CN117689412A - Method, device, equipment and computer readable medium for processing article circulation information - Google Patents

Abstract

Embodiments of the present disclosure disclose an item flow information processing method, apparatus, device, and computer readable medium. One embodiment of the method comprises the following steps: acquiring a predicted circulation information set of a target object and a history circulation information set corresponding to the predicted circulation information set, wherein the predicted circulation information in the predicted circulation information set comprises a predicted circulation amount; for each predicted circulation quantity included in the predicted circulation information set, generating single-point prediction accuracy according to the predicted circulation quantity and the historical circulation quantity of the corresponding predicted circulation quantity included in the historical circulation information set; generating an order prediction accuracy according to each historical circulation quantity included in the historical circulation information set and each prediction circulation quantity included in the prediction circulation information set; and generating accuracy information of the prediction streaming information set according to the generated single-point prediction accuracy and magnitude prediction accuracy. The implementation is related to an intelligent supply chain, and accuracy of the prediction result of the circulation quantity prediction model is improved.

Description

Method, device, equipment and computer readable medium for processing article circulation information

Technical Field

Embodiments of the present disclosure relate to the field of computer technology, and in particular, to an article circulation information processing method, apparatus, device, and computer readable medium.

Background

The item demand prediction is to predict the circulation quantity of the item at the future time, and is particularly critical to the measurement of the accuracy of the prediction result. At present, the commonly adopted prediction result accuracy measurement method is as follows: the accuracy of the prediction result of the flow quantity prediction model is measured by any index of MAPE (Mean Absolute Percentage Error, average absolute percentage error), WMAPE (Weighted Mean Absolute Percentage Error, weighted average absolute percentage error) and SMAPE (Symmetric Mean Absolute Percentage Error, symmetrical average absolute percentage error).

However, when the accuracy of the prediction result is measured in the above manner, there are often the following technical problems: the single-point property and the integrity of the data are not considered at the same time, so that the prediction effect of the current circulation quantity prediction model cannot be accurately measured, and the accuracy of the prediction result of the circulation quantity prediction model is poor.

Disclosure of Invention

The disclosure is in part intended to introduce concepts in a simplified form that are further described below in the detailed description. The disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

Some embodiments of the present disclosure propose an item flow information processing method, apparatus, electronic device, and computer readable medium to solve one or more of the technical problems mentioned in the background section above.

In a first aspect, some embodiments of the present disclosure provide an item circulation information processing method, including: acquiring a predicted circulation information set of a target object and a history circulation information set corresponding to the predicted circulation information set, wherein the predicted circulation information in the predicted circulation information set comprises a predicted circulation amount, and the history circulation information in the history circulation information set comprises a history circulation amount; for each predicted circulation quantity included in the predicted circulation information set, generating single-point prediction accuracy according to the predicted circulation quantity and the historical circulation quantity corresponding to the predicted circulation quantity included in the historical circulation information set; generating an order prediction accuracy according to each historical circulation quantity included in the historical circulation information set and each prediction circulation quantity included in the prediction circulation information set; and generating accuracy information of the prediction circulation information set according to the generated single-point prediction accuracy and the magnitude prediction accuracy.

Optionally, the generating accuracy information of the prediction circulation information set according to the generated single-point prediction accuracy and the magnitude prediction accuracy includes: and determining the product of each generated single-point prediction accuracy and the magnitude prediction accuracy as a single-point magnitude prediction accuracy to obtain a single-point magnitude prediction accuracy set.

Optionally, the generating accuracy information of the prediction circulation information set according to the generated single-point prediction accuracy and the magnitude prediction accuracy further includes: and determining the difference between the preset value and the average value of each single-point magnitude prediction accuracy included in the single-point magnitude prediction accuracy set as accuracy information.

Optionally, the prediction circulation information set is generated by a circulation quantity prediction model; the method further comprises the following steps: in response to determining that the accuracy information meets a preset accuracy condition, determining the circulation quantity prediction model as a trained target circulation quantity prediction model; and generating a target prediction circulation information set according to the target circulation information sequence, the target circulation quantity prediction model and a predicted future time period in response to receiving the target circulation information sequence of the target object, wherein the target prediction circulation information in the target prediction circulation information set comprises the target prediction circulation quantity.

Optionally, the method further comprises: determining the sum of the target predicted circulation amounts included in the target predicted circulation information set as the article demand of the target article; and generating the replenishment quantity of the target object according to the stock quantity of the target object and the object demand quantity in the warehouse corresponding to the historical circulation information set.

Optionally, the method further comprises: and controlling the associated vehicle dispatching equipment to dispatch the replenishment quantity of the target articles to the warehouse.

