Scraped car intelligent warehousing method and system based on transport vehicle

Abstract

The invention discloses a scraped car intelligent warehousing method and system based on a transport vehicle, and belongs to the technical field of scraped car warehousing, and the method comprises the following steps: S1, vehicle warehousing registration and information binding; s2, automatic goods allocation and warehousing task generation; s3, intelligent task decomposition and collaborative scheduling decision making; s4, performing automatic execution and in-library monitoring; s5, responding to the ex-warehouse demand and performing automatic ex-warehouse; and the scheduling and execution processes of the steps S3 and S4 are repeated, the target vehicle is taken out of the goods allocation of the stereoscopic warehouse, and the target vehicle is delivered to a designated warehouse-out area or a disassembly station through the automatic transport vehicle. By integrating the stereoscopic warehouse, all the functional areas and an automatic transport vehicle system and implementing differential goods allocation based on the vehicle safety state, full-process automatic storage of the scraped cars from warehousing to ex-warehouse is achieved, the backward mode of traditional plane stacking and manual operation is changed, and the safety management and control level of the scraped cars is remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of end-of-life vehicle storage technology, specifically relating to an intelligent storage method and system for end-of-life vehicles based on transport vehicles. Background Technology

[0002] In recent years, with the continuous growth of my country's car ownership and the accelerated pace of vehicle replacement, the large-scale and standardized recycling and processing of end-of-life vehicles has become a crucial link in resource recycling and environmental protection. Currently, patent technology research and development in this field mainly focuses on back-end processing stages such as dismantling process optimization, improved crushing and sorting efficiency, and high-value utilization of materials. In contrast, patent technology development for front-end warehousing and logistics stages, such as centralized storage, automated handling, and intelligent scheduling management of end-of-life vehicles before dismantling, is weak, and a systematic solution has not yet been formed.

[0003] Existing technologies include numerous attempts to automate the end-of-life vehicle processing flow. For example, Chinese patent document CN112775204A provides a method for the overall resource recovery of end-of-life vehicles. It uses transport trolleys to sequentially deliver vehicles to different dismantling stations and integrates subsequent crushing and sorting processes, aiming to improve overall processing efficiency. However, this solution focuses primarily on post-dismantling process integration and resource recovery, failing to address effective solutions for large-scale, high-density warehousing management before vehicles enter the dismantling line, as well as core warehousing issues such as automated handling and information traceability within the warehouse. Similarly, Chinese patent document CN212525398U discloses a production line integrating a dismantling line, a pre-crusher, and a crushing line, optimizing the physical process from dismantling to crushing. However, it also lacks an intelligent warehousing and logistics system encompassing vehicle entry, in-warehouse management, and precise delivery. The industry still widely employs traditional methods in the storage of scrapped vehicles, specifically: flat stacking, resulting in extremely low space utilization; reliance on manually operated forklifts for handling, leading to low efficiency and significant safety hazards; insufficient informatization of warehouse management, with vehicle information largely dependent on paper records or isolated spreadsheets, lacking real-time tracking and unified management of vehicle location, status, and flow history, hindering precise scheduling and full-process traceability; and a general lack of dedicated storage solutions with specialized environmental monitoring and fire-fighting linkage for scrapped electric vehicles posing significant safety risks. The root cause of these problems lies in the absence of systematic technical solutions for high-density automated storage and retrieval, full-process information integration, and intelligent collaborative scheduling of multiple devices, addressing the irregular and multi-specification characteristics of scrapped vehicles and their safety storage requirements. Furthermore, these solutions are constrained by the high complexity of technical integration and the cost of retrofitting.

[0004] Therefore, it is necessary to provide a smart warehousing method and system for end-of-life vehicles based on transport vehicles to address the aforementioned problems in existing technologies. Summary of the Invention

[0005] In view of the shortcomings of the above-mentioned background technology, the purpose of this invention is to provide a smart warehousing method and system for scrapped vehicles based on transport vehicles, so as to solve the technical problems in the prior art such as low utilization rate of storage space, low efficiency due to reliance on manual labor, isolated logistics information lacking traceability, rigid task scheduling unable to respond dynamically, and lack of standardized and safe storage for special materials such as scrapped electric vehicles.

