A digital steel bar whole-process management method based on BIM and internet of things

Abstract

The present application relates to the technical field of steel bar process management, and relates to a digital steel bar whole-process management method based on BIM and the Internet of Things. The present application counts the size data and spatial position information of each steel bar based on the target engineering BIM design model, determines the processing priority of each steel bar in combination with the installation serial number of different concrete members, and generates a steel bar processing task sheet. In the steel bar processing process, the actual processing parameters and waste data of the steel bar are collected, compared with the steel bar processing task sheet, the processing data deviation value and processing loss rate are calculated, the finished steel bar is coded after being qualified, the preliminary stacking area is determined based on the representative structure floor of different preset stacking areas and the spatial position information of each finished steel bar, the target stacking area is corrected in combination with the adjacent bundled steel bar set, and the finished steel bar is divided into a steel bar bundling combination to determine the stacking position in combination with the stacking space contour, thereby improving the collaborative efficiency and quality level of the steel bar whole-process management.

Description

Technical Field

[0001] This invention relates to the field of steel reinforcement process management technology, and specifically to a digital and intelligent steel reinforcement full-process management method based on BIM and the Internet of Things. Background Technology

[0002] As the skeleton material of concrete structures, the processing precision, stacking order, and circulation efficiency of steel reinforcement directly affect the construction quality and progress of engineering projects. In large-scale construction projects, the number of steel reinforcement components is enormous, their specifications are diverse, and they need to be precisely matched with the assembly sequence of concrete components. The entire process management of steel reinforcement, from processing to stacking to on-site binding, involves multiple aspects such as dimensional accuracy control, processing loss optimization, spatial planning, and coordination of process connections. How to achieve intelligent and collaborative management of the entire steel reinforcement process is a technical problem to improve the level of lean management in construction.

[0003] In the prior art, Chinese Patent Publication No. CN117670202A discloses a method, device and electronic equipment for intelligent processing and management of reinforcing bars. The method obtains the reinforcing bar demand plan, uses BIM technology to reproduce the reinforcing bars and generate a reinforcing bar material list, and controls the reinforcing bar processing equipment to process the reinforcing bars according to a preset method after the reinforcing bar material list is approved. The preset method includes raw material cutting, reinforcing bar processing, material tag wrapping and reinforcing bar processing, generating the corresponding finished reinforcing bars.

[0004] Chinese Patent Publication No. CN116993269A discloses a comprehensive visual monitoring system and method for reinforcing bars in complex environments. This system utilizes electronic tags, positioning technology, and GIS technology to develop a reinforcing bar positioning system, enabling full life-cycle information management and automated inventory of geographically dispersed reinforcing bars. Simultaneously, it tracks the location and trajectory of reinforcing bars in real time, improving information transparency and reducing the loss and damage of reinforcing bars.

[0005] The existing technology has the following technical defects: 1. The existing technology uses BIM technology to reproduce the steel bars and generate a steel bar material list. After review, the steel bar processing equipment is controlled to cut raw materials and process steel bars in a preset manner. However, this method only realizes the one-way processing instruction based on the demand plan. It does not combine the installation sequence of concrete components and the spatial location information of steel bars to determine the processing priority. This results in the steel bar processing sequence being out of sync with the on-site assembly sequence. There is a problem of unreasonable sorting of processing tasks, which makes it impossible to process steel bars that need to be assembled urgently. This causes on-site waiting and idle work in the process, affecting the continuity of construction and the progress of the project.

[0006] 2. Existing technologies for information management and real-time location tracking of reinforcing bars throughout their entire lifecycle only achieve the tracking and inventory of their location. They do not involve intelligent planning of reinforcing bar stacking areas based on BIM design models or the correlation correction of adjacent tied reinforcing bar sets. This leads to a mismatch between the reinforcing bar stacking location and the requirements of subsequent tying processes, resulting in the mixed stacking and misplacement of reinforcing bars from different components and batches. This causes excessive time spent finding materials during the tying operation and may even lead to quality hazards such as the misuse of reinforcing bars. At the same time, the utilization rate of on-site stacking space is low, increasing the difficulty of on-site management and material loss. Summary of the Invention

[0007] This invention aims to overcome the shortcomings of existing technologies and provide a digital and intelligent steel reinforcement full-process management method based on BIM and IoT, which realizes steel reinforcement processing task sequencing, online control of processing quality, stacking location planning and binding combination arrangement, thereby improving the collaborative efficiency and quality level of steel reinforcement full-process management.

[0008] The technical solution adopted by this invention to solve its technical problem is as follows: This invention provides a digital intelligent steel reinforcement full-process management method based on BIM and IoT, including: S1. Based on the target project BIM design model, collect the dimensional data and spatial location information of each steel bar, combine the installation sequence number of different concrete components in the BIM design model, determine the processing priority of each steel bar, and generate a steel bar processing task sheet.