Optionally, the method further comprises: and in response to determining that the accuracy information does not meet the preset accuracy condition, adjusting parameters of the circulation quantity prediction model to train by using the adjusted circulation quantity prediction model as a circulation quantity prediction model.

In a second aspect, some embodiments of the present disclosure provide an item flow information processing apparatus, the apparatus including: an obtaining unit configured to obtain a predicted circulation information set of a target article and a history circulation information set corresponding to the predicted circulation information set, wherein the predicted circulation information in the predicted circulation information set includes a predicted circulation amount, and the history circulation information in the history circulation information set includes a history circulation amount; a first generation unit configured to generate a single-point prediction accuracy rate for each predicted transfer amount included in the predicted transfer information set, based on the predicted transfer amount and a history transfer amount corresponding to the predicted transfer amount included in the history transfer information set; a second generating unit configured to generate an order prediction accuracy according to each of the history flow amounts included in the history flow information set and each of the prediction flow amounts included in the prediction flow information set; and a third generation unit configured to generate accuracy information of the prediction flow information set according to the generated single-point prediction accuracy and the magnitude prediction accuracy.

Optionally, the third generating unit includes: and the first determining unit is configured to determine the product of each generated single-point prediction accuracy and the magnitude prediction accuracy as a single-point magnitude prediction accuracy, so as to obtain a single-point magnitude prediction accuracy set.

Optionally, the third generating unit further includes: and a second determining unit configured to determine, as accuracy information, a difference between a preset value and a mean value of the single-point magnitude prediction accuracy included in the single-point magnitude prediction accuracy set.

Optionally, the above set of predicted stream information is generated by a stream quantity prediction model.

Optionally, the apparatus further comprises: the device comprises a target circulation quantity prediction model determining unit and a target prediction circulation information set generating unit. Wherein the target flow amount prediction model determining unit is configured to determine the flow amount prediction model as a trained target flow amount prediction model in response to determining that the accuracy information satisfies a preset accuracy condition. The target prediction circulation information set generating unit is configured to generate a target prediction circulation information set according to the target circulation information sequence, the target circulation quantity prediction model and a predicted future time period in response to receiving the target circulation information sequence of the target object, wherein the target prediction circulation information in the target prediction circulation information set comprises a target prediction circulation quantity.

Optionally, the apparatus further comprises: an article demand determining unit and a replenishment quantity generating unit. Wherein the item demand determining unit is configured to determine a sum of the respective target predicted circulation amounts included in the target predicted circulation information set as an item demand of the target item. The replenishment quantity generation unit is configured to generate a replenishment quantity of the target item based on the inventory quantity of the target item and the item demand quantity in the warehouse corresponding to the history flow information set.

Optionally, the apparatus further comprises: and a control unit configured to control the associated vehicle dispatching device to dispatch the replenishment quantity of the target articles to the warehouse.

Optionally, the apparatus further comprises: and an adjustment unit configured to adjust parameters of the circulation quantity prediction model to perform training using the adjusted circulation quantity prediction model as a circulation quantity prediction model in response to determining that the accuracy information does not satisfy the preset accuracy condition.

In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors causes the one or more processors to implement the method described in any of the implementations of the first aspect above.

In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

The above embodiments of the present disclosure have the following advantageous effects: according to the item circulation information processing method, accuracy of the prediction result of the circulation quantity prediction model is improved. Specifically, the reason for the poor accuracy of the prediction result of the flow amount prediction model is that: the single-point property and the integrity of the data are not considered at the same time, so that the prediction effect of the current circulation quantity prediction model cannot be accurately measured, and the accuracy of the prediction result of the circulation quantity prediction model is poor. Based on this, in the item circulation information processing method according to some embodiments of the present disclosure, first, a predicted circulation information set of a target item and a history circulation information set corresponding to the predicted circulation information set are obtained. The prediction flow information in the prediction flow information set comprises a prediction flow amount. The history flow information in the history flow information set includes a history flow amount. Thus, each of the historical circulation amounts in the historical circulation information set can be referred to as a true value of each of the predicted circulation amounts in the predicted circulation information set. Then, for each predicted circulation amount included in the predicted circulation information set, a single-point prediction accuracy is generated based on the predicted circulation amount and a history circulation amount corresponding to the predicted circulation amount included in the history circulation information set. Thus, the accuracy of the predicted result can be measured from the dimension of a single point. And secondly, generating the magnitude prediction accuracy according to each historical circulation quantity included in the historical circulation information set and each prediction circulation quantity included in the prediction circulation information set. Thus, the accuracy of the predicted result can be measured from the overall dimension. And finally, generating accuracy information of the prediction circulation information set according to the generated single-point prediction accuracy and the magnitude prediction accuracy. Thus, the accuracy of the predicted result can be measured from a single point and the whole dimension at the same time. Therefore, the accuracy of the measured prediction effect of the current circulation quantity prediction model can be improved, and the accuracy of the prediction result of the circulation quantity prediction model is further improved.

