Real-time separation method, system and storage medium for temperature-induced deflection and vehicle-induced deflection of bridge

Abstract

The present application discloses a real-time separation method, system and storage medium for temperature-induced deflection and vehicle-induced deflection of a bridge, including the following contents: separating the temperature-induced deflection and vehicle-induced deflection of a preprocessing section; calculating the initial value parameters of the iteration according to the separation results; and t The predicted value of temperature-induced deflection at time t Information before the moment, t The total deflection signal value measured at time +1 is t The temperature-induced deflection value at time +1 is predicted twice; the prediction results of the temperature-induced deflection of the two predictions are combined to obtain the corrected t The predicted value of temperature-induced deflection and vehicle-induced deflection at time +1; the real-time separation of temperature-induced deflection and vehicle-induced deflection is achieved through iteration. This application takes the real-time deflection monitoring data of the bridge as the object, first determines the initial parameters in the preprocessing stage, and then iterates through the three steps of prediction-measurement-correction to continuously separate the temperature-induced deflection and vehicle-induced deflection in real time. The separation result is accurate, and the separation process is controllable, efficient, and highly practical.

Description

Technical Field

[0001] The present application relates to the field of bridge health monitoring, and specifically to a method, system and storage medium for real-time separation of temperature-induced deflection and vehicle-induced deflection of a bridge. Background Art

[0002] In the bridge health monitoring system, the separation technology of bridge temperature-induced deflection and vehicle-induced deflection is an important means to evaluate the health status of the bridge. It aims to extract the deflection contribution caused by vehicle load and daily temperature difference effect respectively. By separating the deflection contribution caused by different factors, we can better understand the deflection characteristics of the bridge under the action of each single factor, and provide a basis for the safety performance evaluation of the bridge; secondly, the separated deflection data can provide support for the improvement of bridge design theory, the development of health monitoring system and the research of sensor setting, so as to optimize the maintenance and management of the bridge.

[0003] At present, some scholars have tried to separate the vehicle-induced and temperature-induced deflections of bridges, but most of them remain at the theoretical level, and the separation effect is poor. In response to this problem, some researchers borrowed the multi-scale system analysis theory and proposed a separation method based on sparse representation for the vehicle-induced deflection of bridges under multi-scale time, in order to improve the separation effect and application practicality. However, this technology still faces challenges in practical applications, such as the need for high-precision measurement equipment, complex data processing algorithms, and professional technicians to operate and analyze. Therefore, the separation technology of temperature-induced and vehicle-induced deflections of bridges still needs further research and improvement to improve its application effect in actual engineering. Secondly, the current academic research on the separation of temperature-induced and vehicle-induced deflections of bridges mainly focuses on processing data that has been collected for one day or even longer, while there is a blank in the corresponding technology for the online and real-time separation of bridge deflections. ‌‌ Summary of the invention

[0004] In response to the problems existing in the prior art, the present application proposes a method, system and storage medium for real-time separation of temperature-induced deflection and vehicle-induced deflection of a bridge, which can separate the temperature-induced deflection and vehicle-induced deflection collected by the bridge deflection sensor in real time and accurately, and the separation process is controllable, efficient and highly practical.

[0005] In a first aspect, a method for real-time separation of temperature-induced deflection and vehicle-induced deflection of a bridge comprises the following steps:

[0006] S1, collecting the total deflection signal including temperature-induced deflection and vehicle-induced deflection;

[0007] S2, selecting a preprocessing section, and separating the temperature-induced deflection and the vehicle-induced deflection of the total deflection signal of the preprocessing section;

[0008] S3, according to the separated pretreatment section temperature-induced deflection value and pretreatment section vehicle-induced deflection value, respectively calculate the standard deviation of the probability distribution of the pretreatment section temperature-induced deflection value increment and the pretreatment section vehicle-induced deflection value, and use the two standard deviations as the initial value parameters of the subsequent iteration process;

[0009] S4, predicting the probability distribution of the temperature-induced deflection at time t+1 to obtain a priori temperature-induced deflection prediction value at time t+1; the mean of the priori temperature-induced deflection prediction value at time t+1 is the temperature-induced deflection prediction value at time t, and the variance is the sum of the variance of the temperature-induced deflection prediction value at time t determined by iterative calculation before time t and the variance of the increment of the temperature-induced deflection value in the pretreatment section;