[0006] The technical solution of this invention is implemented as follows: a smart warehousing method for scrapped vehicles based on transport vehicles, which is executed within a warehousing area including an automated warehouse, a pre-processing area, a dismantling area, a central aisle area, and a parts storage area; wherein, the parts storage area includes a general parts storage area and a separately set up flammable and explosive materials storage area; including the following steps: S1. Vehicle Entry Registration and Information Binding: When a scrapped vehicle enters the storage area, its vehicle identification information, physical size information, and safety status information are collected, and each piece of information is bound to a generated unique identifier to form a digital file; the safety status information is used at least to distinguish whether the vehicle is an electric vehicle that requires special protection or a vehicle with a risk of fuel leakage. S2. Automated Storage Location Allocation and Inbound Task Generation: Based on the vehicle physical size information and safety status information obtained in step S1, combined with the real-time occupancy status, specifications, and attributes of each storage location in the automated warehouse, the optimal storage location is allocated to the vehicle through a storage location allocation algorithm, and a corresponding inbound task is generated; for vehicles identified as requiring special protection, the storage location allocation algorithm prioritizes allocating them to the flammable and explosive materials storage area or designated storage locations in the automated warehouse with specialized environmental monitoring and fire-fighting linkage; S3. Intelligent task decomposition and collaborative scheduling decision: The inbound task is decomposed into sequentially executed transportation sub-tasks and storage sub-tasks; the scheduling decision module receives the sub-tasks and, based on the collaborative scheduling algorithm that integrates static global optimization and dynamic local rescheduling mechanisms, assigns tasks to automated transport vehicles and aisle stacker cranes and plans conflict-free paths. S4. Automated Execution and In-Store Monitoring: The assigned automated transport vehicle goes to the pick-up point to pick up the vehicle and transports it along the planned route to the inbound platform of the target aisle; the aisle stacker crane receives the storage and retrieval sub-task and stores the vehicle in the storage location assigned in step S2; during this process, the transportation and storage status is monitored in real time through IoT sensors, and the vehicle location information and warehousing system data are updated. S5. Responding to Outbound Demands and Automated Outbound Processing: In response to outbound instructions from the dismantling line or other areas, the warehouse management system locates the vehicle in the warehouse according to the instruction target and generates an outbound task; repeating the scheduling and execution process of steps S3 and S4, the target vehicle is retrieved from the storage location in the automated warehouse and delivered by an automated transport vehicle to the designated outbound area or dismantling station.

[0007] This invention integrates automated warehouses, functional areas, and automated transport vehicle systems, and implements differentiated cargo location allocation based on vehicle safety status. It achieves fully automated, high-density storage, and standardized management of end-of-life vehicles from entry to exit, fundamentally changing the outdated traditional flat stacking and manual operation mode, and significantly improving space utilization, operational efficiency, and safety control of special materials such as end-of-life electric vehicles.

[0008] As a further improvement to the above scheme, the static global optimization process of the cooperative scheduling algorithm described in step S3 specifically includes: S31. Static Task Parameter Input: Input the static task parameters to be processed; S32. Objective Function Construction and Fusion Algorithm Definition: Set an objective function with the goal of minimizing the total task completion time and total transportation cost of the system; adopt an optimization algorithm that integrates task allocation and path planning as the fusion algorithm; initialize the population parameters according to the warehouse layout, equipment performance parameters and preset constraints, and generate an initial scheduling scheme population representing different combinations of task allocation and path parameters. S33. Iterative Optimization and Performance Evaluation: During the iteration process, crossover and mutation comparison operations are performed on individuals in the population; for each individual scheme, its single-vehicle performance function is calculated to evaluate the execution efficiency of a single transportation task, and its overall system performance function is calculated to evaluate the performance of the overall scheduling scheme. S34. Solution Output: Through repeated iterations, the task allocation and path parameters corresponding to the individual that makes the overall system performance function value optimal are selected as the final static global optimization scheduling scheme.

[0009] By setting a system-level objective function and using a fusion algorithm to generate and iteratively optimize the population scheme, the system can perform optimal task allocation and path planning for known tasks from a global perspective. This overcomes the limitations of relying on human experience for scheduling and provides an algorithmic guarantee for the system to achieve the highest operating efficiency under deterministic subtasks.

[0010] As a further improvement to the above scheme, the dynamic local rescheduling process of the cooperative scheduling algorithm described in step S3 specifically includes: S35. Dynamic event response: During system operation, respond in real time to dynamic task input, task cancellation, or task sorting change events; S36. Dynamic Model Reconstruction: Based on the current system state and unfinished tasks, merge dynamic event tasks, construct a multivariate objective function, and update dynamic constraints; the dynamic constraints include task occupancy constraints to ensure resource exclusivity. S37. Fast Rescheduling Solution: Based on the updated task set and constraints, new population parameters are initialized, and fast optimization is performed through crossover and mutation comparison within a rolling time window. During the optimization process, parallel computing only considers the static system performance function of the original static task and the overall system performance function considering all tasks, and evaluates the transportation system load. These indicators are combined to generate new task allocation and path planning instructions that can respond to dynamic changes in real time.

[0011] Based on static optimization, a dynamic local rescheduling mechanism is introduced. By responding to dynamic events, reconstructing the optimization model, and solving quickly within a rolling time window, the scheduling system has the ability to respond to new tasks, equipment failures, and other emergencies in real time. This solves the key problems of static scheduling being rigid and unable to adapt to complex changes on site, and greatly improves the system's adaptability and overall throughput.

[0012] As a further improvement to the above scheme, the task occupancy constraint means that for any key resource node within the storage space, including but not limited to track intersections, loading and unloading platforms, lane entrances, and safety buffer zones, only one transportation or storage task is allowed to exclusively use it at any given time. The collaborative scheduling algorithm satisfies this constraint through a spatiotemporal conflict detection and resolution mechanism during the optimization process. By clarifying the "task occupancy constraint" for key resource nodes and the corresponding spatiotemporal conflict detection and resolution mechanism, it is ensured that the planned paths are conflict-free in both time and space dimensions in a multi-vehicle collaborative operation environment, fundamentally avoiding safety and efficiency risks such as equipment collisions and traffic deadlocks.