[0009] S2. During the steel bar processing, collect the actual processing parameters and waste data of the steel bars in real time, compare them with the steel bar processing task sheet, calculate the processing data deviation value and processing loss rate, and determine whether the processing parameters are qualified. When the processing parameters are qualified, encode the finished steel bars after processing.

[0010] S3. Based on the spatial location information of representative structural floors and finished steel bars in different preset stacking areas in the BIM design model, determine the initial stacking area of ​​each finished steel bar. Combine the adjacent tied steel bar sets corresponding to each finished steel bar in the BIM design model to correct the target stacking area of ​​each finished steel bar.

[0011] S4. Divide the finished steel bars stacked in different target stacking areas into various steel bar binding combinations, determine the corresponding stacking positions based on the stacking space outline of each steel bar binding combination, and feed the results back to the BIM design model.

[0012] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention is based on the target project BIM design model to statistically analyze the size data and spatial location information of each steel bar, and determines the processing priority of each steel bar by combining the installation sequence number of different concrete components and sorting them to generate a steel bar processing task list, so as to achieve accurate matching between steel bar processing schedule and on-site assembly sequence, avoid the chaotic sorting of processing tasks leading to on-site waiting and idle work in the process, ensure smooth connection of subsequent construction processes, and improve the collaborative efficiency of steel bar processing and structural assembly.

[0013] (2) In the process of steel bar processing, the present invention collects the actual processing parameters and waste data of steel bars in real time, compares them with the steel bar processing task sheet, calculates the processing data deviation value and processing loss rate, and judges whether the processing parameters are qualified. When the processing parameters are qualified, the finished steel bars are coded to realize real-time quality control and dynamic monitoring of loss in the steel bar processing process. It can intercept processing deviation and excessive loss in time, prevent unqualified finished products from flowing into subsequent processes, and ensure the assembly accuracy of structural components and engineering quality.

[0014] (3) Based on the spatial location information of representative structural floors and finished steel bars in different preset stacking areas in the BIM design model, the present invention determines the initial stacking area, corrects the target stacking area by combining the corresponding adjacent tied steel bar sets of each finished steel bar, and divides the finished steel bars into steel bar tying combinations and determines the stacking position by combining the stacking space outline, thereby realizing steel bar stacking space planning, making the stacking layout fit the on-site tying operation requirements, reducing the time for secondary transport of steel bars and finding materials before tying, avoiding mixed stacking of different batches of steel bars, improving the utilization rate of on-site stacking space, and reducing the quality risk of misuse of steel bars. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the method steps of the present invention.

[0017] Figure 2 This is a schematic diagram of the process for determining whether the processing parameters are qualified in this invention.

[0018] Figure 3 This is a schematic diagram of the steps for modifying the target stacking area corresponding to each finished steel bar in this invention.

[0019] Figure 4 This is a schematic diagram of the steps for determining the stacking location in this invention. Detailed Implementation

[0020] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. Furthermore, it should be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale.

[0021] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use. Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and apparatus should be considered part of the specification.

[0022] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0023] Please see Figure 1 As shown, this invention provides a digital and intelligent steel reinforcement full-process management method based on BIM and IoT, including: S1. Based on the target project BIM design model, collect the dimensional data and spatial location information of each steel bar, combine the installation sequence number of different concrete components in the BIM design model, determine the processing priority of each steel bar, and generate a steel bar processing task sheet.

[0024] S2. During the steel bar processing, collect the actual processing parameters and waste data of the steel bars in real time, compare them with the steel bar processing task sheet, calculate the processing data deviation value and processing loss rate, and determine whether the processing parameters are qualified. When the processing parameters are qualified, encode the finished steel bars after processing.

[0025] S3. Based on the spatial location information of representative structural floors and finished steel bars in different preset stacking areas in the BIM design model, determine the initial stacking area of ​​each finished steel bar. Combine the adjacent tied steel bar sets corresponding to each finished steel bar in the BIM design model to correct the target stacking area of ​​each finished steel bar.

[0026] S4. Divide the finished steel bars stacked in different target stacking areas into various steel bar binding combinations, determine the corresponding stacking positions based on the stacking space outline of each steel bar binding combination, and feed the results back to the BIM design model.

[0027] Considering that the target project's BIM design model contains the geometric attributes, spatial locations, and assembly relationships of the reinforcing bars within the concrete components, without analyzing and extracting the dimensional data and spatial location information of the reinforcing bars from the BIM design model, it will be impossible to establish a connection between the reinforcing bar processing tasks and on-site assembly requirements, leading to chaotic processing sequences and resource imbalances. Therefore, it is necessary to determine the processing priority based on the dimensional data and spatial location information of each reinforcing bar, combined with the installation sequence numbers of different concrete components, and generate a reinforcing bar processing task sheet.

