Real-time monitoring system for bridge construction process

Abstract

The invention relates to the field of bridge construction, and discloses a real-time monitoring system for a bridge construction process, and the system comprises a high-precision sensing module which is used for monitoring the data of all aspects in the bridge construction process in real time; the communication module is used for transmitting the data acquired by the sensor to a monitoring center in real time; the intelligent analysis module is used for preprocessing, storing, comprehensively analyzing and predicting the collected data and generating early warning information; the visual monitoring module is used for displaying an analysis result and real-time data to construction management personnel; the high-precision sensing module, the communication module, the intelligent analysis module and the visual monitoring module are in communication connection through a network. According to the invention, the real-time condition of the construction site can be comprehensively and accurately reflected through the high-precision sensing module, and compared with a traditional manual patrol and regular detection method, potential safety hazards can be quickly found and pre-warned, so that the safety and reliability of the construction process are ensured.

Description

Technical Field

[0001] The invention relates to the field of bridge construction, and in particular to a real-time monitoring system for a bridge construction process. Background Art

[0002] Bridge construction is a key component of modern transportation networks. Especially in complex terrain and harsh environmental conditions, the bridge construction process faces many challenges. Traditional bridge construction monitoring methods mainly rely on manual inspections and regular inspections, which are not only inefficient but also difficult to detect potential safety hazards in a timely manner.

[0003] With the development of information technology, more and more high-tech means are being applied to bridge construction monitoring, such as sensors, drones, remote monitoring systems, etc. After a lot of searches, it is found that the existing monitoring systems focus more on a single factor, and the data processing and analysis capabilities are limited, which still cannot meet the high requirements of real-time and comprehensiveness for large and complex bridge construction. Summary of the invention

[0004] In order to make up for the above deficiencies, the present invention provides a real-time monitoring system for the bridge construction process, aiming to improve the problem of "the traditional monitoring method is inefficient" mentioned in the prior art.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a real-time monitoring system for bridge construction process, comprising:

[0006] High-precision sensor module: used to monitor all aspects of bridge construction data in real time;

[0007] Communication module: used to transmit the data collected by the sensor to the monitoring center in real time;

[0008] Intelligent analysis module: used to pre-process, store, comprehensively analyze and predict the collected data, and generate early warning information;

[0009] Visual monitoring module: used to display analysis results and real-time data to construction management personnel;

[0010] The high-precision sensing module, communication module, intelligent analysis module and visual monitoring module are connected through network communication.

[0011] As a further description of the above technical solution:

[0012] The high-precision sensing module includes a sensing unit and a calibration compensation unit. The sensing unit is used to detect various required data, including but not limited to stress and strain sensors, vibration acceleration sensors, displacement sensors, temperature sensors, humidity sensors, vibration intensity sensors, water level sensors, meteorological sensors and ambient light sensors. The calibration compensation unit mainly automatically corrects the performance drift caused by temperature and humidity factors by dynamically adjusting the sensor offset and gain value parameters, ensuring that high-precision measurement is maintained throughout the entire construction period.

[0013] As a further description of the above technical solution:

[0014] The communication module includes a wireless or wired communication unit, a signal enhancement unit, and an adaptive communication unit. The wireless or wired communication unit can select the most suitable communication method according to the actual application scenario and environmental conditions, thereby enhancing the flexibility and scope of application of the system, including but not limited to Wi-Fi, Bluetooth, LoRa, Ethernet, and optical fiber. The signal enhancement unit is used to improve the signal transmission distance and penetration capability, including but not limited to a built-in or external antenna and a signal amplifier. The adaptive communication unit is used to automatically select the optimal communication path and protocol according to the current communication environment and data transmission requirements.

[0015] As a further description of the above technical solution:

[0016] The intelligent analysis module includes a data preprocessing unit, a data storage unit, a data analysis unit, an edge computing unit and an adaptive data processing unit. The data preprocessing unit is used to clean, convert the format and detect anomalies of the data collected by the sensor. The data storage unit is used to store the processed data in the cloud or a local database. The data analysis unit mainly uses technical means to perform in-depth mining and analysis on the stored data. The edge computing unit is used to perform real-time data processing and analysis on the edge computing device near the sensor to reduce the burden on the monitoring center and improve the response speed of the system. The adaptive data processing unit automatically selects the optimal data processing algorithm mainly according to the current construction status and environmental conditions.

