Geotechnical engineering data processing method and system based on cloud computing

Abstract

The invention relates to the technical field of data processing, in particular to a geotechnical engineering data processing method and system based on cloud computing, and the method comprises the following steps: based on geological survey and field test data of a target area, receiving crack and cavity defect data of a rock stratum structure through a cloud server, and according to the length of a rock stratum crack and the size of a cavity, carrying out the cloud computing; and evaluating the rock stratum anomaly degree to obtain a rock stratum anomaly evaluation result. According to the method, through comprehensive processing of geological survey and field test data, potential geological risks can be rapidly identified, the overall risk of a target area is evaluated in combination with soil stability and rock stratum abnormal data, a scientific basis is provided for geotechnical engineering reinforcement decision, historical data comparative analysis is introduced, and the method has a good application prospect. The method optimizes the selection of the reinforcement method by analyzing the cost of the reinforcement mode, reduces the cost by predicting the material consumption, achieves the high efficiency and refinement of data processing, and improves the safety and economy of geotechnical engineering.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a geotechnical engineering data processing method and system based on cloud computing. Background Art

[0002] The field of data processing technology involves the techniques and methods of extracting useful information from large amounts of data, including data collection, storage, management, analysis and interpretation. This technical field specifically includes data acquisition, storage, analysis and output, supporting a variety of data formats and complexities. Key technologies include database management, big data technology, and data mining, which aim to discover patterns and correlations from data and achieve analytical prediction.

[0003] Geotechnical engineering data processing methods are methods that apply data processing technology to analyze and interpret data in geotechnical engineering in order to better understand the behavior of soil, rock and other geological materials. The main uses of the methods include geological disaster prediction, construction safety assessment, and foundation engineering design optimization. Through these methods, engineers can use the data collected from geological surveys and field tests to perform accurate analysis and risk assessment, providing data support and decision-making basis for geotechnical engineering projects.

[0004] Traditional processing methods rely on decentralized data processing and manual evaluation, which is not only time-consuming, but also affects the accuracy and timeliness of decision-making due to the information island effect. For example, in geotechnical engineering, the prediction of geological disasters and construction safety assessment requires the integration of a large number of decentralized data sources. Traditional methods are difficult to integrate and analyze data in real time, resulting in delays in risk assessment and increased engineering risks and costs. Traditional methods are also not efficient in terms of resource utilization and cost management, resulting in over-allocation of resources and waste of materials, affecting the economic efficiency of the project. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a geotechnical engineering data processing method and system based on cloud computing.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a geotechnical engineering data processing method based on cloud computing, comprising the following steps:

[0007] S1: Based on the geological survey and field test data of the target area, the crack and cavity defect data of the rock formation structure are received through the cloud server. According to the length of the rock formation cracks and the size of the cavity, the degree of rock formation anomaly is evaluated to obtain the rock formation anomaly evaluation results;

[0008] S2: Based on the rock formation anomaly assessment result, combined with the soil type, soil thickness, regional slope and vegetation sparseness of the target area, the soil stability of the target area is assessed, and combined with the degree of rock formation anomaly, the danger level of the target area is assessed, and it is determined whether the target area needs geotechnical reinforcement, and the engineering demand assessment result is obtained;

[0009] S3: Based on the engineering demand assessment results, historical geotechnical engineering data is collected through a cloud server, and the matching degree between the historical reinforcement method and the current project is evaluated according to the engineering objectives, soil type, rock type and regional slope, and the matching reinforcement method is identified. In combination with the cost of the differentiated reinforcement method, the target reinforcement method is selected to obtain the engineering reinforcement method selection result;

[0010] S4: Based on the selection result of the engineering reinforcement method, the engineering material consumption data of the same reinforcement method is collected through the cloud server, and the engineering material consumption is predicted in combination with the project size to obtain the engineering material demand prediction result.

