Method for constructing horizontal rotation force model of long-span bridge based on numerical simulation

Abstract

The present invention relates to the technical field of bridge rotation data processing, and specifically relates to a method for constructing a stress model of the horizontal rotation of a long-span bridge based on numerical simulation. The method includes: constructing an initial stress model of the horizontal rotation of a long-span bridge based on the obtained target bridge information; dividing each simulated rotation into stages; determining the important influence degree of the rotation trend of each preset detection point under each preset detection dimension in each target stage of each simulated rotation; adjusting the simulation sampling frequency of the preset detection point under the preset detection dimension in the target stage of the simulated rotation; constructing a target stress model of the horizontal rotation of a long-span bridge based on the initial stress model of the horizontal rotation of a long-span bridge and the target sampling frequencies of different preset detection points under different target stages of different simulated rotations and different preset detection dimensions. The present invention adaptively sets different target sampling frequencies, improving the accuracy of constructing the stress model of the horizontal rotation of a long-span bridge.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge rotation data processing, and particularly to a method for constructing a horizontal rotation force model of a long-span bridge based on numerical simulation. Background Art

[0002] The bridge horizontal rotation construction method is a method applicable to the construction of long-span bridges. After the bridge precast components are fabricated (cast or spliced) at non-design axis positions, they are rotated to move to the design bridge position, thus completing the bridge installation. Before installation, numerical simulation is usually used to simulate the mechanical response of the bridge during the rotation process, including the forces, displacements, stress distributions, etc. of various parts of the bridge, and to test the changes under different rotation angles, so as to predict the performance of the structure and possible problems.

[0003] During the simulation of the long-span bridge force model, it is necessary to fully consider the possible situations that may be encountered during the actual rotation process of the bridge in order to accurately predict the behavior of the bridge during rotation and ensure the safety and stability during the bridge construction and use. However, in the construction of the horizontal rotation force model of a long-span bridge based on numerical simulation, existing methods often model all parts of the long-span bridge without discrimination, that is, often use a fixed sampling frequency to model different positions of the long-span bridge. In this way, the results obtained during the simulation of the long-span bridge force model may not be able to accurately predict the stress concentration, time-varying characteristics of the friction coefficient, and dynamic deformation response at some positions during the rotation process, resulting in poor accuracy in the construction of the final horizontal rotation force model of the long-span bridge. Summary of the Invention

[0004] In order to solve the technical problem of poor accuracy in the construction of the horizontal rotation force model of a long-span bridge, the present invention proposes a method for constructing a horizontal rotation force model of a long-span bridge based on numerical simulation.

[0005] In a first aspect, the present invention provides a method for constructing a horizontal rotation force model of a long-span bridge based on numerical simulation, the method comprising:

[0006] Based on the obtained target bridge information, construct an initial model of the horizontal rotation force of the long-span bridge, and perform bridge rotation simulation to obtain a bridge simulation record corresponding to each simulated rotation;

[0007] According to the bridge rotation angular velocity in the bridge simulation record corresponding to each simulated rotation, divide each simulated rotation into stages to obtain a preset number of target stages;

[0008] According to the simulation dimension data of each preset detection point under each preset detection dimension in each target stage of each simulated rotation, determine the important influence degree of the rotation trend of each preset detection point under each preset detection dimension in each target stage of each simulated rotation;

[0009] If the importance influence degree of the preset detection point on the rotation trend under the preset detection dimension in the target stage of the simulated rotation is greater than the preset importance influence threshold, then adjust the simulated sampling frequency of the preset detection point under the preset detection dimension in the target stage of the simulated rotation to obtain the target sampling frequency of the preset detection point under the preset detection dimension in the target stage of the simulated rotation;

[0010] Based on the initial model of the horizontal rotation force of the long-span bridge and the target sampling frequencies of different preset detection points under different preset detection dimensions in different target stages of different simulated rotations, construct the target model of the horizontal rotation force of the long-span bridge.

[0011] Combined with the above first aspect, in a possible implementation manner, the method for dividing the stage of each simulated rotation according to the bridge rotation angular velocity in the bridge simulation record corresponding to each simulated rotation to obtain a preset number of target stages includes:

[0012] Determine any one simulated rotation as the marked simulated rotation, and form a marked rotation angular velocity sequence with all the bridge rotation angular velocities in the bridge simulation record corresponding to the marked simulated rotation;

[0013] Determine the maximum value in the marked rotation angular velocity sequence as the marked maximum value, and screen out the marked maximum values that are greater than or equal to the marked representative angular velocity from all the marked maximum values as the candidate maximum values, where the marked representative angular velocity is the average value of all the bridge rotation angular velocities in the marked rotation angular velocity sequence;

[0014] Form each pair of candidate maximum values into a maximum value group, and determine the stage division confidence corresponding to each maximum value group based on the distribution of candidate maximum values between the corresponding positions of the two candidate maximum values in the marked rotation angular velocity sequence in each maximum value group;

[0015] Screen out the maximum value group with the largest corresponding stage division confidence from all the maximum value groups as the target maximum value group;

[0016] Using the two candidate maximum values in the target maximum value group as the segmentation points, segment the marked rotation angular velocity sequence to obtain a preset number of subsequences, and record the time period corresponding to each subsequence as the target stage to obtain a preset number of target stages.

[0017] Combined with the above first aspect, in a possible implementation manner, the determining the stage division confidence corresponding to each maximum value group based on the distribution of candidate maximum values between the corresponding positions of the two candidate maximum values in the marked rotation angular velocity sequence in each maximum value group includes:

[0018] Determine any one of the maximum value groups as the marked maximum value group, and form a reference maximum value sequence from all candidate maximum values between the corresponding positions of the marked maximum value group in the marked body rotation angular velocity sequence, where the reference maximum value sequence includes the marked maximum value group;

[0019] Based on the absolute value of the difference between adjacent candidate maximum values in the reference maximum value sequence, and the duration between the simulation sampling times corresponding to two candidate maximum values in the marked maximum value group, determine the stage division confidence level corresponding to the marked maximum value group.

[0020] Combined with the above first aspect, in a possible implementation manner, the formula for the stage division confidence level corresponding to the marked maximum value group is:

[0021] ; where Q is the stage division confidence level corresponding to the marked maximum value group; is the normalization function; is the absolute value function; is the simulation sampling time corresponding to the first candidate maximum value in the marked maximum value group; is the simulation sampling time corresponding to the second candidate maximum value in the marked maximum value group; is the natural exponential function; A is the number of candidate maximum values in the reference maximum value sequence; a is the serial number of the candidate maximum value in the reference maximum value sequence; is the a th candidate maximum value in the reference maximum value sequence; is the a +1 th candidate maximum value in the reference maximum value sequence.

