CN115759926A - Article scheduling method, device, equipment and computer readable medium - Google Patents

Article scheduling method, device, equipment and computer readable medium Download PDF

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CN115759926A
CN115759926A CN202211432625.6A CN202211432625A CN115759926A CN 115759926 A CN115759926 A CN 115759926A CN 202211432625 A CN202211432625 A CN 202211432625A CN 115759926 A CN115759926 A CN 115759926A
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article
circulation
item
historical
generation model
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宋亚军
陈飘
江熙悦
段珂
张珂瑜
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Multipoint Shenzhen Digital Technology Co ltd
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Multipoint Shenzhen Digital Technology Co ltd
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Abstract

The embodiment of the disclosure discloses an article scheduling method, an article scheduling device, an article scheduling equipment and a computer readable medium. One embodiment of the method comprises: acquiring a first historical article transfer quantity sequence set and a second historical article transfer quantity sequence set; responding to the condition that the current time meets the preset test time, and acquiring a third history article circulation quantity sequence set and an article circulation information generation model set; for each item identification comprised by the set of item identifications, performing the following steps: determining an article circulation information generation model corresponding to an article identifier in an article circulation information generation model set; generating target article circulation information according to the first historical article circulation quantity sequence set, the second historical article circulation quantity sequence set and the article circulation information generation model corresponding to the article identification; and controlling the associated article scheduling equipment to execute article scheduling operation according to the article identification set and the circulation information of each target article. The embodiment can reduce the waste of warehouse space and the consumption of fresh goods.

Description

Article scheduling method, device, equipment and computer readable medium
Technical Field
Embodiments of the present disclosure relate to the field of computer technologies, and in particular, to an article scheduling method, apparatus, device, and computer-readable medium.
Background
With the development of warehouse article scheduling technology, automatic article scheduling can be performed according to the predicted article traffic amount, so that convenience can be provided for warehouse management personnel. At present, when predicting the article flow quantity, the general adopted mode is as follows: the method adopts manual measurement and calculation, and predicts the article flow based on subjective judgment of workers, or predicts the flow by carrying out complex modeling on articles of different types.
However, when the article flow amount is predicted in the above manner, there are often the following technical problems:
first, the subjectivity of manually predicting the article flow is strong, and specific data support is lacked, so that the accuracy of predicting the article flow is low, when the predicted article flow is high, the loss of fresh articles is caused, and when the predicted article flow is low, the waste of warehouse space is caused.
Secondly, the number of the article categories is large, so that a prediction model constructed by a category attribute dividing mode is complex, the calculation complexity is high, and the time for predicting article traffic is long.
Thirdly, the same prediction model is adopted for predicting the commodity circulation amount of different types of commodities, the accuracy rate of the predicted commodity circulation amount of more single commodities is low, when the predicted commodity circulation amount is high, the consumption of fresh commodities is caused, and when the predicted commodity circulation amount is low, the waste of warehouse space is caused.
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 to a person of ordinary skill in the art in this country.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary 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 item scheduling methods, apparatuses, electronic devices and computer readable media 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 article scheduling method, including: acquiring a first historical article transfer quantity sequence set and a second historical article transfer quantity sequence set corresponding to the article identification set; responding to the current time meeting the preset test time condition, and acquiring a third history article circulation quantity sequence set and an article circulation information generation model set corresponding to the article identification set; for each item identifier included in the item identifier set, performing the following steps: determining an article circulation information generation model corresponding to the article identifier in the article circulation information generation model set according to the third history article circulation quantity sequence set and the article circulation information generation model set; generating target article circulation information corresponding to the article identification according to the first historical article circulation quantity sequence set, the second historical article circulation quantity sequence set and an article circulation information generation model corresponding to the article identification, wherein the target article circulation information comprises article circulation quantity; and controlling the associated article dispatching equipment to execute article dispatching operation according to the article identification set and the generated circulation information of each target article.
In a second aspect, some embodiments of the present disclosure provide an article scheduling apparatus, the apparatus comprising: a first acquisition unit configured to acquire a first historical item turnover quantity sequence set and a second historical item turnover quantity sequence set corresponding to the item identification set; the second acquisition unit is configured to respond to the condition that the current time meets the preset test time, and acquire a third history article circulation quantity sequence set and an article circulation information generation model set corresponding to the article identification set; an execution unit configured to execute the following steps for each item identifier included in the item identifier set: determining an article circulation information generation model corresponding to the article identifier in the article circulation information generation model set according to the third history article circulation quantity sequence set and the article circulation information generation model set; generating target article circulation information corresponding to the article identifier according to the first historical article circulation sequence set, the second historical article circulation sequence set and an article circulation information generation model corresponding to the article identifier, wherein the target article circulation information comprises article circulation; and the control unit is configured to control the associated article scheduling equipment to execute article scheduling operation according to the article identification set and the generated target article circulation information.
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, cause the one or more processors to implement the method described in any of the implementations of the first aspect.
In a fourth aspect, some embodiments of the disclosure provide a computer-readable medium on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method described in any implementation manner of the first aspect.
