Large commodity loading strategy generation system and method fusing linear distance and load constraint

Abstract

The invention discloses a large commodity loading strategy generation system and method fusing linear distance and load constraint, and relates to the technical field of logistics scheduling. The method comprises the following steps: constructing a vehicle resource pool and an order pool, maintaining three-dimensional size information of load and capacity of a vehicle, and commodity physical attributes and delivery address information of orders, and carrying out optimal matching and three-dimensional loading calculation on the orders in the order pool based on three-dimensional size constraints of the load and capacity of the vehicle; the method comprises the following steps: generating a loading strategy containing a vehicle-order allocation scheme and three-dimensional placement positions of goods in a vehicle, generating a distribution path sequence meeting a physical unloading sequence of the goods according to a vehicle departure place and a distribution address of a corresponding order in combination with a loading dependency relationship derived from the three-dimensional placement positions of the goods in the vehicle, and loading the goods in the distribution path sequence according to the distribution path sequence. The long-standing core technical difficulties of'loading safety ', 'assembly disjunction', 'experience dependence 'and the like in the field of large-piece logistics are effectively solved, and remarkable industrial application value and economic benefits are achieved.

Description

Technical Field

[0001] This invention relates to the field of logistics scheduling technology, and in particular to a system and method for generating loading strategies for large items that integrates straight-line distance and load constraints. Background Technology

[0002] With the rapid development of e-commerce, especially for large items such as home furnishings, appliances, and building materials, the delivery and scheduling of large-item logistics has become a core link affecting supply chain efficiency and cost. Due to their large size, heavy weight, irregular shape, and high loading and unloading requirements, large items face more complex challenges in logistics and distribution than small-item express delivery. Traditional logistics scheduling methods are no longer sufficient to meet the dual requirements of efficiency and cost.

[0003] Currently, the loading and delivery scheduling of large items relies heavily on manual experience. Dispatchers select vehicles and arrange loading plans based on order addresses, cargo dimensions, weight, and other information, relying on their experience; drivers then make deliveries based on their familiarity with the routes or simple navigation tools. When manually arranging loading, it's difficult to accurately calculate the utilization rate of three-dimensional space, often resulting in vehicles being either "not fully loaded" or "not fully loaded." Furthermore, the lack of scientific load balance calculations can lead to vehicle center of gravity shifts, posing safety hazards such as rollovers during transport. Improper loading, such as heavy goods pressing on lighter goods or fragile items being crushed, also frequently causes cargo damage. In addition, in the existing process, loading plan development and delivery route planning are usually two separate stages. The lack of consideration for delivery order during loading often leads to drivers frequently needing to "reload" cargo in subsequent deliveries, moving later-delivered goods to retrieve earlier-delivered items. This significantly increases loading and unloading time, labor intensity, and exacerbates the risk of cargo damage. Although some studies have optimized the Vehicle Routing Problem (VRP) and Bin Packing Problem separately, they are often solved independently. For example, patent document CN112084528A discloses a vehicle route optimization method that considers cargo loading, but its loading constraints are relatively simple and do not involve precise three-dimensional spatial non-overlapping, center of gravity balance and other realistic constraints; patent document CN110210301A proposes a loading method based on three-dimensional box packing, but does not strongly couple this box packing scheme with the subsequent dynamic delivery route optimization, which may cause the generated scheme to fail in actual execution due to the infeasibility of the unloading sequence.

[0004] In summary, the current field of logistics scheduling for large-item goods urgently needs an intelligent decision-making method that can deeply integrate precise three-dimensional loading constraints with efficient delivery route planning. To this end, a system and method for generating loading strategies for large-item goods that integrates straight-line distance and load constraints are proposed. Summary of the Invention

[0005] The main objective of this invention is to provide a system and method for generating loading strategies for large goods that integrates straight-line distance and load constraints. By establishing a unified mathematical model, vehicle selection, three-dimensional packing (considering load balance), geographical clustering, and route sorting are optimized in an integrated manner, thereby resolving the contradiction between loading and delivery from the source and achieving the comprehensive goals of loading safety, maximizing efficiency, and minimizing cost. This can effectively solve the problems in the background technology.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for generating loading strategies for large goods that integrates straight-line distance and load constraints, characterized by the following steps: S1: Construct a vehicle resource pool and an order pool, and maintain the three-dimensional dimensional information of vehicle load and capacity, as well as the physical attributes of goods and delivery address information of orders; S2: Based on the three-dimensional dimensional constraints of the vehicle's load and capacity, optimize the matching and three-dimensional loading of orders in the order pool to generate at least one loading strategy that includes a vehicle-order allocation scheme and the three-dimensional placement of goods inside the vehicle. S3: For any of the generated loading strategies, based on the vehicle's origin and the corresponding order's delivery address, and combined with the loading dependency derived from the three-dimensional placement of the goods inside the vehicle, generate a delivery path sequence that satisfies the physical unloading order of the goods. The loading dependency relationship is used to ensure that, in the delivery path sequence, for any two goods that are physically blocked, the delivery address of the blocked goods must be accessed after the delivery address of the blocking goods.

