Bridge expansion joint blockage early warning system and method based on convex model correlation analysis

Abstract

The invention discloses a bridge expansion joint blockage early warning system and method based on convex model correlation analysis, relates to the technical field of correlation analysis, and solves the technical problem that the judgment accuracy is reduced due to the fact that the prior art depends on a single linear relation and does not consider the uncertainty of temperature and displacement in different environments. According to the invention, interval processing is carried out on temperature and displacement data; defining interval variables, and constructing a parallelogram interval model by calculating edge intervals; calculating correlation coefficients of the temperature and displacement data, classifying the correlation coefficients, and determining the shape of the interval model; calculating a shape coefficient matrix, and obtaining an uncertain domain corresponding to the temperature and displacement data based on the shape coefficient matrix; combining the correlation classification with the uncertain domain to form a convex model, and forming a parallelogram region through the convex model; judging whether data exists outside the parallelogram area or not; according to the method, the robustness of correlation description between the temperature and the displacement is improved, and the early warning capability of expansion joint blockage is optimized.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent transportation systems and relates to correlation analysis technology, in particular to a bridge expansion joint blockage warning system and method based on convex model correlation analysis. Background Art

[0002] Constructing a bridge expansion joint blockage warning system and method based on convex model correlation analysis provides a more comprehensive analysis tool for displacement and temperature changes under different environmental conditions, so that the prediction of expansion joint blockage is not only based on a single numerical result, but based on the overall trend of the interval, thereby improving the scientificity and accuracy of decision-making; combined with real-time data monitoring, the parameters in the interval convex model can be dynamically adjusted to achieve timely evaluation and early warning of the expansion joint status; this dynamism helps to respond to bridge maintenance in a timely manner and ensure the safety and service life of the bridge.

[0003] When the existing technology is constructed on the linear regression model, it is assumed that there is a linear correlation between temperature and displacement; however, the correlation between temperature and displacement is often affected by multiple factors and is actually a nonlinear relationship. When the actual relationship presents nonlinear characteristics, the prediction accuracy of the linear model will be significantly affected. At the same time, the linear regression model is sensitive to outliers and noise. When there are outliers in the monitoring data, the parameter estimation of the model may be seriously disturbed. This sensitivity may lead to technical problems such as misjudgment or omission of expansion joint blockage.

[0004] The present invention provides a bridge expansion joint blockage early warning system and method based on convex model correlation analysis to solve the above technical problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a bridge expansion joint blockage warning system and method based on convex model correlation analysis, which is used to solve the technical problem that the prior art mainly relies on a single linear relationship and fails to fully consider the variability and uncertainty of temperature and displacement under different environmental conditions. At the same time, the diversity of temperature changes and the randomness of displacement responses will lead to the unreliability of the model, thereby reducing the accuracy of judgment.

[0006] To achieve the above-mentioned object, a first aspect of the present invention provides a bridge expansion joint blockage warning system based on convex model correlation analysis, comprising: a data preprocessing module, a model building module, and a blockage judgment module;

[0007] Data preprocessing module: obtain temperature and displacement data, and process the temperature and displacement data into intervals;

[0008] Model building module: define interval variables, build a parallelogram interval model by calculating edge intervals; calculate the correlation coefficients of temperature and displacement data, classify the correlation coefficients, and determine the shape of the interval model;

[0009] Blockage judgment module: calculate the shape coefficient matrix, and obtain the uncertainty domain corresponding to the temperature and displacement data based on the shape coefficient matrix; form a convex model by combining the uncertainty domains according to the correlation classification, and form a parallelogram area through the convex model; judge whether there is data outside the parallelogram area; if so, the bridge expansion joint is blocked and a warning signal is issued; if not, the bridge expansion joint is not blocked.

[0010] Preferably, the temperature and displacement data are processed in intervals, including:

[0011] Let x and y represent different intervals, where x is the variable in the interval X, and x is the lower endpoint of X. is the upper endpoint of X; y is the variable in the Y interval, and y is the lower endpoint of the Y interval. is the upper endpoint of the Y interval;

[0012] By formula represents the interval X;

[0013] By formula Represents the interval Y.

[0014] Preferably, the definition of interval variables includes:

[0015] The temperature X and the displacement Y are expressed as uncertainty intervals [X_min, X_max] and [Y_min, Y_max] respectively; wherein X_min represents the minimum value of the temperature, and X_max represents the maximum value of the temperature; Y_min represents the minimum value of the displacement, and Y_max represents the maximum value of the displacement; X_min, X_max, Y_min, and Y_max are taken as the four vertices of the parallelogram interval.

