An intelligent adjustment system for prefabricated bridge pier design

Abstract

The present invention relates to the field of pier quality data analysis, and in particular to an intelligent adjustment system for prefabricated pier design, comprising: a difference assessment module for periodically determining whether to perform component load analysis and component erosion analysis for each existing analysis set based on a distribution difference index and a stage difference index; a load analysis module for determining a distribution assessment combination based on a defect distribution overlap index and an overlap distribution proportion index, and determining a load analysis strategy based on an erosion difference coefficient and a characteristic distribution coefficient; an erosion analysis module for determining a growth assessment combination based on an overlap growth correlation index and an overlap edge proportion index, and determining an erosion analysis strategy based on a load difference coefficient and a reference time series correlation coefficient; and an optimization execution module for determining an optimized execution combination of various component preparation parameters of a target analysis component.

Description

Technical Field

[0001] The present invention relates to the field of bridge pier quality data analysis, and in particular to an intelligent adjustment system for prefabricated bridge pier design. Background Art

[0002] Prefabricated bridge piers have been widely used in the current bridge construction process. In the optimization and adjustment process of the design parameters of prefabricated bridge piers, it is often necessary to analyze the defect status of the prefabricated bridge piers that have been put into use, and optimize the design parameters of the prefabricated bridge piers based on the analysis results. The factors that cause pier defects in actual use are mainly attributed to environmental erosion and the self-vibration of the bridge body caused by the use process, and are jointly formed by the above-mentioned factors. The existing design parameter optimization system for prefabricated bridge piers often fails to effectively separate the degree of interference of different influencing factors on the defects of the piers, and cannot fully guarantee the consistency of the environment and the use process during the design parameter optimization process, resulting in low effectiveness of the design parameter optimization process. Therefore, how to separate the degree of interference of different influencing factors on the defects in the optimization process of the design parameters of prefabricated bridge piers, and optimize the design parameters based on the interference situation after separation and the pier defect situation to ensure the effectiveness of the design parameter optimization results of the prefabricated bridge piers, is a problem that needs to be solved urgently by technical personnel in this field.

[0003] Chinese patent publication number CN116910423A discloses a bridge deformation monitoring method and monitoring system, which includes: calculating the overall position offset coefficient and internal structure deflection coefficient of each pier in the target bridge; calculating the comprehensive deformation index of each pier in the target bridge based on the overall position offset coefficient and internal structure deflection coefficient of each pier in the target bridge; for piers in the target bridge that do not have deformation, calculating the linear deflection coefficient and appearance defect coefficient of the bridge deck of the target bridge; calculating the comprehensive deformation index of the bridge deck of the target bridge based on the linear deflection coefficient and appearance defect coefficient of the bridge deck of the target bridge, and judging whether the bridge deck of the target bridge is deformed based on the comprehensive deformation index. Chinese patent publication number CN107273651A discloses a method for designing a super-high pier structure. First, the load size of the pier to be designed is calculated, and then the design parameters of the pier to be designed are determined. The pier design parameters are not limited to simple changes in cross-sectional thickness and width. Instead, the design parameters are selected as the height, cross-sectional dimensions and slope of the pier body and the inclined legs, the bifurcation spacing of the inclined legs, and the height and cross-sectional dimensions of the tie beam, so that the shape of the pier can be optimized to the greatest extent while meeting the use requirements. Next, the value range and value step of the design parameters are determined, and different values ​​of each parameter are scanned and combined. Finite element models are established and calculated using parameterization. The stiffness results, strength results and vibration results of each finite element model are automatically extracted and compared with the requirements of the specification. All pier forms that meet the above three conditions are selected, and the total pier volume is calculated. The pier form with the smallest total pier volume is selected as the optimal pier form under this design method. However, the above technical solution has the following defects: it fails to effectively analyze the usage of existing bridge piers based on the design parameter optimization process, and fails to separate the degree of influence of different influencing factors on the usage of bridge piers, resulting in low effectiveness of the optimization results of design parameters. Summary of the Invention

[0004] To this end, the present invention provides an intelligent adjustment system for the design of prefabricated bridge piers, which is used to overcome the problem that the existing technology fails to effectively analyze the usage of existing piers based on the design parameter optimization process and separate the degree of influence of different influencing factors on the usage of piers, resulting in low effectiveness of the optimization results of design parameters.

[0005] To achieve the above objectives, the present invention provides an intelligent adjustment system for prefabricated bridge pier design, comprising:

[0006] a difference assessment module for periodically determining whether to perform component load analysis and component corrosion analysis for each existing analysis set based on a distribution difference index and a stage difference index;

[0007] a load analysis module connected to the difference evaluation module, configured to determine a distribution evaluation combination based on the defect distribution overlap index and the overlap distribution ratio index, and to determine a load analysis strategy based on the corrosion difference coefficient and the characteristic distribution coefficient, namely, determining an optimization reference coefficient based on the load key correlation and the defect distribution overlap index, or determining an optimization reference coefficient based on the characteristic distribution coefficient and the defect distribution overlap index;

[0008] a load execution module connected to the load analysis module, configured to determine an optimization reference coefficient for each distribution evaluation combination according to the load analysis strategy, and determine whether to adjust the optimization reference coefficient based on the combination edge ratio;

[0009] an erosion analysis module connected to the difference assessment module, configured to determine a growth assessment combination based on an overlapping growth correlation index and an overlapping edge ratio index, and to determine an erosion analysis strategy based on a load difference coefficient and a reference time series correlation coefficient, namely, determining an optimized reference coefficient based on an erosion key correlation and an overlapping growth correlation index, or determining an optimized reference coefficient based on a reference time series correlation coefficient and an overlapping growth correlation index;

[0010] an erosion execution module, connected to the erosion analysis module, for determining an optimized reference coefficient for each growth assessment combination according to the erosion analysis strategy, and determining whether to adjust the optimized reference coefficient based on a reference longitudinal distribution ratio;

[0011] An optimization execution module is connected to the load execution module and the erosion execution module respectively, and is used to determine the reference execution combination of the target analysis component according to the erosion correlation coefficient and the load correlation coefficient, and to determine the combination matching coefficient of each reference execution combination based on the optimization reference coefficient and the reference quality coefficient.

[0012] Furthermore, the difference evaluation module performs defect difference evaluation on each existing analysis set, and determines a defect difference coefficient of each existing analysis set according to a distribution difference index and a stage difference index;

[0013] For a single existing analysis set, if the difference execution condition responded by the difference assessment module is that the defect difference coefficient of the existing analysis set is greater than the preset defect difference coefficient, it is determined that the component load analysis and the component corrosion analysis are performed on the existing analysis set, and the existing analysis set is recorded as an interference processing set;

[0014] The component design difference index of any existing analysis set is smaller than the preset component design difference index.

[0015] Furthermore, the load analysis module responds to the load processing condition and performs distribution evaluation combination division for the interference processing set according to the defect distribution overlap index and the overlap distribution proportion index;

[0016] The load analysis module determines the load execution strategy of each distribution evaluation combination based on the erosion difference coefficient and the characteristic distribution coefficient;

[0017] The load processing condition is that there is any existing analysis set determined by the difference assessment module to perform component load analysis.