[0007] Furthermore, the method also includes a visualization output step, specifically: The loading strategy generated in step S2 is presented in the form of a three-dimensional schematic diagram; The delivery route sequence generated in step S3 is combined with the electronic map to generate a driving guidance map that includes the stopping order.

[0008] Furthermore, the optimization matching and three-dimensional loading calculation of the orders in the order pool specifically involves solving an optimization model with multiple objectives: minimizing vehicle usage costs, maximizing the comprehensive utilization rate of vehicle space and load capacity, and minimizing the estimated delivery distance.

[0009] Furthermore, the constraints of the optimization model include at least: Basic constraints; Three-dimensional dimensional constraints on the vehicle's maximum load capacity and volume; Non-overlapping constraints on all order items allocated to the same vehicle within the vehicle compartment space; Vehicle load balance constraints; Additionally, preliminary path clustering constraints based on the straight-line distance to the order delivery address.

[0010] Furthermore, the multi-objective optimization model is transformed using a weighted sum method, and the transformed optimization model is expressed as follows: ,in, , , All are weighted coefficients.

[0011] Furthermore, The vehicle usage cost function is defined as follows: = In the formula For vehicles The cost per use = Indicates vehicle Whether it is used, When the value is 1, it indicates that the vehicle is in use. When the value is 0, it indicates that the vehicle is in use. This refers to the set of available vehicles in the vehicle resource pool.

[0012] Furthermore, The comprehensive utilization rate function of vehicle space and load capacity is defined as: = In the formula For vehicles utilization rate = , As a weight for volume utilization; For vehicles The volume; For orders volume, = Indicates order Whether to assign to a vehicle , When =1, it indicates an order. Assigned to vehicles , =0 indicates an order Unassigned vehicles ; For vehicles Maximum load capacity; For orders The weight; This refers to the number of orders.

[0013] Furthermore, The function for estimating delivery distance is defined as follows: = In the formula For vehicles The estimated delivery distance = ; For vehicles obtained using the distance estimation method based on minimum spanning trees Delivery distance, = , A function to find the minimum spanning tree length; For vehicles The collection of delivery points = , For vehicles Starting position coordinates For orders The delivery address coordinates.

[0014] Furthermore, the loading dependency relationship is derived based on the loading dependency constraints of the cargo inside the vehicle, and the loading dependency constraints are expressed as follows: For any two goods p and q that are physically blocked, where goods p blocks goods q, the delivery address dest(p) of goods p must be earlier than the delivery address dest(q) of goods q in the path access order u, that is, u(dest(p)) < u(dest(q)).

[0015] Furthermore, the step of generating the delivery route sequence specifically includes: Based on the three-dimensional placement of goods inside the vehicle in the loading strategy, a loading dependency directed acyclic graph describing the physical obstruction relationship between goods is constructed. Starting from the vehicle's current location, and under the premise of satisfying the partial order relation defined by the loaded dependency directed acyclic graph, a heuristic algorithm is used to find the path sequence with the lowest cost that visits all addresses to be delivered and returns to the starting point.

[0016] Furthermore, the heuristic algorithm is an improved farthest neighbor algorithm, which is improved in that: when selecting the next access address at each step, the candidate address set is limited to the addresses corresponding to all the preceding blocked goods that have been visited.

[0017] Furthermore, the basic constraint terms include: The order assignment uniqueness constraint is used to restrict each order to be assigned to one and only one vehicle; Vehicle usage logic constraints are used to restrict that only the vehicles in use can load orders.

[0018] Furthermore, the three-dimensional dimensional constraints on the vehicle's maximum load capacity and volume specifically include: Load constraints are used to ensure that the total weight of all orders allocated to each vehicle does not exceed the vehicle's maximum load limit. Capacity boundary constraints are used to ensure that the placement of each order within the vehicle must be completely within the three-dimensional physical boundaries of the vehicle compartment.

