Multi-warehouse accessory inventory scheduling method and system for sanitation vehicle maintenance

Abstract

The invention relates to a multi-warehouse accessory inventory scheduling method and system for sanitation vehicle maintenance, and relates to the field of intelligent supply chain management, and the method comprises the steps: collecting multi-warehouse real-time inventory data, in-transit logistics data and warehouse network topology data; constructing a global dynamic inventory view based on the multi-warehouse real-time inventory data and the in-transit logistics data; generating a closed loop allocation instruction according to the global dynamic inventory view and the warehouse network topology data; executing the closed-loop circulation allocation instruction to control the accessories to continuously flow along a preset circulation allocation path among a plurality of warehouses, and receiving maintenance work order information; determining an accessory demand list and maintenance position information based on the maintenance work order information; combining the accessory demand list, the maintenance position information and the global dynamic inventory view to determine an optimal supply scheme; and according to the optimal supply scheme, a scheduling execution instruction is generated and issued so as to quickly complete accessory scheduling. The device has the effect of improving the maintenance efficiency of the device.

Description

Technical Field

[0001] This invention relates to the field of intelligent supply chain management, and in particular to a multi-warehouse spare parts inventory scheduling method and system for sanitation vehicle maintenance. Background Technology

[0002] Multi-warehouse spare parts inventory scheduling refers to the management process of collaboratively planning and dynamically adjusting the inventory distribution, replenishment strategies, and logistics routes of spare parts required for maintenance in a distributed warehousing network environment.

[0003] In the current traditional repair system, each warehouse manages its inventory independently. Upon receiving a work order, the local inventory is checked first. If the item is out of stock, other warehouses need to be contacted manually to verify the inventory and then allocate spare parts from those warehouses.

[0004] The disconnect between spare parts inventory and maintenance needs leads to long waiting times for parts, which in turn reduces the efficiency of equipment maintenance and needs to be improved. Summary of the Invention

[0005] To improve equipment maintenance efficiency, this invention provides a multi-warehouse spare parts inventory scheduling method and system for sanitation vehicle maintenance.

[0006] In a first aspect, the present invention provides a multi-warehouse spare parts inventory scheduling method for sanitation vehicle maintenance, employing the following technical solution: A multi-warehouse spare parts inventory scheduling method for sanitation vehicle maintenance includes: Collect real-time inventory data, in-transit logistics data, and warehouse network topology data from multiple warehouses; A global dynamic inventory view is constructed based on real-time inventory data from multiple warehouses and in-transit logistics data. Based on the global dynamic inventory view and warehouse network topology data, a closed-loop cyclical transfer instruction is generated; Execute closed-loop cyclic allocation instructions to control the continuous flow of parts between multiple warehouses along a preset cyclic allocation path, and receive maintenance work order information; Based on the repair work order information, determine the parts demand list and repair location information; The optimal supply plan is determined by combining the parts demand list, repair location information, and a global dynamic inventory view. Based on the optimal supply plan, dispatch execution instructions are generated and issued to quickly complete the dispatch of parts.

[0007] By adopting the above technical solution, a global dynamic inventory view is constructed by collecting multi-warehouse inventory data, in-transit logistics data, and network topology data, enabling a comprehensive understanding of the spare parts inventory status. Based on this view and network topology data, closed-loop cyclical allocation instructions are generated, allowing spare parts to continuously flow within the warehouse network and maintaining dynamic inventory balance. When a maintenance work order is received, the optimal supply plan is quickly determined and executed by combining specific demand information and the global view. This effectively solves the problem of the disconnect between spare parts inventory and maintenance demand in the traditional model, significantly shortens parts waiting time, and improves equipment maintenance efficiency.

[0008] Optionally, a method for generating closed-loop cyclic allocation instructions may also be included: Determine the warehouse to be transferred based on a global dynamic inventory view; The downstream adjacent warehouses are determined based on warehouse network topology data, outgoing warehouses, and cyclical transfer paths. The receiving warehouse is determined based on the downstream adjacent warehouses; Combine the global dynamic inventory view with the incoming warehouse to obtain the incoming inventory level; When the level of the transferred inventory is lower than the preset inventory level threshold, the quantity to be transferred is determined based on the level of the transferred inventory and the inventory level threshold. The closed-loop cyclical transfer instruction is generated by combining the cyclical transfer path, transfer quantity, transfer-out warehouse, and transfer-in warehouse.

[0009] By adopting the above technical solution, the system automatically identifies warehouses with sufficient inventory through a global dynamic inventory view, and intelligently determines downstream warehouses by combining the warehouse network topology and circular transfer paths. By monitoring the inventory level of the warehouses in real time, the system automatically calculates the optimal transfer quantity when it falls below a preset threshold. Finally, the system integrates path information, transfer quantity, and warehouse nodes to generate a complete closed-loop circular transfer instruction. This ensures that spare parts flow continuously in the warehouse network along preset paths, effectively maintaining the dynamic balance of inventory in each warehouse. It avoids the inefficient operation of manually verifying inventory and arranging transfers in the traditional model, and significantly improves inventory turnover and scheduling efficiency.

[0010] Optional, also includes: Collect historical maintenance data; Generate parts demand forecasts based on historical maintenance data; Identify demand hotspots based on component demand forecasts; Combine warehouse network topology data with demand hotspot areas to calculate path optimization coefficients; The node order of the cyclic allocation path is dynamically adjusted based on the path optimization coefficient.

[0011] By adopting the above technical solution, accurate parts demand forecasts are generated by collecting and analyzing historical maintenance data, and hot demand areas are identified accordingly. Combined with warehouse network topology data, a scientific path optimization coefficient is calculated. Finally, based on this coefficient, the node order of the cyclic allocation path is dynamically adjusted, so that the parts inventory layout is more in line with actual maintenance needs, effectively improving inventory turnover and demand response speed, and significantly reducing equipment downtime caused by parts shortages.

[0012] Optionally, a responsive scheduling method for maintenance work orders may also be included: Determine the set of candidate warehouses based on maintenance location information; Collect the current execution status of closed-loop cyclic allocation instructions; Determine the real-time location of the parts based on the current execution status; Combine the candidate warehouse set with the real-time location of parts to calculate the estimated warehouse delivery time; The optimal supply warehouse is determined from the candidate warehouse set based on the estimated delivery time, and inventory scheduling is performed based on the optimal supply warehouse.