[0022] Combined with the above first aspect, in a possible implementation manner, the determining the important influence degree of the body rotation trend for each preset detection point in each preset detection dimension under each target stage of each simulated body rotation includes:

[0023] Determine any one of the preset detection points as the marked detection point, and screen out the preset detection points that detect the same part as the marked detection point from all preset detection points as the reference detection points;

[0024] Determine any one of the target stages as the marked stage, and determine any one of the preset detection dimensions as the marked detection dimension, and determine any one of the simulated body rotations as the marked simulated body rotation;

[0025] Determine the range of all simulated dimension data of the marker detection point under the marker detection dimension in the marker stage of the marker simulated body rotation as the target difference value of the marker detection point under the marker detection dimension in the marker stage of the marker simulated body rotation;

[0026] Determine the range of all simulated dimension data of each reference detection point under the marker detection dimension in the marker stage of the marker simulated body rotation as the target difference value of each reference detection point under the marker detection dimension in the marker stage of the marker simulated body rotation;

[0027] Determine the monitoring attention degree of the marker detection point under the marker detection dimension in the marker stage of the marker simulated body rotation according to the target difference value of the marker detection point under the marker detection dimension in the marker stage of the marker simulated body rotation and the target difference values of all reference detection points under the marker detection dimension in the marker stage of the marker simulated body rotation;

[0028] Determine the important influence degree of the body rotation trend of the marker detection point under the marker detection dimension in the marker stage of the marker simulated body rotation according to the monitoring attention degree and the target difference value of the marker detection point under the marker detection dimension in the marker stage of the marker simulated body rotation, wherein both the monitoring attention degree and the target difference value are positively correlated with the important influence degree of the body rotation trend.

[0029] Combined with the above first aspect, in a possible implementation manner, the determining the monitoring attention degree of the marker detection point under the marker detection dimension in the marker stage of the marker simulated body rotation according to the target difference value of the marker detection point under the marker detection dimension in the marker stage of the marker simulated body rotation and the target difference values of all reference detection points under the marker detection dimension in the marker stage of the marker simulated body rotation includes:

[0030] Determine the average value of the target difference values of all reference detection points under the marker detection dimension in the marker stage of the marker simulated body rotation as the reference difference value of the marker detection point under the marker detection dimension in the marker stage of the marker simulated body rotation;

[0031] Normalize the ratio of the target difference value and the reference difference value of the marker detection point under the marker detection dimension in the marker stage of the marker simulated body rotation to obtain the monitoring attention degree of the marker detection point under the marker detection dimension in the marker stage of the marker simulated body rotation.

[0032] Combined with the above first aspect, in a possible implementation manner, adjusting the simulation sampling frequency of the preset detection point under the preset detection dimension at the target stage of the simulated body rotation to obtain the target sampling frequency of the preset detection point under the preset detection dimension at the target stage of the simulated body rotation includes:

[0033] Determine the maximum value among all the simulated dimension data of the preset detection point under the preset detection dimension at the target stage of the simulated body rotation as the data reference value;

[0034] Adjust the simulation sampling frequency of the preset detection point under the preset detection dimension within the preset time period at the target stage of the simulated body rotation to the maximum to obtain a sequence of simulated dimension data at the maximum sampling frequency within the preset time period, denoted as the sequence of simulated dimension data of the preset detection point under the preset detection dimension within the preset time period at the target stage of the simulated body rotation;

[0035] According to the data reference value and the sequence of simulated dimension data of the preset detection point under the preset detection dimension within the preset time period at the target stage of the simulated body rotation, determine the critical data capture degree of the preset detection point under the preset detection dimension at the target stage of the simulated body rotation;

[0036] According to the critical data capture degree of the preset detection point under the preset detection dimension at the target stage of the simulated body rotation and the initial simulation sampling frequency, determine the target sampling frequency of the preset detection point under the preset detection dimension at the target stage of the simulated body rotation.

[0037] Combined with the above first aspect, in a possible implementation manner, the determining the critical data capture degree of the preset detection point under the preset detection dimension at the target stage of the simulated body rotation according to the data reference value and the sequence of simulated dimension data of the preset detection point under the preset detection dimension within the preset time period at the target stage of the simulated body rotation includes:

[0038] Determine the sequence of simulated dimension data of the preset detection point under the preset detection dimension within the preset time period at the target stage of the simulated body rotation as the target sequence of simulated dimension data;

[0039] Screen out the simulated dimension data greater than or equal to the data reference value from the target sequence of simulated dimension data as the reference dimension data;

[0040] According to the quantity of the reference dimension data, determine the critical data capture degree of the preset detection point under the preset detection dimension at the target stage of the simulated body rotation, where the quantity of the reference dimension data has a positive correlation with the critical data capture degree.

[0041] Combined with the above first aspect, in a possible implementation, the formula for the target sampling frequency of the preset detection point under the preset detection dimension in the target stage of the simulated rotation is as follows:

[0042] ; where G is the target sampling frequency of the preset detection point under the preset detection dimension in the target stage of the simulated rotation; h is the initial simulated sampling frequency of the preset detection point under the preset detection dimension in the target stage of the simulated rotation; ZM is the maximum achievable sampling frequency; H is the critical data capture degree of the preset detection point under the preset detection dimension in the target stage of the simulated rotation.

[0043] Combined with the above first aspect, in a possible implementation, constructing a target model for the horizontal rotation force of a long-span bridge based on the initial model of the horizontal rotation force of a long-span bridge and the target sampling frequencies of different preset detection points under different preset detection dimensions in different target stages of different simulated rotations includes:

[0044] Updating the simulated sampling frequencies of different preset detection points under different preset detection dimensions in different target stages of different simulated rotations in the initial model of the horizontal rotation force of a long-span bridge to their corresponding target sampling frequencies, and determining the updated initial model of the horizontal rotation force of a long-span bridge as the target model for the horizontal rotation force of a long-span bridge.

[0045] In a second aspect, the present invention provides a system for constructing a horizontal rotation force model of a long-span bridge based on numerical simulation, and the system includes:

[0046] A construction simulation module for constructing an initial model of the horizontal rotation force of a long-span bridge based on the obtained target bridge information, and performing bridge rotation simulation to obtain a bridge simulation record corresponding to each simulated rotation;

[0047] A stage division module for dividing each simulated rotation into target stages of a preset number according to the bridge rotation angular velocity in the bridge simulation record corresponding to each simulated rotation;

[0048] A rotation trend importance determination module for determining the rotation trend importance of each preset detection point under each preset detection dimension in each target stage of each simulated rotation according to the simulated dimension data of each preset detection point under each preset detection dimension in each target stage of each simulated rotation;

[0049] A sampling frequency adjustment module, configured to adjust the analog sampling frequency of a preset detection point under a preset detection dimension in a target stage of the analog rotation if the importance degree of the rotation trend of the preset detection point under the preset detection dimension in the target stage of the analog rotation is greater than a preset importance threshold, so as to obtain a target sampling frequency of the preset detection point under the preset detection dimension in the target stage of the analog rotation;

[0050] A model construction module, configured to construct a target model for the horizontal rotation force of a long-span bridge based on an initial model of the horizontal rotation force of the long-span bridge and the target sampling frequencies of different preset detection points under different preset detection dimensions in different target stages of different analog rotations.

