Logistics node scheduling method and device

Abstract

The invention discloses a logistics node scheduling method and device, and relates to the technical field of computers. A specific embodiment of the method comprises the steps of obtaining node data and waybill data, determining a target node and corresponding delay data based on communication relation data between nodes and the waybill data, further determining a delay node and an associated node thereof, and sending early warning information to the associated node. According to the implementation method, the delay condition of each node can be judged according to the real-time logistics data, and the early warning information is sent to the possible delay node in time.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a scheduling method and device for a logistics node. Background Art

[0002] At present, the rapid development of the e-commerce industry has greatly promoted the development of logistics business, and with it a large increase in transportation business. In actual business, transportation platforms have a large number of transportation tasks every month, especially large transportation platforms, whose monthly order volume is in the millions. Scheduling delays often occur due to various reasons such as unreasonable vehicle arrangement, surge in order volume, and lack of vehicles at the site. The delays in these transportation tasks have a great impact on various sites, vehicles, drivers, etc. How to timely identify scheduling delays and issue warnings to downstream is particularly important in real-time business operations.

[0003] In order to analyze the delay data in the prior art, the transportation data of each node is often obtained and analyzed after the transportation task is completed. However, this post-analysis method often has timeliness issues and cannot effectively alleviate delays in the scheduling process. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a scheduling method for logistics nodes, which can determine the delay data of logistics scheduling in real time and issue an early warning.

[0005] To achieve the above object, according to a first aspect of an embodiment of the present invention, a method for scheduling a logistics node is provided, comprising:

[0006] Acquire node data and waybill data, wherein the node data includes connectivity relationship data between nodes;

[0007] Determine the target node according to the connectivity relationship data between the nodes;

[0008] Filtering the delay data of the target node based on the waybill data;

[0009] Determine a delayed node among the target nodes that meets a preset condition based on the delay data;

[0010] Determining the associated nodes of the delayed node based on the connectivity relationship data between the nodes;

[0011] Sending warning information to the associated nodes of the delayed node.

[0012] Optionally, the method further includes: performing delay cause analysis based on multi-dimensional data corresponding to the delay data, the multi-dimensional data including time data.

[0013] Optionally, determining the target node according to the connectivity relationship data between the nodes includes: calculating the centrality score of each node according to the connectivity relationship data between the nodes; and determining the target node based on the centrality score of each node.

[0014] Optionally, the centrality score of each node is calculated based on the connectivity relationship data between the nodes, including: determining the number of nodes connected to each node based on the connectivity relationship between the nodes; and calculating the degree centrality of each node based on the number of nodes connected to each node as the centrality score of each node.

[0015] Optionally, determining the target node based on the centrality scores of the nodes includes: screening the centrality scores of the nodes that are greater than a preset threshold; and determining the node corresponding to the centrality score greater than the preset threshold as the target node.

[0016] Optionally, filtering the delay data of the target node based on the waybill data includes: filtering the waybill data of the target node based on the waybill data; and determining the number of delayed orders in the waybill data of the target node as the delay data of the target node.

[0017] Optionally, determining the delayed node among the target nodes that meets preset conditions based on the delay data includes: calculating the difference between the number of delayed orders leaving the target node and the number of delayed orders arriving at the target node according to the number of delayed orders of the target node; and determining the target node as a delayed node if the difference between the number of delayed orders leaving the target node and the number of delayed orders arriving at the target node is greater than a preset order quantity threshold.

[0018] Optionally, determining the associated nodes of the delayed node based on the connectivity relationship data between the nodes includes: determining all downstream nodes of the delayed node based on the connectivity relationship between the nodes; and determining all downstream nodes as associated nodes of the delayed node.

