CN110458345A - Determine the method, apparatus, equipment and storage medium of machine loss shipment amount - Google Patents
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Abstract
The embodiment of the invention discloses method, apparatus, equipment and the storage mediums of a kind of determining machine loss shipment amount, this method comprises: being recorded according to the shipment of target item in target machine, determine the period out of stock of target item and duration out of stock in target machine, and using the period out of stock as target time section；According to the History Order of machine in the affiliated machine class of target machine, determine that target item is in the shipment speed of the target time section in target machine；According to target item in target machine in the shipment speed and the duration out of stock of the target time section, determine that target item is in the object effects shipment amount of the target time section in target machine.The present embodiment is by the way that time, machine class and type of items to be finely divided, it can be determined that different machines are by availability effect；Meanwhile the marketing efficiency based on every machine every article per period, the machine that can predict next day should have order, facilitate article distribution when adjustment replenishes.
Description
Technical Field
The embodiment of the invention relates to the technical field of automatic retail, in particular to a method, a device, equipment and a storage medium for determining the lost shipment volume of a machine.
Background
"New retail" is all the activities that apply new technologies, new thoughts, the Internet to sell products or services to end consumers. For the vending machine business, in order to realize the integration of people, goods and places and provide more efficient retail service, data support is necessary, and the prediction and analysis of orders are more key nodes.
The existing order forecasting method mainly comprises the following steps: based on historical order data, a prediction equation is made by using a regression fitting method, and then optimization is performed based on a time sequence model, such as an ARMA (autoregressive moving average) model, an ARIMA (autoregressive integrated moving average) model and the like.
The machine learning algorithm is used for calculation, such as a neural network model, a gray prediction model and the like, the algorithm is complex, parameters can be customized based on precision requirements, the algorithm is complex, the calculation pressure is high, and the difficulty is caused when the algorithm is explained with a business party.
Disclosure of Invention
The embodiment of the invention provides a method, a device, equipment and a storage medium for determining the lost shipment volume of a machine, so as to improve the accuracy and the reliability of the lost shipment volume of articles.
In a first aspect, an embodiment of the present invention provides a method for determining a shipment loss of a machine, including:
determining the time period and the time length of the goods shortage of the target object in the target machine according to the delivery record of the target object in the target machine, and taking the time period of the goods shortage as the target time period;
determining the delivery speed of the target object in the target machine in the target time period according to the historical orders of the machines in the machine category to which the target machine belongs;
and determining the target influence shipment volume of the target object in the target machine in the target time period according to the shipment speed and the out-of-stock duration of the target object in the target machine in the target time period.
In a second aspect, an embodiment of the present invention further provides an apparatus for determining a shipment loss of a machine, where the apparatus includes:
the system comprises a goods shortage time period and goods shortage duration determining module, a goods shortage time period and goods shortage duration determining module and a goods shortage time period determining module, wherein the goods shortage time period and the goods shortage duration determining module are used for determining the goods shortage time period and the goods shortage duration of a target object in a target machine according to a goods delivery record of the target object in the target machine and taking the goods shortage time period as the target time period;
the goods delivery speed determining module is used for determining the goods delivery speed of the target object in the target machine in the target time period according to the historical order of the machine in the machine category of the target machine in the target time period;
and the target influence shipment quantity determining module is used for determining the target influence shipment quantity of the target object in the target machine in the target time period according to the shipment speed and the shortage duration of the target object in the target machine in the target time period.
In a third aspect, an embodiment of the present invention further provides an apparatus, including:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a method of determining a shipment loss for a machine as described in any of the embodiments of the invention.
In a fourth aspect, the embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for determining a shipment lost by a machine according to any embodiment of the present invention.
