Logistics transportation information generation method and device, equipment, medium and program product

Abstract

The embodiment of the invention discloses a logistics transportation information generation method and device, equipment, a medium and a program product. A specific embodiment of the method comprises the steps of obtaining a current link transportation state and current logistics transportation information; determining a transportation duration prediction model corresponding to the current link transportation state; inputting the current logistics transportation information and the actual transportation duration into a transportation duration prediction model to obtain an article transportation prediction duration in the current link transportation state; and carrying out transportation adjustment processing on the current logistics transportation information in response to determining that the article transportation prediction duration is greater than a preset article transportation duration threshold value, so as to obtain adjusted logistics transportation information. The embodiment is related to cold chain transportation, and by considering the particularity of the current link transportation state, the accuracy of transportation duration prediction in the current link transportation state (for example, in a cold chain scene, the shelf life of fresh goods and the particularity of logistics) can be improved, and the waste of transportation goods and transportation resources is reduced.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of computer technology, and specifically to methods, devices, equipment, media and program products for generating logistics and transportation information. Background Art

[0002] At present, with the continuous development of the Internet of Things and big data technology, it is crucial to accurately track and manage the transported items (for example, fresh items transported through the cold chain supply chain network) on the cold chain supply chain network of the entire smart logistics park. How to accurately and quickly transport items while ensuring user satisfaction and the freshness of fresh transported items has become a key issue of concern. For the generation of logistics transportation information, the method usually adopted is: using an end-to-end (from the start of transportation of the item to its delivery to the user's location) time prediction model to predict the delivery time of the transported items (for example, fresh items transported through the cold chain supply chain network) to obtain the predicted transportation time of the items. Then, in response to the predicted transportation time of the item being greater than the preset transportation time threshold, the logistics transportation information is dynamically adjusted to obtain the adjusted logistics transportation information.

[0003] However, the inventors have found that when the above method is used to generate physical transportation information, the following technical problems often occur:

[0004] Using the same time prediction model to predict the transportation time for the entire transportation chain has a large prediction range, and different transportation links in the entire transportation chain may have certain particularities, resulting in certain volatility and uncontrollability in the predicted transportation time, waste of transportation items and transportation resources.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0006] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0007] Some embodiments of the present disclosure propose methods, devices, equipment, media and program products for generating logistics and transportation information to solve one or more of the technical problems mentioned in the above background technology section.

[0008] In a first aspect, some embodiments of the present disclosure provide a method for generating logistics transportation information, including: obtaining a current link transportation status and current logistics transportation information corresponding to a target transportation item, wherein the current link transportation status is one of the following: a first status characterizing the turnover of items at a link node, and a second status characterizing the circulation of items on a link path; determining a transportation duration prediction model corresponding to the current link transportation status, wherein the transportation duration prediction model is one of the following: a duration prediction model corresponding to the first status, and a duration prediction model corresponding to the second status; inputting the current logistics transportation information and the actual transportation duration for the previous link transportation status into the transportation duration prediction model to obtain the predicted transportation duration for the item under the current link transportation status; in response to determining that the predicted transportation duration for the item is greater than a preset item transportation duration threshold, performing transportation adjustment processing on the current logistics transportation information to obtain the adjusted logistics transportation information under the current link transportation status.

[0009] Optionally, the above-mentioned transportation adjustment processing of the above-mentioned current logistics transportation information is performed to obtain the adjusted logistics transportation information for the above-mentioned current link transportation state, including: determining the remaining transportation time corresponding to each item of the same category in the same category item set, wherein the item storage place corresponding to the same category item is in proximity to the transportation position corresponding to the above-mentioned target transportation item; screening out the same category items whose corresponding remaining transportation time meets the preset time condition from the above-mentioned same category item set to obtain the target same category items; replacing the target transportation items in the above-mentioned current logistics transportation information with the above-mentioned target same category items to generate the above-mentioned adjusted logistics transportation information.

[0010] Optionally, the current logistics and transportation information includes: item storage information, item order information and item transportation information; and after obtaining the current link transportation status and current logistics and transportation information corresponding to the target transport item, the method further includes: time aligning the item storage information, the item order information and the item transportation information to obtain aligned item storage information, aligned item order information and aligned item transportation information; aggregating the same attribute data of the aligned item storage information, the aligned item order information and the aligned item transportation information to obtain aggregated logistics and transportation information; performing data format conversion on the aggregated logistics and transportation information to obtain converted logistics and transportation information as the current logistics and transportation information.

[0011] Optionally, the method further includes: in response to determining that the predicted item transportation time is greater than the preset item transportation time threshold, generating item transportation time warning information for the target transport item under the current link transportation state.

[0012] Optionally, the above-mentioned current logistics transportation information and the actual transportation time for the previous link transportation status are input into the above-mentioned transportation time prediction model to obtain the predicted transportation time of the items under the above-mentioned current link transportation status, including: in response to determining that the actual transportation time for the above-mentioned previous link transportation status does not exist, setting the actual transportation time of the above-mentioned previous link transportation status to empty; inputting the above-mentioned current logistics transportation information and the actual transportation time for the above-mentioned previous link transportation status into the above-mentioned transportation time prediction model to obtain the predicted transportation time of the items.

[0013] Optionally, the above-mentioned transportation time prediction model is obtained by training through the following steps: obtaining target sample data corresponding to the above-mentioned transportation time prediction model, wherein the above-mentioned target sample data includes: sample logistics transportation information, and sample actual transportation time corresponding to the sample logistics transportation information; based on the above-mentioned target sample data, performing the following training steps: inputting the sample logistics transportation information into the initial transportation time prediction model to obtain the predicted transportation time of the item; determining the transportation time error between the predicted transportation time of the item and the above-mentioned sample actual transportation time; in response to determining that the transportation time error value is less than or equal to a preset transportation time error threshold, determining the initial transportation time prediction model as the transportation time prediction model; in response to determining that the transportation time error value is greater than the above-mentioned preset transportation time error threshold, adjusting relevant parameters of the initial transportation time prediction model, and determining the adjusted initial transportation time prediction model as the initial transportation time prediction model, re-acquiring the target sample data to perform the above-mentioned training steps again.

