Order data and inventory data generation method and device and electronic equipment

Abstract

The embodiment of the invention provides an order data and inventory data generation method and device and electronic equipment, and relates to the technical field of warehouse management. The method comprises the steps of determining a first order feature value of a specified order feature which needs to be accorded with to-be-generated order data according to sample order data and / or a preset feature value of a first type of order features in specified order features; according to the sample inventory data and / or a preset feature value of a first inventory feature in the specified inventory features, determining a first inventory feature value of the specified inventory feature which needs to be accorded with the inventory data to be generated, and obtaining a first association feature value of an association feature which needs to be accorded between the order data to be generated and the inventory data to be generated; order data meeting the obtained first order feature value is generated, and inventory data meeting the obtained first inventory feature value is generated; and performing matching to conform to the first association feature value. Therefore, the validity of the generated order data and inventory data can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of warehouse management, in particular to an order data and inventory data generation method and device and electronic equipment. BACKGROUND

[0002] Order data and inventory data play an important role in many aspects of the technical field of warehouse management. For example, in the business scenarios of developing and testing warehouse management systems and optimizing warehouse inventory management, a large amount of order data and inventory data is indispensable.

[0003] However, the traditional order data and inventory data generation method only involves adjusting the data size, without in-depth analysis of warehouse business characteristics, resulting in generated data that is disconnected from real business and is not highly effective, and cannot support subsequent optimization of warehouse management systems. SUMMARY

[0004] The purpose of the embodiments of the present application is to provide an order data and inventory data generation method, device and electronic equipment to improve the effectiveness of generated order data and inventory data. The specific technical solutions are as follows:

[0005] The first aspect of the embodiments of the present application first provides an order data and inventory data generation method, which comprises:

[0006] According to the preset feature value of the first type of order feature in the sample order data and / or the specified order feature, determine the feature value of the specified order feature required to be met by the order data to be generated as the first order feature value; according to the preset feature value of the first inventory feature in the sample inventory data and / or the specified inventory feature, determine the feature value of the specified inventory feature required to be met by the inventory data to be generated as the first inventory feature value; and obtain the feature value of the association feature required to be met between the order data to be generated and the inventory data to be generated as the first association feature value;

[0007] Generate order data that meets the obtained first order feature value, and generate inventory data that meets the obtained first inventory feature value;

[0008] Match the order materials in the generated order data and the inventory materials in the generated inventory data, so that each type of order material in the matched order data and the same type of inventory material in the matched inventory data meet the first association feature value.

[0009] In some embodiments, the specified order feature includes a fixed value type of specified order feature and a random value type of specified order feature; the fixed value type of specified order feature is used to represent the size of the order data to be generated, and the random value type of specified order feature is used to represent the randomness of the order data to be generated.

[0010] and / or,

[0011] The specified inventory characteristics include: a specified inventory characteristic of a fixed value type, and a specified inventory characteristic of a random value type; the specified inventory characteristic of the fixed value type is used to represent the scale of the inventory data to be generated, and the specified inventory characteristic of the random value type is used to represent the randomness of the inventory data to be generated.

[0012] In some embodiments, the determination of the preset characteristic value of the first type of order characteristic in the sample order data and / or the specified order characteristic as the first order characteristic value required to be met by the order data to be generated comprises:

[0013] obtaining the preset characteristic value of the first type of order characteristic in the specified order characteristic as the first order characteristic value of the first type of order characteristic;

[0014] obtaining the first order characteristic value of the second type of order characteristic in the specified order characteristic according to the sample order data; wherein the second type of order characteristic is an order characteristic other than the first type of order characteristic in the specified order characteristic;

[0015] combining the first order characteristic value of the first type of order characteristic and the first order characteristic value of the second type of order characteristic to obtain the first order characteristic value of the specified order characteristic required to be met by the order data to be generated.

[0016] In some embodiments, the obtaining of the first order characteristic value of the second type of order characteristic in the specified order characteristic according to the sample order data comprises:

[0017] for each second type of order characteristic, if the second type of order characteristic is a specified order characteristic of a fixed value type, determining the value of the sample order data for the second type of order characteristic as the first order characteristic value of the second type of order characteristic;

[0018] if the second type of order characteristic is a specified order characteristic of a random value type, determining the values of each data item of the second type of order characteristic in the sample order data and the frequency of occurrence of each value, and fitting to obtain a random distribution met by each data item, and determining the first order characteristic value of the second type of order characteristic according to the obtained random distribution.

