Disclosure of Invention
In view of the above, the present invention provides a freight loading method and apparatus, so as to solve the problem that the loading space of the intelligent forklift device cannot be fully utilized, and further, the number of times of carrying the intelligent forklift device is increased, so that the loading requirement of a large amount of cargoes is difficult to meet.
In a first aspect, the present invention provides a freight loading method, the method comprising:
Based on loading conditions of a target truck and loading conditions of intelligent forklift equipment for carrying stacking blocks, configuring a plurality of specification combination results of the stacking blocks for stacking the same type of goods;
Selecting a target stacking specification of the stacking block from the combination result of the multiple specifications;
setting a cargo stacking variable, wherein the cargo stacking quantity represents the cargo stacking quantity of cargoes stacked into a target stacking specification along different directions;
Generating a cargo placement model based on a total weight constraint function of cargoes loaded by a truck, a constraint function of total weight of stacking blocks, a constraint function of total volume of stacking blocks, a constraint function of intelligent forklift equipment and the number of cargo stacks of a target stacking specification;
Optimizing cargo stacking variables by using a preset optimization algorithm so that a cargo placing model meets preset conditions;
Based on the optimized goods placement model, the goods of the same type stacked into stacking blocks according to the target stacking specification are loaded into the target truck through intelligent forklift equipment.
In an alternative embodiment, based on the loading conditions of the target truck and the loading conditions of the intelligent forklift equipment for carrying the palletizing blocks, configuring a plurality of specification combination results of the palletizing blocks for stacking the same type of goods, including;
Acquiring a first stacking quantity, a second stacking quantity and a third stacking quantity of the target truck, wherein the first stacking quantity is the maximum stacking quantity along the length direction of the target truck, the second stacking quantity is the maximum stacking quantity along the width direction of the target truck, and the third stacking quantity is the maximum stacking quantity along the height direction of the target truck;
obtaining the maximum carrying quantity of intelligent forklift equipment;
Based on the first stacking quantity, the second stacking quantity, the third stacking quantity, the maximum carrying quantity of the intelligent forklift and the cargo stacking requirements of cargoes in the length direction, the width direction and the height direction of the target forklift, a plurality of different preset numerical configuration stacking blocks are selected from the positive integers to form a plurality of specification combination results.
In an alternative embodiment, the constraint function is created based on the total weight of the truck loaded cargo by the following formula:
where si is the number of preparations required for the ith pallet, a is the number of combinations of the various specifications of the pallet, max is the number of basic loads that ensure that the total weight of the loaded goods of the wagon is maximized, qij is the number of basic loads that the goods are stacked in the j direction in the pallet of the ith target stacking specification, qik is the number of basic loads that the goods are stacked in the k direction in the pallet of the ith target stacking specification, qil is the number of basic loads that the goods are stacked in the l direction in the pallet of the ith target stacking specification, and w0 is the weight of the basic loads.
In an alternative embodiment, the constraint function for the total weight of the palletized block is created by the following formula:
Where a is the number of multiple specification combinations of pallet blocks, qij is the number of basic pallet blocks stacked in the longitudinal direction j of the ith pallet block, qik is the number of basic pallet blocks stacked in the k direction of the ith target pallet block, qil is the number of basic pallet blocks stacked in the l direction of the ith target pallet block, si is the number of preparation required for the ith pallet block, and w is the upper limit of the load of the truck.
In an alternative embodiment, the constraint function for the total volume of palletized blocks is created by the following formula:
Where Mi is the length of the palletized block, Ni is the width of the palletized block, Pi is the height of the palletized block, qij is the number of basic cargos stacked in the ith palletized block in the long side direction j, qik is the number of basic cargos stacked in the ith palletized block in the broadside direction k, qil is the number of basic cargos stacked in the ith palletized block in the high side direction l, M0 is the length of the cargos, N0 is the width of the cargos, P0 is the height of the cargos, M is the length of the cargos, N is the width of the cargos, P is the height of the cargos, Z1 is the width of the doors of the cargos and Z2 is the height of the doors of the cargos.
In an alternative embodiment, the constraint function of the intelligent forklift device is created by the following formula:
(Mi,Ni,Pi)∈Veh
Where Veh is the load volume requirement of the intelligent forklift device loading the cargo, Mi is the length of the palletized block, Ni is the width of the palletized block, and Pi is the height of the palletized block.
