Carriage part splicing planning method and device and storage medium

Abstract

The invention discloses a carriage part splicing and loading planning method and device and a storage medium, and relates to the technical field of automobile logistics. The method comprises the steps that goods taking plan information is acquired, and the goods taking plan information comprises suppliers of to-be-taken parts, the number of the to-be-taken parts and the size of each to-be-taken part; obtaining carriage information of the target vehicle, wherein the carriage information comprises a carriage size and a carriage shape; and a target control strategy set is generated based on the pickup plan information and the compartment size, and the target control strategy set is used for carrying out loading operation on the target vehicle. The technical problem that in the prior art, the space utilization rate and the loading efficiency of a circulating goods taking vehicle are insufficient is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of automotive logistics, and in particular, to a method, device, and storage medium for planning the combined loading of carriage parts. Background Art

[0002] The milk run process in the field of automotive logistics is an efficient and systematic pick-up and delivery model designed to minimize supply chain costs and inventory while ensuring a continuous and stable supply of parts and raw materials to the automobile manufacturing plant. The milk run process generally includes the following steps: Based on the production plan and market forecast, the automobile manufacturing plant forecasts the quantities of various parts and raw materials required in a future period; According to the demand forecast, the logistics department selects appropriate suppliers and formulates pick-up routes; The vehicle makes preparations in advance according to the pick-up route and the cargo list, including cleaning, safety inspection, and loading sequence planning; The vehicle departs according to the optimized route and schedule, and visits the warehouses of each supplier in turn; At each warehouse site, the vehicle quickly unloads the empty containers (if applicable) and loads the required parts and raw materials. After completing the milk run, the vehicle returns to the automobile manufacturing plant, and the goods are unloaded and directly sent to the production line or stored in the warehouse.

[0003] Currently, the goods loaded on a wing van are mostly for combined loading of a single supplier, or the carriage is filled and dispatched, resulting in low utilization rate of the carriage space, poor transportation quality, and high cost.

[0004] In view of the above problems, no effective solution has been proposed yet. Summary of the Invention

[0005] Embodiments of the present invention provide a method, device, and storage medium for planning the combined loading of carriage parts to at least solve the technical problems of insufficient space utilization rate and loading efficiency of milk run vehicles in the prior art.

[0006] According to one aspect of the embodiments of the present invention, a method for planning the combined loading of carriage parts is provided, including: obtaining pick-up plan information, where the pick-up plan information includes: the suppliers of parts to be picked up, the quantities of parts to be picked up, and the dimensions of each part to be picked up; obtaining the carriage information of the target vehicle, where the carriage information includes: the carriage dimensions and the carriage shape; generating a target control strategy set based on the pick-up plan information and the carriage dimensions, where the target control strategy set is used to perform loading operations on the target vehicle.

[0007] Optionally, based on the pickup plan information and the carriage size, a set of target control strategies is generated, including: obtaining the size of the target appliance; determining the number of target appliances based on the number of parts to be picked up, the sizes of the parts to be picked up, and the size of the target appliance; generating an appliance loading simulation scenario based on the size of the target appliance, the number of target appliances, and the carriage size information, where the appliance loading simulation scenario is used to simulate the scenario of loading the target appliance into the carriage of the target vehicle; determining the target scenario for appliance loading based on the appliance loading simulation scenario; and generating a set of target control strategies based on the target scenario for appliance loading.

[0008] Optionally, based on the target scenario for appliance loading, a set of target control strategies is generated, including: determining the number of appliances to be loaded on the target vehicle based on the target scenario for appliance loading; confirming the number of suppliers of parts to be picked up based on the number of appliances to be loaded, the number of parts to be picked up, and the suppliers of each part to be picked up; obtaining the location information of each supplier of parts to be picked up; generating a loading route for the target vehicle based on the location information of each supplier of parts to be picked up; and generating a set of target control strategies based on the loading route.

[0009] Optionally, when generating a set of target control strategies based on the loading route, it further includes: obtaining real-time traffic data, which includes but is not limited to road congestion conditions, accident information, traffic signal status, etc.; predicting the traffic conditions of each loading route based on the real-time traffic data, where the predicted traffic conditions include the estimated travel time, delay probability, etc.; using an optimal path algorithm to generate the optimal loading route for the target vehicle based on the real-time traffic data and the predicted traffic conditions, and the optimal loading route is the loading route with the lowest cost; and generating a set of target control strategies based on the optimal loading route.

[0010] Optionally, obtaining the pickup plan information includes: obtaining production parameters, where the production parameters include: production beat, vehicle type ratio, time period, parts usage per vehicle, and sharing coefficient. The sharing coefficient is the loading ratio of optional parts or accessories. The production beat information is the vehicle production per hour. The vehicle type ratio is used to calculate the parts requirements for different types of vehicles. The time period is used to determine the time period for pickup and loading operations. The parts usage per vehicle is used to calculate the parts demand for a single vehicle; and determining the pickup plan information based on the production parameters.

[0011] Optionally, when generating an appliance loading simulation scenario based on the size of the target appliance, the number of target appliances, and the carriage size information, it includes: obtaining the combinability of the target appliance; determining the loading method of the target appliance based on the size and combinability of the target appliance; and generating an appliance loading simulation scenario based on the size of the target appliance, the number of target appliances, the loading method of the target appliance, the carriage size, and the carriage shape.

[0012] Optionally, based on the size and combinability of the target appliance, determine the loading method of the target appliance, including: in response to the size of the target appliance being less than or equal to a preset size and the combinability being greater than or equal to a preset combinability, determining that the loading method of the target appliance is stacked loading; in response to the size of the target appliance being greater than the preset size and the combinability being less than the preset combinability, determining that the loading method of the target appliance is longitudinal loading.

