Bridge vehicle load analysis method based on temperature compensation and related device

Abstract

The invention relates to the technical field of vehicle load analysis, and provides a bridge vehicle load analysis method based on temperature compensation and a related device, and the method comprises the steps: obtaining a temperature measurement sample data set corresponding to a temperature sensor of a to-be-detected bridge, acquiring a strain measurement sample data set corresponding to a strain sensor of the to-be-detected bridge; performing verification processing on each piece of temperature measurement sample data in the temperature measurement sample data set to obtain a temperature verification sample data set; constructing a temperature strain analysis model according to the temperature verification sample data set and the strain measurement sample data set; based on the temperature strain analysis model, performing temperature compensation processing according to the to-be-processed temperature data set and the to-be-processed strain data set to obtain a target strain compensation value set; and performing vehicle load analysis processing on each target strain compensation value in the target strain compensation value set to obtain a vehicle load analysis result of the to-be-detected bridge, thereby improving the accuracy of the vehicle load analysis process.

Description

Technical Field

[0001] The present application relates to the technical fields of vehicle load analysis and data processing, and in particular to a vehicle load analysis method for a bridge based on temperature compensation and related devices. Background Art

[0002] In the field of bridge engineering, vehicle load analysis is crucial for accurately evaluating the safety, durability, and long-term performance of bridge structures. Currently, with the continuous development of transportation infrastructure, the traffic volume carried by bridges is increasing, and the types of vehicles and driving conditions are becoming more complex and diverse. Among the existing vehicle load analysis methods, most methods only focus on a single or a few influencing factors, and do not fully consider the comprehensive effects of multiple factors. For example, when calculating vehicle loads, they are often based only on the static design parameters of the bridge and the rated load of the vehicle, ignoring the impact of other influencing factors on the mechanical properties of the bridge structure during the actual operation of the bridge, resulting in inaccurate vehicle load analysis results. Therefore, how to improve the accuracy of the vehicle load analysis process has become a problem that needs to be solved urgently. Summary of the Invention

[0003] The embodiments of the present application provide a vehicle load analysis method and related device for a bridge based on temperature compensation, which can construct a temperature-strain analysis model based on a temperature measurement sample data set and a strain measurement sample data set, and perform compensation processing on a to-be-processed temperature data set and a to-be-processed strain data set based on the temperature-strain analysis model to improve the accuracy of the obtained target strain compensation value set.

[0004] A first aspect of an embodiment of the present application provides a vehicle load analysis method for a bridge based on temperature compensation, the method comprising: Obtaining a temperature measurement sample data set corresponding to a temperature sensor of the bridge to be inspected, and obtaining a strain measurement sample data set corresponding to a strain sensor of the bridge to be inspected; Performing verification processing on each temperature measurement sample data in the temperature measurement sample data set to obtain a temperature verification sample data set; Constructing a temperature-strain analysis model based on the temperature calibration sample data set and the strain measurement sample data set; Based on the temperature-strain analysis model, compensation processing is performed according to the temperature data set to be processed and the strain data set to be processed to obtain a target strain compensation value set; A vehicle load analysis process is performed on each target strain compensation value in the target strain compensation value set to obtain a vehicle load analysis result of the bridge to be inspected.

[0005] In this example, by obtaining a temperature measurement sample data set corresponding to the temperature sensor of the bridge to be inspected, and obtaining a strain measurement sample data set corresponding to the strain sensor of the bridge to be inspected, each temperature measurement sample data in the temperature measurement sample data set can be verified to obtain a temperature verification sample data set, and further based on the temperature verification sample data set and the strain measurement sample data set, a temperature strain analysis model can be constructed, so that based on the temperature strain analysis model, temperature compensation processing can be performed according to the temperature data set to be processed and the strain data set to be processed to obtain a target strain compensation value set, and then vehicle load analysis processing can be performed on each target strain compensation value in the target strain compensation value set to obtain a more accurate vehicle load analysis result of the bridge to be inspected, thereby removing the temperature strain value caused by the temperature factor, which is conducive to achieving the purpose of more accurate vehicle load analysis.

[0006] A second aspect of an embodiment of the present application provides a vehicle load analysis device for a bridge based on temperature compensation, the device comprising: an acquisition module, configured to acquire a temperature measurement sample data set corresponding to a temperature sensor of a bridge to be detected, and acquire a strain measurement sample data set corresponding to a strain sensor of the bridge to be detected; A first processing module is configured to perform verification processing on each temperature measurement sample data in the temperature measurement sample data set to obtain a temperature verification sample data set; A second processing module is configured to construct a temperature-strain analysis model based on the temperature verification sample data set and the strain measurement sample data set; a third processing module, configured to perform compensation processing based on the temperature data set to be processed and the strain data set to be processed based on the temperature-strain analysis model to obtain a target strain compensation value set; The fourth processing module is configured to perform vehicle load analysis processing on each target strain compensation value in the target strain compensation value set to obtain a vehicle load analysis result of the bridge to be inspected.

[0007] A third aspect of an embodiment of the present application provides a terminal, comprising a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions and execute the step instructions in the first aspect of the embodiment of the present application.

[0008] The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application.

[0009] A fifth aspect of the embodiments of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0011] Figure 1 A schematic structural diagram of a vehicle load analysis system for a bridge based on temperature compensation is provided for an embodiment of the present application; Figure 2 A flow chart of a vehicle load analysis method for a bridge based on temperature compensation is provided for an embodiment of the present application; Figure 3 A schematic diagram of the structure of a terminal provided in an embodiment of the present application; Figure 4 A schematic structural diagram of a vehicle load analysis device for a bridge based on temperature compensation is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0013] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0014] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0015] To better understand the temperature-compensated bridge vehicle load analysis method provided in the embodiments of the present application, the following first briefly describes the scenarios in which the temperature-compensated bridge vehicle load analysis method is applied. Bridges bear various vehicle loads during use. Accurate vehicle load analysis can determine the maximum load and load distribution borne by the bridge, providing a key basis for bridge management and life assessment. It can also prevent safety issues such as structural damage and excessive deformation caused by excessive loads. However, existing vehicle load analysis methods have significant limitations. Most current vehicle load analysis methods only focus on a single or a few influencing factors and do not fully consider the combined effects of multiple factors. For example, when calculating vehicle loads, they are often based solely on the static design parameters of the bridge and the rated load of the vehicle, ignoring the impact of temperature changes on the mechanical properties of the bridge structure during actual operation. Temperature fluctuations cause the bridge material to expand and contract, changing the bridge's stiffness and stress distribution, thereby affecting the transmission and distribution of vehicle loads on the bridge, and thus resulting in inaccurate vehicle load analysis. The embodiment of the present application aims to solve the problem of inaccurate vehicle load analysis process, and provides a vehicle load analysis method for bridges based on temperature compensation. This method can further construct a temperature-strain analysis model through the acquired temperature measurement sample data set and strain measurement sample data set, and can remove the temperature strain value caused by the temperature factor to obtain strain data that can better reflect the actual strain situation, which is conducive to achieving the purpose of more accurate vehicle load analysis.