[0028] Based on this, the specific implementation of the present invention for statistically analyzing the dimensional data and spatial location information of each reinforcing bar includes: S11. Extract the reinforcement configuration information of each concrete component in the BIM design model. The reinforcement configuration information includes the diameter and bending shape of each reinforcement bar.

[0029] S12. Based on the spatial coordinate system corresponding to the BIM design model, determine the starting and ending spatial coordinates of each steel bar in the corresponding concrete member, calculate the reinforcement direction vector of each steel bar, and determine the axial length of each steel bar by combining the modulus of the reinforcement direction vector. Use the diameter specification, bending shape and axial length as dimensional data.

[0030] S13. Based on the floor elevation of the BIM design model, compare the vertical component of the starting spatial coordinates of each rebar with the floor elevation to determine the structural floor corresponding to each rebar and generate the spatial location information of each rebar.

[0031] It should be noted that the reinforcement direction vector is the difference vector between the end spatial coordinates and the starting spatial coordinates of each reinforcement in the corresponding concrete member, wherein the modulus of the reinforcement direction vector is the Euclidean distance of the difference vector, which represents the actual axial length of the reinforcement in three-dimensional space.

[0032] The floor elevation is the reference height value of each structural floor in the BIM design model. The vertical component of the starting spatial coordinate of each rebar is compared with the reference height value of each structural floor to determine the structural floor to which each rebar belongs.

[0033] Furthermore, the specific implementation details of generating the rebar processing task order include: S14. Based on the BIM design model, the concrete component assembly sequence is obtained. The installation sequence number of each concrete component in the assembly sequence is obtained. Based on the structural floor corresponding to each steel bar, the structural floors are sorted from low to high. Within the same structural floor, the installation sequence number is sorted from small to large to determine the assembly priority value of each steel bar.

[0034] It should be noted that the concrete component assembly sequence is the on-site installation sequence of each concrete component in the pre-determined BIM design model of the target project.

[0035] The method for determining the assembly priority value of each reinforcing bar is as follows: First, based on the structural floors corresponding to each steel bar, the steel bars are sorted in order from low to high structural floors, with the lower the structural floor, the higher the priority value of the steel bar assembly.

[0036] Next, all the reinforcing bars in each concrete component are counted. Combined with the installation sequence number of each concrete component in the assembly sequence, the installation sequence number of each reinforcing bar is determined. The reinforcing bars are then sorted in ascending order of their installation sequence numbers to determine their assembly priority. The smaller the installation sequence number, the higher the assembly priority of the reinforcing bar, ensuring that the reinforcing bars for lower floors and components assembled earlier are processed first, thus guaranteeing consistency between the processing sequence and the on-site assembly sequence.

[0037] S15. Based on the bending shape in the dimensions of each steel bar, obtain the number of bending inflection points and the bending curvature corresponding to the bending shape, evaluate the processing complexity score of each steel bar, and determine the process priority value of each steel bar.

[0038] S16. The process priority value and the assembly priority value are weighted and integrated to obtain the processing priority value. The processing priority of each steel bar is sorted in descending order, and a steel bar processing task sheet is generated by combining the dimensional data.

[0039] It should be noted that the method for obtaining the processing priority value is as follows: the assembly priority value and the process priority value are weighted and summed to obtain the processing priority value of each steel bar, and the processing priority of each steel bar is determined according to the order of processing priority value from largest to smallest. In a specific embodiment of the present invention, the weight coefficients of the assembly priority value and the process priority value are adjusted according to the actual needs of the project. When the construction schedule pressure is large, the weight coefficient of the assembly priority value is increased; when the processing capacity is tight, the weight coefficient of the process priority value is increased. In this embodiment, the assembly priority weight is 0.6 and the process priority weight is 0.4. Implementers can adjust the weight coefficients themselves according to the project needs.

[0040] Preferably, in a specific embodiment of the present invention, the method for determining the process priority value of each reinforcing bar is as follows: S151. Based on the number of bending inflection points corresponding to the bending shape of each steel bar, determine the basic complexity score of each steel bar in descending order of the number of bending inflection points.

[0041] S152. Perform standard deviation analysis on the bending curvature of each bending inflection point and the corresponding average bending curvature to obtain the standard deviation of bending curvature. Obtain the processing complexity score of each steel bar in descending order of the standard deviation of bending curvature.

[0042] S153. The basic complexity score and the processing complexity score are superimposed to obtain the processing technology complexity score. The processing technology complexity scores are sorted in descending order, and the sorting number is used as the process priority value of each steel bar.