[0013] According to another aspect of the embodiments of the present invention, there is also provided a device for planning the combined loading of carriage parts, including: a first acquisition module, configured to acquire pick-up plan information, where the pick-up plan information includes: the number of parts to be picked up and the sizes of each part to be picked up; a second acquisition module, configured to acquire the carriage information of the target vehicle, where the carriage information includes: the carriage size and the carriage shape; a generation module, configured to generate a target control policy set based on the pick-up plan information and the carriage size, where the target control policy set is used to perform a loading operation on the target vehicle.

[0014] According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, where the computer-readable storage medium includes an executable program stored therein, and when the executable program runs, it controls the device where the computer-readable storage medium is located to execute the methods in the various embodiments of the present invention.

[0015] According to another aspect of the embodiments of the present invention, there is also provided a computer program product, including a computer program, where the computer program implements the methods in the various embodiments of the present invention when executed by a processor.

[0016] In the embodiments of the present invention, by considering the part suppliers, the number and size of parts, as well as the size and shape of the carriage, a more refined target control policy is generated, realizing the refined control of the loading operation, and thus solving the technical problems of insufficient space utilization rate and loading efficiency of the existing circulating pick-up truck. Description of the Drawings

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0018] Figure 1 is a flowchart of an optional method for planning the combined loading of carriage parts according to an embodiment of the present invention;

[0019] Figure 2 is a flowchart of an optional method for planning the combined loading of carriage parts according to an embodiment of the present invention;

[0020] Figure 3 is a structural block diagram of an optional device for planning the combined loading of carriage parts according to an embodiment of the present invention. Detailed implementation mode

[0021] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order different from those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0023] According to an embodiment of the present invention, an embodiment of a method for planning the combined loading of carriage parts is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0024] The method embodiments can be executed in an electronic device including a memory and a processor or a similar computing device. Taking running on a vehicle-mounted terminal as an example, the vehicle-mounted terminal may include one or more processors (the processors may include, but are not limited to, a processing device such as a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a Digital Signal Processing (DSP) chip, a Micro Controller Unit (MCU), a Field Programmable Gate Array (FPGA), a Neural-network Processor Unit (NPU), a Tensor Processing Unit (TPU), an Artificial Intelligence (AI) type processor, etc.) and a memory for storing data. Optionally, the above vehicle-mounted terminal may further include a transmission device, an input / output device, and a display device for communication functions. Those of ordinary skill in the art can understand that the above structural description is only illustrative and does not limit the structure of the above vehicle-mounted terminal. For example, the vehicle-mounted terminal may further include more or fewer components than the above structural description, or have a different configuration from the above structural description.

[0025] The memory can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the vehicle compartment part loading and planning method in the embodiments of the present invention. The processor executes various functional applications and data processing by running the computer program stored in the memory, that is, the above vehicle compartment part loading and planning method is implemented. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, a flash memory, or other non-volatile solid-state memories. In some instances, the memory may further include a memory remotely disposed relative to the processor, and these remote memories can be connected to the mobile terminal through a network. Examples of the above network include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0026] The transmission device is used to receive or send data via a network. Specific examples of the above-mentioned network may include a wireless network provided by a communication provider of a mobile terminal. In one example, the transmission device includes a network adapter (Network Interface Controller, abbreviated as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device may be a Radio Frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0027] The display device can be, for example, a touch-screen liquid crystal display (Liquid Crustal Display, LCD) and a touch display (also referred to as a "touch screen" or "touch display screen"). The liquid crystal display enables a user to interact with the user interface of the mobile terminal. In some embodiments, the above-mentioned mobile terminal has a Graphical User Interface (GUI), and the user can perform human-computer interaction with the GUI through finger contacts and / or gestures on the touch-sensitive surface. The human-computer interaction function here may optionally include the following interactions: creating web pages, drawing, word processing, creating electronic documents, games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital videos, playing digital music, and / or web browsing, etc. The executable instructions for performing the above human-computer interaction functions are configured / stored in a computer program product or readable storage medium executable by one or more processors.

[0028] Figure 1 is a method according to an embodiment of the present invention, as Figure 1 shown, the method includes the following steps:

[0029] Step S102, obtain the pick-up plan information, where the pick-up plan information includes: the supplier of the parts to be picked up, the quantity of the parts to be picked up, and the dimensions of each part to be picked up.

[0030] In step S102, the pick-up plan information includes a list of part suppliers, the quantity of each type of part, and the part dimensions, and this information comes from the supply chain management system. For example, the system records that supplier A has 200 units of part 1, and the size of each part is 0.5m x 0.5m x 0.5m; supplier B has 150 units of part 2, and the size of each part is 0.4m x 0.2m x 0.3m.

[0031] Step S104, obtain the carriage information of the target vehicle, where the carriage information includes: the carriage size and the carriage shape.

[0032] In step S104, the target vehicle can be a 9.6-meter wing truck with an inner compartment size of 9.4 meters x 2.45 meters x 2.45 meters. The compartment shape is rectangular, and there are no special obstacles or partitions. After obtaining this information, we can accurately understand the available space and loading limits of the compartment.

[0033] Step S106: Generate a set of target control strategies based on the pick-up plan information and the compartment size. The set of target control strategies is used for the loading operation of the target vehicle.

[0034] In step S106, based on the pick-up plan information and the compartment size, apply a three-dimensional packing optimization algorithm, combine the simulation model and historical loading data, and generate a set of target control strategies. The strategy set includes detailed guidance such as the loading position, loading order, whether stacking loading is allowed, and the number of stacking layers for each part. For example, the loading strategies for part 1 and part 2 may include placing part 1 at the front of the compartment and part 2 at the rear to balance the load distribution; part 1 allows stacking loading, with a maximum of no more than 4 layers; part 2 is flat and can be placed horizontally to improve space utilization.

[0035] Based on steps S102 - S106, by obtaining detailed pick-up plan information and compartment information, and applying intelligent algorithms to generate a set of target control strategies, it not only solves the problems of space utilization and loading efficiency in traditional loading methods, but also has multiple effects such as cost control, environmental protection, supply chain flexibility, and improvement of operation quality, reflecting the innovation and practicality of the technical solution.