[0019] According to a second aspect of an embodiment of the present invention, a scheduling device for a logistics node is provided, comprising:

[0020] An acquisition module, used to acquire node data and waybill data, wherein the node data includes connectivity relationship data between nodes;

[0021] A determination module, used to determine a target node according to the connectivity relationship data between the nodes;

[0022] A screening module, used for screening the delay data of the target node based on the waybill data;

[0023] The determination module is further configured to determine a delayed node that meets a preset condition among the target nodes based on the delay data; and determine an associated node of the delayed node based on the connectivity relationship data between the nodes;

[0024] The sending module is used to send warning information to the associated nodes of the delayed node.

[0025] Optionally, the device further includes: an analysis module, configured to perform delay cause analysis based on multi-dimensional data corresponding to the delay data, wherein the multi-dimensional data includes time data.

[0026] Optionally, the determination module is used to determine the target node based on the connectivity relationship data between the nodes, including: a calculation module, used to calculate the centrality score of each node based on the connectivity relationship data between the nodes; the determination module is used to determine the target node based on the centrality score of each node.

[0027] Optionally, the calculation module is used to calculate the centrality score of each node based on the connectivity relationship data between the nodes, including: the determination module is used to determine the number of nodes connected to each node based on the connectivity relationship between the nodes; the calculation module is used to calculate the degree centrality of each node based on the number of nodes connected to each node as the centrality score of each node.

[0028] Optionally, the determination module is used to determine the target node based on the centrality scores of the nodes, including: the screening module is used to screen the centrality scores of the nodes that are greater than a preset threshold; the determination module is used to determine that the node corresponding to the centrality score greater than the preset threshold is the target node.

[0029] Optionally, the screening module is used to screen the delay data of the target node based on the waybill data, including: the screening module is used to screen the waybill data of the target node based on the waybill data; the determination module is used to determine the delayed order quantity in the waybill data of the target node as the delay data of the target node.

[0030] Optionally, the determination module is also used to determine the delayed nodes among the target nodes that meet preset conditions based on the delay data, including: the calculation module is used to calculate the difference between the number of delayed orders leaving and the number of delayed orders arriving at the target node according to the number of delayed orders of the target node; the determination module is used to determine that the target node whose difference between the number of delayed orders leaving and the number of delayed orders arriving is greater than a preset order quantity threshold is a delayed node.

[0031] Optionally, the determination module is also used to determine the associated nodes of the delayed node based on the connectivity relationship data between the nodes, including: the determination module is also used to determine all downstream nodes of the delayed node based on the connectivity relationship between the nodes; determine that all downstream nodes are associated nodes of the delayed node.

[0032] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, including:

[0033] One or more processors; a storage device for storing one or more programs,

[0034] When the one or more programs are executed by the one or more processors, the one or more processors implement the method provided in the first aspect of the embodiment of the present invention.

[0035] According to a fourth aspect of an embodiment of the present invention, a computer-readable medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method provided by the first aspect of the embodiment of the present invention is implemented.

[0036] An embodiment of the above invention has the following advantages or beneficial effects: determining the target node through the connectivity relationship between nodes, and further determining the delayed node that needs attention based on the data of the delayed order quantity, sending early warning information to the associated nodes of the delayed node, and being able to timely issue early warnings to nodes that may be delayed during the transportation of the waybill, so that the node can take timely measures to alleviate the problem of scheduling delays, avoid the deterioration of the delay problem, and affect the experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a schematic diagram of the main process of the method for scheduling logistics nodes according to an embodiment of the present invention;

[0038] Figure 2 It is a statistical chart of the number of waybills with delay data;

[0039] Figure 3 It is a schematic diagram of the connection relationship between the delay node and the downstream node;

[0040] Figure 4 is a schematic diagram of main modules of a scheduling device for a logistics node according to an embodiment of the present invention;

[0041] Figure 5 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0042] Figure 6 It is a schematic diagram of the structure of a computer system of a terminal device or a server suitable for implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0044] It should be noted that the collection, collection, updating, analysis, processing, use, transmission, storage and other aspects of user personal information involved in the technical solution of this disclosure are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information, network security and national security.

