Bridge detection method based on cloud collaboration

Abstract

The present invention discloses a bridge detection method based on cloud collaboration, which relates to the field of bridge detection technology and includes the following steps: arranging various sensors at key positions of the bridge to obtain real-time data of the bridge structure, wherein the real-time data includes structural deformation information and displacement data information, and after acquisition, the structural deformation information and displacement data information are processed. The present invention arranges various sensors at key positions of the bridge to collect structural deformation information and displacement data information in real time, and uses a cloud collaboration system to process and analyze them, so that the system can detect potential safety hazards in the bridge in a timely manner, trigger an alarm mechanism, transmit abnormal information to the cloud platform through the Internet of Things technology, and notify relevant management personnel, which effectively improves the real-time monitoring capability of the health status of the bridge structure, prevents potential risks, and reduces the risk of damage or collapse of the bridge structure.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge detection, and in particular to a bridge detection method based on cloud collaboration. Background Art

[0002] Cloud-based intelligent bridge detection is an innovative method that uses cloud computing and collaborative technologies to monitor the health status of bridge structures. First, a sensor network is deployed at key locations on the bridge to collect data on structural parameters such as deflection, temperature, and humidity. These sensors transmit real-time data to the cloud platform through the Internet of Things technology to form a digital twin model of the bridge. The cloud platform monitors and analyzes this data in real time through big data analysis and artificial intelligence algorithms, thereby achieving a comprehensive assessment of the bridge structure status.

[0003] Secondly, cloud-based intelligent bridge detection emphasizes the importance of collaborative work. In traditional bridge monitoring systems, data is often collected independently by various sensors, making it difficult to form an overall picture of the structural health status. Cloud-based collaboration, on the other hand, integrates multi-source data to achieve cross-regional and cross-departmental information sharing. The bridge monitoring system can connect multiple management units, maintenance companies, and government regulatory departments to achieve collaborative decision-making. Through information sharing on the cloud platform, different stakeholders can jointly participate in the monitoring and maintenance of bridges, promote resource sharing and collaborative work, and improve the efficiency of bridge maintenance. This collaborative mechanism makes bridge management more flexible and responsive, thereby better ensuring the long-term stability and reliability of the bridge structure.

[0004] In summary, cloud-based collaborative bridge intelligent detection is a bridge monitoring method based on advanced sensing technology, cloud computing and collaborative work, providing bridge managers with a more comprehensive, timely and efficient structural health monitoring solution.

[0005] The prior art has the following deficiencies:

[0006] The existing cloud-based intelligent bridge detection system cannot perceive some potential safety hazards of bridges. During this period, the most direct risk is the damage or collapse of the bridge structure, which may cause major accidents and threaten people's lives. For example, if the structural abnormality of a part of the bridge is not detected in time, it may cause the bridge to collapse during peak hours or in an emergency, causing serious traffic accidents, which not only poses a threat to driving and pedestrians, but also may cause serious social and economic problems. Secondly, failure to detect potential abnormalities of bridges in time may also lead to a sharp increase in maintenance costs. If the problem is not detected in the early stage, more complex and expensive repair work may be required, which will not only increase the financial burden, but also may lead to long-term closure of the bridge and traffic interruption, causing serious obstacles to the normal operation and development of the city.

[0007] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0008] The purpose of the present invention is to provide a bridge detection method based on cloud collaboration. By arranging various sensors at key positions of the bridge, structural deformation information and displacement data information are collected in real time, and the cloud collaboration system is used for processing and analysis. This enables the system to promptly detect potential safety hazards in the bridge, trigger an alarm mechanism, and transmit abnormal information to the cloud platform through the Internet of Things technology to notify relevant management personnel. This effectively improves the real-time monitoring capability of the health status of the bridge structure, prevents potential risks, and reduces the risk of damage or collapse of the bridge structure, so as to solve the problems in the above-mentioned background technology.

[0009] In order to achieve the above object, the present invention provides the following technical solution: a bridge detection method based on cloud collaboration, comprising the following steps:

[0010] Arrange various sensors at key locations of the bridge to obtain real-time data of the bridge structure, where the real-time data includes structural deformation information and displacement data information. After acquisition, the structural deformation information and displacement data information are processed;

[0011] The processed data collected by sensors is transmitted to the cloud platform through the Internet of Things;

[0012] Based on the cloud platform, the processed structural deformation information and displacement data are used to build a data analysis model, generate a hidden danger assessment index, and perform abnormal detection on the real-time status of the bridge;

[0013] When the system detects an abnormal situation, it triggers an alarm mechanism and notifies relevant managers through the cloud so that they can take timely measures to deal with potential safety hazards;

[0014] Collaborative decision-making is achieved on the cloud platform, abnormal alarm information is shared with relevant stakeholders, and maintenance plans and execution decisions are jointly formulated.

[0015] Preferably, the structural deformation information of the bridge includes the vibration amplitude and deflection of the bridge. After acquisition, the vibration amplitude and deflection of the bridge are processed respectively to generate the vibration amplitude abnormal concealment coefficient and the deflection excess coefficient; the displacement data information of the bridge includes the center position of the bridge. After acquisition, the center position of the bridge is processed to generate the center position displacement coefficient.

