Steel bar concentrated intelligent manufacturing aggregate yard management system and method based on digital analysis

Abstract

The application provides a steel bar concentrated intelligent manufacturing stockyard management system and method based on digital analysis, relates to the technical field of steel bar concentrated processing and warehouse management, and solves the technical problems of the existing steel bar concentrated processing stockyard management, such as non-real-time storage state sensing, non-accurate storage and demand matching, and insufficient stockyard space utilization. The method comprises the following steps: acquiring spatial structure data, steel bar storage data and steel bar processing demand data of a stockyard; constructing a three-dimensional digital space model according to the spatial structure data, and mapping the spatial positions and storage attributes of each steel bar stacking position to generate a stockyard storage digital twin model; generating a processing demand time sequence according to the processing demand data, and performing correlation analysis with the storage state data to generate a storage-demand matching degree; generating a storage structure optimization scheme and a stacking strategy instruction based on the matching degree, and updating the digital twin model. The application is used in the intelligent stockyard management process of a steel bar concentrated processing yard.

Description

Technical Field

[0001] This application relates to the field of centralized processing and storage management of reinforcing bars, and in particular to a management system and method for a centralized intelligent manufacturing aggregate yard for reinforcing bars based on digital analysis. Background Technology

[0002] Reinforced concrete structures are the most widely used building structure in my country. Reinforcing steel works account for a significant proportion of the overall construction cost and directly affect project quality. Currently, centralized rebar processing is widely used in bridge engineering, railway beam yards, and nuclear power plant construction. However, the management of material yards in existing centralized rebar processing scenarios still has many shortcomings.

[0003] In terms of rebar inventory management, traditional methods mainly rely on manual ledgers or simple spreadsheets, resulting in delayed data updates and making it difficult for yard managers to keep track of the real-time inventory status of each stockpile. Some solutions utilize BIM technology to build rebar models and generate material lists, or employ digital twin technology to achieve visualized control of the entire rebar processing process. However, these solutions primarily focus on digitizing the processing stage, with little attention paid to stockpile inventory management. Regarding the matching of rebar processing demand with available inventory, current technologies typically rely on manual comparison to determine available inventory, lacking systematic analysis of the temporal characteristics of processing demand, making it difficult to achieve dynamic matching and intelligent scheduling between inventory and demand. In terms of yard space management, existing research has proposed three-dimensional digital yard systems and stockpile digital twin systems, but these mainly focus on visualization and monitoring, lacking functions for inventory structure optimization and dynamic stockpile allocation based on inventory-demand matching analysis.

[0004] Therefore, this application provides a management system and method for a centralized intelligent steel reinforcement aggregate yard based on digital analysis. Summary of the Invention

[0005] This application provides a digital analysis-based intelligent management system and method for centralized steel reinforcement processing and material yards, which solves the technical problems of existing centralized steel reinforcement processing and material yard management, such as non-real-time perception of material status, inaccurate matching of material inventory with demand, and insufficient utilization of material yard space.

[0006] To achieve the above objectives, this application adopts the following technical solution: Firstly, a digital analysis-based intelligent management method for centralized steel reinforcement aggregate yards is provided, including: Acquire spatial structure data of the aggregate yard, steel bar inventory data, and steel bar processing demand data; A three-dimensional digital space model of the aggregate yard is constructed based on the spatial structure data, and the spatial location and storage attributes of each steel bar pile are mapped in the three-dimensional digital space model based on the steel bar storage data, thereby generating a digital twin model of the aggregate yard storage. Based on the steel bar processing demand data, a processing demand time series is generated, and a correlation analysis is performed between the processing demand time series and the storage status data in the digital twin model of the aggregate yard storage to generate a storage-demand matching degree. Based on the matching degree of inventory and demand, a digital analysis is performed on the inventory structure of the material collection yard to generate an optimization scheme for the inventory structure. Based on the material storage structure optimization scheme, stacking strategy instructions for each steel bar stack location are generated, and the digital twin model of the aggregate yard material storage is updated according to the stacking strategy instructions.

[0007] Based on the above technical solutions, the intelligent steel reinforcement storage yard management method based on digital analysis provided in this application constructs a digital twin model of the storage yard, mapping the spatial location of physical stockpiles with storage attribute data in a three-dimensional digital space in real time. This achieves full-element visualization and dynamic monitoring of the storage yard's status, solving the problems of opaque and delayed updates of storage information in traditional management. By generating a processing demand time series and calculating the storage-demand matching degree, and comprehensively considering the available inventory, demand, specification matching coefficient, and spatial distance factors of each stockpile, the method can accurately quantify the matching degree between the overall storage yard and processing demand, providing a quantitative decision-making basis for storage structure optimization. Based on the storage-demand matching degree analysis, the method generates storage structure optimization schemes, automatically identifying low-matching and high-matching stockpiles, generating steel reinforcement adjustment schemes and replenishment procurement schemes between stockpiles, effectively improving steel reinforcement resource utilization and reducing material waste. The optimal stacking location allocation for each rebar is determined with the goal of minimizing the total handling distance within the aggregate yard. By constructing the objective function and constraints and solving it using a heuristic optimization algorithm, the cost of rebar handling can be significantly reduced, and the efficiency of the aggregate yard operation can be improved. The digital twin model is updated in real time by driving stacking strategy instructions, and stacking location merging and partition reorganization schemes are automatically generated based on the updated space utilization rate. This achieves dynamic optimization of the aggregate yard's spatial layout and improves the overall space utilization efficiency of the aggregate yard.

[0008] In conjunction with the first aspect above, in one possible implementation, the construction of the digital twin model of the aggregate yard storage includes: Based on the spatial location information in the steel bar storage data, spatial occupancy areas corresponding to each physical stacking location are marked in the three-dimensional digital space model, and a data index link is established between the spatial occupancy areas and the corresponding steel bar storage attribute data. Based on the data index link, the storage attribute data of each stack location is loaded into the three-dimensional digital space model to form a stack location digital entity with attribute labels. According to the actual spatial arrangement of each physical stack location, the digital entities of each stack location are spatially reorganized in the three-dimensional digital space model to generate the digital twin model of the aggregate yard storage.

