An intelligent water conservancy construction management platform

Abstract

The present invention belongs to the technical field of digital management. The present invention provides an intelligent water conservancy construction management platform, which includes a database module, an AI data analysis module, and a construction management module. The database module is used to obtain multi-source water conservancy project information. Each processing sub-block in the AI data analysis module is used to perform standardized processing on various types of data. The correlation analysis block is used to perform correlation analysis on various types of data to obtain correlation features. The construction management module is used to construct a simulation model of the water conservancy construction site according to the standardized processed various types of data and correlation features, and obtain a construction simulation result based on the current construction data. By using a spatio-temporal convolutional neural network in the correlation analysis block to mine data correlation features and adaptively adjust model parameters, the construction management module constructs a precise model and updates the simulation in real time, improving data availability, model accuracy, the scientific nature and efficiency of construction management, and can effectively optimize resource allocation, ensure construction quality and safety.

Description

Technical Field

[0001] The present invention belongs to the technical field of digital management, and in particular relates to an intelligent water conservancy construction management platform. Background Art

[0002] The construction of water conservancy projects covers multiple specialties and links, and construction management needs to comprehensively consider many factors. With the wide application of digital and intelligent technologies in the water conservancy field, technologies such as digital twins, sensors, and data analysis bring new ways for construction site management and simulation. The data sources of water conservancy projects are extensive, including various types of data such as construction sites, water bodies, geology, and ecological environments, which have the characteristics of multi-source heterogeneity, large volume, and dynamic changes. At the same time, the need to timely master information such as construction progress, resource utilization, and equipment status during construction management and make timely decisions, as well as to consider the impact of the project on the environment to achieve sustainable development, makes it inevitable to establish an accurate and reliable construction site simulation model.

[0003] Currently, water conservancy projects obtain data by means of real-time collection using various sensors combined with manual input, or adopt methods such as data processing and analysis, model construction, and model-based simulation prediction to assist in decision-making. However, there are still many deficiencies in the existing technologies. For example, at the data level, there are quality problems, integration difficulties, and security risks in the existing technologies; in terms of models, their accuracy, calculation efficiency, and adaptability need to be improved; in terms of technology integration, the technology synergy is poor and there is a lack of unified standards, which all lead to difficulties in sharing and reusing data and models between different projects, increasing system costs and affecting the overall operation efficiency and performance. Summary of the Invention

[0004] In view of this, the present invention provides an intelligent water conservancy construction management platform, aiming to solve the problems that there are still many deficiencies in the existing technologies, increasing system costs and affecting the overall operation efficiency and performance.

[0005] The first aspect of the embodiment of the present invention provides an intelligent water conservancy construction management platform, including:

[0006] A database module, an AI data analysis module, and a construction management module;

[0007] The database module is used to obtain multi-source water conservancy project information; among them, the multi-source water conservancy project information includes the following types of data: water conservancy construction site data, water body data, geological data, and ecological environment data;

[0008] The AI data analysis module includes multiple processing sub-blocks and a correlation analysis block;

[0009] Each processing sub-block is used to perform standardization processing on various types of data;

[0010] The association analysis block is used to perform association analysis on various types of data to obtain association features; among them, the association features include: direct time association features, direct space association features, indirect time association features, and indirect space association features;

[0011] The construction management module is used to construct a simulation model of the water conservancy construction site based on various types of data and association features after standardized processing, and to obtain the current construction data in real time. According to the current construction data and the simulation model of the water conservancy construction site, the construction simulation result is obtained.

[0012] In some possible implementation manners, the association analysis block is used to: input various types of data into a pre-established spatio-temporal convolutional neural network model to obtain association features.

[0013] In some possible implementation manners, the spatio-temporal convolutional neural network model includes an input layer, a spatio-temporal convolutional layer, a feature fusion layer, a feature separation layer, an association extraction layer, and an output layer. The association analysis block is used to:

[0014] Input various types of data from the input layer into the spatio-temporal convolutional layer to obtain multiple first feature maps;

[0015] Input the multiple first feature maps into the feature fusion layer to obtain a fused feature;

[0016] Input the fused feature into the feature separation layer to obtain direct time association features and direct space association features;

[0017] Input the fused feature, direct time association features, and direct space association features into the association extraction layer to obtain indirect time association features and indirect space association features;

[0018] Output the direct time association features, direct space association features, indirect time association features, and indirect space association features through the output layer.

[0019] In some possible implementation manners, each processing sub-block is used to:

[0020] Perform data cleaning, standardized conversion, and storage on each type of data to obtain various types of data after standardized processing.

[0021] In some possible implementation manners, the spatio-temporal convolutional neural network model further includes a parameter adjustment layer; the association analysis block is used to:

[0022] Obtain the feature information of the standardized processing in each processing sub-block;

[0023] Input the feature information into the parameter adjustment layer to obtain parameter adjustment information;

[0024] Adjust the parameters of the spatio-temporal convolutional neural network model according to the parameter adjustment information.

[0025] In some possible implementations, the construction management module is used to:

[0026] Establish a physical model of the water conservancy construction site;

[0027] Map the water conservancy construction site based on various types of data and the physical model to establish a digital twin model;

[0028] Perform dynamic simulation on the digital twin model according to the association characteristics to obtain a simulation model of the water conservancy construction site.

[0029] In some possible implementations, the construction management module is used to:

[0030] Determine the dynamic information of the construction site according to the association characteristics and the behavior prediction model;

[0031] Adjust the behavior mapping of the simulation model of the water conservancy construction site according to the dynamic information.

[0032] In some possible implementations, the construction management module is used to:

[0033] Update the model parameters of the simulation model of the water conservancy construction site according to the current construction data;

[0034] Start the updated simulation model of the water conservancy construction site for simulation calculation to obtain the construction simulation result.

[0035] In some possible implementations, the platform further includes an evaluation module, and the evaluation module is used to:

[0036] Evaluate the construction simulation result to obtain an evaluation result.

[0037] In some possible implementations, the evaluation module is further used to:

[0038] Determine the construction management plan for the water conservancy project according to the evaluation result and the current construction data.