[0019] In some embodiments, in the case where the second type of order characteristic includes a characteristic representing the number of orders, the first order characteristic value of the characteristic is the number of orders included in the sample order data.

[0020] and / or,

[0021] In the case that the second type of order feature comprises a feature representing the number of order lines in each order, the first order feature value of the feature is obtained based on the following steps:

[0022] statistically analyzing the number of order lines in each order contained in the sample order data to obtain a random distribution to which the number of order lines in each order contained in the sample order data conforms;

[0023] using the obtained random distribution to sample a first number of numerical values as the first order feature values of the feature representing the number of order lines in each order; wherein the first number is the first order feature value of the feature representing the number of orders;

[0024] and / or,

[0025] In the case that the second type of order feature comprises a feature representing the number of order lines in each order, the first order feature value of the feature is obtained based on the following steps:

[0026] statistically analyzing the number of order lines in each order contained in the sample order data to obtain a random distribution to which the number of order lines in each order contained in the sample order data conforms;

[0027] using the obtained random distribution to sample a first number of numerical values as the first order feature values of the feature representing the number of order lines in each order; wherein the first number is the first order feature value of the feature representing the number of orders;

[0028] and / or,

[0029] In the case that the second type of order feature comprises a feature representing the total number of order materials involved in the order data, the first order feature value of the feature is the total number of order materials involved in the sample order data;

[0030] and / or,

[0031] In the case that the second type of order feature comprises a feature representing the number of orders of each type of order material distribution, the first order feature value of the feature is obtained based on the following steps:

[0032] statistically analyzing the number of orders of each type of order material distribution contained in the sample order data to obtain a random distribution to which the number of orders of each type of order material distribution contained in the sample order data conforms;

[0033] The third number of values are sampled as the first order characteristic values representing the order quantity characteristic of the distribution of each order material category, wherein the third number is the first order characteristic value representing the characteristic of the total number of order material categories involved in the order data;

[0034] and / or,

[0035] In the case that the second order characteristic comprises the characteristic representing the proportion of associated order material categories, the first order characteristic value of the characteristic is obtained based on the following steps:

[0036] For each order material category, a first proportion of the total number of order material categories involved in the sample order data is determined, which is the proportion of the number of order material categories associated with the order material category in the sample order data; and

[0037] The average of the first proportions corresponding to each order material category is calculated as the first order characteristic value representing the characteristic of the proportion of associated order material categories;

[0038] and / or,

[0039] In the case that the second order characteristic comprises the characteristic representing the proportion of associated order material categories, the first order characteristic value of the characteristic is obtained based on the following steps:

[0040] For each order material category, other order materials associated with the order material category in the sample order data are determined as the associated order materials of the order material category;

[0041] For each associated order material of the order material category, the number of orders in the sample order data containing both the order material category and the associated order material is determined as the association number corresponding to the associated order material;

[0042] The maximum value of the association number corresponding to each associated order material is calculated, and the ratio of the maximum value to the number of orders in which the order material category is distributed is determined as a second proportion corresponding to the order material category;

[0043] The average of the second proportions corresponding to each order material category is determined as the first order characteristic value representing the characteristic of the proportion of associated order material categories.

[0044] In some embodiments, the determination of the characteristic value of the specified inventory characteristic required to be met by the generated inventory data as the first inventory characteristic value according to the sample inventory data and / or the preset characteristic value of the first inventory characteristic in the specified inventory characteristic comprises:

[0045] obtaining a preset feature value of a first inventory feature in the specified inventory features as a first inventory feature value of the first inventory feature;

[0046] obtaining, according to the sample inventory data, a first inventory feature value of a second inventory feature in the specified inventory features; wherein the second inventory feature is an inventory feature other than the first inventory feature in the specified inventory features;

[0047] combining the first inventory feature value of the first inventory feature and the first inventory feature value of the second inventory feature to obtain a first inventory feature value of the specified inventory features that the to-be-generated inventory data needs to meet.

[0048] In some embodiments, the obtaining, according to the sample inventory data, a first inventory feature value of a second inventory feature in the specified inventory features comprises:

[0049] for each second inventory feature, if the second inventory feature is a specified inventory feature of a fixed value type, determining a value of the second inventory feature in the sample inventory data as a first inventory feature value of the second inventory feature;

[0050] if the second inventory feature is a specified inventory feature of a random value type, determining values of each data item of the second inventory feature in the sample inventory data and frequencies of occurrence of each value, and fitting a random distribution that each data item meets, and determining a first inventory feature value of the second inventory feature according to the obtained random distribution.