[0043] It should be noted that the number of bending inflection points refers to the number of characteristic points where the curvature changes in the bent shape of the steel bar. The more bending inflection points there are, the more complex the steel bar forming process is, and the higher the basic complexity score is. For example, if there are 5 serial numbers for the number of bending inflection points, the steel bar with the most bending inflection points is serial number 1, and its basic complexity score is 5. Similarly, the steel bar with the fewest bending inflection points is serial number 5, and its basic complexity score is 1.

[0044] The standard deviation of the curvature represents the degree of dispersion of the curvature distribution at each bending inflection point. The larger the standard deviation, the more drastic the curvature change and the higher the processing difficulty. Thus, the processing complexity score is larger. Similarly, the method for determining the basic complexity score is adopted.

[0045] This invention uses the BIM design model of the target project to statistically analyze the dimensional data and spatial location information of each steel bar, and combines the installation sequence number of different concrete components to determine the processing priority of each steel bar and generate a steel bar processing task list. This achieves precise matching between steel bar processing scheduling and on-site assembly sequence, avoids on-site waiting and idle work caused by chaotic processing task sorting, ensures smooth connection of subsequent construction procedures, and improves the collaborative efficiency of steel bar processing and structural assembly.

[0046] Considering that without real-time collection of actual processing parameters and comparison with the rebar processing task sheet during rebar processing, processing deviations and excessive losses cannot be detected in a timely manner, leading to defective products flowing into subsequent processes; furthermore, without dynamic monitoring of processing losses, it is impossible to effectively optimize material utilization, increase construction costs, and affect the assembly accuracy and structural quality of structural components. Therefore, it is necessary to compare processing parameters with the rebar processing task sheet, calculate processing data deviation values ​​and processing loss rates, and achieve real-time quality control and dynamic monitoring of losses during the rebar processing process.

[0047] Based on this, such as Figure 2 As shown, the specific implementation details of calculating the processing data deviation value and processing loss rate in this invention include: S21. Deploy sensor groups at the discharge end of the rebar straightening and cutting equipment and the rebar bending equipment to collect the actual axial length of the rebar being processed, the actual bending curvature of each bending inflection point, and the diameter of each collection point in real time during the rebar processing process, thus forming the actual processing parameters.

[0048] The sensor group includes a laser rangefinder, a curvature detection sensor, and a diameter measurement sensor, which are respectively installed at the discharge end of the rebar straightening and cutting equipment and the rebar bending equipment, and are used to collect the axial length, bending curvature, and diameter of the rebar after processing.

[0049] Each collection point is a diameter detection position arranged along the axis of the reinforcing bar at a preset spacing to ensure full-length diameter coverage detection.

[0050] S22. Compare the actual processing parameters with the corresponding dimensional data of the reinforcing bars in the reinforcing bar processing task sheet, and calculate the deviation value between the actual processing parameters and the dimensional data as the processing data deviation value. The processing data deviation value includes the axial length deviation value, the curvature deviation value of each bending inflection point, and the diameter deviation value of each data collection point.

[0051] S23. The weight of the waste material is collected in real time by the weighing sensor installed below the discharge end of the equipment. The weight of the waste material generated during the processing of the steel bars is counted. Combined with the theoretical material mass of the steel bars to be processed, the ratio of the waste material weight to the theoretical material mass is calculated as the processing loss rate.

[0052] It should be noted that the theoretical material mass is obtained as follows: based on the diameter specification of the steel bar being processed in the steel bar processing task sheet, the theoretical material mass per unit axial length of the steel bar being processed is obtained, and the theoretical material mass per unit axial length is multiplied by the axial length of the steel bar being processed to obtain the theoretical material mass of the steel bar being processed.

[0053] Furthermore, the specific implementation details for determining whether the processing parameters are qualified include: S24. Based on the deviation values ​​of the axis length, the curvature deviation values ​​of each bending inflection point, and the diameter deviation values ​​of each collection point in the processing data deviation values, compare them with the corresponding allowable deviation thresholds.

[0054] S25. When the deviation values ​​of the axis length, the curvature deviation values ​​of each bending inflection point, and the diameter deviation values ​​of each collection point are all less than the corresponding allowable deviation threshold, it is determined that the machining data deviation meets the requirements.

[0055] It should be noted that the allowable deviation thresholds include the allowable deviation threshold for axis length, the allowable deviation threshold for bending curvature, and the allowable deviation threshold for diameter, wherein the allowable deviation thresholds are preset according to the concrete structure construction quality acceptance code.

[0056] S26. When the processing data deviation meets the requirements, the processing loss rate is compared with the allowable loss threshold. When the loss rate is lower than the allowable loss threshold, the processing parameters are deemed qualified.

[0057] The allowable loss threshold is determined based on the statistical data of historical steel bar processing loss. Specifically, it is the sum of the average value of the historical processing loss data and three times the standard deviation, which represents the maximum acceptable material loss ratio during processing.