Drawings

The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.

FIG. 1 is an architecture diagram of an exemplary system in which some embodiments of the present disclosure may be applied;

FIG. 2 is a flow chart of some embodiments of an item flow information processing method according to the present disclosure;

FIG. 3 is a flow chart of other embodiments of an item flow information processing method according to the present disclosure;

FIG. 4 is a flow chart of yet other embodiments of an item flow information processing method according to the present disclosure;

FIG. 5 is a schematic structural view of some embodiments of an item flow information processing apparatus according to the present disclosure;

fig. 6 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.

It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.

It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.

It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.

The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

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

Fig. 1 illustrates an exemplary system architecture 100 to which an item flow information processing method or an item flow information processing apparatus of some embodiments of the present disclosure may be applied.

As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.

The terminal devices 101, 102, 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices having a display screen and supporting information display, including but not limited to smartphones, tablet computers, electronic book readers, laptop and desktop computers, and the like. When the terminal devices 101, 102, 103 are software, they can be installed in the above-listed electronic devices. It may be implemented as a plurality of software or software modules, for example, for providing distributed services, or as a single software or software module. The present invention is not particularly limited herein.

The server 105 may be a server providing various services, such as a background server providing support for information displayed on the terminal devices 101, 102, 103. The background server can analyze the received data and feed back the processing result to the terminal equipment.

Note that, the method for processing the item flow information provided by the embodiment of the present disclosure may be executed by the terminal devices 101, 102, 103, or may be executed by the server 105. Accordingly, the item flow information processing apparatus may be provided in the terminal devices 101, 102, 103 or may be provided in the server 105. The present invention is not particularly limited herein.

The server and the client may be hardware or software. When the server and the client are hardware, the server and the client can be realized as a distributed server cluster/distributed device cluster formed by a plurality of servers/devices, and can also be realized as a single server/single device. When the server and client are software, they may be implemented as a plurality of software or software modules, for example, for providing distributed services, or as a single software or software module. The present invention is not particularly limited herein.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to fig. 2, a flow 200 of some embodiments of an item flow information processing method according to the present disclosure is shown. The method for processing the article circulation information comprises the following steps:

step 201, obtaining a predicted circulation information set of a target object and a history circulation information set corresponding to the predicted circulation information set.

In some embodiments, the execution body of the method for processing the item circulation information (for example, the server shown in fig. 1) may acquire, from the terminal, a predicted circulation information set of item identifiers corresponding to the item identifiers of the target item and a history circulation information set corresponding to the predicted circulation information set through a wired connection manner or a wireless connection manner. Wherein, the terminal stores a predicted circulation information set of the target object and a history circulation information set corresponding to the predicted circulation information set. The target article may be any article where there is a discontinuity in demand. The predicted circulation information set may be information related to circulation amounts of the target items in a predicted history period. Each of the predicted stream information in the set of predicted stream information is continuous over the historical time period. The historical circulation information set may be information related to each real circulation amount corresponding to the predicted circulation information set. The predicted stream information in the above-described predicted stream information set may include a predicted stream amount (predicted sales). The predicted stream information may further include a historical sub-period corresponding to the predicted stream amount. The time granularity of the historical sub-time periods corresponding to the prediction stream information is the same. The history flow information in the above-described history flow information set may include a history flow amount (history sales). The history flow information may further include a history sub-period corresponding to the history flow amount. It can be understood that the correspondence between the above-mentioned prediction circulation information set and the above-mentioned history circulation information set may be: the predicted stream information in the predicted stream information set corresponds to the history stream information in the history stream information set one by one. The corresponding historical circulation information and the predicted circulation information comprise the same historical sub-time period.

In practice, the executing body may further obtain a predicted circulation information set of the target article and a history circulation information set corresponding to the predicted circulation information set locally.

As an example, the target article may be an automobile part "a", and the above-described history period may be "2021 month 1 to 2021 month 5 years". The prediction stream information set may be: [ [2021, 1 month, 107], [2021, 2 months, 23], [2021, 3 months, 68], [2021, 4 months, 73], [2021, 5 months, 9] ]. The predicted stream information [2021, 1 month, 107] includes a predicted stream amount of 107 and includes a history sub-period of 2021, 1 month. The history flow information set may be: [ [2021, 1 month, 132], [2021, 2 month, 0], [2021, 3 month, 56], [2021, 4 month, 59], [2021, 5 month, 0] ].