[0011] The present invention is improved in that the steps of obtaining the rock formation abnormality assessment result are:

[0012] S111: Based on the geological survey and field test data of the target area, the crack and cavity defect data of the rock formation structure are received through the cloud server, and the data are stored according to the receiving time and geographical mark to obtain the rock formation related data;

[0013] S112: Based on the rock formation correlation data, the formula:

[0014]

[0015] Calculate the rock formation anomaly index E, where E represents the rock formation anomaly index, w L and w S is the weight coefficient, G E is the geological environment factor, e is the base of the natural logarithm, L E is the crack length, S E is the size of the cavity;

[0016] S113: Based on the rock formation anomaly index E, according to the value of E, the degree of rock formation anomaly is evaluated to obtain a rock formation anomaly evaluation result.

[0017] The present invention is improved in that the method for evaluating the soil stability of the target area is:

[0018] S211: Collect soil type data of the target area, and integrate soil thickness, slope and vegetation sparseness index of the target area to obtain soil related data;

[0019] S212: Based on the soil related data, the formula:

[0020]

[0021] Calculate the soil stability index S, where T is the soil type index, D S is the soil thickness, θ is the slope, V S is the vegetation sparseness index, α S , β S and γ S is the adjustment parameter;

[0022] S213: Based on the soil stability index S and according to the value of S, the soil stability of the target area is evaluated to obtain soil stability information.

[0023] The present invention is improved in that the steps of obtaining the engineering demand assessment result are:

[0024] S221: Based on the soil stability index S and the rock formation anomaly index E, the formula:

[0025]

[0026] Calculate the geological risk index R, where R is the geological risk index, S is the soil stability index, E is the rock formation anomaly index, and λ R and κ R is the adjustment coefficient;

[0027] S222: Based on the geological risk index R, it is compared with a preset risk threshold. If the R value exceeds the risk threshold, it is determined that the target area needs geotechnical reinforcement, and an engineering demand assessment result is obtained.

[0028] The present invention is improved in that the method for identifying the matching reinforcement mode is:

[0029] S311: Based on the engineering needs assessment results, historical geotechnical engineering data is collected through a cloud server, and the target, soil type, rock type and regional slope information of each project are extracted to obtain reinforcement method related data;

[0030] S312: Based on the reinforcement method association data, the formula:

[0031]

[0032] Calculate the matching index M between the historical reinforcement method and the current project, where M is the matching index, is the weight of the ith attribute, is the sensitivity coefficient of the i-th attribute, x i is the i-th attribute value of the current project, y i is the i-th attribute value of the historical project;

[0033] S313: Based on the matching index M, compare with a preset matching threshold, mark the reinforcement methods whose M values ​​exceed the matching threshold, and obtain a matching reinforcement method list.

[0034] The present invention is improved in that the steps of obtaining the engineering reinforcement method selection result are:

[0035] S321: Based on the matching reinforcement method list, collect the historical average cost of each reinforcement method, and use the formula:

[0036]

[0037] Calculate the benefit score X, where X is the benefit score, M is the matching index, C is the historical average cost of the reinforcement method, and α X is the cost impact coefficient;

[0038] S322: Based on the benefit score X, by comparing the X values ​​of the differentiated reinforcement methods, the reinforcement method with the largest X value is selected as the target reinforcement method, and a project reinforcement method selection result is obtained.

[0039] The present invention is improved in that the steps of obtaining the engineering material demand forecast result are:

[0040] S411: Based on the engineering reinforcement method selection result, the engineering material consumption data of the same reinforcement method is collected through the cloud server, including the material type, quantity, scale and complexity of the historical engineering, to obtain material consumption related data;

[0041] S412: Based on the material consumption related data and the current project size, the formula:

[0042]

[0043] Calculate the predicted material consumption Y and obtain the engineering material demand forecast result, where Y is the predicted material consumption and Y avg is the average material consumption of similar historical projects, U cur is the current project size, U avg is the average size of historical projects, β Y is the adjustment coefficient, γ Y is the attenuation parameter and e is the base of the natural logarithm.