[0045] Figure 1 is a schematic diagram of the main process of the scheduling method of the logistics node according to an embodiment of the present invention, such as Figure 1 As shown, including:

[0046] Step S101, obtaining node data and waybill data, where the node data includes connectivity relationship data between nodes.

[0047] Obtain the data of each transport node in the current transport system, and obtain the waybill data based on the waybill granularity. Among them, the node data includes the connectivity relationship data between nodes, and the waybill data includes the departure time, arrival time, venue, operator and other data of the waybill flowing at each transport node.

[0048] Step S102, determining the target node according to the connectivity relationship data between the nodes.

[0049] The degree center algorithm is used to determine the popular nodes in the transportation system nodes according to the connectivity relationship between the nodes, and the popular nodes are determined as the target nodes. Specifically, the popularity of a node can be determined according to the number of other nodes directly connected to the node.

[0050] Step S103, filtering the delay data of the target node based on the waybill data.

[0051] According to the determined target node and waybill data, the waybill data belonging to the target node is filtered, and the delayed waybill data is determined according to the departure time and arrival time data of the waybill data, and the data of the delayed waybill is determined as the delayed data of the target node. Figure 2As shown, the delay data of the target nodes are counted, where the determined target nodes are A, B, C, and D. The number of arrival delay orders of node A is 164, and the number of departure delay orders is 856; the number of arrival delay orders of node B is 343, and the number of departure delay orders is 332; the number of arrival delay orders of node C is 990, and the number of departure delay orders is 206; the number of arrival delay orders of node D is 1372, and the number of departure delay orders is 1632.

[0052] Step S104: Determine a delayed node among the target nodes that meets a preset condition based on the delay data.

[0053] Delay nodes are determined based on the acquired delay data, that is, the delayed order data of each target node. Specifically, the preset condition is that the difference between the number of delayed orders for departure and the number of delayed orders for arrival is greater than the preset difference threshold. Among them, the number of delayed orders for departure is much smaller than the number of delayed orders for arrival, indicating that the node can handle delayed orders in a timely manner and reduce departure delays, and is not a delay node that requires attention; the number of delayed orders for departure is close to the number of delayed orders for arrival, indicating that the node can handle delayed orders in an orderly manner and will not cause further delays; the number of delayed orders for departure is much larger than the number of delayed orders for arrival, indicating that the node's ability to handle delayed orders is very weak, and it is a delay node that requires special attention.

[0054] Step S105: determining the associated nodes of the delayed node based on the connectivity relationship data between the nodes.

[0055] After the delayed node is determined, all downstream nodes of the delayed node are determined according to the connectivity relationship between the nodes. The downstream nodes may be further delayed due to the delay data of the delayed node.

[0056] Step S106: Send warning information to the associated nodes of the delayed node.

[0057] After determining the downstream node associated with the delayed node, since the downstream node will receive the delay data sent by the delayed node, it is necessary to send early warning information to the downstream node in advance so that the downstream node can take corresponding countermeasures based on the early warning information.

[0058] Optionally, the early warning information may include delayed order quantity data, delayed order estimated arrival time and other information.

[0059] According to the scheduling method of logistics nodes provided by the embodiment of the present invention, during the transportation of waybills, it is possible to determine the target node according to the connectivity relationship between nodes, and determine the delayed node according to the generated waybill delay data, so as to send early warning information to the downstream nodes of the delayed node in a timely manner, locate the delayed node during the transportation of waybills, and send early warning information to the downstream nodes that may receive the delay data in a timely manner, so that the downstream nodes can take corresponding countermeasures to resolve the delayed order quantity, avoid further data delays, and effectively alleviate the scheduling delay problem.

[0060] Optionally, the method further includes: analyzing the cause of delay based on multi-dimensional data corresponding to the delay data, the multi-dimensional data including time data. For sites where the number of delayed orders for departure is much larger than the number of delayed orders for arrival, further analysis can be performed based on dimensions such as time. In addition to time data, data such as the surge in orders, unskilled operation of site personnel, and weather factors can also be included. Corresponding improvement plans can be formulated for each, such as adding staff in advance and operational training, which can effectively alleviate the scheduling delay problem. For example, if the number of delayed orders for node A on a certain day during the statistical period is large and greater than a certain threshold, and this day is marked as the start time of an activity, then the cause of the delay of the node is caused by the activity; if there is no activity mark on this day, then the cause of the delay of the node is the node's own resource problem.

