Geotechnical engineering geological investigation system and method

Abstract

The invention discloses a geotechnical engineering geological investigation system and method, relates to the technical field of geological investigation, and solves the problems of difficulty in multi-source data fusion, low geological anomaly intelligent identification precision and weak regional comprehensive evaluation and visual expression ability in the existing investigation method. According to the method, the unified comprehensive parameter vector is constructed, scattered drilling data, in-situ test parameters and real-time sensor monitoring data are subjected to standardized integration and collaborative analysis, the limitation that various types of data are independently stored in traditional exploration, and a data island is formed is broken through, efficient fusion of multi-source heterogeneous information is achieved, and the method has the advantages of being simple in structure and convenient to use. The problem of data fragmentation is solved, and deep cognition and judgment of complex geological conditions can be more comprehensively carried out.

Description

Technical Field

[0001] This invention relates to the field of geological exploration technology, specifically to a geotechnical engineering geological exploration system and method. Background Technology

[0002] Geotechnical engineering geological investigation is a prerequisite and safety guarantee for the construction of various projects such as highways, high-speed railways, bridges, and buildings. Its core task is to accurately determine the stratigraphic structure, physical and mechanical properties of soil and rock, and potential geological disaster risks of the project site. The accuracy and reliability of the investigation results directly determine the safety, economy, and construction feasibility of the engineering design scheme, and play a vital role in preventing engineering accidents and controlling construction costs.

[0003] Currently, geotechnical engineering geological investigation mainly relies on two methods: traditional manual investigation and existing technical investigation. Traditional manual investigation depends on engineers' field experience, inferring the overall geological conditions through limited drilling points, manual records, and subjective judgment. This not only consumes a lot of manpower and time and has low investigation efficiency, making it difficult to meet the needs of rapid advancement in modern large-scale projects, but more importantly, manual judgment is easily affected by personal experience and subjective cognitive limitations. It lacks the ability to identify subtle changes in strata, spatial variation trends, and hidden adverse geological bodies, resulting in inconsistent and unreliable investigation conclusions, which may lead to potential safety hazards in engineering projects.

[0004] While existing technological exploration methods have improved data management efficiency to some extent, significant technical bottlenecks remain. On the one hand, most methods store and analyze various exploration data, such as drilling, geophysical exploration, and testing, in isolation, forming data silos. They lack effective mechanisms for multi-source information fusion and collaborative analysis, making it difficult to form a holistic and in-depth understanding of geological conditions. On the other hand, the identification of geological anomalies often relies on single parameter thresholds or global statistical indicators, failing to fully consider the spatial correlation of geological parameters and local background changes. This not only fails to effectively capture the hidden defects of normal parameters but abnormal spatial trends, but also easily misjudges normal variations that conform to the local background as anomalies, leading to both misjudgments and omissions. Furthermore, in the process of transforming discrete point data into regional comprehensive evaluation, existing methods often rely on manual sketching or simple interpolation. The resulting maps have a single dimension of information, making it difficult to intuitively and comprehensively reflect the characteristics of geological zoning, the scope of anomaly areas, and spatial transition relationships, resulting in weak decision support capabilities. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a geotechnical engineering geological exploration system and method, which solves the problems of difficulty in multi-source data fusion, low accuracy of intelligent identification of geological anomalies, and weak regional comprehensive evaluation and visualization capabilities in existing exploration methods.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a geotechnical engineering geological investigation method, comprising the following steps:

[0007] Step 1: Deploy array detection nodes within the survey area, collect soil and rock parameters and sensor data from each node, and generate a comprehensive parameter vector for each node;

[0008] Step 2: For each detection node, based on the preset rule base and the comparison with the geographical neighborhood, examine its internal parameter coordination, spatial field coordination and physical response coordination respectively. Calculate the node's comprehensive incoordination index based on the test results, and calculate a specific judgment threshold based on the index of other nodes in its geographical neighborhood. Compare and identify and mark abnormal detection points.

[0009] Step 3: Divide the exploration area into multiple local units, calculate the overall coordination degree based on the node data within the unit, and assign corresponding geological feature labels to each unit based on the comparison results of the overall coordination degree with the preset threshold and the dominant type of disharmony within the unit.

