Multi-store distribution vehicle management method and system, terminal and medium

Abstract

The invention relates to a multi-store distribution vehicle management method and system, a terminal and a medium, and relates to the field of logistics distribution, and the method comprises the steps: obtaining real-time data, the real-time data comprises store data, vehicle data and traffic data, the store data comprises order demands, inventory states and distribution customer priority information of all stores, and the vehicle data comprises the order demands, the inventory states and the distribution customer priority information of all the stores; the vehicle data comprises vehicle load and volume parameters, and the traffic data comprises road traffic conditions; constructing a data model based on the real-time data; based on the data model, a dispatching algorithm is adopted to generate a distribution plan, and the distribution plan comprises vehicle distribution and distribution paths; and pushing the distribution path to the distribution vehicle. The method has the advantages that the fresh goods can be delivered on time in a fresh-keeping manner, the use efficiency of the vehicle is improved, and the delivery cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of logistics distribution, in particular to a multi-store delivery vehicle management method, system, terminal and medium. BACKGROUND

[0002] In modern urban logistics distribution services, multi-store delivery vehicle management is a key technical support to ensure the on-time delivery of fresh goods, improve vehicle use efficiency and reduce distribution costs.

[0003] In related technologies, in the traditional distribution management system, path planning and task allocation are mainly relied on manual experience, and fixed distribution routes are generated, and vehicle monitoring is realized through basic GPS positioning.

[0004] For the related technologies in the above, when delivering fresh goods, there are problems of insufficient temperature control time limit guarantee and missing path feasibility evaluation, which leads to low delivery efficiency and rising operating costs. SUMMARY

[0005] In order to ensure the on-time delivery of fresh goods, improve vehicle use efficiency and reduce distribution costs, the present application provides a multi-store delivery vehicle management method, system, terminal and medium.

[0006] In a first aspect, the present application provides a multi-store delivery vehicle management method, which adopts the following technical solution: A multi-store delivery vehicle management method, comprising: Obtaining real-time data, the real-time data including store data, vehicle data and traffic data, the store data including order demand, inventory status and delivery customer priority information of each store, the vehicle data including vehicle load and volume parameters, and the traffic data including road traffic conditions; Based on the real-time data, a data model is constructed; Based on the data model, a dispatch algorithm is used to generate a distribution plan, the distribution plan including assigned vehicles and distribution paths; The distribution paths are pushed to the assigned vehicles.

[0007] By adopting the above technical solution, store data, vehicle data and real-time traffic data are analyzed, a distribution plan is generated using a dispatch algorithm, and the task order is dynamically adjusted based on customer priority, and the distribution instruction is pushed to the vehicle terminal. This scheme greatly improves the accuracy and response speed of scheduling decisions, reduces path repetition and delivery delay, significantly improves delivery efficiency, customer satisfaction and overall operation reliability.

[0008] Optionally, the business event information of each store is acquired, and the business event information includes at least one of a promotion activity arrangement, a live broadcast goods carrying plan, or an inventory emergency replenishment request. According to the business event information, it is determined whether there is a first store corresponding to a delivery plan with a high-impact business event in an execution period; If so, the priority of the delivery plan corresponding to the first store is raised to the highest priority, and the first delivery path of the first assigned vehicle corresponding to the first store is recalculated; The first delivery path is pushed to the first assigned vehicle; It is determined whether the first assigned vehicle receives a confirmation operation within a preset time; If not, an abnormal warning record is established, and the path confirmation state of the first delivery path is set to be processed.

[0009] By adopting the above technical solution, the store order demand, the vehicle real-time state and the traffic condition are analyzed, the high-impact delivery task is dynamically identified combined with the business event information, the priority is automatically raised and the optimal delivery path is re-planned, and the scheduling instruction is forcibly pushed to the specified vehicle and the confirmation state is tracked. The scheme greatly improves the response speed and execution reliability of the delivery scheduling, and reduces the delivery delay and task omission.

[0010] Optionally, a geofence area and a time window expected to be driven into are set, and the geofence area corresponds to a predetermined section of the first delivery path; Based on the received positioning information of the first assigned vehicle, a driving track of the first assigned vehicle is generated; According to the driving track, it is determined whether the first assigned vehicle drives into the geofence area within the time window; If not, it is determined that the first assigned vehicle does not execute the first delivery path; The target vehicle of the replacement task request is pushed to the first assigned vehicle; The execution of the first delivery path of the highest priority by the first assigned vehicle is suspended.

[0011] By adopting the above technical solution, the positioning information of the first assigned vehicle is monitored, the driving track of the vehicle is generated and analyzed combined with the geofence area and the time window, and it is determined whether it enters the key delivery section as required; When it is found that it is not driven into within the specified time, the task replacement mechanism is triggered, the emergency task is assigned to the adjacent vehicle, and the execution authority of the original vehicle is suspended. The scheme greatly improves the execution reliability of the high-priority delivery task, and reduces the delivery interruption caused by delay or non-performance.

[0012] Optionally, monitor whether the number of location information sent by the first assigned vehicle within a preset time period reaches a quantity threshold; If not, determine that the communication status of the first assigned vehicle is abnormal, and temporarily suspend the step of determining that the first assigned vehicle has not executed the first delivery route; Real-time monitoring of the communication status of the first assigned vehicle; If the communication status is normal, the trajectory data sent by the first allocated vehicle during the period of abnormal communication status is received, and the trajectory data is data locally cached in the first allocated vehicle; A complete driving trajectory is generated based on the trajectory data; The complete driving trajectory is matched with the first delivery route to determine whether the first assigned vehicle has entered the geofenced area.