The above embodiments of the present disclosure have the following advantages: by the article scheduling method of some embodiments of the present disclosure, waste of warehouse space and consumption of fresh articles can be reduced. Specifically, the reasons for the waste of warehouse space or the loss of fresh goods are: the subjectivity of manually predicting the commodity circulation amount is strong, specific data support is lacked, the accuracy of predicting the commodity circulation amount is low, when the predicted commodity circulation amount is high, fresh commodities are consumed, and when the predicted commodity circulation amount is low, the waste of warehouse space is caused. Based on this, the article scheduling method of some embodiments of the present disclosure first obtains a first historical article turnover sequence set and a second historical article turnover sequence set corresponding to the article identification set. Therefore, historical article transfer amount sequence sets corresponding to different time periods can be obtained, and the method can be used for predicting the article transfer amount. And secondly, responding to the condition that the current time meets the preset test time, and acquiring a third history article circulation quantity sequence set and an article circulation information generation model set corresponding to the article identification set. Therefore, when the current time is the preset time for testing each article circulation information generation model, the historical article circulation quantity sequence set and the article circulation information generation model set corresponding to the last half year can be obtained, and the article circulation quantity sequence set and the article circulation information generation model set can be used for determining the article circulation information generation model corresponding to each single article. Then, for each item identifier included in the item identifier set, the following steps are performed: determining an article circulation information generation model corresponding to the article identifier in the article circulation information generation model set according to the third history article circulation quantity sequence set and the article circulation information generation model set; and generating target article circulation information corresponding to the article identification according to the first historical article circulation sequence set, the second historical article circulation sequence set and an article circulation information generation model corresponding to the article identification, wherein the target article circulation information comprises article circulation. Therefore, the accurate article flow quantity can be obtained based on the existing historical data without manual prediction, and the accuracy of the predicted article flow quantity can be improved. And finally, controlling the associated article scheduling equipment to execute article scheduling operation according to the article identification set and the generated target article circulation information. Therefore, the goods can be dispatched in advance to replenish goods according to the predicted more accurate goods circulation amount, and therefore the waste of warehouse space and the loss of fresh goods can be reduced. And when the article scheduling operation is executed, the article flow is automatically generated according to the article flow information generation model and the existing historical data, so that the accuracy of the predicted article flow is improved, and the waste of warehouse space and the loss of fresh articles are reduced.
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The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
Fig. 1 is a flow diagram of some embodiments of an item scheduling method according to the present disclosure;
FIG. 2 is a schematic block diagram of some embodiments of an article scheduling apparatus according to the present disclosure;
FIG. 3 is a schematic block 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 is to be understood that the 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 for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a" or "an" in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will appreciate that references to "one or more" are intended to be exemplary and not limiting unless the context clearly indicates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the 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 a flow 100 of some embodiments of an item scheduling method according to the present disclosure. The article scheduling method comprises the following steps:
step 101, a first historical article transfer amount sequence set and a second historical article transfer amount sequence set corresponding to an article identification set are obtained.
In some embodiments, an executing subject (e.g., a computing device) of the item scheduling method may retrieve a first set of historical item streamlining sequences and a second set of historical item streamlining sequences corresponding to a set of item identifications from a terminal via a wired connection or a wireless connection. The first historical article transit sequence included in the first historical article transit sequence set may be a sequence in which the respective first historical article transit is arranged in ascending order according to historical time. The first historical item turnover number included in the first historical item turnover number sequence may be a turnover number (e.g., sales) of items in a unit time period within the first historical time period. The first history time period may be a time period from a history time to a current time. The time interval of the first historical time period may be a first preset time period. The first preset time period may be a preset time period. The certain unit time period is a time period in units of days. For example, the first preset time period may be 7 days, the current time may be 2022/9/07, the historical time may be 2022/9/01, the first historical time period may be 2022/9/01-2022/9/07, and one unit time period is one day of 2022/9/01-2022/9/07. The second historical item turnover number sequence included in the second historical item turnover number sequence set may be a sequence in which the respective second historical item turnover numbers are arranged in ascending order of historical time. The second historical article turnover number included in the second historical article turnover number sequence may be a turnover number of the article in a certain unit time period in the second historical time period. The second history time period may be a time period from the history time to the current time. The time interval of the second historical time period may be a second preset time period. The second preset time period may be a preset time period. The second preset duration may be greater than the first preset duration. For example, the second time period may be 35 days, the current time may be 2022/9/07, the historical time may be 2022/8/02, and the second historical time period may be 2022/8/02-2022/9/07. The item identifier included in the item identifier set can uniquely identify the item. The second historical article transit amount sequence included in the second historical article transit amount sequence set corresponds to the article identifier included in each article identifier. The correspondence between the second historical item turnover number sequence included in the second historical item turnover number sequence set and the item identifier included in each item identifier may be a one-to-one correspondence. The second historical article transit amount sequence included in the second historical article transit amount sequence set corresponds to the article identifier included in each article identifier. The corresponding relationship between the second historical item transit amount sequence included in the second historical item transit amount sequence set and the item identifier included in each item identifier may be a one-to-one correspondence. It is noted that the wireless connection may include, but is not limited to, a 3G/4G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a UWB (ultra wideband) connection, and other wireless connection now known or developed in the future.
And 102, responding to the condition that the current time meets the preset test time, and acquiring a third history article circulation quantity sequence set and an article circulation information generation model set corresponding to the article identification set.
In some embodiments, the execution subject may obtain a third history item circulation volume sequence set and an item circulation information generation model set corresponding to the item identification set in response to that the current time meets a preset test time condition. The preset test time condition may be a time when the current time is preset to test the accuracy of the prediction of each commodity circulation information generating model included in the commodity circulation information generating model set on the same commodity. The article transfer amount information generating model included in the article transfer amount information generating model set may be: the input data may include, but is not limited to, a sequence of historical item diversions, and the output data may include, but is not limited to, a model of predicted item diversions. For example, the model may be a machine learning model. The machine learning model can be a decision tree or a Markov model. The third history item circulation amount sequence included in the third history item circulation amount sequence set may be a sequence in which the respective third history item circulation amounts are arranged in ascending order of the history time. The third history article circulation amount included in the third history article circulation amount sequence may be a circulation amount of the articles in a unit time period within the third history time period. The third history time period may be a time period from the history time to the current time. The time interval of the third history time period may be a third preset time period. The third preset time period may be a preset time period. The third preset time period may be longer than the second preset time period. For example, the third preset time period may be half a year. In practice, the executing body may obtain the third history item circulation volume sequence set and the item circulation information generation model set from the terminal in a wired connection manner or a wireless connection manner.