[0051] In some embodiments, in the case that the second inventory feature comprises a feature representing a total number of types of inventory materials involved in the inventory data, the first inventory feature value of the feature is the total number of types of inventory materials involved in the sample inventory data.

[0052] and / or,

[0053] in the case that the second inventory feature comprises a feature representing a number of inventories in each container, the first inventory feature value of the feature is obtained based on the following steps:

[0054] statistically analyzing the number of inventories in each container included in the sample inventory data to obtain a random distribution that the number of inventories in each container included in the sample inventory data meets;

[0055] using the obtained random distribution to sample a fourth number of values as the first inventory feature value of the feature representing the number of inventories in each container; wherein the fourth number is the number of required containers in the to-be-generated inventory data.

[0056] and / or,

[0057] In the case that the second type of inventory feature comprises a feature representing the number of inventory materials in each inventory, the first inventory feature value of the feature is obtained based on the following steps:

[0058] statistically analyzing the number of inventory materials in each inventory included in the sample inventory data to obtain a random distribution to which the number of inventory materials in each inventory included in the sample inventory data conforms;

[0059] using the obtained random distribution, sampling to obtain a fifth number of values as the first inventory feature value of the feature representing the number of inventory materials in each inventory; wherein the fifth number is the sum of the first inventory feature values of the feature representing the number of inventories in each container.

[0060] and / or,

[0061] In the case that the second type of inventory feature comprises a feature representing the number of containers of each type of inventory material distribution, the first inventory feature value of the feature is obtained based on the following steps:

[0062] statistically analyzing the number of containers of each type of inventory material distribution included in the sample inventory data to obtain a random distribution to which the number of containers of each type of inventory material distribution included in the sample inventory data conforms;

[0063] using the obtained random distribution, sampling to obtain a sixth number of values as the first inventory feature value of the feature representing the number of containers of each type of inventory material distribution; wherein the sixth number is the first inventory feature value of the feature representing the total number of types of inventory materials involved in the inventory data.

[0064] and / or,

[0065] In the case that the second type of inventory feature comprises a feature representing the proportion of associated types of inventory materials, the first inventory feature value of the feature is obtained based on the following steps:

[0066] for each type of inventory material, determining a third ratio of the number of types of inventory materials associated with the type of inventory material in the sample inventory data to the total number of types of inventory materials involved in the sample inventory data; wherein a type of inventory material is associated with another type of inventory material if the type of inventory material and the other type of inventory material are distributed in the same container.

[0067] calculating the mean of the third ratios corresponding to each type of inventory material as the first inventory feature value of the feature representing the proportion of associated types of inventory materials.

[0068] and / or,

[0069] In the case that the second type of inventory features includes a feature representing the proportion of associated times of inventory materials, the first inventory feature value of the feature is obtained based on the following steps:

[0070] For each type of inventory material, other inventory materials associated with the type of inventory material in the sample inventory data are determined as associated inventory materials of the type of inventory material;

[0071] For each associated inventory material of the type of inventory material, the number of containers in the sample inventory data that contain both the type of inventory material and the associated inventory material is determined as the corresponding associated times of the associated inventory material;

[0072] The maximum value of the associated times corresponding to each associated inventory material is calculated, and the ratio of the maximum value to the number of inventory distributed to the type of inventory material is determined as the fourth ratio corresponding to the type of inventory material;

[0073] The average of the fourth ratios corresponding to each type of inventory material is calculated to determine the first inventory feature value of the feature representing the proportion of associated times of inventory materials.

[0074] In some embodiments, the association feature represents the difference between the ranking of the demand number of each type of order material in the order data and the ranking of the provision number of the same type of inventory material in the inventory data.