[0013] By adopting the above technical solution, the candidate warehouse set can be quickly determined through maintenance location information, and the execution status of closed-loop cyclic allocation instructions can be collected in real time to accurately grasp the real-time location of parts. The estimated delivery time of each supply plan can be intelligently calculated by combining the candidate warehouses and parts locations. Finally, the supply warehouse is determined and the scheduling is executed based on the principle of optimal timeliness. This effectively solves the problems of information lag and slow decision-making in the traditional model, significantly shortens the parts delivery time, and ensures that maintenance work can be carried out in a timely manner.

[0014] Optionally, a method for bundling and scheduling component requirements may also be included: Determine the multi-component combination requirements based on the parts requirement list; Match the distribution warehouses of the parts from the global dynamic inventory view based on the requirements of multiple parts combinations; The optimal bundling and scheduling scheme is calculated based on warehouse network topology data and the distribution of spare parts warehouses. Based on the optimal bundling scheduling scheme, a combined delivery instruction is generated and executed to achieve synchronous and coordinated scheduling of multiple components.

[0015] By adopting the above technical solution, the system identifies the multi-part combination requirements in maintenance work orders and quickly matches the distribution warehouses of each part based on a global dynamic inventory view. Combining warehouse network topology data, the system uses optimization algorithms to calculate the optimal bundled scheduling scheme that comprehensively considers transportation costs, delivery timeliness, and inventory status. Finally, the system generates and executes combined delivery instructions, realizing the synchronous and collaborative scheduling of multiple parts. This significantly reduces the number of deliveries, ensures that the parts required for maintenance are delivered in complete sets on time, and improves the one-time completion rate of maintenance services.

[0016] Optionally, specific methods for calculating the optimal bundling scheduling scheme include: Determine available warehouse combinations based on the distribution of parts and components among warehouses; The overall delivery difficulty value is determined based on warehouse network topology data, available warehouse combinations, and maintenance location information. The available warehouse combinations are sorted based on the overall delivery difficulty value to obtain the available warehouse ranking; The optimal bundling and scheduling scheme is determined based on the available warehouse layout and the preset delivery difficulty threshold.

[0017] By adopting the above technical solution, all feasible available warehouse combinations are first determined based on the distribution of parts; then, the comprehensive delivery difficulty value of each scheme is calculated by combining the warehouse network topology, available warehouse combinations and maintenance location information; based on this difficulty value, the available warehouse combinations are sorted to form an optimization sequence; finally, based on the sorting results and the delivery difficulty threshold, the optimal bundled scheduling scheme is determined, which effectively improves the accuracy of multi-parts collaborative scheduling.

[0018] Optionally, cross-work order merging optimization methods are also included: Collect cross-work order information within a preset work order time period; Based on cross-work order information, determine the cross-work order parts requirement list; Match the distributed warehouses of parts across work orders from the global dynamic inventory view based on the parts demand list across work orders; The optimal delivery plan across work orders is calculated based on warehouse network topology data and the distribution of parts warehouses across work orders. Generate a merged delivery instruction based on the optimal delivery plan across work orders, and execute the merged delivery instruction to achieve simultaneous delivery of parts from multiple work orders.

[0019] By adopting the above technical solution, information from multiple maintenance work orders within a preset time period is collected and integrated to generate a cross-work order parts demand list; the distribution warehouses of each part are quickly matched based on a global dynamic inventory view; combined with warehouse network topology data, a path optimization algorithm is used to calculate the optimal cross-work order delivery plan; finally, a merged delivery instruction is generated and executed, realizing the synchronous and collaborative delivery of parts from multiple work orders, which significantly improves the utilization efficiency of delivery resources and reduces duplicate transportation and empty load rate.

[0020] Optionally, a main component association scheduling method may also be included: The main component items are determined based on the parts requirement list; The list of auxiliary accessories associated with the main accessory item is determined based on the preset accessory association rule library; Determine whether the parts requirement list includes an accessory list; When the parts requirement list does not include the auxiliary parts list, a missing parts replenishment plan is generated based on the global dynamic inventory view. The optimal supply plan and the missing parts replenishment plan are combined to generate associated scheduling instructions, and the associated scheduling instructions are executed to achieve the matching supply of main parts and auxiliary parts.

[0021] By adopting the above technical solution, the main parts item in the maintenance work order is identified, and the necessary auxiliary parts list is automatically determined based on the parts association rule base. When the missing auxiliary parts are detected in the parts demand list, a missing parts replenishment plan is quickly generated based on the global dynamic inventory view. Finally, the optimal supply plan of the main parts and the replenishment plan of the missing parts are intelligently integrated to generate a unified associated scheduling instruction and execute it. This effectively solves the problem of maintenance interruption and repeated scheduling caused by parts mismatch in traditional maintenance, ensures the synchronous supply of main parts and auxiliary parts, and significantly improves the one-time completion rate of maintenance operations and equipment repair efficiency.

[0022] Optionally, a method for evaluating the performance of cyclic paths may also be included: Collect historical operational data of the cyclic allocation path; Calculate path performance indicators based on historical operational data; Identify inefficient path segments based on path performance indicators; The path optimization scheme is generated based on inefficient path segments and cyclic allocation paths, and the cyclic allocation paths are dynamically adjusted according to the path optimization scheme.

[0023] Secondly, this application provides a multi-warehouse spare parts inventory scheduling system for sanitation vehicle maintenance, employing the following technical solution: A multi-warehouse spare parts inventory scheduling system for sanitation vehicle maintenance includes: The data acquisition module is used to collect real-time inventory data, in-transit logistics data, and warehouse network topology data from multiple warehouses. A memory is used to store a program that implements a multi-warehouse spare parts inventory scheduling method for sanitation vehicle maintenance; The processor is used to load and execute programs stored in memory.

