Information acquisition and data processing system and method based on total station
By calculating the entropy and uncertainty of temperature and vibration information in the total station system, automatically adjusting the measurement frequency, and combining quasi-stabilized adjustment and Kalman filtering algorithms, the problem of data uncertainty in the total station system under complex environments is solved, achieving efficient and accurate track geometry state monitoring and early warning.
Patent Information
- Application Number
- CN202511369615.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-24
- Publication Date
- 2025-11-14
AI Technical Summary
Existing total station systems struggle to adapt to environmental changes in complex environments, leading to increased data uncertainty and impacting the accuracy of deformation analysis and the timeliness of early warnings.
By collecting temperature and vibration monitoring values, calculating joint information entropy and comprehensive uncertainty, automatically switching measurement frequencies, and using quasi-steady adjustment algorithm and Kalman filter algorithm for data fusion and early warning, a structured dataset and track deformation analysis report are generated.
It enables efficient and accurate data acquisition and early warning in complex environments, suppresses environmental interference, obtains high-precision three-dimensional deformation, and realizes intelligent monitoring of track geometry.
Smart Images
Figure CN120947593A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of sensor data fusion technology, and in particular to an information acquisition and data processing system and method based on a total station. Background Technology
[0002] Total stations, as high-precision photoelectric measuring instruments, have been widely used in engineering surveying, deformation monitoring, and track geometry detection. Traditional total station systems primarily acquire spatial coordinate data through angle and distance measurements, and then combine this with data processing methods to achieve deformation analysis and early warning of target objects. In recent years, with the advancement of sensor technology, multi-source data fusion methods have gradually been introduced into measurement systems. For example, by integrating environmental sensors such as temperature and vibration sensors, the integrity and reliability of monitoring data can be improved.
[0003] Existing technologies still have limitations in dealing with dynamic disturbances in complex environments. Especially in long-term continuous monitoring scenarios, environmental factors (such as temperature changes and mechanical vibrations) can introduce significant measurement noise, leading to increased data uncertainty. Traditional methods often rely on fixed-frequency data acquisition and independent data processing workflows, making it difficult to dynamically adjust to environmental changes. Existing technologies typically use preset sampling frequencies, which cannot optimize measurement strategies based on real-time environmental disturbances. This may result in data overload during high-noise periods or missing information during low-disturbance periods, thus affecting the accuracy of deformation analysis and the timeliness of early warnings. Summary of the Invention
[0004] In view of the aforementioned existing problems, the present invention is proposed.
[0005] Therefore, this invention provides an information acquisition and data processing method based on a total station, which solves the problem of insufficient monitoring accuracy and early warning timeliness caused by fixed sampling frequency and inefficient data fusion in complex environments.
[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:
[0007] In a first aspect, the present invention provides an information acquisition and data processing method based on a total station, comprising,
[0008] The system collects temperature and vibration monitoring values, calculates the joint information entropy and comprehensive uncertainty to obtain a quantitative index of environmental dynamic disturbance, and automatically switches between low-frequency and high-frequency measurement modes to generate a structured data set.
[0009] After aligning the total station measurement data and environmental monitoring data in the structured dataset with timestamps, they are fused according to the preset initial values of attention weights to generate a spatiotemporal fusion dataset.
[0010] A quasi-stabilized adjustment algorithm is used to process the spatiotemporal fusion dataset to obtain the three-dimensional coordinate deformation of the observation points;
[0011] The three-dimensional coordinate deformation of the observation point is input into the Kalman filter algorithm to calculate the dynamic warning threshold. When the orbital geometric state vector exceeds the dynamic warning threshold, the graded warning mechanism is triggered to generate orbital geometric parameters and graded warning status indicators.
[0012] The track geometry parameters and graded early warning status indicators are correlated with the dynamic sampling frequency recording curve in a multi-dimensional manner to generate a track deformation analysis report.
[0013] As a preferred embodiment of the information acquisition and data processing method based on a total station described in this invention, the following steps are taken: After acquiring temperature monitoring values and vibration monitoring values, the joint information entropy and comprehensive uncertainty are calculated to obtain a quantitative index of environmental dynamic disturbance.
[0014] Temperature and vibration monitoring values are collected, and the joint information entropy is calculated using the discrete probability distribution method.
[0015] The joint information entropy is compared with the preset environmental stability benchmark value to obtain the environmental disturbance degree index, and the comprehensive uncertainty is calculated by the linear weighting method.
[0016] Based on the joint information entropy and comprehensive uncertainty, a sliding window standardization process and dynamic weight adjustment are used to calculate the quantitative index of environmental dynamic disturbance.
[0017] As a preferred embodiment of the information acquisition and data processing method based on a total station according to the present invention, the specific steps for generating the structured data set are as follows:
[0018] When the environmental dynamic disturbance quantification index exceeds the preset mode switching threshold, it automatically switches from low frequency measurement mode to high frequency measurement mode and obtains the measurement mode mark.
[0019] Temperature monitoring values, vibration monitoring values, and measurement modes are labeled, encapsulated and stored according to standard data formats, and a structured data set is generated.
