A multi-dimensional goods location intelligent allocation and path optimization method and system for lithium battery formation and grading, and a medium

Abstract

The application relates to a multi-dimensional goods location intelligent distribution and path optimization method, system and medium for lithium battery formation and capacity grading, and relates to the technical field of warehouse management. The multi-dimensional goods location intelligent distribution and path optimization method comprises the following steps: matching a tray warehouse according to a lithium battery formation and capacity grading process, and screening available warehouse goods locations; positioning and analyzing corresponding lanes of the available warehouse goods locations, calculating lane load weights, and screening candidate warehouse goods locations; according to a tray warehousing station and the candidate warehouse goods locations, combining a station path configuration table, planning a tray conveying path, and calculating a path efficiency score; according to the lane load weights and the path efficiency score, calculating a goods location suitability, and combining process historical storage locations to determine a target warehouse location; monitoring a tray conveying path of the target warehouse location, identifying a target lane running state, and matching a corresponding abnormal solution strategy; and through multi-device state monitoring and static duration weighted correction, dynamic grading alarm of an abnormal lane is realized.

Description

Technical Field

[0001] This application relates to the field of warehouse management technology, and in particular to a multi-dimensional intelligent allocation method, system and medium for lithium battery formation and capacity testing. Background Technology

[0002] In the lithium battery formation and capacity testing process, the cells need to be repeatedly moved between multiple automated warehouses, including high-temperature immersion warehouse, formation warehouse, high-temperature aging warehouse, capacity testing warehouse, high-temperature settling warehouse, and room-temperature settling warehouse. Currently, warehouse management often uses fixed area divisions or simple first-come, first-served strategies for allocating storage locations, lacking comprehensive consideration of multiple factors such as storage location status, equipment load, and process paths.

[0003] Existing patents disclose an object-oriented automated storage and retrieval system (AS / RS) control system and method, including a service startup module, an inbound task module, an inbound task breakdown module, a conveyor line task sub-task distribution module, a conveyor line task completion monitoring module, a stacker crane task sub-task distribution module, and a stacker crane task completion monitoring module. The WCS (Warehouse Control System) and WMS (Warehouse Management System) interact; the WMS knows the location of the pallet required by the user and issues a command to the WCS. The WCS then uses a PLC program to drive the hardware to retrieve the required pallet, and the status and data of the PLC are reflected in real time on the system interface of this invention. The object-oriented AS / RS proposed in this invention, through having retrieval and placement algorithms on both the PLC and WCS ends, can utilize the stacker crane with maximum efficiency and facilitates the management of the AS / RS by the WMS system.

[0004] The existing technical solutions mentioned above have the following drawbacks: 1. In the existing system, pallets in the same process are scattered in different aisles, which increases the round trip of the stacker crane and reduces the efficiency of inbound and outbound operations; 2. The load balance of equipment in each aisle is not considered, with some stacker cranes being busy while others are idle, resulting in a waste of resources; 3. When a certain aisle or equipment fails, there is a lack of dynamic adjustment capability, which leads to the blockage of the entire logistics chain. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this application is to provide a multi-dimensional intelligent allocation and path optimization method, system, and medium for lithium battery formation and capacity testing. This method intelligently allocates storage locations through multi-dimensional comprehensive evaluation, incorporating equipment load balancing, path minimization, and process continuity into a unified decision-making framework. This reduces the empty runs and long-distance movements of stacker cranes, thereby improving the overall efficiency of inbound and outbound operations.

[0006] This was achieved using the following technical solutions: Firstly, this application provides a multi-dimensional intelligent allocation and path optimization method for lithium battery formation and capacity testing, including: Match palletized storage warehouses to the lithium battery formation and capacity testing process, and screen available storage locations; Locate and analyze the aisles corresponding to available storage locations, calculate the aisle load weight, and filter candidate storage locations; Based on the pallet receiving sites and candidate storage locations, and in conjunction with the site route configuration table, plan the pallet transport route and calculate the route efficiency score. Based on the lane load weight and path efficiency score, the suitability of the storage location is calculated, and the target storage location is determined by combining the historical storage locations of the process. Monitor the pallet transport path of the target storage location, identify the operating status of the target aisle, and match the corresponding anomaly resolution strategy.

[0007] By adopting the above technical solution, pallet storage is matched based on the lithium battery formation and capacity testing process. Candidate storage locations are screened by locating aisles and calculating load weights. The transportation path is planned and its efficiency is evaluated in combination with the site path configuration table. The target storage location is determined by combining the weight and efficiency scores with the historical storage locations of the process. At the same time, the abnormal handling strategies for the matching of the transportation path and aisle status are monitored. This realizes intelligent dynamic scheduling and abnormal closed-loop handling of pallet storage, improving storage utilization, logistics efficiency and process continuity.

[0008] This application further specifies: matching palletized storage warehouses according to the lithium battery formation and capacity testing process, and screening available storage locations, including: The lithium battery formation and capacity testing process was analyzed, and process type identifiers were extracted. Match pallet storage warehouses according to process type identifiers to obtain the target process storage warehouse; Conduct warehouse location status checks on the target process storage area and select available or vacant storage locations. Based on the pallet order number and batch number, free and vacant storage locations are filtered in descending order to obtain available storage locations.

[0009] By adopting the above technical solution, the target warehouse is matched based on the process type identifier, and free and vacant warehouses are screened by warehouse location status detection. The priority is then sorted in descending order by pallet order number and batch number. This achieves intelligent and precise warehouse location matching for pallet entry in the lithium battery formation and capacity testing process, which significantly improves the utilization rate of warehousing resources and the efficiency of process connection.

[0010] This application is further configured to: locate and analyze the aisles corresponding to available storage locations, calculate the aisle load weight, and filter candidate storage locations, including: Based on the layout of the warehouse storage locations, the available storage locations are located to obtain the aisle to which each location belongs; The lane to which the cargo location belongs is analyzed to obtain the lane task queue length and average task execution time, and the lane load weight is calculated. Based on the process settling time, the settling time of pallets in the aisle of the storage location is counted to obtain the number of settling pallets and calculate the pallet settling time score. Analyze the historical storage records of the current pallets and calculate the continuity of storage locations in the process. The weighted sum of the aisle load weight, pallet static time score, and process location continuity is used to obtain the comprehensive location score. If the overall score of the storage location is greater than the preset benchmark value, the current storage location is determined to be a candidate storage location.

[0011] By adopting the above technical solution, the load weight is calculated based on the task queue length and average execution time of the aisle to which the storage location belongs. Combined with the pallet static time score and the continuity of the process storage location, the comprehensive score of the storage location is obtained by weighted summation. Candidate storage locations are then screened based on the benchmark value, which realizes accurate evaluation and intelligent screening of multi-dimensional storage resources, and significantly improves the storage utilization rate and logistics scheduling efficiency.

