CN117171922B

CN117171922B - Method and system for parallel correction in steel structure manufacturing

Info

Publication number: CN117171922B
Application number: CN202311446648.7A
Authority: CN
Inventors: 张少荃; 张韵秋
Original assignee: Jiangsu Feierpu Engineering Technology Co ltd
Current assignee: Jiangsu Feierpu Engineering Technology Co ltd
Priority date: 2023-11-02
Filing date: 2023-11-02
Publication date: 2024-01-26
Anticipated expiration: 2043-11-02
Also published as: CN117171922A

Abstract

The invention provides a method and a system for parallel correction in steel structure manufacturing, which relate to the technical field of data processing and comprise the following steps: the method comprises the steps of interactively obtaining structural design information of a steel structure to be detected, obtaining parallel correction constraint, carrying out structural detection to obtain an actually measured structural parameter set, pre-constructing a parallel correction positioning sub-network, carrying out structural deviation analysis to obtain a node sequence to be corrected, including K nodes to be corrected and K structural deviation information identifiers, carrying out correction control parameter analysis to obtain a correction control parameter sequence, carrying out correction region merging analysis to obtain a region sequence to be corrected, carrying out optimization treatment to obtain a parallel correction control sequence, and synchronizing to a correction control module to carry out parallel correction of the steel structure to be detected. The invention solves the technical problems of poor correction precision and accuracy caused by more manual intervention and selection of correction control parameters which usually depend on experience and expertise in the traditional parallel correction method.

Description

Method and system for parallel correction in steel structure manufacturing

Technical Field

The invention relates to the technical field of data processing, in particular to a method and a system for parallel correction in steel structure manufacturing.

Background

In the manufacturing of steel structures, parallel correction is a common process for adjusting the horizontal position and the vertical position in the steel structures, and when the installation of the steel structures has the conditions of inclination, bending, dislocation and the like, the parallel correction technology can achieve the aim of adjusting the structures through pressurization or stretching so as to ensure the precision and the safety of the structures.

The traditional parallel correction method generally requires an operator to carry out correction adjustment, thus being easily influenced by human factors, such as subjective judgment, operation experience and the like, and the manual operation-dependent mode can lead to instability and inconsistency of correction results; in addition, the traditional method often lacks an effective quantitative analysis and optimization method when the correction parameters are determined, and the correction result is not matched with the design requirement or the geometric shape requirement of the structure cannot be met due to the lack of an accurate parameter adjustment mechanism.

There is therefore a need for a new parallel correction method to improve the accuracy, efficiency and consistency of the parallel correction.

Disclosure of Invention

The application aims to solve the technical problems of poor correction precision and accuracy caused by more manual intervention and usually depending on experience and expertise in the selection of correction control parameters in the traditional parallel correction method by providing a method and a system for parallel correction in the steel structure manufacturing.

In view of the above problems, the present application provides a method and a system for parallel correction in manufacturing a steel structure.

In a first aspect of the disclosure, a method for correcting parallelism in manufacturing a steel structure is provided, the method comprising: the method comprises the steps of interactively obtaining structural design information of a steel structure to be detected, and calling to obtain parallel correction constraint based on the structural design information; carrying out structural detection on the steel structure to be detected based on a structural detection module to obtain an actual measurement structural parameter set; pre-constructing a parallel correction positioning sub-network, synchronizing the actually measured structural parameter set and the parallel correction constraint to the parallel correction positioning sub-network for structural deviation analysis, and obtaining a node sequence to be corrected, wherein the node sequence to be corrected comprises K nodes to be corrected, and the K nodes to be corrected have K structural deviation information identifiers; carrying out correction control parameter analysis on the node sequence to be corrected based on the K structural deviation information identifiers to obtain a correction control parameter sequence; carrying out correction region merging analysis based on the correction control parameter sequence to obtain a region sequence to be corrected; carrying out optimization processing on the correction control parameter sequence based on the region sequence to be corrected to obtain a parallel correction control sequence; and synchronizing the parallel correction control sequence to a correction control module, and carrying out parallel correction on the steel structure to be detected based on the correction control module.

In another aspect of the disclosure, there is provided a system for parallel correction in the fabrication of steel structures, the system being used in the above method, the system comprising: the correction constraint acquisition module is used for interactively acquiring structural design information of the steel structure to be detected and calling and acquiring parallel correction constraint based on the structural design information; the structure detection module is used for carrying out structure detection on the steel structure to be detected based on the structure detection module to obtain an actual measurement structure parameter set; the structure deviation analysis module is used for pre-constructing a parallel correction positioning sub-network, synchronizing the actually measured structure parameter set and the parallel correction constraint to the parallel correction positioning sub-network for structure deviation analysis, and obtaining a node sequence to be corrected, wherein the node sequence to be corrected comprises K nodes to be corrected, and the K nodes to be corrected have K structure deviation information identifiers; the parameter analysis module is used for carrying out correction control parameter analysis on the node sequence to be corrected based on the K structural deviation information identifiers to obtain a correction control parameter sequence; the merging analysis module is used for carrying out merging analysis on the correction areas based on the correction control parameter sequence to obtain a to-be-corrected area sequence; the optimization processing module is used for carrying out optimization processing on the correction control parameter sequence based on the region sequence to be corrected to obtain a parallel correction control sequence; and the parallel correction module is used for synchronizing the parallel correction control sequence to the correction control module and carrying out parallel correction on the steel structure to be detected based on the correction control module.

