Logistics early warning method and system, computer equipment and readable storage medium

Abstract

The invention provides a logistics early warning method and system, computer equipment and a readable storage medium. The method comprises the steps that first waybill data of an authorized shop is acquired, a waybill early warning triggering model is determined based on a platform assessment model, and the waybill early warning triggering model comprises at least one of delivery early warning time, collection early warning time and transportation early warning time; each piece of logistics node information of the first waybill data is obtained, actual transportation data between a first logistics node and a second logistics node is determined based on each piece of logistics node information, and each piece of logistics node information comprises delivery node information, collection node information and transportation node information; and determining early warning data based on the actual transportation data and the waybill early warning triggering model, and determining whether to output early warning information based on the early warning data. According to the method, the abnormal logistics waybill number can be automatically identified and early warning can be carried out, so that the labor cost is reduced, and the working efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of logistics technology, and in particular to a logistics early warning method and system, computer equipment and readable storage medium. Background Art

[0002] With the rapid development of e-commerce, logistics services have become an indispensable part of e-commerce platforms. Various problems often occur in the logistics process, such as delayed collection and logistics stagnation. These problems not only affect consumers' shopping experience, but also increase merchants' after-sales processing costs. The corresponding platform will also reduce the merchant's credit score. Therefore, a solution that can provide logistics early warning is urgently needed.

[0003] At present, the main methods of after-sales logistics warning for e-commerce on the market are as follows: the merchant's customer service needs to manually check the logistics track information of the logistics waybill number every day, and then manually identify the number with abnormal track according to the rules of different e-commerce platforms. The merchant's customer service will send the logistics waybill number with abnormal track to the personnel of the logistics outlets through group communication. The personnel of the logistics outlets will then report the logistics order number to the logistics work order system terminal. After the logistics system terminal completes the system processing, the personnel of the logistics outlets will inform the merchant's customer service of the processing results.

[0004] From this we can see that e-commerce logistics early warning is basically entirely done manually, which not only consumes a lot of manpower costs, but also has the problem of low work efficiency. Summary of the invention

[0005] Based on this, it is necessary to provide a logistics early warning method and system, computer equipment and readable storage medium in view of the fact that the existing e-commerce logistics early warning is basically completed manually, which not only consumes a lot of manpower costs but also has the problem of low work efficiency.

[0006] A logistics early warning method, comprising:

[0007] Obtain the first waybill data of the authorized store, and determine the waybill warning trigger model based on the platform assessment model, wherein the waybill warning trigger model includes at least one of the shipment warning time, the collection warning time, and the transportation warning time;

[0008] Obtaining information of each logistics node of the first waybill data, and determining actual transportation data between the first logistics node and the second logistics node based on the information of each logistics node, wherein the information of each logistics node includes shipping node information, collection node information, and transportation node information;

[0009] The warning data is determined based on the actual transportation data and the waybill warning trigger model, and it is determined whether to output the warning information based on the warning data.

[0010] In one embodiment, the step of determining warning data based on the actual transportation data and the waybill warning trigger model, and determining whether to output warning information based on the warning data includes:

[0011] Matching a warning threshold corresponding to the actual transportation data based on the waybill warning trigger model, and processing the warning threshold and the actual transportation data to determine the warning data, wherein the warning threshold is set to at least one of 12 hours, 24 hours, 48 ​​hours, and 72 hours;

[0012] Based on the warning data, determine whether to output visual warning information, wherein the warning information includes real-time warning information of each logistics node corresponding to the first waybill data.

[0013] In one embodiment, the method further comprises:

[0014] Acquire the warning data, and process the warning data based on the timeout ignored warning model;

[0015] If the timeout ignoring warning model determines to ignore the warning data, the warning data is processed, and visual warning information corresponding to the warning data is not output;

[0016] If the timeout ignored warning model does not process the warning data, visual warning information corresponding to the warning data is output.

[0017] In one embodiment, the step of obtaining the first shipping order data of the authorized store and determining the shipping order warning trigger model based on the platform assessment model includes:

[0018] Obtaining an authorization instruction, and determining all authorized stores based on the authorization instruction;

[0019] Determining the first waybill data based on the authorized store;

[0020] The early warning configuration type of the authorized store is determined based on the platform assessment model, and the waybill early warning trigger model is determined according to the early warning configuration type.