According to the embodiment of the invention, by subdividing time, machine types and article types, transaction differences of different articles and different time periods are fully considered, and based on the sales efficiency of each article of each machine in each time period, the estimated order quantity of each machine can be summarized, so that the expected order quantity of the machine in the next day can be predicted, and the adjustment of article distribution during replenishment is facilitated; meanwhile, the degree of influence of the material supply capacity on different machines can be judged by evaluating the lost goods output quantity influenced by sold goods, and the order growth potentials of different machines after the development of the material supply capacity can be visually compared.
Drawings
FIG. 1 is a flow chart of a method for determining a shipment loss for a machine in accordance with a first embodiment of the present invention;
FIG. 2 is a flow chart of a method of determining a lost shipment of a machine in accordance with a second embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a device for determining the lost shipment of a machine according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an apparatus in a third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a method for determining a lost shipment amount of a machine according to an embodiment of the present invention, where the method is applicable to a situation where an article lost due to a machine out-of-stock is estimated, and the method may be performed by a device for determining a lost shipment amount of a machine, and the device may be implemented in software or/and hardware, and may be configured in an apparatus, such as a server, for determining a lost article amount when a machine out-of-stock is performed. As shown in fig. 1, the method for determining the shipment loss of a machine in the embodiment includes:
s110, determining the time period and the time length of the shortage of the target object in the target machine according to the delivery record of the target object in the target machine, and taking the time period of the shortage as the target time period.
Before evaluation, a specific machine or a class of machines needs to be selected as an evaluation object, namely, a target machine. The target machine is a selected machine to be evaluated, which may be a single machine or multiple machines of a certain type. The target item is a particular item type selected. The out-of-stock time periods are time periods when the item is sold out in the machine, e.g., 12:00-13:00, and 15:00-16:00 on a certain day, and when mineral water in machine a is sold out, mineral water in machine a is out-of-stock for the day, e.g., 12:00-13:00, and 15:00-16: 00. The length of the out-of-stock time is the total time of the corresponding out-of-stock time period. And taking the out-of-stock period as a target period, namely evaluating the profit loss of the target item in the machine due to out-of-stock in the out-of-stock period.
S120, according to the historical orders of the machines in the machine category to which the target machine belongs, determining the delivery speed of the target object in the target machine in the target time period.
According to the method, the order quantity increased in unit time of machines of the same type is calculated in a unified mode according to historical orders of machines in machine types to which target machines belong when the sales speed of target articles is determined, machines of different types are distinguished, calculation is carried out according to the types of the machines respectively, the sales speed of each type of machine in a target time period is determined, the influence of the machine types on the sales speed of the articles can be obtained, and therefore the goods channel of the vending machine can be adjusted purposefully.
The delivery rate refers to the amount of orders added in a unit time interval within the target time period, and optionally, in this embodiment, the unit time interval is every minute. Because the articles in the shortage time period have no increase of orders, when determining the shipment speed of the target article in the target machine in the target time period, the space of the sample needs to be enlarged to avoid that the obtained shipment speed is inaccurate because sample data is too sparse and is easily affected by abnormal points, and order data of machines of the same type in a long period needs to be selected as the sample data, and the alternative of the embodiment is as follows: selecting historical data of the machine in the last week as sample data for determining the shipment speed of the target object in the target machine in the target time period. By classifying the selected historical data of the last week according to the types of the articles, and because the sales speeds of the articles in different time periods are different, the sales speed of the target article needs to be determined for each time period. Optionally, each hour is used as a time period, and the increased order quantity per minute in each hour is calculated according to a least square normal regression algorithm, that is, the sales speed of the target item in the time period is obtained. For example, machine a1 and machine a2, both factory floor a level machines, where item c1 of the a1 machine and the a2 machine, at time period 17: 00-18: the number of orders occurring per minute of 00 is shown in table one:
watch 1
| Machine numbering | Number of minutes | Amount of orders | Machine numbering | Number of minutes | Amount of orders |
| A1 | 0 | 1 | A1 | 39 | 1 |
| A1 | 9 | 1 | A1 | 54 | 1 |
| A1 | 10 | 1 | A2 | 34 | 1 |
| A1 | 20 | 1 | A2 | 47 | 1 |
| A1 | 30 | 1 | A2 | 53 | 1 |
| A1 | 33 | 1 | A2 | 54 | 1 |
| A1 | 37 | 1 | A2 | 55 | 1 |
Processing the data, article c1, which may result in machine a1 and machine a2, at time period 17: 00-18: 00, calculating the selling rate of the article c1 according to the formula (1),
o＝f(m)＝km，k＞0 (1)
wherein m is the number of minutes per time period, o is the cumulative order quantity per minute, and k is machine diItem cjSales speed of, indicating machine diSelling k items per minute cj。
For example, in the above example, article c1 to machine a1 and machine a2 during time period 17: 00-18: the sales rate of 00 is: k 0.111267018616282, which is an average growth of 0.111267018616282 orders per minute. If the target time period for the item to be evaluated is 17: 00-18: 00, then the sales rate of the item in the target time period is k 0.111267018616282 accordingly.
