Logistics unmanned vehicle battery replacement scheduling method and system

Abstract

The application discloses a logistics unmanned vehicle battery replacement scheduling method and system, relates to the technical field of logistics unmanned vehicles, and is used for improving the distribution efficiency of the logistics unmanned vehicle. The method comprises the following steps: when receiving a battery replacement request of a target logistics unmanned vehicle, an initial distribution path of the target logistics unmanned vehicle is acquired; the path deviation degree between the battery replacement path of each battery replacement station and the initial distribution path is calculated, and the available resource information of each battery replacement station is acquired; the battery replacement idle degree of each battery replacement station is calculated based on the available resource information; the path deviation degree and the battery replacement idle degree are weighted and summed according to a preset weight distribution rule, the scheduling score of each battery replacement station is calculated, the battery replacement station with the highest scheduling score is determined as a target battery replacement station; the initial distribution path is corrected based on the target battery replacement station, and a target distribution path is obtained; and a path update instruction based on the target distribution path is sent to the target logistics unmanned vehicle.

Description

Technical Field

[0001] This application relates to the field of unmanned logistics vehicle technology, and in particular to a method and system for scheduling battery swapping for unmanned logistics vehicles. Background Technology

[0002] In modern urban logistics and distribution, unmanned delivery vehicles (AWDVs) play an increasingly important role, tasked with efficiently delivering packages to consumers. To ensure the continuous and uninterrupted operation of these vehicles, an energy replenishment mechanism is crucial. Currently, the industry commonly uses rapid battery swapping to recharge AWDVs, involving the quick replacement of batteries at dedicated battery swapping stations.

[0003] During the operation of unmanned delivery vehicles (UGVs), when their battery level is low, they send a battery swap request to the central dispatch system. Upon receiving the request, the central dispatch system plans the nearest battery swapping station for the UGV. However, during peak delivery periods, multiple UGVs may simultaneously flock to the same nearest battery swapping station. The station's battery swapping bays or spare battery reserves may not be able to meet the sudden surge in demand, causing multiple UGVs to queue and wait. This prolongs the downtime of the UGVs, reduces delivery efficiency, and negatively impacts user experience. Summary of the Invention

[0004] This application provides a method and system for scheduling battery swapping for unmanned logistics vehicles, which can reduce the downtime of unmanned logistics vehicles caused by queuing for battery swapping, thereby improving the delivery efficiency of unmanned logistics vehicles.

[0005] The first aspect of this application provides a method for scheduling battery swapping for unmanned logistics vehicles, including:

[0006] When a battery swapping request is received from the target logistics unmanned vehicle, the initial delivery route of the target logistics unmanned vehicle is obtained;

[0007] Calculate the path deviation between the battery swapping path of each battery swapping station and the initial delivery path, and obtain the available resource information of each battery swapping station, including the number of currently idle workstations and the number of swappable batteries.

[0008] The battery swapping idle time of each battery swapping station is calculated based on the available resource information.

[0009] The path deviation and the battery swapping idle time are weighted and summed according to the preset weight allocation rules to obtain the scheduling score of each battery swapping station, and the battery swapping station with the highest scheduling score is determined as the target battery swapping station.

[0010] The initial delivery route is modified based on the target battery swapping station to obtain the target delivery route;

[0011] Send a route update instruction based on the target delivery route to the target logistics unmanned vehicle.

[0012] Optionally, calculating the path deviation between the battery swapping path of each battery swapping station and the initial delivery path includes:

[0013] Obtain the location information of each battery swapping station;

[0014] Based on the location information, determine the nearest adjacent delivery point to each battery swapping station in the initial delivery route;

[0015] Each battery swapping station is embedded into the original path between adjacent delivery points to obtain the battery swapping path corresponding to each battery swapping station.

[0016] Calculate the difference in total driving distance and the difference in total estimated driving time between the battery swapping route and the original route to the corresponding adjacent delivery point;

[0017] The path deviation is determined based on the difference between the total travel distance and the total estimated travel time.

[0018] Optionally, after sending a path update instruction based on the target delivery route to the target logistics unmanned vehicle, the method further includes:

[0019] The battery of the target logistics unmanned vehicle is calibrated to obtain the actual available power of the newly replaced battery.

[0020] The driving range of the target logistics unmanned vehicle is assessed based on the actual available power.

[0021] Determine whether the remaining driving range is greater than the required driving distance for the target delivery task in the target delivery route;

[0022] If not, then activate the emergency battery swapping mode.

[0023] Optionally, the emergency battery swapping mode includes:

[0024] When a second battery swap request is received from the target logistics unmanned vehicle, the distance between each battery swap station and the current location of the target logistics unmanned vehicle is calculated;

[0025] The battery swapping station with the shortest distance is designated as the secondary battery swapping station;

[0026] The target delivery route is modified based on the secondary battery swapping station to obtain the secondary battery swapping delivery route;

[0027] Send a secondary update instruction to the target logistics unmanned vehicle based on the secondary battery swapping delivery route.

[0028] Optionally, after sending a path update instruction based on the target delivery route to the target logistics unmanned vehicle, the method further includes:

[0029] When a new delivery task appears on the target delivery route, the task information of the new delivery task is obtained. The task information includes the target location, expected road conditions, expected package weight, and task urgency.

[0030] The battery power requirement of the new delivery task is estimated based on the task information. The battery power requirement is the total power requirement required to complete the unexecuted delivery tasks in the target delivery route and the new delivery task.

[0031] Obtain the actual available power of the newly replaced battery of the target logistics unmanned vehicle;

[0032] When the actual available power is less than the battery power requirement, search within a preset range of the target logistics unmanned vehicle to see if there are other logistics unmanned vehicles that meet the battery exchange conditions. The battery exchange conditions are that the vehicle is performing a non-emergency delivery task and the backup battery power is greater than the battery power requirement.

[0033] When other unmanned logistics vehicles that meet the battery swapping conditions are found, battery swapping instructions are sent to the target unmanned logistics vehicle and the other unmanned logistics vehicle respectively. The battery swapping instructions are used to instruct the target unmanned logistics vehicle and the other unmanned logistics vehicle to move to a preset location and swap the new replacement battery and the spare battery respectively.