One or more technical solutions provided in the present application have at least the following technical effects or advantages:

the structure detection module is used for accurately detecting the structure of the steel structure to be detected, and an actually measured structure parameter set is obtained, so that accurate basic data is provided for subsequent parallel correction; the structural deviation analysis accuracy of the node sequence to be corrected is effectively improved by pre-constructing a parallel correction positioning sub-network and synchronizing the actually measured structural parameters and the parallel correction constraint to the network for structural deviation analysis; based on the structural deviation information identification, the node sequence to be corrected is subjected to correction control parameter analysis and optimization treatment, so that the correction process is more accurate and efficient; and determining a region sequence to be corrected by analyzing the correction control parameter sequence, and carrying out merging analysis so as to sequentially process the region to be corrected in the subsequent correction operation, and carrying out optimization processing of correction control parameters by utilizing the region sequence to be corrected and the correction control parameter sequence, thereby obtaining a parallel correction control sequence for realizing the parallel correction operation of the steel structure. In summary, the parallel correction method in the steel structure manufacturing achieves the technical effects of improving the parallel correction precision, reducing the manual intervention and improving the correction efficiency and accuracy by introducing the steps of structural deviation analysis, correction control parameter optimization, region merging analysis to be corrected and the like.

The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.

Drawings

Fig. 1 is a schematic flow chart of a method for correcting parallelism in manufacturing a steel structure according to an embodiment of the present application;

fig. 2 is a schematic diagram of a system for parallel correction in manufacturing a steel structure according to an embodiment of the present application.

Reference numerals illustrate: the system comprises a correction constraint acquisition module 10, a structure detection module 20, a structure deviation analysis module 30, a parameter analysis module 40, a combination analysis module 50, an optimization processing module 60 and a parallel correction module 70.

Detailed Description

The embodiment of the application solves the technical problems of poor correction precision and accuracy caused by more manual intervention and usually depending on experience and expertise in the selection of correction control parameters in the traditional parallel correction method by providing the parallel correction method in the steel structure manufacturing.

Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.

Example 1

As shown in fig. 1, an embodiment of the present application provides a method for correcting parallelism in manufacturing a steel structure, where the method includes:

the method comprises the steps of interactively obtaining structural design information of a steel structure to be detected, and calling to obtain parallel correction constraint based on the structural design information;

through interaction with related personnel, such as designers, engineers and the like, structural design information of the steel structure to be detected is obtained, and the information comprises structural dimensions, component connection modes, design loads and the like. Based on the structural design information obtained, constraints for parallelism correction are obtained, which may be predefined specification requirements, standard dimensional parameters, or other known parallelism considerations. According to actual conditions and requirements, parallel correction constraints applicable to the steel structure to be detected are determined, for example, structural design information and calling results are analyzed and screened, so that the selected constraints can effectively meet the requirements of parallel correction.

Carrying out structural detection on the steel structure to be detected based on a structural detection module to obtain an actual measurement structural parameter set;

the structure detection module is a specially designed software program for detecting the structure of the steel structure to be detected, and comprises the technologies of image processing, signal processing, sensors and the like so as to acquire the actual state and parameters of the structure. The steel structure to be detected is provided as input to a structure detection module, which analyzes and processes the steel structure to be detected by utilizing the received input data to obtain actually measured structural parameters, wherein the parameters relate to the size, shape, geometric characteristics and the like of the structure. The actual measurement structure parameter set of the steel structure to be detected is obtained through the processing of the structure detection module, and the parameters are calculated according to the actually observed structure data and reflect the actual state of the steel structure to be detected.

Pre-constructing a parallel correction positioning sub-network, synchronizing the actually measured structural parameter set and the parallel correction constraint to the parallel correction positioning sub-network for structural deviation analysis, and obtaining a node sequence to be corrected, wherein the node sequence to be corrected comprises K nodes to be corrected, and the K nodes to be corrected have K structural deviation information identifiers;

a parallel correction positioning sub-network is pre-constructed, and is a calculation model which comprises a deviation index calculation module and a deviation correction positioning module and is used for analyzing the structural deviation of a steel structure to be detected and determining nodes needing correction.

And taking the actually measured structure parameter set and the parallel correction constraint as input data and simultaneously providing the input data to a parallel correction positioning sub-network, wherein the actually measured structure parameter set comprises actual structure parameters obtained from structure detection, and the parallel correction constraint is a constraint condition obtained according to structure design information. The parallel correction positioning sub-network utilizes the input actual measurement structure parameter set and parallel correction constraint to identify the deviation condition of the structure through analysis and calculation, and compares and evaluates the actual measurement parameter with the constraint condition by using a specific algorithm to determine which nodes need to be corrected.

According to the analysis result, the parallel correction positioning sub-network generates a node sequence to be corrected, wherein the node sequence comprises K nodes to be corrected, and the nodes are determined to be key positions needing to perform parallel correction operation. For each node to be corrected, a corresponding structural deviation information identifier is used to represent the specific deviation condition, and the identifiers indicate the direction, the size or other relevant information of the deviation for further correction control parameter analysis.

