Distribution path planning method and device, program product, equipment and storage medium

Abstract

The invention discloses a distribution path planning method and device, a program product, equipment and a storage medium, and relates to the technical field of logistics, and the method comprises the steps: obtaining order information of new and old commodity replacement, generating a distribution task and a recovery task, and extracting a vehicle estimated driving mileage, a vehicle loading capacity and an estimated service duration; determining that the loading feasibility of the vehicle in each stage is feasible as a first constraint condition, determining the service duration of the mounting stage, the service duration of the dismounting stage and the service duration of the carrying stage based on the estimated service duration, and taking that the service duration does not exceed the customer reservation service time window as a second constraint condition; and based on the first constraint condition, the second constraint condition and the target optimization function, obtaining a distribution path planning scheme of new and old commodity replacement. The purpose of improving the execution reliability of distribution path planning and the logistics collaboration level and the user experience in a new and old commodity replacement business scene is achieved.

Description

Technical Field

[0001] This application relates to the field of logistics and distribution technology, and in particular to a distribution route planning method, apparatus, program product, equipment and storage medium. Background Technology

[0002] Currently, the trade-in of new and used goods is widely used in industries such as home appliances and furniture. However, the existing logistics system typically operates two separate networks for new equipment delivery and used equipment recycling, resulting in high vehicle empty-run rates, high transportation costs, route duplication, and significant resource waste. Furthermore, while existing vehicle routing optimization and its derivative models can achieve route planning in general scenarios, they fail to consider the characteristics of trade-in operations, such as delivery and recycling tasks being located at the same address, having a fixed sequence, dynamically changing vehicle capacity, and uncertain service times. This leads to a disconnect between optimization results and actual execution, making it difficult to achieve the required efficient intermodal transport. Therefore, an optimization method capable of achieving efficient forward and reverse intermodal transport is urgently needed. Summary of the Invention

[0003] In view of the above problems, this application provides a delivery route planning method, apparatus, program product, equipment, and storage medium to improve the execution reliability of delivery route planning and the level of logistics coordination and user experience in the scenario of exchanging old and new goods. The specific solution is as follows:

[0004] The first aspect of this application provides a delivery route planning method, including: obtaining order information for the replacement of old and new goods, and generating delivery tasks and recycling tasks based on the order information;

[0005] Extract the estimated mileage, vehicle loading capacity, and estimated service duration from delivery and recycling tasks respectively;

[0006] The loading status of the vehicle at each stage of the task is calculated based on the vehicle's loading capacity. The loading feasibility of the vehicle at each stage is judged according to the loading status and the preset loading stability judgment method. The first constraint is that the loading feasibility of the vehicle at each stage is feasible. The service time of the installation stage, the service time of the dismantling stage and the service time of the transportation stage are determined by the interval estimation method based on the estimated service time. The second constraint is that the sum of the service time of the installation stage, the service time of the dismantling stage and the service time of the transportation stage does not exceed the customer's scheduled service time window.

[0007] Based on the first constraint, the second constraint, and the objective optimization function, a delivery route planning scheme for replacing old and new goods is obtained. The objective optimization function is an optimization function constructed based on the estimated vehicle mileage, the delayed service penalty factor, and the transfer service penalty factor, with the objective of maximizing the delivery efficiency of the delivery route for replacing old and new goods. Alternatively, the objective optimization function is an optimization function constructed based on the estimated vehicle mileage, the delayed service penalty factor, and the transfer service penalty factor, with the objective of minimizing the delivery cost of the delivery route for replacing old and new goods.

[0008] Optionally, obtain order information for exchanging old and new goods, and generate delivery and recycling tasks based on the order information, including:

[0009] Extract the address information from the order information and parse the address information into latitude and longitude coordinates;

[0010] Extract the model numbers of the delivered and recycled appliances from the order information. Determine the installation difficulty level of the delivered appliance based on its model number, and determine the disassembly difficulty level of the recycled appliance based on its model number.

[0011] The estimated service time for delivery tasks is determined based on the installation difficulty level, and the estimated service time for recycling tasks is determined based on the disassembly difficulty level.

[0012] The estimated mileage of the vehicle is determined based on latitude and longitude coordinates, and delivery and collection tasks are generated based on the estimated mileage, vehicle loading capacity and estimated service duration.

[0013] Optionally, obtain order information for exchanging old and new goods, and generate delivery and recycling tasks based on the order information, including:

[0014] If the number of orders for exchanging old and new goods exceeds the preset number of orders, the geographic location information of all orders for exchanging old and new goods is extracted, and the geographic location information is clustered according to a predefined geographic grid. All orders are divided into multiple task clusters, and delivery and recycling tasks are generated for each task cluster based on the order information of the orders included in each task cluster. Each task cluster includes multiple orders for exchanging old and new goods.

[0015] If there is no preset order quantity for the exchange of new and old goods, the order information of all orders for the exchange of new and old goods is obtained, and delivery and recycling tasks are generated based on the order information.

[0016] Optionally, the method also includes:

[0017] Obtain the maximum value of the estimated installation time range for the current customer location as the pessimistic duration;

[0018] The estimated time for the vehicle to reach the next customer point is obtained based on the pessimistic duration, the time the vehicle takes to reach the current customer point, and the travel time the vehicle takes to reach the next customer point.

[0019] The estimated time for the vehicle to arrive at the next customer point is sent to the device used by the driver.

