Observation column information management system for slope monitoring

Abstract

The invention relates to the technical field of data management, in particular to an observation column information management system for slope monitoring, which comprises a measuring point gradient partition module, a data hierarchical storage module, a measuring point correlation analysis module, a data trend mapping module and a key fragment storage module. According to the method, the height difference and the horizontal distance change are calculated through measuring point space coordinate data, data hierarchical management is optimized, the data organization efficiency is improved, the storage priority is set, the storage frequency of a violent change area is improved, abrupt slope area storage is optimized, redundant data of a gentle slope and a stable area are reduced, measuring point geological parameters are extracted, and the data analysis pertinence is improved; the method comprises the steps of analyzing displacement and inclination trends of measuring points, screening associated measuring points, accurately judging slope stability, optimizing a storage area and layout in combination with spatial distribution of the measuring points and corresponding positions, improving data access safety, realizing fine classification, optimized storage, intelligent analysis and safety management and control, and improving slope monitoring data management efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and particularly to an observation column information management system for slope monitoring. Background Art

[0002] The technical field of data management includes data collection, storage, organization, processing, retrieval, and management, aiming to ensure the integrity, security, and availability of data. This technical field covers database management systems, data storage structures, data retrieval methods, data update strategies, and data consistency control. Database management is divided into relational databases and non-relational databases. The former stores data in a tabular structure and is queried and managed through SQL language, while the latter is suitable for the storage and processing of large-scale, unstructured data. Data storage structures include indexing techniques, hash storage, B-trees and their variants, etc., to achieve efficient data access and management. Data retrieval methods involve full-text retrieval, keyword matching, fuzzy query, etc., to improve data query efficiency. Data update strategies cover transaction management, concurrency control, and data backup and recovery to ensure the correctness and security of data in a multi-user environment. Data consistency control is achieved through the ACID principle, distributed transactions, and data synchronization mechanisms to ensure the reliability and consistency of data in a distributed environment.

[0003] Among them, the observation column information management system refers to a system used to store, organize, analyze, and manage the data of observation columns in slope monitoring. This system manages the layout information, measurement data, data storage, and processing of observation columns, and provides corresponding data query and retrieval functions. Data collection obtains the inclination, displacement, and environmental parameters of the observation column through sensing devices and stores them in a time series. Data storage uses a database structure and is organized according to the observation column number, measuring point location, and time sequence to ensure efficient data management. Data analysis includes numerical calculation and trend extraction to extract key information from the observation data. Data management includes data classification, format conversion, and storage optimization to improve data access efficiency. Data retrieval uses indexing and query optimization techniques to enable users to quickly locate the required observation data. This system also involves data security control, including access permission setting, data encryption, and abnormal data detection, to ensure the integrity and security of data.

[0004] In the management of slope monitoring data in the prior art, there is a lack of dynamic classification of measuring point gradient categories, relying only on a fixed storage strategy, unable to adjust the data storage method according to the changing trend of measuring points, resulting in unbalanced allocation of storage resources, missing key data in high-dynamic areas, and storing a large amount of redundant data in stable areas, affecting storage efficiency. The existing data management method has a relatively rough analysis of the correlation of geological parameters and fails to fully explore the consistency of geological features between measuring points, resulting in weak data correlation and affecting the accurate judgment of slope stability. Data trend analysis relies more on single-parameter analysis and lacks synchronous change analysis of the displacement and inclination trends of measuring points, resulting in insufficient judgment of data correlation and affecting the accurate evaluation of trend changes between measuring points. In terms of storage security management, the existing method uses traditional permission control and encryption technologies, lacks consideration for the storage area division and layout optimization of monitoring data, fails to fully combine the spatial distribution characteristics of measuring points, and has problems of low data access efficiency and security risks. There is still much room for optimization in aspects such as measuring point data storage, analysis, and security management in the prior art, restricting the refined and intelligent development of slope monitoring data management. Summary of the Invention

[0005] To solve the technical problems in the prior art in the management of slope monitoring data, such as the lack of dynamic classification of measuring point gradient categories, relying only on a fixed storage strategy, being unable to adjust the data storage method according to the changing trend of measuring points, resulting in unbalanced allocation of storage resources, missing key data in high-dynamic areas, storing a large amount of redundant data in stable areas, affecting storage efficiency, the existing data management method having a relatively rough analysis of the correlation of geological parameters and failing to fully explore the consistency of geological features between measuring points, resulting in weak data correlation and affecting the accurate judgment of slope stability, data trend analysis relying more on single-parameter analysis and lacking synchronous change analysis of the displacement and inclination trends of measuring points, resulting in insufficient judgment of data correlation and affecting the accurate evaluation of trend changes between measuring points, in terms of storage security management, the existing method using traditional permission control and encryption technologies, lacking consideration for the storage area division and layout optimization of monitoring data, failing to fully combine the spatial distribution characteristics of measuring points, and having problems of low data access efficiency and security risks, and there still being much room for optimization in aspects such as measuring point data storage, analysis, and security management in the prior art, restricting the refined and intelligent development of slope monitoring data management, the embodiment of the present invention provides an observation column information management system for slope monitoring. The technical solution is as follows:

[0006] On the one hand, an observation column information management system for slope monitoring is provided, and the system includes:

[0007] The measurement point gradient zoning module calculates the elevation difference and horizontal distance between measurement points based on the spatial coordinate data of the observation column monitoring points, determines the gradient range to which the measurement points belong, divides them into four gradient regions: stable, gentle slope, steep slope, and drastic change, calculates the spatial distribution density of the measurement points, filters out the high-density regions, and obtains the measurement point gradient zoning result;

[0008] The data hierarchical storage module sets the storage priority based on the measurement point gradient zoning result, adjusts the storage density of the measurement point data, sets the drastic change region as high-frequency storage, adjusts the storage frequency of the steep slope region, and only stores the key time node data of the gentle slope and stable regions to obtain the measurement point data storage density data set;

[0009] The measurement point correlation analysis module calls the measurement point data storage density data set, extracts the geological parameters of the measurement points, classifies them according to the geological characteristics, filters out the measurement points within the same geological characteristic unit, and obtains the measurement point correlation comparison relationship;

[0010] The data trend mapping module analyzes the synchronous change of the displacement and inclination trends of the measurement points based on the measurement point correlation comparison relationship, determines the data correlation degree, sets the data correlation degree threshold, filters out the correlated measurement points, and obtains the slope stability change trend.