Further, the pre-constructing parallel correction positioning sub-network further comprises:

the parallel correction positioning sub-network comprises a deviation index calculation module and a deviation correction positioning module;

constructing a deviation index calculation function, wherein the deviation index calculation function is as follows:

;

wherein,weight for the ith measured structural parameter, < +.>Is the included angle between the ith measured structural parameter and the standard size parameter, +.>Is the steel structure deviation index;

synchronizing the deviation index calculation function to the deviation index calculation module;

presetting deviation correction screening constraint, synchronizing the deviation correction screening constraint to the deviation correction positioning module, and completing construction of the parallel correction positioning sub-network.

The deviation index calculation module and the deviation correction positioning module are two main components of a parallel correction positioning sub-network, and the two modules cooperate together to realize the functions of deviation analysis and correction positioning of the steel structure.

The deviation index calculating module is responsible for calculating the deviation index of the steel structure, and calculates the deviation index corresponding to each measured structural parameter by using a pre-constructed deviation index calculating function based on the included angle between the measured structural parameter and the standard size parameter and the weight.

The deviation correction positioning module is used for correcting and positioning the deviation according to the deviation indexes, deviation correction screening constraints are preset in the module and used for judging which deviation indexes meet the requirements, namely within an allowable range, and then the deviation indexes are judged according to the screening constraints to determine structural deviation information meeting the requirements. Through the deviation correcting and positioning module, structural deviation information meeting requirements can be associated with corresponding nodes, a node sequence to be corrected is obtained, and the nodes to be corrected have structural deviation information marks and are used for subsequent correction operation.

The deviation index calculating function is used for calculating the deviation index of the steel structure Wherein->The weight of the ith measured structural parameter is represented, the importance of the parameter in calculating the deviation index is represented, and higher weight means that the contribution of the parameter to the deviation index is larger; />The included angle between the ith measured structural parameter and the standard size parameter is represented, the included angle is used for measuring the degree of difference between the measured structural parameter and the standard size parameter, if the measured structural parameter and the standard size parameter are completely consistent, the included angle is 0, and if the measured structural parameter and the standard size parameter are different, the included angle value is increased; the final deviation index P ranges between 0 and 1, with a value closer to 0 indicating a smaller deviation of the steel structure and a value closer to 1 indicating a larger deviation of the steel structure.

In summary, the deviation index calculation function calculates the deviation index of the steel structure according to the weight of the actually measured structural parameter and the included angle with the standard dimension parameter, considers the importance of each parameter and the contribution to the deviation, and synthesizes the importance and the contribution to the deviation into an index for measuring the overall deviation degree of the steel structure.

Synchronizing the deviation index calculation function to the deviation index calculation module may be understood as embedding the calculation function into the deviation index calculation module, so that the module may be used to calculate the deviation index in a subsequent structural deviation analysis.

According to actual requirements and specific scenes, deviation correcting screening constraints are defined, wherein the constraints comprise parameter ranges, error limits or other limiting conditions and are used for screening structural deviation information meeting requirements. And associating a preset deviation correction screening constraint with the deviation correction positioning module to ensure that the module can judge and screen according to the constraint.

In the deviation correcting and positioning module, the structural deviation information of the nodes to be corrected can be evaluated by using preset deviation correcting and screening constraints, whether preset constraint conditions are met or not is judged by checking deviation indexes of each node, and according to the results of the constraint conditions, the nodes which need to be corrected are determined so as to meet the required precision requirements.

Through presetting deviation correction screening constraint and synchronizing the deviation correction screening constraint to a deviation correction positioning module, the construction process of the whole parallel correction positioning sub-network is completed, so that the network can be used for carrying out structural deviation analysis and node positioning, and the node sequence to be corrected can be obtained according to the preset constraint condition.

Further, synchronizing the actually measured structural parameter set and the parallel correction constraint to the parallel correction positioning sub-network for structural deviation analysis to obtain a node sequence to be corrected, and further comprising:

The parallel correction constraints comprise N groups of construction size constraints, and each group of construction size constraints comprises i standard size parameters;

the actually measured structure parameter set comprises N groups of actually measured structure data, each group of actually measured structure parameters comprises i actually measured structure parameters, and the parallel correction constraint is mapped in association with the actually measured structure parameter set;

synchronizing the actually measured structural parameter set and the parallel correction constraint to the deviation index calculation module of the parallel correction positioning sub-network to obtain N structural deviation indexes;

and judging whether the N structural deviation indexes meet the deviation correction screening constraint or not at the deviation correction positioning module to obtain the node sequence to be corrected.

The N sets of build dimensional constraints are used to limit the dimensions, e.g., length, width, height, etc., of different portions of the steel structure, each set of constraints including i standard dimensional parameters defining desired build dimensional values.

For example, if there are n=3 sets of construction size constraints, and each set of constraints includes i=2 standard size parameters, then the possible constraints are as follows: building a size constraint 1: the length is 10 meters, and the width is 5 meters; building size constraint 2: the length is 8 meters, and the height is 6 meters; building a size constraint 3: the width is 3 m and the height is 4 m.

These constraints will be used for subsequent bias analysis and bias correction positioning to ensure that the steel structure meets the intended dimensional requirements.

The actually measured structural parameters are the parameter information of the steel structure obtained through actual measurement or detection, and the parameters cover actual values of different aspects such as length, width, height and the like. Assuming that n=3 sets of measured structural data are present and each set of data includes i=2 measured structural parameters, the possible measured structural parameter sets are as follows: measured structural data 1: the length is 9.8 meters, and the width is 4.5 meters; measured structural data 2: the length is 8.5 meters, and the height is 6.2 meters; actual measurement structure data 3: the width is 3.3 m and the height is 4.1 m.