[0024] In summary, this application includes at least one of the following beneficial technical effects: 1. By collecting inventory data from multiple warehouses, in-transit logistics data, and network topology data, a global dynamic inventory view is constructed to achieve a comprehensive understanding of the status of spare parts inventory. Based on this view and network topology data, closed-loop cyclical allocation instructions are generated to ensure the continuous flow of spare parts in the warehouse network and maintain dynamic inventory balance. When a maintenance work order is received, the optimal supply plan is quickly determined and the scheduling is executed by combining specific demand information and the global view. This effectively solves the problem of the disconnect between spare parts inventory and maintenance demand in the traditional model, significantly shortens parts waiting time, and improves equipment maintenance efficiency. 2. By identifying the multi-part combination requirements in maintenance work orders, the distribution warehouses of each part are quickly matched based on the global dynamic inventory view; combined with warehouse network topology data, optimization algorithms are used to calculate the optimal bundled scheduling scheme that comprehensively considers transportation costs, delivery time and inventory status; finally, a combined delivery instruction is generated and executed, realizing the synchronous and collaborative scheduling of multiple parts, significantly reducing the number of deliveries, ensuring that the parts required for maintenance are delivered in complete sets on time, and improving the first-time completion rate of maintenance services; 3. By identifying the main parts item in the maintenance work order and automatically determining the list of required auxiliary parts based on the parts association rule base; when it is detected that the relevant auxiliary parts are missing in the parts requirement list, a missing parts replenishment plan is quickly generated based on the global dynamic inventory view; finally, the optimal supply plan of the main parts and the replenishment plan of the missing parts are intelligently integrated to generate a unified associated scheduling instruction and execute it. This effectively solves the problem of maintenance interruption and repeated scheduling caused by parts mismatch in traditional maintenance, ensures the synchronous supply of main parts and auxiliary parts, and significantly improves the one-time completion rate of maintenance operations and equipment repair efficiency. Attached Figure Description

[0025] Figure 1 This is a flowchart of a multi-warehouse spare parts inventory scheduling method for sanitation vehicle maintenance; Figure 2 This is a flowchart of the method for bundling and scheduling component requirements; Figure 3 This is a flowchart of the main component association scheduling method. Detailed Implementation

[0026] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0027] Reference Figure 1 This application discloses a multi-warehouse spare parts inventory scheduling method for sanitation vehicle maintenance, including the following steps: S10: Collects real-time inventory data, in-transit logistics data, and warehouse network topology data from multiple warehouses.

[0028] Real-time inventory data across multiple warehouses refers to the latest inventory status of various spare parts distributed in central warehouses, regional warehouses, and frontline warehouses, including spare part model, quantity, age, and warehouse location.

[0029] By acquiring real-time inventory snapshots through warehouse management systems deployed in various warehouses, and then aggregating and integrating the inventory status of each warehouse through data interfaces, real-time inventory data across multiple warehouses can be obtained. Specific system interface integration and data collection methods are common knowledge in this field and will not be elaborated upon here.

[0030] In-transit logistics data refers to the logistics information of parts that have been dispatched from the warehouse but have not yet been delivered to their destination, including the model, quantity, shipping warehouse, destination warehouse, and estimated arrival time of parts that are being transferred or procured.

[0031] By obtaining the waybill status through the logistics tracking system interface and combining it with the transfer order and purchase order information in the order management system, data is correlated and integrated to form a complete in-transit view. The specific methods for logistics data docking and correlation are common knowledge in this field and will not be elaborated here.

[0032] Warehouse network topology data refers to data describing the logical connections and physical distances between warehouses, including the geographical location of each warehouse, hierarchical relationships (such as central warehouses and regional warehouses), as well as standard transportation routes and routine transportation times between warehouses.

[0033] This data is pre-entered into the system by those skilled in the art based on the warehouse network plan, and is updated and maintained when the network structure changes.

[0034] S11: Build a global dynamic inventory view based on real-time inventory data from multiple warehouses and in-transit logistics data.

[0035] A global dynamic inventory view is a virtual inventory pool that integrates real-time inventory and in-transit information from all warehouses. It can dynamically reflect the total available quantity and location distribution of each type of component in the entire network.

[0036] By employing data fusion algorithms, real-time inventory data from multiple warehouses is overlaid with in-transit logistics data, and the static location and dynamic flow of inventory are marked to form a globally unified, real-time updated panoramic view of inventory status. The specific data fusion and visualization techniques are common knowledge in the field and will not be elaborated upon here.

[0037] S12: Generate closed-loop cyclical transfer instructions based on the global dynamic inventory view and warehouse network topology data.

[0038] A closed-loop cyclical transfer instruction refers to an instruction used to guide the routine and preventative movement of parts within a warehouse network along a preset path. Its content includes the transfer-out warehouse, the transfer-in warehouse, the list of transferred parts, the quantity, and the suggested logistics route.

[0039] The specific method for generating closed-loop cyclic allocation instructions will be explained in detail in subsequent S20 to S25, and will not be repeated here.

[0040] S13: Execute closed-loop cyclic transfer instructions to control the continuous flow of parts between multiple warehouses along a preset cyclic transfer path, and receive maintenance work order information.

[0041] A circular transfer path refers to a pre-planned, closed-loop route for the flow of spare parts to achieve dynamic inventory balance, such as a circular path from "Warehouse A to Warehouse B to Warehouse C to Warehouse A". The specific circular transfer path is predetermined by those skilled in the art and will not be elaborated here.

[0042] Repair work order information refers to service request data received from the repair business system, including the identifier of the equipment to be repaired, the description of the fault, the list of required parts, the information of the repair engineer and their location.

[0043] Repair work order information is obtained in real time from the repair management system through the system interface.

[0044] Upon receiving a closed-loop cyclic allocation instruction, the system must control the continuous flow of parts between multiple warehouses along the cyclic allocation path, while simultaneously continuously receiving maintenance work order information.

[0045] The movement of spare parts is achieved through a controlled logistics execution system that executes outbound, transportation, and warehousing operations according to the specified outbound and inbound warehouses, and the spare parts list and quantity in the closed-loop transfer instructions. The specific operation procedures of the logistics execution system are common knowledge in this field and will not be elaborated here.

[0046] S14: Determine the parts demand list and repair location information based on the repair work order information.

[0047] A parts requirement list is a specific list of the models, specifications, and quantities of parts necessary to complete the repair work order.

[0048] Repair location information refers to the specific geographical location where the repair service takes place.

[0049] The parts requirement list and repair location information are retrieved directly from the repair work order information. The repair work order information contains the parts requirement list and repair location information.

[0050] S15: Combine parts demand list, repair location information, and global dynamic inventory view to determine the optimal supply solution.

[0051] The optimal supply plan refers to the best response strategy to meet the current maintenance needs.