[0013] As a further improvement to the above solution, in the warehousing area, the automated warehouse, pre-processing area, and dismantling area are connected by a bidirectional track laid in the central aisle and a track-guided vehicle system; the parts storage area uses an automated guided vehicle system for material handling; the track-guided vehicle system and the automated guided vehicle system are coordinated and scheduled by a unified scheduling decision module. The coordinated layout of the RGV system for rigid logistics of whole vehicles across regions and the AGV system for flexible logistics of parts within the area, combining rigid and flexible approaches, ensures both the efficiency and stability of the main logistics through fixed-track RGVs and the flexibility of handling dismantled fragments through autonomous navigation AGVs, achieving optimal configuration of overall logistics system efficiency and cost.

[0014] As a further improvement to the above scheme, the rail-guided vehicle system implements a dynamic task allocation strategy: when a rail-guided vehicle completes its current task, it immediately requests the next task from the scheduling decision module. The scheduling decision module dynamically allocates the nearest or most profitable task from the task pool based on task priority, the real-time location of the rail-guided vehicle, and the path congestion status, in order to minimize the empty running rate. This further defines the dynamic task allocation strategy of the RGV system. Through "request upon completion" and an instant dispatch mechanism based on multiple factors (priority, location, congestion), it achieves continuous balancing of transport vehicle load and minimizes empty running mileage, effectively improving the utilization rate of key transport equipment and system response speed. This is an important optimization of fixed-cycle or simple polling scheduling methods.

[0015] As a further improvement to the above scheme, the cargo location allocation algorithm described in step S2 performs the following steps: S21. Size matching filter: Calculate the matching degree between the physical outline dimensions of the vehicle to be put into the warehouse and the effective capacity dimensions of all available storage spaces, and filter out the candidate storage space set with a matching degree higher than the first threshold. S22. Load Balancing Optimization: In the candidate storage location set, simulate the overall center of gravity distribution or regional load of the rack after the vehicle is stored, and select the storage location that is most conducive to maintaining the stability of the rack structure and load balance. S23. Categorized Cluster Storage: For vehicles with the same model, brand, or belonging to the same batch of goods entering the warehouse, under the premise of meeting the conditions of steps S21 and S22, they are preferentially allocated to the same or adjacent storage areas in terms of space to achieve categorized cluster storage.

[0016] By sequentially performing size matching, load balancing, and categorized clustering storage, not only is the physical compatibility between vehicles and storage locations and the safety of the rack structure ensured, but the efficiency of subsequent outbound and dismantling is also optimized by grouping similar vehicles together.

[0017] As a further improvement to the above solution, the information updates described in steps S4 and S5 constitute a full-process information traceability chain: every change in status and location of a vehicle from its entry into the warehouse, its storage in the warehouse, to its delivery to the dismantling station, is recorded in real time and synchronized to the warehouse management system, manufacturing execution system, and enterprise resource planning system, supporting one-click traceability of a single vehicle throughout its entire lifecycle.

[0018] By integrating vehicle lifecycle data into systems such as WMS, MES, and ERP, information flow accompanies and drives logistics throughout the entire process, changing the traditional state of information silos and providing a data foundation for precise production scheduling, quality traceability, and management decision-making.

[0019] As a further improvement to the above solution, the automated transport vehicle automatically verifies the unique identity of the scrapped vehicle it carries through onboard or fixed-station identification devices before and after performing the transfer task, ensuring that the physical transfer object and the digital archive information always correspond accurately. This further adds an automatic identity verification step before and after transfer, and the dual verification mechanism ensures that the transfer vehicle in the physical world and the archive information in the digital world always maintain strong consistency, effectively preventing operational errors such as mistransfer or omission, and guaranteeing the accuracy and reliability of the information traceability chain.

[0020] A smart warehousing system for end-of-life vehicles based on a transport vehicle, employing the smart warehousing method for end-of-life vehicles based on a transport vehicle as described above, includes: The sensing and information integration unit is used to collect information on the identification, size, and safety status of scrapped vehicles, and to create and bind unique digital identity files. The warehouse management and decision-making unit includes a warehouse management module and a scheduling decision-making module; the warehouse management module runs a location allocation algorithm to generate inbound and outbound tasks; the scheduling decision-making module runs the collaborative scheduling algorithm to perform task decomposition, equipment assignment, and conflict-free path planning. The automated execution unit includes: a three-dimensional warehouse for high-density storage and its supporting aisle stacker crane, a rail-guided vehicle system for transferring whole vehicles across areas on fixed tracks, an automated guided vehicle system for handling parts on flexible paths, and loading and unloading stations located in each functional area. The network communication and monitoring unit is used to connect all the above units, transmit instructions and data, and monitor the equipment status and storage environment in real time. The sensing and information integration unit, the warehouse management and decision-making unit, and the automated execution unit are interconnected through network communication and monitoring units to form an intelligent warehousing system with synchronous closed-loop control of information flow and logistics.

[0021] Compared with the prior art, the present invention has the following advantages: (1) By constructing an integrated storage layout that includes a three-dimensional warehouse, a pre-processing area, a dismantling area, a central passage, and a dedicated storage area, and by using a rail-guided vehicle system and an automated guided vehicle system to achieve automated handling of vehicles and parts across regions, space utilization and logistics efficiency are fundamentally improved.

[0022] (2) By designing an intelligent collaborative scheduling algorithm that integrates static global optimization and dynamic local rescheduling, a system is constructed with the objective function of minimizing the total task completion time and cost, and considering constraints such as task occupancy. The algorithm is then iteratively solved using population optimization methods to achieve static optimization of multi-transport vehicle task allocation and conflict-free path planning. At the same time, a rolling time window mechanism and a dynamic penalty function are introduced to quickly reschedule dynamic events such as new tasks and task cancellations in real time, ensuring the system's responsiveness and overall throughput in complex dynamic environments.