[0010] S5, predicting the probability distribution of the vehicle-induced deflection at time t+1, and calculating the posterior temperature-induced deflection prediction value at time t+1 according to the total deflection signal value at time t+1; the mean of the posterior temperature-induced deflection prediction value at time t+1 is the total deflection signal value at time t+1, and the variance is the variance of the vehicle-induced deflection value in the preprocessing stage;

[0011] S6, fusing the priori temperature-induced deflection prediction value and the posterior temperature-induced deflection prediction value at time t+1 to obtain a corrected temperature-induced deflection prediction value at time t+1; and then calculating the final temperature-induced deflection prediction value and vehicle-induced deflection prediction value at time t+1 based on the total deflection signal value at time t+1;

[0012] S7, iterate steps S4 to S6 to obtain the mean and variance of the temperature-induced deflection prediction value at time t+2, and obtain the vehicle-induced deflection prediction value at time t+2 based on the total deflection signal value collected in real time; and so on, to achieve real-time separation of temperature-induced deflection and vehicle-induced deflection at time t+2 and thereafter.

[0013] In a possible implementation manner of the first aspect, the time interval corresponding to the preprocessing segment is [0, t], and step S4 is executed starting from a subsequent data point of the preprocessing segment.

[0014] In a possible implementation of the first aspect, step S2 uses average mean filtering to process the total deflection signal value of the preprocessing section to obtain the temperature-induced deflection value of the preprocessing section; secondly, the temperature-induced deflection value of the preprocessing section is subtracted from the total deflection signal value to obtain the vehicle-induced deflection value of the preprocessing section.

[0015] In a possible implementation of the first aspect, step S3 calculates the standard deviation of the difference sequence of the temperature-induced deflection values ​​of the pre-processing section and the standard deviation of the vehicle-induced deflection value of the pretreatment section , the standard deviation As the standard deviation of the probability distribution of subsequent temperature-induced deflection value increments, the standard deviation As the standard deviation of the probability distribution of subsequent vehicle-induced deflection; specifically including the following:

[0016] Standard deviation of the difference series of temperature-induced deflection values ​​in the pretreatment section for:

[0017] ;

[0018] in, represents the number of data points of the temperature-induced deflection sequence of the separated pretreatment section, It represents the difference between two adjacent temperature-induced deflection values ​​in the pretreatment section. - , Indicates the temperature-induced deflection value of the pretreatment section, express The average value of , ;

[0019] Standard deviation of vehicle-induced deflection values ​​in the pretreatment section for:

[0020] ;

[0021] in, represents the number of data points of the vehicle-induced deflection sequence of the separated pre-processing section, Indicates the vehicle-induced deflection value of the pretreatment section, It represents the average value of vehicle-induced deflection in the pretreatment section. , .

[0022] In a possible implementation manner of the first aspect, step S4 specifically includes the following content:

[0023] Assuming that the temperature-induced deflection conforms to the normal distribution, the predicted value of the temperature-induced deflection at time t is for:

[0024] ;

[0025] in, , They represent the mean and variance of the predicted value of temperature-induced deflection at time t obtained by the last iterative calculation respectively;

[0026] The prior predicted value of temperature-induced deflection at time t+1 is inferred based on the predicted value of temperature-induced deflection at time t for:

[0027] ;

[0028] ;

[0029] in, Denotes the predicted value of temperature-induced deflection The increment of , which is a normal variable with mean 0 and independent and identical distribution; for The variance of

[0030] The prior temperature-induced deflection prediction value at time t+1 It conforms to the following normal distribution:

[0031] ;

[0032] The a priori temperature-induced deflection prediction value The probability density function of .

[0033] In a possible implementation manner of the first aspect, step S5 specifically includes the following content:

[0034] Assuming that the vehicle-induced deflection conforms to the normal distribution, the predicted value of the vehicle-induced deflection at time t+1 is for:

[0035] ;

[0036] The total deflection signal value at time t+1 is known , calculate the posterior temperature-induced deflection prediction value at time t+1 :

[0037] ;

[0038] The a posteriori temperature-induced deflection prediction value at time t+1 It conforms to the following normal distribution:

[0039] ;

[0040] The a posteriori temperature-induced deflection prediction value The probability density function of .