[0016] Preferably, the logic for obtaining the vibration amplitude abnormal concealment coefficient is as follows:

[0017] Obtain several actual vibration amplitudes generated during bridge operation within T time, and calibrate the actual vibration amplitude as Vib x , x represents the number of actual vibration amplitudes generated during the operation of the bridge within T time, x = 1, 2, 3, 4, ..., p, p is a positive integer;

[0018] The actual vibration amplitudes obtained during the operation of the bridge within T time are compared with the normal vibration amplitude level, and the vibration amplitude abnormal concealment coefficient is calculated. The calculation expression is: Where, Vib represents the vibration amplitude abnormal concealment coefficient, Vib x′ represents the actual vibration amplitude exceeding the normal vibration amplitude level obtained during the operation of the bridge within T time, x′ represents the number of the actual vibration amplitude exceeding the normal vibration amplitude level obtained during the operation of the bridge within T time, x′=1, 2, 3, 4, ..., p′, p′ is a positive integer, Vib 正常 Indicates normal vibration amplitude level.

[0019] Preferably, the logic for obtaining the deflection excess coefficient is as follows:

[0020] Obtain the real-time deflection of the bridge during operation within T time, and represent the real-time deflection in time series using the function Defle(t);

[0021] The real-time deflection obtained during the operation of the bridge within T time is compared with the normal deflection level to calculate the deflection excess coefficient. The calculation expression is: In the formula, [t x , t y ] represents the period during which the real-time deflection of the bridge exceeds the normal deflection level during the operation of the bridge. x <t y , Defule represents the deflection excess coefficient.

[0022] Preferably, the logic for obtaining the center position displacement coefficient is as follows:

[0023] Get the center position of the bridge during operation, place the center position in a three-dimensional rectangular coordinate system, and mark the starting coordinate as A(x 起始 ,y 起始 , z 起始 ), where x 起始 ,y 起始 , z 起始 Respectively represent the horizontal coordinate, vertical coordinate and vertical coordinate of the center position in the three-dimensional rectangular coordinate system;

[0024] Get the center position of the bridge after T time, and mark the center position coordinates of the bridge after T time as B(x T ,yT , z T ), where x T ,y T , z T They respectively represent the horizontal, vertical and vertical coordinates of the center position coordinates after T time when the bridge is in operation;

[0025] The center position coordinate B(x T ,y T , z T ) and the starting coordinates A(x 起始 ,y 起始 , z 起始 ) is used to calculate the center position displacement coefficient. The calculation expression is: Where Displa represents the center position displacement coefficient.

[0026] Preferably, after obtaining the vibration amplitude anomaly concealment coefficient Vib, deflection excess coefficient Defle and center position displacement coefficient Displa generated by the post-processed structural deformation information and displacement data information, the vibration amplitude anomaly concealment coefficient Vib, deflection excess coefficient Defle and center position displacement coefficient Displa are used to construct a data analysis model to generate a hidden danger assessment index Hazardττ, based on the formula:

[0027]

[0028] , where r 1 、r 2 、r 3 are the preset proportional coefficients of the vibration amplitude abnormal concealment coefficient Vib, the deflection excess coefficient Defle, and the center position displacement coefficient Displa, and r 1 、r 2 、r 3 Both are greater than 0.

[0029] Preferably, the hidden danger assessment index generated during the operation of the bridge within T time is compared and analyzed with a preset hidden danger assessment index reference threshold value, and the result of the comparison and analysis is as follows:

[0030] If the hidden danger assessment index is greater than or equal to the hidden danger assessment index reference threshold, an analysis signal is generated;

[0031] If the hidden danger assessment index is less than the hidden danger assessment index reference threshold, a normal signal is generated and the bridge continues to be monitored.

[0032] Preferably, when the bridge operation generates an analysis signal, a plurality of hazard assessment indices subsequently generated by the bridge operation are used to establish an analysis set, and the analysis set is calibrated as L, then L = {Hazard ττk =Hazard ττ 1 , Hazard ττ 2 , Hazard ττ 3 、…、Hazard ττ Q}, Q is a positive integer;

[0033] Several hazard assessment indices in the analysis set are compared with the hazard assessment index reference thresholds, and the hazard assessment index that is greater than or equal to the hazard assessment index reference threshold is marked as Hazard ττ u , u represents the number of the hidden danger assessment index greater than the reference threshold of the hidden danger assessment index in the analysis set, u = 1, 2, 3, 4, ..., s, s is a positive integer;

[0034] Calculate the severity of hidden dangers in bridge operation and mark the severity of hidden dangers as Fx, then: Q represents the total number of hidden danger assessment indices in the analysis set.

[0035] Preferably, the hidden danger severity Fx generated in the data set is compared with the preset J1 and J2, where J1 is less than J2, and the following situation is generated:

[0036] If Fx is greater than or equal to J2, a high hidden danger risk level signal is generated, and a high risk warning is issued for the high hidden danger risk level signal;

[0037] If Fx is greater than or equal to J1 and less than J2, a medium-level hidden danger risk level signal is generated, and a medium-level risk warning prompt is issued for the medium-level hidden danger risk level signal;

[0038] If Fx is less than J1, a low hidden danger risk level signal is generated, and a low risk warning prompt is issued for the low hidden danger risk level signal.