In an alternative embodiment, optimizing the stacking variables using a preset optimization algorithm to make the cargo placement model satisfy a preset condition includes:
using the cargo stacking variable as an initial solution of a preset optimization algorithm;
adjusting cargo stacking variables corresponding to the initial solutions according to a neighborhood searching method to redetermine the cargo stacking quantity of the target stacking specification;
Generating an optimized goods placement model based on a total weight constraint function of goods loaded by the truck, a constraint function of the total weight of the stacking blocks, a constraint function of the total volume of the stacking blocks, a constraint function of intelligent forklift equipment and the number of goods stacked in the redetermined target stacking specification;
and (3) evaluating whether the optimized goods placement model meets the preset condition, and if the goods placement model does not meet the preset condition, returning to the step of stacking the goods until the goods placement model meets the preset condition.
In a second aspect, the present invention provides a freight loading device, the device comprising:
The configuration module is used for configuring a plurality of specification combination results of the stacking blocks for stacking the same type of goods based on the loading conditions of the target truck and the loading conditions of the intelligent forklift equipment for carrying the stacking blocks;
the selecting module is used for selecting the target stacking specification of the stacking block from the combination result of the multiple specifications;
The setting module is used for setting a cargo stacking variable, and the cargo stacking quantity represents the cargo stacking quantity of cargoes stacked into a target stacking specification along different directions;
The generation module is used for generating a goods placement model based on a total weight constraint function of goods loaded by the truck, a constraint function of the total weight of the stacking blocks, a constraint function of the total volume of the stacking blocks, a constraint function of intelligent forklift equipment and the goods stacking quantity of a target stacking specification;
the optimizing module is used for optimizing the number of the goods stacked by utilizing a preset optimizing algorithm so that the goods placing model meets preset conditions;
And the loading module is used for loading the cargoes of the same type stacked into stacking blocks according to the target stacking specification into the target truck through the intelligent forklift equipment based on the optimized cargo placement model.
In a third aspect, the invention provides a computer device comprising a memory and a processor, the memory and the processor being communicatively coupled to each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the freight loading method of the first aspect or any of its corresponding embodiments.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon computer instructions for causing a computer to perform the freight loading method of the first aspect or any of its corresponding embodiments described above.
In a fifth aspect, the present invention provides a computer program product comprising computer instructions for causing a computer to perform the freight loading method of the first aspect or any of its corresponding embodiments described above. The technical scheme of the invention has the following advantages:
The invention relates to the technical field of freight loading and discloses a freight loading method and a freight loading device, wherein the method is used for configuring a plurality of specification combination results of stacking blocks for stacking the same type of cargoes based on loading conditions of a target truck and loading conditions of intelligent forklift equipment for carrying the stacking blocks; the method comprises the steps of selecting a target stacking specification of a stacking block from a combination result of multiple specifications, setting a cargo stacking variable, wherein the cargo stacking number represents the cargo stacking number of the cargos stacked into the target stacking specification along different directions, generating a cargo placing model based on a total weight constraint function of cargos loaded by a truck, a constraint function of the total weight of the stacking block, a constraint function of the total volume of the stacking block, a constraint function of intelligent forklift equipment and the cargo stacking number of the target stacking specification, optimizing the cargo stacking variable by a preset optimization algorithm to enable the cargo placing model to meet preset conditions, and loading the cargos of the same type stacked into the stacking block according to the target stacking specification into the target truck by the intelligent forklift equipment based on the optimized cargo placing model. Therefore, after the stacking block stacking specification of the intelligent forklift equipment is considered, and goods are loaded based on the optimized goods placement model, the loading space of the intelligent forklift equipment can be fully utilized, and the carrying times of the intelligent forklift equipment are further reduced, so that a large number of goods loading requirements are met.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In accordance with an embodiment of the present invention, there is provided a freight loading method embodiment, it being noted that the steps shown in the flow chart of the drawings may be performed in a computer system such as a set of computer executable instructions, and, although a logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in an order other than that shown or described herein.