[0014] In a second aspect, some embodiments of the present disclosure provide a logistics transportation information generating device, including: an acquisition unit, configured to acquire the current link transportation status and current logistics transportation information corresponding to the target transportation item, wherein the above-mentioned current link transportation status is one of the following: a first state characterizing the turnover of the item at the link node, and a second state characterizing the circulation of the item on the link path; a determination unit, configured to determine a transportation duration prediction model corresponding to the above-mentioned current link transportation status, wherein the above-mentioned transportation duration prediction model is one of the following: a duration prediction model corresponding to the above-mentioned first state, and a duration prediction model corresponding to the above-mentioned second state; an input unit, configured to input the above-mentioned current logistics transportation information and the actual transportation duration for the previous link transportation status into the above-mentioned transportation duration prediction model, and obtain the predicted transportation duration of the item under the above-mentioned current link transportation status; a transportation adjustment unit, configured to perform transportation adjustment processing on the above-mentioned current logistics transportation information in response to determining that the above-mentioned predicted transportation duration of the item is greater than a preset item transportation duration threshold, and obtain the adjusted logistics transportation information under the said current link transportation status.

[0015] Optionally, the transport adjustment unit can be configured to: determine the remaining transport time corresponding to each item of the same category in the set of items of the same category, wherein the storage place corresponding to the items of the same category is in proximity to the transport location corresponding to the above-mentioned target transport item; filter out items of the same category whose corresponding remaining transport time meets a preset time condition from the above-mentioned set of items of the same category, and obtain the target items of the same category; replace the target transport items in the above-mentioned current logistics and transportation information with the above-mentioned target items of the same category, so as to generate the above-mentioned adjusted logistics and transportation information.

[0016] Optionally, the current logistics and transportation information includes: item storage information, item order information and item transportation information; and the device also includes: time alignment of the item storage information, the item order information and the item transportation information to obtain aligned item storage information, aligned item order information and aligned item transportation information; aggregating the same attribute data of the aligned item storage information, the aligned item order information and the aligned item transportation information to obtain aggregated logistics and transportation information; performing data format conversion on the aggregated logistics and transportation information to obtain converted logistics and transportation information as the current logistics and transportation information.

[0017] Optionally, the device further includes: in response to determining that the predicted item transportation time is greater than the preset item transportation time threshold, generating item transportation time warning information for the target transport item under the current link transportation state.

[0018] Optionally, the input unit can be configured to: in response to determining that the actual transportation duration for the above-mentioned previous link transportation status does not exist, set the actual transportation duration of the above-mentioned previous link transportation status to empty; input the above-mentioned current logistics transportation information and the actual transportation duration for the above-mentioned previous link transportation status into the above-mentioned transportation duration prediction model to obtain the predicted transportation duration of the items.

[0019] Optionally, the above-mentioned transportation time prediction model is obtained by training through the following steps: obtaining target sample data corresponding to the above-mentioned transportation time prediction model, wherein the above-mentioned target sample data includes: sample logistics transportation information, and sample actual transportation time corresponding to the sample logistics transportation information; based on the above-mentioned target sample data, performing the following training steps: inputting the sample logistics transportation information into the initial transportation time prediction model to obtain the predicted transportation time of the item; determining the transportation time error between the predicted transportation time of the item and the above-mentioned sample actual transportation time; in response to determining that the transportation time error value is less than or equal to a preset transportation time error threshold, determining the initial transportation time prediction model as the transportation time prediction model; in response to determining that the transportation time error value is greater than the above-mentioned preset transportation time error threshold, adjusting relevant parameters of the initial transportation time prediction model, and determining the adjusted initial transportation time prediction model as the initial transportation time prediction model, re-acquiring the target sample data to perform the above-mentioned training steps again.

[0020] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner in the above-mentioned first aspect.

[0021] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any one of the implementation modes in the first aspect above is implemented.

[0022] In a fifth aspect, some embodiments of the present disclosure provide a computer program product, including a computer program, which implements the method described in any implementation manner in the above-mentioned first aspect when executed by a processor.

[0023] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: the logistics and transportation information generation method of some embodiments of the present disclosure takes into account the particularity of the current link transportation state (for example, in the scenario of cold chain transportation of fresh goods, the shelf life of fresh goods and the particularity of logistics), which can improve the accuracy of the transportation time prediction under the current link transportation state and reduce the waste of transportation goods and transportation resources. Specifically, the reasons for the uncontrollability and volatility of the relevant transportation time prediction, the waste of transportation goods and the waste of transportation resources are: the same time prediction model is used for the entire transportation link to predict the transportation time, the prediction range is large, and the different transportation links on the entire transportation link may have certain particularities, resulting in the predicted transportation time having certain volatility and uncontrollability, increasing the waste of transportation goods and transportation resources. Based on this, the logistics and transportation information generation method of some embodiments of the present disclosure can first obtain the current link transportation state and current logistics and transportation information corresponding to the target transportation item, wherein the above-mentioned current link transportation state is one of the following: a first state that characterizes the turnover of the item at the link node, and a second state that characterizes the circulation of the item on the link path. Here, the current link transportation state facilitates the subsequent determination of the transportation time prediction model, and the current logistics transportation information facilitates the subsequent prediction of the item transportation time. Secondly, determine the transportation time prediction model corresponding to the above-mentioned current link transportation state, wherein the above-mentioned transportation time prediction model is one of the following: the time prediction model corresponding to the above-mentioned first state, the time prediction model corresponding to the above-mentioned second state. Here, according to the particularity of the current link transportation state, the corresponding transportation time prediction model is determined, which can improve the degree of consistency between the transportation time prediction model and the current logistics transportation information (for example, in the scenario of cold chain transportation of fresh goods, according to the particularity of the shelf life of fresh goods in the current link transportation state, the degree of consistency between the transportation time prediction model and the current fresh goods in the current link transportation state can be improved). Then, the above-mentioned current logistics transportation information and the actual transportation time for the previous link transportation state are input into the above-mentioned transportation time prediction model to obtain the predicted transportation time of the items under the above-mentioned current link transportation state. Here, the accuracy of the predicted transportation time under the current link transportation state can be improved (for example, in the scenario of cold chain transportation of fresh goods, the accuracy of the predicted transportation time of fresh goods can be improved under the premise of ensuring the freshness of the fresh goods in the cold chain transportation). Finally, in response to determining that the predicted transportation time of the above-mentioned goods is greater than the preset item transportation time threshold, the above-mentioned current logistics transportation information is transport adjusted to obtain the adjusted logistics transportation information under the current link transportation state.Here, the waste of transported goods and the waste of transport resources can be reduced (for example, in the scenario of cold chain transport of fresh goods, when it is detected that the shelf life of the cold chain transported fresh goods is not enough to support the transportation to the destination of the goods, the cold chain transport path in the current logistics and transportation information can be replaced with a path with a shorter required transportation time, or the fresh goods can be replaced with fresh goods of the same category with a longer shelf life, which can reduce the waste of fresh goods and the waste of cold chain transportation resources). It can be obtained that the method for generating logistics and transportation information takes into account the particularity of the current link transportation state, which can improve the accuracy of the transportation time prediction under the current link transportation state and reduce the waste of transported goods and transportation resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0025] Figure 1 is a schematic diagram of an application scenario of a method for generating logistics transportation information according to some embodiments of the present disclosure;