[0034] Optionally, after searching within a preset range for whether other unmanned logistics vehicles that meet the battery swapping conditions exist, the method further includes:

[0035] When no other logistics unmanned vehicles that meet the battery swapping conditions are found, the system detects whether there is an idle mobile battery swapping unit within a preset range of the target logistics unmanned vehicle. The mobile battery swapping unit is a mobile service unmanned vehicle that is pre-deployed in a designated area and equipped with multiple replaceable batteries.

[0036] When an idle mobile battery swapping unit is detected within a preset range of the target logistics unmanned vehicle, a dispatch command is sent to the mobile battery swapping unit. The dispatch command instructs the mobile battery swapping unit to move to the current position of the target logistics unmanned vehicle and exchange batteries with the target logistics unmanned vehicle.

[0037] Optionally, after sending a path update instruction based on the target delivery route to the target logistics unmanned vehicle, the method further includes:

[0038] Real-time calculation of the actual power consumption rate of the newly replaced battery of the target logistics unmanned vehicle;

[0039] Calculate the deviation between the actual power consumption rate and the preset consumption rate;

[0040] When the deviation value exceeds a preset deviation threshold, a battery abnormality warning is sent.

[0041] Optionally, calculating the battery swapping idle time of each battery swapping station based on the available resource information includes:

[0042] Calculate the first ratio of the number of currently available workstations to the total number of workstations in each battery swapping station, and the second ratio of the number of swappable batteries to the total number of batteries;

[0043] The first ratio and the second ratio are normalized.

[0044] The first ratio and the second ratio after normalization are weighted and summed to obtain the battery swapping idle time of each battery swapping station.

[0045] Optionally, after correcting the initial delivery route based on the target battery swapping station to obtain the target delivery route, the method further includes:

[0046] The urgency level of each delivery task in the target delivery route that is located after the target battery swapping station in the delivery order is obtained;

[0047] Determine whether there are delivery tasks with an urgency level greater than a preset urgency threshold;

[0048] If so, a priority battery swapping instruction is sent to the target battery swapping station, which instructs the target battery swapping station to prioritize battery swapping for the target logistics unmanned vehicle.

[0049] The second aspect of this application provides a battery swapping scheduling system for unmanned logistics vehicles, including:

[0050] The acquisition unit is used to acquire the initial delivery route of the target logistics unmanned vehicle when a battery swapping request is received from the target logistics unmanned vehicle.

[0051] The first calculation unit is used to calculate the path deviation between the battery swapping path of each battery swapping station and the initial delivery path, and to obtain the available resource information of each battery swapping station, including the number of currently idle workstations and the number of swappable batteries.

[0052] The second calculation unit is used to calculate the battery swapping idleness of each battery swapping station based on the available resource information.

[0053] The determining unit is used to perform a weighted summation calculation on the path deviation and the battery swapping idleness according to a preset weight allocation rule, to obtain the scheduling score of each battery swapping station, and to determine the battery swapping station with the highest scheduling score as the target battery swapping station.

[0054] The correction unit is used to correct the initial delivery route based on the target battery swapping station to obtain the target delivery route;

[0055] The sending unit is used to send a path update instruction based on the target delivery route to the target logistics unmanned vehicle.

[0056] As can be seen from the above technical solutions, this application has the following effects:

[0057] When a battery swapping request is received from a target logistics drone, the system first obtains the initial delivery route of the drone. Then, it calculates the path deviation between the swapping route of each station and the initial delivery route, and obtains the available resource information for each station, including the number of currently available workstations and the number of swappable batteries. Next, it calculates the battery swapping idleness of each station based on the available resource information. Then, it performs a weighted summation of the path deviation and battery swapping idleness according to a preset weighting rule to obtain a scheduling score for each station, and determines the station with the highest scheduling score as the target station. Finally, it modifies the initial delivery route based on the target station to obtain the target delivery route. Finally, it sends a route update command based on the target delivery route to the target logistics drone. In this way, the impact of going to different swapping stations on the original delivery plan can be quantified by calculating the path deviation between each station and the initial delivery route. And, the battery swapping efficiency of each station can be quantified by calculating its battery swapping idleness. Furthermore, by comprehensively evaluating both factors, the battery swapping station with the least impact on the original delivery plan of the target logistics drones was selected. This ensured the feasibility of the battery swapping service and reduced the likelihood of logistics drones being unable to obtain service promptly upon arrival at the station. This reduces downtime caused by queuing for battery swapping, thereby improving the delivery efficiency of logistics drones. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of an embodiment of a battery swapping scheduling method for unmanned logistics vehicles in this application;

[0059] Figure 2 This is a flowchart illustrating the steps in this application to calculate the path deviation between the battery swapping path and the initial delivery path for each battery swapping station.

[0060] Figure 3 This is a schematic diagram of another embodiment of a battery swapping scheduling method for unmanned logistics vehicles in this application;

[0061] Figure 4 This is a schematic diagram of another embodiment of a battery swapping scheduling method for unmanned logistics vehicles in this application;

[0062] Figure 5 This is a schematic diagram of another embodiment of a battery swapping scheduling method for unmanned logistics vehicles in this application;

[0063] Figure 6 This is a flowchart illustrating the steps in this application to calculate the battery swapping idleness of each battery swapping station based on available resource information.

[0064] Figure 7 This is a schematic diagram of another embodiment of a battery swapping scheduling method for unmanned logistics vehicles in this application;

[0065] Figure 8 This is a schematic diagram of an embodiment of a battery swapping scheduling system for unmanned logistics vehicles in this application. Detailed Implementation

[0066] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0067] It should be understood that, when used in this application specification, the term "comprising" indicates the presence of the described feature, integral, step, operation, element, and / or component, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof.

[0068] It should also be understood that the term “and / or” as used in this application specification means any combination of one or more of the associated listed items, as well as all possible combinations, and includes such combinations.

[0069] As used in this application specification, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [the described condition or event] is detected," or "in response to detection of [the described condition or event]."