[0010] Step 4: Integrate the local units according to geographic coordinates, label the boundary types based on the similarity of the baseline features of adjacent units and the relationship of geological labels, draw graphics in combination with the distribution of anomaly detection points, and integrate the labeling information to generate a global geological distribution map.

[0011] As a further aspect of the present invention: the generation of the comprehensive parameter vector for each node specifically includes:

[0012] The soil layer type at each node was determined by drilling and sampling, and the standard penetration test blow count, the cone dynamic penetration test blow count, the cone tip resistance and the side friction resistance were obtained by penetration test, dynamic cone penetration test and static penetration test, respectively.

[0013] Strain and temperature time series data are collected at each node by deployed sensors; the soil layer type, standard penetration test blow count, cone dynamic penetration test blow count, cone tip resistance, side friction resistance, strain time series data and temperature time series data are standardized and transformed to form a comprehensive parameter vector for that node.

[0014] As a further aspect of the present invention: the verification of intrinsic parameter compatibility specifically includes:

[0015] Based on the soil layer category code of the current detection node, the corresponding standard penetration test blow count range and cone tip resistance range are matched from the preset association rule library;

[0016] Calculate the ratio of the measured cone tip resistance to the measured standard penetration blows at the current node;

[0017] Determine whether the ratio is within the preset ratio range for the soil layer category in the association rule base. If not, it is determined to be an inherent mismatch.

[0018] As a further aspect of the present invention, the specific method for verifying the spatial field coordination is as follows:

[0019] Taking the current detection node as the center, obtain all other detection nodes whose geographical distance from it does not exceed the preset grid side length L as the neighborhood point set;

[0020] Calculate the difference vector between the comprehensive parameter vector of the current node and the comprehensive parameter vector of each node in the neighborhood point set, and determine the orientation angle of each difference vector on the two-dimensional geographic plane.

[0021] Calculate the standard deviation of all orientation angles. If the standard deviation exceeds the preset dispersion threshold, it is determined to be a field incoordination.

[0022] As a further aspect of the present invention: the verification of physical response compatibility specifically includes:

[0023] Extract the strain data sequence of the current detection node within a continuous preset time period, and calculate the standard deviation σi of the sequence;

[0024] Extract the strain data sequences of all other detection points in its preset geographical neighborhood within the same time period, and calculate the average standard deviation σb of each sequence;

[0025] Calculate the strain fluctuation coefficient Rs = σi / σb;

[0026] Extract the temperature data sequence of the current detection point and all other detection points in the same geographical neighborhood at the same time period, calculate the correlation coefficient PT between the temperature sequence of the current point and the average temperature sequence of the neighborhood points, and calculate the temperature response deviation Rt=1-PT;

[0027] If Rs is greater than the preset fluctuation coefficient threshold Ts or Rt is greater than the preset temperature response deviation threshold Tt, it is determined to be a physical response mismatch.

[0028] As a further aspect of the present invention: the specific comprehensive inconsistency index of the computing node is as follows:

[0029] If an inherent misalignment occurs, the first scalar value S1 is calculated based on the deviation of the measured ratio from the median of the preset ratio interval; otherwise, S1 is set to 0.

[0030] If spatial field discrepancy occurs, the second scalar value S2 is calculated based on the ratio of the standard deviation of the orientation angle to the dispersion threshold; otherwise, S2 is set to 0.

[0031] If physical response misalignment occurs, the third scalar value S3 is calculated based on the strain fluctuation coefficient, the temperature response deviation and the degree of exceedance of their respective thresholds; otherwise, S3 is set to 0.

[0032] The comprehensive incoordination index Ii is calculated by the formula Ii = β1×F(S1) + β2×F(S2) + β3×F(S3), where β1, β2, and β3 are preset weight coefficients, and F() is a preset normalization function.

[0033] As a further solution of the present invention: The identifying and calibrating abnormal detection points includes:

[0034] Taking the current detection node as the center, a geographical neighborhood is defined with a preset grid side length L, and the comprehensive incoordination index Ii of all other detection points within this neighborhood is obtained;

[0035] Calculate the median value Iz and the interquartile range Is of the index set;

[0036] According to the formula: Y1 = Iz + K×Is, calculate the exclusive determination threshold Y1, where K is a preset coefficient;

[0037] If the comprehensive incoordination index Ii of the current node is greater than Y1, then it is calibrated as an abnormal detection point.