[0020] Optionally, the loading feasibility of the vehicle at each stage can be determined based on the loading status and a preset loading stability assessment method, including:

[0021] Calculate the center of gravity offset index of the vehicle load at the current stage and compare it with a preset safety threshold. If the center of gravity offset index exceeds the safety threshold, the loading of the vehicle at the current stage is determined to be infeasible. The center of gravity offset index is used to characterize the change of the center of gravity of the cargo during the loading and unloading process.

[0022] Obtain the stacking method of the vehicle load in the current stage, determine whether the stacking method satisfies the stacking constraint rules, and if not, determine that the loading of the vehicle in the current stage is infeasible.

[0023] If the center of gravity offset index does not exceed the safety threshold and the stacking method meets the stacking constraint rules, then the loading feasibility of the vehicle at the current stage is determined to be feasible.

[0024] Optionally, the delayed service penalty factor is the penalty for exceeding the customer's scheduled service time window; the reassignment service penalty factor is the penalty for reassigning a task to another vehicle.

[0025] A second aspect of this application provides a delivery route planning device, comprising:

[0026] The data acquisition module is used to acquire order information for the replacement of old and new goods, and to generate delivery and recycling tasks based on the order information;

[0027] The data extraction module is used to extract the estimated vehicle mileage, vehicle loading capacity, and estimated service duration from the delivery and recycling tasks, respectively.

[0028] The constraint module is used to calculate the loading status of the vehicle at each stage of the task based on the vehicle's loading capacity, and to determine the loading feasibility of the vehicle at each stage according to the loading status and the preset loading stability judgment method. The first constraint condition is that the loading feasibility of the vehicle at each stage is feasible. The service time of the installation stage, the service time of the dismantling stage, and the service time of the transportation stage are determined by the interval estimation method based on the estimated service time. The second constraint condition is that the sum of the service time of the installation stage, the service time of the dismantling stage, and the service time of the transportation stage does not exceed the customer's scheduled service time window.

[0029] The route planning module is used to obtain a delivery route planning scheme for replacing old and new goods based on the first constraint, the second constraint, and the objective optimization function. The objective optimization function is an optimization function constructed based on the estimated vehicle mileage, the delayed service penalty factor, and the transfer service penalty factor, with the objective of maximizing the delivery efficiency of the delivery route for replacing old and new goods. Alternatively, the objective optimization function is an optimization function constructed based on the estimated vehicle mileage, the delayed service penalty factor, and the transfer service penalty factor, with the objective of minimizing the delivery cost of the delivery route for replacing old and new goods.

[0030] A third aspect of this application provides a computer program product including computer-readable instructions that, when executed on an electronic device, cause the electronic device to implement the delivery route planning method of the first aspect or any implementation thereof.

[0031] A fourth aspect of this application provides an electronic device, including at least one processor and a memory connected to the processor, wherein:

[0032] The memory is used to store computer programs;

[0033] The processor is used to execute the computer program so that the electronic device can implement the delivery route planning method of the first aspect or any implementation thereof.

[0034] The fifth aspect of this application provides a computer storage medium carrying one or more computer programs that, when executed by an electronic device, enable the electronic device to perform the delivery route planning method described in the first aspect or any implementation thereof.

[0035] By employing the aforementioned technical solution, this application constrains the target optimization problem by integrating uncertainties such as estimated vehicle mileage, vehicle loading capacity, and estimated service duration into the delivery and recycling tasks during the replacement of old and new goods. This yields a delivery route planning scheme for the replacement of old and new goods. Compared to conventional route optimization methods in existing technologies that can only achieve delivery or recycling tasks, this application introduces an interval estimation method to estimate the installation, disassembly, and handling operation times, mitigating installation delays caused by fluctuations in on-site operations. Furthermore, based on vehicle loading capacity and loading stability rules, it enables the assessment of vehicle loading feasibility during the replacement of old and new goods, thereby improving the reliability of route planning execution and the level of logistics collaboration and user experience in the replacement of old and new goods business scenario. Attached Figure Description

[0036] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0037] Figure 1 A flowchart illustrating a delivery route planning method provided in this application;

[0038] Figure 2 A schematic diagram of the structure of a delivery route planning device provided in this application;

[0039] Figure 3 A schematic block diagram of an electronic device provided in this application. Detailed Implementation

[0040] The embodiments of this application are described below with reference to the accompanying drawings. The terminology used in the implementation section of this application is for explaining specific embodiments only and is not intended to limit the scope of this application.

[0041] The embodiments of this application will now be described with reference to the accompanying drawings. Those skilled in the art will recognize that, with technological advancements and the emergence of new scenarios, the technical solutions provided in the embodiments of this application are equally applicable to similar technical problems.

[0042] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms are interchangeable where appropriate; this is merely a way of distinguishing objects with the same attributes in the embodiments of this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of elements is not necessarily limited to those elements, but may include other elements not explicitly listed or inherent to those processes, methods, products, or apparatuses.

[0043] This application provides a delivery route planning method, such as Figure 1 The method may include the following steps:

[0044] S101: Obtain order information for exchanging old and new goods, and generate delivery and recycling tasks based on the order information.

[0045] Optionally, in this embodiment, the order information is a data set submitted by the user in the new / old goods exchange business scenario, containing delivery and recycling requirements. The order information may include basic fields such as: target service address, delivery appliance model, recycling appliance model, scheduled service time period, and customer contact information. Delivery and recycling tasks are generated based on this information. Through the above steps, the process of constructing delivery and recycling tasks from new / old goods exchange order information can be completed, forming the basic input data for subsequent path planning optimization algorithms.