[0021] In one embodiment, the steps of determining the warning configuration type of the authorized store based on the platform assessment model, and determining the waybill warning trigger model according to the warning configuration type include:

[0022] Determining the early warning configuration type of the authorized store based on the platform assessment model, wherein the early warning configuration type includes a system automatic configuration type and a personalized configuration type;

[0023] If the warning configuration type is the automatic configuration type, automatically determining the waybill warning trigger model according to the automatic configuration type;

[0024] If the warning configuration type is the personalized configuration type, the waybill warning trigger model is manually determined according to the personalized configuration type.

[0025] In one embodiment, the step of obtaining information of each logistics node of the first waybill data and determining actual transportation data between the first logistics node and the second logistics node based on the information of each logistics node includes:

[0026] Obtaining information of each logistics node of the first waybill data, and performing calculation and processing on the information of each logistics node;

[0027] Obtaining first logistics node information and second logistics node information from each logistics node information after calculation and processing, and determining actual transportation data between the first logistics node and the second logistics node based on the first logistics node information and the second logistics node information;

[0028] The shipping time point in the shipping node information is before the collection time point in the collection node information, and the collection time point in the collection node information is before the transportation time point in the transportation node information.

[0029] In one embodiment, the logistics early warning method further includes:

[0030] The first waybill data and the actual transportation data are both stored in a relational database management system and indexed by an ES search engine.

[0031] A logistics early warning system, comprising:

[0032] An authorization module, used to obtain the first waybill data of the authorized store, and determine the waybill warning trigger model based on the platform assessment model, wherein the waybill warning trigger model includes at least one of the shipment warning time, the collection warning time, and the transportation warning time;

[0033] a processing module connected to the authorization module, the processing module being used to obtain information of each logistics node of the first waybill data, and determine actual transportation data between the first logistics node and the second logistics node based on the information of each logistics node, wherein the information of each logistics node includes shipping node information, collection node information, and transportation node information;

[0034] The early warning module is connected to the authorization module and the processing module respectively, and is used to determine early warning data based on the actual transportation data and the waybill early warning trigger model, and determine whether to output early warning information based on the early warning data.

[0035] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the above embodiments when executing the computer program.

[0036] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method described in any one of the above embodiments.

[0037] Compared with traditional technologies, the above-mentioned logistics early warning method and system, computer equipment and readable storage medium, first, obtain the first waybill data of the authorized store, and determine the waybill early warning trigger model based on the platform assessment model, wherein the waybill early warning trigger model includes at least one of the shipping early warning time, the collection early warning time, and the transportation early warning time; secondly, obtain the information of each logistics node of the first waybill data, and determine the actual transportation data between the first logistics node and the second logistics node based on the information of each logistics node, wherein the information of each logistics node includes the shipping node information, the collection node information, and the transportation node information; finally, determine the early warning data based on the actual transportation data and the waybill early warning trigger model, and determine whether to output the early warning information based on the early warning data. The present application adopts the above-mentioned method, which can automatically identify abnormal logistics waybill numbers and issue early warnings, which not only reduces labor costs, but also improves work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the conventional technology, the drawings required for use in the embodiments or the conventional technology descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 A flow chart of a logistics early warning method provided in one embodiment of the present application;

[0040] Figure 2 A flowchart of how to determine a waybill warning trigger model provided in an embodiment of the present application;

[0041] Figure 3 A structural block diagram of a logistics early warning system provided in one embodiment of the present application;

[0042] Figure 4 This is a diagram of the internal structure of a computer device provided in one embodiment of the present application.

[0043] Description of reference numerals:

[0044] 10. Logistics early warning system; 100. Authorization module 100; 200. Processing module; 300. Early warning module 300. DETAILED DESCRIPTION

[0045] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present application, so the present application is not limited by the specific implementation disclosed below.

[0046] The serial numbers assigned to the components herein, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning. The "connection" and "coupling" mentioned in this application, unless otherwise specified, include direct and indirect connections (couplings). In the description of this application, it should be understood that the orientation or position relationship indicated by the terms "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", etc. are based on the orientation or position relationship shown in the accompanying drawings, which is only for the convenience of describing this application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to this application.