Optionally, after step 102, the executing body may further execute the following steps:
firstly, acquiring a historical article circulation value information set corresponding to the article identification set. The article identifier included in the article identifier set may correspond to the historical article circulation value information included in the historical article circulation value information set in a one-to-one manner. The historical item circulation value information included in the historical item circulation value information set may be the sum of the item circulation value information (sales) of all warehouses of the same item in the historical time period. In practice, the execution subject may obtain the historical item circulation value information set corresponding to the item identifier set from a terminal or a server in a wired or wireless connection manner.
And secondly, sequencing each historical article circulation value information in the historical article circulation value information set to obtain a historical article circulation value information sequence. In practice, the execution main body may perform descending processing on each historical article circulation value information in the historical article circulation value information set according to the size of each historical article circulation value information to obtain a historical article circulation value information sequence.
Step 103, for each item identifier included in the item identifier set, performing the following steps:
step 1031, determining an item circulation information generation model corresponding to the item identifier in the item circulation information generation model set according to the third history item circulation quantity sequence set and the item circulation information generation model set.
In some embodiments, the executing body may determine an item circulation information generating model corresponding to the item identifier in the item circulation information generating model set according to the third history item circulation volume sequence set and the item circulation information generating model set. In practice, first, the execution subject may input the third history article circulation volume sequence corresponding to the article identifier in the third history article circulation volume sequence set to each article circulation information generation model included in the article circulation information generation model set, respectively, to obtain a test article circulation information set. The test article circulation information may include, but is not limited to, a test article circulation amount and a test error value. The test article flow rate may be an article flow rate corresponding to a test prediction time period obtained by the test. The test prediction time period may be a time period to be predicted in the test. The test error value may be a weighted average absolute percentage error value. Then, the maximum value of the test error values included in the test article circulation information set is determined as a target test error value. And finally, determining the article circulation information generation model corresponding to the target test error value as the article circulation information generation model corresponding to the article identifier. The article circulation information generation model corresponding to the target test error value may be an article circulation information generation model in which the test error value included in the generated article circulation information is the target test error value.
Optionally, the item circulation information generation model set may include a first item circulation information generation model and a second item circulation information generation model. The first article circulation information generation model may be represented by the following equation:
Figure BDA0003945408950000081
wherein, Y t May represent the sum of predicted commodity turnover t days into the future. X t The sum of the individual historical article runs over the past t days may be represented. X i May represent the volume of commodity runoff over the past t-th day.
The second commodity circulation information generation model may be a model that outputs a predicted commodity circulation amount according to a historical commodity circulation amount sequence. For example, the model may be a machine learning model. The machine learning model may be a markov model.
In some optional implementation manners of some embodiments, the executing entity may determine, according to the third history item circulation volume sequence set and the item circulation information generation model set, an item circulation information generation model corresponding to the item identifier in the item circulation information generation model set by:
the first step is that in response to the fact that the article identification is determined to be the target article identification, the first article circulation information generation model is determined to be the article circulation information generation model corresponding to the article identification.
In response to determining that the item identifier is not a target item identifier, performing the following substeps:
and a first substep of inputting a third history article circulation quantity sequence corresponding to the article identifier, which is included in the third history article circulation quantity sequence set, into the first article circulation information generation model as a test set to obtain first test article circulation information. The first test article circulation information may include a first test article circulation amount and a first error value. The first test article circulation amount may be an article circulation amount obtained through a test of the first article circulation information generation model. The first error value may be a weighted average absolute percentage error value obtained through a test of the first article circulation information generation model.
And a second sub-step of generating second test article circulation information according to a third history article circulation sequence corresponding to the article identifier and included in the third history article circulation sequence set and the second article circulation information generation model. The second test article circulation information may include a second test article circulation amount and a second error value. The second test commodity circulation amount may be a commodity circulation amount obtained through a second commodity circulation information generation model test. The second error value may be a weighted average absolute percentage error value obtained through a second commodity circulation information generation model test.
And a third substep, determining an article circulation information generation model corresponding to the article identifier according to the first error value and the second error value. In practice, first, the execution body may determine a minimum value of the first error value and the second error value as a target error value. And then, determining the article circulation information generation model corresponding to the target error value as the article circulation information generation model corresponding to the article identification.
The related content of the technical scheme is taken as an invention point of the embodiment of the disclosure, and the technical problems mentioned in the background art are solved, namely, the same prediction model is adopted to predict the commodity circulation amount for the commodities of different categories, the accuracy rate of predicting the commodity circulation amount for more single commodities is lower, when the predicted commodity circulation amount is higher, the consumption of fresh commodities is caused, and when the predicted commodity circulation amount is lower, the waste of warehouse space is caused. The factors that lead to the depletion of fresh items or the waste of warehouse space are often as follows: the same prediction model is adopted for predicting the commodity circulation amount of different types of commodities, the accuracy rate of predicting the commodity circulation amount of more single commodities is low, when the predicted commodity circulation amount is high, the consumption of fresh commodities is caused, and when the predicted commodity circulation amount is low, the waste of warehouse space is caused. If the above factors are solved, the effect of reducing the consumption of fresh goods or the waste of warehouse space can be achieved. To achieve this effect, in the article scheduling method according to some embodiments of the present disclosure, first, in response to determining that the article identifier is a target article identifier, the first article circulation information generation model is determined as an article circulation information generation model corresponding to the article identifier. Therefore, the article circulation information generation model corresponding to the article type with the article circulation value information ranked in the top and stable demand can be known as the first article circulation information generation model, and the article circulation value information generation model can be used for predicting the article circulation amount in the future time period. Then, in response to determining that the item identifier is not the target item identifier, performing the following steps: and inputting a third history article circulation quantity sequence corresponding to the article identification and included in the third history article circulation quantity sequence set as a test set into the first article circulation information generation model to obtain first test article circulation information. The first test article circulation information comprises a first test article circulation amount and a first error value. And generating a model according to the third history article circulation quantity sequence corresponding to the article identification and the second article circulation information, wherein the third history article circulation quantity sequence set comprises the second test article circulation information. The second test article circulation information includes a second test article circulation amount and a second error value. And determining an article circulation information generation model corresponding to the article identification according to the first error value and the second error value. Therefore, the error value of the article corresponding to the article identification in each article circulation information generation model in the article circulation information generation model set can be determined, so that the article circulation information generation model with more accurate article prediction corresponding to the article identification can be determined as the article circulation information generation model corresponding to the article identification, and the accuracy of predicting article circulation quantity can be improved. When the article circulation information generation model corresponding to the article identifier is determined, after tests are performed on different models in advance, for different single articles, a more accurate article circulation information generation model is selected as the article circulation information generation model corresponding to the article identifier, so that the accuracy of predicting article circulation quantity is improved, and loss of fresh articles or waste of warehouse space can be reduced.