[0044] A geotechnical engineering data processing system based on cloud computing, the geotechnical engineering data processing system based on cloud computing is used to execute the above-mentioned geotechnical engineering data processing method based on cloud computing, the system comprising:

[0045] The rock formation status assessment module receives the crack and cavity defect data of the rock formation structure through the cloud server based on the geological survey and field test data of the target area, and assesses the degree of rock formation abnormality according to the length of rock formation cracks and the size of cavities to obtain the rock formation abnormality assessment results;

[0046] The soil state assessment module assesses the soil stability of the target area based on the rock formation anomaly assessment result and combines the soil type, soil thickness, regional slope and vegetation sparseness of the target area to obtain soil stability information;

[0047] The reinforcement demand analysis module evaluates the danger level of the target area based on the soil stability information and the rock formation anomaly assessment results, determines whether the target area needs geotechnical engineering reinforcement, and obtains an engineering demand assessment result;

[0048] The reinforcement method identification module collects historical geotechnical engineering data through a cloud server based on the engineering demand assessment results, evaluates the matching degree between the historical reinforcement methods and the current project, identifies matching reinforcement methods, and selects the target reinforcement method in combination with the cost of the differentiated reinforcement methods to obtain the engineering reinforcement method selection result;

[0049] The engineering consumption prediction module collects engineering material consumption data of similar reinforcement methods through a cloud server based on the engineering reinforcement method selection result, predicts the engineering material consumption, and obtains the engineering material demand prediction result.

[0050] Compared with the prior art, the advantages and positive effects of the present invention are:

[0051] In the present invention, through the comprehensive processing of geological survey and field test data, the abnormal degree of rock structure is evaluated, potential geological risks can be quickly identified, and the overall danger of the target area is evaluated in combination with soil stability and rock abnormality data, providing a scientific basis for geotechnical engineering reinforcement decision-making. Historical data comparative analysis is introduced to identify and select appropriate reinforcement methods, and the choice of reinforcement methods is optimized by analyzing the cost of reinforcement methods. The cost is reduced by predicting material consumption, achieving efficient and refined data processing, and improving the safety and economy of geotechnical engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flow chart of the method of the present invention;

[0053] Figure 2 A flow chart for obtaining formation anomaly assessment results for the present invention;

[0054] Figure 3 Flow chart for evaluating soil stability of a target area for the present invention;

[0055] Figure 4A flow chart for obtaining engineering needs assessment results for the present invention;

[0056] Figure 5 A flow chart of identifying a matching reinforcement method for the present invention;

[0057] Figure 6 A flow chart for obtaining the selection result of the engineering reinforcement method for the present invention;

[0058] Figure 7 The present invention is a flow chart for obtaining engineering material demand forecast results. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0060] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0061] See also Figure 1 The present invention provides a technical solution: a geotechnical engineering data processing method based on cloud computing, comprising the following steps:

[0062] S1: Based on the geological survey and field test data of the target area, the crack and cavity defect data of the rock formation structure are received through the cloud server. According to the length of the rock formation cracks and the size of the cavity, the degree of rock formation anomaly is evaluated to obtain the rock formation anomaly evaluation results;

[0063] S2: Based on the results of the rock formation anomaly assessment, combined with the soil type, soil thickness, regional slope and vegetation sparseness of the target area, the soil stability of the target area is assessed. Combined with the degree of rock formation anomaly, the danger level of the target area is assessed, and it is determined whether the target area needs geotechnical reinforcement to obtain the engineering demand assessment results;

[0064] S3: Based on the results of the project demand assessment, historical geotechnical engineering data is collected through the cloud server. According to the project objectives, soil type, rock type and regional slope, the matching degree between the historical reinforcement method and the current project is evaluated, the matching reinforcement method is identified, and the target reinforcement method is selected in combination with the cost of the differentiated reinforcement method to obtain the project reinforcement method selection result;

[0065] S4: Based on the selection result of the engineering reinforcement method, the engineering material consumption data of the same reinforcement method is collected through the cloud server, and the engineering material consumption is predicted in combination with the project size to obtain the engineering material demand prediction result.

[0066] The results of rock formation anomaly assessment include crack width index, void volume ratio and structural integrity rating; the results of engineering needs assessment include stability rating of target area, predicted landslide and collapse risk level and urgency evaluation of geotechnical reinforcement; the results of engineering reinforcement method selection include recommended reinforcement technology type, estimated reinforcement cost and estimated construction time; the results of engineering material demand forecast include types of key materials required, estimated consumption of each material and material procurement cost information.

