Cigarette logistics management system based on intelligent scheduling and flexible distribution

Abstract

The invention provides a cigarette logistics management system based on intelligent scheduling and flexible distribution, and relates to the technical field of logistics management, and the system comprises a data collection module which gathers logistics data which are manually filled and reported and imported by a system in real time, and carries out the preprocessing of the logistics data; the intelligent decision-making module generates a decision-making scheme including a flexible delivery system, differentiated service suggestions and a sorting sequence strategy based on the preprocessed logistics data; the flexible execution module carries out loading capacity calculation, line distribution and personnel cycle management according to the decision scheme, and completes distribution preparation and distribution tasks; the real-time monitoring module carries out distribution progress visual display according to the execution data, and carries out abnormity early warning when an abnormity is found; and the feedback optimization module analyzes an abnormal reason according to the execution abnormal data, and adjusts decision parameters to form an optimized decision scheme. According to the scheme, the problems of low resource utilization rate, high ineffective cost, unreasonable line and goods distribution and the like can be solved, and the adaptability of dynamic market demands is improved.

Description

Technical Field

[0001] This invention relates to the field of logistics management technology, and in particular to a cigarette logistics management system based on intelligent scheduling and flexible distribution. Background Technology

[0002] With the increasing emphasis on small-batch, high-frequency demand at cigarette retail outlets, coupled with the continuous expansion of delivery areas, the ever-increasing scale of retail, and the daily dynamic changes in the number of retailers, the traditional delivery model that relies on fixed routes and static scheduling is finding it difficult to match the reality of the rapid year-on-year growth in cigarette sales.

[0003] For example, traditional delivery management methods suffer from insufficient loading rates during off-seasons, leading to significant idle capacity of individual vehicles, low resource utilization, and uneven distribution of delivery routes with large differences in the number of customers served. Furthermore, traditional delivery management methods result in an empty mileage rate exceeding 18%, with delivery vehicles frequently returning empty, leading to both high fuel costs and carbon emissions. Additionally, traditional delivery management methods rely on manual experience for route planning, lacking dynamic optimization mechanisms, and are labor-intensive and prone to errors.

[0004] In summary, the existing cigarette distribution management scheme has problems such as low resource utilization, high ineffective costs, and unreasonable allocation of routes and cargo volume, making it difficult to adapt to dynamically changing market demands. Summary of the Invention

[0005] In view of this, and to address the above shortcomings, it is necessary to propose a cigarette logistics management system based on intelligent scheduling and flexible delivery to solve at least one of the problems mentioned above, such as low resource utilization, high ineffective costs, and unreasonable allocation of routes and cargo volume, and to improve adaptability to dynamically changing market demands.

[0006] This invention provides a cigarette logistics management system based on intelligent scheduling and flexible delivery. The system includes: a data acquisition module, an intelligent decision-making module, a flexible execution module, a real-time monitoring module, and a feedback optimization module.

[0007] The data acquisition module is used to collect logistics data filled in manually and imported by the system in real time, and after preprocessing the logistics data, transmit the preprocessed logistics data to the intelligent decision-making module; wherein, the logistics data includes: basic business data, order sorting data, dynamic GPS data and historical operation data;

[0008] The intelligent decision-making module is used to generate a decision plan based on the preprocessed logistics data, which includes a flexible delivery system, differentiated service suggestions, and sorting sequence strategy, and transmit the decision plan to the flexible execution module and the real-time monitoring module.

[0009] The flexible execution module is used to calculate the load, allocate routes, and manage personnel cycles according to the decision-making scheme, to complete delivery preparation and execute delivery tasks; and to transmit the execution data generated during the flexible execution process to the real-time monitoring module in real time.

[0010] The real-time monitoring module is used to visualize the delivery progress based on the execution data, issue an alert when an abnormality is detected in the delivery task execution, and transmit the corresponding execution abnormality data to the feedback optimization module.

[0011] The feedback optimization module is used to analyze the cause of the abnormality based on the execution abnormality data, adjust the decision parameters to form an optimized decision plan, and transmit the optimized decision plan to the flexible execution module for use in the subsequent logistics process.

[0012] Preferably, the data acquisition module includes: a basic business data acquisition unit, an order sorting data acquisition unit, a GPS data acquisition unit, a historical operation data acquisition unit, a data cleaning unit, and a data standardization unit;

[0013] The basic business data acquisition unit is used to acquire manually entered basic business data; wherein, the basic business data includes: sorting progress, vehicle status, vehicle basic data, and personnel scheduling data;

[0014] The order sorting data acquisition unit is used to interface with the existing order management system (OMS) and import order sorting data; wherein, the order sorting data includes: order number, cigarette specifications, cigarette quantity, delivery address and delivery time requirements;

[0015] The GPS data acquisition unit is used to access the GPS navigation system APP of the delivery vehicle and receive dynamic GPS data uploaded by the GPS navigation system APP; wherein, the dynamic GPS data includes: the vehicle's real-time location, driving speed and estimated arrival time;

[0016] The historical operation data acquisition unit is used to import historical operation data from the historical database; wherein, the historical operation data includes: historical delivery time, personnel route familiarity, and empty load rate;

[0017] The data cleaning unit is used to remove outliers from the logistics data;

[0018] The data standardization unit is used to unify various logistics data according to a preset format template.

[0019] Preferably, the intelligent decision-making module includes: a flexible delivery system generation unit, a differentiated service suggestion generation unit, and a sorting sequence strategy generation unit;

[0020] The flexible delivery system generation unit is used to collect order data and classify it according to three dimensions: timeliness requirements, regional type, and order fluctuation. The timeliness requirements include T+0 and T+1, and the regional types include core areas, suburbs, and towns. It is also used to dynamically generate flexible delivery systems for different scenarios based on the order volume fluctuation of each regional type.

[0021] The differentiated service suggestion generation unit is used to generate differentiated service suggestions for each imported order, taking into account its timeliness requirements and delivery resources around the delivery address.

[0022] The sorting order strategy generation unit is used to classify and sort orders based on route clustering and time priority.

[0023] Preferably, the differentiated service suggestion generation unit includes: a decision factor assignment subunit, an order score calculation subunit, and a service suggestion generation subunit;

[0024] The decision factor assignment subunit is used to assign scores to different timeliness requirements, different customer levels, and different delivery address surrounding delivery resources.