[0013] By adopting the above technical solution, the number of location information reports from the first assigned vehicle is monitored in real time to determine whether the communication status is abnormal. After communication is restored, the locally cached trajectory data is received to reconstruct the complete driving trajectory, which is then matched and analyzed with the preset geofence. This solution significantly improves the accuracy and fault tolerance of delivery execution judgments, avoiding misjudgments caused by network fluctuations.

[0014] Optionally, when the first assigned vehicle is detected to be traveling within a preset distance range of the geofence area, the location information uploaded by the first assigned vehicle in the current and subsequent period is identified as high-priority cached data. Receive the high-priority cache data uploaded by the first allocated vehicle, and divide the high-priority cache data into multiple sequentially arranged data blocks according to the sequence number; The data blocks are received in the order of the sequence numbers, and a timer is started after the first data block arrives; Determine whether the first allocated vehicle received an acknowledgment signal within the first time threshold. If not, mark the first data block as pending retransmission and send a retransmission prompt to the first allocated vehicle; In subsequent reception processes, the remaining data blocks marked as pending retransmission are processed and received first until the high-priority buffered data is fully received. The remaining data blocks are data blocks other than the first data block.

[0015] By adopting the above technical solution, the behavior of the first assigned vehicle approaching the geofenced area is monitored. The location information uploaded by the vehicle during key periods is identified as high-priority cached data and received sequentially and managed in blocks. In the event of communication abnormalities, a confirmation and retransmission mechanism is triggered, prioritizing the processing of data blocks to be retransmitted until complete reception. This solution significantly improves the reliability of backhauling trajectory data from key delivery nodes, avoids data loss due to network fluctuations, and ensures that the execution process of high-priority tasks is traceable and verifiable.

[0016] Optionally, based on the traffic data, the estimated arrival time of the undelivered second store can be calculated; Determine whether the estimated arrival time of the second store exceeds the second time threshold; If it exists, a second delivery route is generated based on the real-time traffic status of the second allocated vehicle corresponding to the second store. The real-time traffic status includes the vehicle's current location and the customer priority order of the remaining delivery tasks. The second delivery route is verified based on delivery constraints, which include detour distance, external ambient temperature, vehicle insulation performance, expected dwell time, and temperature control timeliness of fresh produce. If the verification is successful, the second delivery route will be sent to the second assigned vehicle; Update the delivery status in the map visualization interface, which includes route display and estimated arrival time.

[0017] By employing the aforementioned technical solution, real-time analysis of traffic data is used to predict arrival times. Alternative delivery routes are dynamically generated based on vehicle location and customer priority. These routes are then validated against delivery constraints such as detour distance, ambient temperature, vehicle insulation performance, and the timeliness of temperature control for fresh produce. Valid routes are then issued and their visual status is updated simultaneously. This solution significantly improves the proactive responsiveness of delivery scheduling and the scientific nature of route adjustments, reducing the risk of product spoilage due to delays.

[0018] Optionally, the energy status of the second allocated vehicle is obtained, the energy status including at least one of the remaining fuel and the remaining battery charge; Based on the route information of the second delivery route and the performance of the second assigned vehicle, the required energy consumption is calculated. The route information includes mileage, gradient, road type, traffic congestion level, and estimated time spent at the store. The performance includes vehicle type, load status, and refrigeration system power. Determine whether the required energy consumption exceeds the energy state; If so, the second delivery route will be marked as unavailable.

[0019] By employing the aforementioned technical solution, the energy status of the second dispatch vehicle is obtained. Combined with parameters such as the delivery route's mileage, gradient, road type, traffic congestion, and refrigeration equipment operating time, the required energy consumption is calculated, and the energy feasibility of the route is determined. This solution significantly improves the scientific rigor and feasibility of delivery route planning, avoids breakdowns or mission interruptions due to insufficient energy, and significantly reduces instances of drivers refusing dispatch instructions.

[0020] Secondly, this application provides a multi-store delivery vehicle management system, which adopts the following technical solution: A multi-store delivery vehicle management system includes: The acquisition module is used to acquire real-time data and data models. A memory for storing the program of the multi-store delivery vehicle management method; The processor and memory can load and execute the program to implement the multi-store delivery vehicle management method.

[0021] By adopting the above technical solution, the acquisition module collects multi-source data in real time, including store orders, vehicle status, and traffic conditions. The processor efficiently executes intelligent scheduling algorithms, and the memory dynamically stores and updates the delivery model and historical trajectories, achieving closed-loop management of the entire process from data perception and route optimization to task issuance and execution monitoring. While ensuring the scientific validity and feasibility of delivery routes, this provides an efficient, reliable, and intelligent solution for urban logistics and fresh food delivery.

[0022] Thirdly, this application provides a smart terminal, which adopts the following technical solution: A smart terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the method described in any of the above-mentioned embodiments.

[0023] Fourthly, this application provides a computer storage medium capable of storing corresponding programs, which facilitates timely and fresh delivery of fresh produce, improves vehicle utilization efficiency, and reduces delivery costs. The technical solution adopted is as follows: A computer-readable storage medium storing a computer program that can be loaded by a processor and executed any of the above-described multi-store delivery vehicle management methods.

