Steel structure construction optimization method and device combined with digital simulation pre-assembly

Abstract

The invention discloses a steel structure construction optimization method and device combined with digital simulation pre-splicing, and relates to the related field of data processing, and the method comprises the steps: carrying out the fixed-point monitoring of construction deviation in the construction of a steel structure based on the Internet of Things, and recording a construction deviation data set; importing the construction deviation data set into a simulation pre-assembling unit, and performing construction risk prediction of a predetermined node according to the accumulated deviation data set to obtain a risk prediction value; if the risk prediction value exceeds a preset risk threshold value, calling a simulation pre-assembling unit, performing construction correction analysis based on the accumulated deviation data set, and determining an optimal correction scheme; and before the nodes are preset, centralized construction correction is carried out according to the optimal correction scheme. The technical problem that the construction efficiency is influenced by low deviation treatment efficiency caused by decentralized treatment of construction deviation in existing steel structure construction is solved, and the technical effect that the construction efficiency is improved by intensively treating the deviation of the current construction stage and improving the deviation treatment efficiency is achieved.

Description

Technical Field

[0001] The present application relates to data processing related fields, and in particular to a steel structure construction optimization method and device combined with digital simulation pre-assembly. Background Art

[0002] In the construction of large-scale steel structure projects, precision control and quality assurance are crucial. Traditional steel structure construction methods often rely on the experience and instant judgment of on-site personnel to adjust deviations during construction. During the construction process, frequent manual adjustments are not only inefficient, but also easily distract the attention of construction personnel, increasing the possibility of errors. In addition, decentralized handling of deviations will result in a lot of time wasted on repeated inspections and adjustments, seriously affecting the construction progress.

[0003] In the current relevant technologies, there is a technical problem in the decentralized processing of construction deviations in steel structure construction, which leads to low efficiency in deviation processing and thus affects the construction efficiency. Summary of the invention

[0004] The present application provides a steel structure construction optimization method and device combined with digital simulation pre-assembly, adopts the Internet of Things technology to monitor the construction deviations in the steel structure construction in real time, uses the simulation pre-assembly unit to predict the construction risk of the predetermined nodes, and performs construction correction analysis when the risk prediction value exceeds the predetermined risk threshold. Before the predetermined node, centralized construction correction is performed according to the optimal correction plan, and other technical means, so that construction personnel can efficiently and centrally handle deviations and avoid wasting time, thereby achieving the technical effect of improving the deviation handling efficiency by centrally handling the deviations in the current construction stage, thereby improving the construction efficiency.

[0005] This application provides a steel structure construction optimization method combined with digital simulation pre-assembly, including:

[0006] Based on the Internet of Things, the construction deviations in the steel structure construction are monitored at fixed points, and the construction deviation data set is recorded; the construction deviation data set is imported into the simulation pre-assembly unit, and the construction risk of the predetermined node is predicted according to the accumulated deviation data set to obtain the risk prediction value; if the risk prediction value exceeds the predetermined risk threshold, the simulation pre-assembly unit is called, and the construction correction analysis is performed based on the accumulated deviation data set to determine the optimal correction plan; before the predetermined node, centralized construction correction is performed according to the optimal correction plan.

[0007] In a possible implementation, the following processing is performed:

[0008] The construction deviation at least includes installation position deviation, installation angle deviation, preload force deviation and welding deviation, and the construction deviation data includes position coordinates.

[0009] In a possible implementation, the construction deviation data set is recorded and the following processing is performed:

[0010] If the construction deviation does not meet the predetermined deviation threshold, the construction deviation is corrected in real time according to a predetermined correction scheme; if the construction deviation meets the predetermined deviation threshold, no real-time correction is performed and the construction deviation data is recorded to obtain the construction deviation data set.

[0011] In a possible implementation, the following processing is performed:

[0012] The accumulated deviation data set includes construction deviation data sets of multiple monitoring nodes and is cleared after each centralized construction correction.