[0016] The vehicle load analysis method for a bridge based on temperature compensation can be applied to a vehicle load analysis system for a bridge based on temperature compensation. Figure 1The figure shows a schematic diagram of the structure of a vehicle load analysis system for a bridge based on temperature compensation. Figure 1 As shown, the vehicle load analysis system for a bridge based on temperature compensation may include: a data acquisition module, a temperature measurement data verification module, a temperature strain analysis model construction module, a temperature compensation processing module and a vehicle load analysis module. Among them, the data acquisition module can be responsible for acquiring the temperature measurement sample data generated by the temperature sensor on the bridge to be inspected, as well as the strain measurement sample data generated by the strain sensor, and can integrate them into a corresponding data set; the temperature measurement data verification module can perform a verification operation on each temperature measurement sample data in the temperature measurement sample data set, so as to output a more accurate and reliable temperature verification sample data set by eliminating outliers, correcting deviations, etc.; the temperature strain analysis model construction module can build a temperature strain analysis model that can reflect the relationship between temperature and strain based on the cooperation between the temperature verification sample data set and the strain measurement sample data set; the temperature compensation processing module can use the constructed temperature strain analysis model to carry out temperature compensation operations on the temperature data set to be processed and the strain data set to be processed, so as to obtain a target strain compensation value set after eliminating the temperature influence; the vehicle load analysis module can further perform vehicle load analysis on each target strain compensation value in the target strain compensation value set, so as to comprehensively consider various factors and ultimately generate a more accurate vehicle load analysis result corresponding to the bridge to be inspected.

[0017] See also Figure 2 , Figure 2 The present invention provides a flow chart of a vehicle load analysis method for a bridge based on temperature compensation, which includes: S10: Acquire a temperature measurement sample data set corresponding to the temperature sensor of the bridge to be detected, and acquire a strain measurement sample data set corresponding to the strain sensor of the bridge to be detected.

[0018] The temperature measurement sample data set may include temperature measurement sample data measured by one or more temperature sensors installed on the bridge to be inspected within one or more time periods. In other words, the temperature measurement sample data can be understood as temperature sample data corresponding to multiple measurement points and multiple different time periods measured by each temperature sensor installed on the bridge to be inspected. It is understood that the temperature measurement sample data may reflect the temperature of the environment in which the bridge to be inspected was located at that time.

[0019] It should be noted that the temperature measurement sample data set and the temperature data set to be processed mentioned later can be temperature data sets corresponding to different time periods. For example, the temperature measurement sample data set can be the temperature data set corresponding to January-December last year, and the temperature data set to be processed can be the temperature data set corresponding to January-March this year. This application does not impose any restrictions on this.

[0020] The strain measurement sample data set may include strain measurement sample data measured by one or more strain sensors installed on the bridge to be inspected within one or more time periods. In other words, the strain measurement sample data can be understood as strain sample data corresponding to multiple measurement points acquired by the strain sensors on the bridge to be inspected during multiple different time periods. It should be noted that the measurement time periods of the strain measurement sample data can correspond one-to-one with the measurement time periods of the aforementioned temperature measurement sample data, so that the temperature and strain values ​​at the same measurement point during the same time period can be analyzed during subsequent processing. It is understood that the strain measurement sample data can reflect the deformation of the bridge structure under the influence of various factors.

[0021] It should be noted that the strain measurement sample data set and the strain data set to be processed mentioned later can be strain data sets corresponding to different time periods. For example, the strain measurement sample data set can be the strain data set corresponding to January-December last year, and the strain data set to be processed can be the strain data set corresponding to January-March this year. This application does not impose any restrictions on this.

[0022] It should be noted that the strain value obtained by the strain sensor may be affected by many factors, among which temperature can be regarded as a more critical influencing factor. Temperature changes will cause thermal expansion and contraction of the bridge structure material, and then the strain value measured by the strain sensor will include the vehicle load strain value caused by the vehicle load and the temperature strain value caused by the temperature. Specifically, when the temperature rises, the bridge structure will expand. Even if the bridge is not affected by external loads at this time, the strain sensor may measure a positive strain value; on the contrary, when the temperature drops, the bridge structure contracts, and the strain sensor may measure a negative strain value.

[0023] The strain caused by temperature changes, referred to as temperature strain, is superimposed on the strain caused by vehicle loads on the bridge structure. Consequently, the strain values ​​measured by the strain sensor cannot accurately reflect the actual vehicle load conditions borne by the bridge structure. Therefore, when analyzing and applying bridge strain measurement sample data, the influence of temperature factors must be considered, and appropriate temperature compensation measures must be implemented to eliminate or reduce the interference of temperature strain in order to obtain more accurate strain information that can be used to assess the health of the bridge structure. This is not a limitation of this application.

[0024] S20: performing verification processing on each temperature measurement sample data in the temperature measurement sample data set to obtain a temperature verification sample data set.

[0025] The temperature calibration sample data set may include one or more temperature calibration sample data. This temperature calibration sample data can be understood as more accurate temperature data obtained after calibration processing of each temperature measurement sample data. Calibration of the temperature measurement sample data can further eliminate measurement errors caused by the temperature sensor, thereby ensuring the accuracy and reliability of the temperature data after calibration processing.

[0026] Specifically, data cleaning can be performed on each temperature measurement sample data set to check for obvious errors or abnormal values ​​in the temperature measurement sample data, such as temperature values ​​outside a reasonable range or missing data, and the abnormal data can be corrected or deleted. Optionally, the temperature sensor can be calibrated, such as by comparing the temperature sensor with a high-precision standard thermometer to obtain a calibration curve or calibration coefficient. The temperature measurement sample data can then be calibrated based on the calibration curve or calibration coefficient to eliminate the systematic errors of the sensor itself.

[0027] Optionally, a repeatability check can be used, such as measuring the temperature of the same test point multiple times to check the repeatability of the temperature measurement data. If the multiple temperature measurements of the same test point differ significantly, the cause can be further analyzed and adjustments can be made. Optionally, data filtering can be used, such as mean filtering or median filtering, to remove noise from the temperature measurement sample data, thereby improving the stability and accuracy of the temperature measurement sample data. This application does not impose any restrictions on this.

[0028] S30: Constructing a temperature-strain analysis model according to the temperature verification sample data set and the strain measurement sample data set.

[0029] The temperature-strain analysis model can be understood as a model that describes the relationship between temperature and strain in a bridge structure, constructed based on a set of temperature calibration sample data and a set of strain measurement sample data. By constructing this model, a better correlation between temperature and strain can be established, allowing the influence of temperature on strain to be eliminated during subsequent temperature compensation, resulting in more accurate and intuitive vehicle load analysis results.