[0023] (3) Establish an information traceability system covering the entire life cycle of vehicles entering, in storage and leaving the warehouse. By binding a unique digital identity to each scrapped vehicle and collecting and synchronizing its location and status data to management systems at all levels in real time, a closed loop of information flow and logistics can be achieved, supporting precise scheduling and one-click traceability.

[0024] (4) Propose a safe storage strategy for special materials such as scrapped electric vehicles. By identifying the safety status of vehicles, prioritize their allocation to dedicated storage areas or locations with special monitoring and fire protection linkage, and implement independent monitoring log management to upgrade safety risk control from passive response to proactive prevention. Attached Figure Description

[0025] Figure 1 This is a layout diagram of the storage area of ​​the present invention; Figure 2 This is a flowchart illustrating the static global optimization process of the present invention; Figure 3 This is a flowchart illustrating the dynamic local rescheduling process of the present invention. Detailed Implementation

[0026] 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 a part of the embodiments of the present invention, and not all of them. 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.

[0027] Example: like Figures 1-3As shown, a smart warehousing method for scrapped vehicles based on transport vehicles is implemented within a warehousing area comprising an automated warehouse, a pre-processing area, a dismantling area, a central aisle area, and a parts storage area. The pre-processing area is used for battery disassembly and electrolyte draining. The dismantling area is used for electric vehicle disassembly, crushing, and sorting. The parts storage area includes a general parts storage area and a separately designated flammable and explosive materials storage area. This flammable and explosive materials storage area is used to centrally store disassembled scrapped vehicle power batteries, airbags, and other hazardous components. The method includes the following steps: S1. Vehicle entry registration and information binding: When a scrapped vehicle enters the storage area, its vehicle identification information (such as VIN code), physical dimension information (such as length, width, and height), and safety status information (such as whether it is an electric vehicle, whether the power battery is in place, and whether there is an oil leak) are collected through RFID readers, vision recognition systems, and manual terminals set up in the entry channel. Each piece of information is then bound to a unique identifier (such as UUID) generated by the system to form a digital file. The safety status information is used at least to distinguish whether the vehicle is an electric vehicle that requires special protection or a vehicle with a risk of fuel leakage. S2, Automated location allocation and inbound task generation; Based on the vehicle's physical dimensions and safety status information obtained in step S1, and combined with the real-time occupancy status, specifications, and attributes of each storage location in the automated warehouse, the optimal storage location is allocated to the vehicle using a storage location allocation algorithm, and a corresponding inbound task is generated. For vehicles marked as requiring special protection, such as electric vehicles marked "power battery in place," the storage location allocation algorithm prioritizes allocating them to dedicated isolated storage locations in the flammable and explosive materials storage area or designated storage locations in the automated warehouse that are equipped with temperature, smoke, and combustible gas concentration sensors and have dedicated environmental monitoring and fire-fighting linkage. Specifically, the cargo location allocation algorithm performs the following steps: S21. Size Matching Filtering: Calculate the physical dimensions (length) of the vehicle to be stored. ,Width ,high ) and the effective capacity dimensions (length) of all available storage spaces ,Width ,high The matching degree is used to filter out a set of candidate storage locations whose matching degree is higher than a first threshold; for example: ; in , , The preset safety gap threshold is used to filter out... Furthermore, the cargo locations with margins in all dimensions exceeding the safety value form a candidate set.

[0028] S22. Load Balancing Optimization: In the candidate set, the algorithm simulates the overall center-of-gravity shift of the rack column or the load distribution of each support point after a vehicle is stored in each candidate location. The location that minimizes the overall center-of-gravity shift or the variance of the load at each support point is selected to ensure the long-term stability and safety of the rack structure. For example, for a two-column rack, the load difference between the left and right columns after a vehicle is stored is calculated, and locations with smaller load differences are prioritized.

[0029] S23. Categorized Cluster Storage: For vehicles of the same model, brand, or belonging to the same recycling batch, provided that conditions S21 and S22 are met, they will be preferentially allocated to the same aisle, the same shelf level, or adjacent storage locations. For example, all vehicles of "Brand A - Model B" can be centrally stored in aisle 01 of area A of the automated warehouse for easy batch-based outbound processing. For vehicles marked as "requiring special protection" in S1, such as electric vehicles with their power batteries still attached, the storage location allocation algorithm will preferentially allocate them to... Figure 1 The independent "flammable and explosive materials storage area" shown, or a few dedicated storage locations within an automated warehouse equipped with temperature, humidity, smoke, and combustible gas concentration monitoring sensors and linked to the fire protection system, are examples of this. After allocation, the WMS (Warehouse Management System) generates an inbound task containing the target storage location coordinates and the vehicle's UUID. By sequentially executing size matching, load balancing, and categorized clustering storage, not only is the physical compatibility between vehicles and storage locations and the safety of the racking structure ensured, but the grouping of similar vehicles also optimizes subsequent outbound and dismantling efficiency.