[0016] The marginal interval definition of the parallelogram interval model in the present invention is similar to the marginal distribution in the probability model, that is, the variation interval of a single variable when the values ​​of other variables are not considered; therefore, geometrically the marginal interval of a single variable is equivalent to the projection of the multivariate joint uncertainty domain in the coordinate direction of the variable.

[0017] Preferably, constructing a parallelogram interval model by calculating edge intervals includes:

[0018] By formula Calculate the midpoint of the interval number X; by the formula Calculate the midpoint of the interval number Y;

[0019] By formula Calculate the deviation of the interval number X; by formula The deviation of the interval number Y is calculated;

[0020] By the formula [x] = [x M -x R , x M +x R ] represents the edge interval of X; through the formula [y] = [y M -y R ,y M +y R ] represents the edge interval of Y.

[0021] Preferably, the calculation of the correlation coefficient between temperature and displacement data includes:

[0022] Let a and b be the lengths of the line segments defined by the geometric features of the parallelogram interval model;

[0023] By formula The correlation coefficient between temperature and displacement is calculated; where φ xy The value range of is [-1, 1];

[0024] Set the interval correlation coefficient φ for interval variables X and Y xy =φ yx =φ;

[0025] Set the interval correlation coefficient φ for the same interval variable X or Y xx =φ yy =1.

[0026] Preferably, the classification of the correlation coefficients includes:

[0027] Retrieve the correlation coefficient between temperature and displacement judge Is it equal to 0? If yes, the temperature interval and displacement interval are independent of each other; if no, the temperature interval and displacement interval are related;

[0028] judge Is it greater than 0? If yes, the temperature range and displacement range are positively correlated; if no, the temperature range and displacement range are negatively correlated.

[0029] Preferably, determining the shape of the interval model includes:

[0030] Assume that the parallelogram domain Ω is surrounded by a rectangular domain ΩS, and the vertices of the parallelogram are located on the diagonals of the rectangular domain;

[0031] When , the temperature interval and displacement interval are independent of each other, and the corresponding parallelogram degenerates into a rectangular domain ΩS;

[0032] When , the temperature interval and displacement interval are completely linearly related, and the parallelogram degenerates into a straight line;

[0033] and When , set A, B, C, and D as the four vertices of the interval model shape;

[0034] The coordinates of point A are

[0035] The coordinates of point B are

[0036] The coordinates of point C are

[0037] The coordinates of point D are

[0038] Connect the four vertices A, B, C, and D to obtain the interval model shape.

[0039] Preferably, the calculating of the shape coefficient matrix comprises:

[0040] By formula The coefficient matrix H is calculated;

[0041] By formula The correlation coefficient matrix is ​​calculated;

[0042] By formula The coefficient matrix Γ is calculated;

[0043] The shape coefficient matrix is ​​calculated using the formula Λ=HΓΦ.

[0044] Preferably, the step of obtaining the uncertainty domain corresponding to the temperature and displacement data based on the shape coefficient matrix includes:

[0045] Label the temperature data as x and the displacement data as y;

[0046] Get the shape factor matrix using the formula The uncertainty domain of the data points is calculated; the data points include: temperature data, displacement data; the value range of Ω is [-η, η], η = (1, 1) T , T is the transpose symbol.

[0047] To achieve the above object, the second aspect of the present invention provides a bridge expansion joint blockage early warning method based on convex model correlation analysis, comprising:

[0048] S1: Acquire temperature and displacement data, and process the temperature and displacement data into intervals;

[0049] S2: Define interval variables and construct a parallelogram interval model by calculating edge intervals; calculate the correlation coefficients of temperature and displacement data, classify the correlation coefficients, and determine the shape of the interval model;

[0050] S3: Calculate the shape coefficient matrix, and obtain the uncertainty domain corresponding to the temperature and displacement data based on the shape coefficient matrix; classify and combine the uncertainty domains according to the correlation to form a convex model, and form a parallelogram area through the convex model;

