BIM shield monitoring system based on fuzzy data query and neighbor algorithm

Abstract

The invention discloses a BIM shield monitoring system based on fuzzy data query and a nearest neighbor algorithm. According to the technical scheme, the BIM shield monitoring system is characterized by comprising a data acquisition module, a data preprocessing module, a fuzzy data query module, a nearest neighbor algorithm module, a path prediction module and a BIM platform integration module; the data acquisition module is used for acquiring tunneling data, including tunneling speed, position and direction, of the shield tunneling machine in real time; the data preprocessing module is used for preprocessing the collected tunneling data, including de-noising processing and abnormal value processing, so as to improve the accuracy and reliability of the data; according to the system, through combination of modular design and an advanced algorithm, comprehensive monitoring of the tunneling process of the shield tunneling machine is realized. All the modules are in close cooperation, accurate and efficient monitoring and decision support is provided for shield construction from data acquisition and preprocessing to fuzzy query, prediction and visual display, and the safety, efficiency and intelligent level of shield engineering are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of shield monitoring systems, and in particular to a BIM shield monitoring system based on fuzzy data query and nearest neighbor algorithm. Background Art

[0002] With the rapid advancement of modern urbanization, the scale of underground projects, especially subways and tunnels, has increased year by year. As the core equipment for tunnel construction, real-time monitoring and efficient management of the tunneling process of shield machines are particularly important. However, in actual applications, the tunneling environment of shield machines is complex, and the data collection process is often affected by noise, outliers, and measurement errors. This places high demands on the accuracy and reliability of tunneling data. Traditional monitoring systems mainly rely on simple real-time data collection and display functions, which makes it difficult to effectively handle the uncertainty and ambiguity in the data, resulting in poor early warning capabilities and decision support during the tunneling process. In addition, under complex underground construction conditions, the tunneling path and direction may deviate from the designed trajectory. If not discovered and corrected in time, it may cause construction progress delays or project quality risks.

[0003] In recent years, some research has begun to introduce fuzzy data processing methods and intelligent algorithms, such as nearest neighbor algorithms and time series analysis, to enhance the intelligence of shield machine monitoring systems. However, a systematic solution that deeply integrates fuzzy data query, intelligent algorithms, and BIM information management platforms is still lacking to achieve comprehensive, real-time monitoring and early warning of the shield tunneling process. To this end, this paper provides a BIM shield monitoring system based on fuzzy data query and nearest neighbor algorithms. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present invention provides a BIM shield monitoring system based on fuzzy data query and nearest neighbor algorithm, which solves the problems mentioned in the above background technology.

[0005] The above technical objectives of the present invention are achieved through the following technical solutions:

[0006] The BIM shield monitoring system based on fuzzy data query and nearest neighbor algorithm includes: data acquisition module, data preprocessing module, fuzzy data query module, nearest neighbor algorithm module, path prediction, and BIM platform integration module;

[0007] The data acquisition module is used to collect tunneling data of the shield machine in real time, including tunneling speed, position and direction;

[0008] The data preprocessing module is used to preprocess the collected excavation data, including noise removal and outlier processing, to improve the accuracy and reliability of the data;

[0009] The fuzzy data query module is used to perform data query based on fuzzy data points and calculate the similarity between data points to deal with uncertainty and fuzziness in the data and improve the accuracy and robustness of data processing;

[0010] The nearest neighbor algorithm module is used to find the data point most similar to the current excavation position using the nearest neighbor algorithm based on the fuzzy data query results, and evaluate the accuracy of the excavation progress and direction by comparing the nearest neighbor data with the information of the current excavation position;

[0011] The path prediction module is used to predict the excavation progress and direction based on the analysis results of the nearest neighbor algorithm module and generate an early warning signal when the progress lags behind or the direction deviates from the expected direction;

[0012] The BIM platform integration module is used to display the excavation trajectory, current position and real-time data of the shield work point through a three-dimensional map in the BIM information management platform, and based on the results of the path prediction module, display the early warning information of the excavation progress and direction in a striking manner in the three-dimensional map.

