Intelligent adjusting system for prefabricated pier design

Abstract

The invention relates to the field of bridge pier quality data analysis, in particular to an intelligent adjusting system for prefabricated bridge pier design, which comprises a difference evaluation module for periodically determining whether to perform component load analysis and component erosion analysis on each existing analysis set based on a distribution difference index and a stage difference index; the load analysis module is used for determining a distribution evaluation combination according to the defect distribution overlapping index and the overlapping distribution proportion index, and determining a load analysis strategy based on the erosion difference coefficient and the feature distribution coefficient; the erosion analysis module is used for determining a growth evaluation combination according to the overlapping growth correlation index and the overlapping edge proportion index, and determining an erosion analysis strategy based on the load difference coefficient and the reference time sequence correlation coefficient; and the optimization execution module is used for determining an optimization execution combination of each component preparation parameter of the target analysis component.

Description

Technical Field

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

[0002] Prefabricated piers have been widely used in the current bridge construction process. For the optimization and adjustment process of the design parameters of precast piers, it is often necessary to analyze the defect states of the precast piers that have been put into use, and optimize the design parameters of the precast piers based on the analysis results. In the actual use process, the influencing factors that cause the defects of the piers are mainly attributed to environmental erosion and the self-vibration of the bridge body during use, and are jointly formed by the above influencing factors. The existing optimization systems for the design parameters of precast piers often fail to effectively separate the interference degrees of different influencing factors on the formation of pier defects, and cannot fully ensure the consistency of the environment and the use process during the process of optimizing the design parameters, resulting in a low effectiveness of the design parameter optimization process. Therefore, how to separate the interference degrees of different influencing factors on the formation of defects during the optimization process of the design parameters of precast piers, and optimize the design parameters based on the separated interference conditions and the pier defect conditions, to ensure the effectiveness of the optimization results of the design parameters of precast piers, is an urgent problem to be solved by those skilled in the art.

[0003] Chinese Patent Publication No. CN116910423A discloses a bridge deformation monitoring method and monitoring system. The monitoring method includes: calculating the overall position offset coefficient and the internal structure flexure coefficient of each pier in the target bridge; calculating the comprehensive deformation index of each pier in the target bridge according to the overall position offset coefficient and the internal structure flexure coefficient of each pier in the target bridge; for the piers without deformation in the target bridge, calculating the linear flexure coefficient and the appearance defect coefficient of the bridge deck of the target bridge; calculating the comprehensive deformation index of the bridge deck of the target bridge according to the linear flexure coefficient and the appearance defect coefficient of the bridge deck of the target bridge, and judging whether there is deformation in the bridge deck of the target bridge according to the comprehensive deformation index. Chinese Patent Publication No. CN107273651A discloses a design method for a super-high pier structure. First, calculate the load size of the pier to be designed, and then determine the design parameters of the pier to be designed. The pier design parameters are not limited to simple changes in the cross-sectional thickness and width, but the design parameters are selected as the height, cross-sectional size and slope ratio of the pier body and the inclined leg, the bifurcation distance of the inclined leg, the height and cross-sectional size of the cross beam, so as to optimize the shape of the pier to the greatest extent under the condition of meeting the use requirements. Then, determine the value range and value step of the design parameters, scan and combine different values of each parameter, establish finite element models parametrically and perform calculations, automatically extract the stiffness results, strength results and vibration results of each finite element model, compare them with the specification requirements, select all pier forms that meet the above three conditions, and calculate the total pier volume, and select the pier form with the smallest total pier volume as the best pier form under this design method. However, the above technical solutions have the following defects: They fail to effectively analyze the usage conditions of the existing piers based on which the design parameters are optimized during the optimization process of the design parameters, and separate the influence degrees of different influencing factors on the usage conditions of the piers, resulting in a low effectiveness of the optimization results of the design parameters. Summary of the Invention

[0004] To this end, the present invention provides an intelligent adjustment system for precast pier design to overcome the problem that the prior art fails to effectively analyze the usage conditions of the existing piers based on which the design parameters are optimized during the optimization process of the design parameters, separate the influence degrees of different influencing factors on the usage conditions of the piers, and result in a low effectiveness of the optimization results of the design parameters.

[0005] To achieve the above object, the present invention provides an intelligent adjustment system for precast pier design, including: A difference evaluation module for periodically determining whether to perform component load analysis and component erosion analysis on each existing analysis set based on the distribution difference index and the stage difference index; A load analysis module, which is connected to the difference evaluation module, is used to determine a distribution evaluation combination according to the defect distribution overlap index and the overlap distribution proportion index, and determine a load analysis strategy based on the erosion difference coefficient and the feature distribution coefficient, that is, to determine an optimization reference coefficient based on the load key relevance and the defect distribution overlap index, or to determine an optimization reference coefficient based on the feature distribution coefficient and the defect distribution overlap index; A load execution module, which is connected to the load analysis module, is used to determine the optimization reference coefficient of each distribution evaluation combination according to the load analysis strategy, and determine whether to adjust the optimization reference coefficient based on the combination edge proportion; An erosion analysis module, which is connected to the difference evaluation module, is used to determine a growth evaluation combination according to the overlap growth correlation index and the overlap edge proportion index, and determine an erosion analysis strategy based on the load difference coefficient and the reference time series correlation coefficient, that is, to determine an optimization reference coefficient based on the erosion key relevance and the overlap growth correlation index, or to determine an optimization reference coefficient based on the reference time series correlation coefficient and the overlap growth correlation index; An erosion execution module, which is connected to the erosion analysis module, is used to determine the optimization reference coefficient of each growth evaluation combination according to the erosion analysis strategy, and determine whether to adjust the optimization reference coefficient based on the reference longitudinal distribution proportion; An optimization execution module, which is respectively connected to the load execution module and the erosion execution module, is used to determine a reference execution combination of the target analysis component according to the erosion correlation coefficient and the load correlation coefficient, and determine a combination matching coefficient of each reference execution combination based on the optimization reference coefficient and the reference quality coefficient.

[0006] Furthermore, 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, the difference execution condition responded by the difference evaluation module is that the defect difference coefficient of the existing analysis set is greater than the preset defect difference coefficient, then it is determined to perform component load analysis and component erosion analysis 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 less than the preset component design difference index.