[0036] As an alternative implementation, optionally, generate a set of target control strategies based on the pick-up plan information and the compartment size, including:

[0037] Step S201: Obtain the size of the target appliance.

[0038] In step S201, first, it is necessary to read the size of the appliance corresponding to the parts to be loaded. For example, the size of appliance A is 1.5 meters x 1.2 meters x 1.0 meters, and the size of appliance B is 1.0 meters x 0.8 meters x 0.5 meters, etc.

[0039] Step S202: Determine the number of target appliances based on the quantity of parts to be picked up, the sizes of each part to be picked up, and the size of the target appliance.

[0040] In step S202, based on the number of parts to be picked and the dimensions of each part obtained in step S102, the number of target appliances required to load all the parts is calculated through an algorithm. For example, if 100 pieces of part 1 need to be loaded and the space occupied by each part in appliance A is 0.05 cubic meters, then the number of appliance A required is at least 2 (100 * 0.05 / (1.5 * 1.2 * 1.0) = 0.4166, rounded up). At the same time, considering the loading efficiency and the remaining space in the carriage, additional spare appliances may be required.

[0041] Step S203, based on the dimensions of the target appliances, the number of target appliances, and the carriage dimension information, generate an appliance loading simulation scenario, where the appliance loading simulation scenario is used to simulate the scenario of loading the target appliances into the carriage of the target vehicle.

[0042] In step S203, three-dimensional visualization software (such as VISIO, AutoCAD, or professional logistics loading planning software) can be used to generate multiple loading simulation scenarios according to the dimensions of the target appliances, the number of target appliances, and the dimensions of the 9.6-meter wing truck carriage. These scenarios simulate the loading effects under different appliance combinations and arrangements to evaluate the space utilization efficiency and load distribution.

[0043] Furthermore, in Visio, we drew the bottom projection of the 9.6-meter wing truck carriage, with dimensions of 9.4 meters x 2.45 meters. The unloading side of the carriage is marked with a red triangle for easy identification. In the picture, the projections of the two appliances are distinguished by different colors. Appliance A is represented by blue, and appliance B is represented by green. The specific quantity and stacking layers are marked above each appliance projection to clearly show the loading plan. For example, the picture shows that 3 pieces of appliance A are placed horizontally and stacked in 2 layers; 4 pieces of appliance B are placed vertically and stacked in 1 layer. The overall picture layout is compact, with high space utilization rate and reasonable load distribution, meeting the requirements of safe and efficient loading.

[0044] Step S204, based on the appliance loading simulation scenario, determine the appliance loading target scenario.

[0045] In step S204, by evaluating the multiple simulation scenarios generated in step S203, select the scenario with the highest space utilization rate and the most balanced load distribution as the target loading scenario. The evaluation process can be completed manually or automatically analyzed through intelligent algorithms to find the optimal solution.

[0046] Furthermore, a specific evaluation example is given:

[0047] Scenario 1: High space utilization rate, uniform load distribution, but some appliances are stacked too high, which may affect the handling safety and efficiency of the operators.

[0048] Scenario 2: The load distribution is reasonable, the center of gravity is maintained at the center of the carriage, but the space utilization rate is slightly lower, and the loading and unloading path is relatively complex, increasing the operation time.

[0049] Scenario 3: It combines high space utilization rate, reasonable load distribution, safe stacking height and convenient loading and unloading path, and at the same time reserves an emergency exit path. The cost-benefit analysis shows that this solution has the lowest cost.

[0050] Through the above evaluation, we can select Scenario 3 as the final loading target scenario because it performs best in multiple key indicators, including space utilization rate, load distribution, operation convenience, safety, cost-benefit and environmental impact, fully reflecting the role of intelligent loading strategies in logistics optimization.

[0051] Step S205, generate a set of target control strategies based on the appliance loading target scenario.

[0052] In step S205, based on the determined appliance loading target scenario, generate a detailed set of target control strategies, including the loading position, loading order, whether stacking loading is allowed and the number of stacking layers, load distribution control strategies, etc. for each appliance. These strategies will guide the actual loading operation to ensure the efficiency and safety of the loading process.

[0053] Based on steps S201 - S205, by obtaining the dimensions of the target appliance, determining the number of target appliances, generating and selecting loading simulation scenarios, and generating a set of target control strategies, not only the problems of inaccurate estimation of the number of appliances, low space utilization rate, and unreasonable load distribution in traditional loading methods are solved, but also the safety and efficiency of the loading operation are improved, and the logistics cost is reduced.

[0054] Optionally, generate the set of target control strategies based on the appliance loading target scenario, including:

[0055] Step S2051, in response to the appliance loading target scenario being the first loading target scenario, generate the first target control strategy in the set of target control strategies, where the first loading target scenario is a scenario where the number of parts that can be loaded in the carriage of the target vehicle is less than the number of parts to be retrieved, and the first target control strategy is used to control the target vehicle to perform a loading operation at a loading destination.

[0056] In step S2051, when the determined appliance loading target scenario is the first loading target scenario, that is, a scenario where the number of parts that can be loaded in the carriage of the target vehicle is less than the number of parts to be retrieved, the generated first target control strategy will focus on preferentially loading high-value or urgently needed parts, and optimizing the load distribution and balance in the carriage to ensure the safety and operation convenience of the vehicle during the loading operation at the loading destination.

[0057] Step S2052: In response to the appliance loading target scenario being the second loading target scenario, generate the second target control strategy in the target control strategy set, where the second loading target scenario is a scenario in which the number of parts that can be loaded in the carriage of the target vehicle is equal to the number of parts to be retrieved, and the second target control strategy is used to control the target vehicle to perform a loading operation at one of the loading destinations.