[0025] The order score calculation subunit is used to calculate the suggested rule score of the current order based on the timeliness requirements of the current order, customer level, delivery resources around the delivery address, and the score assigned by the decision factor assignment subunit.

[0026] The service suggestion generation sub-unit is used to calculate the suggestion rule score calculated by the sub-unit based on the order score, and match the corresponding differentiated service suggestions.

[0027] Preferably, the flexible execution module includes: a load calculation unit, a route allocation unit, and a personnel cycle management unit;

[0028] The loading capacity calculation unit is used to calculate the optimal loading capacity of each vehicle based on the vehicle basic data in the order sorting data and basic business data.

[0029] The route allocation unit is used to construct a personnel-route familiarity database based on historical delivery time and personnel route familiarity in historical operation data, and to match delivery personnel with high route familiarity from the personnel-route familiarity database in combination with the order volume and distance of each route, so that the corresponding delivery personnel can perform loading and delivery tasks.

[0030] The personnel cycle management unit is used to flexibly adjust the delivery cycle of delivery personnel based on personnel scheduling data in basic business data and order fluctuations in statistical order data.

[0031] Preferably, the flexible execution module further includes: an emergency order processing unit;

[0032] The emergency order processing unit is used to push the information of an emergency order to the delivery personnel who meet the timeliness requirements when an emergency order is added during the delivery personnel's delivery task; and to adjust the navigation route to prioritize the delivery address of the emergency order when the delivery personnel accept the emergency order.

[0033] Preferably, the flexible execution module further includes: a sorting anomaly handling unit;

[0034] The sorting anomaly handling unit is used to trigger an anomaly alert when the cigarette inventory of a certain order is insufficient, and synchronize the insufficient inventory information to the intelligent decision-making module so that the intelligent decision-making module can generate a suggestion of partial delivery + remaining replenishment; at the same time, when executing the delivery task, the existing inventory is sorted and loaded first, and the remaining cigarettes are coordinated with the warehouse for replenishment and then delivered according to T+N+n; where T+N is the time limit corresponding to the current order, and n is the replenishment cycle.

[0035] Preferably, the real-time monitoring module includes: a delivery progress display unit and an anomaly warning unit;

[0036] The delivery progress display unit is used to mark the real-time location of each delivery vehicle based on a map and dynamic GPS data, and to distinguish the order status with different colors; wherein, the order status includes: normal delivery, about to time out, and abnormal stoppage;

[0037] The anomaly warning unit is used to issue a warning via a large-screen pop-up window when an abnormality occurs during the execution of a delivery task, and simultaneously displays the cause of the abnormality and historical processing records.

[0038] Preferably, the triggering conditions for the delivery task execution exception include at least one of the following:

[0039] Delivery time warning: Remaining delivery time for T+0 orders < Estimated travel time + First preset time;

[0040] Location alert: The vehicle's GPS remains unchanged for a second consecutive preset period of time, and is not located at the retailer's address or server, or the delivery address has changed;

[0041] Loading warning: The deviation between the actual loading amount and the optimal loading amount exceeds the first preset threshold.

[0042] Preferably, the feedback optimization module includes: a deviation analysis unit and a strategy optimization unit;

[0043] The deviation analysis unit is used to perform deviation analysis on the timeliness, resource optimization accuracy, and service satisfaction of the current delivery task based on abnormal execution data.

[0044] The strategy optimization unit is used to adjust the subsequent decision parameters based on the analysis results obtained by the deviation analysis unit, and push the optimized and adjusted decision scheme to the flexible execution module for use in subsequent delivery tasks.

[0045] As can be seen from the above technical solution, the cigarette logistics management system based on intelligent scheduling and flexible delivery provided by the present invention includes a data acquisition module, an intelligent decision-making module, a flexible execution module, a real-time monitoring module, and a feedback optimization module. The data acquisition module can collect logistics data manually entered and imported by the system in real time, and transmit the preprocessed logistics data to the intelligent decision-making module. The intelligent decision-making module can generate a decision plan based on the preprocessed logistics data, including flexible delivery policies, differentiated service suggestions, and sorting sequence strategies, and transmit this decision plan to the flexible execution module and the real-time monitoring module. The flexible execution module can calculate loading volume, allocate routes, and manage personnel cycles according to the decision plan to ensure accurate delivery and execute delivery tasks, and transmit the execution data to the real-time monitoring module in real time. The real-time monitoring module can visualize the delivery progress based on the execution data, issue anomaly warnings when abnormalities are detected in the delivery task execution, and transmit the corresponding execution anomaly data to the feedback optimization module. The feedback optimization module can analyze the causes of anomalies based on the execution anomaly data, adjust decision parameters to form an optimized decision plan, and transmit the optimized decision plan to the flexible execution module for use in subsequent delivery tasks. Therefore, this solution significantly improves single-vehicle delivery efficiency through intelligent route allocation and load optimization. Furthermore, the decision-making module generates solutions, and the flexible execution module calculates load and allocates routes, effectively reducing empty mileage and thus minimizing ineffective costs. It also achieves a rational allocation of routes and cargo volume, thereby improving adaptability to dynamically changing market demands. In addition, this solution generates flexible delivery policies, differentiated service suggestions, and sorting sequence strategies through intelligent decision-making, making it more adaptable to actual delivery scenarios and thus meeting dynamically changing market demands. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of a cigarette logistics management system based on intelligent scheduling and flexible delivery, provided as an embodiment of the present invention.

[0047] Figure 2 This is a schematic diagram of a data acquisition module provided in an embodiment of the present invention.

[0048] Figure 3 This is a schematic diagram of an intelligent decision-making module provided in an embodiment of the present invention.

[0049] Figure 4This is a schematic diagram of a differentiated service suggestion generation unit provided in an embodiment of the present invention.

[0050] Figure 5 This is a schematic diagram of a flexible execution module provided in an embodiment of the present invention.

[0051] Figure 6 This is a schematic diagram of a real-time monitoring module provided in an embodiment of the present invention.