[0039] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0040] The present invention arranges various sensors at key locations of the bridge to collect structural deformation information and displacement data in real time, and uses a cloud collaborative system to process and analyze them. This enables the system to detect potential safety hazards in the bridge in a timely manner, trigger an alarm mechanism, and transmit abnormal information to the cloud platform through the Internet of Things technology to notify relevant managers. This effectively improves the real-time monitoring capability of the health status of the bridge structure, prevents potential risks, and reduces the risk of damage or collapse of the bridge structure.

[0041] The present invention processes the acquired structural deformation information and displacement data information to generate signals of different hidden danger risk levels, and formulates maintenance plans and execution decisions according to the signals of different levels. For high hidden danger risk level signals, emergency maintenance measures are taken to prevent further damage. For medium hidden danger risk level signals, regular inspections and maintenance are carried out, and preventive maintenance measures are taken. For low hidden danger risk level signals, routine inspections and maintenance are carried out, which helps to optimize the allocation of maintenance resources, improve maintenance efficiency, and reduce maintenance costs.

[0042] The present invention realizes information sharing and collaborative decision-making among relevant stakeholders through a cloud-based collaborative platform. Once the system detects an abnormal situation, it can promptly notify relevant managers to jointly participate in the formulation of maintenance plans and execution decisions. This helps to strengthen bridge management units and improve common understanding of the health status of bridges, thereby better ensuring the safety of bridge structures and reducing traffic accidents and increased maintenance costs caused by potential hidden dangers. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0044] Figure 1 It is a module schematic diagram of the bridge detection method based on cloud collaboration of the present invention. DETAILED DESCRIPTION

[0045] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0046] The present invention provides Figure 1 The bridge detection method based on cloud collaboration shown includes the following steps:

[0047] Arrange various sensors at key locations of the bridge to obtain real-time data of the bridge structure, where the real-time data includes structural deformation information and displacement data information. After acquisition, the structural deformation information and displacement data information are processed;

[0048] Various sensors are placed at key locations on the bridge to obtain real-time data on the bridge structure. The following are some possible sensors and their functions:

[0049] Strain sensors: used to measure the strain in bridge structures, i.e. the deformation caused by the force on the material. They can detect the stress on the structure and find possible cracks or fatigue problems.

[0050] Vibration sensors: used to monitor the vibration of bridges. They can detect vibrations caused by wind, traffic loads or other external factors to assess the stability of the structure.

[0051] Accelerometer: Used to measure the acceleration of a structure to detect possible structural movement or external shocks such as earthquakes. Helps determine the stability and safety of the structure.

[0052] Displacement sensors: These are used to measure the displacement of a structure, i.e. the change in the structure’s position in space. They help understand the overall movement of the structure and are essential for assessing the stability of a bridge.

[0053] Temperature sensors: used to monitor temperature changes in bridge structures. Abnormal temperature fluctuations may indicate local friction, material problems or other anomalies that affect the stability and durability of the structure.

[0054] Humidity sensor: used to detect humidity around the structure. Abnormal humidity may cause corrosion, expansion or contraction of structural materials, affecting the stability of the structure.

[0055] Pressure sensor: used to measure the pressure on the structure. It can be used to monitor the load-bearing conditions of bridge piers or bridge decks, and detect overload or structural deformation.

[0056] Acoustic Sensors: These monitor the sound inside a structure. Unusual sounds may indicate wear, tear, or other problems with materials inside the structure.

[0057] The structural deformation information of the bridge includes the vibration amplitude and deflection of the bridge. After obtaining, the vibration amplitude and deflection of the bridge are processed respectively to generate the vibration amplitude abnormal concealment coefficient and the deflection excess coefficient; the displacement data information of the bridge includes the center position of the bridge. After obtaining, the center position of the bridge is processed to generate the center position displacement coefficient.

[0058] Vibration amplitude refers to the maximum deviation or displacement of vibration generated by a bridge after being excited. This can be characterized by measuring the maximum displacement of a point on the bridge along a specific direction during vibration. Monitoring vibration amplitude is critical for evaluating the structural health and stability of the bridge because the vibration amplitude reflects the dynamic response of the bridge under external forces and helps predict potential structural problems or fatigue damage.

[0059] Secondly, deflection is a measure of the bending deformation of a bridge structure under the action of force. Deflection describes the displacement of a certain point on the bridge in a direction perpendicular to the bridge deck, that is, the degree of bending of the bridge when it is loaded. Monitoring of deflection helps to evaluate the deformation condition of the bridge structure and provide important information about the structural health and load-bearing capacity.

[0060] The vibration amplitude of the bridge itself exceeds the normal vibration amplitude level, which may indicate that the bridge has certain safety hazards. The following is a specific analysis of the reasons:

[0061] Structural health indication: Normally, the vibration amplitude of the bridge is limited by the design and structure itself. Exceeding the normal level may indicate a problem with the health of the structure. Abnormal vibration may be caused by fatigue, damage or other structural problems of the structural material, so further inspection and maintenance may be required.

