Anti-seismic scaffold optimization method and system based on multi-modal data fusion

Abstract

The invention belongs to the technical field of data processing, and particularly discloses an anti-seismic scaffold optimization method and system based on multi-modal data fusion, and the method comprises the steps: collecting soil water content distribution characteristics and penetration depth data in real time from a hydrological environment segment through deploying a sensor array, and obtaining an original data set; according to the original data set, a k-means clustering algorithm is adopted to carry out boundary refining processing on hydrological segments, and segment boundary parameters are determined; obtaining the segment boundary parameters, and constructing a local bearing capacity calculation model for the gradient mutation region by applying a support vector machine algorithm to obtain a model predicted value; fusing the model predicted value and the water content distribution characteristics to generate a basic layout scheme, and determining an initial position coordinate; the objective of the invention is to solve the problems of inaccurate assessment of scaffold foundation bearing capacity and inaccurate foundation layout caused by sudden change of soil parameters in a complex hydrological environment.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method and system for optimizing earthquake-resistant scaffolding based on multimodal data fusion. Background Technology

[0002] In the field of building construction, the assessment of foundation bearing capacity is directly related to the safety and stability of engineering projects. Especially in complex hydrological environments, accurately predicting and adapting to changes in soil mechanical properties is crucial. Research in this area is not only fundamental to ensuring construction safety but also key to improving engineering efficiency and reducing risks. With the acceleration of urbanization, construction environments are becoming increasingly complex, and the impact of hydrological conditions on foundations has become a core factor that cannot be ignored. However, existing methods often face significant limitations in addressing changes in the hydrological environment. Many traditional assessment methods rely heavily on average data for the entire region, making it difficult to capture subtle differences in local hydrological parameters, especially when groundwater levels and soil moisture content are unevenly distributed, often resulting in distorted assessment results. This neglect of local differences leads to a lack of sufficient adaptability in foundation design when facing sudden environmental changes, thus affecting the stability of engineering structures. A more critical technical challenge lies in the coordination between the segmentation of the hydrological environment and the assessment of foundation bearing capacity. Segmentation aims to divide the complex construction area into multiple smaller areas according to hydrological characteristics, in order to more accurately analyze the soil properties of each area.

[0003] However, when hydrological parameters change abruptly at the segment boundaries, such as a sudden rise in groundwater level or uneven distribution of soil moisture content, ensuring that the assessment results for each segment are consistent with the actual soil conditions becomes a pressing problem. This issue directly leads to a decrease in the accuracy of bearing capacity assessments in the boundary areas, thus affecting the rationality of foundation layout during construction. Specifically, during actual construction, a sudden downpour may cause a rapid increase in soil moisture content in a segment boundary area, while adjacent segments remain relatively stable. This abrupt change in hydrological parameters renders the originally defined segment boundaries inapplicable, causing a deviation between the assessment results and the actual soil bearing capacity. Consequently, the placement of the construction foundations faces the risk of adjustment and may even lead to safety hazards.

[0004] Therefore, how to address the impact of abrupt changes in boundary parameters on the assessment of foundation bearing capacity based on hydrological segmentation, and ensure that the assessment results of each segment are highly consistent with the actual soil conditions, has become a key issue that this study urgently needs to overcome. Summary of the Invention

[0005] This invention provides a method and system for optimizing seismic scaffolding based on multimodal data fusion, aiming to solve the problems of inaccurate assessment of scaffolding foundation bearing capacity and inaccurate foundation layout caused by abrupt changes in soil parameters in complex hydrological environments.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for optimizing seismic scaffolding based on multimodal data fusion includes: deploying a sensor array to collect soil moisture distribution characteristics and infiltration depth data in real time from hydrological environmental segments to obtain an original dataset; using the original dataset, employing a k-means clustering algorithm to refine the boundaries of the hydrological segments and determine segment boundary parameters; obtaining the segment boundary parameters, and applying a support vector machine algorithm to construct a local bearing capacity calculation model for gradient abrupt change regions to obtain model prediction values; fusing the model prediction values ​​with moisture distribution characteristics to generate a basic layout scheme and determine preliminary location coordinates; if the deviation between the preliminary location coordinates and the infiltration depth data exceeds a preset threshold, adjusting the gradient abrupt change parameters to obtain an optimized coordinate set; based on the optimized coordinate set, re-collecting verification data from the sensor array to determine the consistency of the hydrological segment discretization; and using the consistency determination result to update the bearing capacity calculation model to obtain the final basic layout scheme.

[0007] In one aspect of this disclosure, the method of deploying a sensor array to collect soil moisture content distribution characteristics and infiltration depth data in real time from hydrological environmental segments, thereby obtaining a raw dataset, includes: By deploying a sensor array, soil moisture content and infiltration depth data are collected in segments from the hydrological environment to obtain the raw dataset; Based on the original dataset, data cleaning methods were used to remove outliers and missing values, and the processed soil moisture content and infiltration depth data were obtained. For the processed soil moisture content and infiltration depth data set, a spatial interpolation method was applied to construct a spatial distribution model of soil moisture content and determine the distribution characteristics data; If the data points in certain regions of the distribution feature data deviate from the preset threshold range, then the data points in those regions are subjected to weighted smoothing to obtain the corrected distribution feature data. Based on the corrected distribution characteristic data, the correlation between infiltration depth and soil moisture content was analyzed, and the correlation index value was obtained. By combining correlation index values ​​with spatial distribution information of the hydrological environment, a prediction model for infiltration depth is constructed, and the prediction results are evaluated. If there is a significant deviation between the predicted data and the actual collected permeation depth data, the parameters of the prediction model are iteratively adjusted to determine the final predicted permeation depth value.

[0008] In one aspect of this disclosure, the step of refining the boundaries of hydrological segments using a k-means clustering algorithm based on the original dataset to determine segment boundary parameters includes: By initially processing the hydrological data, unprocessed data records are obtained from the original dataset. Automated tools are then used to clean the data, resulting in a preliminarily processed dataset.

[0009] Based on the initially organized dataset, the k-means clustering method is used to group the data, generating multiple data groups and determining the location of the center point of each group.