[0052] Figure 7 This is a schematic diagram of a feedback optimization module provided in an embodiment of the present invention. Detailed Implementation

[0053] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] like Figure 1-7 As shown, the present invention provides a cigarette logistics management system based on intelligent scheduling and flexible delivery. The system includes: a data acquisition module 10, an intelligent decision-making module 20, a flexible execution module 30, a real-time monitoring module 40, and a feedback optimization module 50.

[0055] The data acquisition module 10 is used to collect logistics data filled in manually and imported by the system in real time, and after preprocessing the logistics data, transmit the preprocessed logistics data to the intelligent decision-making module 20; wherein, the logistics data includes: basic business data, order sorting data, dynamic GPS data and historical operation data;

[0056] The intelligent decision-making module 20 is used to generate a decision scheme based on the preprocessed logistics data, which includes a flexible delivery system, differentiated service suggestions and sorting sequence strategy, and transmit the decision scheme to the flexible execution module 30 and the real-time monitoring module 40.

[0057] The flexible execution module 30 is used to calculate the load, allocate routes, and manage personnel cycles according to the decision-making scheme, to complete delivery preparation and execute delivery tasks; and to transmit the execution data generated during the flexible execution process to the real-time monitoring module 40 in real time.

[0058] The real-time monitoring module 40 is used to visualize the delivery progress based on the execution data, issue an abnormal warning when an abnormality is detected in the delivery task execution, and transmit the corresponding execution abnormality data to the feedback optimization module 50.

[0059] The feedback optimization module 50 is used to analyze the cause of the abnormality based on the execution abnormality data, adjust the decision parameters to form an optimized decision scheme, and transmit the optimized decision scheme to the flexible execution module 30 for use in the subsequent logistics process.

[0060] In this embodiment, intelligent route allocation and load optimization significantly improve single-vehicle delivery efficiency. Furthermore, the decision-making schemes generated by the intelligent decision-making module 20, along with the load calculations and route allocation by the flexible execution module 30, effectively reduce empty mileage, thereby lowering ineffective costs. Simultaneously, they enable a rational allocation of routes and cargo volume, thus improving adaptability to dynamically changing market demands. In addition, this solution generates flexible delivery systems, differentiated service suggestions, and sorting sequence strategies through intelligent decision-making, making it more adaptable to actual delivery scenarios and thus meeting dynamically changing market demands.

[0061] The data acquisition module 10 aims to aggregate logistics data in real time, including manually entered data, system-imported order sorting data, and GPS dynamic data. It then cleans and verifies this data, such as verifying the consistency between order quantity and sorting quantity, and ensuring the GPS location is within a reasonable delivery range. After ensuring data accuracy, the data is transmitted to the intelligent decision-making module 20. Specifically, the existing intelligent logistics management system can be used as the data hub. On one hand, the system's built-in data entry module requires personnel at each stage (sorting workshop, warehouse outbound, and delivery scheduling) to fill in basic business data such as delivery task progress, vehicle status, and personnel schedules on time and in a standardized manner, ensuring data real-time performance and accuracy. On the other hand, it connects to the existing order management system to automatically import order sorting data (including order number, cigarette specifications / quantity, delivery address, and delivery time requirements). Simultaneously, it connects to the vehicle GPS navigation system via API to collect real-time dynamic data such as vehicle location, speed, and estimated arrival time. Ultimately, this achieves multi-source data aggregation from manually entered data, system-imported data, and GPS dynamic data, solving the problems of single data sources and delayed manual data entry in traditional systems.

[0062] To achieve the above objectives, in one embodiment, the data acquisition module 10 may include: a basic business data acquisition unit 11, an order sorting data acquisition unit 12, a GPS data acquisition unit 13, a historical operation data acquisition unit 14, a data cleaning unit 15, and a data standardization unit 16.

[0063] The basic business data acquisition unit 11 is used to acquire manually filled basic business data; wherein, the basic business data includes: sorting progress, vehicle status, vehicle basic data and personnel scheduling data;

[0064] The order sorting data acquisition unit 12 is used to interface with the existing order management system (OMS) and import order sorting data; wherein, the order sorting data includes: order number, cigarette specifications, cigarette quantity, delivery address and delivery time requirements;

[0065] The GPS data acquisition unit 13 is used to access the GPS navigation system APP of the delivery vehicle and receive dynamic GPS data uploaded by the GPS navigation system APP; wherein, the dynamic GPS data includes: the real-time location of the vehicle, driving speed and estimated arrival time;

[0066] The historical operation data acquisition unit 14 is used to import historical operation data from the historical database; wherein, the historical operation data includes: historical delivery time, personnel route familiarity and empty load rate;

[0067] The data cleaning unit 15 is used to remove outliers from the logistics data;

[0068] The data standardization unit 16 is used to unify the various logistics data according to a preset format template.

[0069] In this embodiment, basic business data can be collected through a combination of manual entry and system-mandated verification. For example, sorting progress is updated hourly, and vehicle and personnel status are reported and uploaded in real time. The intelligent logistics management platform can have a built-in entry module with mandatory fields and format verification. For instance, vehicle status can only be selected as standby, en route, or under maintenance, allowing for real-time entry and upload by personnel. For order sorting data, it can be integrated with the existing Order Management System (OMS) to synchronize data via direct database connection. For dynamic GPS data, it can access navigation system APIs such as vehicle GPS to parse latitude, longitude, speed, and other fields, and upload dynamic GPS data in real time. For historical operational data, a data warehouse can be established to summarize and statistically analyze real-time data on a daily / weekly / monthly basis, and to periodically archive and upload the data.

[0070] For example, suppose a retailer in Wuzhong City submits a "T+0 same-day delivery" order (order number: WZ20250520001, cigarette specifications: 10 cartons of XXX cigarettes, delivery address: No. XXX, XXX Road, Litong District, Wuzhong City). The data collection process needs to complete the following: (1) The order management system automatically imports the order data (including latitude and longitude: N38.02°, E106.24°) into the data collection layer; (2) The sorting workshop staff marks "This order has been sorted (sorting time: 2025-05-20 08:30)" through the filling module; (3) The GPS of the delivery vehicle responsible for the area (license plate number: Ning C・12345) uploads its location in real time (at 08:35, it is located in xxx, Litong District, with a speed of 40km / h); (4) The system automatically associates the historical data of the vehicle (delivery on time rate in Litong District in the past 30 days is 98%, and the proficiency score is 92).

