CN114331257A - Logistics transportation loading management method, device, equipment and storage medium - Google Patents
Logistics transportation loading management method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of computers, and discloses a logistics transportation loading management method, a logistics transportation loading management device, a logistics transportation loading management equipment and a storage medium, wherein the logistics transportation loading management method comprises the following steps: receiving a transportation instruction, and determining a corresponding target transportation route and starting departure time according to the transportation instruction, wherein the target transportation route is a fixed departure route pre-stored in a logistics database; judging whether the target transportation route is a direct route or a transfer route, and acquiring information of each logistics site on the target transportation route, wherein the information of each logistics site comprises site positions and transportation tasks; inputting the information of each logistics network into a preset task allocation model, and outputting the vehicle information of a target truck executing a transportation task; acquiring constraint data to determine the loading position and the loading sequence of each cargo to be loaded and calculating the cargo loading rate of a target truck; the invention improves the space utilization rate of the target boxcar, thereby improving the transportation efficiency and reducing the logistics cost; the operation safety and the reliability of the target truck are improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a logistics transportation loading management method, a logistics transportation loading management device, logistics transportation equipment and a storage medium.
Background
With the rapid development of electronic commerce, national economy is transformed rapidly, and the logistics industry of China is developed greatly; the logistics freight car is an important part in logistics warehouse management, and the improvement of the carriage loading rate of the freight car is beneficial to the improvement of the goods circulation efficiency; the vehicle loading rate is the ratio of the volume of the transport vehicle loaded with goods to the available volume, and is generally used for representing the utilization rate of vehicle space, and the vehicle loading rate reflects the reasonableness of vehicle and express parcel configuration.
At present, logistics loading still adopts artificial mode, and loading is carried out by totally depending on the experience of loading workers, and the loading workers generally place packages at will, so that the waste to a certain degree of vehicle loading space can be caused, logistics enterprises need to send more vehicles to finish transportation, the waste of traffic resources is caused, the loading rate of trucks is unbalanced, the cargo distribution efficiency is low, the waste of logistics resources is caused, and the logistics cost is increased.
Disclosure of Invention
In view of the above, it is desirable to provide a method, an apparatus, a device and a storage medium for managing logistics transportation loading, which can solve the problem of unbalanced loading rate of trucks.
A logistics transportation loading management method comprises the following steps:
receiving a transportation instruction, and determining a corresponding target transportation route and starting departure time according to the transportation instruction, wherein the target transportation route is a fixed departure route pre-stored in a logistics database;
judging whether the target transportation route is a direct route or a transfer route, and acquiring information of each logistics site on the target transportation route, wherein the information of each logistics site comprises site positions and transportation tasks;
inputting the information of each logistics network into a preset task allocation model, and outputting the vehicle information of a target truck executing a transportation task;
and acquiring constraint data to determine the loading position and the loading sequence of each cargo to be loaded and calculating the cargo loading rate of the target truck.
In one embodiment, each logistics point in the target transportation route and a corresponding point position are obtained, wherein the logistics points at least comprise a starting point and an end point;
calculating the distance between adjacent logistics nodes according to the positions of the nodes, and determining the arrival time node of each logistics node according to the originating time, wherein the originating time is the time of the target truck from the originating node;
determining order receiving cut-off time of each logistics network point according to the arrival time node of each logistics network point;
and acquiring the cargo transportation orders generated by each logistics network before the order receiving deadline, and determining the transportation data of the to-be-loaded cargo in each cargo transportation order to generate the transportation tasks corresponding to each logistics network.
In one embodiment, a plurality of transportation tasks with labels to be transported are obtained, and each transportation data in the transportation tasks is classified according to the number, the weight and the volume to form a plurality of training sample subsets;
and training each training sample subset by adopting a machine learning algorithm to obtain a corresponding task allocation submodel, allocating the plurality of task allocation submodels based on the mapping relation among the corresponding quantity, weight and volume in the transportation task, and performing decision fusion by utilizing a decision tree algorithm to form the task allocation model.
In one embodiment, a transportation task in information of each logistics node is obtained, and corresponding transportation data in the transportation task are extracted, wherein the transportation data comprise the number, weight and volume of goods to be loaded;
inputting the transportation data into the task allocation model, and analyzing through a corresponding task allocation sub-model to obtain sum value data corresponding to each logistics network, wherein the sum value data comprises a quantity sum value, a weight sum value, a volume sum value and a value;
and outputting vehicle information of the target truck through the task allocation model based on the sum data, wherein the vehicle information at least comprises vehicle length, vehicle width, vehicle height and bearing capacity.
In one embodiment, the constraint data is determined based on vehicle information of the target truck, and comprises a floor area constraint, a length, width and height constraint, a volume constraint, a load capacity constraint and a stability constraint;
determining the unloading sequence of each cargo to be loaded according to the target transportation route and the transportation tasks of each logistics network;
determining the loading position and the loading sequence of each cargo to be loaded according to the constraint data and the unloading sequence;
and uploading the loading position and the loading sequence of each to-be-loaded cargo to a block chain node for storage.
