Truck loading method and device based on intelligent optimization algorithm and storage medium

Abstract

The invention discloses a truck loading method and device based on an intelligent optimization algorithm and a storage medium, and relates to the technical field of logistics transportation, the method comprises the steps that parameters of goods to be loaded are obtained, and the parameters of the goods to be loaded comprise at least one of the size of the goods to be loaded, the shape of the goods to be loaded and the weight of the goods to be loaded; target carriage information is obtained, and the target carriage information comprises at least one of the following information: the target carriage size, the target carriage shape and the target carriage load limit; generating a target loading strategy based on the parameters of the to-be-loaded goods and the target carriage information; and carrying out loading operation on the to-be-loaded goods based on the target loading strategy. The technical problem that a loading scheme in the prior art is low in loading rate is solved.

Description

Technical Field

[0001] The present invention relates to the field of logistics and transportation technology, and in particular to a truck loading method, device and storage medium based on an intelligent optimization algorithm. Background Art

[0002] In the current logistics and transportation industry, the truck loading rate is one of the important factors affecting transportation costs. Due to the uneven size and weight of goods, as well as the single loading method, problems such as wasted compartment space, low loading rate, and high transportation costs often occur. Therefore, how to optimize the cargo loading plan and improve the truck loading rate has become an urgent problem to be solved in the field of logistics and transportation.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present invention provide a truck loading method, device and storage medium based on an intelligent optimization algorithm, so as to at least solve the technical problem of low loading rate of loading schemes in the prior art.

[0005] According to one aspect of an embodiment of the present invention, a truck loading method based on an intelligent optimization algorithm is provided, comprising: obtaining parameters of goods to be loaded, wherein the parameters of the goods to be loaded include at least one of the following: size of the goods to be loaded, shape of the goods to be loaded, and weight of the goods to be loaded; obtaining target compartment information, wherein the target compartment information includes at least one of the following: target compartment size, target compartment shape, and target compartment load limit; generating a target loading strategy based on the parameters of the goods to be loaded and the target compartment information; and performing loading operations on the goods to be loaded based on the target loading strategy.

[0006] Further, based on the parameters of the cargo to be loaded and the target compartment information, a target loading strategy is generated, including: based on the parameters of the cargo to be loaded and the target compartment information, generating multiple simulated loading schemes; based on the multiple simulated loading schemes, confirming the target loading strategy.

[0007] Furthermore, based on the parameters of the cargo to be loaded and the target compartment information, multiple simulated loading schemes are generated, including: based on the size of the cargo to be loaded and the target compartment size, a first simulated loading scheme among multiple simulated loading schemes is generated; based on the shape of the cargo to be loaded and the target compartment shape, a second simulated loading scheme among multiple simulated loading schemes is generated; based on the weight of the cargo to be loaded and the target compartment load limit, a third simulated loading scheme among multiple simulated loading schemes is generated.

[0008] Furthermore, based on multiple simulated loading schemes, a target loading strategy is generated, including: based on multiple simulated loading schemes, multiple simulated loading scenarios are generated, the simulated loading scenarios are used to simulate the loading positions of the parameters of each cargo to be loaded in each simulated loading scheme; based on multiple simulated loading scenarios, a target loading scenario is confirmed; based on the target loading scenario, a target loading strategy is generated.

[0009] Furthermore, based on the target loading scenario, a target loading strategy is generated, including: obtaining current position information of the goods to be loaded and target position information of the goods to be loaded in the target loading scenario; based on the current position information of the goods to be loaded and the target position information of the goods to be loaded, generating a first loading strategy in the target loading strategy, the first loading strategy being used to control the loading operation of the goods to be loaded along the target path.

[0010] Furthermore, based on the target loading scenario, a target loading strategy is generated, including: obtaining the size of the connecting device in the target loading scenario, wherein the connecting device is used to connect goods to be loaded of different sizes; based on the size of the goods to be loaded and the size of the connecting device, generating a second loading strategy in the target loading strategy, the second loading strategy being used to control the connecting device to connect adjacent goods to be loaded.