[0026] Figure 2 is a flow chart of some embodiments of the method for generating logistics transportation information according to the present disclosure;

[0027] Figure 3 is a schematic diagram of a supply chain network in some embodiments of the method for generating logistics and transportation information according to the present disclosure;

[0028] Figure 4 is a flow chart of other embodiments of the method for generating logistics transportation information according to the present disclosure;

[0029] Figure 5 It is a schematic diagram of the structure of some embodiments of the logistics transportation information generating device according to the present disclosure;

[0030] Figure 6 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0031] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0032] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0033] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0034] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0035] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0036] With regard to the collection, storage, and use of user personal information (such as user order information, user portraits, and user historical behavior) involved in this disclosure, relevant organizations or individuals must fulfill obligations including conducting personal information security impact assessments, fulfilling the obligation to inform the personal information subject, and obtaining the prior authorization and consent of the personal information subject before performing the corresponding operations.

[0037] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0038] Figure 1 It is a schematic diagram of an application scenario of the method for generating logistics and transportation information according to some embodiments of the present disclosure.

[0039] exist Figure 1In the application scenario, the electronic device 101 can first obtain the current link transportation state 103 and the current logistics transportation information 104 corresponding to the target transport item 102. Among them, the above-mentioned current link transportation state 103 is one of the following: a first state characterizing the circulation of items in the link node, and a second state characterizing the transportation of items in the link path. In this application scenario, the adjusted logistics transportation information may include: the transportation path of the target transport item, the shelf life of the target transport item, and the category information of the target transport item. The first state can be the turnover state of the target transport item at the A logistics transfer station. The second state can be the circulation state of the target transport item on the link path from the A logistics transfer station to the B logistics transfer station. Secondly, determine the transportation duration prediction model 105 corresponding to the above-mentioned current link transportation state 103. Among them, the above-mentioned transportation duration prediction model is one of the following: the duration prediction model corresponding to the above-mentioned first state, and the duration prediction model corresponding to the above-mentioned second state. Then, the current logistics transportation information 104 and the actual transportation time 106 for the previous link transportation state are input into the transportation time prediction model 105 to obtain the predicted transportation time 107 of the item under the current link transportation state. Finally, in response to determining that the predicted transportation time 107 of the item is greater than the preset item transportation time threshold, the current logistics transportation information 103 is subjected to transportation adjustment processing to obtain the adjusted logistics transportation information 108.

[0040] It should be noted that the electronic device 101 can be hardware or software. When the electronic device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or it can be implemented as a single server or a single terminal device. When the electronic device is embodied as software, it can be installed in the hardware devices listed above. It can be implemented as multiple software or software modules for providing distributed services, for example, or it can be implemented as a single software or software module. No specific limitation is made here.

[0041] It should be understood that Figure 1 The number of electronic devices in the embodiment is only for illustration. Any number of electronic devices may be provided according to implementation requirements.

[0042] Continue to refer Figure 2 , shows a process 200 of some embodiments of the method for generating logistics and transportation information according to the present disclosure. The method for generating logistics and transportation information comprises the following steps:

[0043] Step 201, obtaining the current link transportation status and current logistics transportation information corresponding to the target transportation item.

[0044] In some embodiments, the execution entity of the above-mentioned logistics transportation information generation method (for example Figure 1The electronic device 101 shown) can obtain the current link transportation status and current logistics transportation information corresponding to the target transport item through a wired connection method or a wireless connection method, wherein the above-mentioned current link transportation status is one of the following: a first state characterizing the turnover of the item at the link node, and a second state characterizing the circulation of the item on the link path. Among them, the above-mentioned target transport item may be an item with a transportation time constraint on the transportation link. For example, the above-mentioned target transport item may be an item transported through a cold chain. The above-mentioned current link transportation status may be a turnover status or a circulation status when the target transport item is transported on the supply chain network. The above-mentioned supply chain network may be an item transportation network composed of multiple supply nodes and transportation links, from the ordering of items to the transportation to the termination node of the target user. For example, the above-mentioned supply chain network may be a cold chain supply chain network for transporting fresh items with a short shelf life. The above-mentioned supply chain network such as Figure 3 As shown. The first state may be the state in which the target transported item is circulated at the link node A. The second state may be the state in which the target transported item is circulated on the link path AB. The link node may include but is not limited to at least one of the following: the logistics park where the target transported item is located, the forward warehouse point, the distribution center, and the first-level distribution site. The current logistics transportation information may be the logistics information of the target transported item under the current link transportation state. The current logistics transportation information may include but is not limited to at least one of the following: the environmental temperature and humidity information of the target transported item, the storage management information, the transportation location information, and the transportation time. The link node may be a node on the transportation link for the target transported item to circulate the item. In practice, the current link transportation state may be the item transportation position of the target transported item obtained at fixed time intervals through a third-party interface. In response to determining that the item transportation position collected at a fixed time interval has not changed, the first state is determined as the current link transportation state. In response to determining that the item transportation position collected at a fixed time interval has changed, the second state is determined as the current link transportation state. Among them, the fixed time interval may be ten minutes.

