model is established to predict and adapt to the impact of climate change on the earthwork filling control system of water conservancy projects. Specifically, data collection and integration are first carried out, and then the collected data is sorted and processed, a machine learning model is established, and scenario simulation and analysis are performed to optimize the earthwork filling control system. Finally, adaptation measures are formulated;

[0035] While the model is predicting, BIM and GIS are integrated to optimize the storage and transportation of materials, reduce waste and costs. During the construction process, the sensor data at the construction site is uploaded in real time and integrated with the BIM model to achieve real-time monitoring of the construction progress and quality. At the same time, GIS technology is used to detect environmental changes in the construction area. While the system is monitoring the construction process, GIS is used to analyze the impact of construction activities on the surrounding ecological environment, including vegetation destruction and interference with wildlife habitats. Environmental protection measures are then planned in the BIM model, including the setting up of temporary fences and real-time soil and water conservation measures. After construction, BIM and GIS data are used to evaluate the performance of the filling project, including the effectiveness of the drainage system and the stability of the filling body. A long-term maintenance plan based on BIM and GIS data is formulated to ensure the continued stable operation of the project.

[0036] (III) Beneficial effects

[0037] The present invention provides a water conservancy project earthwork filling control system based on BIM+GIS. It has the following beneficial effects:

[0038] 1. The present invention provides a water conservancy project earth filling control system based on BIM+GIS. The system establishes a machine learning model to accurately predict the impact of climate change on the water conservancy project earth filling control system, and then make effective response strategies. Moreover, through system adaptability and adjustment strategies, the water conservancy project earth filling control system can flexibly respond to different climatic conditions to ensure construction continuity and project quality.

[0039] 2. The present invention provides a water conservancy project earth filling control system based on BIM+GIS. By integrating BIM and GIS technology into the water conservancy project earth filling control system, the system can improve the planning, design, construction and management efficiency of the project, while reducing environmental impact and improving project quality and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is the overall system flow chart of the present invention;

[0041] Figure 2 It is a flow chart of the environment management module of the present invention;

[0042] Figure 3 It is a flow chart of the integrated management system of the present invention.

[0043] Among them, 1. BIM and GIS-based control system; 2. System components; 3. System functions; 4. Environmental impact warning system; 201. Hardware equipment; 202. Software platform; 203. User interface; 204. Data integration and management module; 301. Design and planning module; 302. Construction simulation module; 303. Real-time monitoring module; 304. Quality management module; 305. Environmental management module; 306. Safety management module; 307. Analysis report module. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] Example:

[0046] like Figure 1-3 As shown, an embodiment of the present invention provides a water conservancy project earthwork filling control system based on BIM+GIS, including a control system based on BIM and GIS, wherein the control system based on BIM and GIS is connected with system components and system functions, and the system components and system functions are connected with an environmental impact early warning system;

[0047] Specifically, in the above specific embodiments, the system can flexibly respond to different climatic conditions and ensure the continuity of construction and project quality through the adaptability and adjustment strategy of the above environmental impact early warning system.

[0048] The environmental impact warning system includes data collection and integration, the data collection and integration is connected with the establishment of a machine learning model, the establishment of a machine learning model is connected with scenario simulation and analysis, the scenario simulation and analysis is connected with the optimization of earth filling, the optimization of earth filling is connected with the formulation of adaptation measures, the system functions include a design and planning module, a construction simulation module, a real-time monitoring module, a quality management module, an environmental management module, a safety management module and an analysis report module, the environmental management module includes an integrated management system, and the integrated management system includes rainy season construction adaptability and adjustment strategies, drought climate adaptability and adjustment strategies, cold climate adaptability and adjustment strategies, high temperature climate adaptability and adjustment strategies and wind and sand climate adaptability and adjustment strategies;

[0049] Specifically, in the above-mentioned specific embodiment, in the process of executing the earth filling control of the water conservancy project, the system first carries out the planning and design of the project. GIS technology is used to perform terrain analysis to provide accurate terrain data for the BIM model. Subsequently, GIS data is integrated into the BIM model to realize the collaborative design of multiple professional teams to ensure that the design plan fully considers geographical and environmental factors. After the planning is completed, preparatory work is carried out before construction, and construction simulation is carried out through BIM software to predict problems that may be encountered during the construction process. In the process of predicting construction, an efficient machine learning model is established, and the impact of climate change on the earth filling control system of water conservancy projects is predicted and adapted. The specific steps include: first, data collection and integration, then sorting and processing the collected data, establishing a machine learning model, then conducting scenario simulation and analysis, optimizing the earth filling control system, and finally formulating adaptation measures;

[0050] The environmental management module can adapt and adjust strategies according to different climate conditions:

[0051] 1. Construction adaptability and adjustment strategies in rainy season

[0052] Adaptability

[0053] Waterproof material: Use waterproof material to cover the fill area to prevent excessive moisture from affecting the stability of the earth.