[0052] The optimal supply solution is determined by first matching all available inventory sources in the global dynamic inventory view based on the parts demand list, including real-time inventory and in-transit logistics data for each warehouse; then, calculating the delivery time and logistics cost for each candidate inventory source by combining maintenance location information; and finally, comprehensively evaluating inventory availability, timeliness requirements, cost control, and work order priority through a multi-objective optimization algorithm to select the supply solution with the highest overall benefit. The specific multi-objective optimization algorithm is configured by those skilled in the art according to business needs and will not be elaborated upon here.

[0053] S16: Generate and issue scheduling execution instructions based on the optimal supply plan to quickly complete parts scheduling.

[0054] Dispatch execution instructions are operation commands that can be directly executed by warehouse management systems and logistics systems, including specific picking lists, packing task lists, and shipping orders.

[0055] The scheduling execution instructions automatically generate standardized sets of operation instructions by parsing the supply warehouses, delivery routes, and parts details in the optimal supply plan. These instructions are then sent in real time to the corresponding warehouse management system and logistics transportation system via system interfaces. Specific instruction generation rules and system interface protocols are pre-defined by those skilled in the art based on the system architecture and will not be elaborated upon here.

[0056] Once the optimal supply plan is obtained, a scheduling execution instruction must first be generated and then issued to quickly complete the parts scheduling.

[0057] It also includes a method for generating closed-loop cyclic allocation instructions: S20: Determine the warehouse to be transferred based on the global dynamic inventory view.

[0058] A warehouse that is being transferred out refers to a warehouse whose current inventory level is higher than a preset threshold for transferring out, and whose spare parts are available for transfer to other warehouses.

[0059] The warehouse transfer threshold refers to the minimum inventory level setting that triggers a warehouse to become a transfer candidate. The warehouse transfer threshold is set in advance by those skilled in the art and will not be elaborated here.

[0060] By analyzing the inventory data of each warehouse in the global dynamic inventory view, warehouses whose inventory exceeds the threshold for being transferred out are identified as warehouses to be transferred out.

[0061] S21: Determine the downstream adjacent warehouses based on warehouse network topology data, outgoing warehouses, and cyclic transfer paths.

[0062] Downstream adjacent warehouses refer to warehouses located at the next node after the originating warehouse in the cyclical transfer path.

[0063] By querying warehouse network topology data and combining it with the cyclic allocation path, the direct downstream node of the warehouse being allocated is determined as the downstream adjacent warehouse in the path flow.

[0064] S22: Obtain the receiving warehouse based on the downstream adjacent warehouse.

[0065] The receiving warehouse refers to the warehouse selected to receive the transferred parts; in this method, it specifically refers to the downstream adjacent warehouse.

[0066] The identified downstream adjacent warehouses will be used directly as the receiving warehouses.

[0067] S23: Combine the global dynamic inventory view and the incoming warehouse to obtain the incoming inventory level.

[0068] The inventory level refers to the current inventory quantity of a specific component transferred into the warehouse.

[0069] By querying the global dynamic inventory view, the real-time inventory quantity of the corresponding parts in the transferred warehouse can be obtained, thus obtaining the transferred inventory level.

[0070] S24: When the level of the transferred inventory is lower than the preset inventory level threshold, the transfer quantity is determined based on the level of the transferred inventory and the inventory level threshold.

[0071] The inventory level threshold refers to the minimum inventory guarantee level set for incoming goods to the warehouse. The inventory level threshold is set in advance by those skilled in the art and will not be elaborated here.

[0072] The transfer quantity refers to the number of parts that need to be transferred from the sending warehouse to the receiving warehouse.

[0073] When the level of the incoming inventory is lower than the inventory level threshold, the difference between the inventory level threshold and the current level of the incoming inventory is calculated. Combined with the available inventory of the outgoing warehouse, the smaller of the two values ​​is taken as the transfer quantity.

[0074] S25: Combine the cyclical transfer path, transfer quantity, transfer-out warehouse, and transfer-in warehouse to generate a closed-loop cyclical transfer instruction.

[0075] By integrating the information on the cyclical transfer route, the determined transfer quantity, the identifiers of the transferring-out warehouse and the transferring-in warehouse, a closed-loop cyclical transfer instruction containing complete transfer elements is generated. The specific instruction generation format is set by those skilled in the art according to system requirements and will not be elaborated here.

[0076] Also includes: S30: Collect historical maintenance data.

[0077] Historical maintenance data refers to maintenance records accumulated over a period of time, including maintenance work order number, maintenance time, maintenance location, faulty equipment model, replacement parts model and quantity, maintenance cycle, and other information.

[0078] Historical maintenance data is obtained by extracting it from the historical database of the maintenance management system. The specific data extraction scope and cycle are determined by those skilled in the art based on predicted needs, and will not be elaborated here.

[0079] S31: Generate parts demand forecasts based on historical maintenance data.

[0080] The demand forecast for spare parts refers to a quantitative estimate of the expected demand for different models of spare parts in various regions during a specific future time period.

[0081] By employing time series analysis algorithms, trend decomposition and seasonal modeling are performed on the parts consumption sequence in historical maintenance data to predict demand in future cycles, thereby generating parts demand forecasts. Specific time series analysis algorithms include, but are not limited to, ARIMA models and exponential smoothing methods. Their model parameters are optimized and set by those skilled in the art based on the characteristics of historical data, and will not be elaborated upon here.

[0082] S32: Identify demand hotspots based on component demand forecasts.

[0083] Demand hotspots refer to physical areas where the predicted demand for components is significantly higher than the average level during a specific future period.

[0084] By setting a demand threshold, the total predicted demand within a unit area is compared with a preset threshold, and areas exceeding the threshold are selected as demand hotspots. The specific demand threshold is dynamically adjusted by those skilled in the art based on business objectives, and will not be elaborated upon here.

[0085] S33: Combine warehouse network topology data and demand hotspot areas to calculate path optimization coefficients.

[0086] The path optimization coefficient refers to the weight used to quantitatively evaluate the service efficiency of each node in a cyclic allocation path relative to the demand hotspot area.

[0087] The path optimization coefficient is derived by calculating the weighted average service distance from each warehouse node to all demand hotspot areas and combining this with the node's position order in the current cyclic allocation path. The weight of the weighted average service distance is determined by the demand forecast value of each demand hotspot area. The specific coefficient calculation formula is set by those skilled in the art based on the optimization objective and will not be elaborated here.