[0067] See also Figure 2 , the steps to obtain the results of rock formation anomaly assessment are:

[0068] S111: Based on the geological survey and field test data of the target area, the crack and cavity defect data of the rock formation structure are received through the cloud server, and the data are stored according to the receiving time and geographical mark to obtain the rock formation related data;

[0069] S112: Based on the rock formation correlation data, the formula is:

[0070]

[0071] Calculate the rock formation anomaly index E, where E represents the rock formation anomaly index, w L and w S is the weight coefficient, G E is the geological environment factor, e is the base of the natural logarithm, L E is the crack length, S E is the size of the cavity;

[0072] S113: Based on the rock formation anomaly index E, according to the size of the E value, the degree of rock formation anomaly is evaluated to obtain a rock formation anomaly evaluation result.

[0073] formula:

[0074]

[0075] Parameter details and how to obtain them:

[0076] LE : Crack length, which is the data obtained through on-site measurement. Usually, geological scanning equipment is used to measure the cracks. The data is in meters. The measurement results are affected by the accuracy of the equipment and operating conditions.

[0077] S E : The size of the cavity is also measured by geological scanning equipment. The results are recorded based on the volume or surface area of ​​the cavity, usually in cubic meters. The size of the cavity directly affects the stability of the rock formation.

[0078] G E : Geological environmental factor, which reflects the complexity of the geological environment of the rock formation. This factor is obtained through expert evaluation based on multiple parameters such as rock formation type, historical seismic activity, and hydrogeological conditions.

[0079] w L and w S : Weight coefficient of crack length and cavity size. The coefficient is obtained based on historical data and empirical statistical analysis of geological engineers, reflecting the weight of the impact of different cracks and cavities on rock stability.

[0080] Calculation example:

[0081] Set the following parameters:

[0082] L E =2 m, S E =3 cubic meters, G E =4,w L =0.5, w S =0.3.

[0083] Calculation process:

[0084]

[0085] The calculated anomaly index E=1.867. This high value indicates that the rock formation has a high degree of anomaly and further inspection or intervention measures are needed.

[0086] See also Figure 3 , the method to evaluate soil stability in the target area is:

[0087] S211: Collect soil type data of the target area, and integrate soil thickness, slope and vegetation sparseness index of the target area to obtain soil related data;

[0088] S212: Based on soil related data, through the formula:

[0089]

[0090] Calculate the soil stability index S, where T is the soil type index, D S is the soil thickness, θ is the slope, V S is the vegetation sparseness index, α S , β S and γ S is the adjustment parameter;

[0091] S213: Based on the soil stability index S, the soil stability of the target area is evaluated according to the value of S to obtain soil stability information.

[0092] formula:

[0093]

[0094] Parameter details and how to obtain them:

[0095] T: Soil type index, obtained through geological exploration techniques such as core sampling or soil analysis. The value is preset by experts according to the type of soil. The more stable the soil type, the larger the index. For example, the index of mud will be greater than that of sand.

[0096] D S : Soil thickness, usually calculated by topographic surveying techniques such as ground penetrating radar or conventional exploration drilling, from field measurements taken at different locations and then averaged.

[0097] θ: Regional slope, obtained by topographic measuring equipment such as inclinometers, which represents the angle of terrain inclination in degrees.

[0098] V S : Vegetation sparse index, obtained using remote sensing technology such as drone or satellite image analysis, is the vegetation coverage rate, reflecting the density and distribution of vegetation coverage.

[0099] α S , β S and γ S : Adjustment coefficient, based on historical analysis of geological and vegetation data and expert experience, is set through a statistical regression model to adjust the contribution weight of soil type, thickness and vegetation to soil stability assessment.

[0100] Calculation example:

[0101] Value settings:

[0102] T = 50 (soil type index), D S = 2 m (average soil thickness measured on site), θ = 15° (slope measured on topography), V S =30% (vegetation coverage obtained by remote sensing technology), α S =0.8,β S=0.5,

[0103] γ S =0.3.

[0104] Calculation process:

[0105]

[0106] The soil stability index S obtained was about 61.74, which is a high value, indicating that the soil in the target area has high stability under the current soil type, thickness, slope and vegetation conditions.

