Settlement monitoring method and system based on data traceability

Abstract

The invention provides a settlement monitoring method and system based on data traceability, and the method comprises the steps: firstly obtaining the time sequence settlement observation data of each monitoring point in a settlement monitoring region in a continuous observation period, including the settlement amount and the environment interference information of a corresponding observation moment, and then constructing a settlement data traceability association network, the method comprises the following steps: associating time sequence settlement observation data with geological base information of a monitoring point, an observation equipment traceability identifier and data processing node information, then tracing a generation link of settlement data of each monitoring point, extracting an equipment calibration record and the like, generating traceability link characteristics, then carrying out coupling analysis on the traceability link characteristics and the settlement amount, determining a traceability reliability index, and finally determining a traceability result. And finally, data are screened according to indexes, a dynamic settlement trend model and a settlement monitoring analysis report containing traceability link characteristics are generated in combination with the spatial distribution characteristics of the monitoring points, and the accuracy and reliability of settlement monitoring are improved.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically, to a settlement monitoring method and system based on data traceability. Background Technology

[0002] In the field of settlement monitoring, accurate acquisition and analysis of settlement data are crucial for assessing the stability of engineering structures and providing early warnings of geological disasters. Currently, settlement monitoring mainly relies on deploying multiple monitoring points within the monitoring area and using various observation equipment to acquire settlement observation data from each point. However, existing settlement monitoring methods have many limitations.

[0003] On the one hand, traditional methods of acquiring settlement monitoring data often focus only on the settlement amount itself, while failing to record sufficient information on environmental disturbances that accompany the observation process. Environmental factors such as temperature changes, groundwater level fluctuations, and surrounding construction activities can all have a significant impact on settlement observation data, but this disturbance information is often overlooked during data collection and analysis, making it difficult to guarantee the accuracy and reliability of settlement data.

[0004] On the other hand, existing settlement data analysis methods lack the ability to trace the data generation process. Settlement data undergoes multiple processing stages from acquisition to final use, including equipment calibration and data correction. However, traditional methods cannot effectively link information from these stages, making it difficult to determine whether the data has been improperly interfered with during generation, and to what extent. This results in a lack of scientific basis for assessing the reliability of settlement monitoring results, thereby affecting the accuracy and effectiveness of decisions made based on settlement monitoring data. Summary of the Invention

[0005] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a settlement monitoring method based on data tracing, the method comprising: Acquire time-series settlement observation data of each monitoring point within the settlement monitoring area. The time-series settlement observation data includes the settlement amount of each monitoring point within a continuous observation period and the environmental disturbance information at the corresponding observation time. A settlement data tracing and association network is constructed, which associates the time-series settlement observation data with the geological base information of the monitoring points, the traceability identifier of the observation equipment, and the data processing node information to form a settlement data tracing and association network information that includes data sources and related elements; Based on the aforementioned settlement data tracing and association network information, the generation link of settlement data at each monitoring point is traced, and equipment calibration records, environmental interference source intensity, and data correction operation records during data collection in different observation periods are extracted to generate settlement data tracing link characteristics. The characteristics of the settlement data tracing link are coupled with the corresponding settlement amount to determine the tracing reliability index of the settlement data at each monitoring point. The tracing reliability index is used to characterize the degree of interference of the settlement data in the generation link. Settlement data that meet the threshold requirements of the traceability reliability index are selected based on the traceability reliability index. Combined with the spatial distribution characteristics of the monitoring points, a dynamic settlement trend model of the settlement monitoring area is generated. At the same time, a settlement monitoring analysis report containing the traceability link characteristics of the settlement data is generated.

[0006] Furthermore, embodiments of the present invention also provide a settlement monitoring system based on data traceability, characterized in that it includes: A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to perform the aforementioned data-based settlement monitoring method by executing the machine-executable instructions.

[0007] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions, the machine-executable instructions being stored in a computer-readable storage medium, a processor of a computer device reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the computer device to execute the aforementioned data-based settlement monitoring method.

[0008] Based on the above, by acquiring time-series settlement observation data from each monitoring point within the settlement monitoring area and recording detailed environmental interference information at each observation time, a settlement data tracing and correlation network is constructed. This network links the time-series settlement observation data with the geological base information of the monitoring points, the traceability identifiers of the monitoring equipment, and the data processing node information, forming a comprehensive information network containing data sources and related elements. Based on this tracing and correlation network information, the generation chain of settlement data at each monitoring point is traced, extracting key information such as equipment calibration records, the intensity of environmental interference sources, and data correction operation records to generate settlement data tracing and correlation chain characteristics. This allows for precise location of each stage in the data generation process. The tracing and correlation chain characteristics are coupled with the corresponding settlement amounts for analysis to determine the tracing reliability index, scientifically characterizing the degree of interference to the settlement data in the generation chain. Settlement data meeting the requirements are selected based on the tracing reliability index, and a dynamic settlement trend model is generated by combining the spatial distribution characteristics of the monitoring points. A settlement monitoring analysis report containing tracing and correlation chain characteristics is then generated, providing accurate, reliable, and traceable results for settlement monitoring. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the execution flow of the settlement monitoring method based on data traceability provided in the embodiments of the present invention.

[0010] Figure 2 This is a schematic diagram of exemplary hardware and software components of the settlement monitoring system based on data traceability provided in an embodiment of the present invention. Detailed Implementation

[0011] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a data-based settlement monitoring method according to an embodiment of the present invention. The following is a detailed description of the data-based settlement monitoring method.

[0012] Step S110: Obtain time-series settlement observation data for each monitoring point within the settlement monitoring area. The time-series settlement observation data includes the settlement amount of each monitoring point within a continuous observation period and the environmental disturbance information at the corresponding observation time.

[0013] Taking a settlement monitoring area along a subway line in a certain city as an example, monitoring points were set up along the subway line. The spacing between the monitoring points was set according to the complexity of the geological conditions, with smaller spacing in sections with complex geological conditions and larger spacing in sections with stable geological conditions. Each monitoring point was equipped with automated settlement monitoring equipment, and the observation cycle was set to once a day, with the observation time selected in the early morning when the external environment was relatively stable.

[0014] Time-series settlement observation data are collected through the equipment's built-in sensors. Settlement amounts are obtained through a high-precision displacement measurement module, with units of millimeters and data retained to three decimal places. Environmental interference information is collected through integrated multi-parameter sensors, including atmospheric temperature, relative humidity, atmospheric pressure, ground vibration acceleration around the monitoring point, and groundwater level depth at the observation time. Atmospheric temperature is in degrees Celsius, retained to one decimal place; relative humidity is expressed as a percentage, retained to the nearest integer; atmospheric pressure is in hectopascals, retained to the nearest integer; ground vibration acceleration is collected by vibration sensors, converted into dimensionless vibration levels, and divided into multiple intervals; groundwater level depth is in meters, retained to two decimal places.

[0015] After data acquisition, the device automatically adds a timestamp to the raw data, accurate to the second, and transmits it to the data center via an encrypted wireless network. Upon receiving the data, the data center first performs a data integrity check, verifying whether the data fields are complete and the format is correct. For incomplete or incorrectly formatted data, a retransmission command is sent to the monitoring device. Data that passes the verification is stored in a distributed database, partitioned by monitoring point number and observation date for easy subsequent data retrieval and processing.

[0016] Step S120: Construct a settlement data tracing and association network, and associate the time-series settlement observation data with the geological base information of the monitoring points, the tracing and identification of the observation equipment, and the data processing node information to form a settlement data tracing and association network information that includes data sources and related elements.

[0017] In the aforementioned subway line settlement monitoring scenario, in order to achieve full-link traceability of settlement data from generation to processing, it is necessary to construct a settlement data source tracing and association network to associate time-series settlement observation data with multi-dimensional factors affecting data quality.

[0018] Step S121: Parse the metadata fields of the time-series settlement observation data, extract the location coding information and observation period identifier of each monitoring point, and establish an index system for the time-series settlement observation data.

[0019] Metadata fields are extracted from the time-series settlement observation data stored in the data center. These fields include monitoring point number, observation date, observation time, data acquisition device number, and data transmission status. The monitoring point number consists of letters and numbers; the first two letters represent the monitoring area code, and the last four numbers represent the monitoring point sequence number. The observation date is represented as a string in a specific format, and the observation time is accurate to the minute. Location coding information is extracted from the monitoring point number. This information includes parameters such as the line number, mileage marker, and offset of the monitoring point, which uniquely determine its location within the subway line. The observation period identifier is composed of the observation date and the observation time period code, which is divided according to different observation time intervals throughout the day. Based on the location coding information and the observation period identifier, an index system for the time-series settlement observation data is established. The index system adopts a multi-level index structure: the first-level index is the monitoring point number, the second-level index is the observation date, and the third-level index is the observation time period code. This index system allows for rapid location of settlement observation data for any monitoring point within any observation period.

[0020] Step S122: Collect geological base information for each monitoring point. The geological base information includes the distribution of strata bearing capacity, soil and rock compressibility parameters, and the distribution of geological structural fault zones. Assign a unique geological information code to each monitoring point.

[0021] Step S1221: Obtain the geological survey report and drilling data of the settlement monitoring area, and extract the physical and mechanical property parameters of each soil layer from them. The physical and mechanical property parameters include the unit weight, internal friction angle and cohesion of the soil.

[0022] Detailed geological survey reports and drilling data for the settlement monitoring area along the subway line were obtained by accessing urban geological survey databases or from surveying units. The geological survey reports included borehole columnar sections and geotechnical test result tables; the drilling data included information such as the depth of each borehole, soil layer name, bottom elevation, and sampling location. Physical and mechanical property parameters for each soil layer were extracted from the geotechnical test result tables. The unit weight of the soil was determined using the ring cutter method, representing the weight of soil per unit volume; the internal friction angle was determined using direct shear tests or triaxial compression tests, reflecting the soil's shear strength characteristics; cohesion was also obtained through shear strength tests, characterizing the bond strength between soil particles. These parameters were then categorized and organized according to soil layer name and depth to create a table of physical and mechanical property parameters for each soil layer, with each record corresponding to a parameter value for a specific soil layer at a specific depth.

[0023] Step S1222: Calculate the characteristic value of the formation bearing capacity at each drilling point based on the physical and mechanical property parameters, and use spatial interpolation to extend the discrete bearing capacity characteristic values ​​of the drilling points to the entire monitoring area to generate a raster map of the formation bearing capacity distribution.

[0024] Based on the calculation formula for the characteristic value of stratum bearing capacity in the geological exploration code, the characteristic value of stratum bearing capacity of each soil layer at each drilling point is calculated using the physical and mechanical property parameters of soil extracted in step S1221, such as unit weight, internal friction angle, and cohesion. For soil layers at different depths at the same drilling point, their characteristic values ​​of bearing capacity are calculated separately, and the corresponding soil layer thicknesses are marked. Kriging interpolation, a spatial interpolation method, is used to spatially extend the discrete characteristic values ​​of bearing capacity at drilling points. First, the interpolation area is determined to be the entire settlement monitoring area, and the size of the interpolation grid is set, with the grid cell side length determined according to the drilling point density. By constructing a semi-variogram model, the spatial correlation of the characteristic values ​​of bearing capacity is analyzed. Then, based on the known bearing capacity values ​​of surrounding drilling points, the characteristic value of bearing capacity of each grid cell is predicted, ultimately generating a raster map of the distribution of stratum bearing capacity. The value of each pixel in the raster map represents the characteristic value of stratum bearing capacity at that location, and the pixel values ​​are represented by different color gradients for easy visual observation of the spatial distribution differences of bearing capacity.

[0025] Step S1223: Extract the compression modulus and compression coefficient of the soil and rock mass from the drilling data, divide the soil and rock mass at different depths into a preset number of compressibility levels according to the compression modulus value, divide the monitoring area into multiple compressibility zones according to the compressibility levels, and form a distribution map of soil and rock compressibility parameters.