[0012] This application further specifies: based on the pallet receiving site and candidate storage locations, and in conjunction with the site route configuration table, planning the pallet transport route and calculating the route efficiency score, including: Match the pallet receiving sites according to the site path configuration table to obtain the receiving point code; Candidate storage locations are mapped based on their location codes, and path start and end node pairs are generated by combining them with the entry point codes. Perform topology transformation on the site path configuration table to construct the conveyor line adjacency graph; Based on the adjacency graph of the conveyor line, the start and end node pairs of the path are traversed and calculated to generate the pallet conveying path and calculate the absolute length of the path. Based on the shortest and longest absolute path length values, normalize all absolute path length values ​​to obtain normalized path values. The path normalization value is mapped to the reachable boundary of the graph, and the path efficiency score is calculated.

[0013] By adopting the above technical solution, the warehouse is matched and candidate storage locations are screened based on the lithium battery formation and capacity testing process. The adjacency graph of the conveyor line is constructed by parsing the site path configuration table. The pallet path is generated by traversing the start and end node pairs. The efficiency score is calculated based on the normalized value of the path length and the reachable boundary of the graph. The automated planning and quantitative evaluation of the pallet conveying path is realized, which improves the efficiency of warehouse scheduling and the speed of logistics response.

[0014] This application further specifies: calculating the suitability of the storage location based on the roadway load weight and path efficiency score, and determining the target storage location by combining the historical storage locations of the process, including: The roadway load weight and path efficiency score are weighted and summed to calculate the basic comprehensive score; Based on the pallet code, perform adjacent searches of historical storage locations for the process, filter historical storage locations, and mark historical adjacent bonus values; The suitability of the cargo location is calculated by weighting and integrating the basic comprehensive score based on the historical adjacent bonus values. The candidate storage locations are constrained and validated, and then sorted in descending order based on their suitability to determine the target storage location.

[0015] By adopting the above technical solution, a basic comprehensive score is obtained by weighted summation of the roadway load weight and path efficiency score. The historical adjacent retrieval results of the process are combined with historical adjacent scores for weighted fusion to calculate the suitability of the storage location. After constraint verification, the target storage location is determined by descending order. This achieves multi-objective collaborative optimization of load balancing, transportation efficiency and process continuity, and improves the scientific nature of warehouse scheduling and the continuity of operations.

[0016] This application is further configured to: monitor the pallet conveying path of the target storage location, identify the operating status of the target aisle, and match corresponding anomaly resolution strategies, including: A global detection of the pallet conveying path of the target storage location is performed to obtain the operation data of the aisle equipment; Based on the data type, the tunnel equipment operation data is denoised and standardized to obtain standard equipment operation data; The standard operating data of the equipment is classified according to the data source, and the equipment type is marked. The equipment's operating status is determined by identifying the status of the standard operating data of the equipment based on a preset fault feature database. Based on the equipment type identification and equipment operating status, the operating status of the target roadway is determined to ascertain the pallet safety transport level. If any equipment malfunctions, the target roadway is classified as a primary abnormal state, and the pallet safety transport level is classified as a Level 1 warning. If the operating status of any two devices is abnormal, the operating status of the target roadway is determined to be a medium-level abnormal state, and the pallet safety transportation level is a level two warning. If all equipment operating statuses are abnormal, the target roadway is determined to be in a high-level abnormal state, and the pallet safety transport level is a level three warning. The pallet safety transport level is adjusted based on the length of time the pallet has been idle, and abnormal aisle levels are screened and corrected, including: If the time a pallet remains stationary beyond the first warning threshold range, the current pallet safety transport level remains unchanged, the original level abnormal aisle is marked, and a pallet expiration warning record is generated. If the time a pallet remains stationary beyond the warning threshold is within the range of the second warning threshold, then the time a pallet remains stationary beyond the warning threshold is mapped to the first correction coefficient, and the current pallet safety transportation level is corrected, the first-level alarm level abnormal lane is marked, and a pallet primary alarm record is generated. If the time a pallet remains stationary beyond the warning threshold is within the range of the third warning threshold, then the time a pallet remains stationary beyond the warning threshold is mapped to the second correction coefficient, and the current pallet safety transportation level is corrected, the second-level alarm level abnormal lane is marked, and a pallet intermediate alarm record is generated. If the time a pallet remains stationary beyond the warning threshold is within the range of the fourth warning threshold, then the time a pallet remains stationary beyond the warning threshold is mapped to the third correction coefficient, and the current pallet safe transport level is corrected, the level three alarm level abnormal lane is marked, and a high-level pallet alarm record is generated. The system matches the abnormal roadway level according to the preset anomaly resolution strategy library to obtain the corresponding anomaly resolution strategy, including automatic reallocation, waiting for recovery in place, detour of the conveyor line path, and equipment disabling.

[0017] By adopting the above technical solutions, dynamic hierarchical alarms for abnormal lanes are achieved through multi-device status monitoring and weighted correction of static time. Based on the level, strategies such as reassignment, detour, or disabling are automatically matched, thereby improving the abnormal response accuracy and continuous operation capability of lithium battery warehousing and logistics.

[0018] Secondly, this application also provides a multi-dimensional intelligent cargo location allocation and path optimization system for lithium battery formation and capacity testing, employing the following technical solution: A multi-dimensional intelligent storage location allocation and path optimization system for lithium battery formation and capacity testing, comprising the following methods for implementing multi-dimensional intelligent storage location allocation and path optimization: The interface interaction module is used for data transmission and business-driven operations, and for sending and uploading business data according to the agreed-upon protocol. The information management module is used to process different business data accordingly, generate task instructions, and receive feedback data from the scheduling and monitoring module for business processing. The scheduling and monitoring module is used to assign task instructions to the corresponding devices, receive status feedback from the device execution modules, and monitor the device operating status. The control execution module is used to decompose task instructions and execute corresponding transport actions, and feed back execution status data to the scheduling and monitoring module for monitoring.

[0019] By adopting the above technical solution, the interface interaction module realizes the distribution and uploading of business data, the information management module generates task instructions based on the data and receives feedback for processing, the scheduling and monitoring module assigns tasks to corresponding devices and monitors the running status in real time, and the control execution module decomposes instructions to complete the handling actions and reports the execution status, forming a closed-loop collaborative mechanism of business-driven, instruction distribution and status feedback, which significantly improves the system's automation level, response speed and operational reliability.

[0020] Thirdly, this application also provides an electronic device, comprising: One or more processors; Memory, used to store one or more programs; When one or more programs are executed by one or more processors, the one or more processors implement any of the methods in the above scheme.