[0007] Furthermore, the load analysis module responds to the load processing condition, and divides the interference processing set into distribution evaluation combinations according to the defect distribution overlap index and the overlap distribution proportion index; The load analysis module determines the load execution strategy of each distribution evaluation combination based on the erosion difference coefficient and the feature distribution coefficient; The load processing condition is that there is any existing analysis set determined by the difference evaluation module for component load analysis.

[0008] Furthermore, the load execution condition for which the load analysis module responds 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, it is determined that the load execution module determines the optimization reference coefficient of this distribution evaluation combination based on the load key relevance and the defect distribution overlap index; The optimization reference coefficient is positively correlated with the load key relevance and the defect distribution overlap index respectively.

[0009] Furthermore, the load execution condition for which the load analysis module responds 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, it is determined that the load execution module determines the optimization reference coefficient of this 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 this distribution evaluation combination based on the combination edge proportion; The optimization regulation condition for which the load execution module responds is that the combination edge proportion is greater than the preset combination edge proportion. Then, it is determined to perform a reduction adjustment on the optimization reference coefficient according to the combination edge proportion; The optimization reference coefficient is positively correlated with the characteristic distribution coefficient and the defect distribution overlap index respectively, and the reduction value of the optimization reference coefficient is positively correlated with the combination edge proportion.

[0010] Furthermore, the erosion analysis module responds to the erosion processing condition, and performs growth evaluation combination division on the interference processing set according to the overlapping growth correlation index and the overlapping edge proportion index; The erosion analysis module determines the erosion analysis strategy for each growth evaluation combination based on the load difference coefficient and the reference time sequence correlation coefficient; The erosion processing condition is that there is any existing analysis set determined by the difference evaluation module for component erosion analysis.

[0011] Furthermore, the erosion execution condition for which the erosion analysis module responds is that the load difference coefficient of the growth evaluation combination is greater than the preset load difference coefficient or the reference time sequence correlation coefficient is greater than the preset time sequence correlation coefficient. Then, it is determined that the erosion execution module determines the optimization reference coefficient of this growth evaluation combination based on the erosion key relevance and the overlapping growth correlation index; The optimization reference coefficient is positively correlated with the erosion key relevance and the overlapping growth correlation index respectively.

[0012] Further, the erosion execution condition responded by the erosion analysis module is that the load difference coefficient of the growth evaluation combination is less than or equal to the preset load difference coefficient and the reference timing correlation coefficient is less than or equal to the preset timing correlation coefficient. Then, it is determined that the erosion execution module determines the optimization reference coefficient of the growth evaluation combination based on the reference timing correlation coefficient and the overlapping growth association index, and determines whether to adjust the optimization reference coefficient of the growth evaluation combination based on the reference longitudinal distribution ratio. The erosion optimization regulation condition responded by the erosion execution module is that the reference longitudinal distribution ratio is greater than the preset reference longitudinal distribution ratio. Then, a reduction adjustment is made to the optimization reference coefficient based on the reference longitudinal distribution ratio. The optimization reference coefficient is positively correlated with the reference timing correlation coefficient and the overlapping growth association index respectively, and the reduction value of the optimization reference coefficient is positively correlated with the reference longitudinal distribution ratio.

[0013] Further, 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 optimization execution combination of the preparation parameters of each component according to the combination matching coefficient. The target optimization condition is that there is a target analysis component that needs to perform parameter optimization.

[0014] Further, 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, 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 that a reduction adjustment is made to 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.

[0015] Compared with the prior art, the beneficial effect of the present invention is that the technical solution of the present invention determines the defect difference coefficient of each existing analysis set according to 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 according to the defect difference coefficient, and determines a targeted analysis method according to the actual defect situation of the existing analysis components in each existing analysis set during the subsequent analysis process to ensure the separation effect of the interference degree of different factors on the defect growth, thereby improving the optimization quality of the subsequent design parameters of the precast bridge pier.

[0016] Further, when performing component load analysis in the present invention, distribution evaluation combination division is carried out for the interference processing set according to the defect distribution overlap index and the overlap distribution proportion index, ensuring that the differences in the degrees of influence of the component usage loads on each existing analysis component in a single distribution evaluation combination are small, providing an analysis basis for separating the reasons for environmental erosion in the subsequent process of determining the optimization reference coefficients of each distribution evaluation combination, and ensuring the analysis efficiency and the effectiveness of the analysis results for the optimization reference coefficients of the distribution evaluation combination.

[0017] Further, in the present invention, based on the erosion difference coefficient and the characteristic distribution coefficient, the load execution strategy of each distribution evaluation combination is determined. By means of the erosion difference coefficient and the characteristic distribution coefficient, the strength of the relationship between the growth reasons of the overlapping defect feature points existing in the distribution evaluation combination and environmental erosion is judged. If the relationship with environmental erosion is weak, only by analyzing the similarity degree of the load conditions of the existing analysis components in the distribution evaluation combination, the reference degree of the defect feature points included in the existing analysis components in the distribution evaluation combination for the optimization analysis process is determined, and the optimization reference coefficient is determined accordingly. If the relationship with environmental erosion is strong, during the process of determining the optimization reference coefficient, according to the degree of influence caused by environmental erosion, the optimization reference coefficient needs to be corrected, ensuring the reliability of the setting results of the reference degrees for different existing analysis components in the optimization process, and further improving the optimization quality of the subsequent design parameters of the precast bridge pier.

[0018] Further, when performing component erosion analysis in the present invention, growth evaluation combination division is carried out for the interference processing set according to the overlapping growth correlation index and the overlapping edge proportion index, ensuring that the differences in the degrees of influence of environmental erosion on each existing analysis component in a single growth evaluation combination are small, providing an analysis basis for separating the reasons for the component usage load in the subsequent process of determining the optimization reference coefficients of each growth evaluation combination, and ensuring the analysis efficiency and the effectiveness of the analysis results for the optimization reference coefficients of the growth evaluation combination.