[0026] Compression modulus and compression coefficient of each soil layer are extracted from the geotechnical test results of drilling data. Compression modulus reflects the soil's ability to compress and deform under lateral confinement conditions, while compression coefficient represents the rate of change of void ratio under a unit pressure increment. The compression modulus is divided into a predetermined number of compressibility grades in ascending order, for example, high compressibility, medium-high compressibility, medium compressibility, medium-low compressibility, and low compressibility, with each grade corresponding to a range of compression modulus. Based on the compression modulus values ​​and their distribution depth of each soil layer at each drilling point, the dominant compressibility grade (i.e., the grade corresponding to the soil layer with the largest proportion of compressibility) at that drilling point is determined. Using a polygon zoning method, areas with the same or similar compressibility grades within the monitoring area are connected to form closed polygons, creating multiple compressibility zones. Each zone is labeled with its corresponding compressibility grade and representative compression modulus and compression coefficient values, thus generating a distribution map of soil and rock compressibility parameters.

[0027] Step S1224: Analyze the geological structure description in the geological survey report, identify the location, orientation and dip of the fault zone in the monitoring area by combining the seismic exploration data, determine the influence width and activity characteristics of the fault zone by the frequency and range of fault zone activity, and generate a geological structure fault zone distribution map.

[0028] Carefully study the sections on regional geological structures in the geological survey report, extracting information such as the names, approximate locations, and scales of the described fault zones. Combine this with seismic exploration data for the region, such as reflection seismic profiles, and analyze areas of anomalous changes in seismic wave propagation velocity to accurately identify the spatial location, strike (direction of the fault zone's extension in the plane), and dip (direction of the fault surface's inclination). For each identified fault zone, collect its historical activity records, statistically analyze the activity frequency (e.g., the number of significant activities within a century), and assess its impact range based on the fault zone's scale and activity intensity. The determination of the impact width considers the actual width of the fault zone's fracture zone and the potential secondary impact areas, including activity characteristics such as activity mode (e.g., creep, sudden slippage) and average slip rate. In Geographic Information System (GIS) software, plot the planar locations of the fault zones to scale, using different line types to represent fault zones with different activity characteristics, and labeling the fault zone's name, strike angle, dip angle, and impact width, generating a geological structural fault zone distribution map.

[0029] Step S1225: Spatially overlay the raster map of stratum bearing capacity distribution, the map of rock and soil compressibility parameters distribution, and the map of geological structural fault zones to form a comprehensive geological basement information map.

[0030] Using the spatial overlay function of Geographic Information System (GIS) software, the raster map of stratum bearing capacity distribution generated in step S1222, the distribution map of soil and rock compressibility parameters generated in step S1223, and the distribution map of geological structural fault zones obtained in step S1224 are loaded into the same coordinate system. First, coordinate registration is performed to ensure accurate spatial correspondence among the three maps. Then, layer overlay is executed to fuse the spatial elements and attribute information of each layer. In the integrated geological basement information map, the bearing capacity values ​​of raster cells, the compressibility partitions of polygons, and the linear elements of fault zones are displayed simultaneously. By adjusting the transparency of each layer, different types of geological information are clearly visible. The integrated geological basement information map can intuitively reflect the spatial relationship between stratum bearing capacity, soil and rock compressibility, and geological structures within the monitoring area.

[0031] Step S1226: The integrated geological base information map is gridded according to the preset grid accuracy, and each grid cell corresponds to a unique spatial coordinate range.

[0032] Based on the accuracy requirements of settlement monitoring and the convenience of data processing, a preset grid accuracy is set, such as a specific side length for each grid cell. A regular square grid is created on the integrated geological basement information map, covering the entire monitoring area. Each grid cell is defined by the coordinates of its upper left and lower right corners, using the Gauss-Kruger projection coordinate system. Grid cells are numbered by row and column; for example, the grid cell in row m and column n is numbered in a specific format. For each grid cell, information such as the average bearing capacity of the strata within its range, the main compressibility grade, and whether it contains fault zones are extracted as the attribute data of the grid cell.

[0033] Step S1227: Assign geological information codes to each grid cell. The geological information coding rules include the stratum bearing capacity level code, soil and rock compressibility level code, and fault zone influence degree code for the area.

[0034] A geological information coding rule is established, with a fixed code length of several digits, each representing a different geological meaning. For example, the first two digits represent the stratum bearing capacity level code, encoded according to the range of the average stratum bearing capacity characteristic value within the grid cell; for instance, one code corresponds to high bearing capacity, another code corresponds to medium-high bearing capacity, and so on. The middle two digits represent the rock and soil compressibility level code, encoded according to the main compressibility level of the grid cell. The last two digits represent the fault zone influence level code; if the grid cell contains a fault zone, it is classified into strong influence, moderate influence, and slight influence levels based on the fault zone's influence width and activity characteristics, and assigned a corresponding code. If there is no fault zone, it is coded with a specific value. A unique geological information code is assigned to each grid cell obtained in step S1226 according to this rule. The code is stored as a string and associated with the spatial coordinate range of the grid cell.

[0035] Step S1228: Match the location coordinates of each monitoring point with the spatial coordinate range of the grid cell so that each monitoring point corresponds to a unique grid cell, thereby obtaining the geological information code of the monitoring point and the corresponding distribution of stratum bearing capacity, rock and soil compressibility parameters and distribution of geological structural fault zones.

[0036] The precise planar coordinates (such as X and Y coordinates) of each monitoring point are extracted from the location coding information. In the geographic information system software, the coordinates of the monitoring points are spatially matched with the grid cells generated in step S1226. By judging the spatial relationship between points and polygons, it is determined which grid cell's spatial coordinate range each monitoring point falls into, thus assigning each monitoring point to a unique grid cell. Based on the geological information code associated with the grid cell, the geological information code of the monitoring point is obtained. At the same time, the corresponding stratigraphic bearing capacity distribution characteristics (such as average bearing capacity value, bearing capacity grade), rock and soil compressibility parameters (such as main compressibility grade, representative compression modulus), and geological structural fault zone distribution (such as whether it is affected by fault zones, and the degree of influence code) are extracted from the attribute data of the grid cells. The above geological base information is bound to the monitoring point number and stored in the monitoring point geological information table.

[0037] Step S123: Obtain the traceability identifier of the observation equipment used at each monitoring point. The traceability identifier includes the equipment production batch number, the calibration certificate number of each time, and the equipment operation and maintenance record number, and establish a full life cycle traceability file for the observation equipment.

[0038] The equipment management department retrieves the archives of the monitoring equipment used at each monitoring point, extracting traceability identification information from them. Equipment production batch numbers are provided by the manufacturer and include information such as the production year, production line code, and batch number. Calibration certificate numbers are obtained from the equipment's calibration record ledger, with a unique certificate number issued by the metrology institution after each calibration. Equipment maintenance record numbers are generated by the equipment maintenance department according to the maintenance sequence, recording operations such as equipment installation, repair, and component replacement. This traceability identification information is linked to the equipment serial number to establish a full lifecycle traceability archive for the monitoring equipment. The archive is stored in a relational database. The main table records basic equipment information (equipment model, serial number, manufacturer, purchase date), and the sub-tables record production batch information, calibration records, and maintenance records, respectively. The main table and sub-tables are linked and queried using the equipment serial number.

[0039] Step S124: Collect information on all data processing nodes involved in the processing of time-series settlement observation data. The data processing node information includes the node processing algorithm version, the qualification number of the processing personnel, and the processing timestamp, forming a data processing link list.

[0040] This study tracks the processing flow of time-series settlement observation data from raw data to final output data, identifying all data processing nodes involved. These nodes include data preprocessing, data quality check, data correction, and statistical analysis. Each node is identified by a unique node number. For each node, information such as the node's processing algorithm version, the operator's qualification number, and the processing timestamp are collected. The node's processing algorithm version refers to the software algorithm version number used by the node; the operator's qualification number refers to the professional qualification certificate number of the person operating the node; and the processing timestamp indicates the start and end times of the node's data processing, accurate to the second. The information of each data processing node is sorted according to the order of data processing to form a data processing chain list.

[0041] Step S125: Establish the correlation between time-series settlement observation data and geological base information. By mapping the location coding information of monitoring points with the geological information coding, bind the time-series settlement observation data of each monitoring point to the corresponding stratum bearing capacity distribution, soil and rock compressibility parameters and geological structural fault zone distribution.

[0042] Based on the monitoring point location coding information extracted in step S121 and the geological information coding assigned in step S122, a mapping relationship is established between the two. This mapping relationship is implemented through an association table, which contains fields such as monitoring point number, location coding information, and geological information coding. By querying the association table, the corresponding geological information coding can be found based on the monitoring point's location coding information. Based on the geological information coding, corresponding geological base information, such as the distribution of strata bearing capacity, soil and rock compressibility parameters, and the distribution of geological structural fault zones, is retrieved from the geological database. This geological base information is then bound to the time-series settlement observation data of the monitoring points. The binding method involves adding a geological information coding field to the attribute fields of the time-series settlement observation data and establishing an association index between the time-series settlement observation data records and the geological base information records.

[0043] Step S126: Establish the association between time-series settlement observation data and the traceability identifier of the observation equipment. By matching the observation cycle identifier with the equipment operation and maintenance record number, bind the settlement data of each observation cycle to the corresponding equipment production batch number and the calibration certificate number of each period.

[0044] The observation period identifier is obtained from the time-series settlement observation data index system established in step S121, and the equipment operation and maintenance record number is obtained from the full life cycle traceability archive of the observation equipment established in step S123. The equipment operation and maintenance record number contains time information such as the equipment installation date and maintenance date. This time information is matched with the observation date in the observation period identifier. The matching rule is as follows: for the settlement data of a certain observation period, find the operation and maintenance record number of the most recent equipment maintenance before that observation period, and bind the equipment production batch number and the previous calibration certificate numbers corresponding to the operation and maintenance record number with the settlement data of that observation period.

[0045] Step S127: Establish the association between time-series settlement observation data and data processing node information. According to the order of data processing, associate the original observation data with the algorithm version, processing personnel qualification number and processing timestamp of the first processing node, and then associate the information of the previous processing node with the information of the next processing node in turn.

[0046] Based on the data processing link list generated in step S124, the sequence of data processing nodes that the time-series settlement observation data undergoes from raw data to the final processed result is determined. Raw observation data refers to unprocessed data directly transmitted from the monitoring equipment to the data center; its data identifier is located in the input data identifier field of the data processing link list. The raw observation data is associated with the first processing node in the data processing link list. The association includes the algorithm version, personnel qualification number, processing time, and end time of the first processing node. The association method involves adding the relevant information field of the first processing node to the metadata of the raw observation data. Then, according to the order of data processing, the output data identifier of the previous processing node is matched with the input data identifier of the next processing node, thereby associating the information of the previous processing node with the information of the next processing node.

[0047] Step S128: Using monitoring points as core nodes and time-series settlement observation data, geological base information, traceability identifiers of observation equipment, and data processing node information as associated edges, construct a multi-dimensional settlement data traceability association network. Each associated edge in the settlement data traceability association network carries a corresponding association strength parameter, forming settlement data traceability association network information that includes data sources and associated elements.

[0048] Each monitoring point serves as the core node in the settlement data tracing and association network. The attributes of the core node include monitoring point number, location coordinates, and installation date. Time-series settlement observation data, geological base information, monitoring equipment tracing identifiers, and data processing node information are used as association edges connected to the core node. Each association edge represents the relationship between the core node and a certain type of information, and its attributes include association type and association strength parameter. The association strength parameter quantifies the tightness of the association between the core node and the associated information, with a value ranging from 0 to 1; a larger value indicates a tighter association. The multi-dimensional settlement data tracing and association network constructed in this way can intuitively display the relationship between the settlement data of each monitoring point and related elements. The association network information is stored in a graph database, facilitating network topology analysis and data tracing queries.

[0049] Step S130: Based on the settlement data tracing and association network information, trace the generation link of settlement data at each monitoring point, extract the equipment calibration records, environmental interference source intensity and data correction operation records during data collection in different observation periods, and generate settlement data tracing link features.

