CN113988974A

CN113988974A - Order generation method, device and storage medium

Info

Publication number: CN113988974A
Application number: CN202111246358.9A
Authority: CN
Inventors: 潘宇通
Original assignee: Beijing Wodong Tianjun Information Technology Co Ltd
Current assignee: Beijing Wodong Tianjun Information Technology Co Ltd
Priority date: 2021-10-26
Filing date: 2021-10-26
Publication date: 2022-01-28

Abstract

The invention provides an order generation method, an order generation device and a storage medium, wherein an empty order corresponding to a first simulation time period is randomly generated; extracting n article coding information from the corresponding historical order data group based on the number of the coding containers in the empty order, and respectively and correspondingly filling the n coding containers with the n article coding information; traversing the article coding information in the n coding containers, and forming order information corresponding to a first simulation time period based on the number of the n article coding information and related data; and determining target order information based on the comparison result of the target trading volume of the historical time period corresponding to the first simulation time period and the order information of other simulation time periods. The target order information determined by the scheme is more matched with the actual transaction amount in the first simulation time period, so that a supply chain scheme matched with the simulation period can be accurately formed through the target order data in each simulation time period.

Description

Order generation method, device and storage medium

Technical Field

The embodiment of the invention relates to the technical field of electronic commerce and computers, in particular to an order generation method, an order generation device and a storage medium.

Background

The supply chain is a core component of enterprise operations. The supply chain is used for supplying the area corresponding to the simulation period. When designing a supply chain scheme, supply chain simulation is often required to analyze the effect of the supply chain, such as whether the trading volume demand can be met, for various scenarios.

Supply chain simulation platforms currently cannot cover different scenarios. The supply chain simulation platform outputs a simulation result based on a historical scene, and the scene of transaction amount change cannot be covered, so that the simulation result is distorted.

The relation between the quantity of the orders and the transaction amount in the historical scene is complex, and the simulation result of the supply chain simulation platform on the relation is difficult to cope with the relation between the quantity of the orders and the transaction amount, so that the simulation result is distorted.

Therefore, the main problem currently faced is that supply chain solutions matching simulation sessions cannot be formed in the face of complex traffic relationships.

Disclosure of Invention

The order generation method, the order generation device and the storage medium provided by the embodiment of the invention can accurately form a supply chain scheme matched with a simulation period.

The technical scheme of the invention is realized as follows:

the embodiment of the invention provides an order generation method, which comprises the following steps:

randomly generating an empty order corresponding to the first simulation time period; the empty order includes: n coding containers; n is an integer of 1 or more;

extracting n article coding information from a corresponding historical order data group based on the number of the coding containers in the empty order, and respectively and correspondingly filling the n coding containers with the n article coding information; the historical order data group is formed based on historical order data in a plurality of historical time periods and corresponds to a set of historical order data of the number of the coding containers;

traversing the article coding information in the n coding containers, and forming order information corresponding to the first simulation time period based on the number of the n article coding information and related data;

and determining target order information based on the order information and the comparison result of the target transaction amount of the historical time period corresponding to the first simulation time period, and further determining the target order information of other simulation time periods, so as to form a supply chain scheme.

In the foregoing solution, the related data includes: the sum of the random number and the frequency correlation is correlated with the transaction amount;

traversing the article coding information in the n coding containers, and forming order information corresponding to the first simulation time period based on the number of the n article coding information and related data, including:

if the n is more than or equal to 2, traversing the article coding information in the ith coding container; i is a positive integer which is more than or equal to 1 and less than n;

adjusting the article coding information in the ith coding container based on the sum of the random number of the article coding information in the ith coding container and the frequency correlation of the article coding information with other article coding information until the article coding information in the n coding containers is traversed;

generating basic transaction amount information corresponding to the n item code information after traversal is completed by combining a transaction amount average value and a transaction amount maximum value preset in the historical order data set;

and adjusting the basic transaction amount information corresponding to the n article code information respectively based on the transaction amount correlation corresponding to the n article code information in the historical order data set, thereby forming the order information.

In the above solution, the adjusting the article code information in the ith code container based on the sum of the random number of the article code information in the ith code container and the frequency correlation with other article code information until the traversal of the article code information in the n code containers is completed, the method further includes:

generating random numbers corresponding to the n article coding information respectively; the random number is a random number which is more than 0 and less than 1;

the adjusting the article coding information in the ith coding container based on the sum of the random number of the article coding information in the ith coding container and the frequency correlation of the article coding information with other article coding information until the traversal of the article coding information in the n coding containers is completed comprises:

processing to obtain an ith random number corresponding to the article coding information in the ith coding container, and comparing the ith random number with a preset threshold value;

if the comparison result represents that the ith random number is smaller than the preset threshold value, counting the sum of the frequency correlations between the article coding information in the ith coding container and other article coding information in the historical order data set; the frequency correlation between the article coding information in the ith coding container and other article coding information is calculated in advance;

and if the sum of the frequency correlation is smaller than a frequency threshold value, taking any other article coding information as the article coding information in the new ith coding container until the n article coding information is traversed.

In the foregoing solution, after the processing obtains a comparison result between an ith random number corresponding to the ith item code information and a preset threshold, the method further includes:

if the comparison result indicates that the ith random number is not smaller than the preset threshold, comparing the (i + 1) th random number of the article coding information in the (i + 1) th coding container with the preset threshold to obtain a comparison result of the article coding information in the (i + 1) th coding container, and continuously executing traversal of the rest article coding information based on the comparison result.

In the above scheme, if the comparison result indicates that the ith random number is smaller than the preset threshold, counting a sum of the frequency correlations between the article coding information in the ith coding container and other article coding information in the historical order data set, the method further includes;

if the sum of the frequency correlations is not less than the frequency threshold, based on the transaction amount correlations corresponding to the current n item code information in the historical order data set, the transaction amount information corresponding to the n item code information respectively is formed, and then the order information is formed.

In the above scheme, the adjusting, based on the correlation between the transaction amounts corresponding to the n item code information in the historical order data set, the basic transaction amount information corresponding to the n item code information, respectively, to form the order information includes:

determining target order data which simultaneously comprise the n item coding information in the historical order data group;

and adjusting the basic transaction amount information corresponding to the n article code information respectively based on the transaction amount correlation corresponding to the n article code information obtained in advance in the target order data to obtain the transaction amount information corresponding to the n article code information respectively, and further forming the order information.

In the above scheme, the adjusting the basic transaction amount information corresponding to the n article code information based on the transaction amount correlation corresponding to the n article code information obtained in advance in the target order data to obtain the transaction amount information corresponding to the n article code information includes:

multiplying the basic transaction amount information corresponding to the t-1 th article code information by the ratio of the historical transaction amount information of the t-1 th article code information to the historical transaction amount information of the t-1 th article code information in the target order data to obtain the transaction amount information corresponding to the t-1 th article code information, and further obtaining the transaction amount information corresponding to the n article code information respectively; t is a positive integer of 2 or more and less than n.

In the above scheme, before the adjusting the basic transaction amount information corresponding to the n article code information based on the transaction amount correlation corresponding to the n article code information in the historical order data set, and further forming the order information, the method further includes:

and if the n is equal to 1, generating basic transaction amount information corresponding to the n article coded information after traversal is completed by combining a preset transaction amount average value and a preset transaction amount maximum value in the historical order data set.

In the above solution, before generating the empty order corresponding to the first simulation time period by using the roulette method, the method further includes:

extracting a plurality of order data corresponding to the plurality of historical time periods in a preset storage space according to a preset condition; each order data includes: at least one item code information, each item code information comprising: corresponding historical transaction amount information;

calculating the target transaction amounts corresponding to the plurality of historical time periods respectively by combining the plurality of order data;

dividing the plurality of order data into a plurality of historical order data groups according to the quantity of the item coding information in the plurality of order data; the order data in each historical order data group contains the same quantity of the article coding information;

and simultaneously calculating a plurality of transaction related information respectively corresponding to the plurality of historical order data groups.

In the above scheme, the preset conditions include: node information, area information and item type information of the plurality of historical time periods;

the extracting of the plurality of order data corresponding to the plurality of historical time periods in the preset storage space according to the preset condition includes:

and extracting the plurality of order data corresponding to the plurality of historical time periods in a preset storage space according to the node information, the area information and the type information of the articles in the plurality of historical time periods.

In the foregoing solution, the calculating the target transaction amounts corresponding to the plurality of historical time periods by combining the plurality of order data includes:

counting the sum of the historical trading volume information of the order data in each historical time period to obtain a reference line of each historical time period;

acquiring a variation coefficient, multiplying the reference line of each historical time period by the variation coefficient, and adding the reference line to obtain the theoretical transaction amount of each historical time period;

and adding the theoretical trading volume of each historical time period to a random variable to obtain the target trading volume of each historical time period, and further obtaining the target trading volumes corresponding to the plurality of historical time periods respectively.

In the above scheme, the transaction-related information includes: the average value of the transaction amount, the maximum value of the transaction amount, the transaction amount distribution frequency information of each item code information, a frequency correlation matrix and a transaction amount correlation matrix;

the simultaneously calculating a plurality of transaction related information respectively corresponding to the plurality of historical order data sets comprises:

and simultaneously calculating the trading volume mean value, the trading volume maximum value, the trading volume distribution frequency information of each item code information, the frequency correlation matrix and the trading volume correlation matrix which are respectively corresponding to the plurality of historical order data sets.