It should be noted that, when determining the selling speed of the target article by the machine of the target type, the historical data of the machines of the same type are all used as sample data to be calculated, so as to obtain the selling speed of the target article in the machine of the target type.
S130, determining the target influence shipment volume of the target object in the target machine in the target time period according to the shipment speed and the out-of-stock duration of the target object in the target machine in the target time period.
Through the steps, the delivery speed of the target object in the target machine in the target time period and the shortage duration of the target time period are obtained, and the target influence delivery amount of the target object in the target machine in the target time period can be obtained according to the formula (2).
ZT＝∑i∈Tkiti (2)
Wherein,
ZTthe goods output is influenced for the target of the target object in the target machine in the target time period;
kithe sales speed of the target object in the target machine in the target time period;
tithe out-of-stock duration of the target object in the target machine in the target time period;
t is a target time period.
The principle of the embodiment is as follows: time, machine types and article types are subdivided, due orders of each article of each machine are estimated firstly, due to the fact that sales speeds of article orders in different time periods of each day are different, the orders are subdivided into different time periods of each day, the sales speeds of the articles of machines in different machine levels and different point location types in different time periods are estimated, and then the sales speeds of the articles in different machine levels and different point location types are aggregated into machine dimensions in a layer mode. The transaction differences of different articles and different time periods and the transaction characteristic differences of machines at different stations are fully considered, the order quantity influenced by selling out of the articles is calculated in a hierarchical mode, and the lost shipment quantity is obtained.
The time, the machine type and the article type are subdivided, the selling speed of the articles in the target type machine within the target time period is obtained based on the historical orders, the affected order quantity of the target articles in the target type machine due to the sold out of the articles is obtained, the affected discharge quantity due to the sold out of the articles is obtained, the articles, the machines and the time are subdivided, and the individual difference of different factors is fully considered, so that the method has good precision in the estimation of the discharge quantity of the articles due to the sold out of the articles. The embodiment generalizes the order data based on the history, so that the method has good interpretability, is convenient for business parties to identify, and can reuse the obtained order growth rate for other businesses. Because the result is obtained by using the induction method, the influence of abnormal data can be effectively eliminated, and the result has good stability.
Example two
Fig. 2 is a flowchart of a method for determining a machine lost shipment amount according to a second embodiment of the present invention, where the second embodiment optimizes a category of a machine based on the first embodiment, and as shown in fig. 2, the method for determining a machine lost shipment amount according to the present embodiment includes:
s210, determining the shipment efficiency of the articles in the existing machine according to the theoretical shipment quantity of the articles in the existing machine.
The shipment efficiency refers to the shipment volume in unit time. In this embodiment, the unit time interval is days when the shipment efficiency is determined, and thus, in this embodiment, the shipment efficiency refers to daily average shipment volume, which is daily average amount of orders from the perspective of orders.