[0061] Optionally, determining the target node according to the connectivity relationship data between the nodes includes: calculating the centrality score of each node according to the connectivity relationship data between the nodes; and determining the target node based on the centrality score of each node. When determining the target node, the centrality score of each node can be calculated according to the connectivity relationship between the nodes to illustrate the importance or influence of the node. For example, the score can be calculated according to the number of other nodes connected to the node. The target node is then determined according to the calculated score, that is, the node with a greater degree of influence / more important. Specifically, the node with a score greater than a certain threshold is determined as the target node.

[0062] Optionally, the centrality score of each node is calculated based on the connectivity relationship data between the nodes, including: determining the number of nodes connected to each node based on the connectivity relationship between the nodes; calculating the degree centrality of each node based on the number of nodes connected to each node as the centrality score of each node. When determining the target node, the degree centrality algorithm can be used for positioning. Degree centrality is used to measure the degree to which a node is directly connected to all other nodes in the network. It can be used to calculate the number of delayed waybills entering and leaving each venue, and evaluate the degree of impact, and select venues with a greater degree of impact for subsequent calculations. Specifically, for a network with g nodes, the degree centrality of node i is the total number of direct connections between i and other g-1 nodes:

[0063]

[0064] Where i represents node i; j = 1, ..., g represents the (g-1) nodes directly connected to node i; represents the weight of the connection between nodes i and j, is the degree centrality value of node i. The larger the degree centrality value, the higher the node centrality and the higher the popularity. Therefore, the node with a larger degree centrality value is selected as the target node.

[0065] Optionally, since the increase in network size will lead to a larger value of degree centrality, in order to avoid the value being too large to be calculated, the data is standardized:

[0066]

[0067] In this standardized degree centrality measurement formula, the degree centrality value of node i is divided by the maximum possible number of connections with other (g-1) nodes to obtain the proportion of network nodes that have direct connections with node i.

[0068] Optionally, when determining the target node, a eigenvector centrality algorithm may be used.

[0069] Optionally, determining the target node based on the centrality score of each node includes: screening the centrality score of each node that is greater than a preset threshold; and determining the node corresponding to the centrality score greater than the preset threshold as the target node. According to the calculated degree centrality value of each node and the preset threshold, screening the degree centrality value greater than the preset threshold, and determining the corresponding node as the target node. The preset threshold can be set according to actual conditions and is not limited to this.

[0070] Optionally, when determining the target node based on the calculated degree centrality, a threshold value for the number of target nodes may be set, and the calculated degree centrality values ​​are selected in order from large to small to select the number of target nodes that meets the threshold value.

[0071] According to the scheduling method of logistics nodes provided in the embodiment of the present invention, it is possible to determine the popular nodes with greater influence in the transportation system, that is, the target nodes, by calculating the degree centrality of the nodes, and determine the delayed nodes therein according to the waybill data, so as to send early warning information to the downstream nodes of the delayed nodes, so that the downstream nodes can take corresponding preparatory measures for the delay data to avoid further delays in the future, thereby alleviating the scheduling delay problem in the waybill transportation process and improving the transportation efficiency.

[0072] Optionally, filtering the delay data of the target node based on the waybill data includes: filtering the waybill data of the target node based on the waybill data; and determining the delayed order quantity in the waybill data of the target node as the delay data of the target node. When determining the delay data, firstly filter the waybill data belonging to the target node from the waybill data obtained from the waybill system, and then filter the delayed order quantity data therein as the delay data of the target node.