[0071] In one embodiment, the data cleaning unit 15 removes outliers using algorithms during data cleaning. For example, if the GPS location remains unchanged for 5 consecutive minutes and is outside the retailer's address range, it is marked as a device malfunction and manual verification is triggered. If the order data deviates from the sorted quantity by more than 5%, it is automatically returned to the sorting stage for re-verification. The data standardization unit 16 is used to unify the data format, for example, converting "delivery address" into the format of "province-city-district-detailed address + latitude and longitude" for easier subsequent map positioning; and calculating "personnel proficiency" as a standardized score of 0-100 based on "delivery frequency (40%) + on-time rate (30%) + customer satisfaction (30%)".

[0072] For the intelligent decision-making module 20, considering breaking through the traditional decision-making model of fixed system + manual judgment, a dynamic and differentiated flexible decision-making mechanism is constructed based on multi-dimensional data from the data collection layer, such as order timeliness requirements, regional delivery volume, and historical delivery efficiency. To achieve this objective, in one embodiment, the intelligent decision-making module 20 may further include: a flexible delivery system generation unit 21, a differentiated service suggestion generation unit 22, and a sorting sequence strategy generation unit 23;

[0073] The flexible delivery system generation unit 21 is used to collect order data and classify it according to three dimensions: timeliness requirements, regional type, and order fluctuation. The timeliness requirements include T+0 and T+1, and the regional types include core areas, suburbs, and towns. It is also used to dynamically generate flexible delivery systems for different scenarios based on the order volume fluctuation of each regional type.

[0074] The differentiated service suggestion generation unit 22 is used to generate differentiated service suggestions for each imported order, taking into account its timeliness requirements and delivery resources around the delivery address.

[0075] The sorting order strategy generation unit 23 is used to classify and sort orders based on route clustering and time priority.

[0076] In this embodiment, the flexible delivery system generation unit 21 considers automatically collecting order data within a certain period (such as weekly) and classifies and analyzes it according to "time-sensitivity requirements" (customer specifies same-day delivery T+0 / next-day delivery T+1), "regional distribution" (urban core area / suburbs / townships), and "order volume fluctuations" (peak season / off-season) to generate flexible delivery systems adapted to different scenarios. For example, when the order volume in the core area is at its peak, the "T+0 priority delivery" system is automatically triggered to prioritize the sorting and outbound delivery of same-day delivery orders; when the order volume in the suburbs is low, the "T+1 consolidated delivery" system is triggered, such as merging T+1 orders within 3 kilometers of the same area into the same vehicle for delivery to reduce the vehicle's empty-run rate; at the same time, the system can also be dynamically adjusted according to real-time data. In the event of a sudden surge in orders, the system parameters are automatically optimized to ensure that the delivery system matches the actual demand.

[0077] The differentiated service suggestion generation unit 22 is used to automatically generate differentiated service suggestions for each imported order, taking into account its timeliness requirements and delivery resources (vehicles en route, available personnel) around the delivery address. For example, if the delivery address is located in a core area and the order contains urgent needs, it is suggested to assign "T+0 service" and push it to the sorting stage for priority sorting; if the delivery address is located in a suburb and there are no urgent needs, it is suggested to assign "T+1 service", which can be combined with other orders in the same area for delivery. The service suggestions are accompanied by data support (such as estimated delivery time and resource occupation costs) for managers to refer to in decision-making, breaking through the limitations of the traditional "one-size-fits-all" service model and improving service accuracy.

[0078] Specifically, in one embodiment, the differentiated service suggestion generation unit 22 may include: a decision factor assignment subunit 221, an order score calculation subunit 222, and a service suggestion generation subunit 223;

[0079] The decision factor assignment subunit 221 is used to assign scores to different timeliness requirements, different customer levels, and different delivery address surrounding delivery resources.

[0080] The order score calculation subunit 222 is used to calculate the suggested rule score of the current order based on the timeliness requirements of the current order, customer level and delivery resources around the delivery address, as well as the score assigned by the decision factor assignment subunit 221.

[0081] The service suggestion generation subunit 223 is used to calculate the suggestion rule score calculated by subunit 222 based on the order score and match the corresponding differentiated service suggestions.

[0082] In this embodiment, an order score can be calculated for each order. Factors may include: timeliness requirements (60 points for T+0, 20 points for T+1), customer level (30 points for core customers, 10 points for ordinary customers), and available surrounding resources (10 points for available vehicles within 3 kilometers, 0 points for none), with a total score of 100 points. If the score is ≥80 points (e.g., T+0 core customers with available vehicles nearby), "T+0 dedicated delivery" is recommended, and the order is simultaneously pushed to the priority sorting queue in the sorting workshop; if the score is 40-79 points (e.g., T+1 ordinary customers with no available vehicles nearby), "T+1 combined delivery" is recommended, and other T+1 orders in the same area are automatically matched; if the score is <40 points (e.g., T+1 temporary customers with <5 orders), "next day combined delivery" is recommended to avoid wasting transportation capacity on small-batch deliveries per vehicle.

[0083] For the sorting sequence strategy generation unit 23, it is considered that it automatically optimizes the sorting sequence based on real-time order sorting data and delivery task arrangement: for example, it sorts orders on the same delivery route and orders of the same time type (T+0) together to reduce the number of times goods are handled during subsequent loading; at the same time, it adjusts the order according to the logic of "orders corresponding to vehicles that depart first are sorted first" based on the vehicle departure time to avoid wasting time waiting for vehicles to sort and improve the efficiency of sorting and delivery.

[0084] Specifically, when optimizing the sorting order, the sorting is based on route clustering and time-priority. For example, all orders (including T+0 and T+1) on the XXX route in Litong District are sorted together, with T+0 orders placed before T+1 orders to reduce the number of times goods are handled during loading. This is also combined with vehicle departure time (e.g., orders from vehicles departing at 09:00 are prioritized over orders from vehicles departing at 09:30). Furthermore, when matching routes with personnel, matching can be achieved based on a personnel-route proficiency database. For example, the XXX route in Litong District (containing 15 retailers, 8 of which are located in narrow alleys) is preferentially assigned to deliveryman Zhang San, who has made 120 deliveries and has a proficiency score of 95. If Zhang San is on leave, Li Si, with the second highest proficiency score (90), is automatically assigned to fill in.