In the above scheme, the simultaneously calculating the average value of the transaction amount, the maximum value of the transaction amount, the frequency information of the transaction amount distribution of the coded information of each item, the frequency correlation matrix, and the correlation matrix of the transaction amount, which correspond to the plurality of historical order data sets, respectively, includes:

adding the historical trading volume information of all the article coding information in each historical order data group, and comparing the number of the article coding information in each historical order data group to obtain the trading volume average value of each historical order data group;

determining the maximum historical trading volume information as the maximum trading volume in the historical trading volume information corresponding to the article coding information in each historical order data set;

in each historical order data group, comparing the quantity of order data respectively containing the coded information of each article with the total quantity of the order data in each historical order data group to obtain the transaction quantity distribution frequency information of the coded information of each article;

calculating the frequency correlation between every two article coding information in each historical order data group, and arranging the frequency correlation of each article coding information corresponding to other article coding information according to the sequence of the article coding information in each historical order data group to form each row of the frequency correlation matrix so as to form the frequency correlation matrix;

and calculating the correlation of the transaction amount between every two article coding information in each historical order data group, and arranging the correlation of the transaction amount of each article coding information corresponding to other article coding information according to the sequence of the article coding information in each historical order data group to form each row of the correlation matrix of the transaction amount, thereby forming the correlation matrix of the transaction amount.

In the above scheme, the calculating a frequency correlation between every two article coding information in each historical order data set, and arranging the frequency correlations of each article coding information corresponding to other article coding information according to an order of the article coding information in each historical order data set to form each row of the frequency correlation matrix, thereby forming the frequency correlation matrix, includes:

according to the sequence of the item coding information in each historical order data set, comparing the number of the order data which simultaneously comprises the kth item coding information and the mth item coding information with the number of the order data which only comprises the kth item coding information to obtain a first frequency correlation between the kth item coding information and the mth item coding information until m is equal to q, and obtaining q-1 first frequency correlations corresponding to each item coding information; k and m are positive integers which are more than or equal to 1 and less than q, and are different from each other; q is the number of the article coding information in each historical order data set;

according to the order of the item coding information in each historical order data set, comparing the number of the order data which simultaneously comprises the kth item coding information and the mth item coding information with the number of the order data which only comprises the mth item coding information to obtain a second frequency correlation between the mth item coding information and the kth item coding information until k is equal to q, and obtaining q-1 second frequency correlations corresponding to each item coding information;

and taking q-1 first frequency correlations and q-1 second frequency correlations of the first article coding information as a first row of the frequency correlation matrix until an nth row of the frequency correlation matrix is formed, and further forming the frequency correlation matrix.

In the above scheme, the calculating a transaction amount correlation between every two article code information in each historical order data group, and arranging the transaction amount correlation of each article code information corresponding to other article code information according to the order of the article code information in each historical order data group to form each row of the transaction amount correlation matrix, thereby forming the transaction amount correlation matrix, includes:

according to the sequence of the article coding information in each historical order data group, determining q-1 order data which simultaneously comprise the kth article coding information and the mth article coding information in each historical order data group; k and m are positive integers which are more than or equal to 1 and less than q, and are different from each other; q is the number of the article coding information in each historical order data set;

comparing the coded information of the kth article included in each order data with the trading volume information corresponding to the coded information of the mth article to obtain q-1 trading volume correlations corresponding to the coded information of the kth article respectively until k is equal to q, and obtaining q-1 trading volume correlations corresponding to the coded information of the q articles respectively;

and taking q-1 transaction amount correlations of the first article coding information as the first row of the transaction amount correlation matrix until the q-th row of the transaction amount correlation matrix is formed, and further forming the transaction amount correlation matrix.

In the above scheme, the determining target order information based on the comparison result of the target transaction amount in the historical time period corresponding to the first simulation time period includes any one of:

if the transaction amount included in the order information is not less than the target transaction amount, determining that the order information is the target order information;

and if the transaction amount included in the order information is smaller than the target transaction amount, continuing to execute the process of forming the order information until the target order information is obtained.

An embodiment of the present invention further provides an order generating apparatus, including:

the order forming unit is used for randomly generating an empty order corresponding to the first simulation time period; the empty order includes: n coding containers; n is an integer of 1 or more;

the data extraction unit is used for extracting n article coding information from a corresponding historical order data set based on the number of the coding containers in the empty order, and filling the n coding containers with the n article coding information respectively; the historical order data group is formed based on historical order data in a plurality of historical time periods and corresponds to a set of historical order data of the number of the coding containers;

the order forming unit is further configured to traverse the article coding information in the n coding containers, and form order information corresponding to the first simulation time period based on the number of the n article coding information and related data;

and the determining unit is used for determining target order information based on the order information and a comparison result of the target transaction amount of the historical time period corresponding to the first simulation time period, and further determining the target order information of other simulation time periods for supply of a supply chain.

The embodiment of the invention also provides an order generating device, which comprises a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the processor realizes the steps of the method when executing the program.

Embodiments of the present invention also provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in the above method.

In the embodiment of the invention, an empty order corresponding to a first simulation time period is randomly generated; the empty order includes: n coding containers; extracting n article coding information from the corresponding historical order data group based on the number of the coding containers in the empty order, and respectively and correspondingly filling the n coding containers with the n article coding information; the historical order data group is formed on the basis of historical order data in a plurality of historical time periods and corresponds to a set of historical order data of the number of the coding containers; traversing the article coding information in the n coding containers, and forming order information corresponding to a first simulation time period based on the number of the n article coding information and related data; and determining target order information based on the order information and the comparison result of the target transaction amount of the historical time period corresponding to the first simulation time period, and further determining the target order information of other simulation time periods, so as to form a supply chain scheme. The target order information is formed on the basis of the corresponding order information, and the order information is formed on the basis of the number of the n article coding information in the randomly generated empty order and the related data, so that the target order data is more matched with the actual transaction amount in the first simulation time period, and the target order data of each simulation time period generated by the scheme can be used for accurately forming a supply chain scheme matched with the simulation period.

Drawings

Fig. 1 is an alternative flow chart of an order generation method according to an embodiment of the present invention;

fig. 2 is a schematic diagram illustrating an optional effect of the order generation method according to the embodiment of the present invention;

fig. 3 is a schematic diagram illustrating an optional effect of the order generation method according to the embodiment of the present invention;

FIG. 4 is a schematic flow chart illustrating an alternative method for generating an order according to an embodiment of the present invention;

FIG. 5 is a schematic flow chart of an alternative order generation method according to an embodiment of the present invention;

fig. 6 is a schematic diagram illustrating an optional effect of the order generation method according to the embodiment of the present invention;

FIG. 7 is a schematic flow chart illustrating an alternative method for generating an order according to an embodiment of the present invention;

FIG. 8 is a schematic flow chart illustrating an alternative method for generating an order according to an embodiment of the present invention;

FIG. 9 is a schematic flow chart illustrating an alternative method for generating an order according to an embodiment of the present invention;

FIG. 10 is a schematic flow chart illustrating an alternative method for generating an order according to an embodiment of the present invention;

FIG. 11 is a schematic flow chart illustrating an alternative method for generating an order according to an embodiment of the present invention;

FIG. 12 is a schematic flow chart illustrating an alternative method for generating an order according to an embodiment of the present invention;

FIG. 13 is a schematic flow chart illustrating an alternative method for generating an order according to an embodiment of the present invention;

fig. 14 is a schematic diagram illustrating an alternative effect of the order generation method according to the embodiment of the present invention;

FIG. 15 is a schematic flow chart illustrating an alternative method for generating an order according to an embodiment of the present invention;

FIG. 16 is a schematic flow chart illustrating an alternative order generation method according to an embodiment of the present invention;

FIG. 17 is a schematic flow chart illustrating an alternative method for generating an order according to an embodiment of the present invention;

FIG. 18 is a schematic flow chart illustrating an alternative method for generating an order according to an embodiment of the present invention;

FIG. 19 is a schematic flow chart illustrating an alternative method for generating an order according to an embodiment of the present invention;

FIG. 20 is a schematic flow chart illustrating an alternative method for generating an order according to an embodiment of the present invention;

fig. 21 is a schematic structural diagram of an order generating apparatus according to an embodiment of the present invention;

fig. 22 is a hardware entity diagram of an order generating apparatus according to an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention are further described in detail with reference to the drawings and the embodiments, the described embodiments should not be construed as limiting the present invention, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.

To the extent that similar descriptions of "first/second" appear in this patent document, the description below will be added, where reference is made to the term "first \ second \ third" merely to distinguish between similar objects and not to imply a particular ordering with respect to the objects, it being understood that "first \ second \ third" may be interchanged either in a particular order or in a sequential order as permitted, to enable embodiments of the invention described herein to be practiced in other than the order illustrated or described herein.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to be limiting of the invention.

Fig. 1 is an alternative flowchart of an order generation method according to an embodiment of the present invention, which will be described with reference to the steps shown in fig. 1.