[0088] S34: Dynamically adjust the node order of the cyclic allocation path based on the path optimization coefficient.

[0089] Based on the calculated path optimization coefficients, the order of nodes in the cyclic allocation path is reordered. For example, nodes with higher efficiency in areas with high service demand are moved to better transfer positions. Specific path adjustment strategies (such as priority-based node swapping algorithms) are predetermined by those skilled in the art and will not be elaborated here.

[0090] It also includes a responsive scheduling method for maintenance work orders: S40: Determine the set of candidate warehouses based on maintenance location information.

[0091] The candidate warehouse set refers to the set of all warehouses that are selected based on maintenance location information, have the capability to serve that location, and whose inventory meets the demand.

[0092] Using a spatial distance calculation algorithm, all reachable warehouses within a preset service radius are screened based on the repair location. This is then combined with a global dynamic inventory view to confirm the availability of spare parts in these warehouses, ultimately forming a candidate warehouse set. The specific service radius setting criteria are determined by those skilled in the art based on logistical capabilities and will not be elaborated upon here.

[0093] S41: Collect the current execution status of the closed-loop cyclic allocation command.

[0094] The current execution status refers to the real-time progress information of the closed-loop transfer instruction during the logistics execution process, including the instruction status (pending execution / in execution / completed), the current warehouse node, the distance already transported, and the estimated time to reach the next node.

[0095] The current execution status is obtained through the real-time data interface of the logistics tracking system. This system integrates GPS positioning data of transport vehicles, inbound and outbound status of the warehouse management system, and scanning records of logistics nodes to generate the real-time execution status of closed-loop cyclical allocation instructions. The specific implementation method of the logistics tracking system is common knowledge in this field and will not be elaborated here.

[0096] S42: Determine the real-time location of the accessory based on the current execution status.

[0097] The real-time location of a component refers to its current coordinates within the logistics network after a closed-loop transfer instruction has been executed.

[0098] By analyzing GPS positioning data, warehouse node information, and the real-time location of transport vehicles in the current execution state, and combining this with coordinate mapping using a geographic information system, the precise location of the parts within the logistics network is determined. The specific location mapping algorithm is set by those skilled in the art based on the positioning technology employed, and will not be elaborated upon here.

[0099] S43: Combine the candidate warehouse set with the real-time location of the parts to calculate the estimated delivery time to the warehouse.

[0100] Estimated delivery time refers to the estimated time required for parts to be transported from the candidate warehouse or its real-time location en route to the repair location.

[0101] The optimal transportation route time from each candidate warehouse to the repair location is calculated using a route planning algorithm. For parts in transit, the remaining transportation time from their current location to the repair location is calculated, and the necessary inbound and outbound operation time is added to obtain the final estimated delivery time. The specific time estimation model is established by those skilled in the art based on historical logistics data and will not be elaborated here.

[0102] S44: Determine the optimal supply warehouse from the candidate warehouse set based on the estimated delivery time of the warehouse, and perform inventory scheduling based on the optimal supply warehouse.

[0103] The optimal supply warehouse is a candidate warehouse that can deliver the required spare parts to the repair location as quickly as possible.

[0104] By comparing the estimated delivery times of each candidate warehouse, the warehouse with the shortest delivery time is selected as the optimal supply warehouse, and inventory is then allocated accordingly.

[0105] Reference Figure 2 It also includes a method for bundling and scheduling component requirements: S50: Determine the requirements for multiple component combinations based on the component requirement list.

[0106] Multi-parts combination requirements refer to a set of multiple different models of parts that need to be supplied simultaneously in a repair work order, and these parts need to be used together in the repair work.

[0107] Multi-part combination requirements are identified by analyzing the functional relationships and repair process requirements of the parts in the parts requirement list, thus determining which parts groups need to be used together in the same repair operation. Specific rules for identifying parts combinations are pre-defined by those skilled in the art based on repair process specifications and will not be elaborated upon here. S51: Match the distribution warehouses of parts from the global dynamic inventory view based on the requirements of multiple parts combinations.

[0108] A parts distribution warehouse refers to a collection of all warehouses that store at least one part in a multi-part combination requirement.

[0109] By querying the inventory records of each component in the global dynamic inventory view, we can obtain all available inventory locations for each component in a multi-component combination requirement, forming a set of component distribution warehouses. The specific inventory query method is common knowledge in this field and will not be elaborated here.

[0110] S52: Calculate the optimal bundling and scheduling scheme based on warehouse network topology data and the distribution of parts warehouses.

[0111] The optimal bundled scheduling scheme refers to a supply scheme that can meet the demand for multiple component combinations in the shortest total time. It specifies the supply warehouse, allocation route and collaborative distribution arrangements for each component.

[0112] The specific optimal bundling scheduling scheme will be explained in detail in subsequent sections S60 to S63, and will not be repeated here.

[0113] S53: Generate combined delivery instructions based on the optimal bundled scheduling scheme, and execute the combined delivery instructions to achieve synchronous and coordinated scheduling of multiple parts.

[0114] A combined delivery instruction is an operational instruction that includes information on the coordinated delivery of multiple components. It specifies the warehouse, delivery route, time point, and coordination requirements for each component.

[0115] By parsing the warehouse allocation results and route planning information in the optimal bundled scheduling scheme, standardized combined delivery instruction messages are generated and sent to the relevant warehouse management system and logistics transportation system for execution via system interfaces. The specific instruction message format and interface protocol are defined by those skilled in the art according to system integration specifications and will not be elaborated here.

[0116] The specific methods for calculating the optimal bundled scheduling scheme include: S60: Determine the available warehouse combinations based on the distribution of parts warehouses.

[0117] An available warehouse combination refers to the set of all possible warehouse supply options that can meet the needs of multiple component combinations. Each combination contains a group of warehouses that collectively provide all the required component models, with each component having at least one source of supply.

[0118] Available warehouse combinations are determined by iterating through all possible combinations of parts distribution warehouses, verifying each combination to ensure it contains all part models required for multi-part combination requirements, and retaining combinations that meet the criteria as available warehouse combinations. Specific verification methods and iteration rules are to be set by those skilled in the art based on business needs and will not be elaborated upon here.

[0119] S61: Determine the overall delivery difficulty value based on warehouse network topology data, available warehouse combinations, and maintenance location information.

