Bridge web shear distribution ratio monitoring method and system based on multi-section collaboration

Abstract

The invention discloses a bridge web shear distribution ratio monitoring method and system based on multi-section collaboration, and relates to the technical field of bridge monitoring, a theoretical model of a bridge is constructed to obtain theoretical shear distribution data, a theoretical shear characteristic flow of the theoretical shear distribution data is extracted, and a theoretical gear of each section is generated based on the theoretical shear characteristic flow; arranging a strain sensor network on a plurality of key sections of the bridge to obtain real shear distribution data of the sections; extracting a real shearing force characteristic flow of the real shearing force distribution data, and generating a dynamic real gear based on the real shearing force characteristic flow; a full-bridge gear transmission chain is generated through the virtual transmission shaft, and the load serves as input torque to drive the full-bridge gear transmission chain; the meshing state of a full-bridge gear transmission chain is monitored in real time, and the shearing force distribution ratio of a bridge web is calculated by analyzing operation data between gears; by means of the theoretical gear and the dynamic reality gear, real-time and dynamic intelligent monitoring of the web shearing force distribution ratio is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bridge monitoring, in particular to a bridge web shear distribution ratio monitoring method and system based on multi-section cooperation. BACKGROUND

[0002] In the construction monitoring and long-term health monitoring of corrugated steel web composite bridges, accurately grasping the shear distribution ratio of the web is the key to evaluating the overall stress performance and cooperative working state of the structure. Currently, the monitoring method in this field mainly relies on arranging a strain sensor network at key sections of the bridge, measuring the strain values of the web and top and bottom plates, and calculating the shear distribution by combining the principles of material mechanics; However, the prior art has some limitations. First, the existing method focuses more on independent analysis and simple comparison of data from each section, lacking effective mining and utilization of the mechanical response cooperation between multiple sections. This method is difficult to capture the dynamic rules of shear redistribution caused by changes in web structure during the longitudinal transmission of loads in the bridge from a system level. Second, the traditional monitoring method lacks the ability to verify the reliability of the data. When some sensors have abnormal data or completely fail, the system often cannot automatically identify and effectively respond, which affects the continuity and accuracy of the shear distribution ratio calculation results, and may mask the real structural state changes or lead to misjudgment. Therefore, it is of great significance to develop a bridge web shear distribution ratio monitoring method based on multi-section cooperation. SUMMARY

[0003] The purpose of the present application is to provide a bridge web shear distribution ratio monitoring method and system based on multi-section cooperation to solve the problems in the background art.

[0004] To achieve the above purpose, the present application provides the following technical solution: a bridge web shear distribution ratio monitoring method based on multi-section cooperation, comprising: Constructing a theoretical model of the bridge, obtaining theoretical shear distribution data of the bridge based on the theoretical model, extracting data features of the theoretical shear distribution data as theoretical shear features, and generating theoretical gears for each section based on the theoretical shear feature flow; Arranging a strain sensor network at multiple key sections of the bridge for synchronous data acquisition to obtain real shear distribution data of the sections; Extracting data features of the real shear distribution data as real shear feature flow, and generating dynamic real gears for each section based on the real shear feature flow; Connecting the theoretical gears and dynamic real gears of each section through a virtual transmission shaft to form a full-bridge gear transmission chain, and driving the full-bridge gear transmission chain with the load as input torque; Real-time monitoring of the meshing state of the full-bridge gear transmission chain, calculating the shear force distribution ratio of the bridge web by analyzing the running data between the gears.

[0005] In a preferred embodiment, the steps of constructing a theoretical model of the bridge, obtaining theoretical shear force distribution data of the bridge based on the theoretical model, extracting data features of the theoretical shear force distribution data as a theoretical shear force feature stream, and generating a theoretical gear for each section based on the theoretical shear force feature stream are as follows: Constructing a finite element model of the bridge based on the finite element method, calculating the theoretical shear force distribution data of each section under different load conditions through the finite element model; Extracting a theoretical shear force feature stream of the theoretical shear force data based on a feature extraction algorithm, the theoretical shear force feature stream including spectral features, gradient features, and time sequence features; Constructing a gear mapping unit, inputting the theoretical shear force feature stream into the gear mapping unit to map it into a gear model with fixed tooth profile parameters and radius; Wherein, the tooth profile parameters include the number of tooth profiles, tooth height, and tooth width, the number of tooth profiles is directly proportional to the time sequence features of the theoretical shear force feature stream, and the tooth height and tooth width are directly proportional to the spectral features and gradient features of the theoretical shear force feature stream.