[0018] Furthermore, if the load execution condition responded by the load analysis module is that the erosion difference coefficient of the distribution evaluation combination is greater than the preset erosion difference coefficient or the characteristic distribution coefficient is greater than the preset characteristic distribution coefficient, the load execution module is determined to determine the optimization reference coefficient of the distribution evaluation combination based on the load key correlation and the defect distribution overlap index;

[0019] The optimization reference coefficient is positively correlated with the load criticality and the defect distribution overlap index respectively.

[0020] Furthermore, the load execution condition responded by the load analysis module is that the erosion difference coefficient of the distribution assessment combination is less than or equal to the preset erosion difference coefficient and the characteristic distribution coefficient is less than or equal to the preset characteristic distribution coefficient, then the load execution module determines the optimization reference coefficient of the distribution assessment combination based on the characteristic distribution coefficient and the defect distribution overlap index, and determines whether to adjust the optimization reference coefficient of the distribution assessment combination based on the combination edge ratio;

[0021] If the optimization control condition of the load execution module response is that the combined edge ratio is greater than the preset combined edge ratio, it is determined that the optimization reference coefficient is to be reduced according to the combined edge ratio;

[0022] The optimized reference coefficient is positively correlated with the characteristic distribution coefficient and the defect distribution overlap index, respectively. The reduced value of the optimized reference coefficient is positively correlated with the combined edge ratio.

[0023] Furthermore, the erosion analysis module responds to the erosion processing conditions and performs growth assessment combination division on the interference processing set according to the overlap growth correlation index and the overlap edge ratio index;

[0024] The erosion analysis module determines the erosion analysis strategy of each growth assessment combination based on the load difference coefficient and the reference time series correlation coefficient;

[0025] The erosion processing condition is that there is any existing analysis set determined by the difference assessment module to perform component erosion analysis.

[0026] Furthermore, if the erosion execution condition responded by the erosion analysis module is that there is a load difference coefficient of a growth assessment combination greater than a preset load difference coefficient or a reference time series correlation coefficient greater than a preset time series correlation coefficient, then the erosion execution module is determined to determine the optimized reference coefficient of the growth assessment combination based on the erosion key correlation and the overlapping growth correlation index;

[0027] The optimized reference coefficient is positively correlated with the erosion key correlation and the overlapping growth correlation index respectively.

[0028] Furthermore, the erosion execution condition responded by the erosion analysis module is that there is a load difference coefficient of a growth assessment combination that is less than or equal to a preset load difference coefficient and a reference time series correlation coefficient that is less than or equal to a preset time series correlation coefficient, then the erosion execution module determines the optimized reference coefficient of the growth assessment combination based on the reference time series correlation coefficient and the overlapping growth correlation index, and determines whether to adjust the optimized reference coefficient of the growth assessment combination based on the reference longitudinal distribution ratio;

[0029] If the erosion optimization control condition responded by the erosion execution module is that the reference longitudinal distribution ratio is greater than the preset reference longitudinal distribution ratio, the optimization reference coefficient is reduced based on the reference longitudinal distribution ratio;

[0030] The optimized reference coefficient is positively correlated with the reference time series correlation coefficient and the overlapping growth correlation index, respectively. The reduced value of the optimized reference coefficient is positively correlated with the reference longitudinal distribution ratio.

[0031] Furthermore, the optimization execution module responds to the target optimization condition, determines a reference execution combination of the target analysis component according to the erosion correlation coefficient and the load correlation coefficient, and determines an optimized execution combination of various component preparation parameters according to the combination matching coefficient;

[0032] The target optimization condition is that there is a target analysis component that needs to perform parameter optimization.

[0033] Furthermore, the optimization execution module determines a combination matching coefficient of each reference execution combination based on the optimization reference coefficient and the reference quality coefficient, and determines whether to adjust the combination matching coefficient according to the combination setting richness;

[0034] For any reference execution combination, if the execution determination condition responded by the optimization execution module is that the combination setting richness of the reference execution combination is greater than the preset combination setting richness, then it is determined to reduce the combination matching coefficient of the reference execution combination based on the execution optimization difference coefficient;

[0035] The reduction value of the combination matching coefficient is positively correlated with the execution optimization difference coefficient.

[0036] Compared with the prior art, the beneficial effect of the present invention lies in that the technical solution of the present invention determines the defect difference coefficient of each existing analysis set based on the distribution difference index and the stage difference index, and determines whether to perform component load analysis and component erosion analysis on the corresponding existing analysis set based on the defect difference coefficient. In the subsequent analysis process, a targeted analysis method is determined based on the actual defect conditions of the existing analysis components in each existing analysis set to ensure the separation effect of the interference degree of different factors on defect growth, thereby improving the subsequent optimization quality of the design parameters of the prefabricated bridge piers.

[0037] Furthermore, when performing component load analysis in the present invention, distribution evaluation combinations are divided for the interference processing set based on the defect distribution overlap index and the overlap distribution ratio index, ensuring that the degree of influence of the component usage load on each existing analysis component in a single distribution evaluation combination is relatively small, providing an analytical basis for separating the causes of environmental erosion in the subsequent process of determining the optimized reference coefficients of each distribution evaluation combination, and ensuring the analysis efficiency of the optimized reference coefficients of the distribution evaluation combination and the validity of the analysis results.

[0038] Furthermore, the present invention determines the load execution strategy of each distribution evaluation combination based on the erosion difference coefficient and the characteristic distribution coefficient, and judges the strength of the relationship between the growth cause of the overlapping defect feature points in the distribution evaluation combination and the environmental erosion through the erosion difference coefficient and the characteristic distribution coefficient. If the relationship with the environmental erosion is weak, the reference degree of the defect feature points contained in the existing analysis components in the distribution evaluation combination for the optimization analysis process is determined only by analyzing the similarity of the load conditions of the existing analysis components in the distribution evaluation combination, and the optimization reference coefficient is determined accordingly. If the relationship with the environmental erosion is strong, it is necessary to correct the optimization reference coefficient according to the degree of influence caused by the environmental erosion in the process of determining the optimization reference coefficient, thereby ensuring the reliability of the setting results of the reference degrees of different existing analysis components in the optimization process, thereby improving the subsequent optimization quality of the design parameters of the prefabricated piers.

[0039] Furthermore, when performing component erosion analysis, the present invention divides the interference processing set into growth assessment combinations according to the overlapping growth correlation index and the overlapping edge ratio index, ensuring that the degree of influence of environmental erosion on each existing analysis component in a single growth assessment combination is relatively small, providing an analysis basis for the reasons for the use load of the separated components in the subsequent determination of the optimized reference coefficients of each growth assessment combination, and ensuring the analysis efficiency of the optimized reference coefficients of the growth assessment combination and the validity of the analysis results.