[0017] As a further description of the above technical solution:

[0018] The visual monitoring module includes a real-time data display unit, a data query unit, a prediction and analysis module, an early warning and alarm module, a construction progress management unit, an uncertainty management unit, a data export unit and a virtual technology unit. The real-time data display unit displays the data collected by various sensors in real time in the form of charts and dashboards. The data query unit supports construction management personnel to query historical data and conduct comparative analysis. The prediction and analysis module predicts future construction status and potential risks through comprehensive analysis of historical data and current data. The early warning and alarm module is used to generate early warning information and notify construction management personnel in various ways. The construction progress management unit displays the completion status of the construction progress in real time by integrating the construction schedule and actual data. The uncertainty management unit displays real-time data caused by weather, humidity, water level and other uncertain factors at the construction site. The data export unit exports the monitoring data into multiple formats to facilitate further analysis and recording. The virtual technology unit displays the real-time monitoring data to construction management personnel in the form of a three-dimensional model.

[0019] As a further description of the above technical solution:

[0020] The sensing unit uses a Kalman filter to fuse different types of sensor data to improve measurement accuracy. The reference formula is as follows:

[0021]

[0022] K k =P k|k-1 H T (HP k|k-1 H T +R) -1

[0023] in, is the current state estimate, is the predicted state, K k is the Kalman gain, z k is the observation value, H is the observation matrix, P k |k-1 is the prediction covariance matrix, R is the observation noise covariance matrix;

[0024] A polynomial regression model can also be used for more accurate temperature and humidity calibration. The reference formula is as follows:

[0025] T comp =α0+α1(T-T0)+α2(T-T0) 2 +α3(H-H0)

[0026] Among them, T compis the temperature compensation value, α0, α1, α2, α3 are regression coefficients, T is the actual temperature, T0 is the reference temperature, H is the actual humidity, and H0 is the reference humidity.

[0027] As a further description of the above technical solution:

[0028] The data preprocessing unit is used for signal denoising and feature extraction, and the following formula can be used:

[0029]

[0030] Among them, W x (a, b) are wavelet coefficients, a is the scale parameter, b is the translation parameter, x(t) is the original signal, and ψ is the wavelet basis function;

[0031] The data storage unit uses discrete cosine transform to perform data compression, and the following formula can be used:

[0032]

[0033] Among them, Y k is the compressed data, x n is the original data, N is the data length, and k is the frequency index.

[0034] As a further description of the above technical solution:

[0035] The virtual technology unit combines augmented reality and virtual reality technologies to display real-time monitoring data to construction management personnel in the form of a three-dimensional model, including but not limited to mapping sensor data into a three-dimensional model, viewing the model in real time at the construction site through AR equipment, or conducting detailed inspection and analysis in a virtual environment through VR equipment.

[0036] As a further description of the above technical solution:

[0037] The early warning alarm module includes a multi-level early warning mechanism and an intelligent notification system. The multi-level early warning mechanism can generate different levels of early warning information according to the degree of abnormality and potential risk level of the data. The intelligent notification system can notify the construction management personnel of the early warning information in real time through methods including but not limited to SMS, email, and APP push, to ensure that timely measures are taken to ensure construction safety.

[0038] As a further description of the above technical solution:

[0039] The system also includes a safety monitoring module for real-time monitoring of the safety status of the construction site, including but not limited to the use of safety equipment, behavior monitoring of personnel and the operating status of construction machinery.

[0040] The present invention has the following beneficial effects:

[0041] 1. In the present invention, the high-precision sensing module can comprehensively and accurately reflect the real-time status of the construction site. Compared with the traditional manual inspection and regular detection methods, it can quickly discover and warn of potential safety hazards, ensuring the safety and reliability of the construction process. The early warning alarm module uses the three sigma rule to set the threshold and performs multi-level early warning through a hierarchical decision tree, which can more accurately judge the risk level and take timely countermeasures.

[0042] 2. In the present invention, the combined use of the communication module and the intelligent analysis module ensures efficient transmission and processing of data from collection to analysis, ensures the stability and real-time performance of data transmission, and the data preprocessing unit, the data storage unit and the data analysis unit can clean, compress, analyze and predict the collected data in real time, thus solving the problem of limited data processing capacity of the existing monitoring system and providing comprehensive and real-time data support for the construction of large and complex bridges.

[0043] 3. In the present invention, the data collected by various sensors are displayed in real time through the visual monitoring module, and the construction process is intuitively displayed in the form of a three-dimensional model through the virtual technology unit, helping managers to quickly understand the conditions of the construction site. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a complete framework schematic diagram of a real-time monitoring system for a bridge construction process in the present invention;

[0045] Figure 2 A detailed schematic diagram of a high-precision sensor module of a real-time monitoring system for a bridge construction process in the present invention;

[0046] Figure 3 It is a detailed framework diagram of a communication module of a real-time monitoring system for a bridge construction process in the present invention;

[0047] Figure 4 It is a detailed framework diagram of an intelligent analysis module of a real-time monitoring system for a bridge construction process in the present invention;

[0048] Figure 5 It is a detailed framework diagram of a visual monitoring module of a real-time monitoring system for a bridge construction process in the present invention.