In one embodiment, the compartment volume of the target truck is obtained;
after the target truck is loaded at each logistics network point, acquiring internal images of a plurality of compartments of the target truck;
detecting a plurality of images in the carriage by adopting an edge detection algorithm, acquiring edge information of the loaded goods, and calculating the volume of the goods by combining the edge information of the loaded goods;
and determining the cargo loading rate of the target truck according to the ratio of the cargo volume to the carriage volume.
A logistics transportation load management apparatus, the logistics transportation load management apparatus comprising:
the receiving module is used for receiving a transportation instruction and determining a corresponding target transportation route and starting departure time according to the transportation instruction, wherein the target transportation route is a fixed departure route pre-stored in a logistics database;
the judging module is used for judging whether the target transportation route is a direct route or a transit route and acquiring information of each logistics site on the target transportation route, wherein the information of each logistics site comprises a site position and a transportation task;
the output module is used for inputting the information of each logistics network point into a preset task allocation model and outputting the vehicle information of a target truck for executing a transportation task;
and the calculation module is used for acquiring the constraint data to determine the loading position and the loading sequence of each cargo to be loaded and calculating the cargo loading rate of the target truck.
In one embodiment, the determining module includes:
the acquisition sub-module is used for acquiring each logistics network point in the target transportation route and corresponding network point positions, wherein the logistics network points at least comprise a starting network point and a tail end network point;
the calculation submodule is used for calculating the distance between adjacent logistics nodes according to the positions of the nodes, and determining the arrival time node of each logistics node according to the originating time, wherein the originating time is the time when the target truck is sent out from the originating node;
the determining submodule is used for determining order receiving cut-off time of each logistics network according to the arrival time node of each logistics network;
and the generation submodule is used for acquiring the cargo transportation orders generated by each logistics network before the order receiving deadline, and determining the transportation data of the to-be-loaded cargo in each cargo transportation order so as to generate the transportation tasks corresponding to each logistics network.
A logistics transportation load management apparatus, the logistics transportation load management apparatus comprising:
a logistics transportation loading management apparatus, the logistics transportation loading management apparatus comprising: a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line; the at least one processor calls the instructions in the memory to cause the logistics transportation load management equipment to execute the steps of the logistics transportation load management method.
A computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the logistics transportation loading management method described above.
According to the logistics transportation loading management method, the logistics transportation loading management device, the logistics transportation loading management equipment and the logistics transportation loading management storage medium, the transportation instruction is received, and the corresponding target transportation route and the starting departure time are determined according to the transportation instruction, wherein the target transportation route is a fixed departure route pre-stored in a logistics database; judging whether the target transportation route is a direct route or a transfer route, and acquiring information of each logistics site on the target transportation route, wherein the information of each logistics site comprises site positions and transportation tasks; inputting the information of each logistics network into a preset task allocation model, and outputting the vehicle information of a target truck executing a transportation task; acquiring constraint data to determine the loading position and the loading sequence of each cargo to be loaded and calculating the cargo loading rate of the target truck; the invention improves the space utilization rate of the target boxcar, thereby improving the transportation efficiency and reducing the logistics cost; the reasonable arrangement and the weight balanced distribution of the goods in the carriage can be ensured, the phenomena of overload and unbalance loading of the vehicle are avoided, and the running safety and the reliability of the target truck are improved.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention.
Fig. 1 is a schematic view of a first embodiment of the logistics transportation loading management method of the invention;
FIG. 2 is a schematic view of a second embodiment of the logistics transportation loading management method of the invention;
fig. 3 is a schematic view of a first embodiment of the logistics transportation loading management apparatus of the invention;
fig. 4 is a schematic view of a second embodiment of the logistics transportation loading management apparatus of the invention;
fig. 5 is a schematic view of an embodiment of the logistics transportation loading management apparatus of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As used herein, the singular forms "a", "an", "the" and "the" include plural referents unless the context clearly dictates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
As a preferred embodiment, as shown in fig. 1, a logistics transportation loading management method is used for logistics transportation loading management, and the logistics transportation loading management method comprises the following steps:
it is to be understood that the executing subject of the present invention may be a logistics transportation loading management apparatus, and may also be a terminal or a server, which is not limited herein. The embodiment of the present invention is described by taking a server as an execution subject.