[0013] In a possible implementation, the construction risk of a predetermined node is predicted based on the accumulated deviation data set, the risk prediction value is obtained, and the following processing is performed:

[0014] According to the current construction progress and the planned construction plan, the historical construction logs are retrieved to obtain the associated construction data set, wherein the associated construction data set includes a historical construction deviation set and a historical construction risk coefficient set, and the historical construction risk coefficient is determined based on the construction quality impact, construction delay duration, rework degree and material loss assessment; the simulation pre-assembly unit is trained with the historical construction deviation set and the historical construction risk coefficient set, and the simulation pre-assembly unit is used to perform construction risk analysis of the predetermined node according to the accumulated deviation data set, and a first risk coefficient is output; based on the building big data, the construction risk of the predetermined node is predicted according to the accumulated deviation data set, and a second risk coefficient is output, which is weighted in combination with the first risk coefficient to obtain the risk prediction value.

[0015] In a possible implementation, the second risk coefficient is outputted, and the following processing is performed:

[0016] Based on the construction big data, a similarity association search is performed according to the predetermined construction plan, the current construction progress and the cumulative deviation data set to obtain a sample construction event set that meets the predetermined similarity threshold; based on the predetermined nodes, the proportion of risky construction events under historical nodes in the sample construction event set is counted to obtain the risky construction ratio; the risky construction ratio is input into a predetermined ratio-coefficient comparison table to match and obtain the second risk coefficient.

[0017] In a possible implementation, a construction correction analysis is performed based on the accumulated deviation data set to determine an optimal correction solution, and the following processing is performed:

[0018] For the purpose of meeting the expected construction quality, multiple expected correction schemes are determined based on the analysis of the cumulative deviation data set; the simulation pre-assembly unit is called to execute simulation correction of the multiple expected correction schemes, and the simulation correction consumption is recorded, wherein the simulation correction consumption includes correction time, correction materials and labor resources; consumption of the multiple expected correction schemes is evaluated according to the correction time, correction materials and labor resources, and the expected correction scheme with the smallest consumption value is selected as the optimal correction scheme.

[0019] The present application also provides a steel structure construction optimization device combined with digital simulation pre-assembly, including:

[0020] A construction deviation data set recording module, the construction deviation data set recording module is used to monitor the construction deviation in the steel structure construction based on the Internet of Things, and record the construction deviation data set; a construction risk prediction module, the construction risk prediction module is used to import the construction deviation data set into the simulation pre-assembly unit, and perform construction risk prediction of the predetermined node according to the accumulated deviation data set to obtain the risk prediction value; a construction correction analysis module, the construction correction analysis module is used to call the simulation pre-assembly unit if the risk prediction value exceeds the predetermined risk threshold, and perform construction correction analysis based on the accumulated deviation data set to determine the optimal correction plan; a centralized construction correction module, the centralized construction correction module is used to perform centralized construction correction according to the optimal correction plan before the predetermined node.

[0021] The steel structure construction optimization method and device combined with digital simulation pre-assembly proposed in this application first monitors the construction deviation in the steel structure construction based on the Internet of Things, records the construction deviation data set, and then imports the construction deviation data set into the simulation pre-assembly unit. The construction risk of the predetermined node is predicted according to the cumulative deviation data set to obtain the risk prediction value. If the risk prediction value exceeds the predetermined risk threshold, the simulation pre-assembly unit is called to perform construction correction analysis based on the cumulative deviation data set to determine the optimal correction plan. Finally, before the predetermined node, centralized construction correction is performed according to the optimal correction plan, thereby achieving the technical effect of improving the deviation processing efficiency by centrally processing the deviations in the current construction stage, thereby improving the construction efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the device according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0023] Figure 1 A schematic flow chart of a steel structure construction optimization method combined with digital simulation pre-assembly provided in an embodiment of the present application.

[0024] Figure 2 A schematic diagram of the structure of a steel structure construction optimization device combined with digital simulation pre-assembly provided in an embodiment of the present application.

[0025] Explanation of reference numerals: construction deviation data set recording module 10 , construction risk prediction module 20 , construction correction analysis module 30 , centralized construction correction module 40 . DETAILED DESCRIPTION

[0026] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0027] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0028] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0029] The present application embodiment provides a steel structure construction optimization method combined with digital simulation pre-assembly, such as Figure 1 As shown, the method includes:

[0030] Step S100, based on the Internet of Things fixed-point monitoring of construction deviations in steel structure construction, records the construction deviation data set. Specifically, high-precision Internet of Things sensors are installed at key locations (such as steel structure connection points, support points, etc.) on the construction site. These sensors are used to monitor various parameters in the construction process in real time, such as position offset, angle deviation, stress change, etc. The sensor collects various data in the construction process in real time and transmits them to the data center via a wireless network or wired method. After receiving the raw data, the data center performs preprocessing steps such as filtering, denoising, and calibration to improve the accuracy and reliability of the data. The preprocessed data is compared with the preset standard value or design value to identify the construction deviation (during the construction process, the actual construction status is inconsistent with the design requirements due to improper operation, material differences, environmental changes, etc.), and these deviation data are recorded as a construction deviation data set, including information such as deviation type, size, location, time, etc.