[0062] Risk Assessment Factors: Abnormal vibration amplitudes may increase the risk to the bridge. Wind loads are a major consideration for bridge structures. When vibrations exceed normal levels, the impact of wind on the structure may increase, affecting the stability and safety of the bridge.

[0063] Problems with structural materials and connections: Exceeding normal vibration amplitude levels may indicate problems with the materials or connections of the bridge structure. This may include cracks, material fatigue, corrosion, etc., which may lead to structural instability and reduced load-bearing capacity.

[0064] Structural stability issues: An abnormal increase in vibration amplitude may mean that the stability of the bridge structure is threatened. The structure will have a certain vibration under normal circumstances, but when the vibration amplitude exceeds the normal level, it may indicate that the structure has an abnormal vibration mode, which may be caused by structural damage or uneven force.

[0065] Fatigue and crack issues: High vibration amplitudes may accelerate the fatigue process in the bridge structure, leading to fatigue damage to the material. In addition, vibrations may also cause crack propagation in the structure, affecting the overall strength and durability of the bridge.

[0066] The logic for obtaining the vibration amplitude anomaly concealment coefficient is as follows:

[0067] Obtain several actual vibration amplitudes generated during bridge operation within T time, and calibrate the actual vibration amplitude as Vib x , x represents the number of actual vibration amplitudes generated during the operation of the bridge within T time, x = 1, 2, 3, 4, ..., p, p is a positive integer;

[0068] It should be noted that accelerometers are usually used to measure vibrations, because they can measure accelerations in all directions on the structure. These sensors can be placed at key locations on the bridge, such as piers, beams or other structural elements, to capture the vibrations of the bridge at different locations. Accelerometers measure the acceleration changes of the structure over time. By integrating the acceleration, the velocity relative to time can be obtained, and the displacement can be integrated again to obtain the amplitude of the vibration.

[0069] The actual vibration amplitudes obtained during the operation of the bridge within T time are compared with the normal vibration amplitude level, and the vibration amplitude abnormal concealment coefficient is calculated. The calculation expression is: Where, Vib represents the vibration amplitude abnormal concealment coefficient, Vib x′ represents the actual vibration amplitude exceeding the normal vibration amplitude level obtained during the operation of the bridge within T time, x′ represents the number of the actual vibration amplitude exceeding the normal vibration amplitude level obtained during the operation of the bridge within T time, x′=1, 2, 3, 4, ..., p′, p′ is a positive integer, Vib 正常 Indicates normal vibration amplitude level;

[0070] It should be noted that the normal vibration amplitude level of the bridge itself refers to the amplitude range of vibration generated by the bridge structure under external excitation or self-load under normal use and operation. This vibration amplitude is the normal response of the bridge structure. The vibration characteristics of the structure are usually taken into account during the design and construction process. The normal vibration amplitude level refers to the bridge under various operating and natural environmental conditions, including traffic loads, wind loads, earthquakes, etc. The structural vibration is kept within an acceptable range. In the normal vibration amplitude level, the bridge structure should show a stable and balanced vibration response without excessive deformation or damage. The natural frequency, structural stiffness, material properties and other factors of the bridge are usually considered in the design stage to ensure that under normal use, the vibration amplitude of the bridge is within a reasonable range and will not affect the safety and stability of the structure.

[0071] From the calculation expression of the vibration amplitude anomaly concealment coefficient, it can be seen that the larger the performance value of the vibration amplitude anomaly concealment coefficient generated within T time when the bridge is in operation, the greater the hidden danger of safety risk in the operation of the bridge, and vice versa.

[0072] The deflection of the bridge itself exceeds the normal level, which may indicate that the bridge has certain safety hazards. The following is an analysis of the specific reasons:

[0073] Structural deformation and stability issues: Deflection is an important parameter that describes the degree of deformation of the bridge structure. If the deflection exceeds the normal level, it may indicate that the structure is subjected to excessive load or structural damage, which will affect the overall stability of the bridge.

[0074] Structural fatigue and cracks: Abnormal increase in deflection may lead to accelerated fatigue of the bridge structure, causing the formation and expansion of cracks. This will reduce the bearing capacity of the structure and increase the fragility of the structure, thus causing potential safety risks.

[0075] Overload risk: Abnormal deflection may be caused by overload, such as excessive traffic load. If the deflection of the bridge exceeds the design range, it may lead to overload of the structure, increase the stress of the structure, and accelerate the fatigue process of the structure.

[0076] Excessive deflection may cause collision with other structural components: If the deflection increases abnormally, it may cause collision between different parts of the bridge, such as collision between the bridge deck and the supporting structure. This may damage key components of the structure and affect the overall safety of the bridge.