[0045] In the cold chain scenario, the target transport items currently being transported correspond to cold chain items (e.g., ice cream, dairy products, perishable foods, fresh fruits). The current link transport status corresponds to the turnover status of the cold chain transport node in the cold chain transport network (e.g., the turnover status of fresh items at link node A in the cold chain supply chain network), or the flow status on the cold chain transport link (e.g., the flow status of fresh items on cold chain path AB in the cold chain supply chain network). The above-mentioned current logistics transportation information corresponds to the information on cold chain logistics transportation of cold chain items in the cold chain supply chain network (e.g., temperature and humidity of fresh items, real-time positioning information of fresh items, order information of fresh items, and cold chain transportation equipment information for fresh item transportation).

[0046] The above-mentioned execution entity can obtain the current link transportation status and current logistics transportation information of fresh goods in the cold chain supply chain network from multiple cold chain transportation fresh goods system databases through a third-party API (Application Programming Interface) interface. Among them, the multiple system databases may include but are not limited to at least one of the following: fresh food storage system database, fresh food cold chain transportation logistics system database, and fresh food order system database.

[0047] In some optional implementations of some embodiments, the above-mentioned current logistics and transportation information includes: item storage information, item order information and item transportation information. Among them, the above-mentioned item storage information may be the storage information of the above-mentioned target transport items in the warehouse. The above-mentioned item storage information may include but is not limited to at least one of the following: item number, item storage location, item entry time, and item exit time. The above-mentioned item order information may be the information of the order corresponding to the above-mentioned target transport items. The above-mentioned item order information may include but is not limited to at least one of the following: order number, user number, order creation time, order completion time, order status, and the number of transport items included in the order. The above-mentioned item transportation information may be the information of the transportation flow of the above-mentioned target transport items in the supply chain network. The above-mentioned item transportation information may include but is not limited to at least one of the following: transportation vehicle number, transportation route, transportation logistics order number, and transportation item information transported by the transportation vehicle.

[0048] Optionally, after obtaining the current link transportation status and current logistics transportation information corresponding to the target transportation item, the method may further include the following steps:

[0049] The first step is to time-align the above-mentioned item storage information, the above-mentioned item order information and the above-mentioned item transportation information to obtain the aligned item storage information, the aligned item order information and the aligned item transportation information. The aligned item storage information, the aligned item order information and the aligned item transportation information may be data under a unified time standard. The above-mentioned time standard may be a time standard in the form of year-month-day under the UTC (Universal Time Coordinated) standard.

[0050] The second step is to aggregate the aligned item storage information, the aligned item order information and the aligned item transportation information with the same attribute data to obtain aggregated logistics and transportation information. The aggregated logistics and transportation information may be the logistics and transportation information obtained by aggregating data with the same attribute type.

[0051] As an example, the execution subject may first select data of the same attribute type from the aligned item storage information, the aligned item order information, and the aligned item transportation information as an attribute type data set. The attribute type data set may include but is not limited to at least one of the following: time series attribute type data, text attribute type data, and numerical attribute type data. Secondly, determine the similarity value group of the aligned item warehouse information, aligned item order information, and aligned item transportation information included in each attribute type data in the attribute type data set to obtain a similarity value group set. The time series attribute type data may determine the similarity value by using a dynamic time warping algorithm. The text attribute type data may determine the similarity value by cosine similarity. Then, select at least one similarity value greater than or equal to a preset similarity threshold from the similarity value group set as a target similarity value group set. The preset similarity threshold may be a preset minimum value of similarity. Finally, map the attribute type data corresponding to each target similarity value in the target similarity value group set to the same attribute type data to obtain aggregated logistics and transportation information.

[0052] The third step is to convert the data format of the aggregated logistics and transportation information to obtain the converted logistics and transportation information as the current logistics and transportation information. The converted logistics and transportation information may be transportation information obtained by converting the aggregated logistics and transportation information into a predetermined format. For example, the predetermined format may be a format for representing data containing numbers in a numerical format.

[0053] Step 202: determine a transportation duration prediction model corresponding to the current link transportation status.

[0054] In some embodiments, the above-mentioned execution entity can determine the transportation time prediction model corresponding to the above-mentioned current link transportation status, wherein the above-mentioned transportation time prediction model is one of the following: a time prediction model corresponding to the above-mentioned first state, a time prediction model corresponding to the above-mentioned second state. Among them, the above-mentioned transportation time prediction model can be a deep neural network that predicts the turnover time or circulation time required for the current link transportation status by inputting the current logistics transportation information and the actual transportation time corresponding to the previous current link transportation status. The above-mentioned transportation time prediction model can be ARIMA (Autoregressive Integrated Moving Average Model).

[0055] As an example, the execution subject may determine the transport duration prediction model corresponding to the current link transport state through the association relationship table between the current link transport state and the transport duration prediction model. The association relationship table between the current link transport state and the transport duration prediction model may be a predefined association relationship between different transport duration prediction models corresponding to different current link transport states.

[0056] In the cold chain scenario, the transportation time prediction model corresponds to a time series prediction model determined by the data features of the current logistics information of fresh goods.

[0057] The execution subject may determine the transportation time prediction model corresponding to the current link transportation state through the data features of the current logistics transportation information. For example, the current logistics transportation information may be the current logistics transportation information including the transportation route, cold chain storage information, and fresh goods order information, and the graph attention model is determined as the transportation time prediction model corresponding to the current link transportation state. The current logistics transportation information may be the logistics transportation information including the cold chain storage information of fresh goods and the order information of fresh goods, and the long short-term memory neural network model is determined as the transportation time prediction model corresponding to the current link transportation state.