[0041] In a possible implementation manner of the first aspect, step S6 specifically includes the following content:

[0042] The a priori temperature-induced deflection prediction value The probability density function of and the a posteriori temperature-induced deflection prediction value The probability density function of Multiply them together and multiply them by a scaling factor to get a new probability density function , the integral of the new probability density function on (-∞,∞) is 1, and the value of the independent variable represents the new normal variable, that is, the predicted value of temperature-induced deflection at time t+1 after correction;

[0043] The new probability density function The independent variable value corresponding to the maximum probability value As the final predicted value of temperature-induced deflection at time t+1;

[0044] The total deflection signal value at time t+1 Subtract the predicted temperature-induced deflection , and obtain the final predicted value of the vehicle-induced deflection at time t+1.

[0045] In a possible implementation manner of the first aspect, the scaling factor is determined by using a Kalman filter method, which specifically includes the following contents:

[0046] Based on the mean at time t and variance , calculate the Kalman gain coefficient K:

[0047] ;

[0048] The Kalman gain coefficient K is used as the scaling coefficient to obtain the corrected mean value of the temperature-induced deflection prediction value at time t+1 and variance They are:

[0049] ;

[0050] ;

[0051] The corrected predicted value of temperature-induced deflection at time t+1 It conforms to the following normal distribution:

[0052] .

[0053] In a second aspect, the present application provides a system for executing the real-time separation method of the bridge temperature-induced deflection and vehicle-induced deflection, comprising:

[0054] A sensor for collecting a total deflection signal including temperature-induced deflection and vehicle-induced deflection in real time;

[0055] A preprocessing module, used for separating the temperature-induced deflection and the vehicle-induced deflection of the total deflection signal of the preprocessing section;

[0056] The initial value calculation module calculates the standard deviation of the probability distribution of the increment of the pretreatment section temperature-induced deflection value and the pretreatment section vehicle-induced deflection value according to the separated pretreatment section temperature-induced deflection value and the pretreatment section vehicle-induced deflection value, and uses the standard deviation as the initial value parameter of the subsequent iteration process;

[0057] The prior prediction module predicts the probability distribution of the temperature-induced deflection value at time t+1 according to the temperature-induced deflection prediction value at time t and the deflection fluctuation information before time t, and obtains the prior temperature-induced deflection prediction value at time t+1; the mean of the prior temperature-induced deflection prediction value at time t+1 is the temperature-induced deflection prediction value at time t, and the variance is the sum of the variance of the temperature-induced deflection prediction value at time t determined by iterative calculation before time t and the variance of the temperature-induced deflection value increment of the preprocessing section;

[0058] The posterior prediction module predicts the probability distribution of the vehicle-induced deflection at time t+1, and calculates the posterior temperature-induced deflection prediction value at time t+1 according to the total deflection measured value at time t+1; the mean of the posterior temperature-induced deflection prediction value at time t+1 is the total deflection measured value at time t+1, and the variance is the variance of the vehicle-induced deflection value in the preprocessing section;

[0059] The fusion correction module fuses and corrects the priori temperature-induced deflection prediction value and the posterior temperature-induced deflection prediction value at time t+1 to obtain the final temperature-induced deflection and vehicle-induced deflection prediction values ​​at time t+1;

[0060] The iteration module is electrically / communicatively connected to the a priori prediction module, the a posteriori prediction module, and the fusion correction module, and iterates steps S4 to S6 to obtain the predicted values ​​of the temperature-induced deflection and the vehicle-induced deflection at time t+2 and thereafter.

[0061] In a third aspect, the present application provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implements the method for real-time separation of the temperature-induced deflection and the vehicle-induced deflection of the bridge.

[0062] Compared with the prior art, the present application can separate the temperature-induced deflection and vehicle-induced deflection of the bridge in real time and accurately, and the separation process is controllable, efficient and highly practical;

[0063] Specifically, the present application takes the real-time deflection monitoring data of bridges as the object. First, filtering means are used in the preprocessing stage to perform static non-real-time deflection separation processing. The temperature-induced deflection value increment and the standard deviation of the probability distribution of the vehicle-induced deflection are calculated based on the vehicle-induced and temperature-induced deflections separated in the preprocessing stage, and then converted into the subsequent real-time separation algorithm, so that the separation effect is more controllable, avoiding the process of manual parameter adjustment, and ensuring the objectivity and accuracy of the corresponding bridge health monitoring evaluation; secondly, through the iteration of the three steps of prediction-measurement-correction, the separation of temperature-induced deflection and vehicle-induced deflection is continuously performed in real time. The calculation process of the separation algorithm has low complexity and high timeliness. In the current big data monitoring environment of a large number of bridge deflection sensors, the separation algorithm with low computational complexity can reduce the computing performance requirements of the system, thereby minimizing the hardware cost, making it easier to achieve popularization and real-time, and can be widely promoted and applied in various types of structural intelligent monitoring systems to meet the real-time and online analysis needs of the monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A flow chart of a method for separating temperature-induced deflection and vehicle-induced deflection of a bridge provided in an embodiment of the present application;