[0050] Step S131: Locate the traceability identifier of the observation equipment at each monitoring point from the settlement data traceability network information, query the initial calibration parameters of the equipment when it left the factory according to the equipment production batch number, and then retrieve the deviation correction value and calibration environment conditions of each calibration through the calibration certificate number to form a sequence of equipment calibration records.

[0051] By utilizing the graph database query function of the settlement data traceability network information, the corresponding traceability identifier of the observation equipment is located based on the monitoring point number, thereby obtaining the equipment production batch number. The equipment manufacturer's database is accessed based on the production batch number to query the initial calibration parameters for that batch of equipment. These initial calibration parameters include standard values ​​and allowable deviation ranges for indicators such as zero drift, sensitivity, and linearity. The calibration certificate numbers for each calibration are extracted from the equipment traceability identifier and arranged in chronological order. Detailed records of each calibration are retrieved from the metrology and calibration institution's database based on the calibration certificate number, including the calibration date, calibration items, standard equipment used, deviation correction values ​​for each calibration point, and calibration environmental conditions. The initial calibration parameters and all calibration records are integrated chronologically to form an equipment calibration record sequence.

[0052] Step S132: For each observation period, extract the calibration deviation correction value that is closest to the observation time from the equipment calibration record sequence, mark it as the valid calibration parameter for that observation period, and record the interval between the calibration time and the observation time.

[0053] For each observation period at each monitoring point, the calibration record with the smallest time interval between the calibration time and the observation time, obtained from the equipment calibration record sequence formed in step S131, is searched. The deviation correction value from this calibration record is extracted and marked as the valid calibration parameter for that observation period. Valid calibration parameters are stored according to calibration items, such as zero drift deviation correction value and sensitivity deviation correction value. The interval between the calibration time and the observation time is calculated in days, accurate to one decimal place. The interval duration is recorded as an additional attribute of the valid calibration parameter.

[0054] Step S133: Analyze the environmental interference information in the settlement data source association network information, and combine it with the distribution of geological structural fault zones in the geological base information to identify the types of environmental interference sources that may affect the settlement data in each observation period. The types of environmental interference sources include surrounding vibration sources, groundwater level changes and atmospheric pressure fluctuations.

[0055] Environmental disturbance information associated with time-series settlement observation data is extracted from the settlement data source correlation network information. This information includes parameters such as ground vibration level, groundwater level depth, and atmospheric pressure at the observation time. Combining this with the distribution of geological structural fault zones in the geological basement information, the relative positional relationship between monitoring points and fault zones is analyzed to determine whether fault zone activity may affect the monitoring points. Based on the parameter characteristics of the environmental disturbance information and the geological structure, the types of environmental disturbance sources that may affect the settlement data in each observation period are identified. Surrounding vibration sources include subway operation vibration, vibration from surrounding construction machinery, and road traffic vibration, which are identified by the magnitude and variation pattern of ground vibration levels. Groundwater level changes are identified by the differences in groundwater level depth across different observation periods. Atmospheric pressure fluctuations are identified by comparing the atmospheric pressure value at the observation time with the historical average atmospheric pressure value.

[0056] Step S134: Retrieve the quantitative evaluation results of the environmental interference source intensity through the processing algorithm version in the data processing node information. The quantitative evaluation results include the influence range and intensity of each interference source at the observation time, forming an environmental interference source intensity dataset.

[0057] Data processing node information is obtained from the sedimentation data source tracing and association network information, and the processing algorithm version of the data processing nodes is extracted. Based on the processing algorithm version, an algorithm model for quantitatively assessing the intensity of environmental disturbance sources is determined. This algorithm model is pre-integrated into the data processing software, and different versions of the algorithm model may employ different quantitative assessment methods.

[0058] Step S1341: Parse the processing algorithm version number in the data processing node information to determine the algorithm model type used to evaluate the intensity of environmental interference sources. The algorithm model type includes statistical regression model, physical field simulation model and machine learning prediction model.

[0059] The processing algorithm version number in the data processing node information is parsed. The version number typically includes a major version number, a minor version number, and a revision number; different version numbers correspond to different algorithm model types. By querying the correspondence table between algorithm versions and model types, the type of environmental interference source intensity assessment model used by the current processing algorithm version is determined. For example, if the version number contains a specific identifier character, it corresponds to a statistical regression model; if it contains another identifier character, it corresponds to a physical field simulation model; if it contains machine learning-related identifiers, it corresponds to a machine learning prediction model. The statistical regression model establishes a regression equation based on the statistical relationship between historical interference source intensity and settlement data; the physical field simulation model simulates physical processes such as vibration wave propagation and groundwater seepage using mathematical models; and the machine learning prediction model uses algorithms such as neural networks and support vector machines to predict the intensity of interference sources.

[0060] Step S1342: Retrieve the corresponding model input parameters according to the algorithm model type. The model input parameters include vibration frequency monitoring data, groundwater level depth monitoring data, and atmospheric pressure monitoring data at the observation time.

[0061] Based on the algorithm model type determined in step S1341, the corresponding model input parameters are retrieved from the environmental interference information of the time-series settlement observation data. For statistical regression models, the input parameters include vibration frequency monitoring data at the observation time (such as the dominant frequency and harmonic components of the vibration signal), groundwater level depth monitoring data (the difference between the depth value of the current observation period and the previous period), and atmospheric pressure monitoring data (the deviation between the air pressure value at the current observation time and the daily average air pressure value). For physical field simulation models, in addition to the above basic monitoring data, medium parameters (such as the density, elastic modulus, and permeability coefficient of soil and rock, obtained from the geological base information) also need to be input. For machine learning prediction models, the input parameters include all the above monitoring data and derived features (such as vibration duration, groundwater level change rate, and air pressure change trend). The above input parameters are organized according to the format required by the model and converted into numerical arrays or matrices to ensure that the dimensions of the parameters are consistent (such as vibration frequency in Hertz, groundwater level depth in meters, and atmospheric pressure in kilopascals).

[0062] Step S1343: Run the algorithm model to calculate the model input parameters and output the intensity quantification value of each environmental interference source. The intensity quantification value is represented by a preset number of relative levels, and the level division is based on the degree of influence of the interference source on the settlement observation.

[0063] The model input parameters, refined in step S1342, are input into the corresponding algorithm model for calculation. For the statistical regression model, the influence coefficients of each interference source are calculated by substituting them into the regression equation, and then the intensity quantification value is determined based on the magnitude of the influence coefficients. For the physical field simulation model, the physical quantities of the interference source at the monitoring point (such as vibration acceleration amplitude and pore water pressure change) are obtained by solving the control equations, and then the physical quantities are converted into intensity quantification values. For the machine learning prediction model, the input parameters are forward-propagated using the trained model to output the intensity prediction values ​​of each interference source. The intensity quantification values ​​are divided into a preset number of relative levels, such as five levels: weak, slight, moderate, relatively strong, and strong. The threshold for division is determined based on the analysis results of the influence of historical interference source intensity on settlement observation data; the greater the influence, the higher the corresponding level.

[0064] Step S1344: Calculate the influence range of each interference source based on the intensity quantification value. The influence range of the vibration source is calculated by the vibration wave propagation attenuation model. The model parameters include medium characteristics, amplitude and frequency. The influence range of groundwater level change is calculated by the groundwater seepage model. The model parameters include permeability coefficient, hydraulic gradient and aquifer thickness. The influence range of atmospheric pressure fluctuation is calculated based on the topographic features of the monitoring area. The topographic features include flat areas, hilly areas and mountainous areas.

[0065] For vibration sources, the influence range is calculated using a combination of geometric and material attenuation methods from the vibration wave propagation attenuation model. The medium properties (such as damping ratio and shear wave velocity of soil and rock) in the model parameters are obtained from the physical and mechanical properties of soil and rock in the geological base information, while the amplitude and frequency are derived from vibration frequency monitoring data. By calculating the energy attenuation of the vibration wave during propagation, the propagation distance corresponding to the reduction of the vibration intensity quantification value to a preset threshold is the radius of influence. For groundwater level changes, Darcy's law from the groundwater seepage model is used to calculate the influence range. The permeability coefficient is extracted from the geological base information based on the soil and rock type, the hydraulic gradient is calculated from groundwater level monitoring data (the ratio of the water level difference between two points to the horizontal distance), and the aquifer thickness is obtained from the geological survey report. The influence radius of groundwater level changes is calculated based on the seepage velocity and time. For atmospheric pressure fluctuations, an influence range calculation coefficient is set according to the topographic features of the monitoring area. Flat areas have a larger influence range, mountainous areas have a smaller influence range due to topographic obstruction, and hilly areas fall in between. The influence range radius is obtained by multiplying the atmospheric pressure fluctuation amplitude by the corresponding topographic influence coefficient.

[0066] Step S1345: Correlate the intensity quantization value, influence range and corresponding observation time of each environmental interference source to form a single interference source quantization record.

[0067] For each environmental disturbance source (surrounding vibration source, groundwater level change, atmospheric pressure fluctuation), a single disturbance source quantification record is created. This record includes the disturbance source type identifier (e.g., using different letters to represent different types), the observation time (timestamp accurate to the minute), the intensity quantification value (corresponding relative level name or level code), and the impact range (radius value, in meters). This information is then combined into structured data according to the field order, for example, using a dictionary or database table to ensure that each record corresponds to a unique observation period and disturbance source type.

[0068] Step S1346: Integrate the single-source quantization records of all environmental interference sources, classify them according to the type of interference source, and arrange the records of interference sources of the same type in chronological order.

[0069] Collect all single-source quantification records generated in step S1345. Classify the records by interference source type identifier, grouping records belonging to the same interference source type together. For example, records of all surrounding vibration sources form a vibration source interference group, records of all groundwater level changes form a groundwater level interference group, and records of all atmospheric pressure fluctuations form a pressure interference group. Within each interference source type group, sort the records according to the order of observation time to form a time series. If multiple interference sources of the same type exist at the same observation time (e.g., two construction vibration sources at different locations at the same time), merge their records and take the maximum or average intensity quantification value as the comprehensive record for that type of interference source at that time.

[0070] Step S1347: Calculate the comprehensive impact index of different types of interference sources at the same observation time. The comprehensive impact index is the weighted sum of the intensity quantification values ​​of each individual interference source. The weight values ​​are determined according to the sensitivity of the interference source to the settlement observation.

[0071] For each observation time, the intensity quantification value for that time is extracted from the records of each group of interference sources after classification and sorting in step S1346 (if there is no record for a certain type of interference source at that time, the intensity quantification value is recorded as 0 or the lowest level). Based on the sensitivity analysis results of each interference source type to settlement observation, a weight value is assigned to each interference source type; the higher the sensitivity, the larger the weight value (the sum of the weight values ​​is 1). For example, groundwater level changes have a high sensitivity to settlement observation and are assigned a larger weight; atmospheric pressure fluctuations have a low sensitivity and are assigned a smaller weight. The intensity quantification value of each individual interference source (converted to the corresponding numerical value, such as 1 for level 1, 2 for level 2, etc.) is multiplied by its respective weight value and then summed to obtain the comprehensive influence index for that observation time. The comprehensive influence index is a continuous numerical value that reflects the total intensity of the combined effect of multiple interference sources.

[0072] Step S1348: Combine the quantified records of single interference sources, the classification and integration results, and the comprehensive impact index to form an environmental interference source intensity dataset that includes the influence range and intensity of each interference source at the observation time.

[0073] The single-source quantification records from step S1345, the classification and integration results from step S1346 (a list of records arranged by source type and time sequence), and the comprehensive impact index calculated in step S1347 are combined to construct an environmental interference source intensity dataset. The dataset is stored in a multi-level structure: the first level is the observation date, the second level is the observation time, and the third level is the interference source type. Under each interference source type, single-source quantification records such as intensity quantification value and impact range are listed. Simultaneously, the comprehensive impact index is recorded at the observation time level. The dataset can be stored as a JSON file or multiple related tables in a relational database for easy subsequent data querying and analysis.

[0074] Step S135: Track the processing timestamp sequence in the data processing node information, determine all correction operations that the time-series settlement observation data has undergone during the processing, extract the correction algorithm name, correction parameter adjustment value and data change before and after each correction operation, and form a data correction operation record.