[0120] The overall delivery difficulty value is a comprehensive indicator used to quantitatively assess the complexity and cost of coordinating the delivery of all spare parts from a given combination of available warehouses to the repair location.

[0121] First, the standard transportation time from each warehouse in the available warehouse combination to the maintenance location is calculated based on warehouse network topology data, and the summation yields the basic transportation time. Then, the overlap of transportation routes from each warehouse to the maintenance location is analyzed; the higher the overlap, the lower the correction coefficient. Finally, the complexity of multi-warehouse collaborative operations is assessed, including the difficulty of synchronizing delivery times and coordinating logistics resources, to determine the collaborative operation coefficient. The basic transportation time is multiplied by the route overlap correction coefficient and the collaborative operation coefficient to obtain the final comprehensive delivery difficulty value. Specific coefficient weights and calculation rules are optimized and determined by those skilled in the art based on actual operational data and will not be elaborated here.

[0122] S62: Sort the available warehouse combinations based on the overall delivery difficulty value to obtain the available warehouse arrangement.

[0123] Available warehouse arrangement refers to the ordered sequence obtained by sorting all available warehouse combinations from low to high according to their corresponding comprehensive delivery difficulty values.

[0124] The available warehouses can be sorted from low to high according to their corresponding comprehensive delivery difficulty value to obtain the available warehouse ranking.

[0125] S63: Determine the optimal bundling scheduling scheme based on the available warehouse arrangement and the preset delivery difficulty threshold.

[0126] The delivery difficulty threshold refers to the highest overall delivery difficulty value limit for determining whether an available warehouse combination is feasible. The delivery difficulty threshold is set in advance by those skilled in the art and will not be elaborated here.

[0127] The optimal bundled scheduling scheme is determined by selecting the combination of available warehouses with the lowest overall delivery difficulty value that meets the delivery difficulty threshold from the available warehouse list. This threshold ensures that the selected scheme achieves optimality within an acceptable difficulty range.

[0128] It also includes cross-work order merging optimization methods: S70: Collect cross-work order information within a preset work order time period.

[0129] A work order time period refers to a time window set for consolidation and optimization, such as the next 4 hours or the current day. This time period is preset by those skilled in the art based on the economic efficiency and service timeliness requirements of logistics consolidation.

[0130] Cross-work order information refers to the collection of information on all repair work orders within the work order time period, including at least the parts requirement list and repair location information for each work order.

[0131] The method for collecting cross-work order information is the same as the method for obtaining maintenance work order information in S13 above, and will not be repeated here.

[0132] S71: Determine the cross-work order parts requirement list based on cross-work order information.

[0133] A cross-work order parts requirement list refers to a unified list of parts requirements obtained by aggregating and deduplicating the parts requirements of all maintenance work orders within a work order time period. It includes all required parts models and their total quantities.

[0134] By extracting the parts requirement details from all relevant repair work orders, classifying and summarizing them according to parts model, calculating the total required quantity of parts for each model, and establishing a unified parts requirement list, a cross-work order parts requirement list is generated.

[0135] S72: Match cross-work-order part distribution warehouses from the global dynamic inventory view based on the cross-work-order part requirement list.

[0136] A cross-work-order parts distribution warehouse refers to the collection of all possible warehouse combinations that can meet the needs of all parts in the cross-work-order parts demand list.

[0137] The cross-work order parts distribution warehouse identifies warehouse combinations that can cover all parts models in the cross-work order parts demand list by traversing the inventory information of all warehouses in the global dynamic inventory view, and uses the collection of these combinations as the cross-work order parts distribution warehouse.

[0138] S73: Calculate the optimal delivery plan across work orders based on warehouse network topology data and cross-work order parts distribution warehouses.

[0139] The optimal cross-work-order delivery solution refers to a logistics plan that can efficiently combine all parts from the selected warehouses in the cross-work-order parts demand list and deliver them to their respective repair locations. This plan specifies the supply warehouse, delivery route, and coordinated delivery sequence for each part across multiple work orders.

[0140] The optimal cross-work-order delivery solution first establishes a delivery network model based on warehouse network topology data; then, for each candidate warehouse combination in the cross-work-order parts distribution warehouses, it calculates the total delivery cost and time for serving all repair locations; finally, it selects the warehouse combination with the lowest total delivery cost and meeting the timeliness requirements as the optimal solution, and plans the specific delivery routes and coordination sequences. The specific network modeling and optimization calculation methods are set by those skilled in the art based on operations research principles, and will not be elaborated here.

[0141] S74: Generate a merged delivery instruction based on the optimal cross-work order delivery plan, and execute the merged delivery instruction to achieve synchronous delivery of parts from multiple work orders.

[0142] Consolidated delivery instructions refer to a set of operational instructions used to guide the execution of optimal delivery plans across work orders. They integrate complete operational information on the picking, packing, coordinated transportation, and final distribution of parts from multiple work orders.

[0143] Once the optimal cross-work-order delivery plan is obtained, a merged delivery instruction must first be generated based on the optimal cross-work-order delivery plan, and then executed according to the merged delivery instruction, so as to achieve synchronous delivery of parts from multiple work orders.

[0144] Reference Figure 3 It also includes a main component association scheduling method: S80: Determine the main accessory items based on the accessory requirement list.

[0145] The main components refer to the key components that play a core role in maintenance work and whose installation or replacement usually requires the use of specific auxiliary components.

[0146] By understanding the parts requirement list, we can identify all the necessary parts. Then, based on the parts' value, their importance in the repair process, and their frequency of association in the parts association rule base, we assign weights to each part in the requirements list. Finally, we select the part with the highest weight as the primary part item. The specific weighting rules are pre-defined by those skilled in the art based on the characteristics of the repair process and will not be elaborated upon here.

[0147] S81: Determine the list of auxiliary accessories associated with the main accessory item based on the preset accessory association rule library.

[0148] The parts association rule base is a database that stores the relationships between main parts and auxiliary parts. It records information on auxiliary parts that typically need to be used or replaced simultaneously when installing or replacing a specific main part. The parts association rule base is established in advance by those skilled in the art and will not be elaborated upon here.

[0149] The accessory list refers to a standard list of one or more auxiliary accessories that have a strong relationship with the identified main accessory item, based on the accessory association rule library.

[0150] The list of auxiliary accessories can be obtained by querying the accessory association rule library.