[0013] Preferably, the data acquisition module includes multiple sensors for collecting multi-dimensional data of the shield machine, including but not limited to:

[0014] Speed sensor, used to monitor the tunneling speed of the shield machine;

[0015] Position sensor, used to determine the real-time position of the shield machine;

[0016] Azimuth sensor, used to monitor the direction of shield machine excavation.

[0017] The data preprocessing module further includes:

[0018] A denoising algorithm unit, used to reduce noise in sensor data through filtering technology;

[0019] The outlier processing unit is used to detect and correct outliers in the data to ensure the validity of the data.

[0020] Preferably, the fuzzy data query module calculates the similarity of data points by the following steps:

[0021] Define fuzzy membership function based on excavation data;

[0022] Calculate the similarity weight of data points using membership function;

[0023] Perform fuzzy queries on data points based on similarity weights;

[0024] The evaluation process of the nearest neighbor algorithm module includes:

[0025] Construct a multidimensional feature space based on the similarity weights output by the fuzzy data query module;

[0026] Use the nearest neighbor algorithm to find the historical data point closest to the current excavation position;

[0027] Compare the current excavation data with the nearest neighbor data to assess the deviation of excavation progress and direction;

[0028] The path prediction module uses the time series analysis method, combined with the output of the nearest neighbor algorithm module, to generate a prediction result of the tunneling path and issue an early warning signal based on the prediction result;

[0029] The BIM platform integration module includes:

[0030] 3D visualization unit, used to display the tunneling trajectory and current position of the shield machine on a 3D map;

[0031] An early warning display unit is used to identify areas that deviate from the expected path or lag behind in progress based on the results of the path prediction module;

[0032] Data interaction unit, used by project managers to query real-time data and early warning information through the platform;

[0033] The BIM platform integration module can connect data with other project management systems to achieve information sharing and collaborative management.

[0034] Preferably, the fuzzy membership function formula is as follows:

[0035]

[0036] Where μ(x) is the membership degree of data point x; c is the center value; σ is the fuzzy width.

[0037] The preferred formula for calculating the similarity between the data points is as follows:

[0038]

[0039] Where S(x,y) is the similarity between data points x and y; μ i is the membership degree of the i-th feature.

[0040] Preferably, the nearest neighbor distance calculation formula is as follows:

[0041]

[0042] Where d(x,y) is the Euclidean distance between data points x and y; i 、y i is the value of the i-th feature.

[0043] Preferably, the time series prediction formula of the path prediction module is as follows:

[0044]

[0045] Where, is the predicted value of the next time step; t is the actual value at the current time; α is the smoothing coefficient.

[0046] Preferably, the filtering formula in the noise algorithm unit is as follows:

[0047]

[0048] Where x[t] is the original data at time t and w is the window size.

[0049] Preferably, the outlier correction formula in the outlier processing unit is as follows:

[0050]

[0051] Where, is the data mean; σ is the data standard deviation; k is the correction threshold.

[0052] The shield monitoring method based on fuzzy data query and nearest neighbor algorithm includes the following steps:

[0053] S1: Collect real-time tunneling data of the shield machine;

[0054] S2: De-noising and outlier processing of the collected data;

[0055] S3: Calculate the similarity of data points based on fuzzy query;

[0056] S4: Apply the nearest neighbor algorithm to find the data point most similar to the current excavation position;

[0057] S5: Predict the tunneling path and direction based on the nearest neighbor results;

[0058] S6: The tunneling trajectory, current position and warning information of the shield machine are displayed in real time on the BIM platform.

[0059] In summary, the present invention mainly has the following beneficial effects:

[0060] Through a modular design and advanced algorithms, the system enables comprehensive monitoring of the shield machine's tunneling process. The modules work closely together, from data acquisition and preprocessing to fuzzy query, prediction, and visualization, providing precise and efficient monitoring and decision-making support for shield construction, enhancing the safety, efficiency, and intelligence of shield engineering projects.