[0030] S3, Intelligent Task Decomposition and Cooperative Scheduling Decisions: The scheduling decision module receives inbound tasks from the WMS. This module is typically integrated into the Warehouse Control System (WCS) or a standalone intelligent scheduling server. It decomposes the inbound task into sequentially executed transportation and storage sub-tasks. For example: Task 1: The RGV transports vehicle A from the inbound area to platform 3 in aisle; Task 2: The stacker crane stores vehicle A from the platform into storage location 3-2-15. The scheduling decision module receives these sub-tasks and, based on a collaborative scheduling algorithm that integrates static global optimization and dynamic local rescheduling mechanisms, assigns tasks to the automated transport vehicle and the stacker crane in the aisle, and plans conflict-free paths. In this embodiment, the static global optimization process of the cooperative scheduling algorithm described in step S3 specifically includes: S31. Static Task Parameter Input: Input the static task parameters to be processed; for example, the parameter set is defined as follows: ,in Indicates the first Each static transportation subtask has attributes including material type (whole vehicle / part), weight, and transportation origin coordinates. End point coordinates and task priority For example, tasks sent to the dismantled partition have higher priority than internal data transfer tasks; S32. Objective Function Construction and Fusion Algorithm Definition: Define an objective function that minimizes the total system task completion time and total transportation cost, and construct the objective function as follows: ; in, For the task Completion time, For the task Release time (start time). For the first Empty driving distance of an automated transport vehicle , These are weighting coefficients; for example, they can be set to 0.7 and 0.3 respectively, to place greater emphasis on time efficiency. This represents the total number of static tasks. The total number of transport vehicles is given. An optimization algorithm that integrates task allocation and path planning is used as the fusion algorithm, such as a genetic algorithm or a particle swarm optimization algorithm. Based on the warehouse layout, equipment performance parameters and preset constraints, the population parameters are initialized, such as setting the population size to 100, the chromosome encoding length to twice the number of tasks, the first half representing task allocation, and the second half representing path order. Finally, an initial scheduling scheme population representing different combinations of task allocation and path parameters is generated. S33. Iterative Optimization and Performance Evaluation: During the iteration process, crossover and mutation comparison operations are performed on individuals in the population; for example, the crossover rate is set to 0.8 and the mutation rate to 0.05; for each individual scheme, its single-vehicle performance function is calculated to evaluate the execution efficiency of a single transportation task. The single-vehicle performance function is: ; in, For the task The theoretical travel time can be calculated based on the path length and vehicle speed. This represents the estimated waiting time for the task along the route due to vehicles occupying resources ahead or intersection conflicts. , These are weighting parameters; for example, all are set to 0.5. Simultaneously, its overall system performance function is calculated to evaluate the performance of the overall scheduling scheme; the overall system performance function is based on the objective function. Calculated; S34. Solution Output: Through repeated iterations, such as 500 iterations, the task allocation and path parameters corresponding to the individual that optimizes the overall system performance function are selected as the final static global optimization scheduling scheme. By setting a system-level objective function and using a fusion algorithm to generate and iteratively optimize the population scheme, optimal task allocation and path planning for known tasks can be performed from a global perspective. This overcomes the limitations of relying on manual experience for scheduling and provides an algorithmic guarantee for the system to achieve the highest operating efficiency under deterministic subtasks.

[0031] In this embodiment, the dynamic local rescheduling process of the cooperative scheduling algorithm described in step S3 specifically includes: S35. Dynamic Event Response: During system operation, respond in real time to dynamic task input, task cancellation, or task reordering events, and define a set of dynamic events: ; in, Indicates the first A dynamic task or event; such as adding a new urgent vehicle to be processed in the warehouse. S36. Dynamic Model Reconstruction: Based on the current system state and unfinished tasks, merge dynamic event tasks and construct a multivariate objective function; the constructed multivariate objective function is as follows: ; in, For dynamic tasks The penalty function for unmet delays, cancellations, or expedited requests. Set its weighting coefficient; for example, take 1.5 to enhance the response to dynamic events; and update the dynamic constraints; the dynamic constraints include task occupancy constraints to ensure resource exclusivity; S37. Fast Rescheduling Solution: Based on the updated task set and constraints, new population parameters are initialized, and fast optimization is performed within a rolling time window through crossover and mutation comparison. During the optimization process, parallel computing considers only the static system performance function of the original static tasks and the overall system performance function considering all tasks, and evaluates the transportation system load. The transportation system load index is: ; in, For vehicles The planned load time, To evaluate the total cycle time, these metrics are used to generate new task allocation and path planning instructions that can respond instantly to dynamic changes. A dynamic local rescheduling mechanism is introduced based on static optimization. By responding to dynamic events, reconstructing the optimization model, and quickly solving within a rolling time window, the scheduling system gains the ability to respond in real time to unexpected situations such as new tasks and equipment malfunctions. This solves the key problems of static scheduling's rigidity and inability to adapt to complex changes in the field, greatly improving the system's adaptability and overall throughput.

[0032] S4. Automated Execution and Inventory Monitoring: The assigned automated transport vehicle goes to the pick-up point to pick up the vehicle and transports it along the planned route to the warehouse platform in the target lane; the lane stacker crane receives the storage and retrieval sub-task and stores the vehicle in the storage location assigned in step S2; during this process, the transportation and storage status is monitored in real time by IoT sensors (such as position sensors, weighing sensors, and visual monitoring cameras) deployed at key nodes and storage locations along the transportation route, and the vehicle location information (such as having arrived at the lane platform) and warehouse system data are updated. S5, Responding to outbound requests and automating outbound processes: In response to outbound instructions from the dismantling line or other areas, the warehouse management system locates the vehicle in the warehouse according to the instruction target and generates an outbound task; repeating the scheduling and execution process of steps S3 and S4, the target vehicle is taken out of the warehouse location and delivered by an automated transport vehicle to the designated outbound area or dismantling station.