[0011] As a further solution of the present invention, the measurement point gradient zoning result includes a stable region, a gentle slope region, a steep slope region, and a drastic change region. The measurement point data storage density data set is specifically a high-frequency storage region, an adjusted storage frequency region, and a key time node storage region. The measurement point correlation comparison relationship includes a measurement point unit with the same soil layer thickness, a measurement point unit with the same rock and soil type, a measurement point unit with the same water content, and a measurement point unit with the same shear strength. The slope stability change trend is specifically the displacement trend of the measurement point, the inclination trend of the measurement point, the data correlation degree threshold, and the correlated measurement points.

[0012] As a further solution of the present invention, the measurement point gradient zoning module includes:

[0013] The elevation difference calculation sub-module measures and records the elevation values based on the spatial coordinate data of the observation column monitoring points, calculates the elevation difference and the horizontal distance between each measurement point, and analyzes the slope according to the elevation difference and the horizontal distance to obtain the measurement point slope value;

[0014] The slope classification sub-module calls the measurement point slope value, identifies the gradient based on the slope change trend of adjacent measurement points, determines the gradient range to which the measurement points belong, sets the gradient classification standard, divides the measurement points into four regions: stable, gentle slope, steep slope, and drastic change, analyzes the distribution of the measurement points within the region, and generates the measurement point gradient zoning result;

[0015] The density screening sub-module calls the measurement point gradient zoning result, calculates the spatial density of the measurement points in the gradient region, filters out the density region, and obtains the measurement point gradient zoning result.

[0016] As a further solution of the present invention, the spatial density of the measurement points in the gradient region is calculated using the formula:

[0017]

[0018] where D represents the density eigenvalue, N represents the total number of measurement points, w i represents the weight factor of measurement point i, and x i represents the x-component of the spatial coordinate of measurement point i, represents the weighted average coordinate of the measurement points in the x direction, and y i represents the y-component of the spatial coordinate of measurement point i, represents the average coordinate of the measurement points in the y direction.

[0019] As a further solution of the present invention, the data hierarchical storage module includes:

[0020] The storage priority setting sub-module calls the measurement point gradient partition result, sets the storage priority according to the measurement point gradient region, sorts according to the degree of gradient change, and obtains the measurement point storage priority data;

[0021] The storage frequency adjustment sub-module calls the measurement point storage priority data, adjusts the storage frequency according to the priority, sets high-frequency storage for the violently changing region, adjusts the storage interval for the steep slope region, and selects key time nodes for storage in the gentle slope and stable regions, analyzes the change of storage density, adjusts the scale of the storage data set, and obtains the measurement point data storage density data set.

[0022] As a further solution of the present invention, the measurement point correlation analysis module includes:

[0023] The geological parameter extraction sub-module extracts the soil layer thickness, rock and soil type, moisture content, and shear strength of the measurement points based on the measurement point data storage density data set, and obtains the measurement point geological parameter set;

[0024] The measurement point classification sub-module calls the measurement point geological parameter set, analyzes the differences in geological characteristics based on the soil layer thickness, rock and soil type, moisture content, and shear strength, screens the measurement points with similar characteristic values, determines the belonging category of the measurement points, divides the same geological characteristic units, optimizes the classification boundary, and obtains the distribution of geological characteristic units;

[0025] The correlation comparison generation sub-module calls the distribution of geological characteristic units, screens the measurement points within the same unit, and obtains the measurement point correlation comparison relationship.

[0026] As a further solution of the present invention, the data trend mapping module includes:

[0027] The measurement point trend analysis sub-module calls the measurement point association comparison relationship, analyzes the displacement and inclination trends of the measurement points, extracts time series data, and obtains the measurement point trend change characteristics;

[0028] The data correlation degree judgment sub-module calls the measurement point trend change characteristics, analyzes the synchronization of the displacement and inclination changes of the measurement points, calculates the measurement point data correlation degree, sets a data correlation degree threshold, screens the measurement point data correlation degree, eliminates low-correlation measurement points, optimizes the data matching relationship, and obtains the measurement point association screening result;

[0029] The stability trend screening sub-module calls the measurement point association screening result, screens the trend change data of the associated measurement points, identifies the slope trend change amplitude, and obtains the slope stability change trend.

[0030] As a further solution of the present invention, the measurement point data correlation degree adopts the formula:

[0031]

[0032] where C represents the measurement point correlation degree, D1 represents the displacement value of measurement point 1, D2 represents the displacement value of measurement point 2, T1 represents the inclination change trend of measurement point 1, T2 represents the inclination change trend of measurement point 2, V1 represents the displacement rate of measurement point 1, V2 represents the displacement rate of measurement point 2, I1 represents the inclination rate of measurement point 1, and I2 represents the inclination rate of measurement point 2.

[0033] As a further solution of the present invention, the system further includes a key fragment storage module:

[0034] The key fragment storage module extracts the spatial distribution information of the monitoring points based on the slope stability change trend, identifies the corresponding positions between the measurement points, divides the storage area according to the distribution characteristics, adjusts the storage partition layout, and obtains a secure storage solution for the slope monitoring data;

[0035] The secure storage solution for the slope monitoring data includes storage area division, storage partition layout adjustment, extraction of spatial distribution information of measurement points, and identification of corresponding positions of measurement points.