[0009] In conjunction with the first aspect above, in one possible implementation, generating a processing demand time sequence based on the rebar processing demand data includes: Obtain the steel bar processing requirement data, which includes the steel bar specification parameters, required quantity, required time node, and processing priority information for each processing task; Using a preset time granularity as the time axis discrete unit, the steel bar processing demand data is classified and merged according to the steel bar specification parameters, and the demand quantity corresponding to each specification is mapped to the corresponding discrete time interval to generate the initial demand time sequence subsequence for each steel bar specification. Based on the processing priority information, the demand quantity in each discrete time interval of the initial demand time sequence is weighted and corrected to generate a weighted demand time sequence for each steel bar specification. The weighted demand time series subsequences of each steel bar are time-series superimposed and merged according to the discrete time interval to generate the processing demand time series sequence.

[0010] In conjunction with the first aspect above, in one possible implementation, the method for obtaining the inventory-demand matching degree includes: The specifications and quantities of each demand item in the processing demand time sequence are matched with each steel bar stack in the inventory status data, and the demand coverage rate of each steel bar stack is calculated. Calculate the overall material storage-demand matching degree of the aggregate yard based on the demand coverage of each steel bar stack and the spatial distance weight of each steel bar stack relative to the processing area. Among them, the overall material storage-demand matching degree of the aggregate yard Calculated using the following formula:

[0011] in, This is the sequence number of the rebar stacking location. This represents the total number of rebar stacking locations. For the first Available inventory at each steel rebar stack location Indicates the relationship with the first The processing requirements corresponding to each rebar stack location. For the first The matching coefficient between the rebar specifications and the required specifications at each rebar stack location is used to characterize the degree of matching between the rebar diameter, material, and required specifications. , Spatial distance attenuation factor, For the first The distance between the steel bar stack and the processing area. This refers to the maximum transport distance within the material collection yard; When the When there is no corresponding project requirement for a particular rebar stacking location. And this term does not participate in the summation; when When >1, take =1.

[0012] In conjunction with the first aspect above, in one possible implementation, the method for generating the optimized storage structure includes: When the overall material storage-demand matching degree of the aggregate yard is lower than a preset threshold, the demand coverage rate of each steel bar stack is traversed to identify low-matching stacks with a demand coverage rate lower than a first preset value and high-matching stacks with a demand coverage rate higher than a second preset value. Based on the shortage specifications and quantities of the low-matching stacks and the surplus specifications and quantities of the high-matching stacks, a steel bar adjustment plan and a replenishment procurement plan between the stacks are generated as the material storage structure optimization plan.

[0013] In conjunction with the first aspect above, in one possible implementation, the method for generating the stacking strategy instructions for each rebar stack location includes: Obtain the current occupancy status and available space parameters of each rebar stack location; based on the rebar specification allocation plan in the material storage structure optimization scheme, combined with the spatial coordinates of each rebar stack location, available space parameters, and the expected usage time of each rebar, determine the optimal stack location allocation for each rebar with the goal of minimizing the total handling distance within the aggregate yard, and generate the stacking strategy instruction.

[0014] In conjunction with the first aspect above, in one possible implementation, determining the optimal stacking location allocation for each steel bar, with the objective of minimizing the total handling distance within the aggregate yard, includes: Construct an objective function and constraints for the allocation of stockpile locations in the aggregate yard. The objective function aims to minimize the total transport distance of all steel bars from their storage locations to the processing area. The total transport distance is the sum of the products of the single transport distance of each steel bar and the frequency of use of that steel bar. The constraints include the space capacity constraints of each stack location, the load-bearing constraints of each stack location, and the matching constraints between the specifications of each steel bar and the specifications of the stack location. The objective function and constraints are solved using a heuristic optimization algorithm to obtain the optimal heap space allocation.

[0015] In conjunction with the first aspect above, in one possible implementation, updating the digital twin model of the aggregate yard inventory according to the stacking strategy instruction includes: The stacking location allocation information in the stacking strategy instruction is mapped to the three-dimensional digital twin space model, and the storage attribute data of the corresponding stacking location is updated. Based on the updated storage attribute data, the space utilization rate of each stacking location and the overall space utilization rate of the aggregate yard are recalculated. When the overall space utilization rate of the aggregate yard is lower than the preset utilization rate threshold, an aggregate yard space layout adjustment scheme is generated. The space layout adjustment scheme includes a stacking location merging scheme and a stacking location partitioning and reorganization scheme.

[0016] In conjunction with the first aspect above, in one possible implementation, the method for acquiring the spatial structure data of the aggregate yard, the steel bar inventory data, and the steel bar processing demand data includes: The spatial structure data of the aggregate yard is obtained by 3D laser scanning or building information model import. The spatial structure data includes the boundary outline of the aggregate yard, the division of each stacking area, the channel layout and the location information of the processing area. The steel bar storage data is collected by weight sensors, RFID readers and barcode scanning terminals installed at each stack location. The steel bar storage data includes the steel bar specifications, quantity, batch information, storage time and spatial coordinates of each stack location. The steel bar processing requirement data is obtained through the data interface of the enterprise resource planning system or the production execution system. The steel bar processing requirement data includes the required steel bar specifications, required quantity, required time nodes, and processing priority information for each processing task.

[0017] Secondly, this application provides a centralized intelligent steel reinforcement material yard management system based on digital analysis, including: a data acquisition module, a modeling module, an analysis module, a decision optimization module, and an instruction execution module; wherein, the data acquisition module is used to acquire spatial structure data of the material yard, steel reinforcement storage data, and steel reinforcement processing demand data; the modeling module is used to construct a three-dimensional digital space model of the material yard based on the spatial structure data, and map the spatial location and storage attributes of each steel reinforcement stack in the three-dimensional digital space model based on the steel reinforcement storage data, generating a digital twin model of the material yard storage; the analysis module is used to generate a processing demand time series based on the steel reinforcement processing demand data, and perform correlation analysis between the processing demand time series and the storage status data to generate a storage-demand matching degree; the decision optimization module is used to perform digital analysis on the storage structure of the material yard based on the storage-demand matching degree, and generate an optimized storage structure scheme; the instruction execution module is used to generate stacking strategy instructions for each steel reinforcement stack based on the optimized storage structure scheme, and update the digital twin model of the material yard storage according to the stacking strategy instructions.