[0065] Figure 2 A comparison diagram of the temperature-induced deflection and the original signal obtained by using average mean filtering in the preprocessing stage of a method for separating the temperature-induced deflection and vehicle-induced deflection of a bridge provided in an embodiment of the present application;

[0066] Figure 3 for Figure 2 A comparison diagram of the real-time separated temperature-induced deflection and the original signal in the illustrated embodiment.

[0067] Figure 4 for Figure 2 Vehicle-induced deflection with real-time separation in the illustrated embodiment. DETAILED DESCRIPTION

[0068] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings.

[0069] It should be clear that the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0070] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0071] In a first aspect, the present application provides a method for real-time separation of temperature-induced deflection and vehicle-induced deflection of a bridge, comprising the following steps:

[0072] S1, collecting the total deflection signal including temperature-induced deflection and vehicle-induced deflection;

[0073] S2, selecting a preprocessing section, and separating the temperature-induced deflection and the vehicle-induced deflection of the total deflection signal of the preprocessing section;

[0074] S3, according to the separated pretreatment section temperature-induced deflection value and vehicle-induced deflection value, respectively calculate the standard deviation of the probability distribution of the pretreatment section temperature-induced deflection value increment and the pretreatment section vehicle-induced deflection value, and use the standard deviation as the initial value parameter of the subsequent iteration process;

[0075] S4, predicting the probability distribution of the temperature-induced deflection at time t+1 according to the predicted value of the temperature-induced deflection at time t and the deflection fluctuation information before time t, and obtaining the prior predicted value of the temperature-induced deflection at time t+1;

[0076] S5, predicting the probability distribution of the vehicle-induced deflection at time t+1 according to the initial value parameters, and calculating the posterior temperature-induced deflection prediction value at time t+1 according to the total deflection signal value at time t+1;

[0077] S6, fusing and correcting the priori temperature-induced deflection prediction value and the posterior temperature-induced deflection prediction value at time t+1 to obtain the final temperature-induced deflection prediction value at time t+1; and then calculating the final vehicle-induced deflection prediction value at time t+1 based on the total deflection signal value at time t+1;

[0078] S7, iterate steps S4 to S6 to obtain the mean and variance of the temperature-induced deflection prediction value at time t+2, and obtain the vehicle-induced deflection prediction value at time t+2 based on the total deflection signal value collected in real time; and so on, to achieve real-time separation of temperature-induced deflection and vehicle-induced deflection at time t+2 and thereafter.

[0079] In a possible implementation, the time interval corresponding to the preprocessing segment in step S2 is [0, t], and step S4 is executed starting from the next data point of the preprocessing segment.

[0080] In a possible implementation, step S2 uses average mean filtering to separate the temperature-induced deflection and vehicle-induced deflection of the preprocessing section; specifically, the following contents are included:

[0081] For any nth data point in the preprocessing segment sequence, the average mean filter is used, and we have:

[0082] ;

[0083] in, is the total deflection signal value of the nth data point in the preprocessing segment sequence, is the temperature-induced deflection value of the separated pretreatment section, and N is the size of the smoothing interval;

[0084] Subtract the temperature-induced deflection value of the pre-processing section from the total deflection signal value to obtain the signal - As the vehicle-induced deflection value of the pretreatment section, it is recorded as .

[0085] In terms of signal characteristics, the overall trend of temperature-induced deflection changing with time is approximately stable, while vehicle-induced deflection is more volatile in a short period of time. Therefore, the volatility of the total deflection signal is reduced by average mean filtering, and the overall trend of the total deflection changing with time is extracted.