S101, randomly generating an empty order corresponding to a first simulation time period; the empty order includes: n coding containers.

In the embodiment of the invention, the server randomly generates the empty order corresponding to the first simulation time period. Wherein the empty order comprises: n coding containers. n is an integer of 1 or more.

Wherein the first simulation time period belongs to one time period of the simulation period. The simulation period may include a plurality of simulation time periods. For example, the simulation period may be 10 months 1 day to 10 months 7 days. Wherein, the first simulation time period may be 10 months and 1 day in the simulation period.

In the embodiment of the invention, the server randomly generates an empty order comprising n coding containers by using a roulette method according to the occurrence frequency corresponding to the coding information of each item in a plurality of pieces of pre-generated historical order data. Wherein the number n of code containers in the empty order is random. Wherein the plurality of historical order data sets are formed based on historical order data over a plurality of historical time periods. The plurality of history time periods are history time periods corresponding to a plurality of simulation time period pairs within the simulation period.

Illustratively, in conjunction with FIG. 2, the server generates an empty order that includes three code containers using a roulette method.

S102, extracting n article coding information from the corresponding historical order data set based on the number of the coding containers in the empty order, and filling the n coding containers with the n article coding information respectively.

In the embodiment of the invention, the server extracts n article coding information from the corresponding historical order data group based on the number of the coding containers in the empty order, and respectively and correspondingly fills the n coding containers by using the n article coding information. The historical order data set is formed based on historical order data in a plurality of historical time periods and corresponds to a set of historical order data of the number of the coding containers.

In the embodiment of the invention, the server forms a plurality of historical order data groups in advance based on historical order data in a plurality of historical time periods. The historical order data within each historical order data set includes the same amount of item code information. The server randomly extracts n item code information from a historical order data set including n item code information. And the server respectively and correspondingly fills n coding containers in the empty order by using n item coding information.

Illustratively, in conjunction with fig. 3, the server extracts 3 item code information including: SKU1, SKU2, and SKU 3. The server correspondingly fills the 3 article coding information into three coding containers of the empty order respectively.

S103, traversing the article coding information in the n coding containers, and forming order information corresponding to the first simulation time period based on the number of the n article coding information and related data.

In the embodiment of the invention, the server traverses the article coding information in the n coding containers, and forms the order information corresponding to the first simulation time period based on the number of the n article coding information and related data.

In the embodiment of the invention, the server sequentially traverses the article coding information in the n coding containers. When the number of the n article coding containers is 1, the server generates basic transaction amount information corresponding to the 1 article coding information respectively by combining a preset transaction amount average value and a preset transaction amount maximum value in the historical order data set. The basic transaction amount information generated by the server is similar to the mean value of the transaction amount and does not exceed the maximum value of the transaction amount. And the server adjusts the basic transaction amount information corresponding to the 1 item code information respectively based on the transaction amount correlation corresponding to the 1 item code information in the historical order data set, so as to form order information.

When the number of the n article coding containers is larger than or equal to 2, the server adjusts the article coding information in the ith coding container based on the sum of the random number of the article coding information in the ith coding container and the frequency correlation of the article coding information with other article coding information until the article coding information in the n coding containers is traversed. And the server generates basic transaction amount information corresponding to the n article coding information after traversing by adopting the same method. And finally, the server adjusts the basic transaction amount information corresponding to the n article code information respectively, so that the basic transaction amount information corresponding to the n article code information respectively does not differ from the historical transaction amount too much, and order information is formed.

In the embodiment of the present invention, the server may also generate order information corresponding to other simulation time periods in the simulation period by using the same method in S103.

And S104, determining target order information based on the comparison result of the order information and the target transaction amount of the historical time period corresponding to the first simulation time period, and further determining the target order information of other simulation time periods, so as to form a supply chain scheme.

In the embodiment of the invention, the server determines the target order information based on the order information and the comparison result of the target transaction amount of the historical time period corresponding to the first simulation time period, and further determines the target order information of other simulation time periods, thereby forming a supply chain scheme.

In the embodiment of the invention, the server calculates the target transaction amounts corresponding to a plurality of historical time periods in advance. The server compares the order information with a target trading volume of a historical time period corresponding to the first simulation time period. And if the transaction amount of the order information is not less than the target transaction amount, determining the order information as the target order information. And if the trading volume is smaller than the target trading volume according to the order information, the server re-executes the processes of S101-S103, determines new order information, and determines target order information according to the new order information and the target trading volume. The server generates target order information of a plurality of simulation time periods in the simulation period by the method. And the server forms a supply chain scheme matched with the simulator by adopting the target order information of the plurality of simulation time periods.

Wherein, at least one article code information can be included in the order information. Each item code information includes corresponding transaction amount information. The server adds the trading volume information of all the article coding information in the order information to obtain total trading volume information, and the server compares the total trading volume information with the target trading volume to obtain a comparison result. And the server determines the final target order information according to the comparison result.

In the embodiment of the invention, an empty order corresponding to a first simulation time period is randomly generated; the empty order includes: n coding containers; extracting n article coding information from the corresponding historical order data group based on the number of the coding containers in the empty order, and respectively and correspondingly filling the n coding containers with the n article coding information; the historical order data group is formed on the basis of historical order data in a plurality of historical time periods and corresponds to a set of historical order data of the number of the coding containers; traversing the article coding information in the n coding containers, and forming order information corresponding to a first simulation time period based on the number of the n article coding information and related data; and determining target order information based on the order information and the comparison result of the target transaction amount of the historical time period corresponding to the first simulation time period, and further determining the target order information of other simulation time periods, so as to form an accurate supply chain scheme. The target order information is formed on the basis of the corresponding order information, and the order information is formed on the basis of the number of the n article coding information in the randomly generated empty order and the related data, so that the target order data is more matched with the actual transaction amount in the first simulation time period, and the target order data of each simulation time period generated by the scheme can be used for accurately forming a supply chain scheme matched with the simulation period.

In some embodiments, referring to fig. 4, fig. 4 is an optional flowchart of the order generation method provided by the embodiment of the present invention, and S103 shown in fig. 1 may be implemented by S105 to S107, which will be described with reference to the steps.

And S105, if n is larger than or equal to 2, adjusting the article coding information in the ith coding container according to the order of the article coding information in the n coding containers based on the sum of the random number of the article coding information in the ith coding container and the frequency correlation of the article coding information with other article coding information until the article coding information in the n coding containers is traversed.

In the embodiment of the invention, the server detects that the number n of the coding containers in the empty order is greater than or equal to 2, and adjusts the article coding information in the ith coding container according to the sequence of the article coding information in the n coding containers based on the sum of the random number of the article coding information in the ith coding container and the frequency correlation of the random number and other article coding information until the article coding information in the n coding containers is traversed.

In the embodiment of the present invention, since the number n of the coding containers in the empty order is randomly determined, the server needs to first detect whether n is greater than or equal to 2. If the number of the articles is larger than 2, the server sequentially traverses the n article coding information according to the sequence of the n article coding information. The server adjusts the article code information based on the random number generated by the article code information in each code container and the sum of the frequency correlations between the code information and other article code information. The server may adjust the n item code information in the same way.

In the embodiment of the invention, the server adjusts the article coding information in such a way that the server replaces the article coding information with other article coding information in the empty order.

And S106, generating basic transaction amount information corresponding to the n item code information after traversal is completed by combining the preset transaction amount average value and the preset transaction amount maximum value in the historical order data set.

In the embodiment of the invention, the server generates basic transaction amount information corresponding to the n article coded information after traversal is completed by combining the preset transaction amount average value and the preset transaction amount maximum value in the historical order data set.

In the embodiment of the invention, the server calculates the average value and the maximum value of the transaction amount in the historical order data set in advance. And the server respectively generates corresponding basic transaction amount information aiming at the n article coding information. And each basic transaction amount information is not more than the maximum value of the transaction amount and is within a certain numerical range of the mean value of the transaction amount.

And S107, adjusting the basic transaction amount information corresponding to the n article code information respectively based on the transaction amount correlation corresponding to the n article code information in the historical order data group, and further forming order information.

In the embodiment of the invention, the server adjusts the basic transaction amount information corresponding to the n article code information respectively based on the transaction amount correlation corresponding to the n article code information in the historical order data set, thereby forming the order information.

In the embodiment of the invention, the server determines target order data which simultaneously comprises n item coding information in the historical order data group. And adjusting the basic transaction amount information corresponding to the n article code information respectively based on the transaction amount correlation corresponding to the n article code information obtained in advance in the target order data to obtain the transaction amount information corresponding to the n article code information respectively, and further forming order information.

In the embodiment of the invention, the server adjusts the basic transaction amount information of each article code information based on the ratio of the transaction amount between each article code information and the previous article code information in the target order data to obtain the transaction amount information corresponding to the n article code information respectively, thereby forming the order information.

In the embodiment of the present invention, the adjusting, by the server, the basic transaction amount information of each article code information according to the ratio of the transaction amount of each article code information to the transaction amount of the previous article code information includes: the server multiplies the basic transaction amount information of the previous article code information by the ratio of the transaction amount to obtain the transaction amount information corresponding to the article code information. For example, the transaction amount information of the SKU2 (the basic transaction amount information of the SKU1 the transaction amount information of the SKU2 in the historical order/the transaction amount information of the SKU1 in the historical order).