[0058] Step 203, input the current logistics transportation information and the actual transportation time for the previous link transportation status into the transportation time prediction model to obtain the predicted transportation time of the items under the current link transportation status.

[0059] In some embodiments, the execution entity may input the current logistics transportation information and the actual transportation time for the previous link transportation state into the transportation time prediction model to obtain the predicted transportation time of the items under the current link transportation state. The previous link transportation state may be a transportation state that is one transportation state above the current link transportation state. The actual transportation time may be the actual turnover time of the previous link transportation state or the actual item circulation time.

[0060] In the cold chain scenario, the actual transportation time corresponds to the actual transportation time information of the fresh goods in the circulation status of the previous cold chain transportation route (for example, 3 hours for fresh goods transportation). The current logistics transportation information corresponds to the storage information of the fresh goods in the fresh refrigerated turnover center (for example, the storage location of the fresh goods, the cold chain storage time). The predicted transportation time of the goods corresponds to the storage turnover time in the fresh refrigerated turnover center (for example, the refrigerated turnover time required for fresh goods is 30 minutes).

[0061] The execution entity may input the current logistics transportation information and the turnover time of the fresh goods in the previous link transportation status into the long short-term memory neural network model to obtain the predicted transportation time of the fresh goods in the current link transportation status.

[0062] In some optional implementations of some embodiments, the above-mentioned inputting the above-mentioned current logistics transportation information and the actual transportation time for the previous link transportation state into the above-mentioned transportation time prediction model to obtain the predicted transportation time of the item under the above-mentioned current link transportation state may include the following steps:

[0063] In the first step, in response to determining that the actual transportation duration for the above-mentioned previous link transportation status does not exist, the actual transportation duration of the above-mentioned previous link transportation status is set to be null.

[0064] The second step is to input the above current logistics transportation information and the actual transportation time for the above previous link transportation status into the above transportation time prediction model to obtain the predicted transportation time of the items.

[0065] In some optional implementations of some embodiments, the above-mentioned transportation time prediction model can be trained by the following steps:

[0066] The first step is to obtain the target sample data corresponding to the above-mentioned transportation time prediction model, wherein the above-mentioned target sample data includes: sample logistics transportation information, and sample actual transportation time corresponding to the sample logistics transportation information. The above-mentioned sample logistics transportation information may include: logistics transportation information of the target transportation item, and actual transportation time corresponding to the transportation status of the previous link.

[0067] In the second step, based on the above target sample data, perform the following training steps:

[0068] Sub-step 1: Input the sample logistics transportation information into the initial transportation time prediction model to obtain the predicted transportation time of the items.

[0069] Sub-step 2, determining the transportation time error between the predicted transportation time of the item and the actual transportation time of the above sample.

[0070] Sub-step 3, in response to determining that the transport time error value is less than or equal to a preset transport time error threshold, determining the initial transport time prediction model as the transport time prediction model. The preset transport time error threshold may be a preset minimum value of the time error.

[0071] The third step, in response to determining that the transport time error value is greater than the above-mentioned preset transport time error threshold, adjust the relevant parameters of the initial transport time prediction model, and determine the adjusted initial transport time prediction model as the initial transport time prediction model, re-acquire the target sample data, and execute the above-mentioned training step again.

[0072] Step 204, in response to determining that the predicted item transportation time is greater than a preset item transportation time threshold, the current logistics transportation information is adjusted to obtain adjusted logistics transportation information under the current link transportation state.

[0073] In some embodiments, the execution subject may, in response to determining that the predicted transportation time of the item is greater than a preset item transportation time threshold, perform transportation adjustment processing on the current logistics transportation information to obtain adjusted logistics transportation information under the current link transportation state. The preset item transportation time threshold may be a pre-set maximum value of the item transportation time under the current link transportation state. The adjusted logistics transportation information may be information that adjusts the logistics path included in the current logistics information to a path with a shorter required transportation time.

[0074] As an example, the execution subject may first, in response to determining that the predicted transportation time of the item is greater than the preset item transportation time threshold, obtain an item transportation path set for the above-mentioned target transportation item. Among them, the item transportation path in the above-mentioned item transportation path set may be the logistics information of the item transportation from the current link node to the target user termination node. The above-mentioned target user termination node is the node where the user whose required item is of the same category as the above-mentioned target transportation item is located. Secondly, determine the target transportation predicted time and the target preset transportation time threshold for each item transportation path in the above-mentioned item transportation path set under the above-mentioned current link transportation state, and obtain the target transportation predicted time set and the target preset transportation time threshold set. Then, arbitrarily select an item transportation path whose target transportation predicted time is less than the target preset transportation time threshold from the above-mentioned item transportation path set to obtain the target item transportation path. Finally, the above-mentioned target item transportation path is determined as the transportation path included in the adjusted logistics transportation information.

[0075] In the cold chain scenario, the preset item transportation time threshold corresponds to the pre-set fresh refrigeration time of the refrigerated turnover center in the cold chain supply chain network. The adjusted logistics transportation information corresponds to the information of cold chain transportation equipment that keeps fresh items fresher and faster (for example, a refrigerated truck equipped with a special cold chain box).

[0076] In response to determining that the predicted transportation time of fresh items is greater than a preset transportation time threshold, the execution entity may replace the cold chain transportation equipment with a faster transportation speed with the cold chain transportation equipment in the current logistics transportation information to generate adjusted logistics transportation information.

[0077] In some optional implementations of some embodiments, after step 204, the above method may further include the following steps:

[0078] In response to determining that the predicted transportation time of the item is greater than a preset item transportation time threshold, an item transportation time warning information is generated for the target transportation item in the current link transportation state. The item transportation time warning information may be information for warning of exceeding the turnover time specified by the link node or the circulation time specified on the link path.

