Freight loading method and device

Abstract

The invention relates to the technical field of freight loading, and discloses a freight loading method and device, and the method comprises the steps: setting a decision variable, and generating a cargo placement model based on a target function of the maximum cargo placement amount, a constraint condition of a cargo placement rule, a constraint condition of a cargo placement direction, a constraint condition of cargo loading, and the decision variable; solving the cargo placement model by using an integer linear programming algorithm to obtain a solving result corresponding to the decision variable; optimizing a solving result corresponding to the decision variable by using a preset optimization algorithm to obtain a target loading strategy; and according to the target loading strategy, the multiple cargoes of the same type are loaded into the target truck through the intelligent forklift equipment. The method is applied to railway freight, a set of target loading strategy is generated through an intelligent algorithm, then the intelligent forklift equipment is used for loading the goods based on the target loading strategy, the goods loading efficiency can be improved, and the labor cost can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of freight loading, and in particular to a freight loading method and device. Background Art

[0002] Freight loading is a critical link in the logistics chain that involves the safe and efficient loading of goods from their point of origin onto a transport vehicle (such as a truck, train, ship or aircraft) for shipment to their destination.

[0003] Related railway loading operations mainly rely on traditional manual operations, which not only has a slow operating speed, but also has high requirements on the physical condition of the operator, which can easily cause personal injury and cargo loss. Moreover, during peak loading periods, it is difficult to meet the loading needs of large quantities of cargo. Summary of the invention

[0004] In view of this, the present invention provides a freight loading method and device to solve the problem that the loading method relying on traditional manual operation has low loading efficiency and is difficult to meet the loading needs of a large amount of goods.

[0005] In a first aspect, the present invention provides a freight loading method, the method comprising:

[0006] Set decision variables;

[0007] Generate a cargo placement model based on the objective function of the maximum cargo placement quantity, the constraints of cargo placement rules, the constraints of cargo placement direction, the constraints of cargo loading, and decision variables;

[0008] An integer linear programming algorithm is used to solve the cargo placement model to obtain the solution results corresponding to the decision variables. The solution results corresponding to the decision variables are used to determine whether cargo can be placed at each target position in the target truck according to the placement direction.

[0009] Utilize the preset optimization algorithm to optimize the solution results corresponding to the decision variables to obtain the target loading strategy;

[0010] According to the target loading strategy, multiple goods of the same type are loaded into the target truck through intelligent forklift equipment.

[0011] In some optional implementations, the objective function of the maximum amount of goods placed is created by the following formula:

[0012]

[0013] Among them, M i is the length of item i, N i is the width of cargo i, P i is the height of item i, V i =M i×N i ×P i is the volume of cargo i, r is the placement direction, R i is the number of placement directions, and Maximize is to ensure that the cargo i is placed at the target position in the target truck according to the placement direction to maximize the cargo placement amount. It is the decision variable that determines whether to allow the placement of cargo i at the target position in the target truck according to the placement direction, and a is the total number of multiple cargoes of the same type.

[0014] In some optional implementations, the constraints of the cargo placement rules are created by the following formula:

[0015]

[0016] Among them, E ir is the coefficient, r is the placement direction, R i is the number of directions of placement. If item i covers position (j, l, l) in the placement direction, then E ir (j,l,l)=1, if the position (j,k,l) ​​is not covered, then E ir (j,k,l)=0, is the decision variable used to determine the target location of cargo i in the target truck according to the placement direction, and a is the total number of multiple cargoes of the same type.

[0017] In some optional implementations, the constraints on the direction of cargo placement are created by the following formula:

[0018]

[0019] in, is the decision variable for determining whether to place cargo i at the target location in the target truck according to the placement direction, r is the placement direction, R i is the number of placement directions, and i,j,k,l are the target positions of cargo i in the target truck.

[0020] In some optional implementations, the constraints for cargo loading are created by the following formula:

[0021]

[0022] Among them, r is the placement direction, It is the decision variable that determines whether the cargo i is allowed to be placed at the target position in the target truck according to the placement direction. Ind is the set of allowed placement areas in the target truck formed according to the truck loading rules.