[0047] In the present application, unless otherwise clearly specified and limited, a first feature being “above” or “below” a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being “above”, “above”, and “above” a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being “below”, “below”, and “below” a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0048] 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 considered to be "connected to" another element, it may be directly connected to the other element or there may be an intermediate element at the same time.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0050] See also Figure 1 , an embodiment of the present application provides a logistics early warning method. The logistics early warning method includes:

[0051] S102: Obtain the first waybill data of the authorized store, and determine the waybill warning trigger model based on the platform assessment model, wherein the waybill warning trigger model includes at least one of the shipment warning time, the collection warning time, and the transportation warning time.

[0052] In some embodiments, the first waybill data of the authorized store can be obtained through the e-commerce after-sales logistics early warning software, and the waybill early warning trigger model can be determined based on the platform assessment model. In some embodiments, before use, the merchant can first authorize the software through the "Merchant Store Authorization" function. Specifically, the merchant selects the store that needs to be warned (you can authorize Taobao, Douyin, Pinduoduo, Douyin and other e-commerce platform stores), clicks authorization and agrees to the authorization, and the e-commerce after-sales logistics early warning software can obtain the merchant's authorized store information that needs to be warned and the first waybill data that needs to be warned in the authorized store. In some embodiments, the first waybill data may include one waybill to be monitored, or may include multiple waybills to be monitored, which can be configured according to actual use.

[0053] Then, the waybill warning trigger model can be determined based on the platform assessment model. Among them, due to the different warning rules of different platforms, it can be configured according to actual needs. In some embodiments, if the platform is a Douyin platform, a waybill warning trigger model with a 24-hour shipping warning, a 24-hour collection warning, and a 48-hour transportation warning can be configured based on the platform assessment model. If the platform is a Taobao platform, a waybill warning trigger model with a 24-hour collection warning can be configured based on the platform assessment model. If the platform is a Pinduoduo platform, a waybill warning trigger model with a 48-hour shipping warning, a 24-hour collection warning, and a 48-hour transportation warning can be configured based on the platform assessment model.

[0054] S104: Obtain information of each logistics node of the first waybill data, and determine actual transportation data between the first logistics node and the second logistics node based on the information of each logistics node, wherein the information of each logistics node includes shipping node information, collection node information, and transportation node information.

[0055] In some embodiments, the information of each logistics node of the first waybill data can be obtained through the e-commerce after-sales logistics early warning software, and the actual transportation data between the first logistics node and the second logistics node can be determined based on the information of each logistics node. In some embodiments, the first logistics node can be a shipping node or a collection node. The second logistics node can be a collection node or a transportation node, and the second logistics node is different from the first logistics node. That is to say, the shipping time point in the shipping node information is before the collection time point in the collection node information, and the collection time point in the collection node information is before the transportation time point in the transportation node information. Specifically, when the first logistics node is a collection node, the second logistics node is a transportation node. And when the first logistics node is a shipping node, the second logistics node is a collection node or a transportation node. Further, the second logistics node is preferably a collection node, so that the first logistics node and the second logistics node are always adjacent nodes.

[0056] In some embodiments, after obtaining the information of each logistics node, the information of each logistics node can be calculated and processed. Specifically, calculating and processing the information of each logistics node is to unify the time format of each logistics node information. Then obtain the first logistics node information and the second logistics node information in the calculated and processed logistics node information, and determine the actual transportation data between the first logistics node and the second logistics node based on the first logistics node information and the second logistics node information. For example, the delivery time is 8 pm on August 8, 2024, and the collection time is 9 pm on August 9, 2024. The actual transportation time between the first logistics node and the second logistics node is 25 hours.

[0057] S106: Determine warning data based on the actual transportation data and the waybill warning trigger model, and determine whether to output warning information based on the warning data.

[0058] In some embodiments, the e-commerce after-sales logistics early warning software can determine the early warning data based on the actual transportation data and the waybill early warning trigger model, and determine whether to output the early warning information based on the early warning data. Specifically, for example: the delivery time is 8 pm on August 8, 2024, and the collection time is 9 pm on August 9, 2024, then the actual transportation time between the first logistics node and the second logistics node is 25 hours. If the collection warning time of the waybill early warning trigger model is 24 hours, it can be determined that the early warning data is 1 hour overdue, and the early warning information can be output according to actual needs. In this way, automatic early warning of the first waybill data can be achieved, thereby improving work efficiency.