[0006] In a preferred embodiment, the steps of arranging a strain sensor network at multiple key sections of the bridge to perform synchronous data acquisition to obtain real shear force distribution data of the sections are as follows: Arranging a strain sensor network at multiple key sections of the bridge, wherein the key sections include inner lining concrete sections, stiffening rib sections, and pure steel web sections; Arranging strain sensors at a preset distance in each key section to generate a shear force monitoring network covering the full section of the box girder; Real-time acquisition of real shear force distribution data of each key section based on the shear force monitoring network, and labeling the real shear force distribution data with time sequence labels.

[0007] In a preferred embodiment, the steps of extracting data features of the real shear force distribution data as a real shear force feature stream, and generating a dynamic real gear for each section based on the real shear force feature stream are as follows: Constructing a real-time signal processing unit, performing multi-dimensional feature extraction on the real shear force distribution data based on the real-time signal processing unit to generate a real shear force feature stream; Wherein, the real shear force feature stream includes time domain amplitude features, frequency domain energy features, and time sequence gradient features; Establishing a dynamic gear generation model, mapping the real shear force feature stream into a dynamic real gear.

[0008] In a preferred embodiment, the steps of establishing a dynamic gear generation model, and mapping the real shear force feature stream into a dynamic real gear are as follows: determining the number of tooth profiles of the dynamic real gear based on the timing characteristics of the theoretical shear force characteristic flow; constructing a dynamic mapping rule of real gear parameters based on the real shear force characteristic flow; mapping the frequency domain energy characteristics in the real shear force characteristic flow into tooth height, and the frequency domain energy characteristics at a moment determine the height of a single tooth profile; mapping the time domain amplitude characteristics in the real shear force characteristic flow into tooth profile fluctuation characteristics, and the change trend of the real-time shear force value determines the steepness of the tooth profile.

[0009] In a preferred embodiment, the step of driving the full-bridge gear transmission chain with the load as input torque by connecting the theoretical gear and the dynamic real gear of each section through the virtual transmission shaft is: engaging the theoretical gear and the dynamic real gear in the same section as a section measurement gear set; constructing a virtual transmission shaft to connect the section measurement gear sets of each section to generate a full-bridge gear transmission chain; setting a torque sensing window in the full-bridge gear transmission chain to monitor the load input torque in real time, and taking the load input torque as the driving force of the full-bridge gear transmission chain.

[0010] In a preferred embodiment, the step of monitoring the engagement state of the full-bridge gear transmission chain in real time and calculating the shear force distribution ratio of the bridge web by analyzing the running data between the gears is: constructing an engagement state monitoring unit and a differential monitoring unit; based on the engagement state monitoring unit, collecting the engagement clearance between the theoretical gear and the dynamic real gear in each section measurement gear set and the tooth profile contact surface as engagement data in real time; based on the differential monitoring unit, collecting the differential data of the full-bridge gear transmission chain in real time; taking the engagement data and the differential data as running data; based on the neural network, analyzing the running data and the load input torque to generate the shear force distribution ratio of the bridge web.

[0011] The application also provides a bridge web shear force distribution ratio monitoring system based on multi-section cooperation, comprising: a theoretical gear generation module: constructing a theoretical model of the bridge, obtaining theoretical shear force distribution data of the bridge based on the theoretical model, extracting data characteristics of the theoretical shear force distribution data as a theoretical shear force characteristic flow, and generating theoretical gears of each section based on the theoretical shear force characteristic flow; a sensor network module: connected with the theoretical gear generation module, arranging a strain sensor network at multiple key sections of the bridge for synchronous data collection to obtain real shear force distribution data of the sections; Real gear generation module: connected with the sensor network module, extract the data characteristics of the real shear force distribution data as the real shear force feature stream, generate the dynamic real gear of each section based on the real shear force feature stream; Gear transmission chain generation module: connected with the real gear generation module, connect the theoretical gear and the dynamic real gear of each section through the virtual transmission shaft to form the full-bridge gear transmission chain, drive the full-bridge gear transmission chain with the load as the input torque; Shear force ratio analysis module: connected with the gear transmission chain generation module, real-time monitor the meshing state of the full-bridge gear transmission chain, calculate the shear force distribution ratio of the bridge web through analyzing the running data between gears.