[0054] Drainage system: Strengthen the on-site drainage system to ensure that rainwater is discharged quickly and reduce water accumulation.

[0055] Adjustment strategy

[0056] Adjust construction schedule: Prioritize work that will be indoors or not affected by rain.

[0057] Increase the number of compaction passes: Since the moisture content of the soil may increase during the rainy season, it may be necessary to increase the number of compaction passes to ensure compaction.

[0058] Use fast-draining soil: Choose sandy soil or dope materials to improve drainage.

[0059] 2. Adaptability and adjustment strategies to drought climate

[0060] Adaptability

[0061] Soil Moisture Monitoring: Use soil moisture sensor to monitor soil moisture in real time.

[0062] Automatic sprinkler system: Equipped with automatic sprinkler system to adjust the moisture content of soil.

[0063] Adjustment strategy

[0064] Adjust compaction parameters: In drought conditions, it may be necessary to reduce the number of compaction passes because the soil may be too dry.

[0065] Increase watering frequency: According to the soil moisture monitoring results, increase watering in time to maintain the moisture content of the soil.

[0066] 3. Cold climate adaptability and adjustment strategies

[0067] Adaptability

[0068] Antifreeze materials: Protect the fill area with antifreeze or insulation materials.

[0069] Heating equipment: In extremely low temperature conditions, heating equipment is used to increase the temperature of the soil.

[0070] Adjustment strategy

[0071] Winter Construction Restrictions: Restrict or suspend construction activities during extreme low temperature conditions.

[0072] Use cold-resistant soil materials: Choose soil materials suitable for construction under low temperature conditions.

[0073] Adjust construction time: try to carry out construction during the day when the temperature is higher.

[0074] 4. Adaptability and adjustment strategies to high temperature climate

[0075] Adaptability

[0076] Temperature monitoring: Use temperature sensors to monitor soil and ambient temperature.

[0077] Shading measures: Provide shading measures for construction equipment and personnel to reduce heat stress.

[0078] Adjustment strategy

[0079] Adjust working times: Avoid working during the hottest hours of the day.

[0080] Increase rest time: Provide workers with more rest time to prevent heat stroke.

[0081] Use cooling equipment: Install cooling systems for construction equipment to prevent overheating.

[0082] 5. Adaptability and adjustment strategies to sandstorm climate

[0083] Adaptability

[0084] Wind speed monitoring: Use an anemometer to monitor wind speed and assess dust risks.

[0085] Dust-proof net: Use dust-proof net to cover the filling area to reduce dust.

[0086] Adjustment strategy

[0087] Increase watering: Increase the frequency of watering in windy and sandy weather to reduce dust.

[0088] Adjust construction methods: In strong wind conditions, it may be necessary to suspend construction activities that are prone to generating dust.

[0089] 6. Integrated management system

[0090] Intelligent decision support system: Integrates climate data and sensor information to provide real-time construction adjustment suggestions.

[0091] Emergency Plan: Develop emergency plans for extreme weather conditions to ensure construction safety and quality.

[0092] In this system, the steps to build a machine learning model are as follows:

[0093] S1. Data collection and integration

[0094] Collect meteorological data including temperature T, precipitation P and wind speed W, soil moisture H, groundwater level G and soil type S, vegetation coverage V, terrain slope R and land use LU;

[0095] And integrate BIM and GIS to obtain geometric information of water conservancy projects, manage and analyze geographic spatial data, and establish spatial data models.