[0058] In step S2052, when the determined appliance loading target scenario is the second loading target scenario, that is, a scenario in which the number of parts that can be loaded in the carriage of the target vehicle is equal to the number of parts to be retrieved, the generated second target control strategy will ensure that all parts can be loaded at one time, while considering maximizing the space utilization rate in the carriage and the balance of the load distribution.

[0059] Step S2053: In response to the appliance loading target scenario being the third loading target scenario, generate the third target control strategy in the target control strategy set, where the third loading target scenario is a scenario in which the number of parts that can be loaded in the carriage of the target vehicle is greater than the number of parts to be retrieved, and the third target control strategy is used to control the target vehicle to perform loading operations at at least two of the loading destinations.

[0060] In step S2053, when the determined appliance loading target scenario is the third loading target scenario, that is, a scenario in which the number of parts that can be loaded in the carriage of the target vehicle is greater than the number of parts to be retrieved, the third target control strategy will include how to perform loading operations at at least two loading destinations (i.e., at least two suppliers) to make full use of the remaining space in the carriage. The strategy may involve secondary loading planning for the remaining space while ensuring a reasonable load distribution, as well as optimizing the route planning among multiple destinations to reduce the overall transportation cost.

[0061] Through steps S2051 to S2053, it is possible to generate a customized control strategy set for different loading target scenarios. These strategies can not only guide the specific operations of the target vehicle during the loading operation, such as the part loading sequence, space layout, load distribution, and unloading sequence, but also perform route planning and optimization to ensure the safety, efficiency, and cost-effectiveness of the loading operation. Implementing this strategy set helps to improve the overall efficiency of the logistics supply chain, reduce resource waste, and at the same time enhance the safety and flexibility of vehicle loading, which has important practical value for the logistics industry, especially the logistics optimization of the manufacturing supply chain.

[0062] Optionally, based on the appliance loading target scenario, generate a target control strategy set, including:

[0063] Step S211: Based on the appliance loading target scenario, determine the number of appliances loaded by the target vehicle.

[0064] In step S211, the number of appliances that can be loaded on the target vehicle is calculated according to the loading target scenario determined in S204. This step ensures that the loading plan matches the actual loading capacity of the vehicle, avoiding the problem of overloading or underloading.

[0065] Step S212, based on the tool loading quantity, the quantity of parts to be picked up and each supplier of parts to be picked up, confirm the quantity of suppliers of parts to be picked up.

[0066] In step S212, based on the determined equipment loading quantity and the number of parts to be picked up, it is determined which suppliers need to pick up the goods and how many parts each supplier needs to provide. This step helps to plan the route and sequence of the loading operation to ensure that the parts of each supplier can be loaded effectively.

[0067] Step S213, obtaining the location information of each supplier of the parts to be picked up.

[0068] In step S213, the specific location information of each supplier, including address, longitude and latitude coordinates, etc., is collected through the supply chain management system or GPS positioning technology to provide basic data for subsequent route planning.

[0069] Step S214: generating a loading route for the target vehicle based on the location information of each supplier of the parts to be picked up.

[0070] In step S214, a path planning algorithm, such as a shortest path algorithm, a traveling salesman problem algorithm, or a genetic algorithm, is used to generate one or more loading routes for the target vehicle based on the location information of each supplier. These routes will take into account transportation costs, travel time, traffic conditions, and the vehicle's stay time at each supplier to ensure efficient loading operations.

[0071] Step S215: generating a target control strategy set based on the loading route.

[0072] In step S215, based on the determined loading route, a target control strategy set is generated, including:

[0073] Loading sequence: Based on the routing, determine which parts are loaded at which supplier and in what order.

[0074] Loading method: Instructions on how to place each part or equipment in the carriage, including stacking, laying flat or special fixing methods.

[0075] Unloading strategy: Plan how to unload parts efficiently after arriving at the destination to ensure the unloading process is fast and orderly.

[0076] Emergency Response Plan: Develop alternate loading routes and contingency strategies in the event of unforeseen events (e.g., traffic jams, weather impacts).

[0077] Through steps S211 - S215, generating a target control strategy set based on the determined target scenarios for appliance loading not only helps improve loading efficiency and reduce costs, but also enhances customer satisfaction, increases the flexibility and responsiveness of the logistics system, and ensures the safety and compliance of operations.

[0078] Optionally, generating a target control strategy set based on the loading route further includes:

[0079] Step S221, obtaining real - time traffic data, which includes but is not limited to road congestion conditions, accident information, traffic signal status, etc.

[0080] In step S221, by means of GPS positioning systems, road sensors, etc., real - time traffic data is collected, including road congestion conditions, accident information, traffic signal status, etc. This data will provide key information for dynamic route planning, helping to predict potential delays and cost increases.

[0081] Step S222, predicting the traffic conditions of each loading route based on the real - time traffic data, where the predicted traffic conditions include the estimated travel time, delay probability, etc.

[0082] In step S222, based on the real - time traffic data, combined with historical data and machine - learning models, key indicators such as the estimated travel time and delay probability of each loading route are predicted. This step helps evaluate the feasibility and cost - effectiveness of different routes, providing data support for selecting the optimal path.

[0083] Step S223, using an optimal path algorithm, generating the optimal loading route for the target vehicle based on the real - time traffic data and the predicted traffic conditions, where the optimal loading route is the loading route with the lowest cost;

[0084] In step S223, using an optimal path algorithm, combined with the real - time traffic data and the predicted traffic conditions, a loading route with the lowest cost is dynamically generated. The optimal loading route will comprehensively consider distance, time, traffic conditions, and potential costs to ensure that the loading task can be completed in the most economical way.

[0085] Step S224, generating a target control strategy set based on the optimal loading route.