[0059] In some embodiments, the step of determining the warning data based on the actual transportation data and the waybill warning trigger model, and determining whether to output the warning information based on the warning data includes: matching the warning threshold corresponding to the actual transportation data based on the waybill warning trigger model, and processing the warning threshold and the actual transportation data to determine the warning data. The warning threshold is set to at least one of 12 hours, 24 hours, 48 ​​hours, and 72 hours. Determine whether to output the visualized warning information based on the warning data. The warning information includes real-time warning information of each logistics node corresponding to the first waybill data.

[0060] Specifically, the matched warning threshold is compared or calculated with the actual transportation data to determine the warning data. For example, the delivery time is 8:00 p.m. on August 8, 2024, and the collection time is 9:00 p.m. on August 9, 2024, then the actual transportation time is 25 hours. The actual transportation data is calculated with the matched warning threshold of 24 hours: 25-24=1, then the warning data is determined to be overdue for 1 hour.

[0061] If the collection warning time of the waybill warning trigger model is 24 hours, it can be determined that the warning data is overdue for 1 hour. At this time, it can be determined whether to output visual warning information based on the warning data according to actual needs. Specifically, if the timeout ignore warning model processes the warning data, that is, if the timeout ignore warning model determines to ignore the warning data, the warning data is processed, and the visual warning information corresponding to the warning data is not output. The warning data that needs to be ignored can be selected through the timeout ignore warning model, and no warning will be issued, thereby realizing real-time elimination of ignored data, so that merchants can better manage warnings.

[0062] On the contrary, if the timeout ignored warning model does not process the warning data, the visual warning information corresponding to the warning data is output. Specifically, the visual warning information corresponding to the warning data can be output in the form of a logistics work order. In some embodiments, the merchant can process the logistics waybill corresponding to the warning data based on the timeout ignored warning model according to the actual situation (i.e., whether to initiate a logistics work order or ignore the warning data), so as to facilitate the merchant to better manage the warning.

[0063] In some embodiments, assuming that the platform is the Douyin platform, the merchant can manually set the shipping warning in the waybill warning trigger model to 48 hours, the collection warning to 24 hours, and the transportation warning to 48 hours. Then, the waybill warning trigger model is used to query the waybill data that meets the conditions in real time to determine whether to output the warning information. In this way, a batch of waybill warning data can be processed centrally, which can further improve work efficiency.

[0064] Please attend Figure 2 In some embodiments, the step of obtaining the first shipping order data of the authorized store and determining the shipping order warning trigger model based on the platform assessment model includes:

[0065] S202: Obtaining an authorization instruction, and determining all authorized stores based on the authorization instruction;

[0066] S204: Determine the first waybill data based on the authorized store;

[0067] S206: Determine the warning configuration type of the authorized store based on the platform assessment model, and determine the waybill warning trigger model according to the warning configuration type.

[0068] In some embodiments, the warning configuration type includes an automatic configuration type and a manual configuration type. In some embodiments, if the warning configuration type is the automatic configuration type, the waybill warning trigger model is automatically determined according to the automatic configuration type. In other words, the built-in configuration rule parameters can be used directly to automatically determine the waybill warning trigger model. In some embodiments, if the warning configuration type is the personalized configuration type, the waybill warning trigger model is manually determined according to the personalized configuration type. In other words, the configuration rule parameters can be manually set to determine the waybill warning trigger model. The manual configuration type can facilitate flexible control by merchants, matching the corresponding warning rules (i.e., the waybill warning trigger model) according to the actual scenarios of each store.

[0069] In some embodiments, the logistics early warning method also includes: the first waybill data and the actual transportation data are stored in a relational database management system, and indexed by an ES search engine. In some embodiments, the relevant waybill data of the authorized store is stored in a relational database management system (MySql) in the server, and then indexed by an ES (Elasticsearch) search engine. The use of MySql+ES ​​in this way greatly improves the efficiency of use. In summary, the present application combines big data storage and real-time computing capabilities to achieve real-time early warning capabilities, which greatly shortens the early warning cycle and improves work efficiency compared to traditional early warnings.