[0030] It should be understood that constructing a temperature-strain analysis model based on the temperature verification sample data and the strain measurement sample data refers to the process of constructing a temperature-strain analysis model that indicates the correlation between temperature and strain based on the temperature verification sample data and the strain measurement sample data. Specifically, step S30, i.e., constructing a temperature-strain analysis model based on the temperature verification sample data and the strain measurement sample data, may include the following steps: S31: performing feature mapping on the temperature calibration sample data and the strain measurement sample data to obtain a high-dimensional linear feature vector; S32: constructing a temperature-strain analysis function according to the temperature verification sample data and the strain measurement sample data; S33: determining a temperature-strain constraint condition according to the temperature verification sample data, the strain measurement sample data, and the high-dimensional linear feature vector; S34: constructing a temperature strain regression optimization function according to the temperature strain analysis function and the temperature strain constraint condition; S35: performing optimal solution processing on the temperature-strain regression optimization function to obtain a target Lagrange multiplier set and a target temperature-strain analysis parameter set; S36: Constructing a temperature strain analysis model according to the target Lagrange multiplier set and the target temperature strain analysis parameter set.

[0031] The high-dimensional linear feature vector can be understood as the vector representation in high-dimensional space obtained by feature mapping the temperature calibration sample data and the strain measurement sample data. This high-dimensional linear feature vector can reflect the characteristic information of the original data (i.e., the temperature calibration sample data and the strain measurement sample data) in high-dimensional space, and this characteristic information can have a linear correlation in the high-dimensional space.

[0032] Specifically, an appropriate feature mapping function can be selected based on the data characteristics of the temperature verification sample data and the strain measurement sample data and the complexity of the problem to be solved (i.e., the correlation between temperature and strain). For example, a radial basis kernel function, a linear kernel function, or a polynomial kernel function can be used to perform feature mapping on the temperature verification sample data and the strain measurement sample data to obtain a high-dimensional linear feature vector. Optionally, the present application selects a radial basis kernel function to perform feature mapping on the temperature verification sample data and the strain measurement sample data to obtain a high-dimensional linear feature vector, which does not constitute a limitation on the present application.

[0033] For example, the temperature calibration sample data and the strain measurement sample data are integrated to obtain the temperature strain data set. Take this as an example to illustrate; specifically, the temperature strain data set is obtained The process can be seen in the following formula: in, represents the temperature-strain dataset; Indicates the first temperature calibration sample data; Represents the first strain measurement sample data; Indicates the second temperature calibration sample data; Represents the second strain measurement sample data; Indicates the nth temperature calibration sample data; Represents the nth strain measurement sample data.

[0034] Optionally, the present application adopts a radial basis kernel function to implement a process of feature mapping the temperature calibration sample data and the strain measurement sample data and obtaining a high-dimensional linear feature vector, as shown in the following formula: in, It is a feature mapping function that maps input data to a high-dimensional feature space. Indicates mapping the i-th temperature calibration sample data to the high-dimensional feature space, Represents the i-th temperature calibration sample data, Indicates mapping the i-th strain measurement sample data to the high-dimensional feature space, represents the i-th strain measurement sample data, Indicates the index of temperature calibration sample data or strain measurement sample data; Indicates that radial basis kernel function is used for calculation and The similarity between them is that this application defines the inner product of two samples (i.e., temperature calibration sample data and strain measurement sample data) in the feature space through the radial basis kernel function, implicitly realizing the mapping to the high-order feature space, and avoiding the direct calculation of the feature mapping function in the subsequent calculation process. and perform complex operations in high-dimensional space, so that the feature mapping function can be used without knowing it explicitly. In the specific form of , the inner product of the sample in the high-dimensional feature space is calculated, which is conducive to better model training and prediction operations; Indicates that the exponential function is used for calculation; Represents a hyperparameter, which is usually called a kernel coefficient in applications related to the radial basis kernel function; represents the norm, usually the Euclidean norm; Represents calculation and The Euclidean distance between them.

[0035] It should be noted that the radial basis kernel function used in this application can be calculated by two samples (i.e. and ) to measure their similarity. The width of the radial basis kernel function can be controlled. The larger it is, the narrower the function is and the stronger the fitting ability to local data is, but it may lead to overfitting; The smaller the value, the wider the function and the stronger the generalization ability of the model, but it may lead to poor fitting effect, which is not limited in this application. Optionally, the radial basis kernel function can be used to calculate the similarity between the two temperature calibration sample data, that is, ; The radial basis kernel function can also be used to calculate the similarity between two strain measurement sample data, that is, , this application does not limit this.

[0036] The temperature-strain analysis function can be understood as a mathematical function constructed based on temperature calibration sample data and strain measurement sample data, and can be used to describe the relationship between temperature and strain. Optionally, the temperature-strain analysis function may generally include model complexity terms and data fit terms, which are not limited in this application. Optionally, the model parameters may be determined based on the principles of solving optimization problems, which are not limited in this application.

[0037] Temperature-strain constraints can be understood as restrictions imposed on model parameters or variables based on the characteristics of the temperature calibration sample data and the strain measurement sample data, as well as the requirements of the actual problem being solved. These temperature-strain constraints can further ensure the rationality and feasibility of the model. For example, they can limit the error range between the model's predicted values ​​and the true values, though this application does not impose any restrictions on this.

[0038] Specifically, the present application constructs a temperature-strain analysis function based on the temperature verification sample data and the strain measurement sample data, and determines the temperature-strain constraint condition based on the temperature verification sample data and the strain measurement sample data. The process can be seen in the following formula: Temperature strain analysis function: in, represents the target weight parameter, represents the target bias parameter, and is the slack variable, 、 、 and are all model parameters; Indicates the model parameters 、 、 and To obtain the temperature strain analysis function The minimum value of Indicates the index of temperature calibration sample data or strain measurement sample data or relaxation variable, Indicates the number of temperature calibration sample data or strain measurement sample data in the temperature-strain dataset; express The norm of is the penalty parameter; represents the temperature strain constraint; Represents the i-th strain measurement sample data; represents the transpose of the target weight parameter; represents the feature mapping function that maps the input data to a high-dimensional feature space, Indicates mapping the i-th temperature calibration sample data to a high-dimensional feature space; is an insensitive loss parameter.

[0039] It should be noted that this application constructs and obtains a temperature strain regression optimization function based on the aforementioned temperature strain analysis function and temperature strain constraints, and based on the principles of a support vector machine regression process. That is, the aforementioned temperature strain analysis function and temperature strain constraints collectively constitute the temperature strain regression optimization function, and this application imposes no restrictions on this. The support vector machine regression process can be understood as finding an optimal hyperplane (in a high-dimensional feature space) to fit the data, minimizing the model's error on the training data while maintaining a low level of model complexity to avoid overfitting.

[0040] in, It can be regarded as an objective function, which can be used to control the complexity of the model and make the model have better generalization ability; Represents the weight vector The smaller it is, the simpler the model is and the stronger the generalization ability is. It can be seen as a penalty for the error in training data. It can be a preset constant used to balance model complexity and training error; The larger the value, the heavier the penalty for the error, and the model will be more inclined to reduce the training error, but it may lead to overfitting. Optionally, the initial value of the penalty parameter can be determined according to the empirical value method. For example, in a support vector machine, when dealing with general classification or regression problems, the C value of the initial attempt can be set to 1. C=1 can achieve a relatively reasonable balance between model complexity and training error. Optionally, a cross-validation method or a grid search method can also be used to determine the initial value of the penalty parameter, and this application does not impose any restrictions on this. The insensitive loss parameter can be expressed as Errors within this range are not penalized and can be used to control the accuracy of the model's fit to the data. and It is a slack variable. During the model training process, the slack variable can allow the data points to deviate from the ideal fitting straight line to a certain extent in order to deal with the noise and outliers in the data. Optionally, a rough range of values ​​for the insensitive loss parameter can be determined based on the empirical value method. For example, in a regression problem, if the noise level of the data is low, a smaller τ value, such as 0.01, can be selected; if the noise level is high, a larger τ value, such as 0.1, can be selected. This application does not impose any restrictions on this. Optionally, a cross-validation method or a grid search method can also be used to determine the initial value of the insensitive loss parameter. This application does not impose any restrictions on this.