[0058] Once the processing parameters are qualified, the finished steel bars are coded using IoT identification technology, such as QR code tags or RFID tags. The coded information is then bound to the dimensional data and spatial location information in the steel bar processing task sheet and synchronized to the BIM design model. In the subsequent stacking stage, the dimensional data and spatial location information of the finished steel bars are retrieved in real time by scanning the coded information, realizing data traceability and visual management of the finished steel bars from processing to stacking.

[0059] This invention collects actual processing parameters and waste data of steel bars in real time during the steel bar processing process, compares them with the steel bar processing task sheet, calculates the processing data deviation value and processing loss rate, and determines whether the processing parameters are qualified. When the processing parameters are qualified, the finished steel bars are coded, realizing real-time quality control and dynamic monitoring of loss in the steel bar processing process. It can promptly intercept processing deviations and excessive losses, prevent unqualified finished products from flowing into subsequent processes, and ensure the assembly accuracy of structural components and the quality of the project.

[0060] Considering that without planning the rebar stacking area in the BIM design model, relying solely on on-site experience or random stacking can lead to a mismatch between the rebar stacking location and the requirements of subsequent binding processes, resulting in mixed and incorrect stacking of rebar from different components and batches, excessively long material retrieval time during the binding operation, and even quality hazards such as misuse of rebar, while also causing low utilization of on-site stacking space, increasing on-site management difficulty and material waste, it is necessary to determine the initial stacking area through the preset stacking area and rebar spatial location information in the BIM design model, and then refine the target stacking area by combining adjacent sets of bound rebar.

[0061] Based on this, such as Figure 3 As shown, the specific implementation details of modifying the target stacking area corresponding to each finished steel bar in this invention include: S31. Obtain the spatial location information of currently stacked steel bars in different preset stacking areas in the BIM design model, count the number of stacked steel bars on each structural floor in different preset stacking areas, and take the structural floor with the largest number of stacked steel bars as the representative structural floor. The representative structural floor represents the main floor to which the currently stacked steel bars in the preset stacking area belong, and is used to match the initial belonging area of ​​the subsequently arriving steel bars.

[0062] It should be noted that the preset stacking area is a temporary storage space for steel bars pre-defined in the BIM design model according to the site conditions of the construction site, and each preset stacking area has a defined three-dimensional boundary range.

[0063] S32. Based on the structural floors in the spatial location information of each finished steel bar, match them with the representative structural floors of different preset stacking areas to determine the initial stacking area of ​​each finished steel bar.

[0064] Preferably, the structural floor of the finished steel bars is compared with the representative structural floors of different preset stacking areas. When there is a preset stacking area where the representative structural floor is consistent with the structural floor of the finished steel bars, the preset stacking area is used as the initial stacking area of ​​the finished steel bars. When there are multiple preset stacking areas where the representative structural floors are consistent, the preset stacking area with the smallest number of steel bars currently stacked is selected as the initial stacking area to balance the stacking load of each preset stacking area.

[0065] S33. Extract the set of adjacent tied steel bars corresponding to each finished steel bar from the BIM design model, and obtain the preliminary stacking area of ​​each adjacent tied steel bar in the set of adjacent tied steel bars.

[0066] The adjacent tied rebar set refers to the set of rebars in the BIM design model that have a tying connection with the current finished rebar in the same or adjacent components. By stacking adjacent tied rebars together, the material retrieval and transportation distance during the tying operation can be reduced, thus improving construction efficiency.

[0067] S34. Take the initial stacking area with the highest concentration of steel bars in the adjacent bundled steel bar sets as the benchmark stacking area, and modify the initial stacking area of ​​each finished steel bar to generate the target stacking area.

[0068] Preferably, in a specific embodiment of the present invention, the method for correcting the initial stacking area of ​​each finished steel bar is as follows: S341. Obtain the initial stacking area of ​​each adjacent tied steel bar in the adjacent tied steel bar set, count the number of adjacent tied steel bars in different initial stacking areas, and calculate the steel bar stacking concentration of each initial stacking area.

[0069] The concentration of steel bar stacking is the ratio of the number of adjacent tied steel bars in each initial stacking area to the total number of steel bars in the set of adjacent tied steel bars. The higher the ratio, the higher the concentration of adjacent tied steel bars in the initial stacking area. The higher the concentration of steel bar stacking, the more adjacent tied steel bars are gathered in the initial stacking area. Storing the current finished steel bars in this area can reduce the material finding distance and hoisting frequency of subsequent tying operations.