[0019] Furthermore, the present invention determines the erosion analysis strategy for 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 existing in 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 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 by analyzing the similarity degree of the environmental erosion conditions of the existing analysis components in the growth evaluation combination, and determine the optimization reference coefficient based on this. 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 influence degree of the bridge body operation load, which ensures the reliability of the setting result of the reference degree for different existing analysis components in the optimization process, and further improves the optimization quality of the design parameters of the precast bridge pier in the follow-up. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a module connection diagram of the intelligent adjustment system for precast bridge pier design of the present invention; Figure 2 It is a flowchart of the difference evaluation module of the present invention for determining whether to perform component load analysis and component erosion analysis based on the defect difference coefficient; Figure 3 It is a flowchart of the load analysis module of the present invention for determining the load analysis strategy based on the operation load difference degree and the application key difference parameter; Figure 4 It is a flowchart of the erosion analysis module of the present invention for determining the erosion analysis strategy based on the load difference coefficient and the reference time series correlation coefficient. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] In order to make the purpose and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principle of the present invention and do not limit the protection scope of the present invention.

[0023] It should be noted that in the description of the present invention, the terms indicating the direction or position relationship such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the direction or position relationship shown in the drawings. This is only for the convenience of description and does not indicate or imply that the device or component must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention.

[0024] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and defined, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the communication inside two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0025] Please refer to Figures 1 to 4 as shown in the figure, the present invention provides an intelligent adjustment system for precast pier design, including: a difference evaluation module, configured to periodically determine whether to perform component load analysis and component erosion analysis on each existing analysis set based on the distribution difference index and the stage difference index; a load analysis module, connected to the difference evaluation module, configured to determine a distribution evaluation combination according to the defect distribution overlap index and the overlap distribution ratio index, and determine a load analysis strategy based on the erosion difference coefficient and the characteristic distribution coefficient. The load analysis strategy is to determine an optimization reference coefficient based on the load key relevance and the defect distribution overlap index, or to determine 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 the optimization reference coefficient of 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 evaluation module, configured to determine a growth evaluation combination according to the overlap growth correlation index and the overlap edge ratio index, and determine an erosion analysis strategy based on the load difference coefficient and the reference time series correlation coefficient. The erosion analysis strategy is to determine an optimization reference coefficient based on the erosion key relevance and the overlap growth correlation index, or to determine an optimization reference coefficient based on the reference time series correlation coefficient and the overlap growth correlation index; an erosion execution module, connected to the erosion analysis module, configured to determine the optimization reference coefficient of each growth evaluation combination according to the erosion analysis strategy, and determine whether to adjust the optimization reference coefficient based on the reference longitudinal distribution ratio; an optimization execution module, respectively connected to the load execution module and the erosion execution module, configured to determine a reference execution combination of the target analysis component according to the erosion correlation coefficient and the load correlation coefficient, and determine a combination matching coefficient of each reference execution combination based on the optimization reference coefficient and the reference quality coefficient.

[0026] Among them, the process of the present invention for optimizing and analyzing the design parameters of precast bridge piers requires the optimization process of the design parameters to be based on the usage conditions of the precast bridge piers that have been put into use. Since there are differences in the usage loads and application environments corresponding to different precast bridge piers, it is necessary to fully consider the influence degree of different influencing factors on the usage conditions during the optimization process to ensure the optimization effect of the design parameters. In the present invention, the precast bridge piers that have been put into use are denoted as existing analysis components, and the precast bridge piers to be prepared for optimizing the design parameters are denoted as target analysis components. Both the existing analysis components and the target analysis components in the present invention are precast bridge piers. The preparation process of precast bridge piers is already mastered by those skilled in the art and will not be elaborated here; The present invention applies an existing monitoring period, and the duration of the existing monitoring period can be determined by the user himself. The higher the user's requirement for the effectiveness of the optimization analysis result of the design parameters of the target analysis component, the shorter the duration of the existing monitoring period. A value for the duration of the existing monitoring period is provided. The duration of the existing monitoring period is 60 days. Defect feature detection is performed on each existing analysis component according to the existing monitoring period. For a single existing analysis component, at the end of each existing monitoring period, defect feature detection is performed on this existing analysis component to determine the defect feature points of this existing analysis component. The defect feature points are the positions where cracks exist in this existing analysis component. How to perform crack detection on each existing analysis component is easily understood by 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; The present invention applies several optimization analysis records. Any optimization analysis record records at least one component design difference index, defect difference coefficient, distribution approximation index, longitudinal distribution ratio, erosion difference coefficient, feature 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, combined setting richness, and combined matching coefficient during the optimization analysis process of the design parameters of the target analysis component. And each optimization analysis record corresponds to a qualified mark, and the qualified mark records whether the effectiveness of the optimization analysis result 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 effectiveness of the optimization analysis result of the design parameters of the target analysis component meets the requirements according to the self-set index. For example, the self-set index can be but is not limited to the optimization quality parameter. The optimization quality parameter is the average value of the interval duration between the moment when the first usage defect is detected after each target analysis component prepared by completing the optimization analysis is put into use and the moment when it is put into use.

[0027] Specifically, 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, the difference execution condition responded by the difference evaluation module is that the defect difference coefficient of this existing analysis set is greater than the preset defect difference coefficient. Then, it is determined that component load analysis and component erosion analysis are to be performed on this existing analysis set, and this existing analysis set is denoted as the interference processing set; The component design difference index of any existing analysis set is less than the preset component design difference index.