[0046] S102: Extract the estimated mileage, vehicle loading capacity, and estimated service duration from the delivery and recycling tasks, respectively.

[0047] Optionally, in this embodiment, the estimated vehicle mileage can be extracted from the delivery task and the recycling task respectively. The estimated vehicle mileage is the distance or time value that the vehicle may travel, calculated by the system based on the latitude and longitude coordinates of the order, the road network model, and the vehicle's current location (e.g., warehouse or distribution center location). In this embodiment, the estimated vehicle mileage for the delivery task and the estimated vehicle mileage for the recycling task can be generated by calling the GIS interface or internal map service through the address resolution program, and combining road condition data, major traffic rules, and common driving routes. This represents the possible transportation path length consumed by the vehicle from its current location to the order's target point. Secondly, the vehicle loading capacity can be extracted from the delivery task and the recycling task. The vehicle loading capacity is composed of the volume and weight parameters of the appliances to be transported in the delivery task or the recycling task. The vehicle loading capacity for the delivery task is the volume and weight attributes corresponding to the model of the delivered appliance, while the vehicle loading capacity for the recycling task is the volume and weight attributes corresponding to the model of the recycled appliance. In this embodiment, the system can obtain the standard volume and weight values ​​of various home appliances by consulting a model parameter database. Combined with the vehicle's current loading status, it dynamically calculates the possible loading states the vehicle might reach before and after performing the task. Vehicle loading capacity is a key parameter for subsequently determining the loading feasibility of the vehicle at different stages, used to limit whether the vehicle can continue to perform the recycling task after the delivery task. Finally, estimated service durations are extracted from both the delivery and recycling tasks. The estimated service duration can be calculated from the installation difficulty level or disassembly difficulty level, resulting in a service time interval or estimated service time value. In the delivery task, based on the installation difficulty level corresponding to the delivered home appliance model, the system retrieves the basic time required for the installation steps, potentially additional installation steps, and incremental time caused by external environmental factors (such as floor level or elevator availability) from a preset installation difficulty time database to obtain the estimated service duration of the delivery task. In the recycling task, based on the disassembly difficulty level corresponding to the recycled home appliance model, the system determines the disassembly operation time, handling time, and the time for basic processing steps such as power and water outages from the disassembly difficulty level and time mapping relationship, thereby obtaining the estimated service duration of the recycling task. This step can extract the estimated mileage, vehicle loading capacity, and estimated service duration for delivery and recycling tasks, laying the foundation for subsequent loading feasibility assessment and service time processing based on interval estimation.

[0048] S103: Calculate the loading status of the vehicle at each stage of the task based on the vehicle's loading capacity, and determine the loading feasibility of the vehicle at each stage according to the loading status and the preset loading stability judgment method. The first constraint is that the loading feasibility of the vehicle at each stage is feasible. Based on the estimated service time, the interval estimation method is used to determine the service time of the installation stage, the service time of the dismantling stage, and the service time of the transportation stage. The second constraint is that the sum of the service time of the installation stage, the service time of the dismantling stage, and the service time of the transportation stage does not exceed the customer's scheduled service time window.

[0049] Optionally, in this embodiment, after generating delivery and recycling tasks, the loading status of the vehicle at each stage of task execution can be calculated based on the vehicle's loading capacity. This is achieved by dynamically calculating the remaining loading capacity after unloading new goods during the delivery stage, and the volumetric load, weight load, and cargo placement after loading old goods during the recycling stage, thus obtaining the vehicle's capacity utilization at different stages. Subsequently, the system judges the loading feasibility of the vehicle at each stage based on the loading status and a preset loading stability judgment method. This is done by calculating the center-of-gravity offset index of the vehicle load at each stage and comparing it with a preset safety threshold, as well as verifying whether the stacking method at the current stage meets stacking constraints (e.g., refrigerators must be placed vertically, washing machines can be stacked but the weight of the upper layer cannot exceed the weight of the lower layer). If any stage does not meet the safety threshold or stacking rules, then loading at that stage is deemed infeasible. Furthermore, the service duration for the installation stage, disassembly stage, and handling stage can be determined using an interval estimation method based on the estimated service duration. The sum of these three service durations is then compared with the user's scheduled service time window to ensure that the total service duration does not exceed the customer's scheduled service time window. Ultimately, the first and second constraints serve together as the basis for judging the feasibility of subsequent path planning solutions, ensuring that the path planning results are executable in terms of vehicle capacity safety and service time windows.

[0050] S104: Based on the first constraint, the second constraint, and the objective optimization function, a delivery route planning scheme for replacing old and new goods is obtained. The objective optimization function is an optimization function constructed based on the estimated vehicle mileage, the delayed service penalty factor, and the transfer service penalty factor, with the objective of maximizing the delivery efficiency of the delivery route for replacing old and new goods. Alternatively, the objective optimization function is an optimization function constructed based on the estimated vehicle mileage, the delayed service penalty factor, and the transfer service penalty factor, with the objective of minimizing the delivery cost of the delivery route for replacing old and new goods.