[0040] Furthermore, the present invention determines the erosion analysis strategy of each growth evaluation combination based on the load difference coefficient and the reference time series correlation coefficient, and judges the strength of the relationship between the growth cause of the overlapping defect feature points of the existing analysis components in the growth evaluation combination and the application load condition of the bridge body through the load difference coefficient and the reference time series correlation coefficient. If the relationship with the application load condition of the bridge body is weak, it is necessary to analyze the similarity of the environmental erosion conditions of the existing analysis components in the growth evaluation combination to determine the reference degree of the defect feature points contained in the existing analysis components in the growth evaluation combination for the optimization analysis process, and thereby determine the optimization reference coefficient. If the relationship with the application load condition of the bridge body is strong, it is necessary to correct the optimization reference coefficient according to the degree of influence of the bridge body's operating load, thereby ensuring the reliability of the setting results of the reference degree of different existing analysis components in the optimization process, thereby improving the subsequent optimization quality of the design parameters of the prefabricated piers. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a module connection diagram of the intelligent adjustment system designed for prefabricated bridge piers according to the present invention;

[0042] Figure 2 This is a flow chart of the difference assessment module of the present invention determining whether to perform component load analysis and component corrosion analysis based on the defect difference coefficient;

[0043] Figure 3 This is a flow chart of the load analysis module of the present invention determining a load analysis strategy based on the operating load difference and the application key difference parameters;

[0044] Figure 4 This is a flow chart of the erosion analysis module of the present invention determining the erosion analysis strategy based on the load difference coefficient and the reference time series correlation coefficient. DETAILED DESCRIPTION

[0045] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0046] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0047] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0048] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0049] See also Figures 1 to 4 As shown, the present invention provides an intelligent adjustment system for prefabricated bridge pier design, comprising:

[0050] a difference assessment module for periodically determining whether to perform component load analysis and component corrosion analysis for each existing analysis set based on a distribution difference index and a stage difference index;

[0051] a load analysis module connected to the difference evaluation module, configured to determine a distribution evaluation combination based on the defect distribution overlap index and the overlap distribution ratio index, and to determine a load analysis strategy based on the corrosion difference coefficient and the characteristic distribution coefficient, namely, determining an optimization reference coefficient based on the load key correlation and the defect distribution overlap index, or determining an optimization reference coefficient based on the characteristic distribution coefficient and the defect distribution overlap index;

[0052] a load execution module connected to the load analysis module, configured to determine an optimization reference coefficient for each distribution evaluation combination according to the load analysis strategy, and determine whether to adjust the optimization reference coefficient based on the combination edge ratio;

[0053] an erosion analysis module connected to the difference assessment module, configured to determine a growth assessment combination based on an overlapping growth correlation index and an overlapping edge ratio index, and to determine an erosion analysis strategy based on a load difference coefficient and a reference time series correlation coefficient, namely, determining an optimized reference coefficient based on an erosion key correlation and an overlapping growth correlation index, or determining an optimized reference coefficient based on a reference time series correlation coefficient and an overlapping growth correlation index;

[0054] an erosion execution module, connected to the erosion analysis module, for determining an optimized reference coefficient for each growth assessment combination according to the erosion analysis strategy, and determining whether to adjust the optimized reference coefficient based on a reference longitudinal distribution ratio;

[0055] An optimization execution module is connected to the load execution module and the erosion execution module respectively, and is used to determine the reference execution combination of the target analysis component according to the erosion correlation coefficient and the load correlation coefficient, and to determine the combination matching coefficient of each reference execution combination based on the optimization reference coefficient and the reference quality coefficient.

[0056] The present invention is used for the process of optimizing and analyzing the design parameters of prefabricated bridge piers. The optimization process of the design parameters needs to be performed based on the usage of the prefabricated bridge piers that have been put into use. Different prefabricated bridge piers have different corresponding usage loads and application environments. Therefore, it is necessary to fully consider the degree of influence of different influencing factors on the usage during the optimization process to ensure the optimization effect of the design parameters. In the present invention, the prefabricated bridge piers that have been put into use are recorded as existing analysis components, and the prefabricated bridge piers that need to be prepared for design parameter optimization are recorded as target analysis components. In the present invention, both the existing analysis components and the target analysis components are prefabricated bridge piers. The preparation process of the prefabricated bridge piers is already known to those skilled in the art and will not be described in detail here.

[0057] The present invention applies an existing monitoring cycle, and the duration of the existing monitoring cycle can be determined by the user. The higher the user's requirements for the effectiveness of the optimization analysis results of the design parameters of the target analysis component, the shorter the duration of the existing monitoring cycle. A value of the duration of the existing monitoring cycle is provided, and the duration of the existing monitoring cycle is 60 days. Defect feature detection is performed on each existing analysis component according to the existing monitoring cycle. For a single existing analysis component, at the end of each existing monitoring cycle, defect feature detection is performed on the existing analysis component to determine the defect feature point of the existing analysis component. The defect feature point is the point where a crack exists in the existing analysis component. How to perform crack detection on each existing analysis component is content that is easy to understand for those skilled in the art. The present invention does not specifically limit the crack detection method. For example, the user can perform crack detection on the existing analysis component through ultrasonic detection;

[0058] The present invention applies several optimization analysis records, and any optimization analysis record records the component design difference index, defect difference coefficient, distribution approximation index, longitudinal distribution ratio, erosion difference coefficient, characteristic distribution coefficient, combined edge ratio, radial depth ratio, growth correlation coefficient, load difference coefficient, reference time series correlation coefficient, reference longitudinal distribution ratio, erosion correlation coefficient, load correlation coefficient, combination setting richness and combination matching coefficient during at least one optimization analysis of the design parameters of the target analysis component. Each optimization analysis record corresponds to a qualified mark, which records whether the validity of the optimization analysis results of the design parameters of the target analysis component meets the user's requirements. It can be understood that the user can determine whether the validity of the optimization analysis results of the design parameters of the target analysis component meets the requirements based on self-set indicators. For example, the self-set indicators can be but are not limited to optimization quality parameters, and the optimization quality parameters are the average value of the interval between the time when the use defect of each target analysis component prepared after the optimization analysis is completed is detected for the first time after it is put into use and the time when it is put into use.

[0059] Specifically, the difference assessment module performs defect difference assessment on each existing analysis set, and determines the defect difference coefficient of each existing analysis set according to the distribution difference index and the stage difference index;

[0060] For a single existing analysis set, if the difference execution condition responded by the difference assessment module is that the defect difference coefficient of the existing analysis set is greater than the preset defect difference coefficient, it is determined that the component load analysis and the component corrosion analysis are performed on the existing analysis set, and the existing analysis set is recorded as an interference processing set;

[0061] The component design difference index of any existing analysis set is smaller than the preset component design difference index.

[0062] The existing analysis set is a set of several existing analysis components. For a single existing analysis set, the component design difference index is the average value of the parameter differences of component preparation parameters of each category. For a single category of component preparation parameters, the parameter difference = (the maximum value of the component preparation parameter of the category during the preparation of each existing analysis component in the existing analysis set - the minimum value of the component preparation parameter of the category during the preparation of each existing analysis component in the existing analysis set) / the average value of the component preparation parameter of the category during the preparation of each existing analysis component in the existing analysis set. The categories of construction preparation parameters used to determine the component design difference index in the present invention include but are not limited to: cross-sectional dimensions, pier height, stressed steel bar diameter, stressed steel bar spacing, and steel bar cover thickness.