[0018] This application provides a centralized intelligent steel reinforcement material yard management system and method based on digital analysis. It can construct a digital twin model of the material yard's inventory, achieving bidirectional mapping between physical stacking locations and the virtual model, and real-time visual monitoring of inventory status. By generating a processing demand time sequence, it incorporates the temporal characteristics and priority information of processing tasks into the analysis and correlates them with inventory status data to accurately quantify the inventory-demand matching degree. Based on this, it can automatically identify low-matching and high-matching stacking locations, generating steel reinforcement adjustment schemes and replenishment procurement schemes between stacking locations. Furthermore, with the goal of minimizing the total handling distance within the material yard, it constructs an objective function and constraints for stacking location allocation and optimizes the solution, generating optimal stacking strategy instructions to effectively reduce handling costs. Simultaneously, through a substitution conversion factor mechanism, it can automatically convert equivalent inventory quantities in scenarios where steel reinforcement substitution is permitted, maximizing the utilization of existing resources. In addition, through dynamic updates of the digital twin model and space utilization monitoring, it can automatically generate stacking location merging and partition reorganization schemes, continuously improving the space utilization efficiency of the material yard. This application realizes the transformation of aggregate yard management from passive recording to proactive intelligent decision-making, which significantly improves the material supply capacity and overall operational efficiency in the scenario of centralized steel bar processing.

[0019] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0020] Figure 1 A system architecture diagram of a centralized intelligent steel reinforcement aggregate yard management system based on digital analysis is provided for embodiments of this application; Figure 2 A flowchart illustrating a centralized intelligent steel reinforcement aggregate yard management method based on digital analysis, provided for an embodiment of this application; Figure 3 A flowchart illustrating a method for constructing a digital twin model of a material storage yard, as provided in an embodiment of this application; Figure 4 This is a flowchart illustrating a processing demand timing sequence method provided in an embodiment of this application. Detailed Implementation

[0021] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] The method for managing centralized steel reinforcement aggregate yards based on digital analysis provided in this application can be applied to, for example... Figure 1 The intelligent management system for the aggregate yard shown includes: a data acquisition module, a modeling module, an analysis module, a decision optimization module, and an instruction execution module.

[0023] The data acquisition module is used to acquire spatial structure data of the material yard through 3D laser scanning or BIM import, collect steel bar inventory data through weight sensors, RFID readers and barcode scanning terminals, and acquire steel bar processing demand data through the data interface of the enterprise resource planning system or production execution system.

[0024] The modeling module is used to construct a three-dimensional digital space model of the aggregate yard based on the spatial structure data, and to map the spatial location and storage attributes of each steel bar pile in the three-dimensional digital space model based on the steel bar storage data, thereby generating a digital twin model of the aggregate yard storage.

[0025] The analysis module is used to generate a time series of processing requirements based on the steel bar processing demand data, and to perform correlation analysis between the time series of processing requirements and the inventory status data to generate the inventory-demand matching degree.

[0026] The decision optimization module is used to perform digital analysis on the material storage structure of the aggregate yard based on the material-demand matching degree, and generate an optimized material storage structure scheme.

[0027] The instruction execution module is used to generate stacking strategy instructions for each steel bar stacking location based on the material storage structure optimization scheme, and update the digital twin model of the aggregate yard storage according to the stacking strategy instructions.

[0028] To address the technical problems in existing centralized steel bar processing and aggregate yard management, such as unreal-time perception of material status, inaccurate matching of material inventory with demand, and insufficient utilization of yard space, this application provides a digital analysis-based intelligent centralized steel bar processing and aggregate yard management method. This method includes: acquiring spatial structure data of the aggregate yard, steel bar inventory data, and steel bar processing demand data; constructing a three-dimensional digital spatial model of the aggregate yard based on the spatial structure data, and mapping the spatial location and inventory attributes of each steel bar stack in the three-dimensional digital spatial model based on the steel bar inventory data, generating a digital twin model of the aggregate yard inventory; generating a processing demand time series based on the steel bar processing demand data, and performing correlation analysis between the processing demand time series and the inventory status data in the aggregate yard inventory digital twin model to generate an inventory-demand matching degree; performing digital analysis on the inventory structure of the aggregate yard based on the inventory-demand matching degree to generate an optimized inventory structure scheme; and generating stacking strategy instructions for each steel bar stack based on the optimized inventory structure scheme, and updating the aggregate yard inventory digital twin model according to the stacking strategy instructions. Based on this, we have achieved visualized perception of the material storage status in the aggregate yard, precise matching of storage and demand, and dynamic optimization of the yard space.

[0029] like Figure 2 As shown in the embodiments of this application, the method for managing centralized intelligent steel reinforcement aggregate yards based on digital analysis includes: S201. Obtain spatial structure data of the aggregate yard, steel bar inventory data, and steel bar processing requirements data.

[0030] The spatial structure data of the aggregate yard includes the boundary outline of the aggregate yard, the division of each stacking area, the layout of passageways, and the location information of processing areas. This data can be obtained through 3D laser scanning or importing from Building Information Modeling (BIM). The steel bar inventory data includes the steel bar specifications, quantity, batch information, warehousing time, and spatial coordinates of each stacking location. This data can be collected through weight sensors, RFID readers, and barcode scanning terminals installed at each stacking location. The steel bar processing demand data includes the required steel bar specifications, quantity, timeframe, and processing priority information for each processing task. This data can be obtained through the data interface of an Enterprise Resource Planning (ERP) system or a Manufacturing Execution System (MES).