[0051] Optionally, in this embodiment, at any segment of the transportation route, the total volume of the loaded goods on the vehicle must not exceed the cargo compartment volume, and the total weight must not exceed the vehicle's maximum load capacity. The load decreases upon completion of each delivery sub-task and increases upon completion of each recycling sub-task. After the vehicle completes a delivery task, the system needs to calculate whether the vehicle's remaining loading capacity is sufficient to load the old goods corresponding to the recycling task, and determine whether the stability requirements are met based on the center of gravity offset index and stacking constraint rules. The formula for the volume constraint condition is: The formula for the weight constraint condition is: ,in For the first One order, For the first A collection of car service orders. and The first Volume and weight of the new order. and These are the vehicle's maximum volume and maximum load capacity, respectively. For volumetric safety factor, and You can choose an initial value of 0.95. For the first The status of new device delivery for this order. Indicates the first The new machines for this order have been delivered. For the first One order for a new machine has not been delivered. For the first The status of the old machine recycling for each order. Indicates the first The old machines for this order have been recycled. For the first The old machines in one order have not been fully recycled. Indicates the first The time the vehicle departed from the warehouse For the first The time it takes for the vehicle to arrive at the recycling center, and The first The volume and weight of the old order. The second constraint is that the sum of the service times for the installation, dismantling, and handling phases, determined using an interval estimation method based on the estimated service time, must not exceed the customer's scheduled service time window. The constraint formula for this second constraint includes the customer scheduled service time window constraint: Service duration constraints: The service duration for the installation phase is as follows: The disassembly service duration is as follows: The service duration for the moving phase is: ,in Based on the basic transport time, for example, 20 minutes, of which For the first The car arrived at the The time for each order point For the first The earliest allowed arrival time for each order. Then it is the first The latest allowed arrival time for each order. Total service time for the nth order. To establish a fixed parking preparation time, For the first Total processing time for each order. Service duration during the installation phase, Service time for the disassembly phase, Based on the transport time, and The first Minimum and maximum installation time for each order. , for the first Number of floors for each order point If the customer's address has an elevator, then... If there is no elevator The objective function is: ,in The cost corresponding to the estimated mileage of a vehicle. For vehicle usage costs, The penalty factor for delayed service is the cost of penalties incurred due to exceeding the customer's scheduled service time window. The service reassignment penalty factor is a high penalty imposed when a solution is deemed high-risk (e.g., insufficient capacity or severe time conflicts) during logical deduction, necessitating the reassignment or postponement of the retrieval task. and The weighting coefficients can be autonomously adjusted based on whether the optimization objective is to maximize the delivery efficiency of delivery routes that replace old and new goods, or to minimize the delivery cost of such routes. For example, maximizing the delivery efficiency of delivery routes that replace old and new goods can be the optimization objective. Under this objective, the weighting coefficients can be increased. and The values ​​of these factors increase the weight of the delayed service penalty factor and the reassignment service penalty factor. If the objective is to minimize the delivery cost of the delivery route involving the replacement of old and new goods, then increasing these values ​​under this objective... and The values ​​of these values ​​increase the weight of the cost corresponding to the estimated mileage of the vehicle and the cost of vehicle use, thereby reducing delivery costs. Finally, the simulated annealing algorithm can be used to solve the optimization problem and obtain the delivery route planning scheme.

[0052] In one embodiment, obtaining order information for exchanging old and new goods, and generating delivery and recycling tasks based on the order information, includes:

[0053] Extract the address information from the order information and parse the address information into latitude and longitude coordinates;

[0054] Extract the model numbers of the delivered and recycled appliances from the order information. Determine the installation difficulty level of the delivered appliance based on its model number, and determine the disassembly difficulty level of the recycled appliance based on its model number.

[0055] The estimated service time for delivery tasks is determined based on the installation difficulty level, and the estimated service time for recycling tasks is determined based on the disassembly difficulty level.

[0056] The estimated mileage of the vehicle is determined based on latitude and longitude coordinates, and delivery and collection tasks are generated based on the estimated mileage, vehicle loading capacity and estimated service duration.

[0057] Specifically, firstly, order information submitted by users can be obtained from the new and used goods trade-in system. Order information may include fields such as the service address filled in by the user, the model of the delivered appliance, the model of the appliance to be recycled, the customer's appointment time window, and the user's contact information. In this embodiment, the address information can be extracted from the order information, and an address parsing program can be called to parse the address information into latitude and longitude coordinates. The address parsing program can be based on internet map services or internal geographic information modules, performing formatted parsing of administrative divisions, street names, and house numbers to obtain latitude and longitude data representing the customer service location, which is used to support subsequent vehicle estimated mileage calculations.

[0058] Secondly, the delivery appliance model and the recycled appliance model are extracted from the order information. In this embodiment, the delivery appliance model can represent the new product category to be delivered to the user's location, such as a refrigerator, television, or washing machine. It can also represent the specific model of the new product category, used to obtain parameters of the new product, such as volume, weight, and installation precautions. The recycled appliance model represents the old product category and related parameters to be recycled from the user's location. Then, based on the delivery appliance model, the corresponding installation difficulty level is queried. This installation difficulty level is determined by a combination of factors such as the number of installation steps, installation tool requirements, and the complexity of fastener installation. Similarly, based on the recycled appliance model, the system queries the corresponding disassembly difficulty level. This disassembly difficulty level is determined by a combination of factors such as the complexity of disassembly operations, wiring structure complexity, and handling conditions. Subsequently, the estimated service time for the delivery task can be determined based on the installation difficulty level. The estimated service time includes the basic installation time and the incremental time caused by the installation environment. In this embodiment, if the delivery appliance model is a product with high installation difficulty, the corresponding standard service time range can be selected based on the difficulty level. For recycling tasks, the system determines the estimated service time based on the dismantling difficulty level, including the dismantling operation time, the time required to handle water and power outages, and the time needed to transport old appliances. Then, the system determines the estimated vehicle mileage based on latitude and longitude coordinates. The estimated vehicle mileage is calculated by the server-side path evaluation module using a map engine. This calculation involves matching the real road network, considering road grade, traffic direction, traffic restrictions, and current traffic conditions to estimate the distance or time the vehicle may need to travel from its starting point to the target service address. Finally, delivery and recycling tasks are generated based on the estimated vehicle mileage, vehicle loading capacity, and estimated service time.