[0061] The data acquisition module realizes the real-time acquisition of multi-dimensional data during the tunneling process of the shield machine, including core parameters such as tunneling speed, position and direction; it provides comprehensive data support, laying the foundation for subsequent processing and analysis, and ensuring the real-time and integrity of the data; the denoising algorithm unit in the data preprocessing module effectively reduces the noise in the data through the filtering formula, ensures the smoothness and stability of the input data, improves the reliability of the data, and provides high-quality data for subsequent fuzzy query and analysis; the outlier processing unit detects and corrects abnormal data points in the acquisition process, eliminates their interference with the analysis results, ensures the validity of the data, and prevents system misjudgment due to outliers; the fuzzy data query module processes the uncertainty and ambiguity in the data, weights the data through the fuzzy membership function, calculates the similarity of data points based on fuzzy query, improves the robustness of data analysis, reduces the impact of noise and deviation on data query, and improves the accuracy of data query, especially when the data is noisy or partially missing, to ensure the stability of the algorithm;

[0062] The nearest neighbor algorithm module uses the nearest neighbor algorithm to find the closest tunneling status from historical data, providing a reference benchmark for the current tunneling progress and direction. By comparing the current tunneling position with the nearest neighbor point, it accurately assesses tunneling progress and direction deviation, improving the system's ability to detect anomalies during shield tunneling and reducing engineering risks caused by tunneling errors.

[0063] The path prediction module combines time series prediction formulas to perform trend analysis on tunneling paths and directions, providing early warning of the shield machine's tunneling progress and direction. This effectively reduces the risk of deviation from the designed trajectory during tunneling, and provides timely alerts to managers through early warning signals, thereby improving the safety and efficiency of shield construction.

[0064] In the BIM platform integration module, the 3D visualization unit intuitively presents the tunneling trajectory, current position and real-time data of the shield machine on a 3D map. By dynamically displaying the tunneling process, managers can grasp the construction progress in real time. The early warning display unit identifies areas of tunneling path deviation or progress lag based on the results of the path prediction module, reminding managers in a striking manner, helping the construction team to quickly locate problem areas and adjust tunneling strategies in a timely manner. The data interaction unit provides interactive functions for real-time data query and early warning information to achieve efficient decision support; it supports data docking with other management systems to achieve information sharing and collaborative management. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0067] refer to Figure 1 ,BIM shield monitoring system based on fuzzy data query and nearest neighbor algorithm, including: data acquisition module, data preprocessing module, fuzzy data query module, nearest neighbor algorithm module, path prediction, BIM platform integration module;

[0068] The data acquisition module is used to collect tunneling data of the shield machine in real time, including tunneling speed, position and direction;

[0069] The data preprocessing module is used to preprocess the collected excavation data, including noise removal and outlier processing, to improve the accuracy and reliability of the data;

[0070] The fuzzy data query module is used to perform data query based on fuzzy data points and calculate the similarity between data points to deal with uncertainty and fuzziness in the data and improve the accuracy and robustness of data processing;

[0071] The nearest neighbor algorithm module is used to find the data point most similar to the current excavation position using the nearest neighbor algorithm based on the fuzzy data query results, and evaluate the accuracy of the excavation progress and direction by comparing the nearest neighbor data with the information of the current excavation position;

[0072] The path prediction module is used to predict the excavation progress and direction based on the analysis results of the nearest neighbor algorithm module and generate an early warning signal when the progress lags behind or the direction deviates from the expected direction;

[0073] The BIM platform integration module is used to display the tunneling trajectory, current location and real-time data of the shield work point through a three-dimensional map in the BIM information management platform, and based on the results of the path prediction module, to display early warning information of the tunneling progress and direction in a striking manner on the three-dimensional map; the data acquisition module includes multiple sensors for collecting multi-dimensional data of the shield machine, including but not limited to:

[0074] Speed sensor, used to monitor the tunneling speed of the shield machine;

[0075] Position sensor, used to determine the real-time position of the shield machine;

[0076] Azimuth sensor, used to monitor the direction of shield machine excavation.

[0077] The data preprocessing module further includes:

[0078] A denoising algorithm unit, used to reduce noise in sensor data through filtering technology;

[0079] The outlier processing unit is used to detect and correct outliers in the data to ensure the validity of the data.