[0151] S82: Determine whether the parts requirement list includes an accessory list.

[0152] The parts requirement list of the repair work order is compared with the auxiliary parts list determined based on the main parts item to determine whether all parts in the auxiliary parts list are included in the parts requirement list.

[0153] S83: When the parts requirement list does not include the auxiliary parts list, generate a missing parts completion plan based on the global dynamic inventory view.

[0154] A missing parts replenishment plan refers to a supply plan developed to replenish missing accessories in the parts demand list. It specifies the supply warehouse, quantity, and supply route for the missing accessories.

[0155] The missing parts replenishment plan first queries the available inventory distribution of each missing part in the global dynamic inventory view; then, based on warehouse network topology data, it selects the available warehouse closest to the repair location as the supply source; finally, it determines the allocation quantity of each missing part and plans the optimal supply route. The specific supply source selection strategy is determined by those skilled in the art based on the principle of proximity, and will not be elaborated here.

[0156] When the parts requirement list does not include the auxiliary parts list, it means that the repair work order does not fully include all the parts necessary to complete the repair work. A missing parts supplementation plan needs to be generated to ensure the completeness and feasibility of the repair work.

[0157] S84: Combine the optimal supply plan with the missing parts replenishment plan to generate associated scheduling instructions, and execute the associated scheduling instructions to achieve the matching supply of main parts and auxiliary parts.

[0158] The associated dispatch instruction refers to a unified dispatch instruction that integrates the optimal supply plan for main parts with the missing parts replenishment plan for auxiliary parts, ensuring that main parts and all necessary auxiliary parts can be coordinated and supplied to the repair location.

[0159] First, the baseline timeline is determined by analyzing the delivery time window and logistics route in the optimal supply plan for main components. Then, the supply time of each auxiliary component in the missing component replenishment plan is aligned with the baseline timeline to ensure that the latest delivery time of all components remains consistent. Next, the delivery routes are optimized and integrated, prioritizing route plans that can share transportation resources to reduce overall delivery costs. Finally, a unified delivery instruction, namely the associated scheduling instruction, is generated to clarify the delivery sequence, transportation route, transit nodes, and final delivery time requirements of each component, and a collaborative monitoring mechanism is set up to ensure that all links are executed synchronously.

[0160] It also includes methods for evaluating the effectiveness of loop paths: S90: Collect historical operation data of the cyclic allocation path.

[0161] Historical operational data refers to the operational records accumulated over past operating cycles for cyclical allocation routes, including data such as actual transportation time, transportation costs, inventory turnover rate, equipment utilization rate, and task completion rate for each route segment.

[0162] By extracting inventory turnover records from each warehouse from the warehouse management system; obtaining actual transportation time and cost data for each route segment from the transportation management system; collecting utilization statistics of loading and unloading equipment and transportation vehicles from the equipment monitoring system; and exporting completion status records of each allocation instruction from the task scheduling system, and by aligning these multi-source data with timestamps and mapping key fields, a complete historical operation dataset of cyclic allocation routes is finally formed.

[0163] S91: Calculate path performance indicators based on historical operational data.

[0164] Route efficiency indicators refer to key performance parameters used to quantitatively evaluate the overall and segmental operational efficiency of a cyclical allocation route, including average transportation time, unit cost-effectiveness, inventory turnover rate, and route reliability index.

[0165] First, historical operational data is preprocessed to remove outliers and fill in missing data. Then, the average transportation time, unit cost-effectiveness, and inventory turnover rate for each route segment are calculated. The average transportation time is calculated using a mileage-weighted average method, the unit cost-effectiveness is obtained by the ratio of total cost to total allocation, and the inventory turnover rate is calculated based on the number of parts turnovers per unit time based on inventory records. Finally, the indicators of each route segment are combined with task completion rate and transportation time stability data to generate a route reliability index using a weighted fusion method, forming a complete route efficiency indicator system. The specific calculation parameters and weight configurations are set by those skilled in the art according to management requirements and will not be elaborated here.

[0166] S92: Identify inefficient path segments based on path performance indicators.

[0167] An inefficient path segment refers to a continuous path interval where the path efficiency index value is consistently lower than the preset benchmark.

[0168] First, a comprehensive evaluation system incorporating transportation timeliness, cost-effectiveness, turnaround rate, and reliability index is established, setting dynamic benchmark values ​​for each indicator. Then, the cyclical allocation routes are monitored in segments, and the comprehensive efficiency score for each segment is calculated in real time. When the comprehensive score of a segment is lower than the corresponding benchmark value for three consecutive monitoring periods, and at least two of the sub-indicators consistently fail to meet the standards, the segment is marked as an inefficient segment. The specific scoring model and benchmark adjustment mechanism are determined by those skilled in the art based on operational requirements and will not be elaborated upon here.

[0169] S93: Generate a path optimization scheme based on inefficient path segments and cyclic allocation paths, and dynamically adjust the cyclic allocation paths according to the path optimization scheme.

[0170] Route optimization schemes refer to improvement strategies proposed for identified inefficient route segments, including specific measures such as route node adjustment, transportation mode change, scheduling strategy optimization, and resource reallocation.

[0171] First, a multi-dimensional diagnostic system is established to analyze the specific problems of inefficient path segments, including quantitative indicators such as transportation timeliness deviation rate, cost overrun ratio, and resource idle rate. Then, based on the overall network characteristics of the cyclical allocation path, network optimization algorithms are used to generate candidate optimization schemes, including: redesigning the network topology for path segments with unreasonable node layouts, establishing multi-mode transportation combination schemes for path segments with incompatible transportation modes, formulating intelligent dynamic scheduling rules for path segments with ineffective scheduling strategies, and establishing a flexible resource allocation mechanism for path segments with unbalanced resource allocation. Finally, the effectiveness of the candidate schemes is evaluated through a simulation verification platform, and the scheme with the best overall improvement is selected as the final path optimization scheme. The specific network optimization algorithms and simulation verification methods are selected by those skilled in the art based on the path characteristics and will not be elaborated here.

[0172] Once the route optimization plan is obtained, the cyclical allocation route needs to be dynamically adjusted according to the route optimization plan.

