Loading time prediction method and system based on multimodal transport

Abstract

The invention provides a multimodal transport-based loading time prediction method and system, and the method comprises the steps: detecting target article information corresponding to a target article in real time when the target article is detected to appear in a preset logistics region in real time; determining a target transfer scheme corresponding to the target article in a preset database in real time according to the target article information, and detecting a plurality of transfer nodes correspondingly contained in the target transfer scheme in real time; splitting the target transfer scheme into a plurality of corresponding local transfer schemes in real time according to the plurality of transfer nodes, and generating a local time axis corresponding to each local transfer scheme in real time based on a preset rule; and performing real-time splicing processing on each local time axis to generate a corresponding target time axis in real time, and correspondingly outputting the truck loading prediction time matched with the target article according to the target time axis. According to the method, the loading time of the article can be comprehensively and accurately predicted, and the loading efficiency is correspondingly improved.

Description

Technical Field

[0001] The present invention relates to the field of logistics technology, and in particular to a loading time prediction method and system based on multimodal transport. Background Art

[0002] With the advancement of science and technology and the rapid development of the times, all industries need to carry out corresponding logistics and transportation in the process of development. Among them, in existing physical manufacturing companies, in order to improve the transportation efficiency of goods, existing companies will preliminarily predict the loading time of goods before loading the goods, so as to facilitate corresponding optimization during the actual transportation process.

[0003] Among them, in the process of real-time prediction of item loading time, most existing technologies collect historical loading records, such as collecting the loading time of similar goods, the same vehicles, and similar loads in the past month or quarter, and using statistical methods to calculate the corresponding average loading time in real time, which serves as the basis for new logistics and transportation tasks.

[0004] Furthermore, in the process of predicting the loading time of items in real time through the above-mentioned method, the existing technology easily ignores the individual differences between items and places high demands on the proficiency of the loaders, resulting in low accuracy of the final predicted loading time. It is also only suitable for logistics and transportation with stable processes, has certain usage limitations, and reduces the efficiency of logistics and transportation. Summary of the Invention

[0005] Based on this, the purpose of the present invention is to provide a loading time prediction method and system based on multimodal transport to solve the problem that the existing technology easily ignores the individual differences between items in the process of real-time prediction of loading time of items, and at the same time puts forward high requirements on the proficiency of loading personnel, resulting in low accuracy of the final predicted loading time.

[0006] The first aspect of the embodiment of the present invention proposes: A method for predicting loading time based on multimodal transport, wherein the method comprises: When a target object is detected in real time in a preset logistics area, target object information corresponding to the target object is detected in real time; Determine in real time a target transshipment plan corresponding to the target item in a preset database according to the target item information, and detect in real time a number of transshipment nodes corresponding to the target transshipment plan; Splitting the target transfer plan into a plurality of corresponding local transfer plans in real time according to the plurality of transfer nodes, and generating a local time axis corresponding to each of the local transfer plans in real time based on preset rules; Each of the local time axes is spliced ​​together in real time to generate a corresponding target time axis in real time, and a loading prediction time adapted to the target item is outputted according to the target time axis.

[0007] The beneficial effect of the present invention is that by detecting the target item information corresponding to the target item in real time, the target transfer plan that is adapted to the current target item can be determined in real time. Based on this, in order to facilitate the prediction of subsequent time, it is necessary to consider the several transfer nodes corresponding to the current target transfer plan. Based on this, in order to improve the prediction accuracy, the present invention will also split into several local transfer plans and generate several local time axes accordingly. Based on this, it is finally spliced ​​into the required target time axis, and the loading of the items can be completed according to the target time axis, so that the loading time of the items can be accurately and effectively predicted, which eliminates the human influence and improves the loading efficiency.

[0008] Furthermore, the step of splitting the target transshipment plan into a number of corresponding local transshipment plans in real time according to the plurality of transshipment nodes includes: When the target transfer plan is obtained in real time, a network topology diagram corresponding to the target transfer plan is created in real time, wherein the network topology diagram is composed of a plurality of topology points and a plurality of topology lines, and the plurality of topology points are connected to each other through the topology lines; Each of the transfer nodes is structurally disassembled to detect in real time a number of transfer elements corresponding to each of the transfer nodes, and the target transfer plan is split into a number of local transfer plans in real time according to the network topology diagram and the number of transfer elements.