[0080] The fuzzy data query module calculates the similarity of data points through the following steps:

[0081] Define fuzzy membership function based on excavation data;

[0082] Calculate the similarity weight of data points using membership function;

[0083] Perform fuzzy queries on data points based on similarity weights;

[0084] The evaluation process of the nearest neighbor algorithm module includes:

[0085] Construct a multidimensional feature space based on the similarity weights output by the fuzzy data query module;

[0086] Use the nearest neighbor algorithm to find the historical data point closest to the current excavation position;

[0087] Compare the current excavation data with the nearest neighbor data to assess the deviation of excavation progress and direction;

[0088] The path prediction module uses the time series analysis method, combined with the output of the nearest neighbor algorithm module, to generate a prediction result of the tunneling path and issue an early warning signal based on the prediction result;

[0089] The BIM platform integration module includes:

[0090] 3D visualization unit, used to display the tunneling trajectory and current position of the shield machine on a 3D map;

[0091] An early warning display unit is used to identify areas that deviate from the expected path or lag behind in progress based on the results of the path prediction module;

[0092] Data interaction unit, used by project managers to query real-time data and early warning information through the platform;

[0093] The BIM platform integration module can connect data with other project management systems to achieve information sharing and collaborative management.

[0094] The fuzzy membership function formula is as follows:

[0095]

[0096] Where μ(x) is the membership degree of data point x; c is the center value; σ is the fuzzy width;

[0097] The formula for calculating the similarity between the data points is as follows:

[0098]

[0099] Where S(x,y) is the similarity between data points x and y; μ i is the membership degree of the i-th feature;

[0100] The nearest neighbor distance calculation formula is as follows:

[0101]

[0102] Where d(x,y) is the Euclidean distance between data points x and y; i 、y i is the value of the i-th feature;

[0103] The time series prediction formula of the path prediction module is as follows:

[0104]

[0105] Where, is the predicted value of the next time step; t is the actual value at the current time; α is the smoothing coefficient;

[0106] The filtering formula in the noise algorithm unit is as follows

[0107]

[0108] Where x[t] is the original data at time t and w is the window size;

[0109] The outlier correction formula in the outlier processing unit is as follows:

[0110]

[0111] Where, is the data mean; σ is the data standard deviation; k is the correction threshold.

[0112] The shield monitoring method based on fuzzy data query and nearest neighbor algorithm includes the following steps:

[0113] S1: Collect real-time tunneling data of the shield machine;

[0114] S2: De-noising and outlier processing of the collected data;

[0115] S3: Calculate the similarity of data points based on fuzzy query;

[0116] S4: Apply the nearest neighbor algorithm to find the data point most similar to the current excavation position;

[0117] S5: Predict the tunneling path and direction based on the nearest neighbor results;

[0118] S6: The tunneling trajectory, current position and warning information of the shield machine are displayed in real time on the BIM platform.

[0119] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. The BIM shield monitoring system based on fuzzy data query and nearest neighbor algorithm is characterized by: include: Data acquisition module, data preprocessing module, fuzzy data query module, nearest neighbor algorithm module, path prediction, and BIM platform integration module; The data acquisition module is used to collect tunneling data of the shield machine in real time, including tunneling speed, position and direction; The data preprocessing module is used to preprocess the collected excavation data, including noise removal and outlier processing, to improve the accuracy and reliability of the data; The fuzzy data query module is used to perform data query based on fuzzy data points and calculate the similarity between data points to deal with uncertainty and fuzziness in the data and improve the accuracy and robustness of data processing; The nearest neighbor algorithm module is used to find the data point most similar to the current excavation position using the nearest neighbor algorithm based on the fuzzy data query results, and evaluate the accuracy of the excavation progress and direction by comparing the nearest neighbor data with the information of the current excavation position; The path prediction module is used to predict the excavation progress and direction based on the analysis results of the nearest neighbor algorithm module and generate an early warning signal when the progress lags behind or the direction deviates from the expected direction; The BIM platform integration module is used to display the excavation trajectory, current position and real-time data of the shield work point through a three-dimensional map in the BIM information management platform, and based on the results of the path prediction module, display the early warning information of the excavation progress and direction in a striking manner in the three-dimensional map.

