Intelligent beam field collaborative optimization system based on CPPS unit autonomous architecture

Abstract

The invention discloses an intelligent beam field collaborative optimization system based on a CPPS unit autonomous architecture, and the system comprises a CPPS unit autonomous module which receives a task instruction through an RFID recognition beam type tag, activates a box beam mold plate after receiving a box beam task, and activates a T beam support when receiving a T beam task; the system architecture comprises a physical layer, a network layer and a virtual layer and is used for cooperating with the CPPS unit autonomous module to realize dynamic adjustment scheduling of the multi-beam production line; the physical layer is used for providing data for the virtual layer, the data middle platform is used for integrating data sources of the network layer and providing data for the virtual layer, the physical layer comprises an intelligent pedestal, and a millimeter wave radar is arranged on the intelligent pedestal. The reconfigurable CPPS unit is adopted, a core plate is based on RFID identification and hydraulic quick change, two-hour dynamic recombination of a production line can be completed, and dynamic adjustment and production scheduling of the production line are achieved.

Description

Technical Field

[0001] This invention relates to the field of industrial Internet of Things (IoT) technology, specifically to a smart beam yard collaborative optimization system based on the CPPS unit autonomous architecture. Background Technology

[0002] The beam yard collaborative system is a management platform based on information technology and Internet technology. It digitally integrates the production, management and transportation of beam yards to support production planning, quality control, inventory management, transportation management and data analysis. Currently, it has become an important tool for improving the operational efficiency and management level of beam yards. However, due to limitations in beam yard production lines, traditional beam yard collaborative systems still have the following problems in their application: 1. When switching beam types (e.g., box girder → T-beam), production must be suspended for ≥3 days, resulting in a 35% loss of production capacity; 2. The distributed beam yard is out of sync with the bridge erection schedule, with a backlog rate of 40% in the storage yard; 3. Simultaneously, heterogeneous equipment (different PLC sensor brands) causes data communication delays >200ms, resulting in data fragmentation; 4. The MES system relies on manual adjustments, and the response time to sudden disturbances is >30 minutes.

[0003] To address these issues, this invention provides a smart beam yard collaborative optimization system based on the CPPS unit autonomous architecture. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a smart beam yard collaborative optimization system based on the CPPS unit autonomous architecture, which solves the aforementioned problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a smart beam yard collaborative optimization system based on a CPPS unit autonomous architecture, comprising: The CPPS unit autonomous module receives task instructions by identifying beam-type tags with RFID. After receiving a box girder task, it activates the box girder mold plate, and after receiving a T-beam task, it activates the T-beam support. The system architecture includes a physical layer, a network layer, and a virtual layer, which are used to work with the CPPS unit autonomous module to achieve dynamic production scheduling adjustment for multi-beam production lines. The data platform is used to integrate network layer data sources and provide data to the virtual layer.

[0006] Preferably, the physical layer includes: The intelligent platform is equipped with a millimeter-wave radar for detecting the deformation of box girders or irregular T-beams; The steam curing kiln is equipped with an infrared temperature control array system to control the steam curing temperature and dynamically compress the steam curing time. AGV, which is equipped with a Beidou positioning module, is used to transport box girder support frames or irregular T-beam support frames; Automatic tensioning machine is used to calibrate the prestress of box girders and irregular T-beams.

[0007] Preferably, the network layer includes: The edge gateway is used to receive pressure data from the automatic tensioning machine, to receive the location information of the AGV in conjunction with the Beidou positioning module, and to receive the strain data of the box girder or irregular T-beam in conjunction with the millimeter-wave radar.

[0008] Preferably, the virtual layer includes: Spatial model, used to generate scene models of beam-type production lines; Digital twins are used to work with spatial models to reflect changes in entities and to simulate and predict entity behavior in real time. The scheduling system is used to control the tensioning path of the automatic tensioning machine, control the vibration parameters of the intelligent platform, and provide navigation instructions to the AGV; An AI decision engine based on a spatiotemporal conflict resolution algorithm is used to process the output information of the digital twin and provide instructions to the scheduling system.

[0009] Preferably, data transmission between the edge gateway and the data center is achieved via the OPC UA over TSN protocol.