[0049] Legend:

[0050] 1. High-precision sensing module; 101. Sensing unit; 102. Calibration and compensation unit; 2. Communication module; 201. Wireless and wired communication unit; 202. Signal enhancement unit; 203. Adaptive communication unit; 3. Intelligent analysis module; 301. Data preprocessing unit; 302. Data storage unit; 303. Data analysis unit; 304. Edge computing unit; 305. Adaptive data processing unit; 4. Visual monitoring module; 401. Real-time data display unit; 402. Data query unit; 403. Prediction and analysis module; 404. Early warning and alarm module; 405. Construction progress management unit; 406. Uncertainty management unit; 407. Data export unit; 408. Virtual technology unit; 5. Safety monitoring module. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Example

[0053] Reference Figure 1-5 , this embodiment provides a real-time monitoring system for a bridge construction process, including:

[0054] High-precision sensing module 1, communication module 2, intelligent analysis module 3, visual monitoring module 4 and safety monitoring module 5. The high-precision sensing module 1, communication module 2, intelligent analysis module 3 and visual monitoring module 4 are connected through network communication. Specifically, the high-precision sensing module 1 is used to monitor various data in real time during the bridge construction process. Specifically, the high-precision sensing module 1 includes a sensing unit 101 and a calibration compensation unit 102. The sensing unit 101 detects various required data, including but not limited to stress strain sensors, vibration acceleration sensors, displacement sensors, temperature sensors, humidity sensors, vibration intensity sensors, water level sensors, meteorological sensors and ambient light sensors. The sensing unit 101 uses a Kalman filter to fuse different types of sensor data to improve measurement accuracy. The reference formula is as follows:

[0055]

[0056]

[0057] in, is the current state estimate, is the predicted state, K kis the Kalman gain, z k is the observation value, H is the observation matrix, P k |k-1 is the prediction covariance matrix, R is the observation noise covariance matrix;

[0058] A polynomial regression model can also be used for more accurate temperature and humidity calibration. The reference formula is as follows:

[0059] T comp =α0+α1(T-T0)+α2(T-T0) 2 +α3(H-H0)

[0060] Among them, T comp is the temperature compensation value, α0, α1, α2, α3 are regression coefficients, T is the actual temperature, T0 is the reference temperature, H is the actual humidity, and H0 is the reference humidity.

[0061] The calibration compensation unit 102 automatically corrects the performance drift caused by factors such as temperature and humidity by dynamically adjusting parameters such as sensor offset and gain value, thereby ensuring continuous high-precision measurement throughout the entire construction period.

[0062] The communication module 2 is used to transmit the data collected by the sensor to the monitoring center in real time. Specifically, the communication module 2 includes a wireless wired communication unit 201, a signal enhancement unit 202 and an adaptive communication unit 203. The wireless wired communication unit 201 can select the most suitable communication method according to the actual application scenario and environmental conditions, which enhances the flexibility and scope of application of the system, including but not limited to Wi-Fi, Bluetooth, LoRa, Ethernet and optical fiber. The signal enhancement unit 202 is used to improve the signal transmission distance and penetration ability, including but not limited to built-in or external antennas and signal amplifiers. The adaptive communication unit 203 is used to automatically select the optimal communication path and protocol according to the current communication environment and data transmission requirements. The intelligent analysis module 3 is used to pre-process, store, comprehensively analyze and predict the collected data, and generate early warning information.

[0063] The intelligent analysis module 3 includes a data preprocessing unit 301, a data storage unit 302, a data analysis unit 303, an edge computing unit 304 and an adaptive data processing unit 305. The data preprocessing unit 301 is used to clean, convert the format and detect anomalies of the data collected by the sensor. The data preprocessing unit 301 is used for signal denoising and feature extraction, and the following formula can be used:

[0064]

[0065] Among them, W x(a, b) are wavelet coefficients, a is the scale parameter, b is the translation parameter, x(t) is the original signal, and ψ is the wavelet basis function.

[0066] The data storage unit 302 is used to store the processed data in the cloud or in a local database. Specifically, the data storage unit 302 uses discrete cosine transform to perform data compression, and the following formula can be used:

[0067]

[0068] Among them, Y k is the compressed data, x n is the original data, N is the data length, and k is the frequency index.