[0028] Among them, 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 difference degrees of the component preparation parameters of each category. For the component preparation parameters of a single category, the parameter difference degree = (the maximum value of the component preparation parameters of this category in the preparation process of each existing analysis component in this existing analysis set - the minimum value of the component preparation parameters of this category in the preparation process of each existing analysis component in this existing analysis set) / the average value of the component preparation parameters of this category in the preparation process of each existing analysis component in this existing analysis set. The categories of component preparation parameters used to determine the component design difference index in the present invention include but are not limited to: cross-sectional dimension, pier height, diameter of stress-bearing steel bars, spacing of stress-bearing steel bars, and thickness of steel bar protective layer; For a single existing analysis set, the defect difference coefficient = distribution difference index × stage difference index. The distribution difference index is the sum of the radial dispersion degree and the longitudinal dispersion degree. Obtain the radial depth ratio and the longitudinal depth ratio of the defect characteristic points of each existing analysis component in this existing analysis set. For any defect characteristic point in a single existing analysis component, the radial depth ratio = the radial distance of this defect characteristic point / the maximum width of this existing analysis component on the horizontal plane where this defect characteristic point is located. The radial distance is the shortest distance between this defect characteristic point and the surface of this existing analysis component on the horizontal plane where this defect characteristic point is located. The longitudinal depth ratio = the shortest distance between this defect characteristic point and the upper surface of this existing analysis component / the height value of this existing analysis component in the direction perpendicular to the horizontal plane. The radial dispersion degree is the absolute value of the difference between the maximum value and the minimum value of the radial depth ratios of the defect characteristic points of each existing analysis component in this existing analysis set. The longitudinal dispersion degree is the absolute value of the difference between the maximum value and the minimum value of the radial depth ratios of the defect characteristic points of each existing analysis component in this existing analysis set. The stage difference index , where e is the number of existing analysis components in the existing analysis set, Lf is the defect growth duration of the f-th existing analysis component in the existing analysis set, and L0 is the average value of the defect growth durations of each existing analysis component in the existing analysis set. For a single existing analysis component, the defect growth duration is the interval duration between the moment when the existing analysis component is put into use and the moment when the defect feature point is first detected; 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 there are significant differences in the usage situations of each existing analysis component in the existing analysis set under relatively close design parameters, that is, the interference differences of different interference factors on the usage situations of the existing analysis components are relatively large. It is necessary to perform component load analysis and component erosion analysis on the existing analysis set to separate the interference degrees of different influencing factors on the usage situations, so as to ensure the effectiveness of the optimization result of the design parameters based on the defect degree of the usage process of the existing analysis components. 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; 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 requirement for the effectiveness of the optimization analysis result 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 obtaining the value of the preset component design difference index is provided. The average value of the component design difference indexes of each existing analysis set in the optimization analysis record that meets the user's requirement for the effectiveness of the optimization analysis result of the design parameters of the target analysis component is recorded as the preset component design difference index. A method for obtaining the value of the preset defect difference coefficient is provided. The minimum value of the defect difference coefficients of each existing analysis set that undergoes component load analysis and component erosion analysis in the optimization analysis record that meets the user's requirement for the effectiveness of the optimization analysis result of the design parameters of the target analysis component is recorded as the preset defect difference coefficient.

[0029] Specifically, the load analysis module responds to the load processing condition 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 for each distribution evaluation combination based on the erosion difference coefficient and the feature distribution coefficient; The load processing condition is that there is any existing analysis set determined by the difference evaluation module to perform component load analysis.

[0030] Among them, for the interference processing set, the differences between the various component preparation parameters of the existing analysis components therein are relatively small during the preparation process. However, there are significant differences in the usage states presented during the use process, indicating that the environmental erosion and usage loads suffered by each existing analysis component within the interference processing set have different effects on the usage state. In order to better judge and distinguish the interferences brought by different factors for component load analysis and component erosion analysis, during which, when performing component load analysis, in the process of evaluating the matching degree of the corresponding component preparation parameters of the interference processing set for different operating load conditions, the influence brought by environmental erosion is reduced to ensure the matching degree between the subsequent process of optimizing parameters and the actual operating load requirements; When performing component load analysis on a single interference processing set, a distribution evaluation combination is determined according to the distribution of defect feature points for subsequent analysis. The distribution evaluation combination is a set of several existing analysis components within the interference processing set, and the distribution correlation coefficient of any distribution evaluation combination is greater than a 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 overlapping distribution proportion index. The defect distribution overlap index = the sum of the number of overlapping defect feature points existing in each existing analysis component within the distribution evaluation combination / the sum of the number of defect feature points existing in each existing analysis component within the distribution evaluation combination. For any two defect feature points within the distribution evaluation combination, if the distribution approximation index between the two defect feature points is greater than a preset distribution approximation index, then the two defect feature points are recorded as a distribution correlation pair, and the two defect feature points are respectively recorded as the distribution correlation feature points of each other. The distribution approximation index = 1 / (the absolute value of the difference between the radial depth ratios of the two defect feature points + the absolute value of the difference between the longitudinal depth ratios of the two defect feature points). If the number of distribution correlation feature points existing for any defect feature point is greater than a preset overlap evaluation parameter, then the defect feature point is recorded as an overlapping defect feature point. The overlapping distribution proportion index = the number of overlapping distribution feature points existing within the distribution evaluation combination / the number of overlapping defect feature points existing within 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 a preset longitudinal angle reference value, then the two overlapping defect feature points are recorded as a longitudinal association combination, and the two overlapping defect feature points are respectively recorded as the longitudinal association feature points of each other. The longitudinal angle reference value is the acute angle value formed by the straight line connecting the two overlapping defect feature points and the axis perpendicular to the horizontal plane of the corresponding existing analysis component. For a single overlapping defect feature point, if the longitudinal association distribution proportion of the overlapping defect feature point is greater than a preset longitudinal distribution proportion, then the overlapping defect feature point is recorded as an overlapping distribution feature point. The longitudinal distribution proportion = the number of longitudinal association feature points existing within the distribution evaluation range of the overlapping defect feature point / the number of overlapping defect feature points existing within the distribution evaluation range of the overlapping defect feature point. The distribution evaluation range is the range corresponding to the sphere with the position of the overlapping defect feature point as the center of the sphere and a 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, and the value of the preset distribution distance is 5% of the maximum width of the existing analysis component to which the overlapping defect feature point belongs; The values of the preset distribution approximation exponent, the preset overlap evaluation parameter, the preset longitudinal angle reference value, the preset longitudinal distribution ratio, and the 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 requirement for the effectiveness of the optimization analysis result of the design parameters of the target analysis component, the larger the value of the preset distribution approximation exponent, 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 obtaining the value of the preset distribution approximation exponent is provided. The minimum value of the distribution approximation exponents between the defect feature points in each distribution correlation pair in the optimization analysis record that meets the user's requirement for the effectiveness of the optimization analysis result of the design parameters of the target analysis component is recorded as the preset distribution approximation exponent. A method for obtaining the value of the preset overlap evaluation parameter is provided. The average value of the number of distribution correlation feature points where each overlap defect feature point exists in the optimization analysis record that meets the user's requirement for the effectiveness of the optimization analysis result of the design parameters of the target analysis component is recorded as the preset overlap evaluation parameter. A method for obtaining the value of the preset longitudinal angle reference value is provided. The value of the preset longitudinal angle reference value is 30°. A method for obtaining the value of the preset longitudinal distribution ratio is provided. The average value of the longitudinal distribution ratios of each overlap distribution feature point in the optimization analysis record that meets the user's requirement for the effectiveness of the optimization analysis result of the design parameters of the target analysis component is recorded as the preset longitudinal distribution ratio. A method for obtaining the value of the preset distribution correlation coefficient is provided. The minimum value of the distribution correlation coefficients of each distribution evaluation combination in the optimization analysis record that meets the user's requirement for the effectiveness of the optimization analysis result of the design parameters of the target analysis component is recorded as the preset distribution correlation coefficient; For a single distribution evaluation combination, the erosion difference coefficient is the average of the erosion difference degrees of the component erosion parameters of each category. For the component erosion parameters of a single category, the erosion difference degree = (the maximum value of the component erosion parameters of this category during the use of each existing analysis component within this distribution evaluation combination - the minimum value of the component erosion parameters of this category during the use of each existing analysis component within this distribution evaluation combination) / the average value of the component erosion parameters of this category during the use of each existing analysis component within this distribution evaluation combination. The user can set the category of the component erosion parameters used to determine the erosion difference coefficient according to the actual working scenario. The categories of the component erosion parameters that can be used to determine the erosion difference coefficient include but are not limited to: the number of freeze-thaw cycles, the concentration of erosive gases, and the water flow scouring speed. The characteristic distribution coefficient is the sum of the radial distribution difference degree and the longitudinal distribution difference degree. The radial distribution difference degree = the standard deviation of the radial depth proportion of each overlapping defect feature point within this distribution evaluation combination / the average value of the radial depth proportion of each overlapping defect feature point within this distribution evaluation combination. The longitudinal distribution difference degree = the standard deviation of the longitudinal depth proportion of each overlapping defect feature point within this distribution evaluation combination / the average value of the longitudinal depth proportion of each overlapping defect feature point within this distribution evaluation combination.