[0073] Optionally, determining the delayed nodes that meet the preset conditions among the target nodes based on the delay data includes: calculating the difference between the number of delayed orders leaving the target node and the number of delayed orders arriving at the target node according to the number of delayed orders at the target node; determining the target node whose difference between the number of delayed orders leaving the target node and the number of delayed orders arriving at the target node is greater than a preset order quantity threshold as a delayed node. Calculate the difference between the number of delayed orders leaving the target node and the number of delayed orders arriving at the target node. If the calculated difference is greater than the preset order quantity threshold, it means that the target node has a poor processing capability for the delayed orders and will cause further delays, and needs to be marked as a delayed node.

[0074] Optionally, the preset condition may be that the number of delayed arrival orders / departure delay orders reaches a preset delayed order threshold, and correspondingly, the target node that meets the preset condition is determined to be the delayed node.

[0075] Optionally, determining the associated nodes of the delayed node based on the connectivity relationship data between nodes includes: determining all downstream nodes of the delayed node based on the connectivity relationship between nodes; and determining all downstream nodes as associated nodes of the delayed node. According to the connectivity relationship between nodes in the acquired node data, all downstream nodes of the delayed node are determined. The downstream nodes are nodes that may receive delay data, and are determined as associated nodes, so as to subsequently send warning information of delay data to the associated nodes. For example, Figure 3 As shown, the downstream nodes E1, E2, E3, E4, E5 of node A and the downstream nodes F1, F2, F3, F4, F5 of node D are found through the connectivity algorithm.

[0076] According to the scheduling method of logistics nodes provided by the embodiment of the present invention, during the transportation process of the waybill, it is possible to determine the target nodes that need to be focused on in the transportation system according to the connectivity relationship between the nodes, and determine the delayed nodes among the target nodes according to the generated waybill delay data, and promptly send early warning information to the downstream nodes of the delayed nodes to locate the delayed nodes during the transportation process of the waybill, and promptly send early warning information to the downstream nodes that may receive the delay data, so that the downstream nodes can take corresponding countermeasures to solve the delayed order quantity, avoid further data delays, and effectively alleviate the scheduling delay problem.

[0077] Figure 4 4 is a schematic diagram of the main process of the scheduling device 400 of the logistics node according to an embodiment of the present invention. Figure 4 As shown, including:

[0078] The acquisition module 401 is used to acquire node data and waybill data, where the node data includes connectivity relationship data between nodes;

[0079] A determination module 402 is used to determine a target node according to the connectivity relationship data between nodes;

[0080] A screening module 403, for screening the delay data of the target node based on the waybill data;

[0081] The determination module 402 is further used to determine the delayed nodes that meet the preset conditions in the target nodes based on the delay data; determine the associated nodes of the delayed nodes based on the connectivity relationship data between the nodes;

[0082] The sending module 404 is used to send warning information to the associated nodes of the delayed node.

[0083] According to the scheduling device of the data logistics node provided by the embodiment of the present invention, during the transportation process of the waybill, it is possible to determine the target node according to the connectivity relationship between the nodes, and determine the delayed node according to the generated waybill delay data, so as to send early warning information to the downstream nodes of the delayed node in a timely manner, locate the delayed node during the transportation process of the waybill, and send early warning information to the downstream nodes that may receive the delay data in a timely manner, so that the downstream nodes can take corresponding countermeasures to solve the delayed order quantity, avoid further data delays, and effectively alleviate the scheduling delay problem.

[0084] Optionally, the device further includes: an analysis module 405, configured to perform delay cause analysis based on multi-dimensional data corresponding to the delay data, wherein the multi-dimensional data includes time data.

[0085] Optionally, the determination module 402 is used to determine the target node based on the connectivity relationship data between nodes, including: a calculation module 406, used to calculate the centrality score of each node based on the connectivity relationship data between nodes; the determination module 402 is used to determine the target node based on the centrality score of each node.