[0085] The flexible execution module 30 is used to achieve precise allocation of vehicles, personnel, routes, etc. In one embodiment, the flexible execution module 30 may include: a load calculation unit 31, a route allocation unit 32, and a personnel cycle management unit 33;

[0086] The loading capacity calculation unit 31 is used to calculate the optimal loading capacity of each vehicle based on the vehicle basic data in the order sorting data and basic business data.

[0087] The route allocation unit 32 is used to construct a personnel-route familiarity database based on historical delivery time and personnel route familiarity in historical operation data, and to match delivery personnel with high route familiarity from the personnel-route familiarity database in combination with the order volume and distance of each route, so that the corresponding delivery personnel can perform loading and delivery tasks.

[0088] The personnel cycle management unit 33 is used to flexibly adjust the delivery cycle of delivery personnel based on the personnel scheduling data in the basic business data and the order fluctuation in the statistical order data.

[0089] In this embodiment, the loading calculation unit 31 calculates the optimal loading capacity for each vehicle based on order sorting data such as cigarette specifications, single box weight, and order quantity, as well as vehicle basic data such as approved load capacity and cargo compartment volume. This avoids problems such as overloading violations, insufficient loading, and empty runs that can occur due to manual calculation. For example, before loading, the driver can view the optimal loading list generated by the system (including the stacking position and quantity of each type of cigarette) through a mobile app. For instance, the 10 cartons of XXX cigarettes for order "WZ20240520001" should be placed on the left side of the middle layer of the cargo compartment to avoid compression. The app tracks the loaded quantity in real time. If the optimal loading capacity is exceeded, such as when 6500 cartons are planned but 6600 cartons have actually been loaded, the app automatically alerts the driver to the risk of overloading. In addition, staggered loading times can also be considered. If the system follows the high-temperature staggered delivery strategy, it will divide the loading batches into 7:00-8:00 (vehicles for orders in the core area) and 17:00-18:00 (vehicles for orders in the suburbs). Zhang San's vehicle mentioned above belongs to the core area order and needs to be loaded between 7:00-8:00. The APP will remind you at 7:50 that there are 10 minutes left to complete the loading.

[0090] For route allocation unit 32, the route allocation function is built on a low-code platform. This allows for flexible adjustment of allocation rules without complex code development. The specific logic is as follows: First, personnel information (including historical delivery route records, delivery completion efficiency of each route, and customer satisfaction rating) is obtained from the data collection module 10 to establish a "personnel-route proficiency" database. For example, if employee A has made 100 deliveries on route A with an average delivery time of 2 hours, their proficiency is marked as "high." Second, based on the regional division of real-time delivery tasks, such as routes A, B, and C, and considering the order volume and distance of each route, personnel with high proficiency are automatically matched. For instance, if route A has a large order volume and includes multiple complex intersections, it is prioritized for allocation to employees with "high proficiency on route A." If new routes need to be added or the route range adjusted, managers can modify the allocation rules through the low-code platform's visual interface (drag and drop components, set parameters) without the need for technical personnel intervention. This achieves flexible and accurate route allocation, improving delivery efficiency and personnel suitability.

[0091] For the personnel cycle management unit 33, it is used by managers to flexibly set and adjust the delivery cycle of delivery personnel based on the regional delivery volume fluctuation data and personnel scheduling needs of the data collection module 10. For example, standardized cycle patterns such as "4+1" (4 days of delivery + 1 day of rest) and "3+2" (3 days of delivery + 2 days of market visits) for county bureaus can be preset and applied directly. When the delivery volume in a certain area surges (such as before a holiday), the system automatically issues an early warning and suggests a temporary adjustment to the cycle, such as changing "4+1" to "5+0" to temporarily increase the number of delivery days. When personnel take leave or are transferred, the system automatically assigns replacement personnel based on the "personnel-route proficiency" database, prioritizing personnel with high proficiency on the same route, and simultaneously updates the delivery plan to the real-time monitoring module 40 to ensure that personnel adjustments do not affect the delivery progress and enhance the adaptability of the delivery team.

[0092] In addition, in one embodiment, the flexible execution module 30 further includes an emergency order processing unit 34;

[0093] The emergency order processing unit 34 is used to push the information of the emergency order to the delivery personnel who meet the time requirements when the delivery personnel add an emergency order while performing a delivery task; and to adjust the navigation route to prioritize the delivery address of the emergency order when the delivery personnel accept the emergency order.

[0094] In this embodiment, if a new "T+0 emergency order" is added during delivery, such as a retailer urgently needing goods, the order information can be pushed through the APP. The driver can confirm whether to accept the order with one click based on the remaining loading space of the vehicle. If accepted, the APP will automatically adjust the navigation route and prioritize going to the address of the retailer with the emergency order.

[0095] In one embodiment, the flexible execution module 30 may further include a sorting anomaly handling unit 35; the sorting anomaly handling unit 35 is used to trigger an anomaly prompt when the cigarette inventory of a certain order is insufficient, and synchronize the insufficient inventory information to the intelligent decision module 20, so that the intelligent decision module 20 can generate a suggestion of partial delivery + remaining replenishment; at the same time, when executing the delivery task, the existing inventory is sorted and loaded first, and the remaining cigarettes are coordinated with the warehouse for replenishment and then delivered according to T+N+n; where T+N is the time limit corresponding to the current order, and n is the replenishment cycle.

[0096] In this embodiment, if the cigarette inventory for a certain order is insufficient, such as only 5 cartons of XXX cigarettes remaining and the order requires 10 cartons, an abnormality prompt will be immediately triggered and synchronously fed back to the intelligent decision-making module 20. The intelligent decision-making module 20 will automatically generate a partial delivery + remaining replenishment suggestion. The flexible execution module 30 will sort 5 cartons according to the suggestion first, and then coordinate with the warehouse to replenish the remaining 5 cartons before delivery on T+1.