[0031] It should be noted that the above data can be acquired using a combination of various technologies. 3D laser scanning can quickly obtain precise spatial geometric information of the aggregate yard, while BIM import is suitable for construction scenarios with existing BIM models. Weight sensors can automatically weigh and record the weight of steel bars, while RFID readers and barcode scanning terminals are used for the automatic identification and input of steel bar batch information and specifications. Obtaining processing demand data through the data interface of ERP or MES systems can achieve automatic synchronization of demand information, avoiding delays and errors caused by manual input.

[0032] S202. Construct a three-dimensional digital space model of the aggregate yard based on the spatial structure data, and map the spatial location and storage attributes of each steel bar pile in the three-dimensional digital space model based on the steel bar storage data to generate a digital twin model of the aggregate yard storage.

[0033] S203. Generate a processing demand time series based on the steel bar processing demand data, and perform correlation analysis between the processing demand time series and the material status data in the digital twin model of the aggregate yard inventory to generate the inventory-demand matching degree.

[0034] S204. Based on the material-demand matching degree, perform digital analysis on the material storage structure of the material yard and generate an optimized material storage structure scheme.

[0035] S205. Based on the material storage structure optimization scheme, generate stacking strategy instructions for each steel bar stacking location, and update the digital twin model of the aggregate yard material storage according to the stacking strategy instructions.

[0036] Based on the above technical solutions, the intelligent steel reinforcement aggregate yard management method based on digital analysis provided in this application realizes bidirectional mapping and real-time synchronization between the physical aggregate yard and the virtual model by constructing a digital twin model of the aggregate yard storage. This allows managers to intuitively grasp the storage status of each stack location through a three-dimensional visualization interface. By generating a processing demand time sequence and calculating the storage-demand matching degree, the time sequence characteristics and spatial distance factors of processing demands are incorporated into the matching analysis, improving the accuracy of the matching between storage and demand. Through the automatic generation of storage structure optimization schemes and stacking strategy instructions, a closed-loop management system is realized from storage status perception to decision optimization and instruction execution, significantly improving the intelligence level of aggregate yard management.

[0037] Furthermore, in this embodiment, the formula for calculating the material-demand matching degree comprehensively considers the ratio of available inventory to demand, the degree of specification matching, and the spatial distance from the stacking location to the processing area, which can fully reflect the contribution of each stacking location to the overall matching degree. When the available inventory of a certain stacking location exceeds the demand, the ratio is taken as 1, avoiding the distortion of matching degree calculation by excessive inventory. The introduction of the spatial distance attenuation factor makes the matching degree calculation more in line with the handling cost considerations in actual production.

[0038] In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 3 As shown, the construction of the digital twin model of the aggregate yard storage in S202 above can be specifically achieved through the following S301-S303, which are explained in detail below: S301. Based on the spatial location information in the steel bar storage data, mark the spatial occupancy area corresponding to each physical stacking location in the three-dimensional digital space model, and establish a data index link between the spatial occupancy area and the corresponding steel bar storage attribute data.

[0039] The spatial occupancy area needs to be defined by drawing corresponding 3D blocks in the 3D digital space model based on the actual boundary coordinates of each physical stack location. Data indexing can be implemented using database primary key associations. That is, a unique identifier is assigned to each stack location digital entity in the 3D digital space model, and this identifier is also used as a foreign key in the material storage attribute database. The linkage between the model and the data is achieved through the mapping of identifiers.

[0040] In some implementations, point cloud data acquired through 3D laser scanning can be used to automatically identify the stacking site boundaries, reducing the workload of manual calibration. For construction scenarios with existing BIM models, the spatial geometric information of the stacking site can be directly extracted from the BIM model.

[0041] It should be noted that the establishment of data index links is the foundation for the dynamic updating capability of the digital twin model. When the storage data of the physical stack location changes, only the corresponding record in the database needs to be updated, and the digital entity of the stack location in the three-dimensional digital space model can automatically obtain the latest data and update its display status through the index links.

[0042] For example, for a steel bar stack numbered A-03, a corresponding spatial occupancy area is established in the three-dimensional digital space model and assigned a unique identifier "STACK_A_03". The steel bar specifications, quantity, batch, and storage time of this stack are stored in the material storage attribute database with "STACK_A_03" as the primary key. The spatial model and attribute data are associated through this identifier.

[0043] S302. Based on data index links, load the storage attribute data of each stack location into a three-dimensional digital space model to form a stack location digital entity with attribute labels.

[0044] The inventory attribute data includes, but is not limited to, rebar specifications (such as diameter, material grade, and length), rebar quantity, rebar batch information, warehousing time, and stacking location spatial coordinates. The stacking location digital entity with attribute tags is not only presented as a three-dimensional geometric block in the 3D digital space model, but also includes visual attribute tags. Users can view detailed inventory information for that stacking location by clicking or hovering over it.

[0045] In some implementations, the digital entities in the stack can be color-coded according to different values ​​of the inventory attribute data. For example, green can indicate sufficient inventory, yellow can indicate moderate inventory, and red can indicate insufficient inventory, thereby achieving intuitive visualization of the inventory status.

[0046] It should be noted that the content and format of the attribute labels can be customized according to management needs. For example, only key information (specifications and quantity) can be displayed to keep the interface simple, or a complete list of inventory attributes can be displayed for detailed query.

[0047] S303. According to the actual spatial arrangement order of each physical stack location, the digital entities of each stack location are spatially reorganized in the three-dimensional digital space model to generate a digital twin model of the aggregate yard storage.

[0048] Spatial reorganization refers to the arrangement of digital entities of each stockpile in a three-dimensional digital space model according to their actual spatial arrangement in the physical aggregate yard, ensuring that the relative positional relationship, channel width, and stockpile spacing between stockpiles in the virtual model are consistent with those in the physical aggregate yard.

[0049] In some implementations, a coordinate transformation matrix can be used to transform the digital entities in each stack position from the local coordinate system to the global coordinate system, achieving precise spatial positioning. After spatial reorganization, path markers need to be added to the passage areas between stack positions to facilitate subsequent calculation of transport distances and path planning.