[0012] In the above technical solution, the technical effects and advantages provided by the present application are: 1、The present application generates theoretical gears and dynamic real gears, and constructs the full-bridge gear transmission chain composed of them, so that the overall mechanical response of the bridge under multiple load conditions can be directly observed and analyzed through the meshing state and transmission efficiency between gears. The traditional monitoring method relies on isolated sensor readings comparison and complex data post-processing, and it is difficult to present the overall mechanical behavior of the structure system in real time. This mechanism reveals the structure cooperative working performance hidden behind the data in a nearly physical entity way. When the real gear of a certain section changes in speed or meshing is stuck due to abnormal shear force, the system can immediately capture this discordance from the overall operation of the transmission chain, thereby realizing early warning and accurate positioning of structural performance degradation, greatly improving the intuitiveness and system-level diagnostic ability of monitoring; 2、Through the operation state of the gear transmission chain, the mechanical state of the structure, the consistency of the model and the actual, the load transmission path efficiency, and the local damage possibility are output synchronously, each gear's speed, tooth shape change and meshing state are the reflection of specific structure behavior, which together constitute a complete dynamic atlas of the bridge health condition. Based on the fusion of multi-dimensional information, not only the accuracy of diagnosis is improved, but also deeper insights for maintenance decision-making are provided, such as identifying local performance degradation trends that have not yet affected overall safety, thereby realizing the leap from passive monitoring to active early warning, from single safety judgment to whole life cycle performance management. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0014] Figure 1 The method flowchart of the present application.

[0015] Figure 2 This is a system block diagram of the present invention.

[0016] Figure 3 This is a logic block diagram of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1, please refer to Figure 1 and Figure 3 As shown in this embodiment, the bridge web shear force distribution ratio monitoring method based on multi-section collaboration includes: S1. Construct a theoretical model of the bridge, obtain theoretical shear force distribution data of the bridge based on the theoretical model, extract the data features of the theoretical shear force distribution data as theoretical shear force feature flow, and generate theoretical gears for each section based on the theoretical shear force feature flow. S2. Arrange a network of strain sensors at multiple key sections of the bridge to acquire real shear force distribution data of the sections synchronously. S3. Extract the data features of the actual shear force distribution data as the actual shear force feature flow, and generate dynamic actual gears for each section based on the actual shear force feature flow; S4. The theoretical gears and dynamic real gears of each section are connected in series through the virtual transmission shaft to form a full-bridge gear transmission chain, and the load is used as the input torque to drive the full-bridge gear transmission chain. S5. Real-time monitoring of the meshing state of the full-bridge gear transmission chain, and calculation of the shear force distribution ratio of the bridge web by analyzing the running data between gears.

[0019] As described in steps S1-S5 above, in the construction monitoring and long-term health monitoring of corrugated steel web composite bridges, accurately grasping the shear force distribution ratio of the web is the key to evaluating the overall stress performance and collaborative working state of the structure. At present, the monitoring methods in this field mainly rely on arranging a strain sensor network on the key sections of the bridge, measuring the strain values ​​of the web and top and bottom plates, and calculating the shear force distribution in combination with the principles of mechanics of materials. However, existing technical solutions have some limitations. First, existing methods focus on independent analysis and simple comparison of data from each section, lacking effective exploration and utilization of the synergistic mechanical response among multiple sections. This method is difficult to capture the dynamic law of shear force redistribution caused by changes in web structure during the longitudinal transmission of load in a bridge from a system level. Second, traditional monitoring methods have insufficient ability to verify the reliability of data. When some sensors show abnormal data or fail completely, the system often has difficulty in automatically identifying and responding effectively. This will affect the continuity and accuracy of the shear force distribution ratio calculation results, and may thus mask the actual changes in structural state or lead to misjudgment. This invention generates theoretical gears and dynamic real gears, and constructs a full-bridge gear transmission chain composed of them. This allows the overall mechanical response of a bridge under multiple load conditions to be directly observed and analyzed through the meshing state and transmission efficiency between gears. Traditional monitoring methods rely on isolated sensor reading comparisons and complex data post-processing, making it difficult to present the overall mechanical behavior of the structural system in real time. This mechanism reveals the structural collaborative performance hidden behind the data in a way that is almost a physical entity. When a real gear at a certain section experiences a change in rotational speed or meshing jamming due to abnormal shear force, the system can immediately capture this incoordination from the overall operation of the transmission chain, thereby achieving early warning and accurate positioning of structural performance degradation, greatly improving the intuitiveness of monitoring and system-level diagnostic capabilities. By analyzing the operational status of the gear transmission chain, multi-dimensional information such as the mechanical state of the structure, the consistency between the model and reality, the efficiency of load transfer paths, and the possibility of local damage are simultaneously output. The rotational speed, tooth profile changes, and meshing state of each gear reflect the specific structural behavior, collectively forming a complete dynamic map of the bridge's health status. The fusion of multi-dimensional information not only improves the accuracy of diagnosis but also provides deeper insights for maintenance decisions. For example, it can identify local performance degradation trends that have not yet affected overall safety, thereby achieving a leap from passive monitoring to proactive early warning and from single safety judgments to full life-cycle performance management.