[0051] In a third aspect, a server is provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the device executes the method in the first aspect or any possible implementation manner of the first aspect.

[0052] In a fourth aspect, a computer program product is provided, including: computer program code, when the computer program code runs on a computer, enabling the computer to execute the method in the first aspect or any possible implementation manner of the first aspect.

[0053] In a fifth aspect, a computer-readable storage medium is provided, storing computer program code, when the computer program code runs on a computer, enabling the computer to execute the method in the first aspect or any possible implementation manner of the first aspect.

[0054] The present invention has the following beneficial effects:

[0055] The method for constructing a horizontal rotation force model of a long-span bridge based on numerical simulation according to the present invention adaptively sets different target sampling frequencies, solves the technical problem of poor accuracy in constructing the horizontal rotation force model of a long-span bridge, and improves the accuracy of constructing the horizontal rotation force model of a long-span bridge. When constructing the target model for the horizontal rotation force of a long-span bridge according to the present invention, the influence of different detection points on the bridge rotation is comprehensively considered, so as to quantify the importance degree of the rotation trend of each preset detection point under each preset detection dimension in each target stage of each analog rotation, realize the adaptive adjustment of the analog sampling frequencies of different preset detection points under different preset detection dimensions in different target stages of different analog rotations, thereby realizing the adaptive modeling of different parts of the long-span bridge, and further improving the accuracy of the finally constructed horizontal rotation force model of the long-span bridge. Description of the Drawings

[0056] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0057] Figure 1 It is a flowchart of the method for constructing a horizontal rotation force model of a long-span bridge based on numerical simulation according to the present invention;

[0058] Figure 2 It is a schematic diagram of the composition structure of the system for constructing a horizontal rotation force model of a long-span bridge based on numerical simulation according to the present invention;

[0059] Figure 3 It is a schematic diagram of the structure of a computer device according to the present invention. Detailed implementation manners

[0060] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in combination with the drawings and preferred embodiments, details the specific implementation manners, structures, features and their effects of the technical solutions proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0062] Reference Figure 1 , which shows the flow of some embodiments of the method for constructing a horizontal rotation force model of a long-span bridge based on numerical simulation according to the present invention. The method for constructing a horizontal rotation force model of a long-span bridge based on numerical simulation includes the following steps:

[0063] Step S1, based on the obtained target bridge information, construct an initial model of the horizontal rotation force of the long-span bridge, and perform bridge rotation simulation to obtain the bridge simulation records corresponding to each simulated rotation.

[0064] Among them, the target bridge information can be information related to the long-span bridge to be detected. For example, the target bridge information can include, but is not limited to: the geometric dimensions and spatial positions of the bridge girders, the geometric dimensions and spatial positions of the bridge piers, the geometric dimensions and spatial positions of the bearings, the geometric dimensions and spatial positions of the slewing mechanisms, the mechanical parameters of the concrete, the mechanical parameters of the steel bars, the static load and the dynamic load. The long-span bridge to be detected can be the long-span bridge to be subjected to the slewing simulation. A long-span bridge refers to a bridge that spans a relatively large space, usually crossing geographical obstacles such as deep valleys, rivers or bays, and the main span lengths of these bridges usually exceed 100 meters. The initial model of the horizontal slewing force of the long-span bridge can be the horizontal slewing force model of the long-span bridge initially constructed based on the target bridge information. The horizontal slewing force model of the long-span bridge refers to the model that studies the actions of various forces on the bridge during the slewing process through the mechanical analysis of the bridge during the slewing process of the long-span bridge. The simulated slewing can be the simulated slewing process obtained by performing the bridge slewing simulation on the initial model of the horizontal slewing force of the long-span bridge. The bridge simulation record corresponding to the simulated slewing can characterize the situation related to the simulated slewing. The bridge simulation record can include, but is not limited to: the bridge slewing angular velocity during the simulated slewing process. The bridge slewing angular velocity can characterize the angular change rate of the bridge rotation.

[0065] It should be noted that during the simulation process of the horizontal slewing of the long-span bridge, it is necessary to fully consider the possible situations that may occur during the actual slewing process, such as uneven stress of the bridge structure, equipment failures or incoordination, bridge deformation or inclination, etc. The possible risk situations are determined through simulation for the structural stability of the bridge during slewing, the adaptability of the slewing platform equipment, the feasibility of the slewing plan, etc., and fully understand the bridge slewing conditions at different slewing angles and loads to avoid unnecessary risks in actual operation.

[0066] As an example, this step can include the following steps:

[0067] First step, obtain the target bridge information.

[0068] For example, geometric data, material parameters, load data, element types, boundary constraints, load cases, and historical rotation records of long-span bridges can be obtained to form the target bridge information. Among them, the method for obtaining geometric data can be as follows: collect the geometric dimensions, spatial positions, and connection relationships of key parts such as the main girder, piers, bearings, and rotation mechanisms of the bridge, and high-precision data needs to be obtained based on design drawings or 3D laser scanning technology. The rotation mechanism can be a spherical hinge. The method for obtaining material parameters can be as follows: input mechanical parameters such as the elastic modulus, Poisson's ratio, and yield strength of materials such as concrete and steel bars, and the parameters need to be based on national standards or laboratory measured data to ensure the accuracy of the parameters. The method for obtaining load data can be as follows: collect static loads, dynamic loads, temporary loads during the rotation process, and special working conditions. Among them, static loads can include but are not limited to: self-weight and secondary dead loads. Dynamic loads can include but are not limited to: vehicle loads and wind loads. The temporary load can be an unbalanced moment. The special working condition can be an earthquake. The method for obtaining element types can be as follows: collect beam elements, shell elements, and special elements. Beam elements can be used to simulate linear structures such as the main girder and piers, and the cross-sectional shape and reinforcement information need to be obtained. The cross-sectional shape can be box-shaped or T-shaped. Shell elements, also known as solid elements, can be used for complex parts such as bridge decks and spherical hinges to capture local stress distributions. Special elements can be but are not limited to: gap elements and spring elements. Gap elements can simulate the sliding of bearings, and spring elements simulate the pile-soil interaction. The method for obtaining boundary constraints can be as follows: set the degree-of-freedom constraints according to the actual support conditions. For example, fixed bearings restrict all displacements, and movable bearings only allow longitudinal sliding. Load cases can include: the rotation stage and environmental loads. During the rotation stage, rotation traction forces, unbalanced moments, and temporary counterweights can be applied to dynamically simulate the time-varying loads under the change of the rotation angle. Environmental loads can consider the effects of wind loads (simulation of pulsating wind) and temperature gradient effects (thermal expansion coefficient) on the structure. The maximum rotation angle and the minimum rotation angle in the historical rotation records of long-span bridges can be used as a reference to determine the rotation angle range of the long-span bridge during simulation; and different load data can be obtained to determine the number of load disturbances available for selection during simulation.