There is an associative mapping between the parallel correction constraints and the set of measured structural parameters, meaning that each measured structural data is associated with a corresponding constraint, the associative mapping being defined by the designer or system settings, which are used in the analysis and correction process to determine which constraints apply to a particular measured structural data. Through the association mapping of the actually measured structure parameter set and the parallel correction constraint, analysis and judgment can be performed according to specific actually measured structure data and corresponding constraint conditions in the subsequent deviation analysis and correction positioning process.

And transmitting the actually measured structural parameter set and the parallel correction constraint as input to a deviation index calculation module. In the deviation index calculation module, a function is calculated according to the deviation indexSubstituting the parameter values in the actual measurement structure parameter set into the function to calculate, and obtaining a structure deviation index value through calculating the function for each group of actual measurement structure data.

N structural deviation indexes and corresponding actually measured structural parameter sets and parallel correction constraints are obtained, and whether the structural deviation indexes meet corresponding deviation correction screening constraints or not is checked one by one according to each structural deviation index by comparing the structural deviation indexes with constraint conditions.

And for the structural deviation index meeting deviation correction screening constraint, taking the corresponding actually measured structural parameter as a node to be corrected and adding the node to be corrected into a node sequence to be corrected. And continuing to traverse the rest structure deviation indexes, and repeating the steps until all the structure deviation indexes are processed. Finally, a node sequence to be corrected is obtained, wherein the node sequence to be corrected comprises nodes meeting deviation correction screening constraint, and the nodes need to be further corrected so as to enable the steel structure to meet the expected precision and size requirements.

Carrying out correction control parameter analysis on the node sequence to be corrected based on the K structural deviation information identifiers to obtain a correction control parameter sequence;

further, performing correction control parameter analysis on the node sequence to be corrected based on the K structural deviation information identifiers to obtain a correction control parameter sequence, and further including:

obtaining a plurality of groups of history control parameter groups which are the same as the steel structure type to be detected according to the steel structure to be detected, wherein each group of history control parameter groups consists of standard size parameters, actually measured size parameters and correction parameter records;

constructing a correction parameter generation sub-network based on a cyclic neural network, and performing supervised training of the correction parameter generation sub-network by adopting the plurality of groups of history control parameters;

obtaining K groups of construction size constraints on the parallel correction constraint mapping call based on the K structural deviation information identifiers;

synchronizing the K structural deviation information identifiers and the K groups of construction size constraints to the correction parameter generation sub-network to obtain K groups of correction control parameters;

and carrying out mapping sequencing of the K groups of correction control parameters based on the node sequence to be corrected to obtain the correction control parameter sequence.

And the model of the steel structure to be detected is defined through related design documents, construction drawings, equipment specifications and the like. According to the model of the steel structure to be detected, a database or a history recording system for storing the history control parameters is inquired, and the database or the system contains the control parameter data which are recorded in the past and are the same as the model of the steel structure to be detected.

Extracting a plurality of groups of history control parameter groups which are the same as the type of the steel structure to be detected from a database or a history recording system, wherein each group of history control parameter groups comprises standard dimension parameters, actual measurement dimension parameters and correction parameter records, and the standard dimension parameters refer to the dimension parameters of the steel structure which are expected or required by design, and the parameters are usually defined by designers or specifications; the actually measured dimensional parameters refer to the dimensional parameters of the steel structure obtained by actual measurement, and the parameters are obtained in the actual construction or detection process; correction parameter records refer to correction parameter information that has been applied to corresponding measured dimensional parameters that were recorded by previous corrective actions. And (3) properly organizing and storing the extracted multiple groups of history control parameter sets to ensure that the information of each group of history control parameter sets is complete and is related to the steel structure to be detected.

And using the previously extracted multiple sets of historical control parameters, wherein the standard size parameters and the actually measured size parameters in each historical control parameter set are used as input, the corresponding correction parameter record is used as target data, and a correction parameter generation sub-network capable of receiving the input data and outputting the correction parameters is constructed based on a cyclic neural network (RNN).

And training the correction parameter generation sub-network by using a plurality of groups of historical control parameters as training data in a supervised learning mode, providing input data for the network for forward propagation, comparing the input data with target data at an output layer, calculating loss, and updating the weight and bias of the network through a backward propagation algorithm to minimize a loss function.

The above steps are repeated until a predetermined convergence condition or training number is reached, and in each iteration, the network parameters are continuously adjusted to improve their performance and accuracy. Through the training process, the sub-network can learn the relation between the input data and the target correction parameters, so that corresponding correction control parameters can be generated for the nodes to be corrected in subsequent application.

And using the K structural deviation information identifiers as input, calling parallel correction constraint mapping to calculate so as to obtain construction size constraints corresponding to each structural deviation information identifier, and repeatedly executing the steps for each structural deviation information identifier so as to obtain K groups of construction size constraints, wherein the constraints are used for limiting the size range of the node to be corrected so as to ensure that the corrected node meets the expected constraint condition.

And transmitting the obtained K structural deviation information identifications and the corresponding K groups of construction size constraints as input data to a correction parameter generation sub-network, wherein the sub-network processes by utilizing the input structural deviation information identifications and the construction size constraints, and obtains K groups of correction control parameters according to the output of the sub-network, wherein each group of correction control parameters is associated with the corresponding structural deviation information identifications and the construction size constraints.