[0041] Specifically, the temperature strain constraint condition means that for each data point , predicted value and the true value The error between range, otherwise the slack variable and To allow a certain deviation and punish it in the objective function.

[0042] Furthermore, the optimal solution for the temperature strain regression optimization function can be understood as the temperature strain data set Based on this, by adjusting the model parameters 、 、 and , in order to further obtain the target Lagrange multiplier set and target temperature strain analysis parameter set (i.e. the adjusted 、 、 and ) to find a balance between minimizing model complexity and minimizing training error; the radial basis kernel function can help the model better handle nonlinear relationships by mapping data to a high-dimensional feature space, thereby better reducing the complexity of the model in the optimal solution process to train a more accurate model.

[0043] The target Lagrangian multiplier set can include one or more target Lagrangian multipliers, which can be understood as a set of Lagrangian multipliers obtained by optimally solving the temperature-strain regression optimization function. When constructing a temperature-strain analysis model, the target Lagrangian multipliers can be used to determine the model's parameters and structure.

[0044] The target temperature-strain analysis parameter set may include one or more target temperature-strain analysis parameters. The target temperature-strain analysis parameters can be understood as a set of parameters obtained by solving the temperature-strain regression optimization function. These parameters can be used together with the target Lagrange multiplier to construct the final temperature-strain analysis model, thereby achieving the goal of describing the correlation between temperature and strain.

[0045] It should be understood that performing an optimal solution processing on the temperature-strain regression optimization function based on the sequential minimum optimization algorithm to obtain a target Lagrangian multiplier set and a target temperature-strain analysis parameter set refers to the process of performing an optimal solution processing on the temperature-strain regression optimization function to obtain optimal model parameters. Step S35, i.e., performing an optimal solution processing on the temperature-strain regression optimization function based on the sequential minimum optimization algorithm to obtain a target Lagrangian multiplier set and a target temperature-strain analysis parameter set, may include the following steps: S351: Based on a sequence minimum optimization algorithm and a Lagrange multiplier method, performing a dual transformation on the temperature-strain regression optimization function to obtain a temperature-strain dual function; S352: Selecting a reference Lagrange multiplier from the set of initialized Lagrange multipliers; S353: Optimizing and solving the temperature-strain dual function based on the reference Lagrangian multiplier to obtain a target Lagrangian multiplier set; S354: Calculating a bias according to each target Lagrangian multiplier in the target Lagrangian multiplier set to obtain a target bias parameter; S355: Calculate a weight vector according to each target Lagrange multiplier in the target Lagrange multiplier set to obtain a target weight parameter; S356: Determine a target temperature strain analysis parameter set according to the target bias parameter and the target weight parameter.

[0046] The Sequential Minimal Optimization (SMO) algorithm can be used to solve quadratic programming problems in support vector machines. Its core approach is to select two Lagrange multipliers for optimization at a time and iterate until convergence is met. The optimal solution process in this application essentially addresses a similar optimization problem: finding the optimal Lagrange multipliers and model parameters for the temperature-strain regression optimization function.

[0047] The temperature-strain dual function can be understood as the function used to further solve the model parameters after dualizing the temperature-strain regression optimization function using the Lagrange multiplier method. During the optimal solution process, the original problem (i.e., the aforementioned temperature-strain regression optimization function) can be transformed into its dual problem. Under certain conditions, the dual problem and the original problem have the same optimal solution, and the dual problem is generally easier to solve.

[0048] Optionally, the temperature-strain regression optimization function is subjected to dual transformation based on a sequential minimum optimization algorithm and a Lagrange multiplier method to obtain a temperature-strain dual function, as shown in the following formula: in, and is a Lagrange multiplier (e.g., called the i-th group of Lagrange multipliers), is the i-th strain measurement sample data, is the temperature calibration sample data sample, Indicates the temperature calibration sample data or strain measurement sample data or the index of the Lagrange multiplier. Indicates the number of temperature calibration sample data or strain measurement sample data in the temperature strain dataset. is an insensitive loss parameter, and is a Lagrange multiplier (e.g., called the jth group of Lagrange multipliers), The radial basis kernel function is used to calculate and similarities between; Indicates that the temperature-strain dual function is calculated for the i-th group of Lagrange multipliers The maximum value of is the constraint condition of the temperature-strain dual function, is the penalty parameter.

[0049] The initialization Lagrangian multiplier set may include one or more initialization Lagrangian multipliers. These initialization Lagrangian multipliers can be understood as initial Lagrangian multiplier values ​​pre-set before the optimization solution begins. It is understood that the initialization Lagrangian multiplier can be considered the starting point for subsequent iterative solutions, and this application does not impose any restrictions on this. The reference Lagrangian multiplier can be a Lagrangian multiplier selected from the initialization Lagrangian multiplier set for optimizing the temperature-strain dual function.

[0050] It should be noted that, during the optimal solution process, the present application can decompose the large optimization problem into multiple small sub-problems, and select a set of Lagrange multipliers (i.e., reference Lagrange multipliers) for optimization solution each time, while fixing other Lagrange multipliers to simplify the quadratic optimal solution problem into a binary quadratic optimal solution problem that is easier to solve, so that the Lagrange multipliers can be continuously iteratively updated in the subsequent process until the convergence conditions are met, thereby obtaining a target Lagrange multiplier set. The target Lagrange multiplier set may include one or more target Lagrange multipliers, which can be understood as a set of optimal Lagrange multipliers obtained after optimizing the temperature-strain dual function. Specifically, the convergence condition can be set as the update amount of the Lagrange multiplier is less than the update threshold, or the change in the objective function value is less than the change threshold, which is not limited by the present application.

[0051] After obtaining the target Lagrange multiplier set, a bias can be further calculated based on the target Lagrange multiplier to obtain a target bias parameter. Specifically, the bias can be calculated by selecting sample points that satisfy the Karush-Kuhn-Tucker (KKT) condition, which is not limited in this application.

[0052] For example, the sample points that meet the KKT condition are , the target Lagrange multiplier obtained is and Take this as an example to illustrate the calculation and solution of the bias The process can be seen in the following formula: in, Indicates the strain measurement sample data that meets the KKT conditions; Indicates temperature calibration sample data that meets KKT conditions; and is the target Lagrange multiplier; Indicates the index of temperature calibration sample data or strain measurement sample data or Lagrange multiplier; Indicates the number of temperature calibration sample data or strain measurement sample data in the temperature-strain dataset; Indicates that radial basis kernel function is used for calculation and similarities between; represents the target bias parameter; represents the insensitive loss parameter.