[0069] In the second step, based on the target bridge information, an initial model of the horizontal rotation force of the long-span bridge is constructed.

[0070] For example, according to the target bridge information, a three-dimensional finite element model of the bridge can be constructed through software tools, and the model constructed at this time is denoted as the initial model of the horizontal rotation force of the long-span bridge. Among them, software tools can include but are not limited to: ANSYS, MIDAS, and SOFiSTiK.

[0071] It should be noted that finite element software such as ANSYS, MIDAS, and SOFiSTiK can be used to establish a three-dimensional model. The main girder and bridge piers are discretized using beam elements or solid elements, and the rotation mechanism needs to be refined in modeling, considering contact nonlinear behavior. The rotation mechanism may include, but is not limited to: spherical hinges.

[0072] In the third step, bridge rotation simulation is carried out through the initial model of the horizontal rotation force of the long-span bridge, and the bridge rotation angular velocity during each simulation rotation process is recorded to form a bridge simulation record corresponding to each simulation rotation.

[0073] Step S2: According to the bridge rotation angular velocity in the bridge simulation record corresponding to each simulation rotation, each simulation rotation is divided into stages to obtain a preset number of target stages.

[0074] Among them, the preset number can be a pre-set number, which can be 3. The three target stages can be, in sequence: the starting stage, the intermediate stage, and the ending stage.

[0075] It should be noted that during the bridge rotation process, when the rotation starts, due to the large static friction of the bridge, it is necessary to overcome the static friction to start moving. Therefore, the speed in this stage is often relatively slow. The speed at this time is often relatively low because a large force is required to overcome the friction; when the bridge starts to rotate and enters a stable rotation state, the speed usually remains relatively stable. This is the main stage in the bridge rotation process, and the rotation equipment pushes the bridge at a relatively constant speed; when the rotation process is approaching the end, the speed of the bridge usually slows down, especially when approaching the final positioning point, the speed gradually decreases because fine adjustments are often required at this time to ensure the accurate positioning of the bridge. Because during the bridge rotation process, the intermediate stage of the bridge rotation is in a stage of relatively stable speed, the period of stable speed can be screened out from the recorded bridge rotation angular velocity, and the corresponding time stage can be divided into the intermediate stage of the bridge rotation. Thus, the corresponding time stages before and after it are the starting stage and the ending stage respectively.

[0076] As an example, this step may include the following steps:

[0077] In the first step, any one simulation rotation is determined as the marked simulation rotation, and all the bridge rotation angular velocities in the bridge simulation record corresponding to the above marked simulation rotation are used to form a marked rotation angular velocity sequence.

[0078] Among them, the marked rotation angular velocity sequence can be a time sequence.

[0079] In the second step, the maximum value in the above marked rotation angular velocity sequence is determined as the marked maximum value, and the marked maximum values that are greater than or equal to the marked representative angular velocity are screened out from all the marked maximum values as candidate maximum values.

[0080] Among them, the marked angular velocity can be the average value of all the bridge rotation angular velocities in the above-mentioned marked rotation angular velocity sequence of the rotating body.

[0081] In the third step, every two candidate maximum values are combined to form a maximum value group, and based on the distribution of candidate maximum values between the corresponding positions of the two candidate maximum values in each maximum value group in the marked rotation angular velocity sequence, determining the stage division confidence corresponding to each maximum value group may include the following sub-steps:

[0082] In the first sub-step, any one maximum value group is determined as the marked maximum value group, and all candidate maximum values between the corresponding positions of the above-mentioned marked maximum value group in the above-mentioned marked rotation angular velocity sequence are used to form a reference maximum value sequence.

[0083] Among them, the reference maximum value sequence may include the marked maximum value group.

[0084] For example, if the marked maximum value group includes: the second candidate maximum value and the sixth candidate maximum value, and the candidate maximum values in the marked rotation angular velocity sequence are successively: the first candidate maximum value, the second candidate maximum value, the third candidate maximum value, the fourth candidate maximum value, the fifth candidate maximum value, the sixth candidate maximum value, the seventh candidate maximum value, and the eighth candidate maximum value, then the formed reference maximum value sequence at this time may be {the second candidate maximum value, the third candidate maximum value, the fourth candidate maximum value, the fifth candidate maximum value, the sixth candidate maximum value}.

[0085] In the second sub-step, based on the absolute value of the difference between adjacent candidate maximum values in the above-mentioned reference maximum value sequence, and the duration between the simulation sampling times corresponding to the two candidate maximum values in the above-mentioned marked maximum value group, determine the stage division confidence corresponding to the above-mentioned marked maximum value group.

[0086] Among them, the simulation sampling time may be the sampling time during the simulated rotation process. The simulation sampling time corresponding to the candidate maximum value may be the sampling time when this candidate maximum value is collected during the simulated rotation process.

[0087] For example, the formula for determining the stage division confidence corresponding to the marked maximum value group may be:

[0088] ; among them, Q is the stage division confidence corresponding to the marked maximum value group. is the normalization function. is the absolute value function. is the simulation sampling time corresponding to the first candidate maximum value in the marked maximum value group. is the simulation sampling time corresponding to the second candidate maximum value in the marked maximum value group. is the natural exponential function. Ais the number of candidate maxima in the reference maximum value sequence. a is the sequence number of the candidate maximum in the reference maximum value sequence. is the a th candidate maximum in the reference maximum value sequence. is the a +1 th candidate maximum in the reference maximum value sequence.

[0089] It should be noted that the intermediate stage is the main stage of the bridge rotation, and its corresponding duration is often relatively long. When is larger, it often indicates that the duration between the simulated sampling times corresponding to the two candidate maxima in the marked maximum value group is larger, often indicating that the stage corresponding to the marked maximum value group is longer, and often indicating that the simulated sampling times corresponding to the two candidate maxima in the marked maximum value group are more likely to be the end times of the intermediate stage. When is smaller, it often indicates that the maxima between the two candidate maxima in the marked maximum value group are more similar, often indicating that the bridge rotation angular velocities within the stage corresponding to the marked maximum value group are more similar, often indicating that the bridge rotation within the stage corresponding to the marked maximum value group is smoother, and often indicating that the simulated sampling times corresponding to the two candidate maxima in the marked maximum value group are more likely to be the end times of the intermediate stage. Therefore, when Q is larger, it often indicates that the simulated sampling times corresponding to the two candidate maxima in the marked maximum value group are more likely to be the end times of the intermediate stage.

[0090] Step 4: Select the maximum value group with the highest corresponding stage division confidence from all the maximum value groups as the target maximum value group.