[0010] Preferably, the digital twin includes: Building Information Modeling (BIM) is used to provide three-dimensional dynamic models. A finite element model of concrete hydration heat is used to simulate the temperature changes of concrete during the hydration process.

[0011] Preferably, the CPPS unit autonomous module integrates a memory and a processor, wherein the memory is used to store resource scheduling programs and the processor is used to execute resource scheduling programs.

[0012] Preferably, the resource scheduling procedure logic is as follows: S1: Obtain current real-time status data from the digital twin, including AGV position, pressure data of the automatic tensioning machine, and strain data of box girder or irregular T-beam; S2: For each beam segment to be processed, perform conflict detection. If a conflict is detected, the system will record the conflict information, including the conflict type and the beam segment involved. S3: If a conflict is detected, the system will initiate a multi-objective optimization rescheduling mechanism, using a hybrid particle swarm genetic algorithm to re-optimize the scheduling scheme. The system will return the optimized scheduling scheme and conflict report. If there is no conflict, the system will directly return to the original plan without rescheduling.

[0013] Preferably, the spatial model construction steps are as follows: S1: Convert the physical space of the beam yard into a digital model through high-precision scanning; S2: The equipment in the beam yard is abstracted into logical units with states and interfaces. Each unit can be quickly reconfigured and state switched through RFID and hydraulic interfaces. S3: Through a dynamic mapping table of tasks and resources, beam-type tasks are assigned to specific equipment units, and task paths and time windows are optimized through a resource scheduler.

[0014] Preferably, the physical space includes: The intelligent pedestal information includes: a unique ID for each intelligent pedestal, coordinates, load-bearing capacity, beam type compatibility list, and current status; AGV lane information, which includes: AGV lane centerline, width, turning radius, and conflict zone; Mold area information, which includes: the storage locations of box girder molds and T-beam molds and the coordinates of hydraulic quick-change joints; Information on the steam curing kiln, including: kiln geometry, temperature zone distribution, kiln entry and exit logic, and kiln door coordinates; The storage yard information includes: storage blocks, stacking layer limits, and gantry crane rails.

[0015] Beneficial effects This invention provides a smart beam yard collaborative optimization system based on the CPPS unit autonomous architecture. Compared with existing technologies, it has the following advantages: (1) The intelligent beam yard collaborative optimization system based on the CPPS unit autonomous architecture adopts reconfigurable CPPS units. The core module is based on RFID identification + hydraulic quick change, which can complete the dynamic reorganization of the production line in 2 hours and realize the dynamic adjustment and scheduling of the production line.

[0016] (2) The intelligent beam yard collaborative optimization system based on the CPPS unit autonomous architecture can solve the problem of matching the progress of "beam fabrication-erection" by integrating Beidou positioning and GIS data through spatiotemporal collaborative algorithm.

[0017] This intelligent beam yard collaborative optimization system, based on the CPPS unit autonomous architecture, uses digital twins to achieve linkage between geometric, physical, and behavioral models, thereby optimizing concrete performance in real time.

[0018] This intelligent beam yard collaborative optimization system, based on the CPPS unit autonomous architecture, uses OPC UAover TSN to achieve millisecond-level interoperability between 200+ devices. Attached Figure Description

[0019] Figure 1 This is an architectural diagram of the present invention; Figure 2 This is a flowchart of the CPPS unit autonomous module of the present invention; Figure 3 This is a flowchart of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0021] Example 1: Please see Figure 1-2 A smart beam yard collaborative optimization system based on CPPS unit autonomous architecture includes: The CPPS unit autonomous module receives task instructions by identifying beam-type tags with RFID. After receiving a box girder task, it activates the box girder mold plate, and after receiving a T-beam task, it activates the T-beam support. The system architecture includes a physical layer, a network layer, and a virtual layer, which are used to work with the CPPS unit autonomous module to achieve dynamic production scheduling adjustment for multi-beam production lines. The data platform is used to integrate network layer data sources and provide data to the virtual layer. Understandably, through the coordination of CPPS unit autonomous modules and system architecture, hydraulic quick-change joints can be realized through unit activation, enabling mold switching within 2 hours and achieving dynamic adjustment of production scheduling; In this embodiment, the physical layer includes: The intelligent support is equipped with a millimeter-wave radar to detect the deformation (±0.1mm) of box girders or irregular T-beams. The steam curing kiln is equipped with an infrared temperature control array system (accuracy ±0.5℃) to control the steam curing temperature and dynamically compress the steam curing time. AGVs are equipped with Beidou positioning modules and are used to transport box girder support frames or irregular T-beam support frames. Automatic tensioning machine, used to calibrate the prestress of box girders and irregular T-beams; In this embodiment, the BeiDou positioning module can receive BeiDou satellite signals and calculate the three-dimensional coordinates (longitude, latitude, and altitude) of the AGV's location, as well as its speed and time information, including: Antenna: Receives signals transmitted by BeiDou satellites.