[0024] In summary, this application includes at least one of the following beneficial technical effects: 1. By analyzing store data, vehicle data, and real-time traffic data, a scheduling algorithm is used to generate delivery plans and dynamically adjust the task order based on customer priority, pushing delivery instructions to the vehicle terminal. This solution significantly improves the accuracy and response speed of scheduling decisions, reduces route duplication and delivery delays, and significantly improves delivery efficiency, customer satisfaction, and overall operational reliability. 2. By monitoring the behavior of the first assigned vehicle approaching the geofenced area, the location information uploaded during its critical time period is identified as high-priority cached data and received sequentially and managed in blocks. In the event of communication anomalies, a confirmation and retransmission mechanism is triggered, prioritizing the processing of data blocks to be retransmitted until complete reception. This solution significantly improves the reliability of backhauling trajectory data from key delivery nodes, avoids data loss due to network fluctuations, and ensures that the execution process of high-priority tasks is traceable and verifiable. 3. Real-time analysis of traffic data predicts arrival times, dynamically generates alternative delivery routes based on vehicle location and customer priority, and verifies these routes against delivery constraints such as detour distance, ambient temperature, vehicle insulation performance, and the timeliness of temperature control for fresh produce. Valid routes are then issued and their status is updated simultaneously via visualization. This solution significantly improves the proactive responsiveness of delivery scheduling and the scientific nature of route adjustments, reducing the risk of product spoilage due to delays. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating a multi-store delivery vehicle management method provided in an embodiment of this application.

[0026] Figure 2 This is a flowchart illustrating a delivery optimization method based on business events provided in an embodiment of this application.

[0027] Figure 3 This is a flowchart illustrating a delivery route monitoring method based on a geofenced area, as provided in an embodiment of this application.

[0028] Figure 4 This is a flowchart illustrating a trajectory completion method based on communication status provided in an embodiment of this application.

[0029] Figure 5 This is a flowchart illustrating a transmission method based on high-priority cached data provided in an embodiment of this application.

[0030] Figure 6 This is a flowchart illustrating a delivery route adjustment method based on traffic anomalies provided in an embodiment of this application.

[0031] Figure 7 This is a flowchart illustrating a delivery route verification method based on energy status provided in an embodiment of this application.

[0032] Figure 8 This is a schematic diagram of the structure of a multi-store delivery vehicle management system provided in an embodiment of this application. Detailed Implementation

[0033] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figures 1 to 8The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0034] This application discloses a method for managing delivery vehicles across multiple stores. (Refer to...) Figure 1 The method includes: Step S101: Obtain real-time data, which includes store data, vehicle data, and traffic data. Store data includes order demand, inventory status, and delivery customer priority information for each store. Vehicle data includes vehicle load and volume parameters. Traffic data includes road traffic conditions.

[0035] Real-time data is acquired based on GPS and the order management system, and reflects the delivery environment and resource status.

[0036] Store data refers to business information related to each delivery store.

[0037] Vehicle data refers to the parameters of the transport vehicles involved in the delivery planning.

[0038] Traffic data refers to road environment information that influences the choice of delivery routes.

[0039] For example, the real-time location of the cold chain truck B is obtained from GPS as "E Logistics Park", and the order requirements of stores A, C, and D, the inventory status of each store, and customer priorities are obtained from the order management system.

[0040] Step S102: Construct a data model based on real-time data.

[0041] A data model refers to the integration and mapping of raw data through pre-defined data structures and rules, typically represented by a graph structure or matrix. Nodes represent warehouses, stores, or transportation hubs, while edges represent road connections. Edge weights can include distance, estimated travel time, energy costs, etc.

[0042] For example, based on the acquired real-time data, warehouses and stores are used as location nodes, and road connections and travel times are used as edges. Combined with vehicle capacity constraints, a structured data model containing nodes, edges, and delivery constraints is constructed.

[0043] Step S103: Based on the data model, a scheduling algorithm is used to generate a delivery plan, which includes vehicle allocation and delivery routes.

[0044] The scheduling algorithm calculates a vehicle allocation scheme and access order that meets order requirements and has a short total travel time based on the node relationships, road conditions and delivery constraints in the data model, thereby forming an executable delivery route.

[0045] Delivery planning refers to determining, based on order demand and vehicle capacity, which vehicles will be responsible for which stores and in what order, through scheduling algorithms.

[0046] For example, based on store order volume, vehicle load capacity, and road travel time, a scheduling algorithm is used to calculate how to allocate orders to vehicles with sufficient load capacity and to plan the shortest travel sequence from the warehouse to each store in sequence, thus forming a delivery plan.

[0047] Step S104: Push the delivery route to the assigned vehicle.

[0048] For example, the generated delivery route "Warehouse → Store A → Store C → Store D" is sent to the vehicle terminal of vehicle B.

[0049] By employing the above technical solution, analyzing store data, vehicle data, and real-time traffic data, a scheduling algorithm is used to generate delivery plans. The task order is dynamically adjusted based on customer priority, and delivery instructions are pushed to the vehicle terminal. This solution significantly improves the accuracy and response speed of scheduling decisions, reduces route duplication and delivery delays, and substantially enhances delivery efficiency, customer satisfaction, and overall operational reliability.

[0050] This application discloses a delivery optimization method based on business events. (Refer to...) Figure 2 The method includes: Step S201: Obtain business event information for each store. Business event information includes at least one of the following: promotional activity arrangements, live-streaming sales plans, or emergency inventory replenishment requests.

[0051] Business event information refers to special activities or urgent needs that a store needs to fulfill due to its operations, including information that may affect delivery priority, such as promotions, live-streaming sales, or emergency restocking.