[0036] As a further solution of the present invention, the key fragment storage module includes:

[0037] The spatial distribution extraction sub-module calls the slope stability change trend, extracts the spatial distribution data of the monitoring points, identifies the corresponding positions of the measurement points, and obtains the spatial distribution information of the measurement points;

[0038] The storage area division sub-module calls the spatial distribution information of the measurement points, calculates the distribution characteristics based on the corresponding positions of the measurement points, screens the measurement point dense areas, divides the storage area, sets the differential storage area boundary, and adjusts the measurement point distribution balance to obtain the storage area division result;

[0039] The sub-module for adjusting the partition layout calls the storage area division result, adjusts the storage partition layout, optimizes the mapping of the measured point storage data, and obtains a secure storage solution for the slope monitoring data.

[0040] The beneficial effects brought by the technical solution provided by the embodiment of the present invention at least include:

[0041] Through the refined calculation of the measured point spatial coordinate data, combined with the change trends of the elevation difference and the horizontal distance, the gradient category of the measured points is dynamically determined, and the data is screened based on the distribution density of the measured points, optimizing the data hierarchical management strategy, improving the organization efficiency of the measured point data, setting the storage priority, enhancing the storage frequency of the data in the violently changing area, optimizing the storage frequency in the steep slope area, and only storing the key time node data in the gentle slope and stable areas, realizing the reasonable allocation of data storage resources, reducing the storage pressure of redundant data, extracting and classifying the geological parameters of the measured points, making the measured points in different geological feature units form an associated comparison, improving the pertinence of data analysis, enhancing the internal connection between data, by analyzing the synchronous changes of the displacement and inclination trends of the measured points, screening relevant measured points based on the data correlation threshold, realizing the accurate trend judgment of slope stability, combining the measured point spatial distribution information, optimizing the storage area division and storage layout according to the corresponding positions between the measured points, improving the access security of the slope monitoring data, realizing the accurate gradient classification of the measured point data, the optimized configuration of storage resources, the intelligent enhancement of data analysis and the secure control of data storage, making the management of slope monitoring data more refined, efficient and secure. Description of the Drawings

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0043] Figure 1 It is a schematic diagram of an observation column information management system for slope monitoring provided by the embodiment of the present invention;

[0044] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0045] Figure 3 It is a flowchart for obtaining the measured point gradient partition module in the present invention;

[0046] Figure 4 It is a flowchart for obtaining the data hierarchical storage module in the present invention;

[0047] Figure 5 It is a flowchart for obtaining the measured point correlation analysis module in the present invention;

[0048] Figure 6 This is the acquisition flowchart of the data trend mapping module in the present invention;

[0049] Figure 7 This is the acquisition flowchart of the key fragment storage module in the present invention. Detailed implementation manners

[0050] The following describes the technical solutions in the present invention with reference to the accompanying drawings.

[0051] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or more advantageous than other embodiments or design solutions. Rather, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either of the two can be selected.

[0052] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0053] In the embodiments of the present invention, sometimes subscripts such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0054] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0055] The embodiments of the present invention provide an observation column information management system for slope monitoring, as Figure 1-2 shown in the schematic diagram of the observation column information management system for slope monitoring. The system includes:

[0056] Based on the spatial coordinate data of the monitoring points of the observation column, the measuring point gradient zoning module calculates the height difference and horizontal distance between the measuring points, determines the gradient range to which the measuring points belong according to the slope change trend between adjacent measuring points, divides the four gradient regions of stable, gentle slope, steep slope, and drastic change, calculates the spatial distribution density of the measuring points, screens the high-density regions, and obtains the measuring point gradient zoning result;

[0057] Based on the measurement point gradient partition result, the data hierarchical storage module sets the storage priority, adjusts the storage density of the measurement point data, sets the violently changing area as the high-frequency storage area, adjusts the storage frequency in the steep slope area, and only stores the key time node data in the gentle slope and stable areas, so as to obtain the data set of the storage density of the measurement point data;

[0058] The measurement point correlation analysis module calls the data set of the storage density of the measurement point data, extracts the geological parameters such as soil layer thickness, geotechnical category, water content, and shear strength of the measurement points, classifies them according to the geological characteristics, screens the measurement points within the same geological characteristic unit, and obtains the measurement point correlation comparison relationship;

[0059] Based on the measurement point correlation comparison relationship, the data trend mapping module analyzes the synchronous change situation of the displacement and inclination trends of the measurement points, judges the data correlation degree of the measurement points, sets the data correlation degree threshold, screens the correlated measurement points, and obtains the change trend of the slope stability;

[0060] Based on the change trend of the slope stability, the key fragment storage module extracts the spatial distribution information of the monitoring points, identifies the corresponding positions between the measurement points, divides the storage areas according to the distribution characteristics, adjusts the storage partition layout, and obtains the secure storage scheme for the slope monitoring data.

[0061] The measurement point gradient partition result includes the stable area, the gentle slope area, the steep slope area, and the violently changing area. The data set of the storage density of the measurement point data is specifically the high-frequency storage area, the area with adjusted storage frequency, and the key time node storage area. The measurement point correlation comparison relationship includes the measurement point units with the same soil layer thickness, the measurement point units with the same geotechnical category, the measurement point units with the same water content, and the measurement point units with the same shear strength. The change trend of the slope stability is specifically the displacement trend of the measurement point, the inclination trend of the measurement point, the data correlation degree threshold, and the correlated measurement points. The secure storage scheme for the slope monitoring data includes the division of the storage area, the adjustment of the storage partition layout, the extraction of the spatial distribution information of the measurement points, and the identification of the corresponding positions of the measurement points.