[0070] S342. The initial stacking area with the highest concentration of steel reinforcement stacking is taken as the benchmark stacking area. The boundary space of the benchmark stacking area and the currently occupied space are extracted and compared to obtain the remaining space capacity. The remaining space capacity is the difference between the boundary space of the benchmark stacking area and the sum of the currently occupied space and the reserved space for the preset safety passage.

[0071] It should be noted that the boundary space of the reference stacking area is the space formed by the coordinates of the smallest circumscribed cuboid vertex of the reference stacking area in the construction site spatial coordinate system, which is used to define the usable space range of the reference stacking area.

[0072] The currently occupied space is the area of ​​the minimum circumscribed cuboid corresponding to the spatial occupancy outline of the stacking area in the BIM design model.

[0073] The pre-set safety passage reserved space is set according to the construction site safety management, representing the space reserved for personnel passage and hoisting equipment turning within the stacking area, and is usually taken as the space within the boundary range of the benchmark stacking area. The larger the boundary area of ​​the baseline stacking area, the smaller the corresponding percentage value.

[0074] S343. Determine whether the remaining space capacity meets the stacking space requirements of the corresponding finished steel bars. If the stacking space requirements are met, then modify the initial stacking area of ​​the corresponding finished steel bars to the benchmark stacking area.

[0075] The required stacking space is determined based on the axial length, diameter, and bending shape of the finished steel bars, which defines the minimum circumscribed cuboid space of the finished steel bars in a horizontal position.

[0076] S344. If the storage space requirement is not met, the remaining space capacity of other preliminary storage areas shall be judged in order of the concentration of steel bar storage from high to low, until the preliminary storage area that meets the storage space requirement is used as the corrected target storage area.

[0077] It should be noted that when all preliminary stacking areas are traversed in order of steel bar stacking concentration, and there are no preliminary stacking areas with remaining space capacity that meet the stacking space requirements, a stacking area expansion warning is triggered. An alarm message indicating insufficient space in the preset stacking area of ​​the structural floor is pushed to the on-site management terminal, prompting the management personnel to make space adjustments.

[0078] This invention determines the initial stacking area based on the representative structural floors and spatial location information of each finished steel bar in different preset stacking areas in the BIM design model. It then corrects the target stacking area by combining the corresponding adjacent tied steel bar sets of each finished steel bar, thereby realizing steel bar stacking space planning. This ensures that the stacking layout meets the needs of on-site tying operations, reduces the time for secondary handling of steel bars and material retrieval before tying, avoids mixing and misplacing different batches of steel bars, improves the utilization rate of on-site stacking space, and reduces the quality risk of misuse of steel bars.

[0079] Considering that failing to classify finished steel bars according to their binding assemblies and determine their stacking locations based on installation sequence numbers would result in steel bars for the same component being stored haphazardly, requiring cross-area sourcing during binding operations and increasing hoisting and transportation costs; furthermore, failing to determine stacking locations based on the installation sequence number of concrete components would lead to steel bars for earlier-assembled components being obscured or occupied by steel bars for later-assembled components, increasing secondary handling workload, it is necessary to classify finished steel bars into binding assemblies, select stacking locations based on the stacking space outline and installation sequence number, and feed this information back into the BIM design model to achieve visualized management.

[0080] Based on this, such as Figure 4 As shown, the specific implementation of determining the corresponding stacking position in this invention based on the stacking space outline of each steel bar binding assembly includes: S41. Count all finished steel bars stacked in different target stacking areas, extract the finished steel bars that have a binding connection with each finished steel bar from the BIM design model, and divide them into a steel bar binding group.

[0081] S42. Based on the dimensional data of each finished steel bar within each steel bar binding assembly, obtain the stacking space outline of each steel bar binding assembly. The stacking space outline is calculated using the minimum bounding rectangle based on the axial length, diameter, and bending shape of each finished steel bar within the steel bar binding assembly, representing the minimum space required for stacking the steel bar binding assembly.

[0082] S43. Based on the installation sequence number of the concrete component corresponding to each rebar binding assembly in the assembly sequence, select the stacking position of each rebar binding assembly in the corresponding target stacking area in ascending order of installation sequence number.

[0083] Preferably, in a specific embodiment of the present invention, the method for selecting the stacking position of each steel bar binding assembly is as follows: S431. The steel bar binding combination with the first installation sequence number is designated as the preferred steel bar binding combination. Based on the stacking space outline of the preferred steel bar binding combination, a stacking location that conforms to the stacking space outline is selected within the corresponding target stacking area.

[0084] S432. Statistically calculate the distance between all stacking locations and entrances / exits corresponding to the preferred rebar binding combination, and select the stacking location with the smallest distance as the stacking position of the preferred rebar binding combination.

[0085] S433. Remove the preferred rebar binding combination from the target stacking area to obtain the remaining target stacking area. Select the stacking location corresponding to the rebar binding combination with the second installation sequence number within the remaining target stacking area. Similarly, determine the stacking location of the rebar binding combination with the second installation sequence number.