[0010] For the generated data groups, the differences between the groups are analyzed. If the distance between the center point of a group and the center points of other groups is less than a preset threshold, the group is merged with other groups to obtain an adjusted set of data groups.

[0011] From the adjusted data group set, obtain the boundary point data of each group, analyze the continuity between the boundary points, and determine the preliminary segment boundary range.

[0012] Based on the initial segment boundary range and the location of the center point of each group, the boundary point data is corrected a second time to obtain the refined segment boundary data.

[0013] By using the refined segment boundary data, the feature parameters of each segment are calculated to generate the final set of segment parameters.

[0014] The final set of segmented parameters is saved to a pre-defined database using a storage tool, thus completing the segmented processing flow of hydrological data.

[0015] In one aspect of this disclosure, obtaining the segmented boundary parameters and applying a support vector machine algorithm to construct a local bearing capacity calculation model for gradient abrupt change regions to obtain model prediction values ​​includes: By analyzing gradient mutation characteristics, key mutation-related information is extracted from the original data to obtain the distribution range of mutation regions.

[0016] Based on the distribution range of the mutation regions, the specific locations of the segment boundaries are identified, and a preliminary set of boundary data is determined.

[0017] For the initial set of boundary data, a filtering method is used to remove outliers, resulting in a processed boundary dataset.

[0018] From the processed boundary dataset, feature parameters of local regions are extracted, and feature matrices of local regions are constructed.

[0019] By using the feature matrix of a local region, a support vector machine algorithm is applied to train the model, thus obtaining a predictive model for carrying capacity.

[0020] Based on the carrying capacity prediction model, new input data is calculated to determine whether the prediction result meets the preset threshold conditions. If not, feature parameters are extracted again to update the model.

[0021] In one aspect of this disclosure, the step of fusing the model predictions with water content distribution characteristics to generate a basic layout scheme and determine preliminary location coordinates includes: Initial result data is obtained through model prediction, and the data is preliminarily organized to obtain structured prediction output.

[0022] Based on the structured forecast output, combined with water content values ​​and distribution data, data integration techniques are used to process the data and determine the comprehensive distribution information.

[0023] Based on the comprehensive distribution information, if the information meets the preset distribution uniformity condition, a basic layout framework is generated, and a preliminary layout structure is determined.

[0024] By establishing a preliminary layout structure, spatial data related to location coordinates is obtained. The support vector machine algorithm is then used for classification to obtain the classified spatial distribution results.

[0025] Based on the spatial distribution results after classification and in conjunction with the layout planning requirements, if there is a deviation between the classification results and the planning objectives, spatial adjustment will be carried out to determine the adjusted layout scheme.

[0026] The adjusted layout scheme is obtained, and the coordinate positioning data in the scheme is verified to obtain the final position coordinate data.

[0027] Based on the final location coordinate data, a corresponding layout planning record is generated to determine the complete business output content.

[0028] In one aspect of this disclosure, the step of adjusting the gradient mutation parameter to obtain an optimized coordinate set if the deviation between the initial position coordinates and the permeation depth data exceeds a preset threshold includes: By obtaining coordinate data and penetration depth data from the initial location, the deviation value between the two is calculated to obtain the deviation result.

[0029] If the deviation results exceed the preset threshold, the data will be compared to determine the distribution of outliers.

[0030] Based on the distribution of outliers, the gradient descent algorithm is used to adjust the gradient mutation parameters to obtain the adjusted parameter values.

[0031] Starting from the adjusted parameter values, position correction is performed on the coordinate data to generate corrected position information.

[0032] The corrected location information is compared with the permeation depth data to determine whether the preset conditions are met.

[0033] If the second comparison result still does not meet the preset conditions, the deviation is recalculated based on the corrected position information to obtain the updated deviation data.

[0034] By combining the updated deviation data with the adjusted parameter values, the final optimized coordinate set is generated.

[0035] In one aspect of this disclosure, the step of determining the consistency of hydrological segment discretization by re-acquiring verification data from the sensor array based on the optimized coordinate set includes: Data is collected using a sensor array device targeting an optimized coordinate set to obtain initial hydrological data records.

[0036] Based on the initial hydrological data records, a segmented analysis method was adopted to divide the data according to the rules of hydrological segmentation, resulting in segmented data groups.

[0037] For the segmented data groups, a consistency assessment is performed using newly acquired verification data to determine whether each segment of data meets the discrete consistency standard.

[0038] If the consistency assessment results show that some segmented data deviates from the discrete consistency standard, the support vector machine algorithm is used to classify the data groups that deviate and determine the range of abnormal data.

[0039] Based on the range of abnormal data after classification and processing, and combined with the real-time acquisition capabilities of the sensor array, targeted verification data is reacquired to obtain updated supplementary data.

[0040] By supplementing the updated data and combining the results of segmented analysis, a secondary evaluation of the discrete consistency of the hydrological segments is conducted to determine whether the overall data meets the consistency requirements.

[0041] Based on the results of the second evaluation, for the segmented data that still do not meet the consistency requirements, the distribution strategy of the coordinate set is adjusted and optimized to obtain the final consistent distribution of hydrological data.

[0042] In one aspect of this disclosure, updating the bearing capacity calculation model using the consistency judgment result to obtain the final foundation layout scheme includes: By analyzing the data output from the consistency judgment, we obtain the input data for the judgment result analysis. We then perform structured processing on the analyzed data stream to obtain a preliminary set of judgment results.

[0043] Based on the preliminary judgment results, the logical framework for bearing capacity calculation is adopted, and combined with the index system for bearing capacity assessment, the intermediate result data for bearing capacity calculation are determined.

[0044] For the intermediate results of the bearing capacity calculation, obtain the input parameters for updating the calculation model. Based on the preset rules for adjusting the model parameters, determine the applicability of the model. If the adjusted parameters meet the preset threshold, output the updated model version.

[0045] The updated model version is used to obtain the basis for generating basic layout schemes. An algorithm framework for optimizing layout schemes is adopted, and the support vector machine algorithm is used to classify the layout schemes in multiple dimensions to obtain an optimized layout scheme dataset.