[0062] Specifically, as Figure 2 、 3 shown, the measurement point gradient partition module includes:

[0063] The elevation difference calculation sub-module measures and records the elevation value based on the spatial coordinate data of the observation column monitoring points, calculates the elevation difference and the horizontal distance between each measurement point, analyzes the slope according to the elevation difference and the horizontal distance, and obtains the slope value of the measurement point;

[0064] First, calculate the elevation difference between each pair of measurement points. The calculation method of the elevation difference is: subtract the elevation value of one measurement point from the elevation value of another measurement point to obtain the vertical distance between the two. Using the spatial coordinate data between these two points, calculate their horizontal distance. The calculation method of the horizontal distance is: according to the Pythagorean theorem, express the horizontal distance between the measurement points as the square root of the sum of the squares of the differences in the horizontal and vertical coordinates between the two points. On this basis, further analyze the ratio of the elevation difference to the horizontal distance to obtain the slope value of the measurement point. The slope can be obtained by dividing the elevation difference by the horizontal distance, and the unit is percentage. If the elevation of a certain measurement point is 100 meters, the elevation of another measurement point is 80 meters, and the horizontal distance between the two is 100 meters, then the elevation difference is 20 meters, and the slope is 20 / 100 = 20%. After calculating the slope, the slope value between each measurement point can be obtained. To ensure the accuracy of the measurement, it is necessary to measure and record the data multiple times to ensure that the elevation value, horizontal distance, and slope calculation of each measurement point can accurately reflect the actual situation and eliminate measurement errors. According to the slope of each pair of measurement points, the slope can be classified to identify areas with larger slopes and flatter areas, providing a basis for subsequent slope analysis.

[0065] The slope classification sub-module calls the slope values of the measurement points, identifies the gradient based on the changing trend of the slopes of adjacent measurement points, determines the gradient range to which the measurement points belong, sets the gradient classification criteria, divides the measurement points into four types of regions: stable, gentle slope, steep slope, and drastic change, analyzes the distribution of the measurement points within the region, and generates the gradient partition result of the measurement points;

[0066] By analyzing the changing trend of the slopes of adjacent measurement points, determine the range of the gradient, calculate the change amplitude of the slopes between adjacent measurement points, and determine its changing trend, whether it increases or decreases, by comparing the slope values of adjacent measurement points. When the increase amplitude of the slope value is large, it indicates that there is a steep slope in this region; otherwise, it is a gentle slope or a stable region. The specific implementation process is as follows: set a slope change threshold. When the difference in the slopes of adjacent measurement points exceeds this threshold, it can be determined that this region has a larger gradient; otherwise, the gradient is smaller. For example, if the slope of measurement point A is 10% and the slope of measurement point B is 40%, and the difference between the two is 30%, which exceeds the set threshold, it can be judged that this region is a steep slope region. When setting the gradient classification criteria, the region to which the measurement points belong can be judged according to different slope ranges. For example, a slope less than 10% can be considered a stable region, 10% to 30% is a gentle slope region, more than 30% is a steep slope region, and a change exceeding 50% can be regarded as a drastic change region. According to this standard, the measurement points can be classified into four gradient regions: stable, gentle slope, steep slope, and drastic change regions. Different gradient thresholds can be set for each region based on the actual measurement data, and the thresholds can be adjusted through historical data or regional experience to ensure that the regional division has practical significance and rationality. By analyzing the distribution of the measurement points, the distribution of each gradient type within the region can be obtained to provide a reference for subsequent engineering planning and environmental analysis.

[0067] The density screening sub-module calls the measurement point gradient partition result, calculates the spatial density of the measurement points in the gradient region, screens the density region, and obtains the measurement point gradient partition result;

[0068] The spatial density of the measurement points in the gradient region adopts the formula:

[0069]

[0070] where D represents the density eigenvalue, N represents the total number of measurement points, w i represents the weight factor of measurement point i, and x i represents the x component of the spatial coordinate of measurement point i, represents the weighted average coordinate of the measurement point in the x direction, and y i represents the y component of the spatial coordinate of measurement point i, represents the average coordinate of the measurement point in the y direction;

[0071] Parameter setting and acquisition:

[0072] N: The total actual number of measurement points is obtained through the monitoring of Geographic Information System (GIS) data. For example, N = 50 measurement points;

[0073] w i : The weight of measurement point i is set according to the importance of the geographical location of the measurement point and the reliability of its measurement data. For example, if the measurement point is located in a key area, the weight is relatively high, and the assumed range is from 0.1 to 1.0;

[0074] x i and y i : The coordinates of each measurement point are obtained through real-time GPS measurement. For example, for a certain measurement point, x i = 32.5m, and y i = 40.7m;

[0075] and are calculated through weighted average, and the formula is and

[0076] Specific calculation example:

[0077] Suppose there are data of five measurement points, w = [0.2, 0.3, 0.1, 0.3, 0.1], x = [32, 33, 34, 35, 36], y = [40, 41, 39, 42, 40]; First, calculate and

[0078]

[0079] Then calculate the density eigenvalue D:

[0080]

[0081] The result shows that the spatial distribution of the measurement points and its density characteristic value is 1.94, which reflects the weighted position deviation and concentration degree of the measurement points on the x and y coordinates, providing a basis for further analyzing the uniformity of the measurement point distribution and the density of the measurement points in the key areas.

