Synchronous monitoring and early warning system for bridge displacement in prestress tensioning stage

Abstract

The invention discloses a synchronous monitoring and early warning system for bridge displacement in a pre-stress tension stage, relates to the technical field of bridge displacement measurement, and aims to solve the technical problem that a traditional monitoring and early warning system adopts single parameter monitoring and is easy to cause inaccurate early warning. The monitoring device is used for monitoring the stress of a prestressed steel beam and the temperature, humidity, stress variation and vertical displacement of each measuring point on a bridge; the data acquisition module is used for calculating corresponding displacement data based on the stress data acquired by the sensor module and storing the data acquired by the sensor module; the data transmission module is used for transmitting the data stored by the data acquisition module, the data processed by the intelligent processing module and the information output by the intelligent monitoring and early warning unit; and the intelligent processing module comprises an early warning calculation unit and a data storage unit. The method has the advantages that the early warning precision is improved, and the accurate capture of the bridge displacement abnormity is ensured.

Description

Technical Field

[0001] This invention relates to the field of bridge displacement measurement technology, and more specifically, to a synchronous monitoring and early warning system for bridge displacement during the prestressing tensioning stage. Background Technology

[0002] During the prestressing tensioning stage of bridge construction, displacement changes are a key indicator reflecting the structural stress state and safety performance. Accurate monitoring and timely early warning are crucial for preventing construction accidents and ensuring project quality. However, existing bridge displacement monitoring systems have significant technical limitations: they often rely on single parameters (such as monitoring only stress or displacement) and do not fully consider the coupling effect of environmental factors such as temperature and humidity with stress and displacement. This results in insufficient monitoring accuracy of bridge displacement changes, and early warnings are prone to false alarms and missed alarms, making it difficult to accurately reflect the real-time safety status of the bridge. Therefore, we propose a synchronous monitoring and early warning system for bridge displacement during the prestressing tensioning stage. Summary of the Invention

[0003] The purpose of this invention is to provide a synchronous monitoring and early warning system for bridge displacement during the prestressing tensioning stage, in order to solve the technical problem that traditional monitoring and early warning systems, which use a single parameter for monitoring, are prone to inaccurate early warnings.

[0004] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a synchronous monitoring and early warning system for bridge displacement during the prestressing tensioning stage, comprising: The sensor module is used to monitor the stress of the prestressed steel strands, as well as the temperature, humidity, stress changes, and vertical displacement at various measurement points on the bridge. The data acquisition module calculates the corresponding displacement data based on the stress data acquired by the sensor module, and stores the data acquired by the sensor module. The data transmission module is used to transmit the data stored in the data acquisition module, the data processed by the intelligent processing module, and the information output by the intelligent monitoring and early warning unit. The intelligent processing module includes an early warning calculation unit and a data storage unit, which are used to store and process the data acquired by the sensor module to obtain corresponding early warning information; The cloud server includes an acquisition unit, a first analysis unit, a second analysis unit, a third analysis unit, a first early warning unit, a second early warning unit, and a judgment unit, which are used to analyze and provide early warnings of the displacement of various measurement points on the bridge. The intelligent monitoring and early warning unit establishes a risk level evaluation model and a prediction model based on displacement data. It is used to conduct real-time risk level evaluation and real-time risk status prediction of bridges and output corresponding early warning information.

[0005] Preferably, the sensor module includes a temperature sensor, a humidity sensor, a stress sensor, and a dual-axis laser displacement sensor, all of which are intelligent monitoring sensors; Temperature sensors are used to acquire the temperature changes at various measurement points on the bridge; Humidity sensors are used to acquire humidity changes at various measurement points on the bridge; Stress sensors are used to acquire stress changes at various measurement points on prestressed steel strands and bridges; Dual-axis laser displacement sensors are used to acquire the vertical displacement of bridges.

[0006] Preferably, the data storage unit of the intelligent processing module is electrically connected to the sensor module and is used to store the data acquired by the stress sensor, temperature sensor, humidity sensor and dual-axis laser displacement sensor; The early warning calculation unit is electrically connected to the sensor module and the data storage unit, and is used to compare the stress change, temperature change, humidity change and vertical displacement with the corresponding early warning limit values ​​to obtain the corresponding early warning information.

[0007] Preferably, the acquisition unit of the cloud server is used to acquire the displacement of each measurement point on the bridge, and organize and store them according to the measurement point and time order; The first analysis unit is used to issue a warning about the displacement of the measurement point based on the comparison result between the displacement of each measurement point and the corresponding warning limit value. The second analysis unit is used to make a warning judgment based on the comparison results of warning information from multiple intelligent monitoring sensors communicating with the same measurement point; The third analysis unit is used to issue an early warning through the first early warning unit or the second early warning unit based on the comparison result between the number of early warnings at each measurement point at the same time and the first preset number. The determination unit is used to make a warning judgment based on the comparison result between the number of measurement points whose warning duration is longer than the first preset duration and the second preset number. The acquisition unit receives information from the data transmission module in real time via a wireless network, ensuring that the displacement data is updated in a timely manner.