[0079] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: the logistics transportation information generation method of some embodiments of the present disclosure takes into account the particularity of the current link transportation state, which can improve the accuracy of the transportation time prediction under the current link transportation state and reduce the waste of transportation items and transportation resources. Specifically, the reasons for the uncontrollability and volatility of the relevant transportation time prediction, the waste of transportation items and the waste of transportation resources are: the same time prediction model is used for the entire transportation link to predict the transportation time, the prediction range is large, and the different transportation links on the entire transportation link may have certain particularities, resulting in the predicted transportation time having certain volatility and uncontrollability, waste of transportation items and waste of transportation resources. Based on this, the logistics transportation information generation method of some embodiments of the present disclosure can first obtain the current link transportation state and current logistics transportation information corresponding to the target transportation item, wherein the above-mentioned current link transportation state is one of the following: a first state characterizing the turnover of items at the link node, and a second state characterizing the circulation of items on the link path. Here, the current link transportation state facilitates the subsequent determination of the transportation time prediction model, and the current logistics transportation information facilitates the subsequent prediction of the transportation time of the item. Secondly, determine the transportation duration prediction model corresponding to the current link transportation state, wherein the transportation duration prediction model is one of the following: the duration prediction model corresponding to the first state, the duration prediction model corresponding to the second state. Here, according to the particularity of the current link transportation state, determine the corresponding transportation duration prediction model, which can improve the degree of consistency between the transportation duration prediction model and the current logistics transportation information. Then, the current logistics transportation information and the actual transportation duration for the previous link transportation state are input into the transportation duration prediction model to obtain the predicted transportation duration of the items under the current link transportation state. Here, the accuracy of the predicted transportation duration under the current link transportation state can be improved. Finally, in response to determining that the predicted transportation duration of the items is greater than the preset item transportation duration threshold, the current logistics transportation information is transported and adjusted to obtain the adjusted logistics transportation information under the current link transportation state. Here, the waste of transported items and the waste of transportation resources can be reduced. It can be obtained that the method for generating logistics transportation information takes into account the particularity of the current link transportation state, can improve the accuracy of the transportation duration prediction under the current link transportation state, and reduce the waste of transported items and transportation resources.

[0080] Further references Figure 4 , shows a process 400 of another embodiment of the method for generating logistics and transportation information according to the present disclosure. The method for generating logistics and transportation information includes the following steps:

[0081] Step 401, obtaining the current link transportation status and current logistics transportation information corresponding to the target transportation item.

[0082] Step 402: determine a transportation duration prediction model corresponding to the current link transportation status.

[0083] Step 403: input the current logistics transportation information and the actual transportation time for the previous link transportation status into the transportation time prediction model to obtain the predicted transportation time of the items under the current link transportation status.

[0084] In some embodiments, the specific implementation of steps 401-403 and the technical effects thereof can be referred to in Figure 2 The steps 201-203 in the corresponding embodiment are not described in detail here.

[0085] Step 404 , in response to determining that the predicted transportation time of the item is greater than a preset item transportation time threshold, determining the remaining transportation time corresponding to each item of the same category in the set of items of the same category.

[0086] In some embodiments, an execution entity (e.g. Figure 1 The electronic device shown) can determine the remaining transportation time corresponding to each item of the same category in the set of items of the same category in response to determining that the predicted transportation time of the item is greater than the preset item transportation time threshold, wherein the storage place corresponding to the item of the same category is in proximity with the transportation position corresponding to the above-mentioned target transportation item. The above-mentioned proximity relationship can be the relationship between the storage place of the same category of items and the proximity range of all storage places within a preset radius distance with the transportation position corresponding to the above-mentioned target transportation item as the center. The above-mentioned preset radius distance range can be the longest distance set in advance. For example, the above-mentioned preset radius distance can be 100 meters. The above-mentioned remaining transportation time can be the difference between the constraint time of the same category of items and the time required for historical transportation. For example, the above-mentioned constraint time can be the constraint time to ensure the freshness of the same category of items, or it can be the constraint time for the user's expedited transportation. The above-mentioned historical transportation required time can be the transportation time taken for the same category of items from the beginning of transportation to the transportation to the transportation position corresponding to the above-mentioned target transportation item.

[0087] In the cold chain scenario, the preset item transportation time threshold corresponds to the remaining time of the cold chain item currently being transported, after deducting the time spent on transportation from the shelf life of the cold chain item. Items of the same category correspond to cold chain items of the same category as the cold chain items currently being transported (for example, if the cold chain items currently being transported are bananas, items of the same category are bananas of the same variety refrigerated in the same turnover center as the bananas currently being transported). The remaining transportation time corresponds to the difference between the shelf life of the fresh items of the same category refrigerated in the same turnover center and the time required for transportation.

[0088] In response to determining that the predicted item transportation time is greater than a preset item transportation time threshold, the above-mentioned execution entity can determine the difference between the shelf life and the fresh transportation time of each fresh item of the same category in the fresh item set as the remaining cold chain transportation time of the fresh items of the same category.

[0089] Step 405 , select the items of the same category whose corresponding remaining transportation time meets the preset time condition from the set of items of the same category, and obtain the target items of the same category.

[0090] In some embodiments, the execution entity may select the same category items whose corresponding remaining transportation time meets the preset time condition from the above-mentioned same category items, and obtain the target same category items. The above-mentioned preset time condition may be the condition that the remaining transportation time of the above-mentioned same category items is the shortest and is greater than or equal to the target remaining transportation time corresponding to the above-mentioned target transportation item.

[0091] In the cold chain scenario, the above-mentioned execution entity can select fresh items of the same category from the set of fresh items of the same category, which are from the same batch as the target fresh items, have the shortest shelf life, and are greater than or equal to the target remaining transportation time corresponding to the above-mentioned target fresh items, as the target fresh items of the same category.

[0092] Step 406, replacing the target transport items in the current logistics and transportation information with target items of the same category to generate adjusted logistics and transportation information.

[0093] In some embodiments, the execution entity may replace the target transport items in the current logistics and transportation information with the target items of the same category to generate the adjusted logistics and transportation information.