According to an embodiment of the present invention, there is provided an embodiment of a freight loading method, which may be used for an intelligent forklift device, a computer device, such as a mobile phone, a tablet computer, a desktop computer, a portable notebook computer, a server, etc., in the embodiment of the present disclosure, fig. 1 is a flowchart of the freight loading method according to the embodiment of the present invention, as shown in fig. 1, the flowchart includes the following steps:
step S101, configuring a combination result of multiple specifications of stacking blocks for stacking the same type of goods based on the loading condition of the target truck and the loading condition of the intelligent forklift device for carrying the stacking blocks.
In a specific example, the freight loading method in the embodiments of the present disclosure is used for loading freight in railway freight. Thus, the target wagon may be a train. When the freight loading method is used in an expressway, the target wagon may be a train.
Specifically, in the presently disclosed embodiments, it is preferred that the target wagon be a train. The loading condition of the target truck may be the number of goods that are allowed to be placed at most according to the full-load requirement in the traveling direction of the train, the number of goods that are allowed to be placed at most according to the full-load requirement in the transverse direction of the train parallel to the traveling direction, and the number of goods that are allowed to be placed according to the requirement in the height direction of the train, and the loading condition of the intelligent forklift device may be the total number of goods that are allowed to be loaded at most at once by the intelligent forklift device.
Further, the same type of cargo means that the shape and weight of the cargo are substantially the same, for example, the same type of cement or rice. Based on the loading conditions of the target truck and the loading conditions of the intelligent forklift equipment for carrying the stacking blocks, the combination result of various specifications of the stacking blocks configured for stacking the same type of goods is exemplified as follows:
For example, the loading condition of the railway carriage is that the maximum allowed number of cargos to be placed according to the full-load requirement is 9 bags of cargos in the running direction of the train, the maximum allowed number of cargos to be placed according to the full-load requirement is 13 bags of cargos in the transverse direction of the train parallel to the running direction, the maximum allowed number of cargos to be placed according to the requirement is 14 bags in the height direction of the train, and meanwhile, the total number of the maximum allowed loaded cargos at one time of the intelligent forklift equipment is 36 bags. And then, carrying out goods placement according to the goods placement standards of the intelligent forklift equipment in the length direction, the width direction and the height direction, for example, placing according to a preset value 3 in the length direction, placing according to a preset value 3 in the width direction and placing according to a preset value 4 in the height direction, wherein the stacking block stacking specification is 3 multiplied by 4, and the intelligent forklift equipment is utilized to just transport 36 bags of goods. Similarly, for example, the placement can be performed according to a preset value 3 in the length direction and according to a preset value 3 in the width direction, and placing according to a preset value of 3 in the height direction, wherein the stacking specification of the stacking blocks is 3 multiplied by 3, and 36 bags of cargoes need to be transported twice. Thus, the first and second substrates are bonded together, palletizing for stacking the same type of goods is configured in this way multiple specification combination results for a block is 3×3×3,3×3×4.
Step S102, selecting a target stacking specification of the stacking block from the combination result of the specifications.
In particular, the method comprises the steps of, based on the above-exemplified combination result of multiple specifications 3×3 selecting target stacking specification of stacking blocks from x 3,3 x 4, 3 x 4 may be selected as the target stacking specification for the palletized block.
In step S103, a cargo stacking variable is set, and the number of cargo stacks indicates the number of cargo stacks in which cargo is stacked in different directions into a target stacking specification.
Specifically, the cargo stacking variable is a variable that participates in calculation, which represents the number of cargo stacks in which cargoes are stacked into a palletized block along a certain direction of the palletized block. For example, the number of stacks of goods may be represented by qij, which represents the number of stacks of goods stacked in the j direction of the stacking block of the ith target stacking specification, which corresponds to the length direction of the stacking block.
Step S104, a cargo placement model is generated based on the total weight constraint function of cargoes loaded by the truck, the constraint function of the total weight of the stacking blocks, the constraint function of the total volume of the stacking blocks, the constraint function of the intelligent forklift equipment and the cargo stacking quantity of the target stacking specification.
In particular, the total weight constraint function of the truck load is to ensure that the total weight of the truck load is maximized, the constraint function of the total weight of the palletizing block is to further constrain the total weight of the palletizing block, the constraint function of the total volume of the palletizing block is to further constrain the total volume of the palletizing block, the constraint function of the intelligent forklift equipment is used for further constraining the size of goods which can be provided by the intelligent forklift equipment, the goods placement model is used for constraining the goods placement mode, and the goods stacking quantity of the target stacking specification is the goods stacking quantity formed according to the target stacking specification of 3 multiplied by 3 or 3 multiplied by 4.