[0039] The intelligent water conservancy construction management platform provided by the embodiments of the present invention includes a database module, an AI data analysis module, and a construction management module. The database module is used to obtain multi-source water conservancy project information. The AI data analysis module includes multiple processing sub-blocks and an association analysis block. Each processing sub-block is used to perform standardized processing on various types of data. The association analysis block is used to perform association analysis on various types of data to obtain association features. The construction management module is used to construct a simulation model of the water conservancy construction site based on the standardized processed various types of data and association features, and to obtain the current construction data in real time. According to the current construction data and the simulation model of the water conservancy construction site, a construction simulation result is obtained. By integrating multi-source data through the database module to break data islands, the processing sub-blocks perform standardized processing to improve data quality, the association analysis block uses a spatio-temporal convolutional neural network to mine data association features and adaptively adjust model parameters, the construction management module constructs an accurate model and updates the simulation in real time, and combines the results of the evaluation module to determine a scientific construction management plan, realizing the full-process optimization from data integration, analysis to model construction, simulation, and decision support, improving data availability, model accuracy, the scientific nature and efficiency of construction management, effectively optimizing resource allocation, ensuring construction quality and safety, and contributing to the sustainable development of water conservancy projects. Description of the Drawings

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0041] Figure 1 It is a schematic structural diagram of the intelligent water conservancy construction management platform provided by the embodiments of the present invention. Detailed Embodiments

[0042] In the following description, specific details such as specific system structures and technologies are proposed for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0043] Figure 1 It is a schematic structural diagram of the intelligent water conservancy construction management platform provided by the embodiments of the present invention. As Figure 1 shown, in some embodiments, the intelligent water conservancy construction management platform includes:

[0044] Database module 11, AI data analysis module 12, and construction management module 13;

[0045] The database module 11 is used to obtain multi-source water conservancy project information; among them, the multi-source water conservancy project information includes the following types of data: water conservancy construction site data, water body data, geological data, and ecological environment data; the AI data analysis module 12 includes multiple processing sub-blocks and an association analysis block; each processing sub-block is used to perform standardization processing on various types of data; the association analysis block is used to perform association analysis on various types of data to obtain association features; among them, the association features include: direct time association features, direct space association features, indirect time association features, and indirect space association features; the construction management module 13 is used to construct a water conservancy construction site simulation model based on the standardized processing of various types of data and the association features, and to obtain the current construction data in real time, and to obtain the construction simulation result based on the current construction data and the water conservancy construction site simulation model.

[0046] In the embodiment of the present invention, the database module plays a crucial role in the intelligent water conservancy construction management platform. It is responsible for obtaining multi-source water conservancy project information, which covers multiple aspects such as water conservancy construction site data, water body data, geological data, and ecological environment data.

[0047] For the operation data of construction equipment: The database module is connected to various sensors installed on construction equipment (such as excavators, cranes, concrete mixers, etc.) to obtain the operation parameters of the equipment in real time. These parameters include the engine speed, temperature, oil pressure, working hours of the equipment, load, etc. For example, for an excavator, the sensor can monitor data such as the working angle of the excavating arm, the excavating depth, and the fuel consumption of the engine in real time, and transmit these data to the database module for storage and management. By analyzing these data, the operation status of the equipment can be understood, potential equipment failures can be discovered in time, the maintenance and repair plan of the equipment can be reasonably arranged, and the use efficiency and service life of the equipment can be improved.

[0048] For the information of construction personnel: With the help of the positioning system and attendance equipment, the database module can obtain information such as the location and attendance of construction personnel. The location information of construction personnel can help managers master the personnel distribution in real time, allocate human resources reasonably, and improve construction efficiency. For example, when more manpower is needed in a certain construction area, managers can quickly find nearby and idle construction personnel for allocation by viewing the personnel location information. The attendance recorded by the attendance equipment can be used to calculate the working hours of personnel, evaluate work performance, and also help to protect the labor rights and interests of construction personnel.

[0049] For construction progress data: Using devices such as cameras and laser scanners, the database module can monitor and record the actual progress of the construction site. For example, by regularly performing three-dimensional laser scanning on the construction site, information such as the shape and dimensions of the building can be obtained and compared with the design model to accurately calculate the construction progress. For large-scale water conservancy projects such as dam construction, displacement sensors installed at key positions can also be used to monitor the deformation of the dam body in real time and promptly detect potential safety hazards during the construction process. In addition, construction workers can also manually enter relevant information on the construction progress, such as the amount of work completed and the progress of the construction area, through devices such as mobile terminals, to ensure that the database module can comprehensively and accurately grasp the construction progress situation.

[0050] For water level and flow data: The database module is connected to devices such as water level gauges and flow meters installed in the water body to obtain data on the water level height and flow rate in real time. These data are crucial for the scheduling and operation of water conservancy projects. For example, in reservoir management, accurately grasping the water level and flow data can reasonably control the water storage capacity and flood discharge volume of the reservoir, ensure the safe operation of the reservoir, and at the same time help to reasonably allocate water resources to meet different needs such as irrigation and power generation. In addition, changes in water level and flow data can also reflect the dynamic changes of the water body and provide important basis for flood control, drought relief and other work.

[0051] For water quality parameter data: Through water quality monitors, the database module can measure water quality parameters such as the pH value, dissolved oxygen, and chemical oxygen demand of the water body. Changes in water quality data can reflect the pollution status of the water body and the health of the ecological environment. For example, when the chemical oxygen demand in the water body exceeds the standard, it may mean that the water body is polluted by organic matter and corresponding measures need to be taken for treatment in a timely manner. The long-term monitoring and recording of these water quality parameters by the database module helps to analyze the trend of water quality changes, evaluate the impact of water conservancy projects on the water environment, and provide data support for water resource protection and ecological restoration.

[0052] For geological structure data: The database module collects underground geological structure information obtained through geological exploration (such as drilling, geophysical exploration, etc.), including the distribution of rock layers, soil types, faults, etc. These data are of great significance for the basic design and construction safety of water conservancy projects. For example, when building a dam, understanding the geological structure at the dam site can determine the type and depth of the foundation to ensure the stability of the dam. Through the analysis of geological structure data, potential geological problems such as landslides and collapses that may be encountered during the construction process can also be predicted and corresponding preventive measures can be taken in advance.

[0053] For geotechnical mechanics parameter data: Laboratory tests and in-situ tests are used to obtain the mechanical property data of rock and soil, such as compressive strength, shear strength, deformation modulus, etc. These parameters are the basis for constructing geological models and play a key role in evaluating the bearing capacity of foundations, calculating soil deformation and stability, etc. The database module stores these geotechnical mechanics parameter data, providing accurate mechanical basis for the structural design and construction of hydraulic engineering to ensure the safety and reliability of the project.

[0054] For meteorological data: The database module interacts with meteorological stations to collect meteorological information of the construction site and its surrounding areas, such as temperature, humidity, wind speed, precipitation, etc. Meteorological conditions have important impacts on construction progress and the ecological environment. For example, extreme weather such as high temperature and heavy rain may cause construction suspension and affect construction progress; at the same time, meteorological factors such as precipitation and wind speed also affect soil erosion and the balance of the ecosystem. By obtaining meteorological data in real time, the database module can provide weather warning information for construction management, helping managers reasonably arrange construction plans and take corresponding protective measures to reduce the adverse impacts of meteorological factors on construction and the ecological environment.