[0059] From the above, it can be concluded that this application realizes the entire process of generating delivery and recycling tasks based on order information, providing accurate and structured task inputs for subsequent constraint judgment, optimization modeling and path planning.

[0060] In one embodiment, obtaining order information for exchanging old and new goods, and generating delivery and recycling tasks based on the order information, includes:

[0061] If the number of orders for exchanging old and new goods exceeds the preset number of orders, the geographic location information of all orders for exchanging old and new goods is extracted, and the geographic location information is clustered according to a predefined geographic grid to divide all orders into multiple task clusters. Based on the order information of the orders included in each task cluster, delivery tasks and recycling tasks are generated for each task cluster. Each task cluster includes multiple orders for exchanging old and new goods.

[0062] If there is no preset order quantity for the exchange of new and old goods, the order information of all orders for the exchange of new and old goods is obtained, and delivery and recycling tasks are generated based on the order information.

[0063] Specifically, firstly, a preset order quantity threshold, called the preset order quantity, can be established to measure the scale of orders. This preset order quantity is determined based on the vehicle scale of the delivery area, computing resource capabilities, and the difficulty of solving the route optimization model. If the number of orders exceeds the preset order quantity, a large-scale order processing flow can be initiated. In this flow, the geographic location information of all orders is first extracted, including latitude and longitude coordinates obtained from the customer's address. Subsequently, spatial clustering is performed based on a predefined geographic grid. The geographic grid can be divided according to urban road density, administrative divisions, or a custom grid size (e.g., a 1km × 1km grid) to group orders with adjacent geographic locations or similar service radii into the same area. After clustering through the geographic grid, all orders can be divided into multiple task clusters. Each task cluster contains multiple orders located in the same geographic grid or multiple adjacent grids, ensuring that the distance between delivery points within the task cluster is short, which is beneficial for subsequent route optimization solutions.

[0064] After the task clusters are divided, order information for all orders within each cluster is obtained. Then, corresponding delivery and collection tasks can be generated based on this order information, resulting in task sets for multiple clusters. Delivery and collection tasks within each cluster can be used as independent optimization units for subsequent path planning to improve computational efficiency and ensure path executability.

[0065] If the number of orders involving the replacement of old and new goods does not exceed the preset order quantity, the system enters a small-scale order processing flow. In this flow, because the number of orders is small, the geographical relationships between orders do not significantly affect the solution efficiency. Therefore, clustering is unnecessary; instead, the system directly obtains the order information for all orders and generates delivery and collection tasks based on this information. This approach avoids unnecessary data partitioning overhead, thus ensuring faster generation of task lists in small-scale scenarios.

[0066] From the above, it can be concluded that this application can select the optimal task construction strategy according to the different number of orders for exchanging old and new goods. When facing "trade-in" business tasks of different scales, it can generate delivery tasks and recycling tasks that can be used for subsequent optimization and solution with high processing efficiency.

[0067] In one embodiment, the maximum value of the estimated installation time range for the current customer point is obtained as the pessimistic duration;

[0068] The estimated time for the vehicle to reach the next customer point is obtained based on the pessimistic duration, the time the vehicle takes to reach the current customer point, and the travel time the vehicle takes to reach the next customer point.

[0069] The estimated time for the vehicle to arrive at the next customer point is sent to the device used by the driver.

[0070] Specifically, firstly, the estimated installation time range for the current customer location can be obtained from the estimated service duration, and the maximum value of the estimated installation time range can be automatically extracted and used as the pessimistic duration for the current customer location. In this embodiment, using the maximum value as the pessimistic duration can reserve a safety buffer in the time planning, avoiding delays in subsequent customer service caused by the actual time taken for on-site installation work exceeding the average expectation due to complex environments (such as high floors, no elevators, complicated installation structures, etc.).

[0071] Subsequently, the estimated arrival time of the vehicle at the next customer point can be calculated using the pessimistic duration, the vehicle's arrival time at the current customer point, and the travel time en route to the next customer point. This calculation first determines the vehicle's actual arrival time at the current customer point, then adds the pessimistic duration to obtain the estimated departure time. Next, the estimated travel time between the current and next customer points in the route planning scheme is used to extrapolate the vehicle's arrival time at the next customer point. Then, the calculated estimated arrival time is sent to the driver's device. This device can be the delivery driver's mobile terminal, a vehicle dispatch terminal, or other devices used to receive route information. The sent content includes the name of the next customer point, the estimated arrival time, and potential time window risk warnings, enabling the driver to understand the vehicle's journey status in real time, prepare for service in advance, and trigger alerts or adjust the plan through the dispatch module when necessary.

[0072] The above demonstrates that the pessimistic time-based mechanism introduced in this application enhances the scheduling scheme's adaptability to actual operational uncertainties, preventing cascading timeouts across the entire route due to delays at a single customer point. Furthermore, by sending estimated arrival times to drivers in real-time, the transparency and controllability of route execution are improved, making delivery and recycling tasks more efficient and stable.