[0091] Step 5: Using the two candidate maxima in the above target maximum value group as the segmentation points, segment the marked rotation angular velocity sequence to obtain a preset number of subsequences, that is, 3 subsequences, and record the time period corresponding to each subsequence as the target stage, obtaining a preset number of target stages, that is, 3 target stages, which can be recorded in sequence as the starting stage, the intermediate stage, and the ending stage.

[0092] Step S3: Determine the important influence degree of the rotation trend of each preset detection point under each preset detection dimension in each target stage of each simulated rotation according to the simulated dimension data of each preset detection point under each preset detection dimension in each target stage of each simulated rotation.

[0093] Among them, the preset detection points can be position points set in advance for status monitoring. The number of preset detection points can be set in advance. Multiple preset detection points can be installed at the same part, located at different positions of the same part. For example, the preset detection points can be, but are not limited to: strut axial force measurement points, rotating hinge stress measurement points, traction force, traction length monitoring points, main girder axis monitoring points, main girder elevation monitoring points, rotation angle, angular velocity, central axis inclination measurement points, and pier tower axis monitoring points. The preset detection dimensions can be dimensions related to the simulated rotation corresponding to the data collected by the preset detection points. The simulated dimension data under the preset detection dimensions can be the preset detection dimension data collected by the preset detection points during the simulated rotation process. For example, taking the strut axial force measurement point as an example, the multiple preset detection dimensions under the strut axial force measurement point can be the axial force dimension, transverse force dimension, bending moment dimension, torque dimension, and shear force dimension respectively, and the simulated dimension data under these preset detection dimensions can be the axial force, transverse force, bending moment, torque, and shear force in sequence.

[0094] It should be noted that during the bridge rotation process, there are often different key parts at different stages of rotation. For example, the key parts can be, but are not limited to: spherical hinge, V-shaped pier support structure, and cantilever end. The selection of these key parts usually depends on factors such as the bridge rotation method, rotation angle, and type of equipment used, and these parts are related to the rotation accuracy, stability of the bridge, reliability of the equipment, and control of mechanical requirements during the construction process. Here, relevant monitoring data of the support system, traction system, and turntable attitude will be involved in the bridge rotation to reflect the relevant data performance during the rotation process. The monitoring parameters of the support system should include turntable settlement, slideway alignment, gap between strut and slideway, turntable stress, auxiliary support stress, etc.; the monitoring parameters of the traction system should include traction force, traction length, traction speed, etc.; the monitoring parameters of the rotation attitude should include rotation inclination, rotation angle, rotation angular velocity, relative elevation difference between the two cantilever ends of the rotation structure, beam axis, pier tower verticality, etc. Therefore, multiple detection points can be set at multiple parts during the monitoring of the support system, traction system, and rotation attitude.

[0095] As an example, this step can include the following steps:

[0096] First step, determine any one of the preset detection points as the marked detection point, and select the preset detection points that detect the same part as the above marked detection point from all the preset detection points as the reference detection points.

[0097] Second step, determine any one of the target stages as the marked stage, determine any one of the preset detection dimensions as the marked detection dimension, and determine any one of the simulated rotations as the marked simulated rotation.

[0098] In the third step, the range of all the simulated dimension data of the above-mentioned marked detection points under the above-mentioned marked detection dimension in the above-mentioned marked stage of the above-mentioned marked simulated rotation is determined as the target difference value of the above-mentioned marked detection points under the above-mentioned marked detection dimension in the above-mentioned marked stage of the above-mentioned marked simulated rotation.

[0099] In the fourth step, the range of all the simulated dimension data of each reference detection point under the above-mentioned marked detection dimension in the above-mentioned marked stage of the above-mentioned marked simulated rotation is determined as the target difference value of each reference detection point under the above-mentioned marked detection dimension in the above-mentioned marked stage of the above-mentioned marked simulated rotation.

[0100] In the fifth step, based on the target difference value of the above-mentioned marked detection points under the above-mentioned marked detection dimension in the above-mentioned marked stage of the above-mentioned marked simulated rotation, and the target difference values of all the reference detection points under the above-mentioned marked detection dimension in the above-mentioned marked stage of the above-mentioned marked simulated rotation, determining the monitoring attention degree of the above-mentioned marked detection points under the above-mentioned marked detection dimension in the above-mentioned marked stage of the above-mentioned marked simulated rotation may include the following sub-steps:

[0101] In the first sub-step, the mean value of the target difference values of all the reference detection points under the above-mentioned marked detection dimension in the above-mentioned marked stage of the above-mentioned marked simulated rotation is determined as the reference difference value of the above-mentioned marked detection points under the above-mentioned marked detection dimension in the above-mentioned marked stage of the above-mentioned marked simulated rotation.

[0102] In the second sub-step, the ratio of the target difference value and the reference difference value of the above-mentioned marked detection points under the above-mentioned marked detection dimension in the above-mentioned marked stage of the above-mentioned marked simulated rotation is normalized to obtain the monitoring attention degree of the above-mentioned marked detection points under the above-mentioned marked detection dimension in the above-mentioned marked stage of the above-mentioned marked simulated rotation.

[0103] For example, the formula corresponding to determining the monitoring attention degree of the marked detection points under the marked detection dimension in the marked stage of the marked simulated rotation may be:

[0104] ; where B is the monitoring attention degree of the marked detection points under the marked detection dimension in the marked stage of the marked simulated rotation. is the normalization function. d is the target difference value of the marked detection points under the marked detection dimension in the marked stage of the marked simulated rotation. D is the reference difference value of the marked detection points under the marked detection dimension in the marked stage of the marked simulated rotation.

[0105] It should be noted that when BWhen it is larger, it often indicates that the data fluctuation of the marker detection point in the marker detection dimension in the marker stage of the marker simulation body rotation is greater than that of most reference detection points in the marker detection dimension in the marker stage of the marker simulation body rotation. It often indicates that the data fluctuation situation of the marker detection point in the marker detection dimension in the marker stage of the marker simulation body rotation is more important, and it often indicates that the monitoring attention degree of the marker detection point in the marker detection dimension in the marker stage of the marker simulation body rotation should be relatively high.

[0106] Step 6: Determine the important influence degree of the body rotation trend of the marker detection point in the marker detection dimension in the marker stage of the marker simulation body rotation according to the monitoring attention degree and the target difference value of the marker detection point in the marker detection dimension in the marker stage of the marker simulation body rotation.

[0107] Among them, both the monitoring attention degree and the target difference value can have a positive correlation with the important influence degree of the body rotation trend.

[0108] For example, the formula for determining the important influence degree of the body rotation trend corresponding to the marker detection point in the marker detection dimension in the marker stage of the marker simulation body rotation can be:

[0109] ; among them, w is the important influence degree of the body rotation trend of the marker detection point in the marker detection dimension in the marker stage of the marker simulation body rotation. is the normalization function. B is the monitoring attention degree of the marker detection point in the marker detection dimension in the marker stage of the marker simulation body rotation. d is the target difference value of the marker detection point in the marker detection dimension in the marker stage of the marker simulation body rotation.