Because the machine's shipment efficiency should be based on the machine's daily order volume, which is affected by the sale of goods, the theoretical order volume of the machine can be used to evaluate the machine's sales efficiency. Wherein, the theoretical shipment volume includes the actual shipment volume of machine and the shipment volume of article selling loss, promptly: the theoretical shipment of the machine is the actual shipment of the machine + the shipment affected by the sold-out of the article, which causes the order quantity affected by the sold-out of the article and the machine level to affect each other, each time a new result is generated, although the iteration is likely to converge to a stable result, a large amount of calculation resources are consumed. To eliminate the effect of sold out of the goods, the alternatives of this embodiment are:
if the out-of-stock item of any machine in any time period is determined to belong to the good-selling item in the time period according to the historical shipment record of the machine in any time period, the historical shipment record of the machine in the time period is removed, and the grade of the machine is determined by adopting the historical shipment record of the machine in other time periods.
Specifically, in the selected sample data (in this embodiment, optionally, historical data of the last week is selected as the sample data), the order amount of each machine in each time period is used as the total sample set, and the total sample set is applied to any machine diIn a period of time hnIf the main sold articles are not sold out, the number of the machine orders in the time period is counted into a statistical sample. Extracting the machine d according to the principle that the time traverses from the present to the pastiThe order amount at all time periods was taken as the order amount for 1 day.
For example machine d1At 16:00:00 to 16:59:59 on day 10 of month 1, 20 orders were placed with item a sold out but belonging to a category of items with few orders, and the 20 orders were counted as a statistical sample.
At 16:00:00 to 16:59:59 on 1/9 days, 20 orders were placed, with the item b sold out and belonging to the class sold at high market, and the machine did not count the number of orders in that time period.
Considering that the goods output of the machine is different between the working day and the non-working day, in order to eliminate the difference between the goods output of the machine on the working day and the goods output of the machine on the non-working day, in the embodiment, when the theoretical goods output of the machine is determined, the working day and the non-working day are separated, and the working day and the non-working day are respectively calculated.
In this embodiment, the time period corresponding to the determination of the category of the machine should be a time period corresponding to the historical order selected for determining the sales speed of the machine, and the time period is not necessarily linked to the out-of-stock time period.
And S220, determining the grade of the existing machine according to the delivery efficiency of the articles in the existing machine.
The machine-level standard is pre-established, and the machine-level division standard can be determined according to the service development conditions of different countries, so that the machine classification standard is established for different countries respectively. For example, in one alternative of this embodiment, the machine-level division criteria are as follows:
classification standard of nation A
| S | Average daily transaction number of the last week is more than or equal to 70 |
| A | In the last week, the daily average transaction number is more than or equal to 50,<70 sheet |
| B | In the last week, the daily average transaction number is more than or equal to 30,<50 sheet |
| C | In the last week, the daily average transaction number is more than or equal to 20,<30 sheet |
| D | In the last week, the daily average transaction number is more than or equal to 10,<20 sheet |
| E | Average number of trades in last week and day<10 sheet |
Classification standard of B country
| S | Average daily transaction number of more than or equal to 150 in last week |
| A | In the last week, the daily average transaction number is more than or equal to 100,<150 sheet |
| B | In the last week, the daily average transaction number is more than or equal to 50,<100 sheet |
| C | In the last week, the daily average transaction number is more than or equal to 20,<50 sheet |
| D | Average number of trades in last week and day<20 sheet |
Therefore, the grade of each machine can be determined according to the preset classification standard of the machine class and the sales efficiency of each machine.
S230, clustering the existing machines according to the point location type and the level of the existing machines, and determining the machine type of the target machine according to the clustering result, wherein the point location type is determined according to the place where the machine is located.