[0086] In step S224, once the optimal loading route is determined, a set of target control strategies will be generated, including the following: Dynamic loading sequence: Determine the loading sequence at each supplier according to the planning of the optimal route. Traffic condition response strategy: Develop alternative routes and emergency measures in case of traffic congestion, accidents or other traffic incidents to ensure the smooth progress of the task. Cost control strategy: Analyze the costs of the optimal route, such as tolls and fuel consumption, and formulate cost control measures. Time management strategy: Based on the estimated travel time and delay probability, plan the residence time at each supplier to ensure the rationality of the overall operation time.

[0087] Through steps S221 - S224, based on real - time traffic data and the optimal path algorithm, a dynamic loading route and a set of target control strategies adapted to the actual traffic conditions are generated. This strategy can not only reduce logistics costs and improve efficiency, but also enhance the emergency response ability and customer satisfaction, and has important application value for the logistics industry, especially for the local milk run loading planning in the manufacturing supply chain.

[0088] Optionally, obtain the pick - up plan information, including:

[0089] Step S231, obtain production parameters, where the production parameters include: production rhythm, vehicle type ratio, time period, parts usage per vehicle, and sharing coefficient. The sharing coefficient is the loading ratio of optional parts or accessories. The production rhythm information is the number of vehicles produced per hour. The vehicle type ratio is used to calculate the parts requirements for different types of vehicles. The time period is used to determine the time period for pick - up and loading operations. The parts usage per vehicle is used to calculate the parts demand for a single vehicle.

[0090] In step S231, for example: the production rhythm is 30 vehicles per hour, the A vehicle type accounts for 60% in the vehicle type ratio, and the B vehicle type accounts for 40%; the time period is from 10 am to 11 am on weekdays; in the parts usage per vehicle, the A vehicle type requires 2 A parts, and the B vehicle type requires 3 B parts; the sharing coefficient shows that the A part is 1 in the A vehicle type (i.e., all vehicles need it), and the B part is 0.5 in the B vehicle type (i.e., half of the vehicles need it).

[0091] Step S232, determine the pick - up plan information based on the production parameters.

[0092] In step S232, based on the above production parameters, the demand and priority of each part within a specific time period are calculated, thereby generating picking plan information. Among them, the part usage of a part at a certain time = model ratio * beat * time period * part usage per vehicle * sharing coefficient. The specific implementation includes: calculating the production quantity of each vehicle model within each time period, and then calculating the total demand for each part; considering the sharing coefficient, calculating the actual demand for various optional parts or accessories; combining the loading capacity of the target vehicle and the optimal loading plan determined in the previous step, allocating the picking order of different suppliers to ensure that the parts can arrive on time according to production requirements; generating a picking schedule, clarifying the picking time window for each supplier and the arrival time of the target vehicle, and reducing waiting and delays.

[0093] For example, according to the calculation results, from 10:00 am to 11:00 am, the demand for part A is 36 (60% * 30 units / hour * 1 hour * 2 units / vehicle * 1), and the demand for part B is 18 (40% * 30 units / hour * 1 hour * 3 units / vehicle * 0.5). Considering the loading capacity of the target vehicle and the optimal loading route, the system generates a picking schedule, arranging the picking order of suppliers for part A and part B according to the priority, ensuring that the parts are loaded on time and in the required quantity.

[0094] Through the implementation of steps S231 and S232, the overall logistics management becomes more scientific and refined, can effectively support the production plan, reduce logistics costs, and improve the efficiency of the supply chain. This is particularly important in industries such as automobile manufacturing that require high coordination, and helps to achieve the goals of just-in-time production and lean logistics.

[0095] Optionally, based on the size of the target appliance, the number of target appliances, and the carriage size information, a simulation scenario for appliance loading is generated, including:

[0096] Step S241, obtaining the combinability of the target appliance.

[0097] In step S241, the combinability refers to the possibility of combining appliances of different models or sizes in the carriage, including whether they can be stacked, placed side by side, or vertically, and under what conditions these combinations can be achieved. For example, for small (KLT) appliances, it is necessary to confirm whether they can be stacked on the pallet and the maximum number of stacking layers.

[0098] Step S242, determining the loading method of the target appliance based on the size and combinability of the target appliance.

[0099] In step S242, based on the size and combinability of the target appliance, the system will determine the optimal loading method for each type of appliance, including laying flat, stacking, or standing upright. This process may involve calculating the space utilization rate, load distribution, and operational convenience under each loading method and selecting the optimal solution. For example, for an appliance ASD103 with dimensions of 1450x1130x745mm, if its combinability allows stacking, the system will analyze the relationship between the number of stacking layers and the space utilization rate to determine the most appropriate number of stacking layers.

[0100] Step S243: Generate an appliance loading simulation scenario based on the size of the target appliance, the number of target appliances, the loading method of the target appliance, the carriage size, and the carriage shape.

[0101] In step S243, after determining the loading method of the target appliance, the system will generate one or more simulation scenarios for appliance loading based on the size, quantity, selected loading method of the target appliance, and the size and shape of the carriage. This scenario can be presented in software through 2D or 3D models, such as a carriage model created using AutoCAD or SolidWorks, and a loading layout diagram calculated based on Excel or professional logistics planning software. For example, if the carriage size is 9400mm x 2450mm x 2450mm, the system will simulate a scenario layout where 3 layers of ASD103 appliances are stacked, with 4 placed on each layer, 2 QWE062 appliances are placed side by side, and 3 ZXC103 appliances are placed upright, ensuring uniform load distribution and efficient space utilization.

[0102] Through steps S241 to S243, we can generate an efficient and safe appliance loading simulation scenario based on the size, quantity, and combinability of the target appliance, combined with the carriage size information. This strategy not only improves space utilization and operational convenience but also ensures the balance of load distribution and the safety of loading and unloading operations, having a significant optimization effect on the loading planning of local loop pickup wing vans in the logistics industry, helping to reduce transportation costs and improve supply chain efficiency.

