Optimization method for unmanned trunk distribution scheme facing heavy livelihood performance

Abstract

The application discloses an unmanned trunk line distribution scheme optimization method for heavy goods livelihood performance, relates to the technical field of intelligent logistics scheduling, and comprises the following steps: S1, obtaining loading space information of an unmanned trunk line heavy truck and distribution information of heavy goods to be loaded; S2, according to the distance of each goods destination from the current distribution starting point, sorting all the goods to be loaded in order from far to near, and generating an initial loading sequence; the standardization operation of the warehouse truck loading link of the heavy goods trunk transportation center of the livelihood is realized, the target goods can be sequentially taken out without searching and moving external goods during unloading, the number of times of carrying is significantly reduced, and the unloading efficiency is greatly improved; meanwhile, through the heavy goods exclusive stacking layer number limitation, the nearby placement of overweight goods and the left-right weight balance constraint of the vehicle, the risk of goods damage and the vehicle driving roll-over hidden danger during transportation are effectively reduced, the loading quality is stable and controllable, and the loading demand of the heavy goods trunk transportation of the livelihood is fully met.

Description

Technical Field

[0001] This invention relates to the field of intelligent logistics scheduling technology, specifically to an optimization method for unmanned trunk line delivery solutions for fulfilling heavy and essential goods deliveries. Background Technology

[0002] With the development of the consumer market, the demand for the delivery of heavy daily necessities such as bottled water, rice, flour and cooking oil continues to grow. These goods are characterized by high unit weight, fast turnover, high risk of damage, and strict requirements for delivery timeliness, which puts forward higher requirements for the logistics fulfillment system.

[0003] Currently, loading scheduling in trunk transportation mainly relies on the transportation management system to aggregate orders and then on-site personnel to arrange operations based on their experience. Existing loading methods mostly follow the order of warehouse picking or order entry. Although some solutions incorporate delivery sequence logic, they usually adopt a first-load-first-delivery strategy, placing the goods to be delivered first on the innermost side of the vehicle. In trunk transportation scenarios for essential goods such as bottled water, rice, flour, and cooking oil, these items are heavy, cylindrical goods are prone to rolling, and packaging bags are easily crushed. During unloading, it is necessary to repeatedly search for and move external goods to retrieve the target goods, resulting in numerous handling operations, long processing times, and a high risk of damage. At the same time, existing methods do not set specific constraints for the physical characteristics of heavy goods such as preventing tipping and stacking, placing overweight single items nearby, and maintaining the left and right weight balance of the vehicle. They also lack a visual output method for loading plans, making it difficult to meet the loading needs of trunk transportation of essential goods.

[0004] Therefore, in view of this, the present invention proposes an optimized method for unmanned trunk line delivery solutions for heavy goods and people's livelihoods to make up for and improve the shortcomings of the existing technology. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides an optimized method for unmanned trunk line delivery solutions for heavy goods and people's livelihoods, thereby resolving the corresponding technical issues raised in the background section.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: an optimized method for unmanned trunk line delivery solutions for fulfilling heavy and essential consumer needs, comprising the following steps: S1, Obtain the loading space information of the unmanned trunk line heavy truck and the delivery information of the heavy goods to be loaded. The delivery information includes the destination, weight, size and form category of each cargo. S2, based on the distance of each cargo destination from the current delivery starting point, sort all cargo to be loaded in order from farthest to nearest, and generate an initial loading sequence; S3. According to the initial loading sequence, the goods are arranged into the vehicle in order from far to near, and the number of stacking layers is limited based on the shape and type of the goods. The single overweight goods are placed in the area near the vehicle door, and the weight difference between the left and right cargo areas of the vehicle is adjusted to a preset range to generate a target loading plan. The target loading plan is used to guide the loading operation of unmanned trunk line heavy trucks in the central warehouse. S4, based on the target loading plan, generates a loading topology map to guide on-site loading.