[0038] As a further solution of the present invention: In the third step, endowing each unit with a geological feature label specifically includes:

[0039] Extract the comprehensive parameter vectors of all nodes within the local unit that are not calibrated as abnormal detection points, and calculate their mean value V_avg and variance V_diff as the benchmark parameter features of this unit;

[0040] Statistically calculate the proportion P_abn of the number of abnormal detection points within the unit;

[0041] According to the formula C_local = (1 - P_abn)×(1 - mean value of Ii within the unit / maximum value of Ii within the unit), calculate the overall coordination degree C_local of the unit;

[0042] Based on the size relationship between C_local and the preset coordination thresholds T1 and T2, and combined with the dominant type of disharmony within the unit, select the corresponding label from a group of predefined geological labels for endowment.

[0043] As a further solution of the present invention: The generating of the global geological distribution map includes:

[0044] Embed each local unit into the global grid according to its geometric boundary and geographical coordinates;

[0045] Calculate the cosine similarity Sim of the benchmark parameter feature vectors V_avg of any two adjacent local units. If Sim ≥ the preset similarity threshold T_sim, then mark the connection boundary as a smooth transition, otherwise mark it as a geological mutation zone;

[0046] If the geological labels of two adjacent units are different and one of them is a significant abnormal aggregation type, then record the connection boundary as an abnormal diffusion boundary;

[0047] Different colors are used to fill the units with different geological labels;

[0048] For a group of units with the same and adjacent geological labels, connect the anomaly detection points on their boundaries to form a closed polygon that surrounds the area;

[0049] Label the geological label name and average anomaly percentage in each unit of the map, and label the overall coordination degree C at the center of the unit.

[0050] Geotechnical engineering geological investigation system, including:

[0051] The data acquisition module is used to drill, sample, conduct penetration tests and deploy sensors at the array detection nodes in the survey area to obtain soil and rock parameters, blow count, cone tip resistance, side friction resistance, strain time series data and temperature time series data.

[0052] The processing module, connected to the acquisition module, is used to standardize the acquired raw data and generate a comprehensive parameter vector for each detection point. Based on the association rule base and the preset algorithm, it performs intrinsic parameter coordination test, spatial field coordination test and physical response coordination test, calculates the comprehensive incoordination index and the exclusive judgment threshold, and compares the two to identify and calibrate abnormal detection points.

[0053] The evaluation module, connected to the processing module, is used to divide the exploration area into local units with a preset grid side length, calculate the baseline parameter characteristics, anomaly point ratio and overall coordination degree of each unit, and assign a predefined geological feature label to each unit based on the comparison result of the overall coordination degree with the preset threshold and the dominant anomaly type within the unit.

[0054] The mapping module, connected to the evaluation module, is used to integrate each local unit into a global grid according to geographic coordinates, calculate and label the boundary type between adjacent units, perform color filling based on geological labels, connect and form a closed geometric shape according to the distribution of anomaly detection points, and integrate the labeling information to generate a complete global geological distribution map.

[0055] The storage module, connected to the above modules, is used to store the association rule base, the collected raw data, the intermediate data generated during processing, and the final map data.

[0056] This invention provides a geotechnical engineering geological exploration system and method. Compared with the prior art, it has the following advantages:

[0057] (1) By constructing a unified comprehensive parameter vector, this invention standardizes and integrates the scattered drilling data, in-situ test parameters and real-time sensor monitoring data for collaborative analysis. This breaks the limitation of independent storage of various types of data and the formation of data silos in traditional exploration, realizes the efficient integration of multi-source heterogeneous information, solves the problem of data fragmentation, and enables a more comprehensive understanding and judgment of complex geological conditions.

[0058] (2) This invention firstly achieves differentiated and refined inspection based on the association rule library of soil layer categories, secondly effectively identifies hidden defects with normal parameters but abnormal spatial variation through spatial field coordination analysis, and finally adopts dynamic and specific judgment threshold based on local statistical features to make the anomaly screening standard adapt to the specific geological environment, which greatly reduces the misjudgment and missed judgment that are common in traditional methods, and makes anomaly identification more scientific and targeted.