[0023] In some optional implementations, optimizing the solution results corresponding to the decision variables using a preset optimization algorithm to obtain a final target loading strategy includes:

[0024] The solution obtained by using the integer linear programming algorithm and satisfying the preset conditions is used as the initial solution of the preset optimization algorithm;

[0025] Based on the target position corresponding to the initial solution, local adjustment is performed according to the neighborhood search method;

[0026] Generate a cargo placement model based on the objective function of the maximum cargo placement quantity, the constraints of the cargo placement rules, the constraints of the cargo placement direction, the constraints of cargo loading, the decision variables, and the locally adjusted decision variables;

[0027] Evaluate whether the cargo placement model meets the preset conditions. If not, return to the step of setting decision variables until the preset conditions are met.

[0028] In some optional embodiments, the method is used for loading cargo in railway freight.

[0029] According to a second aspect, an embodiment of the present disclosure provides a freight loading device, the device comprising:

[0030] Variable setting module, used to set decision variables;

[0031] A parameter generation module is used to generate a cargo placement model based on the objective function of the maximum cargo placement quantity, the constraints of the cargo placement rules, the constraints of the cargo placement direction, the constraints of the cargo loading, and the decision variables;

[0032] A model calculation module is used to solve the cargo placement model using an integer linear programming algorithm to obtain a solution result corresponding to the decision variable, and the solution result corresponding to the decision variable is used to determine whether the cargo is allowed to be placed at each target position in the target truck according to the placement direction;

[0033] A strategy generation module is used to optimize the solution results corresponding to the decision variables using a preset optimization algorithm to obtain a target loading strategy;

[0034] The cargo loading module is used to load multiple cargoes of the same type into a target truck through an intelligent forklift device according to a target loading strategy.

[0035] According to a third aspect, an embodiment of the present disclosure provides a computer device, including:

[0036] The memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the freight loading method in the first aspect or any embodiment of the first aspect by executing the computer instructions.

[0037] According to a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the freight loading method in the first aspect or any one of the embodiments of the first aspect.

[0038] According to a fifth aspect, an embodiment of the present disclosure provides a computer program product, comprising computer instructions, wherein the computer instructions are used to enable a computer to execute the freight loading method in the first aspect or any embodiment of the first aspect.

[0039] The technical solution of the present invention has the following advantages:

[0040] The present invention relates to the field of freight loading technology, and discloses a freight loading method and device, wherein the method includes: setting decision variables; generating a cargo placement model based on the objective function of the maximum amount of cargo placement, the constraints of the cargo placement rules, the constraints of the cargo placement direction, the constraints of the cargo loading, and the decision variables; solving the cargo placement model using an integer linear programming algorithm to obtain a solution result corresponding to the decision variable, and the solution result corresponding to the decision variable is used to determine whether the cargo is allowed to be placed at each target position in the target truck according to the placement direction; optimizing the solution result corresponding to the decision variable using a preset optimization algorithm to obtain a target loading strategy; according to the target loading strategy, multiple cargoes of the same type are loaded into the target truck by an intelligent forklift device. The present invention is applied to railway freight, and a set of target loading strategies are generated by an intelligent algorithm, and then the intelligent forklift device is used to load the cargo based on the target loading strategy, which can improve the cargo loading efficiency and reduce the labor cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 is a flow chart of a freight loading method according to an embodiment of the present invention;

[0043] Figure 2 is a schematic diagram of software and hardware of an intelligent forklift device according to an embodiment of the present invention;

[0044] Figure 3 is a schematic flow chart of another freight loading method according to an embodiment of the present invention;

[0045] Figure 4is a schematic structural diagram of a freight loading device according to an embodiment of the present invention;

[0046] Figure 5 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution 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 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0048] According to an embodiment of the present invention, an embodiment of a freight loading method 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.

[0049] In this embodiment, a freight loading method is provided, which can be used for intelligent forklift equipment, computer equipment, such as mobile phones, tablet computers, desktop computers, portable notebooks, servers, etc. In this disclosed embodiment, Figure 1 is a flow chart of a freight loading method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0050] Step S101, setting decision variables.

[0051] Specifically, the decision variable belongs to the parameter in the integer linear programming algorithm. If the decision variable is represented by x, then x∈{0,1}. In the embodiment of the present disclosure, the decision variable is represented by It represents the situation where cargo i is placed at coordinate (j, j, l) in the cargo compartment and takes placement direction t. This decision variable is used to further determine whether cargo can be placed at each target location in the target truck.