[0070] See also Figure 3Another embodiment of the present application is a logistics early warning system 10. The logistics early warning system 10 includes: an authorization module 100, a processing module 200 and an early warning module 300. The authorization module 100 is used to obtain the first waybill data of the authorized store, and determine the waybill early warning trigger model based on the platform assessment model. Among them, the waybill early warning trigger model includes at least one of the shipping early warning time, the collection early warning time, and the transportation early warning time. The processing module 200 is connected to the authorization module 100. The processing module is used to obtain the information of each logistics node of the first waybill data, and determine the actual transportation data between the first logistics node and the second logistics node based on the information of each logistics node. Among them, the information of each logistics node includes shipping node information, collection node information, and transportation node information. The early warning module 300 is connected to the authorization module 100 and the processing module 200 respectively. The early warning module 300 is used to determine the early warning data based on the actual transportation data and the waybill early warning trigger model, and determine whether to output the early warning information based on the early warning data.

[0071] In some embodiments, the specific structure of the authorization module 100 is not limited, as long as it has the function of obtaining the first waybill data of the authorized store and determining the waybill warning trigger model based on the platform assessment model. Specifically, the authorization module 100 can be a microcontroller or a smart card. The specific structure of the authorization module 100 can be selected according to actual needs and is not specifically limited here.

[0072] In some embodiments, the specific structure of the processing module 200 is not limited, as long as it has the function of obtaining the information of each logistics node of the first waybill data and determining the actual transportation data between the first logistics node and the second logistics node based on the information of each logistics node. Specifically, the processing module 200 can be a microprocessor or an integrated controller. The specific structure of the processing module 200 can be selected according to actual needs and is not specifically limited here.

[0073] In some embodiments, the specific structure of the early warning module 300 is not limited, as long as it has the function of determining the early warning data based on the actual transportation data and the waybill early warning trigger model, and determining whether to output the early warning information based on the early warning data. Specifically, the early warning module 300 can be a microprocessor or an integrated controller. The specific structure of the early warning module 300 can be selected according to actual needs and is not specifically limited here. In this embodiment, the above method can be used to realize automatic early warning of the first waybill data, thereby improving work efficiency.

[0074] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 4As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a logistics early warning method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0075] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0076] See also Figure 4 Another embodiment of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the logistics early warning method described in any one of the above embodiments are implemented.

[0077] In one embodiment, the processor implements the following steps when executing the computer program:

[0078] S102: Acquire the first waybill data of the authorized store, and determine the waybill warning trigger model based on the platform assessment model, wherein the waybill warning trigger model includes at least one of the shipment warning time, the collection warning time, and the transportation warning time;

[0079] S104: Obtaining information of each logistics node of the first waybill data, and determining actual transportation data between the first logistics node and the second logistics node based on the information of each logistics node, wherein the information of each logistics node includes shipping node information, collection node information, and transportation node information;

[0080] S106: Determine warning data based on the actual transportation data and the waybill warning trigger model, and determine whether to output warning information based on the warning data.

[0081] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the logistics early warning method described in any one of the above embodiments are implemented.

[0082] In one embodiment, the computer program, when executed by a processor, implements the following steps:

[0083] S102: Acquire the first waybill data of the authorized store, and determine the waybill warning trigger model based on the platform assessment model, wherein the waybill warning trigger model includes at least one of the shipment warning time, the collection warning time, and the transportation warning time;

[0084] S104: Obtaining information of each logistics node of the first waybill data, and determining actual transportation data between the first logistics node and the second logistics node based on the information of each logistics node, wherein the information of each logistics node includes shipping node information, collection node information, and transportation node information;

[0085] S106: Determine warning data based on the actual transportation data and the waybill warning trigger model, and determine whether to output warning information based on the warning data.

[0086] The above-mentioned computer device and computer-readable storage medium first obtain the first waybill data of the authorized store, and determine the waybill warning trigger model based on the platform assessment model, wherein the waybill warning trigger model includes at least one of the shipping warning time, the collection warning time, and the transportation warning time; secondly, obtain the information of each logistics node of the first waybill data, and determine the actual transportation data between the first logistics node and the second logistics node based on each logistics node information, wherein the information of each logistics node includes the shipping node information, the collection node information, and the transportation node information; finally, determine the warning data based on the actual transportation data and the waybill warning trigger model, and determine whether to output the warning information based on the warning data. The above-mentioned method can automatically identify abnormal logistics waybill numbers and issue warnings, which not only reduces labor costs but also improves work efficiency.