[0021] Fourthly, this application also provides a storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to realize the multi-dimensional intelligent allocation and path optimization method for lithium battery formation and capacity testing as described above.

[0022] In summary, the beneficial technical effects of this application are as follows: By monitoring the status of multiple devices and weighting the correction of static time, dynamic hierarchical alarms for abnormal lanes are realized, and strategies such as reassignment, detour or disabling are automatically matched according to the level, thereby improving the abnormal response accuracy and continuous operation capability of lithium battery warehousing and logistics. By introducing the static time dimension during the location allocation stage, and by assessing the remaining time distribution of static pallets in each aisle, pallets that are about to expire at the same time are proactively dispersed to avoid the concentrated accumulation of outbound tasks, thus reducing the risk of overdue delivery from the source. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the multi-dimensional intelligent allocation and path optimization method for cargo locations in this application; Figure 2 This is a schematic diagram of the multi-dimensional intelligent storage location allocation process in this application; Figure 3 This is a flowchart illustrating step S2 in this application; Figure 4 This is a flowchart illustrating step S3 in this application; Figure 5 This is a schematic diagram of the structure of the multi-dimensional intelligent cargo location allocation and path optimization system in this application. Detailed Implementation

[0024] The present application will be further described in detail below with reference to the accompanying drawings.

[0025] Reference Figure 1 This application discloses a multi-dimensional intelligent allocation and path optimization method for lithium battery formation and capacity testing, comprising: S1: Match pallet storage warehouses according to the lithium battery formation and capacity testing process, and screen available storage locations; S2: Locate and analyze the aisles corresponding to available storage locations, calculate the aisle load weight, and filter candidate storage locations; S3: Based on the pallet receiving site and candidate storage locations, and in conjunction with the site route configuration table, plan the pallet delivery route and calculate the route efficiency score; S4: Calculate the suitability of the storage location based on the lane load weight and path efficiency score, and determine the target storage location by combining the historical storage locations of the process. S5: Monitor the pallet transport path of the target storage location, identify the operating status of the target aisle, and match the corresponding anomaly resolution strategy.

[0026] In this embodiment, refer to Figure 2 By coordinating WMS (Warehouse Control System), MES (Manufacturing Execution System), and ECS (Formation / Capacity Testing System) through WCS (Warehouse Control System), the efficient flow and precise control of battery cells are achieved across multiple processes, including high-temperature wetting, formation, high-temperature aging, capacity testing, high-temperature settling, room-temperature settling, and OCV testing.

[0027] When there is no pallet at the liquid filling and assembly station, the conveyor line PLC sends an idle signal. The WCS reads this signal and requests an empty pallet from the WMS. The WMS generates an empty pallet outbound task, and the WCS directs the stacker crane to move the empty pallet to the outbound gate in front of the warehouse. The pallet is then transported to the assembly station by the conveyor line. The barcode scanner reads the pallet barcode, and the WCS calls the MES interface to verify whether the pallet allows binding of battery cells. If the verification is successful, the robot picks up the battery cell; if the verification fails, the pallet is sent to the error checkpoint for manual handling. After liquid filling, the battery cells are conveyed to the barcode scanning position via a double-speed conveyor. The PLC scans the barcode and uploads it to the WCS. The WCS calls the MES interface to verify whether the battery cell can be bound to the pallet. If the verification is successful, the robot picks up the battery cell and sends a binding request to the WCS. The WCS calls the MES to perform the binding operation. If the binding is successful, the battery cell is placed on the pallet; otherwise, it is placed on the NG conveyor belt for manual handling. After the robot completes the pallet assembly, the PLC sends a request signal to the WCS. The WCS then issues the target location, and the conveyor line delivers the pallet to the full-pallet scanning area. The CCD vision scanner performs a full-pallet scan and sends the barcode information to the WCS. The WCS calls the WMS to perform barcode verification. If the verification is successful, the aisle information is assigned; otherwise, the pallet is conveyed to the abnormal exit. The WCS issues the target address based on the aisle number, and the conveyor line transports the pallet to the aisle entrance. The WCS issues an inbound task to the stacker crane, which moves the pallet to the designated location and sends a completion message back to the WMS. The WMS records the start and end times of the resting period.

[0028] After the settling period, the WMS generates an outbound task, and the WCS issues the outbound task to the stacker crane. The pallet travels along the conveyor line to the barcode scanning position. The WCS reads the barcode and calls the MES to execute the outbound transit operation. The pallet is then transported to the entrance of the next process aisle, where the WCS issues an inbound task, completing the transfer and process time recording. The formation ECS system requests an outbound order from the WMS. After the WCS schedules the stacker crane for outbound processing, barcode scanning, and MES transit, the conveyor line transports the pallet to the entrance of the formation warehouse aisle. The WCS issues an inbound task, and the WMS records the start and end times of the formation process.

[0029] After formation, the WMS generates an outbound task, the WCS schedules the stacker crane to outbound the battery and scans the barcode to execute the MES checkpoint. The conveyor line transports the battery to the second injection unpacking station and scans the pallet barcode. The WCS calls the WMS to obtain the cell information and sends it to the robot PLC. The robot performs unpacking. After unpacking, the pallet is conveyed to the entrance of the rear aisle of the formation warehouse. The WCS issues a transfer task, the stacker crane moves the pallet to the front exit of the warehouse, and then into the high-temperature warehouse. Finally, the empty pallet is returned to the designated location. After formation, the cells that need to be retested are selected and assembled by the robot arm. The cells are then sent to the front entrance of the formation warehouse via the conveyor line, requesting the WCS to issue the target location. The cells are then sent to the front entrance of the formation warehouse, where the formation cabinet completes the retesting process. After the second helium test, the cells are scanned by the double-speed chain. The WCS verifies the MES binding permissions, and the robot completes the tray binding, assembly, full tray barcode scanning, WMS verification, aisle allocation, and warehousing. The process is the same as the aforementioned tray assembly and warehousing.