[0020] As a preferred embodiment of the information acquisition and data processing method based on a total station described in this invention, the specific steps for generating the spatiotemporal fusion dataset are as follows:
[0021] The total station measurement data and environmental monitoring data in the structured dataset are precisely matched and aligned according to millisecond-level timestamps, and records with mismatched timestamps and incomplete data are automatically removed to generate measurement data and environmental data.
[0022] Based on the preset initial values of attention weights, feature weight coefficients are assigned to the measurement data and environmental data;
[0023] The measurement data and environmental data after the assigned feature weight coefficients are weighted and fused to generate a spatiotemporally aligned multimodal feature dataset.
[0024] By combining the spatiotemporally aligned multimodal feature dataset with the original total station measurement data, a spatiotemporally fused dataset is generated.
[0025] As a preferred embodiment of the information acquisition and data processing method based on a total station according to the present invention, the specific steps for obtaining the three-dimensional coordinate deformation of the observation point are as follows:
[0026] The spatiotemporal fusion dataset is standardized to generate a standardized spatiotemporal feature matrix.
[0027] The observation points for horizontal angular fluctuations, vertical angular fluctuations, and vibration parameters were selected from the standardized spatiotemporal feature matrix and verified by temperature gradient, and then used as quasi-stable reference points.
[0028] Based on the horizontal angle eigenvalues, vertical angle eigenvalues, and slant distance eigenvalues of the standardized spatiotemporal feature matrix, an observation residual optimization model is constructed with the quasi-stable reference point as the observation residual constraint condition.
[0029] A weighted iterative adjustment algorithm is used to dynamically adjust the weights and optimize the residuals of the observation values in the residual optimization model, so as to obtain the optimized residuals of the observation values. Combined with the residual constraints, the three-dimensional coordinate deformation of the observation points is obtained.
[0030] As a preferred embodiment of the information acquisition and data processing method based on a total station described in this invention, the specific steps for inputting the three-dimensional coordinate deformation of the observation point into a Kalman filter algorithm to calculate the dynamic early warning threshold are as follows.
[0031] The three-dimensional coordinate deformation of the observation point is input into the Kalman filter algorithm for state estimation, generating the orbital geometric state vector;
[0032] Multi-period sliding window statistical analysis is performed on the orbital geometric state vector to calculate the long-term mean and standard deviation, generate orbital geometric baseline characteristic statistics, and integrate real-time environmental monitoring data for dynamic compensation to generate dynamic early warning thresholds.
[0033] As a preferred embodiment of the information acquisition and data processing method based on a total station according to the present invention, the specific steps for generating track geometric parameters and graded early warning status indicators are as follows:
[0034] The orbital geometric state vector is compared with the dynamic early warning threshold in multiple dimensions, and a graded early warning mechanism is triggered according to the exceedance situation;
[0035] Feedback calibration is performed on the track geometry state vector that triggers the warning. The impact of abnormal data is reduced by reweighting. The covariance matrix of the calibrated track geometry state vector is recalculated to generate track geometry parameters and graded warning status indicators.
[0036] As a preferred embodiment of the information acquisition and data processing method based on a total station described in this invention, the specific steps for performing multi-dimensional correlation analysis between track geometric parameters and graded early warning status indicators and dynamic sampling frequency recording curves are as follows.
[0037] The orbital geometry parameters and graded early warning status indicators are precisely matched with the dynamic sampling frequency recording curve to ensure that the time series of all data are completely synchronized.
[0038] The system establishes a spatial correlation between environmental monitoring data and orbital geometric parameters according to the collection cycle, automatically removes records with mismatched time or missing data, and generates a correlated dataset.
[0039] Based on the associated dataset, a correlation analysis is performed on the orbital geometric parameters and environmental parameters to generate an orbital deformation-environment coupling feature matrix.
[0040] As a preferred embodiment of the information acquisition and data processing method based on total station described in this invention, the track deformation analysis report is generated by locating key anomalies based on graded early warning status indicators, extracting complete event data chains from the time-space dual-dimensional aligned associated dataset, and converting the track deformation-environment coupling feature matrix into an interactive visualization chart.
[0041] Secondly, this invention provides an information acquisition and data processing system based on a total station, comprising:
[0042] The environmental monitoring module is used to collect temperature and vibration monitoring values, calculate the joint information entropy and comprehensive uncertainty to obtain the quantitative index of environmental dynamic disturbance, and automatically switch between low-frequency measurement mode and high-frequency measurement mode to generate a structured data set.
[0043] The data fusion module is used to align the total station measurement data and environmental monitoring data in the structured dataset with timestamps, and then fuse them according to the preset initial value of attention weight to generate a spatiotemporal fusion dataset.
[0044] The deformation calculation module is used to process the spatiotemporal fusion dataset using a quasi-stabilized adjustment algorithm to obtain the three-dimensional coordinate deformation of the observation points.
[0045] The dynamic early warning module is used to input the three-dimensional coordinate deformation of the observation point into the Kalman filter algorithm to calculate the dynamic early warning threshold. When the track geometry state vector exceeds the dynamic early warning threshold, the hierarchical early warning mechanism is triggered to generate track geometry parameters and hierarchical early warning status indicators.
[0046] The correlation analysis module is used to perform multi-dimensional correlation analysis between track geometric parameters and graded early warning status indicators and dynamic sampling frequency recording curves to generate a track deformation analysis report.