[0052] For example, information obtained from the store operation platform that store A will hold a limited-time promotion at 5 PM is identified as a promotional activity and treated as business event information.

[0053] Step S202: Based on the business event information, determine whether there is a first store, and whether there is a high-impact business event in the delivery plan corresponding to the first store during the execution period.

[0054] The first store refers to the store that is identified as having an event corresponding to a business event within the current delivery cycle and whose delivery needs to be prioritized.

[0055] High-impact business events refer to business activities that have a significant impact on store sales performance or customer experience, such as large-scale promotions, live-streaming sales, or emergency restocking, and priority should be given to ensuring timely delivery.

[0056] For example, if we obtain information that store A will be conducting a live-streaming sales event within two hours, and that goods must be restocked two hours in advance with a specific restocking time specified, and failure to restock store A will prevent the live stream from taking place, resulting in significant impact, then this event is classified as a high-impact business event. On the other hand, if we obtain information that store B will be conducting its routine inventory check next Wednesday, without specifying a restocking timeframe, this is also considered a business event.

[0057] Step S203: If it exists, raise the priority of the delivery plan corresponding to the first store to the highest priority, and recalculate the first delivery route of the first allocated vehicle corresponding to the first store.

[0058] Prioritizing to the highest priority means that among the stores in the pending delivery plan, the first store is set as the first to be processed, and the scheduling algorithm will prioritize its delivery time.

[0059] The first assigned vehicle refers to the vehicle originally assigned to the first store in the delivery plan.

[0060] The first delivery route refers to the first assigned vehicle driving order that is replanned to meet high-priority tasks, which may adjust the visit order in the original route or detour to other stores.

[0061] For example, the replenishment task for store A is prioritized to the highest priority, and the path is recalculated. The original path was "Warehouse → Store B → Store A → Store C", which is now adjusted to "Warehouse → Store A → Store B → Store C" to ensure delivery before 16:30.

[0062] Step S204: Push the first delivery route to the first assigned vehicle.

[0063] For example, the first delivery route "warehouse → store A → store B → store C" is sent to the vehicle terminal of the first allocation vehicle.

[0064] Step S205: Determine whether the first assigned vehicle has received the confirmation operation within the preset time.

[0065] The confirmation operation is when the on-board terminal of the first assigned vehicle receives confirmation of the first delivery route. For example, the driver indicates that he has received and accepted the first delivery route by clicking "Confirm" or "Understood" on the on-board terminal of the first assigned vehicle.

[0066] For example, a new route is sent at 17:05, requiring the driver to confirm it before 17:10. If the terminal sends back a confirmation signal at 17:08, it is considered that the confirmation operation has been received within the preset time.

[0067] Step S206: If not, establish an abnormal warning record and set the path confirmation status of the first delivery route to pending.

[0068] Anomaly warning logs refer to the generated log information that records the time of the event, vehicle number, task content, and unconfirmed cause, for subsequent manual intervention or analysis.

[0069] Route confirmation status refers to whether the driver has accepted and confirmed the execution of the first delivery route.

[0070] For example, if vehicle A does not receive confirmation within 5 minutes, an abnormal warning record is generated for vehicle A, and the route confirmation status is set to pending.

[0071] By adopting the above technical solution, the system analyzes store order demand, real-time vehicle status, and traffic conditions. Combined with business event information, it dynamically identifies high-impact delivery tasks, automatically prioritizes them, and replans the optimal delivery route. Simultaneously, it sends dispatch instructions to designated vehicles and tracks their status. This solution significantly improves the response speed and reliability of delivery dispatching, reducing delivery delays and task omissions.

[0072] This application discloses a method for monitoring delivery routes based on geofenced areas. (Refer to...) Figure 3 The method includes: Step S301: Set the geofence area and the expected entry time window. The geofence area corresponds to the predetermined section of the first delivery route.

[0073] A geofence is a virtual geographical area defined on an electronic map for key sections of a delivery route, used to monitor whether the first dispatch vehicle is traveling as planned.

[0074] A time window refers to the time range within which the first assigned vehicle should enter the geofenced area, as determined based on the first delivery route and traffic conditions.

[0075] A predetermined route refers to a key road or area that must be traversed in a pre-planned manner within a geofenced area of ​​the first delivery route.

[0076] For example, for a necessary intersection in the first delivery route, a geofence area is set within 50 meters of the intersection, and the first assigned vehicle is predicted to pass between 15:45 and 15:55 based on the current road conditions, thus forming a dual monitoring condition.

[0077] Step S302: Based on the received location information of the first assigned vehicle, generate the driving trajectory of the first assigned vehicle.

[0078] The driving trajectory refers to the actual movement path of the first assigned vehicle, which is formed by connecting multiple consecutive positioning points in chronological order.

[0079] Location information refers to real-time vehicle location data obtained through GPS or BeiDou navigation.

[0080] For example, location data is obtained from the refrigerated truck with license plate number A every 20 seconds. After collecting 6 points continuously, they are connected into a continuous curve to form the actual driving route after leaving the warehouse.

[0081] Step S303: Based on the driving trajectory, determine whether the first assigned vehicle entered the geofence area within the time window.

[0082] The judgment logic includes two dimensions: space and time. Specifically, it checks whether a certain continuous location point of the first assigned vehicle falls within the geofence area and whether the time corresponding to that point falls within the expected time window for entering. Only when both spatial and temporal conditions are met is the vehicle considered to have executed the delivery route normally.