[0053] Furthermore, the weight vector can be calculated based on the target Lagrangian multiplier to obtain the target weight parameter. For example, the target Lagrangian multiplier obtained by solving is and For example, the process of calculating the weight vector can be seen in the following formula: in, represents the target weight parameter; and represents the target Lagrange multiplier; Indicates the index of temperature calibration sample data or strain measurement sample data or Lagrange multiplier; Indicates the number of temperature calibration sample data or strain measurement sample data in the temperature-strain dataset; represents the feature mapping function that maps the input data to a high-dimensional feature space, Indicates mapping the i-th temperature calibration sample data to a high-dimensional feature space. It should be noted that in actual calculations, it can usually be obtained indirectly through kernel functions. , rather than directly solving , this application does not limit this.

[0054] It should be noted that after the optimal solution is performed, a temperature strain analysis model can be constructed based on the target Lagrangian multiplier set and the target temperature strain analysis parameter set. and For example, the obtained temperature strain analysis model can refer to the following formula: in, It is a temperature strain analysis model used to predict the strain value corresponding to the temperature data to be processed; is any temperature data to be processed in the set of temperature data to be processed; The strain data predicted by the temperature-strain analysis model is the temperature-strain evaluation data to be mentioned later. and represents the target Lagrange multiplier; Indicates the index of temperature calibration sample data or strain measurement sample data or Lagrange multiplier; Indicates the number of temperature calibration sample data or strain measurement sample data in the temperature-strain dataset; Calculation using radial basis kernel function and similarities between; Represents the target bias parameter.

[0055] S40: Based on the temperature-strain analysis model, temperature compensation processing is performed according to the to-be-processed temperature data set and the to-be-processed strain data set to obtain a target strain compensation value set.

[0056] The target strain compensation value set may include one or more target strain compensation values, which may be understood as a strain value obtained after temperature compensation processing that more accurately reflects the actual strain condition of the bridge after eliminating the temperature influence.

[0057] It should be understood that performing compensation processing based on the to-be-processed temperature data set and the to-be-processed strain data set based on the temperature-strain analysis model to obtain a target strain compensation value set refers to the process of performing compensation processing based on the constructed temperature-strain analysis model to obtain a target strain compensation value set that reflects the actual strain conditions after eliminating the influence of temperature. Specifically, step S40, i.e., performing compensation processing based on the to-be-processed temperature data set and the to-be-processed strain data set based on the temperature-strain analysis model to obtain a target strain compensation value set, may include the following steps: S41: Based on the temperature strain analysis model, perform temperature strain analysis processing on each temperature data to be processed in the temperature data set to be processed to obtain a temperature strain evaluation data set; S42: performing compensation processing according to the temperature strain evaluation data set and the strain data set to be processed to obtain a reference compensated strain data set; S43: performing compensation result correction processing on each reference compensation strain data in the reference compensation strain data set to obtain a corrected strain compensation value set; S44: performing stability evaluation processing on each modified strain compensation value in the modified strain compensation value set to obtain a target strain compensation value set.

[0058] The set of temperature data to be processed may include one or more temperature data to be processed, which can be understood as temperature data that needs to be processed for temperature strain analysis and subsequent compensation processing. Specifically, based on the temperature strain analysis model, the temperature data to be processed can be Perform temperature strain analysis to predict the strain value caused by temperature factors, that is, to obtain temperature strain evaluation data , this application does not limit this.

[0059] Optionally, based on the temperature strain analysis model, a temperature strain analysis is performed on each of the to-be-processed temperature data in the to-be-processed temperature data set to obtain a temperature strain evaluation data set. The process can be referred to the following formula: in, represents the temperature strain evaluation data; and represents the target Lagrange multiplier; Represents the radial basis kernel function. In actual calculation, the radial basis kernel function can be used To calculate temperature strain evaluation data; Represents the i-th temperature calibration sample data; Indicates the temperature data to be processed; Represents the target bias parameter.

[0060] The reference compensated strain data set may include one or more reference compensated strain data. The reference compensated strain data can be understood as data that, after temperature compensation, more accurately reflects the actual strain of the vehicle load. Optionally, the process of performing temperature compensation on the temperature strain assessment data set and the strain data set to be processed to obtain the reference compensated strain data set can be described by the following formula: in, is the reference compensation strain data; The strain measurement sample data obtained from actual measurement, i.e., the strain data to be processed; It should be noted that by adopting this formula, the strain component caused by temperature change can be removed from the strain data to be processed, and the strain data that can better reflect the strain caused by vehicle load can be obtained.

[0061] The set of corrected strain compensation values ​​may include one or more corrected strain compensation values, which can be understood as strain data obtained by further correcting the reference compensated strain data. Because temperature strain analysis models may contain certain errors, as well as other unaccounted factors that may exist in the actual environment, the calculated reference compensated strain data can be corrected. Specifically, a correction coefficient can be derived from statistical analysis of historical data, and the reference compensated strain data can be fine-tuned based on this correction coefficient to obtain a more accurate corrected strain compensation value. Specifically, a linear correction coefficient method can be used to find an appropriate linear correction coefficient through statistical analysis of historical data, and then use this coefficient to linearly adjust the reference compensated strain data. Optionally, a filtering algorithm correction method can be used to effectively remove noise and outliers in the data, making the corrected strain compensation value smoother and more accurate. Common filtering algorithms include moving average filtering and Kalman filtering, which are not limited in this application. Optionally, a machine learning-based correction method can be used to correct the reference compensated strain data using machine learning algorithms such as neural networks and support vector regression, which are not limited in this application.

[0062] Furthermore, after the compensation result correction processing is performed, a stability assessment process may be performed on each corrected strain compensation value to evaluate the stability of the resulting strain data. Optionally, the stability assessment process may include data quality assessment processing such as checking for data fluctuations and the presence of outliers, which is not limited in this application.

[0063] Specifically, relevant statistical indicators of the strain compensation value, such as mean, standard deviation, variance, etc., can be calculated based on the corrected strain compensation value to evaluate the stability and reliability of the compensation result. Optionally, if abnormal fluctuations occur in the statistical indicators, it may indicate that there is a problem with the compensation process. In this case, it is necessary to recheck whether there is a problem with the model or data processing steps. Furthermore, the strain compensation value can be compared and verified with historical data or preset reference values. Optionally, if the difference between the two is within an acceptable range, the compensation result is considered reasonable; otherwise, it is necessary to further analyze the cause and make corresponding adjustments, which is not limited in this application.

[0064] S50: Performing vehicle load analysis processing on each target strain compensation value in the target strain compensation value set to obtain a vehicle load analysis result of the bridge to be inspected.

[0065] The vehicle load analysis results indicate the vehicle load characteristics and bridge response obtained after performing vehicle load analysis for each target strain compensation value. This vehicle load analysis provides a more comprehensive understanding of the mechanical behavior of bridges under vehicle loads, allowing for the timely identification of potential safety hazards. This not only allows for the assessment of bridge safety and durability, but also provides strong technical support for bridge design, scientific management, and maintenance.