[0009] Furthermore, the step of splitting the target transport plan into a plurality of local transport plans in real time according to the network topology diagram and the plurality of transport elements includes: When a plurality of transfer elements corresponding to each transfer node are acquired in real time, target attributes corresponding to each transfer element are detected in real time; According to the target attributes, the corresponding transfer elements are mapped to the inside of the network topology map, so as to enable the target topology points corresponding to the transfer elements in real time in the network topology map, and split the target transfer plan into several local transfer plans in real time according to the target topology points.

[0010] Furthermore, the step of splitting the target transport plan into a plurality of local transport plans in real time according to the target topological point includes: In the network topology diagram, target topology points corresponding to each of the transfer elements are marked respectively, and each of the target topology points is unique; A number of target topological points corresponding to each of the transfer nodes are extracted in real time, and a target topological chain adapted to the transfer node is constructed in real time based on the target topological points, and the target topological chain is converted into the local transfer plan.

[0011] Furthermore, the step of generating a local time axis corresponding to each local transfer plan in real time based on preset rules includes: When the local transfer plan is obtained in real time, the transfer information corresponding to the local transfer plan is extracted in real time; The production line information and vehicle model information corresponding to the target item are extracted in real time from the transfer information, and a local time axis adapted to the local transfer plan is generated according to the production line information and the vehicle model information. Each of the local transfer methods is unique.

[0012] Furthermore, the step of generating a local timeline adapted to the local transport plan according to the production line information and the vehicle model information includes: When the production line information is acquired in real time, the circulation time corresponding to the target item is calculated in real time based on the production line information; When the vehicle type information is acquired in real time, the loading time corresponding to the target item is calculated in real time according to the vehicle type information and the target item information; A local time axis adapted to the local transport plan is generated according to the circulation time and the loading time.

[0013] Furthermore, the step of generating a local time axis adapted to the local transport plan according to the circulation time and the loading time includes: When the circulation time and the loading time are respectively obtained, the circulation time and the loading time are integrated in real time to generate a corresponding initial local transit time in real time; The target type corresponding to the target item is determined in real time based on the target item information, and the initial local transit time is adaptively corrected based on the target type to generate a target local transit time in real time, and the target local transit time is converted into the local time axis.

[0014] The second aspect of the embodiment of the present invention proposes: A loading time prediction system based on multimodal transport, wherein the system comprises: A detection module, configured to detect target item information corresponding to the target item in real time when a target item is detected to be present in a preset logistics area; A processing module is used to determine a target transshipment plan corresponding to the target item in a preset database in real time according to the target item information, and to detect a number of transshipment nodes corresponding to the target transshipment plan in real time; A splitting module is used to split the target transfer plan into a plurality of corresponding local transfer plans in real time according to a plurality of transfer nodes, and to generate a local time axis corresponding to each of the local transfer plans in real time based on preset rules; The splicing module is used to perform real-time splicing processing on each of the local time axes to generate a corresponding target time axis in real time, and output a loading prediction time adapted to the target item according to the target time axis.

[0015] Furthermore, the splitting module is specifically used to: When the target transfer plan is obtained in real time, a network topology diagram corresponding to the target transfer plan is created in real time, wherein the network topology diagram is composed of a plurality of topology points and a plurality of topology lines, and the plurality of topology points are connected to each other through the topology lines; Each of the transfer nodes is structurally disassembled to detect in real time a number of transfer elements corresponding to each of the transfer nodes, and the target transfer plan is split into a number of local transfer plans in real time according to the network topology diagram and the number of transfer elements.

[0016] Furthermore, the splitting module is specifically used to: When a plurality of transfer elements corresponding to each transfer node are acquired in real time, target attributes corresponding to each transfer element are detected in real time; According to the target attributes, the corresponding transfer elements are mapped to the inside of the network topology map, so as to enable the target topology points corresponding to the transfer elements in real time in the network topology map, and split the target transfer plan into several local transfer plans in real time according to the target topology points.