The nodes of the sequence of nodes to be rectified need to be subjected to accurate rectification operations.

And mapping each node to be corrected with the K groups of correction control parameters, and sorting the K groups of correction control parameters according to the size, the priority or other indexes of the structural deviation information in the mapping relation, such as descending sorting. And forming a correction control parameter sequence according to the sequencing result and the priority order, wherein each node to be corrected is allocated to the corresponding correction control parameter so as to implement correction operation.

Carrying out correction region merging analysis based on the correction control parameter sequence to obtain a region sequence to be corrected;

further, performing correction region merging analysis based on the correction control parameter sequence to obtain a region sequence to be corrected, and further including:

Constructing a standard twin model of the steel structure to be detected according to the structural design information;

adopting the actually measured structure parameter set to carry out adaptability adjustment of the standard twin model so as to obtain a target twin model;

a first group of correction control parameters are obtained based on the correction control parameter sequence call, and first structure deviation information is obtained on the basis of the first group of correction control parameters in the node sequence call to be corrected;

performing deviation node positioning according to the first structure deviation information to obtain a first deviation node;

performing parallel correction fitting of the target twin model at the first deviation node based on the first set of correction control parameters to obtain a first correction association region;

and so on, obtaining K correction association areas of the K groups of correction control parameters mapped to the correction control parameter sequence;

and carrying out union processing on the K correction association areas to obtain the area sequence to be corrected.

Obtaining structural design information of a steel structure to be detected, creating a blank model in a virtual environment by using Computer Aided Design (CAD) software or other modeling tools, and drawing a basic frame of the steel structure to be detected in a three-dimensional space according to the size and the geometric shape provided by the design information; gradually adding each component into the model according to the specific component type and the connection mode provided in the structural design information; and adjusting the model according to the size and geometric requirements specified in the structural design information so as to ensure that the model is consistent with the design requirements.

Through the steps, the standard twin model of the steel structure to be detected can be created according to the structural design information, the model is accurate copy of the actual structure, the model is presented in a computer environment, and a foundation is provided for subsequent correction operation and analysis.

The measured structural parameter set is obtained from the actual measurement or monitoring. Applying the actually measured structure parameter set to a standard twin model, namely applying the actually measured information such as size, shape change and the like to the corresponding part of the model, and enabling the actually measured information to be more consistent with the actually measured data by modifying the geometric shape, size parameters or other attributes of the model; according to material characteristic data provided by an actually measured structure parameter set, updating the material properties of a target twin model, which involves adjusting parameters such as elastic modulus, yield strength, material nonlinear behavior and the like so as to more accurately simulate the behavior of a steel structure to be detected under the action of external force; according to the deformation, stress and other data in the actual measurement structure parameter set, the standard twin model is adaptively adjusted, including introducing displacement, strain or other deformation into the model, and adjusting the material properties of the relevant part, so that the model is more consistent with the actual measurement data.

The model considers the measured data and the material characteristics, and can reflect the performance influences of deformation, stress and the like of the steel structure under the action of external force.

A first set of remediation control parameters is selected from the sequence of remediation control parameters, applied to each node in the sequence of nodes to be remedied, by calculating or performing a particular remediation operation to alter the position, attitude or other attribute of the node. After the first set of correction control parameters is applied, the structural deviation information of each node is obtained by measuring, analyzing or simulating the sequence of the nodes to be corrected, and the deviation information describes the deformation, displacement, geometric errors and the like of the nodes.

And calculating, comparing or setting a threshold value for the deviation information of each node according to the first structure deviation information so as to judge which nodes have obvious deviation, and further determining the positions of the deviation nodes. And determining a node with significant deviation as a first deviation node according to the deviation node positioning result, wherein the node represents a specific position where the correction operation needs to be performed.

Based on the first deviation node position determined in the previous step, the corrective action is focused on that node, and a parallel corrective fit is performed on the first deviation node using a first set of corrective control parameters, which means that the target twin model is moved or rotated in a parallel direction to align with the deviation node of the actual structure. Based on the results of the parallel correction fit, a first correction association region is determined, which represents a set of nodes associated with the first deviation node by the correction operation, typically a portion adjacent to or having common characteristics with the node to be corrected.

Reviewing a previous sequence of corrective control parameters, including a plurality of sets of corrective control parameters, arranged in a particular order to perform corrective actions on the steel structure, for each set of corrective control parameters, selecting a next set of corrective control parameters from the sequence of corrective control parameters, applying the currently selected corrective control parameters to a previously determined corrective correlation area, determining a current corrective correlation area based on the results of applying the corrective control parameters, the area representing a set of nodes that need to be further adjusted, storing the currently obtained corrective correlation area for subsequent analysis and corrective action, and repeating the steps until all sets of corrective control parameters have been traversed. Through the loop iteration, K correction association areas are obtained, each area corresponding to a set of correction control parameters in the correction control parameter sequence, the areas describing the set of nodes that need to be adjusted in different correction phases.

For K correction-associated regions, all regions are merged into one large region by calculating the boundaries, overlapping portions, etc. of all regions. In the union processing, checking whether the intersection exists between the adjacent areas in the area sequence to be corrected, comparing the areas one by one, if the intersection exists between the rear area and the front area, referring the rear area to the front, and forming the final area sequence to be corrected according to the processing, wherein the sequence contains all nodes to be corrected, and ensuring that the rear area has no intersection with the front area.