[0075] Extract the processing timestamp sequence from the data processing node information and arrange the data processing nodes in timestamp order. Analyze the processing algorithm function of each data processing node to identify nodes involved in data correction operations, such as outlier correction nodes, temperature drift correction nodes, and system error correction nodes. For each data correction operation node, retrieve its processing log and extract detailed information about the correction operation, including the correction algorithm name, correction parameter adjustment value, data value before correction, and data value after correction, and calculate the data change before and after correction. Integrate the above information in the chronological order of the correction operations to form a data correction operation record.

[0076] Step S136: Integrate the effective calibration parameters, interval duration, environmental interference source intensity dataset, and corresponding data correction operation records for each observation period according to the order of observation cycles to form a source tracing element combination for each observation period.

[0077] For each monitoring point, the effective calibration parameters and interval duration extracted in step S132, the environmental interference source intensity dataset formed in step S134, and the data correction operation records formed in step S135 are correlated and integrated according to the time sequence of the observation period. The integration method is to create a source traceability element combination object for each observation period. This source traceability element combination object includes attributes such as observation period identifier, effective calibration parameter set, calibration interval duration, environmental interference source intensity data subset, and data correction operation record subset.

[0078] Step S137: Analyze the differences between the combinations of traceability elements in adjacent observation periods, calculate the rate of change of equipment calibration parameters, the fluctuation range of environmental interference source intensity and the frequency of data correction operations, and generate dynamic change characteristics of traceability elements.

[0079] For two consecutive observation periods, the source element combinations are extracted, and the rate of change of equipment calibration parameters is calculated. The rate of change of equipment calibration parameters is calculated separately for each calibration item, and the rate of change is (the effective calibration parameter value of the current period minus the effective calibration parameter value of the previous period) divided by the effective calibration parameter value of the previous period. The fluctuation amplitude of environmental interference source intensity is calculated. For each interference source type in the subset of environmental interference source intensity data, the effect intensity level value is extracted, and the fluctuation amplitude is the absolute value of the difference between the effect intensity level value of the current period and the effect intensity level value of the previous period. The number of correction operations in the subset of statistical data correction operation records is calculated, and the difference between the number of correction operations in the current period and the number of correction operations in the previous period is used as the frequency change of data correction operations. The rate of change of equipment calibration parameters, the fluctuation amplitude of environmental interference source intensity, and the frequency change of data correction operations are arranged in order of observation period to generate the dynamic change characteristics of source elements.

[0080] Step S138: Associate the source traceability elements of each observation period with the dynamic change characteristics of the source traceability elements to form a settling monitoring data source traceability link feature ordered by the time axis. The settling monitoring data source traceability link feature includes a set of static elements and dynamic change parameters.

[0081] The source traceability elements for each observation period are combined into a static element set, which includes time-invariant elements such as valid calibration parameters, calibration interval duration, environmental interference source intensity data, and data correction operation records for that observation period. The dynamic change characteristics of the source traceability elements are used as dynamic change parameters, reflecting the changes in source traceability elements between adjacent observation periods. The static element set and the dynamic change parameters are associated through the observation period identifier, forming a characteristic source traceability link for settlement monitoring data.

[0082] Step S140: Couple the characteristics of the settlement data tracing link with the corresponding settlement amount for analysis to determine the tracing reliability index of the settlement data at each monitoring point. The tracing reliability index is used to characterize the degree of interference of the settlement data in the generation link.

[0083] In the scenario of settlement monitoring along subway lines, in order to select high-quality settlement data for trend analysis, it is necessary to couple the characteristics of the settlement data traceability link with the actual settlement amount, assess the degree of data interference, and determine the traceability reliability index.

[0084] Step S141: Extract the effective calibration parameters for each observation period from the features of the settlement data traceability link, compare the effective calibration parameters with the nominal accuracy range of the equipment, calculate the calibration deviation rate, and assign a positive calibration weight when the calibration deviation rate is within the preset deviation range; assign a negative calibration weight when the calibration deviation rate exceeds the preset deviation range.

[0085] Effective calibration parameters for each observation period are extracted from the static element set of the settlement data traceability link characteristics. These effective calibration parameters include the deviation correction values ​​for each calibration item. The nominal accuracy range of the equipment is obtained from the technical specifications of the monitoring equipment, with each calibration item corresponding to a nominal allowable deviation range. The calibration deviation rate is calculated as the ratio of the absolute value of the deviation correction value of the effective calibration parameter to the upper limit of the nominal accuracy range of the equipment. The preset deviation range is set according to the importance of the equipment and the monitoring accuracy requirements. When the calibration deviation rate is less than or equal to the upper limit of the preset deviation range, the calibration status is considered good, and a positive calibration weight is assigned; when the calibration deviation rate is greater than the upper limit of the preset deviation range, the calibration status is considered abnormal, and a negative calibration weight is assigned.

[0086] Step S142: Extract the environmental interference source intensity dataset for each observation period, analyze the correlation between the intensity of each interference source and the settlement amount, calculate the interference correlation degree, and assign a first interference influence weight when the interference correlation degree is greater than the first preset correlation degree threshold; assign a second interference influence weight when the interference correlation degree is less than or equal to the first preset correlation degree threshold and greater than the second preset correlation degree threshold; assign a third interference influence weight when the interference correlation degree is less than or equal to the second preset correlation degree threshold, wherein the first interference influence weight value is greater than the second interference influence weight value, and the second interference influence weight value is greater than the third interference influence weight value.

[0087] Environmental disturbance source intensity datasets for each observation period are extracted from the static element set of settlement data source tracing characteristics. These datasets include the intensity level values ​​of each disturbance source type. Corresponding settlement observation values ​​are also extracted. The correlation between the intensity of each disturbance source and the settlement is analyzed, and statistical methods are used to calculate the disturbance correlation degree. The interference correlation degree ranges from -1 to 1, with a larger absolute value indicating a stronger correlation. A first and second preset correlation degree thresholds are set based on experience regarding the impact of disturbance sources on settlement observations, with the first preset correlation degree threshold being greater than the second preset correlation degree threshold. When the interference correlation degree is greater than the first preset correlation degree threshold, a first disturbance influence weight is assigned; when the interference correlation degree is less than or equal to the first preset correlation degree threshold but greater than the second preset correlation degree threshold, a second disturbance influence weight is assigned; and when the interference correlation degree is less than or equal to the second preset correlation degree threshold, a third disturbance influence weight is assigned.

[0088] Step S143: Extract the data correction operation records for each observation period, count the number of correction operations and the cumulative amount of correction parameter adjustment values. When the number of correction operations and the cumulative amount of correction parameter adjustment values ​​are both within the preset standard correction range, assign a positive correction weight; when the number of correction operations or the cumulative amount of correction parameter adjustment values ​​exceed the preset standard correction range, assign a negative correction weight.

[0089] Data correction operation records for each observation period are extracted from the static element set of the settlement data traceability chain characteristics. The number of data correction operations within that observation period and the sum of the correction parameter adjustments for each operation are counted. A preset standard correction range is set based on historical correction data and data processing specifications, including the normal range for the number of correction operations and the normal range for the cumulative amount of correction parameter adjustments. When the number of correction operations in a given observation period is within the preset range and the cumulative amount of correction parameter adjustments is within the preset range, a positive correction weight is assigned; when the number of correction operations exceeds the preset range or the cumulative amount of correction parameter adjustments exceeds the preset range, a negative correction weight is assigned.

[0090] Step S144: The positive calibration weight, negative calibration weight, first interference influence weight, second interference influence weight, third interference influence weight, positive correction weight, and negative correction weight are weighted and summed to obtain the initial reliability score for each observation period.

[0091] The calibration weights determined in step S141, the interference impact weights determined in step S142, and the correction weights determined in step S143 are weighted and summed. The weighting coefficients are set according to the degree of influence of each factor on data reliability, with factors having a greater impact being assigned larger weighting coefficients. Each weight is multiplied by its corresponding weighting coefficient and then summed to obtain the initial reliability score.

[0092] Step S145: Combining the dynamic change characteristics of the traceability elements in the settlement data traceability link, calculate the change gradient of the initial reliability score in adjacent periods. When the change gradient is less than the preset gradient threshold, smooth the initial reliability score; when the change gradient is greater than or equal to the preset gradient threshold, mark it as a reliability mutation point and record the cause of the mutation.

[0093] Step S1451: Obtain the rate of change of equipment calibration parameters, the amplitude of fluctuation of environmental interference source intensity, and the frequency of data correction operations from the dynamic change characteristics of the traceability elements in adjacent observation periods.

[0094] Accessing the dynamic change feature data structure of traceability elements stored in the settlement data traceability link feature, which is arranged in order of observation period, the dynamic change features of each period include an array of equipment calibration parameter change rate, an array of environmental interference source intensity fluctuation amplitude, and a data correction operation frequency change value. For two adjacent observation periods (such as period t and period t-1), the equipment calibration parameter change rate (change rate of each calibration item), the environmental interference source intensity fluctuation amplitude (fluctuation amplitude of each interference source type), and the data correction operation frequency change value (difference between the number of corrections in the current period and the number of corrections in the previous period) are extracted for period t, reflecting the dynamic changes of key elements affecting data reliability within adjacent periods.

[0095] Step S1452: Calculate the difference between the initial reliability scores of adjacent observation periods, and perform a ratio operation between the difference and the time interval of adjacent periods to obtain the gradient of the change in the initial reliability scores.

[0096] From the initial reliability score sequence calculated in step S144, extract the initial reliability scores for two adjacent observation periods (period t and period t-1), and calculate the difference between them (initial reliability score t minus initial reliability score t-1). Obtain the time interval between these two adjacent observation periods, which is the number of days between the observation date of period t and the observation date of period t-1 (if the observation period is once a day, the time interval is usually 1 day; if there is missing data, calculate according to the actual interval). Divide the difference in initial reliability scores by the time interval to obtain the gradient of change in initial reliability scores, expressed in "scores / day", representing the average change in reliability scores per day.

[0097] Step S1453: Compare the changing gradient with a preset gradient threshold. If the changing gradient is less than the preset gradient threshold, smooth the initial reliability score. If the changing gradient is greater than or equal to the preset gradient threshold, indicate that there is a reliability mutation in the observation period.

[0098] The preset gradient threshold is determined by analyzing historical reliability score change data and is set as an empirical value, reflecting the maximum allowable rate of change of the reliability score under normal circumstances. The change gradient calculated in step S1452 is numerically compared with the preset gradient threshold. If the absolute value of the change gradient is less than the absolute value of the preset gradient threshold, it indicates that the reliability score fluctuates within the normal range and no special processing is required; smoothing processing is directly performed. If the absolute value of the change gradient is greater than or equal to the absolute value of the preset gradient threshold, it indicates that the reliability score has changed abnormally rapidly, indicating a reliability mutation in the current observation period (period t).

[0099] Step S1454: When performing smoothing, the initial reliability score is processed using the moving average method, and the size of the sliding window is adaptively determined according to the total number of observation periods based on a preset ratio.

[0100] When smoothing is deemed necessary, the moving average method is selected as the smoothing algorithm. The size of the moving window is adaptively determined based on the total number of observation periods N according to a preset proportion (e.g., 5%). The window size is calculated by multiplying N by the preset proportion, and the result is rounded to the nearest integer (or 1 if the result is less than 1). For the current observation period t, the moving window includes period t and several periods before and after it (e.g., when the window size is 5, it includes periods t-2, t-1, t, t+1, and t+2; if it is the first or last period of the sequence, boundary padding is performed, and the reliability score of the missing period is filled by copying the first and last values). The arithmetic mean of the initial reliability scores of all periods within the window is calculated, and this mean is used as the smoothed reliability score to replace the original initial reliability score.

[0101] Step S1455: When a reliability mutation is identified, extract the timestamp of the mutation observation period and the corresponding initial reliability score.

[0102] For an observation period t identified as having a reliability mutation, the timestamp (accurate to the observation date and time) of that period is extracted from the metadata of the time-series settlement observation data. Simultaneously, the initial reliability score (the raw, unsmoothed score) corresponding to that period is recorded. The timestamp and initial reliability score are stored in temporary variables for subsequent analysis and recording of the mutation's causes.