[0033] In this embodiment, the task occupancy constraint means that for any critical resource node within the storage space, including but not limited to track intersections, loading / unloading platforms, tunnel entrances, and safety buffer zones, only one transportation or storage task is allowed to exclusively use it at any given time. The collaborative scheduling algorithm satisfies this constraint through a spatiotemporal conflict detection and resolution mechanism during the optimization process. The spatiotemporal conflict detection is specifically implemented through the following collision function constraint: for any two different transport vehicles A and B, and any future time point t, the following must be satisfied: ; in, For the two cars in The Euclidean distance function between the path points at any given time. and vehicles and exist The planned coordinates of the moment. The minimum safe distance threshold is preset. The "task occupancy constraints" for key resource nodes and the corresponding spatiotemporal conflict detection and resolution mechanism are clearly defined, ensuring that the planned paths are conflict-free in both time and space dimensions in a multi-vehicle collaborative operation environment, fundamentally avoiding safety and efficiency risks such as equipment collisions and traffic jams.

[0034] In this embodiment, the automated warehouse, pre-processing area, and dismantling area are connected by a bidirectional track laid in the central passageway and a track-guided vehicle system. The parts warehouse uses an automated guided vehicle (AGV) system for material handling. The track-guided vehicle system and the AGV system are coordinated and scheduled by a unified scheduling decision module. The RGV system handles the rigid logistics of whole vehicles across areas, while the AGV system handles the flexible logistics of parts within the area. This "rigid-flexible" approach ensures both the efficiency and stability of the main logistics through fixed-track RGVs and the flexibility of handling dismantled, fragmented materials through autonomous AGVs, achieving optimal configuration of overall logistics system efficiency and cost.

[0035] In this embodiment, the rail-guided vehicle system executes a dynamic task allocation strategy: when a rail-guided vehicle completes its current task, it immediately requests the next task from the scheduling decision module. The scheduling decision module dynamically allocates the nearest or most profitable task from the task pool based on the task priority, the real-time location of the rail-guided vehicle, and the path congestion status, in order to minimize the empty running rate.

[0036] The system's revenue can be calculated using an evaluation function, for example: Revenue = Task Base Score / (Estimated Travel Distance * (1 + Path Congestion Coefficient)). The scheduling decision module selects the task with the highest revenue value for allocation. This further defines the dynamic task allocation strategy of the RGV system. Through "request upon completion" and an instant dispatch mechanism based on multiple factors (priority, location, congestion), it achieves continuous balancing of transport vehicle load and minimizes empty mileage, effectively improving the utilization rate of key transport equipment and system response speed. This represents a significant optimization of fixed-cycle or simple polling scheduling methods.

[0037] In this embodiment, the information updates described in steps S4 and S5 constitute a full-process information traceability chain: every status change and location movement information of a vehicle from its entry into the warehouse, its storage, to its delivery to the dismantling station is recorded in real time and synchronized to the warehouse management system, manufacturing execution system, and enterprise resource planning system, supporting one-click traceability of a single vehicle's entire lifecycle. For example, by entering the vehicle's VIN code into the system interface, one can query its entry time, storage location, exit time, and the dismantling station to which it flows, among other full-process information. By integrating the vehicle's entire lifecycle data with systems such as WMS, MES (Manufacturing Execution System), and ERP (Enterprise Resource Planning), the information flow accompanies and drives the logistics process throughout, changing the traditional state of information silos and providing a data foundation for precise production scheduling, quality traceability, and management decision-making.

[0038] In this embodiment, before and after performing the transfer task, the automated transport vehicle automatically verifies the unique identification of the scrapped vehicle it carries through onboard or fixed-station identification devices, ensuring that the physical transfer object and the digital archive information always accurately correspond. Specifically, at the pick-up point, the RGV reads the vehicle's RFID tag using its onboard RFID reader and compares it with the identification in the task instruction; at the unloading point, the station's fixed reader reads and verifies it again. Only when both verifications are successful is the task confirmed as completed. This further adds automatic identification verification before and after transfer. The dual verification mechanism ensures that the transfer vehicle in the physical world and the archive information in the digital world always maintain strong consistency, effectively preventing operational errors such as mistransfer or omission, and ensuring the accuracy and reliability of the information traceability chain.

[0039] This embodiment integrates the automated warehouse, various functional areas, and automated transport vehicle system, and implements differentiated storage location allocation based on vehicle safety status. It achieves full-process automation, high-density storage, and standardized management of scrapped vehicles from entry to exit, fundamentally changing the outdated mode of traditional flat stacking and manual operation, and significantly improving space utilization, operational efficiency, and safety control of special materials such as scrapped electric vehicles.

[0040] Example 2: This embodiment provides an intelligent warehousing system for end-of-life vehicles based on a transport vehicle, applying the intelligent warehousing method for end-of-life vehicles based on a transport vehicle as described in Embodiment 1. The system includes: The sensing and information integration unit is used to collect information on the identification, size, and safety status of scrapped vehicles, and to create and bind unique digital identity files; its hardware may include RFID reading and writing stations at the entrance, visual measurement systems, manual data entry terminals, and corresponding data acquisition software.