[0083] For example, if a vehicle is detected entering a geofenced area at 15:42, earlier than the time window of 15:45-15:55, or entering at 16:00, it is considered as not entering within the time window.

[0084] Step S304: If not, determine that the first assigned vehicle did not execute the first delivery route.

[0085] For example, if the first assigned vehicle does not pass through the geofenced area within the time window, it is determined that the first assigned vehicle has not executed the first delivery route. In addition, because the first store has a high-impact business event, it is necessary to supply goods to the first store in a timely manner. Therefore, in subsequent steps, a vehicle will be requested to supply goods to the first store to meet the high-impact business event.

[0086] Step S305: Push the target vehicle for the task replacement request to the first assigned vehicle.

[0087] The target vehicle refers to a nearby vehicle that is displayed on the onboard terminal of the first assigned vehicle, has the capability to take over, and meets the task requirements. For example, the driver can view nearby vehicles on a map on the onboard terminal and contact them by phone.

[0088] The task request is based on factors such as the distance between nearby vehicles and the geofenced area, whether the direction of travel is consistent with the first delivery route, whether the current cargo status is capable of taking over, and whether the estimated arrival time meets the task timeliness requirements.

[0089] For example, the driver of the first assigned vehicle can see a nearby vehicle traveling in the same direction and close to the geofence area through the vehicle terminal, and contact it by phone to see if the current cargo status is available to take over, and coordinate whether it can be used as the target vehicle to complete the subsequent delivery task.

[0090] Step S306: Suspend the execution of the first assigned vehicle on the highest priority first delivery route.

[0091] For example, the first assigned vehicle will take over the task request that has been pushed to the target vehicle, so the first assigned vehicle stops traveling on the first delivery route and continues to travel along the original route.

[0092] By employing the aforementioned technical solution, the location information of the first assigned vehicle is monitored. Combined with geofenced areas and time windows, the vehicle's trajectory is generated and analyzed to determine whether it has entered the critical delivery section as required. If it is found that the vehicle has not entered within the specified time, a task takeover mechanism is triggered, assigning an emergency task to a nearby vehicle and suspending the original vehicle's execution privileges. This solution significantly improves the reliability of high-priority delivery tasks and reduces delivery interruptions caused by delays or non-compliance.

[0093] This application discloses a trajectory completion method based on communication status. (Refer to...) Figure 4 The method includes: Step S401: Monitor whether the number of location information sent by the first assigned vehicle within a preset time period reaches the quantity threshold.

[0094] For example, the preset duration is set to 60 seconds, and the quantity threshold is 2. If only one location information from the first assigned vehicle is received between 10:15 and 10:16, it is determined that the quantity threshold has not been reached.

[0095] Step S402: If not, determine that the communication status of the first assigned vehicle is abnormal, and temporarily suspend the step of determining that the first assigned vehicle has not executed the first delivery route.

[0096] An abnormal communication status means that the data transmission link of the first assigned vehicle is unstable or interrupted, resulting in incomplete uploading of location information.

[0097] "Suspension of execution" means temporarily suspending the path deviation judgment logic based on geofence areas to avoid misjudging that the first assigned vehicle did not travel along the first delivery route due to missing data.

[0098] For example, if a vehicle enters a subway tunnel area and only uploads one location record within 60 consecutive seconds, it is determined that the communication is abnormal, and the "determination of execution" in step S304 is temporarily suspended.

[0099] Step S403: Real-time detection of the communication status of the first assigned vehicle.

[0100] Communication status refers to the network connection status of the first assigned vehicle, which is divided into two states: "abnormal" and "normal".

[0101] For example, after the first assigned vehicle exits the tunnel, it continuously uploads two location information records. Once the data stream is detected to have stabilized, the communication status is determined to change from "abnormal" to "normal".

[0102] Step S404: If the communication status is normal, receive the trajectory data sent by the first assigned vehicle during the period of abnormal communication status. The trajectory data is the data locally cached in the first assigned vehicle.

[0103] Trajectory data refers to the local location information cached by the on-board terminal of the first assigned vehicle during the communication interruption, which is then uploaded in batches after the network is restored.

[0104] Local caching refers to the temporary data storage mechanism built into the onboard terminal of the first allocated vehicle, used to temporarily store critical information when the network is unavailable, preventing data loss.

[0105] For example, after the first assigned vehicle exits the tunnel, communication is restored, and the vehicle receives the trajectory data cached locally on the first assigned vehicle.

[0106] Step S405: Generate a complete driving trajectory based on the trajectory data.

[0107] The complete driving trajectory refers to the continuous movement path of the first assigned vehicle from the push of the first delivery route to the current moment, including the missing road segments during the communication interruption, which is used for subsequent matching with the first delivery route.

[0108] For example, after the first assigned vehicle communicates normally, the cached location data is spliced ​​with the trajectories of the preceding and following time periods to form a continuous and complete driving trajectory.

[0109] Step S406: Match the complete driving trajectory with the first delivery route to determine whether the first assigned vehicle has entered the geofence area.

[0110] For example, the complete driving trajectory is compared with the first delivery route to confirm whether the vehicle has entered the geofenced area and completed the critical road segment driving on time.

[0111] By adopting the above technical solution, the number of location information reports from the first assigned vehicle is monitored in real time to determine whether the communication status is abnormal. After communication is restored, the locally cached trajectory data is received to reconstruct the complete driving trajectory, which is then matched and analyzed with the preset geofence. This solution significantly improves the accuracy and fault tolerance of delivery execution judgments, avoiding misjudgments caused by network fluctuations.