[0094] In the cold chain scenario, the above-mentioned execution entity can replace the target fresh items in the above-mentioned current logistics and transportation information with target fresh items of the same category (for example, bananas of the same category from the same batch, with the shortest shelf life, and the remaining transportation time is greater than or equal to the remaining transportation time of the currently transported bananas) to generate the above-mentioned adjusted logistics and transportation information (for example, fresh logistics and transportation information after replacing bananas).

[0095] from Figure 4 It can be seen that Figure 2 Compared with the description of some corresponding embodiments, Figure 4In the corresponding process 400 of the logistics transportation information generation method in some embodiments, by replacing the target transport item with an item of the same category that conforms to the current transport path and belongs to the same category as the target transport item, it can be ensured that the item is transported to the user node within the constraint time of the transport item, thereby reducing the waste of transported items and the waste of transportation resources, and improving user satisfaction (for example, the remaining transportation time of fresh items of the same category is longer than the time required for the cold chain transportation path corresponding to the fresh items currently being transported, which can reduce the waste of fresh items and the waste of cold chain transportation resources, and ensure the freshness of the fresh items transported by cold chain, thereby improving user satisfaction).

[0096] Further references Figure 5 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a logistics transportation information generating device. These device embodiments are similar to Figure 2 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0097] like Figure 5 As shown, a logistics transportation information generating device 500 includes: an acquisition unit 501, a determination unit 502, an input unit 503 and a transportation adjustment unit 504. The acquisition unit 501 is configured to: acquire the current link transportation state and the current logistics transportation information corresponding to the target transportation item, wherein the current link transportation state is one of the following: a first state characterizing the turnover of the item at the link node, and a second state characterizing the circulation of the item on the link path. The determination unit 502 is configured to: determine the transportation duration prediction model corresponding to the current link transportation state, wherein the transportation duration prediction model is one of the following: a duration prediction model corresponding to the first state, and a duration prediction model corresponding to the second state. The input unit 503 is configured to: input the current logistics transportation information and the actual transportation duration for the previous link transportation state into the transportation duration prediction model to obtain the predicted transportation duration of the item under the current link transportation state. The transport adjustment unit 504 is configured to: in response to determining that the predicted transport time of the above-mentioned item is greater than the preset item transport time threshold, perform transport adjustment processing on the above-mentioned current logistics transport information to obtain adjusted logistics transport information for the above-mentioned current link transport state.

[0098] In some optional implementations of some embodiments, the transportation adjustment unit is further configured to: determine the remaining transportation time corresponding to each item of the same category in the set of items of the same category, wherein the storage place corresponding to the item of the same category is in proximity to the transportation location corresponding to the above-mentioned target transportation item; filter out the items of the same category whose corresponding remaining transportation time meets the preset time condition from the above-mentioned set of items of the same category, and obtain the target items of the same category; replace the target transportation items in the above-mentioned current logistics transportation information with the above-mentioned target items of the same category, so as to generate the above-mentioned adjusted logistics transportation information.

[0099] In some optional implementations of some embodiments, the current logistics and transportation information includes: item storage information, item order information and item transportation information; and the logistics and transportation information generating device 500 further includes: a time alignment unit, a same attribute data aggregation unit, and a data style conversion unit (not shown in the figure). Among them, the time alignment unit can be configured to: perform time alignment on the item storage information, the item order information and the item transportation information to obtain the aligned item storage information, the aligned item order information and the aligned item transportation information. The same attribute data aggregation unit can be configured to: perform same attribute data aggregation on the aligned item storage information, the aligned item order information and the aligned item transportation information to obtain the aggregated logistics and transportation information. The data style conversion unit can be configured to: perform data format conversion on the aggregated logistics and transportation information to obtain the converted logistics and transportation information as the current logistics and transportation information.

[0100] In some optional implementations of some embodiments, the logistics transportation information generating device 500 further includes: a generating unit (not shown in the figure). The generating unit may be configured to: in response to determining that the predicted transportation time of the item is greater than a preset item transportation time threshold, generate item transportation time warning information for the target transportation item in the current link transportation state.

[0101] In some optional implementations of some embodiments, the input unit can be further configured to: in response to determining that the actual transportation duration for the above-mentioned previous link transportation status does not exist, set the actual transportation duration of the above-mentioned previous link transportation status to empty; input the above-mentioned current logistics transportation information and the actual transportation duration for the above-mentioned previous link transportation status into the above-mentioned transportation duration prediction model to obtain the predicted transportation duration of the items.

[0102] In some optional implementations of some embodiments, the above-mentioned transportation time prediction model is trained through the following steps: obtaining target sample data corresponding to the above-mentioned transportation time prediction model, wherein the above-mentioned target sample data includes: sample logistics transportation information, and sample actual transportation time corresponding to the sample logistics transportation information; based on the above-mentioned target sample data, performing the following training steps: inputting the sample logistics transportation information into the initial transportation time prediction model to obtain the predicted transportation time of the item; determining the transportation time error between the predicted transportation time of the item and the above-mentioned sample actual transportation time; in response to determining that the transportation time error value is less than or equal to the preset transportation time error threshold, determining the initial transportation time prediction model as the transportation time prediction model; in response to determining that the transportation time error value is greater than the above-mentioned preset transportation time error threshold, adjusting relevant parameters of the initial transportation time prediction model, and determining the adjusted initial transportation time prediction model as the initial transportation time prediction model, re-acquiring the target sample data to perform the above-mentioned training steps again.

[0103] It is understood that the units described in the device 500 are similar to those described in the reference Figure 2 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the device 500 and the units included therein, and will not be described in detail here.

[0104] Reference below Figure 6 , which shows an electronic device (eg, Figure 1 Schematic diagram of the structure of the electronic device 101)600. Figure 6 The electronic 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 disclosure.