[0075] In some embodiments, the feature value of the association feature required to be met between the order data to be generated and the inventory data to be generated is obtained as the first association feature value, including:

[0076] Each type of order material in the sample order data is sorted according to the size order of the demand number to obtain the first ranking of each type of order material, and each type of inventory material in the sample inventory data is sorted according to the size order of the provision number to obtain the second ranking of each type of inventory material; wherein the sorting method of each type of order material in the sample order data is consistent with the sorting method of each type of inventory material in the sample inventory data;

[0077] For each type of order material in the sample order data, the difference between the first ranking of the type of order material and the second ranking of the inventory material matched with the type of order material is calculated;

[0078] The calculated differences are statistically analyzed to obtain a random distribution of the association feature met between the order data to be generated and the inventory data to be generated;

[0079] The third number of values are sampled as the characteristic values of the required matching association characteristics between the generated order data and the generated inventory data, as the first association characteristic values, by using the obtained random distribution; wherein the third number is a first order characteristic value representing a total number of characteristic of order materials involved in the order data.

[0080] In some embodiments, the matching of the order materials in the generated order data and the inventory materials in the generated inventory data is performed so that each type of order material in the matched order data and each type of inventory material in the matched inventory data meet the first association characteristic values, including:

[0081] The order materials in the generated order data are sorted in a size order of the required numbers, to obtain a third ranking of each type of order material; and the inventory materials in the generated inventory data are sorted in a size order of the provided numbers, to obtain a fourth ranking of each type of inventory material; wherein the sorting manner of the order materials in the generated order data is consistent with the sorting manner of the inventory materials in the generated inventory data; and the sorting manner of the order materials in the generated order data is consistent with the sorting manner of the order materials in the sample order data;

[0082] The order materials in the generated order data and the inventory materials in the generated inventory data are matched by using the first association characteristic values; wherein each type of order material in the matched order data corresponds to a first association characteristic value, and the difference between the third ranking of the order material and the fourth ranking of the matched inventory material is the first association characteristic value.

[0083] In some embodiments, the specified order characteristics include: a characteristic representing a number of orders, a characteristic representing a number of order lines in each order, a characteristic representing a required number of order materials in each order line, a characteristic representing a total number of types of order materials involved in the order data, and a characteristic representing a number of orders distributed by each type of order material;

[0084] The specified inventory characteristics include: a characteristic representing a total number of types of inventory materials involved in the inventory data, a characteristic representing a number of inventories in each container, a characteristic representing a provided number of inventory materials in each inventory, and a characteristic representing a number of containers distributed by each type of inventory material.

[0085] In some embodiments, the specified order characteristics further include: a characteristic representing a proportion of associated types of order materials, and / or, a characteristic representing a proportion of associated times of order materials;

[0086] The order material association category proportion represents a proportion of a category of order materials having an association relationship in the order data in a total category of order materials involved in the order data;

[0087] The order material association times proportion represents a ratio of a maximum association times of a category of order materials to a number of orders in which the category of order materials is distributed; and the maximum association times of the category of order materials represents a maximum value of a number of times that the category of order materials is distributed in the same order with other order materials having an association relationship;

[0088] and / or,

[0089] The specified inventory characteristics further include a characteristic representing an inventory material association category proportion and / or a characteristic representing an inventory material association times proportion;

[0090] The inventory material association category proportion represents a proportion of a category of inventory materials having an association relationship in the inventory data in a total category of inventory materials involved in the inventory data;

[0091] The inventory material association times proportion represents a ratio of a maximum association times of a category of inventory materials to a number of containers in which the category of inventory materials is distributed; and the maximum association times of the category of inventory materials represents a maximum value of a number of times that the category of inventory materials is distributed in the same container with other inventory materials having an association relationship.

[0092] In some embodiments, before the order data satisfying the obtained first order characteristic value is generated and the inventory data satisfying the obtained first inventory characteristic value is generated, the method further includes:

[0093] determining whether the currently obtained first order characteristic value and the first inventory characteristic value satisfy a preset verification rule;

[0094] The verification rule includes at least one of the following:

[0095] The first order characteristic value of the characteristic representing the total number of categories of order materials involved in the order data is not greater than a total sum of the first order characteristic values of the characteristic representing the number of order lines in each order;

[0096] The first order characteristic value of the characteristic representing the total number of categories of order materials involved in the order data is not less than a maximum value in the first order characteristic values of the characteristic representing the number of order lines in each order;

[0097] The first inventory characteristic value of the characteristic representing the total number of categories of inventory materials involved in the inventory data is not less than a maximum value in the first inventory characteristic values of the characteristic representing the number of inventories in each container;

[0098] the first order characteristic value representing the total number of types of order materials involved in the order data is not greater than the first inventory characteristic value representing the total number of types of inventory materials involved in the inventory data;

[0099] the average of the number of orders of each type of order material is not greater than the average of the number of orders of each type of inventory material;

[0100] If the first order characteristic value and the first inventory characteristic value obtained at present do not satisfy the preset verification rule, the characteristic value of the specified order characteristic, and / or the characteristic value of the specified inventory characteristic that do not satisfy the verification rule are corrected until the first order characteristic value and the first inventory characteristic value satisfying the verification rule are obtained.