[0103] Step S1456: Based on the dynamic change characteristics of the traceability elements before and after the mutation observation period, determine the main factors causing the reliability mutation: if the change rate of equipment calibration parameters exceeds the preset change threshold, the main factor is equipment calibration change; if the fluctuation amplitude of the intensity of environmental interference sources exceeds the preset fluctuation threshold, the main factor is environmental interference change; if the change in the frequency of data correction operations is zero and the fluctuation amplitude of other factors is lower than the preset fluctuation threshold, the main factor is data processing abnormality.

[0104] The dynamic change characteristics of source elements are retrieved from several cycles before and after the mutation observation period t (e.g., the first 3 cycles and the last 3 cycles). The analysis focuses on the changes in the rate of change of equipment calibration parameters, the amplitude of fluctuations in the intensity of environmental interference sources, and the frequency of data correction operations during cycle t. Preset change thresholds (for the rate of change of equipment calibration parameters) and preset fluctuation thresholds (for the amplitude of fluctuations in the intensity of environmental interference sources) are set. These thresholds are determined based on three times the standard deviation of the normal fluctuation range in historical data. If the absolute value of the rate of change of any calibration item in the rate of change of equipment calibration parameters exceeds the preset change threshold, the main factor is determined to be a change in equipment calibration (e.g., equipment recalibration or adjustment of calibration parameters). If the amplitude of fluctuations in the intensity of environmental interference sources exceeds the preset fluctuation threshold, the main factor is determined to be a change in environmental interference (e.g., sudden strong vibrations or sudden changes in groundwater levels). If the change value of the frequency of data correction operations is zero (i.e., the number of corrections is the same as the previous cycle), and neither the rate of change of equipment calibration parameters nor the amplitude of fluctuations in the intensity of environmental interference sources exceeds their respective thresholds, the main factor is determined to be an abnormal data processing condition (e.g., changes not recorded by the processing algorithm or errors in operation by processing personnel).

[0105] Step S1457: Link and store the timestamps of the mutation observation period, the initial reliability scores, and the identified main factors to generate mutation point analysis data.

[0106] Create a mutation point analysis data record containing the following fields: mutation cycle timestamp, initial reliability score, main factors, and detailed description (briefly describing the specific changes in the elements that caused the mutation, such as "the rate of change of equipment sensitivity calibration parameters exceeds the threshold by 2 times"). Fill the record with the timestamp and initial reliability score extracted in step S1455, as well as the main factors and detailed description determined in step S1456. Then, append this record to the mutation point analysis dataset. The dataset is stored in tabular form to support subsequent queries and statistical analysis.

[0107] Step S1458: Standardize the data format of the reliability score after smoothing and the reliability score after the abrupt change, so that the reliability score of all observation periods has a unified data structure.

[0108] The smoothed reliability scores (for non-mutation periods) and the reliability scores marked with mutations (for mutation periods, retaining the original scores and adding mutation markers) are uniformly converted to the same data format. The data structure includes: observation period ID, processed reliability score, whether it is a mutation (Boolean value), and the reason for the mutation (if it is a mutation period, fill in the main factor; otherwise, leave it blank). The standardized data of all observation periods are arranged in chronological order and stored in the reliability score processing result array for subsequent geological adaptability adjustment steps.

[0109] Step S146: Based on the distribution of strata bearing capacity in the geological base information, adjust the initial reliability score according to geological adaptability. When the strata bearing capacity value is greater than the first bearing capacity threshold, and the settlement data reliability score is less than the first reliability threshold, increase the initial reliability score by a preset percentage. When the strata bearing capacity value is less than the second bearing capacity threshold, and the settlement data reliability score is greater than the second reliability threshold, decrease the initial reliability score by a preset percentage.

[0110] The bearing capacity values ​​of the strata at each monitoring point are obtained from the geological basement information. These bearing capacity values ​​are characteristic values ​​derived from the standard penetration test hammer blow count or static cone tip resistance of the area. A first bearing capacity threshold and a second bearing capacity threshold are set based on geological conditions, with the first threshold being greater than the second. A first reliability threshold and a second reliability threshold are also set based on the general reliability level of the settlement data, with the first reliability threshold being greater than the second. When the bearing capacity value is greater than the first bearing capacity threshold, if the initial reliability score of the monitoring point in that area is less than the first reliability threshold, the initial reliability score is increased by a preset percentage. Conversely, when the bearing capacity value is less than the second bearing capacity threshold, if the initial reliability score of the monitoring point in that area is greater than the second reliability threshold, the initial reliability score is decreased by a preset percentage.

[0111] Step S147: Normalize the reliability scores after smoothing, mutation point marking and geological adaptability adjustment to obtain a traceability reliability index for settlement data of each monitoring point with a uniform value range. The traceability reliability index is used to characterize the degree of interference of settlement data in the generation link.

[0112] Reliability scores for all observation periods were collected after smoothing, abrupt change marking, and geological adaptability adjustments. The maximum and minimum values ​​were identified. A linear normalization method was used to subtract the minimum value from each reliability score and then divide by (maximum minus minimum value) to obtain a traceability reliability index ranging from 0 to 1. The closer the index value is to 1, the less interference the settlement data experienced in the generation process, and the more reliable the data; the closer the index value is to 0, the greater the interference and the less reliable the data.

[0113] Step S150: Based on the traceability reliability index, select settlement data that meet the threshold requirements of the traceability reliability index, combine the spatial distribution characteristics of the monitoring points, generate a dynamic settlement trend model of the settlement monitoring area, and generate a settlement monitoring analysis report containing the traceability link characteristics of the settlement data.

[0114] In the scenario of settlement monitoring along subway lines, a dynamic settlement trend model is constructed by using the filtered high-reliability settlement data and combining it with the spatial relationship of the monitoring points. This model can intuitively display the changes in regional settlement and generate an analysis report containing source tracing information.

[0115] Step S151: Select settlement data from all monitoring points whose source traceability reliability index is higher than the source traceability reliability index threshold within all observation periods, and use them as settlement data that meet the source traceability reliability index threshold requirements.

[0116] A threshold for the traceability reliability index is set, and the value of the threshold is determined based on the accuracy requirements and data quality objectives of settlement monitoring. Settlement data from all observation periods at each monitoring point are traversed, and data records with traceability reliability indices higher than the threshold are selected to form a high-reliability settlement dataset. The high-reliability settlement dataset is then subjected to validity checks, including the temporal continuity and spatial uniformity of the data.

[0117] Step S152: Extract the location coordinates of each monitoring point and the settlement amount of the corresponding observation period from the settlement data that meets the threshold requirements of the traceability reliability index, and establish a three-dimensional dataset.

[0118] The monitoring point number, observation date, and settlement amount for each data record are extracted from the high-reliability settlement dataset. Based on the monitoring point number, the corresponding plane and elevation coordinates are obtained from the location coding information. The location coordinates, settlement amount, and observation date are combined to create a 3D dataset. Each record in the 3D dataset contains fields such as X-coordinate, Y-coordinate, Z-coordinate, observation date, and settlement amount. The 3D dataset is stored in a spatial database, supporting spatial query and spatial analysis operations.

[0119] Step S153: Based on the three-dimensional dataset, analyze the spatial distribution characteristics of the monitoring points, divide the settlement monitoring area into multiple spatial grid units according to the location coordinates, calculate the average settlement of all monitoring points in each grid unit during the same observation period, and generate a grid unit settlement matrix.

[0120] Spatial clustering analysis was performed on the location coordinates of monitoring points in the 3D dataset, and a density clustering algorithm was used to identify densely distributed and sparsely distributed areas of the monitoring points. Based on the range of the monitoring area and the density of the monitoring points, the settlement monitoring area was divided into multiple square spatial grid cells of equal size, with the side length of the grid cell determined by the spacing between the monitoring points. For each spatial grid cell, all monitoring points falling within that cell were counted. For each observation period, the arithmetic mean of the settlement amounts of all monitoring points within the grid cell was calculated as the average settlement amount of that grid cell in that observation period. The average settlement amounts of all grid cells in each observation period were arranged in order of grid cell number and observation period to form a grid cell settlement matrix.

[0121] Step S154: Perform time series analysis on the settlement matrix of the grid cells, extract the settlement change trend of each grid cell in the continuous observation period, and determine the grid cells whose settlement growth rate is greater than the preset growth rate threshold and the grid cells whose settlement growth rate is less than or equal to the preset stability threshold.

[0122] Trend analysis was performed on the time series of average settlement for each grid cell in the grid cell settlement matrix. Linear regression was used to calculate the rate of change of settlement over time (i.e., settlement rate increase). Preset rate increase thresholds and preset stability thresholds were set according to settlement control standards, with the preset rate increase threshold being greater than the preset stability threshold. A grid cell with a settlement rate increase greater than the preset rate increase threshold was marked as an active settlement cell; a cell with a settlement rate increase less than or equal to the preset stability threshold was marked as a stable settlement cell; and cells in between were classified as slow settlement cells.

[0123] Step S155: Based on the settlement variation trend of each grid cell, construct a spatial interpolation model to predict the settlement in areas where no monitoring points are set up, and generate spatial distribution data of settlement covering the entire monitoring area.

[0124] Step S1551: Determine the type of spatial interpolation model as a Kriging interpolation model based on geological basement information constraints. This Kriging interpolation model is configured to correct the interpolation results by incorporating the distribution of formation bearing capacity.

[0125] Considering the complexity of the geological conditions in the settlement monitoring area, a Kriging interpolation model constrained by geological basement information was selected as the spatial interpolation model type. This Kriging interpolation model, based on traditional Kriging interpolation, introduces the distribution of strata bearing capacity from the geological basement information as an auxiliary variable to constrain and correct the interpolation process. The core configuration of the model includes: variogram model type (e.g., spherical model), geological constraint factor (correlation coefficient between strata bearing capacity and settlement), and interpolation search radius (dynamically adjusted according to the density of monitoring points). By inputting the strata bearing capacity distribution raster map as a covariate into the model, the model considers not only spatial distance factors but also the strata bearing capacity characteristics of the point when predicting settlement at unknown points, thereby improving interpolation accuracy.

[0126] Step S1552: The settlement amount of the known monitoring points in the settlement matrix of the grid unit is used as input data, and the geological information code corresponding to each known monitoring point is used as covariate, and then input into the spatial interpolation model.

[0127] Settlement data for all grid cells containing monitoring points (grid cells with known monitoring points) are extracted from the grid cell settlement matrix. This data serves as the primary input data (i.e., attribute values ​​of known sample points) for the spatial interpolation model. Simultaneously, the geological information code corresponding to each known monitoring point is obtained from the monitoring point geological information table. This code contains information such as the stratum bearing capacity level and the compressibility level of the soil and rock mass. The geological information code is then converted into numerical covariates (e.g., different levels are mapped to different integer codes). The planar coordinates (grid cell center point coordinates), settlement (attribute values), and geological information code covariates of the known sample points are combined into an input dataset, formatted as a two-dimensional array. Each row represents a sample point, and the columns are the X-coordinate, Y-coordinate, settlement, stratum bearing capacity level code, compressibility level code, etc.

[0128] Step S1553: Configure the semivariogram parameters of the spatial interpolation model, wherein the type of the semivariogram is determined based on the spatial correlation characteristics of the settlement in the monitoring area.

[0129] The spatial correlation characteristics of settlement in the monitoring area were analyzed using experimental semivariogram analysis. First, the distances between all known sample point pairs and the squares of the settlement differences were calculated. Then, the data were grouped by distance intervals, and the semivariogram value (half the average of the squared differences) for each interval was calculated. An experimental semivariogram graph was plotted. Based on the trend of the semivariogram values ​​with distance, an appropriate theoretical semivariogram type was selected: if the semivariogram values ​​first increased and then stabilized with increasing distance, a spherical model was chosen; if they showed an exponential growth trend, an exponential model was chosen; and if they resembled a Gaussian curve, a Gaussian model was chosen. After determining the semivariogram type, the experimental data were fitted using the least squares method to estimate the model parameters: range (spatial autocorrelation distance), sill value (asymptotic value of the semivariogram), and nugget value (semivariogram value at the origin). These parameters describe the spatial variation structure of the settlement.