[0103] Optionally, based on the size and combinability of the target appliance, determine the loading method of the target appliance, including:

[0104] Step S244: In response to the size of the target appliance being less than or equal to the preset size and the combinability being greater than or equal to the preset combinability, determine the loading method of the target appliance as stacking loading.

[0105] In step S244, when the size of the target appliance is less than or equal to the preset size (e.g., the preset size of small appliances), and its combinability is greater than or equal to the preset combinability (i.e., it can be stacked with other identical or different appliances, and the stacking stability meets the requirements), the system will determine the loading method of the appliance as stacked loading. This method can make full use of the vertical space and increase the loading capacity.

[0106] For example, for a parts box with dimensions of 600mm x 400mm x 300mm, if its combinability is high and it is allowed to be stacked with other appliances of the same or different models, the system will determine its loading method as stacked loading. Assuming the height of the carriage allows, 4 can be stacked in each layer and the stacking layer is 5 layers.

[0107] Step S245, in response to the size of the target appliance being greater than the preset size and the combinability being less than the preset combinability, determine the loading method of the target appliance as longitudinal loading.

[0108] In step S245, when the size of the target appliance is greater than the preset size (e.g., the preset size of large appliances), and its combinability is lower than the preset combinability (i.e., it is not easy to be stacked or combined with other appliances), the system will determine the loading method of the appliance as longitudinal loading. This method is suitable for long or special-sized appliances to ensure their stability and safety in the carriage.

[0109] For example, for a large appliance with dimensions of 2000mm x 700mm x 500mm, if its combinability is low and the width of the carriage is limited, the system will determine its loading method as longitudinal loading, that is, the appliance is placed along the length direction of the carriage to reduce the occupancy of the width space.

[0110] Through steps S244 and S245, we can determine the most suitable loading method according to the size and combinability of the target appliance. Whether it is stacked loading or longitudinal loading, it can ensure the efficient use of space, the reasonable distribution of load, as well as the convenience and safety of operation.

[0111] Figure 2 It is a flowchart of another method for planning the combined loading of carriage parts according to an embodiment of the present invention. As Figure 2 shown, the method includes the following steps:

[0112] Step S201, obtain the size of the target appliance.

[0113] Step S202, based on the number of parts to be taken, the sizes of each part to be taken, and the size of the target appliance, determine the number of target appliances.

[0114] Step S203: Generate an appliance loading simulation scenario based on the dimensions of the target appliance, the number of target appliances, and the carriage size information, where the appliance loading simulation scenario is used to simulate the scenario of loading the target appliance into the carriage of the target vehicle.

[0115] Step S204: Determine the target scenario for appliance loading based on the appliance loading simulation scenario.

[0116] Step S205: Generate a set of target control strategies based on the target scenario for appliance loading.

[0117] Step S211: Determine the number of appliances to be loaded on the target vehicle based on the target scenario for appliance loading.

[0118] Step S212: Confirm the number of suppliers for parts to be retrieved based on the number of appliances to be loaded, the number of parts to be retrieved, and each supplier of parts to be retrieved.

[0119] Step S213: Obtain the location information of each supplier of parts to be retrieved.

[0120] Step S214: Generate a loading route for the target vehicle based on the location information of each supplier of parts to be retrieved.

[0121] Step S215: Generate a set of target control strategies based on the loading route.

[0122] Step S221: Obtain real-time traffic data, which includes but is not limited to road congestion conditions, accident information, traffic signal status, etc.

[0123] Step S222: Predict the traffic conditions of each loading route based on the real-time traffic data, where the predicted traffic conditions include the estimated travel time, delay probability, etc.

[0124] Step S223: Use the optimal path algorithm to generate the optimal loading route for the target vehicle based on the real-time traffic data and the predicted traffic conditions, where the optimal loading route is the loading route with the lowest cost.

[0125] Step S224: Generate a set of target control strategies based on the optimal loading route.

[0126] Step S231: Obtain production parameters, where the production parameters include: production rhythm, vehicle type ratio, time period, parts usage per vehicle, and sharing coefficient. The sharing coefficient is the loading ratio of optional parts or accessories. The production rhythm information is the vehicle production per hour. The vehicle type ratio is used to calculate the parts requirements for different types of vehicles. The time period is used to determine the time period for picking up goods and loading operations. The parts usage per vehicle is used to calculate the parts demand for a single vehicle.

[0127] Step S232: Determine the picking-up plan information based on the production parameters.

[0128] Step S241, obtain the combinability of the target appliance.

[0129] Step S242, determine the loading method of the target appliance based on the size and combinability of the target appliance.

[0130] Step S243, generate an appliance loading simulation scenario based on the size of the target appliance, the number of target appliances, the loading method of the target appliance, the carriage size, and the carriage shape.

[0131] Step S244, in response to the size of the target appliance being less than or equal to the preset size and the combinability being greater than or equal to the preset combinability, determine that the loading method of the target appliance is stacked loading.

[0132] Step S245, in response to the size of the target appliance being greater than the preset size and the combinability being less than the preset combinability, determine that the loading method of the target appliance is longitudinal loading.

[0133] Based on the above steps S201 to S245, in the embodiments of the present invention, by obtaining detailed pick-up plan information and carriage information, applying an intelligent algorithm to generate a target control strategy set, and applying a three-dimensional bin packing optimization algorithm, combined with the part size and the carriage shape, the purpose of achieving more efficient part combination loading is achieved, thereby achieving the technical effect of significantly improving the utilization rate of the carriage space, and further solving the technical problems of insufficient space utilization rate and loading efficiency of the existing loop delivery vehicle. By generating a target control strategy set, it can guide the operator to load in the optimal order and manner, reduce loading errors, improve the efficiency of the loading operation, and ensure the safety of the parts, thereby improving the quality of the operation.