The point location type is determined according to the location where the machine is located, for example, a factory, a station, a residential area, and the like, and because the flow of people in different locations is different, the sales capacities of the machines are also different, so that the point location type is used as one dimension information for dividing the machine category, and the sales speed of the machine in different location types can be evaluated in a targeted manner. Therefore, the category of the machine is determined according to the point location type and the sales efficiency of the machine, the sales speed of the machine can be correspondingly evaluated from the two dimensions, and the influence of the point location type and the machine level of the machine on the sales speed is definitely obtained.
After the type of the machine is determined, the machines are clustered according to the type of the machine, that is, the machines of the same type are classified into one sample data to be counted, so that after the type of the machine is clustered, the sales speed of the machine of the type in a selected time period can be calculated according to the sample order of the machine type of the type, and the obtained sales speed reflects the influence of the machine type on the sales speed.
Clustering refers to classifying machines according to the point location type and the two dimensions of the machine level, so that historical orders of machines of the same category are divided into a category to be processed uniformly, and the article sale speed of the machines of the category is determined. For example, the existing sample data includes a1 machine, a2 machine, b1 machine and b2 machine, wherein a1 machine and a2 machine are all factory floor a-level machines; b1 machine and b2 machine are all machines in b level of residential area, so that when machines are clustered, historical orders of a1 and a2 are grouped into one type, and historical orders of b1 and b2 are grouped into one type, so that machines in different point location types and different levels are clustered.
It can be seen that the clustering result in this embodiment is a classification result including two dimension information of a machine level and a machine point location type, for example, a level a mall machine, a level B school machine, and the like. After the class of each machine is determined, the sales rate of each class of machine can be calculated. Accordingly, the sales speed of the article of the category to which the target machine belongs can be obtained.
S240, determining the time period and the time length of the shortage of the target object in the target machine according to the delivery record of the target object in the target machine, and taking the time period of the shortage as the target time period.
And S250, determining the delivery speed of the target object in the target machine in the target time period according to the historical orders of the machines in the machine category to which the target machine belongs.
S260, determining the target influence shipment volume of the target object in the target machine in the target time period according to the shipment speed and the out-of-stock duration of the target object in the target machine in the target time period.
S270, determining the influenced shipment volume of the target object in the target machine in the selected time according to the influenced shipment volume of the target object in the target machine in the target time period and the selected time length.
The target machine can be a single machine or a certain type of machine, and when the shortage time period is determined to be directed to the single machine, the target machine is also the same as the single machine; accordingly, when it is determined that the out-of-stock period is for a certain type of machine, the target machine is all machines of that type.
The selected time is the time to be evaluated determined according to the user requirements, the affected shipment volume of each sub-time period is calculated by dividing the selected time into each sub-time period, and the affected shipment volumes of each sub-time period are accumulated, so that the affected shipment volume of the target object in the target machine in the selected time can be obtained.
S280, determining the influenced shipment volume of all the objects in the target machine in the selected time according to the influenced shipment volume of the target objects in the target machine in the selected time and the types of the objects.
After determining the affected shipment volume of a category of articles in the selected time, according to the same method, the affected shipment volumes of other types of articles in the target machine in the selected time can be calculated, and the affected shipment volumes of various types of articles in the target machine in the selected time are accumulated, so that the affected shipment volumes of all articles in the target machine in the selected time can be obtained.
S290, determining the influenced shipment volume of all the articles in all the machines in the selected time according to the influenced shipment volume of all the articles in the target machine in the selected time and the types of the machines.
The target machine is the same as the machine determined by the out-of-stock duration, and when the target machine determined in the out-of-stock duration is a single machine, the target machine is a single machine corresponding to the target machine; when the determined target machine in the out-of-stock period is a certain type of machine, the target machine is the corresponding certain type of machine.
After determining the influenced shipment volume of all the articles in the target machine in the selected time, according to the same method, the influenced shipment volume of all the articles in other machines in the selected time can be determined, and the influenced shipment volumes of all the articles in all the machines in the selected time are accumulated to obtain the influenced shipment volume of all the articles in all the machines in the selected time, namely the total influenced shipment volume in the selected time.