[0107] See also Figure 4 , the steps to obtain the results of engineering needs assessment are:

[0108] S221: Based on the soil stability index S and the rock anomaly index E, the formula is:

[0109]

[0110] Calculate the geological risk index R, where R is the geological risk index, S is the soil stability index, E is the rock formation anomaly index, and λ R and κ R is the adjustment coefficient;

[0111] S222: Based on the geological risk index R, it is compared with the preset risk threshold. If the R value exceeds the risk threshold, it is determined that the target area needs geotechnical reinforcement, and the engineering demand assessment result is obtained.

[0112] formula:

[0113]

[0114] Parameter details and how to obtain them:

[0115] S: Soil stability index, calculated by the previous steps.

[0116] E: Rock formation anomaly index, calculated by the previous steps.

[0117] λ R : Parameters that adjust the influence of the rock anomaly index, based on historical geological data and analysis of regional-specific geological activity, are used to adjust the weight of rock anomalies in the overall risk assessment.

[0118] κ R : Parameters that balance the impact of abnormal increases in rock formations, obtained through statistical analysis of historical data and simulation of rock formation behavior, with the aim of ensuring a balanced treatment of risk assessment when abnormally high values ​​are present.

[0119] Calculation example:

[0120] Set the following parameters:

[0121] S = 70, the soil stability index is high, indicating that the soil conditions are relatively stable, E = 0.3, the rock formation anomaly index is relatively low, λ R =0.5,κ R =0.2.

[0122] Calculation process, using natural logarithm:

[0123]

[0124] The calculated geological risk index R is about 60.3, which is relatively high but still within an acceptable range. This shows that although the rock layer has slight anomalies, the overall geological risk is still at a low level due to the high soil stability. This result is used to decide whether to carry out geotechnical reinforcement. If the risk threshold T R If it is set at 50, it is judged that reinforcement measures are needed.

[0125] See also Figure 5 , the method to identify the matching reinforcement method is:

[0126] S311: Based on the results of the project demand assessment, historical geotechnical engineering data is collected through the cloud server, and the target, soil type, rock type and regional slope information of each project are extracted to obtain the reinforcement method related data;

[0127] S312: Correlate data based on reinforcement method, by formula:

[0128]

[0129] Calculate the matching index M between the historical reinforcement method and the current project, where M is the matching index, is the weight of the ith attribute, is the sensitivity coefficient of the i-th attribute, x i is the i-th attribute value of the current project, y i is the i-th attribute value of the historical project;

[0130] S313: Based on the matching index M, compare with the preset matching threshold, mark the reinforcement methods whose M values ​​exceed the matching threshold, and obtain a matching reinforcement method list.

[0131] formula:

[0132]

[0133] Parameter details and how to obtain them:

[0134] This is the weight of the ith attribute, used to evaluate the importance of different attributes in the matching calculation. The weight is obtained based on the statistical analysis of the success rate of historical projects and the stability of the attributes, and is comprehensively derived from professional databases and expert evaluations.

[0135] The sensitivity coefficient of the i-th attribute is used to adjust the influence of the differences between attributes. The coefficient reflects the sensitivity of the difference between different attributes to the matching degree, which is usually obtained through regression analysis of historical data to ensure that the matching degree reflects the actual engineering needs.

[0136] x i and i : Represents the values ​​of the current project and the historical project on the i-th attribute. The values ​​are directly extracted from the project database. For example, the soil type can be represented by its corresponding soil type index T for easy processing and comparison.

[0137] Calculation example:

[0138] Set the current project and a history project to have the following property values:

[0139] Current project property value (x i ):

[0140] Soil type index = 30, rock type index = 20, area slope = 10°.

[0141] Historical project attribute value (y i ):

[0142] Soil type index = 20, rock type index = 20, area slope = 15°.

[0143] Weight

[0144] Soil type = 0.5, rock type = 0.3, area slope = 0.2.

[0145] Sensitivity coefficient

[0146] Soil type = 0.01, rock type = 0.01, area slope = 0.05.

[0147] Calculation process:

[0148]

[0149] The calculated matching degree M is about 0.908, which is a large value, indicating that the current project and the selected historical project have a high degree of similarity in key attributes and a high matching degree. This means that the reinforcement method used in the historical project is very suitable for the current project, so the same or similar reinforcement strategy can be considered.