[0097] The real-time monitoring module 40 can display dynamic information of all delivery tasks in transit in real time through the system's large screen or mobile dashboard: based on a map, it marks the real-time location of each delivery vehicle (updated synchronously via GPS data) and uses different colors to distinguish task status (green = normal delivery, yellow = about to exceed timeout, red = abnormal stoppage); it also lists and displays information such as the timeliness type (T+0 / T+1), responsible personnel, and loading volume of each task, and supports clicking on any task to view details; when an anomaly occurs (such as vehicle delay, change of delivery address), the module automatically pops up a warning and displays the cause of the anomaly and historical processing records, which facilitates managers to quickly trace the problem and formulate response plans, filling the technical gap of traditional delivery "black box operation" and the difficulty in timely detection of anomalies. Specifically, in one embodiment, the real-time monitoring module 40 may include: a delivery progress display unit 41 and an anomaly warning unit 42;

[0098] The delivery progress display unit 41 is used to mark the real-time location of each delivery vehicle based on a map and dynamic GPS data of each delivery vehicle, and to distinguish the order status with different colors; wherein, the order status includes: normal delivery, about to time out, and abnormal stoppage;

[0099] The abnormality warning unit 42 is used to issue a warning through a large screen pop-up window when the delivery task is abnormal, and at the same time, it displays the cause of the abnormality and historical processing records.

[0100] In this embodiment, the delivery progress display unit 41 is based on an electronic map, marking the real-time location of all delivery vehicles and using different colors to distinguish order statuses. For example, green indicates normal delivery, such as Zhang San's vehicle being "delivering" at 08:50, marked in green; yellow indicates impending timeout, such as a T+0 order requiring delivery before 12:00, where the vehicle is still 5 kilometers away at 11:40, marked in yellow; red indicates abnormal stoppage, such as a vehicle whose GPS has not changed for 10 consecutive minutes and is not at the retailer's address, marked in red. In addition, an order details list can be displayed simultaneously, specifically including order number, time type, responsible personnel, current status, estimated arrival time, and actual arrival time (delivered orders), supporting filtering by status and region, such as filtering for T+0 orders in the Litong District. Simultaneously, core operational indicators are displayed at the bottom of the large screen and updated in real time: specifically including delivery completion rate, T+0 order on-time rate, and vehicle empty load rate.

[0101] Anomaly warning unit 42 considers triggering an anomaly warning when at least one of the following occurs:

[0102] Timeliness warning: The remaining delivery time of a T+0 order is less than the estimated travel time plus the first preset time. For example, if an order is due at 12:00, and it is currently 11:30, the estimated travel time is 25 minutes, and the remaining time is 30 minutes, which is less than 25 + 30 minutes, triggering a warning.

[0103] Location alert: The vehicle's GPS remains unchanged for a second preset time period and is not located at the retailer's address or server, or the delivery address has changed; for example, the second preset time period is 20 minutes.

[0104] Loading warning: The deviation between the actual loading volume and the optimal loading volume exceeds the first preset threshold. For example, if the plan is to load 6,500 units but the actual loading volume is 7,200 units, the deviation is 10.8% > 10%, triggering a warning.

[0105] When issuing an alert, the alert information can be displayed on a large visual screen, annotating the abnormal order / vehicle number, such as: "Order WZ20240520002 is about to time out." SMS / APP notifications can also be sent to the dispatcher and driver responsible for the order. For example, a notification could be sent to Zhang San: "Your order WZ20240520002 is about to time out; please expedite delivery." Furthermore, the system automatically records alert events, such as trigger time, cause, and handling result, creating an abnormal alert ledger for subsequent analysis.

[0106] In addition, the delivery progress display unit 41 also allows managers to enter keywords such as order number, delivery vehicle number or delivery area through the system interface (computer / mobile terminal) to query the entire process progress of the corresponding delivery task with one click. This includes key node information such as "sorting completion status (sorted / pending sorting), warehouse outbound time, vehicle departure time, current location, estimated delivery time, and receipt status". The query results are presented in the form of charts (progress bars, timelines), which are intuitive and clear, solving the cumbersome problem of traditional queries that require checking paper documents one by one or switching between multiple systems.

[0107] The feedback optimization module 50 can automatically analyze the causes of deviations based on abnormal feedback and execution data (such as the deviation between actual and expected delivery time, and load utilization rate), such as unreasonable route planning or insufficient personnel proficiency. It then adjusts subsequent decision parameters, such as optimizing the route plan or prioritizing the allocation of highly skilled personnel. Simultaneously, the optimized decision plan is pushed back to the flexible execution module 30, forming a closed loop of dynamic optimization. Specifically, in one embodiment, the feedback optimization module 50 may include a deviation analysis unit 51 and a strategy optimization unit 52.

[0108] The deviation analysis unit 51 is used to perform deviation analysis on the timeliness, resource optimization accuracy, and service satisfaction of the current delivery task based on the execution anomaly data.

[0109] The strategy optimization unit 52 is used to adjust the subsequent decision parameters based on the analysis results obtained by the deviation analysis unit 51, and push the optimized and adjusted decision scheme to the flexible execution module 30 for use in subsequent delivery tasks.

[0110] In this embodiment, the first step is to establish an order file, which associates and stores the collected data (order information), decision data (service suggestions), execution data (delivery time), and feedback data (satisfaction) for each order. For example, the order file for "WZ20240520001" includes: collected data: order number, cigarette specifications, latitude and longitude; decision data: service priority score of 100 points, suggested T+0 delivery; execution data: sorting time 08:40, loading time 07:50, delivery time 09:23; feedback data: retailer satisfaction score of 5 points, no complaints.

[0111] Furthermore, based on the archived data, decision-making optimization analyses are conducted to assess timeliness, resource optimization accuracy, and service satisfaction. For timeliness analysis, the estimated delivery time can be compared with the actual delivery time to analyze the reasons for the discrepancy. For example, if a route repeatedly experiences actual delivery times 10-15 minutes later than estimated, and the main reason for the discrepancy is morning rush hour congestion, then the estimated travel time for that route is underestimated. Similarly, if a driver's orders repeatedly exceed the time limit, and the main reason for the discrepancy is unfamiliarity with the routes, then the driver's proficiency with some routes is insufficient. For resource optimization accuracy analysis, data such as load utilization and empty load rate can be analyzed. For example, if the empty load rate of vehicles in suburban areas consistently exceeds 20% (target 18%), and the main reason is "no orders on the return trip," then the T+1 consolidated delivery capacity in suburban areas is insufficient. If the actual load of a vehicle consistently falls below the optimal load capacity by more than 10%, and the main reason is "scattered orders," then the order clustering logic for that area needs optimization. For service satisfaction analysis, data such as customer satisfaction and complaint content can be analyzed. For example, if core customers' satisfaction with "T+0 delivery" is below 90% (target 95%), and most complaints are about "sorting delays", then it is determined that "the priority of sorting T+0 orders needs to be further improved"; if rural retailers complain about "excessively long delivery cycles", and data shows that "the average delivery cycle in rural areas is 5 days" (3 days in core areas), then it is determined that "the delivery cycle in rural areas needs to be shortened".