[0041] The warehouse management and decision-making unit includes a warehouse management module and a scheduling decision-making module. The warehouse management module runs a location allocation algorithm to generate inbound and outbound tasks. The scheduling decision-making module runs the collaborative scheduling algorithm to perform task decomposition, equipment assignment, and conflict-free path planning. This unit is typically deployed on a server or industrial computer.

[0042] The automated execution unit includes: an automated warehouse for high-density storage and its supporting aisle stacker cranes; a rail-guided vehicle (RGV) system for transferring whole vehicles across areas on fixed tracks; an automated guided vehicle (AGV) system for handling parts on flexible paths; and loading and unloading platforms located in each functional area. The automated warehouse consists of high-rise racks, stacker cranes, and inbound / outbound conveyors. The RGV system includes a track network, RGV trolleys, and a ground control system. The AGV system includes AGV trolleys and navigation infrastructure.

[0043] The network communication and monitoring unit is used to connect all the above units, transmit instructions and data, and monitor the equipment status and storage environment in real time; it includes industrial Ethernet, wireless AP, PLC, sensor network (temperature and humidity, smoke, video monitoring) and central monitoring screen.

[0044] The sensing and information integration unit, warehouse management and decision-making unit, and automated execution unit are interconnected through network communication and monitoring units to form an intelligent warehousing system with synchronized closed-loop control of information flow and logistics. Data acquired by the sensing unit drives the management and decision-making unit to generate instructions, which are then sent to the automated execution unit via the network to complete physical operations. The operation results and status are then fed back to the management and decision-making unit through sensing and the network, thereby enabling the entire intelligent warehousing system to operate efficiently, accurately, and adaptively.

[0045] Based on the disclosure and teachings of the foregoing specification, those skilled in the art can make changes and modifications to the above embodiments. Therefore, the present invention is not limited to the specific embodiments disclosed and described above, and some modifications and changes to the invention should also fall within the protection scope of the claims of the present invention. Furthermore, although some specific terms are used in this specification, these terms are only for convenience of explanation and do not constitute any limitation on the present invention.

Claims

1. A method for intelligent warehousing of scrapped vehicles based on transport vehicles, characterized in that, This method is implemented within a storage area that includes an automated warehouse, a pre-processing area, a dismantling area, a central aisle area, and a parts storage area; wherein, the parts storage area includes a general parts storage area and a separately set up flammable and explosive materials storage area; and includes the following steps: S1. Vehicle Entry Registration and Information Binding: When a scrapped vehicle enters the storage area, its vehicle identification information, physical size information, and safety status information are collected, and each piece of information is bound to a generated unique identifier to form a digital file; the safety status information is used at least to distinguish whether the vehicle is an electric vehicle that requires special protection or a vehicle with a risk of fuel leakage. S2. Automated Storage Location Allocation and Inbound Task Generation: Based on the vehicle physical size information and safety status information obtained in step S1, combined with the real-time occupancy status, specifications, and attributes of each storage location in the automated warehouse, the optimal storage location is allocated to the vehicle through a storage location allocation algorithm, and a corresponding inbound task is generated; for vehicles identified as requiring special protection, the storage location allocation algorithm prioritizes allocating them to the flammable and explosive materials storage area or designated storage locations in the automated warehouse with specialized environmental monitoring and fire-fighting linkage; S3. Intelligent task decomposition and collaborative scheduling decision: The inbound task is decomposed into sequentially executed transportation sub-tasks and storage sub-tasks; the scheduling decision module receives the sub-tasks and, based on the collaborative scheduling algorithm that integrates static global optimization and dynamic local rescheduling mechanisms, assigns tasks to automated transport vehicles and aisle stacker cranes and plans conflict-free paths. S4. Automated Execution and In-Store Monitoring: The assigned automated transport vehicle goes to the pick-up point to pick up the vehicle and transports it along the planned route to the inbound platform of the target aisle; the aisle stacker crane receives the storage and retrieval sub-task and stores the vehicle in the storage location assigned in step S2; during this process, the transportation and storage status is monitored in real time through IoT sensors, and the vehicle location information and warehousing system data are updated. S5. Responding to Outbound Demands and Automated Outbound Processing: In response to outbound instructions from the dismantling line or other areas, the warehouse management system locates the vehicle in the warehouse according to the instruction target and generates an outbound task; repeating the scheduling and execution process of steps S3 and S4, the target vehicle is retrieved from the storage location in the automated warehouse and delivered by an automated transport vehicle to the designated outbound area or dismantling station.

2. The intelligent warehousing method for scrapped vehicles based on transport vehicles according to claim 1, characterized in that, The static global optimization process of the cooperative scheduling algorithm described in step S3 specifically includes: S31. Static Task Parameter Input: Input the static task parameters to be processed; S32. Objective Function Construction and Fusion Algorithm Definition: Set an objective function with the goal of minimizing the total task completion time and total transportation cost of the system; adopt an optimization algorithm that integrates task allocation and path planning as the fusion algorithm; initialize the population parameters according to the warehouse layout, equipment performance parameters and preset constraints, and generate an initial scheduling scheme population representing different combinations of task allocation and path parameters. S33. Iterative Optimization and Performance Evaluation: During the iteration process, crossover and mutation comparison operations are performed on individuals in the population; for each individual scheme, its single-vehicle performance function is calculated to evaluate the execution efficiency of a single transportation task, and its overall system performance function is calculated to evaluate the performance of the overall scheduling scheme. S34. Solution Output: Through repeated iterations, the task allocation and path parameters corresponding to the individual that makes the overall system performance function value optimal are selected as the final static global optimization scheduling scheme.