In the embodiment of the invention, when the number of the article coding information in the empty order is more than or equal to 2, the server adjusts each article coding information based on the random number of the article coding information in each coding container and the sum of frequency correlation of the article coding information and other article coding information, and finally generates corresponding order information by combining with the correlation of the transaction amount, so that the empty order is more matched with the actual transaction amount in the first simulation time period to form an accurately matched supply chain.

In some embodiments, referring to fig. 5, fig. 5 is an optional flowchart of the order generation method provided by the embodiment of the present invention, and S105 shown in fig. 4 may be implemented through S108 to S111, which will be described with reference to the steps.

And S108, generating random numbers corresponding to the n article coding information respectively.

In the embodiment of the invention, the server generates random numbers corresponding to the n article coding information respectively.

Wherein the random number may be a random number between 0 and 1.

And S109, processing to obtain the ith random number corresponding to the article coding information in the ith coding container, and comparing the ith random number with a preset threshold value.

In the embodiment of the invention, the server processes the ith random number corresponding to the article coding information in the ith coding container and obtains the comparison result between the ith random number and the preset threshold.

In the embodiment of the invention, the server compares the ith random number corresponding to the ith article coding information with the preset threshold value to obtain the comparison result.

The preset threshold value can also be a random number between 0 and 1.

And S110, counting the sum of frequency correlations between the article coding information in the ith coding container and other article coding information in the historical order data set if the comparison result represents that the ith random number is smaller than the preset threshold value.

In the embodiment of the invention, if the server detects that the comparison result indicates that the ith random number is smaller than the preset threshold value, the sum of frequency correlations between the article coding information in the ith coding container and other article coding information in the historical order data set is counted.

In the embodiment of the invention, the server generates a random number R between 0 and 1 for each SKU in the empty order, if the SKU is not the same as the SKU, the server generates a random number R for each SKU in the empty order_iR of (A) to (B)<And P. Calculate SKU by equation (1)_iSum of frequency correlations C with other n-1 SKUs_SKUi。

Wherein n is the number of coded information of n articles, SKU_i，SKU_jThe frequency dependence of the coded information for the ith item on the coded information for the jth item. The server adds the frequency correlation of the ith item code information and the rest n-1 item code information to obtain the sum C of the frequency correlation_SKUi。

And S111, if the sum of the frequency correlation is smaller than a frequency threshold, taking any other article coding information as the article coding information in the new ith coding container until the traversal of the n article coding information is completed.

In the embodiment of the invention, if the sum of the frequency correlations detected by the server is smaller than the frequency threshold, any other article coding information is used as the article coding information in the new ith coding container until the traversal of the n article coding information is completed.

In the embodiment of the invention, if the sum of the frequency correlations detected by the server is smaller than the frequency threshold, any other article coding information is used as the article coding information in the new ith coding container. Then, the server continuously traverses the (i + 1) th item coding information and continuously executes the traversing process.

In the embodiment of the present invention, referring to fig. 6, when the server detects that the sum of the frequency correlations of the SKUs 1 is smaller than the frequency threshold, the server randomly determines NEW _ SKU1 in SKU2 and SKU 3. The server fills the first encoded container with NEW _ SKU1 and proceeds through the remaining item encoding information.

In the embodiment of the invention, when the number of the article coding information in the empty order is more than or equal to 2, the server generates the random number corresponding to each article coding information. And the server replaces and updates each item code information based on the random number of the item code information in each code container and the size of a preset threshold value, and the sum of the frequency correlation of the random number and other item code information and the size of a frequency threshold value, so that the empty order is more matched with the actual transaction amount in the first simulation time period to form an accurately matched supply chain.

In some embodiments, referring to fig. 7, fig. 7 is an optional flowchart of the order generation method provided by the embodiment of the present invention, and S109 shown in fig. 5 may be implemented by S112 later, which will be described with reference to the steps.

And S112, if the comparison result represents that the ith random number is not less than the preset threshold, comparing the (i + 1) th random number of the article coding information in the (i + 1) th coding container with the preset threshold to obtain a comparison result of the article coding information in the (i + 1) th coding container, and continuously executing traversal of the residual article coding information based on the comparison result.

In the embodiment of the invention, if the server detects that the comparison result indicates that the ith random number is not less than the preset threshold, the i +1 th random number of the article coding information in the i +1 th coding container is compared with the preset threshold to obtain the comparison result of the article coding information in the i +1 th coding container, and traversal of the remaining article coding information is continuously executed based on the comparison result.

Illustratively, if the server detects that the comparison result corresponding to the 1 st item code information indicates that the 1 st random number is not less than the preset threshold, the server compares the 2 nd random number of the item code information in the 2 nd coding container with the preset threshold to obtain the comparison result of the item code information in the 2 nd coding container, and based on the comparison result, the traversal of the remaining item code information is continuously performed.

In the embodiment of the invention, if the random number of the article coding information detected and obtained by the server is not less than the preset threshold value, the traversal of the next article coding information is continued until all article coding information is traversed, and the scheme can adjust each article coding information, so that the order information is more matched with the first simulation time period.

In some embodiments, referring to fig. 8, fig. 8 is an optional flowchart of the order generation method provided by the embodiment of the present invention, and S110 shown in fig. 5 may be implemented through S113, which will be described with reference to steps.

And S113, if the sum of the frequency correlations is not less than the frequency threshold, based on the correlation of the transaction amount corresponding to the current n article code information in the historical order data set, forming the transaction amount information corresponding to the n article code information respectively, and further forming the order information.

In the embodiment of the invention, if the sum of the frequency correlations detected by the server is not less than the frequency threshold, the trading volume information corresponding to the n article code information respectively is formed based on the trading volume correlations corresponding to the current n article code information in the historical order data set, and then the order information is formed.

In the embodiment of the invention, if the sum of the frequency correlation of the article coding information is not less than the frequency threshold, the article coding information has stronger correlation with other article coding information, and the process of forming order information is executed without executing the process of adjusting the article coding information, so that the order generation time is saved, and the efficiency of forming a supply chain is improved.

In some embodiments, referring to fig. 9, fig. 9 is an optional flowchart of the order generation method provided by the embodiment of the present invention, and S107 shown in fig. 4 may be implemented by S114 to S115, which will be described with reference to the steps.

And S114, determining target order data which simultaneously comprise n items of coded information in the historical order data group.

In the embodiment of the invention, the server determines target order data which simultaneously comprises n item coding information in the historical order data group.

Wherein the target order data may include z item code information, z being greater than or equal to n.

In an embodiment of the present invention, the server may determine target order data that includes both SKU1, SKU2, and SKU3 in the historical order data set.

S115, based on the correlation of the transaction amount of the n corresponding article code information obtained in advance in the target order data, adjusting the basic transaction amount information corresponding to the n article code information respectively to obtain the transaction amount information corresponding to the n article code information respectively, and further forming order information.

In the embodiment of the invention, the server adjusts the basic transaction amount information corresponding to the n article code information respectively based on the transaction amount correlation corresponding to the n article code information obtained in advance in the target order data to obtain the transaction amount information corresponding to the n article code information respectively, thereby forming the order information.

In the embodiment of the invention, the server adjusts the basic transaction amount information corresponding to the n article code information respectively based on the correlation of the transaction amount of each article code information and the next article code information obtained in advance in the target order data to obtain the transaction amount information corresponding to the n article code information respectively, thereby forming the order information.

In the embodiment of the invention, the server adjusts the basic transaction amount corresponding to each article code information by utilizing the correlation of the transaction amount corresponding to each article code information, thereby avoiding the formation of transaction amount information with larger error and enabling the order information to be more matched with the actual transaction amount in the first simulation time period.

In some embodiments, referring to fig. 10, fig. 10 is an optional flowchart of the order generation method provided by the embodiment of the present invention, and S115 shown in fig. 9 may be implemented by S116, which will be described with reference to the steps.

S116, multiplying basic transaction amount information corresponding to the t-1 th article coding information by the ratio of the historical transaction amount information of the t-1 th article coding information to the historical transaction amount information of the t-1 th article coding information in the target order data to obtain transaction amount information corresponding to the t-1 th article coding information, and further obtaining transaction amount information corresponding to the n article coding information respectively.

In the embodiment of the invention, the server multiplies the basic transaction amount information corresponding to the t-1 th article coding information by the ratio of the historical transaction amount information of the t-1 th article coding information in the target order data to the historical transaction amount information of the t-1 th article coding information to obtain the transaction amount information corresponding to the t-1 th article coding information until t is equal to n, and further obtains the transaction amount information corresponding to the n article coding information respectively. Wherein t is a positive integer greater than or equal to 2 and less than n.

In the embodiment of the invention, the server multiplies the basic transaction amount information corresponding to the 1 st article code information by the ratio of the historical transaction amount information of the 2 nd article code information in the target order data to the historical transaction amount information of the 1 st article code information to obtain the transaction amount information corresponding to the 2 nd article code information, and the server obtains the rest and further obtains the transaction amount information corresponding to the n article code information respectively by adopting the same method.