[0020] In one embodiment, step S1, which involves constructing a theoretical model of the bridge, obtaining theoretical shear force distribution data of the bridge based on the theoretical model, extracting data features from the theoretical shear force distribution data as a theoretical shear force feature stream, and generating theoretical gears for each cross-section based on the theoretical shear force feature stream, includes: S11. Construct a finite element model of the bridge based on the finite element method, and calculate the theoretical shear force distribution data of each section under different load conditions using the finite element model; S12. Extract the theoretical shear force feature flow from the theoretical shear force data based on the feature extraction algorithm. The theoretical shear force feature flow includes spectral features, gradient features, and time series features. S13. Construct a gear mapping unit, and map the theoretical shear force characteristic flow into the gear mapping unit as a gear model with fixed tooth profile parameters and radius; S14. Among them, the tooth profile parameters include the number of teeth, tooth height and tooth width. The number of teeth is proportional to the temporal characteristics of the theoretical shear force characteristic flow, and the tooth height and tooth width are proportional to the spectral characteristics and gradient characteristics of the theoretical shear force characteristic flow. As described in steps S11-S14 above, in the process of constructing the theoretical gear, a refined numerical model reflecting the actual geometric dimensions, material properties, and boundary conditions of the bridge is first established using finite element analysis technology. Through systematic loading of various typical working conditions such as vehicle loads and wind loads, the theoretical shear force distribution data of each target section under the standard load sequence is calculated. The theoretical shear force distribution data is then processed based on a multi-dimensional feature extraction algorithm. The spectral characteristics are obtained by using fast Fourier transform to acquire the dominant frequency components, the gradient characteristics are calculated using the central difference method to calculate the shear force change rate, and the temporal characteristics are determined by the load action time series to determine the phase relationship. These three together constitute a complete theoretical shear force feature flow. A gear mapping unit is then constructed to map the theoretical shear force feature flow into a gear model. The steps are as follows: This unit receives feature flow data containing spectral characteristics, gradient characteristics, and temporal characteristics, and processes it in conjunction with predefined basic mapping coefficients. The predefined basic mapping coefficients are a set of key scaling factors stored within the gear mapping unit, used as calibration parameters to convert the dimensionless feature data into actual gear geometric dimensions. These coefficients are obtained through prior analysis of the bridge structure characteristics. The system is derived from the systematic analysis and calibration of the theoretical model. The tooth shape coefficient establishes the quantitative correspondence between temporal characteristics and the physical number of teeth; the tooth height coefficient determines the scaling ratio from the amplitude of the spectral characteristics to the tooth height; the tooth width coefficient regulates the sensitivity of the inverse change between the gradient characteristics and the tooth width dimension; and the base radius provides a unified dimensional benchmark for all gears. These coefficients collectively ensure that theoretical gears with different cross-sections can be generated under a unified measurement system, preserving the differences in the mechanical characteristics of each cross-section while maintaining the comparability and coordination of gear collaboration. During the conversion process, the tooth shape number is determined through a linear mapping relationship based on the number of load application time nodes in the temporal characteristics, ensuring that the number of gear teeth matches the frequency of load changes. The tooth height parameter is determined proportionally based on the dominant frequency amplitude in the spectral characteristics, reflecting the response intensity of the theoretical shear force. The tooth width parameter is calculated using an inverse relationship through the shear force change rate data in the gradient characteristics, resulting in a larger tooth width when the shear force changes gently and a narrower tooth width when the shear force changes drastically. Based on the above steps, a rigid gear model with a specific tooth shape is generated, which can participate in meshing operations in a virtual transmission chain.