[0059] (3) This invention grids the exploration area, calculates the overall coordination degree by integrating the distribution of anomalies within the unit and the coordination of parameters, and assigns standardized geological feature labels to achieve the scientific classification of regional engineering geological conditions. The final global geological distribution map integrates and visualizes spatial distribution, attribute characteristics and relational logic through color partitioning, anomaly point outline connection and transition relationship labeling, which greatly enhances the intuitiveness and decision support capability of the results and effectively solves the problems of abstract expression and single information in traditional results. Attached Figure Description

[0060] Figure 1 This is a flowchart of the method of the present invention;

[0061] Figure 2 This is a system flowchart of the present invention. Detailed Implementation

[0062] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] Example 1

[0064] Please see Figure 1 This application provides a geotechnical engineering geological investigation method, including the following steps:

[0065] Step 1: Deploy array detection nodes within the survey area, collect soil and rock parameters and sensor data from each node, and generate a comprehensive parameter vector for each node;

[0066] Step 2: For each detection node, based on the preset rule base and the comparison with the geographical neighborhood, examine its internal parameter coordination, spatial field coordination and physical response coordination respectively. Calculate the node's comprehensive incoordination index based on the test results, and calculate a specific judgment threshold based on the index of other nodes in its geographical neighborhood. Compare and identify and mark abnormal detection points.

[0067] Step 3: Divide the exploration area into multiple local units, calculate the overall coordination degree based on the node data within the unit, and assign corresponding geological feature labels to each unit based on the comparison results of the overall coordination degree with the preset threshold and the dominant type of disharmony within the unit.

[0068] Step 4: Integrate the local units according to geographic coordinates, label the boundary types based on the similarity of the baseline features of adjacent units and the relationship of geological labels, draw graphics in combination with the distribution of anomaly detection points, and integrate the labeling information to generate a global geological distribution map.

[0069] Example 2

[0070] This illustrates another embodiment of the present invention. Based on Embodiment 1, the content of Embodiment 1 is further described in detail. This application provides a geotechnical engineering geological investigation method, specifically including the following steps:

[0071] Step 1: Identify the area to be surveyed. Using the survey area as a plane, set up array detection nodes at equal intervals along the X and Y axes. Drill and sample at each detection node to obtain information on soil depth and soil type. Conduct penetration tests, dynamic cone penetration tests, and static cone penetration tests to obtain data on blow count N, dynamic penetration blow count, cone tip resistance qc, and friction resistance fs, respectively. Summarize the raw data collected from all detection nodes into an initial survey dataset. Standardize and transform the various test data in the initial survey dataset to generate a comprehensive parameter vector Vi for each detection point. At the same time, deploy data acquisition sensors at each detection node to collect and record the strain data and temperature distribution time series data of the soil layer along the sensing path in real time.

[0072] Step 2: Based on the preset association rule library with soil layer category as the main parameter, when performing the verification, extract the corresponding soil layer category code Ci, measured blow count Ni, and cone tip resistance qc from the comprehensive parameter vector Vi of the current detection point. Match the soil layer code Ci with the soil layer code Ci in the association rule library. If the match is successful, extract the standard penetration test blow count N and static cone tip resistance qc of the corresponding soil layer category from the association rule library. Obtain the ratio interval a1 and a2 of static cone tip resistance qc and standard penetration test blow count N, where a1 and a2 are preset boundary values ​​in the association library. Then calculate the actual ratio Ri = qc / Ni. If the value of Ri is outside the preset [a1, a2], it is determined that there is an internal misalignment at this point. Record an internal misalignment mark and obtain its deviation S1 through the formula: |Ri-(a1+a2) / 2| / ((a1+a2) / 2). If no misalignment occurs or there is no match in the rule library, then S1 = 0.

[0073] Centered on the current detection point, obtain all other detection points (P1, P2, ..., Pi) in geographic space that are not less than the preset grid side length L as the neighborhood point set. Using the formula: Vi = Vi of this detection point minus Vi of the neighboring points, obtain the difference vectors (ΔV1, ΔV2, ..., ΔVi) between this detection point and each neighboring point j in the neighborhood point set in parameter space. For these difference vectors, calculate the angle between each difference vector on the two-dimensional geographic plane with due east as 0 degrees and increasing counterclockwise using the arctangent function. This angle is taken as the direction angle of the difference vector. After obtaining the direction angles of each difference vector, calculate the standard deviation α of all the obtained direction angles. Compare the standard deviation α with the preset dispersion threshold H. If the standard deviation > H, it is determined that the spatial parameter change trend of this detection point and its neighborhood is inconsistent, and a field inconsistency marker is recorded. The scalar value S2 of the dispersion degree of this detection point is obtained using the formula: α / H. If there is no field inconsistency, then S2 = 0.