[0046] Based on the optimized layout scheme dataset, the input information for the scheme generation process is obtained, and the final layout scheme structure is determined through a standardized process of result data processing.

[0047] For the final layout scheme structure, obtain the formatted data of the layout result output, and perform data mapping through the preset output template to obtain the final basic layout result.

[0048] From the final basic layout results, the feedback data of the verification stage is obtained, and the feedback data is processed in the secondary processing stage of model verification to determine the stability output of the bearing capacity calculation model.

[0049] In another aspect, this disclosure also relates to a seismic scaffolding optimization system based on multimodal data fusion, comprising: The sensor array module is used to collect soil moisture content distribution characteristics and infiltration depth data in real time from hydrological environment segments to obtain the raw dataset. The data processing module, based on the original dataset, uses the k-means clustering algorithm to refine the boundaries of the hydrological segments and determine the segment boundary parameters. The model building module is used to obtain the segment boundary parameters, apply the support vector machine algorithm to construct a local bearing capacity calculation model for gradient abrupt change regions, and obtain the model prediction value. The layout generation module is used to generate a basic layout scheme by fusing the model prediction values ​​with the water content distribution characteristics, and to determine the preliminary location coordinates. The optimization module is used to determine whether the deviation between the initial position coordinates and the penetration depth data exceeds a preset threshold. If it exceeds the threshold, the gradient mutation parameters are adjusted to obtain an optimized coordinate set. The verification module is used to determine the consistency of hydrological segment discretization by re-collecting verification data from the sensor array based on the optimized coordinate set. The update module is used to update the load-bearing capacity calculation model based on the consistency judgment result, so as to obtain the final basic layout scheme.

[0050] Compared with the prior art, the present invention has the following beneficial effects: This invention achieves high-precision acquisition and segmented modeling of hydrological features such as soil moisture content and infiltration depth through multimodal data fusion and intelligent algorithm collaboration. It accurately identifies gradient change regions and constructs a bearing capacity calculation model by combining support vector machine and K-means clustering algorithm. Through iterative optimization and closed-loop verification mechanism, it dynamically adjusts the foundation layout coordinates, which significantly improves the structural stability, construction safety and deployment reliability of scaffolding under uneven foundation settlement and seismic scenarios. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0052] Figure 1 This is a flowchart of an earthquake-resistant scaffolding optimization method based on multimodal data fusion according to the present invention. Detailed Implementation

[0053] The present invention will be further described below with reference to embodiments. These embodiments are merely some, not all, of the embodiments described. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the protection scope of the present invention.

[0054] Please see Figure 1 As shown in the figure, this embodiment discloses an optimization method for seismic scaffolding based on multimodal data fusion. The specific steps are as follows: S1. By deploying a sensor array, real-time data on soil moisture content distribution characteristics and infiltration depth are collected from hydrological environmental segments to obtain the raw dataset. S1-1. By deploying a sensor array, soil moisture content and infiltration depth data are collected in segments from the hydrological environment to obtain the raw dataset; S1-2. Based on the original dataset, use data cleaning methods to remove outliers and missing values, and obtain the processed soil moisture content and infiltration depth data set. S1-3. For the treated soil moisture content and infiltration depth data set, a spatial interpolation method is applied to construct a spatial distribution model of soil moisture content and determine the distribution characteristic data. S1-4. If the data points in certain regions of the distribution feature data deviate from the preset threshold range, then the data points in those regions are subjected to weighted smoothing to obtain the corrected distribution feature data. S1-5. Based on the corrected distribution characteristic data, analyze the correlation between infiltration depth and soil moisture content, and obtain the correlation index value. S1-6. By combining the correlation index values ​​with the spatial distribution information of the hydrological environment, construct a prediction model for infiltration depth and judge the prediction results data. S1-7. If there is a significant deviation between the predicted data and the actual collected permeation depth data, the parameters of the prediction model shall be iteratively adjusted to determine the final predicted permeation depth value.

[0055] For example, by deploying a sensor array, soil moisture content distribution characteristics and infiltration depth data can be collected in segments in real time from the hydrological environment. The specific implementation methods can be integrated into a complete technical process.

[0056] First, within the target hydrological environment area, assuming an area of ​​1000 square meters, it is divided into 10 sub-areas, each with an area of ​​100 square meters. A high-precision soil moisture sensor and a permeability depth detector are deployed at the center of each sub-area. The sensor's measurement range is from 0 to 1.5 meters in soil depth, and the sampling frequency is set to once per hour. The data is automatically transmitted to a cloud database via a wireless network. The data format is timestamp, location coordinates, water content percentage, and permeability depth value. For example, the data at a certain moment is "20XX-XX-XX,XX:XX,(X1,Y1),35.2%,0.8m".

[0057] Next, for the collected raw dataset, a data preprocessing algorithm is used to remove outliers. Specifically, the statistical 3σ principle is used to calculate the mean and standard deviation of water content in each sub-region. If a data point exceeds the mean ± 3 times the standard deviation, it is considered an outlier and is removed. For example, if the mean of 10 data points in a sub-region is 30.5% and the standard deviation is 2.1%, then the range is 24.2% to 36.8%, and data outside this range will be removed.

[0058] Subsequently, interpolation algorithms such as Kriging interpolation are used to fill in the missing data to ensure data integrity. The interpolation results will generate a continuous soil moisture distribution map with an accuracy controlled within ±0.5%.

[0059] Finally, the soil moisture content distribution characteristics and infiltration depth data were analyzed, and spatial analysis algorithms were used to calculate the average moisture content and infiltration depth gradient of each sub-region. For example, gradient analysis revealed that the infiltration depth of a certain sub-region changed from 0.6m to 0.9m at a rate of 0.3m / h. Combined with historical rainfall data, it was inferred that groundwater seepage may exist, and a hydrological environmental risk assessment report was generated. The report includes a moisture content distribution heat map and an infiltration depth change curve for subsequent decision-making reference.