[0021] In one embodiment, step S2, which involves arranging a strain sensor network at multiple key sections of the bridge to synchronously acquire data and obtain the actual shear force distribution data of the sections, includes: S21. Arrange a network of strain sensors at multiple key sections of the bridge, including the inner concrete lining section, the stiffening rib section, and the pure steel web section. S22. Arrange strain sensors at preset distances at each key section to generate a shear force monitoring network covering the entire cross section of the box girder; S23. Based on the shear force monitoring network, collect the actual shear force distribution data of each key section in real time, and label the actual shear force distribution data with time series labels; As described in steps S21-S23 above, when implementing bridge web shear monitoring, firstly, based on the structural characteristics of corrugated steel web composite bridges, a strain sensor network is systematically deployed at three key sections: the inner concrete lining section, the stiffening rib section, and the pure steel web section. The sensor installation positions must strictly adhere to the preset distance requirements. Strain sensors are symmetrically deployed at the four corners and the middle key points of each section of the box girder to form a complete shear monitoring network covering the top plate, bottom plate, and web. This network adopts a distributed data acquisition architecture, using a unified clock source to control each acquisition node to achieve microsecond-level synchronous sampling, acquiring strain data at each measuring point in real time, and calculating the actual shear force distribution data of the section accordingly. In the data preprocessing stage, the system labels each data packet with a timestamp accurate to the millisecond level as a time sequence label to ensure that the subsequent feature extraction and theoretical model data have a strict time correspondence. Furthermore, the acquisition system adopts an industrial-grade network communication protocol to ensure the integrity and real-time performance of the data during transmission, providing reliable raw data support for the subsequent generation of dynamic real gears.

[0022] In one embodiment, step S3, which involves extracting the data features of the actual shear force distribution data as an actual shear force feature stream and generating dynamic actual gears for each cross-section based on the actual shear force feature stream, includes: S31. Construct a real-time signal processing unit, and extract multi-dimensional features from the real-time shear force distribution data based on the real-time signal processing unit to generate a real-time shear force feature stream. S32, where the actual shear force characteristic flow includes time-domain amplitude characteristics, frequency-domain energy characteristics, and time-series gradient characteristics; S33. Establish a dynamic gear generation model to map the real shear force feature flow into a dynamic real gear; As described in steps S31-S33 above, a real-time signal processing unit is first constructed. This unit uses a multi-channel parallel processing architecture to perform online feature extraction on the input real-time shear force distribution data. The processing unit segments the data through a sliding time window mechanism, calculates the average shear force within each window as the time domain amplitude feature in the unit time domain dimension, extracts the energy of the main frequency bands through fast Fourier transform in the unit frequency domain dimension to form the frequency domain energy feature, and obtains the temporal gradient feature based on the difference operation of adjacent timestamp data. The three together constitute the real shear force feature stream. A dynamic gear generation model is constructed to receive the real shear force feature stream. This model adopts a real-time update mechanism to dynamically adjust the gear geometric parameters according to the changes in the feature stream, ensuring that the dynamic real gear can accurately reflect the continuous changes in the actual stress state of the bridge web.

[0023] In one embodiment, step S33, which establishes a dynamic gear generation model and maps the real shear force feature flow to a dynamic real gear, includes: S331. Determine the number of teeth of dynamic real gears based on the temporal characteristics of theoretical shear force characteristic flow; S332. Construct dynamic mapping rules for real tooth profile parameters based on real shear force characteristic flow; S333. Map the frequency domain energy characteristics in the real shear force characteristic flow to the tooth height. The frequency domain energy characteristics at a certain moment determine the height of a single tooth profile. S334. Map the temporal amplitude features in the real shear force feature stream to the tooth surface profile fluctuation features. The changing trend of the real-time shear force value determines the steepness of the tooth profile. As described in steps SS331-SS334 above, the dynamic gear generation model first determines the number of basic tooth profiles of the dynamic real gear based on the temporal characteristics in the theoretical shear force feature flow. This number strictly corresponds to the load action time sequence of the theoretical model. Then, a dynamic mapping rule for tooth profile parameters is established based on the real shear force feature flow. Real-time feature data is mapped to gear geometric parameters through predefined conversion coefficients. Specifically, the frequency domain energy features are normalized and proportionally converted into tooth height parameters, so that the spectral energy distribution at each moment directly determines the longitudinal dimension of a single tooth profile. At the same time, the gradient change obtained by differentiating the temporal amplitude features is converted into tooth surface profile fluctuation features. The increase or decrease trend of the real-time shear force value determines the steepness of the tooth profile curvature. The entire mapping process uses a sliding time window mechanism for dynamic updates to ensure that the gear geometric features can reflect the changes in the structural stress state in real time. The generated dynamic real gear maintains the temporal synchronization with the theoretical gear and intuitively presents the difference between the actual stress state and the theoretical model through variable tooth profile parameters.