The calculation process of summarizing the machine type, the article type and the time information according to the steps to obtain the influence of all articles in all machines on the shipment in the selected time can be carried out by a formula (3),
wherein,
z, ordering quantity influenced by the shortage of the goods of all the machines in the selected time;
d, selecting a set of all machines in operation in time;
c, the collection of all articles of the current machine;
h set of time of day: [1,2,3,.., 24 ];
hnat different times of the day;
vijn: machine diArticle CjIn a period of time hnThe rate of sale of the article in units of units/minute;
tijn: machine diArticle CjIn a period of time hnTotal time to empty in minutes.
According to the method, the class of the existing machine is determined according to the sales efficiency of the machine, the class of the machine is divided according to the class of the machine and the point type, the sales speed of the machine is summarized according to the accumulated order quantity of the machines of the same type, and the influence of the point type and the class of the machine on the sales speed can be obtained, so that the shipment quantity lost due to the shortage of the machines of different types is obtained, and the calculated loss quantity of the articles due to the shortage of the goods is more accurate and reliable. According to the embodiment, by constructing two data dimensions of the machine level and the machine point location type, the data discrimination between different machines is enhanced, and the data can be used as a summary layer, so that the estimated order data is smoother. In the embodiment, the degree of influence of the supply capacity on different machines can be judged according to the evaluation of the orders influenced by sold out of the machines, and the order growth potentials of the different machines after the development of the supply capacity can be visually compared; meanwhile, based on the sales efficiency of each machine for each article in each time period, the estimated order quantity of each machine can be summarized, the expected order quantity of the machine in the next day can be predicted, and adjustment of article distribution during replenishment is facilitated, for example, when a driver replys the articles, the machines are supplied with articles and the articles are carried by the articles, and the quantity of each article is increased, so that the logistics efficiency is improved.
EXAMPLE III
Fig. 3 is a schematic structural diagram of an apparatus for determining a lost shipment volume of a machine according to a third embodiment of the present invention, where this embodiment is applicable to a case of determining a lost shipment volume due to a shortage of a machine, and the apparatus may be implemented by software or/and hardware, and may be configured in a device, such as a server, for determining a lost shipment volume of a machine.
As shown in fig. 3, an apparatus for determining a shipment loss of a machine according to an embodiment of the present invention may include: a time out of stock period and length of out of stock determination module 310, a shipment speed determination module 320, and a target impact shipment volume determination module 330, wherein:
the out-of-stock time period and out-of-stock time length determining module 310 is used for determining the out-of-stock time period and the out-of-stock time length of the target object in the target machine according to the delivery record of the target object in the target machine, and taking the out-of-stock time period as the target time period;
the shipment speed determining module 320 is configured to determine a shipment speed of the target item in the target machine in the target time period according to a historical order of the machine in the machine category to which the target machine belongs in the target time period;
and the target influence shipment quantity determining module 330 is configured to determine the target influence shipment quantity of the target item in the target machine in the target time period according to the shipment speed of the target item in the target machine in the target time period and the shortage duration.
Optionally, the shipment speed determining module 320 includes:
the order quantity determining unit of the unit time interval is used for determining the order quantity of the target object in the target machine at the unit time interval in the target time period according to the historical order of the machine in the machine type of the target machine in the target time period;
and the shipment speed determining unit is used for determining the shipment speed of the target object in the target machine in the target time period according to the order quantity of the unit time interval and the stock shortage duration.
Optionally, the apparatus further comprises:
the clustering module is used for clustering the existing machines according to the point location type and the level of the existing machines, wherein the point location type is determined according to the places where the machines are located;
the machine type determining module is used for determining the machine type of the target machine according to the clustering result;
the machine shipment efficiency determining module is used for determining the shipment efficiency of the articles in the existing machine according to the theoretical shipment quantity of the articles in the existing machine;
the first machine level determining module is used for determining the level of the existing machine according to the shipment efficiency of the articles in the existing machine;
and the second machine level determining module is used for removing the historical shipment records of the machine in the time period and determining the level of the machine by adopting the historical shipment records of the machine in other time periods if the out-of-stock articles of the machine in the time period belong to the good-selling articles in the time period according to the historical shipment records of any machine in any time period.