[0063] For a single existing analysis set, the defect difference coefficient = distribution difference index × stage difference index, the distribution difference index is the sum of radial dispersion and longitudinal dispersion, and the radial depth ratio and longitudinal depth ratio of the defect feature point of each existing analysis component in the existing analysis set are obtained. For any defect feature point in a single existing analysis component, the radial depth ratio = the radial distance of the defect feature point / the maximum width of the existing analysis component on the horizontal plane where the defect feature point is located. The radial distance is the shortest distance between the defect feature point and the surface of the existing analysis component on the horizontal plane where the defect feature point is located. The longitudinal depth ratio = the shortest distance between the defect feature point and the upper surface of the existing analysis component / the height of the existing analysis component in the direction perpendicular to the horizontal plane. The radial dispersion is the absolute value of the difference between the maximum and minimum values ​​of the radial depth ratio of the defect feature points of each existing analysis component in the existing analysis set. The longitudinal dispersion is the absolute value of the difference between the maximum and minimum values ​​of the radial depth ratio of the defect feature points of each existing analysis component in the existing analysis set. The stage difference index , e is the number of existing analysis components in the existing analysis set, Lf is the defect growth time of the f-th existing analysis component in the existing analysis set, L0 is the average defect growth time of each existing analysis component in the existing analysis set. For a single existing analysis component, the defect growth time is the interval between the time when the existing analysis component is put into use and the time when the defect feature point is first detected;

[0064] For a single existing analysis set, if the defect difference coefficient of the existing analysis set is greater than the preset defect difference coefficient, it indicates that under relatively similar design parameters, there are significant differences in the usage conditions of the existing analysis components within the existing analysis set. That is, different interference factors have significant interference with the usage conditions of the existing analysis components. Therefore, it is necessary to perform component load analysis and component erosion analysis on the existing analysis set to separate the degree of interference of different influencing factors on the usage conditions, thereby ensuring the effectiveness of the design parameter optimization results based on the defect degree of the existing analysis components during their usage. If the defect difference coefficient of the existing analysis set is less than or equal to the preset defect difference coefficient, component load analysis and component erosion analysis are not performed on the existing analysis set.

[0065] The values ​​of the preset component design difference index and the preset defect difference coefficient can be determined by the user according to the actual working scenario. For example, the user can set them according to the optimization analysis record. The higher the user's requirements for the effectiveness of the optimization analysis results of the design parameters of the target analysis component, the smaller the value of the preset component design difference index and the smaller the value of the preset defect difference coefficient. A method for determining the value of the preset component design difference index is provided, and the average value of the component design difference index of each existing analysis set in the optimization analysis record that meets the user's requirements for the effectiveness of the optimization analysis results of the design parameters of the target analysis component is recorded as the preset component design difference index. A method for determining the value of the preset defect difference coefficient is provided, and the minimum value of the defect difference coefficient of each existing analysis set for component load analysis and component erosion analysis in the optimization analysis record that meets the user's requirements for the effectiveness of the optimization analysis results of the design parameters of the target analysis component is recorded as the preset defect difference coefficient.

[0066] Specifically, the load analysis module responds to the load processing conditions and performs distribution evaluation combination division on the interference processing set according to the defect distribution overlap index and the overlap distribution proportion index;

[0067] The load analysis module determines the load execution strategy of each distribution evaluation combination based on the erosion difference coefficient and the characteristic distribution coefficient;

[0068] The load processing condition is that there is any existing analysis set determined by the difference assessment module to perform component load analysis.

[0069] Among them, for the interference processing set, the differences between the various component preparation parameters during the preparation process of the existing analysis components are small, but there are large differences between the usage states presented during use, indicating that the environmental erosion and usage loads suffered by the existing analysis components in the interference processing set have different impacts on the usage states. In order to better judge and distinguish the interference caused by different factors, component load analysis and component erosion analysis are carried out. When performing component load analysis, the matching degree of the various construction preparation parameters corresponding to the interference processing set for different operating load conditions is evaluated, and the impact caused by environmental erosion is reduced to ensure the matching degree of the subsequent parameter optimization process with the actual operating load requirements;

[0070] When performing component load analysis on a single interference processing set, a distribution evaluation combination is determined based on the distribution of defect feature points for subsequent analysis. The distribution evaluation combination is a set of several existing analysis components in the interference processing set, and the distribution correlation coefficient of any distribution evaluation combination is greater than the preset distribution correlation coefficient. For a single distribution evaluation combination, the distribution correlation coefficient is the sum of the defect distribution overlap index and the overlap distribution proportion index. The defect distribution overlap index = the sum of the number of overlapping defect feature points of each existing analysis component in the distribution evaluation combination / the sum of the number of defect feature points of each existing analysis component in the distribution evaluation combination. For the distribution For any two defect feature points in the evaluation combination, if the distribution approximation index between the two defect feature points is greater than the preset distribution approximation index, the two defect feature points are recorded as a distribution-related pair, and the two defect feature points are respectively recorded as each other's distribution-related feature points, the distribution approximation index = 1 / (the absolute value of the difference between the radial depth proportions of the two defect feature points + the absolute value of the difference between the longitudinal depth proportions of the two defect feature points). If the number of distribution-related feature points of any defect feature point is greater than the preset overlap evaluation parameter, the defect feature point is recorded as an overlapping defect feature point, the overlapping distribution proportion index = the distribution evaluation group. The number of overlapping distribution feature points in the combination / the number of overlapping defect feature points in the distribution evaluation combination. For any two overlapping defect feature points, if the longitudinal angle reference value between the two overlapping defect feature points is less than the preset longitudinal angle reference value, the two overlapping defect feature points are recorded as a longitudinal correlation combination, and the two overlapping defect feature points are respectively recorded as each other's longitudinal correlation feature points. The longitudinal angle reference value is the acute angle formed by the straight line connecting the two overlapping defect feature points and the axis of the corresponding existing analysis component perpendicular to the horizontal plane. For a single overlapping defect feature point, if the longitudinal correlation distribution of the overlapping defect feature point accounts for a large proportion, If the overlapped defect feature point has a preset longitudinal distribution ratio, the overlapping defect feature point is recorded as the overlapping distribution feature point. The longitudinal distribution ratio = the number of longitudinally associated feature points within the distribution evaluation range of the overlapping defect feature point / the number of overlapping defect feature points within the distribution evaluation range of the overlapping defect feature point. The distribution evaluation range is the range corresponding to the sphere formed by the location of the overlapping defect feature point as the sphere center and the preset distribution distance as the radius of the sphere. The value of the preset distribution distance can be set by the user according to the actual working scenario. A value of the preset distribution distance is provided, which is 5% of the maximum width of the existing analysis component to which the overlapping defect feature point belongs.

[0071] The values ​​of the preset distribution approximation index, preset overlap evaluation parameter, preset longitudinal angle reference value, preset longitudinal distribution ratio and preset distribution correlation coefficient can be determined by the user according to the actual working scenario. For example, the user can set them according to the optimization analysis record. The higher the user's requirements for the validity of the optimization analysis results of the design parameters of the target analysis component, the larger the value of the preset distribution approximation index, the larger the value of the preset overlap evaluation parameter, the smaller the value of the preset longitudinal angle reference value, the larger the value of the preset longitudinal distribution ratio, and the larger the value of the preset distribution correlation coefficient. A method for determining the value of the preset distribution approximation index is provided, and the minimum value of the distribution approximation index between the defect feature points in each distribution correlation pair in the optimization analysis record that meets the user's requirements for the validity of the optimization analysis results of the design parameters of the target analysis component is recorded as the preset distribution approximation index. A preset A method for determining the value of an overlap evaluation parameter, recording the average value of the number of distribution-related characteristic points existing in each overlapping defect characteristic point in the optimization analysis record that meets the user's requirements for the validity of the optimization analysis results of the design parameters of the target analysis component as a preset overlap evaluation parameter, providing a value for a preset longitudinal angle reference value, the value of the preset longitudinal angle reference value being 30°, providing a method for determining the value of a preset longitudinal distribution ratio, recording the average value of the longitudinal distribution ratio of each overlapping distribution characteristic point in the optimization analysis record that meets the user's requirements for the validity of the optimization analysis results of the design parameters of the target analysis component as a preset longitudinal distribution ratio, providing a value for a preset distribution correlation coefficient, recording the minimum value of the distribution correlation coefficient of each distribution evaluation combination in the optimization analysis record that meets the user's requirements for the validity of the optimization analysis results of the design parameters of the target analysis component as the preset distribution correlation coefficient;