[0047] The beneficial effects of this invention are as follows: by calculating the joint information entropy and comprehensive uncertainty of temperature and vibration, the measurement frequency is adaptively switched, ensuring efficient and accurate data acquisition; furthermore, through spatiotemporal data fusion and quasi-stabilization adjustment algorithm, environmental interference is effectively suppressed, and high-precision three-dimensional deformation is obtained; finally, Kalman filtering is used to calculate dynamic thresholds and trigger graded early warnings, and a comprehensive report is generated through multi-dimensional correlation analysis, thereby realizing intelligent and highly reliable monitoring of the entire chain of track geometry from perception, processing to early warning. Attached Figure Description
[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0049] Figure 1 This is a flowchart of a total station-based information acquisition and data processing method.
[0050] Figure 2 This is a schematic diagram of an information acquisition and data processing system based on a total station.
[0051] Figure 3 A flowchart for calculating quantitative indicators of environmental dynamic disturbances.
[0052] Figure 4 Flowchart for generating spatiotemporal fusion dataset. Detailed Implementation
[0053] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0054] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for information acquisition and data processing based on a total station, including the following steps:
[0055] S1. Collect temperature and vibration monitoring values, calculate the joint information entropy and comprehensive uncertainty to obtain the environmental dynamic disturbance quantitative index, and automatically switch between low-frequency measurement mode and high-frequency measurement mode to generate a structured data set.
[0056] S1.1 Collect temperature and vibration monitoring values, and calculate the joint information entropy using the discrete probability distribution method;
[0057] It should be noted that after collecting temperature and vibration monitoring values, the numerical ranges of the temperature and vibration monitoring values are divided into several discrete intervals. The frequency of occurrence of the temperature and vibration monitoring values falling into each temperature-vibration interval combination is statistically analyzed, and the frequency of occurrence within the interval combination is used as an estimate of the joint probability distribution.
[0058] Based on the joint probability distribution, the probability of all possible combinations of temperature and vibration monitoring values is summed using the information entropy formula. The expression is:
[0059] ;
[0060] in, Represents the comprehensive information entropy. This represents a discrete random variable representing temperature monitoring values. Represents a discrete random variable of vibration monitoring values. The joint probability of temperature and vibration, This indicates the temperature monitoring value. This indicates the vibration monitoring value.
[0061] S1.2. Compare the joint information entropy with the preset environmental stability benchmark value to obtain the environmental disturbance degree index, and calculate the comprehensive uncertainty through the linear weighting method.
[0062] It should be noted that the calculated joint information entropy is compared with a preset environmental stability benchmark value to obtain an environmental disturbance level index. The environmental stability benchmark value is the statistical average of the joint information entropy of temperature monitoring values and vibration monitoring values. Based on a linear weighting coefficient, the differences between the two are combined according to preset weights to obtain the comprehensive uncertainty value. The calculation result of the comprehensive uncertainty provides a basis for subsequently generating quantitative indicators of environmental dynamic disturbances.
[0063] It should also be noted that the preset environmental stability benchmark value is obtained by collecting a large number of temperature monitoring values and vibration monitoring values samples, calculating the joint information entropy of the temperature monitoring values and vibration monitoring values samples, and setting the statistical average value of the joint information entropy as the environmental stability benchmark value.
[0064] S1.3. Based on the joint information entropy and comprehensive uncertainty, the dynamic environmental disturbance quantification index is calculated through sliding window standardization and dynamic weight adjustment.
[0065] It should be noted that, based on the joint information entropy and comprehensive uncertainty, the numerical sequences of the joint information entropy and comprehensive uncertainty are standardized within a fixed-length sliding time window to eliminate the influence of dimensions. Dynamic weights are assigned to the joint information entropy at each time point within the sliding window according to the magnitude of the comprehensive uncertainty. When the comprehensive uncertainty is large, it indicates a strong environmental disturbance at that time point, and the joint information entropy at that time point will be assigned a high weight; when the comprehensive uncertainty is small, the weight of the joint information entropy is low. The standardized joint information entropy values within the sliding window are combined with the corresponding dynamic weights to finally calculate the quantitative index of environmental dynamic disturbance.
[0066] The quantitative index of environmental dynamic disturbance is calculated, and its expression is:
[0067] ;
[0068] in, It is a quantitative indicator of dynamic environmental disturbances. At a certain point in time The calculated joint information entropy, It is a preset environmental stability benchmark value. It is a point in time. The overall uncertainty It is a point in time. Dynamic weights, It is the length of the sliding window.
[0069] S1.4 When the environmental dynamic disturbance quantification index exceeds the preset mode switching threshold, automatically switch from low frequency measurement mode to high frequency measurement mode and obtain the measurement mode mark.
[0070] It should be noted that the environmental dynamic disturbance quantification index is compared with the preset mode switching threshold in real time. When the value of the environmental dynamic disturbance quantification index is greater than the mode switching threshold, the measurement mode switching operation is immediately triggered, switching from the current low-frequency measurement mode to the high-frequency measurement mode. This mode switching event is recorded and a corresponding measurement mode marker is generated. The measurement mode marker is used to identify that the subsequently collected temperature monitoring values and vibration monitoring values are obtained in the high-frequency measurement mode.