2. The BIM shield monitoring system based on fuzzy data query and nearest neighbor algorithm according to claim 1 is characterized in that: The data acquisition module includes multiple sensors for collecting multi-dimensional data of the shield machine, including but not limited to: Speed sensor, used to monitor the tunneling speed of the shield machine; Position sensor, used to determine the real-time position of the shield machine; Azimuth sensor, used to monitor the direction of shield machine excavation. The data preprocessing module further includes: A denoising algorithm unit, used to reduce noise in sensor data through filtering technology; The outlier processing unit is used to detect and correct outliers in the data to ensure the validity of the data.

3. The BIM shield monitoring system based on fuzzy data query and nearest neighbor algorithm according to claim 2 is characterized in that: The fuzzy data query module calculates the similarity of data points through the following steps: Define fuzzy membership function based on excavation data; Calculate the similarity weight of data points using membership function; Perform fuzzy queries on data points based on similarity weights; The evaluation process of the nearest neighbor algorithm module includes: Construct a multidimensional feature space based on the similarity weights output by the fuzzy data query module; Use the nearest neighbor algorithm to find the historical data point closest to the current excavation position; Compare the current excavation data with the nearest neighbor data to assess the deviation of excavation progress and direction; The path prediction module uses the time series analysis method, combined with the output of the nearest neighbor algorithm module, to generate a prediction result of the tunneling path and issue an early warning signal based on the prediction result; The BIM platform integration module includes: 3D visualization unit, used to display the tunneling trajectory and current position of the shield machine on a 3D map; An early warning display unit is used to identify areas that deviate from the expected path or lag behind in progress based on the results of the path prediction module; Data interaction unit, used by project managers to query real-time data and early warning information through the platform; The BIM platform integration module can connect data with other project management systems to achieve information sharing and collaborative management.

4. The BIM shield monitoring system based on fuzzy data query and nearest neighbor algorithm according to claim 3 is characterized in that: The fuzzy membership function formula is as follows: Where μ(x) is the membership degree of data point x; c is the center value; σ is the fuzzy width.

5. The BIM shield monitoring system based on fuzzy data query and nearest neighbor algorithm according to claim 4 is characterized in that: The formula for calculating the similarity between the data points is as follows: Where S(x,y) is the similarity between data points x and y; μ i is the membership degree of the i-th feature.

6. The BIM shield monitoring system based on fuzzy data query and nearest neighbor algorithm according to claim 5 is characterized in that: The nearest neighbor distance calculation formula is as follows: Where d(x,y) is the Euclidean distance between data points x and y; i 、y i is the value of the i-th feature.

7. The BIM shield monitoring system based on fuzzy data query and nearest neighbor algorithm according to claim 6 is characterized in that: The time series prediction formula of the path prediction module is as follows: Where, is the predicted value of the next time step; t is the actual value at the current time; α is the smoothing coefficient.

8. The BIM shield monitoring system based on fuzzy data query and nearest neighbor algorithm according to claim 7 is characterized in that: The filtering formula in the noise algorithm unit is as follows Where x[t] is the original data at time t and w is the window size.

9. The BIM shield monitoring system based on fuzzy data query and nearest neighbor algorithm according to claim 8 is characterized in that: The outlier correction formula in the outlier processing unit is as follows: Where, is the data mean; σ is the data standard deviation; k is the correction threshold.

10. A shield monitoring method based on fuzzy data query and nearest neighbor algorithm, according to a BIM shield monitoring system based on fuzzy data query and nearest neighbor algorithm according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1: Collect real-time tunneling data of the shield machine; S2: De-noising and outlier processing of the collected data; S3: Calculate the similarity of data points based on fuzzy query; S4: Apply the nearest neighbor algorithm to find the data point most similar to the current excavation position; S5: Predict the tunneling path and direction based on the nearest neighbor results; S6: The tunneling trajectory, current position and warning information of the shield machine are displayed in real time on the BIM platform.

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