In this embodiment, the ideal of quickly improving the delivery efficiency and the reality of increasing the logistics operation difficulty span more than one difficulty, which mainly includes: complex scheduling cable and urban road conditions, balanced maximum loading rate and optimal delivery path and the like. The intelligent cloud automatic single-row line arranging intelligent scheduling is adopted, the scene selection with the optimal selectable path or the shortest mileage is provided by means of the algorithm of a road network matrix, the problems caused by manual scheduling and line arranging are solved, the efficiency based on transportation instruction line arranging is improved, the intelligent cloud outputs a target transportation line and the time of starting and departure, and the target transportation line is a fixed departure line stored in a logistics database in advance.
in this embodiment, the purpose of this step is to determine the number of points on the target transportation route, so as to plan transportation and loading, for example, if the target transportation route is a transit route, it indicates that the target transportation route will pass through one or more transit points on the target transportation route, and after determining each logistics point, it is convenient to perform logistics point information statistics for the logistics point, where each logistics point information includes a point position and a transportation task, and the transportation task is a sum of the goods to be loaded corresponding to the logistics point; the logistics point information determination in one embodiment includes steps 1021 through 1024.
Step 1021, acquiring each logistics network point in the target transportation route and the corresponding network point position;
specifically, the logistics nodes at least comprise a starting node and an end node, if the target transportation route is a transit route, the logistics nodes comprise the starting node, one or more transit nodes and the end node, and the corresponding node positions are obtained through an electronic map.
Step 1022, calculating the distance between adjacent logistics nodes according to the positions of the nodes, and determining the arrival time node of each logistics node according to the originating and departure time;
specifically, after the positions of the logistics nodes on the target transportation route are determined, the server can calculate the distance between any two adjacent logistics nodes, and the time of starting departure is the time of the target truck from the starting node, namely the server can determine the arrival time nodes of the logistics nodes according to the time of starting departure; for example, the distance between the logistics point a and the logistics point B is 500 meters.
1023, determining order receiving cutoff time of each logistics network according to the arrival time node of each logistics network;
specifically, in order to reserve certain operation time for picking operators and subsequent operators for rechecking, packaging and the like, the corresponding order receiving deadline is determined for the arrival time node of each truck. For example, the time corresponding to the first 1 hour (which may be adjusted according to actual conditions) of the arrival time node may be used as the order receiving deadline.
Step 1024, acquiring the freight transportation orders generated by the logistics nodes before the order receiving deadline, and determining the transportation data of the goods to be loaded in each freight transportation order to generate the transportation tasks corresponding to the logistics nodes.
Specifically, when the current time is the order receiving deadline, the system stops receiving orders, a server of each logistics network calculates a plurality of goods transportation orders received by the network at the order receiving deadline, the goods transportation orders comprise information such as consignee addresses, consignment addresses and transportation data, the transportation data are the goods information of the consignment, the quantity, weight and volume of goods to be loaded are included, and each logistics network calculates all the transportation data to generate a transportation task corresponding to each logistics network.
103, inputting information of each logistics network point into a preset task allocation model, and outputting vehicle information of a target truck for executing a transportation task;
in this embodiment, the purpose of this step is to determine a corresponding target truck and obtain vehicle information of the target truck based on a transportation task and a network point position of a logistics network point on a target transportation route; in one embodiment, the specific process is shown as steps 1031-1033.
Step 1031, obtaining the transportation tasks in the information of each logistics node, and extracting corresponding transportation data in the transportation tasks;
specifically, the server acquires a transportation task in each logistics network information, and corresponding transportation data are acquired from the transportation task, wherein the transportation data include the number, weight and volume of goods to be loaded.
Step 1032, inputting the transportation data into the task allocation model, and analyzing through a corresponding task allocation sub-model to obtain sum value data corresponding to each logistics network, wherein the sum value data comprises a quantity sum value, a weight sum value, a volume sum value;
specifically, the task allocation model is obtained based on machine learning algorithm training, the sum value data is an optimal numerical value of the goods to be loaded in the logistics network, for example, the volume and the value are combined in consideration of left and right/front and back/upper and lower spaces, small spaces can continuously appear in the space division process, excessive small spaces are not beneficial to continuous loading of the goods, and a new large space can be formed through space combination, so that the volume of the goods to be loaded is minimum, and the loading rate is improved.
And step 1033, outputting the vehicle information of the target truck through the task allocation model based on the sum value data.
Specifically, vehicle information of a target truck is output through a task allocation model according to the number and value, the weight and value, and the volume and value; for example, the load of a 4.2-meter full-closed van is 5 tons, the load of a 9.6-meter front four-rear four-full-closed van is 15 tons, the load of a 8.6-meter full-closed van is 15 tons, and the like; the vehicle information of the target truck at least comprises vehicle length, vehicle width, vehicle height and bearing capacity.
And step 104, acquiring constraint data to determine the loading position and the loading sequence of each cargo to be loaded, and calculating the cargo loading rate of the target truck.
In the embodiment, the conditions of maximum utilization rate of loading weight, maximum utilization rate of space, maximum utilization rate of area, minimum total transportation cost and the like are ensured in the transportation process, the logistics cost can be reduced, and the transportation efficiency is improved, so that the compartment space of a target truck needs to be reasonably planned and utilized; based on the target transportation route, if the target transportation route is a transfer route, the target truck needs to be thrown and delivered when arriving at a transfer network point, so that it is necessary to plan the loading of goods and update the target goods loading rate. In one embodiment, the loading positions and the loading sequence of the goods to be loaded are determined as shown in steps 1041 to 1044.