[0073] In one embodiment, the loading feasibility of the vehicle at each stage is determined based on the loading status and a preset loading stability assessment method, including:

[0074] Calculate the center of gravity offset index of the vehicle load at the current stage and compare it with a preset safety threshold. If the center of gravity offset index exceeds the safety threshold, the loading of the vehicle at the current stage is determined to be infeasible. The center of gravity offset index is used to characterize the change of the center of gravity of the cargo during the loading and unloading process.

[0075] Obtain the stacking method of the vehicle load in the current stage, determine whether the stacking method satisfies the stacking constraint rules, and if not, determine that the loading of the vehicle in the current stage is infeasible.

[0076] If the center of gravity offset index does not exceed the safety threshold and the stacking method meets the stacking constraint rules, then the loading feasibility of the vehicle at the current stage is determined to be feasible.

[0077] Specifically, firstly, at any stage of a vehicle's delivery or retrieval mission, the center of gravity offset index of the vehicle load can be calculated based on the vehicle's loading status at the current stage. The center of gravity offset index is an indicator representing the overall change in the vehicle's center of gravity, calculated based on the weight distribution and position of the currently loaded items. The index can be calculated using a pre-defined center of gravity calculation model by recording the placement, weight, size, and loading level of each item in the cargo compartment. Secondly, the center of gravity offset index can be compared with a pre-defined safety threshold. If the calculated index exceeds the safety threshold, the loading feasibility for the vehicle at the current stage is deemed infeasible. Thirdly, the stacking method of the vehicle load at the current stage is obtained, and it is determined whether the stacking method satisfies the stacking constraint rules. These stacking constraint rules are pre-defined based on the shape, size, weight, and transportation requirements of different types of home appliances. For example, refrigerators must be placed vertically due to their structure; washing machines can be stacked, but the weight of the upper layer must not exceed the weight of the lower layer; televisions must avoid heavy objects pressing on the screen area. The system determines whether the stacking method violates the stacking constraint rules by reading the loading position structure at the current stage. If the loading method does not meet the stacking constraint rules, the system determines that the loading of the vehicle at the current stage is not feasible.

[0078] Finally, if the vehicle load's center of gravity offset index in the current stage does not exceed the preset safety threshold and the stacking method meets the stacking constraint rules, the system determines that the vehicle's loading feasibility in the current stage is feasible. At this point, the vehicle can safely continue to perform the subsequent delivery or retrieval tasks according to the route planning scheme.

[0079] From the above, it can be concluded that this application realizes a multi-dimensional judgment mechanism for vehicle loading stability, which can not only check the volume and weight capacity, but also monitor the changes in the center of gravity and the rationality of stacking of vehicles during loading and unloading, thereby improving the safety and feasibility of the route planning scheme.

[0080] In one embodiment, the delayed service penalty factor is a penalty for exceeding the customer's scheduled service time window; the reassignment service penalty factor is a penalty for reassigning a task to another vehicle.

[0081] Specifically, firstly, the system sets a delay service penalty factor based on the customer's scheduled service time window for each order. This penalty factor is incurred when a vehicle exceeds the customer's scheduled service time window while performing a delivery or retrieval task. It is achieved by recording the vehicle's arrival time at the customer's location, the service duration during the installation, dismantling, and handling phases in real time, and comparing this with the customer's scheduled service time. If the actual operation time exceeds the customer's scheduled service time window due to route extensions, service delays, or complex on-site conditions, the cost of that route in the overall evaluation is increased by adding a time default cost to the objective optimization function. This encourages the system to prioritize task sequences and routes that do not cause service delays during the route planning phase. Secondly, the reassignment service penalty factor represents the cost of reassigning a retrieval task to another vehicle because loading is infeasible, time conflicts, or unforeseen events prevent it from being performed by the original vehicle. If, during route planning, a retrieval task is determined to be unable to meet loading feasibility or time window constraints, the task is marked as needing reassignment, and the corresponding reassignment service penalty factor is added to the objective optimization function.

[0082] From the above, it can be concluded that by incorporating the delay service penalty factor and the transfer service penalty factor into the objective optimization function, this application tends to select route schemes that are time-controllable, loading feasible, and do not require additional transfer during the route optimization process, thereby improving the overall efficiency and execution reliability of the new and old goods replacement and delivery route.

[0083] The above describes a delivery route planning method provided by the embodiments of this application. The delivery route planning device that performs the above description will be described below.

[0084] Please see Figure 2 , Figure 2 This is a schematic diagram of a delivery route planning device provided in an embodiment of this application. Figure 2 As shown, the delivery route planning device includes:

[0085] The data acquisition module 301 is used to acquire order information for the replacement of old and new goods, and to generate delivery and recycling tasks based on the order information;

[0086] Data extraction module 302 is used to extract the estimated mileage, vehicle loading capacity and estimated service duration of vehicles in delivery tasks and recycling tasks respectively;

[0087] The constraint module 303 is used to calculate the loading status of the vehicle in each stage of the task based on the vehicle's loading capacity, and to determine the loading feasibility of the vehicle in each stage according to the loading status and the preset loading stability judgment method. The first constraint condition is that the loading feasibility of the vehicle in each stage is feasible. The service time of the installation stage, the service time of the dismantling stage, and the service time of the transportation stage are determined by the interval estimation method based on the estimated service time. The second constraint condition is that the sum of the service time of the installation stage, the service time of the dismantling stage, and the service time of the transportation stage does not exceed the customer's scheduled service time window.