[0011] According to another aspect of an embodiment of the present invention, a truck loading device based on an intelligent optimization algorithm is also provided, including: a first acquisition module, the first acquisition module is used to acquire parameters of the goods to be loaded, wherein the parameters of the goods to be loaded include at least one of the following: the size of the goods to be loaded, the shape of the goods to be loaded, and the weight of the goods to be loaded; a second acquisition module, the second acquisition module is used to acquire target compartment information, wherein the target compartment information includes at least one of the following: target compartment size, target compartment shape, target compartment load limit; a confirmation module, the confirmation module is used to confirm the target loading strategy based on the parameters of the goods to be loaded and the target compartment information; and a loading module, the loading module is used to perform loading operations based on the target loading strategy.

[0012] Furthermore, the loading module comprises: a connecting device module, and the connecting device module is used to control the connecting device to connect goods to be loaded of different sizes.

[0013] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0014] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0015] In an embodiment of the present invention, the parameters of the goods to be loaded and the target compartment information are obtained, a target loading strategy is generated, and the goods to be loaded are loaded based on the target loading strategy. Through data collection, intelligent analysis and automatic control, the optimization of the truck during the loading process is achieved, thereby improving transportation efficiency and safety, thereby solving the technical problem of low loading rate of loading schemes in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0017] Figure 1 is a flow chart of a truck loading method based on an intelligent optimization algorithm according to an embodiment of the present invention;

[0018] Figure 2 is a schematic diagram of a truck loading device based on an intelligent optimization algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0020] 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 are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] According to an embodiment of the present invention, a truck loading method embodiment based on an intelligent optimization algorithm 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 a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0022] Figure 1 is a method according to an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0023] Step S100, obtaining parameters of the cargo to be loaded, wherein the parameters of the cargo to be loaded include at least one of the following: size of the cargo to be loaded, shape of the cargo to be loaded, and weight of the cargo to be loaded.

[0024] In step S100, detailed information of each piece of cargo to be loaded needs to be collected, including but not limited to size (length, width, height), shape (cube, cylinder, irregular shape, etc.) and weight. This information can be automatically read from the label data on the cargo by the data acquisition module, or manually input into the system. Accurate cargo parameters are the basis for subsequent optimization algorithms, ensuring that the algorithm can accurately calculate the optimal placement of the cargo in the carriage.

[0025] Step S200, obtaining target compartment information, wherein the target compartment information includes at least one of the following: target compartment size, target compartment shape, and target compartment load limit.

[0026] In step S200, the system needs to obtain detailed information about the target compartment, including the size (internal length, width, height), shape (e.g., whether it is rectangular, whether there are compartments of special shapes, etc.), and the load limit of the compartment. This data can also be automatically obtained through preset sensors or manually input into the system by the operator. The acquisition of compartment information is another key factor in formulating loading strategies, ensuring that the loading plan fully utilizes the space without exceeding the load limit.

[0027] Step S300, generating a target loading strategy based on the parameters of the goods to be loaded and the target compartment information.

[0028] In step S300, after collecting detailed information about the cargo and the carriage, the intelligent optimization algorithm module starts working. It uses the previously mentioned intelligent optimization algorithms, such as gradient descent, momentum, Adagrad, RMSprop, and Adam, to calculate how to optimally load all the cargo into the carriage. The goal of the algorithm is to maximize the loading rate while ensuring the stability and safety of the cargo. The generated loading strategy includes the specific location of the cargo in the carriage, the placement direction, and the method of using the equipment connection mechanism to keep the cargo stable.

[0029] Step S400: Loading the cargo based on the target loading strategy.

[0030] In step S400, the last step is to actually load the goods according to the loading strategy generated in step S300. This step can be performed by loaders or automated equipment. Loaders will use the 3D simulation display module to view the visual display of the loading plan. After confirming that it is correct, they will use the tool connection mechanism module to automatically adjust the position and load the goods into the carriage according to the instructions in the plan. Automated equipment can automatically adjust the position and angle of the loading manipulator directly according to the instructions output by the algorithm to accurately place each piece of cargo in the optimal position.