[0070] Furthermore, in the description of this application, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0071] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0072] This application discloses a battery swapping scheduling method and system for unmanned logistics vehicles, which can reduce the downtime of unmanned logistics vehicles caused by queuing for battery swapping, thereby improving the delivery efficiency of unmanned logistics vehicles.

[0073] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0074] The battery swapping scheduling method for unmanned logistics vehicles described in this application is implemented on systems, terminals, servers, or other devices with logical analysis and processing capabilities. This application uses a central scheduling system as the implementation entity for illustrative purposes. Please refer to [link to relevant documentation]. Figure 1 As shown, one embodiment of the battery swapping scheduling method for unmanned logistics vehicles in this application includes:

[0075] 101. When a battery swapping request is received from the target logistics unmanned vehicle, obtain the initial delivery route of the target logistics unmanned vehicle;

[0076] In this embodiment, during operation, the target logistics unmanned vehicle's internal battery management system monitors its remaining battery power in real time. When the remaining battery power falls below a preset lower limit, the battery management system sends a battery swap request to the central dispatch system. Upon receiving the battery swap request, the central dispatch system obtains the target logistics unmanned vehicle's initial delivery path. This initial delivery path can be generated by the target logistics unmanned vehicle based on its current delivery task and destination planning, and then reported to the central dispatch system; alternatively, it can be calculated by the central dispatch system based on the target logistics unmanned vehicle's current location and the delivery tasks to be executed, using a path planning algorithm. For example, the Dijkstra algorithm or the A* algorithm, combined with real-time traffic information, can be used to calculate the shortest or fastest path from the target logistics unmanned vehicle's current location to all its delivery points and back to the base station, serving as the initial delivery path. It should be noted that the initial delivery path includes the target logistics unmanned vehicle's starting point, transit delivery points, destination, current location, the driving path planning between each transit delivery point, and the time window requirements for each delivery task; specific details are not limited here. For example, a target logistics drone is performing a three-stage delivery mission from a warehouse center to Community A, Office Building B, and Shopping Mall C. After completing the first delivery at Community A, the drone's battery level drops to 18%, triggering a battery swap request. Upon receiving this request, the central dispatch system obtains its initial delivery route: sequentially traveling through the warehouse center, Community A, Office Building B, and Shopping Mall C. Its current location is at the east gate of Community A, and its next destination is Office Building B, with an estimated remaining distance of 12 kilometers.

[0077] 102. Calculate the path deviation between the battery swapping path and the initial delivery path of each battery swapping station, and obtain the available resource information of each battery swapping station, which includes the number of currently idle workstations and the number of swappable batteries.

[0078] In this embodiment, the battery swapping path represents the complete route taken by the target logistics drone from its current location, deviating from the initial delivery path to the battery swapping station, completing the battery swap, and then returning to the initial delivery path to continue delivery. The path deviation can be determined by the deviation between the battery swapping path and the initial delivery path in terms of total travel distance and total estimated time; specific details will be described in subsequent embodiments. Simultaneously, available resource information for each battery swapping station is obtained in real-time from its management system, including the number of currently available workstations and the number of swappable batteries. The number of currently available workstations represents the number of unoccupied battery swapping workstations within the station, and the number of available batteries represents the number of fully charged and quality-inspected spare batteries. This available resource information can be monitored and reported in real-time through the internal sensors, battery management system, and workstation occupancy detection system of the battery swapping station.

[0079] 103. Calculate the battery swapping idle time of each battery swapping station based on available resource information;

[0080] In this embodiment, after obtaining the available resource information of all battery swapping stations, the specific quantities of currently idle workstations and swappable batteries are detected. Then, based on the specific quantities of currently idle workstations and swappable batteries, the battery swapping idleness of each battery swapping station is calculated. The higher the battery swapping idleness, the higher the battery swapping efficiency of the target logistics unmanned vehicle entering that battery swapping station for battery swapping.

[0081] 104. According to the preset weight allocation rules, the path deviation and battery swapping idle time are weighted and summed to obtain the scheduling score of each battery swapping station, and the battery swapping station with the highest scheduling score is determined as the target battery swapping station.

[0082] Since the battery swapping time for the target logistics drone mainly consists of the additional distance traveled to and from the battery swapping station and the battery swapping time itself, and path deviation can quantify the impact of traveling to different battery swapping stations on the current delivery task of the target logistics drone, while battery swapping idle time can quantify battery swapping efficiency, this embodiment comprehensively evaluates these two variables to determine the target battery swapping station with the minimum battery swapping time. Specifically, these two variables can first be normalized so that both path deviation and battery swapping idle time become constants between 0 and 1, and then different weighting coefficients can be assigned to path deviation and battery swapping idle time according to the distribution of battery swapping stations in different regions. For example, weighting coefficients are set based on the density of battery swapping stations in different areas of region A: Core urban area (station density ≥ 2 / km²): Path deviation weighting coefficient is set to 0.6, and battery swapping idleness weighting coefficient is set to 0.4 (prioritizing control of detour costs); Suburbs (station density < 1 / km²): Path deviation weighting coefficient is set to 0.4, and battery swapping idleness weighting coefficient is set to 0.6 (prioritizing avoiding queues at battery swapping stations); Urban-rural fringe areas: Both path deviation and battery swapping idleness weighting coefficients are set to 0.5. The different weighting coefficients are multiplied by the corresponding path deviation and battery swapping idleness values, and the products are summed to obtain the scheduling score for each battery swapping station. After calculating the scheduling score, all scheduling scores are sorted in descending order, and the highest-ranked scheduling score is selected as the target battery swapping station.

[0083] 105. Based on the target battery swapping station, the initial delivery route is modified to obtain the target delivery route;

[0084] In this embodiment, the correction process includes guiding the target logistics unmanned vehicle from its current location to the target battery swapping station. After completing the battery swap, the vehicle is then guided back to a point on the initial delivery path from the target battery swapping station to continue executing the remaining delivery tasks. For example, if the initial delivery path is P1-P2-P3-P4 and the target battery swapping station is H, the initial delivery path may be corrected to P1-H-P2-P3-P4 or P1-P2-H-P3-P4, depending on the location of the target battery swapping station and the optimal access point for returning to the initial delivery path from the battery swapping station. The corrected path is the target delivery path.