[0031] 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, it is determined that the load execution module determines the optimization reference coefficient of this distribution evaluation combination based on the load key relevance and the defect distribution overlap index; The optimization reference coefficient has a positive correlation with the load key relevance and the defect distribution overlap index respectively.

[0032] Among them, for a single distribution evaluation combination, if the erosion difference coefficient of this distribution evaluation 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 significant differences in the environmental conditions among the application scenarios of each existing analysis component within this distribution evaluation combination or the distribution of the existing defect feature points is relatively scattered. It can indicate that the relationship between the growth cause of the overlapping defect feature points existing within this distribution evaluation combination and environmental erosion is weak. Therefore, the effectiveness of the overlapping defect feature points within the distribution evaluation combination for reference analysis during the optimization process is judged by analyzing the similarity degree of the load conditions of the existing analysis components within the distribution evaluation combination; The values of the preset erosion difference coefficient and the preset feature distribution coefficient can be determined by the user according to the actual working scenario. For example, the user can set according to the optimization analysis record. A method for obtaining the value of the preset erosion difference coefficient is provided. The optimization analysis record for determining the optimization reference coefficient of the distribution evaluation combination based on the load key relevance and the defect distribution overlap index is recorded as the distribution reference record. The average value of the erosion difference coefficients in the distribution reference records that meet the effectiveness requirements of the user for the optimization analysis results of the design parameters of the target analysis component is recorded as the preset erosion difference coefficient. A method for obtaining the value of the preset feature distribution coefficient is provided. The average value of the feature distribution coefficients in the distribution reference records that meet the effectiveness requirements of the user for the optimization analysis results of the design parameters of the target analysis component is recorded as the preset feature distribution coefficient; For a single distribution evaluation combination, if the erosion difference coefficient of this distribution evaluation combination is greater than the preset erosion difference coefficient or the feature distribution coefficient is greater than the preset feature distribution coefficient, then the optimization reference coefficient of this distribution evaluation combination is determined based on the load key relevance and the defect distribution overlap index. The optimization reference coefficient has a positive correlation with the load evaluation coefficient. The load evaluation coefficient is the sum of the load key relevance and the defect distribution overlap index of this distribution evaluation combination. The load key relevance = 1 / the average value of the load difference degrees of the component load parameters of each category. For the component load parameters of a single category, the load difference degree , where m is the number of existing analysis components within this distribution evaluation combination, sk is the value of the component load parameter of this category during the use of the k-th existing analysis component within this distribution evaluation combination, and s0 is the average value of the values of the component load parameter of this category during the use of each existing analysis component within this distribution evaluation combination. The user can set the category of the component load parameters used to determine the load key relevance according to the actual working scenario. The categories of the component load parameters used to determine the load key relevance include but are not limited to: the maximum value of the self-vibration intensity of the bridge body, the dead load pressure caused by the upper structure of the bridge pier, the train passing frequency, and the average value of the accelerations during each train passage.

[0033] Specifically, when the load execution condition responded to 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 feature distribution coefficient is less than or equal to the preset feature distribution coefficient, it is determined that the load execution module determines the optimization reference coefficient of this distribution evaluation combination based on the feature distribution coefficient and the defect distribution overlap index, and determines whether to adjust the optimization reference coefficient of this distribution evaluation combination based on the combination edge ratio; When the load optimization regulation condition responded to by the load execution module is that the combination edge ratio is greater than the preset combination edge ratio, it is determined that the optimization reference coefficient is reduced according to the combination edge ratio; The optimization reference coefficients are positively correlated with the feature distribution coefficient and the defect distribution overlap index respectively, and the decrease value of the optimization reference coefficient is positively correlated with the combined edge ratio.