[0082] Specifically, as Figure 2 、 4 shown, the data hierarchical storage module includes:

[0083] The storage priority setting sub-module calls the measurement point gradient partition result, sets the storage priority according to the measurement point gradient area, sorts according to the degree of gradient change from high to low, and obtains the measurement point storage priority data;

[0084] By analyzing the gradient area to which each measurement point belongs, the storage priority of the measurement point is set according to the gradient range where the measurement point is located. For the area with drastic changes, due to the large change amplitude of the measurement points, the storage priority is high and storage is performed first. First, it is necessary to quantitatively analyze the change amplitude of different gradient areas. For the area with drastic changes, such as the slope change exceeding 50%, all the measurement points in this area are given a high storage priority. For the steep slope area, although the slope change is large, it is not as large as that in the area with drastic changes. The set slope range is 30% to 50%, and the storage priority of the measurement points in this area is set to medium, and its priority is processed in turn. For the gentle slope area, the slope range is between 10% and 30%. Because the slope of the area is gentle and the change is small, the storage priority is relatively low. The storage priority of the measurement points in the stable area is the lowest, and the storage can be carried out for a long time. Specifically in practical applications, assuming that the slopes of measurement point A and measurement point B in a certain area are 40% and 15% respectively, then the storage priority of measurement point A is high and the storage priority of measurement point B is low. The measurement points can be sorted according to the priority in turn according to different gradient areas, providing a decision basis for storage management, and then obtaining the measurement point storage priority data.

[0085] The storage frequency adjustment sub-module calls the measurement point storage priority data, adjusts the storage frequency according to the priority, sets high-frequency storage for the area with drastic changes, adjusts the storage interval for the steep slope area, and filters the key time nodes for storage in the gentle slope and stable areas, analyzes the change of storage density, and adjusts the scale of the storage data set to obtain the measurement point data storage density data set;

[0086] First, it is necessary to analyze the storage requirements of each area. Combining the storage priorities of measurement points, adjust the data storage frequency. For areas with drastic changes, the storage frequency is set to high-frequency storage, which means that the measurement point data in this area needs to be stored frequently at short time intervals to ensure real-time reflection of terrain changes. According to the set storage period, data recording can be carried out every 10 minutes in areas with drastic changes, and the slope change in this area can be measured in real time through sensors. When the change amplitude exceeds 50%, data storage is automatically triggered. For example, if the slope change reaches 60% during a certain period, the data will be immediately recorded and stored. In steep slope areas, due to the relatively significant slope change but less frequently than in areas with drastic changes, the storage frequency is set to medium, and data can be stored every 30 minutes. In the case where the slope change exceeds 30%, it can be adjusted to store data every 15 minutes. For gentle slope areas, because the change is relatively gentle, the storage interval is relatively long, and it is set to store data every 1 hour. Only at key time nodes, such as weather changes or external factor influences, will data storage be selected. In stable areas, the storage interval can be longer, once every 2 hours, and storage is only triggered in special cases. By adjusting the storage frequencies of different areas, the storage space can be effectively optimized to avoid unnecessary frequent storage. It is necessary to adjust the scale of the storage data set through the analysis of the change in storage density to ensure the rationality of the data storage set. For example, in stable areas, due to the relatively small amount of data, the storage frequency can be compressed, while in areas with drastic changes, due to the large amount of data, the capacity and processing power of storage devices can be selected to increase to ensure that data can be stored in a timely manner and accurately reflect the changes in the terrain, and a data set of the storage density of measurement point data is obtained.

[0087] Specifically, as Figure 2 、 5 shown, the measurement point correlation analysis module includes:

[0088] The geological parameter extraction sub-module extracts the soil layer thickness, rock and soil type, water content, and shear strength of the measurement points based on the data set of the storage density of measurement point data, and obtains the geological parameter set of the measurement points;

[0089] First, extract geological parameters such as soil layer thickness, rock and soil type, water content, and shear strength. For each measurement point, obtain the soil properties of each measurement point at different levels from the data set. The extraction methods of geological parameters include: obtaining the soil layer thickness from the soil layer depth record, determining the rock and soil type from the rock and soil analysis data, extracting the water content value from the laboratory test data, and calculating the shear strength from the soil sample analysis. For example, at a certain measurement point, assuming the soil layer thickness is 5 meters, the rock and soil type is clay, the water content is 12%, and the shear strength is 30 kPa, then the geological parameter set of this measurement point is the specific value. The geological parameter set of the measurement points provides basic data for subsequent classification, analysis, correlation comparison, etc., and obtains the geological parameter set of the measurement points for further analysis and division of different geological feature units.

[0090] The measuring point classification sub-module calls the geological parameter set of the measuring points, analyzes the differences in geological characteristics based on the soil layer thickness, rock and soil types, water content, and shear strength, screens the measuring points with similar characteristic values, determines the belonging categories of the measuring points, divides the same geological characteristic units, optimizes the classification boundary, and obtains the distribution of geological characteristic units.

[0091] First, based on the differences in soil layer thickness, the measuring points are preliminarily screened. For example, the measuring points with a soil layer thickness greater than 10 meters are distinguished from those less than 10 meters. According to the rock and soil types, different rock and soil types such as clay, sand, and gravel soil are classified. Based on the differences in water content, the measuring points with higher or lower water content are screened. A threshold can be set. For example, the area with a water content higher than 20% is regarded as a high water content area, and the area with a water content lower than 5% is regarded as a low water content area. Through the shear strength data, it is divided into high shear strength (greater than 50 kPa), medium shear strength (greater than 20 kPa and less than 50 kPa), and low shear strength (less than 20 kPa) areas. Based on the characteristic values, the measuring points with similar geological characteristics can be further screened and classified into the same category. For example, if the soil layer thickness of measuring point A and measuring point B is similar, the rock and soil types are the same, and the differences in water content and shear strength are within a certain range, it can be determined that they belong to the same category. Regarding the similarity between the measuring points, by optimizing the classification boundary, the measuring points are divided into the same geological characteristic units, and the distribution of geological characteristic units is obtained, which not only reflects the geological attributes of the measuring points but also provides data support for subsequent geological analysis and engineering applications.

[0092] The correlation comparison generation sub-module calls the distribution of geological characteristic units, screens the measuring points within the same unit, and obtains the correlation comparison relationship of the measuring points.