[0007] As a preferred embodiment, the specific process for generating the initial loading sequence is as follows: Obtain the destination information for each shipment and calculate the distance between each destination and the current delivery origin. All goods to be loaded are arranged in descending order of distance from farthest to nearest, with the goods corresponding to the farthest destination placed at the beginning of the sequence and the goods corresponding to the nearest destination placed at the end of the sequence. The sorted cargo sequence is output as the initial loading sequence.

[0008] As a preferred method, the specific process for limiting the number of stacking layers based on the form category of the goods is as follows: Obtain the shape category information of each cargo, including cylindrical cargo and square cargo; For cylindrical goods, they are limited to single-layer stacking only, and no two cylindrical goods may be stacked together vertically. For square goods, they are restricted to being stacked at the bottom layer, and the total number of stacked layers shall not exceed 3.

[0009] As a preferred option, the specific process for arranging a single overweight item in the area near the vehicle door is as follows: Based on the weight information of each item, items weighing more than 20kg are selected and marked as overweight items. The loading location for overweight cargo is allocated to the area near the vehicle door within 1 meter of the loading space door; Non-overweight cargo is arranged in the remaining areas of the loading space.

[0010] As a preferred method, the specific process for adjusting the weight difference between the left and right cargo areas of the vehicle to a preset range is as follows: Divide the left and right sides of the loading space into a left cargo area and a right cargo area, and obtain the total weight of all goods in the left cargo area and the total weight of all goods in the right cargo area. Calculate the absolute value of the weight difference between the left and right cargo areas, and calculate the percentage of this absolute value of weight difference to the total weight of the vehicle when fully loaded. Based on a preset percentage threshold, the percentage is determined as follows: If the percentage exceeds the preset percentage threshold, then, without exceeding the constraints of reverse order and stacking layer, the loading position of some goods between the left and right cargo areas will be adjusted so that the percentage of the weight difference between the left and right cargo areas to the total weight of the vehicle does not exceed the preset percentage threshold.

[0011] As a preferred method, the specific process for generating the target loading scheme is as follows: Using the initial loading sequence as input, obtain the delivery information of each item in the sequence; According to the initial loading sequence, the goods are sequentially assigned to the candidate loading positions within the loading space; When assigning a loading location to each cargo, the location is constrained to meet the following verification: The constraints include the number of stacking layers based on cargo type, the area near the vehicle door for single overweight cargo, and the weight difference between the left and right cargo areas of the vehicle. If the conditions are met, then lock the position; If the conditions are not met, the next candidate position is searched in order of distance from the car door from closest to furthest until a position that satisfies all constraints is found. Once all cargo has been assigned a loading location, a data set containing the correspondence between each cargo and its loading location is output as the target loading plan.

[0012] As a preferred embodiment, the specific process for generating a loading topology diagram to guide on-site loading is as follows: Obtain the target loading plan and extract the loading position coordinates of each cargo within the loading space; A three-dimensional spatial mesh model is generated based on the dimensions of the loading space and the dimensions of each cargo. The loading location coordinates of each cargo are mapped onto a three-dimensional spatial mesh model to generate a three-dimensional loading topology map that includes cargo distribution, partition identification, and stacking hierarchy. Output a 3D loading topology map to a display terminal or printing device to guide on-site loading personnel to complete the loading operation according to the location shown in the topology map.