[0071] It should also be noted that the range of the preset mode switching threshold is determined based on historical data analysis. It is usually set in the high percentile range of the historical statistical values of the environmental dynamic disturbance quantitative index. For example, the example value is the 95th percentile of the historical values of the environmental dynamic disturbance quantitative index. The specific value needs to be determined comprehensively based on the environmental disturbance level and measurement accuracy requirements in the actual monitoring scenario.
[0072] S1.5. Mark the temperature monitoring values, vibration monitoring values and measurement modes, encapsulate and store them according to the standard data format, and generate a structured data set.
[0073] It should be noted that the collected temperature monitoring values, vibration monitoring values, and corresponding measurement mode labels are organized according to a standard data format. The temperature monitoring values, vibration monitoring values, and measurement mode labels are collectively input into a structured data record, ultimately forming a structured dataset.
[0074] It should also be noted that standard data format refers to the organization and representation of data such as temperature monitoring values, vibration monitoring values, and measurement mode markings in accordance with unified specifications during data storage and transmission.
[0075] The data originates from temperature and vibration monitoring data acquired through external data sources. All data includes millisecond-level timestamps to ensure time-series consistency. All collected data is packaged according to standard data formats to ensure consistent data structure and facilitate subsequent data processing and analysis.
[0076] The standard data format specifies the field names, data types, and order of temperature monitoring values, vibration monitoring values, measurement mode markers, and millisecond-level timestamps.
[0077] S2. After aligning the total station measurement data and environmental monitoring data in the structured dataset with timestamps, the data is fused according to the preset initial attention weight values to generate a spatiotemporal fusion dataset.
[0078] S2.1. Accurately match and align the total station measurement data and environmental monitoring data in the structured dataset according to millisecond-level timestamps, automatically remove records with mismatched timestamps and incomplete data, and generate measurement data and environmental data;
[0079] It should be noted that the structured dataset is traversed to extract millisecond-level timestamps from the total station measurement data and environmental monitoring data. Records with identical millisecond-level timestamps in the total station measurement data and environmental monitoring data are paired and joined. Records for which no matching millisecond-level timestamp can be found, and records with matching millisecond-level timestamps but missing fields in temperature monitoring values, vibration monitoring values, or total station measurement data, are discarded. After precise matching and discarding, the remaining one-to-one matching total station measurement data records and environmental monitoring data records are used to generate measurement data and environmental data, respectively.
[0080] S2.2. Assign feature weight coefficients to the measurement data and environmental data according to the preset initial value of attention weight;
[0081] It should be noted that, based on the preset initial attention weight values, corresponding feature weight coefficients are assigned to the measurement data and the environmental data respectively. The initial attention weight values consist of two parts: one part is the initial attention weight value for the measurement data, and the other part is the initial attention weight value for the environmental data. The feature weight coefficients for the measurement data are directly assigned to the measurement data, and the feature weight coefficients for the environmental data are directly assigned to the environmental data.
[0082] It should also be noted that the initial values of the preset attention weights are set based on the importance of the data and the priority of the features, and are usually determined through historical data analysis. For measurement data and environmental data, different initial weights can be assigned according to their degree of impact on the overall system. Ensure that important data features have high weights, while less important features are assigned low weights.
[0083] S2.3. Perform weighted fusion calculation on the measurement data and environmental data of the assigned feature weight coefficients to generate a spatiotemporally aligned multimodal feature dataset;
[0084] It should be noted that when combining the feature weight coefficients of the measurement data with the numerical values of the measurement data, the numerical value of each measurement data point is associated with its corresponding feature weight coefficient to obtain weighted measurement data. Similarly, the feature weight coefficients of the environmental data are also associated with the numerical values of the environmental data to generate weighted environmental data values. The combined measurement data results and environmental data results are then merged to form a new fused data record. All fused data records are aggregated in chronological order to ultimately generate a spatiotemporally aligned multimodal feature dataset.
[0085] S2.4 Combine the spatiotemporally aligned multimodal feature dataset with the original total station measurement data to generate a spatiotemporally fused dataset.
[0086] It should be noted that each fused data record in the spatiotemporally aligned multimodal feature dataset is associated with a record in the original total station measurement data that has the same millisecond-level timestamp. The fused data record in the spatiotemporally aligned multimodal feature dataset and the corresponding original total station measurement data record together form a new, more complete data entry. All data entries generated in this way are arranged and aggregated in chronological order, ultimately forming a spatiotemporally fused dataset that simultaneously contains fused features and original measurement values.
[0087] S3. Use the quasi-stabilized adjustment algorithm to process the spatiotemporal fusion dataset and obtain the three-dimensional coordinate deformation of the observation points;
[0088] S3.1 Perform data standardization processing on the spatiotemporal fusion dataset to generate a standardized spatiotemporal feature matrix;
[0089] It should be noted that when processing the spatiotemporal fusion dataset, the mean and standard deviation of each numerical feature column in the dataset are calculated. Based on the mean and standard deviation of each feature column, the corresponding feature values of each record in the spatiotemporal fusion dataset are standardized to ensure that all feature values are on a uniform scale. The standardized spatiotemporal fusion dataset is arranged in chronological order, with rows corresponding to time points and columns corresponding to standardized features, ultimately forming a standardized spatiotemporal feature matrix.