[0074] Extract a continuous strain data sequence from the sensor data of the current detection point, calculate the standard deviation αi of the sequence, and simultaneously obtain the standard deviation αb of the strain data sequence of all other detection points in the same time period within all neighborhood point sets within the preset grid side length L. Obtain the strain fluctuation coefficient Rs through the formula: αi / ab.

[0075] Next, extract the temperature data sequences of the current detection point and all other detection points in the same geographical neighborhood during the same time period. Simultaneously, extract the temperature data sequence of the current detection point during the same time period from the data of the same time period. Calculate the correlation coefficient PT between the temperature sequence of the current detection point and the average temperature sequence of all other points in the neighborhood. Obtain the temperature response deviation Rt using the formula: 1-PT. If Rs>the fluctuation coefficient threshold Ts or Rt>Tt the temperature response deviation threshold, then the physical response of the current detection point is determined to be abnormal, and a response misalignment mark is recorded. At the same time, obtain its severity scalar value S3 using the formula: max((Rs / Ts)-1, (Rt / Tt)-1). If there is no response misalignment, then S3=0.

[0076] For the current detection point, based on the above test results, its comprehensive inconsistency index Ii is calculated using the formula: β1×F(S1)+β2×F(S1)+β3×F(S1), where β1, β2, and β3 are preset weight coefficients that satisfy β1+β2+β3=1, and F() is a preset normalization function.

[0077] Centered on the current detection point, other geographical neighborhoods are delineated with a preset grid side length L. The median value Iz and interquartile range Is of the comprehensive inconsistency index li of all other detection points in the neighborhood are obtained. The specific judgment threshold Y1 is obtained by the formula: Iz+K×Is, where K is a preset coefficient.

[0078] If Ii>Y1, the current detection point is determined to be significantly deviating from the expected range of its local geological background. A geological anomaly sign signal is then generated, and its geographical location is marked as an anomaly detection point Pi. If Ii≤Y1, the current detection point is in normal condition. The above steps are repeated for each detection point until all detection points have been judged.

[0079] Step 3: Divide the survey area into several non-overlapping and continuously covered local units (each unit is a local puzzle piece) with a preset grid side length L. Extract the mean V_mean and variance V_difference of the comprehensive parameter vector Vi of all normal detection points in this local unit to form the benchmark parameter features of the local unit. At the same time, calculate the proportion Pabn of all abnormal detection points Pi in the local unit (number of abnormal points / total number of detection points in the unit).

[0080] The overall coordination degree C of the local unit is calculated by the formula: (1 - Pabn) × (1 - the average value of Ii in the unit / the maximum value of Ii in the unit). When C ≥ the preset coordination threshold T1, if there are no internal incoordination abnormal points, it is labeled as: a geological label of uniform stability type. If there are abnormal points and the internal incoordination is the main factor, it is labeled as: a geological label of local defect stability type. If there are abnormal points and the field incoordination is the main factor, it is labeled as: a geological label of slightly spatially incoordinated stability type. If there are abnormal points and the response incoordination is the main factor, it is labeled as: a geological label of environmental response stability type. If there is no single abnormal proportion exceeding 40% of the total anomalies, it is labeled as: a geological label of mixed defect stability type;

[0081] When T2 ≤ C < T1, if the abnormal points are mainly field incoordination, it is labeled as: a geological label of slightly uneven type. If the abnormal points are mainly internal incoordination, it is labeled as: a geological label of local defect uneven type. If the abnormal points are mainly response incoordination, it is labeled as: a geological label of environmental response uneven type. If there is no single abnormal proportion exceeding 40% of the total anomalies, it is labeled as: a geological label of mixed slightly uneven type;

[0082] The judgment condition for the main abnormal type is: when the proportion of a single anomaly exceeds 40% of the total anomalies, it is determined that the abnormal type is the main one;

[0083] Both T1 and T2 are preset coordination thresholds, and T1 > T2;

[0084] When C < T2 or the proportion of abnormal points Pabn ≥ the preset abnormal proportion threshold Tabn, it is labeled as: a geological label of significant abnormal aggregation type;