[0101] In some embodiments, the generating of the order data satisfying the obtained first order characteristic value comprises:

[0102] determining the number of orders contained in the order data to be generated according to the first order characteristic value representing the number of orders;

[0103] determining the number of order lines in each order contained in the order data to be generated according to the first order characteristic value representing the number of order lines in each order;

[0104] determining the number of order materials in each order line contained in the order data to be generated according to the first order characteristic value representing the number of order materials in each order line;

[0105] determining the total number of types of order materials involved in the order data to be generated according to the first order characteristic value representing the total number of types of order materials involved in the order data;

[0106] determining the number of orders of each type of order material distributed in the order data to be generated according to the first order characteristic value representing the number of orders of each type of order material distributed in the order data;

[0107] allocating the types and the number of order materials for each order line according to the number of order materials in each order line contained in the order data to be generated, the total number of types of order materials involved in the order data to be generated, and the number of orders of each type of order material distributed in the order data to be generated;

[0108] allocating order lines for each order according to the number of order lines in each order contained in the order data to be generated and the number of orders contained in the order data to be generated, so that the orders allocated satisfy the first order characteristic value representing the proportion of associated types of order materials, and / or the first order characteristic value representing the proportion of associated times of order materials.

[0109] In some embodiments, the generating the inventory data satisfying the obtained first inventory characteristic value comprises:

[0110] calculating the required number of containers in the to-be-generated inventory data according to a first formula; wherein the first formula is:

[0111]

[0112] m1 is a first inventory characteristic value representing a total number of types of inventory materials involved in the inventory data; m2 is a mean value of first inventory characteristic values representing a number of containers in which each type of inventory material is distributed; m3 is a mean value of first inventory characteristic values representing a number of inventories in each container;

[0113] determining the number of inventories in each container in the to-be-generated inventory data according to the first inventory characteristic value representing the number of inventories in each container;

[0114] determining the total number of types of inventory materials involved in the to-be-generated inventory data according to the first inventory characteristic value representing a total number of types of inventory materials involved in the inventory data;

[0115] determining the number of inventories of inventory materials in each inventory in the to-be-generated inventory data according to the first inventory characteristic value representing a number of inventories of inventory materials in each inventory;

[0116] determining the number of containers in which each type of inventory material is distributed in the to-be-generated inventory data according to the first inventory characteristic value representing a number of containers in which each type of inventory material is distributed;

[0117] allocating types and numbers of inventory materials for each inventory according to the number of inventories of inventory materials in each inventory in the to-be-generated inventory data, the total number of types of inventory materials involved in the to-be-generated inventory data, and the number of containers in which each type of inventory material is distributed in the to-be-generated inventory data;

[0118] allocating inventories for each container in the to-be-generated inventory data according to the required number of containers in the to-be-generated inventory data and the number of inventories in each container in the to-be-generated inventory data, so that the allocated containers satisfy the first inventory characteristic value representing a proportion of associated types of inventory materials, and / or the first inventory characteristic value representing a proportion of associated times of inventory materials.

[0119] In a second aspect, the embodiments of the present application provide an order data and inventory data generation device, which comprises:

[0120] The characteristic value acquisition module is configured to: determine, according to preset characteristic values of first order characteristics in the specified order characteristics and / or sample order data, a characteristic value of the specified order characteristics required to be met by the order data to be generated as a first order characteristic value; determine, according to preset characteristic values of first inventory characteristics in the specified inventory characteristics and / or sample inventory data, a characteristic value of the specified inventory characteristics required to be met by the inventory data to be generated as a first inventory characteristic value; and acquire a characteristic value of an association characteristic required to be met between the order data to be generated and the inventory data to be generated as a first association characteristic value.

[0121] The data generation module is configured to generate order data meeting the obtained first order characteristic value, and generate inventory data meeting the obtained first inventory characteristic value.