[0031] Through the above steps, not only the truck loading rate is improved and the transportation cost is reduced, but also the loading efficiency and safety are significantly improved through automation and intelligent means.

[0032] Furthermore, based on the parameters of the goods to be loaded and the target compartment information, a target loading strategy is generated, including:

[0033] Step S310, generating multiple simulated loading plans based on the parameters of the goods to be loaded and the target compartment information.

[0034] In step S310, the algorithm can randomly generate an initial cargo layout, and then adjust the cargo position through the optimization algorithm to generate different loading configurations. The algorithm can try to generate loading solutions under different constraints, for example, giving priority to weight distribution, center of gravity position, cargo stability or cargo sequence, etc. In addition to maximizing the stowage rate, the algorithm can also consider multiple goals at the same time, such as minimizing loading and unloading time, reducing the risk of cargo damage, etc., to generate loading solutions that meet different goals.

[0035] Step S320: confirming a target loading strategy based on multiple simulated loading schemes.

[0036] In step S320, after generating multiple simulated loading schemes, the system (or operator) needs to evaluate and compare these schemes to confirm the final target loading strategy. The evaluation may include the following aspects: 1. Evaluate which scheme can maximize the space utilization of the cargo in the carriage. Check whether each scheme can ensure the stability and safety of the cargo during transportation, and avoid the center of gravity shift or cargo collapse. 2. Analyze which scheme has the simplest and fastest loading and unloading process, reducing loading and unloading time. 3. Consider the transportation costs, including fuel consumption, labor costs, etc., and select the most cost-effective scheme. Finally, according to the above evaluation criteria, select one or combine the advantages of several schemes to generate the final target loading strategy. This strategy will serve as a guide for the loading operation to ensure that the loading and unloading process is both efficient and safe. At the same time, step S320 may also include converting the final loading strategy into a 3D visualization model so that the operator can intuitively understand the loading scheme and improve the accuracy and efficiency of execution.

[0037] Through steps S310 and S320, the system can more comprehensively consider various possibilities and constraints, thereby generating an optimal loading strategy.

[0038] Furthermore, based on the parameters of the goods to be loaded and the target compartment information, multiple simulated loading plans are generated, including:

[0039] Step S311 : generating a first simulated loading scheme among a plurality of simulated loading schemes based on the size of the cargo to be loaded and the target compartment size.

[0040] In step S311, the intelligent optimization algorithm focuses on the size parameters, namely the length, width, height of the goods and the length, width, height of the interior of the carriage. The algorithm can generate the first simulated loading plan in the following ways: 1. Analyze the size of each piece of goods and try to find the best placement plan to fill every corner of the carriage as much as possible while ensuring that each piece of goods can be loaded without exceeding the physical boundaries of the carriage. 2. Divide the interior space of the carriage to find the best space configuration that can accommodate all cargo sizes. For goods whose height is less than the interior height of the carriage, the algorithm will consider the feasibility of stacking to maximize the utilization of vertical space.

[0041] Step S312: generating a second simulated loading scheme among a plurality of simulated loading schemes based on the shape of the cargo to be loaded and the shape of the target carriage.

[0042] In step S312, the algorithm will deal with the complexity of the shape of the cargo and how to adapt it to the internal shape of the carriage. For example, if there are pillars or special compartments in the carriage, the algorithm will try to find the most suitable placement of the cargo without changing the original structure of the carriage. For irregularly shaped cargo, the algorithm can adopt the following strategy: consider the matching degree of the cargo shape with the available space in the carriage, and try to find a solution that can arrange the cargo tightly. For some special-shaped cargo, the algorithm can allow rotation or tilting to find the best placement that can adapt to the shape of the carriage.