[0085] The reference parameter characteristics, overall coordination degree C, and geological characteristic label of each local unit

[0086] Step 4: Precisely embed all local units into the coordinate grid of the global exploration area according to their geometric boundaries and geographical coordinates to form the initial distribution framework of the global exploration area;

[0087] Extract the average reference parameter V of adjacent local units, calculate the vector similarity Sim through the cosine similarity formula. If Sim ≥ the preset similarity Tsim, it is determined that the parameters of the two units are smoothly transitioned at their connection boundary. If Sim < Tsim, it is determined that there is a parameter mutation, and a geological mutation zone is marked at their connection boundary, and the trend and start and end coordinates of the geological mutation zone are recorded synchronously;

[0088] If the geological labels of adjacent local units are the same and one of them is of the significant abnormal focus type and the other is of other types, then record at their connection boundary as: abnormal diffusion boundary, and synchronously mark the boundary side length;

[0089] Based on the geological label of each local unit, different colors are filled to intuitively distinguish different geological types. The independent geometric boundary contour of each local unit is preserved at the same time. For two adjacent local units with the same geological label, all anomaly detection points Pi on the boundary of the unit are traversed to obtain the pair of Pi points that are closest to each other on the boundary of the two units and connect them. If a local unit has multiple adjacent units with the same label, the nearest Pi point is connected to each adjacent unit to form a Pi point connection chain between units.

[0090] For regions with contiguous distribution (≥2 adjacent units with the same label), the Pi point connecting chains between eye units are connected sequentially, and the Pi points on the boundaries of all contiguous units are closed sequentially according to the principle of proximity, forming a closed geometric figure around the contiguous region, and the lines of the figure are consistent with the Pi point connecting lines;

[0091] If a local unit is an isolated unit, then all Pi points within that unit are traversed and connected sequentially in clockwise order according to their coordinates to form a closed geometric figure. If there are no Pi points within a local unit, then only the color fill of that unit is retained, and no closed figure is drawn.

[0092] Adjacent local units with inconsistent geological labels are not connected by Pi points. The independent boundaries of each unit are kept clear. The connection features are intuitively distinguished by color differences and smooth transitions, geological abrupt change zones, and abnormal diffusion boundaries. If one side of the unit has Pi points and the other side does not, only the closed shape with Pi points is retained. Units without Pi points are only shown with color filling and connection feature marks, and no additional lines are drawn.

[0093] In the blank area inside each closed geometric shape, label the corresponding geological label name and the average percentage of outliers in that contiguous area, Pabn.

[0094] At the center of each local unit, mark its overall coordination degree C.

[0095] Set a legend on the side of the global map to clarify the correspondence between colors (geological labels), lines (Pi point connections / closed shapes), and markers (smooth transitions / geological abrupt change zones / abnormal diffusion boundaries), and also mark the geographic coordinate scale and X and Y axis coordinate ranges;

[0096] Finally, by integrating all the elements mentioned above, such as coordinate embedding, connection feature annotation, color filling, Pi point connection, closed shape drawing, and key information annotation, a complete global geological distribution map is formed, which serves as the comprehensive survey result of the geotechnical engineering geological survey of this area.

[0097] Example 3

[0098] Please see Figure 2This application provides a geotechnical engineering geological investigation method for implementing the method described in any one of Embodiment 1, comprising:

[0099] The data acquisition module is used to drill, sample, conduct penetration tests and deploy sensors at the array detection nodes in the survey area to obtain soil and rock parameters, blow count, cone tip resistance, side friction resistance, strain time series data and temperature time series data.

[0100] The processing module, connected to the acquisition module, is used to standardize the acquired raw data and generate a comprehensive parameter vector for each detection point. Based on the association rule base and the preset algorithm, it performs intrinsic parameter coordination test, spatial field coordination test and physical response coordination test, calculates the comprehensive incoordination index and the exclusive judgment threshold, and compares the two to identify and calibrate abnormal detection points.

[0101] The evaluation module, connected to the processing module, is used to divide the exploration area into local units with a preset grid side length, calculate the baseline parameter characteristics, anomaly point ratio and overall coordination degree of each unit, and assign a predefined geological feature label to each unit based on the comparison result of the overall coordination degree with the preset threshold and the dominant anomaly type within the unit.