[0150] See also Figure 6 , the steps to obtain the results of engineering reinforcement method selection are:

[0151] S321: Based on the matching reinforcement method list, collect the historical average cost of each reinforcement method, through the formula:

[0152]

[0153] Calculate the benefit score X, where X is the benefit score, M is the matching index, C is the historical average cost of the reinforcement method, and α X is the cost impact coefficient;

[0154] S322: Based on the benefit score X, by comparing the X values ​​of the differentiated reinforcement methods, the reinforcement method with the largest X value is selected as the target reinforcement method, and the engineering reinforcement method selection result is obtained.

[0155] formula:

[0156]

[0157] Parameter details and how to obtain them:

[0158] M: Matching index, calculated by the previous step.

[0159] C: The historical average cost of the reinforcement method, which represents the total cost required to implement a specific reinforcement method. The cost data is collected from historical records and supplier quotations, taking into account factors such as materials, labor and time.

[0160] α X : Cost impact coefficient, determined through historical data analysis, used to adjust the impact of cost factors in the total score. It reflects the suppressive effect of cost increase on the score.

[0161] Calculation example:

[0162] The setting conditions are as follows:

[0163] The matching degree M is 0.9, indicating a highly similar engineering situation. The cost C is 50,000 yuan, which is the reinforcement cost. The cost impact coefficient α X is 0.1, and lower values ​​indicate that the cost increase has less impact on the score.

[0164] Calculation process:

[0165]

[0166] Calculate the square of the match:

[0167] M 2 =0.92 =0.81

[0168] Compute the inverse transform of the cost, taking the natural logarithm:

[0169]

[0170] Calculate the square root of the cost and multiply it by the cost impact factor:

[0171]

[0172]

[0173] Calculate the score X:

[0174]

[0175] The calculated score X is approximately 0.1205, which is used to evaluate the comprehensive benefits of different reinforcement methods to ensure that the selected reinforcement method is the most appropriate in terms of technology and cost.

[0176] See also Figure 7 , the steps to obtain the engineering material demand forecast results are:

[0177] S411: Based on the selection result of the engineering reinforcement method, the engineering material consumption data of the same reinforcement method is collected through the cloud server, including the material type, quantity, scale and complexity of the historical engineering, and the material consumption related data is obtained;

[0178] S412: Based on the material consumption data and the current project size, the formula is:

[0179]

[0180] Calculate the predicted material consumption Y and obtain the engineering material demand forecast result, where Y is the predicted material consumption and Y avg is the average material consumption of similar historical projects, U cur is the current project size, U avg is the average size of historical projects, β Y is the adjustment coefficient, γ Y is the attenuation parameter and e is the base of the natural logarithm.

[0181] formula:

[0182]

[0183] Parameter details and how to obtain them:

[0184] Y avg:The average material consumption calculated from historical data of similar projects is obtained by analyzing the actual material usage records of completed projects to ensure that the prediction model is optimized based on actual conditions.

[0185] U cur and U avg :U cur It is the size of the current project, which is the volume or area of ​​the project and is obtained from the project design document. avg It is the average size of historical projects, which is the volume or area of ​​historical projects, and is calculated by collecting data from similar past projects.

[0186] β Y and γ Y : The effect of coefficient adjustment scale changes on material consumption forecasts. β Y Usually based on regression analysis of historical data, indicating the nonlinear effect of increasing scale on material demand. γ Y is the attenuation parameter, which reduces the impact of large-scale deviations by optimizing the model to match the sensitivity adjustment of historical data deviations.

[0187] Calculation Example

[0188] The setting conditions are as follows:

[0189] Historical average material consumption Y avg =200 units.

[0190] Current project volume U cur =1500 square meters.

[0191] Historical average volume U avg =1000 square meters.

[0192] Adjustment coefficient β Y =0.8.

[0193] Attenuation parameter γ Y =0.0002.

[0194] Calculation process

[0195] Calculation of scale ratio:

[0196]

[0197] Calculation of exponential decay factor:

[0198] e -γY ·(U cur -U avg )=e -0.0002 (1500-1000)

[0199] e-0 .0002 500

[0200] =e -0.1

[0201] ≈0.9048

[0202] Material consumption calculation:

[0203] Y=200·1.383·0.9048≈250.27

[0204] The calculated and predicted material consumption Y is approximately 250.27 units, reflecting the actual demand of the project.