[0077] The logic for obtaining the deflection excess coefficient is as follows:

[0078] Obtain the real-time deflection of the bridge during operation within T time, and represent the real-time deflection in time series using the function Defle(t);

[0079] It should be noted that displacement sensors are sensors used to measure the position or deformation of objects and are widely used in the field of structural health monitoring. In bridge intelligent detection systems, laser displacement sensors, fiber optic sensors or other similar devices are usually used. These sensors are usually installed at key locations on bridges, such as beams, piers or other structural elements to capture the deflection changes of bridges. Displacement sensors measure the displacement changes of structures over time through different technical principles (such as laser ranging, fiber grating, etc.). This measurement can provide accurate data on the deformation of bridges.

[0080] The real-time deflection obtained during the operation of the bridge within T time is compared with the normal deflection level to calculate the deflection excess coefficient. The calculation expression is: In the formula, [t x , t y ] represents the period during which the real-time deflection of the bridge exceeds the normal deflection level during the operation of the bridge. x <t y , Defle represents the deflection excess coefficient;

[0081] It should be noted that the normal deflection level of the bridge itself refers to the degree of bending or deformation of the structure under external loads or self-loads when the bridge is in normal operation and use. The normal deflection level means that the deflection of the bridge structure is within a reasonable range under normal working conditions and does not exceed the limits specified by the design and safety specifications. This parameter is part of the dynamic characteristics of the bridge structure, and its amplitude is affected by many factors, including traffic loads, wind loads, temperature changes, etc. When designing and building bridges, engineers usually consider factors such as structural stiffness, elastic modulus, cross-sectional shape, and material properties to ensure that the deflection of the bridge is maintained within an acceptable range under normal operation. The definition of the normal deflection level is relative to the design requirements and engineering specifications of the specific bridge, because bridges of different types and sizes have different deflection limits when designed;

[0082] From the calculation expression of the deflection excess coefficient, it can be seen that the larger the value of the deflection excess coefficient generated within the time T when the bridge is in operation, the greater the hidden danger of safety risk in the operation of the bridge; vice versa, the smaller the hidden danger of safety risk in the operation of the bridge.

[0083] The deviation of the center position of the bridge itself may cause certain safety hazards to the bridge. The following is an analysis of the specific reasons:

[0084] Impaired structural balance: The center of a bridge structure is usually the balance point considered during design. If the center is offset, it may mean that the structure has lost its balance, resulting in uneven load distribution. This may cause a non-uniform response of the structure under load, affecting the overall stability of the structure.

[0085] Increased structural stress: The deviation of the center position will change the geometry of the structure, causing different parts to bear different loads. This may cause some structural components to bear stress exceeding the design load, increase the risk of structural deformation and damage, and thus affect the safety of the structure.

[0086] Overall structural distortion: The deviation of the center position may cause overall structural distortion, especially for symmetrical structures. Distortion may cause structural asymmetry, which in turn affects the stability and seismic performance of the structure and increases the risk of the structure under external forces.

[0087] Foundation and bearing load issues: The offset of the center position may have an uneven impact on the load of the bridge foundation and bearings. This may cause uneven settlement of the foundation, causing the bearings to bear additional loads, increasing the risk of uneven stress on the structure.

[0088] Traffic safety risks: The deviation of the center position may cause the geometry of the bridge to change, which may affect traffic safety. For example, the bridge deck may no longer be level, making it difficult for drivers to adapt, increasing the risk of traffic accidents.

[0089] The logic of obtaining the center position displacement coefficient is as follows:

[0090] Get the center position of the bridge during operation, place the center position in a three-dimensional rectangular coordinate system, and mark the starting coordinate as A(x 起始 ,y 起始 , z 起始 ), where x 起始 ,y 起始 , z 起始 Respectively represent the horizontal coordinate, vertical coordinate and vertical coordinate of the center position in the three-dimensional rectangular coordinate system;

[0091] Get the center position of the bridge after T time, and mark the center position coordinates of the bridge after T time as B(x T ,y T , z T ), where x T ,y T , z T They respectively represent the horizontal, vertical and vertical coordinates of the center position coordinates after T time when the bridge is in operation;

[0092] It should be noted that displacement sensors are sensors used to measure the position or deformation of objects, and are suitable for monitoring changes in the center position of structures. Commonly used displacement sensors include laser displacement sensors, optical fiber sensors, capacitive displacement sensors, etc. Displacement sensors should be arranged at key positions of bridge structures to accurately measure the displacement of the center position. Usually, these positions include key nodes of structures such as bridge main beams and piers. Displacement sensors measure the displacement changes of structures over time through different technical principles (such as laser ranging, optical fiber grating, etc.). This measurement provides accurate data on the center position of the bridge.

[0093] The center position coordinate B(x T ,y T , z T ) and the starting coordinates A(x 起始 ,y 起始 , z 起始 ) is used to calculate the center position displacement coefficient. The calculation expression is: Where Displa represents the center position displacement coefficient.

[0094] From the calculation expression of the center position displacement coefficient, it can be seen that the larger the performance value of the center position displacement coefficient generated within T time when the bridge is in operation, the greater the hidden danger of safety risk in the operation of the bridge. Conversely, the smaller the hidden danger of safety risk in the operation of the bridge.