[0052] Step S102, generating a cargo placement model based on the objective function of the maximum cargo placement quantity, the constraints of the cargo placement rules, the constraints of the cargo placement direction, the constraints of the cargo loading, and the decision variables.

[0053] Specifically, the objective function of the maximum amount of cargo placement is generated based on the volume of each cargo, the decision variables, and the total number of cargoes. This objective function is used to maximize the calculation result of the amount of cargo loaded on the target truck, thereby improving the utilization rate of the truck space. The cargo placement model is a model that constrains the way cargo is placed.

[0054] Furthermore, the constraints of the cargo placement rules are used to constrain the placement of each cargo in the truck, for example, to ensure that the cargo is placed within the boundaries of the truck and to prevent overlap.

[0055] Furthermore, the constraint condition of the direction of placing the goods is used to constrain the placement of each good in the truck in a specific manner, for example, at a specific location, each good is placed in one direction.

[0056] Furthermore, the cargo loading constraint is used to constrain the requirements of each cargo placement area in combination with the truck industry loading standards. For example, the set Ind is a set of placement areas that are allowed to be formed based on industry constraints such as loading specifications.

[0057] In a specific example, the freight loading method in the embodiment of the present disclosure is used to load freight in railway freight. Therefore, the target freight car can be a train.

[0058] The freight loading method in the embodiment of the present disclosure can also be used to load freight on a highway, so in this case, the target freight vehicle can be a truck.

[0059] Specifically, since the current railway freight loading and unloading operations mainly rely on traditional manual operations, there are problems such as low efficiency, high labor intensity and high safety risks. This method is not only slow in operation, but also requires high physical conditions of operators, which is easy to cause personal injury and cargo loss. In addition, with the increase in cargo transportation volume, traditional loading and unloading methods have been unable to meet the growing logistics needs, especially during peak periods, the inefficiency of traditional methods is more prominent.

[0060] Therefore, the freight loading method in the embodiment of the present disclosure is applied to railway freight. A set of target loading strategies is generated through an intelligent algorithm, and then intelligent forklift equipment is used to load goods based on the target loading strategy, which can improve cargo loading efficiency and reduce labor costs.

[0061] Step S103, using an integer linear programming algorithm to solve the cargo placement model, and obtaining solution results corresponding to decision variables, wherein the solution results corresponding to the decision variables are used to determine whether cargo can be placed at each target position in the target truck according to the placement direction.

[0062] Specifically, the integer linear programming algorithm is a linear programming in which variables take integer values. Its general form is min Z, satisfies the condition Ax=b, or>0, and takes integer values. In addition to the constraints of general linear programming, the requirement that variables take integer values ​​is added, which makes a profound change in the problem, affecting both theory and application, thus forming a unique branch of integer linear programming. In the embodiment of the present disclosure, a linear programming solution algorithm is used to solve the above-mentioned cargo placement model, and the solution obtained will indicate whether each target location is suitable for placing cargo, thereby forming a cargo placement method. The result of the solution corresponds to the calculation result of the decision variables in the cargo placement model mentioned above. For example, the decision variable of cargo i is calculated by a linear programming algorithm. As j, k, l gradually change, do they satisfy the objective function of the maximum amount of goods placed, the constraints of the goods placement rules, the constraints of the goods placement direction, and the constraints of the goods loading? If so, it means that the target position corresponding to j, k, l is suitable for placing goods.

[0063] Step S104, optimizing the solution corresponding to the decision variables using a preset optimization algorithm to obtain a target loading strategy.

[0064] Specifically, the preset optimization algorithm may be a commonly used genetic algorithm or simulated annealing algorithm or particle swarm optimization algorithm, through which the solution of the integer linear programming algorithm is further optimized to ensure the feasibility and accuracy of the target loading strategy.

[0065] Specifically, through the preset optimization algorithm, the position or direction of the cargo is gradually adjusted, and the cargo loading model is continuously optimized to improve stability and loading speed.

[0066] Step S105 , according to the target loading strategy, multiple goods of the same type are loaded into the target truck by the intelligent forklift equipment.