Step 1041, determining constraint data based on the vehicle information of the target truck;
specifically, the constraint data comprises a base area constraint, a length, a width and a height constraint, a volume constraint, a load constraint, a bearing capacity constraint and a stability constraint; for example: sigmaili≤L、∑iwi≤W、∑ihi≤H、∑ili×wi×hiLess than or equal to L multiplied by W multiplied by H, wherein the length of goods to be loaded is LiWidth wiHigh h, hi(i ═ 1, 2., n), the length L, width W and height H of the target freight wagon, the length, width and height of the goods loaded into the target wagon compartment do not exceed the length, width and height of the wagon compartment, and the total volume of the goods does not exceed the volume of the wagon compartment.
1042, determining the unloading sequence of the goods to be loaded according to the target transportation route and the transportation tasks of the logistics nodes;
specifically, if the target transportation route is a transfer route, the target truck needs to be thrown and delivered when arriving at a transfer network point, so that the problem that some goods need to be unloaded when arriving at the transfer network point exists; for example, the implementation of determining the unloading sequence of each cargo to be loaded according to the target transportation route and the transportation task of each logistics node may be: and determining the unloading sequence according to the sequence of the logistics network points corresponding to the goods to be loaded in the target transportation route.
1043, determining the loading position and the loading sequence of each cargo to be loaded according to the constraint data and the unloading sequence;
specifically, the planning of the loading position and the loading sequence can improve the utilization rate of the target boxcar; the cargo loading position and loading sequence can be implemented as follows: boxes for each cargoIn sequence according to volume a1(l × w × h), base area a2(l × w), height a3(h) The descending rules of the loading space are sorted, namely, boxes with larger volume are loaded preferentially according to the residual change of the loading space; when the sizes of the 2 goods boxes are the same, the box with larger bottom area is preferentially loaded, larger bottom supporting area is provided for the goods loaded subsequently, and finally, in order to effectively utilize the space, the goods with higher height are preferentially loaded, and the goods are not extruded; the cargo loading position and loading sequence can be implemented as follows: when a box is loaded into a carriage, the remaining space of the carriage is divided into 3 parts of a front part (Y-axis direction), an upper part (Z-axis direction) and a right part (X-axis direction), and in order to prevent space waste to the maximum extent, the space with the smallest remaining volume is preferably selected; the present invention is not limited to the implementation of the loading position and the loading sequence.
And step 1044, uploading the loading positions and the loading sequence of the goods to be loaded to the block chain nodes for storage.
Specifically, the loading position and the loading sequence of each article to be loaded are uploaded to a block chain network for storage, and the authenticity of information is ensured due to the fact that the block chain technology has non-tamper-ability, so that the accurate source of the information is confirmed, and the traceability of the loading position and the loading sequence of each article to be loaded is ensured.
In one embodiment, the cargo loading rate of the target truck is calculated as shown in steps 1045-1048.
Step 1045, obtaining a compartment volume of the target truck;
specifically, the compartment volume of the target truck is calculated according to the length l, the width w and the height h of the target truck.
Step 1046, obtaining internal images of a plurality of compartments of the target truck after the target truck is loaded at each logistics site;
specifically, when the train is loaded into a carriage, a camera is arranged at the position where a target truck stops, data collected by each camera is accessed to a server, a behavior monitoring system is instantiated for each path of video, one frame of picture in a video stream can be captured from the monitoring video, and a plurality of carriage internal images of the target truck are obtained after processing.
Step 1047, detecting the internal images of the multiple carriages by adopting an edge detection algorithm, acquiring edge information of the loaded cargos, and calculating the cargo volume by combining the edge information of the loaded cargos;
specifically, the edge detection of the image is realized by searching the gray level jump position of the image gray level matrix according to the gradient vector of the two-dimensional gray level matrix by using a discretization gradient approximation function, and then connecting the points of the positions in the image to form the so-called image edge, so that the specific edge detection process is not described herein; linking the edges into the contour in the high-threshold image, when the end point of the contour is reached, searching a point meeting a low threshold value in 8 adjacent points of a breakpoint by the algorithm, collecting a new edge according to the point until the edge of the whole image is closed, and calculating the cargo volume by combining the edge information of the loaded cargo.
And 1048, determining the cargo loading rate of the target truck according to the ratio of the cargo volume to the carriage volume.
Specifically, the cargo loading rate is updated in real time based on the arrived logistics network points, so that the logistics intelligent planning is facilitated, and the bottom-of-delivery efficiency of the target truck driver is improved.