[0050] Based on the above technical solutions, the constructed digital twin model of the aggregate yard not only has a spatial geometric structure that corresponds one-to-one with the physical aggregate yard, but also can reflect the storage attribute status of each stockpile in real time, providing an accurate data foundation for subsequent storage-demand matching degree analysis and storage structure optimization.

[0051] In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 4 As shown, in S203 above, the processing demand time sequence is generated based on the steel bar processing demand data. This can be specifically achieved through S401-S404, which are explained in detail below: S401. Obtain steel bar processing requirement data, which includes steel bar specification parameters, required quantity, required time node, and processing priority information for each processing task.

[0052] Among them, the specifications of the steel bars include the diameter, material grade, and length of the steel bars; the required quantity is the quantity of steel bars of various specifications required for the processing task; the required time node is the planned start or completion time of the processing task; and the processing priority information reflects the importance and urgency of each processing task.

[0053] In some implementations, processing demand data can be periodically synchronized with the data interface of the ERP system or MES system, or a message queue can be used to push demand data in real time.

[0054] It should be noted that the acquired processing demand data should contain clear time attributes so that subsequent demand time series analysis can be performed according to the time dimension.

[0055] S402. Using a preset time granularity as the time axis discrete unit, classify and merge the steel bar processing demand data according to the steel bar specification parameters, and map the demand quantity corresponding to each specification to the corresponding discrete time interval to generate the initial demand time sequence subsequence for each steel bar specification.

[0056] The preset time granularity can be set according to the management needs of the aggregate yard, such as by day, shift, or hour. The smaller the time granularity, the higher the accuracy of the demand time series analysis, but the computational complexity also increases accordingly. For scenarios involving centralized rebar processing, a time granularity of day or shift is usually more appropriate.

[0057] Categorization and consolidation refers to combining the processing demands of steel bars with the same specifications into a single statistical count. Specifically, for steel bars of the same specification, the demand for that specification across different processing tasks is aggregated into the corresponding time interval. For example, if there are three processing tasks on July 15, 2026, requiring 2 tons, 3 tons, and 5 tons of HRB400 steel bars with a diameter of 25mm, the combined demand for that specification on July 15th would be 10 tons.

[0058] It should be noted that when classifying and merging, the temporal distribution characteristics of the demand for the same specification of steel bars by different processing tasks should also be considered in order to generate an accurate demand time sequence.

[0059] S403. Based on the processing priority information, the demand quantity in each discrete time interval of the initial demand time sequence is weighted and corrected to generate a weighted demand time sequence for each steel bar specification.

[0060] The weighted adjustment is designed to reflect the higher requirements for material availability for high-priority processing tasks. Specifically, different weighting coefficients can be assigned to different priorities, with higher priorities having larger weighting coefficients. Then, the demand quantity in each time interval is multiplied by the corresponding weighting coefficient to obtain the weighted demand quantity.

[0061] In some implementations, a three-tier priority system can be used: high priority (such as urgent tasks and critical path tasks) has a weighting coefficient of 1.2, medium priority has a weighting coefficient of 1.0, and low priority has a weighting coefficient of 0.8. After weighting adjustment, the needs of high priority tasks will receive a higher weight in the matching degree calculation, thus ensuring their material supply is guaranteed first.

[0062] It should be noted that the specific values ​​of the weighting coefficients can be adjusted according to actual management needs, but the relative size relationship between each priority should be kept reasonable.

[0063] S404. The weighted demand time sequence subsequences of each steel bar are time-stacked and merged according to discrete time intervals to generate the processing demand time sequence.

[0064] The temporal overlay and merging refers to superimposing the weighted demand time series subsequences of each steel rebar specification on the same time interval to form a comprehensive demand time series sequence covering all steel rebar specifications. This sequence uses the time interval as the horizontal axis and the total weighted demand of all steel rebar specifications in each time interval as the vertical axis, reflecting the distribution characteristics of the overall processing demand of the aggregate yard in the time dimension.

[0065] Based on the above technical solution, the generated processing demand time series not only considers the demand distribution of different steel bar specifications, but also reflects the impact of processing priority through weighted correction, making the subsequent inventory-demand matching degree analysis more in line with actual production management needs.

[0066] In one possible implementation of this application embodiment, the "obtaining the inventory-demand matching degree" in S203 above can be specifically implemented through the following S501-S502, which are described in detail below: S501. Match the specifications and quantities of each demand item in the processing demand time sequence with each steel bar stack in the inventory status data, and calculate the demand coverage rate of each steel bar stack.

[0067] Specification matching refers to comparing the specifications of the steel bars in the demand project with the specifications of the materials in stock at each storage location to determine whether they match and the degree of matching. Quantity matching refers to comparing the demand quantity of the demand project with the available inventory quantity at the corresponding storage location to calculate the ratio of available inventory quantity to demand quantity, i.e., demand coverage ratio.

[0068] In some implementations, specification matching can be further divided into complete matching and partial matching: complete matching means that the diameter, material grade, length, and other parameters of the reinforcing bars are all consistent; partial matching means that the main parameters (such as diameter and material grade) are consistent, but the secondary parameters (such as length) differ. For partial matching, the matching coefficient can be determined based on the degree of difference. The value of .

[0069] S502. Calculate the overall inventory-demand matching degree of the aggregate yard based on the demand coverage rate of each rebar stack and the spatial distance weight of each rebar stack relative to the processing area; where the overall inventory-demand matching degree of the aggregate yard... Calculated using the following formula:

[0070] in, This is the sequence number of the rebar stacking location. This represents the total number of rebar stacking locations. For the first Available inventory at each steel rebar stack location Indicates the relationship with the first The processing requirements corresponding to each rebar stack location. For the first The matching coefficient between the rebar specifications and the required specifications at each rebar stack location is used to characterize the degree of matching between the rebar diameter, material, and required specifications. , Spatial distance attenuation factor, For the first The distance between the steel bar stack and the processing area. This refers to the maximum transport distance within the material collection yard; When the When there is no corresponding project requirement for a particular rebar stacking location. And this term does not participate in the summation; when When >1, take =1.