[0102] The mapping module, connected to the evaluation module, is used to integrate each local unit into a global grid according to geographic coordinates, calculate and label the boundary type between adjacent units, perform color filling based on geological labels, connect and form a closed geometric shape according to the distribution of anomaly detection points, and integrate the labeling information to generate a complete global geological distribution map.

[0103] The storage module, connected to the above modules, is used to store the association rule base, the collected raw data, the intermediate data generated during processing, and the final map data.

[0104] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0105] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for geotechnical engineering geological investigation, characterized in that, Includes the following steps: Step 1: Deploy array detection nodes within the survey area, collect soil and rock parameters and sensor data from each node, and generate a comprehensive parameter vector for each node; Step 2: For each detection node, based on the preset rule base and the comparison with the geographical neighborhood, examine its internal parameter coordination, spatial field coordination and physical response coordination respectively. Calculate the node's comprehensive incoordination index based on the test results, and calculate a specific judgment threshold based on the index of other nodes in its geographical neighborhood. Compare and identify and mark abnormal detection points. Step 3: Divide the exploration area into multiple local units, calculate the overall coordination degree based on the node data within the unit, and assign corresponding geological feature labels to each unit based on the comparison results of the overall coordination degree with the preset threshold and the dominant type of disharmony within the unit. Step 4: Integrate the local units according to geographic coordinates, label the boundary types based on the similarity of the baseline features of adjacent units and the relationship of geological labels, draw graphics in combination with the distribution of anomaly detection points, and integrate the labeling information to generate a global geological distribution map.

2. The geotechnical engineering geological investigation method according to claim 1, characterized in that, The generation of the comprehensive parameter vector for each node specifically includes: The soil layer type at each node was determined by drilling and sampling, and the standard penetration test blow count, the cone dynamic penetration test blow count, the cone tip resistance and the side friction resistance were obtained by penetration test, dynamic cone penetration test and static penetration test, respectively. Strain and temperature time series data are collected at each node by deployed sensors; the soil layer type, standard penetration test blow count, cone dynamic penetration test blow count, cone tip resistance, side friction resistance, strain time series data and temperature time series data are standardized and transformed to form a comprehensive parameter vector for that node.

3. The geotechnical engineering geological investigation method according to claim 1, characterized in that, The verification of the inherent parameter consistency specifically includes: Based on the soil layer category code of the current detection node, the corresponding standard penetration test blow count range and cone tip resistance range are matched from the preset association rule library; Calculate the ratio of the measured cone tip resistance to the measured standard penetration blows at the current node; Determine whether the ratio is within the preset ratio range for the soil layer category in the association rule base. If not, it is determined to be an inherent mismatch.

4. The geotechnical engineering geological investigation method according to claim 1, characterized in that, The specific method for verifying the spatial field coordination is as follows: Taking the current detection node as the center, obtain all other detection nodes whose geographical distance from it does not exceed the preset grid side length L as the neighborhood point set; Calculate the difference vector between the comprehensive parameter vector of the current node and the comprehensive parameter vector of each node in the neighborhood point set, and determine the orientation angle of each difference vector on the two-dimensional geographic plane. Calculate the standard deviation of all orientation angles. If the standard deviation exceeds the preset dispersion threshold, it is determined to be a field incoordination.

5. The geotechnical engineering geological investigation method according to claim 1, characterized in that, The verification of physical response consistency specifically includes: Extract the strain data sequence of the current detection node within a continuous preset time period, and calculate the standard deviation σi of the sequence; Extract the strain data sequences of all other detection points in its preset geographical neighborhood within the same time period, and calculate the average standard deviation σb of each sequence; Calculate the strain fluctuation coefficient Rs = σi / σb; Extract the temperature data series of the current detection point and all other detection points within the same geographical neighborhood at the same time period, calculate the correlation coefficient PT between the temperature series of the current point and the average temperature series of the neighborhood points, and calculate the temperature response deviation degree Rt = 1 - PT; If Rs is greater than the preset fluctuation coefficient threshold Ts or Rt is greater than the preset temperature response deviation threshold Tt, it is determined that the physical response is out of coordination.