[0067] Specifically, multiple goods of the same type refer to goods that are basically the same in shape and weight. The freight loading method in the embodiment of the present disclosure can be applied to computer equipment, or directly applied to intelligent forklift equipment. If the freight loading method in the embodiment of the present disclosure is directly applied to an intelligent forklift device, the intelligent forklift device loads multiple goods of the same type in a target truck according to the target loading strategy. Intelligent forklift equipment is designed for automatic cargo and loading of goods. The equipment is equipped with high-precision sensors and machine vision, which can identify the type and location of goods in real time and automatically plan the optimal loading path. The intelligent forklift equipment receives and executes operation instructions through integration with the central controller to ensure accurate stacking of goods. In addition, the intelligent forklift equipment also has automatic obstacle avoidance and fault self-diagnosis functions to enhance operation safety and continuity. Intelligent unmanned forklifts not only improve loading and unloading efficiency, but also significantly reduce labor costs and operating error rates. Figure 2 The schematic diagram of the hardware, software and mechanical system design of the intelligent forklift equipment is shown. In order to meet the existing railway loading standards and optimize the loading layout of goods, efficient stacking of goods and maximum utilization of space are achieved. By executing the above steps S101 to S105, the intelligent forklift equipment can automatically calculate the best way to place the goods, thereby reducing loading and unloading time and improving the utilization rate of the compartment space.

[0068] In some optional implementations, the objective function of the maximum amount of goods placed is created by the following formula (1):

[0069]

[0070] Among them, M i is the length of item i, N i is the width of cargo i, P i is the height of item i, V i =M i ×N i ×P i is the volume of cargo i, r is the placement direction, R i is the number of placement directions, and Maximize is to ensure that the cargo i is placed at the target position in the target truck according to the placement direction to maximize the cargo placement amount. is the decision variable that determines whether to allow the placement of cargo i at the target position in the target truck according to the placement direction. The placement direction is the direction formed by placing the cargo along the direction of the long side of the cargo, or the direction formed by placing the cargo along the direction of the short side of the cargo. The direction of the long side or the short side of the cargo is parallel to the driving direction of the target truck. a is the total number of multiple cargoes of the same type.

[0071] The disclosed embodiment is generated based on the volume of each cargo, decision variables, and the total number of cargoes. The objective function is used to ensure that the calculation result of the target truck loading amount is maximized, thereby improving the utilization rate of the truck space.

[0072] In some optional implementations, the constraints of the cargo placement rules are created by the following formula (2):

[0073]

[0074] Among them, E ir is the coefficient, r is the placement direction, R i is the number of directions of placement. If item i covers position (j, k, l) in the placement direction, then E ir (j,k,l)=1, if the position (j,k,l) ​​is not covered, then E ir (j,k,l)=0, is the decision variable used to determine the target position of cargo i in the target truck according to the placement direction, a is the total number of multiple cargoes of the same type, and the placement direction is the direction formed by placing the cargoes along the direction of the long side of the cargoes, or the direction formed by placing the cargoes along the direction of the short side of the cargoes, and the direction of the long side or short side of the cargoes is parallel to the driving direction of the target truck.

[0075] Specifically, the constraints of the cargo placement rule are space filling constraints, which are used to constrain the placement of each cargo in the truck, for example, to ensure that the cargo does not exceed the boundaries of the truck and prevent overlap. If cargo i covers position (j, k, l) in placement direction r, then X ir (j,k,l) ​​= 1. If the position (j,k,l) ​​is not covered, then χ ir (j,k,l)=0.

[0076] In some optional implementations, the constraints on the direction of cargo placement are created by the following formula:

[0077] in, is the decision variable for determining whether to place cargo i at the target location in the target truck according to the placement direction, r is the placement direction, R i is the number of placement directions, and i,j,k,l are the target positions of cargo i in the target truck.

[0078] Specifically, the constraints on the direction of cargo placement are used to constrain the placement of each cargo in a truck in a specific way. For example, at a specific location, a placement direction is selected from a direction formed by placing the cargo along the direction of the long side of the cargo, or a direction formed by placing the cargo along the direction of the short side of the cargo.

[0079] In some optional embodiments, the constraints for cargo loading are created by the following formula (4):

[0080]

[0081] Among them, r is the placement direction, It is the decision variable that determines whether the cargo i is allowed to be placed at the target position in the target truck according to the placement direction. Ind is the set of allowed placement areas in the target truck formed according to the truck loading rules.