[0090] S3.2 Select observation points for horizontal angular fluctuation, vertical angular fluctuation and vibration parameters from the standardized spatiotemporal feature matrix, and use them as quasi-stable reference points after verification by temperature gradient;
[0091] It should be noted that, by using a standardized spatiotemporal feature matrix, observation points in the matrix whose horizontal angular fluctuation values, vertical angular fluctuation values, and vibration parameter values are all below a preset stability threshold are identified. Temperature gradient verification is then performed on candidate observation points. The temperature gradient method is used to check whether the horizontal angular fluctuations, vertical angular fluctuations, and vibration parameters remain stable across different temperature ranges, and whether the fluctuation range consistently remains below the stability threshold. Observation points that pass the temperature gradient verification are selected as quasi-stable reference points.
[0092] It should also be noted that the range of the stability threshold is determined based on the statistics of long-term historical monitoring data. It is usually taken as the low percentile of the standardized horizontal angle fluctuation, vertical angle fluctuation and vibration parameter historical data. The specific value is determined comprehensively based on the actual monitoring accuracy requirements and the distribution characteristics of historical data.
[0093] S3.3 Based on the horizontal angle eigenvalues, vertical angle eigenvalues, and slant distance eigenvalues of the standardized spatiotemporal feature matrix, construct an observation residual optimization model with the quasi-stable reference point as the observation residual constraint condition;
[0094] It should be noted that the observation equations are established using the horizontal angle eigenvalues, vertical angle eigenvalues, and slant distance eigenvalues from the standardized spatiotemporal characteristic matrix. The three-dimensional coordinates of the selected quasi-stable reference point are incorporated into the observation equations as fixed constraints. A mathematical model is constructed with the objective of minimizing the difference between the observed values at all observation points and the theoretical values calculated through the observation equations; this mathematical model is the observation residual optimization model.
[0095] It should also be noted that the mathematical model is constructed based on measurement adjustment theory, using the horizontal angle eigenvalues, vertical angle eigenvalues, and slant distance eigenvalues in the standardized spatiotemporal characteristic matrix as observed values to establish the error equation. The coordinates of the quasi-stabilized reference point are used as fixed parameters, and the coordinate correction is forcibly set to zero, constituting a strong constraint condition. The error equation is combined with the quasi-stabilized constraint condition to form an observation residual optimization model.
[0096] S3.4. The weighted iterative adjustment algorithm is used to dynamically adjust the weights and optimize the residuals of the observation values to obtain the optimized residuals of the observation values. Combined with the constraints of the residuals of the observation values, the three-dimensional coordinate deformation of the observation points is obtained.
[0097] It should be noted that a weighted iterative adjustment algorithm is used to process the observation residual optimization model. In each iteration, the weights of the observations are dynamically adjusted according to the magnitude of the residuals, with observations having larger residuals receiving smaller weights. Through iterative calculation, the observation residuals are continuously optimized, causing the sum of squared residuals to gradually decrease until convergence. The optimized observation residuals, under the constraint that the coordinates of the quasi-stable reference point remain unchanged, are used to calculate the coordinate corrections for each observation point, thereby obtaining the three-dimensional coordinate deformation of the observation points.
[0098] S4. Input the three-dimensional coordinate deformation of the observation point into the Kalman filter algorithm to calculate the dynamic warning threshold. When the track geometry state vector exceeds the dynamic warning threshold, trigger the graded warning mechanism to generate track geometry parameters and graded warning status indicators.
[0099] S4.1 Input the three-dimensional coordinate deformation of the observation point into the Kalman filter algorithm for state estimation and generate the orbital geometric state vector;
[0100] It should be noted that the three-dimensional coordinate shape variables of the observation points are used as the input to the Kalman filter algorithm. The Kalman filter algorithm is based on the state transition equation and the observation equation, and performs recursive calculations through two steps: prediction and update. The predicted state value for the current moment is estimated based on the orbital geometric state vector from the previous moment, and then corrected using the three-dimensional coordinate shape variables of the observation points at the current moment, resulting in the orbital geometric state vector.
[0101] S4.2 Perform multi-period sliding window statistical analysis on the track geometric state vector, calculate the long-term mean and standard deviation, generate track geometric baseline characteristic statistics, and integrate real-time environmental monitoring data for dynamic compensation to generate dynamic early warning thresholds.
[0102] It should be noted that within a sliding time window encompassing multiple historical periods, statistical analysis is performed on the historical numerical sequence of the track geometry state vector. The values of the track geometry state vector at different time points are extracted, and analysis is conducted separately for each dimension. The mean, standard deviation, and other statistical measures of the horizontal angle, vertical angle, and slant distance parameters in the track geometry state are calculated to understand the overall trend and fluctuation range of the track geometry state. The long-term mean and long-term standard deviation together constitute the baseline characteristic statistics of the track geometry. The baseline characteristic statistics of the track geometry are dynamically compensated and corrected by combining real-time environmental monitoring data. The dynamically compensated and corrected baseline characteristic statistics of the track geometry are used to determine the specific numerical range of the dynamic early warning threshold.