[0122] The material matching module is configured to match order materials in the generated order data and inventory materials in the inventory data, so that each type of order material in the matched order data and each type of inventory material in the matched inventory data meet the first association characteristic value.

[0123] In some embodiments, the specified order characteristics include: a fixed value type of specified order characteristic, and a random value type of specified order characteristic; the fixed value type of specified order characteristic is used to represent the scale of the order data to be generated, and the random value type of specified order characteristic is used to represent the randomness of the order data to be generated; and / or, the specified inventory characteristics include: a fixed value type of specified inventory characteristic, and a random value type of specified inventory characteristic; the fixed value type of specified inventory characteristic is used to represent the scale of the inventory data to be generated, and the random value type of specified inventory characteristic is used to represent the randomness of the inventory data to be generated.

[0124] In some embodiments, the characteristic value acquisition module includes:

[0125] The first order characteristic value acquisition submodule is configured to acquire the preset characteristic value of the first type of order characteristic in the specified order characteristics as the first order characteristic value of the first type of order characteristic.

[0126] The second order characteristic value acquisition submodule is configured to obtain, according to sample order data, the first order characteristic value of the second type of order characteristic in the specified order characteristics; wherein the second type of order characteristic is an order characteristic in the specified order characteristics other than the first type of order characteristic.

[0127] The first combination submodule is configured to combine the first order characteristic value of the first type of order characteristic and the first order characteristic value of the second type of order characteristic to obtain the first order characteristic value of the specified order characteristics required to be met by the order data to be generated.

[0128] In some embodiments, the second order feature value obtaining submodule is specifically configured to:

[0129] For each second order feature, if the second order feature is a specified order feature of a fixed value type, a value of the sample order data for the second order feature is determined as a first order feature value of the second order feature.

[0130] If the second order feature is a specified order feature of a random value type, values of each data item of the sample order data for the second order feature and a frequency of occurrence of each value are determined, a random distribution to which each data item conforms is fitted, and a first order feature value of the second order feature is determined according to the obtained random distribution.

[0131] In some embodiments, when the second type of order feature includes a feature representing the number of orders, the first order feature value of the feature is: the number of orders included in the sample order data; and / or, when the second type of order feature includes a feature representing the number of order lines in each order, the first order feature value of the feature is obtained based on the following steps: performing statistical analysis on the number of order lines in each order included in the sample order data to obtain a random distribution that the number of order lines in each order included in the sample order data conforms to; using the obtained random distribution, sampling to obtain a first number of numerical values ​​as the first order feature value of the feature representing the number of order lines in each order; wherein, the first number The first order characteristic value is a characteristic representing the number of orders; and / or, in the case where the second type of order characteristics includes a characteristic representing the number of order materials required in each order line, the first order characteristic value of the characteristic is obtained based on the following steps: statistically analyzing the number of order materials required in each order line included in the sample order data to obtain a random distribution that conforms to the number of order materials required in each order line included in the sample order data; using the obtained random distribution, sampling a second number of values ​​as the first order characteristic value representing the number of order materials required in each order line; wherein the second number is: the first order characteristic value of each first order characteristic representing the number of order lines in each order The sum value; and / or, in the case where the second type of order feature includes a feature representing the total number of types of order materials involved in the order data, the first order feature value of the feature is: the total number of types of order materials involved in the sample order data; and / or, in the case where the second type of order feature includes a feature representing the number of orders distributed for each type of order material, the first order feature value of the feature is obtained based on the following steps: statistically analyzing the number of orders distributed for each type of order material included in the sample order data to obtain a random distribution that the number of orders distributed for each type of order material included in the sample order data conforms to; using the obtained random distribution, sampling to obtain a third number of values ​​as a representation of each type of order a first order characteristic value representing a characteristic of the number of orders for material distribution; wherein the third number is: a first order characteristic value representing a characteristic of the total number of types of order materials involved in the order data; and / or, in a case where the second type of order characteristic includes a characteristic representing a ratio of associated types of order materials, the first order characteristic value of the characteristic is obtained based on the following steps: for each type of order material, determining, in the sample order data, a first ratio of the types of order materials that have an associated relationship with the order materials of that type to the total types of order materials involved in the sample order data; wherein the existence of an associated relationship between one type of order material and another type of order material indicates that the order materials of that type and the order materials of the other type are distributed in the same order;The average of the first ratios of each type of order material is calculated as a first order feature value representing the proportion of the type of order material; and / or, in the case that the second type of order feature comprises a feature representing the proportion of the number of associations, the first order feature value of the feature is obtained based on the following steps: for each type of order material, determining other order materials associated with the type of order material in the sample order data as associated order materials of the type of order material; for each associated order material of the type of order material, determining the number of orders in the sample order data containing both the type of order material and the associated order material as the number of associations corresponding to the associated order material; calculating the ratio of the maximum value of the number of associations corresponding to each associated order material to the number of orders distributed with the type of order material as a second ratio corresponding to the type of order material; and determining the average of the second ratios corresponding to each type of order material as the first order feature value representing the proportion of the number of associations.