[0031] In a possible implementation, step S100 further includes step S110, the construction deviation includes at least installation position deviation, installation angle deviation, preload force deviation and welding deviation, and the construction deviation data includes position coordinates.

[0032] Specifically, the installation position deviation is the deviation between the actual position and the designed position of the steel structure component during the installation process. By deploying high-precision position sensors (such as GPS, laser rangefinders or high-precision encoders) at key installation points of the steel structure, the installation position data of the steel structure components is monitored and recorded in real time, and compared with the theoretical position on the design drawings, the position deviation is calculated, and the specific value of the deviation, the time of occurrence, the component number involved and the position coordinates are recorded. The installation angle deviation is the deviation between the actual installation angle and the designed angle of the steel structure component during installation. Angle measuring instruments (such as total stations, inclination sensors) are used to monitor the installation angle of the steel structure component, compare the actual installation angle with the designed angle, calculate the angle deviation, and record the deviation data, including the deviation angle, direction, time of occurrence, component number and position coordinates. Preload deviation is the deviation between the actual preload applied and the preload required by the design in fasteners such as bolt connections. Preload sensors are installed at locations such as bolts and connectors that need to be preloaded. The preload value is monitored and recorded in real time. It is compared with the preload required by the design to calculate the preload deviation. The specific value, occurrence time, component number and position coordinates of the deviation are recorded. Welding deviation is the deviation between the actual size, shape, position and other parameters of the weld and the design requirements or welding standards during the welding process. For welded connections, ultrasonic, X-ray or other non-destructive testing technologies are used to detect welds, evaluate the size, shape, position and other parameters of the weld, compare them with welding standards or design requirements, identify welding deviations, and record the type of deviation (such as size deviation, position deviation, crack, etc.), severity, location, component number and welding process parameters. For each deviation, it is necessary to accurately record the location coordinates of its occurrence for precise positioning in subsequent analysis and correction. The location coordinates are represented by a three-dimensional coordinate system, including coordinate values ​​in the three directions of X, Y and Z. This implementation method provides reliable data support for subsequent construction quality control by comprehensively and accurately monitoring and recording construction deviations, helping to ensure the stability and reliability of construction quality.

[0033] In a possible implementation, the construction deviation data set is recorded, and step S100 further includes step S120, if the construction deviation does not meet the predetermined deviation threshold, the construction deviation is corrected in real time according to the predetermined correction scheme. Specifically, after the IoT sensor detects the construction deviation, it is first determined whether the deviation meets (i.e., is greater than or equal to) the predetermined deviation threshold. The predetermined deviation threshold is set according to construction specifications, design requirements and historical experience, and is used to determine the severity of the deviation and whether it needs to be processed immediately. If the deviation meets the predetermined deviation threshold, it means that the deviation is more serious and immediate measures need to be taken to correct it. At this time, the most suitable real-time correction scheme is selected according to the preset correction scheme library. The construction personnel quickly correct the deviation according to the selected real-time correction scheme. After the correction is completed, the IoT sensor is used again to verify the correction effect to ensure that the deviation has been effectively controlled within an acceptable range. Step S130, if the construction deviation meets the predetermined deviation threshold, no real-time correction is performed and the construction deviation data is recorded to obtain the construction deviation data set. Specifically, it is also first determined whether the construction deviation does not meet (i.e., is less than) the predetermined deviation threshold. If the deviation does not meet the predetermined deviation threshold, it means that the deviation is small and has limited impact on the current construction, so real-time correction is not performed immediately. However, for subsequent analysis and prediction, the deviation data, including deviation type, size, location coordinates, occurrence time, etc., are recorded in detail and included in the construction deviation data set. After recording the deviation data, the IoT sensor continues to monitor the location and related locations to track the changes in the deviation and take further measures when necessary. This implementation method sets a predetermined deviation threshold to prioritize those serious deviations that have a greater impact on construction quality. For smaller deviations, immediate correction will increase construction costs and time, and the effect is not obvious. Therefore, by recording these deviation data and continuously monitoring them, unified processing can be performed in subsequent stages. This method of centrally processing accumulated deviations and correcting serious deviations in real time reduces the number of interruptions and rework during the construction process and improves construction efficiency.