[0022] RF front end: processes the signals received by the antenna, including amplification, filtering, and downconversion.

[0023] Baseband processor: Decodes the signals processed by the RF front end and calculates the location information.

[0024] Memory: Stores firmware, algorithms, and location data.

[0025] Interface circuit: Provides an interface for communication with external devices, including but not limited to UART, SPI, and I2C; In this embodiment, the network layer includes: Edge gateways are used to receive pressure data from automatic tensioning machines, receive AGV location information in conjunction with Beidou positioning modules, and receive strain data of box girders or irregular T-beams in conjunction with millimeter-wave radar. In this embodiment, the virtual layer includes: Spatial model, used to generate scene models of beam-type production lines; Digital twins are used to work with spatial models to reflect changes in entities and to simulate and predict entity behavior in real time. The scheduling system is used to control the tensioning path of the automatic tensioning machine, control the vibration parameters of the intelligent platform, and provide navigation instructions to the AGV; An AI decision engine based on a spatiotemporal conflict resolution algorithm is used to process the output information of the digital twin and provide instructions to the scheduling system. More specifically, the spatiotemporal conflict resolution algorithm adopts a four-step closed-loop process of "prediction-detection-optimization-verification". Based on real-time data from the digital twin, it continuously optimizes the production plan and uses the occupancy grid method to dynamically mark the area occupancy status of AGV paths, platforms, and molds, predicting conflicts 15 minutes in advance. At the same time, based on the finite element model of concrete hydration heat, it predicts the strength compliance time of each beam (±30 minutes error) to avoid time conflicts such as "lifting before the strength meets the standard". It can establish a mold life decay model based on the number of stress cycles, predict the remaining usable number of times the mold can be used, and prevent resource conflicts such as "production scheduling despite insufficient mold life". In this embodiment, data transmission between the edge gateway and the data platform is achieved through the OPC UA over TSN protocol. By using the OPC UA over TSN protocol as the core communication protocol of the network layer, all device parameters can be uniformly mapped to standard variables such as "beam type, temperature, and coordinates", eliminating protocol barriers and enabling millisecond-level interoperability of 200+ devices. In this embodiment, the digital twin includes: Building Information Modeling (BIM) is used to provide three-dimensional dynamic models. A finite element model of concrete hydration heat is used to simulate the temperature changes of concrete during the hydration process. In this embodiment, the CPPS unit autonomous module integrates a memory and a processor. The memory is used to store the resource scheduling program, and the processor is used to execute the resource scheduling program. In this embodiment, the resource scheduler logic is as follows: S1: Obtain current real-time status data from the digital twin, including AGV position, pressure data of the automatic tensioning machine, and strain data of box girder or irregular T-beam; S2: For each beam segment to be processed, perform conflict detection. If a conflict is detected, the system will record the conflict information, including the conflict type and the beam segment involved. S3: If a conflict is detected, the system will initiate a multi-objective optimization rescheduling mechanism, using a hybrid particle swarm genetic algorithm to re-optimize the scheduling scheme. The system will return the optimized scheduling scheme and conflict report. If there is no conflict, the system will directly return to the original plan without rescheduling. Furthermore, in this embodiment, collision detection includes: Spatial conflict: Detects whether the beam segment has a spatial conflict with the current position of the AGV.

[0026] Time conflict: Detect whether the planned execution time of this beam segment conflicts with the existing plan.

[0027] Resource conflict: Check whether the equipment resources required for the beam segment are available and whether there are any resource conflicts.