[0095] The processed data collected by sensors is transmitted to the cloud platform through the Internet of Things;

[0096] Use secure communication protocols to ensure data integrity and privacy;

[0097] It is critical to ensure the integrity and privacy of data transmitted in a cloud-based bridge intelligent detection system. Here are some secure communication protocols and methods to ensure data integrity and privacy:

[0098] Secure communication protocol:

[0099] HTTPS (Hypertext Transfer Protocol Secure): Use SSL / TLS encryption protocol to ensure data encryption during transmission and prevent man-in-the-middle attacks. This is a protocol commonly used in Internet security and can effectively protect the privacy of data transmission.

[0100] MQTT (Message Queuing Telemetry Transport): A lightweight, open IoT protocol that supports SSL / TLS encryption and is used to transmit sensor data. MQTT is designed to be suitable for situations with low bandwidth, high latency or unstable networks.

[0101] CoAP (Constrained Application Protocol): An application layer protocol for constrained devices that supports the DTLS (Datagram Transport Layer Security) protocol and is used for secure communication on resource-constrained devices.

[0102] Data encryption: Use symmetric encryption algorithms (such as AES) or asymmetric encryption algorithms (such as RSA) to encrypt transmitted data to ensure that only legitimate clouds and terminals can decrypt and read data.

[0103] Digital signature: By adding a digital signature to the data, the integrity of the data can be verified to ensure that the data has not been tampered with during transmission. Public key infrastructure (PKI) is generally used to manage digital certificates.

[0104] Access control and authentication: Implement strict access control and authentication mechanisms on the cloud platform to ensure that only authorized users or devices can access sensor data. Use tokens or API keys for authentication.

[0105] Data compression and segmentation: During the transmission process, data compression technology can be used to reduce the size of the data packet and improve transmission efficiency. In addition, segmenting the data into small pieces for transmission can reduce the risk of data leakage.

[0106] Change keys regularly: Use regularly changed keys to reduce potential risks caused by key leakage. Key rotation can effectively increase the security of the system.

[0107] Logging and monitoring: Establish a detailed logging system in the cloud to record the access and modification history of sensor data. Monitor the system in real time to quickly identify and respond to any abnormal activities.

[0108] Based on the cloud platform, the processed structural deformation information and displacement data are used to build a data analysis model, generate a hidden danger assessment index, and perform abnormal detection on the real-time status of the bridge;

[0109] After obtaining the vibration amplitude anomaly concealment coefficient Vib, deflection excess coefficient Defle and center position displacement coefficient Displa generated by the post-processed structural deformation information and displacement data information, the vibration amplitude anomaly concealment coefficient Vib, deflection excess coefficient Defle and center position displacement coefficient Displa are used to construct a data analysis model to generate the hidden danger assessment index Hazardττ, based on the following formula:

[0110]

[0111] , where r 1 、r 2 、r 3 are the preset proportional coefficients of the vibration amplitude abnormal concealment coefficient Vib, the deflection excess coefficient Defle, and the center position displacement coefficient Displa, and r 1 、r 2 、r 3 Both are greater than 0.

[0112] It can be seen from the calculation formula that the greater the abnormal concealment coefficient of the vibration amplitude generated within T time when the bridge is in operation, the greater the deflection excess coefficient, and the greater the center position displacement coefficient, that is, the greater the performance value of the hidden danger assessment index Hazarddττ generated within T time when the bridge is in operation, the greater the hidden danger of safety risk in the operation of the bridge, and vice versa.

[0113] When the system detects an abnormal situation, it triggers an alarm mechanism and notifies relevant managers through the cloud so that they can take timely measures to deal with potential safety hazards;

[0114] The hidden danger assessment index generated during the operation of the bridge within T time is compared and analyzed with the preset hidden danger assessment index reference threshold. The results of the comparison and analysis are as follows:

[0115] If the hidden danger assessment index is greater than or equal to the reference threshold of the hidden danger assessment index, an analysis signal is generated. When the bridge operation generates an analysis signal, it indicates that there are abnormal hidden dangers in the bridge operation;

[0116] If the hidden danger assessment index is less than the reference threshold of the hidden danger assessment index, a normal signal is generated. When the bridge operation generates a normal signal, it indicates that the bridge is in normal operation and the bridge continues to be monitored.

[0117] When the bridge operation generates an analysis signal, several hidden danger assessment indices generated by the bridge operation are used to establish an analysis set, and the analysis set is calibrated as L, then L = {Hazardττk} = Hazardττ 1 、Hazarddττ 2 Hazard 3 ,…,HazardττQ}, Q is a positive integer;

[0118] Compare several hidden danger assessment indices in the analysis set with the hidden danger assessment index reference thresholds respectively, and mark the hidden danger assessment indices greater than or equal to the hidden danger assessment index reference threshold as Hazardττu, where u represents the number of the hidden danger assessment index greater than the hidden danger assessment index reference threshold in the analysis set, and u=1, 2, 3, 4, ..., s, where s is a positive integer;

[0119] Calculate the severity of hidden dangers in bridge operation and mark the severity of hidden dangers as Fx, then: Q represents the total number of hidden danger assessment indices in the analysis set;