[0085] 106. Send a route update instruction based on the target delivery route to the target logistics unmanned vehicle.

[0086] After obtaining the target delivery route, a route update instruction containing this route is generated and sent to the target logistics drone, instructing it to update its initial delivery route to the target route. This route update instruction includes detailed information about the target delivery route, such as a series of latitude and longitude coordinates, speed limits, and the specific location of the battery swapping station. Upon receiving this instruction, the target logistics drone updates its internal navigation system and follows the new target delivery route to the target battery swapping station for a battery replacement, then continues its delivery mission.

[0087] In this way, the impact of going to different battery swapping stations on the original delivery plan can be quantified by calculating the path deviation between each battery swapping station and the initial delivery route. Furthermore, the battery swapping efficiency of each station can be quantified by calculating its battery swapping idleness. By comprehensively evaluating both, the battery swapping station with the least impact on the original delivery plan of the target logistics drone can be selected, ensuring the feasibility of the battery swapping service and reducing situations where drones cannot obtain service in a timely manner after arriving at the station. This reduces the downtime of logistics drones due to queuing for battery swapping, thereby improving the delivery efficiency of logistics drones.

[0088] In some embodiments, please refer to Figure 2 As shown, the steps for calculating the path deviation between the battery swapping path of each battery swapping station and the initial delivery path can specifically include:

[0089] 201. Obtain the location information of each battery swapping station;

[0090] 202. Based on location information, determine the nearest adjacent delivery point to each battery swapping station in the initial delivery route;

[0091] 203. Embed each battery swapping station into the original path between the corresponding adjacent delivery points to obtain the battery swapping path corresponding to each battery swapping station;

[0092] 204. Calculate the difference in total travel distance and the difference in total estimated travel time between the battery swapping route and the original route to the corresponding adjacent delivery point;

[0093] 205. Determine the path deviation based on the difference between the total travel distance and the total estimated travel time.

[0094] Specifically, the location information of each battery swapping station can be obtained through methods such as Global Positioning System (GPS), cellular network positioning, or pre-stored Geographic Information System (GIS) data. After obtaining the location information of each battery swapping station, the nearest adjacent delivery point in the initial delivery path is determined based on the location information. Specifically, for each battery swapping station, all delivery points on the initial delivery path of the target logistics AWACS are traversed first, and then the straight-line distance or actual driving distance between the battery swapping station and each delivery point is calculated to find the two closest and adjacent delivery points. Next, each battery swapping station is embedded into the original path between the corresponding adjacent delivery points to obtain the battery swapping path corresponding to each battery swapping station. For example, if the initial delivery path is A->B->C, and battery swapping station X is closest to the path between B and C, then battery swapping station X will be embedded between B and C, forming a new battery swapping path B->X->C. Thus, the battery swapping path refers to the complete path of the target logistics AWACS, starting from a point on the initial delivery path, passing through a battery swapping station, and returning to another point on the initial delivery path to continue completing the delivery task. After determining the battery swapping routes for each battery swapping station, the total travel distance difference and the total estimated travel time difference between the battery swapping route and the original routes of the corresponding adjacent delivery points are calculated. The total travel distance difference refers to the difference between the total travel distance of the battery swapping route and the total travel distance of the initial delivery route, reflecting the increased mileage due to battery swapping. The total estimated travel time difference refers to the difference between the total estimated travel time of the battery swapping route and the total estimated travel time of the initial delivery route, reflecting the increased time cost due to battery swapping. This time cost can take into account factors such as traffic conditions and road speed limits. Finally, the total travel distance difference and the total estimated travel time difference are divided by the total travel distance and the total estimated travel time of the initial delivery route, respectively, to obtain a first ratio and a second ratio. Weighting coefficients are then assigned to the first and second ratios, and the weighted first and second ratios are summed to obtain the path deviation. This comprehensive consideration of the spatial distance and time cost between the battery swapping station and the initial delivery route makes the assessment of path deviation more accurate and objective. This effectively avoids suboptimal choices due to a single-dimensional consideration, ensuring that the selected battery swapping stations can minimize the impact on the delivery efficiency of unmanned logistics vehicles and optimize the overall scheduling effect.

[0095] In some embodiments, please refer to Figure 3As shown, after the above-described step of sending a path update instruction based on the target delivery route to the target logistics unmanned vehicle, the logistics unmanned vehicle battery swapping scheduling method in this application may further include:

[0096] 301. Perform power calibration on the newly replaced battery of the target logistics unmanned vehicle to obtain the actual available power of the newly replaced battery;

[0097] 302. Evaluate the driving range of the target logistics unmanned vehicle based on the actual available power.

[0098] 303. Determine whether the remaining driving range is greater than the required driving distance of the target delivery task in the target delivery route. If not, proceed to step 304.

[0099] 304. Activate emergency battery swapping mode.

[0100] Specifically, after the target logistics drone completes its battery swap according to the target delivery route, the actual usable capacity of the newly replaced battery may deviate from the expected value, potentially leading to insufficient power for subsequent delivery tasks. Therefore, after the battery swap, the new battery's capacity should be calibrated. Capacity calibration refers to accurately measuring and calibrating the current state of charge of the newly replaced battery using specific detection equipment or algorithms. This eliminates inaccurate capacity display caused by factors such as battery aging, storage conditions, or measurement errors, thus obtaining a more accurate and reliable estimate of the actual usable capacity. Actual usable capacity can be understood as the actual energy output that the newly replaced battery can provide in its current state. Capacity calibration can be achieved as follows: First, continuously collect the open-circuit voltage of the newly replaced battery and match the collected voltage value with a preset "voltage-capacity correspondence curve" to initially determine the battery's initial capacity value. Then, collect the voltage change and current consumption rate of the newly replaced battery under dynamic load in real time. Combined with the energy consumption model of the target logistics drone, correct the initial capacity value to finally obtain the actual usable capacity of the newly replaced battery. After obtaining the actual available power, the baseline mileage of the newly replaced battery is calculated by combining it with the standard energy consumption data of the target logistics drone. Then, the baseline mileage is adjusted based on various factors such as current load, expected road conditions, and ambient temperature to obtain the final driving range. The assessed driving range is then compared with the total distance required for all unfinished delivery tasks on the target delivery route. If the driving range is less than the required distance, the emergency battery swapping mode is activated. The emergency battery swapping mode aims to provide a rapid-response backup plan for the logistics drone to avoid mission interruptions due to insufficient power. This effectively solves the range risk problem caused by the new battery's actual capacity not matching expectations after battery replacement. By introducing a power calibration and range assessment mechanism, the reliability and safety of the target logistics drone during delivery tasks are significantly improved, avoiding mission interruptions or delays due to depleted power. Furthermore, the activation of the emergency battery swapping mode provides a rapid response solution for emergencies, further ensuring the continuity and efficiency of the target logistics drone's logistics services.