[0051] S4: Determine whether there is data outside the parallelogram area; if yes, the expansion joint of the bridge is blocked and a warning signal is issued; if no, the expansion joint of the bridge is not blocked.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] The present invention can effectively capture the interval correlation between temperature and displacement through an interval convex model, and improve the modeling ability of displacement changes in complex environments. This method can flexibly adapt to the diversity of temperature changes, thereby enhancing the accuracy and reliability of the model; the interval convex model shows higher robustness when processing outliers; by performing interval processing on the data, the influence of a single outlier on the model can be effectively suppressed, thereby improving the applicability of the model in actual monitoring; the present invention can describe uncertainty through intervals, reflecting the diversity between displacement and temperature under different environmental conditions; this method provides a more comprehensive analysis tool, so that the prediction of expansion joint blockage is not only based on a single numerical result, but based on the overall trend of the interval, thereby improving the scientificity and accuracy of the decision; by combining real-time data monitoring, the parameters in the interval convex model can be dynamically adjusted to achieve timely evaluation and early warning of the expansion joint status. This dynamicity helps to respond to bridge maintenance in a timely manner and ensure the safety and service life of the bridge.

[0054] In summary, the present invention aims to improve the accuracy and robustness of the description of the correlation between temperature and displacement by introducing an interval convex model, thereby optimizing the prediction ability of expansion joint blockage and promoting the advancement of bridge intelligent monitoring technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 This is a schematic diagram of the relationship between modules included in the present invention;

[0057] Figure 2 It is a schematic diagram of the interval correlation between the displacement and temperature of the expansion joint of the present invention;

[0058] Figure 3 It is a schematic diagram of the interval variable positive correlation parallelogram interval model of the present invention;

[0059] Figure 4 It is a schematic diagram of the interval variable negative correlation parallelogram interval model of the present invention;

[0060] Figure 5 It is a schematic diagram of the process of early warning of bridge expansion joint blockage according to the present invention. DETAILED DESCRIPTION

[0061] The technical scheme of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] See also Figure 1 , the first aspect of the present invention provides a bridge expansion joint blockage warning system based on convex model correlation analysis, including: a data preprocessing module, a model building module, and a blockage judgment module;

[0063] Data preprocessing module: obtain temperature and displacement data, and process the temperature and displacement data into intervals;

[0064] Model building module: define interval variables, build a parallelogram interval model by calculating edge intervals; calculate the correlation coefficients of temperature and displacement data, classify the correlation coefficients, and determine the shape of the interval model;

[0065] Blockage judgment module: calculate the shape coefficient matrix, and obtain the uncertainty domain corresponding to the temperature and displacement data based on the shape coefficient matrix; form a convex model by combining the uncertainty domains according to the correlation classification, and form a parallelogram area through the convex model; judge whether there is data outside the parallelogram area; if so, the bridge expansion joint is blocked and a warning signal is issued; if not, the bridge expansion joint is not blocked.

[0066] See also Figure 2 , the interval correlation between expansion joint displacement and temperature, marking the temperature data as x and the displacement data as y;

[0067] Get the shape factor matrix using the formula The uncertainty domain of the data points is calculated; the data points include: temperature data, displacement data; the value range of Ω is [-η, η], η = (1, 1) T, T is the transpose symbol.

[0068] For example, the uncertainty domains of interval variables X and Y are Figure 2 The minimum parallelogram region Ω containing all sample points is shown; the construction of the parallelogram interval model follows the following four principles:

[0069] Principle 1: The parallelogram domain Ω is surrounded by a rectangular domain ΩS;

[0070] Principle 2: The vertices of a parallelogram lie on the diagonals of a rectangular domain;

[0071] Principle 3: When X and Y are independent, the parallelogram domain degenerates into a rectangular domain;

[0072] Principle 4: When X and Y are completely related, the parallelogram domain becomes a diagonal in the rectangular domain;

[0073] Therefore, the uncertainty and correlation of interval variables X and Y can be measured by the geometric characteristics of the parallelogram.

[0074] The present invention aims to analyze the interval correlation between temperature and displacement using the parallelogram interval model, by considering temperature and displacement as uncertain interval variables, and constructing a binary interval function using the parallelogram interval model (see Figure 2 ), thereby characterizing the degree of correlation between temperature and displacement data.

[0075] It should be noted that the use of the parallelogram interval model to analyze binary interval function problems is helpful in overcoming the interval expansion phenomenon that occurs in interval operations.