[0030] The capacity allocation ECS system requests outbound data from the WMS. After the WCS schedules the stacker crane to leave the warehouse, scans the barcode, and the MES passes through the station, the conveyor line transports the cells to the cell selection station. The WCS reads the barcode and calls the MES to obtain the cell information, which is then sent to the robot PLC to execute the selection and report completion. Subsequently, the conveyor line transports the cells to the restraint and pallet changing station. The WCS sends data to the robot PLC to execute the pallet changing. After the pallet is unpacked, the empty pallet is transferred back to the capacity allocation warehouse or the pallet assembly station. After the restraint pallet is transferred to the injection molding pallet, the conveyor line sends it to the high-temperature settling warehouse for storage. The WCS issues the storage task, and the WMS records the start and end times of the high-temperature settling. After the end, the WMS sends it out of the warehouse. The WCS schedules the stacker crane to send it out of the warehouse, scans the barcode, and passes it through the MES. The conveyor line sends it to the ambient temperature settling warehouse 1 for storage. The WMS records the start and end times of the ambient temperature settling warehouse 1. After the end, the WMS sends it out of the warehouse, scans the barcode, passes it through the MES, and sends it to the OCV inspection station. After the inspection is completed, if all are OK, it is sent to the ambient temperature settling warehouse 2. If there are NGs, it is sent to the cell selection station. The WCS reads the barcode, calls the MES to obtain information, and sends the robot to perform the selection. After the ambient temperature settling period of 2 hours, the battery is released from the warehouse via WMS. After scanning and passing through the MES station, OCV detection and cell selection are performed. Once OK or selected, the pallet is sent to the wrapping and unpacking station. WCS calls WMS to obtain cell information and sends it to the robot PLC to perform unpacking. After unpacking, the empty pallet is transferred back to the exit before the ambient temperature settling warehouse, then passes through the high temperature settling warehouse before entering the warehouse, and finally returns to the designated location.

[0031] When there are no pallets at the injection molding pallet changing station, the conveyor line PLC sends an idle signal, and the WCS requests an empty pallet from the WMS. The WMS generates an outbound task, and the WCS schedules the stacker crane to issue the outbound pallet. The empty pallet is transported by the conveyor line to the primary injection assembly station, where it awaits robot cell picking after being scanned and verified by the MES. Cells requiring retesting are assembled by the robot arm, then sent to the capacity grading warehouse inlet via the conveyor line after requesting the target location from the WCS. Cells requiring retesting are then put into storage and retesting at the capacity grading warehouse inlet. Cells requiring retesting are assembled by the robot arm, then sent to the capacity grading warehouse inlet via the conveyor line after requesting the target location from the WCS. They are then moved to the ambient temperature settling warehouse and the high temperature settling warehouse, and finally sent to the capacity grading warehouse for retesting. Cells requiring re-ambient temperature settling are assembled by the robot arm, then sent to the ambient temperature settling warehouse inlet via the conveyor line for ambient temperature settling. Under the unified scheduling of WCS, the entire system realizes automatic transfer, real-time verification and anomaly handling of battery cells between various processes, effectively supporting intelligent management and control of the entire lithium battery production process.

[0032] Preferably, step S1 includes: The lithium battery formation and capacity testing process was analyzed, and process type identifiers were extracted. Match pallet storage warehouses according to process type identifiers to obtain the target process storage warehouse; Conduct warehouse location status checks on the target process storage area and select available or vacant storage locations. Based on the pallet order number and batch number, free and vacant storage locations are filtered in descending order to obtain available storage locations.

[0033] In this embodiment, when the conveyor line transports a pallet fully loaded with battery cells to the entrance of an aisle in a storage warehouse (high-temperature wetting warehouse, formation warehouse, high-temperature aging warehouse, capacity testing warehouse, high-temperature storage warehouse, or ambient temperature storage warehouse), the conveyor line PLC sends a request signal to the WCS. The WCS calls the WMS interface to request the allocation of a storage location. After receiving the request, the WMS first identifies the process type to which the pallet belongs (such as high-temperature wetting, formation, high-temperature aging, etc.) and filters out all storage locations in the warehouse that are "idle" and not locked or disabled. At the same time, the WMS queries the pallet's order number (MONumber) and batch number to obtain the storage location distribution information of other pallets already stored in the same batch and under the same process. The initial candidate set contains all available storage locations.

[0034] Reference Figure 3 Preferably, step S2 includes: S21: Locate available storage locations based on the warehouse layout to obtain the aisle to which the location belongs; S22: Analyze the lane to which the cargo location belongs to obtain the lane task queue length and average task execution time, and calculate the lane load weight; S23: Based on the process settling time, count the number of pallets in the aisle to which the storage location belongs, obtain the number of settling pallets, and calculate the pallet settling time score. S24: Analyze the historical storage records of the current pallet and calculate the continuity of the storage location in the process; In this embodiment, process location continuity is an indicator that measures the spatial clustering of pallets for the same process in the warehouse. When allocating locations, the WMS prioritizes grouping pallets for the same process into the same or adjacent aisles to reduce the long-distance movement of stacker cranes during subsequent centralized outbound shipments. Continuity can be quantified as the proportion of the number of pallets for the same process stored in the same aisle to the total number of pallets for that process, or the reciprocal of the average aisle distance between all pairs of locations for the same process.

[0035] S25: Weighted summation of aisle load weight, pallet static time score and process location continuity to obtain a comprehensive location score; S26: If the overall score of the storage location is greater than the preset benchmark value, the current storage location is determined to be a candidate storage location.

[0036] In this embodiment, the WMS queries the aisle to which each candidate storage location belongs to obtain the current task queue length and average task execution time of the stacker crane in that aisle. The load weight is calculated as: Task queue length × Average task execution time / Aisle rated throughput. Aisles with lower load weights receive higher scores.

[0037] The WMS queries the process dwell time of the pallet's corresponding operation (read from the basic database) and the number of pallets already in a "dwelling" state in the same or adjacent aisles. If a certain aisle already has a large number of pallets in a dwelling for the same process (indicating that the aisle will be used for concentrated outbound shipments when the dwelling ends), the WMS considers calculating the expected outbound pressure of that aisle within the current time window (the remaining time distribution of the dwelling pallets) when assigning the current pallet to that aisle. If a certain aisle has many pallets that will gradually finish dwelling in the next few hours, the current pallet is avoided from being assigned to that aisle to distribute the time distribution of subsequent outbound tasks. The WMS deducts points from the candidate storage locations based on the expected outbound pressure.

[0038] WMS queries the historical inbound records of the current pallet to determine whether it has been stored in the same warehouse since its last outbound. If so, bonus points are awarded to the storage locations adjacent to the last storage location (same aisle, similar floor).

[0039] The WMS (Workstation Management System) weights and sums the equipment load score, settling time score, and process continuity score according to preset proportions (e.g., 50%, 30%, 20%) to obtain a comprehensive score for each storage location. If a candidate storage location has a deduction in its settling time score, causing its comprehensive score to fall below the benchmark value (e.g., below 60% of the full score), the WMS will proactively select the storage location with the highest comprehensive score as the allocation target for this time and record the estimated settling end time of that storage location for subsequent settling time monitoring.