[0086] In a possible implementation, step S3 includes the following contents:

[0087] The standard deviation of the probability distribution of the temperature-induced deflection increment of the preset segment Set as the standard deviation of the difference sequence of the temperature-induced deflection values ​​of the pretreatment section, and record the temperature-induced deflection sequence of the pretreatment section as , Represents the number of data points of the temperature-induced deflection series of the separated pretreatment section;

[0088] Define the standard deviation of the difference series of the temperature-induced deflection values ​​of the pre-processing section for:

[0089] ;

[0090] in, It represents the difference between two adjacent temperature-induced deflection values ​​in the pretreatment section. - , ; express The average value of ;

[0091] The standard deviation of the probability distribution of the vehicle-induced deflection value in the preset section Set as the standard deviation of the sequence of vehicle-induced deflection values ​​in the pre-processing section, and record the sequence of vehicle-induced deflection in the pre-processing section as , The number of data points representing the vehicle-induced deflection sequence of the separated pre-processing section;

[0092] Define the standard deviation of the vehicle-induced deflection values ​​in the pre-treatment section for:

[0093] ;

[0094] in, It represents the average value of vehicle-induced deflection in the pretreatment section. , ;

[0095] The two standard deviations calculated by the above process are , will be used as the initial value parameter for the real-time separation step, where the standard deviation is the standard deviation of the probability distribution of the subsequent temperature-induced deflection value increment, the standard deviation is the standard deviation of the probability distribution of subsequent vehicle-induced deflections.

[0096] Further, based on the assumption that the increment of the temperature-induced deflection value conforms to the normal distribution, step S4 is based on the prediction result of the previous moment and the initial value parameter ( ), predict the temperature-induced deflection value at the next moment; specifically, the mean of the prior temperature-induced deflection prediction value at time t+1 is taken as the temperature-induced deflection prediction value at time t, and the variance is taken as the variance of the temperature-induced deflection prediction value at time t determined by iterative calculation before time t and the variance of the temperature-induced deflection value increment of the preprocessing section The specific process is as follows:

[0097] Assuming that the temperature-induced deflection conforms to the normal distribution, the predicted value of the temperature-induced deflection at time t is for:

[0098] ;

[0099] in, , They represent the mean and variance of the predicted value of temperature-induced deflection at time t obtained by the last iterative calculation respectively;

[0100] The prior predicted value of temperature-induced deflection at time t+1 is inferred based on the predicted value of temperature-induced deflection at time t for:

[0101] ;

[0102] ;

[0103] in, Denotes the predicted value of temperature-induced deflection The increment of , which is a normal variable with mean 0 and independent and identical distribution; The variance of , The standard deviation of the difference series of the temperature-induced deflection values ​​of the above pretreatment section is Sure;

[0104] Prior prediction value of temperature-induced deflection at time t+1 It conforms to the following normal distribution:

[0105] ;

[0106] A priori prediction of temperature-induced deflection The probability density function of .

[0107] When predicting the temperature-induced deflection value at time t+1, due to , It is obtained by multiple iterations before time t. Therefore, the predicted temperature-induced deflection value at time t+1 is not only based on the temperature-induced deflection value at time t, but also based on the information before time t. This information is transmitted through parameters that are continuously iterated over time, and can accurately and completely reflect the deflection fluctuation before time t.

[0108] Further, based on the assumption that the vehicle-induced deflection conforms to the normal distribution, step S5 calculates the total deflection signal value at time t+1 and the initial value parameter ( ), and then predict the temperature-induced deflection prediction value at time t+1; specifically, the mean of the posterior temperature-induced deflection prediction value at time t+1 is the total deflection signal value at time t+1, and the variance is the variance of the vehicle-induced deflection value in the preprocessing section The specific process is as follows:

[0109] Assuming that the vehicle-induced deflection conforms to the normal distribution, the predicted value of the vehicle-induced deflection at time t+1 is for:

[0110] ;

[0111] The total deflection signal value measured at time t+1 is received from the sensor , calculate the posterior temperature-induced deflection prediction value at time t+1 :

[0112] ;

[0113] Posterior temperature-induced deflection prediction value at time t+1 It conforms to the following normal distribution:

[0114] ;

[0115] A posteriori prediction of temperature-induced deflection The probability density function of .

[0116] The above steps S4 and S5 respectively predict the probability distribution of the temperature-induced deflection value at time t+1 based on the empirical prediction information at time t and the measured information at time t+1; however, the variance and mean of the normal distribution of the temperature-induced deflection prediction value predicted by the two methods are different, so in step S6, the information contained in the two normal distributions is fused to achieve the correction of the temperature-induced deflection prediction value; the specific process is as follows:

[0117] The a priori predicted value of temperature-induced deflection and the a posteriori temperature-induced deflection prediction value The probability density function of is multiplied by a scaling factor to obtain a new probability density function whose integral on (-∞,∞) is 1. The independent variable value is the corrected predicted value of the temperature-induced deflection at time t+1.