[0130] Step S1554: Run the spatial interpolation model to predict the settlement of grid cells without monitoring points, and obtain the predicted settlement and corresponding prediction error of each unmonitored grid cell.

[0131] The input dataset prepared in step S1552 is input into the configured Kriging interpolation model constrained by geological basement information. The model first calculates the spatial weight matrix between sample points (based on semi-variogram parameters), and then uses geological covariates to correct the weights (sample points in areas with high stratum bearing capacity are assigned higher weights). For the center point of each grid cell without monitoring points, the model searches for known sample points within a certain range around its coordinates and calculates the predicted settlement of that center point by weighted average. Simultaneously, the model outputs the prediction error (e.g., Kriging variance) corresponding to each predicted settlement. The prediction error reflects the uncertainty of the predicted value; the smaller the error, the more reliable the prediction. The predicted settlement and prediction error are stored according to grid cell number to form a prediction result table.

[0132] Step S1555: The predicted settlement amount is fused with the settlement amount of known monitoring points to generate preliminary spatial distribution data of settlement amount covering the entire monitoring area.

[0133] The settlement values ​​of known monitoring points (from the grid cell settlement matrix) are integrated with the settlement values ​​of unmonitored grid cells predicted in step S1554 to ensure that each grid cell has a corresponding settlement value (measured average settlement for known points, predicted settlement for unknown points). These settlement values ​​are arranged in spatial coordinate order of the grid cells to form a two-dimensional array, where the rows and columns correspond to the row and column numbers of the grid cells, and the array elements are the settlement values. This two-dimensional array is then converted into raster data format, with the raster cell size matching the grid cell size and the cell value being the settlement value, thereby generating preliminary spatial distribution data of settlement covering the entire monitoring area.

[0134] Step S1556: Based on the distribution map of soil and rock compressibility parameters in the geological base information, the preliminary settlement spatial distribution data is corrected: for grid cells whose soil and rock compressibility level belongs to the preset high compressibility level range, the predicted settlement is increased by the first preset adjustment coefficient; for grid cells whose soil and rock compressibility level belongs to the preset low compressibility level range, the predicted settlement is decreased by the second preset adjustment coefficient.

[0135] The distribution map of soil and rock compressibility parameters is retrieved from the geological base information, dividing the monitoring area into different compressibility level zones. A high compressibility level zone is preset for zones with a compression modulus less than a certain value (e.g., compression modulus < 4 MPa), and a low compressibility level zone is preset for zones with a compression modulus greater than a certain value (e.g., compression modulus > 15 MPa). Based on the positive correlation between soil and rock compressibility and settlement, a first preset adjustment coefficient (greater than 1, e.g., 1.1) and a second preset adjustment coefficient (less than 1, e.g., 0.9) are set. Each grid cell in the preliminary settlement spatial distribution data is traversed, and the corresponding soil and rock compressibility level is queried through spatial overlay. If it belongs to the high compressibility level zone, its settlement is multiplied by the first preset adjustment coefficient; if it belongs to the low compressibility level zone, it is multiplied by the second preset adjustment coefficient; the settlement of grid cells in the medium compressibility level zone remains unchanged.

[0136] Step S1557: Based on the prediction error, perform a confidence assessment on the corrected spatial distribution data of settlement, and divide the region where the prediction error is less than the first preset error threshold into a high confidence region, the region where the prediction error is between the first preset error threshold and the second preset error threshold into a medium confidence region, and the region where the prediction error is greater than the second preset error threshold into a low confidence region.

[0137] The prediction error of each grid cell is extracted from the prediction result table in step S1554. A first preset error threshold and a second preset error threshold are set (the first preset error threshold is less than the second preset error threshold). These thresholds are determined based on the statistical distribution of all prediction errors (e.g., the first threshold is the mean error minus the standard deviation, and the second threshold is the mean error plus the standard deviation). For each grid cell in the corrected spatial distribution data of settlement, its prediction error is compared with the preset thresholds: if the prediction error is less than the first preset error threshold, the area where the grid cell is located is a high-confidence area; if the prediction error is greater than or equal to the first preset error threshold and less than or equal to the second preset error threshold, it is a medium-confidence area; if the prediction error is greater than the second preset error threshold, it is a low-confidence area. Spatially, grid cells with the same confidence level are connected to form polygons representing high, medium, and low-confidence areas.

[0138] Step S1558: Integrate the confidence assessment results with the corrected settlement data to generate the final spatial distribution data of settlement covering the entire monitoring area. This spatial distribution data of settlement includes the settlement value of each grid cell and its corresponding confidence level.

[0139] The final spatial distribution data structure for settlement is created, comprising two layers: a settlement raster layer and a confidence level raster layer. The settlement raster layer stores the settlement values ​​of each grid cell after correction in step S1556; the confidence level raster layer stores the confidence levels of each grid cell divided in step S1557 (using numbers 1, 2, and 3 to represent high, medium, and low confidence levels, respectively). The two layers are spatially registered in the geographic information system to ensure a one-to-one correspondence between grid cells. The final spatial distribution data for settlement is stored in GeoTIFF format, supporting opening and analysis in professional GIS software. Each grid cell's attributes include both the settlement value and the confidence level.

[0140] Step S156: Overlay the spatial distribution data of settlement from different observation periods in time series, analyze the expansion direction of the settlement area and the positional changes of the contraction area, and extract the movement trajectory of the settlement boundary.

[0141] The spatial distribution data of settlement in each observation period are superimposed and displayed in chronological order, with different colors representing different settlement magnitudes, forming a dynamic settlement change map. By comparing the spatial distribution of settlement in adjacent observation periods, areas of increasing and decreasing settlement are identified. For expanding areas, the direction and distance of movement of their geometric centers are calculated; similarly, for contracting areas, the direction and distance of movement of their geometric centers are calculated. Settlement boundaries are extracted using an edge detection algorithm. Edge detection is performed on the spatial distribution data of settlement in each observation period to obtain the boundary polygons of the settlement areas. The boundary polygons from different periods are superimposed, and the movement trajectories of the boundary polygon vertices are analyzed. A curve fitting method is used to obtain the continuous movement trajectory line of the settlement boundary.

[0142] Step S157: Combining the movement trajectory of the settlement boundary and the settlement rate of the grid cells, construct a dynamic settlement trend model. The dynamic settlement trend model includes the spatial distribution of settlement at each time point, the movement speed of the settlement boundary, and the migration path of the settlement core area.

[0143] The model integrates spatial distribution data of settlement, settlement boundary movement trajectory, and settlement rate of grid cells into a dynamic settlement trend model. The model uses time-series animation to display the spatial distribution of settlement at each time point, visually reflecting changes in settlement magnitude through color gradients. The settlement boundary movement speed is calculated as the ratio of the distance moved by the boundary in adjacent periods to the time interval. The settlement core region is defined as a set of continuous grid cells with the largest settlement rate and an area exceeding a preset threshold. The migration path of the settlement core region is generated by tracking the positional changes of its geometric center. The dynamic settlement trend model supports interactive operation; users can select any observation period using a time slider to view the settlement distribution for that period.

[0144] Step S158: Verify the dynamic settlement trend model by comparing the subsequent periodic settlement predicted by the dynamic settlement trend model with the actual observed settlement data that meets the threshold requirements of the traceability reliability index, and adjust the spatial interpolation parameters of the dynamic settlement trend model so that the deviation between the output of the dynamic settlement trend model and the actual data is within the preset deviation range.

[0145] Actual settlement data from several recent observation periods are selected as validation samples. The settlement amounts predicted by the dynamic settlement trend model for these periods are compared with the actual observed values, and the prediction bias for each grid cell is calculated. The average absolute value and root mean square error of the prediction biases for all grid cells are calculated to evaluate the model's prediction accuracy. If the average absolute error or root mean square error exceeds the preset bias range, the variogram parameters or interpolation method of the spatial interpolation model are adjusted, and interpolation prediction is performed again. The adjustment and validation process is repeated until the model's prediction bias is within the preset bias range.

[0146] Step S159: Generate a settlement monitoring and analysis report that includes the characteristics of the settlement data traceability link.

[0147] For example, step S1591: extract key feature parameters from the dynamic settlement trend model. The key feature parameters include the location coordinates of the settlement core area, the movement speed of the settlement boundary, and the settlement growth rate of each grid unit.

[0148] The key feature parameters calculated by the model are queried and extracted through the data interface of the dynamic settlement trend model. The location coordinates of the settlement core area are obtained by calculating the geometric center of the set of continuous grid cells with the largest settlement growth rate. The coordinates of the geometric center are the weighted average of the coordinates of the center points of all grid cells in the area (the weight is the settlement growth rate of each grid cell). The movement speed of the settlement boundary is calculated by dividing the distance difference between corresponding vertices of the settlement boundary polygons of adjacent periods by the time interval, and the average of the movement speeds of all vertices is taken as the overall boundary movement speed. The settlement growth rate of each grid cell is directly extracted from the grid cell attribute table, which is generated in step S154, and the linear regression settlement growth rate value of each grid cell is recorded. The above key feature parameters are organized into time series data according to the observation period to analyze their variation over time.

[0149] Step S1592: Based on a preset impact significance threshold, determine significant traceability elements from the features of the settlement data traceability link. The significant traceability elements include the observation period in which the equipment calibration deviation rate exceeds the preset deviation range, the time period in which the intensity of the environmental interference source reaches the preset impact level, and the processing node in which the data correction operation exceeds the preset standard correction range.

[0150] Set significant impact thresholds, including the upper limit of the preset deviation range for equipment calibration deviation rate, the preset impact level of environmental interference source intensity (e.g., "strong" or higher), and the upper limit of the preset standard correction range for data correction operations (the upper limit of the number of corrections and the cumulative amount of correction parameters). Traverse the settlement data tracing link characteristics of each monitoring point. For each observation period, check whether the equipment calibration deviation rate exceeds the preset deviation range; if so, mark the observation period as a significant equipment calibration element. Check whether there are interference source types and time periods in the environmental interference source intensity dataset that reach the preset impact level; if so, mark the time period as a significant environmental interference element. Check whether there are processing nodes in the data correction operation records where the number of corrections or the cumulative amount of correction parameter adjustments exceeds the preset standard correction range; if so, mark the processing node as a significant data correction element. Classify all marked significant tracing elements by type, and record their timestamps, associated monitoring points, and specific exceedance values.

[0151] Step S1593: Associate the key feature parameters with the significant source-tracing elements to identify the source-tracing influencing factors corresponding to each settlement feature; when the settlement growth rate in the core settlement area is greater than the preset growth rate threshold, analyze the changes in the intensity of equipment calibration records and environmental interference sources during the same period.

[0152] A correlation analysis mechanism is established between key characteristic parameters and significant source-tracing elements. Using a time window matching method, for each key characteristic parameter's abnormal change point (such as a sudden increase in settlement rate), a search is conducted within a certain range before and after its timestamp (e.g., ±3 observation periods) to determine if significant source-tracing elements exist. If the settlement rate in the core settlement area exceeds a preset rate threshold (which is consistent with the preset rate threshold in step S154), then equipment calibration records (obtained from the equipment calibration record sequence) and environmental interference source intensity change data (obtained from the dynamic change characteristics of the environmental interference source intensity dataset) from the same period (same observation period) are extracted. The temporal correlation between the changing trend of equipment calibration deviation rate, the fluctuation of environmental interference source intensity, and the abnormal settlement rate is analyzed. For example, if there is a record of excessive equipment calibration deviation rate in the period preceding the sudden increase in settlement rate, then equipment calibration is considered to be one of the influencing factors leading to the abnormal settlement rate; if the environmental interference source intensity reaches a preset influence level during the same period, then environmental interference is also considered to be an influencing factor.