[0072] For a single distribution assessment combination, the erosion difference coefficient is the average of the erosion differences of each category of component erosion parameters. For a single category of component erosion parameters, the erosion difference = (the maximum value of the component erosion parameter of that category during the use of each existing analysis component in the distribution assessment combination - the minimum value of the component erosion parameter of that category during the use of each existing analysis component in the distribution assessment combination) / the average value of the component erosion parameter of that category during the use of each existing analysis component in the distribution assessment combination. Users can set the category of component erosion parameters used to determine the erosion difference coefficient based on actual work scenarios. The categories of component erosion parameters that can be used to determine the erosion difference coefficient include but are not limited to: number of freeze-thaw cycles, corrosive gas concentration, and water flow rate. The characteristic distribution coefficient is the sum of the radial distribution difference and the longitudinal distribution difference. The radial distribution difference = the standard deviation of the radial depth ratio of each overlapping defect feature point in the distribution assessment combination / the average value of the radial depth ratio of each overlapping defect feature point in the distribution assessment combination. The longitudinal distribution difference = the standard deviation of the longitudinal depth ratio of each overlapping defect feature point in the distribution assessment combination / the average value of the longitudinal depth ratio of each overlapping defect feature point in the distribution assessment combination.

[0073] Specifically, the load execution condition responded by the load analysis module is that the erosion difference coefficient of the distribution evaluation combination is greater than the preset erosion difference coefficient or the characteristic distribution coefficient is greater than the preset characteristic distribution coefficient, then the load execution module is determined to determine the optimization reference coefficient of the distribution evaluation combination based on the load key correlation and the defect distribution overlap index;

[0074] The optimization reference coefficient is positively correlated with the load criticality and the defect distribution overlap index respectively.

[0075] Among them, for a single distribution assessment combination, if the erosion difference coefficient of the distribution assessment combination is greater than the preset erosion difference coefficient or the characteristic distribution coefficient is greater than the preset characteristic distribution coefficient, it indicates that there are large differences in the environmental conditions between the application scenarios of the existing analysis components in the distribution assessment combination or the distribution of the existing defect characteristic points is relatively dispersed, which can indicate that the growth cause of the overlapping defect characteristic points in the distribution assessment combination is weakly related to environmental erosion. Therefore, by analyzing the similarity of the load conditions of the existing analysis components in the distribution assessment combination, the effectiveness of the overlapping defect characteristic points in the distribution assessment combination for reference analysis in the optimization process is judged;

[0076] The values ​​of the preset erosion difference coefficient and the preset characteristic distribution coefficient can be determined by the user according to the actual working scenario. For example, the user can set them according to the optimization analysis record, and a method for determining the value of the preset erosion difference coefficient is provided, in which the optimization analysis record for determining the optimization reference coefficient of the distribution evaluation combination based on the load key correlation and the defect distribution overlap index is recorded as the distribution reference record, and the average value of the erosion difference coefficient in the distribution reference record that meets the user's requirements for the validity of the optimization analysis results of the design parameters of the target analysis component is recorded as the preset erosion difference coefficient. A method for determining the value of the preset characteristic distribution coefficient is provided, in which the average value of the characteristic distribution coefficient in the distribution reference record that meets the user's requirements for the validity of the optimization analysis results of the design parameters of the target analysis component is recorded as the preset characteristic distribution coefficient;

[0077] For a single distribution assessment combination, if the erosion difference coefficient of the distribution assessment combination is greater than the preset erosion difference coefficient or the characteristic distribution coefficient is greater than the preset characteristic distribution coefficient, the optimization reference coefficient of the distribution assessment combination is determined based on the load key correlation and the defect distribution overlap index. The optimization reference coefficient is positively correlated with the load assessment coefficient. The load assessment coefficient is the sum of the load key correlation and the defect distribution overlap index of the distribution assessment combination. The load key correlation = 1 / the average value of the load difference of the component load parameters of each category. For the component load parameters of a single category, the load difference is , m is the number of existing analysis components in the distribution evaluation combination, sk is the value of the component load parameter of this category during the use of the kth existing analysis component in the distribution evaluation combination, s0 is the average value of the component load parameter of this category during the use of each existing analysis component in the distribution evaluation combination. The user can set the category of component load parameters for determining the critical relevance of loads according to the actual working scenario. The category of component load parameters for determining the critical relevance of loads includes but is not limited to: the maximum value of the self-seismic strength of the bridge body, the constant load pressure caused by the superstructure of the pier, the frequency of train passage, and the average value of the acceleration of each train passage process.

[0078] Specifically, the load execution condition responded by the load analysis module is that the erosion difference coefficient of the distribution evaluation combination is less than or equal to the preset erosion difference coefficient and the characteristic distribution coefficient is less than or equal to the preset characteristic distribution coefficient, then the load execution module determines the optimization reference coefficient of the distribution evaluation combination based on the characteristic distribution coefficient and the defect distribution overlap index, and determines whether to adjust the optimization reference coefficient of the distribution evaluation combination based on the combination edge ratio;

[0079] If the load optimization control condition responded by the load execution module is that the combined edge ratio is greater than the preset combined edge ratio, it is determined that the optimization reference coefficient is to be reduced according to the combined edge ratio;

[0080] The optimized reference coefficient is positively correlated with the characteristic distribution coefficient and the defect distribution overlap index, respectively. The reduced value of the optimized reference coefficient is positively correlated with the combined edge ratio.

[0081] Among them, for a single distribution assessment combination, if the erosion difference coefficient of the distribution assessment combination is less than or equal to the preset erosion difference coefficient and the characteristic distribution coefficient is less than or equal to the preset characteristic distribution coefficient, it indicates that the difference in environmental conditions between the application scenarios of the existing analysis components in the distribution assessment combination is small and the distribution of defect characteristic points is relatively concentrated, which can indicate that the overlapping defect characteristic points in the distribution assessment combination are highly correlated with the factors of environmental erosion. In the process of determining the optimized reference coefficient, it is necessary to correct the optimized reference coefficient according to the degree of influence caused by environmental erosion;

[0082] For a single distribution assessment combination, if the erosion difference coefficient of the distribution assessment combination is less than or equal to the preset erosion difference coefficient and the characteristic distribution coefficient is less than or equal to the preset characteristic distribution coefficient, the optimization reference coefficient is positively correlated with the load separation coefficient, the load separation coefficient is the sum of the characteristic distribution coefficient and the defect distribution overlap index, the combination edge proportion = the number of overlapping edge feature points in the distribution assessment combination / the number of overlapping defect feature points in the distribution assessment combination, and the overlapping edge feature point is an overlapping defect feature point whose radial depth proportion is less than the preset radial depth proportion;

[0083] The value of the preset combination edge ratio can be determined by the user according to the actual working scenario. For example, the user can set it according to the optimization analysis record. The higher the user's requirement for the effectiveness of the optimization analysis results of the design parameters of the target analysis component, the smaller the value of the preset combination edge ratio. A method for determining the value of the preset combination edge ratio is provided, and the optimization analysis record that is reduced and adjusted according to the optimization reference coefficient based on the combination edge ratio is recorded as a load correction record, and the average value of the combination edge ratio in the load correction record that meets the user's requirement for the effectiveness of the optimization analysis results of the design parameters of the target analysis component is recorded as the preset combination edge ratio.