[0069] The data analysis unit 303 mainly uses technical means to deeply mine and analyze the stored data. The edge computing unit 304 is used to perform real-time data processing and analysis on the edge computing device near the sensor, reducing the burden on the monitoring center and improving the response speed of the system. The adaptive data processing unit 305 mainly automatically selects the optimal data processing algorithm based on the current construction status and environmental conditions.

[0070] The visual monitoring module 4 is used to display the analysis results and real-time data to the construction management personnel. Specifically, the visual monitoring module 4 includes a real-time data display unit 401, a data query unit 402, a prediction analysis module 403, an early warning alarm module 404, a construction progress management unit 405, an uncertainty management unit 406, a data export unit 407 and a virtual technology unit 408. The real-time data display unit 401 displays the data collected by various sensors in real time in the form of charts, dashboards, etc. The data query unit 402 supports the construction management personnel to query historical data and conduct comparative analysis. The prediction analysis module 403 predicts the future construction status and potential risks through a comprehensive analysis of historical data and current data. The early warning alarm module 404 is used to generate early warning information and notify the construction management personnel in various ways. Specifically, the early warning alarm module 404 uses the three sigma rule to set the threshold and uses a hierarchical decision tree for multi-level early warning. The three sigma rule sets the threshold using the following formula:

[0071] Upper Threshold=μ+3σ

[0072] Lower Threshold=μ-3σ

[0073] Among them, μ is the data mean, σ is the data standard deviation;

[0074] The hierarchical decision tree uses the following formula for multi-level warning:

[0075] If x>T1, then Warning Level 1

[0076] If x>T2, then Warning Level 2

[0077] If x>T3, then Warning Level 3

[0078] Among them, x is the current monitoring data, T1, T2, T3 are the set warning thresholds.

[0079] The construction progress management unit 405 displays the completion status of the construction progress in real time by integrating the construction schedule and actual data. The uncertainty management unit 406 displays the real-time data caused by the weather, humidity, water level and other uncertain factors at the construction site. The data export unit 407 exports the monitoring data into multiple formats for further analysis and recording. The virtual technology unit 408 displays the real-time monitoring data to the construction management personnel in the form of a three-dimensional model. The virtual technology unit 408 uses point cloud data to reconstruct the three-dimensional model or uses a particle filter for dynamic visual display. The following formula is used for the reconstruction of the three-dimensional model using point cloud data:

[0080]

[0081] Among them, P is the reconstructed point cloud, R is the rotation matrix, T is the translation vector, and X, Y, and Z are the original point cloud data;

[0082] The formula for dynamic visual display using particle filters is as follows:

[0083]

[0084] in, is the estimated state, is the particle weight, z k is the observed value, is the particle state, is the observation model, It is a state transition model.

[0085] The safety monitoring module 5 is used to monitor the safety status of the construction site in real time, including but not limited to the use of safety equipment, personnel behavior monitoring and the operating status of construction machinery.

[0086] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A real-time monitoring system for bridge construction process, characterized by: include: High-precision sensor module (1): used to monitor various data during the bridge construction process in real time; Communication module (2): used to transmit the data collected by the sensor to the monitoring center in real time; Intelligent analysis module (3): used to pre-process, store, comprehensively analyze and predict the collected data, and generate early warning information; Visual monitoring module (4): used to display the analysis results and real-time data to the construction management personnel; The feature of the invention is that the high-precision sensing module (1), the communication module (2), the intelligent analysis module (3) and the visual monitoring module (4) are connected via network communication.

2. A real-time monitoring system for bridge construction process according to claim 1, characterized in that: The high-precision sensing module (1) comprises a sensing unit (101) and a calibration compensation unit (102); the sensing unit (101) detects various required data, including but not limited to a stress strain sensor, a vibration acceleration sensor, a displacement sensor, a temperature sensor, a humidity sensor, a vibration intensity sensor, a water level sensor, a meteorological sensor and an ambient light sensor; the calibration compensation unit (102) mainly automatically corrects performance drift caused by temperature and humidity factors by dynamically adjusting sensor offset and gain value parameters, thereby ensuring that high-precision measurement is continuously maintained throughout the entire construction period.

3. A real-time monitoring system for bridge construction process according to claim 1, characterized in that: The communication module (2) comprises a wireless wired communication unit (201), a signal enhancement unit (202) and an adaptive communication unit (203); the wireless wired communication unit (201) can select the most suitable communication method according to actual application scenarios and environmental conditions, thereby enhancing the flexibility and scope of application of the system, including but not limited to Wi-Fi, Bluetooth, LoRa, Ethernet and optical fiber; the signal enhancement unit (202) is used to improve the signal transmission distance and penetration ability, including but not limited to a built-in or external antenna and a signal amplifier; and the adaptive communication unit (203) is used to automatically select the optimal communication path and protocol according to the current communication environment and data transmission requirements.