[0060] The above process forms a closed-loop logic through automated sensor data acquisition, algorithm processing, and cloud analysis, ensuring data real-time performance and analytical accuracy.

[0061] S2. Based on the original dataset, the k-means clustering algorithm is used to refine the boundaries of the hydrological segments and determine the segment boundary parameters. S2-1. Through preliminary processing of hydrological data, unprocessed data records are obtained from the original dataset. Automated tools are used to clean the data to obtain a preliminarily processed dataset.

[0062] S2-2. Based on the initially organized dataset, the k-means clustering method is used to group the data, generating multiple data groups and determining the center point of each group.

[0063] S2-3. For the generated data groups, analyze the differences between each group. If the distance between the center point of a group and the center points of other groups is less than a preset threshold, then merge the group with other groups to obtain the adjusted data group set.

[0064] S2-4. From the adjusted data group set, obtain the boundary point data of each group, analyze the continuity between the boundary points, and determine the preliminary segment boundary range.

[0065] S2-5. Based on the preliminary segment boundary range and the center point position of each group, the boundary point data is corrected a second time to obtain the refined segment boundary data.

[0066] S2-6. Using the refined segment boundary data, calculate the feature parameters of each segment to generate the final segment parameter set.

[0067] S2-7. For the final set of segmented parameters, use a storage tool to save it to a preset database to complete the segmented processing flow of hydrological data.

[0068] For example, for the refinement of hydrological segment boundaries, the k-means clustering algorithm is used to analyze and optimize the original dataset. The specific implementation method is as follows.

[0069] First, suppose we have a hydrological dataset containing flow observation data along a river, with a total of 100 observation points. The data for each point includes the flow value and the location of the river section. For example, the data for a certain point is a flow value of 25.3 and a location of 10.5 kilometers.

[0070] The initial segmentation may be based on expert experience and divided into 3 segments, but the boundaries are vague and need to be refined.

[0071] We input the data into the k-means clustering algorithm, setting k to 3, indicating that we want to cluster the data into 3 groups, representing 3 river sections.

[0072] The algorithm first randomly selects three observation points as initial cluster centers. For example, the flow values ​​are 20.1, 30.5, and 40.2, and the corresponding locations are 5.0, 15.0, and 25.0 kilometers.

[0073] Next, the algorithm calculates the Euclidean distance from each observation point to these three centers, assigns each point to the nearest center, and forms the initial cluster.

[0074] Then, the center point of each group is recalculated; for example: The new center flow rate for the first category group is 21.5, and its location is 6.2 kilometers away. The central flow value of the second group is 29.8, and its location is 14.8 kilometers away. The central flow value of the third group is 39.7, and its location is 24.5 kilometers away.

[0075] Repeat this process until the center point no longer changes significantly or the maximum number of iterations (e.g., 10 times) is reached, eventually resulting in three stable groups.

[0076] Analysis of the clustering results revealed that the cluster boundary points were located at 12.3 km and 22.7 km, with significant changes in flow values. For example, the flow at 12.3 km changed abruptly from 28.5 to 31.2, indicating that this was a segment boundary.

[0077] By comparing the original segment boundaries, assuming they are 10 kilometers and 20 kilometers, it was found that the refined boundaries better reflect the flow change trend.

[0078] Finally, these boundary parameters were output as 12.3 km and 22.7 km as the basis for the optimized hydrological segmentation. Combined with the flow change rate, such as a flow change of 2.5 cubic meters per second per kilometer, the rationality was verified to ensure that the segmentation was consistent with the hydrological characteristics.

[0079] This entire process is automated through algorithms, forming a complete chain logically from data input and clustering calculation to boundary determination, demonstrating the efficiency of information technology.

[0080] S3. Obtain the segment boundary parameters, and apply the support vector machine algorithm to construct a local bearing capacity calculation model for the gradient abrupt region to obtain the model prediction value; S3-1. By analyzing the gradient mutation characteristics, key information related to mutations is extracted from the original data to obtain the distribution range of mutation regions.

[0081] S3-2. Based on the distribution range of the mutation region, identify the specific location of the segment boundary and determine the preliminary set of boundary data.

[0082] S3-3. For the initial set of boundary data, use filtering methods to remove outliers and obtain the processed boundary dataset.

[0083] S3-4. Extract the feature parameters of the local region from the processed boundary dataset and construct the feature matrix of the local region.

[0084] S3-5. Using the feature matrix of the local region, the support vector machine algorithm is applied to train the model and obtain the predictive model of carrying capacity.

[0085] S3-6. Based on the load-bearing capacity prediction model, calculate the new input data and determine whether the prediction result meets the preset threshold conditions. If not, re-extract the feature parameters and update the model.

[0086] For example, in the process of constructing a local bearing capacity calculation model, the key boundary points can be determined first by analyzing the gradient change rate to obtain the segment boundary parameters.

[0087] For example, suppose we perform stress distribution analysis on a bridge structure and collect stress data at 100 points, with the data ranging from 0 to 500 MPa. By calculating the stress gradient between adjacent points, we find that the gradient change rates at points 30 and 70 reach 5.2 and 4.8 respectively, which are much higher than the average of 1.5 at other points. Therefore, we use these two points as segmentation boundaries to divide the area into three regions for subsequent modeling.

[0088] Next, for the application of gradient mutation regions, image processing technology combined with gradient detection algorithms can be used to further confirm the range of mutation regions. Assuming that the Sobel operator is used to calculate the first derivative of the data, regions with gradient mutation values ​​greater than the threshold of 3.0 are extracted, and finally, points 28 to 32 and 68 to 72 are determined to be mutation regions.

[0089] Subsequently, for the construction of a local bearing capacity model using the support vector machine algorithm, the radial basis function can be used as the kernel function, with the penalty parameter C set to 10 and the kernel parameter γ set to 0.1. By fitting the training set (80% of the data, i.e., 80 points), the mean square error of the model on the test set (20 points) is 2.5 MPa, indicating that the model has high accuracy.