[0034] Among them, for a single distribution evaluation combination, if the erosion difference coefficient of the distribution evaluation combination is less than or equal to the preset erosion difference coefficient and the feature distribution coefficient is less than or equal to the preset feature distribution coefficient, it indicates that the environmental conditions between the application scenarios of each existing analysis component in the distribution evaluation combination are less different and the distribution of defect feature points is relatively concentrated, which can indicate that there is a large correlation between the overlapping defect feature points existing in the distribution evaluation combination and the factors of environmental erosion. During the process of determining the optimization reference coefficient, it is necessary to correct the optimization reference coefficient according to the degree of influence caused by environmental erosion; For a single distribution evaluation combination, if the erosion difference coefficient of the distribution evaluation combination is less than or equal to the preset erosion difference coefficient and the feature distribution coefficient is less than or equal to the preset feature distribution coefficient, the optimization reference coefficient is positively correlated with the load separation coefficient. The load separation coefficient is the sum of the feature distribution coefficient and the defect distribution overlap index. The combined edge ratio = the number of overlapping edge feature points existing in the distribution evaluation combination / the number of overlapping defect feature points existing in the distribution evaluation combination. The overlapping edge feature points are overlapping defect feature points with a radial depth ratio less than the preset radial depth ratio; For the value of the preset combined edge ratio, the user can determine it 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 result of the design parameters of the target analysis component, the smaller the value of the preset combined edge ratio. A method for obtaining the value of the preset combined edge ratio is provided. The optimization analysis record that adjusts the optimization reference coefficient according to the combined edge ratio is recorded as the load correction record, and the average value of the combined edge ratio in the load correction records that meet the user's requirement for the effectiveness of the optimization analysis result of the design parameters of the target analysis component is recorded as the preset combined edge ratio.

[0035] Specifically, the erosion analysis module responds to the erosion processing condition and divides the interference processing set into growth evaluation combinations according to the overlapping growth correlation index and the overlapping edge ratio index; The erosion analysis module determines the erosion analysis strategy of each growth evaluation 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 evaluation module for component erosion analysis.

[0036] Among them, when performing component erosion analysis on the interference processing set, during the process of evaluating the matching of various component preparation parameters corresponding to the interference processing set under different environmental erosion conditions, it is necessary to reduce the influence brought by the bridge load during the use of the bridge pier to ensure the matching degree between the subsequent process of optimizing parameters and the actual environmental protection requirements; When performing component erosion analysis on a single interference processing set, determine the growth evaluation combination division according to the formation timing of the defect feature points for subsequent analysis. The growth evaluation combination is a set of several existing analysis components within 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 association index and the overlapping edge ratio index. The overlapping growth association index , where n is the number of overlapping defect feature points existing in the growth evaluation combination, gi is the characteristic growth parameter of the i-th overlapping defect feature point existing in the growth evaluation combination, g0 is the average value of the characteristic growth parameters of each overlapping defect feature point existing in the growth evaluation combination. For a single overlapping defect feature point, the characteristic growth parameter is the interval duration 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 ratio index = the number of overlapping edge feature points existing in the growth evaluation combination / the number of overlapping defect feature points existing in the growth evaluation combination; 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 according to the optimization analysis record. The higher the user's requirement for the effectiveness of the optimization analysis result 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. Provide a method for obtaining the value of the preset radial depth ratio. Denote the average value of the radial depth ratios of each overlapping edge feature point in the optimization analysis record that meets the user's requirement for the effectiveness of the optimization analysis result of the design parameters of the target analysis component as the preset radial depth ratio. Provide a value of the preset radial depth ratio, and the value of the preset radial depth ratio is 0.05. Provide a method for obtaining the value of the preset growth correlation coefficient. Denote the average value of the growth correlation coefficients of each growth evaluation combination in the optimization analysis record that meets the user's requirement for the effectiveness of the optimization analysis result of the design parameters of the target analysis component as the preset growth correlation coefficient; For a single growth evaluation combination, the load difference coefficient is the average value of the combined load difference degrees of the component load parameters of each category. For the component load parameter of a single category, the combined load difference degree , where p is the number of existing analysis components in the growth assessment combination, gt is the 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 values of the component load parameter of this category during the use of each existing analysis component in the growth assessment combination, and the reference time series correlation coefficient is the average value of the time series correlation coefficients 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 existing in this existing analysis component / the average value of the characteristic growth parameters of the overlapping defect feature points existing in this existing analysis component.

[0037] Specifically, the erosion execution condition for the erosion analysis module to respond 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, it is determined that the erosion execution module determines the optimization reference coefficient of the growth assessment combination based on the erosion key relevance and the overlapping growth association index; The optimization reference coefficient has a positive correlation with the erosion key relevance and the overlapping growth association index respectively.

[0038] 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 significant differences in the operating loads during the use of each existing analysis component in the growth assessment combination or there are significant differences in the growth moments of the overlapping defect feature points. Furthermore, it shows that the relationship between the growth causes of the overlapping defect feature points existing in the existing analysis components in this growth assessment combination and the application load situation of the bridge body is weak. Therefore, the effectiveness of the overlapping defect feature points in the growth assessment combination for reference analysis during the optimization process is judged by analyzing the similarity of the environmental erosion situations of the existing analysis components in the growth assessment combination; For the values of the preset load difference coefficient and the preset time series correlation coefficient, the user can determine them according to the actual working scenario. For example, the user can set them according to the optimization analysis record. A method for obtaining the value of the preset load difference coefficient is provided. The optimization analysis record for determining the optimization reference coefficient of the growth assessment combination based on the erosion key relevance and the overlapping growth association index is recorded as the growth reference record. The average value of the load difference coefficients of each growth assessment combination in the growth reference record that meets the effectiveness requirement of the optimization analysis result of the design parameters of the target analysis component is recorded as the preset load difference coefficient. A method for obtaining the value of the preset time series correlation coefficient is provided. The average value of the reference time series correlation coefficients of each growth assessment combination in the growth reference record that meets the effectiveness requirement of the optimization analysis result of the design parameters of the target analysis component is recorded as the preset time series correlation coefficient; For a single growth evaluation combination, if the load difference coefficient of the growth evaluation 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 optimization reference coefficient and the erosion evaluation coefficient are in a positive correlation relationship. The erosion evaluation coefficient is the sum of the erosion key correlation degree and the overlapping growth association index of the growth evaluation combination. The erosion key correlation degree = 1 / the average value of the combined erosion differences 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 parameters of this category during the use of each existing analysis component in the growth evaluation combination - the minimum value of the component erosion parameters of this category during the use of each existing analysis component in the growth evaluation combination) / the average value of the component erosion parameters of this category during the use of each existing analysis component in the growth evaluation combination.