In some embodiments, referring to fig. 11, fig. 11 is an optional flowchart of the order generation method provided by the embodiment of the present invention, and S107 shown in fig. 2 may be implemented by S117 before, which will be described with reference to the steps.

And S117, if n is equal to 1, generating basic transaction amount information corresponding to the n item code information after traversal is completed by combining the preset transaction amount average value and the preset transaction amount maximum value in the historical order data set.

In the embodiment of the invention, if the number of the coding containers in the empty order detected by the server is 1, the server directly combines the preset transaction amount average value and the preset transaction amount maximum value in the historical order data set to generate the basic transaction amount information respectively corresponding to the n article coding information after traversal is completed.

In some embodiments, referring to fig. 12, fig. 12 is an optional flowchart of an order generation method provided by the embodiment of the present invention, and will be described with reference to steps.

And S118, extracting a plurality of order data corresponding to a plurality of historical time periods in a preset storage space according to preset conditions.

In the embodiment of the invention, the server extracts a plurality of order data corresponding to a plurality of historical time periods in the preset storage space according to the preset conditions.

In the embodiment of the invention, the server can extract at least one order data corresponding to each time period of the plurality of historical time periods from the memory of the server according to the preset condition. The at least one order data for each time period is combined to form a plurality of order data.

In the embodiment of the invention, the server needs to extract the SKU list, the order quantity data and the sales quantity data of the target by using the relevant data of the order to the main website of the mall, specifically including the data of the time period of the order occurrence, the data of the area of the order occurrence and the data of the target class. The order data is stored in a Distributed File System (HDFS), and the server needs to obtain required related data from the HDFS through a Hive calculation engine.

And S119, calculating target transaction amounts corresponding to the plurality of historical time periods by combining the plurality of order data.

In the embodiment of the invention, the server calculates the target transaction amounts corresponding to a plurality of historical time periods respectively by combining a plurality of order data.

In the embodiment of the invention, the server calculates the reference line of each historical time period by combining the order data of each historical time period. And the server calculates the target transaction amount of each historical time period by combining the variation coefficient and the random variable.

And S120, dividing the plurality of order data into a plurality of historical order data groups according to the quantity of the item coding information in the plurality of order data.

In the embodiment of the invention, the server divides the plurality of order data into a plurality of historical order data groups according to the quantity of the article coding information in the plurality of order data. And the order data in each historical order data group contains the same quantity of the item coding information.

In an embodiment of the present invention, the historical order data set is structured such that the order contains the number of SKUs. All order data may be divided into multiple historical order data sets depending on the number of SKUs involved.

And S121, simultaneously calculating a plurality of transaction related information corresponding to the plurality of historical order data sets.

In the embodiment of the invention, the server simultaneously calculates a plurality of transaction related information respectively corresponding to a plurality of historical order data sets.

In the embodiment of the invention, the server simultaneously calculates the transaction amount mean value, the transaction amount maximum value, the transaction amount distribution frequency information of each item code information, the frequency correlation matrix and the transaction amount correlation matrix which are respectively corresponding to a plurality of historical order data groups.

In the embodiment of the invention, the server utilizes the spark distributed computing framework to improve the efficiency of order data processing. The server imports all historical order data sets into a flexible Distributed data set (RDD) of spark, and then the server can simultaneously calculate a transaction amount mean value, a transaction amount maximum value, transaction amount distribution frequency information of each item code information, a frequency correlation matrix and a transaction amount correlation matrix corresponding to a plurality of historical order data sets.

In the embodiment of the invention, the server calculates and obtains the target transaction amounts of a plurality of historical time periods through a plurality of order data of a plurality of historical time periods, and calculates and obtains the average value of the transaction amount, the maximum value of the transaction amount, the transaction amount distribution frequency information of each item code information, the frequency correlation matrix and the transaction amount correlation matrix of each historical order data group, so that a data base is provided for forming order information subsequently, and the order information is more matched with the actual transaction amount of the first simulation time period.

In some embodiments, referring to fig. 13, fig. 13 is an optional flowchart of the order generation method provided by the embodiment of the present invention, and S118 to S119 shown in fig. 12 can be implemented by S122 to S125, which will be described with reference to the steps.

And S122, extracting a plurality of order data corresponding to a plurality of historical time periods in a preset storage space according to the node information, the area information and the item type information of the plurality of historical time periods.

In the embodiment of the invention, the server extracts a plurality of order data corresponding to a plurality of historical time periods in the preset storage space according to the node information, the area information and the item type information of the plurality of historical time periods.

In the embodiment of the invention, the server acquires node information, area information and article type information of a plurality of historical time periods configured by a user. And the server extracts at least one order data corresponding to each historical time period from a preset storage space according to the node information, the area information and the item type information of the plurality of historical time periods. The at least one order data for each historical time period is combined to form a plurality of order data.

And S123, counting the sum of the historical trading volume information of the order data in each historical time period to obtain a reference line of each historical time period.

In the embodiment of the invention, the server counts the sum of the historical trading volume information of the order data in each historical time period to obtain the reference line of each historical time period.

In the embodiment of the invention, the server carries out aggregation operation on the order data according to time, and sums the transaction amounts of all SKUs to obtain the reference line of each historical time period.

And S124, acquiring a change coefficient, multiplying the reference line of each historical time period by the change coefficient, and adding the reference line to obtain the theoretical transaction amount of each historical time period.

In the embodiment of the invention, the server acquires the change coefficient, multiplies the reference line of each historical time period by the change coefficient, and adds the reference line to obtain the theoretical transaction amount of each historical time period.

In the embodiment of the invention, the server acquires the change coefficient configured by the user, and the server multiplies the reference line of each historical time period by the change coefficient and then adds the reference line to obtain the theoretical transaction amount of each historical time period. Illustratively, the server multiplies each datum line by a corresponding demand change coefficient, and then performs rounding operation on the calculation result by using a rounding function to obtain the theoretical transaction amount of each historical time period. For example, the server may calculate the theoretical transaction amount target _ s _ t by formula (2)

target_s_t＝round(s_t*(1+demand_ratio)) (2)

Wherein round is an integer function, s _ t is a reference line, and demand _ ratio is a variation coefficient. And the server multiplies the datum line by the value obtained by adding 1 to the variable coefficient, and then obtains the theoretical transaction amount by using an integer function.

And S125, adding the theoretical transaction amount of each historical time period and the random variable to obtain the target transaction amount of each historical time period, and further obtaining the target transaction amounts corresponding to the multiple historical time periods.

In the embodiment of the invention, the server adds the theoretical transaction amount of each historical time period with the random variable to obtain the target transaction amount of each historical time period, and further obtains the target transaction amounts respectively corresponding to a plurality of historical time periods.

In the embodiment of the invention, the server introduces volatility: on the basis of the theoretical trading volume, a disturbance item of normal distribution is introduced to obtain the target trading volume. The introduction of the volatility is mainly the volatility capable of increasing the sales volume, and the market environment is more reasonably restored.

In the embodiment of the invention, the server uses the historical sales as a reference, adds the sales change and the disturbance, and generates the target transaction amount. Taking the type of coffee machine as an example, in fig. 14, curve 1 is a reference line, curve 3 is a curve with an amplitude of 30% in curve 1, and curve 3 is a curve with an amplitude of 30% in curve 1 + a final target transaction amount after disturbance.

In the embodiment of the invention, the server adjusts the datum line by combining the variation coefficient and the random variable to obtain the final target transaction amount, so that the target transaction amount is closer to the market environment.

In some embodiments, referring to fig. 13, fig. 13 is an optional flowchart of the order generation method provided by the embodiment of the present invention, and S121 shown in fig. 12 may be implemented by S126, which will be described with reference to each step.

And S126, calculating a transaction amount mean value, a transaction amount maximum value, transaction amount distribution frequency information of each item code information, a frequency correlation matrix and a transaction amount correlation matrix which correspond to the plurality of historical order data groups at the same time.

In some embodiments, referring to fig. 15, fig. 15 is an optional flowchart of the order generation method provided by the embodiment of the present invention, and S121 shown in fig. 12 may be implemented through S127 to S131, which will be described with reference to the steps.

And S127, adding the historical trading volume information of all the article code information in each historical order data group, and comparing the number of the article code information in each historical order data group to obtain the trading volume average value of each historical order data group.

In the embodiment of the invention, the server adds the historical trading volume information of all the article coding information in each historical order data group, and obtains the trading volume average value of each historical order data group by comparing the number of the article coding information in each historical order data group.

And S128, determining the maximum historical trading volume information as the maximum trading volume value in the historical trading volume information corresponding to the article coding information in each historical order data set.

In the embodiment of the invention, the server determines the maximum historical trading volume information as the maximum trading volume value in the historical trading volume information corresponding to the article coding information in each historical order data set.

And S129, comparing the quantity of the order data respectively containing the coded information of each article with the total quantity of the order data in each historical order data group to obtain the transaction quantity distribution frequency information of the coded information of each article.

In the embodiment of the invention, the server compares the quantity of the order data respectively containing the coded information of each article with the total quantity of the order data in each historical order data group to obtain the transaction quantity distribution frequency information of the coded information of each article.

For example, the transaction amount distribution frequency information of SKU1 includes the number of order data of SKU 1/the number of total order data.