Step S105, optimizing the cargo stacking variable by using a preset optimization algorithm so that the cargo placing model meets preset conditions.
Specifically, the preset optimization algorithm may be a commonly used genetic algorithm, a simulated annealing algorithm or a particle swarm optimization algorithm, and the cargo placement model is further solved by the algorithms to optimize the result so as to ensure the feasibility and the accuracy of the target loading strategy.
And S106, loading the cargoes of the same type stacked into stacking blocks according to the target stacking specification into a target truck through intelligent forklift equipment based on the optimized cargo placement model.
Specifically, the freight loading method in the embodiment of the disclosure can be applied to computer equipment or directly applied to intelligent forklift equipment. If the freight loading method in the embodiment of the disclosure is directly applied to the intelligent forklift equipment, the intelligent forklift equipment loads the same type of cargoes stacked into stacking blocks according to the target stacking specification to the target truck based on the cargo placement model. The design of intelligent fork truck equipment is used for automatic goods, loading goods. The equipment is provided with a high-precision sensor and machine vision, can identify the type and the position of goods in real time, and automatically plans the optimal loading path. The intelligent forklift equipment receives and executes the operation instructions through integration with the central controller, and ensures accurate stacking of goods. In addition, the intelligent forklift equipment also has the functions of automatic obstacle avoidance and fault self-diagnosis, and operation safety and continuity are enhanced. The intelligent unmanned forklift not only improves the loading and unloading efficiency, but also obviously reduces the labor cost and the operation error rate. Fig. 2 shows a schematic diagram of the software and hardware and the mechanical system design of the intelligent forklift equipment. In order to meet the existing railway loading standard and optimize the loading layout of cargoes, efficient stacking of cargoes and space maximum utilization are achieved. By executing the steps S101 to S106, the intelligent forklift device can automatically calculate the optimal cargo placement mode, thereby reducing loading and unloading time and improving the utilization rate of the carriage space.
In this embodiment, a freight loading method is provided, which may be used in an intelligent forklift device, a computer device, such as a mobile phone, a tablet computer, a desktop computer, a portable notebook computer, a server, etc., fig. 3 is a flowchart of the freight loading method according to an embodiment of the present invention, and as shown in fig. 3, step S101, based on a loading condition of a target truck and a loading condition of the intelligent forklift device carrying a palletized block, a combination result of multiple specifications of palletized blocks for stacking the same type of freight is configured, and the flowchart includes the following steps:
In step S1011, a first number of stacks of the target wagon, which is the maximum number of stacks along the length direction of the target wagon, a second number of stacks, which is the maximum number of stacks along the width direction of the target wagon, and a third number of stacks, which is the maximum number of stacks along the height direction of the target wagon, are acquired.
Specifically, the first number of stacks is the maximum number of stacks in the length direction (traveling direction) of the target truck, for example, the maximum number of stacks in the direction is 9 bags of goods, the second number of stacks is the maximum number of stacks in the width direction (transverse direction) of the target truck, for example, the maximum number of stacks in the direction is 13 bags of goods, and the third number of stacks is the maximum number of stacks in the height direction of the target truck, for example, the maximum number of stacks in the direction is 14 bags.
Step S1012, obtaining the maximum carrying quantity of the intelligent forklift equipment.
Specifically, the maximum number of shipments of the smart forklift device, for example, the maximum number of shipments of the smart forklift device may be 36 bags.
In step S1013, based on the first stacking number, the second stacking number, the third stacking number, the maximum carrying number of the intelligent forklift, and the cargo stacking requirement of the cargo in the length direction, the width direction, and the height direction of the target truck, a plurality of different preset numerical values are selected from the positive integers to configure a plurality of specification combination results of stacking blocks.