[0055] For biodiversity data: Through field investigations and monitoring, the database module records biodiversity data such as the types and quantities of animals and plants in the construction site and its surrounding areas. Biodiversity data can reflect the stability and health status of the ecosystem. For example, in the process of hydraulic engineering construction, protecting biodiversity is crucial for maintaining ecological balance and sustainable development. The recording and analysis of biodiversity data by the database module help evaluate the impact of project construction on the ecological environment, take corresponding ecological protection measures, protect biological habitats, and reduce the damage to biodiversity.

[0056] In some embodiments, the association analysis block is used to: input various types of data into a pre-established spatio-temporal convolutional neural network model to obtain association features. In some embodiments, the spatio-temporal convolutional neural network model includes an input layer, a spatio-temporal convolutional layer, a feature fusion layer, a feature separation layer, an association extraction layer, and an output layer. The association analysis block is used to: input various types of data from the input layer into the spatio-temporal convolutional layer to obtain multiple first feature maps; input the multiple first feature maps into the feature fusion layer to obtain a fused feature; input the fused feature into the feature separation layer to obtain a direct time association feature and a direct space association feature; input the fused feature, the direct time association feature, and the direct space association feature into the association extraction layer to obtain an indirect time association feature and an indirect space association feature; output the direct time association feature, the direct space association feature, the indirect time association feature, and the indirect space association feature through the output layer.

[0057] In the embodiments of the present invention, the spatio-temporal convolutional neural network model is designed specifically for processing data with spatio-temporal characteristics. It consists of an input layer, a spatio-temporal convolutional layer, a feature fusion layer, a feature separation layer, an association extraction layer, and an output layer. This structure can deeply explore the spatio-temporal association information in the data layer by layer, from basic feature extraction to the acquisition of complex association features, providing comprehensive and accurate association features for water conservancy construction management.

[0058] In the embodiments of the present invention, the spatio-temporal convolutional layer is a core component of the spatio-temporal convolutional neural network, specifically used for processing data with time and space dimension information. In the scenario of water conservancy construction management, data such as water conservancy construction site data, water body data, geological data, and ecological environment data all contain rich spatio-temporal characteristics. The spatio-temporal convolutional layer can effectively capture the local features of these data in the spatio-temporal dimension, explore the spatio-temporal association patterns in the data, and provide an important basis for subsequent feature fusion, analysis, and final decision-making.

[0059] The convolution operation is the basis of the spatio-temporal convolutional layer. It slides a convolution kernel (also called a filter) over the input data and performs a weighted summation calculation on the data. In traditional two-dimensional convolution, the convolution kernel slides in the spatial dimensions (length and width) of the image; while in spatio-temporal convolution, the convolution kernel not only slides in the spatial dimension but also moves in the time dimension, thus considering both the time and space information of the data simultaneously. The spatio-temporal convolution kernel is a three-dimensional filter, which has a certain size in the three dimensions of time, length, and width in space. At a water conservancy construction site, the construction progress changes in different spatial areas over time. The spatio-temporal convolutional layer can capture such spatio-temporal change features. For example, by performing a convolution operation on the construction completion data at different time points and different construction areas, the convolution kernel can learn the advancement pattern of the construction progress, such as whether there are periodic accelerations or decelerations in the construction of certain areas, and the mutual influence relationship of the construction progress between different areas. If the foundation construction progress in a certain area speeds up, it may promote the subsequent construction in adjacent areas, and this association information can be discovered through the feature extraction of the spatio-temporal convolutional layer.

[0060] Water body data (such as water level, flow, water quality, etc.) also has obvious spatiotemporal characteristics. The spatiotemporal convolution layer can process water body data at different time and spatial locations to explore the laws of water body changes. For example, for water level data in rivers, the spatiotemporal convolution layer can identify the change patterns of water levels in different seasons and different river sections, as well as the propagation characteristics of extreme events such as floods in time and space. By analyzing these characteristics, the trend of water level changes can be predicted in advance, providing a decision-making basis for the scheduling of water conservancy projects and flood prevention and disaster reduction. There are also complex associations between geological data (such as soil type, geological structure, etc.) and ecological environment data (such as vegetation coverage, biodiversity, etc.) in time and space. The spatiotemporal convolution layer can extract the spatiotemporal correlation characteristics between these data. For example, changes in geological conditions may affect the growth and distribution of vegetation in the ecological environment, and changes in vegetation may in turn affect soil erosion and water quality. The spatiotemporal convolution layer can capture this complex interaction relationship and provide support for the ecological environment protection and sustainable development of water conservancy projects.

[0061] Unlike traditional convolutional neural networks that can only process spatial information or simply flatten the time dimension, the spatiotemporal convolution layer can simultaneously consider the temporal and spatial dimensions of the data and capture the characteristics of the data more comprehensively. This makes it more accurate and effective when processing data with spatiotemporal characteristics. The spatiotemporal convolution layer can automatically learn spatiotemporal patterns and associated features from the data through the weight learning mechanism of the convolution kernel, without the need for manual extraction of complex features. This automatic learning capability enables the model to adapt to different water conservancy construction management scenarios and data changes, improving the generalization ability of the model.

[0062] In an embodiment of the present invention, the spatiotemporal convolution layer extracts multiple first feature maps from the input multi-source water conservancy project data (water conservancy construction site data, water body data, geological data, and ecological environment data, etc.) through a convolution operation. Each of these first feature maps contains information about the data at different spatiotemporal dimensions, different local areas, or different feature levels. However, the information expressed by a single first feature map is relatively limited and cannot fully reflect the overall picture and internal connection of the data. The task of the feature fusion layer is to fuse these scattered first feature maps with different focuses and organically combine the information they contain. In essence, the feature fusion layer is a process of comprehensive processing of multiple first feature maps, and through certain calculations or operations, they are converted into a new, more comprehensive feature representation, namely, a fusion feature. This fusion feature can more comprehensively capture the spatiotemporal characteristics of the data and the potential relationship between different data types, providing a richer information basis for subsequent analysis and decision-making.

[0063] Specifically, fusion based on the attention mechanism can be adopted. The attention mechanism can automatically learn the importance of each feature map at different positions or channels according to the content of the input feature map, and then perform fusion based on these importance levels. This method can more flexibly capture the relationships between different feature maps and improve the quality of the fused features.

[0064] In the embodiments of the present invention, the feature fusion layer can effectively integrate the feature information from different data sources. For example, the feature maps of the equipment operation data (such as the working status and operation parameters of the equipment) at the water conservancy construction site are fused with the water body data (such as water level, flow rate, water quality, etc.). Through the fusion, the potential impact of equipment operation on the water body environment can be discovered, such as whether the operation of certain construction equipment causes water pollution or water flow changes; or whether the changes in the water body environment will affect the normal operation of the equipment. Similarly, fusing the feature maps of geological data (such as geological structure, geotechnical mechanics parameters, etc.) and ecological environment data (such as vegetation coverage, biodiversity, etc.) helps to analyze the impact of geological conditions on the ecological environment and the feedback effect of ecological environment changes on geological stability. For example, through the fused features, it can be judged whether the destruction of vegetation will cause soil erosion under specific geological conditions, which in turn affects the foundation stability of the water conservancy project.