[0034] Step S200, import the construction deviation data set into the simulation pre-assembly unit, predict the construction risk of the predetermined node according to the cumulative deviation data set, and obtain the risk prediction value. Specifically, the recorded construction deviation data set is imported into the digital simulation pre-assembly unit, and the simulation pre-assembly unit is a calculation unit that uses computer simulation technology to pre-assemble and simulate the construction of steel structures in a virtual environment to predict and evaluate the problems and risks that may be encountered in actual construction. The simulation pre-assembly unit performs a cumulative analysis on the imported deviation data set, that is, analyzes the accumulation of deviations over time or construction progress according to the cumulative effect of the deviations to evaluate its impact on the overall construction quality and progress. Based on the cumulative deviation analysis results, the construction risks of the predetermined nodes are predicted, and the prediction content includes but is not limited to structural stability risks, function realization risks, cost overrun risks, etc. The predicted risks are quantified and output in the form of risk prediction values ​​for subsequent judgment and decision-making.

[0035] In a possible implementation, step S200 further includes step S210, wherein the cumulative deviation data set includes construction deviation data sets of multiple monitoring nodes, and is cleared after each centralized construction correction. Specifically, during the steel structure construction process, the IoT sensor continuously monitors the construction deviation of each key node, and records these deviation data in real time into the construction deviation data set, which includes construction deviation information of multiple monitoring nodes, such as position deviation, angle deviation, preload deviation and welding quality. The collected construction deviation data are integrated and classified according to the monitoring nodes to form multiple sub-data sets, each of which corresponds to a construction deviation record of a monitoring node. For each monitoring node, its cumulative deviation is calculated, and the cumulative deviation refers to the sum or weighted sum of all deviation values ​​of the node from the start of construction to the current time point. Based on the cumulative deviation data set, the pre-assembly unit is simulated to predict the construction risk of the predetermined node, and the corresponding risk prediction value is calculated. After each centralized construction correction, the cumulative deviation data set is updated, that is, for the monitoring node whose deviation has been resolved through centralized construction correction, its cumulative deviation value is cleared or reset to the corrected new value. This implementation method comprehensively and accurately evaluates the accumulated deviations in the construction process through the accumulated deviation data set, providing reliable data support for construction risk prediction. The construction deviation data set is cleared after each centralized construction correction, ensuring that the accumulated deviation data set always reflects the actual situation of the current construction status, thereby providing accurate data support for subsequent risk prediction and construction correction.

[0036] In a possible implementation, the construction risk of a predetermined node is predicted according to the accumulated deviation data set to obtain the risk prediction value. Step S200 further includes step S220, retrieving the historical construction log according to the current construction progress and the predetermined construction plan to obtain the associated construction data set, wherein the associated construction data set includes the historical construction deviation set and the historical construction risk coefficient set, and the historical construction risk coefficient is determined based on the construction quality impact, construction delay duration, rework degree and material loss assessment. Specifically, according to the current construction progress and the predetermined construction plan, the time range, construction type, structural part and other conditions of the historical construction logs to be retrieved are determined. In the database or storage system, the above conditions are used to retrieve the relevant historical construction logs, which contain detailed construction records, such as construction deviation, construction risk, construction quality, construction delay, rework degree and material loss information. From the retrieved historical construction logs, the historical construction deviation set and the historical construction risk coefficient set related to the current construction situation are extracted. Among them, the historical construction deviation set records the deviation data under similar construction conditions, and the historical construction risk coefficient set is obtained based on these deviation data and their impact assessment on construction quality, delay, rework and material loss. Step S230, the simulation pre-assembly unit is trained with the historical construction deviation set and the historical construction risk coefficient set, and the simulation pre-assembly unit is used to perform construction risk analysis of the predetermined node according to the cumulative deviation data set, and a first risk coefficient is output. Specifically, the extracted historical construction deviation set and historical construction risk coefficient set are preprocessed, including data cleaning, format unification, normalization, etc., to ensure data quality and consistency. The simulation pre-assembly unit is trained using the preprocessed historical data. The simulation pre-assembly unit is constructed based on digital twin simulation, which can simulate various deviation conditions in the actual construction process and predict its impact on subsequent construction. Through training, the simulation pre-assembly unit learns the complex relationship between historical construction deviations and construction risks. The current cumulative deviation data set is input into the trained simulation pre-assembly unit to perform construction risk analysis of the predetermined node. The simulation pre-assembly unit outputs a first risk coefficient based on the input deviation data and combined with its internal learning model. The coefficient represents the possibility of construction risk occurring at the predetermined node under given deviation conditions. Step S240, based on the building big data, the construction risk of the predetermined node is predicted according to the cumulative deviation data set, and the second risk coefficient is output, and the risk prediction value is obtained by weighting in combination with the first risk coefficient. Specifically, based on the building big data platform, historical project data and industry standards similar to the current construction situation are queried. These data cover a wide range of construction scenarios and deviation situations, providing rich reference information for construction risk prediction. Using the relevant information in the building big data, combined with the current cumulative deviation data set, the construction risk of the predetermined node is predicted, and the second risk coefficient is output.According to the prediction accuracy and reliability of the first risk factor and the second risk factor, the corresponding weights are set, and the two risk factors are weighted and fused to obtain the final risk prediction value. This implementation method combines historical construction data and building big data, comprehensively considers the impact of multiple factors on construction risks, and improves the accuracy and reliability of construction risk prediction.