Optionally, before step 2031, the executing body may further execute the following steps:
the first step, obtain the historical article flow quantity sequence corresponding to the above-mentioned article label. The historical article transit amount sequence may be a sequence in which the historical article transit amounts are arranged in ascending order according to historical time. The historical article turnover number included in the historical article turnover number sequence can be the turnover number of the articles in a certain unit time period in the historical time period. The historical time period may be a time period from a time when the item corresponding to the item identifier is first circulated (sold) to a current time. In practice, the execution subject may obtain the historical article transit amount sequence corresponding to the article identifier from a terminal or a server in a wired or wireless connection manner.
And secondly, determining the type of the article corresponding to the article identifier according to the historical article circulation quantity sequence. The article types can be obtained by classifying according to the historical article circulation value information and the historical article circulation quantity sequence.
In some optional implementations of some embodiments, the executing body may determine the item type corresponding to the item identifier according to the historical item turnover number sequence by:
the first substep is to select historical item circulation value information corresponding to the item identifier from the historical item circulation value information set as target historical item circulation value information.
And a second substep of determining the arrangement position of the target historical item circulation value information in the historical item circulation value information sequence as a target arrangement position.
And a third substep of generating an item traffic variation coefficient corresponding to the item identifier according to each historical item traffic in the historical item traffic sequence. The variation coefficient of the commodity circulation amount can be a variation coefficient of the commodity circulation amount. In practice, first, the execution body may determine an average value of the respective historical article transit amounts in the historical article transit amount sequence as a first numerical value. Then, the standard deviation of each historical article flow in the historical article flow sequence is determined as a second numerical value. And finally, determining the ratio of the second value to the first value as the variation coefficient of the commodity circulation quantity.
And a fourth substep of determining the article type corresponding to the article identifier according to the target arrangement position and the variation coefficient of the article flow rate. In practice, the executing entity may determine preset item type information satisfying a preset type condition in the preset item type information set as the target item type information. The preset article type information in the preset article type information set may be information representing an article type. The preset item type information may include, but is not limited to: item type, coefficient of variation information, and arrangement location information. The above-mentioned item types may be represented in the form of numbers. The variation coefficient information can represent the range of the article flow variation coefficient of the article. The arrangement position information may represent a range of a section in which the article value information arrangement position of the article is located. Each article type can be in one-to-one correspondence with the variation coefficient information and the arrangement position information. The article value information arrangement position may be an arrangement position of the historical article circulation value information corresponding to the article identifier in the historical article circulation value information set corresponding to all articles. The preset type condition may be that the target arrangement position is within an interval range where an article circulation variation coefficient of an article represented by variation coefficient information included in the preset article type information is located, and the article circulation variation coefficient is within an interval range where arrangement position information included in the preset article type information is located, where the arrangement position information included in the preset article type information is located.
As an example, the target arrangement position may be 75%, the commodity circulation amount variation coefficient may be 0.3, and the preset commodity type information set may be "01"; (0, 0.5; (0, 80% ],02; (0.5, 1; (80%, 95% ]. The target arrangement position 75% is within (0, 80% ]), the item runoff variance factor is within (0, 0.5], and the item type corresponding to the item identification may be "01".
The related content of the technical scheme is taken as an invention point of the embodiment of the disclosure, and the technical problem mentioned in the background art is solved, namely that the number of the article categories is large, so that a prediction model constructed by a classification mode of category attributes is complex, the calculation complexity is high, and the time consumed for predicting article traffic is long. Factors that contribute to the long time taken to predict the volume of a commodity flow tend to be as follows: the number of the article categories is large, so that a prediction model constructed by a classification mode of category attributes is complex, the calculation complexity is high, and the time for predicting article traffic is long. If the above-mentioned factors are solved, the effect of shortening the time taken to predict the article drift amount can be achieved. In order to achieve this effect, in the article scheduling method according to some embodiments of the present disclosure, first, historical article circulation value information corresponding to the article identifier is selected from the historical article circulation value information set as target historical article circulation value information. Therefore, the article circulation value information of the articles in the historical time period can be obtained, and the arrangement positions of the article circulation value information of the articles in the historical article circulation value information sequences corresponding to all the articles can be determined. And then, determining the arrangement position of the target historical article circulation value information in the historical article circulation value information sequence as a target arrangement position. Therefore, the arrangement position of the historical article circulation value information of the article in the historical article circulation value information sequence can be obtained, and the method can be used for reclassifying the types of the articles according to the historical article circulation value information and the historical article circulation amount. And then, generating an article traffic variation coefficient corresponding to the article identifier according to each historical article traffic in the historical article traffic sequence. Therefore, the variation coefficient of the commodity circulation quantity of the commodity with the characteristic fluctuation of the demand can be obtained, and the method can be used for reclassifying the types of the commodities according to the historical commodity circulation value information and the historical commodity circulation quantity. And finally, determining the type of the article corresponding to the article identifier according to the target arrangement position and the variation coefficient of the article flow quantity. Therefore, the type of reclassification of the article according to the historical article circulation value information and the historical article circulation quantity can be obtained, so that each article with the requirement fluctuation of the historical article circulation value information and the historical article circulation quantity in the same range can be classified into the same type, and the types of the articles are further reduced. When the articles are classified, the articles are not classified according to the attributes of the articles, but the types of the articles are re-classified according to the historical article circulation value information and the historical article circulation amount, so that the types of the articles are reduced, a simpler prediction model can be constructed, the calculation complexity is reduced, and the time for predicting the article circulation amount is shortened.