S130, calculating the frequency correlation between every two article coding information in each historical order data set, arranging the frequency correlation of each article coding information corresponding to other article coding information according to the sequence of the article coding information in each historical order data set, forming each row of a frequency correlation matrix, and further forming the frequency correlation matrix.

In the embodiment of the invention, the server calculates the frequency correlation between every two article coding information in each historical order data set, arranges the frequency correlation of each article coding information corresponding to other article coding information according to the sequence of the article coding information in each historical order data set, forms each row of a frequency correlation matrix, and further forms the frequency correlation matrix.

The server may count the sum of the frequency correlations in the frequency correlation matrix.

S131, calculating the correlation of the transaction amount between every two article coding information in each historical order data set, and arranging the correlation of the transaction amount of each article coding information corresponding to other article coding information according to the sequence of the article coding information in each historical order data set to form each row of a transaction amount correlation matrix, thereby forming the transaction amount correlation matrix.

In the embodiment of the invention, the server calculates the correlation of the transaction amount between every two article coding information in each historical order data group, arranges the correlation of the transaction amount of each article coding information corresponding to other article coding information according to the sequence of the article coding information in each historical order data group to form each row of a transaction amount correlation matrix, and further forms the transaction amount correlation matrix.

In the embodiment of the invention, the server can obtain the transaction amount correlation of the n article code information corresponding to other article code information in the transaction amount correlation matrix, so as to adjust the transaction amount of the corresponding article code information.

In some embodiments, referring to fig. 16, fig. 16 is an optional flowchart of the order generation method provided in the embodiment of the present invention, and S129 shown in fig. 15 may be implemented by S132 to S134, which will be described with reference to the steps.

S132, according to the order of the item coding information in each historical order data set, comparing the number of order data which simultaneously comprise the kth item coding information and the mth item coding information with the number of order data which only comprise the kth item coding information to obtain a first frequency correlation between the kth item coding information and the mth item coding information until m is equal to q, and obtaining q-1 first frequency correlations corresponding to each item coding information.

In the embodiment of the invention, the server compares the number of order data simultaneously comprising the kth item coding information and the mth item coding information with the number of order data only comprising the kth item coding information according to the order of the item coding information in each historical order data group to obtain the first frequency correlation between the kth item coding information and the mth item coding information until m is equal to q, and obtains q-1 first frequency correlations respectively corresponding to each item coding information. Wherein k and m are positive integers which are more than or equal to 1 and less than q, and k and m are different; q is the number of item encoded information in each historical order data set.

In the embodiment of the invention, the server compares the number of order data simultaneously comprising the 1 st article coding information and the 2 nd article coding information with the number of order data only comprising the 1 st article coding information to obtain the first frequency correlation between the 1 st article coding information and the 2 nd article coding information. The server compares the number of order data simultaneously comprising the 1 st article coding information and the 3 rd article coding information with the number of order data only comprising the 1 st article coding information to obtain a first frequency correlation between the 1 st article coding information and the 3 rd article coding information. The server can obtain q-1 first frequency correlations corresponding to the q article coding information in each historical order data set according to the method.

S133, according to the order of the article coding information in each historical order data set, comparing the number of order data which simultaneously comprise the kth article coding information and the mth article coding information with the number of order data which only comprise the mth article coding information to obtain a second frequency correlation between the mth article coding information and the kth article coding information until k is equal to q, and obtaining q-1 second frequency correlations corresponding to each article coding information.

In the embodiment of the invention, the server compares the number of order data simultaneously comprising the kth item coding information and the mth item coding information with the number of order data only comprising the mth item coding information according to the order of the item coding information in each historical order data group to obtain the second frequency correlation between the mth item coding information and the kth item coding information until k is equal to q, and then obtains q-1 second frequency correlations respectively corresponding to each item coding information.

In the embodiment of the invention, the server compares the number of the order data simultaneously comprising the 1 st article coding information and the 2 nd article coding information with the number of the order data only comprising the 2 nd article coding information to obtain the second frequency correlation between the 1 st article coding information and the 2 nd article coding information. The server compares the number of the order data simultaneously comprising the 1 st article coding information and the 3 rd article coding information with the number of the order data only comprising the 3 rd article coding information to obtain a second frequency correlation between the 1 st article coding information and the 3 rd article coding information. The server can obtain q-1 second frequency correlations corresponding to the q article coding information in each historical order data set according to the method.

S134, taking the q-1 first frequency correlations and the n-1 second frequency correlations of the first article coding information as a first row of a frequency correlation matrix until a q-th row of the frequency correlation matrix is formed, and further forming the frequency correlation matrix.

In the embodiment of the invention, the server takes q-1 first frequency correlations and n-1 second frequency correlations of the first article coding information as a first row of a frequency correlation matrix until a q-th row of the frequency correlation matrix is formed, and then the frequency correlation matrix is formed.

In the embodiment of the invention, the server forms the frequency correlation matrix of each historical order data set, and the sum of the frequency correlations of the coding information in the corresponding historical order data set can be more conveniently extracted in the traversal process of the subsequent empty orders.

In some embodiments, referring to fig. 17, fig. 17 is an optional flowchart of the order generation method provided in the embodiment of the present invention, and S131 shown in fig. 15 may be implemented by S135-S137, which will be described with reference to the steps.

S135, according to the sequence of the item code information in each historical order data group, determining q-1 pieces of intermediate order data which simultaneously comprise the kth item code information and the mth item code information in each historical order data group.

In the embodiment of the invention, the server determines q-1 intermediate order data which simultaneously comprise the kth item coding information and the mth item coding information in each historical order data group according to the order of the item coding information in each historical order data group. Wherein k and m are positive integers which are more than or equal to 1 and less than q, and k and m are different; q is the number of item encoded information in each historical order data set.

S136, comparing the kth article coding information included in each order data with the transaction amount information corresponding to the mth article coding information, and further obtaining q-1 transaction amount correlations corresponding to the kth article coding information respectively until k is equal to q, and obtaining q-1 transaction amount correlations corresponding to the q article coding information respectively.

In the embodiment of the invention, the server compares the kth article coding information included in each order data with the transaction amount information corresponding to the mth article coding information, so as to obtain q-1 transaction amount correlations corresponding to the kth article coding information respectively until k is equal to q, and obtain q-1 transaction amount correlations corresponding to the q article coding information respectively.

In the embodiment of the invention, the server compares the 1 st article code information included in each order data with the transaction amount information corresponding to the 2 nd article code information to obtain the transaction amount correlation between the 1 st article code information and the 2 nd article code information. The server compares the 1 st article code information included in each order data with the transaction amount information corresponding to the 3 rd article code information to obtain the transaction amount correlation between the 1 st article code information and the 3 rd article code information. The server can obtain q-1 transaction amount correlations corresponding to the q article coding information in each historical order data set according to the method.

S137, the q-1 transaction amount correlation of the first article coding information is used as the first row of the transaction amount correlation matrix until the q-th row of the transaction amount correlation matrix is formed, and then the transaction amount correlation matrix is formed.

In the embodiment of the invention, the server takes the q-1 transaction amount correlations of the first article coding information as the first row of the transaction amount correlation matrix until the q-th row of the transaction amount correlation matrix is formed, and further the transaction amount correlation matrix is formed.

In the embodiment of the invention, the server forms the transaction amount correlation matrix of each historical order data set, and the transaction amount correlation of the corresponding coded information in the corresponding historical order data set can be more conveniently extracted in the formation process of the subsequent order information.

In some embodiments, referring to fig. 18, fig. 18 is an optional flowchart of the order generation method provided by the embodiment of the present invention, and S104 shown in fig. 1 may be implemented by S138, which will be described with reference to steps.

And S137, if the trading volume included in the order information is not less than the target trading volume, determining the order information as the target order information.

In the embodiment of the invention, if the transaction amount included in the order information detected and obtained by the server is not less than the target transaction amount, the order information is determined to be the target order information.

In some embodiments, referring to fig. 19, fig. 19 is an optional flowchart of the order generation method provided by the embodiment of the present invention, and S104 shown in fig. 1 may be implemented by S139, which will be described with reference to each step.

And S138, if the transaction amount included in the order information is smaller than the target transaction amount, continuing to execute the process of forming the order information until the target order information is obtained.

In the embodiment of the invention, if the server detects that the transaction amount included in the obtained order information is smaller than the target transaction amount, the process of forming the order information is continuously executed until the target order information is obtained. I.e., the process of S101-S103 is continued.

In some embodiments, referring to fig. 20, fig. 20 is an optional flowchart of the order generation method according to the embodiment of the present invention, and the description will be made with reference to the steps.

S201, transaction amount target.

In the embodiment of the invention, the data extraction module of the server is used for extracting the simulation time, the area information and the category information in the distributed file system. Order data is extracted. Each order data includes: at least one sku message, each sku message including corresponding transaction amount data. And the server calculates the transaction amount target of each simulation time period corresponding to the simulation period by combining the extracted order data through the data processing module.

And S202, distributing the order structure.

In the embodiment of the invention, the server forms a plurality of historical order data groups according to the extracted order data.

And S203, counting SKU transaction amount information.