Specifically, the preset value selected from the positive integers may be 3 or 4, and the cargo stacking requirements of the cargoes in the length direction, the width direction and the height direction of the target truck are a full stack requirement allowed in the length direction, a full stack requirement allowed in the width direction and a full stack requirement not allowed in the height direction. The various combinations of stacking blocks selected from the positive integers for a plurality of different predetermined numerical configurations may be 3 x 3, or alternatively, the first and second heat exchangers may be, formed of 3X 4 number of stacks of goods. Step S102 to step S106 are described in detail in the above embodiments, and are not described herein.
Therefore, in the freight loading method in the embodiment of the disclosure, based on the loading conditions of the target truck and the loading conditions of the intelligent forklift equipment for carrying the stacking blocks, the combination result of multiple specifications of the stacking blocks for stacking the same type of cargoes is configured, so that the intelligent forklift equipment is beneficial to stacking the cargoes of the same type into cargoes of different target stacking specifications.
In an alternative embodiment, the constraint function is created by the following formula (1) based on the total weight of the truck loaded cargo:
Where si is the number of preparations required for the ith pallet, a is the number of combinations of the various specifications of the pallet, max is the number of basic loads that ensure that the total weight of the loaded goods of the wagon is maximized, qjj is the number of basic loads that the goods are stacked in the j direction in the pallet of the ith target stacking specification, qik is the number of basic loads that the goods are stacked in the k direction in the pallet of the ith target stacking specification, qil is the number of basic loads that the goods are stacked in the l direction in the pallet of the ith target stacking specification, and w0 is the weight of the basic loads.
In the above formula (1), the numerator in the formula (1) represents the total weight of the freight loaded by the freight car, and the denominator in the formula represents the number of various specification combinations and the number of preparation needed by the palletizing block, and in the calculation process, the total weight of the freight loaded by the freight car is ensured to be maximized.
The total weight constraint function of the freight car is used for preventing the overload phenomenon from occurring when the total weight of the freight car is overlarge.
In an alternative embodiment, the constraint function for the total weight of the palletized block is created by the following formula:
Where a is the number of multiple specification combinations of pallet blocks, qij is the number of basic pallet blocks stacked in the j direction in the pallet block of the i-th target stacking specification, qik is the number of basic pallet blocks stacked in the k direction in the pallet block of the i-th target stacking specification, qil is the number of basic pallet blocks stacked in the l direction in the pallet block of the i-th target stacking specification, si is the number of preparation required for the i-th pallet block, and w is the upper load limit of the truck.
The constraint function of the total weight of the stacking blocks is used for preventing the total weight of the stacking blocks loaded in the intelligent forklift equipment from being too large and overload.
In an alternative embodiment, the constraint function of the total volume of palletized blocks is created by the following formula (2):
Where Mi is the length of the palletized block, Ni is the width of the palletized block, Pi is the height of the palletized block, qij is the number of basic cargos stacked in the ith palletized block in the long side direction j, qik is the number of basic cargos stacked in the ith palletized block in the broadside direction k, qil is the number of basic cargos stacked in the ith palletized block in the high side direction l, M0 is the length of the cargos, N0 is the width of the cargos, P0 is the height of the cargos, M is the length of the cargos, N is the width of the cargos, P is the height of the cargos, Z1 is the width of the doors of the cargos and Z2 is the height of the doors of the cargos.
The length, the width and the height of the stacking blocks are constrained through the formula (3), so that the purpose of constraining the total volume of the stacking blocks is achieved.
The constraint function of the total volume of the stacking blocks is used for preventing the total volume of cargoes from exceeding the loading volume of the intelligent forklift equipment and affecting loading of the stacking blocks.
In an alternative embodiment, the constraint function of the intelligent forklift device is created by the following equation (4):
(mi,Ni,Pi)∈Veh (4)
Where Veh is the load volume requirement of the intelligent forklift device loading the cargo, Mi is the length of the palletized block, Ni is the width of the palletized block, and Pi is the height of the palletized block.
Through retraining Mi,Ni,Pi in above-mentioned formula (4) simultaneously, prevent that the pile up neatly piece from taking place the phenomenon of rolling, when pile up neatly piece length, width and height simultaneously in intelligent fork truck equipment's space of bearing volume, satisfy intelligent fork truck equipment's preset condition.
The constraint function of the intelligent forklift device is to prevent violating the cargo loading requirements of the intelligent forklift device when loading cargo.