[0043] Step S313: Based on the weight of the cargo to be loaded and the target carriage load limit, a third simulated loading scheme among the multiple simulated loading schemes is generated.

[0044] In step S313, the weight distribution of the cargo and the load capacity of the carriage are mainly considered to ensure that the loading plan does not exceed the load limit of the carriage, while also maintaining the stability of the cargo during transportation and avoiding safety hazards caused by the center of gravity shift. The algorithm can use the following methods: 1. Calculate the total weight and distribution of the cargo to ensure that the weight is evenly distributed in the carriage to avoid excessive weight in one area. 2. Ensure that the total weight in the loading plan does not exceed the load limit of the carriage, and adjust the quantity or type of loaded cargo if necessary.

[0045] Through the subdivision of these three steps, the system is able to generate more comprehensive and detailed simulated loading plans, which will be comprehensively evaluated and compared to generate the final target loading strategy. This means that in the actual loading process, not only whether the goods can be loaded is considered, but also the rationality, stability and compliance of the loading plan with the loading rules are considered, thus ensuring the optimization of the entire loading process and the safety of transportation.

[0046] Furthermore, based on multiple simulated loading scenarios, a target loading strategy is generated, including:

[0047] Step S321, based on the multiple simulated loading schemes, multiple simulated loading scenarios are generated, and the simulated loading scenarios are used to simulate the loading positions of the parameters of each cargo to be loaded in each simulated loading scheme.

[0048] In step S321, a virtual carriage model is constructed using 3D modeling technology, and then each piece of cargo is placed at a designated location in the model according to its size, shape and weight parameters. In the simulated loading scenario, physical simulation is performed to evaluate the stability of the cargo to ensure that it will not move or collapse during transportation. The system will check whether the weight distribution of the cargo in the simulated scenario is reasonable and whether it meets the requirements of load balance.

[0049] Step S322: confirming a target loading scenario based on a plurality of simulated loading scenarios.

[0050] In step S322, after generating multiple simulated loading scenarios, the system (or operator) will comprehensively consider factors such as loading rate, loading efficiency, cargo stability, and load balance, evaluate and compare these scenarios, and select a target loading scenario that best meets transportation needs and safety standards. The evaluation process includes the following aspects: 1. Check which scenario can maximize the use of the compartment space and improve the loading rate. 2. Evaluate the stability of the cargo during transportation in different scenarios and select the safest loading layout. 3. Confirm whether the weight distribution in the scenario is reasonable and whether the load limit is complied with to avoid overweight or center of gravity shift. 4. Analyze which scenario has the simplest and most efficient loading and unloading operations, reducing manpower and time costs.

[0051] Step S323: Generate a target loading strategy based on the target loading scenario.

[0052] In step S323, the system will eventually generate a specific target loading strategy based on the selected target loading scenario. This includes the exact location of each piece of cargo in the carriage, the placement direction, the stacking order, and how to use the equipment connection mechanism to fix the cargo to ensure their stability during transportation. In addition, the strategy will also include guidance on loading and unloading operations, such as which cargo should be loaded or unloaded first, and how to make the most effective use of loading and unloading equipment.

[0053] Through the above steps, the system can not only generate the optimal loading plan, but also ensure that this plan is feasible and safe in actual operation, and can effectively improve work efficiency and reduce transportation costs.

[0054] Furthermore, based on the target loading scenario, a target loading strategy is generated, including:

[0055] Step S3231, obtaining current position information of the goods to be loaded and target position information of the goods to be loaded in the target loading scene.

[0056] In step S323, in this step, the system determines the actual location of each piece of cargo in the current environment (such as a warehouse or loading area), as well as the predetermined location where the cargo should be placed in the target loading scenario. The current location information can be obtained through RFID or QR code tags, visual recognition systems or sensor networks. 1. Each piece of cargo is attached with an RFID tag or QR code, and the system obtains the unique identification and current location of the cargo by scanning these tags. 2. Using cameras and image processing technology, the system can automatically identify the cargo and determine its location in the warehouse. 3. The sensor network arranged in the loading area can track the location of the cargo in real time and provide accurate current location information. The target location information is derived from the target loading scenario selected in step S322, and the specific location and direction in which each piece of cargo should be placed in the carriage.