[0065] In the embodiments of the present invention, the feature separation layer receives the fused features from the feature fusion layer. Although the fused features integrate the information of multiple first feature maps, the time and space correlation features therein are intertwined. The core principle of the feature separation layer is to use specific algorithms and models to deeply analyze and process the fused features, identify and extract the direct correlation in the time dimension and the direct correlation in the space dimension of the data. The time correlation reflects the laws and mutual relationships of data changes over time, such as the progress of construction work over time and the change trend of water levels at different time points. The space correlation reflects the mutual connections in the spatial distribution of data, such as the differences in construction conditions in different areas of the construction site and the water quality distribution at different geographical locations. The role of the feature separation layer is to strip these two different-dimensional direct correlation features from the fused features for more detailed analysis and application in the future.

[0066] In the embodiments of the present invention, the feature separation layer can extract direct time correlation features from the fused features. It is usually composed of a series of operation units designed for the time dimension, and common structures include one-dimensional convolutional layers, recurrent neural networks and their variants (such as long short-term memory networks, gated recurrent units), etc. Among them, in time feature separation, the convolutional kernels of the one-dimensional convolutional layer slide in the time dimension and perform convolutional operations on the fused features. By setting different convolutional kernels, different patterns and features of the fused features in the time series can be captured. For example, smaller convolutional kernels can capture short-term time change features, while larger convolutional kernels are more suitable for extracting long-term time trend features. Network structures such as recurrent neural networks can effectively process data with time series characteristics. They control the flow and memory of information through gating mechanisms and can better capture long-term dependencies in the data. In the feature separation layer, these network structures can deeply analyze the time dimension of the fused features and extract direct time correlation features, such as the change law of the construction progress of hydraulic engineering over time, the correlation of the equipment operation status at different time points, etc.

[0067] If a one-dimensional convolutional layer is used, the convolutional kernel slides in the time dimension according to the set stride and performs convolutional calculations on the fused features. Each convolutional operation obtains a scalar value, and these scalar values constitute a time-related feature representation. Through the operations of multiple convolutional kernels, multiple time-related feature maps can be obtained. These feature maps contain the responses of the fused features in different time patterns, and finally, direct time correlation features are extracted through further processing.

[0068] If network structures such as recurrent neural networks are adopted, the fused features are input into the network in sequence according to the time order. The gating units (input gate, forget gate, output gate) in the network will determine which information to retain, which information to forget, and which information to output according to the input information and the previous memory state. In this way, the dependencies of the fused features in the time series can be learned, and direct time correlation features can be separated.

[0069] In an embodiment of the present invention, the feature separation layer can also separate direct spatial correlation features from the fused features. It is generally composed of a two-dimensional convolution layer, a pooling layer, etc. Among them, the convolution kernel of the two-dimensional convolution layer performs a sliding operation on the two dimensions of height and width of the space, and performs a convolution operation on the fused features. Through the convolution operation, the two-dimensional convolution layer can extract the local features and patterns of the fused features in space, such as the spatial relationship between different areas of the water conservancy construction site, the distribution characteristics of water quality in different geographical locations, etc. Convolution kernels of different sizes and numbers can capture spatial features of different scales. The pooling layer is usually used in conjunction with the two-dimensional convolution layer, and the most common ones are maximum pooling and average pooling. The role of the pooling layer is to downsample the feature map output by the convolution layer, reducing the amount of data while retaining important spatial features. Through the pooling operation, the dimension of the feature map can be reduced, the computational efficiency of the model can be improved, and overfitting can be prevented to a certain extent.

[0070] The convolution kernel slides in the height and width directions of the space, and performs convolution calculations on each position of the fused feature to obtain a series of feature maps. These feature maps reflect the local features of the fused features at different spatial positions. Next, the pooling layer downsamples the feature maps output by the convolution layer. Maximum pooling selects the maximum value in each pooling area as the output, while average pooling calculates the average value of each pooling area as the output. Through the pooling operation, the dimension of the feature map is reduced while retaining important spatial features, and finally the direct spatial correlation features are obtained.

[0071] In an embodiment of the present invention, in the spatiotemporal convolutional neural network, the association extraction layer is a key component, which inherits the processing results of the feature fusion layer and the feature separation layer, and further mines the indirect time correlation features and indirect space correlation features in the data, providing more comprehensive and in-depth information support for water conservancy construction management.

[0072] The association extraction layer receives the fusion features from the feature fusion layer, as well as the direct time association features and direct space association features output by the feature separation layer. The fusion features integrate the information of multiple first feature maps output by the spatiotemporal convolution layer, and contain rich but complex spatiotemporal feature information; the direct time association features reflect the direct connection of data in the time dimension, such as the trend of construction progress over time, the association of equipment operating parameters at different time points, etc.; the direct space association features reflect the direct relationship of data in the spatial dimension, such as the spatial position relationship between different construction areas, the spatial distribution characteristics of water quality, etc. These input data provide a rich information basis for the association extraction layer to mine indirect association features.

[0073] The association extraction layer uses deep learning models, such as long short-term memory networks (LSTM), gated recurrent units (GRU), graph neural networks (GNN), etc., to mine the indirect association features in the data. The association extraction layer mines the indirect associations between them by interacting and combining the input fusion features, direct time association features, and direct space association features. It is not just an analysis of individual features, but rather discovers potential and complex association relationships through the interaction and influence between features. For example, combining the water body data features in the fusion features with the construction area location information in the direct space association features to analyze how the flow and change of the water body indirectly affect the construction progress and quality of different construction areas; or interacting the equipment operation time information in the direct time association features with the construction environment data features in the fusion features to mine the indirect impact of the long-term operation of the equipment under different environmental conditions on its performance.

[0074] The deep learning model of the association extraction layer needs to be trained with a large amount of data to learn the indirect association feature patterns in the data. During the training process, the parameters of the model are adjusted according to the difference between the predicted results of the model and the actual data to optimize the performance of the model. Through continuous training and parameter adjustment, the association extraction layer can more accurately extract the indirect time association features and indirect space association features in the data, improving the model's understanding and expression ability of complex association relationships in water conservancy construction management.

[0075] In some embodiments, each processing sub-block is used to: perform data cleaning, standardization conversion, and storage on each type of data to obtain various types of data after standardized processing.

[0076] In the embodiments of the present invention, in the intelligent water conservancy construction management platform, the processing sub-block undertakes the important task of preprocessing each type of data in the multi-source water conservancy project information, specifically including three key steps: data cleaning, standardization conversion, and storage, to obtain various types of data after standardized processing, providing a high-quality data basis for subsequent analysis and model construction.