In the embodiment of the invention, a transportation instruction is received, and a corresponding target transportation route and starting departure time are determined according to the transportation instruction, wherein the target transportation route is a fixed departure route pre-stored in a logistics database; judging whether the target transportation route is a direct route or a transfer route, and acquiring information of each logistics site on the target transportation route, wherein the information of each logistics site comprises site positions and transportation tasks; inputting the information of each logistics network into a preset task allocation model, and outputting the vehicle information of a target truck executing a transportation task; acquiring constraint data to determine the loading position and the loading sequence of each cargo to be loaded and calculating the cargo loading rate of a target truck; the invention improves the space utilization rate of the target boxcar, thereby improving the transportation efficiency and reducing the logistics cost; the reasonable arrangement and the weight balanced distribution of the goods in the carriage can be ensured, the phenomena of overload and unbalance loading of the vehicle are avoided, and the running safety and the reliability of the target truck are improved.
Referring to fig. 2, a second embodiment of the method for managing logistics transportation loading according to the embodiment of the present invention includes:
in this embodiment, during machine learning, a plurality of training sample subsets are formed according to actual needs of a problem to be solved, and according to differences in processing problems and differences in required training samples. The server acquires the transportation task, the transportation task is the sum of data of all logistics points, so the server needs to extract transportation data in the transportation task and classify each data in the transportation data, and in the loading process, the quantity, weight and volume of goods have determining factors, so the transportation data can be divided into three types, namely quantity, weight and volume, so as to form a corresponding training sample subset.
In this embodiment, the machine learning algorithm may be: spark machine learning and XGboost (eXtreme Gradient Boosting) machine learning, because each transportation task contains transportation data of a plurality of orders, the sum of the quantity, the weight and the volume in the transportation task of each logistics network point can be respectively calculated through a corresponding task allocation sub-model, the mapping relation among the quantity, the weight and the volume is established, and the accuracy of the association among the quantity, the weight and the volume of goods to be loaded can be improved; a decision tree is a tree structure similar to a flow chart, where each node inside the tree represents a test for a feature, the branches of the tree represent each test result of the feature, and each leaf node of the tree represents a category. The highest level of the tree is the root node. The process of using the decision tree to make a decision is to start from the root node, test the corresponding characteristic attributes in the items to be classified, select an output branch according to the value of the characteristic attributes until the leaf node is reached, and take the category stored by the leaf node as a decision result. The decision process of the decision tree is equivalent to traversal from the root node to one of the leaf nodes in the tree. How each step is traversed is determined by the specific feature attributes of the individual features of the data. And fusing the plurality of task allocation sub-models based on a weighting or simple voting integration algorithm to form a task allocation model.
In the embodiment of the invention, a plurality of transportation tasks with labels to be transported are obtained, and each transportation data in the transportation tasks is classified according to the quantity, the weight and the volume to form a plurality of training sample subsets; training each training sample subset by adopting a machine learning algorithm to obtain a corresponding task allocation sub-model, allocating a plurality of task allocation sub-models based on the mapping relation among the corresponding quantity, weight and volume in the transportation task, and performing decision fusion by utilizing a decision tree algorithm to form a task allocation model; the invention forms a task allocation model for determining the target truck for executing the transportation task, effectively provides customized service for transportation and improves the accuracy and timeliness of delivery of drivers.
Referring to fig. 3, in an embodiment, a logistics transportation loading management apparatus is provided, including:
the receiving module 301 is configured to receive a transportation instruction, and determine a corresponding target transportation route and an originating departure time according to the transportation instruction, where the target transportation route is a fixed departure route pre-stored in the logistics database;
the judging module 302 is configured to judge whether the target transportation route is a direct route or a transit route, and acquire information of each logistics node on the target transportation route, where the information of each logistics node includes a node position and a transportation task;
the output module 303 is configured to input information of each logistics node into a preset task allocation model, and output vehicle information of a target truck that executes a transportation task;
the calculating module 304 is configured to obtain the constraint data, determine a loading position and a loading sequence of each to-be-loaded cargo, and calculate a cargo loading rate of the target truck.
Referring to fig. 4, in a second embodiment of the logistics transportation loading management apparatus according to the embodiment of the present invention, the determining module 302 specifically includes:
an obtaining submodule 3021, configured to obtain each logistics node in the target transportation route and a corresponding node position, where the logistics node includes at least a start node and an end node;
the calculating submodule 3022 is configured to calculate a distance between adjacent logistics nodes according to the node position, and determine an arrival time node of each logistics node according to the originating time, where the originating time is a time when the target truck is sent from the originating node;
the determining submodule 3023 is configured to determine order receiving deadline of each logistics node according to the arrival time node of each logistics node;
the generating submodule 3024 is configured to obtain the transportation orders of the goods generated by each logistics node before the order receiving deadline, and determine transportation data of the goods to be loaded in each transportation order of the goods, so as to generate a transportation task corresponding to each logistics node.