[0028] Furthermore, the AI ​​decision engine has a built-in strategy library that automatically matches the optimal strategy based on the type of conflict. In summary, two case studies illustrate the use of a reconfigurable CPPS unit-based autonomous beam yard collaborative optimization system for beam type switching and sudden changes in beam erection schedule on the Shenzhen Dapeng branch line: (1) Beam type switching: When the cross-sectional shape of the bridge changes, such as when the beam fabrication needs to switch from "box girder" to "irregular T-beam", the beam type label is identified by RFID, and the hydraulic quick-change joint switches the mold. The specific steps are: 1. AGV transports the T-beam support frame to the target platform; 2. The hydraulic system locks the mold (positioning accuracy ±1mm). The total time can be controlled within 1.8 hours.

[0029] (2) Sudden change in beam erection progress: When the bridge erection progress changes abruptly, the bridge erection machine speed can be increased by 20% by using the reconfigurable CPPS unit autonomous beam yard collaborative optimization system. The production cycle is simulated by digital twins to dynamically adjust the bridge erection progress, while the steam curing time is dynamically compressed by controlling the steam curing temperature (temperature control ±0.3℃).

[0030] The "beam type switching" in the Shenzhen Dapeng branch line, from "24m box girder" to "irregular T-beam", is carried out in two steps: (1) The AGV transports the T-beam support frame to the target platform; (2) The hydraulic system is used to lock the mold (positioning accuracy ±1mm), and the total time is 1.8 hours, while the traditional "beam type switching" takes 72 hours, and the time saving rate is 97.5%.

[0031] When the bridge girder erection progress suddenly changes, the bridge erecting machine accelerates by 20%. Digital twin simulation of the production cycle is used to dynamically compress the steam curing time (temperature control ±0.3℃), resulting in a 40% reduction in steam curing energy consumption. Specific measured data comparisons are as follows:

[0032] Compared with other collaborative control systems, the CPPS unit-based autonomous beam yard collaborative optimization system has the following technical advantages:

[0033] In this embodiment, the spatial model construction steps are as follows: S1: The physical space of the beam yard is transformed into a digital model through high-precision scanning, providing a precise physical basis for subsequent dynamic reconstruction and ensuring that the location, size and relationship of all equipment and resources are accurate. S2: The equipment in the beam yard is abstracted into logical units with states and interfaces. Each unit can be quickly reconfigured and state switched through RFID and hydraulic interfaces to realize the dynamic reorganization capability of the equipment, support the rapid switching of beam types and dynamic adjustment of production tasks. S3: Through a dynamic mapping table of tasks and resources, beam-type tasks are assigned to specific equipment units, and task paths and time windows are optimized through a resource scheduling program to achieve dynamic scheduling of tasks and efficient utilization of resources, ensuring efficient execution of production tasks and real-time resolution of conflicts. In summary, by moving from physics to logic and then to dynamic optimization, a precise and flexible digital twin model is gradually constructed to support the efficient production and dynamic reconfiguration of beam yards. The formula for converting the physical space of the beam yard into a digital model through high-precision scanning is as follows: ; In the formula: The thin plate spline transformation function to be determined is... For optimal data transformation, For BeiDou control points, For laser points, For Euclidean distance, To sum over all corresponding points, For bending energy penalty weights, For Hessian operators, The square of the Frobenius norm of the Hessian; For example, eight BeiDou control points are set up at the four corners of the beam yard's physical space, with a coordinate accuracy of 2cm. A large image is created by stitching together 2000 photos from a drone. Due to attitude and lens distortion, the control points are offset by 0.3–0.8m on the large image. Thin-plate spline transformation is then performed on the pixel coordinates of each photo. By "attaching" the eight BeiDou control points to the RTK ground truth, the entire image is stretched accordingly. A value of 0.1 was used to prevent local "wavy" distortion in the image and to ensure that straight roads remain straight. After the transformation, the maximum residual of the image control points was 1.4cm, and the average was 0.7cm, which met the specifications of 1:500 topographic maps. The actual measured distance between two points 100m apart on the field was 0.9cm different from the total station measurement on the map. In summary, like control points when pixel coordinates when After completing the puzzle, use With a simple swipe, you can transform an aerial photograph into a centimeter-level map; The formula for abstracting the equipment in the beam yard into logical units with states and interfaces is as follows: ; In the formula: For a cluster to be determined, all The set of points constitutes the division of the entire point cloud. For the optimal cluster, For intra-block similarity weights, For cross-block edge weights, i.e., connections The sum of the weights of all edges in the remaining part, for" and non Sum all edges between ' and '. For all possible subsets Find the one that maximizes the score; For example, the laser point cloud of the pedestal area with 2.1M points has been meshed with a 0.5cm grid, resulting in 200k hyperpoints. Each hyperpoint is treated as a node, and k=10 nearest neighbor edges are connected, with edge weights... S = normal angle + height difference. Then, the formula is run to find the 5 clusters that maximize "internal similarity – cross-regional similarity", such as: C1 = pedestal area (42k points), C2 = box girder mold (12k), C3 = T-beam mold (15k), C4 = AGV lane (78k), C5 = steam curing kiln (53k). Score 1.8 × 10 5 , minimum 1.2×10 4 The highest net score is ; The formula for assigning beam-type tasks to specific equipment units using a dynamic mapping table of tasks and resources is as follows: ; In the formula, The smaller the objective function value, the better. The weighting for transportation time is 0.5 based on actual measurements. The single-task AGV travel time (in seconds) is calculated as A * path / vehicle speed. This represents the total transportation time for all tasks in the current production schedule. The utilization penalty weight is set to 0.3. Average utilization rate of the pedestal = pedestal hours used / total pedestal hours, between 0 and 1. A lower value indicates higher utilization; therefore, minimizing this value maximizes utilization. The conflict penalty weight is set to 0.2. For hard collisions, including interface incompatibility, overlapping time windows, insufficient radius, etc., increment by 1 for each occurrence. For constraints; The constraints include: Mold interface matching: Task beam type interface = pedestal hydraulic interface, otherwise x ij =0; Base state machine: can only be assigned when State=IDLE and maintenance is complete; AGV turning radius: minimum path curvature ≥ 1 / R_min, R_min = 6 m; For example, the production scheduling task for switching from box girder to T-beam is: 6 24m T-beams, requiring 6 free support platforms + 6 sets of T-beam molds + 6 trips of AGV transportation; Candidates: 8 pedestals, 3 still under maintenance, 10 sets of molds (7 sets of T-beam interfaces), 3 AGVs; Optimization: PSO-GA provides the optimal chromosome after searching 200 individuals and 50 generations; The result is: =312s (28% lower than the initial plan) =92% (initially 68%) = 0 (interface, radius, and time window are all satisfied), objective function ; In summary, the mold quick change was completed in 1.8 hours according to this plan, with a measured deviation of 0.4cm, which meets the positioning requirement of ±1mm. In this embodiment, the physical space includes: The information of the intelligent pedestal includes: a unique ID, coordinates, load-bearing capacity, beam type compatibility list, and current status for each intelligent pedestal. The current status of the intelligent pedestal is idle / occupied / under maintenance. AGV lane information includes: AGV lane centerline, width, turning radius, and conflict zone. Mold area information includes: storage locations of box girder molds and T-beam molds, and coordinates of hydraulic quick-change joints; Information on the steam curing kiln includes: kiln geometry, temperature zone distribution, kiln entry and exit logic, and kiln door coordinates. Yard information includes: stacking blocks, stacking layer limits, and gantry crane rails.

[0034] In summary, by adopting a reconfigurable CPPS unit, the core components are based on RFID identification and hydraulic quick-change, enabling dynamic reconfiguration of the production line within 2 hours. By integrating BeiDou positioning and GIS data through a spatiotemporal collaborative algorithm, the problem of matching the progress of beam fabrication and erection can be solved. By employing digital twins, the geometric, physical, and behavioral models are linked to optimize concrete performance in real time. The heterogeneous device protocol uses OPC UA over TSN to achieve millisecond-level interoperability between 200+ devices.

[0035] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0036] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0037] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart beam yard collaborative optimization system based on CPPS unit autonomous architecture, characterized in that: include: The CPPS unit autonomous module receives task instructions by identifying beam-type tags with RFID. After receiving a box girder task, it activates the box girder mold plate, and after receiving a T-beam task, it activates the T-beam support. The system architecture includes a physical layer, a network layer, and a virtual layer, which are used to work with the CPPS unit autonomous module to achieve dynamic adjustment and scheduling of multi-beam production lines; The data platform is used to integrate network layer data sources and provide data to the virtual layer.