And thirdly, determining whether the type of the article corresponding to the article identifier is the same as a preset article type. The preset article type may be a preset article type. For example, the preset item type may be an item type corresponding to coefficient of variation information of "(0, 0.5]" and corresponding arrangement position information of "(0, 80% ]". The fourth step, in response to determining that the item type corresponding to the item identification is the same as the preset item type, determines the item identification as a target item identification.
And 1032, generating target article circulation information corresponding to the article identification according to the first historical article circulation sequence set, the second historical article circulation sequence set and the article circulation information generation model corresponding to the article identification.
In some embodiments, the execution body may generate the target article circulation information corresponding to the article identifier according to the first historical article circulation amount sequence set, the second historical article circulation amount sequence set, and an article circulation information generation model corresponding to the article identifier. Wherein, the target article circulation information comprises article circulation amount. The article circulation amount may be a sum of circulation amounts of the articles in each unit time period within a predicted future time period. The future time period may be a time period from the second day of the current time to the future time. The time interval of the future time period may be a preset future time period. The preset future time period may be a preset time period, for example, the current time may be 2022/9/07, the preset future time period may be 7 days, and the future time period may be 2022/9/08-2022/9/14. In practice, first, the execution main body may input the first historical article transit amount sequence set and the second historical article transit amount sequence set into an article transit information generation model corresponding to the article identifier, respectively, to obtain a first transit amount and a second transit amount. Then, an average value of the first and second diversion amounts is determined as an article diversion amount corresponding to the article identification. And finally, combining the article identification with the article circulation amount corresponding to the article identification to obtain target article circulation information corresponding to the article identification.
In some optional implementations of some embodiments, the executing body may generate the target article circulation information corresponding to the article identifier according to the first historical article circulation amount sequence set, the second historical article circulation amount sequence set, and an article circulation information generation model corresponding to the article identifier by:
in response to determining that the item circulation information generation model corresponding to the item identifier is the first item circulation information generation model, selecting a first historical item circulation quantity sequence corresponding to the item identifier from the first historical item circulation quantity sequence set as a first item circulation quantity sequence.
And secondly, generating a model according to the first article circulation quantity sequence and the first article circulation information, and generating first target article circulation information as target article circulation information. The first target article circulation information may represent an article circulation amount in a future time period. In practice, first, the execution main body may input each first commodity circulation quantity in the first commodity circulation quantity sequence into the first commodity circulation information generation model, so as to obtain the commodity circulation quantity in a future time period. Then, the obtained article circulation amount in the future time period and the corresponding date range of the future time period may be combined to obtain the first target article circulation information as the target article circulation information.
And thirdly, in response to the fact that the article circulation information generating model corresponding to the article identification is determined to be the second article circulation information generating model, selecting a second historical article circulation sequence corresponding to the article identification from the second historical article circulation sequence set as the second article circulation sequence.
And fourthly, generating a model according to the second commodity circulation quantity sequence and the second commodity circulation information, and generating second target commodity circulation information as target commodity circulation information. The first target article circulation information may represent an article circulation amount in a future time period. In practice, the execution body may generate second target article circulation information as the target article circulation information in various ways according to the second article circulation amount sequence and the second article circulation information generation model.
Therefore, for different articles, the article circulation quantity in the future time period can be predicted according to the article circulation information generation model corresponding to the article identification obtained through testing and the required historical article circulation quantity sequence, and therefore the accuracy of the predicted article circulation quantity can be improved.
In some optional implementation manners of some embodiments, the executing body may generate second target circulation information as the target circulation information according to the second circulation quantity sequence and the second circulation information generation model by:
and step one, carrying out polymerization treatment on the second article flow rate sequence to obtain a polymer article flow rate sequence. In practice, first, the execution main body may sequentially perform a segmentation process on each second article traffic in the second article traffic sequence according to a preset number, so as to obtain a second article traffic group set. The number of the second article runout included in the second article runout group set may be a preset number. The preset number may be a preset number. The preset number may be 7. Then, for each second article traffic volume group included in the second article traffic volume group set, the following substeps are performed:
a first substep of determining the sum of the individual second article runouts included in the set of second article runouts as a polymeric article runout.
And a second substep of determining the arrangement position of the second article traffic volume group in the second article traffic volume group set as the target serial number.
And finally, arranging the obtained flow quantities of the polymer articles in an ascending order according to the corresponding target sequence numbers to obtain a flow quantity sequence of the polymer articles.
And secondly, determining the article flow quantity boundary value according to the polymer article flow quantity sequence. The article flow rate threshold value may be a value that divides each polymer article flow rate into different grades. In practice, the execution body may determine a median of each of the polymer commodity runouts included in the polymer commodity runout amount sequence as a commodity runout amount boundary value.