[0066] Specifically, the vehicle load analysis results may include, but are not limited to, load size (such as the specific values ​​of static and dynamic loads generated by different types of vehicles on the bridge), load distribution (i.e., the distribution pattern of vehicle loads at different locations on the bridge (such as the mid-span, support, etc.)) or on different lanes (such as the load near the outer lane may be relatively small, while the middle lane may bear a larger load due to the high frequency of vehicle traffic), changes in the load borne by the bridge in different time periods (such as during peak traffic hours, the bridge may frequently bear large loads, while at night or during traffic off-peak hours, the load is relatively small), load change frequency (specifically, the frequency characteristics of load changes over time can be determined through time domain and frequency domain analysis, such as high-frequency load changes may be related to factors such as vehicle vibration and impact, while low-frequency load changes may be related to factors such as changes in vehicle speed and long-term deformation of the bridge), and bridge response characteristics (such as the strain of the bridge under the action of vehicle loads, including the strain size, distribution, and change pattern), as well as the vibration frequency, amplitude, and other characteristics of the bridge under vehicle load excitation. This application does not impose any restrictions on this.

[0067] It should be understood that performing vehicle load analysis on each target strain compensation value in the target strain compensation value set to obtain the vehicle load analysis result for the bridge to be inspected refers to the process of performing vehicle load analysis based on the target strain compensation value after temperature compensation to obtain a more accurate vehicle load analysis result. Specifically, step S50, i.e., performing vehicle load analysis on each target strain compensation value in the target strain compensation value set to obtain the vehicle load analysis result for the bridge to be inspected, may include the following steps: S51: performing time domain feature extraction on each target strain compensation value in the target strain compensation value set to obtain a strain time domain feature data set; S52: performing frequency domain feature extraction on each target strain compensation value in the target strain compensation value set to obtain a strain frequency domain feature data set; S53: performing static load calculation based on the strain time domain feature data set, the strain frequency domain feature data set, and the bridge structure parameters of the bridge to be inspected to obtain a vehicle static load data set; S54: performing dynamic load calculation based on the strain time domain feature data set, the strain frequency domain feature data set, the vehicle dynamic driving data set, and the vehicle static load data set to obtain a vehicle dynamic load data set; S55: Perform vehicle load analysis processing based on the vehicle static load data set and the vehicle dynamic load data set to obtain a vehicle load analysis result of the bridge to be inspected.

[0068] The strain time-domain feature data set may include one or more strain time-domain feature data, which may be used to indicate feature data reflecting the time-varying strain after time-domain feature extraction of the target strain compensation value. Optionally, the strain time-domain feature data set may include, but is not limited to, the peak value of strain, the mean value of strain, the rate of change of strain over time, the vibration period, and the like, which are not limited in this application.

[0069] The strain frequency domain feature data set may include one or more strain frequency domain feature data. This strain frequency domain feature data may be used to indicate characteristic data converted to the frequency domain, obtained after frequency domain feature extraction of the target strain compensation value, that can reflect the dynamic vibration of the bridge caused by the vehicle during driving. Optionally, this strain frequency domain feature data set may include, but is not limited to, the bridge vibration frequency distribution, the energy percentage of each frequency component, and low-frequency components, etc., which are not limited in this application.

[0070] The vehicle static load data set may include one or more vehicle static load data, which can be used to indicate the load data obtained after static load calculation based on the strain time domain feature data set, such as the strain peak value, strain mean value, etc., the strain frequency domain feature data set, which can reflect the low-frequency components of the static or slow movement of the vehicle, and the bridge structure parameters of the bridge to be tested. Optionally, the strain time domain feature data can be used to perform impact load calculation or load time history analysis, and the strain frequency domain feature data can be used to perform vibration frequency analysis or load identification and separation processing, which is not limited in this application.

[0071] The structural parameters of the bridge to be inspected can be understood as parameters related to the bridge structure. These parameters may include, but are not limited to, span, beam height, cross-sectional characteristics (such as cross-sectional area, moment of inertia, etc.), and elastic modulus, and are not limited in this application. Specifically, the principles of structural mechanics can be utilized to calculate and obtain vehicle static load data based on the aforementioned data. This can help understand the stress conditions on the bridge when the vehicle is stationary or nearly stationary.

[0072] The vehicle dynamic load data set may include one or more vehicle dynamic load data, which can be used to indicate the load data obtained after dynamic load calculation is performed on the vehicle dynamic driving data set and the vehicle static load data set based on the strain time domain feature data set, such as the rate of change of strain over time, the vibration period, etc., and the strain frequency domain feature data set, such as the bridge vibration frequency distribution, the energy proportion of each frequency component, etc.

[0073] The vehicle dynamic driving data set may include one or more vehicle dynamic driving data, and the vehicle dynamic driving data can be understood as data used to reflect the dynamic effects of the vehicle during driving. The vehicle dynamic driving data may include but is not limited to driving speed, acceleration, vibration and impact, etc., and this application does not impose any restrictions on this. Specifically, the static load can be corrected based on the aforementioned data by introducing methods such as dynamic coefficients, thereby obtaining vehicle dynamic load data. Specifically, the vehicle dynamic driving data can be obtained by using on-board sensors installed on the vehicle, such as speed sensors, acceleration sensors, vibration sensors or impact sensors. Optionally, the vehicle dynamic driving data can also be obtained through a bridge monitoring system, such as strain gauges or optical fiber sensors installed on the bridge, or cameras or image recognition equipment on the bridge, and this application does not impose any restrictions on this.

[0074] Furthermore, the obtained vehicle dynamic load data set and vehicle static load data set can be further analyzed, such as the load conditions in different time periods and different lanes can be counted, or the load distribution of different types of vehicles can be calculated, etc., so that the vehicle load analysis results of the bridge to be inspected can be further obtained based on the analysis results, which is conducive to more accurate assessment of the safety of the bridge and more accurate bridge maintenance and bridge management. This application does not impose any restrictions on this.

[0075] It can be seen that in the above scheme, by obtaining the temperature measurement sample data set corresponding to the temperature sensor of the bridge to be inspected, and obtaining the strain measurement sample data set corresponding to the strain sensor of the bridge to be inspected, each temperature measurement sample data in the temperature measurement sample data set can be verified to obtain a temperature verification sample data set, and further based on the temperature verification sample data set and the strain measurement sample data set, a temperature strain analysis model can be constructed, so that based on the temperature strain analysis model, temperature compensation processing can be performed according to the temperature data set to be processed and the strain data set to be processed to obtain a target strain compensation value set, and then vehicle load analysis processing can be performed on each target strain compensation value in the target strain compensation value set to obtain a more accurate vehicle load analysis result of the bridge to be inspected, and the temperature strain value caused by the temperature factor can be removed, which is conducive to achieving the purpose of more accurate vehicle load analysis.