If the freight loading method in the embodiment of the disclosure is directly applied to intelligent forklift equipment, scene modeling and positioning of the intelligent forklift equipment are key for ensuring efficient operation of the intelligent forklift equipment. As shown in fig. 4, by adopting SLAM (simultaneous localization and mapping) technology, the intelligent forklift device can perform self-localization in an unknown environment and create an accurate map of the surrounding environment in real time. By combining a laser radar, a camera and an IMU (inertial measurement unit), the intelligent forklift equipment can perform high-precision environment sensing and obstacle detection, and accurate navigation and operation under a complex scene are realized. In addition, the intelligent forklift equipment integrates various sensor data through an advanced data fusion algorithm, so that positioning accuracy and operation reliability are improved, and stability and efficiency are ensured to be maintained in a dynamically-changing working environment.
The intelligent forklift equipment adopts Yolov target detection algorithm aiming at the identification of the cargo stacking in the actual scene. In order to fully meet the use requirement of the intelligent forklift equipment in an open scene, various sample data in an actual use scene need to be fully collected, and meanwhile, the problem that the sample collection difficulty is high in part of special scenes is considered, such as inconsistent data distribution in cloudy days, foggy days and other environments. In order to solve the problems of diversity and unbalanced distribution of data distribution, the embodiment of the disclosure adopts various data enhancement methods to enhance and generate existing data, and expands the sample size, including but not limited to the traditional Crop, rotation and other data transformation, frog, contrastion and other image processing modes and similar sample generation based on a diffusion model.
Further, with respect to existing data, embodiments of the present disclosure perform dataset distillation on a sample set and perform additional data enhancement and similar sample mining based on the distilled data. The task belongs to single-class multi-target detection, and in order to enhance model generalization, negative sample generation is needed to be further carried out. Specifically, in the embodiment of the disclosure, the negative sample and the partial sample are generated according to a ratio of 1:1:3 by randomly moving the position of the detection frame, wherein the partial sample is data between 0.4 and 0.6 with the positive sample IOU, and the negative sample is data with the IOU less than 0.3.
In addition, in order to improve the training efficiency and performance of the model, a dual-stage training mode is adopted for model training. And step one, training a model backup outside the detection head by adopting a contrast learning mode based on the positive and negative samples. And step two, introducing part of samples, and training YoLov by adopting an end-to-end training mode to perform single-class multi-target detection task.
In the actual reasoning stage after model deployment, the embodiment of the disclosure counts the stacking number based on the detected target stacking boxes. In order to improve accuracy of target positioning, the embodiment of the disclosure adopts a multi-machine-position multi-view fusion mode to detect a stacker type in a current scene. Specifically, for the stacking example in the current scene, the robot first makes a round to collect surrounding environment information, constructs a rough three-dimensional description of the whole environment based on the NERF algorithm, and then calculates a stacking overview in the current scene. And further detecting and stacking the holes under the current machine position, and then positioning the holes until the holes capable of moving the target to be stacked are obtained.
Based on a canny operator and combining visual image information, a high-precision and rapid rectangular hole positioning method is formulated, and the specific process can be composed of the following steps:
a) And (3) Gaussian filtering, namely, certain noise exists in the image and directly affects the rectangular detection effect, so that Gaussian filtering is firstly carried out on the target image, and a part of noise information is removed, and the effect of image smoothing is achieved.
B) Edge detection, namely designing an edge information detection algorithm in the image based on a canny operator aiming at the smoothed image. Specifically, by setting two thresholds maxVal and minVal, a pixel is discriminated once, wherein all greater than maxVal are detected as edges and all lower than minVal are detected as non-edges. And judging the middle pixel point as an edge if the middle pixel point is adjacent to the pixel point determined as the edge, and judging the middle pixel point as a non-edge if the middle pixel point is not adjacent to the pixel point determined as the edge.
C) The method comprises the steps of carrying out straight line detection and fitting by adopting a Hough operator, firstly mapping a planar scattered point set into a binary image after canny detection through a parameter space of a polar coordinate system, and then finding out points with more intersecting curves in the parameter space, wherein the points are the straight lines detected in the original scattered point set.
D) Rectangular hole location, namely performing rectangular detection on the detected linear image, setting a certain threshold value, and filtering the rectangle obtained by detection to finally obtain a target hole location.