[0103] It should also be noted that the range of dynamic warning threshold values is determined based on the statistical values of the orbital geometric reference characteristics after dynamic compensation correction, with the long-term mean as the center and the range being formed by offsetting the long-term mean by several times the long-term standard deviation.
[0104] S4.3. Compare the track geometry state vector with the dynamic early warning threshold in multiple dimensions, and trigger a graded early warning mechanism based on the exceedance situation;
[0105] It should be noted that the values of each component of the orbital geometric state vector are compared one by one with the corresponding limits of the dynamic warning threshold. Different levels of tiered warning mechanisms are triggered based on the degree to which the values of the orbital geometric state vector components exceed the dynamic warning threshold limits; a level-two warning is triggered when the values of the orbital geometric state vector components exceed the level-two warning limit. The tiered warning mechanism outputs the corresponding warning level identifier according to preset rules.
[0106] It should also be noted that the orbital geometry state vector typically includes multiple warning levels, commonly including Level 1, Level 2, and Level 3. A Level 1 warning usually indicates a minor anomaly, a Level 2 warning indicates a moderate anomaly, and a Level 3 warning indicates a severe anomaly. When the value of a component of the orbital geometry state vector exceeds a certain warning threshold, the corresponding level of warning is triggered.
[0107] The dynamic early warning threshold is determined by statistical analysis of long-term historical data. The mean and standard deviation of the orbital geometric state vector are calculated, and then dynamic compensation and correction are performed in combination with real-time monitoring data to determine the specific numerical range of the threshold.
[0108] The range of dynamic warning threshold values is usually determined by offsetting the sample value by twice the standard deviation based on the long-term mean of the orbital geometric state vector.
[0109] S4.4 Perform feedback calibration on the track geometric state vector that triggers the warning, reduce the impact of abnormal data through the reweighting method, recalculate the covariance matrix of the calibrated track geometric state vector, and generate track geometric parameters and graded warning status indicators.
[0110] It should be noted that feedback calibration is performed on the track geometric state vector that triggers the warning. A reweighting method is used to assign new weights to each component of the track geometric state vector, with components of higher anomaly severity receiving lower weights. The track geometric state vector is then calibrated based on these new weights to obtain the calibrated track geometric state vector. The covariance matrix is recalculated using the calibrated track geometric state vector. Finally, the calibrated track geometric state vector is combined with the recalculated covariance matrix to generate track geometric parameters and a graded warning status identifier.
[0111] S5. Perform multi-dimensional correlation analysis between the track geometric parameters and graded early warning status indicators and the dynamic sampling frequency recording curve to generate a track deformation analysis report.
[0112] S5.1. Accurately match the orbital geometry parameters and graded early warning status indicators with the dynamic sampling frequency recording curve using timestamps to ensure that the time series of all data are completely synchronized;
[0113] It should be noted that millisecond-level timestamp fields are extracted from the orbital geometry parameters, graded warning status indicators, and dynamic sampling frequency recording curves. Records with completely identical timestamp values in the orbital geometry parameters, graded warning status indicators, and dynamic sampling frequency recording curves are paired and concatenated. Records for which no matching timestamp can be found are discarded to ensure that the final retained orbital geometry parameters, graded warning status indicators, and dynamic sampling frequency records are completely synchronized in the time series.
[0114] It should also be noted that the dynamic sampling frequency recording curve records the process of automatically switching from low-frequency measurement mode to high-frequency measurement mode when the environmental dynamic disturbance quantification index exceeds the preset mode switching threshold, and the change of environmental dynamic disturbance quantification index value during the high-frequency measurement mode. The horizontal axis of the curve is the millisecond-level timestamp, and the vertical axis is the environmental dynamic disturbance quantification index value and the measurement mode mark.
[0115] S5.2 Establish spatial correlation between environmental monitoring data and orbital geometric parameters according to the collection cycle, automatically remove records with mismatched time or missing data, and generate a correlated dataset;
[0116] It should be noted that, based on the shared spatial location identifiers of environmental monitoring data and orbital geometric parameters, environmental monitoring data records and orbital geometric parameter records at the same spatial location are associated. The timestamp field in the associated record pairs is checked to ensure consistency with the environmental monitoring data collection period, and it is verified that neither the environmental monitoring data nor the orbital geometric parameters contain missing fields. Record pairs with mismatched timestamps or missing data are removed. Record pairs with matching timestamps and complete data are retained, and these pairs are organized by spatial location to generate an associated dataset.
[0117] S5.3. Based on the associated dataset, perform correlation analysis on the orbital geometric parameters and environmental parameters to generate the orbital deformation-environment coupling feature matrix;
[0118] It should be noted that, through correlation and regression analysis between orbital geometric parameters and environmental parameters in the associated dataset, their mutual influence is revealed. The orbital geometric parameter values, environmental parameter values, and corresponding correlation index values for each spatial location point in the associated dataset are combined. The combined results of all spatial location points are arranged in a unified format, with rows corresponding to spatial location points and columns corresponding to orbital deformation characteristics, environmental characteristics, and correlation characteristics, ultimately forming an orbital deformation-environment coupling feature matrix.