[0134] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware, but in many cases, the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes a contribution to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions for causing a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0135] In an embodiment of the present invention, a device for planning the combined loading of carriage parts is further provided. This device is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0136] Figure 3 It is a structural block diagram of a device for planning the combined loading of carriage parts according to an embodiment of the present invention. As Figure 3 shown, the device includes:

[0137] A first acquisition module 301, configured to acquire picking plan information, where the picking plan information includes: the number of parts to be picked and the dimensions of each part to be picked.

[0138] A second acquisition module 302, configured to acquire the carriage information of the target vehicle, where the carriage information includes: the carriage dimensions and the carriage shape.

[0139] A generation module 303, configured to generate a target control strategy set based on the picking plan information and the carriage dimensions, where the target control strategy set is used to perform loading operations on the target vehicle.

[0140] Optionally, the first acquisition module 301 is further configured to acquire the dimensions of the target appliance; the generation module 303 is further configured to determine the number of target appliances based on the number of parts to be picked, the dimensions of each part to be picked, and the dimensions of the target appliance; generate an appliance loading simulation scenario based on the dimensions of the target appliance, the number of target appliances, and the carriage dimension information, where the appliance loading simulation scenario is used to simulate the scenario of loading the target appliance into the carriage of the target vehicle; determine the target scenario for appliance loading based on the appliance loading simulation scenario; and generate a target control strategy set based on the target scenario for appliance loading.

[0141] Optionally, the generation module 303 is further configured to determine the number of appliances loaded on the target vehicle based on the target scenario for appliance loading; confirm the number of suppliers of parts to be picked based on the number of appliances loaded, the number of parts to be picked, and the suppliers of each part to be picked; acquire the location information of each supplier of parts to be picked; generate a loading route for the target vehicle based on the location information of each supplier of parts to be picked; and generate a target control strategy set based on the loading route.

[0142] Optionally, the generation module 303 is further configured to obtain real-time traffic data, which includes but is not limited to road congestion conditions, accident information, traffic signal states, etc.; based on the real-time traffic data, predict the traffic conditions of each loading route, where the predicted traffic conditions include the estimated travel time, delay probability, etc.; adopt an optimal path algorithm, and based on the real-time traffic data and the predicted traffic conditions, generate an optimal loading route for the target vehicle, where the optimal loading route is the loading route with the lowest cost; based on the optimal loading route, generate a set of target control strategies.

[0143] Optionally, the first acquisition module 301 is further configured to obtain production parameters, where the production parameters include: production cycle, vehicle type ratio, time period, parts usage per vehicle, and sharing coefficient, and the sharing coefficient is the loading ratio of optional parts or accessories, the production cycle information is the vehicle production per hour, the vehicle type ratio is used to calculate the parts requirements for different types of vehicles, the time period is used to determine the time period for picking up goods and loading operations, and the parts usage per vehicle is used to calculate the parts requirements for a single vehicle; based on the production parameters, determine the picking-up plan information.

[0144] Optionally, the second acquisition module 302 is further configured to obtain the combinability of the target appliance; the generation module 303 is further configured to determine the loading method of the target appliance based on the size and combinability of the target appliance; based on the size of the target appliance, the number of target appliances, the loading method of the target appliance, the carriage size, and the carriage shape, generate an appliance loading simulation scenario.

[0145] Optionally, the carriage parts loading and planning device further includes a determination module 304, configured to determine that the loading method of the target appliance is stacking loading in response to the size of the target appliance being less than or equal to a preset size and the combinability being greater than or equal to a preset combinability; determine that the loading method of the target appliance is longitudinal loading in response to the size of the target appliance being greater than the preset size and the combinability being less than the preset combinability.

[0146] Optionally, the generation module 303 is further configured to generate a first target control policy in the target control policy set in response to the appliance loading target scenario being a first loading target scenario, where the first loading target scenario is a scenario in which the number of parts that can be loaded in the carriage of the target vehicle is less than the number of parts to be retrieved, and the first target control policy is used to control the target vehicle to perform a loading operation at one loading destination; generate a second target control policy in the target control policy set in response to the appliance loading target scenario being a second loading target scenario, where the second loading target scenario is a scenario in which the number of parts that can be loaded in the carriage of the target vehicle is equal to the number of parts to be retrieved, and the second target control policy is used to control the target vehicle to perform a loading operation at one of the loading destinations; generate a third target control policy in the target control policy set in response to the appliance loading target scenario being a third loading target scenario, where the third loading target scenario is a scenario in which the number of parts that can be loaded in the carriage of the target vehicle is greater than the number of parts to be retrieved, and the third target control policy is used to control the target vehicle to perform loading operations at at least two of the loading destinations.

[0147] It should be noted that the above-mentioned modules can be implemented by software or hardware. For the latter, it can be achieved in the following ways, but not limited to: the above-mentioned modules are all located in the same processor; or, the above-mentioned modules are respectively located in different processors in any combination form.

[0148] According to an embodiment of the present invention, an electronic device is further provided, including: a memory storing an executable program; a processor for running the program, where when the program runs, it executes the above-mentioned carriage part consolidation planning method.

[0149] Optionally, in this embodiment, the above-mentioned processor can be set to execute the following steps through a computer program:

[0150] Step S102, obtain the pick-up plan information, where the pick-up plan information includes: the supplier of the parts to be retrieved, the number of parts to be retrieved, and the dimensions of each part to be retrieved.

[0151] Step S104, obtain the carriage information of the target vehicle, where the carriage information includes: the carriage dimensions and the carriage shape.

[0152] Step S106, generate a target control policy set based on the pick-up plan information and the carriage dimensions, and the target control policy set is used to perform a loading operation on the target vehicle.

[0153] According to one embodiment of the present invention, there is also provided a computer-readable storage medium, which includes a stored executable program. When the executable program runs, it controls the device where the storage medium is located to execute the above-mentioned carriage part loading and planning method.