It should be noted that the wireless connection may include, but is not limited to, 3G/4G connections, wiFi connections, bluetooth connections, wiMAX connections, zigbee connections, UWB (ultra wideband) connections, and other now known or later developed wireless connection means.

Step 202, for each predicted circulation quantity included in the predicted circulation information set, generating single-point prediction accuracy according to the predicted circulation quantity and the historical circulation quantity corresponding to the predicted circulation quantity included in the historical circulation information set.

In some embodiments, for each predicted transfer amount included in the predicted transfer information set, the execution body may generate a single-point prediction accuracy according to the predicted transfer amount and a historical transfer amount corresponding to the predicted transfer amount included in the historical transfer information set. In practice, the execution subject may generate the single-point prediction accuracy by:

in the first step, the absolute value of the difference between the predicted stream quantity and the history stream quantity is determined as a single-point stream difference.

And a second step of determining the sum of the predicted stream quantity and the historical stream quantity as a single-point stream quantity sum.

And thirdly, generating single-point prediction accuracy according to the single-point flow difference and the single-point flow sum. In practice, first, the ratio of the single-point flow difference to the single-point flow sum may be determined as a single-point ratio. Then, the product of the single-point ratio and the preset multiple can be determined as the single-point prediction accuracy. Here, the specific setting of the above-mentioned preset multiple is not limited. For example, the preset multiple may be 2.

And 203, generating the magnitude prediction accuracy according to each historical circulation quantity included in the historical circulation information set and each prediction circulation quantity included in the prediction circulation information set.

In some embodiments, the executing body may generate the magnitude prediction accuracy according to each historical circulation amount included in the historical circulation information set and each predicted circulation amount included in the predicted circulation information set. In practice, the execution subject may generate the magnitude prediction accuracy by:

and a first step of determining the sum of the predicted circulation amounts included in the predicted circulation information set as a predicted circulation amount sum.

And a second step of determining the sum of the historical circulation amounts included in the historical circulation information set as the sum of the historical circulation amounts.

And thirdly, determining the absolute value of the difference between the predicted flow quantity sum and the historical flow quantity sum as an order flow difference.

And step four, determining the sum of the predicted flow quantity sum and the historical flow quantity sum as an order flow quantity sum.

And fifthly, generating the magnitude prediction accuracy according to the magnitude flow difference and the magnitude flow sum. In practice, first, the ratio of the magnitude flow difference to the magnitude flow sum may be determined as a magnitude ratio. Then, the product of the magnitude ratio and the preset multiple can be determined as magnitude prediction accuracy.

And 204, generating accuracy information of the prediction streaming information set according to the generated single-point prediction accuracy and magnitude prediction accuracy.

In some embodiments, the executing entity may generate the accuracy information of the prediction circulation information set according to the generated single-point prediction accuracy and the magnitude prediction accuracy. The number of the single-point prediction accuracy rates generated is the same as the number of the historical circulation amounts included in the historical circulation information set. The number of single-point prediction accuracy rates generated is the same as the number of each prediction stream quantity included in the above-described prediction stream information set. In practice, first, the execution body may determine, as the first value, a product of each generated single-point prediction accuracy and the magnitude prediction accuracy, to obtain a first value set. Then, a difference between 1 and each of the first values in the first set of values may be determined as a second value, resulting in a second set of values. Finally, the average value of the second values included in the second value set may be determined as accuracy information. Thus, the degree of numerical reduction of the index corresponding to the overestimated predicted value and the underestimated predicted value can be equalized.

In some optional implementations of some embodiments, first, the executing entity may determine, as a single-point magnitude prediction accuracy, a product of each generated single-point prediction accuracy and the magnitude prediction accuracy, to obtain a single-point magnitude prediction accuracy set. Then, a difference between the preset value and the average value of the single-point magnitude prediction accuracy included in the single-point magnitude prediction accuracy set can be determined as accuracy information. Wherein, the preset value may be 1.