[0017] Furthermore, the splitting module is specifically used to: In the network topology diagram, target topology points corresponding to each of the transfer elements are marked respectively, and each of the target topology points is unique; A number of target topological points corresponding to each of the transfer nodes are extracted in real time, and a target topological chain adapted to the transfer node is constructed in real time based on the target topological points, and the target topological chain is converted into the local transfer plan.

[0018] Furthermore, the splitting module is specifically used to: When the local transfer plan is obtained in real time, the transfer information corresponding to the local transfer plan is extracted in real time; The production line information and vehicle model information corresponding to the target item are extracted in real time from the transfer information, and a local time axis adapted to the local transfer plan is generated according to the production line information and the vehicle model information. Each of the local transfer methods is unique.

[0019] Furthermore, the splitting module is specifically used to: When the production line information is acquired in real time, the circulation time corresponding to the target item is calculated in real time based on the production line information; When the vehicle type information is acquired in real time, the loading time corresponding to the target item is calculated in real time according to the vehicle type information and the target item information; A local time axis adapted to the local transport plan is generated according to the circulation time and the loading time.

[0020] Furthermore, the splitting module is specifically used to: When the circulation time and the loading time are respectively obtained, the circulation time and the loading time are integrated in real time to generate a corresponding initial local transit time in real time; The target type corresponding to the target item is determined in real time based on the target item information, and the initial local transit time is adaptively corrected based on the target type to generate a target local transit time in real time, and the target local transit time is converted into the local time axis.

[0021] The third aspect of the embodiment of the present invention proposes: A computer comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for predicting loading time based on multimodal transport as described above is implemented.

[0022] The fourth aspect of the embodiments of the present invention proposes: A readable storage medium stores a computer program thereon, wherein when the program is executed by a processor, the method for predicting loading time based on multimodal transport as described above is implemented.

[0023] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flowchart of a method for predicting loading time based on multimodal transport provided in a first embodiment of the present invention; Figure 2This is a structural block diagram of a multimodal transport-based loading time prediction system provided in the third embodiment of the present invention.

[0025] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0026] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate several embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0027] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is referred to as being "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0029] See also Figure 1 , shown is a loading time prediction method based on multimodal transport provided by the first embodiment of the present invention. The loading time prediction method based on multimodal transport provided by this embodiment can comprehensively and accurately predict the loading time of items, while avoiding interference from human factors, thereby correspondingly improving efficiency.

[0030] Specifically, this embodiment provides: A method for predicting loading time based on multimodal transport specifically includes the following steps: Step S10, when a target item is detected in real time to be present in a preset logistics area, target item information corresponding to the target item is detected in real time; Among them, it should be noted that the products produced by existing factories need to undergo corresponding logistics processing before they can reach the sales location. Based on this, before existing items are transported, the produced products need to be loaded into the interior of the transport vehicle. In this process, in order to improve the logistics efficiency of items, the existing technology will predict in advance the loading time of the items from the production site to the interior of the transport vehicle for subsequent management and control. Based on this, in order to be able to comprehensively and accurately predict the loading time of a batch of items produced in real time, the present invention will first set up a logistics area. Among them, it should be pointed out that when an item appears in the logistics area, it corresponds to that the item is a qualified product that needs to be transported. Based on this, in the actual application process, when a batch of target items is detected in real time to appear in the logistics area, in order to facilitate subsequent loading, the target item information corresponding to the current target item can be detected in real time in the existing item database. Specifically, through the target item information, the type, size, transportation requirements and other information of the current target item can be directly obtained.

[0031] Step S20: determining a target transshipment plan corresponding to the target item in a preset database in real time according to the target item information, and detecting a number of transshipment nodes corresponding to the target transshipment plan in real time; Among them, it should be noted that after the target item information corresponding to the target item is determined in real time through the above steps, corresponding transfer processing is required at this time. Specifically, the present invention will immediately determine the target transfer plan corresponding to the current target item in the existing database in real time based on the current target item information, and at the same time detect the several transfer nodes corresponding to the internal part of the current target transfer plan. Among them, it should be pointed out that, for the sake of ease of understanding, when it is detected in real time that the target item is a refrigerated item, it is necessary to execute the corresponding cold chain transportation plan. Correspondingly, when it is detected in real time that the target item is a fragile item, it is necessary to execute the corresponding protective transportation plan for subsequent processing.