[0071] The physical meaning of this formula is: the contribution of each stack location to the overall matching degree is determined by the product of three factors, namely, the inventory satisfaction rate ( (Maximum value is 1), specification matching degree ( The contribution of a stacking site is determined by its spatial distance and the attenuation factor (exponential term). The farther the stacking site is in space, the lower its contribution. This encourages the priority use of stacking sites closer to the processing area to reduce handling costs.

[0072] It should be noted that, in one possible implementation of the embodiments of this application, the processing of specification matching, in addition to directly calculating the matching coefficient, In addition, a refined matching method can be used by converting equivalent inventory quantities. Specifically, when the specifications of the steel reinforcement stored at a certain location differ from those of the required project, but the relevant engineering specifications (such as the "Code for Design of Concrete Structures") explicitly allow for steel reinforcement substitution, the available inventory quantity at that location can be used as the basis for calculation. After converting the stock to equivalent inventory using a substitution conversion factor, the equivalent inventory is then substituted into the overall inventory-demand matching formula for the aggregate yard. The substitution conversion factor is determined based on the ratio of the mechanical performance indicators of the substitute steel bars to the required steel bars. For example, when using "equal strength substitution," the conversion factor is the ratio of the tensile strength design value of the substitute steel bars to the required steel bars; when using "equal area substitution," the conversion factor is the ratio of their cross-sectional areas. This conversion method maximizes the utilization of existing steel bars in the aggregate yard while ensuring the structural safety of the project, effectively reducing the frequency of replenishment purchases due to minor specification differences and further saving material costs.

[0073] Based on the above technical solution, a quantitative index of the overall inventory-demand matching degree of the collection yard can be obtained. This index comprehensively reflects the matching status of three dimensions: inventory quantity, specification matching, and spatial layout, providing a clear quantitative basis for subsequent inventory structure optimization.

[0074] In one possible implementation of this application embodiment, the above-mentioned S204 (generation of the inventory structure optimization scheme) can be specifically implemented by the following S601-S602, which will be described in detail below: S601. When the overall material storage-demand matching degree of the aggregate yard is lower than the preset threshold, traverse the demand coverage of each steel bar pile and identify the low matching pile with a demand coverage of lower than the first preset value and the high matching pile with a demand coverage of higher than the second preset value.

[0075] The preset threshold is used to determine whether the current inventory structure needs optimization. When the matching degree M is lower than the threshold, the optimization process is triggered. The first preset value and the second preset value are used to identify low-matching and high-matching stack locations. The first preset value is usually low (e.g., 0.3), indicating that the inventory of the stack location is severely insufficient; the second preset value is usually high (e.g., 0.9), indicating that the inventory of the stack location is relatively abundant.

[0076] In some implementations, the preset threshold, the first preset value, and the second preset value can be dynamically adjusted based on historical data and actual management experience. For example, during peak construction periods, the preset threshold can be appropriately lowered to tolerate a certain decrease in matching degree, while during off-seasons, the preset threshold can be appropriately raised to pursue a higher matching degree.

[0077] S602. Based on the shortage specifications and quantities of materials in the low-matching stacks and the surplus specifications and quantities in the high-matching stacks, generate a steel bar adjustment plan and a replenishment procurement plan between stacks as an optimization plan for the material storage structure.

[0078] The rebar relocation plan refers to transferring surplus rebar from high-matching stockpiles to low-matching stockpiles to alleviate material shortages in the low-matching stockpiles. The generation of the relocation plan needs to consider the following factors: whether the stockpiling location has sufficient surplus quantity, whether the receiving stockpiles have sufficient space, whether the rebar specifications match, and whether the relocation route is reasonable.

[0079] A replenishment procurement plan refers to a plan for purchasing materials from external suppliers to address shortages that cannot be met even after adjustments. The procurement plan should include information such as the specifications and quantity of the steel bars to be procured, the suggested procurement time, and recommended suppliers.

[0080] In some implementations, the inter-stall allocation plan can be prioritized because it does not involve external procurement costs and has a faster response time; for the material shortage that cannot be covered by the allocation, the replenishment procurement plan can then be implemented.

[0081] Based on the above technical solutions, through the implementation of S601 to S602, targeted optimization solutions can be automatically generated when the material inventory-demand matching is insufficient. By combining internal adjustments and external procurement, the rationality of the material inventory structure can be quickly restored, ensuring that processing needs are effectively guaranteed.

[0082] In one possible implementation of this application embodiment, the "generation of stacking strategy instructions for each rebar stack location" in S205 above can be specifically implemented through the following S701-S702, which will be described in detail below: S701. Obtain the current occupancy status and available space parameters for each rebar stack location.

[0083] The current occupancy status includes the specifications, quantity, and volume of the steel bars already stored in each stack location; the available space parameters include the maximum capacity of each stack location (by weight or volume), the remaining available space, and the size limitations of the stack location (such as the maximum stacking height).

[0084] In some implementations, the available space parameters can be directly calculated using a three-dimensional digital twin space model, that is, the remaining available space is automatically calculated based on the geometric dimensions of the digital entity of the stack and the currently occupied volume.

[0085] S702. Based on the steel bar specification allocation plan in the material storage structure optimization scheme, combined with the spatial coordinates of each steel bar stacking location, the available space parameters, and the expected usage time of each steel bar, with the goal of minimizing the total handling distance within the aggregate yard, determine the optimal stacking location allocation for each steel bar and generate stacking strategy instructions.

[0086] The optimal stockpile allocation is determined with the goal of minimizing the total transport distance within the aggregate yard. Specifically, this includes: constructing an objective function and constraints for the stockpile allocation; the objective function aims to minimize the total transport distance of all steel bars from their storage locations to the processing area; the total transport distance is the sum of the products of the single transport distance of each steel bar and the frequency of use of that steel bar; the constraints include the spatial capacity constraints of each stockpile location, the load-bearing constraints of each stockpile location, and the matching constraints between the specifications of each steel bar and the specifications of the stockpile location; and using heuristic optimization algorithms (such as genetic algorithms, simulated annealing algorithms, or particle swarm optimization algorithms) to solve the objective function and constraints to obtain the optimal stockpile allocation.