[0039] Specifically, the erosion execution condition for which the erosion analysis module responds is that the load difference coefficient of the growth evaluation 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 it is determined that the erosion execution module determines the optimization reference coefficient of the growth evaluation combination based on the reference time series correlation coefficient and the overlapping growth association index, and determines whether to adjust the optimization reference coefficient of the growth evaluation combination based on the reference longitudinal distribution ratio; The erosion optimization control condition for which the erosion execution module responds is that the reference longitudinal distribution ratio is greater than the preset reference longitudinal distribution ratio. Then a reduction adjustment is made to the optimization reference coefficient based on the reference longitudinal distribution ratio; The optimization reference coefficient is in a positive correlation relationship with the reference time series correlation coefficient and the overlapping growth association index respectively. The reduction value of the optimization reference coefficient is in a positive correlation relationship with the reference longitudinal distribution ratio.

[0040] Among them, for a single distribution evaluation combination, if the load difference coefficient of the growth evaluation 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 are no excessive differences between the operating loads during the use of each existing analysis component in the growth evaluation combination and the growth moments of the overlapping defect feature points. Furthermore, it shows that the relationship between the growth reasons of the overlapping defect feature points existing in the existing analysis components in the growth evaluation combination and the application load situation of the bridge body is relatively strong. Therefore, it is necessary to correct the optimization reference coefficient according to the influence degree of the bridge body operating load. For a single distribution evaluation combination, if the load difference coefficient of the growth evaluation 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 and the erosion separation coefficient are positively correlated, the erosion separation coefficient is the sum of the reference time series correlation coefficient and the overlapping growth association index, and the reference longitudinal distribution ratio is the average value of the longitudinal association distribution ratios of each overlapping defect feature point in the distribution evaluation combination; 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 result of the design parameters of the target analysis component, the smaller the value of the preset longitudinal association distribution ratio. A method for determining the value of the preset reference longitudinal distribution ratio is provided. The optimization analysis record that reduces the optimized reference coefficient based on the reference longitudinal distribution ratio is recorded as the erosion correction record, and 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 result of the design parameters of the target analysis component is recorded as the preset reference longitudinal distribution ratio.

[0041] 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 optimized matching 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.

[0042] Among them, for a single target analysis component, the growth evaluation combination with an erosion correlation coefficient greater than the preset erosion correlation coefficient and the distribution evaluation combination with a load correlation coefficient greater than the preset load correlation coefficient are recorded as the reference execution combination. For a single growth evaluation combination, the erosion correlation coefficient is the average value of the target erosion correlation degrees of the component erosion parameters of each category. For the component erosion parameters of a single category, the target erosion correlation degree = the value of the component erosion parameter of this category during the use of the target analysis component / the average value of the absolute value of the difference between the value of the component erosion parameter of this category during the use of the target analysis component and the value of the component erosion parameter of this category during the use of each existing analysis component in the growth evaluation combination. For a single distribution evaluation combination, the load correlation coefficient is the average value of the target load correlation degrees of the component load parameters of each category. For the component load parameters of a single category, the target load correlation degree = the value of the component load parameter of this category during the use of the target analysis component / the average value of the absolute value of the difference between the value of the component load parameter of this category during the use of the target analysis component and the value of the component load parameter of this category during the use of each existing analysis component in the distribution evaluation combination; 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 requirement for the effectiveness of the optimization analysis result 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 obtaining the value of the preset erosion correlation coefficient is provided. The average value of the erosion correlation coefficients of the growth evaluation combinations used as the reference analysis combinations in the optimization analysis records that meet 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 obtaining the value of the preset load correlation coefficient is provided. The average value of the load correlation coefficients of the distribution evaluation combinations used as the reference analysis combinations in the optimization analysis records that meet 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.

[0043] 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. For any reference execution combination, the execution determination condition responded by the optimization execution module is that the combination setting richness of this reference execution combination is greater than the preset combination setting richness, then it is determined that the combination matching coefficient of this reference execution combination is reduced based on the execution optimization difference coefficient. The reduction value of the combination matching coefficient is positively correlated with the execution optimization difference coefficient.

[0044] Among them, the combination setting richness is the average value of the setting range degrees of each component preparation parameter. For any component preparation parameter of a single target analysis component, the setting range degree = (the maximum value of the component preparation reference for this item in the reference analysis combination of this target analysis component - the minimum value of the component preparation reference for this item in the reference analysis combination of this target analysis component) / the maximum value of the component preparation reference for this item in the reference analysis combination of this 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 this reference execution combination. The reference quality coefficient is the average value of the number of defect feature points existing in each existing analysis component within this reference analysis combination. The execution optimization difference coefficient = the optimization reference coefficient of this reference analysis combination / the average value of the optimization reference coefficients of each reference analysis combination of this target analysis component. The reference analysis combinations with the combination matching coefficient greater than the preset combination matching coefficient are recorded as the optimization execution combinations. Based on the values of each component preparation parameter of each existing analysis component within each optimization execution combination, the component preparation parameters of the target analysis component are optimized. This is easy to understand for those skilled in the art and will not be elaborated here. The values of the richness of the preset combination settings and the matching coefficient of the preset combination can be determined by the user according to the actual working scenario. For example, the user can set according to the optimization analysis record. The higher the user's requirement for the effectiveness of the optimization analysis result of the design parameters of the target analysis component, the smaller the value of the richness of the preset combination settings and the larger the value of the matching coefficient of the preset combination. A method for obtaining the value of the richness of the preset combination is provided. The optimization analysis record that reduces and adjusts the matching coefficient of the reference execution combination based on the execution optimization difference coefficient is recorded as the matching control record. The average value of the richness of the combination settings in the matching control records that meet the user's requirement for the effectiveness of the optimization analysis result of the design parameters of the target analysis component is recorded as the richness of the preset combination settings. A method for obtaining the value of the matching coefficient of the preset combination is provided. The average value of the matching coefficients of the optimization execution combinations in the optimization analysis records that meet the user's requirement for the effectiveness of the optimization analysis result of the design parameters of the target analysis component is recorded as the matching coefficient of the preset combination.