[0040] Preferably, refer to Figure 4 Step S3 includes: S31: Match the pallet receiving sites according to the site path configuration table to obtain the receiving point code; S32: Map candidate storage locations based on storage location codes, and generate path start and end node pairs by combining them with inbound point codes; S33: Perform topology transformation on the site path configuration table and construct the conveyor line adjacency graph; S34: Based on the adjacency graph of the conveyor line, traverse and calculate the start and end node pairs of the path to generate the pallet conveying path and calculate the absolute length of the path. S35: Normalize all absolute path length values ​​based on the shortest and longest values ​​to obtain normalized path values. S36: Map the path normalization value to the graph reachability boundary and calculate the path efficiency score.

[0041] In this embodiment, the graph reachability boundary is the outer edge of the set of stations reachable from the current inbound station along the directed edges defined in the station path configuration table in the directed graph of the warehouse conveyor topology. When the aisle entrance station of a candidate storage location is outside the graph reachability boundary (i.e., there is no connected path in the graph), the storage location is determined as unreachable and directly removed from the candidate set. The graph reachability boundary is determined by the physical connection relationship of the conveyor line and is dynamically determined by the WMS through graph connectivity verification before each path calculation.

[0042] In this embodiment, the WMS queries the equipment configuration table based on the logical position of the conveyor line currently bound to the pallet to obtain the unique code of the current inbound station where the pallet is located.

[0043] For each candidate storage location selected, the WMS parses its corresponding lane number based on the storage location code, and then reads the lane entrance station code corresponding to that lane from the basic database. Each candidate storage location is mapped to a unique lane entrance station.

[0044] For each candidate storage location, a pair of start and end nodes for path calculation are generated. Multiple candidate storage locations constitute multiple path calculation requests that start from the same inbound site and arrive at different aisle entrance sites.

[0045] The site path configuration table defines the connectivity between sites in the warehouse conveyor network. Each record includes the starting site code, the ending site code, and the path length between the two sites. The path length can be the actual conveying distance in meters or a standardized number of site hops. The configuration table itself has been manually maintained in advance based on the site layout or generated by importing from CAD.

[0046] WMS maintains a directed weighted graph in memory that is periodically updated based on the site path configuration table. The vertices of the graph represent all conveyor line sites, and the directed edges are the connected paths defined in the configuration table. The weight of each edge is its path length.

[0047] After each update, the system automatically checks whether the start and end nodes exist in the graph. If a candidate roadway entrance site is not reachable from the current inbound site in the graph structure, the candidate location is marked as "path unavailable" and excluded from the candidate sequence.

[0048] Since the network size of conveyor line sites is usually limited and the site path configuration table is static data, the WMS uses Dijkstra's algorithm or a pre-computed Floyd-Warshall full-source shortest path matrix for fast lookup. If the network is large but the path configuration table is stable, it is preferred to use the pre-computed path length matrix and directly look up dist[start][end] in the table each time a calculation is performed.

[0049] Iterate through each pair, calculate the shortest path from the inbound station to the entrance station of the alley, and obtain the total path length value LiLi, in meters or station jumps, the unit of which depends on the weight definition in the station path configuration table.

[0050] The absolute length values ​​Li of each candidate path are converted into normalized values ​​that can be compared laterally. The shortest path Lmin and the longest path Lmax are taken, and the Min-Max normalization method is applied. Spath_raw(i)=1−(Li−Lmin) / (Lmax−Lmin), The shorter the path, the closer Spath_raw is to 1; the longer the path, the closer the value is to 0.

[0051] The calculated normalized value Spath_raw is directly mapped to the path efficiency score SpathSpath, with a value ranging from 0 to 100. The formula is: Spath(i) = Spath_raw(i) × 100. Candidate locations with a path length equal to Lmin get 100 points, those equal to Lmax get 0 points, and the rest are distributed proportionally.

[0052] If only one candidate storage location passes the graph reachability check, its path efficiency score is automatically assigned the full score of 100, without the need for normalization calculation. If all candidate paths have the same length, then all candidate storage locations will have a path efficiency score of 100.

[0053] WMS appends the path efficiency score Spath(i) of each candidate storage location to its attribute structure and passes it to the comprehensive scoring module. The comprehensive scoring module weights and sums the path efficiency score and the equipment load score according to preset weights to complete the final storage location selection ranking.

[0054] Preferably, step S4 includes: The roadway load weight and path efficiency score are weighted and summed to calculate the basic comprehensive score; Based on the pallet code, perform adjacent searches of historical storage locations for the process, filter historical storage locations, and mark historical adjacent bonus values; The suitability of the cargo location is calculated by weighting and integrating the basic comprehensive score based on the historical adjacent bonus values. The candidate storage locations are constrained and validated, and then sorted in descending order based on their suitability to determine the target storage location.

[0055] In this embodiment, the path efficiency score Spath(i) of each candidate storage location i is obtained. This score has been normalized to 0-100 points, and the shorter the path, the higher the score.

[0056] WMS monitors the current task queue length and busy level of stacker cranes in each lane in real time, and generates a load score Sload(i) for each lane. The lower the load and the higher the idle level of the lane, the higher its load score, which is also mapped to the range of 0-100 points.

[0057] The two scores are weighted and summed according to a preset weight ratio to generate a basic comprehensive score: Sbase(i)=wpath⋅Spath(i)+wload⋅Sload(i), Here, wpath and wload are the path efficiency weight and load weight, respectively, and their sum is 1. If the system is configured to each account for 50%, then wload=0.5 and wpath=0.5.

[0058] Using the unique barcode of the current pallet as an index, query the historical inbound record table in the WMS database, sort by inbound time in descending order, and extract the storage location code that was most recently stored in this warehouse.

[0059] Check if the historical storage location is still located in the aisle of the currently available candidate storage locations. If the aisle of the historical storage location is full or occupied by other pallets, the historical reference is invalid; if it is valid, record its aisle number, layer number, and column number as the anchor point for adjacent recommendations.

[0060] For pallets with valid historical storage locations, WMS marks the storage locations in the candidate storage location list that are in the same aisle and in a similar layer position as the historical storage location, and assigns them the "historical adjacent preferred" label, providing a basis for subsequent bonus points.

[0061] Retrieve the batch number and process code of the current pallet from the current inbound task.

[0062] Retrieve all pallets in the same batch and process that have been put into storage and are in a "standing" or "in stock" status, count their distribution in each aisle, and generate an aisle-batch inventory distribution table.

[0063] For each candidate storage location, calculate the proportion of the number of pallets of the same batch already stored in that aisle to the total number of pallets already received in that batch. The aisle with the higher proportion has a higher clustering score Sbatch(i), which is also normalized to 0–100.

[0064] For candidate storage locations marked as "historical adjacent preferred", a fixed adjacent bonus value Δhist is directly added to their basic comprehensive score Sbase(i). This bonus value is a system preset constant, and its magnitude should be sufficient to make adjacent storage locations win under similar conditions, but should not cover significant load or path disadvantages.