[0082] Through some of the above optional implementations, different objective functions or constraints are created to ensure that the goods are placed in the target truck according to the specification requirements, thereby improving the efficiency of goods placement and making full use of the truck loading space.

[0083] In this embodiment, a freight loading method is provided, which can be used for intelligent forklift equipment, computer equipment, such as mobile phones, tablet computers, desktop computers, portable notebooks, servers, etc. Figure 3 is a flow chart of a freight loading method according to an embodiment of the present invention. Figure 3 As shown, in the above step S104, a preset optimization algorithm is used to optimize the solution corresponding to the decision variable to obtain the final target loading strategy. The process includes the following steps:

[0084] Step S1041, using the integer linear programming algorithm to obtain a solution that meets the preset conditions as an initial solution of the preset optimization algorithm.

[0085] Specifically, the solution obtained by using the above integer linear programming algorithm is used as the initial solution of the cargo placement model.

[0086] Step S1042: Perform local adjustments based on the target position corresponding to the initial solution according to the neighborhood search method to redetermine the decision variables.

[0087] Specifically, local adjustments are made to the current initial solution, for example, the position of the goods is moved or the direction of the goods is changed.

[0088] Step S1043, based on the objective function of the maximum amount of goods placed, the constraints of the goods placement rules, the constraints of the goods placement direction, the constraints of the goods loading, the decision variables, and the re-determined decision variables, an optimized goods placement model is generated.

[0089] Specifically, based on the locally adjusted actions, the cargo placement model is regenerated.

[0090] Step S1044, evaluating whether the optimized cargo placement model meets the preset conditions. If not, returning to the step of setting decision variables until the preset conditions are met.

[0091] Specifically, the preset conditions are the objective functions or constraints in the above formulas (1)-(4), which verify whether the optimized cargo placement model meets the requirements of the above formulas (1)-(4), and then generate the optimal target loading strategy to improve cargo loading efficiency and meet the loading needs of a large number of cargoes.

[0092] In this embodiment, a cargo loading device is also provided, which is used to implement the above-mentioned embodiments and preferred implementations, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0093] This embodiment provides a cargo loading device, such as Figure 4 As shown, including:

[0094] A variable setting module 401 is used to set decision variables;

[0095] A parameter generation module 402 is used to generate a cargo placement model based on the objective function of the maximum cargo placement quantity, the constraints of the cargo placement rules, the constraints of the cargo placement direction, the constraints of the cargo loading, and the decision variables;

[0096] The model calculation module 403 is used to solve the cargo placement model using an integer linear programming algorithm to obtain a solution result corresponding to the decision variable, and the solution result corresponding to the decision variable is used to determine whether the cargo is allowed to be placed at each target position in the target truck according to the placement direction;

[0097] A strategy generation module 404 is used to optimize the solution corresponding to the decision variables using a preset optimization algorithm to obtain a target loading strategy;

[0098] The cargo loading module 405 is used to load multiple cargoes of the same type into a target truck through an intelligent forklift device according to a target loading strategy.

[0099] In some optional embodiments, the objective function of the maximum amount of goods placed is created using the above formula (1).

[0100] In some optional implementations, the constraints of the cargo placement rules are created using the above formula (2).

[0101] In some optional implementations, the constraint condition of the cargo placement direction is created by the above formula (3).

[0102] In some optional embodiments, the constraints for cargo loading are created by the above formula (4).

[0103] In some optional implementations, the strategy generation module 404 includes:

[0104] The initial solution determination submodule is used to use the solution result that meets the preset conditions obtained by using the integer linear programming algorithm as the initial solution of the preset optimization algorithm.

[0105] The local adjustment submodule is used to perform local adjustment according to the neighborhood search method based on the target position corresponding to the initial solution.

[0106] The model generation submodule is used to generate a cargo placement model based on the objective function of the maximum cargo placement quantity, the constraints of the cargo placement rules, the constraints of the cargo placement direction, the constraints of the cargo loading, the decision variables, and the locally adjusted decision variables.

[0107] The model evaluation submodule is used to evaluate whether the cargo placement model meets the preset conditions. If the preset conditions are not met, it returns to the step of setting decision variables until the preset conditions are met.

[0108] In some optional embodiments, the method is used for loading cargo in railway freight.

[0109] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0110] The freight loading device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0111] The embodiment of the present invention also provides a computer device having the above Figure 5 Freight loading device shown.