In the embodiment of the invention, a transportation instruction is received, and a corresponding target transportation route and starting departure time are determined according to the transportation instruction, wherein the target transportation route is a fixed departure route pre-stored in a logistics database; judging whether the target transportation route is a direct route or a transfer route, and acquiring information of each logistics site on the target transportation route, wherein the information of each logistics site comprises site positions and transportation tasks; inputting the information of each logistics network into a preset task allocation model, and outputting the vehicle information of a target truck executing a transportation task; acquiring constraint data to determine the loading position and the loading sequence of each cargo to be loaded and calculating the cargo loading rate of a target truck; the invention improves the space utilization rate of the target boxcar, thereby improving the transportation efficiency and reducing the logistics cost; the reasonable arrangement and the weight balanced distribution of the goods in the carriage can be ensured, the phenomena of overload and unbalance loading of the vehicle are avoided, and the running safety and the reliability of the target truck are improved.
Fig. 3 to 4 describe the logistics transportation loading management apparatus in the embodiment of the present invention in detail from the perspective of the modular functional entity, and the logistics transportation loading management apparatus in the embodiment of the present invention is described in detail from the perspective of hardware processing.
Fig. 5 is a schematic structural diagram of a logistics transportation loading management apparatus 500 according to an embodiment of the present invention, where the logistics transportation loading management apparatus 500 may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 510 (e.g., one or more processors) and a memory 520, and one or more storage media 530 (e.g., one or more mass storage devices) for storing applications 533 or data 532. Memory 520 and storage media 530 may be, among other things, transient or persistent storage. The program stored in the storage medium 530 may include one or more modules (not shown), and each module may include a series of instructions for the logistics transportation load management apparatus 500. Further, the processor 510 may be configured to communicate with the storage medium 530, and execute a series of instruction operations in the storage medium 530 on the logistics transportation loading management apparatus 500.
The logistics transport loading management apparatus 500 may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input-output interfaces 560, and/or one or more operating systems 531, such as Windows service, Mac OS X, Unix, Linux, FreeBSD, etc. It will be understood by those skilled in the art that the configuration of the logistics transport loading management apparatus shown in fig. 5 does not constitute a limitation of the logistics transport loading management apparatus provided herein, and may include more or less components than those shown, or some components in combination, or a different arrangement of components.
A logistics transportation loading management device is used for realizing the following logistics transportation loading management method, and the logistics transportation loading management method specifically comprises the following steps:
receiving a transportation instruction, and determining a corresponding target transportation route and starting departure time according to the transportation instruction, wherein the target transportation route is a fixed departure route pre-stored in a logistics database;
judging whether the target transportation route is a direct route or a transfer route, and acquiring information of each logistics site on the target transportation route, wherein the information of each logistics site comprises site positions and transportation tasks;
inputting the information of each logistics network into a preset task allocation model, and outputting the vehicle information of a target truck executing a transportation task;
and acquiring constraint data to determine the loading position and the loading sequence of each cargo to be loaded and calculating the cargo loading rate of the target truck.
In one embodiment, each logistics site in the target transportation route and a corresponding site position are obtained, wherein the logistics sites at least comprise a starting site and an end site;
calculating the distance between adjacent logistics nodes according to the positions of the nodes, and determining the arrival time node of each logistics node according to the originating time, wherein the originating time is the time of the target truck from the originating node;
determining order receiving cut-off time of each logistics network point according to the arrival time node of each logistics network point;
and acquiring the cargo transportation orders generated by each logistics network before the order receiving deadline, and determining the transportation data of the to-be-loaded cargo in each cargo transportation order to generate the transportation tasks corresponding to each logistics network.
In one embodiment, a plurality of transportation tasks with labels to be transported are obtained, and each transportation data in the transportation tasks is classified according to the number, the weight and the volume to form a plurality of training sample subsets;
and training each training sample subset by adopting a machine learning algorithm to obtain a corresponding task allocation submodel, allocating the plurality of task allocation submodels based on the mapping relation among the corresponding quantity, weight and volume in the transportation task, and performing decision fusion by utilizing a decision tree algorithm to form the task allocation model.
In one embodiment, a transportation task in each logistics node information is obtained, and corresponding transportation data in the transportation task are extracted, wherein the transportation data comprise the number, weight and volume of goods to be loaded;
inputting the transportation data into the task allocation model, and analyzing through a corresponding task allocation sub-model to obtain sum value data corresponding to each logistics network, wherein the sum value data comprises a quantity sum value, a weight sum value, a volume sum value and a value;
and outputting vehicle information of the target truck through the task allocation model based on the sum data, wherein the vehicle information at least comprises vehicle length, vehicle width, vehicle height and bearing capacity.
In one embodiment, the constraint data is determined based on vehicle information of the target truck, the constraint data includes a floor area constraint, a length, width, height constraint, a volume constraint, a load capacity constraint, and a stability constraint;
determining the unloading sequence of each cargo to be loaded according to the target transportation route and the transportation tasks of each logistics network;
determining the loading position and the loading sequence of each cargo to be loaded according to the constraint data and the unloading sequence;
and uploading the loading position and the loading sequence of each to-be-loaded cargo to a block chain node for storage.