[0013] Compared with existing technologies, the beneficial effects of this invention are as follows: By acquiring the delivery information of heavy goods to be loaded, an initial loading sequence is generated according to the order of destination from far to near, and the loading position of each item in the loading space is determined based on this sequence. On this basis, constraints based on the stacking layer number, the near-door area constraint of a single overweight item, and the weight difference constraint of the left and right cargo areas of the vehicle are superimposed to generate a target loading scheme to guide the loading operation of unmanned trunk heavy trucks in the central warehouse and output a loading topology map. This enables loading position planning for unloading efficiency to be completed at the loading stage, so that the target goods can be taken out sequentially without searching and moving external goods during unloading, significantly reducing the number of handling operations. At the same time, specific constraints are set for the physical characteristics of heavy objects such as anti-tipping stacking, placement of overweight goods nearby, and left and right counterweight balance of the vehicle, which effectively reduces the risk of cargo damage and vehicle tilting hazards during transportation. Through standardized scheme generation and visualization topology map output, on-site loading operations are freed from dependence on human experience, and the loading quality is stable and controllable, fully meeting the loading needs of trunk transportation of heavy goods for people's livelihood. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the overall structure of a preferred embodiment of the present invention; Figure 2 This is a flowchart of the method shown in this invention; Figure 3 This is a flowchart illustrating the process of generating a loading topology diagram to guide on-site loading, as shown in this invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Embodiments of the present invention: Please refer to Figures 1 to 3 As shown, the optimization method for unmanned trunk line delivery solutions for heavy goods essential to people's livelihoods is applied to the loading scenario of trunk line transportation center warehouses for heavy goods such as bottled water, rice, flour, and cooking oil. Taking the shipment of bottled water from the center warehouse to the city distributor as an example, the delivered goods are 18.9L bottled water (each bottle weighs approximately 20KG and is cylindrical). The optimization method of this invention is used for center warehouse loading planning: Specifically, the following steps are included: S1, obtain the loading space information of the unmanned trunk line heavy truck, and at the same time obtain the delivery information of the heavy cargo to be loaded. The delivery information includes the destination, weight, size and form category of each cargo. In this embodiment, the system obtains that the unmanned trunk heavy truck model is a 13-meter semi-trailer with a cargo volume of 85 cubic meters and a maximum load of 32 tons. The cargo to be loaded is bottled water, which needs to be delivered to multiple urban distribution company transit warehouses. The weight of a single bottle is 20KG, and the shape is cylindrical. The system summarizes the information obtained into a loading task dataset. S2, based on the distance of each cargo destination from the current delivery starting point, sort all cargo to be loaded in order from farthest to nearest, and generate an initial loading sequence; S3, based on the initial loading sequence, arrange the goods into the vehicle in order from far to near, and limit the number of stacking layers based on the shape and type of the goods. Place the single overweight goods in the area near the vehicle door, adjust the weight difference between the left and right cargo areas of the vehicle to a preset range, and generate a target loading plan. The target loading plan is used to guide the loading operation of unmanned trunk line heavy trucks in the central warehouse. S4, based on the target loading plan, generates a loading topology map to guide on-site loading.

[0017] Compared with existing technologies, this invention achieves standardized operations in the loading and unloading process of the heavy cargo trunk transportation center. During unloading, the target goods can be retrieved sequentially without having to search for or move external goods, significantly reducing the number of handling operations and greatly improving unloading efficiency. At the same time, through the specific stacking layer limit for heavy goods, the placement of overweight goods nearby, and the left and right weight balance constraint of the vehicle, the risk of cargo damage and vehicle tilting hazards during transportation are effectively reduced. The loading quality is stable and controllable, fully meeting the loading needs of heavy cargo trunk transportation for people's livelihood.

[0018] The specific process for generating the initial load sequence is as follows: Obtain the destination information for each shipment and calculate the distance between each destination and the current delivery origin. All goods to be loaded are arranged in descending order of distance from farthest to nearest, with the goods corresponding to the farthest destination placed at the beginning of the sequence and the goods corresponding to the nearest destination placed at the end of the sequence. Output the sorted cargo sequence as the initial loading sequence; Let the current central warehouse be the delivery origin, and the distance between each destination and the origin be... The system sorts all goods to be loaded in order of distance from farthest to nearest, i.e.: Sort in descending order; In this embodiment, the system sorts the goods of each transit warehouse from farthest to closest to the central warehouse, with the goods of the farthest transit warehouse at the beginning of the sequence and the goods of the closest transit warehouse at the end of the sequence.