[0101] Furthermore, the aforementioned emergency battery swapping mode may specifically include: when a secondary battery swapping request is received from the target logistics unmanned vehicle, calculating the distance between each battery swapping station and the current location of the target logistics unmanned vehicle; determining the battery swapping station with the shortest distance as the secondary battery swapping station; correcting the target delivery route based on the secondary battery swapping station to obtain the secondary battery swapping delivery route; and sending a secondary update instruction based on the secondary battery swapping delivery route to the target logistics unmanned vehicle.

[0102] Specifically, when a target logistics drone is executing its target delivery route, if the actual usable charge of its newly replaced battery falls below the minimum charge limit, or if the central dispatch system detects through real-time monitoring that its range is insufficient to meet the remaining delivery tasks, the target logistics drone will send a secondary battery swap request to the central dispatch system. Upon receiving this secondary battery swap request, the central dispatch system will immediately initiate an emergency battery swap process. This process begins by acquiring the location information of all available battery swap stations and calculating the straight-line distance or actual driving distance between these stations and the target logistics drone's current location. After considering current traffic conditions and road conditions, the system selects the battery swap station that allows the target logistics drone to reach it fastest and designates it as the secondary battery swap station. It should be noted that this "shortest distance" refers to the shortest actual driving distance calculated based on a third-party map interface (not a straight-line distance). After determining the secondary battery swap station, the target logistics drone's current target delivery route is adjusted based on its location to obtain the secondary battery swap delivery route. Finally, the central dispatch system sends a secondary update instruction, including the secondary battery swapping delivery route, to the target logistics RV, instructing it to update its target delivery route to the secondary battery swapping delivery route. The updated route guides the RV to a secondary battery swapping station for emergency battery swapping, and then it continues its remaining delivery tasks after the swap is complete. In emergency battery swapping mode, the nearest battery swapping station to the RV's current location is prioritized, significantly shortening the RV's travel time and reducing the risk of delivery delays due to insufficient power. This, in turn, improves the RV's operational reliability and emergency response capabilities in complex delivery environments.

[0103] In some embodiments, please refer to Figure 4 As shown, after the above-described step of sending a path update instruction based on the target delivery route to the target logistics unmanned vehicle, the logistics unmanned vehicle battery swapping scheduling method in this application may further include:

[0104] 401 When a new delivery task appears on the target delivery route, obtain the task information of the new delivery task, which includes the target location, expected road conditions, expected package weight, and task urgency.

[0105] 402. Estimate the battery power requirements for new delivery tasks based on task information. The battery power requirements are the total power requirements needed to complete the unexecuted delivery tasks and new delivery tasks in the target delivery route.

[0106] 403. Obtain the actual available power of the newly replaced battery of the target logistics unmanned vehicle;

[0107] 404. When the actual available power is less than the battery power requirement, search within the preset range of the target logistics unmanned vehicle to see if there are other logistics unmanned vehicles that meet the battery exchange conditions. The battery exchange conditions are that the vehicle is performing a non-emergency delivery task and the backup battery power is greater than the battery power requirement.

[0108] 405. When other unmanned logistics vehicles that meet the battery exchange conditions are found, send battery exchange instructions to the target unmanned logistics vehicle and the other unmanned logistics vehicles respectively. The battery exchange instructions are used to instruct the target unmanned logistics vehicle and the other unmanned logistics vehicles to move to the preset location and exchange the new replacement battery and the spare battery respectively.

[0109] Specifically, a new delivery task refers to an additional delivery task received by the target logistics drone after it has received the target delivery route and begun execution. The task information includes: target location (the destination of the new delivery task); expected road conditions (the road traffic conditions that may be encountered during the execution of the new task, such as congestion or smooth traffic); expected package weight (the weight of the package carried in the new delivery task, which directly affects the energy consumption of the target logistics drone); and task urgency (the priority of the new task, such as "urgent" or "normal"). Battery power requirement refers to the total power required to complete all these tasks, calculated comprehensively based on the task information of the new delivery task and the currently planned but unexecuted delivery tasks of the target logistics drone. For example, it can be estimated using a preset energy consumption model or historical data based on parameters such as target location, expected road conditions, expected package weight, and task urgency to determine whether the current battery power is sufficient to support all subsequent tasks. The process of obtaining the actual available power of the newly replaced battery is similar to that described in the previous embodiments and will not be repeated here. The preset range is a certain geographical area centered on the current location of the target logistics drone, such as an area within 5 kilometers of the target drone's current location. Other logistics drones that meet the battery swapping conditions refer to those within this preset range that meet specific conditions. These conditions can include two aspects: first, the other drone is currently performing a non-emergency delivery task, meaning its task has flexibility and lower priority, allowing for a brief stop to complete the battery swap without severely impacting critical tasks; second, the other drone's spare battery capacity exceeds its battery demand, ensuring its spare battery can meet the target drone's power requirements for completing all tasks. When other drones meeting the battery swapping conditions are found, battery swapping instructions are sent to both the target and other drones to coordinate the swap. The preset location can be a convenient intermediate meeting point planned based on the current locations, driving paths, and surrounding environment of the two drones, such as an open parking lot or a dedicated battery swapping area. In response to the battery swap command, the two unmanned logistics vehicles will move to a predetermined location, where their onboard automated mechanisms or manual assistance will complete the battery swap. Specifically, the target unmanned logistics vehicle will exchange its newly replaced battery with a spare battery from another unmanned logistics vehicle. This allows for battery resource sharing, optimizes the overall operational efficiency of the logistics network, reduces operating costs and time losses caused by power issues, and alleviates the battery swapping pressure on battery swapping stations.