[0087] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0088] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0089] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A logistics early warning method, characterized in that: include: Obtain the first waybill data of the authorized store, and determine the waybill warning trigger model based on the platform assessment model, wherein the waybill warning trigger model includes at least one of the shipment warning time, the collection warning time, and the transportation warning time; Obtaining information of each logistics node of the first waybill data, and determining actual transportation data between the first logistics node and the second logistics node based on the information of each logistics node, wherein the information of each logistics node includes shipping node information, collection node information, and transportation node information; The warning data is determined based on the actual transportation data and the waybill warning trigger model, and it is determined whether to output the warning information based on the warning data.

2. The logistics early warning method according to claim 1, characterized in that: The step of determining warning data based on the actual transportation data and the waybill warning trigger model, and determining whether to output warning information based on the warning data includes: Matching a warning threshold corresponding to the actual transportation data based on the waybill warning trigger model, and processing the warning threshold and the actual transportation data to determine the warning data, wherein the warning threshold is set to at least one of 12 hours, 24 hours, 48 ​​hours, and 72 hours; Based on the warning data, determine whether to output visual warning information, wherein the warning information includes real-time warning information of each logistics node corresponding to the first waybill data.

3. The logistics early warning method according to claim 2, characterized in that: The method further comprises: Acquire the warning data, and process the warning data based on the timeout ignored warning model; If the timeout ignoring warning model determines to ignore the warning data, the warning data is processed, and visual warning information corresponding to the warning data is not output; If the timeout ignored warning model does not process the warning data, visual warning information corresponding to the warning data is output.

4. The logistics early warning method according to claim 1, characterized in that: The step of obtaining the first shipping order data of the authorized store and determining the shipping order warning trigger model based on the platform assessment model includes: Obtaining an authorization instruction, and determining all authorized stores based on the authorization instruction; Determining the first waybill data based on the authorized store; The early warning configuration type of the authorized store is determined based on the platform assessment model, and the waybill early warning trigger model is determined according to the early warning configuration type.

5. The logistics early warning method according to claim 4, characterized in that: The steps of determining the warning configuration type of the authorized store based on the platform assessment model, and determining the waybill warning trigger model according to the warning configuration type include: Determining the early warning configuration type of the authorized store based on the platform assessment model, wherein the early warning configuration type includes a system automatic configuration type and a personalized configuration type; If the warning configuration type is the automatic configuration type, automatically determining the waybill warning trigger model according to the automatic configuration type; If the warning configuration type is the personalized configuration type, the waybill warning trigger model is manually determined according to the personalized configuration type.

6. The logistics early warning method according to claim 1, characterized in that: The step of obtaining the information of each logistics node of the first waybill data and determining the actual transportation data between the first logistics node and the second logistics node based on the information of each logistics node includes: Obtaining information of each logistics node of the first waybill data, and performing calculation and processing on the information of each logistics node; Obtaining first logistics node information and second logistics node information from each logistics node information after calculation and processing, and determining actual transportation data between the first logistics node and the second logistics node based on the first logistics node information and the second logistics node information; The shipping time point in the shipping node information is before the collection time point in the collection node information, and the collection time point in the collection node information is before the transportation time point in the transportation node information.

7. The logistics early warning method according to claim 1, characterized in that: The method further comprises: The first waybill data and the actual transportation data are both stored in a relational database management system and indexed by an ES search engine.

8. A logistics early warning system, characterized in that: include: An authorization module, used to obtain the first waybill data of the authorized store, and determine the waybill warning trigger model based on the platform assessment model, wherein the waybill warning trigger model includes at least one of the shipment warning time, the collection warning time, and the transportation warning time; a processing module connected to the authorization module, the processing module being used to obtain information of each logistics node of the first waybill data, and determine actual transportation data between the first logistics node and the second logistics node based on the information of each logistics node, wherein the information of each logistics node includes shipping node information, collection node information, and transportation node information; The early warning module is connected to the authorization module and the processing module respectively, and is used to determine early warning data based on the actual transportation data and the waybill early warning trigger model, and determine whether to output early warning information based on the early warning data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images