[0008] Preferably, the first analysis unit has a built-in comparator that compares the displacement of each measurement point with the warning limit value one by one, and immediately issues a warning signal when the displacement exceeds the limit. The second analysis unit is connected to multiple intelligent monitoring sensors through a data interface. After receiving the early warning information, it analyzes its type, occurrence time, and duration. When multiple sensors issue the same type of early warning information for the same measurement point and the time overlaps, it determines that the point needs to issue an early warning. The third analysis unit counts the number of warnings at the same time. When the number exceeds the first preset number, the first warning unit is triggered to issue a high-level warning; otherwise, the second warning unit is triggered to issue a low-level warning. The judgment unit counts the number of measurement points whose warning duration exceeds the first preset duration. When the number exceeds the second preset number, a warning is issued; otherwise, no warning is issued.

[0009] Preferably, the second analysis unit includes a first acquisition subunit, a first calculation subunit, and a first determination subunit; The first acquisition subunit is used to acquire the number of early warning messages received at each measurement point; The first calculation subunit is used to calculate the number of measurement points for continuous early warning. The first judgment subunit is used to make a warning judgment based on the comparison result between the number of measurement points for continuous warning and the first preset number.

[0010] Preferably, the third analysis unit includes a second acquisition subunit, a second calculation subunit, and a second determination subunit; The second acquisition subunit is used to acquire the number of early warning messages received simultaneously by multiple intelligent monitoring sensors that communicate with the measurement point. The second calculation subunit is used to calculate the number of warning messages received at the same time; The second determination subunit is used to issue an early warning based on the comparison result between the number of early warning messages received at the same time and the second preset number.

[0011] Preferably, the determination unit includes a third acquisition subunit, a third calculation subunit, and a third determination subunit; The third acquisition subunit is used to acquire the number of measurement points where the warning duration is longer than the first preset duration; The third calculation subunit is used to calculate the number of measurement points where the warning duration is longer than the first preset duration; The third judgment subunit is used to make a warning judgment based on the comparison result between the number of measurement points whose warning duration is longer than the first preset duration and the third preset number.

[0012] A method for synchronous monitoring and early warning of bridge displacement during the prestressing tensioning stage includes the following steps: S1. Data monitoring: Temperature changes, humidity changes, stress changes of prestressed steel strands and bridge measurement points, as well as vertical displacement of the bridge, are obtained at various measurement points on the bridge through temperature sensors, humidity sensors, stress sensors and biaxial laser displacement sensors. S2. Data Processing and Storage: Calculate the corresponding displacement data based on the stress changes collected in S1, and store the temperature changes, humidity changes, stress changes, vertical displacement, and the calculated displacement data. S3. Data transmission: Transmit the raw data stored in S2, the data obtained from subsequent processing, and the output warning information; S4. Preliminary processing of early warning information: The stress change, temperature change, humidity change and vertical displacement obtained in S1 are compared with the corresponding early warning limit values ​​to obtain preliminary early warning information and store it. S5. Multi-dimensional early warning analysis: S51. Single-point displacement early warning: The displacement of each measurement point is compared with the corresponding early warning limit value. An early warning signal is issued when the displacement exceeds the limit. S52. Multi-sensor collaborative early warning: Analyze the early warning information issued by multiple sensors corresponding to the same measurement point. When multiple sensors issue the same type of early warning information for the point and the time overlaps, it is determined that the point needs to issue an early warning. S53. Multi-point quantity warning: Count the number of warnings for each measurement point at the same time. When the number exceeds the first preset number, a high-level warning is issued; otherwise, a low-level warning is issued. S54. Duration Warning: Count the number of measurement points whose warning duration exceeds the first preset duration, and issue a warning when the number exceeds the second preset number; S6. Risk Assessment and Prediction: Based on displacement data, establish risk level assessment and prediction models to conduct real-time risk level assessment and real-time risk status prediction for bridges and output corresponding early warning information.

[0013] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention synchronously collects temperature, humidity, stress, and vertical displacement data through a sensor module, combines the comparison and analysis of each parameter with the warning limit value by the early warning calculation unit of the intelligent processing module, and performs multi-dimensional early warning judgments by the first analysis unit and the second analysis unit of the cloud server, to achieve multi-parameter collaborative monitoring and early warning. This effectively solves the problem of inaccurate early warning caused by single parameter monitoring, significantly improves early warning accuracy, and ensures accurate capture of bridge displacement anomalies.

[0014] 2. This invention also relies on the data transmission module to efficiently transmit the data stored in the data acquisition module, the data processed by the intelligent processing module, and the information output by the intelligent monitoring and early warning unit. In conjunction with the cloud server acquisition unit, it receives information in real time through the wireless network to ensure timely updates of displacement data. Combined with the rapid data processing capabilities of the intelligent processing module and the cloud server, it solves the problem of data processing lag in multi-parameter monitoring scenarios, ensures the timeliness of early warning information, and enables construction personnel to adjust construction plans in a timely manner based on early warnings, thereby ensuring construction safety.

[0015] 3. This invention also establishes a risk level evaluation model and a prediction model based on displacement data through an intelligent monitoring and early warning unit. The risk level evaluation model can quantify bridge risks through indicators such as displacement change rate, and the prediction model can predict the next displacement data in advance based on the displacement matrix. This solves the problem that traditional early warning can only reflect the current state and cannot predict risks in advance, and realizes a forward-looking assessment of bridge risks, helping the construction party to take preventive measures in advance and further reduce construction safety hazards. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the system framework of the present invention. Detailed Implementation

[0017] To facilitate understanding of the technical solution of the present invention by those skilled in the art, the technical solution of the present invention will now be further described in conjunction with the accompanying drawings.