[0110] For more details, please refer to further information. Figure 4 As shown, the battery swapping scheduling method for unmanned logistics vehicles in this application may further include:

[0111] 406. When no other logistics unmanned vehicles that meet the battery swapping conditions are found, check whether there is an idle mobile battery swapping unit within the preset range of the target logistics unmanned vehicle. The mobile battery swapping unit is a mobile service unmanned vehicle that is pre-deployed in a designated area and equipped with multiple replaceable batteries.

[0112] 407. When an idle mobile battery swapping unit is detected within a preset range of the target logistics unmanned vehicle, a dispatch command is sent to the mobile battery swapping unit. The dispatch command is used to instruct the mobile battery swapping unit to move to the current position of the target logistics unmanned vehicle and perform battery swapping with the target logistics unmanned vehicle.

[0113] If no other unmanned logistics vehicles (AWDVs) meeting the battery swapping criteria are found within the target WADV's preset range, a detection process for mobile battery swapping units will be initiated to ensure the target WADV can continue its delivery mission. A mobile battery swapping unit can be understood as a service WADV pre-deployed in a specific area, equipped with multiple swappable batteries and possessing autonomous mobility. These mobile battery swapping units are typically strategically placed at key nodes or in high-demand areas of the logistics delivery network for rapid response when needed. The detection process may include querying the mobile battery swapping unit's management system to obtain information such as its current location, battery status, and whether it is idle. When an idle mobile battery swapping unit is detected within the target WADV's preset range, a dispatch command is sent to that unit. The dispatch command may include the target WADV's current location, estimated arrival time, and the type of battery to be swapped. Upon receiving the dispatch command, the mobile battery swapping unit is instructed to move to the target WADV's current location. Upon arrival at the designated location, the mobile battery swapping unit will swap batteries with the target WADV, providing it with sufficient power to meet the needs of any additional delivery missions. By introducing mobile battery swapping units as a backup power supply mechanism, the dilemma of a target logistics drone having additional delivery tasks and insufficient power, but being unable to find other logistics drones for battery swapping, is effectively solved. When other logistics drone resources are unavailable, the system can quickly switch to the mobile battery swapping unit scheduling mode. The deployment and mobility of the mobile battery swapping units allow them to proactively travel to the location of the target logistics drone for battery swapping, rather than waiting for the target logistics drone to go to a fixed battery swapping station, thereby greatly improving the flexibility and response speed of power replenishment.

[0114] In some embodiments, please refer to Figure 5 As shown, after the above-described step of sending a path update instruction based on the target delivery route to the target logistics unmanned vehicle, the logistics unmanned vehicle battery swapping scheduling method in this application may further include:

[0115] 501. Real-time calculation of the actual power consumption rate of newly replaced batteries in the target logistics unmanned vehicle;

[0116] 502. Calculate the deviation between the actual power consumption rate and the preset consumption rate;

[0117] 503. When the deviation value is greater than the preset deviation threshold, send a battery abnormality warning.

[0118] Specifically, after the target logistics drone completes battery replacement, the battery level of the newly replaced battery can be monitored. Combined with time factors, the battery power consumption per unit time can be dynamically calculated to determine the actual power consumption rate. For example, the current battery percentage or voltage value reported by the battery management system can be read periodically, and the corresponding timestamps recorded. Then, the actual power consumption rate can be calculated using differential calculation. The real-time monitored actual power consumption rate is then compared with a pre-set standard power consumption rate. The preset consumption rate can be comprehensively evaluated and set based on various factors such as the target logistics drone model, load, expected road conditions, and ambient temperature. The deviation value obtained from the difference is then compared with a preset deviation threshold. When the deviation value is greater than the preset deviation threshold, it indicates that the newly replaced battery may have an abnormality. In this case, maintenance personnel can send a battery abnormality warning to prompt them to detect and repair the fault. For example, if the preset deviation threshold is set to 15%, a battery abnormality warning will be sent when the deviation between the actual power consumption rate and the preset consumption rate is greater than 15%. Early warnings can take the form of audible and visual alarms to the dispatch center, SMS or email notifications to maintenance personnel, or highlighting abnormal autonomous vehicle status on the dispatch interface. This allows maintenance personnel to promptly detect and address potential battery malfunctions or abnormal operating conditions, preventing delivery interruptions due to battery issues. Based on this, when abnormal battery consumption is detected, timely warnings can be issued, enabling maintenance personnel to intervene quickly, such as arranging emergency battery swaps, adjusting delivery tasks, or conducting remote diagnostics. This ensures that the logistics autonomous vehicles can stably and reliably complete delivery tasks, reducing operational risks.

[0119] In some embodiments, please refer to Figure 6 As shown, the steps described above for calculating the battery swapping idleness of each battery swapping station based on available resource information may specifically include:

[0120] 601. Calculate the first ratio of the number of currently available workstations to the total number of workstations in each battery swapping station, and the second ratio of the number of swappable batteries to the total number of batteries.

[0121] 602. Normalize the first ratio and the second ratio;

[0122] 603. The first and second ratios after normalization are weighted and summed to obtain the battery swapping idle time of each battery swapping station.