[0090] Finally, the process of obtaining the model's predicted value can be achieved by inputting stress data from the test area. For example, inputting the stress value of 300 MPa at point 29 will result in the model outputting a predicted bearing capacity of 450 kN, with an error of only 1.1% compared to the actual value of 455 kN, thus verifying the effectiveness of the model.

[0091] The above steps form a closed-loop logic through data analysis, algorithm processing, and error verification, ensuring the rigor and reliability of the process from boundary delineation to prediction results.

[0092] S4. Generate a basic layout scheme by fusing the model predictions with the water content distribution characteristics, and determine the preliminary location coordinates; S4-1. Obtain initial result data through model prediction, perform preliminary processing on the data, and obtain structured prediction output.

[0093] S4-2. Based on the structured prediction output, combined with the water content value and distribution data, data integration technology is used to process the data and determine the comprehensive distribution information.

[0094] S4-3. For the comprehensive distribution information, if the information meets the preset distribution uniformity condition, a basic layout framework is generated, and the preliminary layout structure is determined.

[0095] S4-4. Through the initial layout structure, obtain spatial data related to the position coordinates, and use the support vector machine algorithm for classification to obtain the classified spatial distribution results.

[0096] S4-5. Based on the spatial distribution results after classification and in conjunction with the layout planning requirements, if there is a deviation between the classification results and the planning objectives, spatial adjustment processing shall be carried out to determine the adjusted layout scheme.

[0097] S4-6. Obtain the adjusted layout scheme, and perform verification processing on the coordinate positioning data in the scheme to obtain the final position coordinate data.

[0098] S4-7. Using the final location coordinate data, generate the corresponding layout planning record to determine the complete business output content.

[0099] For example, the specific implementation method of generating a basic layout scheme and determining preliminary location coordinates by fusing model predictions with water content distribution characteristics can be designed from the complete process of data processing to result output.

[0100] First, suppose we collect soil moisture content data for a certain area using sensors. The data matrix is ​​a 100x100 grid, and the moisture content value of each grid cell ranges from 0 to 1. For example, the moisture content of a certain point (50,50) is 0.75.

[0101] The region was predicted using a deep learning model such as a convolutional neural network. The model input was a water content distribution matrix, and the output was a probability distribution map of suitable layout. The prediction results showed that the suitability near point (50,50) was 0.85, which was higher than the average of 0.6 in the surrounding area.

[0102] Next, the model predictions and water content distribution characteristics were fused together using a weighted average algorithm. The weights were set to 0.7 for the predicted values ​​and 0.3 for the water content values. The overall suitability after fusion was calculated to be 0.85x0.7+0.75x0.3=0.82, indicating that this point has a high priority.

[0103] Subsequently, a basic layout scheme is generated based on the comprehensive suitability. Clustering algorithms such as K-means are used to group the grid points. K=5 is set, and the clustering result shows that the point (50,50) is assigned as the center point of the priority layout area, and its coordinates are used as the initial position.

[0104] Finally, the initial position was fine-tuned using spatial optimization algorithms such as simulated annealing. The initial temperature was set to 100 and the cooling rate to 0.95. After 100 iterations, the coordinates were adjusted from (50,50) to (51,49), and the overall suitability was improved to 0.83.

[0105] The entire process is automated through algorithms. Data analysis and coordinate determination form a closed-loop logic to ensure the scientific nature of the layout plan. At the same time, it is combined with soil management operations to optimize resource allocation efficiency.

[0106] S5. If the deviation between the preliminary position coordinates and the penetration depth data exceeds a preset threshold, the gradient mutation parameter is adjusted to obtain an optimized coordinate set. S5-1. Obtain coordinate data and penetration depth data from the initial position, calculate the deviation between the two, and obtain the deviation result.

[0107] S5-2. If the deviation result exceeds the preset threshold, the data of the deviation result is compared to determine the distribution of abnormal points.

[0108] S5-3. Based on the distribution of outliers, use the gradient descent algorithm to adjust the gradient mutation parameters and obtain the adjusted parameter values.

[0109] S5-4. Starting from the adjusted parameter values, perform position correction on the coordinate data to generate corrected position information.

[0110] S5-5. Compare the corrected location information with the penetration depth data a second time to determine whether the preset conditions are met.

[0111] S5-6. If the second comparison result still does not meet the preset conditions, the deviation is recalculated based on the corrected position information to obtain the updated deviation data.

[0112] S5-7. Using the updated deviation data and the adjusted parameter values, generate the final optimized coordinate set.

[0113] For example, when dealing with the deviation between location coordinates and permeation depth data, assume that the initial location coordinates are (x1, y1) = (10.5, 20.3), the permeation depth data is d = 15.2m, and the preset threshold is that the deviation does not exceed 2.0m.

[0114] By calculating the theoretical depth value corresponding to the preliminary coordinates, assuming the theoretical depth is d theory =18.0 meters, the actual deviation is |15.2-18.0|=2.8 meters, which is greater than the threshold of 2.0 meters, so the system automatically triggers the adjustment mechanism.

[0115] Next, the system calls the gradient mutation parameter optimization algorithm, with the initial gradient parameter set to k=0.5 and the step size s=0.1. Iterative calculations are performed using the gradient descent method, updating the coordinates in each iteration as follows: ; Where dx is the partial derivative of the coordinates with respect to depth; x new The optimized x-coordinate; x1 is the initial x-coordinate; k is the gradient mutation parameter; s represents the learning step size; d represents the actual measured penetration depth; d theory This is a predicted value for the theoretical penetration depth; Assuming dx = 1.2, after 5 iterations, the coordinates are adjusted to (x... new ,y new ) = (10.2, 20.1), corresponding to a theoretical depth update of d. theory =16.5 meters, the deviation is reduced to |15.2-16.5|=1.3 meters, which is less than the threshold and meets the requirements.

[0116] The system further analyzes and optimizes the stability of the coordinate set. By calculating the variance of the coordinate set, assuming the variance is 0.05, which is less than the preset stability threshold of 0.1, the system confirms that the optimized coordinate set is reliable.

[0117] If the variance is too large, the system will automatically increase the number of iterations or adjust the step size to s=0.05 to continue optimization until the conditions are met.