[0173] Based on the same inventive concept, embodiments of the present invention provide a multi-warehouse spare parts inventory scheduling system for sanitation vehicle maintenance, comprising: The data acquisition module is used to collect real-time inventory data from multiple warehouses, in-transit logistics data, warehouse network topology data, historical maintenance data, current execution status, cross-work order information, and historical operation data. A memory is used to store a program that implements a multi-warehouse spare parts inventory scheduling method for sanitation vehicle maintenance; The processor is used to load and execute programs stored in memory.

[0174] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0175] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for scheduling multi-warehouse spare parts inventory for sanitation vehicle maintenance, characterized in that, include: Collect real-time inventory data, in-transit logistics data, and warehouse network topology data from multiple warehouses; A global dynamic inventory view is constructed based on real-time inventory data from multiple warehouses and in-transit logistics data. Based on the global dynamic inventory view and warehouse network topology data, a closed-loop cyclical transfer instruction is generated; Execute closed-loop cyclic allocation instructions to control the continuous flow of parts between multiple warehouses along a preset cyclic allocation path, and receive maintenance work order information; Based on the repair work order information, determine the parts demand list and repair location information; The optimal supply plan is determined by combining the parts demand list, repair location information, and a global dynamic inventory view. Based on the optimal supply plan, dispatch execution instructions are generated and issued to quickly complete the dispatch of parts.

2. The multi-warehouse spare parts inventory scheduling method for sanitation vehicle maintenance according to claim 1, characterized in that, It also includes a method for generating closed-loop cyclic allocation instructions: Determine the warehouse to be transferred based on a global dynamic inventory view; The downstream adjacent warehouses are determined based on warehouse network topology data, outgoing warehouses, and cyclical transfer paths. The receiving warehouse is determined based on the downstream adjacent warehouses; Combine the global dynamic inventory view with the incoming warehouse to obtain the incoming inventory level; When the level of the transferred inventory is lower than the preset inventory level threshold, the quantity to be transferred is determined based on the level of the transferred inventory and the inventory level threshold. The closed-loop cyclical transfer instruction is generated by combining the cyclical transfer path, transfer quantity, transfer-out warehouse, and transfer-in warehouse.

3. A multi-warehouse spare parts inventory scheduling method for sanitation vehicle maintenance according to claim 2, characterized in that, Also includes: Collect historical maintenance data; Generate parts demand forecasts based on historical maintenance data; Identify demand hotspots based on component demand forecasts; Combine warehouse network topology data with demand hotspot areas to calculate path optimization coefficients; The node order of the cyclic allocation path is dynamically adjusted based on the path optimization coefficient.

4. A multi-warehouse spare parts inventory scheduling method for sanitation vehicle maintenance according to claim 1, characterized in that, It also includes a responsive scheduling method for maintenance work orders: Determine the set of candidate warehouses based on maintenance location information; Collect the current execution status of closed-loop cyclic allocation instructions; Determine the real-time location of the parts based on the current execution status; Combine the candidate warehouse set with the real-time location of parts to calculate the estimated warehouse delivery time; The optimal supply warehouse is determined from the candidate warehouse set based on the estimated delivery time, and inventory scheduling is performed based on the optimal supply warehouse.

5. A multi-warehouse spare parts inventory scheduling method for sanitation vehicle maintenance according to claim 1, characterized in that, This also includes a method for bundling and scheduling component requirements: Determine the multi-component combination requirements based on the parts requirement list; Match the distribution warehouses of the parts from the global dynamic inventory view based on the requirements of multiple parts combinations; The optimal bundling and scheduling scheme is calculated based on warehouse network topology data and the distribution of spare parts warehouses. Based on the optimal bundling scheduling scheme, a combined delivery instruction is generated and executed to achieve synchronous and coordinated scheduling of multiple components.

6. A multi-warehouse spare parts inventory scheduling method for sanitation vehicle maintenance according to claim 5, characterized in that, The specific methods for calculating the optimal bundled scheduling scheme include: Determine available warehouse combinations based on the distribution of parts and components among warehouses; The overall delivery difficulty value is determined based on warehouse network topology data, available warehouse combinations, and maintenance location information. The available warehouse combinations are sorted based on the overall delivery difficulty value to obtain the available warehouse ranking; The optimal bundling and scheduling scheme is determined based on the available warehouse layout and the preset delivery difficulty threshold.

7. A multi-warehouse spare parts inventory scheduling method for sanitation vehicle maintenance according to claim 5, characterized in that, It also includes cross-work order merging optimization methods: Collect cross-work order information within a preset work order time period; Based on cross-work order information, determine the cross-work order parts requirement list; Match the distributed warehouses of parts across work orders from the global dynamic inventory view based on the parts demand list across work orders; The optimal delivery plan across work orders is calculated based on warehouse network topology data and the distribution of parts warehouses across work orders. Generate a merged delivery instruction based on the optimal delivery plan across work orders, and execute the merged delivery instruction to achieve simultaneous delivery of parts from multiple work orders.

8. A multi-warehouse spare parts inventory scheduling method for sanitation vehicle maintenance according to claim 1, characterized in that, It also includes a main component association scheduling method: The main component items are determined based on the parts requirement list; The list of auxiliary accessories associated with the main accessory item is determined based on the preset accessory association rule library; Determine whether the parts requirement list includes an accessory list; When the parts requirement list does not include the auxiliary parts list, a missing parts replenishment plan is generated based on the global dynamic inventory view. The optimal supply plan and the missing parts replenishment plan are combined to generate associated scheduling instructions, and the associated scheduling instructions are executed to achieve the matching supply of main parts and auxiliary parts.

9. A multi-warehouse spare parts inventory scheduling method for sanitation vehicle maintenance according to claim 1, characterized in that, It also includes methods for evaluating the effectiveness of loop paths: Collect historical operational data of the cyclic allocation path; Calculate path performance indicators based on historical operational data; Identify inefficient path segments based on path performance indicators; The path optimization scheme is generated based on inefficient path segments and cyclic allocation paths, and the cyclic allocation paths are dynamically adjusted according to the path optimization scheme.

10. A multi-warehouse spare parts inventory scheduling system for sanitation vehicle maintenance, characterized in that, include: The data acquisition module is used to collect real-time inventory data, in-transit logistics data, and warehouse network topology data from multiple warehouses. A memory for storing a program that implements a multi-warehouse spare parts inventory scheduling method for sanitation vehicle maintenance as described in any one of claims 1 to 9; The processor is used to load and execute programs stored in memory.

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