[0087] When determining the optimal stacking location allocation, the expected usage time is an important consideration: rebars with an earlier expected usage time should be prioritized for allocation to stacking locations closer to the processing area to reduce handling distance; rebars with a later expected usage time can be allocated to stacking locations further away. This allocation strategy of "placing near-term demand with far-term demand" can further optimize the overall handling cost over time.

[0088] In some implementations, a multi-objective optimization method can be used to minimize the total handling distance while also taking into account auxiliary objectives such as stack load balancing and minimizing channel congestion, in order to obtain a more comprehensive optimal allocation scheme.

[0089] Based on the above technical solution, the generated stacking strategy instructions can achieve the optimal spatial layout of each steel bar in the material yard while meeting the capacity and load-bearing constraints of each stacking position, thereby minimizing the handling cost.

[0090] In one possible implementation of the embodiments of this application, after S205, the method for managing centralized intelligent steel reinforcement aggregate yards based on digital analysis provided in the embodiments of this application further includes the following S801-S802: S801. Map the stacking location allocation information in the stacking strategy instruction to the three-dimensional digital twin space model, and update the storage attribute data of the corresponding stacking location.

[0091] Specifically, the stacking location allocation information mapping refers to writing the correspondence between each steel bar and the target stacking location determined in the stacking strategy instruction into the three-dimensional digital space model, and updating the material storage attribute data (such as steel bar specifications, quantity, batch, etc.) of each stacking location digital entity. After the update is completed, the material storage status in the three-dimensional digital space model is consistent with the actual material storage status in the physical aggregate yard.

[0092] In some implementations, the update operation can be triggered automatically, meaning the system automatically updates the model after the stacking strategy instruction is generated; alternatively, a semi-automatic approach can be used, where the update is manually triggered after the administrator confirms the instruction.

[0093] S802. Based on the updated material storage attribute data, recalculate the space utilization rate of each stockpile and the overall space utilization rate of the aggregate yard. When the overall space utilization rate of the aggregate yard is lower than the preset utilization rate threshold, generate an aggregate yard space layout adjustment plan. The space layout adjustment plan includes a stockpile merging plan and a stockpile partitioning and reorganization plan.

[0094] Among them, the utilization rate of the stacking space refers to the ratio of the space occupied by the stacking space to the total space that can be accommodated; the overall space utilization rate of the aggregate yard is the ratio of the sum of the occupied space of all stacking spaces to the sum of the total space that can be accommodated by all stacking spaces. When the overall space utilization rate is lower than the preset threshold, it indicates that there is a lot of idle space in the aggregate yard, and layout adjustments are needed.

[0095] The stacking location consolidation scheme refers to merging multiple adjacent stacking locations with low utilization rates into a larger stacking location to reduce space loss between stacking locations and improve space utilization. The stacking location zoning and reorganization scheme refers to re-dividing stacking location areas according to attributes such as the specification type and usage frequency of the reinforcing bars, concentrating frequently used reinforcing bars in areas closer to the processing area.

[0096] Based on the above technical solution, the dynamic updating of the digital twin model of the aggregate yard and the continuous monitoring of space utilization are realized. When the space utilization is insufficient, the layout adjustment plan is automatically generated to ensure that the aggregate yard space is always in a state of efficient utilization.

[0097] Some of the data in the above formula are calculated by removing dimensions and taking their numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

Claims

1. A method for managing centralized intelligent steel reinforcement material yards based on digital analysis, characterized in that: include: Acquire spatial structure data of the aggregate yard, steel bar inventory data, and steel bar processing demand data; A three-dimensional digital space model of the aggregate yard is constructed based on the spatial structure data, and the spatial location and storage attributes of each steel bar pile are mapped in the three-dimensional digital space model based on the steel bar storage data, thereby generating a digital twin model of the aggregate yard storage. Based on the steel bar processing demand data, a processing demand time series is generated, and a correlation analysis is performed between the processing demand time series and the storage status data in the digital twin model of the aggregate yard storage to generate a storage-demand matching degree. Based on the matching degree of inventory and demand, a digital analysis is performed on the inventory structure of the material collection yard to generate an optimization scheme for the inventory structure. Based on the material storage structure optimization scheme, stacking strategy instructions for each steel bar stack location are generated, and the digital twin model of the aggregate yard material storage is updated according to the stacking strategy instructions.

2. The method for managing centralized intelligent steel reinforcement material yards based on digital analysis according to claim 1, characterized in that, The construction of the digital twin model of the aggregate yard storage includes: Based on the spatial location information in the steel bar storage data, spatial occupancy areas corresponding to each physical stacking location are marked in the three-dimensional digital space model, and a data index link is established between the spatial occupancy areas and the corresponding steel bar storage attribute data. Based on the data index link, the storage attribute data of each stack location is loaded into the three-dimensional digital space model to form a stack location digital entity with attribute labels. According to the actual spatial arrangement of each physical stack location, the digital entities of each stack location are spatially reorganized in the three-dimensional digital space model to generate the digital twin model of the aggregate yard storage.

3. The method for managing centralized intelligent steel reinforcement material yards based on digital analysis according to claim 1, characterized in that, The step of generating a processing demand time sequence based on the steel bar processing demand data includes: Obtain the steel bar processing requirement data, which includes the steel bar specification parameters, required quantity, required time node, and processing priority information for each processing task; Using a preset time granularity as the time axis discrete unit, the steel bar processing demand data is classified and merged according to the steel bar specification parameters, and the demand quantity corresponding to each specification is mapped to the corresponding discrete time interval to generate the initial demand time sequence subsequence for each steel bar specification. Based on the processing priority information, the demand quantity in each discrete time interval of the initial demand time sequence is weighted and corrected to generate a weighted demand time sequence for each steel bar specification. The weighted demand time series subsequences of each steel bar are time-series superimposed and merged according to the discrete time interval to generate the processing demand time series sequence.