[0110] It should be noted that when B is larger, it often indicates that the data fluctuation of the marker detection point in the marker detection dimension in the marker stage of the marker simulation body rotation is greater than that of most reference detection points in the marker detection dimension in the marker stage of the marker simulation body rotation. It often indicates that the monitoring attention degree of the marker detection point in the marker detection dimension in the marker stage of the marker simulation body rotation should be relatively high. When d is larger, it often indicates that the data fluctuation of the marker detection point in the marker detection dimension in the marker stage of the marker simulation body rotation is greater. It often indicates that the data fluctuation situation of the marker detection point in the marker detection dimension in the marker stage of the marker simulation body rotation is more important, and it often indicates that the monitoring attention degree of the marker detection point in the marker detection dimension in the marker stage of the marker simulation body rotation should be relatively high. Therefore, when wWhen it is larger, it often indicates that the monitoring attention degree of the marked detection point under the marked detection dimension in the marked stage of the marked simulated rotation should be relatively high.

[0111] Step S4, if the importance influence degree of the preset detection point on the rotation trend under the preset detection dimension in the target stage of the simulated rotation is greater than the preset importance influence threshold, then adjust the simulated sampling frequency of the preset detection point under the preset detection dimension in the target stage of the simulated rotation to obtain the target sampling frequency of the preset detection point under the preset detection dimension in the target stage of the simulated rotation.

[0112] Among them, the preset importance influence threshold can be a threshold set in advance, and it can be 0.6. The simulated sampling frequency under the preset detection dimension can be the sampling frequency of the data under the preset detection dimension during the simulated rotation, and it can be set manually.

[0113] As an example, this step may include the following steps:

[0114] First step, determine the maximum value among all the simulated dimension data of the preset detection point under the preset detection dimension in the target stage of the simulated rotation as the data reference value.

[0115] Second step, adjust the simulated sampling frequency of the preset detection point under the preset detection dimension within the preset time period in the target stage of the simulated rotation to the maximum to obtain the simulated dimension data sequence at the maximum sampling frequency within the preset time period, denoted as the simulated dimension data sequence of the preset detection point under the preset detection dimension within the preset time period in the target stage of the simulated rotation.

[0116] Among them, the preset time period can be a time period set in advance, and its corresponding duration can be less than the duration corresponding to the target stage. For example, if the duration corresponding to the preset time period is half of the duration corresponding to the target stage, then the preset time period in the target stage can be the first half of the target stage. The maximum sampling frequency can be the maximum sampling frequency that the corresponding sampling device can reach. The simulated dimension data sequence at the maximum sampling frequency can include: the simulated dimension data under the preset detection dimension collected at the maximum sampling frequency. The simulated dimension data sequence can be a time series.

[0117] Third step, according to the above data reference value and the simulated dimension data sequence of the preset detection point under the preset detection dimension within the preset time period in the target stage of the simulated rotation, determining the critical data capture degree of the preset detection point under the preset detection dimension in the target stage of the simulated rotation may include the following sub-steps:

[0118] The first sub-step is to determine the target simulated dimension data sequence as the sequence of simulated dimension data under the preset detection dimension within the preset time period of the target stage of the simulated rotation.

[0119] The second sub-step is to screen out the simulated dimension data greater than or equal to the data reference value from the above-mentioned target simulated dimension data sequence as the reference dimension data.

[0120] The third sub-step is to determine the critical data capture degree of the preset detection point under the preset detection dimension at the target stage of the simulated rotation according to the quantity of the reference dimension data.

[0121] Among them, the quantity of the reference dimension data can have a positive correlation with the critical data capture degree.

[0122] For example, the formula for determining the critical data capture degree of the preset detection point under the preset detection dimension at the target stage of the simulated rotation can be:

[0123] ; where H is the critical data capture degree of the preset detection point under the preset detection dimension at the target stage of the simulated rotation. n is the quantity of the reference dimension data. N is the quantity of the simulated dimension data in the target simulated dimension data sequence.

[0124] It should be noted that to a certain extent, the reference dimension data can represent the abnormal data captured after refined modeling. Among them, the modeling after increasing the sampling frequency is equivalent to refined modeling. When H is larger, it often means that the reference dimension data is relatively more, often means that the abnormal situations captured after refined modeling are relatively more, and often means that the corresponding sampling frequency should be increased when constructing the final horizontal rotation force model of the long-span bridge in the subsequent stage to achieve refined modeling.

[0125] The fourth step is to determine the target sampling frequency of the preset detection point under the preset detection dimension at the target stage of the simulated rotation according to the critical data capture degree of the preset detection point under the preset detection dimension at the target stage of the simulated rotation and the initial simulated sampling frequency.

[0126] Among them, the initial simulated sampling frequency can be the sampling frequency under the preset detection dimension during the simulated rotation process, that is, the sampling frequency without adjustment.

[0127] For example, the formula for determining the target sampling frequency of the preset detection point under the preset detection dimension at the target stage of the simulated rotation can be:

[0128] ; wherein, G is the target sampling frequency of the preset detection point under the preset detection dimension in the target stage of the simulated rotation. h is the initial simulated sampling frequency of the preset detection point under the preset detection dimension in the target stage of the simulated rotation. ZM is the maximum sampling frequency that can be achieved. H is the critical data capture degree of the preset detection point under the preset detection dimension in the target stage of the simulated rotation.

[0129] It should be noted that when H is larger, it often indicates that there are relatively more reference dimension data, often indicating that there are relatively more abnormal situations captured after refined modeling, and often indicating that the corresponding sampling frequency should be increased when constructing the final horizontal rotation force model of the long-span bridge in the subsequent stage to achieve refined modeling. Therefore, G can represent the sampling frequency required after refined modeling of the preset detection point under the preset detection dimension in the target stage of the simulated rotation.

[0130] Step S5, based on the initial horizontal rotation force model of the long-span bridge and the target sampling frequencies of different preset detection points under different preset detection dimensions in different target stages of different simulated rotations, construct the target horizontal rotation force model of the long-span bridge.

[0131] Among them, the target horizontal rotation force model of the long-span bridge is the final constructed horizontal rotation force model of the long-span bridge.

[0132] As an example, the simulated sampling frequencies of different preset detection points under different preset detection dimensions in different target stages of different simulated rotations in the initial horizontal rotation force model of the long-span bridge can be updated to their corresponding target sampling frequencies, and the updated initial horizontal rotation force model of the long-span bridge is determined as the target horizontal rotation force model of the long-span bridge.