[0019] Furthermore, the non-overlapping constraint term for all orders allocated to the same vehicle within the vehicle compartment space is used to ensure that all orders allocated to the same vehicle do not overlap with each other within the vehicle compartment space, that is, no two different orders can occupy the same three-dimensional space area at the same time.

[0020] Furthermore, the vehicle load balance constraint is used to ensure that the weight distribution of the vehicle is balanced after loading, and to avoid excessive shift of the center of gravity that could lead to instability. It is achieved by limiting the weight ratio of the front, middle and rear areas of the vehicle.

[0021] Furthermore, the preliminary path clustering constraint based on the straight-line distance of order delivery addresses is used to ensure that orders assigned to the same vehicle are relatively concentrated in spatial geographic distribution, avoiding overly scattered delivery points. Specifically, it includes: Maximum delivery radius constraint is used to limit the number of vehicles assigned to them. All orders are no more than the maximum radius from the vehicle's starting point. : The maximum distance constraint between orders limits the distance between any two orders assigned to the same vehicle to a threshold value. ; Cluster-based assignment constraints are used to limit orders for the same vehicle to come from the same or adjacent clusters.

[0022] A system for generating loading strategies for large goods that integrates straight-line distance and load constraints is provided to implement a method for generating loading strategies for large goods that integrates straight-line distance and load constraints, including: The data management module is used to maintain the vehicle resource pool and the order pool; The intelligent scheduling and loading optimization module is used to execute the intelligent scheduling and loading strategy generation steps and output the loading strategy. The route planning module is used to execute the route optimization steps based on the loading order and output the delivery route sequence; In addition, there is an interactive display module, which is used to visually output the loading strategy and delivery route sequence.

[0023] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for generating a loading strategy for large goods that integrates straight-line distance and load constraints.

[0024] The present invention has the following beneficial effects: Compared with existing technologies, this solution constructs an optimization model with multiple objectives, including minimizing vehicle usage costs, maximizing the comprehensive utilization rate of vehicle space and load capacity, and minimizing the estimated delivery distance. This achieves the global optimal allocation of logistics resources (vehicles, space, and manpower), which not only improves the comprehensive utilization rate of vehicle volume and load capacity but also optimizes vehicle scheduling, directly reducing vehicle fixed costs. Compared with experience-based scheduling, the globally superior driving sequence generated by the optimization model can significantly reduce the total delivery mileage and time, saving fuel costs.

[0025] Compared with existing technologies, this solution can automatically calculate and ensure that the center of gravity of the cargo is within a safe range by setting load balance constraints, thereby fundamentally preventing the risk of reduced vehicle handling or rollover caused by center of gravity shift.

[0026] Compared with existing technologies, this solution transforms the complex decision-making process, which relies heavily on personal experience, into an automated and intelligent process driven by data and algorithms. It provides dispatchers with visualized, data-driven optimal solutions, reduces the intensity and error rate of manual decision-making, and significantly improves the ability to respond to order fluctuations. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating the method for generating loading strategies for large goods that integrates straight-line distance and load constraints according to the present invention. Figure 2 This is a schematic diagram of the structure of the large-item loading strategy generation system that integrates straight-line distance and load constraints according to the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0029] See Figure 1 The flowchart shown is a method for generating loading strategies for large goods that integrates straight-line distance and load constraints according to the present invention. It specifically includes the following steps: Step 1: Build a vehicle resource pool and an order pool, and maintain the three-dimensional dimensional information of vehicle load and capacity, as well as the physical attributes of goods and delivery address information of orders; Step 2: Based on the three-dimensional dimensional constraints of vehicle load and capacity, optimize the matching and three-dimensional loading of orders in the order pool to generate at least one loading strategy that includes vehicle-order allocation scheme and three-dimensional placement of goods in the vehicle. Specifically, the optimization matching and 3D loading calculation of orders in the order pool are transformed into solving a multi-objective optimization model that minimizes vehicle usage costs, maximizes the comprehensive utilization rate of vehicle space and load capacity, and minimizes the estimated delivery distance. The multi-objective optimization model is then transformed into a single-objective optimization model using a weighted sum method. The transformed optimization model is expressed as follows: ; in, , , All are weighted coefficients; The vehicle usage cost function is defined as follows: = In the formula For vehicles The cost per use = Indicates vehicle Whether it is used, When the value is 1, it indicates that the vehicle is in use. When the value is 0, it indicates that the vehicle is in use. The set of available vehicles in the vehicle resource pool; The comprehensive utilization rate function of vehicle space and load capacity is defined as: = In the formula For vehicles utilization rate = , As a weight for volume utilization; For vehicles The volume; For orders volume, = Indicates order Whether to assign to a vehicle , When =1, it indicates an order. Assigned to vehicles , =0 indicates an order Unassigned vehicles ; For vehicles Maximum load capacity; For orders The weight; For order quantity; The function for estimating delivery distance is defined as follows: = In the formula For vehicles The estimated delivery distance = ; For vehicles obtained using the distance estimation method based on minimum spanning trees Delivery distance, = , A function to find the minimum spanning tree length; For vehicles The collection of delivery points = , For vehicles Starting position coordinates For orders The delivery address coordinates.