[0018] Example 1, such as Figure 1 As shown, the present invention provides a synchronous monitoring and early warning system for bridge displacement during the prestressing tensioning stage, including prestressed steel strands, sensor modules, data acquisition modules, data transmission modules, intelligent processing modules, cloud servers, and intelligent monitoring and early warning units; The sensor module is connected to the prestressed steel strands and the bridge structure, and is used to monitor the stress of the prestressed steel strands as well as the temperature, humidity, stress change and vertical displacement at various measurement points on the bridge, and collect the corresponding data in real time. The data acquisition module is electrically connected to the sensor module, calculates the corresponding displacement data based on the stress data acquired by the sensor module, and stores the data acquired by the sensor module. The data transmission module is communicatively connected to the data acquisition module, the intelligent processing module, the cloud server, and the intelligent monitoring and early warning unit, respectively, and is used to transmit the data stored in the data acquisition module, the data processed by the intelligent processing module, and the information output by the intelligent monitoring and early warning unit. The intelligent processing module is electrically connected to the data acquisition module and the data transmission module, and includes an early warning calculation unit and a data storage unit, which are used to store and process the data acquired by the sensor module to obtain the corresponding early warning information; The cloud server is communicatively connected to the data transmission module and includes an acquisition unit, a first analysis unit, a second analysis unit, a third analysis unit, a first early warning unit, a second early warning unit, and a judgment unit, used to analyze and provide early warnings of the displacement of each measurement point on the bridge. The intelligent monitoring and early warning unit is connected to the cloud server and data transmission module. It establishes a risk level evaluation model and a prediction model based on displacement data, which are used to evaluate the bridge's risk level in real time, predict its risk status in real time, and output corresponding early warning information. This system achieves full automation of the entire process from data acquisition and processing to analysis and early warning through the collaborative work of multiple modules. The sensor module comprehensively collects various parameters affecting bridge displacement, the data acquisition module converts stress data into displacement data, the data transmission module ensures efficient data flow, the intelligent processing module and cloud server perform in-depth data analysis, and the intelligent monitoring and early warning unit realizes risk assessment and prediction, forming a complete monitoring and early warning closed loop.

[0019] In embodiments of the present invention, the sensor module includes a temperature sensor, a humidity sensor, a stress sensor, and a dual-axis laser displacement sensor, all of which are intelligent monitoring sensors; Temperature sensors are used to acquire the temperature changes at various measurement points on the bridge. ; Humidity sensors are used to acquire humidity changes at various measurement points on the bridge. ; Stress sensors are used to acquire stress changes at various measurement points on prestressed steel strands and bridges. ; Dual-axis laser displacement sensors are used to acquire the vertical displacement of bridges. .

[0020] The temperature sensor employs a high-precision thermocouple sensor, installed inside and on the surface of the concrete at the bridge measurement points. The sampling frequency can be dynamically adjusted according to the construction stage: sampling every 10 seconds during critical tensioning stages and every 60 seconds during non-critical stages, accurately capturing the impact of temperature changes on bridge displacement. The humidity sensor uses a capacitive humidity sensor, embedded in the pores at the bridge measurement points. It achieves a measurement accuracy of ±2%RH within a humidity range of 0-100%, effectively reflecting the effect of humidity. The stress sensor uses a resistance strain gauge sensor, closely fitted to the surface of the prestressed steel strands and critical stress-bearing parts of the bridge. It has a measurement range of 0-2000MPa and a resolution of 1MPa, providing reliable stress data for displacement calculation. The biaxial laser displacement sensor is installed on fixed brackets directly above and to the side of the measurement points. Working on the principle of laser reflection, it measures distances of 0.5-50 meters with an accuracy of ±0.01mm, directly acquiring vertical displacement.

[0021] In an embodiment of the present invention, the sensor module is assumed to be within a certain time period. The stress data collected internally is ,in, Representing stress data The data acquisition time is accurate to the millisecond level. The stress sensor number is indicated, starting from 1 and incrementing sequentially. This indicates the number of stress data acquisitions, starting from 1. The data acquisition module calculates the corresponding displacement data based on the following formula: ; ; in, Indicates the time period of prestressed steel strands Inner The displacement data calculated in mm is consistent with the sampling frequency of the stress sensor and reflects the total elongation of the steel strand during the calculation. Indicates the time period of prestressed steel strands Inner The stress amplitude calculated in this calculation is the difference between the maximum and minimum values ​​in the stress data during this calculation, expressed in MPa. It reflects the fluctuation range of stress within the corresponding time period of this calculation. The elastic modulus of prestressed steel strands in bridges is expressed in MPa. It is a physical quantity that measures the ability of steel strand materials to resist elastic deformation. Its value is determined by the material of the steel strands and is determined in advance through experiments. It represents the cross-sectional area of ​​the prestressed steel strands in a bridge, with the unit being mm². It is calculated by measuring the diameter of the steel strands and is a parameter reflecting the size of the cross-section of the steel strands. The calculation cycle is consistent with the sampling frequency of the stress sensor, so that the displacement data and stress data are synchronized in time, improving the timeliness and accuracy of displacement calculation; The calculation logic of the above algorithm formula is as follows: First, the prestressed steel strands are collected by stress sensors within a time period. Stress data at different times within the time frame are processed to obtain the corresponding stress amplitude for each calculation. Then, each stress amplitude Divide by the elastic modulus of the prestressed steel strand respectively With cross-sectional area The product of these values ​​yields the displacement component corresponding to each stress amplitude; next, these displacement components are summed to obtain the total displacement increment caused by stress change; finally, the initial elongation of the prestressed steel strand is added. Thus, the prestressed steel strands are obtained within a certain time period. Inner Displacement data calculated this time This logic embodies the transformation process from stress data to displacement data, combining the physical properties of the material and its initial state, and can accurately reflect the displacement of the prestressed steel strands.