[0112] This application discloses a method for transmitting high-priority cached data. (Refer to...) Figure 5 The method includes: Step S501: When the first assigned vehicle is detected to have traveled within a preset distance range of the geofence area, the location information uploaded by the first assigned vehicle in the current period and in the subsequent period is identified as high-priority cached data.

[0113] The preset distance range refers to the outer perimeter of the geofence area.

[0114] High-priority cached data refers to the location information that is marked as high-priority cached data when the first assigned vehicle enters the preset distance range. It enjoys priority processing and priority retransmission during transmission, which prevents the loss of critical data due to network fluctuations.

[0115] For example, when the first assigned vehicle travels to a distance of 300 meters from the perimeter of the geofence area, the location information uploaded by the first assigned vehicle in the current period and for a subsequent period of time is identified as high-priority cached data.

[0116] Step S502: Receive the high-priority cached data uploaded by the first allocated vehicle, and divide the high-priority cached data into multiple sequentially arranged data blocks according to the sequence number.

[0117] A data block is a transmission unit that divides high-priority cached data into fixed-size or logical units. Each data block has a unique sequence number to identify its position in the high-priority cached data.

[0118] For example, a high-priority cached data containing 10 location points is divided into 5 data blocks, which are labeled with sequence numbers 1 to 5 respectively.

[0119] Step S503: Receive data blocks in sequence according to their sequence numbers, and start timing after the first data block arrives.

[0120] Timing refers to a timeout mechanism set up to ensure data integrity. Timing is started after the first data block arrives.

[0121] For example, if a data block with sequence number 1 is received at 10:25, a timer is immediately started and the time threshold is set to 10 seconds.

[0122] Step S504: Determine whether the first assigned vehicle has received an acknowledgment signal within the first time threshold.

[0123] The confirmation signal refers to the signal that the on-board terminal of the first allocated vehicle has successfully received the data.

[0124] For example, after sending the first data block and receiving the confirmation, check whether a confirmation signal is received from the first assigned vehicle within ten seconds.

[0125] Step S505: If not, mark the first data block as pending retransmission and send a retransmission prompt to the first allocated vehicle.

[0126] The state of pending retransmission refers to the state in which the first data block is marked as needing to be retransmitted because the first allocated vehicle did not receive an acknowledgment signal, and thus enters the retransmission queue.

[0127] The retransmission prompt is an instruction message sent to the on-board terminal of the first allocated vehicle, notifying that the first data block was not completely received and needs to be retransmitted.

[0128] For example, if it is found that the data block with serial number 1 has not received an acknowledgment signal, it is marked as "to be retransmitted", and an instruction "Please retransmit the data block with serial number 1" is sent to the first allocation vehicle.

[0129] Step S506: In the subsequent reception process, the remaining data blocks marked as pending retransmission are processed and received first until the high-priority buffered data is completely received. The remaining data blocks are the data blocks other than the first data block.

[0130] For example, when receiving subsequent data blocks, data blocks marked as not yet to be retransmitted are received first, until all 5 data blocks are successfully received, confirming the integrity of the high-priority cached data.

[0131] By adopting the above technical solution, the behavior of the first assigned vehicle approaching the geofenced area is monitored. The location information uploaded by the vehicle during key periods is identified as high-priority cached data and received sequentially and managed in blocks. In the event of communication abnormalities, a confirmation and retransmission mechanism is triggered, prioritizing the processing of data blocks to be retransmitted until complete reception. This solution significantly improves the reliability of backhauling trajectory data from key delivery nodes, avoids data loss due to network fluctuations, and ensures that the execution process of high-priority tasks is traceable and verifiable.

[0132] This application discloses a method for adjusting delivery routes based on traffic anomalies. (Refer to...) Figure 6 The method includes: Step S601: Based on traffic data, calculate the estimated arrival time of the undelivered second store.

[0133] The second store refers to any store that has been planned along the delivery route but has not yet been actually delivered to.

[0134] Estimated arrival time refers to the time predicted when the vehicle will arrive at the second store, based on its current location, current speed, remaining distance, and real-time traffic conditions.

[0135] The estimated arrival time is calculated based on the real-time location of the second assigned vehicle and the remaining delivery route, combined with the current road congestion level, traffic accidents, speed limit information, and the historical average travel time of the road segment. The estimated arrival time to the second store is calculated by comprehensively assessing the time required for each segment of the journey and summing them up.

[0136] For example, if a traffic accident occurs on the main road causing slow traffic, the arrival time of the vehicle heading to store B is recalculated and updated from the original 15:20 to 15:45.

[0137] Step S602: Determine whether there is a second store whose estimated arrival time exceeds the second time threshold.

[0138] For example, the estimated arrival time of store B is 15:45, while its time threshold is 15:30. If it exceeds 15 minutes, it is determined that the estimated arrival time exceeds the time threshold.

[0139] Step S603: If it exists, generate a second delivery route based on the real-time traffic status of the second allocated vehicle corresponding to the second store. The real-time traffic status includes the current location of the vehicle and the customer priority order of the remaining delivery tasks.

[0140] The second delivery route refers to an alternative route that is replanned to avoid delays and will bypass congested areas.

[0141] For example, based on the current location of the second assigned vehicle and the customer priority order of the remaining delivery tasks, a second delivery route is generated: "Detour via the auxiliary road to the accident section → Prioritize delivery to store B → Continue to complete the delivery to store C".