[0076] See also Figure 3-Figure 4 , Schematic diagram of the parallelogram interval model with different correlation coefficients, assuming that a and b are the lengths of the line segments defined by the geometric characteristics of the parallelogram interval model;

[0077] By formula The correlation coefficient between temperature and displacement is calculated; where φ xy The value range of is [-1, 1];

[0078] Set the interval correlation coefficient φ for interval variables X and Y xy =φ yx =φ;

[0079] Set the interval correlation coefficient φ for the same interval variable X or Y xx =φ yy =1;

[0080] Retrieve the correlation coefficient between temperature and displacement judge Is it equal to 0? If yes, the temperature interval and displacement interval are independent of each other; if no, the temperature interval and displacement interval are related;

[0081] judge Is it greater than 0? If yes, the temperature range and displacement range are positively correlated; if no, the temperature range and displacement range are negatively correlated.

[0082] For example, Figure 3 Indicates that the interval variables temperature and displacement are positively correlated, and the correlation coefficient

[0083] Figure 4 Indicates that the two interval variables of temperature and displacement are negatively correlated, and the correlation coefficient

[0084] When two interval variables are independent, the correlation coefficient φ xy = 0, when the correlation is completely linear, φ xy =±1; the stronger the correlation between two interval variables, the larger the absolute value of the correlation coefficient.

[0085] See also Figure 5 The second aspect of the present invention provides a bridge expansion joint blockage warning method based on convex model correlation analysis, including:

[0086] S1: Acquire temperature and displacement data, and process the temperature and displacement data into intervals;

[0087] S2: Define interval variables and construct a parallelogram interval model by calculating edge intervals; calculate the correlation coefficients of temperature and displacement data, classify the correlation coefficients, and determine the shape of the interval model;

[0088] S3: Calculate the shape coefficient matrix, and obtain the uncertainty domain corresponding to the temperature and displacement data based on the shape coefficient matrix; classify and combine the uncertainty domains according to the correlation to form a convex model, and form a parallelogram area through the convex model;

[0089] S4: Determine whether there is data outside the parallelogram area; if yes, the expansion joint of the bridge is blocked and a warning signal is issued; if no, the expansion joint of the bridge is not blocked.

[0090] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0091] The working principle of the present invention is as follows: the present invention obtains temperature and displacement data, performs interval processing on the temperature and displacement data; defines interval variables, and constructs a parallelogram interval model by calculating edge intervals; calculates the correlation coefficients of the temperature and displacement data, classifies the correlation coefficients, and determines the shape of the interval model; calculates a shape coefficient matrix, and obtains the uncertainty domain corresponding to the temperature and displacement data based on the shape coefficient matrix; constructs a convex model by combining the uncertainty domains according to the correlation classification, and forms a parallelogram area through the convex model; determines whether there is data outside the parallelogram area; if yes, the expansion joint of the bridge is blocked and a warning signal is issued; if no, the expansion joint of the bridge is not blocked.

[0092] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A bridge expansion joint blockage warning system and method based on convex model correlation analysis, characterized in that: Data preprocessing module, model building module, and congestion judgment module; Data preprocessing module: obtain temperature and displacement data, and process the temperature and displacement data into intervals; Model building module: define interval variables, build a parallelogram interval model by calculating edge intervals; calculate the correlation coefficients of temperature and displacement data, classify the correlation coefficients, and determine the shape of the interval model; Blockage judgment module: calculate the shape coefficient matrix, and obtain the uncertainty domain corresponding to the temperature and displacement data based on the shape coefficient matrix; classify and combine the uncertainty domains according to the correlation to form a convex model, and form a parallelogram area through the convex model; Determine whether there is data outside the parallelogram area; If yes, the bridge expansion joint is blocked and an early warning signal is issued; No, the bridge expansion joint is not blocked.

2. The bridge expansion joint blockage early warning system and method based on convex model correlation analysis according to claim 1 is characterized in that: The temperature and displacement data are processed in intervals, including: Let x and y represent different intervals, where x is the variable in the interval X, and x is the lower endpoint of X. is the upper endpoint of X; y is the variable in the Y interval, and y is the lower endpoint of the Y interval. is the upper endpoint of the Y interval; By formula represents the interval X; By formula Represents the interval Y.