[0077] The data sources of water conservancy projects are extensive and complex. During the acquisition process, problems such as noise, missing values, and outliers will inevitably be introduced, which will affect the quality of the data and the accuracy of subsequent analysis. Therefore, data cleaning is required. Among them, data cleaning specifically includes noise removal, missing value processing, outlier identification and processing, etc.

[0078] Noise removal: Due to factors such as the accuracy limitation of sensors and environmental interference, the collected data may contain noise. The processing sub-block will use filtering algorithms to remove noise, such as mean filtering, median filtering, Gaussian filtering, etc. For example, for the vibration noise in the equipment operation data at the water conservancy construction site, the data can be smoothed through mean filtering to reduce the interference of noise on the judgment of the equipment operation state.

[0079] Missing value handling: Data missing is a common problem, which may be caused by reasons such as sensor failures and data transmission interruptions. The processing sub-block will select appropriate methods to handle missing values according to the characteristics of the data. For a small number of missing values, interpolation methods such as linear interpolation and spline interpolation can be used to estimate the missing values based on the trend of the existing data; for a large number of missing values, it may be necessary to re-collect the data or use other statistical methods for processing. For example, in water body data, if the water level data for a certain period is missing, the missing value can be filled by linear interpolation using the water level data at the previous and subsequent time points.

[0080] Outlier identification and handling: Outliers refer to observed values that deviate significantly from other data, which may be caused by measurement errors, equipment failures or other special reasons. The processing sub-block will identify outliers by setting reasonable threshold ranges or using statistical methods (such as box plots, 3σ principle, etc.). For outliers, they are generally corrected or removed according to specific circumstances. For example, in geological data, if the rock hardness value of a certain sample significantly exceeds the normal range and is found to be caused by a measurement error after inspection, the outlier can be removed.

[0081] In the embodiments of the present invention, since the water conservancy project data includes various types, such as numerical type, categorical type, etc., and the dimensions and value ranges of different types of data vary greatly, in order to make the data comparable and facilitate subsequent processing, it is necessary to perform standardization conversion.

[0082] In the embodiments of the present invention, the data after data cleaning and standardization conversion needs to be stored for subsequent analysis and use. The processing sub-block will store various types of data after standardization processing in a database. The database can be a relational database (such as MySQL, Oracle) or a non-relational database (such as MongoDB, Redis), depending on the characteristics of the data and application requirements. The stored data should have good organization and indexing for quick retrieval and query. For example, the water conservancy construction site data, water body data, geological data and ecological environment data are stored in different tables respectively, and corresponding indexes are established to facilitate data reading during correlation analysis and model construction.

[0083] In some embodiments, the spatio-temporal convolutional neural network model further includes a parameter adjustment layer; the association analysis block is used to: obtain the feature information after standardization processing in each processing sub-block; input the feature information into the parameter adjustment layer to obtain parameter adjustment information; adjust the parameters of the spatio-temporal convolutional neural network model according to the parameter adjustment information.

[0084] In the embodiments of the present invention, the operation of the parameter adjustment layer is specifically as follows:

[0085] 1. Feature information acquisition. The primary operation of the parameter adjustment layer is to obtain the standardized feature information from each processing sub-block. These feature information cover the key features of multi-source data in the field of water conservancy projects after processing, specifically as follows:

[0086] Characteristics of construction site data in water conservancy projects: Include the numerical characteristics after standardization of the operating parameters of construction equipment (such as rotational speed, temperature, load, etc.), and the feature representations after processing of the attendance and location information of construction personnel. For example, statistical features such as the mean and variance of equipment operating parameters can reflect the stability of equipment operation and the changing trend of working conditions. These information are crucial for the parameter adjustment layer to judge whether the model's learning of equipment operation is accurate.

[0087] Characteristics of water body data: Include the characteristic values after standardization of water level, flow rate, water quality indicators (such as pH value, dissolved oxygen, etc.). For example, the amplitude and frequency characteristics of water level changes, and the correlation characteristics between different water quality indicators, etc., can help the parameter adjustment layer understand the performance of the model in processing water body data and judge whether it is necessary to adjust parameters to better capture the dynamic changes of the water body.

[0088] Characteristics of geological data: Involve the characteristic information after standardization of geological structures (such as rock layer distribution, soil type, etc.) and geotechnical mechanical parameters (such as compressive strength, shear strength, etc.). For example, the spatial distribution characteristics of geological structures and the variation degree of geotechnical mechanical parameters are of great significance for the parameter adjustment layer to evaluate the model's simulation and analysis ability of geological conditions, so as to determine whether to adjust the model parameters accordingly.

[0089] Characteristics of ecological environment data: Include the characteristics after processing of meteorological data (such as temperature, humidity, wind speed, etc.) and biodiversity data (such as types and quantities of animals and plants, etc.). For example, the seasonal change characteristics of temperature and humidity, and the changing trend of biodiversity indicators, etc. The parameter adjustment layer judges the accuracy and adaptability of the model in processing ecological environment data based on these characteristic information, and then adjusts the model parameters.

[0090] 2. Input and analysis of feature information

[0091] After inputting the various standardized feature information obtained into the parameter adjustment layer, this layer will conduct in-depth analysis on this information, mainly through the following methods:

[0092] Statistical analysis: Calculate various statistics of the input feature information, such as mean, median, standard deviation, coefficient of variation, etc. Through these statistics, the parameter adjustment layer can understand the data distribution, and judge whether there are abnormalities or deviations from expectations in the data. For example, if it is found that the standard deviation of a certain type of data is too large, it may mean that there is a large uncertainty in the model's processing of this data, and parameters need to be adjusted to improve the stability of the model.

[0093] 3. Generate parameter adjustment information

[0094] Based on the analysis result of the input feature information, the parameter adjustment layer will generate corresponding parameter adjustment information, which specifically includes the following aspects:

[0095] Parameter adjustment direction: Determine the direction in which the model parameters need to be adjusted, whether to increase or decrease the value of a certain parameter. For example, if the analysis finds that the predicted values of the model are generally low when processing water flow data, the parameter adjustment layer may decide to increase the parameter values related to flow to improve the prediction accuracy of the model.

[0096] Parameter adjustment amplitude: Calculate the specific amplitude by which each parameter needs to be adjusted. This is usually determined based on the analysis of feature information and the performance evaluation of the model. For example, by comparing the error between the predicted results of the model and the actual data and combining the changes in feature information, the parameter adjustment layer can determine the appropriate parameter adjustment amplitude to improve the performance of the model without compromising its stability.

[0097] Parameter adjustment priority: Determine the adjustment priorities of different parameters according to the degree of influence of the feature information on the model performance. For key parameters that have a greater impact on the model performance, adjustments will be made first; for some secondary parameters, fine-tuning can be carried out according to the performance of the model after the key parameters are adjusted. For example, when processing data at a water conservancy construction site, parameters directly related to the construction progress may have a higher adjustment priority, while the adjustment priority of some parameters related to the operation of auxiliary equipment is relatively low.