[0205] A geotechnical engineering data processing system based on cloud computing, the geotechnical engineering data processing system based on cloud computing is used to execute the above-mentioned geotechnical engineering data processing method based on cloud computing, the system comprises:

[0206] The rock formation status assessment module receives the crack and cavity defect data of the rock formation structure through the cloud server based on the geological survey and field test data of the target area, and assesses the degree of rock formation abnormality according to the length of rock formation cracks and the size of cavities to obtain the rock formation abnormality assessment results;

[0207] The soil state assessment module assesses the soil stability of the target area based on the rock formation anomaly assessment results, combined with the soil type, soil thickness, regional slope and vegetation sparseness of the target area, and obtains soil stability information;

[0208] The reinforcement demand analysis module evaluates the danger level of the target area based on soil stability information and rock formation anomaly assessment results, determines whether the target area needs geotechnical reinforcement, and obtains the engineering demand assessment results;

[0209] The reinforcement method identification module collects historical geotechnical engineering data through the cloud server based on the project demand assessment results, evaluates the matching degree between the historical reinforcement methods and the current project, identifies the matching reinforcement methods, and selects the target reinforcement method based on the cost of the differentiated reinforcement methods to obtain the project reinforcement method selection result;

[0210] The engineering consumption prediction module is based on the engineering reinforcement method selection results. It collects engineering material consumption data of similar reinforcement methods through the cloud server, predicts the engineering material consumption, and obtains the engineering material demand prediction results.

[0211] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A geotechnical engineering data processing method based on cloud computing, characterized in that: The following steps are involved: Based on the geological survey and field test data of the target area, the crack and cavity defect data of the rock formation structure are received through the cloud server. According to the length of the rock formation cracks and the size of the cavity, the degree of rock formation anomaly is evaluated to obtain the rock formation anomaly evaluation results; Based on the rock formation anomaly assessment results, combined with the soil type, soil thickness, regional slope and vegetation sparseness of the target area, the soil stability of the target area is assessed, and combined with the degree of rock formation anomaly, the danger level of the target area is assessed, and it is determined whether the target area needs geotechnical reinforcement to obtain the engineering demand assessment results; Based on the engineering needs assessment results, historical geotechnical engineering data is collected through a cloud server, and the matching degree between the historical reinforcement method and the current project is evaluated according to the engineering objectives, soil type, rock type and regional slope, and the matching reinforcement method is identified. In combination with the cost of the differentiated reinforcement method, the target reinforcement method is selected to obtain the engineering reinforcement method selection result; Based on the selection result of the engineering reinforcement method, the engineering material consumption data of the same reinforcement method is collected through the cloud server, and the engineering material consumption is predicted in combination with the project size to obtain the engineering material demand prediction result.

2. The geotechnical engineering data processing method based on cloud computing according to claim 1, characterized in that: The steps for obtaining the rock formation anomaly assessment result are as follows: Based on the geological survey and field test data of the target area, the crack and cavity defect data of the rock structure are received through the cloud server, and the data is stored according to the receiving time and geographical tags to obtain rock formation related data; Based on the rock formation correlation data, the formula: Calculate the rock formation anomaly index E, where E represents the rock formation anomaly index, w L and w S is the weight coefficient, G E is the geological environment factor, e is the base of the natural logarithm, L E is the crack length, S E is the size of the cavity; Based on the rock formation anomaly index E, the degree of rock formation anomaly is evaluated according to the value of E, and a rock formation anomaly evaluation result is obtained.

3. The geotechnical engineering data processing method based on cloud computing according to claim 1, characterized in that: The method for evaluating soil stability in a target area is: Collect soil type data of the target area, and integrate soil thickness, slope and vegetation sparseness index of the target area to obtain soil correlation data; Based on the soil association data, the formula: Calculate the soil stability index S, where T is the soil type index, D S is the soil thickness, θ is the slope, V S is the vegetation sparseness index, α S , β S and γ S is the adjustment parameter; Based on the soil stability index S, the soil stability of the target area is evaluated according to the value of S to obtain soil stability information.