[0086] S434. By analogy, the stacking positions of each steel bar binding combination are selected, and the stacking positions of each steel bar binding combination are fed back to the BIM design model to realize the visualization update and dynamic management of the stacking layout.

[0087] This invention divides the finished steel bars stacked in different target stacking areas into various steel bar binding combinations, determines the corresponding stacking position based on the stacking space outline of each steel bar binding combination, and feeds it back to the BIM design model. This enables visualized management and dynamic updating of the stacking layout, facilitating quick location and retrieval by on-site operators, shortening the steel bar transportation path, further reducing steel bar loss and damage, and ensuring construction continuity.

[0088] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0089] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0090] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0091] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0092] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A digital and intelligent steel reinforcement full-process management method based on BIM and IoT, characterized in that, include: Based on the BIM design model of the target project, the dimensional data and spatial location information of each steel bar are statistically analyzed. Combined with the installation sequence number of different concrete components in the BIM design model, the processing priority of each steel bar is determined, and a steel bar processing task sheet is generated. During the steel bar processing, the actual processing parameters and waste data of the steel bars are collected in real time, compared with the steel bar processing task sheet, and the processing data deviation value and processing loss rate are calculated to determine whether the processing parameters are qualified. When the processing parameters are qualified, the finished steel bars are coded. Based on the spatial location information of representative structural floors and finished steel bars in different preset stacking areas in the BIM design model, the initial stacking area of ​​each finished steel bar is determined. Combined with the set of adjacent tied steel bars corresponding to each finished steel bar in the BIM design model, the target stacking area of ​​each finished steel bar is corrected. The finished steel bars stacked in different target stacking areas are divided into various steel bar binding combinations. The corresponding stacking position is determined by combining the stacking space outline of each steel bar binding combination and fed back to the BIM design model.

2. The method for intelligent digital steel reinforcement management based on BIM and IoT according to claim 1, characterized in that, The method for collecting the dimensional data and spatial location information of each steel bar is as follows: Extract the reinforcement configuration information of each concrete component in the BIM design model. The reinforcement configuration information includes the diameter and bending shape of each reinforcement bar. Based on the spatial coordinate system corresponding to the BIM design model, the starting point and ending point spatial coordinates of each steel bar in the corresponding concrete member are determined, the reinforcement direction vector of each steel bar is calculated, and the axial length of each steel bar is determined by combining the modulus of the reinforcement direction vector. The diameter specification, bending shape and axial length are used as dimensional data. Based on the floor elevation of the BIM design model, the vertical component of the starting spatial coordinates of each rebar is compared with the floor elevation to determine the structural floor corresponding to each rebar and generate the spatial location information of each rebar.

3. The method for intelligent digital steel reinforcement management based on BIM and IoT according to claim 2, characterized in that, The method for generating the steel bar processing task order is as follows: Based on the BIM design model, the concrete component assembly sequence is obtained, the installation sequence number of each concrete component in the assembly sequence is obtained, and based on the structural floor corresponding to each steel bar, the structural floors are sorted in order from low to high. Within the same structural floor, they are sorted in order from small to large installation sequence number to determine the assembly priority value of each steel bar. Based on the bending shape in the size data of each steel bar, obtain the number of bending inflection points and bending curvature corresponding to the bending shape, evaluate the processing complexity score of each steel bar, and determine the process priority value of each steel bar. The processing priority value is obtained by weighting and integrating the process priority value and the assembly priority value. The processing priority of each steel bar is sorted in descending order, and a steel bar processing task sheet is generated by combining the dimensional data.

4. The method for intelligent digital steel reinforcement management based on BIM and IoT according to claim 3, characterized in that, The method for determining the process priority value of each steel bar is as follows: Based on the number of bending inflection points corresponding to the bending shape of each steel bar, the basic complexity score of each steel bar is determined in descending order of the number of bending inflection points. The standard deviation analysis of the bending curvature of each bending inflection point and the corresponding average bending curvature is performed to obtain the standard deviation of bending curvature. The processing complexity score of each steel bar is obtained in descending order of the standard deviation of bending curvature. The basic complexity score is superimposed with the processing complexity score to obtain the processing technology complexity score. The processing technology complexity scores are then sorted from high to low, and the sorting number is used as the process priority value of each steel bar.

5. The method for intelligent digital steel reinforcement management based on BIM and IoT according to claim 1, characterized in that, The method for calculating the processing data deviation and processing loss rate is as follows: The actual axial length of the steel bar being processed, the actual curvature of each bending inflection point, and the diameter of each collection point are collected in real time during the steel bar processing process to form the actual processing parameters. The actual processing parameters are compared with the corresponding steel bar dimensions in the steel bar processing task sheet, and the deviation value between the actual processing parameters and the dimensions is calculated as the processing data deviation value. The weight of waste material generated during the current processing of steel bars is counted. Combined with the theoretical material weight of the steel bars currently being processed, the ratio of the waste material weight to the theoretical material weight is calculated as the processing loss rate.