[0037] In a possible implementation, the second risk coefficient is output, and step S240 further includes step S241, based on the building big data, similarity association retrieval is performed according to the predetermined construction plan, current construction progress and cumulative deviation data set to obtain a sample construction event set that meets the predetermined similarity threshold. Specifically, a large amount of historical construction data is collected from the building big data platform, and these data include but are not limited to the progress information of historical construction projects, construction plans, construction deviation records, construction risk events and their consequences, etc. The collected data is cleaned and sorted, and noise data is removed to ensure the accuracy and consistency of the data. The construction plan, construction progress and cumulative deviation data set of the current project are compared with the historical data using the cosine similarity algorithm to calculate the similarity. According to the similarity calculation result, the sample construction event set that meets the predetermined similarity threshold is screened out, and these samples have a high similarity and can represent the situation that may occur in the current project in the future. Step S242, based on the predetermined node, the proportion of risk construction events under the historical node in the sample construction event set is counted to obtain the risk construction ratio. Specifically, in the screened sample construction event set, the historical node corresponding to the predetermined node of the current project is found. Traverse the construction events under these historical nodes and identify the risky construction events, such as construction delays, substandard quality, safety accidents, etc. Count the proportion of risky construction events in all construction events under historical nodes to obtain the risky construction ratio, which reflects the possibility of risky events occurring at similar nodes under the historical conditions. Step S243, input the risky construction ratio into a predetermined ratio-coefficient comparison table, and match to obtain the second risk coefficient. Specifically, according to the obtained risky construction ratio, search in a predetermined ratio-coefficient comparison table, which is constructed based on historical experience and expert knowledge and is used to convert the risky construction ratio into a specific risk coefficient. Find the risk coefficient that matches the risky construction ratio in the comparison table and output it as the second risk coefficient. This risk coefficient represents the possibility of construction risks occurring at a predetermined node under the current construction conditions. This implementation method improves the accuracy of obtaining the second risk coefficient by performing similar association retrieval and matching the predetermined ratio-coefficient comparison table in building big data.

[0038] Step S300, if the risk prediction value exceeds the predetermined risk threshold, call the simulation pre-assembly unit, perform construction correction analysis based on the cumulative deviation data set, and determine the optimal correction plan. Specifically, compare the obtained risk prediction value with the predetermined risk threshold to determine whether the risk is acceptable. If the risk prediction value exceeds the threshold, the simulation pre-assembly unit is automatically triggered to perform construction correction analysis. The simulation pre-assembly unit generates multiple possible correction plans based on the cumulative deviation data set, conducts a comprehensive evaluation of the generated multiple correction plans, and determines the optimal correction plan by considering factors such as correction effect, cost, and construction difficulty.