[0022] In an embodiment of the present invention, the data storage unit of the intelligent processing module is electrically connected to the sensor module and is used to store the data acquired by the stress sensor, temperature sensor, humidity sensor and dual-axis laser displacement sensor. The early warning calculation unit is electrically connected to the sensor module and the data storage unit, and is used to calculate the stress change. Temperature change Humidity change and vertical displacement Compare each value with the corresponding warning limit value and obtain the corresponding warning information; The data storage unit uses a large-capacity solid-state drive, connected to the sensor module via a data bus. It stores all raw and processed data in CSV format for easy querying and analysis, and automatically backs up data to the cloud hourly to prevent data loss. The early warning calculation unit uses a high-performance microprocessor, connected to each module via circuitry. After reading sensor data in real time, it first filters the data to remove noise interference, then compares each parameter with the early warning limit value. When a parameter exceeds the limit, it generates an early warning message containing the name of the parameter exceeding the limit, the value exceeding the limit, the time exceeding the limit, and the corresponding measurement point, ensuring the completeness and accuracy of the early warning information.

[0023] In an embodiment of the present invention, the early warning calculation unit is used to calculate an early warning limit value that matches the amount of stress change according to the following formula: ; ; The warning limit value is used to calculate the temperature change according to the following formula: ; ; in, The preset weighting coefficient has no unit. Its value is determined based on a large amount of experimental data and engineering experience. It is adjusted to the optimal value through trial and error. It is used to quantify the influence of different parameters on the warning limit value. The larger the weighting coefficient, the more significant the influence of the corresponding parameter. It represents a warning limit value that matches the amount of stress change, comprehensively reflecting the critical impact of stress, temperature, and humidity changes on bridge safety. The unit is determined based on the specific combination of parameters. This represents another warning limit value that matches the amount of stress change, reflecting the safety critical value under the combined action of stress change and vertical displacement. The unit is determined according to the specific combination of parameters. It represents a warning limit value that matches the amount of temperature change, taking into account the critical impact of temperature, humidity changes and vertical displacement on bridge safety, and the unit is determined based on the specific combination of parameters; This represents another warning limit value that matches the amount of temperature change, reflecting the safety critical value under the combined effect of temperature and stress changes. The unit is determined based on the specific combination of parameters. The above algorithm formula operates as follows: the early warning calculation unit performs weighted calculations on key parameters affecting bridge safety (stress change, temperature change, humidity change, and vertical displacement) to obtain early warning limit values ​​under different scenarios. For early warning limit values ​​related to stress change, the formula... Taking into account the effects of stress changes, temperature changes, and humidity changes, and using their respective weighting coefficients... , , Perform weighted summation; formula Then, combining the stress change and vertical displacement, based on the weighting coefficient... , The calculation yields the following formula for the warning limit values ​​related to temperature changes: Integrating temperature changes, humidity changes, and vertical displacement, and using weighting coefficients. , , Weighted calculation; formula Then, the temperature change and stress change are correlated, and weighted by a coefficient. , The results were obtained. These formulas, through reasonable weight allocation, enable the calculated warning limit values ​​to accurately reflect the critical safety state of the bridge under the combined effect of various parameters, providing a scientific basis for subsequent warning judgments.

[0024] In an embodiment of the present invention, the acquisition unit of the cloud server is used to acquire the displacement data obtained from each measurement point on the bridge. They are organized and stored according to the measurement points and time sequence; The first analysis unit is used to analyze the displacement of each measurement point. The comparison result with the corresponding warning limit value provides a warning of the displacement of the measurement point. The second analysis unit is used to make a warning judgment based on the comparison results of warning information from multiple intelligent monitoring sensors communicating with the same measurement point; The third analysis unit is used to determine the number of warnings issued by each measurement point at the same time. With the first preset quantity The comparison results are used to issue an early warning through the first early warning unit or the second early warning unit; The determination unit is used to determine whether the warning duration is greater than a first preset duration. Number of measurement points With the second preset quantity The comparison results are used to make early warning judgments; The acquisition unit receives information from the data transmission module in real time via a wireless network, ensuring that the displacement data is updated in a timely manner. The first analysis unit has a built-in comparator that compares the displacement of each measurement point with the warning limit value one by one. When the displacement exceeds the limit, a warning signal is immediately issued. The second analysis unit is connected to multiple intelligent monitoring sensors through a data interface. After receiving the early warning information, it analyzes its type, occurrence time, and duration. When multiple sensors issue the same type of early warning information for the same measurement point and the time overlaps, it determines that the point needs to issue an early warning. The third analysis unit counts the number of warnings issued at the same time. ,when When triggered, the first early warning unit issues a high-level warning. The second early warning unit is triggered to issue a low-level warning. Judgment Unit Statistics ,when Issue warnings in a timely manner. Without warning, The time is set according to the characteristics of the bridge structure and construction requirements, and is generally 30-300 seconds.