[0030] The constraints of the optimization model include at least the following: 1) Basic constraints, including: The order assignment uniqueness constraint, which restricts each order to be assigned to one and only one vehicle, can be represented as: ; Vehicle-based logical constraints, used to limit the loading of orders to only the vehicles in use, can be represented as: ; 2) Three-dimensional dimensional constraints on the vehicle's maximum load capacity and volume, including: Load constraints, used to ensure that the total weight of all orders allocated to each vehicle does not exceed the vehicle's maximum load limit, can be expressed as: ; Capacity boundary constraints are used to ensure that the placement of each order within the vehicle must be completely within the three-dimensional physical boundaries of the vehicle compartment. and If the order Assigned to vehicles ( Then its placement within the carriage satisfies: ; in, For orders In the vehicle The position coordinates of the carriage in the spatial coordinate system; For orders In the vehicle Actual dimensions of the space inside the carriage; vehicles The interior length, width, and height of the carriage; It is a very large positive number, used when The constraints will naturally take effect in time; 3) Non-overlapping constraints on all order items allocated to the same vehicle within the vehicle compartment space; This constraint is used to ensure that all orders assigned to the same vehicle do not overlap within the vehicle compartment space, meaning that no two different orders can occupy the same three-dimensional space area at the same time. For those assigned to the same vehicle Any two different orders and ( < (), at least one of the following conditions must be met: when exist When on the left: ; when exist When on the right: ; when exist When on the front side: ; when exist When on the back: ; when exist When on the lower side: ; when exist When on the upper side: ; 4) Vehicle load and balance constraints; This constraint is used to ensure that the weight distribution of the vehicle is balanced after loading, and to avoid excessive shift of the center of gravity that could lead to instability. It is achieved by limiting the weight ratio of the front, middle and rear areas of the vehicle. Specifically, it can be defined through the following steps: For vehicles The interior of the carriage is divided into three regions along its length (x-axis): Front area range: ; Central region scope: ; Rear area range: ; Define indicator functions , Similarly, define indicator functions and ,have: ; ; Then the vehicle The weight of each area within the carriage must meet the following conditions: ; in, The deviation coefficient is in the interval [0,1].

[0031] 5) Preliminary path clustering constraints based on the straight-line distance of the order delivery address.

[0032] This constraint is used to ensure that orders assigned to the same vehicle are relatively concentrated in spatial geographic distribution, avoiding overly dispersed delivery points. Specifically, it includes: Maximum delivery radius constraint is used to limit the number of vehicles assigned to them. All orders are no more than the maximum radius from the vehicle's starting point. , can be represented as:

[0033] The maximum distance constraint between orders limits the distance between any two orders assigned to the same vehicle to a threshold value. , can be represented as:

[0034] Cluster-based assignment constraints, used to limit orders for the same vehicle to come from the same or adjacent clusters, can be achieved through the following steps:

[0035] Spatial clustering of orders yields several clusters. , ,..., ; Constraining orders for the same vehicle to originate from the same or adjacent clusters can be represented as:

[0036] in, ; For clusters Neighbor clustering.

[0037] Step 3: For any generated loading strategy, based on the vehicle's origin and the corresponding order's delivery address, and combined with the loading dependencies derived from the three-dimensional placement of the goods inside the vehicle, generate a delivery path sequence that satisfies the physical unloading order of the goods.