[0039] In a possible implementation, construction correction analysis is performed based on the cumulative deviation data set to determine the optimal correction scheme. Step S300 further includes step S310, in order to meet the expected construction quality, multiple expected correction schemes are determined based on the cumulative deviation data set analysis. Specifically, the cumulative deviation data set is analyzed in detail, including identifying the type of deviation (such as dimensional deviation, position deviation, etc.), degree and their possible impact on subsequent construction. According to the analysis results, combined with construction experience, technical specifications and design requirements, multiple possible expected correction schemes are generated. A preliminary evaluation is performed on each generated scheme, and schemes that are obviously unfeasible or do not meet safety and quality standards are excluded, and schemes with potential feasibility are retained to enter the next step. Step S320, calling the simulation pre-assembly unit, performing simulation correction of the multiple expected correction schemes, and recording the simulation correction consumption, wherein the simulation correction consumption includes correction time, correction materials and labor resources. Specifically, calling the simulation pre-assembly unit, setting the simulation environment according to the actual situation of the current construction project (such as structural form, material properties, etc.). In the simulation environment, the multiple expected correction schemes determined in step S310 are executed one by one. During the simulation correction process, the correction time, the type and quantity of correction materials required, and the required labor resources of each scheme are recorded. Step S330, the consumption of the multiple expected correction schemes is evaluated according to the correction time, correction materials and labor resources, and the expected correction scheme with the smallest consumption value is selected as the optimal correction scheme. Specifically, a comprehensive evaluation is performed on each expected correction scheme based on the recorded consumption data. The evaluation criteria include but are not limited to correction time (reflecting efficiency), correction material cost (reflecting economy) and labor resource consumption (reflecting labor demand). According to the specific needs and priorities of the project, weights are assigned to different evaluation criteria, and each scheme is weighted and scored to calculate the total consumption value. Compare the total consumption values ​​of each scheme, and select the scheme with the smallest consumption value as the optimal correction scheme. This scheme achieves the best balance in efficiency, economy and feasibility. This implementation method simulates multiple schemes and performs consumption evaluation by simulating pre-assembly units, reduces the uncertainty caused by subjective judgment, and improves the scientific nature of the determination of the optimal correction scheme.

[0040] Step S400, before the predetermined node, centralized construction correction is performed according to the optimal correction scheme. Specifically, according to the optimal correction scheme, construction materials and human resources are organized to make necessary construction preparations. Before the predetermined node, centralized construction correction is performed according to the optimal correction scheme, that is, within a specific time period, concentrated resources and forces are uniformly corrected for deviations in construction to improve correction efficiency and accuracy. After the correction is completed, the correction effect is verified to ensure that the construction deviation is effectively controlled and the construction quality and progress meet expectations. The embodiment of the present application adopts real-time monitoring of construction deviations in steel structure construction through Internet of Things technology, uses simulated pre-assembly units to predict construction risks of predetermined nodes, performs construction correction analysis when the risk prediction value exceeds the predetermined risk threshold, and before the predetermined node, centralized construction correction and other technical means are performed according to the optimal correction scheme to centrally handle deviations in the current construction stage, reduce the number of reworks, and enable construction personnel to efficiently and centrally handle deviations, avoid time waste, and achieve the technical effect of improving deviation handling efficiency and thus improving construction efficiency by centrally handling deviations in the current construction stage.

[0041] In the above, refer to Figure 1 The steel structure construction optimization method combined with digital simulation pre-assembly according to an embodiment of the present invention is described in detail. Figure 2 A steel structure construction optimization device combined with digital simulation pre-assembly according to an embodiment of the present invention is described.

[0042] The steel structure construction optimization device combined with digital simulation pre-assembly according to the embodiment of the present invention is used to solve the technical problem that the existing steel structure construction has decentralized processing of construction deviations, resulting in low deviation processing efficiency, which in turn affects the construction efficiency, and achieves the technical effect of improving the deviation processing efficiency by centrally processing the deviations in the current construction stage, thereby improving the construction efficiency. The steel structure construction optimization device combined with digital simulation pre-assembly includes: a construction deviation data set recording module 10, a construction risk prediction module 20, a construction correction analysis module 30, and a centralized construction correction module 40.

[0043] The construction deviation data set recording module 10 is used to monitor the construction deviation in the steel structure construction based on the Internet of Things, and record the construction deviation data set; the construction risk prediction module 20 is used to import the construction deviation data set into the simulation pre-assembly unit, and perform construction risk prediction of the predetermined node according to the cumulative deviation data set to obtain the risk prediction value; the construction correction analysis module 30 is used to call the simulation pre-assembly unit if the risk prediction value exceeds the predetermined risk threshold, and perform construction correction analysis based on the cumulative deviation data set to determine the optimal correction plan; the centralized construction correction module 40 is used to perform centralized construction correction according to the optimal correction plan before the predetermined node.