[0057] Step S3232: Based on the current position information of the goods to be loaded and the target position information of the goods to be loaded, a first loading strategy in the target loading strategy is generated, and the first loading strategy is used to control the loading operation of the goods to be loaded along the target path.

[0058] In step S3232, the system will generate detailed loading paths and operating instructions based on the current location and target location of the goods to form the first loading strategy. This strategy may include path planning, operating instructions, sequence arrangement and stability assurance: 1. Plan the shortest or optimal path from the current location to the target location for each piece of cargo. 2. Issue operating instructions to loading and unloading equipment (such as robotic arms, forklifts, etc.) to guide them to move the goods from the current location to the target location according to the planned path. 3. Determine the loading order of the goods to ensure that all goods can be efficiently loaded into the carriage according to the strategy. 4. In path planning, take into account the weight and shape of the goods to ensure stability and safety during loading.

[0059] The generation of the first loading strategy not only guides the loading and unloading equipment or personnel on how to move the goods, but also plans the entire loading process, making the loading process more efficient and orderly. Through precise path planning and operation instructions, the system can reduce the time and labor costs in the process of cargo handling, while ensuring the reasonable layout of the goods in the carriage, improving the loading rate and transportation safety.

[0060] Furthermore, based on the target loading scenario, a target loading strategy is generated, including:

[0061] Step S3233, obtaining the size of the connecting device in the target loading scene, wherein the connecting device is used to connect goods to be loaded of different sizes.

[0062] In step S3233, the system obtains the size parameters of the connecting devices, which are used to connect adjacent goods to be loaded during the loading process to enhance the overall stability of the goods and reduce the risk of movement during transportation. The information on the size of the connecting device can be obtained in the following ways: Database query: The system queries its size information from the stored connecting device database to ensure that it matches the requirements in the target loading scenario. Visual recognition: For connecting devices located in the loading area, the system can use cameras and image processing technology to automatically identify their size. Manual input: If the system cannot automatically obtain the size information, the operator may need to manually enter the size of the connecting device for the system to use.

[0063] Step S3234: Based on the size of the goods to be loaded and the size of the connecting device, a second loading strategy in the target loading strategy is generated, and the second loading strategy is used to control the connecting device to connect adjacent goods to be loaded.

[0064] In step S3234, after obtaining the size information of the connecting device, the system will generate a second loading strategy based on the size of the goods to be loaded, and instruct how to use these devices to connect the goods. This strategy may include selecting suitable connecting devices, path planning and operation instructions, generating connection strategies, etc.: 1. According to the size and shape of the goods, as well as the size of the connecting device, the system selects the most suitable type of device to enhance the stability of the cargo connection. 2. Plan a path from the loading area to the target location (between the goods) for the connecting device, and generate operation instructions to instruct the loading and unloading equipment or personnel on how to correctly place and connect the connecting device. 3. The system can generate a connection strategy that explains how to use the connecting device to connect goods of different sizes to form a whole, so as to improve the loading rate and ensure the stability of the goods during transportation.

[0065] The generation of the second loading strategy is the key to ensure that the goods are safely and securely loaded in the carriage. By using the connection device, not only can the stability of the goods during transportation be improved, but also the use of the carriage space can be further optimized by connecting the goods tightly, thereby improving the loading rate. The implementation of this strategy requires the system to accurately match the size of the connection device and the goods, as well as plan the use of the connection device to ensure that the entire loading process is both efficient and safe.

[0066] Example 2

[0067] According to an embodiment of the present invention, a truck loading device embodiment based on an intelligent optimization algorithm is provided. It should be noted that the device can be used to execute the truck loading method based on the intelligent optimization algorithm in the above embodiment.