[0093] For all the measuring points within each geological characteristic unit, the correlation between their geological parameters is analyzed, and the measuring points that are close in some key parameters (such as soil layer thickness, rock and soil types, etc.) are screened. Assuming that within a geological characteristic unit, the soil layer thickness and rock and soil types of measuring point C, measuring point D, and measuring point E are the same, the measuring points are regarded as associated measuring points within this unit. By analyzing the spatial relationship and geological parameters between the measuring points, it helps to understand the distribution law of each measuring point within the geological characteristic unit, further optimize the resource utilization and construction design. Through such screening and association, a detailed comparison relationship of the measuring points within each geological characteristic unit can be formed, and the correlation comparison relationship of the measuring points can be obtained to support subsequent engineering applications and analysis.

[0094] Specifically, as Figure 2 、 6 shown, the data trend mapping module includes:

[0095] The measuring point trend analysis sub-module calls the measuring point association control relationship, analyzes the displacement and inclination trends of the measuring points, extracts time series data, and obtains the trend change characteristics of the measuring points;

[0096] First, analyze the displacement and inclination trends of the measuring points, extract the time series data of each measuring point, and combine its spatial position and time stamp to analyze the displacement values and inclination angle changes of each measuring point at different time periods, and gradually draw the trend change curve of the measuring points. The displacement data can be calculated by the difference method, that is, the displacement at each moment minus the displacement at the previous moment to obtain the displacement change amount during this period. The inclination trend can be obtained by calculating the inclination angle difference within each time period. Suppose the displacement of measuring point A at time t1 is 10 mm and the displacement at time t2 is 15 mm, then the displacement change amount is 15 - 10 = 5 mm. Suppose the inclination angle of measuring point A at time t1 is 2 degrees and at time t2 is 2.5 degrees, then the inclination change amount is 2.5 degrees - 2 degrees = 0.5 degrees. It can effectively extract the trend change characteristics of each measuring point, reflect the displacement and inclination trends of the measuring points at different time periods, and provide data support for further analysis.

[0097] The data correlation degree judgment sub-module calls the trend change characteristics of the measuring points, analyzes the synchronization of the displacement and inclination changes of the measuring points, calculates the data correlation degree of the measuring points, sets the data correlation degree threshold, screens the data correlation degree of the measuring points, eliminates the measuring points with low correlation degree, optimizes the data matching relationship, and obtains the measuring point association screening result;

[0098] The data correlation degree of the measuring points adopts the formula:

[0099]

[0100] Among them, C represents the measuring point correlation degree, D1 represents the displacement value of measuring point 1, D2 represents the displacement value of measuring point 2, T1 represents the inclination change trend of measuring point 1, T2 represents the inclination change trend of measuring point 2, V1 represents the displacement rate of measuring point 1, V2 represents the displacement rate of measuring point 2, I1 represents the inclination rate of measuring point 1, and I2 represents the inclination rate of measuring point 2;

[0101] Definition and calculation method of parameters:

[0102] C represents the measuring point correlation degree, which measures the correlation degree between two measuring points;

[0103] D1 and D2 respectively represent the displacement values of measuring point 1 and measuring point 2. The displacement values are monitored and collected from the displacement data measured by the sensor. The displacement data is measured by a high-precision displacement sensor (such as a laser rangefinder or a fiber optic sensor), and the unit is meter (m). Suppose the displacement value of measuring point 1 is 0.12 m and the displacement value of measuring point 2 is 0.10 m;

[0104] T1 and T2 represent the tilt change trends of measurement point 1 and measurement point 2 respectively. The tilt change trend is obtained by measuring the change in the tilt angle of the measurement point, acquired using a tilt sensor (such as an electronic inclinometer), and the sensor provides real-time data on the angle change. Assume the tilt change trend of measurement point 1 is 0.02°, and that of measurement point 2 is 0.03°;

[0105] V1 and V2 represent the displacement rates of measurement point 1 and measurement point 2 respectively. The displacement rate is the change in displacement per unit time, with the unit of meters per second (m / s). The displacement rate is calculated by dividing the difference in displacement values by the time difference. Assume the displacement rate of measurement point 1 is 0.01 m / s, and that of measurement point 2 is 0.008 m / s;

[0106] I1 and I2 represent the tilt rates of measurement point 1 and measurement point 2 respectively. The tilt rate is the rate of change of the tilt angle, with the unit of degrees per second. The tilt rate is calculated by the ratio of the change in tilt angle to time. Assume the tilt rate of measurement point 1 is 0.001, and that of measurement point 2 is 0.002;

[0107] Formula calculation process:

[0108] Calculate the displacement difference: |D1 - D2| = |0.12 - 0.10| = 0.02 m;

[0109] Calculate the tilt trend difference:

[0110] (T1 - T2) = (0.02 - 0.03) = -0.01°;

[0111]

[0112] Calculate the sum of the squares of the displacement rates:

[0113] |V1| 2 +|V2| 2 = (0.01) 2 +(0.008) 2 = 0.0001 + 0.000064 = 0.000164;

[0114]

[0115] Calculate the tilt rate difference: |I1 - I2| = |0.001 - 0.002| = 0.001;

[0116] Substitute into the formula to calculate the correlation degree C of the measurement point:

[0117] The result shows that the correlation degree between measuring point 1 and measuring point 2 is 2.17, indicating a relatively high degree of correlation. This value reflects the consistency of the displacement and tilt change trends between the two. The larger the correlation degree value, the stronger the synchronization between the two. Thus, measuring points with a relatively high correlation are selected for further analysis and optimization.

[0118] The stability trend screening sub-module calls the measuring point correlation screening result, screens the trend change data of the correlated measuring points, identifies the trend change amplitude of the slope, and obtains the stability change trend of the slope.