[0120] From the calculation formula of the severity of hidden dangers during bridge operation, it can be seen that the greater the performance value of the hidden danger severity generated in the analysis set, the more serious the abnormal hidden dangers during bridge operation, and vice versa, the less serious the abnormal hidden dangers during bridge operation;

[0121] Compare the hidden danger severity Fx generated in the data set with the pre-set J1 and J2, where J1 is less than J2, and generate the following situation:

[0122] If Fx is greater than or equal to J2, a high hidden danger risk level signal is generated, and a high risk warning is issued for the high hidden danger risk level signal;

[0123] If Fx is greater than or equal to J1 and less than J2, a medium-level hidden danger risk level signal is generated, and a medium-level risk warning prompt is issued for the medium-level hidden danger risk level signal;

[0124] If Fx is less than J1, a low hidden danger risk level signal is generated, and a low risk warning prompt is issued for the low hidden danger risk level signal.

[0125] Achieve collaborative decision-making on the cloud platform, share abnormal alarm information with relevant stakeholders, and jointly formulate maintenance plans and execute decisions;

[0126] Relevant stakeholders include bridge management units, maintenance companies, etc.;

[0127] After obtaining the high, medium and low hidden danger risk level signals generated by bridge operation, collaborative decision-making is achieved on the cloud platform, abnormal alarm information is shared with relevant stakeholders, and maintenance plans and execution decisions are jointly formulated;

[0128] According to different hidden danger risk level signals, different levels of maintenance plans and execution decisions can be formulated. The following is an explanation of high hidden danger risk level signals, medium hidden danger risk level signals and low hidden danger risk level signals:

[0129] High risk level signal:

[0130] Maintenance Plan: In response to high hazard risk level signals, an emergency maintenance plan should be initiated immediately, which may include immediate closure of the bridge and detailed structural assessment and inspection.

[0131] Executive Decision: Take emergency measures, which may include immediate repair or replacement of damaged structural elements to prevent further damage, while notifying relevant stakeholders, including transportation authorities and municipalities, to ensure public safety.

[0132] Intermediate hidden danger risk level signal:

[0133] Maintenance plan: In response to signals of medium risk levels of hazards, a plan is developed to schedule regular inspections and maintenance work to monitor the evolution of the structure.

[0134] Execution decisions: Take preventive maintenance measures to repair or replace potential problem components in response to detected problems to ensure the stability of the structure. At the same time, report the structural health status to relevant stakeholders on a regular basis.

[0135] Low hidden danger risk level signal:

[0136] Maintenance plan: For low risk level signals, regular routine inspections and maintenance are carried out to ensure the good operating condition of the structure.

[0137] Execution decisions: Take preventive and planned maintenance actions for minor issues found during routine inspections to keep the structure in good condition, update files in a timely manner and provide feedback to relevant stakeholders.

[0138] The present invention arranges various sensors at key locations of the bridge to collect structural deformation information and displacement data in real time, and uses a cloud collaborative system to process and analyze them. This enables the system to detect potential safety hazards in the bridge in a timely manner, trigger an alarm mechanism, and transmit abnormal information to the cloud platform through the Internet of Things technology to notify relevant managers. This effectively improves the real-time monitoring capability of the health status of the bridge structure, prevents potential risks, and reduces the risk of damage or collapse of the bridge structure.

[0139] The present invention processes the acquired structural deformation information and displacement data information to generate signals of different hidden danger risk levels, and formulates maintenance plans and execution decisions according to the signals of different levels. For high hidden danger risk level signals, emergency maintenance measures are taken to prevent further damage. For medium hidden danger risk level signals, regular inspections and maintenance are carried out, and preventive maintenance measures are taken. For low hidden danger risk level signals, routine inspections and maintenance are carried out, which helps to optimize the allocation of maintenance resources, improve maintenance efficiency, and reduce maintenance costs.

[0140] The present invention realizes information sharing and collaborative decision-making among relevant stakeholders through a cloud-based collaborative platform. Once the system detects an abnormal situation, it can promptly notify relevant managers to jointly participate in the formulation of maintenance plans and execution decisions. This helps to strengthen bridge management units and improve common understanding of the health status of bridges, thereby better ensuring the safety of bridge structures and reducing traffic accidents and increased maintenance costs caused by potential hidden dangers.