[0118] Finally, the system outputs an optimized coordinate set (10.2, 20.1) and related parameter records for subsequent business modules such as geological modeling depth calibration, ensuring data consistency.

[0119] Through this automated process, the system achieves a complete closed loop from deviation detection to parameter adjustment and result verification, logically ensuring the accuracy and stability of coordinate optimization.

[0120] S6. Based on the optimized coordinate set, re-collect verification data from the sensor array to determine the consistency of hydrological segment discretization. S6-1. Data is collected using a sensor array device for the optimized coordinate set to obtain initial hydrological data records.

[0121] S6-2. Based on the initial hydrological data records, a segmented analysis method is used to divide the data according to the rules of hydrological segmentation, resulting in segmented data groups.

[0122] S6-3. For the segmented data groups, perform a consistency assessment using the newly acquired verification data to determine whether each segment of data meets the discrete consistency standard.

[0123] S6-4. If the consistency assessment results show that some segmented data deviates from the discrete consistency standard, the support vector machine algorithm is used to classify the data groups that deviate and determine the range of abnormal data.

[0124] S6-5. Based on the range of abnormal data after classification and processing, and combined with the real-time acquisition capability of the sensor array, re-acquire targeted verification data to obtain updated supplementary data.

[0125] S6-6. By supplementing the updated data and combining the results of the segmented analysis, a secondary evaluation of the discrete consistency of the hydrological segments is conducted to determine whether the overall data meets the consistency requirements.

[0126] S6-7. Based on the results of the second evaluation, for the segmented data that still do not meet the consistency requirements, adjust and optimize the distribution strategy of the coordinate set to obtain the final hydrological data consistency distribution.

[0127] For example, in the process of re-collecting verification data from the sensor array based on the optimized coordinate set to determine the discretization consistency of hydrological segments, the system first uses the optimized coordinate point (assumed to be x=15.7, y=25.4) as a reference, and collects hydrological data at this location through the sensor array, specifically including water flow velocity and water level. Assume the collected water flow velocity is 3.2 m / s and the water level is 4.5 m. Subsequently, the system compares this data with the historical hydrological segmentation model, calculates the discretization consistency index using the Euclidean distance algorithm, and the formula is: ; Where v model and h model The predicted water flow velocity is 3.0 m / s and the water level is 4.8 m, respectively. actual and h actual The collected water flow velocity and water level were recorded. If the value is below the preset consistency threshold of 0.5, it indicates that the preliminary data meets the requirements.

[0128] Next, the system performs multi-point verification on the collected data. Assuming three auxiliary collection points are set within a 1.0-meter radius around the coordinate point, the water flow velocity data are obtained as 3.1 m / s, 3.3 m / s, and 3.0 m / s respectively. The standard deviation is calculated to be 0.15, which is less than the preset fluctuation threshold of 0.2, confirming data stability. If the standard deviation exceeds the threshold, the system automatically expands the collection points to five and recalculates until the condition is met. Finally, the system integrates the consistency index and stability data to generate a hydrological segmented discretized consistency report, which is automatically linked to the water resources distribution analysis module for subsequent calibration of hydrological prediction models, ensuring the applicability of the data in different business scenarios. Through this process, the system achieves automated processing from data collection to consistency judgment, forming a complete data verification chain.

[0129] S7. Using the consistency results of the judgment, update the load-bearing capacity calculation model to obtain the final basic layout scheme.

[0130] S7-1. Through the data output of consistency judgment, obtain the input data for judgment result analysis, perform structured processing on the analyzed data stream, and obtain a preliminary judgment result set.

[0131] S7-2. Based on the preliminary judgment results, adopt the logical framework of bearing capacity calculation and combine it with the index system of bearing capacity assessment to determine the intermediate result data of bearing capacity calculation.

[0132] S7-3. For the intermediate result data of the bearing capacity calculation, obtain the input parameters for updating the calculation model. Based on the preset rules for adjusting the model parameters, determine the applicability of the model. If the adjusted parameters meet the preset threshold, output the updated model version.

[0133] S7-4. Obtain the basis for generating the basic layout scheme from the updated model version, adopt the layout scheme optimization algorithm framework, and classify the layout scheme in multiple dimensions through the support vector machine algorithm to obtain the optimized layout scheme dataset.

[0134] S7-5. Based on the optimized layout scheme dataset, obtain the input information for the scheme generation process, and determine the final layout scheme structure through the standardized process of result data processing.

[0135] S7-6. For the final layout scheme structure, obtain the formatted data of the layout result output, and perform data mapping through the preset output template to obtain the final basic layout result.

[0136] S7-7. Obtain feedback data from the verification stage from the final basic layout results, perform secondary processing on the feedback data in the model verification stage, and determine the stability output of the bearing capacity calculation model.

[0137] For example, in the process of updating the bearing capacity calculation model and obtaining the final foundation layout scheme by using the consistency judgment result, the initial data is first analyzed by the consistency judgment. It is assumed that the foundation bearing capacity design value of a certain project needs to meet 1000kPa, and the bearing capacity mean values ​​obtained by multiple sets of test data are 950kPa, 980kPa and 1020kPa, respectively, with a standard deviation of 30kPa.

[0138] Using a consistency test algorithm, the deviation of each data point from the mean was calculated. The threshold was set to twice the standard deviation, i.e., 60 kPa. It was found that the deviations of 950 kPa and 980 kPa were within the threshold, and the deviation of 40 kPa for 1020 kPa was also within the range, so the data consistency was passed.

[0139] Next, the bearing capacity calculation model was updated based on the consistency results. The weighted average algorithm was used, and the weights of the three sets of data were set to 0.3, 0.3 and 0.4 (because 1020 kPa is closer to the target value). The updated bearing capacity was calculated as 950×0.3+980×0.3+1020×0.4=986 kPa.

[0140] Subsequently, based on geological survey data, the distribution of the foundation soil layers was analyzed. Assuming a soil layer thickness of 5m and a permeability coefficient of 0.001m / s, the bearing capacity distribution was simulated using finite element analysis software. It was found that under a bearing capacity of 986kPa, the foundation settlement was 2.5cm, which meets the design specification requirement of less than 3cm.