[0119] S5.4. Based on the hierarchical early warning status identifier, locate key anomalies, extract complete event data chains from the time-space dual-dimensional aligned associated dataset, and convert the track deformation-environment coupling feature matrix into an interactive visualization chart to generate a track deformation analysis report.
[0120] It should be noted that key anomalies with high warning levels are identified using tiered early warning status identifiers. Based on the timestamps and spatial location information of these key anomalies, all relevant data records from the same spatial location within a time period before and after the anomaly occurred are extracted from the temporally and spatially aligned correlation dataset, forming a complete event data chain. The data corresponding to the event data chain in the orbital deformation-environment coupling feature matrix is converted into interactive visualization charts. The event data chain and visualization charts are integrated to generate an orbital deformation analysis report that includes anomaly event descriptions, causal analysis, and visualizations.
[0121] This embodiment also provides an information acquisition and data processing system based on a total station, including:
[0122] The environmental monitoring module is used to collect temperature and vibration monitoring values, calculate the joint information entropy and comprehensive uncertainty to obtain the quantitative index of environmental dynamic disturbance, and automatically switch between low-frequency measurement mode and high-frequency measurement mode to generate a structured data set.
[0123] The data fusion module is used to align the total station measurement data and environmental monitoring data in the structured dataset with timestamps, and then fuse them according to the preset initial value of attention weight to generate a spatiotemporal fusion dataset.
[0124] The deformation calculation module is used to process the spatiotemporal fusion dataset using a quasi-stabilized adjustment algorithm to obtain the three-dimensional coordinate deformation of the observation points.
[0125] The dynamic early warning module is used to input the three-dimensional coordinate deformation of the observation point into the Kalman filter algorithm to calculate the dynamic early warning threshold. When the track geometry state vector exceeds the dynamic early warning threshold, the hierarchical early warning mechanism is triggered to generate track geometry parameters and hierarchical early warning status indicators.
[0126] The correlation analysis module is used to perform multi-dimensional correlation analysis between track geometric parameters and graded early warning status indicators and dynamic sampling frequency recording curves to generate a track deformation analysis report.
[0127] This embodiment also provides a computer device applicable to the information acquisition and data processing method based on a total station, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the information acquisition and data processing method based on a total station as proposed in the above embodiment.
[0128] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.
[0129] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the information acquisition and data processing method based on a total station as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.
[0130] In summary, this invention provides a total station-based information acquisition and processing method for track monitoring. By calculating the joint information entropy and comprehensive uncertainty of temperature and vibration, it adaptively switches the measurement frequency, ensuring efficient and accurate data acquisition. Furthermore, through spatiotemporal data fusion and a quasi-stabilization adjustment algorithm, it effectively suppresses environmental interference and obtains high-precision three-dimensional deformation data. Finally, it uses Kalman filtering to calculate dynamic thresholds and trigger graded early warnings, and then generates a comprehensive report through multi-dimensional correlation analysis. This achieves intelligent and highly reliable monitoring of the entire track geometry from perception and processing to early warning.
[0131] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A method for information acquisition and data processing based on a total station, characterized in that: include, The system collects temperature and vibration monitoring values, calculates the joint information entropy and comprehensive uncertainty to obtain a quantitative index of environmental dynamic disturbance, and automatically switches between low-frequency and high-frequency measurement modes to generate a structured dataset. After aligning the total station measurement data and environmental monitoring data in the structured dataset with timestamps, they are fused according to the preset initial values of attention weights to generate a spatiotemporal fusion dataset. A quasi-stabilized adjustment algorithm is used to process the spatiotemporal fusion dataset to obtain the three-dimensional coordinate deformation of the observation points; The three-dimensional coordinate deformation of the observation point is input into the Kalman filter algorithm to calculate the dynamic warning threshold. When the orbital geometric state vector exceeds the dynamic warning threshold, the graded warning mechanism is triggered to generate orbital geometric parameters and graded warning status indicators. The track geometry parameters and graded early warning status indicators are correlated with the dynamic sampling frequency recording curve in a multi-dimensional manner to generate a track deformation analysis report.
2. The information acquisition and data processing method based on a total station as described in claim 1, characterized in that: The collected temperature and vibration monitoring values are used to calculate the joint information entropy and comprehensive uncertainty to obtain a quantitative index of environmental dynamic disturbance. The specific steps are as follows: Temperature and vibration monitoring values are collected, and the joint information entropy is calculated using the discrete probability distribution method. The joint information entropy is compared with the preset environmental stability benchmark value to obtain the environmental disturbance degree index, and the comprehensive uncertainty is calculated by the linear weighting method. Based on the joint information entropy and comprehensive uncertainty, a sliding window standardization process and dynamic weight adjustment are used to calculate the quantitative index of environmental dynamic disturbance.
3. The information acquisition and data processing method based on a total station as described in claim 2, characterized in that: The specific steps for generating the structured data set are as follows: When the environmental dynamic disturbance quantification index exceeds the preset mode switching threshold, it automatically switches from low frequency measurement mode to high frequency measurement mode and obtains the measurement mode mark. Temperature monitoring values, vibration monitoring values, and measurement modes are labeled, encapsulated and stored according to standard data formats, and a structured data set is generated.