Carrying out optimization processing on the correction control parameter sequence based on the region sequence to be corrected to obtain a parallel correction control sequence;

further, the optimizing process of the correction control parameter sequence is performed based on the region sequence to be corrected, so as to obtain a parallel correction control sequence, and the method further includes:

calling to obtain a first region sequence to be corrected based on the region sequence to be corrected;

traversing the correction control parameter sequence according to the region composition of the first region sequence to be corrected to obtain a first correction control parameter set;

presetting a parameter adjustment step length, optimizing the control parameters of the first region sequence to be corrected by taking the first correction control parameter set as an optimizing starting point and taking the preset parameter adjustment step length as a constraint, and obtaining a first parallel correction control parameter;

and similarly, the region sequence to be corrected is subjected to optimization processing of the correction control parameter sequence, and the parallel correction control sequence is obtained.

The first region sequence to be corrected is selected from the region sequences to be corrected, and can be selected randomly or a specific region sequence to be corrected can be selected according to the requirement.

The region composition of the first region to be corrected, i.e. the nodes involved in the region, including the number of the nodes, the position information or other characteristics related to the correction operation, is analyzed. Traversing the correction control parameter sequence, checking each group of correction control parameters one by one, judging whether each group of correction control parameters is matched with the region constitution of the first region to be corrected, and if a certain group of correction control parameters is matched with the region constitution of the first region to be corrected, classifying the group of correction control parameters into a first correction control parameter set. Finally, a first set of correction control parameters is obtained, which contains correction control parameters that match the region composition of the first region to be corrected.

According to actual conditions and requirements, a parameter adjustment step length is preset, and the parameter adjustment amplitude in each optimization iteration is determined by the step length. And taking the first correction control parameter set as a starting point of optimizing, namely taking the first correction control parameter set as an initial parameter value, and optimizing the control parameters of the first region sequence to be corrected by using a constraint optimizing algorithm such as a gradient descent method or a genetic algorithm, wherein the optimization aims at finding the optimal correction control parameters so that the first region to be corrected can be aligned with a target twin model.

When the control parameter is optimized, the range of parameter adjustment is limited based on the preset parameter adjustment step length so as to avoid excessive or insufficient parameter change, and the parameter change is ensured not to exceed the set step length in each optimization iteration. Finally, a first parallel correction control parameter is obtained, which is the control parameter obtained after optimization treatment and can align the first area to be corrected with the target twin model and is used for correcting the steel structure.

And repeating the control parameter optimizing operation for each region to be corrected to obtain corresponding parallel correction control parameters after optimization processing, and adding the obtained parallel correction control parameters into a parallel correction control sequence so as to sequentially perform correction operation. Finally, all the areas to be corrected are subjected to an optimization process and corresponding parallel correction control parameters are generated, and the parameters form a parallel correction control sequence according to the sequence of the areas to be corrected, wherein the sequence describes the parallel correction control parameters required by the sequential execution of the correction operation.

And synchronizing the parallel correction control sequence to a correction control module, and carrying out parallel correction on the steel structure to be detected based on the correction control module.

The parallel correction control sequence describes the parallel correction operation sequence, mode and related parameters of the area to be corrected. The correction control module is a specially designed software program for implementing parallel correction of the steel structure to be detected, and comprises an executing mechanism, a sensor, a control algorithm and other components for executing correction operation and monitoring correction process.

The parallel orthotic control sequence is synchronized to the orthotic control module, which means that the orthotic instructions, parameters, and sequence in the sequence are transferred to the orthotic control module to perform the orthotic operation in a predetermined order and manner. Based on the correction control module, the steel structure to be detected is subjected to parallel correction, and the correction control module executes corresponding operations, such as adjusting supporting equipment, applying force or performing other forms of adjustment, according to instructions and parameters in the parallel correction control sequence, so as to realize parallel correction of the structure. By transmitting the corrective instruction, parameters and sequence, the corrective control module can accurately perform corrective operations, ensuring that the structure is properly adjusted and modified during processing.

Further, the method further comprises:

presetting a parallel correction monitoring window;

carrying out multi-period extraction on the node sequence to be corrected by taking the parallel correction monitoring window as a reference to obtain M groups of historical correction node sequences;

presetting structural deviation constraint, node position deviation constraint and system error constraint;

performing aggregation processing on the M groups of history correction node sequences based on the structural deviation constraint and the node position deviation constraint, and screening the aggregation processing results based on the system error constraint to obtain H system error nodes, wherein each system error node has a structural deviation interval;

and optimizing the processing parameters of the production line where the steel structure to be detected is located based on the H system error nodes and the structure deviation interval.

According to specific requirements and design requirements, a time range needing to be monitored is selected in the whole parallel correction process and used as a monitoring window, wherein the time range can be the duration of the whole correction process or the time period of a specific stage or a key step. By presetting a monitoring window for parallel correction, the structural change can be monitored in real time in the parallel correction process, which is helpful for finding any abnormality in time, and corresponding measures are taken to ensure the accuracy and safety of correction.