[0038] The specific steps for generating the delivery route sequence include: Based on the three-dimensional placement of goods inside the vehicle in the loading strategy, a loading dependency directed acyclic graph describing the physical obstruction relationship between goods is constructed. Starting from the vehicle's current location, and under the premise of satisfying the partial order relation defined by the directed acyclic graph of loading dependency, a heuristic algorithm is used to find the path sequence with the lowest cost that visits all addresses to be delivered and returns to the starting point. The heuristic algorithm is an improved farthest neighbor algorithm, which is improved in that: when selecting the next address to visit at each step, the candidate address set is limited to the addresses corresponding to all the goods blocking the way in front of it that have been visited.

[0039] Loading dependencies are used to ensure that, in a delivery route sequence, for any two goods that are physically blocked, the delivery address of the blocked goods must be accessed after the delivery address of the blocking goods.

[0040] The loading dependency is derived based on the loading dependency constraints of the cargo inside the vehicle. The loading dependency constraints are expressed as follows: For any two goods p and q that are physically blocked, where goods p blocks goods q, the delivery address dest(p) of goods p must be earlier than the delivery address dest(q) of goods q in the path access order u, that is, u(dest(p)) < u(dest(q)).

[0041] Step 4: Display the generated loading strategy in the form of a 3D schematic diagram, and combine the generated delivery route sequence with the electronic map to generate a driving guidance map including the stopping order.

[0042] Example 2: This invention also provides a system for generating loading strategies for large goods that integrates straight-line distance and load constraints, used to implement the steps of the above-mentioned method for generating loading strategies for large goods that integrates straight-line distance and load constraints. See [link to relevant documentation]. Figure 2 The system architecture diagram shown includes: The data management module is used to maintain the vehicle resource pool and the order pool; The intelligent scheduling and loading optimization module is used to execute the intelligent scheduling and loading strategy generation steps and output the loading strategy; The route planning module is used to perform route optimization steps based on the loading order and output the delivery route sequence; In addition, there is an interactive display module for visually displaying loading strategies and delivery route sequences.

[0043] Example 2: The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method for generating a loading strategy for large goods that integrates straight-line distance and load constraints.

[0044] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for generating loading strategies for large goods that integrates straight-line distance and load constraints, characterized in that, Includes the following steps: S1: Construct a vehicle resource pool and an order pool, and maintain the three-dimensional dimensional information of vehicle load and capacity, as well as the physical attributes of goods and delivery address information of orders; S2: Based on the three-dimensional dimensional constraints of the vehicle's load and capacity, optimize the matching and three-dimensional loading of orders in the order pool to generate at least one loading strategy that includes a vehicle-order allocation scheme and the three-dimensional placement of goods inside the vehicle. S3: For any of the generated loading strategies, based on the vehicle's origin and the corresponding order's delivery address, and combined with the loading dependency derived from the three-dimensional placement of the goods inside the vehicle, generate a delivery path sequence that satisfies the physical unloading order of the goods. The loading dependency relationship is used to ensure that, in the delivery path sequence, for any two goods that are physically blocked, the delivery address of the blocked goods must be accessed after the delivery address of the blocking goods.

2. The method for generating a loading strategy for large goods that integrates straight-line distance and load constraints according to claim 1, characterized in that, The optimization matching and three-dimensional loading calculation of orders in the order pool specifically involves solving an optimization model with multiple objectives: minimizing vehicle usage costs, maximizing the comprehensive utilization rate of vehicle space and load capacity, and minimizing the estimated delivery distance. The constraints of the optimization model include at least the following: Basic constraints; Three-dimensional dimensional constraints on the vehicle's maximum load capacity and volume; Non-overlapping constraints on all order items allocated to the same vehicle within the vehicle compartment space; Vehicle load balance constraints; Additionally, preliminary path clustering constraints based on the straight-line distance to the order delivery address.

3. The method for generating a loading strategy for large goods that integrates straight-line distance and load constraints according to claim 2, characterized in that, The multi-objective optimization model is transformed using a weighted sum method, and the transformed optimization model is expressed as follows: ,in, , , All are weighted coefficients; The vehicle usage cost function is defined as follows: = In the formula For vehicles The cost per use = Indicates vehicle Whether it is used, When the value is 1, it indicates that the vehicle is in use. When the value is 0, it indicates that the vehicle is in use. The set of available vehicles in the vehicle resource pool; The comprehensive utilization rate function of vehicle space and load capacity is defined as: = In the formula For vehicles utilization rate = , As a weight for volume utilization; For vehicles The volume; For orders volume, = Indicates order Whether to assign to a vehicle , When =1, it indicates an order. Assigned to vehicles , =0 indicates an order Unassigned vehicles ; For vehicles Maximum load capacity; For orders The weight; For order quantity; The function for estimating delivery distance is defined as follows: = In the formula For vehicles The estimated delivery distance = ; For vehicles obtained using the distance estimation method based on minimum spanning trees Delivery distance, = , A function to find the minimum spanning tree length; For vehicles The collection of delivery points = , For vehicles Starting position coordinates For orders The delivery address coordinates.