And thirdly, determining the grade state information corresponding to the flow quantity of the polymer according to the article flow quantity boundary value for each polymer flow quantity in the polymer flow quantity sequence. Wherein the grade state information may characterize a grade state of the flow of the polymeric article. The above-mentioned level status may be, but is not limited to, one of the following: low commodity circulation state, medium commodity circulation state and high commodity circulation state. The low commodity flow condition can be indicative of the polymeric commodity flow being less than the commodity flow cutoff value. The article run-out condition may be indicative of the polymeric article run-out being equal to the article run-out cutoff. The high commodity run condition may be indicative of the polymeric commodity run being greater than the commodity run boundary value. In practice, for each of the series of polymer item flowrates, the executive body may determine a low item flowrate status as the class status information corresponding to the polymer item flowrate in response to determining that the polymer item flowrate is less than the item flowrate cutoff value. In response to determining that the article run-out is equal to the article run-out cutoff value, determining a medium article run-out status as the class status information corresponding to the article run-out. In response to determining that the article run out is greater than the article run out cutoff, determining a high article run out status as the class status information corresponding to the article run out.
And fourthly, determining the grade state information of the polymer article flow quantity which meets the preset position condition in the polymer article flow quantity sequence as the target grade state information. Wherein the predetermined position condition may be that the position of the polymer run-out in the polymer run-out sequence is the last one. A fifth step of, for each of the determined individual level state information, performing the following substeps:
the first sub-step, at least one polymer article flow quantity corresponding to the grade state information is determined as a target flow quantity set.
And a second substep, in response to determining that the number of each target traffic volume included in the target traffic volume set satisfies a preset number condition, determining an input traffic volume corresponding to the level state information according to the target traffic volume set. The preset quantity condition may be that the quantity of each target traffic volume included in the target traffic volume set is greater than 1. In practice, the execution main body may determine a minimum value of each target traffic in the target traffic set as an input traffic corresponding to the level state information.
A third sub-step, in response to determining that the number of each target traffic volume included in the target traffic volume set does not satisfy the preset number condition, determining the target traffic volume in the target traffic volume set as the input traffic volume corresponding to the level state information.
And sixthly, inputting the target level state information and the determined input flow into the second commodity circulation information generation model to obtain second target commodity circulation information serving as target commodity circulation information. The second commodity circulation information generation model may be represented by the following formula:
Y t ＝C j ×P ij +C k ×P ik +C l ×P il
wherein, Y t May represent the sum of the predicted commodity turnover t days into the future. C y Can represent the input flow rate P corresponding to the rank state y xy The state transition probability of a transition from rank state x to rank state y may be represented. The state transition probability may be obtained by pre-training. i may indicate a target level state included in the target level state information.
It should be noted that, when generating the second test article circulation information according to the third history article circulation amount sequence corresponding to the article identifier and the second article circulation information generation model included in the third history article circulation amount sequence set, the second test article circulation information may also be generated according to the above formula, and therefore, details are not repeated.
Therefore, the commodity circulation amount in the future time period can be predicted through the model with low calculation degree, so that the calculation complexity can be reduced, and the time for predicting the commodity circulation amount can be shortened.
And 104, controlling the associated article scheduling equipment to execute article scheduling operation according to the article identification set and the generated information of each target article circulation.
In some embodiments, the executing body may control the associated article scheduling device to execute the article scheduling operation according to the article identification set and the generated target article circulation information. The article scheduling device may be a device for transporting an article to schedule the article. For example, the article scheduling apparatus may be a robot. In practice, for each item identifier in the set of item identifiers, first, the executing entity may obtain an existing stock quantity corresponding to the item identifier. The existing inventory amount may be the number of the articles stored in the warehouse at the current time corresponding to the article identifier. Then, the target article flow amount corresponding to the article mark in the target article flow amounts is compared with the existing warehouseThe difference in inventory is determined as the replenishment quantity. And then, filling the replenishment quantity and the article identification into a preset character string template to obtain replenishment quantity information. Wherein, the preset character string template can be'-------The replenishment quantity is-------". The underlined part in the preset character string template can represent the part to be filled in. The first underlined portion of the preset string template may be used to populate the item identifier. The second underlined portion of the predetermined string template may be used to fill the replenishment quantity. And finally, sending the replenishment quantity information to the article scheduling equipment, so that the article scheduling equipment executes article scheduling operation for scheduling articles after receiving the replenishment quantity information. The article scheduling operation may be an operation of scheduling the replenishment quantity articles included in the replenishment quantity information to a warehouse storing each article.
In some optional implementation manners of some embodiments, for each item identifier included in the item identifier set, the executing body may control the associated item scheduling device to execute an item scheduling operation according to the item identifier set and the generated target item circulation information by the following steps:
and step one, generating a daily article circulation volume ratio information set corresponding to the article identification according to the third history article circulation volume sequence set. The daily commodity circulation volume proportion information included in the daily commodity circulation volume proportion information set can represent the ratio of the daily commodity circulation volume to the sum of the commodity circulation volumes. The total amount of the article runout can be the total amount of the article runout in the third history time period. The daily commodity circulation amount may be a commodity circulation amount in a unit time period. In practice, first, the executing body may determine a third history item circulation amount sequence corresponding to the item identifier included in the third history item circulation amount sequence set as a target third history item circulation amount sequence. Then, for each preset date identification in the set of preset date identifications, the following substeps are performed:
the first substep is to determine the sum of the target third history article circulation amounts corresponding to the preset date mark in the target third history article circulation amount sequence as a first numerical value. The preset date identifier set may include monday, tuesday, wednesday, thursday, friday, saturday, and sunday.
And a second substep of determining the sum of the target third history item circulation quantity sequences included in the target third history item circulation quantity sequences as a second numerical value.
And a third substep of determining the ratio of the first value to the second value as the daily commodity circulation ratio.
And a fourth substep, combining the preset date identification and the daily commodity circulation volume ratio to obtain daily commodity circulation volume ratio information. The combination mode can be character splicing.