[0019] The specific process for limiting the number of stacking layers based on the form category of goods is as follows: Obtain the shape category information of each cargo, including cylindrical cargo and square cargo; For cylindrical goods, they are limited to single-layer stacking only, and no two cylindrical goods may be stacked together vertically. For square goods, they are restricted to being stacked at the bottom layer, and the total number of stacked layers shall not exceed 3. In this embodiment, bottled water is a cylindrical cargo, and the system restricts it to single-layer stacking, meaning only one layer of bottled water can be stacked at a time, and stacking bottled water on top of each other is prohibited. Let the stacking height at any location within the vehicle be H, and the height of a single layer of bottled water be h. Then the stacking constraint for cylindrical cargo is: h.

[0020] The specific process of placing a single overweight item in the area near the vehicle door is as follows: Based on the weight information of each item, items weighing more than 20kg are selected and marked as overweight items. The loading location for overweight cargo is allocated to the area near the vehicle door within 1 meter of the loading space door; Non-overweight cargo is arranged in the remaining areas of the loading space; Let L be the distance from the location of the cargo to the vehicle door. The loading constraints for overweight cargo are: ; In this embodiment, the weight of a single bottle of water is 20KG, which reaches the overweight threshold, so the system allocates it to the area near the vehicle door.

[0021] The specific process for adjusting the weight difference between the left and right cargo areas of the vehicle to a preset range is as follows: Divide the left and right sides of the loading space into a left cargo area and a right cargo area, and obtain the total weight of all goods in the left cargo area and the total weight of all goods in the right cargo area. Calculate the absolute value of the weight difference between the left and right cargo areas, and calculate the percentage of this absolute value of weight difference to the total weight of the vehicle when fully loaded. Based on a preset percentage threshold, the percentage is determined as follows: If the percentage exceeds the preset percentage threshold, then, without exceeding the constraints of reverse order and stacking layer, the loading position of some goods between the left and right cargo areas will be adjusted so that the percentage of the weight difference between the left and right cargo areas after adjustment to the total weight of the vehicle does not exceed the preset percentage threshold. Let the total weight of the left cargo area be... The total weight of the right cargo area is The total weight of the vehicle when fully loaded is If the preset percentage threshold is k, then the counterweight balance constraint is: ; In this embodiment, a preset percentage threshold is used. When the system allocates goods to the left and right cargo areas in reverse order, it calculates the percentage difference in weight between the left and right sides in real time to ensure that it does not exceed 3%. Under the condition that the above three constraints are satisfied, the system determines the specific loading position of each item in the carriage and finally generates a target loading plan, which is used to guide the loading operation of unmanned trunk heavy trucks in the central warehouse.

[0022] The specific process for generating the target loading scheme is as follows: Using the initial loading sequence as input, obtain the delivery information of each item in the sequence; According to the initial loading sequence, the goods are sequentially assigned to the candidate loading positions within the loading space; When assigning a loading location to each cargo, the location is constrained to meet the following verification: The constraints include the number of stacking layers based on the cargo type, the area near the vehicle door for single overweight cargo, and the weight difference between the left and right cargo areas of the vehicle. If the conditions are met, then lock the position; If the conditions are not met, the next candidate position is searched in order of distance from the car door from closest to furthest until a position that satisfies all constraints is found. Once all cargo has been assigned a loading location, a data set containing the correspondence between each cargo and its loading location is output as the target loading plan.