[0084] Specifically, the erosion analysis module responds to the erosion processing conditions and performs growth assessment combination division on the interference processing set according to the overlap growth correlation index and the overlap edge ratio index;

[0085] The erosion analysis module determines the erosion analysis strategy of each growth assessment combination based on the load difference coefficient and the reference time series correlation coefficient;

[0086] The erosion processing condition is that there is any existing analysis set determined by the difference assessment module to perform component erosion analysis.

[0087] Among them, when conducting component erosion analysis for the interference treatment set, in the process of evaluating the matching of the various component preparation parameters corresponding to the interference treatment set with different environmental erosion conditions, it is necessary to reduce the impact of the bridge load during the use of the piers to ensure that the subsequent parameter optimization process matches the actual environmental protection requirements;

[0088] When performing component erosion analysis on a single interference processing set, the growth evaluation combination division is determined according to the formation sequence of the defect feature points for subsequent analysis. The growth evaluation combination is a set of several existing analysis components in the interference processing set, and the growth correlation coefficient of any growth evaluation combination is greater than the preset growth correlation coefficient. For a single growth evaluation combination, the growth correlation coefficient is the sum of the overlapping growth correlation index and the overlapping edge ratio index. The overlapping growth correlation index is the sum of the overlapping growth correlation index and the overlapping edge ratio index. , n is the number of overlapping defect feature points in the growth assessment combination, gi is the characteristic growth parameter of the i-th overlapping defect feature point in the growth assessment combination, g0 is the average of the characteristic growth parameters of each overlapping defect feature point in the growth assessment combination, for a single overlapping defect feature point, the characteristic growth parameter is the interval between the time when the existing analysis component where the overlapping defect feature point exists is put into use and the time when the overlapping defect feature point is first detected, the overlapping edge proportion index = the number of overlapping edge feature points in the growth assessment combination / the number of overlapping defect feature points in the growth assessment combination;

[0089] The values ​​of the preset radial depth ratio and the preset growth correlation coefficient can be determined by the user according to the actual working scenario. For example, the user can set them according to the optimization analysis record. The higher the user's requirements for the effectiveness of the optimization analysis results of the design parameters of the target analysis component, the smaller the value of the preset radial depth ratio and the larger the value of the preset growth correlation coefficient. A method for determining the value of the preset radial depth ratio is provided, and the average value of the radial depth ratio of each overlapping edge feature point in the optimization analysis record that meets the user's requirements for the effectiveness of the optimization analysis results of the design parameters of the target analysis component is recorded as the preset radial depth ratio. A value of the preset radial depth ratio is provided, and the value of the preset radial depth ratio is 0.05. A method for determining the value of the preset growth correlation coefficient is provided, and the average value of the growth correlation coefficient of each growth evaluation combination in the optimization analysis record that meets the user's requirements for the effectiveness of the optimization analysis results of the design parameters of the target analysis component is recorded as the preset growth correlation coefficient.

[0090] For a single growth assessment combination, the load difference coefficient is the average value of the combined load difference of the component load parameters of each category. For a single category of component load parameters, the combined load difference is , p is the number of existing analysis components in the growth assessment combination, gt is the numerical value of the component load parameter of this category during the use of the t-th existing analysis component in the growth assessment combination, g0 is the average value of the component load parameter of this category during the use of each existing analysis component in the growth assessment combination, the reference time series correlation coefficient is the average value of the time series correlation coefficient of each existing analysis component in the growth assessment combination, for a single existing analysis component, the time series correlation coefficient = the standard deviation of the characteristic growth parameters of the overlapping defect feature points of the existing analysis component / the average value of the characteristic growth parameters of the overlapping defect feature points of the existing analysis component.

[0091] Specifically, the erosion execution condition responded by the erosion analysis module is that the load difference coefficient of the growth assessment combination is greater than the preset load difference coefficient or the reference time series correlation coefficient is greater than the preset time series correlation coefficient, then the erosion execution module is determined to determine the optimized reference coefficient of the growth assessment combination based on the erosion key correlation and the overlapping growth correlation index;

[0092] The optimized reference coefficient is positively correlated with the erosion key correlation and the overlapping growth correlation index respectively.

[0093] Among them, for a single growth assessment combination, if the load difference coefficient of the growth assessment combination is greater than the preset load difference coefficient or the reference time series correlation coefficient is greater than the preset time series correlation coefficient, it indicates that there are large differences in the operating load conditions of the existing analysis components in the growth assessment combination during use, or there are large differences between the growth moments of the overlapping defect feature points. This further indicates that the growth cause of the overlapping defect feature points in the existing analysis components in the growth assessment combination is weakly related to the applied load conditions of the bridge body. Therefore, by analyzing the similarity of the environmental erosion conditions of the existing analysis components in the growth assessment combination, the effectiveness of the overlapping defect feature points in the growth assessment combination for reference analysis in the optimization process can be judged;

[0094] The values ​​of the preset load difference coefficient and the preset timing correlation coefficient can be determined by the user according to the actual working scenario. For example, the user can set them according to the optimization analysis record, and a method for determining the value of the preset load difference coefficient is provided, in which the optimization analysis record for determining the optimization reference coefficient of the growth evaluation combination based on the erosion key correlation and the overlapping growth correlation index is recorded as the growth reference record, and the average value of the load difference coefficients of each growth evaluation combination in the growth reference record that meets the user's requirements for the validity of the optimization analysis results of the design parameters of the target analysis component is recorded as the preset load difference coefficient. A method for determining the value of the preset timing correlation coefficient is provided, in which the average value of the reference timing correlation coefficients of each growth evaluation combination in the growth reference record that meets the user's requirements for the validity of the optimization analysis results of the design parameters of the target analysis component is recorded as the preset timing correlation coefficient;

[0095] For a single growth assessment combination, if the load difference coefficient of the growth assessment combination is greater than the preset load difference coefficient or the reference time series correlation coefficient is greater than the preset time series correlation coefficient, the optimized reference coefficient is positively correlated with the erosion assessment coefficient, and the erosion assessment coefficient is the sum of the erosion key correlation and the overlapping growth correlation index of the growth assessment combination. The erosion key correlation = 1 / the average value of the combined erosion difference of the component erosion parameters of each category. For the component erosion parameters of a single category, the combined erosion difference = (the maximum value of the component erosion parameter of the category during the use of each existing analysis component in the growth assessment combination - the minimum value of the component erosion parameter of the category during the use of each existing analysis component in the growth assessment combination) / the average value of the component erosion parameter of the category during the use of each existing analysis component in the growth assessment combination.