[0119] For each correlated measuring point, extract its trend change data, analyze its impact on the slope stability, select the measuring points with a relatively high correlation degree, and analyze the change trends of their displacement and tilt data, so as to identify the trend change amplitude of the slope. For example, for the measuring points in a specific area, by comparing the changes in their displacement and tilt angles, if the change amplitude is greater than a set threshold (such as 0.5 degrees / hour or 10 mm / hour), it is considered that there is a large trend change in the slope of this area, and then it is judged that there is a risk in the slope stability of this area. By calculating the trend change amplitude of each measuring point, the stability change trend of the slope can be obtained. The data can reflect the stability change of the slope at different time periods, and finally provide a basis for the monitoring and evaluation of the slope. Suppose the displacement change of a certain measuring point within 24 hours is 20 mm and the tilt change is 1 degree. Such a change amplitude is greater than the set threshold, indicating that the slope in this area is in an unstable state and further monitoring and intervention are required.

[0120] Specifically, as Figure 2 、 7 shown, the key fragment storage module includes:

[0121] The spatial distribution extraction sub-module calls the slope stability change trend, extracts the spatial distribution data of the monitoring points, identifies the corresponding positions of the measuring points, and obtains the spatial distribution information of the measuring points.

[0122] Collect the spatial distribution data of the monitoring points, collect the coordinates of each measuring point and the measurement data related to it, analyze the corresponding positions by calculating the distances between the measuring points, and obtain the spatial distribution of each measuring point in the geographic coordinate system. According to the geographical locations of each measuring point, use the spatial clustering analysis method to determine the distance relationship and distribution pattern between the measuring points. For example, use the K-means clustering algorithm, set an initial clustering center, and calculate the distances between each measuring point and the clustering center, and gradually adjust the clustering center until all measuring points are assigned to the nearest clustering center, and finally form several spatially dense areas. In this way, it can be identified which areas have relatively dense measuring points, providing a basis for the subsequent storage area division and obtaining the spatial distribution information of the measuring points.

[0123] The storage area division sub-module calls the measured point spatial distribution information, calculates the distribution characteristics based on the corresponding positions of the measured points, screens the densely measured point areas, divides the storage areas, sets the boundaries of the differentiated storage areas, adjusts the distribution balance of the measured points, and obtains the storage area division result;

[0124] Based on the spatial distribution information of the measured points, according to the corresponding positions between the measured points, the clustering algorithm is used to calculate the distribution characteristics of each area. Through the comprehensive evaluation of distance, distribution density, and area division, the areas with dense measured points are screened out. The number of measured points in the area is high, the distribution is concentrated, and it affects the change trend of slope stability. Next, the boundaries of the differentiated storage areas are set, and the areas with uneven distribution of measured points are adjusted according to the distribution characteristics of the measured points to make their distribution more uniform. This process can use the Voronoi diagram algorithm to divide into multiple areas based on the position of each measured point. The measured points inside each area are closer to the measured point, and the influence of external measured points is smaller. Through this division method, the spatial distribution of the measured points can be optimized, so that the storage area can better meet the needs of different areas. For example, in a certain slope area, the measured points are relatively dense. If a uniform storage method is adopted, it will lead to a waste of storage resources. On the contrary, if it is divided according to the dense area, the storage efficiency and security can be effectively improved, and finally the storage area division result is obtained.

[0125] The partition layout adjustment sub-module calls the storage area division result, adjusts the storage partition layout, optimizes the mapping of the measured point storage data, and obtains the safe storage scheme for slope monitoring data;

[0126] Evaluate the storage area division result and analyze the storage requirements of different areas. According to the density of the measured points, the reading frequency of the data, and the requirements of the storage capacity, adjust the layout of each storage area. By optimizing the storage partition, ensure the rationality of the mapping of the measured point storage data. First, considering that the storage requirements of the dense area are higher, more storage resources need to be allocated, while for the sparse area, the configuration of the storage space can be appropriately reduced. Data compression technology can be used to compress and store the sparse area to reduce the volume of the data and the burden of reading. At the same time, through the mapping algorithm, map the data of each measured point to the appropriate storage unit according to the spatial distribution to ensure the safe storage of the data. During the implementation process, the hash mapping algorithm can be used to map the spatial position of the measured point to a specific position in the storage area to avoid over-concentration or waste of the storage area, and finally generate an effective safe storage scheme for slope monitoring data.