[0044] The specific configuration of the construction deviation data set recording module 10 will be described in detail below. As described above, the construction deviation data set recording module 10 may further include: a construction deviation construction unit for the construction deviation to include at least installation position deviation, installation angle deviation, preload deviation and welding deviation, and the construction deviation data includes position coordinates.

[0045] Among them, the construction deviation data set is recorded, and the construction deviation data set recording module 10 may further include: a real-time correction unit is used to correct the construction deviation in real time according to a predetermined correction scheme if the construction deviation does not meet the predetermined deviation threshold; a construction deviation data recording unit is used to not perform real-time correction and record the construction deviation data if the construction deviation meets the predetermined deviation threshold, so as to obtain the construction deviation data set.

[0046] The specific configuration of the construction risk prediction module 20 will be described in detail below. As described above, the construction risk prediction module 20 may further include: a cumulative deviation data set construction unit for the cumulative deviation data set to include construction deviation data sets of multiple monitoring nodes and to be cleared after each centralized construction correction.

[0047] Among them, the construction risk of the predetermined node is predicted according to the cumulative deviation data set to obtain the risk prediction value. The construction risk prediction module 20 may further include: an associated construction data set acquisition unit is used to retrieve the historical construction log according to the current construction progress and the predetermined construction plan, and obtain the associated construction data set, wherein the associated construction data set includes a historical construction deviation set and a historical construction risk coefficient set, and the historical construction risk coefficient is determined based on the construction quality impact, construction delay duration, rework degree and material loss assessment; a first risk coefficient output unit is used to train the simulation pre-assembly unit with the historical construction deviation set and the historical construction risk coefficient set, and use the simulation pre-assembly unit to perform construction risk analysis of the predetermined node according to the cumulative deviation data set, and output a first risk coefficient; a risk prediction value acquisition unit is used to perform construction risk prediction of the predetermined node according to the cumulative deviation data set based on the building big data, output a second risk coefficient, and obtain the risk prediction value by weighting in combination with the first risk coefficient.

[0048] Among them, the second risk coefficient is output, and the risk prediction value acquisition unit can further include: a similar association retrieval subunit is used to perform similar association retrieval based on the building big data, according to the predetermined construction plan, the current construction progress and the cumulative deviation data set, to obtain a sample construction event set that meets the predetermined similarity threshold; a risk construction proportion acquisition subunit is used to, based on the predetermined node, count the proportion of risk construction events under the historical nodes in the sample construction event set to obtain the risk construction proportion; the second risk coefficient matching subunit is used to input the risk construction proportion into a predetermined proportion-coefficient comparison table, and match it to obtain the second risk coefficient.

[0049] The specific configuration of the construction correction analysis module 30 will be described in detail below. As described above, the construction correction analysis is performed based on the cumulative deviation data set to determine the optimal correction scheme. The construction correction analysis module 30 may further include: a plurality of expected correction scheme determination units for the purpose of meeting the expected construction quality, and for determining a plurality of expected correction schemes based on the analysis of the cumulative deviation data set; a simulation correction consumption recording unit for calling the simulation pre-assembly unit, performing simulation correction of the plurality of expected correction schemes, and recording simulation correction consumption, wherein the simulation correction consumption includes correction time, correction materials, and labor resources; a consumption evaluation unit for performing consumption evaluation on the plurality of expected correction schemes according to the correction time, correction materials, and labor resources, and selecting the expected correction scheme with the smallest consumption value as the optimal correction scheme.

[0050] The steel structure construction optimization device combined with digital simulation pre-assembly provided in an embodiment of the present invention can execute the steel structure construction optimization method combined with digital simulation pre-assembly provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0051] Although the present application makes various references to certain modules in the device according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0052] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A steel structure construction optimization method combined with digital simulation pre-assembly, characterized in that: include: Based on the Internet of Things, fixed-point monitoring of construction deviations in steel structure construction is carried out to record construction deviation data sets; Importing the construction deviation data set into the simulation pre-assembly unit, performing construction risk prediction of a predetermined node according to the accumulated deviation data set, and obtaining a risk prediction value; If the risk prediction value exceeds a predetermined risk threshold, the simulation pre-assembly unit is called to perform construction correction analysis based on the accumulated deviation data set to determine an optimal correction plan; Before the predetermined node, centralized construction correction is performed according to the optimal correction plan.