6. The method for intelligent digital steel reinforcement management based on BIM and IoT according to claim 5, characterized in that, The method for determining whether the processing parameters are qualified is as follows: Based on the deviation values ​​of the axis length, the curvature deviation values ​​of each bending inflection point, and the diameter deviation values ​​of each collection point in the processing data deviation values, they are compared with the corresponding allowable deviation thresholds. When the deviation values ​​of the axis length, the curvature deviation values ​​of each bending inflection point, and the diameter deviation values ​​of each collection point are all less than the corresponding allowable deviation threshold, the machining data deviation is judged to meet the requirements. When the deviation of the processing data meets the requirements, the processing loss rate is compared with the allowable loss threshold. When the loss rate is lower than the allowable loss threshold, the processing parameters are deemed to be qualified.

7. The method for intelligent digital steel reinforcement management based on BIM and IoT according to claim 1, characterized in that, The method for correcting the target stacking area corresponding to each finished steel bar is as follows: Obtain the spatial location information of the currently stacked steel bars in different preset stacking areas in the BIM design model, count the number of stacked steel bars in each structural floor in different preset stacking areas, and take the structural floor with the largest number of stacked steel bars as the representative structural floor. Based on the structural floors in the spatial location information of each finished steel bar, they are matched with representative structural floors in different preset stacking areas to determine the initial stacking area of ​​each finished steel bar. Extract the set of adjacent tied steel bars corresponding to each finished steel bar from the BIM design model, and obtain the preliminary stacking area of ​​each adjacent tied steel bar in the set of adjacent tied steel bars; The initial stacking area with the highest concentration of steel bars in adjacent bundled steel bar sets is used as the benchmark stacking area. The initial stacking areas of each finished steel bar are then modified to generate the target stacking area.

8. The method for intelligent digital steel reinforcement management based on BIM and IoT according to claim 7, characterized in that, The method for correcting the initial stacking area of ​​each finished steel bar is as follows: Obtain the initial stacking area of ​​each adjacent tied steel bar in the adjacent tied steel bar set, count the number of adjacent tied steel bars in different initial stacking areas, and calculate the steel bar stacking concentration of each initial stacking area; The initial stacking area with the highest concentration of steel bars is taken as the benchmark stacking area. The boundary space of the benchmark stacking area and the currently occupied space are extracted and compared to obtain the remaining space capacity. Determine whether the remaining space capacity meets the stacking space requirements of the corresponding finished steel bars. If it meets the stacking space requirements, then modify the initial stacking area of ​​the corresponding finished steel bars to the benchmark stacking area. If the storage space requirement is not met, the remaining space capacity of other preliminary storage areas will be judged in descending order of the concentration of steel bar storage until the preliminary storage area that meets the storage space requirement is selected as the corrected target storage area.

9. The method for intelligent digital steel reinforcement management based on BIM and IoT according to claim 1, characterized in that, The method for determining the corresponding stacking position by combining the stacking space outline of each steel bar binding assembly is as follows: Count all finished steel bars stacked in different target stacking areas, extract the finished steel bars that have tying connections with each finished steel bar from the BIM design model, and divide them into a steel bar tying group; Based on the dimensional data of each finished steel bar in each steel bar binding assembly, the stacking space outline of each steel bar binding assembly is obtained. Based on the installation sequence number of the concrete component corresponding to each rebar binding assembly in the assembly sequence, and in ascending order of installation sequence number, the stacking position of each rebar binding assembly is selected within the corresponding target stacking area.

10. A method for intelligent digital steel reinforcement management based on BIM and IoT according to claim 9, characterized in that, The method for selecting the stacking position of each steel bar binding assembly is as follows: The first installation sequence number of the steel bar binding combination is recorded as the preferred steel bar binding combination. Based on the stacking space outline of the preferred steel bar binding combination, a stacking location that conforms to the stacking space outline is selected in the corresponding target stacking area. The distances between all stacking locations and entrances / exits corresponding to the optimal rebar binding combination are statistically analyzed, and the stacking location with the smallest distance is selected as the stacking location of the optimal rebar binding combination. The preferred rebar binding combination is removed from the target stacking area to obtain the remaining target stacking area. The stacking location corresponding to the rebar binding combination with the second installation sequence number is selected from the remaining target stacking area. Similarly, the stacking location of the rebar binding combination with the second installation sequence number is determined. By analogy, the stacking location of each steel bar binding assembly is selected.

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