[0024] In one embodiment, step S4, which connects the theoretical gears and dynamic real gears of each cross-section in series via a virtual drive shaft to form a full-axle gear transmission chain and uses the load as input torque to drive the full-axle gear transmission chain, includes: S41. Meshing together the theoretical gear and the dynamic actual gear at the same cross section to form a cross section measurement gear set; S42. Construct a virtual transmission shaft to connect the cross-sectional measurement gear sets of each section in series to generate a full-bridge gear transmission chain; S43. Set a torque sensing window in the full axle gear transmission chain to monitor the load input torque in real time and use the load input torque as the driving force of the full axle gear transmission chain. As described in steps S41-S43 above, the theoretical gear and the dynamic real gear of the same cross section are first precisely meshed to form a cross-section measurement gear set. This meshing process ensures that the two gears can achieve interlocking engagement through a uniform number of tooth profiles. The theoretical gear serves as a reference, while the dynamic real gear adjusts its tooth profile parameters according to the real-time feature flow. The two gears transmit motion through tooth surface contact, which is achieved through parameter matching and state coupling algorithms. The system performs phase synchronization numerical alignment of the fixed tooth profile parameters of the theoretical gear and the real-time tooth profile parameters of the dynamic real gear, and calculates the tooth profile parameters of the two gears in the corresponding time sequence. The system establishes a virtual meshing state based on geometric compatibility within the window. When parameters such as tooth height and tooth width meet the preset meshing tolerance range, the system determines that tooth surface contact is established and derives the motion transmission ratio based on the mathematical relationship between the parameters. The change in meshing state is quantified by the deviation of the real-time rotational speed of the dynamic real gear from the reference rotational speed of the theoretical gear. The step of deriving the motion transmission ratio is as follows: when the tooth profile parameters of the two gears meet the meshing conditions, their angular velocity ratio is equal to the inverse ratio of their effective radii. The system calculates the equivalent meshing radius using the real-time tooth height parameter of the dynamic real gear and compares this radius with the fixed radius of the theoretical gear. A proportional relationship between the rotational speeds of the two gears is established. This motion transmission ratio directly reflects the correspondence between the theoretical shear force distribution and the actual measured data. When changes in tooth profile parameters lead to changes in the equivalent radius, the motion transmission ratio is adjusted accordingly, thereby quantifying the degree of deviation between the dynamic real gear and the theoretical gear. Subsequently, a virtual transmission shaft is constructed that runs through each cross-section. This transmission shaft serves as the mechanical transmission medium connecting all cross-section measured gear sets. By establishing a unified angular displacement coordinate system, torque and rotational speed are transmitted across cross-sections. The virtual transmission shaft manifests as a mathematical modeling unit with torsional stiffness in the system. The dynamics of the transmission chain are solved in real time. The design concept of using load input torque as the driving force of the transmission chain is based on the principle of mechanical equivalence. The external load borne by the bridge generates a corresponding shear force distribution inside the structure. The load input torque monitored through the torque sensing window is a concentrated manifestation of this mechanical transformation. When the load input torque acts on the starting end of the transmission chain, it will generate a corresponding dynamic response in the entire gear transmission chain. The speed difference and torque transmission efficiency between the gear sets measured at each section directly reflect the actual shear force distribution law in various parts of the bridge, thus establishing a visual monitoring mechanism from external load to internal shear force distribution.

[0025] In one embodiment, step S5, which involves real-time monitoring of the meshing state of the full-bridge gear transmission chain and calculating the shear force distribution ratio of the bridge web by analyzing the operating data between the gears, includes: S51. Construct a meshing state monitoring unit and a differential speed monitoring unit; S52. The meshing clearance and tooth contact surface of the theoretical gear and the dynamic real gear in each section of the gear set are collected in real time by the meshing state monitoring unit as meshing data. S53. Real-time acquisition of differential data of the full-axle gear transmission chain based on the differential monitoring unit; S54. Use meshing data and differential data as operating data; S55. The shear force distribution ratio of the bridge web is generated based on the analysis of operating data and load input torque using neural networks. As described in steps S51-S55 above, the system first analyzes the tooth profile parameter matching degree between the theoretical gear and the dynamic real gear in each cross-section of the gear set in real time through the meshing state monitoring unit. When the tooth height ratio and tooth width ratio of the two exceed the preset threshold, it is recorded as a meshing abnormality. At the same time, the fluctuation frequency and amplitude of the tooth surface contact surface are continuously monitored as meshing state data. The differential speed monitoring unit calculates the ratio change of the theoretical gear speed and the dynamic real gear speed in real time through a high-precision time alignment mechanism, and monitors the speed gradient distribution of adjacent gear sets in the transmission chain. These operating data and the load input torque together constitute a multi-dimensional feature vector, which is input into a pre-trained deep neural network. The network adopts a multi-branch architecture, in which the convolutional neural network branch extracts the spatial features of the meshing data, and the long short-term memory network branch captures the temporal pattern of the differential speed data. Finally, the load torque is used as the working condition weight and the extracted features are weighted by an attention mechanism through the feature fusion layer. A nonlinear mapping relationship from the dynamic characteristics of the gear transmission chain to the shear force distribution ratio is established in the fully connected layer. The network output layer is processed using SoftMax normalization. The system ultimately generates the real-time shear force distribution ratio of the bridge web and continuously optimizes the model parameters through an online learning mechanism to adapt to changes in the bridge's operational state. Furthermore, this scheme can quickly identify potential shear force imbalances at various bridge sections. Specifically, when irreversible damage occurs at a point on a section, rendering it unable to withstand the corresponding shear force, the dynamic real-world gear at that section may fail to mesh with the theoretical gear. This is because shear force imbalance means the tooth profile parameters of the dynamic real-world gear are almost zero, making it physically impossible to mesh with the ideal gear. Conversely, if the structure at a certain point experiences shear force exceeding its capacity, the dynamic real-world gear will become too large and also fail to mesh with the theoretical gear. Therefore, this scheme can not only detect the shear force distribution ratio of the bridge web but also monitor the stress conditions of other sections. Moreover, when abnormal shear force data appears on the bridge web, fault diagnosis can be performed based on the meshing status of the gear sets measured at other sections. In other words, this scheme achieves early warning and precise location of structural performance degradation, greatly improving the intuitiveness of monitoring and system-level diagnostic capabilities.