[0076] For the same example as above, please refer to Figure 3 , Figure 3 A schematic diagram of the structure of a terminal provided in an embodiment of the present application is shown in FIG. Figure 3As shown, the system comprises a processor, an input device, an output device and a memory, which are connected to each other, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions, and the program includes instructions for executing the following steps; Acquire a temperature measurement sample data set corresponding to a temperature sensor of the bridge to be inspected, and acquire a strain measurement sample data set corresponding to a strain sensor of the bridge to be inspected; Performing verification processing on each temperature measurement sample data in the temperature measurement sample data set to obtain a temperature verification sample data set; Constructing a temperature-strain analysis model based on the temperature verification sample data set and the strain measurement sample data set; Based on the temperature-strain analysis model, compensation processing is performed according to the to-be-processed temperature data set and the to-be-processed strain data set to obtain a target strain compensation value set; A vehicle load analysis process is performed on each target strain compensation value in the target strain compensation value set to obtain a vehicle load analysis result of the bridge to be inspected.

[0077] In this example, by obtaining a temperature measurement sample data set corresponding to the temperature sensor of the bridge to be inspected, and obtaining a strain measurement sample data set corresponding to the strain sensor of the bridge to be inspected, each temperature measurement sample data in the temperature measurement sample data set can be verified to obtain a temperature verification sample data set, and further based on the temperature verification sample data set and the strain measurement sample data set, a temperature strain analysis model can be constructed, so that based on the temperature strain analysis model, compensation processing can be performed according to the temperature data set to be processed and the strain data set to be processed to obtain a target strain compensation value set, and then vehicle load analysis processing can be performed on each target strain compensation value in the target strain compensation value set to obtain a more accurate vehicle load analysis result of the bridge to be inspected, and the temperature strain value caused by the temperature factor can be removed, which is conducive to achieving the purpose of more accurate vehicle load analysis.

[0078] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that, in order to implement the above functions, the terminal includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of the various examples described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in a hardware or computer software driven hardware manner 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.

[0079] The embodiment of the present application can divide the terminal into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical functional division. In actual implementation, there may be other division methods.

[0080] In line with the above, please see Figure 4 , Figure 4 The present invention provides a schematic diagram of a vehicle load analysis device for a bridge based on temperature compensation. Figure 4 As shown, the device includes: An acquisition module 101 is configured to acquire a temperature measurement sample data set corresponding to a temperature sensor of a bridge to be inspected, and acquire a strain measurement sample data set corresponding to a strain sensor of the bridge to be inspected; A first processing module 102 is configured to perform verification processing on each temperature measurement sample data in the temperature measurement sample data set to obtain a temperature verification sample data set; A second processing module 103 is configured to construct a temperature-strain analysis model based on the temperature verification sample data set and the strain measurement sample data set; The third processing module 104 is configured to perform compensation processing based on the temperature data set to be processed and the strain data set to be processed based on the temperature strain analysis model to obtain a target strain compensation value set; The fourth processing module 105 is configured to perform vehicle load analysis processing on each target strain compensation value in the target strain compensation value set to obtain a vehicle load analysis result of the bridge to be inspected.

[0081] In one possible implementation, the second processing module is configured to construct a temperature-strain analysis model based on the temperature verification sample data set and the strain measurement sample data set, specifically to: Performing feature mapping on the temperature calibration sample data and the strain measurement sample data to obtain a high-dimensional linear feature vector; constructing a temperature-strain analysis function based on the temperature calibration sample data and the strain measurement sample data; determining a temperature-strain constraint condition based on the temperature verification sample data, the strain measurement sample data, and the high-dimensional linear feature vector; Constructing a temperature strain regression optimization function according to the temperature strain analysis function and the temperature strain constraint condition; Based on the sequential minimum optimization algorithm, the temperature strain regression optimization function is optimally solved to obtain a target Lagrange multiplier set and a target temperature strain analysis parameter set; A temperature strain analysis model is constructed according to the target Lagrange multiplier set and the target temperature strain analysis parameter set.

[0082] In one possible implementation, the second processing module is configured to perform optimal solution processing on the temperature-strain regression optimization function based on a sequential minimum optimization algorithm to obtain a target Lagrange multiplier set and a target temperature-strain analysis parameter set, specifically for: Based on the sequence minimum optimization algorithm and the Lagrange multiplier method, the temperature-strain regression optimization function is subjected to a dual transformation to obtain a temperature-strain dual function; Select a reference Lagrange multiplier from the set of initialized Lagrange multipliers; Optimizing and solving the temperature-strain dual function based on the reference Lagrangian multiplier to obtain a target Lagrangian multiplier set; Calculating a bias according to each target Lagrangian multiplier in the target Lagrangian multiplier set to obtain a target bias parameter; Calculate the weight vector according to each target Lagrange multiplier in the target Lagrange multiplier set to obtain the target weight parameter; A target temperature strain analysis parameter set is determined according to the target bias parameter and the target weight parameter.

[0083] In one possible implementation, the third processing module is configured to perform compensation processing based on the temperature data set to be processed and the strain data set to be processed based on the temperature-strain analysis model to obtain a target strain compensation value set, specifically for: Based on the temperature strain analysis model, performing temperature strain analysis processing on each temperature data to be processed in the temperature data set to be processed to obtain a temperature strain evaluation data set; Performing compensation processing on the temperature strain evaluation data set and the strain data set to be processed to obtain a reference compensated strain data set; performing compensation result correction processing on each reference compensation strain data in the reference compensation strain data set to obtain a corrected strain compensation value set; A stability evaluation process is performed on each modified strain compensation value in the modified strain compensation value set to obtain a target strain compensation value set.

[0084] In one possible implementation, the fourth processing module is configured to perform vehicle load analysis on each target strain compensation value in the target strain compensation value set to obtain a vehicle load analysis result for the bridge to be inspected, specifically for: performing time domain feature extraction on each target strain compensation value in the target strain compensation value set to obtain a strain time domain feature data set; performing frequency domain feature extraction on each target strain compensation value in the target strain compensation value set to obtain a strain frequency domain feature data set; Performing static load calculation based on the strain time domain feature data set, the strain frequency domain feature data set, and the bridge structure parameters of the bridge to be inspected to obtain a vehicle static load data set; Performing dynamic load calculation based on the strain time domain feature data set, the strain frequency domain feature data set, the vehicle dynamic driving data set, and the vehicle static load data set to obtain a vehicle dynamic load data set; Vehicle load analysis processing is performed based on the vehicle static load data set and the vehicle dynamic load data set to obtain a vehicle load analysis result of the bridge to be inspected.

[0085] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any vehicle load analysis method for a bridge based on temperature compensation as described in the above method embodiments.

[0086] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program enables a computer to execute part or all of the steps of any vehicle load analysis method for a bridge based on temperature compensation as described in the above method embodiments.

[0087] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0088] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0089] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, 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, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0090] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0091] In addition, the functional units in the various embodiments of the application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software program modules.

[0092] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, magnetic disk, or optical disk, etc., various media that can store program code.