[0112] When optimizing decisions, the decision optimization unit can consider improvements in decision parameters, rule models, and process flows. For example, regarding decision parameters, to address the issue of "short estimated travel time on XX Road in Litong District," the estimated travel time coefficient for this route during the morning peak (7:30-9:00) can be adjusted from 1.0 to 1.2. For instance, if the original estimated time was 20 minutes, the adjusted time would be 24 minutes, ensuring more accurate estimated times. To address the issue of "high empty load rate in suburban areas," the merging range for T+1 orders in suburban areas can be expanded from within 3 kilometers to within 5 kilometers, reducing empty return trips. Regarding rule models, to address the issue of "Li Si's insufficient route familiarity," the "personnel-route matching model" can be updated, increasing the weight of "delivery on-time rate in the past month." If Li Si's on-time rate for a certain route is below 90%, the allocation frequency for that route will be reduced. To address the issue of "T+0 sorting delay," the "sorting priority rule" can be optimized, advancing the sorting deadline for T+0 orders by 30 minutes. Regarding the delivery process, to address the issue of "long delivery cycles in rural areas," the delivery cycle for some rural routes can be adjusted from twice a week to three times a week, and an additional emergency vehicle can be added to handle urgent orders in rural areas. To address the issue of "insufficient order clustering logic," the "order clustering algorithm" can be optimized by using a two-factor clustering approach of "retailer address latitude and longitude + order volume" to ensure that the load capacity of each vehicle is closer to the optimal value.

[0113] In summary, the cigarette logistics management system based on intelligent scheduling and flexible delivery provided by this invention can have at least the following beneficial effects:

[0114] (1) Improve delivery efficiency and resource utilization: Through intelligent route allocation, load optimization and delivery cycle compression, the daily average transportation capacity efficiency has been significantly improved, and the average load per vehicle has increased to more than 6,000 routes. The problems of insufficient loading rate and idle capacity during the off-season have been effectively alleviated, the resource utilization rate has approached the industry average level, and the unit delivery cost has been reduced.

[0115] (2) Reduce operating costs and ineffective losses: The proportion of empty mileage of vehicles (especially empty return trips) is significantly reduced, reducing fuel consumption (e.g., shortening vehicle return time by 1 hour / day) and carbon emissions, which is in line with the development trend of green logistics. By avoiding peak loading times (e.g., "high-temperature off-peak loading"), morning peak congestion is avoided, waiting time is shortened, and operating costs are indirectly reduced.

[0116] (3) Optimize the scientific nature of route planning and scheduling: Change the traditional route planning method that relies on manual experience, and reduce the overlap of delivery routes between urban and rural areas and reduce invalid mileage through electronic maps, intelligent matching models and dynamic optimization algorithms. Achieve automatic and intelligent matching of "personnel-route" during shift changes, covering 44 routes, ensuring the risk resistance and stability of the delivery network, and improving scheduling efficiency and fairness.

[0117] (4) Enhance demand response flexibility and service quality: Construct an emergency delivery mechanism and a "T+0" tiered service standard to achieve rapid response and dedicated delivery for emergency orders, thereby improving the satisfaction of core customers. Through a flexible delivery model, effectively cope with fluctuations in loading rates exceeding 20% ​​during peak and off-peak seasons, and avoid delivery delays during peak seasons and resource waste during off-peak seasons.

[0118] (5) Enhance management and decision-making capabilities: The visualized delivery screen enables real-time monitoring of vehicle, progress, and retailer information, improving scheduling transparency and management sophistication. Historical trajectory records and data analysis provide data support for route optimization, personnel assessment, and process improvement, assisting in scientific decision-making.

[0119] (6) Improve the working environment and labor intensity of front-line workers: strategies such as "high temperature staggered delivery" avoid operational safety risks and shorten the working time of front-line personnel. For example, the return time is shortened, which reduces labor intensity.

[0120] This specification also provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the methods in any of the embodiments of the specification.

[0121] This specification also provides a computing device, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, it implements the method in any of the embodiments of the specification.

[0122] The modules or units in the device of this invention can be merged, divided, and deleted according to actual needs. The above-disclosed embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of the invention. Those skilled in the art will understand that implementing all or part of the processes of the above embodiments and making equivalent changes according to the claims of this invention still fall within the scope of the invention.

Claims

1. A cigarette logistics management system based on intelligent scheduling and flexible delivery, characterized in that, The system includes: a data acquisition module, an intelligent decision-making module, a flexible execution module, a real-time monitoring module, and a feedback optimization module; The data acquisition module is used to collect logistics data filled in manually and imported by the system in real time, and after preprocessing the logistics data, transmit the preprocessed logistics data to the intelligent decision-making module; wherein, the logistics data includes: basic business data, order sorting data, dynamic GPS data and historical operation data; The intelligent decision-making module is used to generate a decision plan based on the preprocessed logistics data, which includes a flexible delivery system, differentiated service suggestions, and sorting sequence strategy, and transmit the decision plan to the flexible execution module and the real-time monitoring module. The flexible execution module is used to calculate the load, allocate routes, and manage personnel cycles according to the decision-making scheme, to complete delivery preparation and execute delivery tasks; and to transmit the execution data generated during the flexible execution process to the real-time monitoring module in real time. The real-time monitoring module is used to visualize the delivery progress based on the execution data, issue an alert when an abnormality is detected in the delivery task execution, and transmit the corresponding execution abnormality data to the feedback optimization module. The feedback optimization module is used to analyze the cause of the abnormality based on the execution abnormality data, adjust the decision parameters to form an optimized decision plan, and transmit the optimized decision plan to the flexible execution module for use in the subsequent logistics process.