[0123] Specifically, the total number of workstations represents the total number of workstations designed or configured at the battery swapping station for battery swapping operations of the target logistics unmanned vehicles, while the total number of batteries represents the total number of batteries available for replacement with those equipped in the target logistics unmanned vehicles. After obtaining the available resource information of the battery swapping station, the number of currently idle workstations in the available resource information is divided by the total number of workstations to obtain the first ratio. At the same time, the number of swappable batteries is divided by the total number of batteries to obtain the second ratio. Since both the first and second ratios are percentages with values ​​ranging from 0 to 1, in actual scenarios, the weights of their impact on battery swapping service capabilities may need to be adjusted due to different scenario requirements. Therefore, normalization can be used to further eliminate potential numerical biases, ensuring that the processed values ​​are mapped to the [0,1] interval, and ensuring that the two types of indicators are comparable and superimposed when subsequently weighted and summed. Then, based on the actual needs of the battery swapping scenario, appropriate weights are assigned to the normalized first and second ratios. For example, during peak delivery periods, the target logistics drones have higher requirements for battery swapping speed, and the proportion of available workstations is more important; in this case, the first ratio can be assigned a higher weight coefficient. During periods of tight battery reserves, the proportion of swappable batteries is more important; in this case, the second ratio can be assigned a higher weight coefficient. The weighted first and second ratios are then summed to obtain the battery swapping station's idle rate. It can be understood that a higher idle rate indicates a more sufficient service capacity of the battery swapping station, making it more suitable to dispatch target logistics drones for battery swapping; conversely, a lower idle rate indicates a tighter service capacity of the battery swapping station, requiring fewer or no dispatching of drones. In this way, the idle status of workstations and battery reserve status of the battery swapping station can be comprehensively considered to accurately quantify the service capacity of the station.

[0124] In some embodiments, please refer to Figure 7 As shown, after the steps described above for correcting the initial delivery path based on the target battery swapping station to obtain the target delivery path, the battery swapping scheduling method for unmanned logistics vehicles in this application may further include:

[0125] 701. Obtain the urgency level of each delivery task in the target delivery route that is located after the target battery swapping station in the delivery order;

[0126] 702. Determine if there are any delivery tasks with an urgency level greater than the preset urgency threshold. If so, proceed to step 703.

[0127] 703. Send a priority battery swapping instruction to the target battery swapping station. This priority battery swapping instruction is used to instruct the target battery swapping station to give priority to the target logistics unmanned vehicle.

[0128] Specifically, the process first identifies all subsequent delivery tasks that need to be performed after the target logistics RV completes its battery swap, and then queries or calculates the urgency of each task. For example, urgency can be quantified based on factors such as delivery time requirements, cargo type, or customer-specified priority. Then, the urgency of each delivery task is compared to a pre-set urgency threshold. If at least one subsequent delivery task exceeds this threshold, it indicates that the target logistics RV needs priority for battery swapping, and a priority swapping instruction can be sent to the target battery swapping station. For example, if the preset urgency threshold is "delivery time ≤ 2 hours," then any subsequent delivery task with a delivery time ≤ 2 hours will trigger priority swapping. Upon receiving this instruction, the target battery swapping station will prioritize the target logistics RV according to its internal scheduling rules, such as reserving an idle workstation or prioritizing battery swapping operations to shorten its waiting time, thereby improving the efficiency and timeliness of urgent delivery tasks.

[0129] Please see Figure 8 As shown, one embodiment of the unmanned logistics vehicle battery swapping scheduling system in this application includes:

[0130] The acquisition unit 801 is used to acquire the initial delivery route of the target logistics unmanned vehicle when a battery swapping request is received from the target logistics unmanned vehicle.

[0131] The first calculation unit 802 is used to calculate the path deviation between the battery swapping path of each battery swapping station and the initial delivery path, and to obtain the available resource information of each battery swapping station, including the number of currently idle workstations and the number of swappable batteries.

[0132] The second calculation unit 803 is used to calculate the battery swapping idleness of each battery swapping station based on available resource information;

[0133] Unit 804 is used to perform weighted summation calculation of path deviation and battery swapping idle time according to preset weight allocation rules, obtain the scheduling score of each battery swapping station, and determine the battery swapping station with the highest scheduling score as the target battery swapping station.

[0134] The correction unit 805 is used to correct the initial delivery route based on the target battery swapping station to obtain the target delivery route;

[0135] The sending unit 806 is used to send a path update instruction based on the target delivery route to the target logistics unmanned vehicle.

[0136] In this embodiment, the impact of going to different battery swapping stations on the original delivery plan can be quantified by calculating the path deviation between each battery swapping station and the initial delivery route. Furthermore, the battery swapping efficiency of each battery swapping station can be quantified by calculating its battery swapping idleness. By comprehensively evaluating both, the battery swapping station with the least impact on the original delivery plan of the target logistics drone can be selected, ensuring the feasibility of the battery swapping service and reducing situations where drones cannot obtain service in a timely manner after arriving at the battery swapping station. This reduces the downtime of logistics drones due to queuing for battery swapping, thereby improving the delivery efficiency of logistics drones.

[0137] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0138] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0139] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0140] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0141] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for scheduling battery swapping for unmanned logistics vehicles, characterized in that, include: When a battery swapping request is received from the target logistics unmanned vehicle, the initial delivery route of the target logistics unmanned vehicle is obtained; Calculate the path deviation between the battery swapping path of each battery swapping station and the initial delivery path, and obtain the available resource information of each battery swapping station, including the number of currently idle workstations and the number of swappable batteries. The battery swapping idle time of each battery swapping station is calculated based on the available resource information. The path deviation and the battery swapping idle time are weighted and summed according to the preset weight allocation rules to obtain the scheduling score of each battery swapping station, and the battery swapping station with the highest scheduling score is determined as the target battery swapping station. The initial delivery route is modified based on the target battery swapping station to obtain the target delivery route; Send a path update instruction based on the target delivery route to the target logistics unmanned vehicle; The calculation of the path deviation between the battery swapping path of each battery swapping station and the initial delivery path includes: Obtain the location information of each battery swapping station; Based on the location information, determine the nearest adjacent delivery point to each battery swapping station in the initial delivery route; Each battery swapping station is embedded into the original path between adjacent delivery points to obtain the battery swapping path corresponding to each battery swapping station. Calculate the difference in total driving distance and the difference in total estimated driving time between the battery swapping route and the original route to the corresponding adjacent delivery point; The path deviation is determined based on the difference between the total travel distance and the total estimated travel time.