The above embodiments of the present disclosure have the following advantageous effects: according to the item circulation information processing method, accuracy of the prediction result of the circulation quantity prediction model is improved. Specifically, the reason for the poor accuracy of the prediction result of the flow amount prediction model is that: the single-point property and the integrity of the data are not considered at the same time, so that the prediction effect of the current circulation quantity prediction model cannot be accurately measured, and the accuracy of the prediction result of the circulation quantity prediction model is poor. Based on this, in the item circulation information processing method according to some embodiments of the present disclosure, first, a predicted circulation information set of a target item and a history circulation information set corresponding to the predicted circulation information set are obtained. The prediction flow information in the prediction flow information set comprises a prediction flow amount. The history flow information in the history flow information set includes a history flow amount. Thus, each of the historical circulation amounts in the historical circulation information set can be referred to as a true value of each of the predicted circulation amounts in the predicted circulation information set. Then, for each predicted circulation amount included in the predicted circulation information set, a single-point prediction accuracy is generated based on the predicted circulation amount and a history circulation amount corresponding to the predicted circulation amount included in the history circulation information set. Thus, the accuracy of the predicted result can be measured from the dimension of a single point. And secondly, generating the magnitude prediction accuracy according to each historical circulation quantity included in the historical circulation information set and each prediction circulation quantity included in the prediction circulation information set. Thus, the accuracy of the predicted result can be measured from the overall dimension. And finally, generating accuracy information of the prediction circulation information set according to the generated single-point prediction accuracy and the magnitude prediction accuracy. Thus, the accuracy of the predicted result can be measured from a single point and the whole dimension at the same time. Therefore, the accuracy of the measured prediction effect of the current circulation quantity prediction model can be improved, and the accuracy of the prediction result of the circulation quantity prediction model is further improved.

With further reference to fig. 3, a flow 300 of further embodiments of the item flow information processing method is shown. The flow 300 of the method for processing the information of the article circulation comprises the following steps:

step 301, obtaining a predicted circulation information set of a target object and a history circulation information set corresponding to the predicted circulation information set.

Step 302, for each predicted circulation quantity included in the predicted circulation information set, generating a single-point prediction accuracy according to the predicted circulation quantity and the historical circulation quantity corresponding to the predicted circulation quantity included in the historical circulation information set.

Step 303, generating the magnitude prediction accuracy according to each historical circulation quantity included in the historical circulation information set and each prediction circulation quantity included in the prediction circulation information set.

And step 304, generating accuracy information of the prediction streaming information set according to the generated single-point prediction accuracy and magnitude prediction accuracy.

In some embodiments, the specific implementation of steps 301-304 and the technical effects thereof may refer to steps 201-204 in those embodiments corresponding to fig. 2, and will not be described herein.

In step 305, in response to determining that the accuracy information meets the preset accuracy condition, the flow quantity prediction model is determined as a trained target flow quantity prediction model.

In some embodiments, an execution subject (e.g., a server shown in fig. 1) of the item circulation information processing method may determine the circulation quantity prediction model as a trained target circulation quantity prediction model in response to determining that the accuracy information satisfies a preset accuracy condition. The prediction flow information set may be generated by a flow amount prediction model. The flow amount prediction model may be a time-series prediction model that predicts a flow amount of an item whose demand has a discontinuity. For example, the flow amount prediction model may be, but is not limited to: ARIMA (Autoregressive Integrated Moving Average model, differential integration moving average autoregressive model), croston, ETS (Error-Trend-Seaseness), SBA (Syntetos-Boylan Approximation). The predicted circulation amount included in the predicted circulation information output by the circulation amount prediction model may be a non-zero value. The preset accuracy condition may be that "the value represented by the accuracy information is greater than or equal to a preset accuracy threshold". Here, the specific setting of the preset accuracy threshold is not limited. Therefore, when the obtained accuracy information meets the preset accuracy condition for determining whether the model is trained, the circulation quantity prediction model can be determined to be a target circulation quantity prediction model which is trained.

Optionally, in response to determining that the accuracy information does not meet the preset accuracy condition, parameters of the circulation quantity prediction model are adjusted to perform training using the adjusted circulation quantity prediction model as a circulation quantity prediction model. Wherein, the parameters can be obtained by fitting. Therefore, when the accuracy information does not meet the preset accuracy condition for determining whether the model is trained, the parameters of the circulation quantity prediction model can be adjusted to continue training the circulation quantity prediction model.

Step 306, in response to receiving the target circulation information sequence of the target item, generating a target prediction circulation information set according to the target circulation information sequence, the target circulation quantity prediction model and the predicted future time period.