[0112] See also Figure 5 , Figure 5 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 5 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0113] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0114] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0115] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0116] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0117] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0118] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0119] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0120] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for loading freight, characterized in that: The method comprises: Set decision variables; Generate a cargo placement model based on the objective function of the maximum cargo placement quantity, the constraints of the cargo placement rules, the constraints of the cargo placement direction, the constraints of the cargo loading, and the decision variables; Solving the cargo placement model using an integer linear programming algorithm to obtain a solution result corresponding to the decision variable, wherein the solution result corresponding to the decision variable is used to determine whether cargo is allowed to be placed at each target position in the target truck according to the placement direction; Optimizing the solution results corresponding to the decision variables using a preset optimization algorithm to obtain a target loading strategy; According to the target loading strategy, multiple goods of the same type are loaded into the target truck by intelligent forklift equipment.

2. The method according to claim 1, characterized in that The objective function of the maximum amount of goods placed is created by the following formula: Among them, M i is the length of item i, N i is the width of cargo i, P i is the height of item i, V i =M i ×N i ×P i is the volume of cargo i, r is the placement direction, R i is the number of placement directions, and Maximize is to ensure that the cargo i is placed at the target position in the target truck according to the placement direction to maximize the cargo placement amount. It is the decision variable that determines whether to allow the placement of cargo i at the target position in the target truck according to the placement direction, and a is the total number of multiple cargoes of the same type.

3. The method according to claim 1, characterized in that The constraints of the cargo placement rules are created by the following formula: for allj,k,l Among them, E ir is the coefficient, r is the placement direction, R i is the number of directions of placement. If item i covers position (j, k, l) in the placement direction, then E ir (j,k,l)=1, if the position (j,k,l) ​​is not covered, then E ir (j,k,l)=0, is the decision variable used to determine the target location of cargo i in the target truck according to the placement direction, and a is the total number of multiple cargoes of the same type.

4. The method according to claim 1, characterized in that: The constraints of the cargo placement direction are created by the following formula: for alli,j,k,l in, is the decision variable for determining whether to place cargo i at the target location in the target truck according to the placement direction, r is the placement direction, R i is the number of placement directions, and i,j,k,l are the target positions of cargo i in the target truck.

5. The method according to claim 1, characterized in that: The constraints for cargo loading are created by the following formula: Among them, r is the placement direction, It is the decision variable that determines whether the cargo i is allowed to be placed at the target position in the target truck according to the placement direction. Ind is the set of allowed placement areas in the target truck formed according to the truck loading rules.

6. The method according to claim 1, characterized in that The method of optimizing the solution results corresponding to the decision variables by using a preset optimization algorithm to obtain a final target loading strategy includes: The solution obtained by using the integer linear programming algorithm and satisfying the preset conditions is used as the initial solution of the preset optimization algorithm; Based on the target position corresponding to the initial solution, local adjustment is performed according to a neighborhood search method to redetermine the decision variables; Generate an optimized cargo placement model based on the objective function of the maximum cargo placement quantity, the constraints of the cargo placement rules, the constraints of the cargo placement direction, the constraints of cargo loading, the decision variables, and the re-determined decision variables; Evaluate whether the optimized cargo placement model meets the preset conditions. If not, return to the step of setting the decision variables until the preset conditions are met.

7. The method according to any one of claims 1 to 6, characterized in that Used for loading cargo in railway freight.

8. A freight loading device, characterized in that: The device comprises: Variable setting module, used to set decision variables; A parameter generation module, used to generate a cargo placement model based on the objective function of the maximum cargo placement quantity, the constraints of the cargo placement rules, the constraints of the cargo placement direction, the constraints of the cargo loading, and the decision variables; A model calculation module, used to solve the cargo placement model using an integer linear programming algorithm to obtain a solution result corresponding to the decision variable, wherein the solution result corresponding to the decision variable is used to determine whether cargo is allowed to be placed at each target position in the target truck according to the placement direction; A strategy generation module, used to optimize the solution corresponding to the decision variables using a preset optimization algorithm to obtain a target loading strategy; The cargo loading module is used to load multiple cargoes of the same type into the target truck through an intelligent forklift device according to the target loading strategy.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the freight loading method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the freight loading method according to any one of claims 1 to 7.

11. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the freight loading method according to any one of claims 1 to 7.

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