2. The intelligent beam yard collaborative optimization system based on CPPS unit autonomous architecture according to claim 1, characterized in that: The physical layer includes: The intelligent platform is equipped with a millimeter-wave radar for detecting the deformation of box girders or irregular T-beams; The steam curing kiln is equipped with an infrared temperature control array system to control the steam curing temperature and dynamically compress the steam curing time. AGV, which is equipped with a Beidou positioning module, is used to transport box girder support frames or irregular T-beam support frames; Automatic tensioning machine is used to calibrate the prestress of box girders and irregular T-beams.

3. The intelligent beam yard collaborative optimization system based on CPPS unit autonomous architecture according to claim 2, characterized in that: The network layer includes: The edge gateway is used to receive pressure data from the automatic tensioning machine, to receive the location information of the AGV in conjunction with the Beidou positioning module, and to receive the strain data of the box girder or irregular T-beam in conjunction with the millimeter-wave radar.

4. The intelligent beam yard collaborative optimization system based on CPPS unit autonomous architecture according to claim 3, characterized in that: The virtual layer includes: Spatial model, used to generate scene models of beam-type production lines; Digital twins are used to work with spatial models to reflect changes in entities and to simulate and predict entity behavior in real time. The scheduling system is used to control the tensioning path of the automatic tensioning machine, control the vibration parameters of the intelligent platform, and provide navigation instructions to the AGV; An AI decision engine based on a spatiotemporal conflict resolution algorithm is used to process the output information of the digital twin and provide instructions to the scheduling system.

5. The intelligent beam yard collaborative optimization system based on CPPS unit autonomous architecture according to claim 3, characterized in that: Data transmission between the edge gateway and the data platform is achieved via the OPC UA over TSN protocol.

6. The intelligent beam yard collaborative optimization system based on CPPS unit autonomous architecture according to claim 4, characterized in that: The digital twin includes: Building Information Modeling (BIM) is used to provide three-dimensional dynamic models. A finite element model of concrete hydration heat is used to simulate the temperature changes of concrete during the hydration process.

7. The intelligent beam yard collaborative optimization system based on CPPS unit autonomous architecture according to claim 1, characterized in that: The CPPS unit autonomous module integrates a memory and a processor. The memory is used to store resource scheduling programs, and the processor is used to execute resource scheduling programs.

8. The intelligent beam yard collaborative optimization system based on CPPS unit autonomous architecture according to claim 7, characterized in that: The resource scheduling procedure logic is as follows: S1: Obtain current real-time status data from the digital twin, including AGV position, pressure data of the automatic tensioning machine, and strain data of box girder or irregular T-beam; S2: For each beam segment to be processed, perform conflict detection. If a conflict is detected, the system will record the conflict information, including the conflict type and the beam segment involved. S3: If a conflict is detected, the system will initiate a multi-objective optimization rescheduling mechanism, using a hybrid particle swarm genetic algorithm to re-optimize the scheduling scheme. The system will return the optimized scheduling scheme and conflict report. If there is no conflict, the system will directly return to the original plan without rescheduling.

9. A smart beam yard collaborative optimization system based on CPPS unit autonomous architecture according to claim 4, characterized in that: The steps for constructing the spatial model are as follows: S1: Convert the physical space of the beam yard into a digital model through high-precision scanning; S2: The equipment in the beam yard is abstracted into logical units with states and interfaces. Each unit can be quickly reconfigured and state switched through RFID and hydraulic interfaces. S3: Through a dynamic mapping table of tasks and resources, beam-type tasks are assigned to specific equipment units, and task paths and time windows are optimized through a resource scheduler.

10. A smart beam yard collaborative optimization system based on CPPS unit autonomous architecture according to claim 9, characterized in that: The physical space includes: The intelligent pedestal information includes: a unique ID for each intelligent pedestal, coordinates, load-bearing capacity, beam type compatibility list, and current status; AGV lane information, which includes: AGV lane centerline, width, turning radius, and conflict zone; Mold area information, which includes: the storage locations of box girder molds and T-beam molds and the coordinates of hydraulic quick-change joints; Information on the steam curing kiln, including: kiln geometry, temperature zone distribution, kiln entry and exit logic, and kiln door coordinates; The storage yard information includes: storage blocks, stacking layer limits, and gantry crane rails.

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