[0096] Specifically, the erosion execution condition responded by the erosion analysis module is that there is a load difference coefficient of the growth assessment combination that is less than or equal to the preset load difference coefficient and the reference time series correlation coefficient is less than or equal to the preset time series correlation coefficient, then the erosion execution module determines the optimized reference coefficient of the growth assessment combination based on the reference time series correlation coefficient and the overlapping growth correlation index, and determines whether to adjust the optimized reference coefficient of the growth assessment combination based on the reference longitudinal distribution ratio;

[0097] If the erosion optimization control condition responded by the erosion execution module is that the reference longitudinal distribution ratio is greater than the preset reference longitudinal distribution ratio, the optimization reference coefficient is reduced based on the reference longitudinal distribution ratio;

[0098] The optimized reference coefficient is positively correlated with the reference time series correlation coefficient and the overlapping growth correlation index, respectively. The reduced value of the optimized reference coefficient is positively correlated with the reference longitudinal distribution ratio.

[0099] Among them, for a single distribution assessment combination, if the load difference coefficient of the growth assessment combination is less than or equal to the preset load difference coefficient and the reference time series correlation coefficient is less than or equal to the preset time series correlation coefficient, it indicates that there is no excessive difference between the operating load conditions of the existing analysis components in the growth assessment combination and the growth moments of the overlapping defect feature points during use. This further indicates that the growth cause of the overlapping defect feature points in the existing analysis components in the growth assessment combination is closely related to the applied load conditions of the bridge body. Therefore, the optimization reference coefficient needs to be modified according to the degree of influence of the bridge body's operating load.

[0100] For a single distribution assessment combination, if the load difference coefficient of the growth assessment combination is less than or equal to the preset load difference coefficient and the reference time series correlation coefficient is less than or equal to the preset time series correlation coefficient, the optimized reference coefficient is positively correlated with the erosion separation coefficient, the erosion separation coefficient is the sum of the reference time series correlation coefficient and the overlapping growth correlation index, and the reference longitudinal distribution ratio is the average longitudinal correlation distribution ratio of each overlapping defect feature point in the distribution assessment combination;

[0101] The value of the preset reference longitudinal distribution ratio can be determined by the user according to the actual working scenario. For example, the user can set it according to the optimization analysis record. The higher the user's requirement for the effectiveness of the optimization analysis results of the design parameters of the target analysis component, the smaller the value of the preset longitudinal correlation distribution ratio. A method for determining the value of the preset reference longitudinal distribution ratio is provided, and the optimization analysis record that is adjusted to reduce the optimization reference coefficient based on the reference longitudinal distribution ratio is recorded as an erosion correction record. The minimum value of the reference longitudinal distribution ratio in the erosion correction record that meets the user's requirement for the effectiveness of the optimization analysis results of the design parameters of the target analysis component is recorded as the preset reference longitudinal distribution ratio.

[0102] Specifically, the optimization execution module responds to the target optimization condition, determines the reference execution combination of the target analysis component according to the erosion correlation coefficient and the load correlation coefficient, and determines the optimal matching combination of the preparation parameters of each component according to the combination matching coefficient;

[0103] The target optimization condition is that there is a target analysis component that needs to perform parameter optimization.

[0104] Among them, for a single target analysis component, a growth assessment combination with an erosion correlation coefficient greater than a preset erosion correlation coefficient and a distribution assessment combination with a load correlation coefficient greater than a preset load correlation coefficient are recorded as reference execution combinations. For a single growth assessment combination, the erosion correlation coefficient is the average value of the target erosion correlation degrees of component erosion parameters of each category. For a single category of component erosion parameters, the target erosion correlation degree=the average value of the absolute value of the difference between the numerical value of the component erosion parameter of the category during the use of the target analysis component / the numerical value of the component erosion parameter of the category during the use of the target analysis component and the numerical value of the component erosion parameter of the category during the use of each existing analysis component in the growth assessment combination. For a single distribution assessment combination, the load correlation coefficient is the average value of the target load correlation degrees of component load parameters of each category. For a single category of component load parameters, the target load correlation degree=the numerical value of the component load parameter of the category during the use of the target analysis component / the average value of the absolute value of the difference between the numerical value of the component load parameter of the category during the use of the target analysis component and the numerical value of the component load parameter of the category during the use of each existing analysis component in the distribution assessment combination.

[0105] The values ​​of the preset erosion correlation coefficient and the preset load correlation coefficient can be determined by the user according to the actual working scenario. For example, the user can set them according to the optimization analysis record. The higher the user's requirements for the effectiveness of the optimization analysis results of the design parameters of the target analysis component, the larger the value of the preset erosion correlation coefficient and the larger the value of the preset load correlation coefficient. A method for determining the value of the preset erosion correlation coefficient is provided, and the average value of the erosion correlation coefficient of the growth evaluation combination serving as the reference analysis combination in the optimization analysis record that meets the user's requirements for the effectiveness of the optimization analysis results of the design parameters of the target analysis component is recorded as the preset erosion correlation coefficient. A method for determining the value of the preset load correlation coefficient is provided, and the average value of the load correlation coefficient of the distribution evaluation combination serving as the reference analysis combination in the optimization analysis record that meets the user's requirements for the effectiveness of the optimization analysis results of the design parameters of the target analysis component is recorded as the preset load correlation coefficient.

[0106] Specifically, the optimization execution module determines the combination matching coefficient of each reference execution combination based on the optimization reference coefficient and the reference quality coefficient, and determines whether to adjust the combination matching coefficient according to the combination setting richness;

[0107] For any reference execution combination, if the execution determination condition responded by the optimization execution module is that the combination setting richness of the reference execution combination is greater than the preset combination setting richness, then it is determined to reduce the combination matching coefficient of the reference execution combination based on the execution optimization difference coefficient;

[0108] The reduction value of the combination matching coefficient is positively correlated with the execution optimization difference coefficient.

[0109] Among them, the combination setting richness is the average value of the setting range of each component preparation parameter. For any component preparation parameter of a single target analysis component, the setting range = (the maximum value of the component preparation reference in the reference analysis combination of the target analysis component - the minimum value of the component preparation reference in the reference analysis combination of the target analysis component) / the maximum value of the component preparation reference in the reference analysis combination of the target analysis component. For any reference execution combination, the combination matching coefficient is the product of the optimization reference coefficient and the reference quality coefficient of the reference execution combination. The reference quality coefficient is the average value of the number of defect feature points existing in each existing analysis component in the reference analysis combination. The execution optimization difference coefficient = the optimization reference coefficient of the reference analysis combination / the average value of the optimization reference coefficients of each reference analysis combination of the target analysis component. The reference analysis combination whose combination matching coefficient is greater than the preset combination matching coefficient is recorded as the optimized execution combination. The component preparation parameter of the target analysis component is optimized based on the numerical value of the component preparation parameter in each existing analysis component in each optimized execution combination. This is easy to understand for those skilled in the art and will not be elaborated here.

[0110] The user can determine the value of the preset combination setting richness and the preset combination matching coefficient according to the actual work scenario. For example, the user can set it according to the optimization analysis record. The higher the user's requirement for the effectiveness of the optimization analysis results of the design parameters of the target analysis component, the smaller the value of the preset combination setting richness and the larger the value of the preset combination matching coefficient. A method for determining the value of the preset combination setting richness is provided, and the optimization analysis record that reduces and adjusts the combination matching coefficient of the reference execution combination based on the execution optimization difference coefficient is recorded as a matching control record. The average value of the combination setting richness in the matching control record that meets the user's requirement for the effectiveness of the optimization analysis results of the design parameters of the target analysis component is recorded as the preset combination setting richness. A method for determining the value of the preset combination matching coefficient is provided, and the average value of the combination matching coefficient of the optimized execution combination in the optimization analysis record that meets the user's requirement for the effectiveness of the optimization analysis results of the design parameters of the target analysis component is recorded as the preset combination matching coefficient.