[0098] 4. Adjust the model parameters according to the parameter adjustment information

[0099] The parameter adjustment layer transmits the generated parameter adjustment information to other layers of the spatio-temporal convolutional neural network model to achieve the adjustment of the model parameters. The specific operations are as follows:

[0100] Parameter adjustment of the spatio-temporal convolutional layer: For the parameters of the spatio-temporal convolutional layer, such as the weights and biases of the convolutional kernels, they are updated according to the parameter adjustment information. For example, if the parameter adjustment information indicates that the model's ability to extract certain features needs to be enhanced, the weight value of the convolutional kernel may be increased to make it more focused on such features. At the same time, adjusting the bias parameter can change the output range of the convolutional layer to adapt to the changes in the data.

[0101] In some embodiments, the construction management module is used for: establishing a physical model of the water conservancy construction site; mapping the water conservancy construction site based on various types of data and the physical model to establish a digital twin model; and dynamically simulating the digital twin model according to the associated features to obtain a simulation model of the water conservancy construction site.

[0102] In the embodiments of the present invention, various types of real-time data collected (such as current construction progress data, equipment operation status data, environmental monitoring data, etc.) are integrated and fused with the relevant data of the physical model. Through preprocessing operations such as data cleaning and standardization, the quality and consistency of the data are ensured. For example, the real-time operation data of construction equipment is fused with the design parameters of the equipment in the physical model to more accurately evaluate the operation status and performance of the equipment. Subsequently, a mapping relationship can be established, where the mapping relationship includes geometric mapping, attribute mapping, and behavior mapping.

[0103] Geometric mapping: Using geographic information system (GIS) technology and 3D modeling software, the geometric shape and spatial position of the physical model are accurately mapped to the actual water conservancy construction site. Ensure that the buildings, terrain, etc. in the digital twin model are geometrically consistent with the actual scene, providing an accurate spatial reference for subsequent simulations and analyses.

[0104] Attribute mapping: Associate and map various attributes in the physical model (such as material properties, mechanical parameters, construction techniques, etc.) with the corresponding attributes of the actual construction site. At the same time, dynamically update the attribute information in the real-time monitoring data (such as the real-time status of equipment, the work conditions of construction personnel, etc.) to the digital twin model, enabling the model to reflect the actual situation in real time.

[0105] Behavior mapping: Establish a mapping relationship between various physical processes in the physical model (such as water flow movement, structural stress, construction activities, etc.) and the corresponding behaviors of the actual construction site. Through the simulation of the physical model and the verification of actual data, ensure that the digital twin model can accurately simulate various behaviors and changes in the actual construction process.

[0106] In the embodiments of the present invention, the digital twin model architecture constructed based on the above mappings includes a data layer, a model layer, and an application layer.

[0107] Data layer: Establish a data storage and management system for storing various types of data, including physical model data, real-time monitoring data, historical data, etc. Adopt database management systems (such as MySQL, Oracle, etc.) and data warehouse technologies to achieve efficient storage, retrieval, and management of data.

[0108] Model layer: Integrate the physical model and real-time data processing modules to construct the core algorithms and modules of the digital twin model. Through real-time updating and adjustment of the physical model, enable it to accurately reflect the changes in the actual construction site. At the same time, use data analysis and machine learning algorithms to analyze and predict real-time data, providing decision-making support for construction management.

[0109] Application layer: Develop various application functions, such as construction progress monitoring, equipment fault diagnosis, quality control, safety warning, etc. Through the visualization interface, the simulation results and analysis information of the digital twin model are intuitively displayed to the construction management personnel to facilitate their decision-making and management.

[0110] In the embodiment of the present invention, various association features are obtained from the association analysis block, including direct time association features, direct space association features, indirect time association features, and indirect space association features. These association features are deeply analyzed to understand their roles and influence mechanisms in the water conservancy construction process. For example, analyze the direct time association feature between construction progress and equipment operation time, and the indirect space association feature between geological conditions and construction safety, providing a basis for dynamic simulation.

[0111] Among them, the dynamic simulation process includes initial condition setting, model update, and simulation calculation.

[0112] Initial condition setting: Set the initial conditions for the digital twin model according to the current construction status and real-time data. It includes the current status of construction progress, the operation status of equipment, environmental parameters, etc. For example, input the current construction progress data into the model to determine the completion status and remaining workload of each construction task; input the real-time operation parameters of the equipment into the model to set the initial state of the equipment.

[0113] Model update: Update the digital twin model in real time according to the association features and real-time data. For example, if it is found that the geological conditions in a certain construction area have changed, update the geological parameters and construction difficulty assessment in the model according to the indirect space association feature; if the construction progress is delayed, adjust the time arrangement and resource allocation of subsequent construction tasks according to the direct time association feature.

[0114] Simulation calculation: Use the updated digital twin model to perform simulation calculations to predict the development of the construction process in the future for a period of time. By solving various equations and logical relationships in the model, simulate the progress of construction, the change of equipment operation status, environmental impacts, etc. For example, simulate the construction progress and resource requirements under different construction plans, and evaluate the feasibility and efficiency of the construction plans.

[0115] In some embodiments, the construction management module is used to: determine the dynamic information of the construction site according to the association features and the behavior prediction model; adjust the behavior mapping of the water conservancy construction site simulation model according to the dynamic information.

[0116] In an embodiment of the present invention, in some embodiments of the intelligent water conservancy construction management platform, the construction management module determines the dynamic information of the construction site by means of associated features and a behavior prediction model, and adjusts the behavior mapping of the water conservancy construction site simulation model based on this information. This process is of great significance for realizing precise and intelligent construction management.

[0117] The construction management module first obtains various associated features from the association analysis block, including direct time association features, direct space association features, indirect time association features, and indirect space association features. These associated features reveal the internal relationships between various factors in the water conservancy construction process. For example, the direct time association feature between the construction progress and the equipment operation time can help the construction management module understand the impact of the continuous operation of the equipment on the progress of the construction. If it is found that the construction progress shows an obvious acceleration or deceleration trend after the equipment has been running at a high load for a long time, this associated feature can be used to predict the changes in the subsequent construction progress. The construction sequence and progress of adjacent construction areas restrict each other. By analyzing the direct space association feature, the impact of the construction progress change in a certain area on the surrounding areas can be predicted in advance. For the indirect time association feature of the early construction decision (such as the construction technology selection) on the later project quality, the construction management module can, based on this feature and combined with the current implementation situation of the construction technology, predict the possible problems in the future project quality and take corresponding quality control measures in advance. For the indirect space association feature that the geological conditions indirectly affect the stability of the surrounding construction areas by affecting the groundwater flow, the construction management module can use these features to evaluate the potential impact of the geological condition changes on the entire construction site and adjust the construction plan in a timely manner.