[0153] Step S1594: Statistically analyze the traceability reliability index of each monitoring point, calculate the proportion of data that meets the threshold requirements of the traceability reliability index in the total data volume, analyze the data distribution characteristics that meet the threshold requirements in different spatial areas, and generate data reliability assessment results.

[0154] For each monitoring point, the total number of traceability reliability indicators for all observation periods and the number of data records exceeding the traceability reliability indicator threshold are counted. The ratio (proportion) between the two is calculated to obtain the high-reliability data proportion for that monitoring point. The high-reliability data proportions of all monitoring points are spatially marked on the monitoring area map, using a hierarchical color scheme to represent different proportion ranges (e.g., 0-20%, 20-40%, ..., 80-100%), and the spatial distribution characteristics of the proportions are analyzed (e.g., which areas have high proportions, which areas have low proportions). Simultaneously, the average high-reliability data proportion for the entire monitoring area is calculated, and the number and proportion of monitoring points with proportions higher than the regional average are counted. The above statistical results, spatial distribution characteristics, and proportion analysis of typical areas are integrated to generate a data reliability assessment report, which includes statistical tables and a spatial distribution map.

[0155] Step S1595: Combining the geological base information, assess the correlation between the distribution of stratum bearing capacity and the subsidence trend, generate analysis results on the impact of geological conditions on subsidence development, and generate a basis for distinguishing between geological factors and external interference factors based on the source information.

[0156] A raster map of stratum bearing capacity distribution was extracted from geological basement information and spatially overlaid with the spatial distribution data of settlement in a dynamic settlement trend model. A zonal statistical method was used to calculate the average settlement and average settlement rate within regions of different stratum bearing capacity levels. The correlation between the two was analyzed by comparing and analyzing their correlation (e.g., plotting a scatter plot of bearing capacity level and average settlement rate and calculating the correlation coefficient) to assess the impact of stratum bearing capacity distribution on settlement trends (e.g., whether low-bearing-capacity areas have higher settlement rates). Based on the environmental disturbance source intensity and equipment calibration records in the settlement data tracing link characteristics, geological factors and external disturbance factors were distinguished: when the stratum bearing capacity level is low and there are no significant external disturbance sources (environmental disturbance source intensity does not reach the preset level, equipment calibration is normal), settlement is mainly caused by geological factors (compressibility of soil and rock); when there are significant external disturbance sources and the settlement anomaly highly coincides with the occurrence time of the disturbance source, settlement is mainly caused by external disturbance factors. The correlation assessment results, impact degree analysis, and distinction criteria were integrated to form a geological condition impact analysis report.

[0157] Step S1596: According to the preset report template, integrate the visualization data of the dynamic subsidence trend model, the analysis results of significant source elements, the data reliability assessment results, and the geological correlation analysis results.

[0158] The pre-defined settlement monitoring and analysis report template is used, which includes a cover, table of contents, introduction, overview of the monitoring area, data sources and processing, dynamic settlement trend analysis, data reliability assessment, geological condition impact analysis, conclusions and recommendations. Visualized data from the dynamic settlement trend model (such as a time-series map of settlement spatial distribution, animation of settlement boundary movement trajectory, and core area migration path map) are inserted into the "Dynamic Settlement Trend Analysis" section; the significant source elements identified in step S1592 and their impact analysis on settlement are inserted into the source analysis section of the "Data Sources and Processing" section; the data reliability assessment results generated in step S1594 are inserted into the "Data Reliability Assessment" section; and the geological correlation analysis results from step S1595 are inserted into the "Geological Condition Impact Analysis" section. Ensure that the content of each section is logically coherent, that charts and tables are numbered correctly, and that data citations are accurate.

[0159] Step S1597: During the integration process, each analysis result is associated with the characteristics of the settlement data traceability link on which it is based, and finally a settlement monitoring analysis report containing the characteristics of the settlement data traceability link is generated.

[0160] During the report integration process, for each analytical conclusion or data chart, the source of the settlement data traceability link is clearly indicated in its footnotes or explanatory text. For example, when describing an abnormal settlement rate increase in a certain area, it is noted that "this conclusion is based on the environmental disturbance source intensity dataset (atmospheric pressure fluctuation level reaches 'strong') and equipment calibration records (calibration deviation rate exceeds the preset range by 1.5 times)"; when displaying a high reliability data proportion chart, it is noted that "the data comes from the calculation results of the traceability reliability index of each monitoring point, see the calculation results of processing algorithm version V2.3 in the data processing node information table for details." Through the above-mentioned association markers, users of the report can trace the original data and traceability information of each analytical result, ensuring the scientific nature and verifiability of the report. After completing all content integration and association markers, the report is formatted, checked for typos, and logically reviewed, finally generating a complete settlement monitoring and analysis report containing the settlement data traceability link characteristics.

[0161] Based on the same inventive concept, please refer to Figure 2 The diagram shows a schematic block diagram of a data traceability-based settlement monitoring system 100 provided in this application embodiment for performing the above-described data traceability-based settlement monitoring method. The data traceability-based settlement monitoring system 100 may include a communication unit 110, a machine-readable storage medium 120, and a processor 130.

[0162] The machine-readable storage medium 120 is used to store machine-executable instructions for executing the scheme of this application, and the processor 130 is used to execute the machine-executable instructions stored in the machine-readable storage medium 120 to implement the settlement monitoring method based on data traceability provided in the aforementioned method embodiments.

[0163] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A settlement monitoring method based on data traceability, characterized in that, The method includes: Acquire time-series settlement observation data of each monitoring point within the settlement monitoring area. The time-series settlement observation data includes the settlement amount of each monitoring point within a continuous observation period and the environmental disturbance information at the corresponding observation time. A settlement data tracing and association network is constructed, which associates the time-series settlement observation data with the geological base information of the monitoring points, the traceability identifier of the observation equipment, and the data processing node information to form a settlement data tracing and association network information that includes data sources and related elements; Based on the aforementioned settlement data tracing and association network information, the generation link of settlement data at each monitoring point is traced, and equipment calibration records, environmental interference source intensity, and data correction operation records during data collection in different observation periods are extracted to generate settlement data tracing link characteristics. The characteristics of the settlement data tracing link are coupled with the corresponding settlement amount to determine the tracing reliability index of the settlement data at each monitoring point. The tracing reliability index is used to characterize the degree of interference of the settlement data in the generation link. Settlement data that meet the threshold requirements of the traceability reliability index are selected based on the traceability reliability index. Combined with the spatial distribution characteristics of the monitoring points, a dynamic settlement trend model of the settlement monitoring area is generated. At the same time, a settlement monitoring analysis report containing the traceability link characteristics of the settlement data is generated.

2. The settlement monitoring method based on data traceability according to claim 1, characterized in that, The construction of the settlement data tracing and association network involves associating the time-series settlement observation data with the geological base information of the monitoring points, the traceability identifiers of the observation equipment, and the data processing node information, forming a settlement data tracing and association network information that includes data sources and associated elements, including: The metadata fields of the time-series settlement observation data are analyzed, the location coding information and observation period identifier of each monitoring point are extracted, and an index system for the time-series settlement observation data is established. Geological base information of each monitoring point is collected. The geological base information includes the distribution of strata bearing capacity, rock and soil compressibility parameters and the distribution of geological structural fault zones. A unique geological information code is assigned to each monitoring point. Obtain the traceability identifiers of the observation equipment used at each monitoring point. The traceability identifiers include the equipment production batch number, the calibration certificate number, and the equipment operation and maintenance record number. Establish a full life cycle traceability file for the observation equipment. Collect information on all data processing nodes involved in the processing of time-series settlement observation data. The data processing node information includes the node processing algorithm version, the qualification number of the processing personnel, and the processing timestamp, forming a data processing link list. Establish the correlation between time-series settlement observation data and geological base information. By mapping the location coding information of monitoring points with the geological information coding, bind the time-series settlement observation data of each monitoring point to the corresponding stratum bearing capacity distribution, rock and soil compressibility parameters and geological structural fault zone distribution. Establish the association between time-series settlement observation data and the traceability identifier of the observation equipment. By matching the observation cycle identifier with the equipment operation and maintenance record number, the settlement data of each observation cycle is bound to the corresponding equipment production batch number and the calibration certificate number of each period. Establish the association between time-series settlement observation data and data processing node information. According to the order of data processing, associate the original observation data with the algorithm version, personnel qualification number and timestamp of the first processing node, and then associate the information of the previous processing node with the information of the next processing node in turn. Using monitoring points as core nodes and time-series settlement observation data, geological base information, traceability identifiers of observation equipment, and data processing node information as associated edges, a multi-dimensional settlement data traceability association network is constructed. Each associated edge in the settlement data traceability association network carries a corresponding association strength parameter, forming settlement data traceability association network information that includes data sources and associated elements.

3. The settlement monitoring method based on data traceability according to claim 2, characterized in that, The geological foundation information collected at each monitoring point includes the distribution of strata bearing capacity, soil and rock compressibility parameters, and the distribution of geological structural fault zones. A unique geological information code is assigned to each monitoring point, including: Obtain geological survey reports and drilling data of the settlement monitoring area, and extract physical and mechanical property parameters of each soil layer from them. The physical and mechanical property parameters include the unit weight, internal friction angle and cohesion of the soil. The characteristic value of the formation bearing capacity at each drilling point is calculated based on the physical and mechanical properties parameters. The spatial interpolation method is used to extend the discrete bearing capacity characteristic value of the drilling point to the entire monitoring area, generating a raster map of the formation bearing capacity distribution. The compression modulus and compression coefficient of the soil and rock mass are extracted from the drilling data. Soil and rock mass at different depths are divided into a preset number of compressibility levels according to the compression modulus value. The monitoring area is divided into multiple compressibility zones according to the compressibility level, forming a distribution map of soil and rock compressibility parameters. By analyzing the geological structure description in the geological survey report and combining it with seismic exploration data, the location, orientation, and dip of fault zones within the monitoring area are identified. The influence width and activity characteristics of the fault zones are determined by the frequency and range of their activity, and a geological structure fault zone distribution map is generated. The distribution of strata bearing capacity grid map, rock and soil compressibility parameter distribution map and geological structure fault zone distribution map are spatially superimposed to form a comprehensive geological basement information map. The integrated geological base information map is gridded according to the preset grid precision, and each grid cell corresponds to a unique spatial coordinate range; Each grid cell is assigned a geological information code. The geological information coding rules include the code for the bearing capacity level of the strata, the code for the compressibility level of the soil and rock mass, and the code for the degree of influence of the fault zone in the area. The location coordinates of each monitoring point are matched with the spatial coordinate range of the grid unit, so that each monitoring point corresponds to a unique grid unit, thereby obtaining the geological information code of the monitoring point and the corresponding distribution of strata bearing capacity, soil and rock compressibility parameters and distribution of geological structural fault zones.

4. The settlement monitoring method based on data traceability according to claim 1, characterized in that, The process of tracing the generation link of settlement data at each monitoring point based on the settlement data tracing and association network information involves extracting equipment calibration records, environmental interference source intensity, and data correction operation records during data acquisition in different observation periods to generate settlement data tracing link features, including: The equipment traceability identifiers of each monitoring point are located from the settlement data traceability network information. The initial calibration parameters of the equipment at the time of leaving the factory are retrieved according to the equipment production batch number. Then, the deviation correction value and calibration environment conditions of each calibration are retrieved through the calibration certificate number to form a sequence of equipment calibration records. For each observation period, the calibration deviation correction value closest to the observation time is extracted from the equipment calibration record sequence and marked as the valid calibration parameter for that observation period. At the same time, the interval between the calibration time and the observation time is recorded. The environmental interference information in the settlement data source tracing and association network information is analyzed, and combined with the distribution of geological structural fault zones in the geological base information, the types of environmental interference sources that may affect the settlement data in each observation period are identified. The types of environmental interference sources include surrounding vibration sources, groundwater level changes and atmospheric pressure fluctuations. By using the processing algorithm version in the data processing node information, the quantitative evaluation results of the environmental interference source intensity are retrieved. The quantitative evaluation results include the influence range and intensity of each interference source at the observation time, forming an environmental interference source intensity dataset. By tracking the processing timestamp sequence in the data processing node information, we can determine all the correction operations that the time-series settlement observation data has undergone during the processing. We can extract the correction algorithm name, correction parameter adjustment value, and data change before and after each correction operation to form a data correction operation record. According to the order of the observation cycles, the effective calibration parameters, interval duration, environmental interference source intensity dataset and corresponding data correction operation records of each cycle are integrated to form a source traceability element combination for each observation cycle. The differences between the combinations of traceability elements in adjacent observation periods are analyzed, the rate of change of equipment calibration parameters, the fluctuation range of environmental interference source intensity and the frequency of data correction operations are calculated, and the dynamic change characteristics of traceability elements are generated. By associating the source-tracing elements of each observation period with the dynamic change characteristics of the source-tracing elements, a settling data source-tracing link feature ordered by the time axis is formed. The settling data source-tracing link feature includes a set of static elements and dynamic change parameters.