[0023] The specific process for generating a loading topology diagram to guide on-site loading is as follows: Obtain the target loading plan and extract the loading position coordinates of each cargo within the loading space; A three-dimensional spatial mesh model is generated based on the dimensions of the loading space and the dimensions of each cargo. The loading location coordinates of each cargo are mapped onto a three-dimensional spatial mesh model to generate a three-dimensional loading topology map that includes cargo distribution, partition identification, and stacking hierarchy. Output a 3D loading topology map to a display terminal or printing device to guide on-site loading personnel to complete the loading operation according to the location shown in the topology map; In this embodiment, the system constructs a three-dimensional spatial mesh model based on the dimensions of the 13-meter semi-trailer's cargo compartment and the dimensions of a single water barrel. The loading position coordinates of each cargo in the target loading plan are mapped to the three-dimensional spatial mesh model to generate a three-dimensional loading topology map. This topology map clearly displays the partition markings, stacking levels, and unloading sequence markings in a visual manner. The system outputs the three-dimensional loading topology map to the display terminal in the central warehouse. On-site loading personnel place the cargo one by one according to the positions shown in the topology map, without having to determine the placement position and order themselves.

[0024] In another embodiment, the goods to be loaded are rice, flour, and cooking oil (including rice, cooking oil, flour, and other essential consumer goods) and delivered to multiple terminal stores. Some of the goods are in square packaging (rice and flour), while others are cylindrical (cooking oil drums). After obtaining the shape and category information of each type of goods in the S3 stage, the system restricts the square goods to be stacked at the bottom and the total number of stacked layers to no more than 3. The cylindrical goods are restricted to being stacked in a single layer. At the same time, goods weighing more than 20KG are marked as overweight goods and are all arranged in the area near the vehicle door. After loading, the square goods are stably stacked at the bottom, and the cylindrical goods are stacked in a single layer. The weight difference between the left and right cargo areas is controlled within 3%.

[0025] Compared with existing technologies, this invention obtains the delivery information of heavy goods to be loaded, generates an initial loading sequence according to the destination from farthest to nearest, and determines the loading position of each item in the loading space based on this sequence. On this basis, it superimposes constraints based on the stacking layer number based on shape category, the near-door area constraint of single overweight items, and the weight difference constraint of the left and right cargo areas of the vehicle, to generate a target loading scheme to guide the loading operation of unmanned trunk heavy trucks in the central warehouse and outputs a loading topology map. It can complete the loading position planning for unloading efficiency during the loading process, so that the target goods can be retrieved sequentially without searching and moving external goods during unloading, significantly reducing the number of handling operations. At the same time, it sets specific constraints for the physical characteristics of heavy objects such as anti-tipping stacking, placement of overweight goods nearby, and left and right counterweight balance of the vehicle, effectively reducing the risk of cargo damage and vehicle tilting hazards during transportation. Through standardized scheme generation and visualization topology map output, on-site loading operations are freed from dependence on human experience, and the loading quality is stable and controllable, fully meeting the loading needs of trunk transportation of heavy goods for people's livelihood.

[0026] The size of the interval and threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value.

[0027] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation. In the two embodiments provided in this application, it should be understood that the disclosed apparatus and system can be implemented in other ways; for example, the apparatus embodiments described above are merely illustrative, and the division of modules is merely a logical functional division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed; furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of the apparatus or module may be electrical, mechanical or other forms. The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An optimization method for unmanned trunk line delivery solutions for fulfilling heavy and essential consumer needs, characterized in that: Includes the following steps: S1, Obtain the loading space information of the unmanned trunk line heavy truck and the delivery information of the heavy goods to be loaded. The delivery information includes the destination, weight, size and form category of each cargo. S2, based on the distance of each cargo destination from the current delivery starting point, sort all cargo to be loaded in order from farthest to nearest, and generate an initial loading sequence; S3. According to the initial loading sequence, the goods are arranged into the vehicle in order from far to near, and the number of stacking layers is limited based on the shape and type of the goods. The single overweight goods are placed in the area near the vehicle door, and the weight difference between the left and right cargo areas of the vehicle is adjusted to a preset range to generate a target loading plan. The target loading plan is used to guide the loading operation of unmanned trunk line heavy trucks in the central warehouse. S4, based on the target loading plan, generates a loading topology map to guide on-site loading.