[0154] Optionally, in this embodiment, the above storage medium can be set to store a computer program for executing the following steps:

[0155] Step S102, obtain the pick-up plan information, where the pick-up plan information includes: the supplier of the parts to be picked up, the quantity of the parts to be picked up, and the dimensions of each part to be picked up.

[0156] Step S104, obtain the carriage information of the target vehicle, where the carriage information includes: the carriage dimensions and the carriage shape.

[0157] Step S106, generate a target control strategy set based on the pick-up plan information and the carriage dimensions, and the target control strategy set is used for the loading operation of the target vehicle.

[0158] Optionally, in this embodiment, the above storage medium may include but is not limited to: various media such as USB flash drives, read-only memories (ROM for short), random access memories (RAM for short), mobile hard disks, magnetic disks, or optical discs that can store computer programs.

[0159] According to one embodiment of the present invention, there is also provided a computer program product, which includes a computer program that implements the above-mentioned carriage part loading and planning method when executed by a processor.

[0160] Optionally, in this embodiment, the above computer program product can be set to execute a computer program for the following steps:

[0161] Step S102, obtain the pick-up plan information, where the pick-up plan information includes: the supplier of the parts to be picked up, the quantity of the parts to be picked up, and the dimensions of each part to be picked up.

[0162] Step S104, obtain the carriage information of the target vehicle, where the carriage information includes: the carriage dimensions and the carriage shape.

[0163] Step S106, generate a target control strategy set based on the pick-up plan information and the carriage dimensions, and the target control strategy set is used for the loading operation of the target vehicle.

[0164] In the above embodiments of the present invention, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0165] In several embodiments provided in the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are merely illustrative. For example, the division of the units can be a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed with each other can be through some interfaces. The indirect couplings or communication connections of units or modules can be in electrical or other forms.

[0166] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0167] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0168] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks or optical discs that can store program codes.

[0169] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for planning the assembly of car parts, characterized in that: include: Acquire pickup plan information, wherein the pickup plan information includes: the supplier of the parts to be picked up, the number of parts to be picked up, and the size of each part to be picked up; Acquire the compartment information of the target vehicle, wherein the compartment information includes: compartment size and compartment shape; Based on the pickup plan information and the vehicle compartment size, a target control strategy set is generated, and the target control strategy set is used to perform a loading operation on the target vehicle.

2. The method according to claim 1, characterized in that Based on the pickup plan information and the carriage size, the target control strategy set is generated, including: Get the size of the target device; Determining the number of the target tools based on the number of parts to be taken, the size of each of the parts to be taken, and the size of the target tools; Based on the size of the target appliance, the number of the target appliances and the compartment size information, generating an appliance loading simulation scenario, wherein the appliance loading simulation scenario is used to simulate a scenario in which the target appliance is loaded in the compartment of the target vehicle; Determining a target scenario for equipment loading based on the equipment loading simulation scenario; Based on the appliance loading target scenario, the target control strategy set is generated.

3. The method according to claim 2, characterized in that Based on the device loading target scenario, generating the target control strategy set includes: Determining the number of equipment loaded on the target vehicle based on the equipment loading target scenario; Based on the loading quantity of the tool, the quantity of the parts to be picked up and each supplier of the parts to be picked up, confirm the quantity of the suppliers of the parts to be picked up; Obtaining location information of each supplier of the parts to be picked up; Based on the location information of each of the suppliers of the parts to be picked up, generating a loading route for the target vehicle; Based on the loading route, the target control strategy set is generated.

4. The method according to claim 3, characterized in that Based on the loading route, generating the target control strategy set also includes: Acquire real-time traffic data, including but not limited to road congestion, accident information, traffic light status, etc.; Based on the real-time traffic data, predict the traffic conditions of each loading route, wherein the predicted traffic conditions include estimated travel time, delay probability, etc.; Using an optimal path algorithm, based on the real-time traffic data and the predicted traffic conditions, to generate an optimal loading route for the target vehicle, wherein the optimal loading route is a loading route with the lowest cost; Based on the optimal loading route, the target control strategy set is generated.

5. The method according to any one of claims 1 to 4, characterized in that Obtain the pickup plan information, including: Obtaining production parameters, wherein the production parameters include: production tact, vehicle type ratio, time period, single vehicle parts usage and sharing coefficient, the sharing coefficient is the loading ratio of optional parts or optional accessories, the production tact information is the hourly vehicle output, the vehicle type ratio is used to calculate the parts demand of different types of vehicles, the time period is used to determine the time period for picking up and loading operations, and the single vehicle parts usage is used to calculate the parts demand of a single vehicle; Based on the production parameters, the pickup plan information is determined.

6. The method according to any one of claims 1 to 4, generating the equipment loading simulation scenario based on the size of the target equipment, the number of the target equipment and the compartment size information, comprising: Obtaining the combinability of the target device; Determining a loading method of the target device based on the size of the target device and the degree of combinability; The equipment loading simulation scenario is generated based on the size of the target equipment, the number of the target equipment, the loading method of the target equipment, the compartment size, and the compartment shape.

7. The method according to claim 6, characterized in that Determining a loading method of the target device based on the size of the target device and the degree of combinability includes: In response to the size of the target utensil being less than or equal to a preset size, and the combinability being greater than or equal to a preset combinability, determining that the loading mode of the target utensil is stacked loading; In response to the size of the target device being larger than a preset size and the combinability being smaller than a preset combinability, it is determined that the loading mode of the target device is longitudinal loading.

8. A vehicle compartment parts assembly planning device, characterized in that: include: A first acquisition module is used to acquire pickup plan information, wherein the pickup plan information includes: the number of parts to be picked up and the size of each part to be picked up; The second acquisition module acquires the compartment information of the target vehicle, wherein the compartment information includes: the compartment size and the compartment shape; A generation module is used to generate a target control strategy set based on the pickup plan information and the vehicle compartment size, and the target control strategy set is used to perform loading operations on the target vehicle.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is executed, the device where the storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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