[0086] Optionally, a calculation module 406 is used to calculate the centrality score of each node based on the connectivity relationship data between the nodes, including: a determination module 402 is used to determine the number of nodes connected to each node based on the connectivity relationship between the nodes; and a calculation module 406 is used to calculate the degree centrality of each node based on the number of nodes connected to each node as the centrality score of each node.

[0087] Optionally, the determination module 402 is used to determine the target node based on the centrality scores of each node, including: a screening module 403, used to screen the centrality scores of each node that are greater than a preset threshold; the determination module 402 is used to determine that the node corresponding to the centrality score greater than the preset threshold is the target node.

[0088] Optionally, a screening module 403 is used to screen the delay data of the target node based on the waybill data, including: a screening module 403 is used to screen the waybill data of the target node based on the waybill data; a determination module 402 is used to determine the number of delayed orders in the waybill data of the target node as the delay data of the target node.

[0089] Optionally, the determination module 402 is also used to determine the delayed nodes that meet the preset conditions among the target nodes based on the delay data, including: a calculation module 406, used to calculate the difference between the number of delayed orders leaving the target node and the number of delayed orders arriving at the target node according to the number of delayed orders of the target node; the determination module 402 is used to determine that the target node whose difference between the number of delayed orders leaving the target node and the number of delayed orders arriving is greater than a preset order quantity threshold is a delayed node.

[0090] Optionally, the determination module 402 is further used to determine the associated nodes of the delayed node based on the connectivity relationship data between the nodes, including: the determination module 402 is further used to determine all downstream nodes of the delayed node based on the connectivity relationship between the nodes; determine all downstream nodes as associated nodes of the delayed node.

[0091] The scheduling device for logistics nodes provided by the embodiment of the present invention can determine the target nodes that need to be focused on in the transportation system according to the connectivity relationship between nodes during the waybill transportation process, and determine the delayed nodes among the target nodes according to the generated waybill delay data, and promptly send early warning information to the downstream nodes of the delayed nodes to locate the delayed nodes during the waybill transportation process, and promptly send early warning information to the downstream nodes that may receive the delay data, so that the downstream nodes can take corresponding countermeasures to solve the delayed order quantity, avoid further data delays, and effectively alleviate the scheduling delay problem.

[0092] Figure 5 An exemplary system architecture 500 is shown to which the method for scheduling a logistics node or the device for scheduling a logistics node according to an embodiment of the present invention can be applied.

[0093] like Figure 5 As shown, system architecture 500 may include terminal devices 501, 502, 503, a network 504 and a server 505. Network 504 is used to provide a medium for communication links between terminal devices 501, 502, 503 and server 505. Network 504 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0094] Users can use terminal devices 501, 502, 503 to interact with server 505 through network 504 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 501, 502, 503, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0095] The terminal devices 501 , 502 , and 503 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0096] The server 505 may be a server that provides various services, such as a backend management server that provides support for shopping websites browsed by users using the terminal devices 501, 502, and 503. The backend management server may analyze and process the received delay data request and other data, and feed back the processing results (such as target node data, waybill data - just an example) to the terminal device.

[0097] It should be noted that the logistics node scheduling method provided in the embodiment of the present invention is generally executed by the server 505, and accordingly, the logistics node scheduling device is generally set in the server 505.

[0098] It should be understood that Figure 5 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0099] Reference below Figure 6 , which shows a schematic diagram of the structure of a computer system 600 of a terminal device suitable for implementing an embodiment of the present invention. Figure 6 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0100] like Figure 6 As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the system 600 are also stored. The CPU 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0101] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage section 608 as needed.

[0102] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the above-mentioned functions defined in the system of the present invention are executed.

[0103] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0104] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0105] The modules involved in the embodiments of the present invention may be implemented in software or hardware. The modules described may also be provided in a processor. For example, they may be described as follows: a processor includes an acquisition module, a determination module, a screening module, and a sending module. The names of these modules do not, in some cases, constitute limitations on the modules themselves. For example, the acquisition module may also be described as a "module for acquiring node data and waybill data."