And secondly, generating a target article circulation quantity sequence according to target article circulation information corresponding to the article identification and the daily article circulation quantity ratio information set which are included in each target article circulation information. The target article traffic sequence may be a sequence in which the target article traffic is arranged in an ascending order of time in a future prediction time period. The target commodity circulation amount may be a commodity circulation amount of a unit time period in a future prediction time period. In practice, first, for each preset date identifier in the preset date identifier set, the executing body may perform the following substeps:
the first substep is to determine the product of the target article circulation amount included in the target article circulation information and the ratio of the daily article circulation amount corresponding to the preset date mark as the target article circulation amount.
And a second substep of determining the arrangement position of the unit time interval corresponding to the preset date mark in the future prediction time interval.
And then sequencing each determined target article flow quantity according to the arrangement position corresponding to the target article flow quantity to obtain a target article flow quantity sequence.
And thirdly, controlling the associated article scheduling equipment to execute article scheduling operation according to the target article flow quantity sequence in response to the fact that the current time meets a preset scheduling time condition. The preset scheduling time condition may be that the current time is a preset time for scheduling an article. The predetermined time for scheduling the item may be 22 o' clock per day. In practice, first, the execution subject may determine, as the adjustment amount, a target item circulation amount at which the arrangement position in the target item circulation amount sequence is the first. Then, the inventory of the article corresponding to the article identifier at the current time is obtained. And determining the difference between the dispatching quantity and the stock quantity as the dispatching replenishment quantity. And finally, controlling the associated article scheduling equipment to execute article scheduling operation according to the scheduling replenishment quantity.
The above embodiments of the present disclosure have the following advantages: by the article scheduling method of some embodiments of the present disclosure, waste of warehouse space and consumption of fresh articles can be reduced. Specifically, the reasons for the waste of warehouse space or the loss of fresh goods are: the subjectivity of manually predicting the commodity circulation volume is strong, specific data support is lacked, the accuracy of predicting the commodity circulation volume is low, when the predicted commodity circulation volume is high, the consumption of fresh commodities is caused, and when the predicted commodity circulation volume is low, the waste of warehouse space is caused. Based on this, the article scheduling method of some embodiments of the present disclosure first obtains a first historical article transit amount sequence set and a second historical article transit amount sequence set corresponding to the article identification set. Therefore, historical article transfer amount sequence sets corresponding to different time periods can be obtained, and the method can be used for predicting the article transfer amount. And secondly, responding to the condition that the current time meets the preset test time, and acquiring a third history article circulation quantity sequence set and an article circulation information generation model set corresponding to the article identification set. Therefore, when the current time is the preset time for testing each article circulation information generation model, the historical article circulation quantity sequence set and the article circulation information generation model set corresponding to the last half year can be obtained, and the article circulation quantity sequence set and the article circulation information generation model set can be used for determining the article circulation information generation model corresponding to each single article. Then, for each item identifier included in the item identifier set, the following steps are performed: determining an article circulation information generation model corresponding to the article identifier in the article circulation information generation model set according to the third history article circulation quantity sequence set and the article circulation information generation model set; and generating target article circulation information corresponding to the article identification according to the first historical article circulation quantity sequence set, the second historical article circulation quantity sequence set and an article circulation information generation model corresponding to the article identification, wherein the target article circulation information comprises article circulation quantity. Therefore, the more accurate predicted article flow amount can be obtained based on the existing historical data without manual prediction, and the accuracy of the predicted article flow amount can be improved. And finally, controlling the associated article dispatching equipment to execute article dispatching operation according to the article identification set and the generated circulation information of each target article. Therefore, the goods can be dispatched in advance to replenish according to the predicted more accurate goods circulation amount, so that the waste of warehouse space and the loss of fresh goods can be reduced. And when the article scheduling operation is executed, the predicted article transfer amount is automatically generated according to the article transfer information generation model and the existing historical data, so that the accuracy of the predicted article transfer amount is improved, and the waste of warehouse space and the loss of fresh articles are reduced.
With further reference to fig. 2, as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of an article scheduling apparatus, which correspond to those shown in fig. 1, and which may be applied in various electronic devices.
As shown in fig. 2, the article scheduling apparatus 200 of some embodiments includes: a first acquisition unit 201, a second acquisition unit 202, an execution unit 203, and a control unit 204. Wherein the first obtaining unit 201 is configured to obtain a first historical item turnover sequence set and a second historical item turnover sequence set of the corresponding item identification set; the second obtaining unit 202 is configured to obtain a third history item circulation volume sequence set and an item circulation information generation model set corresponding to the item identification set in response to the current time meeting a preset test time condition; the execution unit 203 is configured to, for each item identifier included in the item identifier set, perform the following steps: determining an article circulation information generation model corresponding to the article identifier in the article circulation information generation model set according to the third history article circulation quantity sequence set and the article circulation information generation model set; generating target article circulation information corresponding to the article identifier according to the first historical article circulation sequence set, the second historical article circulation sequence set and an article circulation information generation model corresponding to the article identifier, wherein the target article circulation information comprises article circulation; the control unit 204 is configured to control the associated article scheduling device to perform article scheduling operations according to the above article identification set and the generated target article circulation information.
It will be appreciated that the units described in the apparatus 200 correspond to the various steps in the method described with reference to figure 1. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 200 and the units included therein, and are not described herein again.