2. The optimization method for unmanned trunk line delivery solutions for heavy goods and people's livelihood fulfillment as described in claim 1, characterized in that, The specific process for generating the initial load sequence is as follows: Obtain the destination information for each shipment and calculate the distance between each destination and the current delivery origin. All goods to be loaded are arranged in descending order of distance from farthest to nearest, with the goods corresponding to the farthest destination placed at the beginning of the sequence and the goods corresponding to the nearest destination placed at the end of the sequence. The sorted cargo sequence is output as the initial loading sequence.

3. The optimization method for unmanned trunk line delivery solutions for heavy goods and people's livelihood fulfillment as described in claim 1, characterized in that, The specific process for limiting the number of stacking layers based on the form category of goods is as follows: Obtain the shape category information of each cargo, including cylindrical cargo and square cargo; For cylindrical goods, they are limited to single-layer stacking only, and no two cylindrical goods may be stacked together vertically. For square goods, they are restricted to being stacked at the bottom layer, and the total number of stacked layers shall not exceed 3.

4. The optimization method for unmanned trunk line delivery scheme for fulfilling heavy goods and people's livelihood needs according to claim 1, characterized in that, The specific process of placing a single overweight item in the area near the vehicle door is as follows: Based on the weight information of each item, items weighing more than 20kg are selected and marked as overweight items. The loading location for overweight cargo is allocated to the area near the vehicle door within 1 meter of the loading space door; Non-overweight cargo is arranged in the remaining areas of the loading space.

5. The optimization method for unmanned trunk line delivery solutions for heavy goods and people's livelihood fulfillment as described in claim 1, characterized in that, The specific process for adjusting the weight difference between the left and right cargo areas of the vehicle to a preset range is as follows: Divide the left and right sides of the loading space into a left cargo area and a right cargo area, and obtain the total weight of all goods in the left cargo area and the total weight of all goods in the right cargo area. Calculate the absolute value of the weight difference between the left and right cargo areas, and calculate the percentage of this absolute value of weight difference to the total weight of the vehicle when fully loaded. Based on a preset percentage threshold, the percentage is determined as follows: If the percentage exceeds the preset percentage threshold, then, without exceeding the constraints of reverse order and stacking layer, the loading position of some goods between the left and right cargo areas will be adjusted so that the percentage of the weight difference between the left and right cargo areas to the total weight of the vehicle does not exceed the preset percentage threshold.

6. The optimization method for unmanned trunk line delivery scheme for heavy goods and people's livelihood fulfillment according to claim 1, characterized in that, The specific process for generating the target loading scheme is as follows: Using the initial loading sequence as input, obtain the delivery information of each item in the sequence; According to the initial loading sequence, the goods are sequentially assigned to the candidate loading positions within the loading space; When assigning a loading location to each cargo, the location is constrained to meet the following verification: The constraints include the number of stacking layers based on cargo type, the area near the vehicle door for single overweight cargo, and the weight difference between the left and right cargo areas of the vehicle. If the conditions are met, then lock the position; If the conditions are not met, the next candidate position is searched in order of distance from the car door from closest to furthest until a position that satisfies all constraints is found. Once all cargo has been assigned a loading location, a data set containing the correspondence between each cargo and its loading location is output as the target loading plan.

7. The optimization method for unmanned trunk line delivery solutions for heavy goods and people's livelihood fulfillment as described in claim 1, characterized in that, The specific process for generating a loading topology diagram to guide on-site loading is as follows: Obtain the target loading plan and extract the loading position coordinates of each cargo within the loading space; A three-dimensional spatial mesh model is generated based on the dimensions of the loading space and the dimensions of each cargo. The loading location coordinates of each cargo are mapped onto a three-dimensional spatial mesh model to generate a three-dimensional loading topology map that includes cargo distribution, partition identification, and stacking hierarchy. Output a 3D loading topology map to a display terminal or printing device to guide on-site loading personnel to complete the loading operation according to the location shown in the topology map.

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