[0133] It should be noted that the embodiments of the present invention can divide different stages in the rotation process summary for the historical bridge rotation simulation records, and analyze the key parts existing in different stages based on different stages, so as to understand the corresponding key parts in each stage of the rotation, so as to perform refined modeling on these key parts in the actual modeling process, so as to fully monitor various sudden situations that may be encountered in the actual rotation process, and replace the data measured during the actual rotation with the corresponding data during the simulation, so as to improve the construction of the model.

[0134] Reference Figure 2, based on the same inventive concept as the above method embodiments, the present invention provides a system for constructing a horizontal rotation force model of a long-span bridge based on numerical simulation. The system includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the method for constructing a horizontal rotation force model of a long-span bridge based on numerical simulation, which may specifically include:

[0135] Construct a simulation module 201, which is used to construct an initial model of the horizontal rotation force of a long-span bridge based on the obtained target bridge information, and perform bridge rotation simulation to obtain the bridge simulation records corresponding to each simulated rotation.

[0136] A stage division module 202, which is used to divide each simulated rotation into stages according to the bridge rotation angular velocity in the bridge simulation records corresponding to each simulated rotation, to obtain a preset number of target stages.

[0137] A rotation trend importance determination module 203, which is used to determine the rotation trend importance degree of each preset detection point under each preset detection dimension in each target stage of each simulated rotation according to the simulated dimension data of each preset detection point under each preset detection dimension in each target stage of each simulated rotation.

[0138] A sampling frequency adjustment module 204, which is used to adjust the simulation sampling frequency of the preset detection point under the preset detection dimension in the target stage of the simulated rotation if the rotation trend importance degree of the preset detection point under the preset detection dimension in the target stage of the simulated rotation is greater than a preset importance threshold, to obtain the target sampling frequency of the preset detection point under the preset detection dimension in the target stage of the simulated rotation.

[0139] A model construction module 205, which is used to construct a target model of the horizontal rotation force of a long-span bridge based on the initial model of the horizontal rotation force of a long-span bridge and the target sampling frequencies of different preset detection points under different preset detection dimensions in different target stages of different simulated rotations.

[0140] Figure 3 is a schematic structural diagram of a computer device provided by an embodiment of the present invention. Exemplarily, as Figure 3 shown, the computer device 300 includes: a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and running on the processor 302. Among them, when the processor 302 executes the computer program 303, the computer device can execute any one of the above-described methods for constructing a horizontal rotation force model of a long-span bridge based on numerical simulation.

[0141] Based on the same inventive concept as the above method embodiments, the present invention provides a server, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the device executes any one of the above-mentioned methods for constructing a horizontal rotation force model of a long-span bridge based on numerical simulation.

[0142] Based on the same inventive concept as the above method embodiments, the present invention provides a computer program product, which includes: computer program code, when the computer program code runs on a computer, it causes the computer to execute any one of the above-mentioned methods for constructing a horizontal rotation force model of a long-span bridge based on numerical simulation.

[0143] Based on the same inventive concept as the above method embodiments, the present invention provides a computer-readable storage medium, which stores computer program code, when the computer program code runs on a computer, it causes the computer to execute any one of the above-mentioned methods for constructing a horizontal rotation force model of a long-span bridge based on numerical simulation.

[0144] In summary, when constructing the horizontal rotation force target model of the long-span bridge, the present invention comprehensively considers the influence of different detection points on the bridge rotation, thereby quantifying the important influence degree of the rotation trend of each preset detection point under each preset detection dimension in each target stage of each simulated rotation, realizing the adaptive adjustment of the simulation sampling frequency of different preset detection points under different target stages of different simulated rotations and different preset detection dimensions, thus realizing the adaptive modeling of different parts of the long-span bridge, and further improving the accuracy of the final construction of the horizontal rotation force model of the long-span bridge.

[0145] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for constructing a force model of a large-span bridge horizontal rotation based on numerical simulation, characterized in that: The following steps are involved: Based on the acquired target bridge information, an initial force model of the horizontal rotation of the long-span bridge is constructed, and the bridge rotation simulation is performed to obtain the bridge simulation records corresponding to each simulated rotation; According to the bridge rotation angular velocity in the bridge simulation record corresponding to each simulated rotation, each simulated rotation is divided into stages to obtain a preset number of target stages; Determine the rotation trend importance of each preset detection point in each preset detection dimension in each target stage of each simulated rotation according to the simulated dimension data of each preset detection point in each preset detection dimension in each target stage of each simulated rotation; If the rotation trend important influence degree of the preset detection point in the preset detection dimension at the target stage of the simulated rotation is greater than the preset important influence threshold, the simulation sampling frequency of the preset detection point in the preset detection dimension at the target stage of the simulated rotation is adjusted to obtain the target sampling frequency of the preset detection point in the preset detection dimension at the target stage of the simulated rotation; Based on the initial force model of horizontal rotation of large-span bridges and the target sampling frequencies of different preset detection points under different preset detection dimensions at different target stages of different simulated rotations, a target force model of horizontal rotation of large-span bridges is constructed.

2. According to the method for constructing a force model of a large-span bridge horizontal rotation based on numerical simulation according to claim 1, it is characterized in that: The method divides each simulated rotation into stages according to the bridge rotation angular velocity in the bridge simulation record corresponding to each simulated rotation, and obtains a preset number of target stages, including: Determine any simulated rotation as a marked simulated rotation, and form a marked rotation angular velocity sequence with all bridge rotation angular velocities in the bridge simulation record corresponding to the marked simulated rotation; Determine the maximum value in the marked rotation angular velocity sequence as the marked maximum value, and select the marked maximum value greater than or equal to the marked representative angular velocity from all the marked maximum values ​​as the candidate maximum value, wherein the marked representative angular velocity is the average of all the bridge rotation angular velocities in the marked rotation angular velocity sequence; Every two candidate maxima form a maximum value group, and based on the candidate maximum value distribution between the corresponding positions of the two candidate maxima in each maximum value group in the marked rotation angular velocity sequence, determine the stage division confidence corresponding to each maximum value group; Select the maximum value group with the largest confidence in the corresponding stage division from all the maximum value groups as the target maximum value group; The marked rotation angular velocity sequence is segmented using two candidate maxima in the target maximum group as segmentation points to obtain a preset number of subsequences, and the time period corresponding to each subsequence is recorded as a target stage to obtain a preset number of target stages.

3. The method for constructing a force model of a large-span bridge horizontal rotation based on numerical simulation according to claim 2 is characterized in that: The step of determining the stage division confidence corresponding to each maximum value group based on the candidate maximum value distribution between the corresponding positions of two candidate maximum values ​​in each maximum value group in the marked rotation angular velocity sequence comprises: Determine any maximum value group as a marked maximum value group, and form a reference maximum value sequence with all candidate maximum values ​​of the marked maximum value group between corresponding positions in the marked rotation angular velocity sequence, wherein the reference maximum value sequence includes the marked maximum value group; Based on the absolute value of the difference between adjacent candidate maxima in the reference maximum sequence and the duration between the simulated sampling moments corresponding to two candidate maxima in the marked maximum group, the confidence level of the stage division corresponding to the marked maximum group is determined.