[0127] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An observation column information management system for slope monitoring, characterized in that, The system includes: The measuring point gradient zoning module calculates the elevation difference and horizontal distance between measuring points based on the spatial coordinate data of the observation column monitoring points, determines the gradient range to which the measuring points belong, divides them into four gradient regions: stable, gentle slope, steep slope, and drastic change, calculates the spatial distribution density of the measuring points, filters out the high-density regions, and obtains the measuring point gradient zoning result; The data hierarchical storage module sets the storage priority based on the measuring point gradient zoning result, adjusts the storage density of the measuring point data, sets the drastic change region as high-frequency storage, adjusts the storage frequency of the steep slope region, and only stores the key time node data of the gentle slope and stable regions, and obtains the measuring point data storage density data set; The measuring point correlation analysis module calls the measuring point data storage density data set, extracts the geological parameters of the measuring points, classifies them according to the geological characteristics, filters out the measuring points within the same geological characteristic unit, and obtains the measuring point correlation comparison relationship; The data trend mapping module analyzes the synchronous change situation of the displacement and inclination trends of the measuring points based on the measuring point correlation comparison relationship, judges the data correlation degree of the measuring points, sets the data correlation degree threshold, filters out the correlated measuring points, and obtains the slope stability change trend; 2. The observation column information management system for slope monitoring according to claim 1, characterized in that: The measuring point gradient zoning result includes a stable region, a gentle slope region, a steep slope region, and a drastic change region. The measuring point data storage density data set is specifically a high-frequency storage region, an adjusted storage frequency region, and a key time node storage region. The measuring point correlation comparison relationship includes a measuring point unit with the same soil layer thickness, a measuring point unit with the same rock and soil type, a measuring point unit with the same water content, and a measuring point unit with the same shear strength. The slope stability change trend is specifically the displacement trend of the measuring points, the inclination trend of the measuring points, the data correlation degree threshold, and the correlated measuring points; 3. The observation column information management system for slope monitoring according to claim 1, characterized in that: The measuring point gradient zoning module includes: The elevation difference calculation sub-module measures and records the elevation value based on the spatial coordinate data of the observation column monitoring points, calculates the elevation difference and the horizontal distance between each measuring point, and analyzes the slope according to the elevation difference and the horizontal distance to obtain the measuring point slope value; The slope classification sub-module calls the measuring point slope value, identifies the gradient based on the slope change trend of adjacent measuring points, determines the gradient range to which the measuring points belong, sets the gradient classification standard, divides the measuring points into four regions: stable, gentle slope, steep slope, and drastic change, analyzes the distribution of the measuring points within the region, and generates the measuring point gradient zoning result; The density screening sub-module calls the measuring point gradient zoning result, calculates the spatial density of the measuring points in the gradient region, filters out the density region, and obtains the measuring point gradient zoning result; 4. The observation column information management system for slope monitoring according to claim 3, wherein: The spatial density of the measuring points in the gradient region adopts the formula: Among them, D represents the density eigenvalue, N represents the total number of measurement points, w i represents the weight factor of measurement point i, x i represents the x-component of the spatial coordinate of measurement point i, represents the weighted average coordinate of the measurement point in the x direction, y i represents the y-component of the spatial coordinate of measurement point i, represents the average coordinate of the measurement point in the y direction.

5. The observation column information management system for slope monitoring according to claim 1, characterized in that: The data hierarchical storage module includes: The storage priority setting sub-module calls the measuring point gradient zoning result, sets the storage priority according to the measuring point gradient region, sorts according to the degree of gradient change from high to low, and obtains the measuring point storage priority data; The storage frequency adjustment sub-module calls the measuring point storage priority data, adjusts the storage frequency according to the priority, sets high-frequency storage for the drastic change region, adjusts the storage interval for the steep slope region, and filters out the key time nodes for storage in the gentle slope and stable regions, analyzes the change in storage density, adjusts the scale of the storage data set, and obtains the measuring point data storage density data set; 6. The observation column information management system for slope monitoring according to claim 1, characterized in that: The measuring point correlation analysis module includes: The geological parameter extraction sub-module extracts the soil layer thickness, rock and soil type, water content, and shear strength of the measuring points based on the measuring point data storage density data set, and obtains the measuring point geological parameter set; The measuring point classification sub-module calls the measuring point geological parameter set, analyzes the geological feature differences based on the soil layer thickness, rock and soil type, water content, and shear strength, screens the measuring points with similar characteristic values, determines the belonging category of the measuring points, divides the same geological feature units, optimizes the classification boundary, and obtains the geological feature unit distribution; The association comparison generation sub-module calls the geological feature unit distribution, screens the measuring points within the same unit, and obtains the measuring point association comparison relationship.

7. The observation column information management system for slope monitoring according to claim 1, characterized in that: The data trend mapping module includes: The measuring point trend analysis sub-module calls the measuring point association comparison relationship, analyzes the displacement and tilt trends of the measuring points, extracts the time series data, and obtains the measuring point trend change characteristics; The data correlation degree judgment sub-module calls the measuring point trend change characteristics, analyzes the synchronization of the displacement and tilt changes of the measuring points, calculates the measuring point data correlation degree, sets the data correlation degree threshold, screens the measuring point data correlation degree, eliminates the low-correlation measuring points, optimizes the data matching relationship, and obtains the measuring point association screening result; The stability trend screening sub-module calls the measuring point association screening result, screens the trend change data of the associated measuring points, identifies the slope trend change amplitude, and obtains the slope stability change trend.

8. The observation column information management system for slope monitoring according to claim 7, characterized in that: The measuring point data correlation degree adopts the formula: where C represents the measuring point correlation degree, D1 represents the displacement value of measuring point 1, D2 represents the displacement value of measuring point 2, T1 represents the tilt change trend of measuring point 1, T2 represents the tilt change trend of measuring point 2, V1 represents the displacement rate of measuring point 1, V2 represents the displacement rate of measuring point 2, I1 represents the tilt rate of measuring point 1, and I2 represents the tilt rate of measuring point 2.

9. The observation column information management system for slope monitoring according to claim 1, characterized in that: The system also includes a key fragment storage module: The key fragment storage module extracts the spatial distribution information of the monitoring points based on the slope stability change trend, identifies the corresponding positions between the measuring points, divides the storage areas according to the distribution characteristics, adjusts the storage partition layout, and obtains the slope monitoring data secure storage plan; The slope monitoring data secure storage plan includes storage area division, storage partition layout adjustment, monitoring point spatial distribution information extraction, and measuring point corresponding position identification.

10. The observation column information management system for slope monitoring according to claim 9, wherein: The key fragment storage module includes: The spatial distribution extraction sub-module calls the slope stability change trend, extracts the spatial distribution data of the monitoring points, identifies the corresponding positions of the measuring points, and obtains the monitoring point spatial distribution information; The storage area division sub-module calls the monitoring point spatial distribution information, calculates the distribution characteristics based on the corresponding positions of the measuring points, screens the dense areas of the measuring points, divides the storage areas, sets the differential storage area boundaries, and adjusts the distribution balance of the measuring points to obtain the storage area division result; The partition layout adjustment sub-module calls the storage area division result, adjusts the storage partition layout, and optimizes the mapping of the measuring point stored data to obtain the slope monitoring data secure storage plan.

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