[0025] In an embodiment of the present invention, the second analysis unit includes a first acquisition subunit, a first calculation subunit, and a first determination subunit; The first acquisition subunit is used to acquire the number of early warning messages received at each measurement point. ; The first calculation subunit is used to calculate the number of measurement points for continuous early warning according to the following formula: ; The first determination subunit is used to determine the number of measurement points for continuous early warning and the first preset number. The comparison results are used to make early warning judgments; in, The number of measurement points with continuous warnings is the result of summing the continuous warning status of each measurement point. It has no unit and its value reflects the number of points on the bridge with continuous warnings. The characteristic function is used to identify the first... Is each measurement point in a continuous early warning state? When the first... When a measurement point issues an early warning message in three consecutive data collection cycles ,otherwise No unit; This represents the total number of measurement points, that is, the sum of all measurement points set up on the bridge. It has no unit and its value is determined based on factors such as the length and structural complexity of the bridge. This indicates the number of warning messages received at each measurement point. It has no unit and reflects the frequency with which each measurement point receives warning messages. This represents the first preset number, a threshold pre-set based on factors such as the importance of the bridge and the distribution of measurement points. It has no unit and is used to compare with the calculated number. The values ​​are compared to determine whether an alert needs to be issued; The operational logic of the above algorithm formula is as follows: First, the first acquisition subunit collects the number of early warning messages received at each measurement point, providing basic data for subsequent judgment; then, the first calculation subunit uses the feature function... Determine whether each measurement point is in a continuous early warning state. When a measurement point issues an early warning message within three consecutive data collection cycles... A value of 1 indicates that the location is in a continuous warning state; otherwise... Take 0, and finally, sum all The values ​​are added together to obtain the total number of measurement points for continuous early warning. This calculation logic can accurately filter out measurement points that are continuously in a warning state, providing a reliable basis for subsequent warning judgments and avoiding misjudgments due to short-term warnings.

[0026] In an embodiment of the present invention, the third analysis unit includes a second acquisition subunit, a second calculation subunit, and a second determination subunit; The second acquisition subunit is used to acquire the number of early warning messages received simultaneously by multiple intelligent monitoring sensors that communicate with the measurement point. ; The second calculation subunit is used to calculate the number of warning messages received at the same time, and its calculation formula is as follows: ; The second determination subunit is used to determine the number of warning messages received at the same time and the second preset number. The comparison results will trigger an early warning; in, The number of intelligent monitoring sensors that receive early warning information at the same time is the result of summing up the results after judging the situation of each sensor receiving early warning information at the same time. It has no unit, and its value reflects the concentration of early warning information at a certain moment. The characteristic function is used to identify the first... Whether each intelligent monitoring sensor receives an early warning message at the same time, when the first one receives an early warning message. When multiple intelligent monitoring sensors receive warning information at the same time... ,otherwise, ; This represents the total number of intelligent monitoring sensors, that is, the total number of all sensors participating in the monitoring of this measurement point. It has no unit and its value is determined based on the monitoring requirements of the measurement point and the redundancy of the sensors. This indicates the number of warning messages received simultaneously by multiple intelligent monitoring sensors communicating with the measurement point. It has no unit and is the raw data directly collected by the second acquisition subunit, reflecting the initial reception status of warning messages at a certain moment. This indicates the second preset number, a threshold pre-set based on sensor redundancy and reliability requirements. It is unitless and used in conjunction with the calculated value. The values ​​are compared to determine whether to trigger the first or second warning unit; The computational logic of the above algorithm formula is as follows: First, the second acquisition subunit uses time synchronization technology to collect data on the reception of early warning information by multiple intelligent monitoring sensors communicating with the measurement point at the same time, ensuring that the time synchronization accuracy is within ±10 milliseconds, thereby guaranteeing the number of early warning information received at the same time. Accurate and error-free; next, the second calculation subunit utilizes the characteristic function It is determined whether each intelligent monitoring sensor receives an early warning message at the same time. When multiple intelligent monitoring sensors receive warning information at the same time... The value is 1 if it is not 1, and 0 otherwise. Finally, all values ​​are summed. The values ​​are added together to obtain the total number of sensors that received the warning information at the same time. This calculation logic can accurately count the number of sensors receiving early warning information at the same time, providing reliable data support for subsequent judgment of the early warning level based on this number, and helping to more scientifically assess the real-time risk status of bridges.