[0105] like Figure 6 As shown, the electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 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 device 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0106] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 6 The electronic device 600 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 6 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0107] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0108] It should be noted that the computer-readable medium in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the 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 some embodiments of the present disclosure, the 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 some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. 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. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, 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 suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0109] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0110] The computer-readable medium may be included in the electronic device; or it may exist independently without being assembled into the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains the current link transportation state and current logistics transportation information corresponding to the target transportation item, wherein the current link transportation state is one of the following: a first state characterizing the turnover of the item at the link node, and a second state characterizing the circulation of the item on the link path; determines the transportation duration prediction model corresponding to the current link transportation state, wherein the transportation duration prediction model is one of the following: a duration prediction model corresponding to the first state, and a duration prediction model corresponding to the second state; inputs the current logistics transportation information and the actual transportation duration for the previous link transportation state into the transportation duration prediction model to obtain the predicted transportation duration of the item under the current link transportation state; in response to determining that the predicted transportation duration of the item is greater than the preset item transportation duration threshold, performs transportation adjustment processing on the current logistics transportation information to obtain the adjusted logistics transportation information under the current link transportation state.

[0111] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0112] 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 disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square 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 square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / 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.

[0113] The units described in some embodiments of the present disclosure may be implemented by software or hardware. The described units may also be set in a processor, for example, it may be described as: a processor includes an acquisition unit, a determination unit, an input unit, and a transportation adjustment unit. The names of these units do not constitute a limitation on the units themselves in some cases. For example, the acquisition unit may also be described as "a unit for acquiring the current link transportation status and current logistics transportation information corresponding to the target transportation item."

[0114] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0115] Some embodiments of the present disclosure also provide a computer program product, including a computer program, which implements any of the above-mentioned logistics and transportation information generation methods when executed by a processor.

[0116] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for generating logistics and transportation information, comprising: Acquire the current link transportation status and current logistics transportation information corresponding to the target transported item, wherein the current link transportation status is one of the following: a first status representing the turnover of the item at the link node, and a second status representing the circulation of the item on the link path; Determine a transportation duration prediction model corresponding to the current link transportation state, wherein the transportation duration prediction model is one of the following: a duration prediction model corresponding to the first state, and a duration prediction model corresponding to the second state; Input the current logistics transportation information and the actual transportation time for the previous link transportation status into the transportation time prediction model to obtain the predicted transportation time of the item under the current link transportation status; In response to determining that the predicted item transportation time is greater than a preset item transportation time threshold, the current logistics transportation information is subjected to transportation adjustment processing to obtain adjusted logistics transportation information under the current link transportation state.

2. The method according to claim 1, wherein: The performing transportation adjustment processing on the current logistics transportation information to obtain the adjusted logistics transportation information under the current link transportation state includes: Determine the remaining transportation time corresponding to each item of the same category in the set of items of the same category, wherein the storage location corresponding to the items of the same category is in proximity to the transportation location corresponding to the target transportation item; Filter out the same category items whose corresponding remaining transportation time meets the preset time condition from the same category items, and obtain the target same category items; The target transport item in the current logistics and transportation information is replaced with the target item of the same category to generate the adjusted logistics and transportation information.

3. The method according to claim 1, wherein: The current logistics and transportation information includes: item storage information, item order information and item transportation information; and After obtaining the current link transportation status and current logistics transportation information corresponding to the target transportation item, the method further includes: Performing time alignment on the item storage information, the item order information, and the item transportation information to obtain aligned item storage information, aligned item order information, and aligned item transportation information; Aggregating the same attribute data of the aligned item storage information, the aligned item order information and the aligned item transportation information to obtain aggregated logistics transportation information; The aggregated logistics and transportation information is subjected to data format conversion to obtain converted logistics and transportation information as current logistics and transportation information.

4. The method according to claim 1, wherein: The method further comprises: In response to determining that the predicted item transportation time is greater than the preset item transportation time threshold, item transportation time warning information is generated for the target transportation item under the current link transportation state.

5. The method according to claim 1, wherein: The step of inputting the current logistics transportation information and the actual transportation time for the previous link transportation state into the transportation time prediction model to obtain the predicted transportation time of the item under the current link transportation state includes: In response to determining that the actual transport duration for the previous link transport state does not exist, setting the actual transport duration for the previous link transport state to null; The current logistics transportation information and the actual transportation time for the previous link transportation status are input into the transportation time prediction model to obtain the predicted transportation time of the items.

6. The method according to claim 1, wherein: The transport time prediction model is trained by the following steps: Acquire target sample data corresponding to the transportation time prediction model, wherein the target sample data includes: sample logistics transportation information, and sample actual transportation time corresponding to the sample logistics transportation information; Based on the target sample data, the following training steps are performed: Input the sample logistics transportation information into the initial transportation time prediction model to obtain the predicted transportation time of the items; Determining a shipping time error between a predicted shipping time of an item and an actual shipping time of the sample; In response to determining that the transport time error value is less than or equal to a preset transport time error threshold, determining the initial transport time prediction model as the transport time prediction model; In response to determining that the transport time error value is greater than the preset transport time error threshold, the relevant parameters of the initial transport time prediction model are adjusted, and the adjusted initial transport time prediction model is determined as the initial transport time prediction model, and the target sample data is reacquired to perform the training step again.

7. A logistics and transportation information generating device, comprising: An acquisition unit is configured to acquire a current link transport state and current logistics transport information corresponding to a target transported item, wherein the current link transport state is one of the following: a first state representing that the item is circulated at a link node, and a second state representing that the item is circulated at a link path; A determination unit is configured to determine a transport duration prediction model corresponding to the current link transport state, wherein the transport duration prediction model is one of the following: a duration prediction model corresponding to the first state, a duration prediction model corresponding to the second state; An input unit is configured to input the current logistics transportation information and the actual transportation time for the previous link transportation state into the transportation time prediction model to obtain the predicted transportation time of the item under the current link transportation state; The transportation adjustment unit is configured to perform transportation adjustment processing on the current logistics transportation information in response to determining that the predicted transportation time of the item is greater than a preset item transportation time threshold, so as to obtain adjusted logistics transportation information under the current link transportation state.

8. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, 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 6.

9. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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