[0096] S2. Build a machine learning model

[0097] Feature set: Define the input feature set X and the target variable Y

[0098] Input features: X = [H, P, T, S, G, W, V, R, LU]

[0099] Target variable: Y represents the strength and stability of earth fill, specifically the compression strength C or settlement D;

[0100] Select algorithm: Artificial neural network ANN

[0101] Model Training

[0102] Loss function: Define the loss function L suitable for the target, including the mean square error (MSE):

[0103]

[0104] Among them, $Y_i$ is the true value, $\hat{Y}_i$ is the predicted value, and $n$ is the number of samples;

[0105] Optimization algorithm: Use gradient descent to update model parameters θ:

[0106]

[0107] Among them, $\alpha$ is the learning rate, $\nablaL(\theta)$ is the gradient of the loss function with respect to the parameters;

[0108] S3. Scenario simulation and analysis

[0109] Using specific climate model RCPs to predict future climate scenarios C f

[0110] Climate change data C f Input into machine learning model to obtain prediction of earthfill strength

[0111]

[0112] pass Assess the risk of earthfill under different climate scenarios, where R is the risk value and g is the risk assessment function;

[0113] Use gradients or other methods to estimate the impact of different features on the model output, such as the rate of change of the output after changing an input feature.

[0114] S4. Optimize earth filling control system

[0115] Real-time collection of meteorological and soil data t , which is fed back into the model;

[0116] Using feedback data (D t) Updated models to make earthwork decisions more accurately;

[0117] The data visualization platform built on BIM and GIS provides engineers with real-time data and model output to optimize construction decisions.

[0118] S5. Development of adaptation measures

[0119] Based on the analysis results, specific policy recommendations are put forward, including improving earth filling standards to enhance risk resistance.

[0120] At this point, this system can effectively establish a machine learning model to accurately predict the impact of climate change on the earthfill control system of water conservancy projects, and then make effective response strategies.

[0121] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The water conservancy project earthwork filling control system based on BIM+GIS includes a control system based on BIM and GIS, which is characterized by: The BIM and GIS-based control system is connected with system components and system functions, and the system components and system functions are connected with an environmental impact early warning system; The environmental impact warning system includes data collection and integration, which is connected to the establishment of a machine learning model, which is connected to scenario simulation and analysis, which is connected to the optimization of earth filling, and which is connected to the formulation of adaptive measures.

2. The water conservancy project earthwork filling control system based on BIM+GIS according to claim 1 is characterized in that: The system functions include design and planning module, construction simulation module, real-time monitoring module, quality management module, environmental management module, safety management module and analysis report module.

3. The water conservancy project earthwork filling control system based on BIM+GIS according to claim 2 is characterized in that: The environmental management module includes an integrated management system, which includes rainy season construction adaptability and adjustment strategies, drought climate adaptability and adjustment strategies, cold climate adaptability and adjustment strategies, high temperature climate adaptability and adjustment strategies, and wind and sand climate adaptability and adjustment strategies.

4. The water conservancy project earthwork filling control system based on BIM+GIS according to claim 1 is characterized in that: The environmental impact early warning system specifically includes: Data collection and integration Collect meteorological data including temperature T, precipitation P and wind speed W, soil moisture H, groundwater level G and soil type S, vegetation coverage V, terrain slope R and land use LU; Building a machine learning model Input features: X = [H, P, T, S, G, W, V, R, LU] Target variable: Y represents the strength and stability of earth fill, specifically the compression strength C or settlement D; Select algorithm: Artificial neural network ANN Model Training Loss function: Define the loss function L suitable for the target, including the mean square error (MSE): Among them, $Y_i$ is the true value, $\hat{Y}_i$ is the predicted value, and $n$ is the number of samples; Optimization algorithm: Use gradient descent to update model parameters θ: Among them, $\alpha$ is the learning rate, $\nabla L(\theta)$ is the gradient of the loss function with respect to the parameters; Scenario simulation and analysis Using specific climate model RCPs to predict future climate scenarios C f Climate change data C f Input into machine learning model to obtain prediction of earthfill strength pass Assess the risk of earthfill under different climate scenarios, where R is the risk value and g is the risk assessment function; Use gradients or other methods to estimate the impact of different features on the model output, such as the rate of change of the output after changing an input feature.

5. The water conservancy project earthwork filling control system based on BIM+GIS according to claim 1 is characterized in that: The environmental impact early warning system also includes optimizing the earth filling control system Real-time collection of meteorological and soil data t , which is fed back into the model; Using feedback data (D t) Update models to make earthwork decisions more accurately.

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