In some embodiments, the executing entity may generate a target predicted circulation information set according to the target circulation information sequence, the target circulation quantity prediction model, and a predicted future time period in response to receiving the target circulation information sequence of the target item. The target prediction circulation information in the target prediction circulation information set comprises a target prediction circulation quantity. The target flow information sequence may be input data for the target flow amount prediction model. The target circulation information sequence may be information related to each circulation amount of the target object in each sub-time period in a time period before the current time. The target stream information in the target stream information sequence may include a stream quantity. The target circulation information may further include a sub-period corresponding to the circulation amount. The predicted future period may be a future period in which the amount of flow needs to be predicted. The predicted future time period may include N future sub-time periods having the same granularity as any of the sub-time periods. For example, the current time may be 2022, 1 month, 1 day. The time period corresponding to the target stream information sequence may be 2019, 1 month, 2021 and 12 months. The sub-time period may correspond to a month granularity. The predicted future time period may be 2022, 1-2022, 6. The future sub-time period may correspond to a month granularity. In practice, the execution subject may input the target circulation information sequence and the predicted future time period into the target circulation quantity prediction model to obtain a target prediction circulation information set. Thus, the circulation quantity of the target object in the future time period can be predicted through the trained target circulation quantity prediction model.

As can be seen in fig. 3, the flow 300 of the method of processing item circulation information in some embodiments corresponding to fig. 3 embodies the steps extended to predict the circulation volume of a target item in a future time period, as compared to the description of some embodiments corresponding to fig. 2. Thus, the embodiments describe a solution that can predict the amount of circulation of a target item in a future time period by training a completed target circulation amount prediction model. And because the target circulation quantity prediction model is trained by taking the accuracy information as a measurement index, the accuracy of the prediction result of the target circulation quantity prediction model is improved.

With further reference to fig. 4, a flow 400 of yet further embodiments of an item flow information processing method is shown. The flow 400 of the method for processing the information of the article circulation comprises the following steps:

step 401, obtaining a predicted circulation information set of a target article and a history circulation information set corresponding to the predicted circulation information set.

Step 402, for each predicted circulation amount included in the predicted circulation information set, generating a single-point prediction accuracy according to the predicted circulation amount and the historical circulation amount corresponding to the predicted circulation amount included in the historical circulation information set.

Step 403, generating the magnitude prediction accuracy according to each historical circulation quantity included in the historical circulation information set and each prediction circulation quantity included in the prediction circulation information set.

Step 404, generating accuracy information of the predicted streaming information set according to the generated single-point prediction accuracy and magnitude prediction accuracy.

In some embodiments, the specific implementation of steps 401 to 404 and the technical effects thereof may refer to steps 201 to 204 in those embodiments corresponding to fig. 2, and will not be described herein.

And step 405, determining the circulation quantity prediction model as a trained target circulation quantity prediction model in response to determining that the accuracy information meets the preset accuracy condition.

In response to receiving the target circulation information sequence of the target item, a target prediction circulation information set is generated according to the target circulation information sequence, the target circulation quantity prediction model and the predicted future time period, step 406.

In some embodiments, the specific implementation of steps 405-406 and the technical effects thereof may refer to steps 305-306 in those embodiments corresponding to fig. 3, which are not described herein.

Step 407, determining the sum of the respective target predicted circulation amounts included in the target predicted circulation information set as the article demand of the target article.

In some embodiments, the execution subject of the item circulation information processing method (e.g., the server shown in fig. 1) may determine the sum of the respective target predicted circulation amounts included in the target predicted circulation information set as the item demand amount of the target item. Wherein the demand for the item may be an overall demand for the target item over the predicted future time period.

Step 408, generating the replenishment quantity of the target object according to the inventory quantity and the object demand quantity of the target object in the warehouse corresponding to the historical circulation information set.

In some embodiments, the execution entity may generate the replenishment quantity of the target item according to the inventory quantity of the target item and the item demand quantity in the warehouse corresponding to the historical circulation information set. Wherein, the warehouse may be a warehouse storing the target object. The target object is circulated through the warehouse. In practice, the execution body may determine a difference between the item demand amount and the stock amount as the replenishment amount of the target item. In practice, the executing entity may also replace the replenishment amount with zero in response to determining that the replenishment amount is less than zero. Thus, the replenishment quantity may characterize the number of target items that the warehouse needs to replenish.

In step 409, control of the associated vehicle scheduling device schedules the restocking volume of the target items to a warehouse.

In some embodiments, the executing entity may control an associated vehicle dispatching device to dispatch the restocking volume of target items to the warehouse. The vehicle dispatching device may be a device for dispatching an article. For example, the vehicle dispatching device may be an unmanned vehicle.

As can be seen in fig. 4, the flow 400 of the method for processing the item circulation information in some embodiments corresponding to fig. 4 represents the step of restocking according to the prediction result, compared with the description of some embodiments corresponding to fig. 2. Therefore, the schemes described in the embodiments can supplement the target articles according to the accurate target predicted transfer amounts, so that article loss and warehouse space resource waste caused by more supplement amounts and stock backlog can be reduced, and transportation resource waste caused by less guide supplement amounts and repeated transportation can be reduced.