3. The bridge expansion joint blockage early warning system and method based on convex model correlation analysis according to claim 1 is characterized in that: The definition of interval variables includes: The temperature X and the displacement Y are expressed as uncertainty intervals [X_min, X_max] and [Y_min, Y_max] respectively; wherein X_min represents the minimum value of the temperature, and X_max represents the maximum value of the temperature; Y_min represents the minimum value of the displacement, and Y_max represents the maximum value of the displacement; X_min, X_max, Y_min, and Y_max are taken as the four vertices of the parallelogram interval.

4. The bridge expansion joint blockage early warning system and method based on convex model correlation analysis according to claim 1 is characterized in that: The step of constructing a parallelogram interval model by calculating edge intervals includes: By formula Calculate the midpoint of the interval number X; by the formula Calculate the midpoint of the interval number Y; By formula Calculate the deviation of the interval number X; by formula Calculate the deviation of the interval number Y; By the formula [x] = [x M -x R , x M +x R ] represents the edge interval of X; through the formula [y] = [y M -y R ,y M +y R ] represents the edge interval of Y.

5. The bridge expansion joint blockage early warning system and method based on convex model correlation analysis according to claim 1 is characterized in that: The calculation of the correlation coefficient between temperature and displacement data includes: Let a and b be the lengths of the line segments defined by the geometric features of the parallelogram interval model; By formula The correlation coefficient between temperature and displacement is calculated; where φ xy The value range of is [-1, 1]; Set the interval correlation coefficient φ for interval variables X and Y xy =φ yx =φ; Set the interval correlation coefficient φ for the same interval variable X or Y xx =φ yy =1.

6. The bridge expansion joint blockage early warning system and method based on convex model correlation analysis according to claim 1 is characterized in that: The correlation coefficients are classified into categories, including: Retrieve the correlation coefficient between temperature and displacement judge Is it equal to 0? If yes, the temperature interval and displacement interval are independent of each other; if no, the temperature interval and displacement interval are related; judge Is it greater than 0? If yes, the temperature range and displacement range are positively correlated; if no, the temperature range and displacement range are negatively correlated.

7. The bridge expansion joint blockage early warning system and method based on convex model correlation analysis according to claim 1 is characterized in that: The determining the shape of the interval model comprises: Assume that the parallelogram domain Ω is surrounded by a rectangular domain ΩS, and the vertices of the parallelogram are located on the diagonals of the rectangular domain; When , the temperature interval and displacement interval are independent of each other, and the corresponding parallelogram degenerates into a rectangular domain ΩS; When , the temperature interval and displacement interval are completely linearly related, and the parallelogram degenerates into a straight line; and When , set A, B, C, and D as the four vertices of the interval model shape; The coordinates of point A are The coordinates of point B are The coordinates of point C are The coordinates of point D are Connect the four vertices A, B, C, and D to obtain the interval model shape.

8. The bridge expansion joint blockage early warning system and method based on convex model correlation analysis according to claim 1 is characterized in that: The calculating shape coefficient matrix comprises: By formula The coefficient matrix H is calculated; By formula The correlation coefficient matrix is ​​calculated; By formula The coefficient matrix Γ is calculated; The shape coefficient matrix is ​​calculated using the formula Λ=HΓΦ.

9. The bridge expansion joint blockage early warning system and method based on convex model correlation analysis according to claim 1 is characterized in that: The obtaining of the uncertainty domain corresponding to the temperature and displacement data based on the shape coefficient matrix includes: Label the temperature data as x and the displacement data as y; Get the shape factor matrix using the formula The uncertainty domain of the data points is calculated; the data points include: temperature data, displacement data; the value range of Ω is [-η, η], η = (1, 1) T , T is the transpose symbol.

10. A bridge expansion joint blockage warning method based on convex model correlation analysis, applied to a bridge expansion joint blockage warning system based on convex model correlation analysis as claimed in any one of claims 1 to 9, characterized in that: include: S1: Acquire temperature and displacement data, and process the temperature and displacement data into intervals; S2: Define interval variables and construct a parallelogram interval model by calculating edge intervals; calculate the correlation coefficients of temperature and displacement data, classify the correlation coefficients, and determine the shape of the interval model; S3: Calculate the shape coefficient matrix, and obtain the uncertainty domain corresponding to the temperature and displacement data based on the shape coefficient matrix; classify and combine the uncertainty domains according to the correlation to form a convex model, and form a parallelogram area through the convex model; S4: Determine whether there is data outside the parallelogram area; If yes, the bridge expansion joint is blocked and an early warning signal is issued; No, the bridge expansion joint is not blocked.

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