In the embodiment of the invention, the server can simultaneously calculate the transaction amount basic statistics of the transaction amount distribution, the frequency correlation matrix and the transaction amount correlation matrix corresponding to a plurality of historical order data groups respectively

And S204, generating an empty order with the number of the SKUs n.

In the embodiment of the invention, the server generates the empty order with the number of the SKUs n through the random order generation module.

S205, roulette draws n SKUs.

S206、n＝1。

In the embodiment of the invention, the server judges whether n is equal to 1. If n is equal to 1, S212 is performed. If n is not equal to 1, S207 is executed.

S207、R<P。

In the embodiment of the invention, the server judges the SKU_iWhether the corresponding random number R is less than P. If R is less than P, S211 is executed. If R is not less than P, then S208 is performed.

And S208, calculating the correlation.

S209, correlation < C.

In the embodiment of the invention, the server judges whether the statistical relevance is smaller than a threshold value C. If the correlation is smaller than C, S212 is performed. If not, go to step S210.

And S210, adjusting the correlation of the SKUs.

And S211, traversing n times.

And S212, generating a transaction amount.

And S213, adjusting the relationship of the transaction amount.

And S214, order generation.

S215, the target daily transaction amount is achieved.

In the embodiment of the invention, the server judges whether the transaction amount in the order reaches the corresponding target transaction amount through the order time sequence control module. If so, determining the order as a target order. If not, the process returns to step S204.

And S216, finishing order generation.

Please refer to fig. 21, which is a schematic structural diagram of an order generating apparatus according to an embodiment of the present invention.

An embodiment of the present invention further provides an order generating apparatus 800, including: an order forming unit 803, a data extracting unit 804 and a determining unit 805.

An order forming unit 803, configured to randomly generate an empty order corresponding to the first simulation time period; the empty order includes: n coding containers; n is an integer of 1 or more;

a data extraction unit 804, configured to extract n item code information from a corresponding historical order data set based on the number of code containers in the empty order, and fill the n code containers with the n item code information respectively; the historical order data group is formed based on historical order data in a plurality of historical time periods and corresponds to a set of historical order data of the number of the coding containers;

the order forming unit 803 is further configured to traverse the article coding information in the n coding containers, and form order information corresponding to the first simulation time period based on the number of the n article coding information and related data;

the determining unit 805 is configured to determine target order information based on the comparison result between the order information and the target transaction amount in the historical time period corresponding to the first simulation time period, and further determine the target order information in other simulation time periods for the supply chain to supply.

In an embodiment of the present invention, the related data includes: the sum of the random number and the frequency correlation is correlated with the transaction amount; the order forming unit 803 in the order generating apparatus 800 is configured to traverse the article coding information in the ith coding container if n is greater than or equal to 2; i is a positive integer which is more than or equal to 1 and less than n; adjusting the article coding information in the ith coding container based on the sum of the random number of the article coding information in the ith coding container and the frequency correlation of the article coding information with other article coding information until the article coding information in the n coding containers is traversed; generating basic transaction amount information corresponding to the n item code information after traversal is completed by combining a transaction amount average value and a transaction amount maximum value preset in the historical order data set; and adjusting the basic transaction amount information corresponding to the n article code information respectively based on the transaction amount correlation corresponding to the n article code information in the historical order data set, thereby forming the order information.

In this embodiment of the present invention, the order forming unit 803 in the order generating apparatus 800 is configured to generate random numbers corresponding to the n article coding information, respectively; the random number is a random number which is more than 0 and less than 1; processing to obtain an ith random number corresponding to the article coding information in the ith coding container, and comparing the ith random number with a preset threshold value; if the comparison result represents that the ith random number is smaller than the preset threshold value, counting the sum of the frequency correlations between the article coding information in the ith coding container and other article coding information in the historical order data set; the frequency correlation between the article coding information in the ith coding container and other article coding information is calculated in advance; and if the sum of the frequency correlation is smaller than a frequency threshold value, taking any other article coding information as the article coding information in the new ith coding container until the n article coding information is traversed.

In this embodiment of the present invention, the order forming unit 803 in the order generating apparatus 800 is configured to compare, if the comparison result indicates that the ith random number is not less than the preset threshold, the (i + 1) th random number of the article coding information in the (i + 1) th coding container with the preset threshold, to obtain a comparison result of the article coding information in the (i + 1) th coding container, and based on the comparison result, continue to perform traversal of the remaining article coding information.

In this embodiment of the present invention, the order forming unit 803 in the order generating apparatus 800 is configured to form, based on the correlation between the transaction amounts corresponding to the current n item code information in the historical order data set, the transaction amount information corresponding to the n item code information, and further form the order information, if the sum of the frequency correlations is not less than the frequency threshold.

In this embodiment of the present invention, the order forming unit 803 in the order generating apparatus 800 is configured to determine, in the historical order data set, target order data that simultaneously includes the n item code information; and adjusting the basic transaction amount information corresponding to the n article code information respectively based on the transaction amount correlation corresponding to the n article code information obtained in advance in the target order data to obtain the transaction amount information corresponding to the n article code information respectively, and further forming the order information.

In this embodiment of the present invention, the order forming unit 803 in the order generating apparatus 800 is configured to multiply the basic transaction amount information corresponding to the t-1 th item code information by a ratio of the historical transaction amount information of the t-1 th item code information to the historical transaction amount information of the t-1 th item code information in the target order data to obtain the transaction amount information corresponding to the t-1 th item code information, and further obtain the transaction amount information corresponding to each of the n item code information; t is a positive integer of 2 or more and less than n.

In this embodiment of the present invention, the order forming unit 803 in the order generating device 800 is configured to, if n is equal to 1, generate basic transaction amount information corresponding to the n item code information after traversal is completed, by combining a transaction amount average value and a transaction amount maximum value preset in the historical order data set.

In this embodiment of the present invention, the data extraction unit 804 in the order generating apparatus 800 is configured to extract a plurality of order data corresponding to the plurality of historical time periods in a preset storage space according to a preset condition; each order data includes: at least one item code information, each item code information comprising: corresponding historical transaction amount information; calculating the target transaction amounts corresponding to the plurality of historical time periods respectively by combining the plurality of order data; dividing the plurality of order data into a plurality of historical order data groups according to the quantity of the item coding information in the plurality of order data; the order data in each historical order data group contains the same quantity of the article coding information; and simultaneously calculating a plurality of transaction related information respectively corresponding to the plurality of historical order data groups.

In the embodiment of the present invention, the preset conditions include: node information, area information and item type information of the plurality of historical time periods; the data extraction unit 804 in the order generating apparatus 800 is configured to extract, in the preset storage space, the plurality of order data corresponding to the plurality of historical time periods according to the node information, the area information, and the category information of the article in the plurality of historical time periods.

In this embodiment of the present invention, the determining unit 805 in the order generating apparatus 800 is configured to count a sum of the historical transaction amount information of the order data in each historical time period, so as to obtain a reference line of each historical time period; acquiring a variation coefficient, multiplying the reference line of each historical time period by the variation coefficient, and adding the reference line to obtain the theoretical transaction amount of each historical time period; and adding the theoretical trading volume of each historical time period to a random variable to obtain the target trading volume of each historical time period, and further obtaining the target trading volumes corresponding to the plurality of historical time periods respectively.

In this embodiment of the present invention, the determining unit 805 in the order generating apparatus 800 is configured to simultaneously calculate the average value of the transaction amount, the maximum value of the transaction amount, the frequency information of the transaction amount distribution of the encoded information of each item, the frequency correlation matrix, and the correlation matrix of the transaction amount, which correspond to the plurality of historical order data sets, respectively.

In this embodiment of the present invention, the determining unit 805 in the order generating apparatus 800 is configured to add the historical trading volume information of all the item encoding information in each historical order data group, and obtain the mean value of the trading volume of each historical order data group by comparing the number of the item encoding information in each historical order data group; determining the maximum historical trading volume information as the maximum trading volume in the historical trading volume information corresponding to the article coding information in each historical order data set; in each historical order data group, comparing the quantity of order data respectively containing the coded information of each article with the total quantity of the order data in each historical order data group to obtain the transaction quantity distribution frequency information of the coded information of each article; calculating the frequency correlation between every two article coding information in each historical order data group, and arranging the frequency correlation of each article coding information corresponding to other article coding information according to the sequence of the article coding information in each historical order data group to form each row of the frequency correlation matrix so as to form the frequency correlation matrix; and calculating the correlation of the transaction amount between every two article coding information in each historical order data group, and arranging the correlation of the transaction amount of each article coding information corresponding to other article coding information according to the sequence of the article coding information in each historical order data group to form each row of the correlation matrix of the transaction amount, thereby forming the correlation matrix of the transaction amount.