[0088] The route planning module 304 is used to obtain a delivery route planning scheme for replacing old and new goods based on the first constraint, the second constraint, and the objective optimization function. The objective optimization function is an optimization function constructed based on the estimated vehicle mileage, the delayed service penalty factor, and the transfer service penalty factor, with the objective of maximizing the delivery efficiency of the delivery route for replacing old and new goods, or an optimization function constructed based on the estimated vehicle mileage, the delayed service penalty factor, and the transfer service penalty factor, with the objective of minimizing the delivery cost of the delivery route for replacing old and new goods.

[0089] In one embodiment, the data acquisition module 301 is specifically used to: extract address information from order information and parse the address information into latitude and longitude coordinates;

[0090] Extract the model numbers of the delivered and recycled appliances from the order information. Determine the installation difficulty level of the delivered appliance based on its model number, and determine the disassembly difficulty level of the recycled appliance based on its model number.

[0091] The estimated service time for delivery tasks is determined based on the installation difficulty level, and the estimated service time for recycling tasks is determined based on the disassembly difficulty level.

[0092] The estimated mileage of the vehicle is determined based on latitude and longitude coordinates, and delivery and collection tasks are generated based on the estimated mileage, vehicle loading capacity and estimated service duration.

[0093] In one embodiment, the data acquisition module 301 is specifically used to: if the number of orders for exchanging old and new goods exceeds the preset number of orders, extract the geographic location information from all orders for exchanging old and new goods, perform clustering operations on the geographic location information according to a predefined geographic grid, divide all orders into multiple task clusters, and generate delivery and recycling tasks for each task cluster based on the order information of the orders included in each task cluster, wherein each task cluster includes multiple orders for exchanging old and new goods.

[0094] If there is no preset order quantity for the exchange of new and old goods, the order information of all orders for the exchange of new and old goods is obtained, and delivery and recycling tasks are generated based on the order information.

[0095] In one embodiment, the data acquisition module 301 is specifically used to: acquire the maximum value of the estimated installation time interval of the current customer point as the pessimistic duration;

[0096] The estimated time for the vehicle to reach the next customer point is obtained based on the pessimistic duration, the time the vehicle takes to reach the current customer point, and the travel time the vehicle takes to reach the next customer point.

[0097] The estimated time for the vehicle to arrive at the next customer point is sent to the device used by the driver.

[0098] In one embodiment, the constraint module 303 is specifically used to: calculate the center of gravity offset index of the vehicle load in the current stage, and compare the center of gravity offset index with a preset safety threshold. If the center of gravity offset index exceeds the safety threshold, the loading feasibility of the vehicle in the current stage is determined to be infeasible. The center of gravity offset index is used to characterize the change of the center of gravity of the cargo during the loading and unloading process.

[0099] Obtain the stacking method of the vehicle load in the current stage, determine whether the stacking method satisfies the stacking constraint rules, and if not, determine that the loading of the vehicle in the current stage is infeasible.

[0100] If the center of gravity offset index does not exceed the safety threshold and the stacking method meets the stacking constraint rules, then the loading feasibility of the vehicle at the current stage is determined to be feasible.

[0101] This application also provides an electronic device in its embodiments. (See reference...) Figure 3 The diagram illustrates a structural schematic of an electronic device suitable for implementing the delivery route planning method in the embodiments of this application. The electronic device in the embodiments of this application may include, but is not limited to, fixed terminals such as mobile phones, laptops, PDAs (personal digital assistants), PADs (tablet computers), desktop computers, etc. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0102] like Figure 3As shown, the electronic device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. When the electronic device is powered on, the RAM 603 also stores various programs and data required for the operation of the electronic device. The processing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0103] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 608 including, for example, memory cards, hard drives, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have instead.

[0104] This application also provides a computer program product including computer-readable instructions, which, when executed on an electronic device, cause the electronic device to implement any of the delivery route planning methods provided in this application.

[0105] This application also provides a computer-readable storage medium that carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement any of the delivery route planning methods provided in this application.

[0106] It should also be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. In addition, in the device embodiment drawings provided in this application, the connection relationship between modules indicates that they have a communication connection, which can be implemented as one or more communication buses or signal lines.

[0107] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware, or it can be implemented by special-purpose hardware including application-specific integrated circuits, special-purpose CPUs, special-purpose memory, special-purpose components, etc. Generally, any function performed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be diverse, such as analog circuits, digital circuits, or special-purpose circuits. However, for this application, software program implementation is more often the preferred implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk, or optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, training equipment, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0108] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0109] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, training device, or data center to another website, computer, training device, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

Claims

1. A delivery route planning method, characterized in that, include: Obtain order information for exchanging old and new goods, and generate delivery and recycling tasks based on the order information; Extract the estimated vehicle mileage, vehicle loading capacity, and estimated service duration from the delivery and recycling tasks, respectively. Based on the vehicle's loading capacity, the loading status of the vehicle at each stage of the task is calculated, and the loading feasibility of the vehicle at each stage is judged according to the loading status and the preset loading stability judgment method. The first constraint condition is that the loading feasibility of the vehicle at each stage is feasible. Based on the estimated service time, the interval estimation method is used to determine the service time of the installation stage, the service time of the dismantling stage, and the service time of the transportation stage. The second constraint condition is that the sum of the service time of the installation stage, the service time of the dismantling stage, and the service time of the transportation stage does not exceed the customer's scheduled service time window. Based on the first constraint, the second constraint, and the objective optimization function, a delivery route planning scheme for replacing old and new goods is obtained. The objective optimization function is an optimization function constructed based on the estimated vehicle mileage, the delayed service penalty factor, and the transfer service penalty factor, with the objective of maximizing the delivery efficiency of the delivery route for replacing old and new goods. Alternatively, the objective optimization function is an optimization function constructed based on the estimated vehicle mileage, the delayed service penalty factor, and the transfer service penalty factor, with the objective of minimizing the delivery cost of the delivery route for replacing old and new goods.