[0026] Example 2, please refer to Figure 2 As shown in this embodiment, the bridge web shear force distribution ratio monitoring system based on multi-section collaboration includes: Theoretical Gear Generation Module: Constructs a theoretical model of the bridge, obtains theoretical shear force distribution data of the bridge based on the theoretical model, extracts data features of the theoretical shear force distribution data as theoretical shear force feature flow, and generates theoretical gears for each section based on the theoretical shear force feature flow; Sensor network module: Connected to the theoretical gear generation module, a strain sensor network is deployed at multiple key sections of the bridge to synchronously acquire the actual shear force distribution data of the sections; Realistic gear generation module: Connected to the sensor network module, it extracts the data features of the real shear force distribution data as the real shear force feature stream, and generates dynamic realistic gears for each section based on the real shear force feature stream; Gear transmission chain generation module: Connected to the real gear generation module, it connects the theoretical gears and dynamic real gears of each section in series through the virtual transmission shaft to form a full-bridge gear transmission chain, and uses the load as the input torque to drive the full-bridge gear transmission chain; Shear force distribution analysis module: Connected to the gear transmission chain generation module, it monitors the meshing state of the entire bridge gear transmission chain in real time and calculates the shear force distribution ratio of the bridge web by analyzing the running data between the gears.

[0027] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for monitoring the shear distribution ratio of a bridge web based on multi-section coordination, characterized in that, a theoretical model of the bridge is constructed, theoretical shear distribution data of the bridge is obtained based on the theoretical model, data features of the theoretical shear distribution data are extracted as a theoretical shear feature flow, and theoretical gears of each section are generated based on the theoretical shear feature flow; a strain sensor network is arranged at multiple key sections of the bridge, real-time data acquisition is performed to obtain real shear distribution data of the sections; data features of the real shear distribution data are extracted as a real shear feature flow, and dynamic real gears of each section are generated based on the real shear feature flow; theoretical gears and dynamic real gears of each section are connected through virtual transmission shafts to form a full-bridge gear transmission chain, and the load is taken as an input torque to drive the full-bridge gear transmission chain; the engagement state of the full-bridge gear transmission chain is monitored in real time, and the shear distribution ratio of the bridge web is calculated by analyzing the operation data between the gears.

2. The multi-cross-section synergy based monitoring method of a bridge web shear distribution ratio according to claim 1, wherein, The steps of constructing a theoretical model of the bridge, obtaining theoretical shear distribution data of the bridge based on the theoretical model, extracting data features of the theoretical shear distribution data as a theoretical shear feature flow, and generating theoretical gears of each section based on the theoretical shear feature flow are as follows: a finite element model of the bridge is constructed based on a finite element method, and theoretical shear distribution data of each section under different load conditions are calculated through the finite element model; theoretical shear features of the theoretical shear data are extracted based on a feature extraction algorithm, and the theoretical shear features include spectral features, gradient features, and time sequence features; a gear mapping unit is constructed, and the theoretical shear feature flow is input into the gear mapping unit to be mapped into a gear model with fixed gear shape parameters and a radius; wherein the gear shape parameters include the number of gear shapes, the gear height, and the gear width, the number of gear shapes is directly proportional to the time sequence features of the theoretical shear feature flow, and the gear height and the gear width are directly proportional to the spectral features and the gradient features of the theoretical shear feature flow.