[0068] Specifically, combined Figure 2 As shown, the truck loading device based on the intelligent optimization algorithm includes a first acquisition module 51, a second acquisition module 52, a confirmation module 53 and a loading module 54, the first acquisition module 51 is used to obtain parameters of the goods to be loaded, wherein the parameters of the goods to be loaded include at least one of the following: the size of the goods to be loaded, the shape of the goods to be loaded and the weight of the goods to be loaded; the second acquisition module 52 is used to obtain target compartment information, wherein the target compartment information includes at least one of the following: target compartment size, target compartment shape, target compartment load limit; the confirmation module 53 is used to confirm the target loading strategy based on the parameters of the goods to be loaded and the target compartment information; the loading module 54 is used to perform loading operations based on the target loading strategy.

[0069] Through data collection, intelligent analysis and automatic control, the truck loading process is optimized to improve transportation efficiency and safety. Each module has its specific function, and together they ensure that the goods can be loaded into the carriage in the most reasonable way, whether from the perspective of space utilization, load distribution or cargo stability.

[0070] Furthermore, the loading module 54 includes: a connecting device module, which is used to control the connecting device to connect goods of different sizes to be loaded. In a specific embodiment of the present application, the connecting device module has the following functions:

[0071] 1. Size matching: The connection device module first needs to identify and match the size of each piece of cargo. This is usually based on the cargo parameters obtained from the first acquisition module 51, including the length, width, height and shape characteristics of the cargo. Based on this information, the module will determine which cargoes need to use connection devices and which type of connection device is most suitable.

[0072] 2. Device selection: According to the size and shape of the goods, the connection device module will select the most suitable type from the preset connection device library. The connection device can include but is not limited to various fixing belts, brackets, locks, etc., with different sizes, shapes and load-bearing capacities to meet the needs of connecting different goods.

[0073] 3. Path planning: After confirming the use of the connection device, the module will plan the best movement path for the connection device from the storage location to the target goods. This helps to reduce the handling time and operation complexity of the connection device during loading, ensuring the efficiency of the entire loading process.

[0074] 4. Operational control: The connection device module generates detailed control instructions to instruct the loading machinery (such as robotic arms, automated loading equipment, etc.) on how to accurately place the connection device at the specified position between the goods. This can include how to adjust the rotation angle of the robotic arm, the movement speed, and how to perform locking or fixing actions.

[0075] 5. Stability verification: After the connection device is placed, the module will verify the stability of the cargo to ensure that the cargo can remain stable during transportation through the connection of the connection device to avoid movement or damage of the cargo due to vibration, collision or other external factors.

[0076] 6. Dynamic adjustment: If it is found during the loading process that the originally planned connection method is inappropriate or there are safety hazards, the connection device module can adjust the strategy in real time and re-plan the use of the connection device to adapt to the current loading conditions.

[0077] Through the intelligent control of the connection device module, it can not only ensure the stable loading of goods in the carriage, but also effectively improve the loading efficiency, reduce manual intervention and reduce transportation risks. This reflects the flexibility and reliability of the truck loading device based on the intelligent optimization algorithm in practical applications, and is one of the key components to improve the level of loading automation and achieve efficient logistics transportation.

[0078] Example 3

[0079] An embodiment of the present application further provides an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of the present invention when running.

[0080] Example 4

[0081] An embodiment of the present application further provides a computer-readable storage medium, which includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0082] Example 5

[0083] An embodiment of the present application further provides a computer program product, including a computer program, which implements the methods in various embodiments of the present invention when executed by a processor.

[0084] Example 6

[0085] An embodiment of the present application further provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0086] Example 7

[0087] The embodiments of the present application further provide a computer program, which implements the methods in the above-mentioned embodiments of the present invention when executed by a processor.