4. The method for managing centralized intelligent steel reinforcement material yards based on digital analysis according to claim 1, characterized in that, The method for obtaining the inventory-demand matching degree includes: The specifications and quantities of each demand item in the processing demand time sequence are matched with each steel bar stack in the inventory status data, and the demand coverage rate of each steel bar stack is calculated. Calculate the overall material storage-demand matching degree of the aggregate yard based on the demand coverage of each steel bar stack and the spatial distance weight of each steel bar stack relative to the processing area. Among them, the overall material storage-demand matching degree of the aggregate yard Calculated using the following formula: in, This is the sequence number of the rebar stacking location. This represents the total number of rebar stacking locations. For the first Available inventory at each steel rebar stack location Indicates the relationship with the first The processing requirements corresponding to each rebar stack location. For the first The matching coefficient between the rebar specifications and the required specifications for each rebar stack location. , Spatial distance attenuation factor, For the first The distance between the steel bar stack and the processing area. This refers to the maximum transport distance within the material collection yard; When the When there is no corresponding project requirement for a particular rebar stacking location. And this term does not participate in the summation; when When >1, take =1.

5. The method for managing centralized intelligent steel reinforcement material yards based on digital analysis according to claim 4, characterized in that, The method for generating the optimized storage structure includes: When the overall material storage-demand matching degree of the aggregate yard is lower than a preset threshold, the demand coverage rate of each steel bar stack is traversed to identify low-matching stacks with a demand coverage rate lower than a first preset value and high-matching stacks with a demand coverage rate higher than a second preset value. Based on the shortage specifications and quantities of the low-matching stacks and the surplus specifications and quantities of the high-matching stacks, a steel bar adjustment plan and a replenishment procurement plan between the stacks are generated as the material storage structure optimization plan.

6. The method for managing centralized intelligent steel reinforcement material yards based on digital analysis according to claim 1, characterized in that, The method for generating the stacking strategy instructions for each rebar stack location includes: Obtain the current occupancy status and available space parameters of each rebar stack location; based on the rebar specification allocation plan in the material storage structure optimization scheme, combined with the spatial coordinates of each rebar stack location, available space parameters, and the expected usage time of each rebar, determine the optimal stack location allocation for each rebar with the goal of minimizing the total handling distance within the aggregate yard, and generate the stacking strategy instruction.

7. The method for managing centralized intelligent steel reinforcement material yards based on digital analysis according to claim 6, characterized in that, The determination of the optimal stacking location for each steel bar, with the goal of minimizing the total handling distance within the aggregate yard, includes: Construct an objective function and constraints for the allocation of stockpile locations in the aggregate yard. The objective function aims to minimize the total transport distance of all steel bars from their storage locations to the processing area. The total transport distance is the sum of the products of the single transport distance of each steel bar and the frequency of use of that steel bar. The constraints include the space capacity constraints of each stack location, the load-bearing constraints of each stack location, and the matching constraints between the specifications of each steel bar and the specifications of the stack location. The objective function and constraints are solved using a heuristic optimization algorithm to obtain the optimal heap space allocation.

8. The method for managing centralized intelligent steel reinforcement material yards based on digital analysis according to claim 1, characterized in that, The step of updating the digital twin model of the aggregate yard inventory according to the stacking strategy instruction includes: The stacking location allocation information in the stacking strategy instruction is mapped to the three-dimensional digital twin space model, and the storage attribute data of the corresponding stacking location is updated. Based on the updated storage attribute data, the space utilization rate of each stacking location and the overall space utilization rate of the aggregate yard are recalculated. When the overall space utilization rate of the aggregate yard is lower than the preset utilization rate threshold, an aggregate yard space layout adjustment scheme is generated. The space layout adjustment scheme includes a stacking location merging scheme and a stacking location partitioning and reorganization scheme.

9. The method for managing centralized intelligent steel reinforcement material yards based on digital analysis according to claim 1, characterized in that, The methods for obtaining the spatial structure data of the aggregate yard, the steel bar inventory data, and the steel bar processing demand data include: The spatial structure data of the aggregate yard is obtained by 3D laser scanning or building information model import. The spatial structure data includes the boundary outline of the aggregate yard, the division of each stacking area, the channel layout and the location information of the processing area. The steel bar storage data is collected by weight sensors, RFID readers and barcode scanning terminals installed at each stack location. The steel bar storage data includes the steel bar specifications, quantity, batch information, storage time and spatial coordinates of each stack location. The steel bar processing requirement data is obtained through the data interface of the enterprise resource planning system or the production execution system. The steel bar processing requirement data includes the required steel bar specifications, required quantity, required time nodes, and processing priority information for each processing task.

10. A centralized intelligent steel reinforcement aggregate yard management system based on digital analysis, operating based on the centralized intelligent steel reinforcement aggregate yard management method based on digital analysis as described in any one of claims 1-9, characterized in that, It includes a data acquisition module, a modeling module, an analysis module, a decision optimization module, and an instruction execution module; The acquisition module is used to acquire spatial structure data of the aggregate yard, steel bar storage data, and steel bar processing demand data. The modeling module is used to construct a three-dimensional digital space model of the aggregate yard based on the spatial structure data, and to map the spatial location and storage attributes of each steel bar pile in the three-dimensional digital space model based on the steel bar storage data, thereby generating a digital twin model of the aggregate yard storage. The analysis module is used to generate a processing demand time series based on the steel bar processing demand data, and to perform correlation analysis between the processing demand time series and the inventory status data to generate an inventory-demand matching degree. The decision optimization module is used to perform digital analysis on the storage structure of the material yard based on the material-demand matching degree, and generate an optimized storage structure scheme. The instruction execution module is used to generate stacking strategy instructions for each steel bar stacking position based on the material storage structure optimization scheme, and update the digital twin model of the aggregate yard storage according to the stacking strategy instructions.

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