[0032] Step S30: splitting the target transfer plan into a plurality of corresponding local transfer plans in real time according to the plurality of transfer nodes, and generating a local time axis corresponding to each of the local transfer plans in real time based on a preset rule; Among them, it should be noted that after the required target transfer plan and the several transfer nodes contained therein are determined in real time through the above steps, in order to be able to comprehensively analyze the current target transfer plan, the current target transfer plan can be immediately split and processed in real time according to the current several transfer nodes, and several local transfer plans can be split accordingly. Among them, it should be pointed out that the above-mentioned several transfer nodes can specifically be item input nodes, item conveying nodes, and item loading nodes, etc., so that the loading links of the current target items can be fully obtained, so that the time required for each link can be calculated accordingly, so as to facilitate subsequent processing.

[0033] Step S40 , performing real-time splicing processing on each of the local time axes to generate a corresponding target time axis in real time, and outputting a loading prediction time adapted to the target item according to the target time axis.

[0034] Among them, it should be noted that after obtaining several local transfer plans in real time through the above steps, the local time axis corresponding to each current local transfer plan can be immediately generated in real time according to the pre-set rules, that is, the time period corresponding to each local transfer plan can be generated in real time. Based on this, it is only necessary to detect the starting time node and the ending time node of each current local time axis in turn, and then perform real-time splicing processing according to the sequence of each time node. In this way, a complete target time axis can be spliced ​​out, and the time threshold corresponding to the current target time axis can be extracted in real time, and the loading prediction time required for the current target item can be output accordingly. Based on this, manual participation can be eliminated in the above process, thereby avoiding unnecessary errors and improving loading efficiency.

[0035] Second embodiment Furthermore, the step of splitting the target transshipment plan into a number of corresponding local transshipment plans in real time according to the plurality of transshipment nodes includes: When the target transfer plan is obtained in real time, a network topology diagram corresponding to the target transfer plan is created in real time, wherein the network topology diagram is composed of a plurality of topology points and a plurality of topology lines, and the plurality of topology points are connected to each other through the topology lines; Each of the transfer nodes is structurally disassembled to detect in real time a number of transfer elements corresponding to each of the transfer nodes, and the target transfer plan is split into a number of local transfer plans in real time according to the network topology diagram and the number of transfer elements.

[0036] Among them, it should be noted that after the target transfer plan corresponding to the current target item is determined in real time through the above steps, in order to be able to complete the split objectively and accurately, the current target transfer plan will first be quantified. Specifically, the present invention will convert the current target transfer plan into a network topology diagram that can be calculated in real time. Specifically, the interior of the network topology diagram is composed of several topological points and several topological lines. Correspondingly, in order to obtain the information corresponding to the interior of the above-mentioned transfer node, the several transfer elements corresponding to the interior of each current transfer node will be detected again at this time. Specifically, the several transfer elements can be time requirements, cargo attributes, and resource constraints, etc., to facilitate subsequent processing.

[0037] Furthermore, the step of splitting the target transport plan into a plurality of local transport plans in real time according to the network topology diagram and the plurality of transport elements includes: When a plurality of transfer elements corresponding to each transfer node are acquired in real time, target attributes corresponding to each transfer element are detected in real time; According to the target attributes, the corresponding transfer elements are mapped to the inside of the network topology map, so as to enable the target topology points corresponding to the transfer elements in real time in the network topology map, and split the target transfer plan into several local transfer plans in real time according to the target topology points.

[0038] Among them, it should be noted that after the several transfer elements corresponding to each current transfer node are determined in real time through the above steps, the information contained in each current transfer element will be immediately determined at this time to facilitate subsequent classification. Specifically, the present invention will detect the target attributes corresponding to each current transfer element in real time. Specifically, the target attribute can be a cargo attribute, a time attribute, and a resource attribute, etc. Among them, it should be pointed out that each topological point in the network topology diagram provided by the present invention contains an independent attribute, and the attribute of the topological point can correspond to the target attribute of the above transfer element. Based on this, the present invention can map the corresponding transfer element to the inside of the above network topology diagram in real time according to the current target attribute. Correspondingly, the target topology point corresponding to the current transfer element can be enabled in real time in the current network topology diagram to facilitate subsequent processing.