5. The settlement monitoring method based on data traceability according to claim 4, characterized in that, The process retrieves the quantitative assessment results of the environmental interference source intensity by using the processing algorithm version in the data processing node information. These quantitative assessment results include the influence range and intensity of each interference source at the observation time, forming an environmental interference source intensity dataset, including: The processing algorithm version number in the data processing node information is parsed to determine the type of algorithm model used to evaluate the intensity of environmental interference sources. The algorithm model type includes statistical regression model, physical field simulation model and machine learning prediction model. The corresponding model input parameters are retrieved according to the algorithm model type. The model input parameters include vibration frequency monitoring data, groundwater level depth monitoring data, and atmospheric pressure monitoring data at the observation time. The algorithm model is run to calculate the input parameters of the model and output the intensity quantification value of each environmental interference source. The intensity quantification value is represented by a preset number of relative levels, and the level division is based on the degree of influence of the interference source on the settlement observation. The influence range of each interference source is calculated based on the intensity quantification value. The influence range of the vibration source is calculated by the vibration wave propagation attenuation model, and the model parameters include medium characteristics, amplitude and frequency. The influence range of groundwater level change is calculated by the groundwater seepage model, and the model parameters include permeability coefficient, hydraulic gradient and aquifer thickness. The influence range of atmospheric pressure fluctuation is calculated based on the topographic features of the monitoring area, which include flat areas, hilly areas and mountainous areas. The intensity quantification value, impact range and corresponding observation time of each environmental interference source are correlated to form a single interference source quantification record; The single-source quantitative records of all environmental interference sources are integrated and classified according to the type of interference source. Records of interference sources of the same type are arranged in chronological order. Calculate the comprehensive impact index of different types of interference sources at the same observation time. The comprehensive impact index is the weighted sum of the intensity quantification values ​​of each individual interference source. The weight values ​​are determined according to the sensitivity of the interference source to the settlement observation. By combining the quantitative records of individual interference sources, the classification and integration results, and the comprehensive impact index, an environmental interference source intensity dataset is formed, which includes the influence range and intensity of each interference source at the observation time.

6. The settlement monitoring method based on data traceability according to claim 1, characterized in that, The process of coupling the characteristics of the settlement data tracing link with the corresponding settlement amount to determine the reliability index of the settlement data tracing at each monitoring point includes: The effective calibration parameters for each observation period are extracted from the features of the settlement data traceability link. The effective calibration parameters are compared with the nominal accuracy range of the equipment to calculate the calibration deviation rate. When the calibration deviation rate is within the preset deviation range, a positive calibration weight is assigned; when the calibration deviation rate exceeds the preset deviation range, a negative calibration weight is assigned. Extract the environmental interference source intensity dataset for each observation period, analyze the correlation between the intensity of each interference source and the settlement amount, calculate the interference correlation degree, and assign a first interference influence weight when the interference correlation degree is greater than the first preset correlation degree threshold; assign a second interference influence weight when the interference correlation degree is less than or equal to the first preset correlation degree threshold and greater than the second preset correlation degree threshold; assign a third interference influence weight when the interference correlation degree is less than or equal to the second preset correlation degree threshold, wherein the first interference influence weight value is greater than the second interference influence weight value, and the second interference influence weight value is greater than the third interference influence weight value. Extract data correction operation records for each observation period, and count the number of correction operations and the cumulative amount of correction parameter adjustment values. When the number of correction operations and the cumulative amount of correction parameter adjustment values ​​are both within the preset standard correction range, a positive correction weight is assigned; when the number of correction operations or the cumulative amount of correction parameter adjustment values ​​exceed the preset standard correction range, a negative correction weight is assigned. The initial reliability score for each observation period is obtained by weighting and summing the positive calibration weight, negative calibration weight, first interference impact weight, second interference impact weight, third interference impact weight, positive correction weight, and negative correction weight. By combining the dynamic change characteristics of the traceability elements in the settlement data traceability link, the change gradient of the initial reliability score in adjacent periods is calculated. When the change gradient is less than the preset gradient threshold, the initial reliability score is smoothed. When the change gradient is greater than or equal to the preset gradient threshold, it is marked as a reliability mutation point and the cause of the mutation is recorded. Based on the distribution of strata bearing capacity in the geological base information, the initial reliability score is adjusted for geological adaptability. In areas where the strata bearing capacity value is greater than the first bearing capacity threshold, if the settlement data reliability score is less than the first reliability threshold, the initial reliability score is increased by a preset percentage. In areas where the strata bearing capacity value is less than the second bearing capacity threshold, if the settlement data reliability score is greater than the second reliability threshold, the initial reliability score is decreased by a preset percentage. The reliability scores, after smoothing, mutation point marking, and geological adaptability adjustment, are normalized to obtain a traceability reliability index for settlement data at each monitoring point with a uniform value range. This traceability reliability index is used to characterize the degree of interference to the settlement data in the generation process.

7. The settlement monitoring method based on data traceability according to claim 6, characterized in that, The dynamic change characteristics of the traceability elements in the sedimentation data traceability link are combined to calculate the change gradient of the initial reliability score in adjacent periods. When the change gradient is less than the preset gradient threshold, the initial reliability score is smoothed. When the gradient change is greater than or equal to a preset gradient threshold, it is marked as a reliability mutation point and the cause of the mutation is recorded, including: The dynamic change characteristics of the traceability elements are obtained from the change rate of equipment calibration parameters, the fluctuation amplitude of environmental interference source intensity, and the change in the frequency of data correction operations in adjacent observation periods. Calculate the difference in initial reliability scores between adjacent observation periods, and then perform a ratio operation between this difference and the time interval between adjacent periods to obtain the gradient of change in the initial reliability scores. The changing gradient is compared with a preset gradient threshold. If the changing gradient is less than the preset gradient threshold, the initial reliability score is smoothed. If the changing gradient is greater than or equal to the preset gradient threshold, a reliability mutation is identified in the observation period. When performing smoothing, the initial reliability score is processed using the moving average method, and the size of the sliding window is adaptively determined according to the total number of observation periods based on a preset ratio. When a reliability mutation is identified, the timestamp of the mutation observation period and the corresponding initial reliability score are extracted. Based on the dynamic change characteristics of traceability elements before and after the mutation observation period, the main factors causing reliability mutations are determined: if the change rate of equipment calibration parameters exceeds the preset change threshold, the main factor is equipment calibration change; if the fluctuation amplitude of environmental interference source intensity exceeds the preset fluctuation threshold, the main factor is environmental interference change; if the change in data correction operation frequency is zero and the fluctuation amplitude of other factors is lower than the preset fluctuation threshold, the main factor is data processing anomaly. The timestamps of the mutation observation period, the initial reliability scores, and the main factors identified are associated and stored to generate mutation point analysis data. The reliability scores after smoothing and after abrupt changes are standardized in data format so that the reliability scores for all observation periods have a unified data structure.

8. The settlement monitoring method based on data traceability according to claim 1, characterized in that, The process involves selecting settlement data that meets the threshold requirements of the traceability reliability index based on the aforementioned traceability reliability index, and combining this with the spatial distribution characteristics of the monitoring points to generate a dynamic settlement trend model for the settlement monitoring area, including: Settlement data with traceability reliability indices higher than the traceability reliability index threshold in all observation periods at each monitoring point are selected as settlement data that meet the traceability reliability index threshold requirements. Extract the location coordinates of each monitoring point and the settlement amount of the corresponding observation period from the settlement data that meets the threshold requirements of the traceability reliability index, and establish a three-dimensional dataset; Based on the three-dimensional dataset, the spatial distribution characteristics of the monitoring points are analyzed, and the settlement monitoring area is divided into multiple spatial grid units according to the location coordinates. The average settlement of all monitoring points in each grid unit during the same observation period is calculated, and a grid unit settlement matrix is ​​generated. Time series analysis is performed on the settlement matrix of the grid cells to extract the settlement change trend of each grid cell in the continuous observation period, and to determine the grid cells whose settlement growth rate is greater than a preset growth rate threshold and the grid cells whose settlement growth rate is less than or equal to a preset stability threshold. Based on the settlement variation trend of each grid unit, a spatial interpolation model is constructed to predict the settlement in areas where no monitoring points are set up, and generate spatial distribution data of settlement covering the entire monitoring area. By overlaying spatial distribution data of settlement from different observation periods over time, we can analyze the expansion direction of the settlement area and the positional changes of the contraction area, and extract the movement trajectory of the settlement boundary. By combining the movement trajectory of the settlement boundary and the settlement rate of the grid cells, a dynamic settlement trend model is constructed. The dynamic settlement trend model includes the spatial distribution of settlement at each time point, the movement speed of the settlement boundary, and the migration path of the settlement core area. The dynamic settlement trend model was validated by comparing the subsequent periodic settlement predicted by the dynamic settlement trend model with the actual observed settlement data that met the threshold requirements of the traceability reliability index. The spatial interpolation parameters of the dynamic settlement trend model were adjusted so that the deviation between the output of the dynamic settlement trend model and the actual data was within the preset deviation range.

9. The settlement monitoring method based on data traceability according to claim 8, characterized in that, Based on the settlement variation trend of each grid cell, a spatial interpolation model is constructed to predict settlement in areas without monitoring points, generating spatial distribution data of settlement covering the entire monitoring area, including: The spatial interpolation model was determined to be a kriging interpolation model constrained by geological basement information. This kriging interpolation model was configured to correct the interpolation results by incorporating the distribution of formation bearing capacity. The settlement amount of the known monitoring points in the settlement matrix of the grid unit is used as input data, and the geological information code corresponding to each known monitoring point is used as covariate, and then input into the spatial interpolation model. Configure the semivariogram parameters of the spatial interpolation model, wherein the type of the semivariogram is determined based on the spatial correlation characteristics of the settlement in the monitoring area; Run the spatial interpolation model to predict the settlement of grid cells without monitoring points, and obtain the predicted settlement and corresponding prediction error of each unmonitored grid cell. The predicted settlement is fused with the settlement at known monitoring points to generate preliminary spatial distribution data of settlement covering all grid cells in the entire monitoring area. Based on the distribution map of soil and rock compressibility parameters in the geological base information, the spatial distribution data of the preliminary settlement is corrected: for grid cells whose soil and rock compressibility level belongs to the preset high compressibility level range, the predicted settlement is increased by the first preset adjustment coefficient; for grid cells whose soil and rock compressibility level belongs to the preset low compressibility level range, the predicted settlement is decreased by the second preset adjustment coefficient. Based on the prediction error, the confidence level of the corrected spatial distribution data of settlement is evaluated, and the regions with prediction errors less than the first preset error threshold are divided into high confidence regions, the regions with prediction errors between the first preset error threshold and the second preset error threshold are divided into medium confidence regions, and the regions with prediction errors greater than the second preset error threshold are divided into low confidence regions. The confidence assessment results are integrated with the corrected settlement data to generate the final spatial distribution data of settlement covering the entire monitoring area. This spatial distribution data of settlement includes the settlement value of each grid cell and its corresponding confidence level.

10. A settlement monitoring system based on data traceability, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the data-based settlement monitoring method according to any one of claims 1 to 8 by executing the machine-executable instructions.

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