[0106] As another aspect, the present invention further provides a computer-readable medium, which may be included in the device described in the above embodiment; or may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes:

[0107] Obtain node data and waybill data. Node data includes connectivity data between nodes.

[0108] Determine the target node according to the connectivity relationship data between the nodes;

[0109] Filter the delay data of the target node based on the waybill data;

[0110] Determine the delayed nodes that meet the preset conditions among the target nodes based on the delay data;

[0111] Determine the associated nodes of the delayed node based on the connectivity relationship data between the nodes;

[0112] Send warning information to the associated nodes of the delayed node.

[0113] According to the technical solution of the embodiment of the present invention, during the waybill transportation process, the target node can be determined according to the connectivity relationship between the nodes, and the delayed node can be determined according to the generated waybill delay data, so that early warning information can be sent to the downstream nodes of the delayed node in a timely manner, the delayed node can be located during the waybill transportation process, and early warning information can be sent to the downstream nodes that may receive the delay data in a timely manner, so that the downstream nodes can take corresponding countermeasures to solve the delayed order quantity, avoid further data delays, and effectively alleviate the scheduling delay problem.

[0114] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for scheduling logistics nodes, characterized in that: The method comprises: Acquire node data and waybill data, wherein the node data includes connectivity relationship data between nodes; Determine the target node according to the connectivity relationship data between the nodes; Filtering the delay data of the target node based on the waybill data; Determine a delayed node among the target nodes that meets a preset condition based on the delay data; Determining the associated nodes of the delayed node based on the connectivity relationship data between the nodes; Sending warning information to the associated nodes of the delayed node.

2. The method according to claim 1, characterized in that: The method further comprises: Delay cause analysis is performed based on multi-dimensional data corresponding to the delay data, wherein the multi-dimensional data includes time data.

3. The method according to claim 1, characterized in that The determining of the target node according to the connectivity relationship data between the nodes comprises: Calculate the centrality score of each node according to the connectivity relationship data between the nodes; A target node is determined based on the centrality scores of the nodes.

4. The method according to claim 3, characterized in that: The calculating the centrality score of each node according to the connectivity relationship data between the nodes includes: Determine the number of nodes connected to each node according to the connectivity relationship between the nodes; The degree centrality of each node is calculated based on the number of nodes connected to each node as the centrality score of each node.

5. The method according to claim 3, characterized in that: The determining the target node based on the centrality scores of the nodes comprises: Filter the centrality scores of the nodes that are greater than a preset threshold; The node corresponding to the centrality score greater than the preset threshold is determined as the target node.

6. The method according to claim 1, characterized in that The screening of the delay data of the target node based on the waybill data includes: Filtering the waybill data of the target node based on the waybill data; Determine the delayed order quantity in the waybill data of the target node as the delay data of the target node.

7. The method according to claim 6, characterized in that The determining, based on the delay data, a delayed node among the target nodes that meets a preset condition comprises: Calculate the difference between the number of delayed orders leaving the target node and the number of delayed orders arriving at the target node according to the number of delayed orders at the target node; Determine that a target node whose difference between the number of delayed outgoing orders and the number of delayed arriving orders is greater than a preset order quantity threshold is a delayed node.

8. The method according to claim 1, characterized in that The determining the associated nodes of the delayed node based on the connectivity relationship data between the nodes includes: Determine all downstream nodes of the delayed node based on the connectivity relationship between the nodes; Determine that all downstream nodes are associated nodes of the delayed node.

9. A logistics node scheduling device, characterized in that: include: An acquisition module, used to acquire node data and waybill data, wherein the node data includes connectivity relationship data between nodes; A determination module, used to determine a target node according to the connectivity relationship data between the nodes; A screening module, used for screening the delay data of the target node based on the waybill data; The determination module is further used to determine a delayed node among the target nodes that meets a preset condition based on the delay data; Determining the associated nodes of the delayed node based on the connectivity relationship data between the nodes; The sending module is used to send warning information to the associated nodes of the delayed node.

10. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 8.

11. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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