[0027] In an embodiment of the present invention, the determination unit includes a third acquisition subunit, a third calculation subunit, and a third determination subunit; The third acquisition subunit is used to acquire information indicating that the warning duration is greater than the first preset duration. Number of measurement points ; The third calculation subunit is used to calculate the number of measurement points where the warning duration is longer than the first preset duration. The calculation formula is as follows: ; The third determination subunit is used to determine the number of measurement points whose warning duration exceeds the first preset duration and the third preset number. The comparison results are used to make early warning judgments; in, This indicates that the warning duration is longer than the first preset duration. The number of measurement points is the result of summing up the warning duration of each measurement point. It has no unit and its value intuitively reflects the scale of points on the bridge that have been in a warning state for a long time. The characteristic function is used to identify the first... Does the warning duration at each measurement point exceed the first preset duration? When the first The duration of the early warning for each measurement point. hour, ,otherwise, No unit; The serial number of the measurement point starts from 1 and increases sequentially, used to distinguish different measurement points; This indicates the total number of measurement points, that is, the total number of all measurement points set on the bridge. It has no unit and its value is determined based on factors such as the length and structural complexity of the bridge. Indicates the first The duration of the early warning for each measurement point, in seconds, refers to the time interval from the first issuance of the early warning information at that point to the current moment; The first preset duration, in seconds, is a time threshold set in advance based on factors such as the structural characteristics and construction requirements of the bridge. It is used to determine whether the warning at the measurement point is a continuous warning. This indicates that the duration of the initial warning acquired by the third acquisition subunit is greater than [a certain value]. The number of measurement points is given, without units, for subsequent precise calculation. Provide basic data; This indicates the third preset quantity, without units. It is a threshold set based on factors such as the bridge's load-bearing capacity and safety margin, used in conjunction with... Compare the data to determine whether to issue a warning; The calculation logic of the above algorithm formula is as follows: First, the third acquisition subunit is linked with the early warning timer to track the early warning duration of each measurement point in real time. It also filters out those whose warning duration exceeds the first preset duration. The measurement points were initially counted. Next, the third computational subunit introduces a characteristic function. For each measurement point, does it meet the requirement that "the duration of the early warning is greater than..." This condition is quantified and determined when the first... Each measurement point hour, Assign a value of 1 if the condition is not met, and 0 otherwise. Finally, sum all the values. The values ​​are accumulated, and the warning duration exceeds [a certain value]. The exact total number of measurement points This logic, through quantitative screening and cumulative calculation, accurately identifies the number of measurement points that have been in a long-term warning state, providing reliable data support for subsequent warning decisions based on this number, and helping to identify potential persistent risks to bridges.

[0028] In an embodiment of the present invention, the risk level evaluation model of the intelligent monitoring and early warning unit is as follows: ; When the rate of change of displacement When the risk level assessment model is in a favorable condition, it outputs an early warning message; otherwise, it outputs a risk level assessment report. in, This represents the rate of change of displacement, is dimensionless, and is used to quantify a time period. The overall variation range of the displacement data of the internally prestressed steel strands is related to the displacement threshold. The larger the value, the more drastic the displacement change. Indicates the time period of prestressed steel strands Inner Next and first The absolute value of the difference between the two calculated displacement data, in mm, reflects the magnitude of the fluctuation between the two displacement data. Indicates time period The number of displacement data points, i.e. the total number of times displacement data is calculated within this time period, has no unit, and its value is determined by the frequency of data collection and the length of the time period. This represents the displacement threshold, measured in mm. It is a reference value determined based on the bridge's construction conditions, environmental factors, and relevant specifications and design documents, used to measure whether displacement changes are within the allowable range. This represents the displacement change rate threshold, which has no unit. It is a critical value set based on factors such as the allowable deformation of the bridge structure. It is used to determine whether the displacement change rate exceeds the normal range and thus decide whether to output a warning message. The operational logic of the above algorithm formula is as follows: First, collect data on prestressed steel strands over a time period. Displacement data obtained from multiple calculations and By calculating the absolute value of the difference between each pair of displacement data This reflects the fluctuations in displacement data at different times; then, these absolute values ​​are summed to obtain the time period. The total fluctuation of the internal displacement data; then, divide the total fluctuation by the number of displacement data. With displacement threshold The product of these factors yields the rate of change of displacement. This rate of change comprehensively reflects the relative relationship between the overall displacement change amplitude and the displacement threshold over a time period; finally, the displacement change rate... With the set threshold If a comparison is made, If the displacement change exceeds the normal range, the risk level assessment model will output an early warning message; otherwise, it will output a risk level assessment report to provide a basis for assessing the safety status of the bridge.

[0029] In an embodiment of the present invention, the intelligent monitoring and early warning unit evaluates the risk level of the bridge based on the following model: ; in, This represents the average risk value in real-time risk level assessment, without units, and comprehensively reflects the risk level over a period of time. The overall deviation of internal bridge displacement data from the risk threshold is a core indicator for measuring the real-time risk level of bridges. For prestressed steel strands in a time period Inner The displacement data calculated in mm is obtained by the data acquisition module based on the stress data and reflects the displacement of the steel strand during the calculation. Indicates the corresponding number The risk threshold calculated each time is in mm. It is dynamically adjusted according to the construction conditions (such as tension force, tension speed, etc.) and environment (such as temperature, humidity, etc.) at the time of each calculation. It is set lower at the beginning of tensioning and gradually increases as the tensioning process progresses. Indicates time period The number of internal risk thresholds, without units, is the same as the number of times the displacement data within that time period is calculated, ensuring the comprehensiveness and relevance of the risk assessment; then, the displacement data for each time is calculated. and the corresponding risk threshold

[0030] The operational logic of the above algorithm formula is as follows: First, collect data on prestressed steel strands over a time period. Displacement data obtained from each internal calculation and the corresponding risk threshold Then, calculate the displacement data for each step. and corresponding risk threshold The difference This difference reflects the degree of deviation of a single displacement data point from the risk threshold; a positive value indicates that the displacement exceeds the risk threshold, while a negative value indicates that the displacement is within the safe range. Next, these differences are summed to obtain the time period. The total deviation of the internal displacement data from the risk threshold; finally, divide the total deviation by the number of risk thresholds. To obtain the average risk value ,pass The size of the bridge can be used to comprehensively evaluate its real-time risk level over a given period of time. A positive value, and the larger it is, the higher the overall risk. A negative value indicates that the overall situation is relatively safe.