The multiple periods are divided in the preset time range of the parallel correction monitoring window according to the preset time range, and the periods can be fixed periods with equal intervals or can be divided according to specific events. And in each period, extracting corresponding node data from the node sequence to be corrected, wherein the node data comprises information of recording the position, displacement, deformation and the like of the node, so that the subsequent analysis and comparison can be performed. And repeating the process until the whole preset parallel correction monitoring window is covered, so that M groups of historical correction node sequences can be obtained, and each group of sequences represents node data in one period. By performing multi-cycle extraction, historical data of the node sequence to be corrected in different time periods can be obtained, and the historical correction node sequence is used for subsequent analysis, comparison or evaluation of the effect and stability of the correction process.

The structural deviation constraint defines the allowed structural deviation range of the steel structure to be detected in the parallel correction process, and the structural deviation range comprises limitations in aspects of morphological change, displacement, angle change and the like, for example, a maximum allowed structural variable or displacement value is preset, and the corrected structure still meets the design requirement.

The node position deviation constraint prescribes a position deviation range of each node in the correction process, which relates to the difference between the nodes and the target positions thereof and the relative position requirement between the nodes, and by setting the maximum allowable position deviation value, the corrected node distribution can be ensured to meet the precision requirement.

The system error constraint is used for limiting the error or uncertainty of the correction system, which includes consideration of the precision of a mechanical device, the measurement error of a sensor and the like, and by setting the upper limit of the system error, the influence of the system error on the correction result can be reduced.

If two steel structures with the same design information have defects of relatively close positions and relatively close deviation amounts, the two defects are considered to be caused by control parameter defects of the same processing technology, and the non-random errors can be avoided by carrying out process parameter adjustment; the system error constraint is the constraint that the same error occurs frequently in different steel structures, and exceeding the constraint represents the system error.

By presetting these constraints, it is possible to ensure that deviations and errors in the parallel correction process are within acceptable ranges, which helps control the accuracy and precision of the correction operation to meet structural design requirements and improve the reliability and stability of the correction effect.

For each periodic node sequence in the M groups of history correction node sequences, alignment and standardization operations are carried out, for example, least square fitting is adopted to realize the alignment of the sequences, so that the node position data of different periods have the same starting point and coordinate system. And carrying out aggregation processing on the aligned node sequences, wherein the aggregation processing comprises calculation of an average value, a median value or a weighted average value and the like so as to obtain an aggregation position of each node in the whole history correction process, and the aggregated position can represent the average performance of the node in the whole correction process.

Comparing the difference between the position of each node and the target position according to the preset structure deviation constraint, and removing the nodes exceeding the set limit, so as to screen out the positions of the aggregation nodes exceeding the allowable deviation range; the remaining nodes are further filtered according to preset node position deviation constraints, which involves considering the relative positions between the nodes, ensuring that they meet specific spatial relationship requirements.

Finally, the position result of the aggregation node after the structure deviation constraint and the node position deviation constraint screening is obtained, the position data represent the average performance of the system in the whole history correction process, and preset constraint conditions are considered.

And aiming at the node position result after the aggregation processing, calculating the systematic error between each node and the target position by comparing the difference between the node position and the target position. According to preset system error constraint, nodes meeting constraint requirements are screened out, nodes exceeding the system error constraint are excluded, and only nodes meeting the constraint are reserved. For each node that has undergone systematic error screening, a structural deviation interval is calculated, which is achieved by considering the range of variation of the node position and the structural deviation value, and the structural deviation interval of each systematic error node is calculated, which reflects the structural deviation range.

And determining the final H system error nodes according to the results of screening and structural deviation interval calculation, wherein each system error node is provided with a structural deviation interval and is used for indicating the structural deviation range.

By analyzing the positions of the H system error nodes and the corresponding structure deviation intervals, specific system error modes and influence factors are identified, wherein the specific system error modes and influence factors comprise different process links, equipment settings, material characteristics and the like. From the analysis of the systematic errors, machining parameters that may be correlated to the systematic errors are determined, including machining parameters (e.g., cutting speed, feed speed, tool selection, etc.), process control parameters (e.g., temperature, pressure, spray amount, etc.), and the like.

Based on the determined processing parameters, experimental schemes are designed, for example, the relation between the processing parameters and the system errors is explored through actual processing, numerical simulation and the like, and the influence of the processing parameters and the system errors is further evaluated. By analyzing experimental data, an optimization algorithm is used to find the optimal parameter setting capable of reducing the system error, and the optimal processing parameter combination is determined.

The optimized processing parameters are applied to actual production, verification and adjustment are carried out, and whether the optimized processing parameters can effectively reduce the system errors and improve the product quality is confirmed by monitoring and evaluating the change condition of the system error nodes. This helps to improve product accuracy and consistency and optimizes the process flow to meet design requirements.

In summary, the method and system for parallel correction in steel structure manufacturing provided by the embodiments of the present application have the following technical effects:

1. the structure detection module is used for accurately detecting the structure of the steel structure to be detected, and an actually measured structure parameter set is obtained, so that accurate basic data is provided for subsequent parallel correction;

2. the structural deviation analysis accuracy of the node sequence to be corrected is effectively improved by pre-constructing a parallel correction positioning sub-network and synchronizing the actually measured structural parameters and the parallel correction constraint to the network for structural deviation analysis;

3. based on the structural deviation information identification, the node sequence to be corrected is subjected to correction control parameter analysis and optimization treatment, so that the correction process is more accurate and efficient;

4. and determining a region sequence to be corrected by analyzing the correction control parameter sequence, and carrying out merging analysis so as to sequentially process the region to be corrected in the subsequent correction operation, and carrying out optimization processing of correction control parameters by utilizing the region sequence to be corrected and the correction control parameter sequence, thereby obtaining a parallel correction control sequence for realizing the parallel correction operation of the steel structure.