4. The information acquisition and data processing method based on a total station as described in claim 3, characterized in that: The specific steps for generating the spatiotemporal fusion dataset are as follows: The total station measurement data and environmental monitoring data in the structured dataset are precisely matched and aligned according to millisecond-level timestamps, and records with mismatched timestamps and incomplete data are automatically removed to generate measurement data and environmental data. Based on the preset initial values of attention weights, feature weight coefficients are assigned to the measurement data and environmental data; The measurement data and environmental data after the assigned feature weight coefficients are weighted and fused to generate a spatiotemporally aligned multimodal feature dataset. By combining the spatiotemporally aligned multimodal feature dataset with the original total station measurement data, a spatiotemporally fused dataset is generated.
5. The information acquisition and data processing method based on a total station as described in claim 4, characterized in that: The specific steps for obtaining the three-dimensional coordinate deformation of the observation point are as follows: The spatiotemporal fusion dataset is standardized to generate a standardized spatiotemporal feature matrix. The observation points for horizontal angular fluctuations, vertical angular fluctuations, and vibration parameters were selected from the standardized spatiotemporal feature matrix and verified by temperature gradient, and then used as quasi-stable reference points. Based on the horizontal angle eigenvalues, vertical angle eigenvalues, and slant distance eigenvalues of the standardized spatiotemporal feature matrix, an observation residual optimization model is constructed with the quasi-stable reference point as the observation residual constraint condition. A weighted iterative adjustment algorithm is used to dynamically adjust the weights and optimize the residuals of the observation values in the residual optimization model, so as to obtain the optimized residuals of the observation values. Combined with the residual constraints, the three-dimensional coordinate deformation of the observation points is obtained.
6. The information acquisition and data processing method based on a total station as described in claim 5, characterized in that: The specific steps for inputting the three-dimensional coordinate deformation of the observation point into the Kalman filter algorithm to calculate the dynamic early warning threshold are as follows: The three-dimensional coordinate deformation of the observation point is input into the Kalman filter algorithm for state estimation, generating the orbital geometric state vector; Multi-period sliding window statistical analysis is performed on the orbital geometric state vector to calculate the long-term mean and standard deviation, generate orbital geometric baseline characteristic statistics, and integrate real-time environmental monitoring data for dynamic compensation to generate dynamic early warning thresholds.
7. The information acquisition and data processing method based on a total station as described in claim 6, characterized in that: The specific steps for generating orbital geometric parameters and graded early warning status indicators are as follows. The orbital geometric state vector is compared with the dynamic early warning threshold in multiple dimensions, and a graded early warning mechanism is triggered according to the exceedance situation; Feedback calibration is performed on the track geometry state vector that triggers the warning. The impact of abnormal data is reduced by reweighting. The covariance matrix of the calibrated track geometry state vector is recalculated to generate track geometry parameters and graded warning status indicators.
8. The information acquisition and data processing method based on a total station as described in claim 7, characterized in that: The specific steps for performing multi-dimensional correlation analysis between the orbital geometric parameters and graded early warning status indicators and the dynamic sampling frequency recording curve are as follows. The orbital geometry parameters and graded early warning status indicators are precisely matched with the dynamic sampling frequency recording curve to ensure that the time series of all data are completely synchronized. The system establishes a spatial correlation between environmental monitoring data and orbital geometric parameters according to the collection cycle, automatically removes records with mismatched time or missing data, and generates a correlated dataset. Based on the associated dataset, a correlation analysis is performed on the orbital geometric parameters and environmental parameters to generate an orbital deformation-environment coupling feature matrix.
9. The information acquisition and data processing method based on a total station as described in claim 1, characterized in that: The track deformation analysis report is generated by locating key anomalies based on graded early warning status indicators, extracting complete event data chains from a temporally and spatially aligned associated dataset, and converting the track deformation-environment coupling feature matrix into an interactive visualization chart.
10. A total station-based information acquisition and data processing system, based on the total station-based information acquisition and data processing method according to any one of claims 1 to 9, characterized in that: include, The environmental monitoring module is used to collect temperature and vibration monitoring values, calculate the joint information entropy and comprehensive uncertainty to obtain the quantitative index of environmental dynamic disturbance, and automatically switch between low-frequency measurement mode and high-frequency measurement mode to generate a structured data set. The data fusion module is used to align the total station measurement data and environmental monitoring data in the structured dataset with timestamps, and then fuse them according to the preset initial value of attention weight to generate a spatiotemporal fusion dataset. The deformation calculation module is used to process the spatiotemporal fusion dataset using a quasi-stabilized adjustment algorithm to obtain the three-dimensional coordinate deformation of the observation points. The dynamic early warning module is used to input the three-dimensional coordinate deformation of the observation point into the Kalman filter algorithm to calculate the dynamic early warning threshold. When the track geometry state vector exceeds the dynamic early warning threshold, the hierarchical early warning mechanism is triggered to generate track geometry parameters and hierarchical early warning status indicators. The correlation analysis module is used to perform multi-dimensional correlation analysis between track geometric parameters and graded early warning status indicators and dynamic sampling frequency recording curves to generate a track deformation analysis report.