[0039] Furthermore, the step of splitting the target transport plan into a plurality of local transport plans in real time according to the target topological point includes: In the network topology diagram, target topology points corresponding to each of the transfer elements are marked respectively, and each of the target topology points is unique; A number of target topological points corresponding to each of the transfer nodes are extracted in real time, and a target topological chain adapted to the transfer node is constructed in real time based on the target topological points, and the target topological chain is converted into the local transfer plan.

[0040] Among them, it should be noted that, after the mapping processing of the above steps, the present invention can mark the target topology points corresponding to each current transfer element in the above network topology diagram respectively. At the same time, it can extract in real time several target topology points respectively contained in each current transfer node, and immediately arrange and combine the current several target topology points, so as to construct in real time a target topology chain that is adapted to the current transfer node and is unique. Based on this, the current target topology chain is encoded into a corresponding target character string in real time through the existing encoder, and the required local transfer plan can finally be generated according to the target character string for subsequent processing.

[0041] Furthermore, the step of generating a local time axis corresponding to each local transfer plan in real time based on preset rules includes: When the local transfer plan is obtained in real time, the transfer information corresponding to the local transfer plan is extracted in real time; The production line information and vehicle model information corresponding to the target item are extracted in real time from the transfer information, and a local time axis adapted to the local transfer plan is generated according to the production line information and the vehicle model information. Each of the local transfer methods is unique.

[0042] Among them, it should be noted that after the required local transfer plan is generated in real time through the above steps, the final time prediction is required at this time. Specifically, the present invention will extract the transfer information contained in the current local transfer plan in real time. Among them, it should be pointed out that the transfer information also includes the production line information corresponding to the current target item during the transportation process, and the vehicle model information corresponding to the transport vehicle. Based on this, the current two types of information are used as the basis for calculation to facilitate subsequent processing.

[0043] Furthermore, the step of generating a local timeline adapted to the local transport plan according to the production line information and the vehicle model information includes: When the production line information is acquired in real time, the circulation time corresponding to the target item is calculated in real time based on the production line information; When the vehicle type information is acquired in real time, the loading time corresponding to the target item is calculated in real time according to the vehicle type information and the target item information; A local time axis adapted to the local transport plan is generated according to the circulation time and the loading time.

[0044] Among them, it should be noted that after the production line information of the target item is obtained in real time through the above steps, the length of the conveyor belt that transfers the current target item from the detection location to the transport vehicle, as well as the conveying speed of the current conveyor belt can be obtained. Based on this, the circulation time of the current target item on the conveyor belt can be calculated first. Correspondingly, the loading capacity of the transport vehicle can be determined according to the above vehicle model information. Similarly, the size of the current target item can be determined in real time according to the above target item information. Based on this, the loading time for transferring all the current target items to the interior of the current transport vehicle can be calculated in real time. Therefore, it is only necessary to integrate the current circulation time and loading time to generate the above-mentioned local time axis for subsequent processing.

[0045] Furthermore, the step of generating a local time axis adapted to the local transport plan according to the circulation time and the loading time includes: When the circulation time and the loading time are respectively obtained, the circulation time and the loading time are integrated in real time to generate a corresponding initial local transit time in real time; The target type corresponding to the target item is determined in real time based on the target item information, and the initial local transit time is adaptively corrected based on the target type to generate a target local transit time in real time, and the target local transit time is converted into the local time axis.

[0046] Among them, it should be noted that after the required circulation time and loading time are respectively obtained through the above steps, specifically, corresponding integration processing can be performed in real time according to the time nodes of the current circulation time and loading time, so that an initial local transit time can be integrated accordingly. Based on this, in order to improve the accuracy of the initial local transit time, the target type of the current target item will also be determined in real time. Among them, it should be pointed out that for the sake of ease of understanding, when the current target item is detected in real time as a fragile item, the current initial local transit time will be appropriately extended. Similarly, when the current target item is detected in real time as a refrigerated item, the current initial local transit time will be appropriately shortened, so that the required target local transit time can be generated. Based on this, the current target local transit time can eventually be converted into the required local time axis for subsequent processing.