4. The method for constructing a force model of a large-span bridge horizontal rotation based on numerical simulation according to claim 3 is characterized in that: The formula for the confidence level of the stage division corresponding to the marked maximum value group is: ;in, Q is the confidence level of the stage division corresponding to the marked maximum value group; is the normalization function; It is the absolute value function; is the simulated sampling time corresponding to the first candidate maximum in the marked maximum group; is the simulated sampling time corresponding to the second candidate maximum in the marked maximum group; is a natural exponential function; A is the number of candidate maxima in the reference maximum sequence; a is the sequence number of the candidate maximum value in the reference maximum value sequence; is the first a Candidate maxima; is the first a +1 candidate maximum.

5. The method for constructing a force model of a large-span bridge horizontal rotation based on numerical simulation according to claim 1 is characterized in that: The determining, according to the simulated dimension data of each preset detection point in each preset detection dimension in each target stage of each simulated rotation, the rotation trend importance influence degree of each preset detection point in each preset detection dimension in each target stage of each simulated rotation comprises: Determine any preset detection point as a marked detection point, and select a preset detection point that detects the same part as the marked detection point from all preset detection points as a reference detection point; Determine any target phase as a marking phase, determine any preset detection dimension as a marking detection dimension, and determine any simulated rotation as a marking simulated rotation; Determine the range of all simulation dimension data of the mark detection point in the mark detection dimension in the marking stage of the mark simulation rotor as the target difference value of the mark detection point in the mark detection dimension in the marking stage of the mark simulation rotor; Determine the range of all simulated dimensional data of each reference detection point in the mark detection dimension at the mark stage of the mark simulation rotor as a target difference value of each reference detection point in the mark detection dimension at the mark stage of the mark simulation rotor; Determine the monitoring attention degree of the mark detection point in the mark detection dimension at the marking stage of the mark simulation rotor according to the target difference value of the mark detection point in the mark detection dimension at the marking stage of the mark simulation rotor and the target difference values ​​of all reference detection points in the mark detection dimension at the marking stage of the mark simulation rotor; According to the monitoring attention and target difference value of the mark detection point in the mark detection dimension during the marking phase of the mark simulation rotation, the important influence of the rotation trend of the mark detection point in the mark detection dimension during the marking phase of the mark simulation rotation is determined, wherein the monitoring attention and target difference value are positively correlated with the important influence of the rotation trend.

6. The method for constructing a force model of a large-span bridge horizontal rotation based on numerical simulation according to claim 5 is characterized in that: The step of determining the monitoring attention of the mark detection point in the mark detection dimension at the mark stage of the mark simulation rotor according to the target difference value of the mark detection point in the mark detection dimension at the mark stage of the mark simulation rotor and the target difference values ​​of all reference detection points in the mark detection dimension at the mark stage of the mark simulation rotor comprises: Determine the average of the target difference values ​​of all reference detection points in the mark detection dimension at the mark stage of the mark simulation rotor as the reference difference value of the mark detection point in the mark detection dimension at the mark stage of the mark simulation rotor; The ratio of the target difference value and the reference difference value of the mark detection point in the mark detection dimension during the marking phase of the mark simulation rotor is normalized to obtain the monitoring attention degree of the mark detection point in the mark detection dimension during the marking phase of the mark simulation rotor.

7. The method for constructing a force model of a large-span bridge horizontal rotation based on numerical simulation according to claim 1 is characterized in that: The step of adjusting the simulated sampling frequency of the preset detection point in the preset detection dimension at the target stage of the simulated rotation to obtain the target sampling frequency of the preset detection point in the preset detection dimension at the target stage of the simulated rotation includes: Determine the maximum value of all simulated dimension data of the preset detection point in the preset detection dimension at the target stage of the simulated rotation as a data reference value; The simulated sampling frequency of the preset detection point under the preset detection dimension within the preset time period in the target phase of the simulated rotation is adjusted to the maximum, and a simulated dimension data sequence under the maximum sampling frequency within the preset time period is obtained, which is recorded as the simulated dimension data sequence of the preset detection point under the preset detection dimension within the preset time period in the target phase of the simulated rotation; Determine the critical data capture degree of the preset detection point in the preset detection dimension at the target stage of the simulated rotation according to the data reference value and the simulated dimension data sequence of the preset detection point in the preset detection dimension within the preset time period at the target stage of the simulated rotation; According to the critical data capture degree of the preset detection point in the preset detection dimension in the target stage of the simulated rotation and the initial simulation sampling frequency, the target sampling frequency of the preset detection point in the preset detection dimension in the target stage of the simulated rotation is determined.

8. The method for constructing a force model of a large-span bridge horizontal rotation based on numerical simulation according to claim 7 is characterized in that: The step of determining the critical data capture degree of the preset detection point in the preset detection dimension at the target stage of the simulated rotation body according to the data reference value and the simulated dimension data sequence of the preset detection point in the preset detection dimension within the preset time period at the target stage of the simulated rotation body comprises: Determine the simulation dimension data sequence of the preset detection point under the preset detection dimension within the preset time period in the target stage of the simulated rotation as the target simulation dimension data sequence; Filter out simulated dimensional data greater than or equal to a data reference value from the target simulated dimensional data sequence as reference dimensional data; According to the amount of reference dimension data, the critical data capture degree of the preset detection point in the preset detection dimension at the target stage of the simulated rotation is determined, wherein the amount of reference dimension data is positively correlated with the critical data capture degree.

9. The method for constructing a force model of a large-span bridge horizontal rotation based on numerical simulation according to claim 7, characterized in that: The formula corresponding to the target sampling frequency of the preset detection point in the preset detection dimension at the target stage of the simulated rotation is: ;in, G is the target sampling frequency of the preset detection point in the preset detection dimension at the target stage of the simulated rotation; h is the initial simulation sampling frequency of the preset detection point in the preset detection dimension at the target stage of the simulated rotation; ZM is the maximum sampling frequency that can be achieved; H It is the critical data capture degree of the preset detection point under the preset detection dimension at the target stage of the simulated rotation.

10. The method for constructing a force model of a large-span bridge horizontal rotation based on numerical simulation according to claim 1, characterized in that: The method of constructing a target model of horizontal rotation force of a large-span bridge based on the initial model of horizontal rotation force of the large-span bridge and the target sampling frequencies of different preset detection points under different preset detection dimensions at different target stages of different simulated rotations includes: The simulation sampling frequencies of different preset detection points in the initial force model of horizontal rotation of large-span bridges under different preset detection dimensions at different target stages of different simulated rotations are updated to their corresponding target sampling frequencies, and the updated initial force model of horizontal rotation of large-span bridges is determined as the target force model of horizontal rotation of large-span bridges.

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