[0142] Step S604: Verify the second delivery route based on delivery constraints, which include detour distance, external ambient temperature, vehicle compartment insulation performance, estimated dwell time, and temperature control timeliness of fresh produce.

[0143] The verification is based on whether the detour distance exceeds the standard, whether the external ambient temperature affects the preservation of goods, whether the insulation performance of the carriage can maintain a low temperature, and whether the expected dwell time at each station and the temperature control timeliness of fresh goods meet the requirements, to comprehensively judge whether the second delivery route is feasible.

[0144] For example, it was verified that although the second delivery route was 5 kilometers longer, it was still within the allowable range, and the temperature of the vehicle compartment was controllable throughout the journey, and the temperature control time for fresh produce was sufficient, thus determining that the second delivery route was feasible.

[0145] Step S605: If the verification is successful, send the second delivery route to the second assigned vehicle.

[0146] For example, after verifying the feasibility of the second delivery route, the second delivery route is sent to the vehicle terminal of the second allocation vehicle.

[0147] Step S606: Update the delivery status in the map visualization interface. The delivery status includes route display and estimated arrival time.

[0148] For example, the delivery status in the visualization interface is updated, the green line shows the second delivery route, and the estimated arrival time of the second store is refreshed simultaneously.

[0149] By employing the aforementioned technical solution, real-time analysis of traffic data is used to predict arrival times. Alternative delivery routes are dynamically generated based on vehicle location and customer priority. These routes are then validated against delivery constraints such as detour distance, ambient temperature, vehicle insulation performance, and the timeliness of temperature control for fresh produce. Valid routes are then issued and their visual status is updated simultaneously. This solution significantly improves the proactive responsiveness of delivery scheduling and the scientific nature of route adjustments, reducing the risk of product spoilage due to delays.

[0150] This application discloses a delivery route verification method based on energy status. (Refer to...) Figure 7 The method includes: Step S701: Obtain the energy status of the second allocated vehicle, which includes at least one of the remaining fuel and the remaining battery charge.

[0151] Energy status refers to the current available energy reserves of the second-allocated vehicle, including remaining fuel or battery charge, and also includes the remaining energy of hybrid vehicles.

[0152] For example, information obtained from the second allocated vehicle with license plate number B indicates that the current battery charge is 45%, with a remaining range of approximately 120 kilometers, or that the second allocated vehicle has 30 liters of fuel remaining.

[0153] Step S702: Based on the route information of the second delivery route and the performance of the second assigned vehicle, calculate the required energy consumption. The route information includes mileage, gradient, road type, traffic congestion level and estimated dwell time at the store. The performance includes vehicle type, load status and refrigeration system power.

[0154] Required energy consumption refers to the total energy consumed to complete the second delivery route. The calculation formula is: Required energy consumption = (base fuel consumption × mileage) + (slope correction factor × slope × mileage) + (additional energy consumption for cooling × estimated running time). Among them, the base fuel consumption is obtained from the table based on the vehicle model and load, the slope correction factor reflects the additional energy consumption for climbing, and the additional energy consumption for cooling is based on the cooling power and the total running time, including driving and stopping.

[0155] For example, the base fuel consumption of the second assigned vehicle executing the second delivery route is 0.3 liters per kilometer, the distance is 20 kilometers, the gradient is 5%, the gradient correction factor is 0.02, the additional energy consumption of the air conditioner is 2 liters per hour, and the estimated running time is 3 hours. The calculated energy consumption is (0.3×20)+(0.02×5×20)+(2×3)=6+2+6=14 liters of fuel.

[0156] Step S703: Determine whether the required energy consumption exceeds the energy state.

[0157] For example, the calculated fuel requirement is 14 liters, but the second vehicle only has 10 liters of fuel left, indicating that the required energy consumption exceeds the energy state.

[0158] Step S704: If so, mark the second delivery route as unusable.

[0159] For example, if the calculation shows that the second delivery route requires 14 liters of fuel, but the second dispatch vehicle only has 10 liters left, it is determined that the energy consumption exceeds the limit, and the second delivery route is marked as "unusable".

[0160] By employing the aforementioned technical solution, the energy status of the second dispatch vehicle is obtained. Combined with parameters such as the delivery route's mileage, gradient, road type, traffic congestion, and refrigeration equipment operating time, the required energy consumption is calculated, and the energy feasibility of the route is determined. This solution significantly improves the scientific rigor and feasibility of delivery route planning, avoids breakdowns or mission interruptions due to insufficient energy, and significantly reduces instances of drivers refusing dispatch instructions.

[0161] Based on the same inventive concept, embodiments of this application provide a multi-store delivery vehicle management system, including: Module 801 is used to acquire real-time data and data models; Memory 802 is used to store programs for managing multi-store delivery vehicles; The processor 803 enables the program in memory to be loaded and executed by the processor, thus implementing a multi-store delivery vehicle management method.

[0162] By adopting the above technical solution, the acquisition module collects multi-source data in real time, including store orders, vehicle status, and traffic conditions. The processor efficiently executes intelligent scheduling algorithms, and the memory dynamically stores and updates the delivery model and historical trajectories, achieving closed-loop management of the entire process from data perception and route optimization to task issuance and execution monitoring. While ensuring the scientific validity and feasibility of delivery routes, this provides an efficient, reliable, and intelligent solution for urban logistics and fresh food delivery.

[0163] 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.

[0164] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as a multi-store delivery vehicle management method.

[0165] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0166] Based on the same inventive concept, embodiments of this application provide a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as a multi-store delivery vehicle management method.