[0141] Finally, based on the updated bearing capacity model and settlement analysis, the foundation layout scheme was optimized, and the foundation size was determined to be 2m×2m with a spacing of 3m. The layout diagram was generated by the algorithm, and the foundation points were automatically adjusted to avoid underground pipeline data to ensure that they matched the engineering constraints.

[0142] The above process, through data processing, consistency verification, model updating, and layout optimization, forms a complete logical chain, all of which is completed automatically by the system to ensure the accuracy and efficiency of the results.

[0143] In some different embodiments, this embodiment also relates to a seismic scaffolding optimization system based on multimodal data fusion, comprising: The sensor array module is used to collect soil moisture content distribution characteristics and infiltration depth data in real time from hydrological environment segments to obtain the raw dataset. The data processing module, based on the original dataset, uses the k-means clustering algorithm to refine the boundaries of the hydrological segments and determine the segment boundary parameters. The model building module is used to obtain the segment boundary parameters, apply the support vector machine algorithm to construct a local bearing capacity calculation model for gradient abrupt change regions, and obtain the model prediction value. The layout generation module is used to generate a basic layout scheme by fusing the model prediction values ​​with the water content distribution characteristics, and to determine the preliminary location coordinates. The optimization module is used to determine whether the deviation between the initial position coordinates and the penetration depth data exceeds a preset threshold. If it exceeds the threshold, the gradient mutation parameters are adjusted to obtain an optimized coordinate set. The verification module is used to determine the consistency of hydrological segment discretization by re-collecting verification data from the sensor array based on the optimized coordinate set. The update module is used to update the load-bearing capacity calculation model based on the consistency judgment result, so as to obtain the final basic layout scheme.

[0144] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing seismic scaffolding based on multimodal data fusion, characterized in that, include: By deploying a sensor array, soil moisture content distribution characteristics and infiltration depth data are collected in real time from hydrological environment segments to obtain the raw dataset; Based on the original dataset, the k-means clustering algorithm is used to refine the boundaries of the hydrological segments and determine the segment boundary parameters. Obtain the segment boundary parameters, apply the support vector machine algorithm to construct a local bearing capacity calculation model for the gradient abrupt change region, and obtain the model prediction value; A basic layout scheme is generated by fusing the model's predicted values ​​with water content distribution characteristics, and preliminary location coordinates are determined. If the deviation between the initial position coordinates and the penetration depth data exceeds a preset threshold, the gradient mutation parameter is adjusted to obtain an optimized coordinate set. Based on the optimized coordinate set, the consistency of hydrological segment discretization is determined by re-collecting verification data from the sensor array. Using the consistency results of the judgment, the load-bearing capacity calculation model is updated to obtain the final basic layout scheme.

2. The method for optimizing seismic scaffolding based on multimodal data fusion according to claim 1, characterized in that: The process involves deploying a sensor array to collect real-time data on soil moisture content distribution characteristics and infiltration depth from different hydrological environmental segments, resulting in a raw dataset including: By deploying a sensor array, soil moisture content and infiltration depth data are collected in segments from the hydrological environment to obtain the raw dataset; Based on the original dataset, data cleaning methods were used to remove outliers and missing values, and the processed soil moisture content and infiltration depth data were obtained. For the processed soil moisture content and infiltration depth data set, a spatial interpolation method was applied to construct a spatial distribution model of soil moisture content and determine the distribution characteristics data; If the data points in certain regions of the distribution feature data deviate from the preset threshold range, then the data points in those regions are subjected to weighted smoothing to obtain the corrected distribution feature data. Based on the corrected distribution characteristic data, the correlation between infiltration depth and soil moisture content was analyzed, and the correlation index value was obtained. By combining correlation index values ​​with spatial distribution information of the hydrological environment, a prediction model for infiltration depth is constructed, and the prediction results are evaluated. If there is a significant deviation between the predicted data and the actual collected permeation depth data, the parameters of the prediction model are iteratively adjusted to determine the final predicted permeation depth value.

3. The method for optimizing seismic scaffolding based on multimodal data fusion according to claim 1, characterized in that: The step involves refining the boundaries of hydrological segments using the k-means clustering algorithm based on the original dataset to determine the segment boundary parameters, including: By initially processing the hydrological data, unprocessed data records are obtained from the original dataset, and automated tools are used to clean the data to obtain a preliminarily processed dataset. Based on the initially organized dataset, the k-means clustering method was used to group the data, generating multiple data groups and determining the location of the center point of each group; For the generated data groups, analyze the differences between each group. If the distance between the center point of a group and the center points of other groups is less than a preset threshold, then merge the group with other groups to obtain the adjusted data group set. From the adjusted data group set, obtain the boundary point data of each group, analyze the continuity between the boundary points, and determine the preliminary segment boundary range; Based on the initial segment boundary range and the location of the center point of each group, the boundary point data is corrected a second time to obtain the refined segment boundary data. By using the refined segment boundary data, the feature parameters of each segment are calculated to generate the final set of segment parameters; The final set of segmented parameters is saved to a pre-defined database using a storage tool, thus completing the segmented processing flow of hydrological data.

4. The method for optimizing seismic scaffolding based on multimodal data fusion according to claim 1, characterized in that: The process of obtaining the segmented boundary parameters and constructing a local bearing capacity calculation model using a support vector machine algorithm for gradient abrupt change regions to obtain model prediction values ​​includes: By analyzing gradient mutation characteristics, key mutation-related information is extracted from the original data to obtain the distribution range of mutation regions. Based on the distribution range of the mutation regions, identify the specific locations of the segment boundaries and determine the initial set of boundary data; For the initial set of boundary data, a filtering method is used to remove outliers and obtain the processed boundary dataset; Extract feature parameters of local regions from the processed boundary dataset and construct feature matrices of local regions; By using the feature matrix of a local region, a support vector machine algorithm is applied to train the model, thus obtaining a predictive model for carrying capacity. Based on the load-bearing capacity prediction model, new input data is calculated to determine whether the prediction result meets the preset threshold conditions. If not, feature parameters are extracted again to update the model.