[0065] The score, after historical adjacency adjustment, is then weighted and merged with the batch clustering score in a second weighted manner. Sfinal(i)=wbase⋅(Sbase(i)+Δhist(i))+wbatch⋅Sbatch(i), Δhist(i) is greater than 0 only when the candidate location has the "historical adjacent preferred" label, otherwise it is 0. wbase and wbatch are the base score weight and batch aggregation weight, respectively, and their sum is 1. The default values ​​can be set to 0.7 and 0.3.

[0066] Before sorting, a final constraint check is performed on all candidate storage locations to exclude locked, allocated, or mismatched storage locations, ensuring that all candidates entering the sorting process are physically available.

[0067] Sort all candidate storage locations that pass the constraint verification in descending order of Sfinal(i). If there are equal scores, the priority rules are as follows: historically adjacent storage locations take precedence over non-historical storage locations, lanes with higher batch clustering take precedence over lanes with lower clustering, and shorter paths take precedence over longer paths.

[0068] The first candidate storage location after sorting is the target storage location. The WMS records the storage location code, aisle number, and corresponding final suitability score for this location.

[0069] WMS encapsulates the selected target storage location code and aisle number into an inbound instruction and returns it to WCS for execution. WCS then sends the target address to the corresponding stacker crane and conveyor line based on the aisle number, completing the physical inbound handling of the pallet.

[0070] After receiving the task completion feedback from WCS, WMS records the current system time as the settling start time, reads the process settling time of the process from the basic database, calculates the theoretical settling end time, updates the pallet status to "Settling in progress" and writes it to the settling time record table.

[0071] Preferably, step S5 includes: A global detection of the pallet conveying path of the target storage location is performed to obtain the operation data of the aisle equipment; Based on the data type, the tunnel equipment operation data is denoised and standardized to obtain standard equipment operation data; The standard operating data of the equipment is classified according to the data source, and the equipment type is marked. The equipment's operating status is determined by identifying the status of the standard operating data of the equipment based on a preset fault feature database. Based on the equipment type identification and equipment operating status, the operating status of the target roadway is determined to ascertain the pallet safety transport level. If any equipment malfunctions, the target roadway is classified as a primary abnormal state, and the pallet safety transport level is classified as a Level 1 warning. If the operating status of any two devices is abnormal, the operating status of the target roadway is determined to be a medium-level abnormal state, and the pallet safety transportation level is a level two warning. If all equipment operating statuses are abnormal, the target roadway is determined to be in a high-level abnormal state, and the pallet safety transport level is a level three warning. The pallet safety transport level is adjusted based on the length of time the pallet has been idle, and abnormal aisle levels are screened and corrected, including: If the time a pallet remains stationary beyond the first warning threshold range, the current pallet safety transport level remains unchanged, the original level abnormal aisle is marked, and a pallet expiration warning record is generated. If the time a pallet remains stationary beyond the warning threshold is within the range of the second warning threshold, then the time a pallet remains stationary beyond the warning threshold is mapped to the first correction coefficient, and the current pallet safety transportation level is corrected, the first-level alarm level abnormal lane is marked, and a pallet primary alarm record is generated. If the time a pallet remains stationary beyond the warning threshold is within the range of the third warning threshold, then the time a pallet remains stationary beyond the warning threshold is mapped to the second correction coefficient, and the current pallet safety transportation level is corrected, the second-level alarm level abnormal lane is marked, and a pallet intermediate alarm record is generated. If the time a pallet remains stationary beyond the warning threshold is within the range of the fourth warning threshold, then the time a pallet remains stationary beyond the warning threshold is mapped to the third correction coefficient, and the current pallet safe transport level is corrected, the level three alarm level abnormal lane is marked, and a high-level pallet alarm record is generated. The system matches the abnormal roadway level according to the preset anomaly resolution strategy library to obtain the corresponding anomaly resolution strategy, including automatic reallocation, waiting for recovery in place, detour of the conveyor line path, and equipment disabling.

[0072] In this embodiment, the WCS periodically polls the PLCs or controllers of each stacker crane in each aisle using an industrial communication protocol to obtain the current status word of the stacker crane. The status word includes the operating mode, current loading status, fault code, communication heartbeat count, and limit sensor level.

[0073] The status of auxiliary equipment such as conveyors, track switching vehicles, and safety light curtains associated with the roadway is collected simultaneously to confirm whether the entire roadway is in a usable state.

[0074] If the WCS does not receive a communication heartbeat response from the stacker crane within a preset period, it determines that the communication is interrupted, marks the aisle as "unreachable", and immediately caches the instructions to be sent.

[0075] When the fault code bit of the stacker crane status word changes from zero to a non-zero value, the WCS determines that the stacker crane is faulty. The specific fault category is then analyzed based on the fault code field, including drive overload, lifting limit exceeding limits, fork action timeout, inverter alarm, and emergency stop button press.

[0076] If any safety device or critical actuator in the status bits of the roadway-related equipment is in an abnormal state, or if the operator manually sets the roadway to "maintenance mode" or "disabled" on the WCS interface, WCS will identify the roadway as "roadway disabled".

[0077] If the entrance conveyor station of the target lane is continuously timed out by the occupancy sensor, and the downstream stacker crane does not perform the picking action, WCS identifies it as "conveyor congestion". This type of anomaly is managed separately from equipment failure.

[0078] Based on the redundancy design principle of warehouse automation systems, single equipment failures are often localized and temporary, allowing the system to maintain basic operation; dual equipment failures indicate that the overall stability of the system is compromised, requiring proactive intervention; and failures of all equipment mean that the aisle is completely ineffective, necessitating emergency measures to prevent derivative accidents (such as pallet jamming or stacker crane collisions).

[0079] Based on the timeliness and safety factor of warehouse logistics, as shown in Table 1 Table 1. Thresholds for the Classification of Pallet Static Expiration Time and Correction Factor First warning threshold range ≤10 minutes No correction Primary-level abnormal roadway Second warning threshold range 10 minutes < T ≤ 30 minutes 1.2 Level 1 Alarm-Level Abnormal Roadway Third warning threshold range 30 minutes < T ≤ 60 minutes 1.5 Level 2 alarm level abnormal roadway Fourth warning threshold range T>60 minutes 2.0 Level 3 Alarm Level Abnormal Lane The 10-minute window is within the normal scheduling buffer period, which is the inherent tolerance of equipment response time and conveyor line cycle time, and requires no intervention.

[0080] 10 to 30 minutes corresponds to the baseline duration of a single process cycle (such as formation and settling). If this time is exceeded, it indicates that the subsequent production cycle has been affected and the risk level needs to be adjusted.