[0045] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0046] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent adjustment system for precast pier design, characterized in that, Including: The difference evaluation module is used to periodically determine whether to perform component load analysis and component erosion analysis on each existing analysis set based on the distribution difference index and the stage difference index; The load analysis module is connected to the difference evaluation module and is used to determine the distribution evaluation combination according to the defect distribution overlap index and the overlap distribution ratio index, and determine the load analysis strategy based on the erosion difference coefficient and the feature distribution coefficient as determining the optimization reference coefficient based on the load key relevance and the defect distribution overlap index, or determining the optimization reference coefficient based on the feature distribution coefficient and the defect distribution overlap index; The load execution module is connected to the load analysis module and is used to determine the optimization reference coefficient of 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; The erosion analysis module is connected to the difference evaluation module and is used to determine the growth evaluation combination according to the overlap growth correlation index and the overlap edge ratio index, and determine the erosion analysis strategy based on the load difference coefficient and the reference time series correlation coefficient as determining the optimization reference coefficient based on the erosion key relevance and the overlap growth correlation index, or determining the optimization reference coefficient based on the reference time series correlation coefficient and the overlap growth correlation index; The erosion execution module is connected to the erosion analysis module and is used to determine the optimization reference coefficient of each growth evaluation combination according to the erosion analysis strategy, and determine whether to adjust the optimization reference coefficient based on the reference longitudinal distribution ratio; The optimization execution module is respectively connected to the load execution module and the erosion execution module, 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 determine the combination matching coefficient of each reference execution combination based on the optimization reference coefficient and the reference quality coefficient.

2. The intelligent adjustment system for precast pier design according to claim 1, wherein 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, the difference execution condition responded by the difference evaluation module is that the defect difference coefficient of the existing analysis set is greater than the preset defect difference coefficient, then it is determined to perform component load analysis and component erosion analysis on the existing analysis set, and the existing analysis set is recorded as the interference processing set; The component design difference index of any existing analysis set is less than the preset component design difference index.

3. The intelligent adjustment system for precast pier design according to claim 2, wherein, The load analysis module responds to the load processing condition and divides the distribution evaluation combination for 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 feature distribution coefficient; The load processing condition is that there is any existing analysis set determined by the difference evaluation module to perform component load analysis.

4. The intelligent adjustment system for precast pier design according to claim 3, characterized in that, The load execution condition responded by the load analysis module is that there is an erosion difference coefficient of the distribution evaluation combination greater than the preset erosion difference coefficient or a characteristic distribution coefficient greater than the preset characteristic distribution coefficient, and it is determined that the load execution module determines the optimization reference coefficient of the distribution evaluation combination based on the load key relevance and the defect distribution overlap index; The optimization reference coefficient is positively correlated with the load key relevance and the defect distribution overlap index respectively.

5. The intelligent adjustment system for precast pier design according to claim 4, wherein, The load execution condition responded by the load analysis module is that there is an erosion difference coefficient of the distribution evaluation combination less than or equal to the preset erosion difference coefficient and a characteristic distribution coefficient less than or equal to the preset characteristic distribution coefficient, and it is determined that 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; The optimization regulation condition responded by the load execution module is that the combination edge ratio is greater than the preset combination edge ratio, and it is determined that the optimization reference coefficient is reduced according to the combination edge ratio; The optimization reference coefficient is positively correlated with the characteristic distribution coefficient and the defect distribution overlap index respectively, and the reduction value of the optimization reference coefficient is positively correlated with the combination edge ratio.

6. The intelligent adjustment system for precast pier design according to claim 2, characterized in that, The erosion analysis module responds to the erosion treatment condition and divides the interference treatment set into growth evaluation combinations according to the overlapping growth correlation index and the overlapping edge ratio index; The erosion analysis module determines the erosion analysis strategy of each growth evaluation combination based on the load difference coefficient and the reference time series correlation coefficient; The erosion treatment condition is that there is any existing analysis set determined by the difference evaluation module to perform component erosion analysis.

7. The intelligent adjustment system for precast pier design according to claim 6, characterized in that, The erosion execution condition responded by the erosion analysis module is that there is a load difference coefficient of the growth evaluation combination greater than the preset load difference coefficient or a reference time series correlation coefficient greater than the preset time series correlation coefficient, and it is determined that the erosion execution module determines the optimization reference coefficient of the growth evaluation combination based on the erosion key relevance and the overlapping growth correlation index; The optimization reference coefficient is positively correlated with the erosion key relevance and the overlapping growth correlation index respectively.

8. The intelligent adjustment system for precast pier design according to claim 7, characterized in that, The erosion execution condition responded by the erosion analysis module is that there is a load difference coefficient of the growth evaluation combination less than or equal to the preset load difference coefficient and a reference time series correlation coefficient less than or equal to the preset time series correlation coefficient, and it is determined that the erosion execution module determines the optimization reference coefficient of the growth evaluation combination based on the reference time series correlation coefficient and the overlapping growth correlation index, and determines whether to adjust the optimization reference coefficient of the growth evaluation combination based on the reference longitudinal distribution ratio; The erosion optimization regulation condition responded by the erosion execution module is that the reference longitudinal distribution ratio is greater than the preset reference longitudinal distribution ratio, and the optimization reference coefficient is reduced based on the reference longitudinal distribution ratio; The optimization reference coefficient is positively correlated with the reference time series correlation coefficient and the overlapping growth correlation index respectively, and the reduction value of the optimization reference coefficient is positively correlated with the reference longitudinal distribution ratio.

9. The intelligent adjustment system for precast pier design according to claim 1, wherein 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 optimization 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.

10. The intelligent adjustment system for precast pier design according to claim 9, 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, 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 that the combination matching coefficient of the reference execution combination is reduced based on the execution optimization difference coefficient; The reduction value of the combination matching coefficient is positively correlated with the execution optimization difference coefficient.

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