3. The intelligent warehousing method for scrapped vehicles based on transport vehicles according to claim 2, characterized in that, The dynamic local rescheduling process of the cooperative scheduling algorithm described in step S3 specifically includes: S35. Dynamic event response: During system operation, respond in real time to dynamic task input, task cancellation, or task sorting change events; S36. Dynamic Model Reconstruction: Based on the current system state and unfinished tasks, merge dynamic event tasks, construct a multivariate objective function, and update dynamic constraints; the dynamic constraints include task occupancy constraints to ensure resource exclusivity. S37. Fast Rescheduling Solution: Based on the updated task set and constraints, new population parameters are initialized, and fast optimization is performed through crossover and mutation comparison within a rolling time window. During the optimization process, parallel computing only considers the static system performance function of the original static task and the overall system performance function considering all tasks, and evaluates the transportation system load. These indicators are combined to generate new task allocation and path planning instructions that can respond to dynamic changes in real time.

4. The intelligent warehousing method for scrapped vehicles based on transport vehicles according to claim 3, characterized in that, The task occupancy constraint means that any key resource node in the storage space, including but not limited to track intersections, loading and unloading platforms, lane entrances and safety buffer zones, can only be exclusively used by one transportation or storage task at any given time; the cooperative scheduling algorithm satisfies this constraint through a spatiotemporal conflict detection and resolution mechanism during the optimization process.

5. The intelligent warehousing method for scrapped vehicles based on transport vehicles according to claim 1, characterized in that, In the storage area, the automated warehouse, pre-processing area and dismantling area are connected by a two-way track laid in the central passageway and a track-guided vehicle system; the parts storage area uses an automated guided vehicle system for material handling; the track-guided vehicle system and the automated guided vehicle system are coordinated and scheduled by a unified scheduling decision module.

6. A method for intelligent warehousing of scrapped vehicles based on transport vehicles according to claim 5, characterized in that, The track-guided vehicle system executes a dynamic task allocation strategy: when a track-guided vehicle completes its current task, it immediately requests the next task from the scheduling decision module. The scheduling decision module dynamically allocates the nearest or most profitable task from the task pool based on the task priority, the real-time location of the track-guided vehicle, and the path congestion status, in order to minimize the empty running rate.

7. The intelligent warehousing method for scrapped vehicles based on transport vehicles according to claim 1, characterized in that, The storage location allocation algorithm described in step S2 executes the following steps: S21. Size matching filter: Calculate the matching degree between the physical outline dimensions of the vehicle to be put into the warehouse and the effective capacity dimensions of all available storage spaces, and filter out the candidate storage space set with a matching degree higher than the first threshold. S22. Load Balancing Optimization: In the candidate storage location set, simulate the overall center of gravity distribution or regional load of the rack after the vehicle is stored, and select the storage location that is most conducive to maintaining the stability of the rack structure and load balance. S23. Categorized Cluster Storage: For vehicles with the same model, brand, or belonging to the same batch of goods entering the warehouse, under the premise of meeting the conditions of steps S21 and S22, they are preferentially allocated to the same or adjacent storage areas in terms of space to achieve categorized cluster storage.

8. The intelligent warehousing method for scrapped vehicles based on transport vehicles according to claim 1, characterized in that, The information updates described in steps S4 and S5 constitute a full-process information traceability chain: every status change and location movement information of a vehicle from its entry into the warehouse, storage in the warehouse, to its delivery to the dismantling station, is recorded in real time and synchronized to the warehouse management system, manufacturing execution system, and enterprise resource planning system, supporting one-click traceability of a single vehicle throughout its entire lifecycle.

9. A method for intelligent warehousing of scrapped vehicles based on transport vehicles according to claim 1, characterized in that, Before and after performing the transfer task, the automated transport vehicle automatically verifies the unique identification of the scrapped vehicle it carries through an onboard or fixed-station identification device to ensure that the physical transfer object and the digital file information always correspond accurately.

10. A smart warehousing system for end-of-life vehicles based on a transport vehicle, characterized in that, The system employs a smart warehousing method for end-of-life vehicles based on a transport vehicle, as described in any one of claims 1-9. The system comprises: The sensing and information integration unit is used to collect information on the identification, size, and safety status of scrapped vehicles, and to create and bind unique digital identity files. The warehouse management and decision-making unit includes a warehouse management module and a scheduling decision-making module; the warehouse management module runs a location allocation algorithm to generate inbound and outbound tasks; the scheduling decision-making module runs the collaborative scheduling algorithm to perform task decomposition, equipment assignment, and conflict-free path planning. The automated execution unit includes: a three-dimensional warehouse for high-density storage and its supporting aisle stacker crane, a rail-guided vehicle system for transferring whole vehicles across areas on fixed tracks, an automated guided vehicle system for handling parts on flexible paths, and loading and unloading stations located in each functional area. The network communication and monitoring unit is used to connect all the above units, transmit instructions and data, and monitor the equipment status and storage environment in real time. The sensing and information integration unit, the warehouse management and decision-making unit, and the automated execution unit are interconnected through network communication and monitoring units to form an intelligent warehousing system with synchronous closed-loop control of information flow and logistics.

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