[0081] A 30-60 minute window corresponds to an emergency repair response window. If this window is exceeded, it indicates that the anomaly has not been resolved for an extended period, the risk of roadway congestion has increased significantly, and the alarm level needs to be raised.

[0082] If the tray remains in the water for more than 60 minutes, it means that the tray has been in the water for too long, which may cause the battery cells to overcharge, over-discharge or abnormal temperature, requiring the highest level of alarm and forced intervention.

[0083] Based on the equipment anomaly classification, a risk weighting factor is added to adjust the time-overdue period: final anomaly level = original anomaly level × adjustment factor; if the level is improved after adjustment, the corresponding anomaly resolution strategy is associated.

[0084] To address the scenario where "the equipment has been restored but the tray has become ineffective due to prolonged inactivity"—the risks accumulated over time cannot be eliminated simply because the equipment status is instantly restored; their residual impact must be reflected through a correction factor.

[0085] Intervention intensity is matched according to the increasing severity of the anomaly: minor anomalies allow the system to reschedule autonomously; moderate anomalies require local waiting or detour avoidance; severe anomalies require physical isolation of equipment to ensure the safety of personnel and goods.

[0086] WCS uses the alarm device code to look up the device configuration table to determine the roadway number to which the anomaly belongs and the affected physical area.

[0087] Scan all import commands in the WCS Task Manager that are in a "issued" or "executed" state, filter out the tasks whose target roadway is equal to the abnormal roadway number, and generate a list of affected tasks.

[0088] WCS will temporarily set the pallet status of the affected task to "transportation pending reassignment" and report the abnormal event to WMS one by one. The reported information includes the pallet barcode, the original assigned storage location code, the abnormal lane number, and the abnormal type code.

[0089] In the event of a stacker crane malfunction that cannot be reset or an aisle being disabled for an extended period, the WCS sends a "reassignment request" signal to the WMS. Upon receiving this signal, the WMS automatically removes the originally assigned location from the candidate set, recalculates the location suitability, assigns the selected location, and returns the new target address.

[0090] For stacker crane communication interruptions or temporary faults that can be quickly reset, WCS will suspend the affected tasks and start a waiting timer. If the equipment status returns to normal within the set timeout period, WCS will automatically resume the original inbound command; if the timeout period does not result in a recovery, it will escalate to requesting a reallocation.

[0091] If the anomaly only affects the stacker crane in that aisle, but the pallet is still on the conveyor line and there is a physical path to other aisles, the WCS requests a detour and reallocation from the WMS, and at the same time controls the conveyor line to transfer the pallet to the designated buffer station, and continues conveying after the new target aisle is determined.

[0092] For serious faults requiring downtime for maintenance, WCS automatically locks the roadway status to "disabled," preventing new tasks from being assigned to that roadway, and pushes fault details and maintenance work orders to maintenance personnel. Tasks already in that roadway will either request reassignment or have their transport paths rerouted based on the actual situation.

[0093] Reference Figure 5 A multi-dimensional intelligent storage location allocation and path optimization system for lithium battery formation and capacity testing, applied to a multi-dimensional intelligent storage location allocation and path optimization method, including: The interface interaction module is used for data transmission and business-driven operations, and for sending and uploading business data according to the agreed-upon protocol. The information management module is used to process different business data accordingly, generate task instructions, and receive feedback data from the scheduling and monitoring module for business processing. The scheduling and monitoring module is used to assign task instructions to the corresponding devices, receive status feedback from the device execution modules, and monitor the device operating status. The control execution module is used to decompose task instructions and execute corresponding transport actions, and feed back execution status data to the scheduling and monitoring module for monitoring.

[0094] In this embodiment, the interface interaction module is used for data transmission and business driving between the computer system and the MES system. It serves as a data bridge between the information management module and the peripheral system, sending and uploading business data according to agreements. The information management module is used for unified management of storage locations, materials, process flows, statistical reports, etc. It receives data input from the interface interaction module and processes it according to different business data. Simultaneously, it processes business data based on feedback from the scheduling and monitoring module and processes the data input from the interface interaction layer before feeding it back to the interface interaction module. The scheduling and monitoring module refers to the WCS scheduling system receiving task instructions from the information management module, decomposing the instructions, and rationally scheduling and controlling the various devices of the execution module using scheduling algorithms, then sending out instructions to drive the equipment to perform handling operations. The scheduling and monitoring module also receives real-time status feedback from the equipment execution layer and reflects the equipment's operating status in real time. The control and execution module refers to each handling device communicating with the WCS via Profinet industrial Ethernet technology. After receiving task instructions from the WCS, the device decomposes the instructions and executes the corresponding handling actions. Simultaneously, it feeds back the execution status data to the equipment monitoring module for monitoring.

[0095] The system comprises an information management module, a scheduling and monitoring module, and a control execution module. Each system is equipped with different hardware facilities at different levels and connected via gigabit Ethernet to achieve seamless integration, real-time interoperability, and rapid response. Application servers and database servers are deployed in the formation and capacity distribution control rooms. These servers are designed with redundancy in mind, employing a dual-machine hot standby configuration to automatically switch to the backup server in the event of a primary server failure or application malfunction, ensuring high service availability. The core switch in the control room achieves stable communication with field access switches via a gigabit network. Field switches connect to equipment such as stacker cranes, conveyor lines, robots, and bolt insertion / removal machines via Ethernet, enabling real-time and stable communication between the scheduling system and the equipment, facilitating process control and information transmission during equipment operation.

[0096] An electronic device, comprising: One or more processors; Memory, used to store one or more programs; When one or more programs are executed by one or more processors, the one or more processors implement any of the methods in the above scheme.

[0097] A storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the multi-dimensional intelligent allocation and path optimization method for cargo locations as described above.

[0098] The embodiments described in this specific implementation are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A multi-dimensional intelligent allocation and path optimization method for lithium battery formation and capacity testing, characterized in that, include: Match palletized storage warehouses to the lithium battery formation and capacity testing process, and screen available storage locations; Locate and analyze the aisles corresponding to the available storage locations, calculate the aisle load weight, and filter candidate storage locations; Based on the pallet receiving sites and candidate storage locations, and in conjunction with the site route configuration table, plan the pallet transport route and calculate the route efficiency score. Based on the lane load weight and path efficiency score, the suitability of the storage location is calculated, and the target storage location is determined by combining the historical storage locations of the process. Monitor the pallet transport path of the target storage location, identify the operating status of the target aisle, and match the corresponding anomaly resolution strategy.