In this embodiment of the present invention, the determining unit 805 in the order generating apparatus 800 is configured to compare the number of the order data that includes the kth item coding information and the mth item coding information at the same time with the number of the order data that includes only the kth item coding information according to the order of the item coding information in each historical order data set, to obtain a first frequency correlation between the kth item coding information and the mth item coding information until m equals q, and to obtain q-1 first frequency correlations that correspond to each item coding information respectively; k and m are positive integers which are more than or equal to 1 and less than q, and are different from each other; q is the number of the article coding information in each historical order data set; according to the order of the item coding information in each historical order data set, comparing the number of the order data which simultaneously comprises the kth item coding information and the mth item coding information with the number of the order data which only comprises the mth item coding information to obtain a second frequency correlation between the mth item coding information and the kth item coding information until k is equal to q, and obtaining q-1 second frequency correlations corresponding to each item coding information; and taking q-1 first frequency correlations and q-1 second frequency correlations of the first article coding information as a first row of the frequency correlation matrix until an nth row of the frequency correlation matrix is formed, and further forming the frequency correlation matrix.

In this embodiment of the present invention, the determining unit 805 in the order generating apparatus 800 is configured to determine, in the order of the item code information in each historical order data group, q-1 order data that simultaneously includes the kth item code information and the mth item code information in each historical order data group; k and m are positive integers which are more than or equal to 1 and less than q, and are different from each other; q is the number of the article coding information in each historical order data set; comparing the coded information of the kth article included in each order data with the trading volume information corresponding to the coded information of the mth article to obtain q-1 trading volume correlations corresponding to the coded information of the kth article respectively until k is equal to q, and obtaining q-1 trading volume correlations corresponding to the coded information of the q articles respectively; and taking q-1 transaction amount correlations of the first article coding information as the first row of the transaction amount correlation matrix until the q-th row of the transaction amount correlation matrix is formed, and further forming the transaction amount correlation matrix.

In this embodiment of the present invention, the determining unit 805 in the order generating apparatus 800 is configured to determine that the order information is the target order information if the transaction amount included in the order information is not less than the target transaction amount; and if the transaction amount included in the order information is smaller than the target transaction amount, continuing to execute the process of forming the order information until the target order information is obtained.

In the embodiment of the present invention, an order forming unit 803 randomly generates an empty order corresponding to the first simulation time period; the empty order includes: n coding containers; extracting n article coding information from the corresponding historical order data group through a data extraction unit 804 based on the number of the coding containers in the empty order, and respectively filling the n coding containers with the n article coding information; the historical order data group is formed on the basis of historical order data in a plurality of historical time periods and corresponds to a set of historical order data of the number of the coding containers; traversing the article coding information in the n coding containers through the order forming unit 803, and forming order information corresponding to a first simulation time period based on the number of the n article coding information and related data; the determination unit 805 determines target order information based on the comparison result of the order information and the target transaction amount in the historical time period corresponding to the first simulation time period, and further determines target order information in other simulation time periods, so as to form an accurate supply chain scheme. The target order information is formed on the basis of the corresponding order information, and the order information is formed on the basis of the number of the n article coding information in the randomly generated empty order and the related data, so that the target order data is more matched with the actual transaction amount in the first simulation time period, and the target order data of each simulation time period generated by the scheme can be used for accurately forming a supply chain scheme matched with the simulation period.

It should be noted that, in the embodiment of the present invention, if the order generation method is implemented in the form of a software functional module and is sold or used as an independent product, the order generation method may also be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing an order generating apparatus (which may be a personal computer or the like) to execute all or part of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, or an optical disk. Thus, embodiments of the invention are not limited to any specific combination of hardware and software.

Correspondingly, the embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the above-mentioned method.

Correspondingly, the embodiment of the present invention provides an order generating apparatus, which includes a memory 802 and a processor 801, where the memory 802 stores a computer program that is executable on the processor 801, and the processor 801 executes the computer program to implement the steps in the above method.

Here, it should be noted that: the above description of the storage medium and apparatus embodiments is similar to the description of the method embodiments above, with similar beneficial effects as the method embodiments. For technical details not disclosed in the embodiments of the storage medium and the apparatus according to the invention, reference is made to the description of the embodiments of the method according to the invention.

Fig. 22 is a schematic diagram of a hardware entity of an order generating apparatus according to an embodiment of the present invention, and as shown in fig. 22, the hardware entity of the order generating apparatus 800 includes: a processor 801 and a memory 802, wherein;

the processor 801 generally controls the overall operation of the order generating apparatus 800.

The Memory 802 is configured to store instructions and applications executable by the processor 801, and may also buffer data (e.g., image data, audio data, voice communication data, and video communication data) to be processed or already processed by each module in the processor 801 and the order generation apparatus 800, and may be implemented by a FLASH Memory (FLASH) or a Random Access Memory (RAM).

It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in various embodiments of the present invention, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention. The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.

Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media capable of storing program codes, such as a removable storage device, a Read Only Memory (ROM), a magnetic disk, and an optical disk.

Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media that can store program codes, such as a removable storage device, a ROM, a magnetic disk, or an optical disk.

The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present invention, and all such changes or substitutions are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

Claims

1. An order generation method, comprising:

2. The order generation method of claim 1, wherein the related data comprises: the sum of the random number and the frequency correlation is correlated with the transaction amount;

if the n is more than or equal to 2, adjusting the article coding information in the ith coding container according to the sequence of the article coding information in the n coding containers and based on the sum of the random number of the article coding information in the ith coding container and the frequency correlation of the article coding information with other article coding information until the article coding information in the n coding containers is traversed; i is a positive integer which is more than or equal to 1 and less than n;

3. The order generating method as claimed in claim 2, wherein said adjusting the item code information in the ith code container based on the sum of said random number of the item code information in the ith code container and said frequency correlation with other item code information in the order of said item code information in said n code containers until the item code information in said n code containers is completely traversed further comprises:

adjusting the article coding information in the ith coding container based on the sum of the random number of the article coding information in the ith coding container and the frequency correlation with other article coding information according to the order of the article coding information in the n coding containers until the article coding information in the n coding containers is traversed, including:

4. The order generation method according to claim 3, wherein before the processing obtains an ith random number corresponding to the item code information in the ith code container, and after a comparison result between the ith random number and a preset threshold value, and by combining a preset transaction amount mean value and a preset transaction amount maximum value in the historical order data set, and generating basic transaction amount information corresponding to the n item code information after traversal is completed, the method further comprises:

5. The order generating method according to claim 3, wherein if the comparison result indicates that the ith random number is smaller than the preset threshold, then counting the sum of the frequency correlations between the item code information in the ith code container and the other item code information in the historical order data set, the method further comprising;

6. The order generation method according to any one of claims 2 to 5, wherein the adjusting, based on the correlation between the transaction amounts corresponding to the n item code information in the historical order data set, the basic transaction amount information corresponding to each of the n item code information to form the order information comprises:

7. The order generating method according to claim 6, wherein the adjusting the basic transaction amount information corresponding to the n item code information, respectively, based on the transaction amount correlation corresponding to the n item code information obtained in advance in the target order data, to obtain the transaction amount information corresponding to the n item code information, respectively, comprises:

multiplying the basic transaction amount information corresponding to the t-1 th article code information by the ratio of the historical transaction amount information of the t-1 th article code information to the historical transaction amount information of the t-1 th article code information in the target order data to obtain the transaction amount information corresponding to the t-1 th article code information until t is equal to n, and further obtaining the transaction amount information corresponding to the n article code information respectively; t is a positive integer of 2 or more and less than n.

8. The order generation method according to claim 2, wherein before adjusting the basic transaction amount information corresponding to the n item code information, respectively, based on the transaction amount correlation corresponding to the n item code information in the historical order data set, and further forming the order information, the method further comprises:

9. The order generation method of claim 1, wherein before randomly generating the empty order corresponding to the first simulation time period, the method further comprises:

10. The order generating method according to claim 9, wherein the preset condition includes: node information, area information and item type information of the plurality of historical time periods;

11. The order generating method according to claim 9, wherein said calculating the target transaction amounts corresponding to the plurality of historical time periods, respectively, in combination with the plurality of order data, comprises:

12. The order generation method of claim 9, wherein the plurality of transaction-related information comprises: the average value of the transaction amount, the maximum value of the transaction amount, the transaction amount distribution frequency information of each item code information, a frequency correlation matrix and a transaction amount correlation matrix;

13. The order generating method according to claim 12, wherein said simultaneously calculating the trade volume mean value, the trade volume maximum value, the trade volume distribution frequency information of the coded information of each item, the frequency correlation matrix, and the trade volume correlation matrix corresponding to the plurality of historical order data sets, respectively, comprises:

14. The order generating method according to claim 13, wherein said calculating a frequency correlation between every two item code information in each historical order data set, and arranging the frequency correlation of each item code information corresponding to other item code information according to an order of the item code information in each historical order data set to form each row of the frequency correlation matrix, thereby forming the frequency correlation matrix, comprises:

15. The order generation method according to claim 13, wherein the calculating of the transaction amount correlation between every two item code information in each historical order data group, and arranging the transaction amount correlation of each item code information corresponding to other item code information according to the order of the item code information in each historical order data group to form each row of the transaction amount correlation matrix, thereby forming the transaction amount correlation matrix, includes:

16. The order generation method according to claim 1, wherein the determining target order information based on the comparison result of the target transaction amount in the historical time period corresponding to the first simulation time period with the order information includes any one of:

17. An order generating apparatus, comprising:

18. An order generating apparatus comprising a memory and a processor, the memory storing a computer program operable on the processor, the processor implementing the steps of the method of any one of claims 1 to 16 when executing the program.

19. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 16.