[0031] In an embodiment of the present invention, the intelligent monitoring and early warning unit is based on a displacement matrix. Establish a prediction model, which is as follows: ; The displacement matrix for: ; in, Indicates the time period of prestressed steel strands Inner The displacement data calculated in this step, i.e. the predicted displacement data for the next step, is in mm and is the output of the prediction model, used to know the displacement trend of the steel strand in advance. , , , All are displacement matrices The parameters, which are unitless, are determined through regression analysis of historical displacement data and are used to quantify the first... The degree of influence of sub-displacement data, time interval, initial elongation, and displacement matrix on the prediction results; Indicates the time period of prestressed steel strands Inner The displacement data calculated in mm is one of the input parameters of the prediction model and reflects the most recent known displacement. Indicates time period Inner The time interval between calculations, in seconds, is used to reflect the time span between two displacement calculations and is an important time factor affecting displacement changes. The displacement matrix is ​​a unitless matrix containing historical displacement data and corresponding time data, providing historical patterns for the prediction model. Represents the displacement matrix The first row contains the historical displacement data sequence, in mm. This is the initial displacement. arrive The displacement data from previous calculations were used to record the past displacement changes of the steel strands; Represents the displacement matrix The second line contains the corresponding time series, in seconds. At the initial moment, arrive The time for each previous calculation corresponds one-to-one with the historical displacement data.

[0032] Example 2: A method for synchronous monitoring and early warning of bridge displacement during the prestressing tensioning stage, comprising the following steps: S1. Data monitoring: Temperature changes, humidity changes, stress changes of prestressed steel strands and bridge measurement points, as well as vertical displacement of the bridge, are obtained at various measurement points on the bridge through temperature sensors, humidity sensors, stress sensors and biaxial laser displacement sensors. S2. Data Processing and Storage: Calculate the corresponding displacement data based on the stress changes collected in S1, and store the temperature changes, humidity changes, stress changes, vertical displacement, and the calculated displacement data. S3. Data transmission: Transmit the raw data stored in S2, the data obtained from subsequent processing, and the output warning information; S4. Preliminary processing of early warning information: The stress change, temperature change, humidity change and vertical displacement obtained in S1 are compared with the corresponding early warning limit values ​​to obtain preliminary early warning information and store it. S5. Multi-dimensional early warning analysis: S51. Single-point displacement early warning: The displacement of each measurement point is compared with the corresponding early warning limit value. An early warning signal is issued when the displacement exceeds the limit. S52. Multi-sensor collaborative early warning: Analyze the early warning information issued by multiple sensors corresponding to the same measurement point. When multiple sensors issue the same type of early warning information for the point and the time overlaps, it is determined that the point needs to issue an early warning. S53. Multi-point quantity warning: Count the number of warnings for each measurement point at the same time. When the number exceeds the first preset number, a high-level warning is issued; otherwise, a low-level warning is issued. S54. Duration Warning: Count the number of measurement points whose warning duration exceeds the first preset duration, and issue a warning when the number exceeds the second preset number; S6. Risk Assessment and Prediction: Based on displacement data, establish risk level assessment and prediction models to conduct real-time risk level assessment and real-time risk status prediction for bridges and output corresponding early warning information.

[0033] The embodiments disclosed in this invention are preferred embodiments, but are not limited thereto. Those skilled in the art can easily understand the spirit of this invention based on the above embodiments and make different extensions and variations, but as long as they do not depart from the spirit of this invention, they are all within the protection scope of this invention.

Claims

1. A prestressed tension stage bridge displacement synchronous monitoring and early warning system, characterized in that, The system comprises a sensor module, a data acquisition module, a data transmission module, an intelligent processing module, a cloud server and an intelligent monitoring and early warning unit. The sensor module is used for monitoring the stress of the prestressed tendon and the temperature, humidity, stress change and vertical displacement of each measuring point on the bridge. The data acquisition module calculates the corresponding displacement data based on the stress data collected by the sensor module and stores the data collected by the sensor module. The data transmission module is used for transmitting the data stored by the data acquisition module, the data processed by the intelligent processing module and the information output by the intelligent monitoring and early warning unit. The intelligent processing module comprises an early warning calculation unit and a data storage unit, which are used for storing and processing the data collected by the sensor module to obtain corresponding early warning information. The cloud server comprises an acquisition unit, a first analysis unit, a second analysis unit, a third analysis unit, a first early warning unit, a second early warning unit and a determination unit, which are used for analyzing and warning the displacement of each measuring point on the bridge. The intelligent monitoring and early warning unit establishes a risk level evaluation model and a prediction model based on the displacement data, and is used for real-time risk level evaluation, real-time risk state prediction and output of corresponding early warning information.

2. The pre-stressed tension stage bridge displacement synchronous monitoring and early warning system according to claim 1, characterized in that, The sensor module comprises temperature sensors, humidity sensors, stress sensors and dual-axis laser displacement sensors, all of which are intelligent monitoring sensors. The temperature sensors are used for obtaining the temperature change of each measuring point on the bridge. The humidity sensors are used for obtaining the humidity change of each measuring point on the bridge. The stress sensors are used for obtaining the stress change of the prestressed tendon and each measuring point on the bridge. The dual-axis laser displacement sensors are used for obtaining the vertical displacement of the bridge.

3. The pre-stressed tension stage bridge displacement synchronous monitoring and early warning system according to claim 2, characterized in that, The data storage unit of the intelligent processing module is electrically connected with the sensor module, and is used for storing the data collected by the stress sensors, temperature sensors, humidity sensors and dual-axis laser displacement sensors. The early warning calculation unit is electrically connected with the sensor module and the data storage unit, and is used for comparing the stress change, temperature change, humidity change and vertical displacement with corresponding early warning limit values and obtaining corresponding early warning information.