Referring now to fig. 3, a block diagram of an electronic device 300 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device in some embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle-mounted terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 3, the electronic device 300 may include a processing means 301 (e.g., a central processing unit, a graphics processor, etc.) that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage means 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data necessary for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
Generally, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage devices 308 including, for example, magnetic tape, hard disk, etc.; and a communication device 309. The communication means 309 may allow the electronic device 300 to communicate wirelessly or by wire with other devices to exchange data. While fig. 3 illustrates an electronic device 300 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 3 may represent one device or may represent multiple devices, as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams 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 illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. The computer program, when executed by the processing apparatus 301, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described in some embodiments of the present disclosure may be a computer readable information medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination 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 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, a computer readable information medium may include data information propagated in baseband or as part of a carrier wave in which computer readable program code is carried. Such propagated data information may take many forms, including, but not limited to, electromagnetic information, optical information, or any suitable combination of the foregoing. A computer readable information 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, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers 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"), 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 network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled 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 first historical article transfer quantity sequence set and a second historical article transfer quantity sequence set corresponding to the article identification set; responding to the current time meeting the preset test time condition, and acquiring a third history article circulation quantity sequence set and an article circulation information generation model set corresponding to the article identification set; for each item identifier included in the item identifier set, performing the following steps: determining an article circulation information generation model corresponding to the article identifier in the article circulation information generation model set according to the third history article circulation quantity sequence set and the article circulation information generation model set; generating target article circulation information corresponding to the article identifier according to the first historical article circulation sequence set, the second historical article circulation sequence set and an article circulation information generation model corresponding to the article identifier, wherein the target article circulation information comprises article circulation; and controlling the associated article dispatching equipment to execute article dispatching operation according to the article identification set and the generated circulation information of each target article.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of 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 type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart 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 that 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 software, and may also be implemented by hardware. The described units may also be provided in a processor, which may be described as: a processor includes a first acquisition unit, a second acquisition unit, an execution unit, and a control unit. Where the names of these units do not in some cases constitute a limitation of the unit itself, for example, the first acquisition unit may also be described as a "unit that acquires a first set of historical item streamlining sequences and a second set of historical item streamlining sequences corresponding to a set of item identifications".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, hardware logic components of exemplary types of information that may be used include: field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems on a chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology 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-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.

Claims (7)

1. An item scheduling method, comprising:
acquiring a first historical article transfer quantity sequence set and a second historical article transfer quantity sequence set corresponding to the article identification set;
responding to the condition that the current time meets the preset test time, and acquiring a third history article circulation quantity sequence set and an article circulation information generation model set corresponding to the article identification set;
for each item identification comprised by the set of item identifications, performing the following steps:
determining an article circulation information generation model corresponding to the article identifier in the article circulation information generation model set according to the third history article circulation quantity sequence set and the article circulation information generation model set;
generating target article circulation information corresponding to the article identification according to the first historical article circulation sequence set, the second historical article circulation sequence set and an article circulation information generation model corresponding to the article identification, wherein the target article circulation information comprises article circulation;
and controlling the associated article scheduling equipment to execute article scheduling operation according to the article identification set and the generated target article circulation information.
2. The method according to claim 1, wherein said controlling associated item scheduling devices to perform item scheduling operations according to the item identification set and the generated respective target item circulation information comprises:
for each item identification comprised by the set of item identifications, performing the following steps:
generating a daily article circulation volume ratio information set corresponding to the article identification according to the third history article circulation volume sequence set;
generating a target article circulation quantity sequence according to target article circulation information corresponding to the article identification and the daily article circulation quantity ratio information set which are included in each target article circulation information;
and controlling the associated article scheduling equipment to execute article scheduling operation according to the target article flow transfer quantity sequence in response to the fact that the current time meets a preset scheduling time condition.
3. The method of claim 1, wherein before determining the item circulation information generation model corresponding to the item identifier in the item circulation information generation model set according to the third history item circulation volume sequence set and the item circulation information generation model set, the method further comprises:
obtaining a historical article flow quantity sequence corresponding to the article identification;
determining the type of the article corresponding to the article identifier according to the historical article flow quantity sequence;
determining whether the type of the article corresponding to the article identifier is the same as a preset article type;
in response to determining that the item type corresponding to the item identification is the same as the preset item type, determining the item identification as a target item identification.
4. The method of claim 3, wherein after acquiring a third historical item circulation volume set and an item circulation information generation model set corresponding to the item identification set in response to the current time satisfying a preset test time condition, the method further comprises:
acquiring a historical article circulation value information set corresponding to the article identification set;
and sequencing all the historical article circulation value information in the historical article circulation value information set to obtain a historical article circulation value information sequence.
5. An article scheduling apparatus comprising:
a first acquisition unit configured to acquire a first historical item turnover quantity sequence set and a second historical item turnover quantity sequence set corresponding to the item identification set;
the second acquisition unit is configured to respond to the condition that the current time meets the preset test time, and acquire a third history article circulation quantity sequence set and an article circulation information generation model set corresponding to the article identification set;
an execution unit configured to, for each item identification comprised by the set of item identifications, perform the following steps:
determining an article circulation information generation model corresponding to the article identifier in the article circulation information generation model set according to the third history article circulation quantity sequence set and the article circulation information generation model set;
generating target article circulation information corresponding to the article identification according to the first historical article circulation sequence set, the second historical article circulation sequence set and an article circulation information generation model corresponding to the article identification, wherein the target article circulation information comprises article circulation;
and the control unit is configured to control the associated article scheduling equipment to execute article scheduling operation according to the article identification set and the generated target article circulation information.
6. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method recited in any of claims 1-4.
7. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-4.
CN202211432625.6A 2022-11-16 2022-11-16 Article scheduling method, device, equipment and computer readable medium Pending CN115759926A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116662672A (en) * 2023-07-27 2023-08-29 中信证券股份有限公司 Value object information transmitting method, device, equipment and computer readable medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116662672A (en) * 2023-07-27 2023-08-29 中信证券股份有限公司 Value object information transmitting method, device, equipment and computer readable medium
CN116662672B (en) * 2023-07-27 2024-02-06 中信证券股份有限公司 Value object information transmitting method, device, equipment and computer readable medium

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