3. The multi-cross-section synergy based bridge web shear distribution ratio monitoring method according to claim 1, wherein, The steps of arranging a strain sensor network at multiple key sections of the bridge, performing synchronous data acquisition to obtain real shear distribution data of the sections are as follows: the strain sensor network is arranged at multiple key sections of the bridge, wherein the key sections include a lining concrete section, a stiffening rib section, and a pure steel web section; strain sensors are arranged at a preset distance in each key section to generate a shear monitoring network covering the full section of the box girder; real shear distribution data of each key section are collected in real time based on the shear monitoring network, and time sequence labels are labeled for the real shear distribution data.

4. The multi-cross-section synergy based bridge web shear distribution ratio monitoring method according to claim 1, wherein, The steps of extracting data features of the real shear distribution data as a real shear feature flow, and generating dynamic real gears of each section based on the real shear feature flow are as follows: a real-time signal processing unit is constructed, multi-dimensional feature extraction is performed on the real shear distribution data based on the real-time signal processing unit, and a real shear feature flow is generated; wherein the real shear feature flow includes time domain amplitude features, frequency domain energy features, and time sequence gradient features; a dynamic gear generation model is established, and the real shear feature flow is mapped into dynamic real gears.

5. The multi-slice synergy based bridge web shear distribution ratio monitoring method according to claim 1, wherein, The steps of establishing a dynamic gear generation model and mapping the real shear feature flow into dynamic real gears are as follows: the number of gear shapes of the dynamic real gears is determined based on the time sequence features of the theoretical shear feature flow; A dynamic mapping rule of the real gear profile parameters is constructed based on the real shear force feature flow; The frequency domain energy feature in the real shear force feature flow is mapped to the tooth height, and the frequency domain energy feature at a moment determines the height of a single gear profile; The time domain amplitude feature in the real shear force feature flow is mapped to the tooth surface profile fluctuation feature, and the change trend of the real-time shear force value determines the steepness of the gear profile.

6. The multi-cross-section synergy based bridge web shear distribution ratio monitoring method according to claim 1, wherein, The steps of driving the full-bridge gear transmission chain by taking the load as the input torque are as follows: The theoretical gear and the dynamic real gear in the same section are meshed to form a section measurement gear set; The section measurement gear sets in different sections are connected by the virtual transmission shaft to form the full-bridge gear transmission chain; The torque sensing window is arranged in the full-bridge gear transmission chain to monitor the load input torque in real time, and the load input torque is taken as the driving force of the full-bridge gear transmission chain.

7. The multi-cross-section synergy based bridge web shear distribution ratio monitoring method according to claim 6, wherein, The steps of monitoring the meshing state of the full-bridge gear transmission chain in real time and calculating the shear force distribution ratio of the bridge web by analyzing the running data between the gears are as follows: The meshing state monitoring unit and the differential monitoring unit are constructed; The meshing gap between the theoretical gear and the dynamic real gear in the section measurement gear set and the gear contact surface are taken as the meshing data based on the meshing state monitoring unit; The differential data of the full-bridge gear transmission chain are collected in real time based on the differential monitoring unit; The meshing data and the differential data are taken as the running data; The shear force distribution ratio of the bridge web is generated based on the neural network analysis of the running data and the load input torque. 8.A bridge web shear force distribution ratio monitoring system based on multi-section cooperation, used to realize the bridge web shear force distribution ratio monitoring method based on multi-section cooperation according to any one of claims 1-7, characterized in that, a theoretical gear generation module: a theoretical model of the bridge is constructed, the theoretical shear force distribution data of the bridge are obtained based on the theoretical model, the data features of the theoretical shear force distribution data are extracted as the theoretical shear force feature flow, and the theoretical gears in different sections are generated based on the theoretical shear force feature flow; a sensor network module: connected with the theoretical gear generation module, a strain sensor network is arranged at multiple key sections of the bridge to collect the real shear force distribution data of the sections synchronously; a real gear generation module: connected with the sensor network module, the data features of the real shear force distribution data are extracted as the real shear force feature flow, and the dynamic real gears in different sections are generated based on the real shear force feature flow; a gear transmission chain generation module: connected with the real gear generation module, the theoretical gears and the dynamic real gears in different sections are connected by a virtual transmission shaft to form a full-bridge gear transmission chain, and the load is taken as the input torque to drive the full-bridge gear transmission chain; a shear force ratio analysis module: connected with the gear transmission chain generation module, the meshing state of the full-bridge gear transmission chain is monitored in real time, and the shear force distribution ratio of the bridge web is calculated by analyzing the running data between the gears.

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