4. The pre-stressed tension stage bridge displacement synchronous monitoring and early warning system according to claim 1, characterized in that, The acquisition unit of the cloud server is used for obtaining the displacement of each measuring point on the bridge and storing the displacement in the order of measuring points and time. The first analysis unit is used for warning the displacement of each measuring point based on the comparison result of the displacement of the measuring point with the corresponding early warning limit value. The second analysis unit is used for warning judgment based on the comparison result of the early warning information of multiple intelligent monitoring sensors in communication with the same measuring point. The third analysis unit is used for warning through the first early warning unit or the second early warning unit based on the comparison result of the number of early warnings of each measuring point at the same time with the first preset number. The determination unit is used for warning judgment based on the comparison result of the number of measuring points with the first preset time length greater than the first preset time length with the second preset number. The acquisition unit receives the information of the data transmission module in real time through a wireless network to ensure that the displacement data is updated in time.

5. The prestressed tensioning stage bridge displacement synchronous monitoring and early warning system according to claim 4, wherein The first analysis unit has a built-in comparator that compares the displacement of each measurement point with the warning limit value one by one. When the displacement exceeds the limit, a warning signal is immediately issued. The second analysis unit is connected to multiple intelligent monitoring sensors through a data interface. After receiving the early warning information, it analyzes its type, occurrence time, and duration. When multiple sensors issue the same type of early warning information for the same measurement point and the time overlaps, it determines that the point needs to issue an early warning. The third analysis unit counts the number of warnings at the same time. When the number exceeds the first preset number, the first warning unit is triggered to issue a high-level warning; otherwise, the second warning unit is triggered to issue a low-level warning. The judgment unit counts the number of measurement points whose warning duration exceeds the first preset duration. When the number exceeds the second preset number, a warning is issued; otherwise, no warning is issued.

6. The pre-stressed tension stage bridge displacement synchronous monitoring and early warning system according to claim 5, characterized in that, The second analysis unit includes a first acquisition subunit, a first calculation subunit, and a first determination subunit; The first acquisition subunit is used to acquire the number of early warning messages received at each measurement point; The first calculation subunit is used to calculate the number of measurement points for continuous early warning. The first judgment subunit is used to make a warning judgment based on the comparison result between the number of measurement points for continuous warning and the first preset number.

7. The pre-stressed tension stage bridge displacement synchronous monitoring and early warning system according to claim 5, characterized in that, The third analysis unit includes a second acquisition subunit, a second calculation subunit, and a second determination subunit; The second acquisition subunit is used to acquire the number of early warning messages received simultaneously by multiple intelligent monitoring sensors that communicate with the measurement point. The second calculation subunit is used to calculate the number of warning messages received at the same time; The second determination subunit is used to issue an early warning based on the comparison result between the number of early warning messages received at the same time and the second preset number.

8. The pre-stressed tension stage bridge displacement synchronous monitoring and early warning system according to claim 5, characterized in that, The determination unit includes a third acquisition subunit, a third calculation subunit, and a third determination subunit; The third acquisition subunit is used to acquire the number of measurement points where the warning duration is longer than the first preset duration; The third calculation subunit is used to calculate the number of measurement points where the warning duration is longer than the first preset duration; The third judgment subunit is used to make a warning judgment based on the comparison result between the number of measurement points whose warning duration is longer than the first preset duration and the third preset number.

9. A method applied to the pre-stressed tension stage bridge displacement synchronous monitoring and early warning system according to any one of claims 1-8, characterized in that, Includes the following steps: S1. Data monitoring: Temperature changes, humidity changes, stress changes of prestressed steel strands and bridge measurement points, as well as vertical displacement of the bridge, are obtained at various measurement points on the bridge through temperature sensors, humidity sensors, stress sensors and biaxial laser displacement sensors. S2. Data Processing and Storage: Calculate the corresponding displacement data based on the stress changes collected in S1, and store the temperature changes, humidity changes, stress changes, vertical displacement, and the calculated displacement data. S3. Data transmission: Transmit the raw data stored in S2, the data obtained from subsequent processing, and the output warning information; S4. Preliminary processing of early warning information: The stress change, temperature change, humidity change and vertical displacement obtained in S1 are compared with the corresponding early warning limit values ​​to obtain preliminary early warning information and store it. S5. Multi-dimensional early warning analysis: S51. Single-point displacement early warning: The displacement of each measurement point is compared with the corresponding early warning limit value. An early warning signal is issued when the displacement exceeds the limit. S52. Multi-sensor collaborative early warning: Analyze the early warning information issued by multiple sensors corresponding to the same measurement point. When multiple sensors issue the same type of early warning information for the point and the time overlaps, it is determined that the point needs to issue an early warning. S53. Multi-point quantity warning: Count the number of warnings for each measurement point at the same time. When the number exceeds the first preset number, a high-level warning is issued; otherwise, a low-level warning is issued. S54. Duration Warning: Count the number of measurement points whose warning duration exceeds the first preset duration, and issue a warning when the number exceeds the second preset number; S6. Risk Assessment and Prediction: Based on displacement data, establish risk level assessment and prediction models to conduct real-time risk level assessment and real-time risk status prediction for bridges and output corresponding early warning information.

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