[0093] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0094] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A vehicle load analysis method for a bridge based on temperature compensation, characterized in that: The method comprises: Acquire a temperature measurement sample data set corresponding to a temperature sensor of the bridge to be inspected, and acquire a strain measurement sample data set corresponding to a strain sensor of the bridge to be inspected; Performing verification processing on each temperature measurement sample data in the temperature measurement sample data set to obtain a temperature verification sample data set; Constructing a temperature-strain analysis model based on the temperature verification sample data set and the strain measurement sample data set; Based on the temperature-strain analysis model, compensation processing is performed according to the to-be-processed temperature data set and the to-be-processed strain data set to obtain a target strain compensation value set; A vehicle load analysis process is performed on each target strain compensation value in the target strain compensation value set to obtain a vehicle load analysis result of the bridge to be inspected.

2. The vehicle load analysis method for a bridge based on temperature compensation according to claim 1, characterized in that: The step of constructing a temperature-strain analysis model based on the temperature verification sample data set and the strain measurement sample data set includes: Performing feature mapping on the temperature calibration sample data and the strain measurement sample data to obtain a high-dimensional linear feature vector; constructing a temperature-strain analysis function based on the temperature calibration sample data and the strain measurement sample data; determining a temperature-strain constraint condition based on the temperature verification sample data, the strain measurement sample data, and the high-dimensional linear feature vector; Constructing a temperature strain regression optimization function according to the temperature strain analysis function and the temperature strain constraint condition; Based on the sequential minimum optimization algorithm, the temperature strain regression optimization function is optimally solved to obtain a target Lagrange multiplier set and a target temperature strain analysis parameter set; A temperature strain analysis model is constructed according to the target Lagrange multiplier set and the target temperature strain analysis parameter set.

3. The vehicle load analysis method for a bridge based on temperature compensation according to claim 2, characterized in that: The temperature strain regression optimization function is optimally solved based on the sequential minimum optimization algorithm to obtain a target Lagrange multiplier set and a target temperature strain analysis parameter set, including: Based on the sequence minimum optimization algorithm and the Lagrange multiplier method, the temperature-strain regression optimization function is subjected to a dual transformation to obtain a temperature-strain dual function; Select a reference Lagrange multiplier from the set of initialized Lagrange multipliers; Optimizing and solving the temperature-strain dual function based on the reference Lagrangian multiplier to obtain a target Lagrangian multiplier set; Calculating a bias according to each target Lagrangian multiplier in the target Lagrangian multiplier set to obtain a target bias parameter; Calculate the weight vector according to each target Lagrange multiplier in the target Lagrange multiplier set to obtain the target weight parameter; A target temperature strain analysis parameter set is determined according to the target bias parameter and the target weight parameter.

4. The vehicle load analysis method for a bridge based on temperature compensation according to any one of claims 1 or 2, characterized in that: The method of performing compensation processing based on the temperature-strain analysis model and the to-be-processed temperature data set and the to-be-processed strain data set to obtain a target strain compensation value set includes: Based on the temperature strain analysis model, performing temperature strain analysis processing on each temperature data to be processed in the temperature data set to be processed to obtain a temperature strain evaluation data set; Performing compensation processing on the temperature strain evaluation data set and the strain data set to be processed to obtain a reference compensated strain data set; performing compensation result correction processing on each reference compensation strain data in the reference compensation strain data set to obtain a corrected strain compensation value set; A stability evaluation process is performed on each modified strain compensation value in the modified strain compensation value set to obtain a target strain compensation value set.

5. The vehicle load analysis method for a bridge based on temperature compensation according to claim 4, characterized in that: The performing vehicle load analysis processing on each target strain compensation value in the target strain compensation value set to obtain a vehicle load analysis result of the bridge to be inspected includes: performing time domain feature extraction on each target strain compensation value in the target strain compensation value set to obtain a strain time domain feature data set; performing frequency domain feature extraction on each target strain compensation value in the target strain compensation value set to obtain a strain frequency domain feature data set; Performing static load calculation based on the strain time domain feature data set, the strain frequency domain feature data set, and the bridge structure parameters of the bridge to be inspected to obtain a vehicle static load data set; Performing dynamic load calculation based on the strain time domain feature data set, the strain frequency domain feature data set, the vehicle dynamic driving data set, and the vehicle static load data set to obtain a vehicle dynamic load data set; Vehicle load analysis processing is performed based on the vehicle static load data set and the vehicle dynamic load data set to obtain a vehicle load analysis result of the bridge to be inspected.

6. A vehicle load analysis device for a bridge based on temperature compensation, characterized in that: The device comprises: an acquisition module, configured to acquire a temperature measurement sample data set corresponding to a temperature sensor of a bridge to be detected, and acquire a strain measurement sample data set corresponding to a strain sensor of the bridge to be detected; A first processing module is configured to perform verification processing on each temperature measurement sample data in the temperature measurement sample data set to obtain a temperature verification sample data set; A second processing module is configured to construct a temperature-strain analysis model based on the temperature verification sample data set and the strain measurement sample data set; a third processing module, configured to perform temperature compensation processing based on the temperature data set to be processed and the strain data set to be processed based on the temperature-strain analysis model, to obtain a target strain compensation value set; The fourth processing module is configured to perform vehicle load analysis processing on each target strain compensation value in the target strain compensation value set to obtain a vehicle load analysis result of the bridge to be inspected.

7. The vehicle load analysis device for a bridge based on temperature compensation according to claim 6, characterized in that: The second processing module is configured to construct a temperature-strain analysis model based on the temperature verification sample data set and the strain measurement sample data set, specifically for: Performing feature mapping on the temperature calibration sample data and the strain measurement sample data to obtain a high-dimensional linear feature vector; constructing a temperature-strain analysis function based on the temperature calibration sample data and the strain measurement sample data; determining a temperature-strain constraint condition based on the temperature verification sample data, the strain measurement sample data, and the high-dimensional linear feature vector; Constructing a temperature strain regression optimization function according to the temperature strain analysis function and the temperature strain constraint condition; Based on the sequential minimum optimization algorithm, the temperature strain regression optimization function is optimally solved to obtain a target Lagrange multiplier set and a target temperature strain analysis parameter set; A temperature strain analysis model is constructed according to the target Lagrange multiplier set and the target temperature strain analysis parameter set.

8. The vehicle load analysis device for a bridge based on temperature compensation according to claim 7, characterized in that: The second processing module is used to perform optimal solution processing on the temperature-strain regression optimization function based on a sequential minimum optimization algorithm to obtain a target Lagrange multiplier set and a target temperature-strain analysis parameter set, specifically for: Performing a dual transformation on the temperature-strain regression optimization function based on a sequence minimum optimization algorithm and a Lagrange multiplier method to obtain a temperature-strain dual function; Select a reference Lagrange multiplier from the set of initialized Lagrange multipliers; Optimizing and solving the temperature-strain dual function based on the reference Lagrangian multiplier to obtain a target Lagrangian multiplier set; Calculating a bias according to each target Lagrangian multiplier in the target Lagrangian multiplier set to obtain a target bias parameter; Calculate the weight vector according to each target Lagrange multiplier in the target Lagrange multiplier set to obtain the target weight parameter; A target temperature strain analysis parameter set is determined according to the target bias parameter and the target weight parameter.

9. A terminal, characterized in that: The method comprises a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the vehicle load analysis method for a bridge based on temperature compensation as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, the processor executes the vehicle load analysis method for a bridge based on temperature compensation according to any one of claims 1 to 5.

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