6. The geotechnical engineering geological investigation method according to claim 1, characterized in that, The specific method for calculating the comprehensive incoordination index of the calculation node is as follows: If internal incoordination occurs, calculate the first scalar value S1 based on the deviation degree between the measured ratio and the median of the preset ratio interval, otherwise set S1 = 0; If spatial field incoordination occurs, calculate the second scalar value S2 based on the ratio of the standard deviation of the direction angle to the dispersion threshold, otherwise set S2 = 0; If physical response incoordination occurs, calculate the third scalar value S3 based on the excess degree of the strain fluctuation coefficient and the temperature response deviation degree from their respective thresholds, otherwise set S3 = 0; Calculate the comprehensive incoordination index Ii through the formula Ii = β1×F(S1)+β2×F(S2)+β3×F(S3), where β1, β2, and β3 are preset weight coefficients, and F() is a preset normalization function.

7. The geotechnical engineering geological investigation method according to claim 1, characterized in that, The identification and calibration of abnormal detection points include: Taking the current detection node as the center,划定 a geographical neighborhood with a preset grid side length L, and obtain the comprehensive incoordination index Ii of all other detection points within this neighborhood; Calculate the median Iz and the interquartile range Is of the index set; Calculate the exclusive determination threshold Y1 according to the formula: Y1 = Iz + K×Is, where K is a preset coefficient; If the comprehensive incoordination index Ii of the current node is greater than Y1, mark it as an abnormal detection point.

8. The geotechnical engineering geological investigation method according to claim 1, characterized in that, In step three, endowing each unit with a geological feature label specifically includes: Extract the comprehensive parameter vectors of all nodes within the local unit that are not marked as abnormal detection points, and calculate their mean V_avg and variance V_var as the benchmark parameter features of this unit; Count the proportion P_abn of the number of abnormal detection points within the unit; Calculate the overall coordination degree C_local of the unit according to the formula C_local = (1 - P_abn)×(1 - the mean of Ii within the unit / the maximum value of Ii within the unit); According to the magnitude relationship between C_local and the preset coordination thresholds T1 and T2, and combined with the dominant out-of-coordination type within the unit, select the corresponding label from a group of predefined geological labels for endowment.

9. A geotechnical engineering geological investigation method according to claim 1, characterized in that, The generation of the global geological distribution map includes: Embed each local unit into the global grid according to its geometric boundary and geographical coordinates; Calculate the cosine similarity Sim of the benchmark parameter feature vectors V_avg of any two adjacent local units. If Sim ≥ the preset similarity threshold T_sim, mark the connection boundary as a smooth transition, otherwise mark it as a geological mutation zone; If the geological labels of two adjacent units are different and one of them is a significant abnormal aggregation type, record the connection boundary as an abnormal diffusion boundary; Fill different colors for units with different geological labels; For groups of adjacent units with the same geological label, connect the abnormal detection points on their boundaries to form a closed polygon surrounding this area; Mark the name of the geological label and the average proportion of abnormal points within each unit in the figure, and mark the overall coordination degree C_local at the center of the unit.

10. A geotechnical engineering geological investigation system, used to implement the geotechnical engineering geological investigation method according to any one of claims 1 to 9, characterized in that, Include: The data acquisition module is used to drill, sample, conduct penetration tests and deploy sensors at the array detection nodes in the survey area to obtain soil and rock parameters, blow count, cone tip resistance, side friction resistance, strain time series data and temperature time series data. The processing module, connected to the acquisition module, is used to standardize the acquired raw data and generate a comprehensive parameter vector for each detection point. Based on the association rule base and the preset algorithm, it performs intrinsic parameter coordination test, spatial field coordination test and physical response coordination test, calculates the comprehensive incoordination index and the exclusive judgment threshold, and compares the two to identify and calibrate abnormal detection points. The evaluation module, connected to the processing module, is used to divide the exploration area into local units with a preset grid side length, calculate the baseline parameter characteristics, anomaly point ratio and overall coordination degree of each unit, and assign a predefined geological feature label to each unit based on the comparison result of the overall coordination degree with the preset threshold and the dominant anomaly type within the unit. The mapping module, connected to the evaluation module, is used to integrate each local unit into a global grid according to geographic coordinates, calculate and label the boundary type between adjacent units, perform color filling based on geological labels, connect and form a closed geometric shape according to the distribution of anomaly detection points, and integrate the labeling information to generate a complete global geological distribution map. The storage module, connected to the above modules, is used to store the association rule base, the collected raw data, the intermediate data generated during processing, and the final map data.

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