[0118] The behavior prediction model is constructed based on historical data and machine learning algorithms and is used to predict the occurrence probability and development trend of various behaviors at the construction site. The behavior prediction model learns from a large amount of historical construction data (including construction progress, equipment operation data, personnel activity data, etc.) and extracts the patterns and rules therein. For example, learning information such as the time, cause, and related construction environment factors of equipment failures in past similar water conservancy projects. Combining with the associated features, the behavior prediction model can predict various behaviors at the construction site. For example, predicting the possibility of equipment failure in a certain period in the future, or predicting the probability of construction progress delay in a certain construction area due to weather changes. At the same time, it can also predict the behavior patterns of construction personnel, such as changes in work efficiency and the occurrence probability of safety violations.

[0119] The construction management module combines the associated features and the prediction results of the behavior prediction model to comprehensively determine the dynamic information of the construction site. Such dynamic information includes, but is not limited to, the changing trend of the construction progress, the change in the operating status of equipment (such as fault warning), the fluctuation of the work efficiency of personnel, the impact of environmental factors on construction (such as the change in construction conditions caused by weather changes), etc. For example, by analyzing the associated features, it is found that the operating parameters of a certain piece of equipment have a similar associated pattern with the historical fault data, and at the same time, the behavior prediction model predicts a relatively high probability of the equipment failing in the next few days. Then, the construction management module can determine the dynamic information that there is a fault risk for this equipment.

[0120] The behavior mapping of the water conservancy construction site simulation model refers to the simulation and representation in the model of various behaviors in the actual construction site (such as the operation of construction equipment, the operation of construction personnel, the movement of water flow, etc.). The accuracy of the behavior mapping directly affects the degree to which the simulation model reflects the actual construction situation. If the dynamic information shows that the construction progress may be delayed, the construction management module will adjust the time arrangement of relevant construction tasks in the simulation model, extend the duration of the corresponding tasks, or re-plan the construction sequence to reflect the actual possible progress changes. For example, due to weather reasons, the progress of the earthwork project in a certain construction area lags behind. The time of the earthwork project task in this area in the simulation model will be extended accordingly, and it may affect the start time of subsequent related construction tasks. When determining the dynamic information that there is a fault risk for the equipment, the construction management module will update the operating status of the equipment in the simulation model, mark it as a possible fault state, and adjust the construction tasks related to this equipment. For example, reduce the workload borne by this equipment, or arrange other equipment for substitution, and at the same time adjust the construction progress plan to adapt to the impact that the equipment failure may bring.

[0121] If the dynamic information indicates that the work efficiency of the construction personnel fluctuates, the construction management module will adjust the work parameters of the personnel in the simulation model, such as working hours, working intensity, etc. For example, if it is found that the work efficiency of a certain construction team decreases, the amount of work completed by this team per unit time in the simulation model will be reduced accordingly, thus affecting the simulation result of the overall construction progress. For the change in construction conditions caused by environmental factors, such as the rising water level, the deterioration of geological conditions and other dynamic information, the construction management module will adjust the relevant environmental parameters and construction behavior mapping in the simulation model. For example, when the rising water level may affect the foundation construction of water conservancy facilities, flood control measures will be added to the simulation in the simulation model, and the construction method and time arrangement of the foundation will be adjusted to cope with the impact brought by the environmental change.

[0122] In some embodiments, the construction management module is used to: update the model parameters of the water conservancy construction site simulation model according to the current construction data; start the updated water conservancy construction site simulation model for simulation calculation to obtain the construction simulation result.

[0123] In the process of constructing a simulation model for a water conservancy construction site, the data used are all historical data in the database. Therefore, it is necessary to collect real-time data to fine-tune the model to ensure accuracy. In some embodiments of the intelligent water conservancy construction management platform in the embodiments of the present invention, the construction management module updates the parameters of the water conservancy construction site simulation model and the simulation calculation process based on the current construction data, which is a key link to achieve precise construction management and scientific decision-making.

[0124] The construction management module first obtains the current construction data from the database module. These data cover all aspects of the water conservancy construction site, including but not limited to construction equipment operation data (such as engine speed, working hours, fuel consumption, etc.), construction personnel information (attendance, working location, etc.), construction progress data (completed engineering volume, progress of each construction link, etc.), water body data (water level, flow rate, water quality, etc.), geological data (real-time monitored geological changes, etc.), and ecological environment data (meteorological conditions, biodiversity changes, etc.). After obtaining the data, the construction management module preprocesses these data, including data cleaning (removing noise, handling missing values and outliers), data standardization (unifying data formats and dimensions), etc., to ensure the quality and usability of the data. For example, for the noise interference in the equipment operation data, it is removed through a filtering algorithm; for the missing construction progress data, a reasonable interpolation method is used for filling.

[0125] If the current construction data shows that there is a difference between the actual progress of a certain construction task and the progress predicted by the simulation model, the construction management module will update the duration parameter, resource allocation parameter, etc. of this task. For example, if it is found in actual construction that the concrete pouring speed is faster than predicted by the model, then the duration parameter of the concrete pouring task will be correspondingly shortened, and the relevant manpower and equipment resource parameters will be adjusted. When it is monitored that the actual stress condition of the building structure is inconsistent with the result calculated by the simulation model, the construction management module will update the parameters of the structural mechanics model, such as the elastic modulus and Poisson's ratio of the material. For example, if it is detected through a stress sensor that the actual stress at a certain part of the dam body exceeds the range predicted by the model, it may be necessary to adjust the mechanical parameters of the material at this part to more accurately reflect the stress state of the structure. Based on the current water body data, such as changes in water level and flow rate, the construction management module will update the parameters of the hydraulic model, such as the flow resistance coefficient and boundary condition parameters. For example, when the actual water level is higher than the water level predicted by the model, it may be necessary to adjust the flow resistance coefficient to more accurately simulate the movement of water flow and its impact on the surrounding environment.

[0126] After the model parameters are updated, the construction management module starts the updated simulation model of the water conservancy construction site for simulation calculation. The simulation calculation process is based on the updated model parameters, and by solving the mathematical equations and logical relationships of each sub-model, various physical processes and construction activities on the water conservancy construction site are simulated. Construction process simulation: According to the updated construction process model parameters, simulate the execution sequence of construction tasks, the allocation and use of resources, and predict the development trend of the construction progress. For example, calculate the completion time of each construction task after the parameter update, and the estimated completion time of the entire project. Using the updated structural mechanics model parameters, simulate the stress and deformation of the building structure under different working conditions. By solving the mechanical equations, obtain the stress and strain distributions of the structure, and evaluate the safety and stability of the structure. Based on the updated hydraulics model parameters, simulate the changes in parameters such as the velocity, pressure, and flow rate of the water flow, as well as the impact of the water flow on water conservancy facilities and the surrounding environment. For example, simulate the evolution process of the water flow in the case of a flood, and evaluate the effectiveness of flood control facilities.