[0118] In a possible implementation, the Kalman gain coefficient K is used as the scaling coefficient, and the specific process is as follows:

[0119] Based on the mean at time t and variance , calculate the Kalman gain coefficient K:

[0120] ;

[0121] Then the final mean value of the predicted temperature-induced deflection at time t+1 is and variance for:

[0122] ;

[0123] ;

[0124] Define the new probability density function as , then the predicted value of temperature-induced deflection at time t+1 after correction is It conforms to the following normal distribution:

[0125] ;

[0126] The new probability density function The independent variable value corresponding to the maximum probability value As the final predicted value of temperature-induced deflection at time t+1;

[0127] The total deflection signal value at time t+1 and predicted value of temperature-induced deflection Subtract, and we get , as the final predicted value of the vehicle-induced deflection at time t+1.

[0128] Steps S4 to S6 are iterated to achieve real-time separation of vehicle-induced deflection and temperature-induced deflection.

[0129] In a second aspect, the present application provides a system for executing the above-mentioned method for real-time separation of temperature-induced deflection and vehicle-induced deflection of a bridge, comprising the following devices and modules:

[0130] A sensor for collecting a total deflection signal including temperature-induced deflection and vehicle-induced deflection in real time;

[0131] A preprocessing module, used for separating the temperature-induced deflection and the vehicle-induced deflection of the total deflection signal of the preprocessing section;

[0132] The initial value calculation module calculates the standard deviation of the probability distribution of the increment of the temperature-induced deflection and the vehicle-induced deflection of the pre-processing section according to the separated temperature-induced deflection and the vehicle-induced deflection, and uses the standard deviation as the initial value parameter of the subsequent iteration process;

[0133] The prior prediction module predicts the probability distribution of the temperature-induced deflection at time t+1 according to the predicted value of the temperature-induced deflection at time t and the deflection fluctuation information before time t, and obtains the prior predicted value of the temperature-induced deflection at time t+1; specifically, the mean of the prior predicted value of the temperature-induced deflection at time t+1 is the predicted value of the temperature-induced deflection at time t, and the variance is the sum of the variance of the predicted value of the temperature-induced deflection at time t determined by iterative calculation before time t and the variance of the increment of the temperature-induced deflection value in the preprocessing section;

[0134] The posterior prediction module predicts the probability distribution of the vehicle-induced deflection at time t+1, and calculates the posterior temperature-induced deflection prediction value at time t+1 according to the total deflection signal value at time t+1; specifically, the mean of the posterior temperature-induced deflection prediction value at time t+1 is the total deflection signal value at time t+1, and the variance is the variance of the vehicle-induced deflection value in the preprocessing stage;

[0135] The fusion correction module fuses and corrects the priori temperature-induced deflection prediction value and the posterior temperature-induced deflection prediction value at time t+1 to obtain the final temperature-induced deflection and vehicle-induced deflection prediction values ​​at time t+1;

[0136] The iteration module is electrically / communicatively connected to the a priori prediction module, the a posteriori prediction module, and the fusion correction module, and iterates steps S4 to S6 to obtain the predicted values ​​of the temperature-induced deflection and the vehicle-induced deflection at time t+2 and thereafter.

[0137] In a third aspect, the present application further provides a computer-readable storage medium on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method in the above embodiment is implemented;

[0138] For example, it can be achieved Figure 1 The steps of the real-time separation method of beam temperature-induced deflection and vehicle-induced deflection are shown in .

[0139] Computer readable storage media can be tangible devices that can hold and store instructions used by instruction execution devices. Computer readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the above.

[0140] The computer program instructions used to perform the operations of the present application may be source code or object code written in any combination of one or more programming languages, and the computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server.

[0141] The following takes the bridge deflection data collected by the deflection sensor of a bridge in Huizhou on a certain day as an example to illustrate the specific implementation process of the present invention.