And multi-information fusion, namely processing depth image data by combining camera depth information, and improving the positioning accuracy of the rectangular hole.
In this embodiment, a freight loading device is further provided, and the device is used to implement the foregoing embodiments and preferred embodiments, and will not be described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The present embodiment provides a freight loading device, as shown in fig. 5, including:
the configuration module 51 is configured to configure a combination result of multiple specifications of the palletizing blocks for stacking the same type of goods based on the loading condition of the target truck and the loading condition of the intelligent forklift device for carrying the palletizing blocks;
a selecting module 52, configured to select a target stacking specification of the stacking block from the multiple specification combination results;
A setting module 53 for setting a cargo stacking variable, the cargo stacking number representing the number of cargo stacks in which cargo is stacked in different directions into a target stacking specification;
The generating module 54 is configured to generate a cargo placement model based on a total weight constraint function of cargos loaded by the truck, a constraint function of total weight of the stacking blocks, a constraint function of total volume of the stacking blocks, a constraint function of the intelligent forklift device, and a cargo stacking number of a target stacking specification;
The optimizing module 55 is configured to optimize the number of stacked goods by using a preset optimizing algorithm, so that the goods placement model meets a preset condition;
the loading module 56 is configured to load, based on the optimized cargo placement model, the same type of cargo stacked into a stacking block according to the target stacking specification into the target truck through the intelligent forklift device.
In some alternative embodiments, the constraint function is created by equation (1) above based on the total weight of the truck loaded cargo.
In some alternative embodiments, a constraint function for the total weight of the palletized block is created by equation (2) above.
In some alternative embodiments, a constraint function of the total volume of palletized blocks is created by equation (3) above.
In some alternative embodiments, the constraint function of the intelligent forklift device is created by equation (4) above.
In some alternative embodiments, the optimization module includes:
An initial solution determining sub-module, configured to use the cargo stacking variable as an initial solution of a preset optimization algorithm;
the stacking number determining submodule is used for adjusting the cargo stacking variable corresponding to the initial solution according to a neighborhood search method so as to redetermine the cargo stacking number of the target stacking specification;
the placing model generating sub-module is used for generating an optimized goods placing model based on the total weight constraint function of goods loaded by the truck, the constraint function of the total weight of the stacking blocks, the constraint function of the total volume of the stacking blocks, the constraint function of the intelligent forklift equipment and the goods stacking quantity of the redetermined target stacking specification;
and the placement model evaluation sub-module is used for evaluating whether the optimized goods placement model meets the preset conditions, and returning to the step of stacking the goods until the preset conditions are met if the goods placement model does not meet the preset conditions.
Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.
The shipping and loading device of the present embodiment is presented in terms of functional units, referred to herein as ASIC (Application SPECIFIC INTEGRATED Circuit) circuits, processors and memory that execute one or more software or firmware programs, and/or other devices that provide the functionality described above.
The embodiment of the invention also provides computer equipment, which is provided with the freight loading device shown in the figure 6.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, and as shown in fig. 6, the computer device includes one or more processors 10, a memory 20, and interfaces for connecting components, including a high-speed interface and a low-speed interface. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 6.
The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific 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.
Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform the methods shown in implementing the above embodiments.
The memory 20 may include a storage program area that may store an operating system, application programs required for at least one function, and a storage data area that may store data created according to the use of the computer device, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The memory 20 may comprise volatile memory, such as random access memory, or nonvolatile memory, such as flash memory, hard disk or solid state disk, or the memory 20 may comprise a combination of the above types of memory.
The computer device also includes a communication interface 30 for the computer device to communicate with other devices or communication networks.
The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium may be a magnetic disk, an optical disk, a read-only memory, a random-access memory, a flash memory, a hard disk, a solid state disk, or the like, and further, the storage medium may further include a combination of the above types of memories. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.
Portions of the present invention may be implemented as a computer program product, such as computer program instructions, which when executed by a computer, may invoke or provide methods and/or aspects in accordance with the present invention by way of operation of the computer. Those skilled in the art will appreciate that the existence of computer program instructions in a computer-readable medium includes, but is not limited to, source files, executable files, installation package files, and the like, and accordingly, the manner in which computer program instructions are executed by a computer includes, but is not limited to, the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled programs, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed programs. Herein, a computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by a computer.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.