2. The delivery route planning method according to claim 1, characterized in that, The process of obtaining order information for exchanging old and new goods, and generating delivery and recycling tasks based on the order information, includes: Extract the address information from the order information and parse the address information into latitude and longitude coordinates; Extract the model numbers of the delivered and recycled home appliances from the order information respectively. Determine the installation difficulty level of the delivered home appliance based on the model number of the delivered home appliance, and determine the disassembly difficulty level of the recycled home appliance based on the model number of the recycled home appliance. The estimated service time for the delivery task is determined based on the installation difficulty level, and the estimated service time for the recycling task is determined based on the disassembly difficulty level. Based on the latitude and longitude coordinates, the estimated mileage of the vehicle is determined, and delivery and collection tasks are generated according to the estimated mileage, vehicle loading capacity, and estimated service duration.

3. The delivery route planning method according to claim 1, characterized in that, The process of obtaining order information for exchanging old and new goods, and generating delivery and recycling tasks based on the order information, includes: If the number of orders for exchanging old and new goods exceeds the preset number of orders, the geographic location information of all orders for exchanging old and new goods is extracted, and the geographic location information is clustered according to a predefined geographic grid to divide all orders into multiple task clusters. Based on the order information of the orders included in each task cluster, delivery tasks and recycling tasks for each task cluster are generated respectively. Each task cluster includes multiple orders for exchanging old and new goods. If there is no preset order quantity for the exchange of new and old goods, then the order information of all orders for the exchange of new and old goods is obtained, and delivery and recycling tasks are generated based on the order information.

4. The delivery route planning method according to claim 1, characterized in that, Also includes: Obtain the maximum value of the estimated installation time range for the current customer location as the pessimistic duration; The estimated time for the vehicle to reach the next customer point is obtained based on the pessimistic duration, the time the vehicle arrives at the current customer point, and the travel time the vehicle takes to reach the next customer point. The estimated time for the vehicle to arrive at the next customer point is sent to the device used by the driver.

5. The delivery route planning method according to claim 1, characterized in that, The step of determining the loading feasibility of the vehicle at each stage based on the loading status and a preset loading stability judgment method includes: Calculate the center of gravity offset index of the vehicle load at the current stage, and compare the center of gravity offset index with a preset safety threshold. If the center of gravity offset index exceeds the safety threshold, the loading feasibility of the vehicle at the current stage is determined to be infeasible. The center of gravity offset index is used to characterize the change of the center of gravity of the cargo during the loading and unloading process. Obtain the stacking method of the vehicle load at the current stage, determine whether the stacking method satisfies the stacking constraint rules, and if not, determine that the loading of the vehicle at the current stage is not feasible. If the center of gravity offset index does not exceed the safety threshold and the stacking method satisfies the stacking constraint rules, then the loading feasibility of the vehicle at the current stage is determined to be feasible.

6. The delivery route planning method according to claim 1, characterized in that, The delayed service penalty factor is a penalty for exceeding the customer's scheduled service time window; the reassignment service penalty factor is a penalty for reassigning the retrieval task to another vehicle.

7. A delivery route planning device, characterized in that, include: The data acquisition module is used to acquire order information for the replacement of old and new goods, and to generate delivery and recycling tasks based on the order information; The data extraction module is used to extract the estimated vehicle mileage, vehicle loading capacity, and estimated service duration from the delivery and recycling tasks, respectively. The constraint module is used to calculate the loading status of the vehicle at each stage of the task based on the vehicle's loading capacity, and to determine the loading feasibility of the vehicle at each stage according to the loading status and the preset loading stability judgment method. The first constraint condition is that the loading feasibility of the vehicle at each stage is feasible. The service time of the installation stage, the service time of the dismantling stage, and the service time of the transportation stage are determined by the interval estimation method based on the estimated service time. The second constraint condition is that the sum of the service time of the installation stage, the service time of the dismantling stage, and the service time of the transportation stage does not exceed the customer's scheduled service time window. The route planning module is used to obtain a delivery route planning scheme for replacing old and new goods based on the first constraint, the second constraint, and the objective optimization function. The objective optimization function is an optimization function constructed based on the estimated vehicle mileage, the delayed service penalty factor, and the transfer service penalty factor, with the objective of maximizing the delivery efficiency of the delivery route for replacing old and new goods. Alternatively, the objective optimization function is an optimization function constructed based on the estimated vehicle mileage, the delayed service penalty factor, and the transfer service penalty factor, with the objective of minimizing the delivery cost of the delivery route for replacing old and new goods.

8. A computer program product, characterized in that, It includes computer-readable instructions that, when executed on an electronic device, cause the electronic device to implement the delivery route planning method as described in any one of claims 1 to 6.

9. An electronic device, characterized in that, It includes at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program to enable the electronic device to implement the delivery route planning method as described in any one of claims 1 to 6.

10. A computer storage medium, characterized in that, The storage medium carries one or more computer programs that, when executed by an electronic device, enable the electronic device to implement the delivery route planning method as described in any one of claims 1 to 6.

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