[0142] This embodiment uses the deflection data collected in the first hour as the preprocessing section, and uses the average filtering method to separate the temperature-induced deflection and the vehicle-induced deflection. Figure 2 The figure shows the comparison of the temperature-induced deflection obtained by using the average mean filter in the preprocessing stage and the original signal; the vehicle-induced deflection and the temperature-induced deflection are separated in real time, and the prior temperature-induced deflection is inferred from the information at time t and before time t, and the posterior temperature-induced deflection is inferred from the measured value of the deflection at time t+1, and the two are merged; through continuous iteration, the comparison of the separated temperature-induced deflection and the total deflection is shown in Figure 3 The information of vehicle-induced deflection separated by this method is as follows: Figure 4 shown.

[0143] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A real-time separation method for temperature-induced deflection and vehicle-induced deflection of a bridge, characterized in that: The steps include: S1, collecting the total deflection signal including temperature-induced deflection and vehicle-induced deflection; S2, selecting a preprocessing section, and separating the temperature-induced deflection and the vehicle-induced deflection of the total deflection signal of the preprocessing section; S3, according to the separated pretreatment section temperature-induced deflection value and pretreatment section vehicle-induced deflection value, calculate the standard deviation of the difference sequence of the pretreatment section temperature-induced deflection value and the standard deviation of the vehicle-induced deflection value of the pretreatment section , the standard deviation As the standard deviation of the probability distribution of subsequent temperature-induced deflection value increments, the standard deviation As the standard deviation of the probability distribution of the subsequent vehicle-induced deflection, the above two standard deviations are used as the initial value parameters of the subsequent iteration process; specifically, they include the following contents: Standard deviation of the difference series of temperature-induced deflection values ​​in the pretreatment section for: ； in, represents the number of data points of the temperature-induced deflection sequence of the separated pretreatment section, It represents the difference between two adjacent temperature-induced deflection values ​​in the pretreatment section. - , Indicates the temperature-induced deflection value of the pretreatment section, express The average value of , ; Standard deviation of vehicle-induced deflection values ​​in the pretreatment section for: ； in, represents the number of data points of the vehicle-induced deflection sequence of the separated pre-processing section, It represents the vehicle-induced deflection value of the pretreatment section. It represents the average value of vehicle-induced deflection in the pretreatment section. , ; S4, predicting the probability distribution of the temperature-induced deflection at time t+1, and obtaining the prior temperature-induced deflection prediction value at time t+1; the mean of the prior temperature-induced deflection prediction value at time t+1 is the temperature-induced deflection prediction value at time t, and the variance is the sum of the variance of the temperature-induced deflection prediction value at time t determined by iterative calculation before time t and the variance of the increment of the temperature-induced deflection value in the pretreatment section; specifically including the following contents: Assuming that the temperature-induced deflection conforms to the normal distribution, the predicted value of the temperature-induced deflection at time t is for: ； in, , They represent the mean and variance of the predicted value of temperature-induced deflection at time t obtained by the last iterative calculation respectively; The prior predicted value of temperature-induced deflection at time t+1 is inferred based on the predicted value of temperature-induced deflection at time t for: ； ； in, Indicates the predicted value of temperature-induced deflection The increment of , which is a normal variable with mean 0 and independent and identical distribution; for The variance of The prior temperature-induced deflection prediction value at time t+1 It conforms to the following normal distribution: ； The a priori temperature-induced deflection prediction value The probability density function of ; S5, predicting the probability distribution of the vehicle-induced deflection at time t+1, and calculating the posterior temperature-induced deflection prediction value at time t+1 according to the total deflection signal value at time t+1; the mean of the posterior temperature-induced deflection prediction value at time t+1 is the total deflection signal value at time t+1, and the variance is the variance of the vehicle-induced deflection value in the preprocessing stage; specifically including the following contents: Assuming that the vehicle-induced deflection conforms to the normal distribution, the predicted value of the vehicle-induced deflection at time t+1 is for: ； The total deflection signal value at time t+1 is known , calculate the posterior temperature-induced deflection prediction value at time t+1 : ； The a posteriori temperature-induced deflection prediction value at time t+1 is It conforms to the following normal distribution: ； The a posteriori temperature-induced deflection prediction value The probability density function of ; S6, fusing the a priori temperature-induced deflection prediction value and the a posteriori temperature-induced deflection prediction value at time t+1 to obtain a corrected temperature-induced deflection prediction value at time t+1; then, based on the total deflection signal value at time t+1, calculating the final temperature-induced deflection prediction value and vehicle-induced deflection prediction value at time t+1; specifically including the following contents: The a priori temperature-induced deflection prediction value The probability density function of and the a posteriori temperature-induced deflection prediction value The probability density function of Multiply them together and multiply them by a scaling factor to get a new probability density function , the integral of the new probability density function on (-∞,∞) is 1, and the value of the independent variable represents the new normal variable, that is, the predicted value of temperature-induced deflection at time t+1 after correction; The new probability density function The independent variable value corresponding to the maximum probability value As the final predicted value of temperature-induced deflection at time t+1; The total deflection signal value at time t+1 Subtract the predicted temperature-induced deflection , and obtain the final predicted value of vehicle-induced deflection at time t+1; S7, iterate steps S4 to S6 to obtain the mean and variance of the temperature-induced deflection prediction value at time t+2, and obtain the vehicle-induced deflection prediction value at time t+2 based on the total deflection signal value collected in real time; and so on, to achieve real-time separation of temperature-induced deflection and vehicle-induced deflection at time t+2 and thereafter.