[0167] 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.

[0168] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A multi-store delivery vehicle management method, characterized by, The method comprises: acquiring real-time data, the real-time data comprising store data, vehicle data, and traffic data, the store data comprising order demand, inventory status, and delivery customer priority information of each store, the vehicle data comprising vehicle load and volume parameters, and the traffic data comprising road traffic conditions; constructing a data model based on the real-time data; generating a delivery plan based on the data model using a scheduling algorithm, the delivery plan comprising assigned vehicles and delivery paths; pushing the delivery paths to the assigned vehicles.

2. The multi-store delivery vehicle management method according to claim 1, characterized by, The method further comprises: acquiring business event information of the stores, the business event information comprising at least one of a promotion activity arrangement, a live streaming e-commerce plan, or an inventory emergency replenishment request; determining whether there is a first store corresponding to a delivery plan having a high-impact business event within an execution period; if so, raising the priority of the delivery plan corresponding to the first store to the highest priority, and recalculating a first delivery path of a first assigned vehicle corresponding to the first store; pushing the first delivery path to the first assigned vehicle; determining whether the first assigned vehicle receives a confirmation operation within a preset time; if not, establishing an abnormality warning record, and setting a path confirmation state of the first delivery path to be pending.

3. The multi-store delivery vehicle management method according to claim 2, characterized by, After the first delivery path is pushed to the first assigned vehicle, the method further comprises: setting a geofence area and an expected time window for driving into the geofence area, the geofence area corresponding to a predetermined section of the first delivery path; generating a driving trajectory of the first assigned vehicle based on received positioning information of the first assigned vehicle; determining whether the first assigned vehicle drives into the geofence area within the time window according to the driving trajectory; if not, determining that the first assigned vehicle does not execute the first delivery path; pushing a target vehicle that takes over a task request to the first assigned vehicle; suspending execution of the first delivery path of the highest priority by the first assigned vehicle.

4. The multi-store delivery vehicle management method according to claim 3, characterized by, The method further comprises: monitoring whether the number of the positioning information sent by the first assigned vehicle within a preset time period reaches a quantity threshold; if not, determining that the communication state of the first assigned vehicle is abnormal, and suspending the step of determining that the first assigned vehicle does not execute the first delivery path; real-time detecting the communication state of the first assigned vehicle; if the communication state is normal, receiving trajectory data sent by the first assigned vehicle during the period when the communication state is abnormal, the trajectory data being locally cached data in the first assigned vehicle; generating a complete driving trajectory based on the trajectory data; matching the complete driving trajectory with the first delivery path to determine whether the first assigned vehicle drives into the geofence area.

5. The multi-store delivery vehicle management method according to claim 4, wherein Local cache data upload communication state abnormality comprises: when it is monitored that the first assigned vehicle drives to within a preset distance range of the geofence area, identifying the positioning information uploaded by the first assigned vehicle in the current and subsequent time period as high-priority cache data; receiving the high-priority cache data uploaded by the first distribution vehicle, and dividing the high-priority cache data into a plurality of sequentially arranged data blocks according to sequence numbers; receiving the data blocks in the order of the sequence numbers, and timing after the first data block arrives; determining whether the first distribution vehicle receives an acknowledgement signal within a first time threshold; if not, marking the first data block as a retransmission pending state, and sending a retransmission prompt to the first distribution vehicle; in subsequent receiving processes, preferentially processing and receiving the remaining data blocks marked as the retransmission pending state until the high-priority cache data is completely received, the remaining data blocks being data blocks other than the first data block.

6. The multi-store delivery vehicle management method according to claim 1, wherein when there is a traffic condition or an emergency situation in the distribution path, comprising: based on the traffic data, calculating an estimated arrival time of a second store that has not been delivered to; determining whether the estimated arrival time of the second store exceeds a second time threshold; if so, generating a second distribution path according to a real-time traffic state of a second distribution vehicle corresponding to the second store, the real-time traffic state including a current position of the vehicle and a customer priority order of remaining distribution tasks; verifying the second distribution path according to distribution constraints, the distribution constraints including a detour distance, an external environment temperature, a refrigeration performance of a vehicle cabin, an estimated stay time, and a temperature control time limit of fresh goods; if verified, sending the second distribution path to the second distribution vehicle; updating a distribution state in a map visualization interface, the distribution state including path display and estimated arrival time.

7. The multi-store delivery vehicle management method according to claim 6, wherein further comprising: obtaining an energy state of the second distribution vehicle, the energy state including at least one of a remaining fuel amount and a remaining battery capacity; based on path information of the second distribution path and performance of the second distribution vehicle, calculating a required energy consumption, the path information including mileage, slope, road type, traffic congestion degree, and store estimated stay time, the performance including vehicle type, load state, and refrigeration system power; determining whether the required energy consumption exceeds the energy state; if so, marking the second distribution path as unusable.

8. A multi-store delivery vehicle management system, characterized by, The system is used to execute the multi-store distribution vehicle management method according to any one of claims 1 to 7, comprising: an obtaining module for obtaining real-time data and a data model; a memory for storing a program of the multi-store distribution vehicle management method; a processor, the program in the memory being loadable and executable by the processor and realizing the multi-store distribution vehicle management method.

9. A smart terminal, characterized by comprising a memory and a processor, the memory storing a computer program loadable and executable by the processor and realizing the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, a computer program loadable and executable by the processor and realizing the method according to any one of claims 1 to 7.

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