2. The multi-dimensional intelligent allocation and path optimization method for lithium battery formation and capacity testing as described in claim 1, characterized in that, The process of matching palletized storage warehouses based on the lithium battery formation and capacity testing process, and screening available storage locations, includes: The lithium battery formation and capacity testing process was analyzed, and process type identifiers were extracted. Match pallet storage warehouses according to process type identifiers to obtain the target process storage warehouse; Conduct warehouse location status checks on the target process storage area and select available or vacant storage locations. Based on the pallet order number and batch number, free and vacant storage locations are filtered in descending order to obtain available storage locations.

3. The multi-dimensional intelligent allocation and path optimization method for lithium battery formation and capacity testing according to claim 1, characterized in that, The process of locating and analyzing the aisles corresponding to the available storage locations, calculating the aisle load weight, and filtering candidate storage locations includes: Based on the layout of the warehouse storage locations, the available storage locations are located to obtain the aisle to which each location belongs; The lane to which the cargo location belongs is analyzed to obtain the lane task queue length and average task execution time, and the lane load weight is calculated. Based on the process settling time, the settling time of pallets in the aisle of the storage location is counted to obtain the number of settling pallets and calculate the pallet settling time score. Analyze the historical storage records of the current pallets and calculate the continuity of storage locations in the process. The weighted sum of the aisle load weight, pallet static time score, and process location continuity is used to obtain the comprehensive location score. If the overall score of the storage location is greater than the preset benchmark value, the current storage location is determined to be a candidate storage location.

4. The multi-dimensional intelligent allocation and path optimization method for lithium battery formation and capacity testing according to claim 1, characterized in that, The process of planning pallet transport routes and calculating route efficiency scores based on pallet receiving stations and candidate storage locations, combined with a station route configuration table, includes: Match the pallet receiving sites according to the site path configuration table to obtain the receiving point code; Candidate storage locations are mapped based on their location codes, and path start and end node pairs are generated by combining them with the entry point codes. Perform topology transformation on the site path configuration table to construct the conveyor line adjacency graph; Based on the adjacency graph of the conveyor line, the start and end node pairs of the path are traversed and calculated to generate the pallet conveying path and calculate the absolute length of the path. Based on the shortest and longest absolute path length values, normalize all absolute path length values ​​to obtain normalized path values. The path normalization value is mapped to the reachable boundary of the graph, and the path efficiency score is calculated.

5. The multi-dimensional intelligent allocation and path optimization method for lithium battery formation and capacity testing as described in claim 1, characterized in that, The process of calculating the suitability of storage locations based on lane load weight and path efficiency score, and determining target storage locations by combining historical storage locations of processes, includes: The roadway load weight and path efficiency score are weighted and summed to calculate the basic comprehensive score; Based on the pallet code, perform adjacent searches of historical storage locations for the process, filter historical storage locations, and mark historical adjacent bonus values; The suitability of the cargo location is calculated by weighting and integrating the basic comprehensive score based on the historical adjacent bonus values. The candidate storage locations are constrained and validated, and then sorted in descending order based on their suitability to determine the target storage location.

6. The multi-dimensional intelligent allocation and path optimization method for lithium battery formation and capacity testing according to claim 1, characterized in that, The monitoring of pallet transport paths at target storage locations, identification of target aisle operating status, and matching of corresponding anomaly resolution strategies include: A global detection of the pallet conveying path of the target storage location is performed to obtain the operation data of the aisle equipment; Based on the data type, the tunnel equipment operation data is denoised and standardized to obtain standard equipment operation data; The standard operating data of the equipment is classified according to the data source, and the equipment type is marked. The equipment's operating status is determined by identifying the status of the standard operating data of the equipment based on a preset fault feature database. Based on the equipment type identification and equipment operating status, the operating status of the target roadway is determined to ascertain the pallet safety transport level. If any equipment malfunctions, the target roadway is classified as a primary abnormal state, and the pallet safety transport level is classified as a Level 1 warning. If the operating status of any two devices is abnormal, the operating status of the target roadway is determined to be a medium-level abnormal state, and the pallet safety transportation level is a level two warning. If all equipment operating statuses are abnormal, the target roadway is determined to be in a high-level abnormal state, and the pallet safety transport level is a level three warning. The pallet safety transport level is adjusted based on the length of time the pallet has been left idle, and the abnormal aisle level is screened and corrected. The system matches the anomaly level to a pre-defined anomaly resolution strategy library to obtain the corresponding anomaly resolution strategy.

7. The multi-dimensional intelligent allocation and path optimization method for lithium battery formation and capacity testing according to claim 6, characterized in that: The process of adjusting the pallet safety transport level based on the pallet's duration of inactivity, and screening and correcting abnormal aisle levels, includes: If the time a pallet remains stationary beyond the first warning threshold range, the current pallet safety transport level remains unchanged, the original level abnormal aisle is marked, and a pallet expiration warning record is generated. If the time a pallet remains stationary beyond the warning threshold is within the range of the second warning threshold, then the time a pallet remains stationary beyond the warning threshold is mapped to the first correction coefficient, and the current pallet safety transportation level is corrected, the first-level alarm level abnormal lane is marked, and a pallet primary alarm record is generated. If the time a pallet remains stationary beyond the warning threshold is within the range of the third warning threshold, then the time a pallet remains stationary beyond the warning threshold is mapped to the second correction coefficient, and the current pallet safety transportation level is corrected, the second-level alarm level abnormal lane is marked, and a pallet intermediate alarm record is generated. If the time a pallet remains stationary beyond the warning threshold is within the range of the fourth warning threshold, then the time a pallet remains stationary beyond the warning threshold is mapped to the third correction coefficient, and the current pallet safe transport level is corrected, the third-level alarm level abnormal lane is marked, and a high-level pallet alarm record is generated.

8. The multi-dimensional intelligent allocation and path optimization method for lithium battery formation and capacity testing according to claim 6, characterized in that: The anomaly resolution strategies include automatic reassignment, waiting for recovery in place, detouring the conveyor line, and disabling the equipment.

9. A multi-dimensional intelligent storage location allocation and path optimization system for lithium battery formation and capacity testing, used to implement the multi-dimensional intelligent storage location allocation and path optimization method as described in any one of claims 1-8, characterized in that, include: The interface interaction module is used for data transmission and business-driven operations, and for sending and uploading business data according to the agreed-upon protocol. The information management module is used to process different business data accordingly, generate task instructions, and receive feedback data from the scheduling and monitoring module for business processing. The scheduling and monitoring module is used to assign task instructions to the corresponding devices, receive status feedback from the device execution modules, and monitor the device operating status. The control execution module is used to decompose task instructions and execute corresponding transport actions, and feed back execution status data to the scheduling and monitoring module for monitoring.

10. A storage medium storing at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the multi-dimensional intelligent allocation and path optimization method for cargo locations as described in any one of claims 1 to 8.

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