[0127] After the simulation calculation is completed, the construction management module obtains the construction simulation results, which include but are not limited to information such as construction progress prediction, resource demand prediction, equipment status prediction, quality risk assessment, and environmental impact assessment.

[0128] In some embodiments, the platform further includes an evaluation module, and the evaluation module is used to: evaluate the construction simulation results to obtain an evaluation result. In some embodiments, the evaluation module is further used to: determine the construction management plan for the water conservancy project according to the evaluation result and the current construction data.

[0129] In the embodiment of the present invention, the evaluation module first needs to construct a comprehensive and scientific evaluation index system to quantitatively evaluate the construction simulation results. The evaluation module selects a suitable evaluation method for analysis and calculation according to the characteristics and data types of the evaluation indexes. After obtaining the evaluation result, the evaluation module deeply analyzes the relationship between the evaluation result and the current construction data. By comparing the evaluation result and the current construction data, identify the existing problems and potential risks. For example, if the evaluation result shows that the construction progress is lagging behind, and it is found in the current construction data that the failure rate of a certain key equipment is relatively high, then it can be inferred that the equipment failure may be one of the reasons for the progress lag.

[0130] According to the analysis results, the evaluation module formulates a targeted construction management plan for the water conservancy project, and the specific measures include:

[0131] Construction Progress Management: If the construction progress lags behind, measures such as increasing construction personnel and equipment, adjusting the construction sequence, and optimizing the construction process can be taken to speed up the progress; if the progress is ahead of schedule, the resource allocation can be appropriately adjusted to avoid resource waste. For example, for the task of dam concrete pouring with a lagging progress, increase the number of concrete mixers and transport vehicles, and at the same time optimize the pouring process to improve construction efficiency.

[0132] Resource Management: In response to unreasonable resource utilization, re-plan the allocation and scheduling of resources. For example, reasonably arrange the usage time of equipment to avoid equipment idling; optimize the material procurement plan to ensure that the material supply matches the construction progress. For example, according to the construction progress prediction, purchase the required construction materials in advance to avoid construction interruption caused by material shortage.

[0133] Quality Management: If there are problems with the project quality, strengthen quality inspection and testing, strictly implement construction specifications and standards, and promptly rectify unqualified construction parts. For example, increase the detection frequency of concrete strength and rework the parts with substandard strength.

[0134] Safety Management: For situations with high safety risks, strengthen safety training and education, improve safety management systems, and increase safety protection facilities. For example, train construction personnel on safety operation procedures and set obvious safety warning signs at the construction site.

[0135] Environmental Management: If the environmental impact is significant, take corresponding environmental protection measures, such as strengthening soil and water loss control, controlling water pollution, and protecting the ecological environment. For example, set up sedimentation ponds at the construction site to treat construction wastewater before discharging it to reduce pollution to the surrounding water bodies.

[0136] In addition, the evaluation module also needs to optimize and adjust the formulated construction management plan, and update the management plan in a timely manner according to the actual construction situation and newly emerging problems. For example, during the implementation of the construction management plan, if new safety hazards or quality problems are discovered, promptly adjust the management plan and add corresponding measures to solve the problems. At the same time, regularly evaluate the implementation effect of the management plan, continuously improve and perfect the management plan to improve the level and efficiency of water conservancy project construction management.

[0137] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0138] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. An intelligent water conservancy construction management platform, characterized in that, It includes: a database module, an AI data analysis module, and a construction management module; The database module is used to obtain multi-source water conservancy project information; among them, the multi-source water conservancy project information includes the following types of data: water conservancy construction site data, water body data, geological data, and ecological environment data; The AI data analysis module includes multiple processing sub-blocks and a correlation analysis block; Each processing sub-block is used to perform standardized processing on various types of data; The correlation analysis block is used to perform correlation analysis on various types of data to obtain correlation features; among them, the correlation features include: direct time correlation features, direct space correlation features, indirect time correlation features, and indirect space correlation features; The construction management module is used to construct a water conservancy construction site simulation model based on the standardized processed various types of data and the correlation features, and to obtain the current construction data in real time, and to obtain a construction simulation result based on the current construction data and the water conservancy construction site simulation model; The correlation analysis block is used for: Inputting various types of data into a pre-established spatio-temporal convolutional neural network model to obtain the correlation features; The spatio-temporal convolutional neural network model includes an input layer, a spatio-temporal convolutional layer, a feature fusion layer, a feature separation layer, a correlation extraction layer, and an output layer. The correlation analysis block is used for: Inputting various types of data from the input layer into the spatio-temporal convolutional layer to obtain multiple first feature maps; Inputting the multiple first feature maps into the feature fusion layer to obtain a fusion feature; Inputting the fusion feature into the feature separation layer to obtain direct time correlation features and direct space correlation features; Inputting the fusion feature, the direct time correlation features, and the direct space correlation features into the correlation extraction layer to obtain indirect time correlation features and indirect space correlation features; Outputting the direct time correlation features, direct space correlation features, indirect time correlation features, and indirect space correlation features through the output layer.

2. The intelligent water conservancy construction management platform according to claim 1, characterized in that Each processing sub-block is used for: Performing data cleaning, standardized conversion, and storage on each type of data to obtain the standardized processed various types of data.

3. The intelligent water conservancy construction management platform according to claim 2, wherein The spatio-temporal convolutional neural network model further includes a parameter adjustment layer. The correlation analysis block is used for: Obtaining the feature information of the standardized processing in each processing sub-block; Inputting the feature information into the parameter adjustment layer to obtain parameter adjustment information; Adjusting the parameters of the spatio-temporal convolutional neural network model according to the parameter adjustment information.

4. The intelligent water conservancy construction management platform according to claim 3, characterized in that, The construction management module is used for: Establishing a physical model of the water conservancy construction site; Mapping the water conservancy construction site according to various types of data and the physical model to establish a digital twin model; Performing dynamic simulation on the digital twin model according to the correlation features to obtain a water conservancy construction site simulation model.

5. The intelligent water conservancy construction management platform according to claim 4, wherein The construction management module is used for: Determining the dynamic information of the construction site according to the correlation features and the behavior prediction model; Adjusting the behavior mapping of the water conservancy construction site simulation model according to the dynamic information.

6. The intelligent water conservancy construction management platform according to claim 5, characterized in that, The construction management module is used for: Updating the model parameters of the water conservancy construction site simulation model according to the current construction data; Starting the updated water conservancy construction site simulation model for simulation calculation to obtain a construction simulation result.

7. The intelligent water conservancy construction management platform according to claim 4, characterized in that The platform further includes an evaluation module, and the evaluation module is configured to: evaluate the construction simulation result to obtain an evaluation result.

8. The intelligent water conservancy construction management platform according to claim 7, characterized in that, The evaluation module is further configured to: determine a construction management plan for the water conservancy project according to the evaluation result and the current construction data.

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