2. The real-time separation method of bridge temperature-induced deflection and vehicle-induced deflection according to claim 1 is characterized in that: In step S2, the time interval corresponding to the preprocessing segment is [0, t], and step S4 is executed starting from the next data point of the preprocessing segment.

3. The real-time separation method of bridge temperature-induced deflection and vehicle-induced deflection according to claim 1 is characterized in that: In step S2, the total deflection signal value of the preprocessing section is processed by average mean filtering to obtain the temperature-induced deflection value of the preprocessing section; secondly, the temperature-induced deflection value of the preprocessing section is subtracted from the total deflection signal value to obtain the vehicle-induced deflection value of the preprocessing section.

4. The real-time separation method of bridge temperature-induced deflection and vehicle-induced deflection according to claim 1 is characterized in that: The scaling factor is determined by using a Kalman filter method, which specifically includes the following contents: Based on the mean at time t and variance , calculate the Kalman gain coefficient K: ； The Kalman gain coefficient K is used as the scaling coefficient to obtain the corrected mean value of the temperature-induced deflection prediction value at time t+1 and variance They are: ； ； The corrected predicted value of temperature-induced deflection at time t+1 It conforms to the following normal distribution: 。 5. A system for executing the real-time separation method of bridge temperature-induced deflection and vehicle-induced deflection as claimed in any one of claims 1 to 4, characterized in that: include: A sensor for collecting a total deflection signal including temperature-induced deflection and vehicle-induced deflection in real time; A preprocessing module, used for separating the temperature-induced deflection and the vehicle-induced deflection of the total deflection signal of the preprocessing section; The initial value calculation module calculates the standard deviation of the probability distribution of the increment of the pretreatment section temperature-induced deflection value and the pretreatment section vehicle-induced deflection value according to the separated pretreatment section temperature-induced deflection value and the pretreatment section vehicle-induced deflection value, and uses the standard deviation as the initial value parameter of the subsequent iteration process; The prior prediction module predicts the probability distribution of the temperature-induced deflection value at time t+1 according to the temperature-induced deflection prediction value at time t and the deflection fluctuation information before time t, and obtains the prior temperature-induced deflection prediction value at time t+1; the mean of the prior temperature-induced deflection prediction value at time t+1 is the temperature-induced deflection prediction value at time t, and the variance is the sum of the variance of the temperature-induced deflection prediction value at time t determined by iterative calculation before time t and the variance of the temperature-induced deflection value increment of the preprocessing section; The posterior prediction module predicts the probability distribution of the vehicle-induced deflection at time t+1, and calculates the posterior temperature-induced deflection prediction value at time t+1 according to the total deflection measured value at time t+1; the mean of the posterior temperature-induced deflection prediction value at time t+1 is the total deflection measured value at time t+1, and the variance is the variance of the vehicle-induced deflection value in the preprocessing section; The fusion correction module fuses and corrects the priori temperature-induced deflection prediction value and the posterior temperature-induced deflection prediction value at time t+1 to obtain the final temperature-induced deflection and vehicle-induced deflection prediction values ​​at time t+1; The iteration module is electrically / communicatively connected to the a priori prediction module, the a posteriori prediction module, and the fusion correction module, and iterates steps S4 to S6 to obtain the predicted values ​​of the temperature-induced deflection and the vehicle-induced deflection at time t+2 and thereafter.

6. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the real-time separation method of the temperature-induced deflection and vehicle-induced deflection of a bridge as described in any one of claims 1 to 4 is implemented.

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