In one embodiment, a car volume of the target truck is obtained;
after the target truck is loaded at each logistics network point, acquiring internal images of a plurality of compartments of the target truck;
detecting a plurality of images in the carriage by adopting an edge detection algorithm, acquiring edge information of the loaded goods, and calculating the volume of the goods by combining the edge information of the loaded goods;
and determining the cargo loading rate of the target truck according to the ratio of the cargo volume to the carriage volume.
The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium, and may also be a volatile computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed on a computer, the instructions cause the computer to execute the following logistics transportation load management method, where the logistics transportation load management method specifically includes the following steps:
receiving a transportation instruction, and determining a corresponding target transportation route and starting departure time according to the transportation instruction, wherein the target transportation route is a fixed departure route pre-stored in a logistics database;
judging whether the target transportation route is a direct route or a transfer route, and acquiring information of each logistics site on the target transportation route, wherein the information of each logistics site comprises site positions and transportation tasks;
inputting the information of each logistics network into a preset task allocation model, and outputting the vehicle information of a target truck executing a transportation task;
and acquiring constraint data to determine the loading position and the loading sequence of each cargo to be loaded and calculating the cargo loading rate of the target truck.
In one embodiment, each logistics site in the target transportation route and a corresponding site position are obtained, wherein the logistics sites at least comprise a starting site and an end site;
calculating the distance between adjacent logistics nodes according to the positions of the nodes, and determining the arrival time node of each logistics node according to the originating time, wherein the originating time is the time of the target truck from the originating node;
determining order receiving cut-off time of each logistics network point according to the arrival time node of each logistics network point;
and acquiring the cargo transportation orders generated by each logistics network before the order receiving deadline, and determining the transportation data of the to-be-loaded cargo in each cargo transportation order to generate the transportation tasks corresponding to each logistics network.
In one embodiment, a plurality of transportation tasks with labels to be transported are obtained, and each transportation data in the transportation tasks is classified according to the number, the weight and the volume to form a plurality of training sample subsets;
and training each training sample subset by adopting a machine learning algorithm to obtain a corresponding task allocation submodel, allocating the plurality of task allocation submodels based on the mapping relation among the corresponding quantity, weight and volume in the transportation task, and performing decision fusion by utilizing a decision tree algorithm to form the task allocation model.
In one embodiment, a transportation task in each logistics node information is obtained, and corresponding transportation data in the transportation task are extracted, wherein the transportation data comprise the number, weight and volume of goods to be loaded;
inputting the transportation data into the task allocation model, and analyzing through a corresponding task allocation sub-model to obtain sum value data corresponding to each logistics network, wherein the sum value data comprises a quantity sum value, a weight sum value, a volume sum value and a value;
and outputting vehicle information of the target truck through the task allocation model based on the sum data, wherein the vehicle information at least comprises vehicle length, vehicle width, vehicle height and bearing capacity.
In one embodiment, the constraint data is determined based on vehicle information of the target truck, the constraint data includes a floor area constraint, a length, width, height constraint, a volume constraint, a load capacity constraint, and a stability constraint;
determining the unloading sequence of each cargo to be loaded according to the target transportation route and the transportation tasks of each logistics network;
determining the loading position and the loading sequence of each cargo to be loaded according to the constraint data and the unloading sequence;
and uploading the loading position and the loading sequence of each to-be-loaded cargo to a block chain node for storage.
In one embodiment, a car volume of the target truck is obtained;
after the target truck is loaded at each logistics network point, acquiring internal images of a plurality of compartments of the target truck;
detecting a plurality of images in the carriage by adopting an edge detection algorithm, acquiring edge information of the loaded goods, and calculating the volume of the goods by combining the edge information of the loaded goods;
and determining the cargo loading rate of the target truck according to the ratio of the cargo volume to the carriage volume.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A logistics transportation loading management method is characterized by comprising the following steps:
receiving a transportation instruction, and determining a corresponding target transportation route and starting departure time according to the transportation instruction, wherein the target transportation route is a fixed departure route pre-stored in a logistics database;
judging whether the target transportation route is a direct route or a transfer route, and acquiring information of each logistics site on the target transportation route, wherein the information of each logistics site comprises site positions and transportation tasks;
inputting the information of each logistics network into a preset task allocation model, and outputting the vehicle information of a target truck executing a transportation task;
and acquiring constraint data to determine the loading position and the loading sequence of each cargo to be loaded and calculating the cargo loading rate of the target truck.
2. The method for managing the logistics transportation loading of the truck according to claim 1, wherein the determining whether the target transportation route is a direct route or a transit route and the obtaining of the information of each logistics point on the target transportation route comprises:
acquiring each logistics network point and a corresponding network point position in the target transportation route, wherein the logistics network points at least comprise a starting network point and a tail end network point;
calculating the distance between adjacent logistics nodes according to the positions of the nodes, and determining the arrival time node of each logistics node according to the originating time, wherein the originating time is the time of the target truck from the originating node;
determining order receiving cut-off time of each logistics network point according to the arrival time node of each logistics network point;
and acquiring the cargo transportation orders generated by each logistics network before the order receiving deadline, and determining the transportation data of the to-be-loaded cargo in each cargo transportation order to generate the transportation tasks corresponding to each logistics network.