5. The method for optimizing seismic scaffolding based on multimodal data fusion according to claim 1, characterized in that: The process of fusing the model's predicted values ​​with water content distribution characteristics to generate a basic layout scheme and determine preliminary location coordinates includes: Initial result data is obtained through model prediction, and the data is preliminarily organized to obtain structured prediction output; Based on the structured prediction output, combined with water content values ​​and distribution data, data integration techniques are used to process the data and determine the comprehensive distribution information. Based on the comprehensive distribution information, if the information meets the preset distribution uniformity condition, a basic layout framework is generated, and a preliminary layout structure is determined. By establishing a preliminary layout structure, spatial data related to location coordinates is obtained. The support vector machine algorithm is then used for classification to obtain the classified spatial distribution results. Based on the spatial distribution results after classification and in conjunction with the layout planning requirements, if there is a deviation between the classification results and the planning objectives, spatial adjustment processing will be carried out to determine the adjusted layout scheme. Obtain the adjusted layout scheme, verify the coordinate positioning data in the scheme, and obtain the final position coordinate data. Based on the final location coordinate data, a corresponding layout planning record is generated to determine the complete business output content.

6. The method for optimizing seismic scaffolding based on multimodal data fusion according to claim 1, characterized in that: If the deviation between the initial position coordinates and the permeation depth data exceeds a preset threshold, the gradient mutation parameter is adjusted to obtain an optimized coordinate set, including: By acquiring coordinate data and penetration depth data from the initial location, the deviation between the two is calculated to obtain the deviation result. If the deviation results exceed the preset threshold, the data will be compared to determine the distribution of outliers. Based on the distribution of outliers, the gradient descent algorithm is used to adjust the gradient mutation parameters to obtain the adjusted parameter values. Starting from the adjusted parameter values, position correction is performed on the coordinate data to generate corrected position information; The corrected location information is compared with the permeation depth data a second time to determine whether the preset conditions are met. If the second comparison result still does not meet the preset conditions, the deviation is recalculated based on the corrected position information to obtain the updated deviation data; By combining the updated deviation data with the adjusted parameter values, the final optimized coordinate set is generated.

7. The method for optimizing seismic scaffolding based on multimodal data fusion according to claim 1, characterized in that: The step of determining the consistency of hydrological segment discretization by re-collecting verification data from the sensor array based on the optimized coordinate set includes: Data is collected using a sensor array device to optimize the coordinate set and obtain initial hydrological data records. Based on the initial hydrological data records, a segmented analysis method was adopted to divide the data according to the rules of hydrological segmentation, resulting in segmented data groups; For the segmented data groups, a consistency assessment is performed using newly acquired verification data to determine whether each segment of data meets the discrete consistency standard. If the consistency assessment results show that some segmented data deviates from the discrete consistency standard, the support vector machine algorithm is used to classify the data groups that deviate and determine the range of abnormal data. Based on the range of abnormal data after classification and processing, and combined with the real-time acquisition capability of the sensor array, targeted verification data is reacquired to obtain updated supplementary data. By supplementing the updated data and combining the results of segmented analysis, a secondary evaluation of the discrete consistency of the hydrological segments is conducted to determine whether the overall data meets the consistency requirements. Based on the results of the second evaluation, for the segmented data that still do not meet the consistency requirements, the distribution strategy of the coordinate set is adjusted and optimized to obtain the final hydrological data consistency distribution.

8. The method for optimizing seismic scaffolding based on multimodal data fusion according to claim 1, characterized in that: The step of updating the load-bearing capacity calculation model based on the consistency judgment result to obtain the final basic layout scheme includes: By analyzing the data output from the consistency judgment, we obtain the input data for the judgment result analysis. We then perform structured processing on the analyzed data stream to obtain a preliminary set of judgment results. Based on the preliminary judgment results, the logical framework for bearing capacity calculation is adopted, and combined with the index system for bearing capacity assessment, the intermediate result data for bearing capacity calculation are determined. For the intermediate results data of the bearing capacity calculation, obtain the input parameters for updating the calculation model. Based on the preset rules for adjusting the model parameters, determine the applicability of the model. If the adjusted parameters meet the preset threshold, output the updated model version. The basis for generating basic layout schemes is obtained from the updated model version. An algorithm framework for optimizing layout schemes is adopted, and the layout schemes are classified in multiple dimensions using the support vector machine algorithm to obtain an optimized layout scheme dataset. Based on the optimized layout scheme dataset, the input information for the scheme generation process is obtained, and the final layout scheme structure is determined through a standardized process of result data processing. For the final layout scheme structure, obtain the formatted data of the layout result output, and perform data mapping through the preset output template to obtain the final basic layout result; From the final basic layout results, the feedback data of the verification stage is obtained, and the feedback data is processed in the secondary processing stage of model verification to determine the stability output of the bearing capacity calculation model.

9. A seismic scaffolding optimization system based on multimodal data fusion, characterized in that, include: The sensor array module is used to collect soil moisture content distribution characteristics and infiltration depth data in real time from hydrological environment segments to obtain the raw dataset. The data processing module, based on the original dataset, uses the k-means clustering algorithm to refine the boundaries of the hydrological segments and determine the segment boundary parameters. The model building module is used to obtain the segment boundary parameters, apply the support vector machine algorithm to construct a local bearing capacity calculation model for gradient abrupt change regions, and obtain the model prediction value. The layout generation module is used to generate a basic layout scheme by fusing the model prediction values ​​with the water content distribution characteristics, and to determine the preliminary location coordinates. The optimization module is used to determine whether the deviation between the initial position coordinates and the penetration depth data exceeds a preset threshold. If it exceeds the threshold, the gradient mutation parameters are adjusted to obtain an optimized coordinate set. The verification module is used to determine the consistency of hydrological segment discretization by re-collecting verification data from the sensor array based on the optimized coordinate set. The update module is used to update the load-bearing capacity calculation model based on the consistency judgment result, so as to obtain the final basic layout scheme.

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