[0047] See also Figure 2 , the third embodiment of the present invention provides: A loading time prediction system based on multimodal transport, wherein the system comprises: A detection module, configured to detect target item information corresponding to the target item in real time when a target item is detected to be present in a preset logistics area; A processing module is used to determine a target transshipment plan corresponding to the target item in a preset database in real time according to the target item information, and to detect a number of transshipment nodes corresponding to the target transshipment plan in real time; A splitting module is used to split the target transfer plan into a plurality of corresponding local transfer plans in real time according to a plurality of transfer nodes, and to generate a local time axis corresponding to each of the local transfer plans in real time based on preset rules; The splicing module is used to perform real-time splicing processing on each of the local time axes to generate a corresponding target time axis in real time, and output a loading prediction time adapted to the target item according to the target time axis.

[0048] Furthermore, the splitting module is specifically used to: When the target transfer plan is obtained in real time, a network topology diagram corresponding to the target transfer plan is created in real time, wherein the network topology diagram is composed of a plurality of topology points and a plurality of topology lines, and the plurality of topology points are connected to each other through the topology lines; Each of the transfer nodes is structurally disassembled to detect in real time a number of transfer elements corresponding to each of the transfer nodes, and the target transfer plan is split into a number of local transfer plans in real time according to the network topology diagram and the number of transfer elements.

[0049] Furthermore, the splitting module is specifically used to: When a plurality of transfer elements corresponding to each transfer node are acquired in real time, target attributes corresponding to each transfer element are detected in real time; According to the target attributes, the corresponding transfer elements are mapped to the inside of the network topology map, so as to enable the target topology points corresponding to the transfer elements in real time in the network topology map, and split the target transfer plan into several local transfer plans in real time according to the target topology points.

[0050] Furthermore, the splitting module is specifically used to: In the network topology diagram, target topology points corresponding to each of the transfer elements are marked respectively, and each of the target topology points is unique; A number of target topological points corresponding to each of the transfer nodes are extracted in real time, and a target topological chain adapted to the transfer node is constructed in real time based on the target topological points, and the target topological chain is converted into the local transfer plan.

[0051] Furthermore, the splitting module is specifically used to: When the local transfer plan is obtained in real time, the transfer information corresponding to the local transfer plan is extracted in real time; The production line information and vehicle model information corresponding to the target item are extracted in real time from the transfer information, and a local time axis adapted to the local transfer plan is generated according to the production line information and the vehicle model information. Each of the local transfer methods is unique.

[0052] Furthermore, the splitting module is specifically used to: When the production line information is acquired in real time, the circulation time corresponding to the target item is calculated in real time based on the production line information; When the vehicle type information is acquired in real time, the loading time corresponding to the target item is calculated in real time according to the vehicle type information and the target item information; A local time axis adapted to the local transport plan is generated according to the circulation time and the loading time.

[0053] Furthermore, the splitting module is specifically used to: When the circulation time and the loading time are respectively obtained, the circulation time and the loading time are integrated in real time to generate a corresponding initial local transit time in real time; The target type corresponding to the target item is determined in real time based on the target item information, and the initial local transit time is adaptively corrected based on the target type to generate a target local transit time in real time, and the target local transit time is converted into the local time axis.

[0054] A fourth embodiment of the present invention provides a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for predicting loading time based on multimodal transport as described above is implemented.

[0055] A fifth embodiment of the present invention provides a readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the method for predicting loading time based on multimodal transport as described above is implemented.

[0056] In summary, the multimodal transport-based loading time prediction method and system provided by the above embodiments of the present invention can comprehensively and accurately predict the loading time of items while avoiding human interference, thereby correspondingly improving efficiency.

[0057] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0058] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0059] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0060] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0061] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0062] The above-described embodiments merely illustrate several embodiments of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for predicting loading time based on multimodal transport, characterized in that: The method comprises: When a target object is detected in real time in a preset logistics area, target object information corresponding to the target object is detected in real time; Determine in real time a target transshipment plan corresponding to the target item in a preset database according to the target item information, and detect in real time a number of transshipment nodes corresponding to the target transshipment plan; Splitting the target transfer plan into a plurality of corresponding local transfer plans in real time according to the plurality of transfer nodes, and generating a local time axis corresponding to each of the local transfer plans in real time based on preset rules; Each of the local time axes is spliced ​​together in real time to generate a corresponding target time axis in real time, and a loading prediction time adapted to the target item is outputted according to the target time axis.