Optionally, the apparatus further comprises:
the influence shipment quantity determining module of the target object in the selected time is used for determining the influence shipment quantity of the target object in the target machine in the selected time according to the target influence shipment quantity of the target object in the target machine in the target time period and the selected time length;
the influence shipment quantity determining module of all the articles in the selected time is used for determining the influence shipment quantity of all the articles in the target machine in the selected time according to the influence shipment quantity of the target articles in the target machine in the selected time and the categories of the articles;
and the total influence shipment quantity determining module in the selected time is used for determining the influence shipment quantities of all the articles in all the machines in the selected time according to the influence shipment quantities of all the articles in the target machine in the selected time and the classes of the machines.
The device for determining the machine loss shipment volume provided by the embodiment of the invention can execute the method for determining the machine loss shipment volume provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. Reference may be made to the description of any method embodiment of the invention not specifically described in this embodiment.
Example four
Fig. 4 is a schematic structural diagram of an apparatus according to a fourth embodiment of the present invention. FIG. 4 illustrates a block diagram of an exemplary device 412 suitable for use in implementing embodiments of the present invention. The device 412 shown in fig. 4 is only an example and should not impose any limitation on the functionality or scope of use of embodiments of the present invention.
As shown in FIG. 4, device 412 is in the form of a general purpose computing device. The components of device 412 may include, but are not limited to: one or more processors or processing units 416, a system memory 428, and a bus 418 that couples the various system components including the system memory 428 and the processing unit 416.
Bus 418 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Device 412 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by device 412 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 428 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)430 and/or cache memory 432. The device 412 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 434 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 418 by one or more data media interfaces. Memory 428 can include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 440 having a set (at least one) of program modules 442 may be stored, for instance, in memory 428, such program modules 442 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. The program modules 442 generally perform the functions and/or methodologies of the described embodiments of the invention.
The device 412 may also communicate with one or more external devices 414 (e.g., keyboard, pointing device, display 424, etc.), with one or more devices that enable a user to interact with the device 412, and/or with any devices (e.g., network card, modem, etc.) that enable the device 412 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 422. Also, the device 412 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) through the network adapter 420. As shown, network adapter 420 communicates with the other modules of device 412 over bus 418. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the device 412, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 416 executes programs stored in the system memory 428 to perform various functional applications and data processing, such as implementing a method for determining a shipment loss for a machine provided by an embodiment of the present invention, the method comprising:
determining the time period and the time length of the goods shortage of the target object in the target machine according to the delivery record of the target object in the target machine, and taking the time period of the goods shortage as the target time period;
determining the delivery speed of the target object in the target machine in the target time period according to the historical orders of the machines in the machine category to which the target machine belongs;
and determining the target influence shipment volume of the target object in the target machine in the target time period according to the shipment speed and the out-of-stock duration of the target object in the target machine in the target time period.
EXAMPLE five
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for determining a shipment lost by a machine according to any embodiment of the present invention, where the method includes:
determining the time period and the time length of the goods shortage of the target object in the target machine according to the delivery record of the target object in the target machine, and taking the time period of the goods shortage as the target time period;
determining the delivery speed of the target object in the target machine in the target time period according to the historical orders of the machines in the machine category to which the target machine belongs;
and determining the target influence shipment volume of the target object in the target machine in the target time period according to the shipment speed and the out-of-stock duration of the target object in the target machine in the target time period.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. 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 (a non-exhaustive list) of the computer readable storage medium would include the following: 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 the context of this document, 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.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. 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 wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like 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).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method of determining a lost shipment of a machine, comprising:
determining the time period and the time length of the goods shortage of the target object in the target machine according to the delivery record of the target object in the target machine, and taking the time period of the goods shortage as the target time period;
determining the delivery speed of the target object in the target machine in the target time period according to the historical orders of the machines in the machine category to which the target machine belongs;
and determining the target influence shipment volume of the target object in the target machine in the target time period according to the shipment speed and the out-of-stock duration of the target object in the target machine in the target time period.