[0111] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0112] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. An intelligent adjustment system for prefabricated bridge pier design, characterized in that: include: a difference assessment module for periodically determining whether to perform component load analysis and component corrosion analysis for each existing analysis set based on a distribution difference index and a stage difference index; a load analysis module connected to the difference evaluation module, configured to determine a distribution evaluation combination based on the defect distribution overlap index and the overlap distribution ratio index, and to determine a load analysis strategy based on the corrosion difference coefficient and the characteristic distribution coefficient, namely, determining an optimization reference coefficient based on the load key correlation and the defect distribution overlap index, or determining an optimization reference coefficient based on the characteristic distribution coefficient and the defect distribution overlap index; a load execution module connected to the load analysis module, configured to determine an optimization reference coefficient for each distribution evaluation combination according to the load analysis strategy, and determine whether to adjust the optimization reference coefficient based on the combination edge ratio; an erosion analysis module connected to the difference assessment module, configured to determine a growth assessment combination based on an overlapping growth correlation index and an overlapping edge ratio index, and to determine an erosion analysis strategy based on a load difference coefficient and a reference time series correlation coefficient, namely, determining an optimized reference coefficient based on an erosion key correlation and an overlapping growth correlation index, or determining an optimized reference coefficient based on a reference time series correlation coefficient and an overlapping growth correlation index; an erosion execution module, connected to the erosion analysis module, for determining an optimized reference coefficient for each growth assessment combination according to the erosion analysis strategy, and determining whether to adjust the optimized reference coefficient based on a reference longitudinal distribution ratio; an optimization execution module, connected to the load execution module and the erosion execution module respectively, for determining a reference execution combination of a target analysis component according to the erosion correlation coefficient and the load correlation coefficient, and determining a combination matching coefficient of each reference execution combination based on the optimization reference coefficient and the reference quality coefficient; The load execution condition responded by the load analysis module is that the erosion difference coefficient of the distribution evaluation combination is greater than the preset erosion difference coefficient or the characteristic distribution coefficient is greater than the preset characteristic distribution coefficient, then the load execution module is determined to determine the optimization reference coefficient of the distribution evaluation combination based on the load key correlation and the defect distribution overlap index; The optimization reference coefficient is positively correlated with the load critical relevance and the defect distribution overlap index respectively; If the erosion execution condition responded by the erosion analysis module is that there is a load difference coefficient of the growth assessment combination greater than the preset load difference coefficient or the reference time series correlation coefficient greater than the preset time series correlation coefficient, then the erosion execution module determines the optimized reference coefficient of the growth assessment combination based on the erosion key correlation and the overlapping growth correlation index; The optimized reference coefficient is positively correlated with the erosion key correlation and the overlapping growth correlation index respectively.

2. The intelligent adjustment system for prefabricated bridge pier design according to claim 1 is characterized in that: The difference evaluation module performs defect difference evaluation on each existing analysis set and determines the defect difference coefficient of each existing analysis set according to the distribution difference index and the stage difference index; For a single existing analysis set, if the difference execution condition responded by the difference assessment module is that the defect difference coefficient of the existing analysis set is greater than the preset defect difference coefficient, it is determined that the component load analysis and the component corrosion analysis are performed on the existing analysis set, and the existing analysis set is recorded as an interference processing set; The component design difference index of any existing analysis set is smaller than the preset component design difference index.

3. The intelligent adjustment system for prefabricated bridge pier design according to claim 2 is characterized in that: The load analysis module responds to the load processing conditions and performs distribution evaluation combination division on the interference processing set according to the defect distribution overlap index and the overlap distribution ratio index; The load analysis module determines the load execution strategy of each distribution evaluation combination based on the erosion difference coefficient and the characteristic distribution coefficient; The load processing condition is that there is any existing analysis set determined by the difference assessment module to perform component load analysis.

4. The intelligent adjustment system for prefabricated bridge pier design according to claim 3 is characterized in that: The load execution condition responded by the load analysis module is that the erosion difference coefficient of the distribution assessment combination is less than or equal to the preset erosion difference coefficient and the characteristic distribution coefficient is less than or equal to the preset characteristic distribution coefficient, then the load execution module determines the optimization reference coefficient of the distribution assessment combination based on the characteristic distribution coefficient and the defect distribution overlap index, and determines whether to adjust the optimization reference coefficient of the distribution assessment combination based on the combination edge ratio; If the optimization control condition of the load execution module response is that the combined edge ratio is greater than the preset combined edge ratio, it is determined that the optimization reference coefficient is to be reduced according to the combined edge ratio; The optimized reference coefficient is positively correlated with the characteristic distribution coefficient and the defect distribution overlap index, respectively. The reduced value of the optimized reference coefficient is positively correlated with the combined edge ratio.

5. The intelligent adjustment system for prefabricated bridge pier design according to claim 2 is characterized in that: The erosion analysis module responds to the erosion processing conditions and performs growth assessment combination division on the interference processing set according to the overlap growth correlation index and the overlap edge ratio index; The erosion analysis module determines the erosion analysis strategy of each growth assessment combination based on the load difference coefficient and the reference time series correlation coefficient; The erosion processing condition is that there is any existing analysis set determined by the difference assessment module to perform component erosion analysis.

6. The intelligent adjustment system for prefabricated bridge pier design according to claim 5, characterized in that: The erosion execution condition responded by the erosion analysis module is that the load difference coefficient of the growth assessment combination is less than or equal to the preset load difference coefficient and the reference time series correlation coefficient is less than or equal to the preset time series correlation coefficient, then the erosion execution module determines the optimized reference coefficient of the growth assessment combination based on the reference time series correlation coefficient and the overlapping growth correlation index, and determines whether to adjust the optimized reference coefficient of the growth assessment combination based on the reference longitudinal distribution ratio; If the erosion optimization control condition responded by the erosion execution module is that the reference longitudinal distribution ratio is greater than the preset reference longitudinal distribution ratio, the optimization reference coefficient is reduced based on the reference longitudinal distribution ratio; The optimized reference coefficient is positively correlated with the reference time series correlation coefficient and the overlapping growth correlation index, respectively. The reduced value of the optimized reference coefficient is positively correlated with the reference longitudinal distribution ratio.

7. The intelligent adjustment system for prefabricated bridge pier design according to claim 1 is characterized in that: The optimization execution module responds to the target optimization condition, determines the reference execution combination of the target analysis component according to the erosion correlation coefficient and the load correlation coefficient, and determines the optimized execution combination of each component preparation parameter according to the combination matching coefficient; The target optimization condition is that there is a target analysis component that needs to perform parameter optimization.

8. The intelligent adjustment system for prefabricated bridge pier design according to claim 7, characterized in that: The optimization execution module determines the combination matching coefficient of each reference execution combination based on the optimization reference coefficient and the reference quality coefficient, and determines whether to adjust the combination matching coefficient according to the combination setting richness; For any reference execution combination, if the execution determination condition responded by the optimization execution module is that the combination setting richness of the reference execution combination is greater than the preset combination setting richness, then it is determined to reduce the combination matching coefficient of the reference execution combination based on the execution optimization difference coefficient; The reduction value of the combination matching coefficient is positively correlated with the execution optimization difference coefficient.

Images