2. The steel structure construction optimization method combined with digital simulation pre-assembly according to claim 1 is characterized in that: The construction deviation at least includes installation position deviation, installation angle deviation, preload force deviation and welding deviation, and the construction deviation data includes position coordinates.

3. The steel structure construction optimization method combined with digital simulation pre-assembly according to claim 2 is characterized in that: Record construction deviation data sets, including: If the construction deviation does not meet the predetermined deviation threshold, the construction deviation is corrected in real time according to a predetermined correction scheme; If the construction deviation meets the predetermined deviation threshold, no real-time correction is performed and the construction deviation data is recorded to obtain the construction deviation data set.

4. The steel structure construction optimization method combined with digital simulation pre-assembly according to claim 1 is characterized in that: The accumulated deviation data set includes construction deviation data sets of multiple monitoring nodes, and is cleared after each centralized construction correction.

5. The steel structure construction optimization method combined with digital simulation pre-assembly according to claim 1 is characterized in that: According to the cumulative deviation data set, the construction risk of the predetermined node is predicted to obtain the risk prediction value, including: Retrieve historical construction logs according to the current construction progress and the planned construction plan to obtain a related construction data set, wherein the related construction data set includes a historical construction deviation set and a historical construction risk coefficient set, and the historical construction risk coefficient is determined based on the construction quality impact, construction delay duration, rework degree, and material loss assessment; The simulation pre-assembly unit is trained with the historical construction deviation set and the historical construction risk coefficient set, and the simulation pre-assembly unit is used to perform construction risk analysis of a predetermined node according to the accumulated deviation data set, and output a first risk coefficient; Based on the building big data, the construction risk of the predetermined node is predicted according to the cumulative deviation data set, and a second risk coefficient is output, which is weighted in combination with the first risk coefficient to obtain the risk prediction value.

6. The steel structure construction optimization method combined with digital simulation pre-assembly according to claim 5 is characterized in that: Output the second risk factor, including: Based on the building big data, similarity association retrieval is performed according to the predetermined construction plan, the current construction progress and the accumulated deviation data set to obtain a sample construction event set that meets a predetermined similarity threshold; Based on the predetermined nodes, the proportion of risky construction events at the historical nodes in the sample construction event set is counted to obtain the risky construction ratio; The risk construction ratio is input into a predetermined ratio-coefficient comparison table, and the second risk coefficient is obtained by matching.

7. The steel structure construction optimization method combined with digital simulation pre-assembly according to claim 1 is characterized in that: Perform construction correction analysis based on the accumulated deviation data set to determine the optimal correction solution, including: For the purpose of satisfying the expected construction quality, determining a plurality of expected correction schemes based on the analysis of the accumulated deviation data set; Calling the simulation pre-assembly unit to perform simulation correction of the multiple expected correction schemes, and recording the simulation correction consumption, wherein the simulation correction consumption includes correction time, correction materials and labor resources; The consumption of the multiple expected correction schemes is evaluated according to the correction time, correction materials and labor resources, and the expected correction scheme with the smallest consumption value is selected as the optimal correction scheme.

8. A steel structure construction optimization device combined with digital simulation pre-assembly, characterized in that: The device is used to implement the steel structure construction optimization method combined with digital simulation pre-assembly according to any one of claims 1 to 7, and the device comprises: A construction deviation data set recording module, wherein the construction deviation data set recording module is used to monitor the construction deviation in the steel structure construction based on the Internet of Things fixed point, and record the construction deviation data set; A construction risk prediction module, wherein the construction risk prediction module is used to import the construction deviation data set into a simulation pre-assembly unit, perform construction risk prediction of a predetermined node according to the accumulated deviation data set, and obtain a risk prediction value; A construction correction analysis module, wherein if the risk prediction value exceeds a predetermined risk threshold, the construction correction analysis module calls the simulation pre-assembly unit, performs construction correction analysis based on the accumulated deviation data set, and determines an optimal correction solution; A centralized construction correction module is used to perform centralized construction correction according to the optimal correction plan before the predetermined node.

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