In summary, the parallel correction method in the steel structure manufacturing achieves the technical effects of improving the parallel correction precision, reducing the manual intervention and improving the correction efficiency and accuracy by introducing the steps of structural deviation analysis, correction control parameter optimization, region merging analysis to be corrected and the like.

Example two

Based on the same inventive concept as the method of parallel correction in the fabrication of a steel structure in the foregoing embodiment, as shown in fig. 2, the present application provides a system of parallel correction in the fabrication of a steel structure, the system comprising:

the correction constraint acquisition module 10 is used for interactively acquiring structural design information of the steel structure to be detected, and calling to acquire parallel correction constraint based on the structural design information;

the structure detection module 20 is used for carrying out structure detection on the steel structure to be detected based on the structure detection module, and an actual measurement structure parameter set is obtained;

the structural deviation analysis module 30 is configured to pre-construct a parallel correction positioning sub-network, synchronize the actually measured structural parameter set and the parallel correction constraint to the parallel correction positioning sub-network for structural deviation analysis, and obtain a node sequence to be corrected, where the node sequence to be corrected includes K nodes to be corrected, and the K nodes to be corrected have K structural deviation information identifiers;

the parameter analysis module 40 is configured to perform correction control parameter analysis on the node sequence to be corrected based on the K structural deviation information identifiers, so as to obtain a correction control parameter sequence;

The merging analysis module 50 is used for carrying out merging analysis on the correction areas based on the correction control parameter sequence to obtain a to-be-corrected area sequence;

the optimization processing module 60 is configured to perform optimization processing on the correction control parameter sequence based on the region sequence to be corrected, so as to obtain a parallel correction control sequence;

and the parallel correction module 70 is used for synchronizing the parallel correction control sequence to a correction control module, and carrying out parallel correction of the steel structure to be detected based on the correction control module.

Further, the system also includes a process parameter optimization module to perform the following operational steps:

presetting a parallel correction monitoring window;

Further, the system also comprises a parallel correction positioning sub-network construction module for executing the following operation steps:

;

Further, the system further comprises a node sequence acquisition module to be rectified, so as to execute the following operation steps:

Further, the system further comprises a correction control parameter sequence acquisition module for executing the following operation steps:

Further, the system further comprises a region sequence acquisition module to be rectified, so as to execute the following operation steps:

Further, the system also comprises a parallel correction control sequence acquisition module for executing the following operation steps:

The foregoing detailed description of a method for correcting parallelism in manufacturing a steel structure will be clear to those skilled in the art, and the description of the apparatus disclosed in this embodiment is relatively simple, and the relevant points refer to the description of the method section.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for parallel correction in the fabrication of a steel structure, the method comprising:

2. The method of claim 1, wherein the method further comprises:

presetting a parallel correction monitoring window;

3. The method of claim 1, wherein the pre-building of the parallel correction positioning sub-network further comprises:

；

4. The method of claim 3, wherein synchronizing the measured structural parameter set and the parallel correction constraint to the parallel correction positioning sub-network for structural deviation analysis, obtaining a sequence of nodes to be corrected, further comprises:

5. The method of claim 4, wherein performing a correction control parameter analysis on the sequence of nodes to be corrected based on the K structural deviation information identifiers, obtaining a correction control parameter sequence, further comprises:

6. The method of claim 5, wherein performing a correction region merge analysis based on the correction control parameter sequence to obtain a sequence of regions to be corrected, further comprising:

7. The method of claim 6, wherein the optimizing the sequence of corrective control parameters based on the sequence of regions to be corrected to obtain a parallel corrective control sequence, further comprising:

8. A system for parallel correction in steel construction, characterized in that it is adapted to perform a method for parallel correction in steel construction according to any one of claims 1-7, comprising:

the correction constraint acquisition module is used for interactively acquiring structural design information of the steel structure to be detected and calling and acquiring parallel correction constraint based on the structural design information;

the structure detection module is used for carrying out structure detection on the steel structure to be detected based on the structure detection module to obtain an actual measurement structure parameter set;

the structure deviation analysis module is used for pre-constructing a parallel correction positioning sub-network, synchronizing the actually measured structure parameter set and the parallel correction constraint to the parallel correction positioning sub-network for structure deviation analysis, and obtaining a node sequence to be corrected, wherein the node sequence to be corrected comprises K nodes to be corrected, and the K nodes to be corrected have K structure deviation information identifiers;

the parameter analysis module is used for carrying out correction control parameter analysis on the node sequence to be corrected based on the K structural deviation information identifiers to obtain a correction control parameter sequence;

The merging analysis module is used for carrying out merging analysis on the correction areas based on the correction control parameter sequence to obtain a to-be-corrected area sequence;

the optimization processing module is used for carrying out optimization processing on the correction control parameter sequence based on the region sequence to be corrected to obtain a parallel correction control sequence;

and the parallel correction module is used for synchronizing the parallel correction control sequence to the correction control module and carrying out parallel correction on the steel structure to be detected based on the correction control module.