[0141] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0142] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0143] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0144] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0145] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0146] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0147] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0148] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0149] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0150] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0151] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0152] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. The bridge detection method based on cloud collaboration is characterized by: The following steps are involved: Arrange various sensors at key locations of the bridge to obtain real-time data of the bridge structure, where the real-time data includes structural deformation information and displacement data information. After acquisition, the structural deformation information and displacement data information are processed; The processed data collected by sensors is transmitted to the cloud platform through the Internet of Things; Based on the cloud platform, the processed structural deformation information and displacement data are used to build a data analysis model, generate a hidden danger assessment index, and perform abnormal detection on the real-time status of the bridge; When the system detects an abnormal situation, it triggers the alarm mechanism and notifies relevant managers through the cloud; Achieve collaborative decision-making on the cloud platform, share abnormal alarm information with relevant stakeholders, and jointly formulate maintenance plans and execute decisions; The structural deformation information of the bridge includes the vibration amplitude and deflection of the bridge. After obtaining, the vibration amplitude and deflection of the bridge are processed respectively to generate the vibration amplitude abnormal concealment coefficient and the deflection excess coefficient; the displacement data information of the bridge includes the center position of the bridge. After obtaining, the center position of the bridge is processed to generate the center position displacement coefficient; The logic for obtaining the vibration amplitude anomaly concealment coefficient is as follows: Obtain several actual vibration amplitudes generated during the operation of the bridge within T time, and calibrate the actual vibration amplitude as , x Indicates the number of actual vibration amplitudes generated during the operation of the bridge within T time. x =1, 2, 3, 4, ..., p , p is a positive integer; The actual vibration amplitudes obtained during the operation of the bridge within T time are compared with the normal vibration amplitude level, and the vibration amplitude abnormal concealment coefficient is calculated. The calculation expression is: , where Indicates the vibration amplitude abnormal concealment coefficient, It represents the actual vibration amplitude exceeding the normal vibration amplitude level obtained during the operation of the bridge within T time. The number indicating the actual vibration amplitude exceeding the normal vibration amplitude level obtained during the operation of the bridge within the time T. , is a positive integer, Indicates normal vibration amplitude level; The logic for obtaining the deflection excess coefficient is as follows: Get the real-time deflection of the bridge during operation within T time, and use the function to convert the real-time deflection into time series To express; The real-time deflection obtained during the operation of the bridge within T time is compared with the normal deflection level to calculate the deflection excess coefficient. The calculation expression is: , where It indicates the period of time when the real-time deflection of the bridge exceeds the normal deflection level during the operation of the bridge. , It indicates the deflection excess coefficient; The logic of obtaining the center position displacement coefficient is as follows: Get the center position of the bridge during operation, place the center position in a three-dimensional rectangular coordinate system, and mark the starting coordinates as ,in, , , Respectively represent the horizontal coordinate, vertical coordinate and vertical coordinate of the center position in the three-dimensional rectangular coordinate system; Get the center position of the bridge after T time when it is running, and mark the coordinates of the center position after T time when the bridge is running as ,in, , , They represent the horizontal, vertical and vertical coordinates of the center position of the bridge after T time in operation; The center position coordinates after T time when passing through the bridge and the starting coordinates The distance between the center position displacement coefficient is calculated, and the calculation expression is: , where represents the center position displacement coefficient; Obtain the vibration amplitude anomaly concealment coefficient generated by the post-processed structural deformation information and displacement data information , Deflection Exceeding Limit Coefficient And the center position displacement coefficient Then, the vibration amplitude abnormal concealment coefficient , Deflection Exceeding Limit Coefficient And the center position displacement coefficient Build a data analysis model to generate a hidden danger assessment index , based on the formula: , where , , They are the vibration amplitude anomaly concealment coefficient , Deflection Exceeding Limit Coefficient And the center position displacement coefficient The preset scaling factor of , , Both are greater than 0.

2. The bridge detection method based on cloud collaboration according to claim 1 is characterized in that: The hidden danger assessment index generated during the operation of the bridge within T time is compared and analyzed with the preset hidden danger assessment index reference threshold. The results of the comparison and analysis are as follows: If the hidden danger assessment index is greater than or equal to the hidden danger assessment index reference threshold, an analysis signal is generated; If the hidden danger assessment index is less than the hidden danger assessment index reference threshold, a normal signal is generated and the bridge continues to be monitored.

3. The bridge detection method based on cloud collaboration according to claim 2 is characterized in that: When the bridge operation generates an analysis signal, several hidden danger assessment indices generated by the bridge operation are used to establish an analysis set, and the analysis set is calibrated as L ,but , Q is a positive integer; The several hidden danger assessment indices in the analysis set are compared with the hidden danger assessment index reference thresholds respectively, and the hidden danger assessment indices that are greater than or equal to the hidden danger assessment index reference thresholds are calibrated as , u Indicates the number of the hidden danger assessment index in the analysis set that is greater than the reference threshold of the hidden danger assessment index. u =1, 2, 3, 4, ..., s , s is a positive integer; Calculate the severity of hidden dangers in bridge operation and mark the severity of hidden dangers as Fx ,but: , Q Represents the total number of hidden danger assessment indices in the analysis set.

4. The bridge detection method based on cloud collaboration according to claim 3 is characterized in that: The severity of the hidden dangers generated in the data set Fx With the preset J 1 and J 2 for comparison, where J 1 is less than J 2. Generate the following situation: like Fx Greater than or equal to J 2, a high hidden danger risk level signal is generated, and a high risk warning is issued for the high hidden danger risk level signal; if Fx Greater than or equal to J 1 and less than J 2. A medium-level hidden danger risk level signal is generated, and a medium-level risk warning is issued for the medium-level hidden danger risk level signal; like Fx Less than J 1, a low hidden danger risk level signal is generated, and a low risk warning prompt is issued for the low hidden danger risk level signal.

Images