4. The geotechnical engineering data processing method based on cloud computing according to claim 3 is characterized in that: The steps for obtaining the engineering needs assessment results are as follows: Based on the soil stability index S and the rock formation anomaly index E, the formula: Calculate the geological risk index R, where R is the geological risk index, S is the soil stability index, E is the rock formation anomaly index, and λ R and κ R is the adjustment coefficient; Based on the geological risk index R, it is compared with a preset risk threshold. If the R value exceeds the risk threshold, it is determined that the target area needs geotechnical reinforcement, and the engineering demand assessment result is obtained.

5. The geotechnical engineering data processing method based on cloud computing according to claim 1, characterized in that: The method for identifying the matching reinforcement method is: Based on the engineering needs assessment results, historical geotechnical engineering data is collected through a cloud server, and the target, soil type, rock type and regional slope information of each project are extracted to obtain reinforcement method related data; Based on the reinforcement method associated data, through the formula: Calculate the matching index M between the historical reinforcement method and the current project, where M is the matching index, is the weight of the ith attribute, is the sensitivity coefficient of the i-th attribute, x i is the i-th attribute value of the current project, y i is the i-th attribute value of the historical project; Based on the matching index M, a comparison is made with a preset matching threshold, and reinforcement methods whose M values ​​exceed the matching threshold are marked to obtain a matching reinforcement method list.

6. The geotechnical engineering data processing method based on cloud computing according to claim 5, characterized in that: The steps for obtaining the engineering reinforcement method selection result are as follows: Based on the matching reinforcement method list, the historical average cost of each reinforcement method is collected, and the formula is: Calculate the benefit score X, where X is the benefit score, M is the matching index, C is the historical average cost of the reinforcement method, and α X is the cost impact coefficient; Based on the benefit score X, by comparing the X values ​​of the differentiated reinforcement methods, the reinforcement method with the largest X value is selected as the target reinforcement method, and the engineering reinforcement method selection result is obtained.

7. The geotechnical engineering data processing method based on cloud computing according to claim 1, characterized in that: The steps for obtaining the engineering material demand forecast result are as follows: Based on the selection result of the engineering reinforcement method, the engineering material consumption data of the same reinforcement method is collected through the cloud server, including the material type, quantity, scale and complexity of the historical engineering, to obtain the material consumption related data; Based on the material consumption related data and the current project size, the formula is: Calculate the predicted material consumption Y and obtain the engineering material demand forecast result, where Y is the predicted material consumption and Y avg is the average material consumption of similar historical projects, U cur is the current project size, U avg is the average size of historical projects, β Y is the adjustment coefficient, γ Y is the attenuation parameter and e is the base of the natural logarithm.

8. A geotechnical engineering data processing system based on cloud computing, characterized in that: According to the cloud computing-based geotechnical engineering data processing method according to any one of claims 1 to 7, the system comprises: The rock formation status assessment module receives the crack and cavity defect data of the rock formation structure through the cloud server based on the geological survey and field test data of the target area, and assesses the degree of rock formation abnormality according to the length of rock formation cracks and the size of cavities to obtain the rock formation abnormality assessment results; The soil state assessment module assesses the soil stability of the target area based on the rock formation anomaly assessment result and combines the soil type, soil thickness, regional slope and vegetation sparseness of the target area to obtain soil stability information; The reinforcement demand analysis module evaluates the danger level of the target area based on the soil stability information and the rock formation anomaly assessment results, determines whether the target area needs geotechnical engineering reinforcement, and obtains an engineering demand assessment result; The reinforcement method identification module collects historical geotechnical engineering data through a cloud server based on the engineering demand assessment results, evaluates the matching degree between the historical reinforcement methods and the current project, identifies matching reinforcement methods, and selects the target reinforcement method in combination with the cost of the differentiated reinforcement methods to obtain the engineering reinforcement method selection result; The engineering consumption prediction module collects engineering material consumption data of similar reinforcement methods through a cloud server based on the engineering reinforcement method selection result, predicts the engineering material consumption, and obtains the engineering material demand prediction result.

Images