2. The battery swapping scheduling method for unmanned logistics vehicles according to claim 1, characterized in that, After sending a path update instruction based on the target delivery route to the target logistics unmanned vehicle, the method further includes: The battery of the target logistics unmanned vehicle is calibrated to obtain the actual available power of the newly replaced battery. The driving range of the target logistics unmanned vehicle is assessed based on the actual available power. Determine whether the remaining driving range is greater than the required driving distance for the target delivery task in the target delivery route; If not, then activate the emergency battery swapping mode.

3. The battery swapping scheduling method for unmanned logistics vehicles according to claim 2, characterized in that, The emergency battery swapping mode includes: When a second battery swap request is received from the target logistics unmanned vehicle, the distance between each battery swap station and the current location of the target logistics unmanned vehicle is calculated; The battery swapping station with the shortest distance is designated as the secondary battery swapping station; The target delivery route is modified based on the secondary battery swapping station to obtain the secondary battery swapping delivery route; Send a secondary update instruction to the target logistics unmanned vehicle based on the secondary battery swapping delivery route.

4. The battery swapping scheduling method for unmanned logistics vehicles according to claim 1, characterized in that, After sending a path update instruction based on the target delivery route to the target logistics unmanned vehicle, the method further includes: When a new delivery task appears on the target delivery route, the task information of the new delivery task is obtained. The task information includes the target location, expected road conditions, expected package weight, and task urgency. The battery power requirement of the new delivery task is estimated based on the task information. The battery power requirement is the total power requirement required to complete the unexecuted delivery tasks in the target delivery route and the new delivery task. Obtain the actual available power of the newly replaced battery of the target logistics unmanned vehicle; When the actual available power is less than the battery power requirement, search within a preset range of the target logistics unmanned vehicle to see if there are other logistics unmanned vehicles that meet the battery exchange conditions. The battery exchange conditions are that the vehicle is performing a non-emergency delivery task and the backup battery power is greater than the battery power requirement. When other unmanned logistics vehicles that meet the battery swapping conditions are found, battery swapping instructions are sent to the target unmanned logistics vehicle and the other unmanned logistics vehicle respectively. The battery swapping instructions are used to instruct the target unmanned logistics vehicle and the other unmanned logistics vehicle to move to a preset location and swap the new replacement battery and the spare battery respectively.

5. The battery swapping scheduling method for unmanned logistics vehicles according to claim 4, characterized in that, After determining whether other unmanned logistics vehicles meeting the battery swapping conditions exist within a preset range of the target unmanned logistics vehicle, the method further includes: When no other logistics unmanned vehicles that meet the battery swapping conditions are found, the system detects whether there is an idle mobile battery swapping unit within a preset range of the target logistics unmanned vehicle. The mobile battery swapping unit is a mobile service unmanned vehicle that is pre-deployed in a designated area and equipped with multiple replaceable batteries. When an idle mobile battery swapping unit is detected within a preset range of the target logistics unmanned vehicle, a dispatch command is sent to the mobile battery swapping unit. The dispatch command instructs the mobile battery swapping unit to move to the current position of the target logistics unmanned vehicle and exchange batteries with the target logistics unmanned vehicle.

6. The battery swapping scheduling method for unmanned logistics vehicles according to claim 1, characterized in that, In the direction of the target After the unmanned logistics vehicle sends a route update instruction based on the target delivery route, the method further includes: Real-time calculation of the actual power consumption rate of the newly replaced battery of the target logistics unmanned vehicle; Calculate the deviation between the actual power consumption rate and the preset consumption rate; When the deviation value exceeds a preset deviation threshold, a battery abnormality warning is sent.

7. The method for scheduling battery swapping for unmanned logistics vehicles according to any one of claims 1 to 6, characterized in that, The calculation of battery swapping idle time for each battery swapping station based on the available resource information includes: Calculate the first ratio of the number of currently available workstations to the total number of workstations in each battery swapping station, and the second ratio of the number of swappable batteries to the total number of batteries; The first ratio and the second ratio are normalized. The first ratio and the second ratio after normalization are weighted and summed to obtain the battery swapping idle time of each battery swapping station.

8. The method for scheduling battery swapping for unmanned logistics vehicles according to any one of claims 1 to 6, characterized in that, After correcting the initial delivery route based on the target battery swapping station to obtain the target delivery route, the method further includes: The urgency level of each delivery task in the target delivery route that is located after the target battery swapping station in the delivery order is obtained; Determine whether there are delivery tasks with an urgency level greater than a preset urgency threshold; If so, a priority battery swapping instruction is sent to the target battery swapping station, which instructs the target battery swapping station to prioritize battery swapping for the target logistics unmanned vehicle.

9. A battery swapping scheduling system for unmanned logistics vehicles, characterized in that, include: The acquisition unit is used to acquire the initial delivery route of the target logistics unmanned vehicle when a battery swapping request is received from the target logistics unmanned vehicle. The first calculation unit is used to calculate the path deviation between the battery swapping path of each battery swapping station and the initial delivery path, and to obtain the available resource information of each battery swapping station, including the number of currently idle workstations and the number of swappable batteries. It is also used to obtain the location information of each battery swapping station; Based on the location information, determine the nearest adjacent delivery point to each battery swapping station in the initial delivery route; Each battery swapping station is embedded into the original path between adjacent delivery points to obtain the battery swapping path corresponding to each battery swapping station. Calculate the difference in total driving distance and the difference in total estimated driving time between the battery swapping route and the original route to the corresponding adjacent delivery point; The path deviation is determined based on the difference between the total travel distance and the total estimated travel time. The second calculation unit is used to calculate the battery swapping idleness of each battery swapping station based on the available resource information. The determining unit is used to perform a weighted summation calculation on the path deviation and the battery swapping idleness according to a preset weight allocation rule, to obtain the scheduling score of each battery swapping station, and to determine the battery swapping station with the highest scheduling score as the target battery swapping station. The correction unit is used to correct the initial delivery route based on the target battery swapping station to obtain the target delivery route; The sending unit is used to send a path update instruction based on the target delivery route to the target logistics unmanned vehicle.

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