[0088] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0089] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0090] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0091] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0092] If the 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 the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0093] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A truck loading method based on an intelligent optimization algorithm, characterized in that: include: Acquire parameters of the cargo to be loaded, wherein the parameters of the cargo to be loaded include at least one of the following: size of the cargo to be loaded, shape of the cargo to be loaded, and weight of the cargo to be loaded; Acquiring target compartment information, wherein the target compartment information includes at least one of the following: target compartment size, target compartment shape, and target compartment load limit; Generate a target loading strategy based on the parameters of the cargo to be loaded and the target compartment information; Based on the target loading strategy, a loading operation is performed on the cargo to be loaded.

2. The truck loading method based on intelligent optimization algorithm according to claim 1 is characterized in that: Based on the parameters of the cargo to be loaded and the target compartment information, generating the target loading strategy includes: Based on the parameters of the cargo to be loaded and the target compartment information, generating a plurality of simulated loading schemes; Based on the multiple simulated loading scenarios, the target loading strategy is confirmed.

3. The truck loading method based on intelligent optimization algorithm according to claim 2 is characterized in that: Based on the parameters of the cargo to be loaded and the target compartment information, a plurality of simulated loading schemes are generated, including: Based on the size of the cargo to be loaded and the target compartment size, generating a first simulated loading scheme among the plurality of simulated loading schemes; generating a second simulated loading scheme among the plurality of simulated loading schemes based on the shape of the cargo to be loaded and the target carriage shape; Based on the weight of the cargo to be loaded and the target carriage load limit, a third simulated loading scheme among the plurality of simulated loading schemes is generated.

4. The truck loading method based on intelligent optimization algorithm according to claim 2 or 3, characterized in that: Generating the target loading strategy based on the plurality of simulated loading schemes includes: Based on the plurality of simulated loading schemes, a plurality of simulated loading scenarios are generated, wherein the simulated loading scenarios are used to simulate the loading positions of the parameters of the goods to be loaded in the respective simulated loading schemes; Based on the plurality of simulated loading scenarios, confirming a target loading scenario; Based on the target loading scenario, the target loading strategy is generated.

5. The truck loading method based on intelligent optimization algorithm according to claim 4 is characterized in that: Based on the target loading scenario, generating the target loading strategy includes: Acquire current position information of the cargo to be loaded and target position information of the cargo to be loaded in the target loading scene; Based on the current position information of the cargo to be loaded and the target position information of the cargo to be loaded, a first loading strategy in the target loading strategy is generated, and the first loading strategy is used to control the cargo to be loaded to perform the loading operation along the target path.

6. The truck loading method based on intelligent optimization algorithm according to claim 4 is characterized in that: Based on the target loading scenario, generating the target loading strategy includes: Acquiring a size of a connecting device in the target loading scenario, wherein the connecting device is used to connect the goods to be loaded of different sizes; Based on the size of the goods to be loaded and the size of the connecting device, a second loading strategy in the target loading strategy is generated, and the second loading strategy is used to control the connecting device to connect the adjacent goods to be loaded.

7. A truck loading device based on intelligent optimization algorithm, characterized in that: include: A first acquisition module, the first acquisition module is used to acquire parameters of the cargo to be loaded, wherein the parameters of the cargo to be loaded include at least one of the following: size of the cargo to be loaded, shape of the cargo to be loaded, and weight of the cargo to be loaded; A second acquisition module, the second acquisition module is used to acquire target compartment information, wherein the target compartment information includes at least one of the following: target compartment size, target compartment shape, and target compartment load limit; A confirmation module, the confirmation module is used to confirm the target loading strategy based on the parameters of the goods to be loaded and the target compartment information; A loading module is used to perform a loading operation based on the target loading strategy.

8. The truck loading device based on intelligent optimization algorithm according to claim 7, characterized in that: The loading module comprises: A connecting device module is used to control the connecting device to connect the goods to be loaded of different sizes.

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 run, the device where the storage medium is located is controlled to execute the truck loading method based on the intelligent optimization algorithm as described in any one of claims 1 to 6.

10. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, implements the truck loading method based on the intelligent optimization algorithm according to any one of claims 1 to 6.

Images