2. The method of claim 1, wherein determining the shipment speed of the target item in the target machine before the target time period according to the historical order of the machine in the machine category to which the target machine belongs in the target time period further comprises:
clustering the existing machines according to the point location type and the level of the existing machines, wherein the point location type is determined according to the places where the machines are located;
and determining the machine type of the target machine according to the clustering result.
3. The method of claim 2, wherein before clustering the existing machines according to the point location types and levels of the existing machines, the method further comprises:
determining the shipment efficiency of the articles in the existing machine according to the theoretical shipment quantity of the articles in the existing machine, wherein the theoretical shipment quantity is the sum of the actual shipment quantity and the affected shipment quantity;
the class of the existing machine is determined based on the shipment efficiency of the items in the existing machine.
4. The method of claim 2, wherein before clustering the existing machines according to the point location types and levels of the existing machines, the method further comprises:
if the out-of-stock item of any machine in any time period is determined to belong to the good-selling item in the time period according to the historical shipment record of the machine in any time period, the historical shipment record of the machine in the time period is removed, and the grade of the machine is determined by adopting the historical shipment record of the machine in other time periods.
5. The method of claim 2, wherein determining the shipment speed of the target item in the target machine for the target time period according to the historical order of the machine in the machine category to which the target machine belongs for the target time period comprises:
according to the historical orders of the machines in the machine category to which the target machine belongs in the target time period, determining the order quantity of the target object in the target machine in the unit time interval in the target time period;
and determining the delivery speed of the target object in the target machine in the target time period according to the order quantity of the unit time interval and the stock shortage duration.
6. The method according to any one of claims 1-5, further comprising:
determining the influence shipment volume of the target object in the target machine within the selected time according to the target influence shipment volume of the target object in the target machine in the target time period and the selected time length;
determining the influence shipment volume of all the objects in the target machine in the selected time according to the influence shipment volume of the target objects in the target machine in the selected time and the types of the objects;
and determining the influence shipment volume of all the articles in all the machines in the selected time according to the influence shipment volume of all the articles in the target machine in the selected time and the categories of the machines.
7. An apparatus for determining a lost shipment of a machine, the apparatus comprising:
the system comprises a goods shortage time period and goods shortage duration determining module, a goods shortage time period and goods shortage duration determining module and a goods shortage time period determining module, wherein the goods shortage time period and the goods shortage duration determining module are used for determining the goods shortage time period and the goods shortage duration of a target object in a target machine according to a goods delivery record of the target object in the target machine and taking the goods shortage time period as the target time period;
the goods delivery speed determining module is used for determining the goods delivery speed of the target object in the target machine in the target time period according to the historical order of the machine in the machine category of the target machine in the target time period;
and the target influence shipment quantity determining module is used for determining the target influence shipment quantity of the target object in the target machine in the target time period according to the shipment speed and the shortage duration of the target object in the target machine in the target time period.
8. The apparatus of claim 7, further comprising:
the machine clustering module is used for clustering the existing machines according to the point location type and the level of the existing machines, wherein the point location type is determined according to the places where the machines are located;
and the machine type determining module is used for determining the machine type of the target machine according to the clustering result.
9. An apparatus, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a method of determining lost shipment of a machine as claimed in any one of claims 1 to 6.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out a method of determining a shipment loss for a machine according to any one of claims 1 to 6.
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| CN112906953B (en) * | 2021-02-04 | 2023-12-22 | 杭州涂鸦信息技术有限公司 | People flow prediction method, device, computer equipment and readable storage medium |
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