With further reference to fig. 5, as an implementation of the method shown in the foregoing figures, the present disclosure provides some embodiments of an article circulation information processing apparatus, which correspond to those method embodiments shown in fig. 2, and which are particularly applicable to various electronic devices.

As shown in fig. 5, the item flow information processing apparatus 500 of some embodiments includes: an acquisition unit 501, a first generation unit 502, a second generation unit 503, and a third generation unit 504. Wherein the obtaining unit 501 is configured to obtain a predicted circulation information set of a target article and a history circulation information set corresponding to the predicted circulation information set, where predicted circulation information in the predicted circulation information set includes a predicted circulation amount and history circulation information in the history circulation information set includes a history circulation amount; the first generating unit 502 is configured to generate, for each predicted circulation amount included in the predicted circulation information set, a single-point prediction accuracy rate according to the predicted circulation amount and a historical circulation amount corresponding to the predicted circulation amount included in the historical circulation information set; the second generating unit 503 is configured to generate an order prediction accuracy according to each of the history flow amounts included in the history flow information set and each of the prediction flow amounts included in the prediction flow information set; the third generating unit 504 is configured to generate accuracy information of the above-described prediction circulation information set according to the generated single-point prediction accuracy and the above-described magnitude prediction accuracy.

Optionally, the third generating unit 504 may include: a first determining unit (not shown in the figure) configured to determine, as a single-point magnitude prediction accuracy, a product of each generated single-point prediction accuracy and the magnitude prediction accuracy, and obtain a single-point magnitude prediction accuracy set.

Optionally, the third generating unit 504 may further include: a second determining unit (not shown in the figure) configured to determine, as accuracy information, a difference between a preset value and a mean value of the individual single-point magnitude prediction accuracy included in the above single-point magnitude prediction accuracy set.

Optionally, the above set of predicted stream information is generated by a stream quantity prediction model.

Optionally, the item circulation information processing apparatus 500 may further include: a target flow amount prediction model determination unit and a target prediction flow information set generation unit (not shown in the figure). Wherein the target flow amount prediction model determining unit is configured to determine the flow amount prediction model as a trained target flow amount prediction model in response to determining that the accuracy information satisfies a preset accuracy condition. The target prediction circulation information set generating unit is configured to generate a target prediction circulation information set according to the target circulation information sequence, the target circulation quantity prediction model and a predicted future time period in response to receiving the target circulation information sequence of the target object, wherein the target prediction circulation information in the target prediction circulation information set comprises a target prediction circulation quantity.

Optionally, the item circulation information processing apparatus 500 may further include: an item demand amount determination unit and a replenishment amount generation unit (not shown in the figure). Wherein the item demand determining unit is configured to determine a sum of the respective target predicted circulation amounts included in the target predicted circulation information set as an item demand of the target item. The replenishment quantity generation unit is configured to generate a replenishment quantity of the target item based on the inventory quantity of the target item and the item demand quantity in the warehouse corresponding to the history flow information set.

Optionally, the item circulation information processing apparatus 500 may further include: a control unit (not shown in the figures) configured to control the associated vehicle dispatching apparatus to dispatch the replenishment quantity of the target items to the warehouse.

Optionally, the item circulation information processing apparatus 500 may further include: an adjustment unit (not shown in the figure) configured to adjust parameters of the circulation quantity prediction model to perform training using the adjusted circulation quantity prediction model as a circulation quantity prediction model in response to determining that the accuracy information does not satisfy the preset accuracy condition.

It will be appreciated that the elements recited in the item flow information processing apparatus 500 correspond to the respective steps in the method described with reference to fig. 2. Thus, the operations, features and advantages described above with respect to the method are equally applicable to the item flow information processing apparatus 500 and the units contained therein, and are not described herein.

Referring now to fig. 6, a schematic diagram of an electronic device (e.g., server in fig. 1) 600 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 6 is merely an example and should not impose any limitations on the functionality and scope of use of embodiments of the present disclosure.

As shown in fig. 6, the electronic device 600 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 601, which may 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 means 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data required for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

In general, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, magnetic tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 shows an electronic device 600 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 6 may represent one device or a plurality of devices as needed.

In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communications device 609, or from storage device 608, or from ROM 602. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing device 601.

It should be noted that, the computer readable medium described 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 can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (Hyper Text 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"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.