3. The logistics transportation loading management method of claim 1, wherein before inputting information of each logistics site into a preset task allocation model and outputting vehicle information of a target truck for performing a transportation task, the logistics transportation loading management method further comprises:
acquiring a plurality of transportation tasks with labels to be transported, and classifying each transportation data in the transportation tasks according to the number, weight and volume to form a plurality of training sample subsets;
and training each training sample subset by adopting a machine learning algorithm to obtain a corresponding task allocation submodel, allocating the plurality of task allocation submodels based on the mapping relation among the corresponding quantity, weight and volume in the transportation task, and performing decision fusion by utilizing a decision tree algorithm to form the task allocation model.
4. The logistics transportation loading management method of claim 1, wherein the inputting of the information of each logistics site into a preset task allocation model and the outputting of the vehicle information of a target truck performing a transportation task comprises:
acquiring a transportation task in information of each logistics network, and extracting corresponding transportation data in the transportation task, wherein the transportation data comprises the number, weight and volume of goods to be loaded;
inputting the transportation data into the task allocation model, and analyzing through a corresponding task allocation sub-model to obtain sum value data corresponding to each logistics network, wherein the sum value data comprises a quantity sum value, a weight sum value, a volume sum value and a value;
and outputting vehicle information of the target truck through the task allocation model based on the sum data, wherein the vehicle information at least comprises vehicle length, vehicle width, vehicle height and bearing capacity.
5. The logistics transportation loading management method of claim 1, wherein the obtaining of constraint data to determine the loading position and the loading sequence of each cargo to be loaded comprises:
determining the constraint data based on the vehicle information of the target truck, wherein the constraint data comprises a bottom area constraint, a length, a width and a height constraint, a volume constraint, a load constraint, a bearing capacity constraint and a stability constraint;
determining the unloading sequence of each cargo to be loaded according to the target transportation route and the transportation tasks of each logistics network;
determining the loading position and the loading sequence of each cargo to be loaded according to the constraint data and the unloading sequence;
and uploading the loading position and the loading sequence of each to-be-loaded cargo to a block chain node for storage.
6. The logistics transportation loading management method of claim 1, wherein the calculating the cargo loading rate of the target truck comprises:
obtaining the carriage volume of the target truck;
after the target truck is loaded at each logistics network point, acquiring internal images of a plurality of compartments of the target truck;
detecting a plurality of images in the carriage by adopting an edge detection algorithm, acquiring edge information of the loaded goods, and calculating the volume of the goods by combining the edge information of the loaded goods;
and determining the cargo loading rate of the target truck according to the ratio of the cargo volume to the carriage volume.
7. A logistics transportation loading management apparatus, characterized in that the logistics transportation loading management apparatus comprises:
the receiving module is used for receiving a transportation instruction and determining a corresponding target transportation route and starting departure time according to the transportation instruction, wherein the target transportation route is a fixed departure route pre-stored in a logistics database;
the judging module is used for judging whether the target transportation route is a direct route or a transit route and acquiring information of each logistics site on the target transportation route, wherein the information of each logistics site comprises a site position and a transportation task;
the output module is used for inputting the information of each logistics network point into a preset task allocation model and outputting the vehicle information of a target truck for executing a transportation task;
and the calculation module is used for acquiring the constraint data to determine the loading position and the loading sequence of each cargo to be loaded and calculating the cargo loading rate of the target truck.
8. The logistics transportation loading management device of claim 7, wherein the determining module comprises:
the acquisition sub-module is used for acquiring each logistics network point in the target transportation route and corresponding network point positions, wherein the logistics network points at least comprise a starting network point and a tail end network point;
the calculation submodule is used for calculating the distance between adjacent logistics nodes according to the positions of the nodes, and determining the arrival time node of each logistics node according to the originating time, wherein the originating time is the time when the target truck is sent out from the originating node;
the determining submodule is used for determining order receiving cut-off time of each logistics network according to the arrival time node of each logistics network;
and the generation submodule is used for acquiring the cargo transportation orders generated by each logistics network before the order receiving deadline, and determining the transportation data of the to-be-loaded cargo in each cargo transportation order so as to generate the transportation tasks corresponding to each logistics network.
9. A logistics transportation loading management apparatus, characterized in that the logistics transportation loading management apparatus comprises: a memory having instructions stored therein and at least one processor, the memory and the at least one processor interconnected by a line; the at least one processor invokes the instructions in the memory to cause the logistics transport loading management apparatus to perform the steps of the logistics transport loading management method of any one of claims 1-6.
10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the logistics transport loading management method of any one of claims 1-6.
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