4. The method for generating a loading strategy for large goods that integrates straight-line distance and load constraints according to claim 1, characterized in that, The loading dependency relationship is derived based on the loading dependency constraints of the cargo inside the vehicle, which are expressed as follows: For any two goods p and q that are physically blocked, where goods p blocks goods q, the delivery address dest(p) of goods p must be earlier than the delivery address dest(q) of goods q in the path access order u, that is, u(dest(p)) < u(dest(q)).

5. The method for generating a loading strategy for large goods that integrates straight-line distance and load constraints according to claim 1, characterized in that, The steps for generating the delivery route sequence specifically include: Based on the three-dimensional placement of goods inside the vehicle in the loading strategy, a loading dependency directed acyclic graph describing the physical obstruction relationship between goods is constructed. Starting from the vehicle's current location, and under the premise of satisfying the partial order relation defined by the loaded dependency directed acyclic graph, a heuristic algorithm is used to find the path sequence with the lowest cost that visits all addresses to be delivered and returns to the starting point.

6. The method for generating a loading strategy for large goods that integrates straight-line distance and load constraints according to claim 5, characterized in that, The heuristic algorithm is an improved farthest neighbor algorithm. The improvement is that when selecting the next access address at each step, the candidate address set is limited to addresses whose addresses have been visited by all the preceding blocked goods.

7. The method for generating a loading strategy for large goods that integrates straight-line distance and load constraints according to claim 2, characterized in that, The basic constraints include: The order assignment uniqueness constraint is used to restrict each order to be assigned to one and only one vehicle; Vehicle usage logic constraints are used to restrict that only the vehicle being used can load orders; The specific three-dimensional dimensional constraints on the vehicle's maximum load capacity and volume include: Load constraints are used to ensure that the total weight of all orders allocated to each vehicle does not exceed the vehicle's maximum load limit. Capacity boundary constraints are used to ensure that the placement of each order within the vehicle must be completely within the three-dimensional physical boundaries of the vehicle compartment; The non-overlapping constraint of all orders assigned to the same vehicle within the vehicle compartment space is used to ensure that all orders assigned to the same vehicle do not overlap with each other within the vehicle compartment space, that is, no two different orders can occupy the same three-dimensional space area at the same time. The vehicle load balance constraint is used to ensure that the weight distribution of the vehicle is balanced after loading, and to avoid excessive shift of the center of gravity leading to unstable driving. It is achieved by limiting the weight ratio of the front, middle and rear areas of the vehicle. The preliminary path clustering constraint based on the straight-line distance of order delivery addresses is used to ensure that orders assigned to the same vehicle are relatively concentrated in spatial geographic distribution, avoiding overly scattered delivery points. Specifically, it includes: Maximum delivery radius constraint is used to limit the number of vehicles assigned to them. All orders are no more than the maximum radius from the vehicle's starting point. : The maximum distance constraint between orders limits the distance between any two orders assigned to the same vehicle to a threshold value. ; Cluster-based assignment constraints are used to limit orders for the same vehicle to come from the same or adjacent clusters.

8. The method for generating a loading strategy for large goods that integrates straight-line distance and load constraints according to any one of claims 1-7, characterized in that, The method also includes a visualization output step, specifically: The loading strategy generated in step S2 is presented in the form of a three-dimensional schematic diagram; The delivery route sequence generated in step S3 is combined with the electronic map to generate a driving guidance map that includes the stopping order.

9. A system for generating loading strategies for large goods that integrates straight-line distance and load constraints, characterized in that, The method for generating a loading strategy for large goods that integrates straight-line distance and load constraints as described in any one of claims 1-8 includes: The data management module is used to maintain the vehicle resource pool and the order pool; The intelligent scheduling and loading optimization module is used to execute the intelligent scheduling and loading strategy generation steps and output the loading strategy; The route planning module is used to execute the route optimization steps based on the loading order and output the delivery route sequence; In addition, there is an interactive display module, which is used to visually output the loading strategy and delivery route sequence.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method for generating a loading strategy for large goods that integrates straight-line distance and load constraints as described in any one of claims 1-8.

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