The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a predicted circulation information set of a target object and a history circulation information set corresponding to the predicted circulation information set, wherein the predicted circulation information in the predicted circulation information set comprises a predicted circulation amount, and the history circulation information in the history circulation information set comprises a history circulation amount; for each predicted circulation quantity included in the predicted circulation information set, generating single-point prediction accuracy according to the predicted circulation quantity and the historical circulation quantity corresponding to the predicted circulation quantity included in the historical circulation information set; generating an order prediction accuracy according to each historical circulation quantity included in the historical circulation information set and each prediction circulation quantity included in the prediction circulation information set; and generating accuracy information of the prediction circulation information set according to the generated single-point prediction accuracy and the magnitude prediction accuracy.

Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in some embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The described units may also be provided in a processor, for example, described as: a processor includes an acquisition unit, a first generation unit, a second generation unit, and a third generation unit. The names of these units do not limit the unit itself in some cases, and for example, the acquisition unit may also be described as "a unit that acquires a predicted-circulation information set of a target item and a history-circulation information set corresponding to the predicted-circulation information set described above".

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: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.

The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.

Claims (10)

1. An item circulation information processing method, comprising:

acquiring a predicted circulation information set of a target object and a history circulation information set corresponding to the predicted circulation information set, wherein the predicted circulation information in the predicted circulation information set comprises a predicted circulation amount, and the history circulation information in the history circulation information set comprises a history circulation amount;

for each predicted transfer amount included in the predicted transfer information set, generating single-point prediction accuracy according to the predicted transfer amount and the historical transfer amount corresponding to the predicted transfer amount included in the historical transfer information set;

generating an order prediction accuracy according to each historical circulation quantity included in the historical circulation information set and each prediction circulation quantity included in the prediction circulation information set;

and generating accuracy information of the prediction circulation information set according to the generated single-point prediction accuracy and the magnitude prediction accuracy.

2. The method of claim 1, wherein the generating accuracy information of the set of predicted stream information from the generated single-point prediction accuracy and the magnitude prediction accuracy comprises:

And determining the product of each generated single-point prediction accuracy and the magnitude prediction accuracy as a single-point magnitude prediction accuracy, and obtaining a single-point magnitude prediction accuracy set.

3. The method of claim 2, wherein the generating accuracy information of the set of predicted stream information from the generated single-point prediction accuracy and the magnitude prediction accuracy further comprises:

and determining the difference between the preset value and the average value of each single-point magnitude prediction accuracy included in the single-point magnitude prediction accuracy set as accuracy information.

4. The method of claim 3, wherein the set of predicted flow information is generated by a flow quantity prediction model; and

the method further comprises the steps of:

in response to determining that the accuracy information meets a preset accuracy condition, determining the circulation quantity prediction model as a trained target circulation quantity prediction model;

and generating a target prediction circulation information set according to the target circulation information sequence, the target circulation quantity prediction model and a predicted future time period in response to receiving the target circulation information sequence of the target object, wherein target prediction circulation information in the target prediction circulation information set comprises target prediction circulation quantity.

5. The method of claim 4, wherein the method further comprises:

determining the sum of all target predicted circulation amounts included in the target predicted circulation information set as the article demand of the target article;

and generating the replenishment quantity of the target object according to the stock quantity of the target object and the object demand quantity in the warehouse corresponding to the historical circulation information set.

6. The method of claim 5, wherein the method further comprises:

controlling an associated vehicle dispatching device to dispatch the restocking volume of target items to the warehouse.

7. The method according to one of claims 4-6, wherein the method further comprises:

and in response to determining that the accuracy information does not meet the preset accuracy condition, adjusting parameters of the circulation quantity prediction model to train by using the adjusted circulation quantity prediction model as a circulation quantity prediction model.

8. An article transfer information processing apparatus comprising:

an obtaining unit configured to obtain a predicted circulation information set of a target article and a history circulation information set corresponding to the predicted circulation information set, wherein the predicted circulation information in the predicted circulation information set includes a predicted circulation amount, and the history circulation information in the history circulation information set includes a history circulation amount;

A first generation unit configured to generate, for each predicted transfer amount included in the predicted transfer information set, a single-point prediction accuracy rate according to the predicted transfer amount, a historical transfer amount corresponding to the predicted transfer amount included in the historical transfer information set;

a second generating unit configured to generate an order prediction accuracy according to each of the historical circulation amounts included in the historical circulation information set and each of the predicted circulation amounts included in the predicted circulation information set;

and a third generation unit configured to generate accuracy information of the prediction circulation information set according to the generated single-point prediction accuracy and the magnitude prediction accuracy.

9. An electronic device, comprising:

one or more processors;

a storage device having one or more programs stored thereon,

when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-7.

10. A computer readable medium having stored thereon a computer program, wherein the program when executed by a processor implements the method of any of claims 1-7.