4. A real-time monitoring system for bridge construction process according to claim 1, characterized in that: The intelligent analysis module (3) comprises a data preprocessing unit (301), a data storage unit (302), a data analysis unit (303), an edge computing unit (304) and an adaptive data processing unit (305); the data preprocessing unit (301) is used to clean, convert the format and detect anomalies of the data collected by the sensor; the data storage unit (302) is used to store the processed data in the cloud or in a local database; the data analysis unit (303) mainly uses technical means to perform in-depth mining and analysis on the stored data; the edge computing unit (304) is used to perform real-time data processing and analysis on an edge computing device near the sensor, thereby reducing the burden on the monitoring center and improving the response speed of the system; and the adaptive data processing unit (305) automatically selects the optimal data processing algorithm mainly according to the current construction status and environmental conditions.

5. A real-time monitoring system for bridge construction process according to claim 1, characterized in that: The visual monitoring module (4) includes a real-time data display unit (401), a data query unit (402), a prediction analysis module (403), an early warning alarm module (404), a construction progress management unit (405), an uncertainty factor management unit (406), a data export unit (407) and a virtual technology unit (408). The real-time data display unit (401) displays data collected by various sensors in real time in the form of charts and dashboards. The data query unit (402) supports construction management personnel to query historical data and conduct comparative analysis. The prediction analysis module (403) comprehensively analyzes historical data and current data to obtain the data collected by various sensors. The early warning alarm module (404) is used to generate early warning information and notify construction management personnel in various ways. The construction progress management unit (405) displays the completion status of the construction progress in real time by integrating the construction schedule and actual data. The uncertainty management unit (406) displays the real-time data caused by the weather, humidity, water level and other uncertain factors at the construction site. The data export unit (407) exports the monitoring data into various formats to facilitate further analysis and recording. The virtual technology unit (408) displays the real-time monitoring data to the construction management personnel in the form of an X-dimensional model.

6. A real-time monitoring system for bridge construction process according to claim 2, characterized in that: The sensing unit (101) uses a Kalman filter to fuse different types of sensor data to improve measurement accuracy. The reference formula is as follows: K k =P k|k-1 H T (HP k|k-1 H T +R) -1 in, is the current state estimate, is the predicted state, Kk is the Kalman gain, z k is the observation value, H is the observation matrix, P k |k-1 is the prediction covariance matrix, R is the observation noise covariance matrix; A polynomial regression model can also be used for more accurate temperature and humidity calibration. The reference formula is as follows: T comp =α0+α1(T-T0)+α2(T-T0) 2 +α3(H-H0) Among them, T comp is the temperature compensation value, α0, α1, α2, α3 are regression coefficients, T is the actual temperature, T0 is the reference temperature, H is the actual humidity, and H0 is the reference humidity.

7. A real-time monitoring system for bridge construction process according to claim 4, characterized in that: The data preprocessing unit (301) is used for signal denoising and feature extraction, and the following formula may be used: Among them, W x (a, b) are wavelet coefficients, a is the scale parameter, b is the translation parameter, x(t) is the original signal, and ψ is the wavelet basis function; The data storage unit (302) uses discrete cosine transform to perform data compression, and the following formula can be used: Among them, Y k is the compressed data, x n is the original data, N is the data length, and k is the frequency index.

8. A real-time monitoring system for bridge construction process according to claim 5, characterized in that: The virtual technology unit (408) combines augmented reality and virtual reality technology to display real-time monitoring data to construction management personnel in the form of a three-dimensional model, including but not limited to mapping sensor data into a three-dimensional model, viewing the model in real time at the construction site through an AR device, or conducting detailed inspection and analysis in a virtual environment through a VR device.

9. A real-time monitoring system for bridge construction process according to claim 5, characterized in that: The early warning alarm module (404) includes a multi-level early warning mechanism and an intelligent notification system. The multi-level early warning mechanism can generate early warning information of different levels according to the abnormality degree and potential risk level of the data. The intelligent notification system can notify the construction management personnel of the early warning information in real time through, including but not limited to, text messages, emails, and APP push notifications, to ensure that timely measures are taken to ensure construction safety.

10. A real-time monitoring system for bridge construction process according to claim 1, characterized in that: The system also includes a safety monitoring module (5) for real-time monitoring of the safety status of the construction site, including but not limited to the use of safety equipment, personnel behavior monitoring and the operating status of construction machinery.

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