2. The method for predicting loading time based on multimodal transport according to claim 1, characterized in that: The step of splitting the target transfer plan into a number of corresponding local transfer plans in real time according to the plurality of transfer nodes comprises: When the target transfer plan is obtained in real time, a network topology diagram corresponding to the target transfer plan is created in real time, wherein the network topology diagram is composed of a plurality of topology points and a plurality of topology lines, and the plurality of topology points are connected to each other through the topology lines; Each of the transfer nodes is structurally disassembled to detect in real time a number of transfer elements corresponding to each of the transfer nodes, and the target transfer plan is split into a number of local transfer plans in real time according to the network topology diagram and the number of transfer elements.

3. The method for predicting loading time based on multimodal transport according to claim 2, characterized in that: The step of splitting the target transport plan into a plurality of local transport plans in real time according to the network topology diagram and the plurality of transport elements comprises: When a plurality of transfer elements corresponding to each transfer node are acquired in real time, target attributes corresponding to each transfer element are detected in real time; According to the target attributes, the corresponding transfer elements are mapped to the inside of the network topology map, so as to enable the target topology points corresponding to the transfer elements in real time in the network topology map, and split the target transfer plan into several local transfer plans in real time according to the target topology points.

4. The method for predicting loading time based on multimodal transport according to claim 3, characterized in that: The step of splitting the target transport plan into a plurality of local transport plans in real time according to the target topological point comprises: In the network topology diagram, target topology points corresponding to each of the transfer elements are marked respectively, and each of the target topology points is unique; A number of target topological points corresponding to each of the transfer nodes are extracted in real time, and a target topological chain adapted to the transfer node is constructed in real time based on the target topological points, and the target topological chain is converted into the local transfer plan.

5. The method for predicting loading time based on multimodal transport according to claim 1, characterized in that: The step of generating a local time axis corresponding to each local transfer plan in real time based on preset rules includes: When the local transfer plan is obtained in real time, the transfer information corresponding to the local transfer plan is extracted in real time; The production line information and vehicle model information corresponding to the target item are extracted in real time from the transfer information, and a local time axis adapted to the local transfer plan is generated according to the production line information and the vehicle model information. Each of the local transfer methods is unique.

6. The method for predicting loading time based on multimodal transport according to claim 5, characterized in that: The step of generating a local time axis adapted to the local transport plan according to the production line information and the vehicle model information includes: When the production line information is acquired in real time, the circulation time corresponding to the target item is calculated in real time based on the production line information; When the vehicle type information is acquired in real time, the loading time corresponding to the target item is calculated in real time according to the vehicle type information and the target item information; A local time axis adapted to the local transport plan is generated according to the circulation time and the loading time.

7. The method for predicting loading time based on multimodal transport according to claim 6, characterized in that: The step of generating a local time axis adapted to the local transport plan according to the circulation time and the loading time includes: When the circulation time and the loading time are respectively obtained, the circulation time and the loading time are integrated in real time to generate a corresponding initial local transit time in real time; The target type corresponding to the target item is determined in real time based on the target item information, and the initial local transit time is adaptively corrected based on the target type to generate a target local transit time in real time, and the target local transit time is converted into the local time axis.

8. A loading time prediction system based on multimodal transport, characterized in that: The system comprises: A detection module, configured to detect target item information corresponding to the target item in real time when a target item is detected to be present in a preset logistics area; A processing module is used to determine a target transshipment plan corresponding to the target item in a preset database in real time according to the target item information, and to detect a number of transshipment nodes corresponding to the target transshipment plan in real time; A splitting module is used to split the target transfer plan into a plurality of corresponding local transfer plans in real time according to a plurality of transfer nodes, and to generate a local time axis corresponding to each of the local transfer plans in real time based on preset rules; The splicing module is used to perform real-time splicing processing on each of the local time axes to generate a corresponding target time axis in real time, and output a loading prediction time adapted to the target item according to the target time axis.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for predicting loading time based on multimodal transport according to any one of claims 1 to 7 is implemented.

10. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for predicting loading time based on multimodal transport as claimed in any one of claims 1 to 7 is implemented.

Images