2. The cigarette logistics management system based on intelligent scheduling and flexible delivery according to claim 1, characterized in that, The data acquisition module includes: a basic business data acquisition unit, an order sorting data acquisition unit, a GPS data acquisition unit, a historical operation data acquisition unit, a data cleaning unit, and a data standardization unit; The basic business data acquisition unit is used to acquire manually entered basic business data; wherein, the basic business data includes: sorting progress, vehicle status, vehicle basic data, and personnel scheduling data; The order sorting data acquisition unit is used to interface with the existing order management system (OMS) and import order sorting data; wherein, the order sorting data includes: order number, cigarette specifications, cigarette quantity, delivery address and delivery time requirements; The GPS data acquisition unit is used to access the GPS navigation system APP of the delivery vehicle and receive dynamic GPS data uploaded by the GPS navigation system APP; wherein, the dynamic GPS data includes: the vehicle's real-time location, driving speed and estimated arrival time; The historical operation data acquisition unit is used to import historical operation data from the historical database; wherein, the historical operation data includes: historical delivery time, personnel route familiarity, and empty load rate; The data cleaning unit is used to remove outliers from the logistics data; The data standardization unit is used to unify various logistics data according to a preset format template.

3. The cigarette logistics management system based on intelligent scheduling and flexible delivery according to claim 1, characterized in that, The intelligent decision-making module includes: a flexible delivery system generation unit, a differentiated service suggestion generation unit, and a sorting sequence strategy generation unit; The flexible delivery system generation unit is used to collect order data and classify it according to three dimensions: timeliness requirements, regional type, and order fluctuation. The timeliness requirements include T+0 and T+1, and the regional types include core areas, suburbs, and towns. It is also used to dynamically generate flexible delivery systems for different scenarios based on the order volume fluctuation of each regional type. The differentiated service suggestion generation unit is used to generate differentiated service suggestions for each imported order, taking into account its timeliness requirements and delivery resources around the delivery address. The sorting order strategy generation unit is used to classify and sort orders based on route clustering and time priority.

4. The cigarette logistics management system based on intelligent scheduling and flexible delivery according to claim 3, characterized in that, The differentiated service suggestion generation unit includes: a decision factor assignment subunit, an order score calculation subunit, and a service suggestion generation subunit; The decision factor assignment subunit is used to assign scores to different timeliness requirements, different customer levels, and different delivery address surrounding delivery resources. The order score calculation subunit is used to calculate the suggested rule score of the current order based on the timeliness requirements of the current order, customer level, delivery resources around the delivery address, and the score assigned by the decision factor assignment subunit. The service suggestion generation sub-unit is used to calculate the suggestion rule score calculated by the sub-unit based on the order score, and match the corresponding differentiated service suggestions.

5. The cigarette logistics management system based on intelligent scheduling and flexible distribution according to claim 1, characterized in that, The flexible execution module includes: a load calculation unit, a route allocation unit, and a personnel cycle management unit; The loading capacity calculation unit is used to calculate the optimal loading capacity of each vehicle based on the vehicle basic data in the order sorting data and basic business data. The route allocation unit is used to construct a personnel-route familiarity database based on historical delivery time and personnel route familiarity in historical operation data, and to match delivery personnel with high route familiarity from the personnel-route familiarity database in combination with the order volume and distance of each route, so that the corresponding delivery personnel can perform loading and delivery tasks. The personnel cycle management unit is used to flexibly adjust the delivery cycle of delivery personnel based on personnel scheduling data in basic business data and order fluctuations in statistical order data.

6. The cigarette logistics management system based on intelligent scheduling and flexible delivery according to claim 5, characterized in that, The flexible execution module also includes: an emergency order processing unit; The emergency order processing unit is used to push the information of an emergency order to the delivery personnel who meet the timeliness requirements when an emergency order is added during the delivery personnel's delivery task; and to adjust the navigation route to prioritize the delivery address of the emergency order when the delivery personnel accept the emergency order.

7. The cigarette logistics management system based on intelligent scheduling and flexible delivery according to claim 5, characterized in that, The flexible execution module also includes: a sorting anomaly handling unit; The sorting anomaly handling unit is used to trigger an anomaly alert when the cigarette inventory of a certain order is insufficient, and synchronize the insufficient inventory information to the intelligent decision-making module so that the intelligent decision-making module can generate a suggestion of partial delivery + remaining replenishment; at the same time, when executing the delivery task, the existing inventory is sorted and loaded first, and the remaining cigarettes are coordinated with the warehouse for replenishment and then delivered according to T+N+n; where T+N is the time limit corresponding to the current order, and n is the replenishment cycle.

8. The cigarette logistics management system based on intelligent scheduling and flexible delivery according to claim 1, characterized in that, The real-time monitoring module includes: a delivery progress display unit and an anomaly warning unit; The delivery progress display unit is used to mark the real-time location of each delivery vehicle based on a map and dynamic GPS data, and to distinguish the order status with different colors; wherein, the order status includes: normal delivery, about to time out, and abnormal stoppage; The anomaly warning unit is used to issue a warning via a large-screen pop-up window when an abnormality occurs during the execution of a delivery task, and simultaneously displays the cause of the abnormality and historical processing records.

9. The cigarette logistics management system based on intelligent scheduling and flexible delivery according to claim 8, characterized in that, The triggering conditions for the delivery task execution exception include at least one of the following: Delivery time warning: Remaining delivery time for T+0 orders < Estimated travel time + First preset time; Location alert: The vehicle's GPS remains unchanged for a second consecutive preset period of time, and is not located at the retailer's address or server, or the delivery address has changed; Loading warning: The deviation between the actual loading amount and the optimal loading amount exceeds the first preset threshold.

10. The cigarette logistics management system based on intelligent scheduling and flexible delivery according to claim 1, characterized in that, The feedback optimization module includes: a deviation analysis unit and a strategy optimization unit; The deviation analysis unit is used to perform deviation analysis on the timeliness, resource optimization accuracy, and service satisfaction of the current delivery task based on abnormal execution data. The strategy optimization unit is used to adjust the subsequent decision parameters based on the analysis results obtained by the deviation analysis unit, and push the optimized and adjusted decision scheme to the flexible execution module for use in subsequent delivery tasks.

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