[0132] In some embodiments, the feature value obtaining module comprises:

[0133] The first inventory feature value obtaining submodule is configured to obtain a preset feature value of a first inventory feature in the specified inventory features as a first inventory feature value of a first type of inventory feature.

[0134] The second inventory feature value obtaining submodule is configured to obtain, according to the sample inventory data, a first inventory feature value of a second type of inventory feature in the specified inventory features; wherein the second type of inventory feature is an inventory feature other than the first type of inventory feature in the specified inventory features.

[0135] The second combining submodule is configured to combine the first inventory feature value of the first type of inventory feature and the first inventory feature value of the second type of inventory feature to obtain a first inventory feature value of the specified inventory features that the to-be-generated inventory data needs to meet.

[0136] In some embodiments, the second inventory feature value obtaining submodule is specifically configured to:

[0137] For each second type of inventory feature, if the second type of inventory feature is a specified inventory feature of a fixed value type, the value of the sample inventory data for the second type of inventory feature is determined as the first inventory feature value of the second type of inventory feature.

[0138] If the second type of inventory feature is a specified inventory feature of a random value type, the values of each data item of the sample inventory data for the second type of inventory feature and the frequency of occurrence of each value are determined, and a random distribution that each data item conforms to is fitted, and the first inventory feature value of the second type of inventory feature is determined according to the obtained random distribution.

[0139] In some embodiments, in the case that the second type of inventory feature comprises a feature representing the total number of types of inventory items involved in the inventory data, the first inventory feature value of this feature is: the total number of types of inventory items involved in the sample inventory data; and / or, in the case that the second type of inventory feature comprises a feature representing the number of inventories in each container, the first inventory feature value of this feature is obtained based on the following steps: performing statistical analysis on the number of inventories in each container included in the sample inventory data to obtain a random distribution to which the number of inventories in each container included in the sample inventory data conforms; and using the obtained random distribution to sample a fourth number of values as the first inventory feature values of the feature representing the number of inventories in each container; wherein the fourth number is: the number of containers required in the inventory data to be generated; and / or, in the case that the second type of inventory feature comprises a feature representing the number of provides of inventory items in each inventory, the first inventory feature value of this feature is obtained based on the following steps: performing statistical analysis on the number of provides of inventory items in each inventory included in the sample inventory data to obtain a random distribution to which the number of provides of inventory items in each inventory included in the sample inventory data conforms; and using the obtained random distribution to sample a fifth number of values as the first inventory feature values of the feature representing the number of provides of inventory items in each inventory; wherein the fifth number is: the total sum of the first inventory feature values of the feature representing the number of inventories in each container; and / or, in the case that the second type of inventory feature comprises a feature representing the number of containers distributed with each type of inventory item, the first inventory feature value of this feature is obtained based on the following steps: performing statistical analysis on the number of containers distributed with each type of inventory item included in the sample inventory data to obtain a random distribution to which the number of containers distributed with each type of inventory item included in the sample inventory data conforms; and using the obtained random distribution to sample a sixth number of values as the first inventory feature values of the feature representing the number of containers distributed with each type of inventory item; wherein the sixth number is: the first inventory feature value of the feature representing the total number of types of inventory items involved in the inventory data; and / or, in the case that the second type of inventory feature comprises a feature representing the proportion of associated types of inventory items, the first inventory feature value of this feature is obtained based on the following steps: for each type of inventory item, determining a third ratio of the number of types of inventory items associated with this type of inventory item in the sample inventory data to the total number of types of inventory items involved in the sample inventory data, wherein a type of inventory item is associated with another type of inventory item if the two types of inventory items are distributed in the same container; and calculating the mean of the third ratios corresponding to each type of inventory item as the first inventory feature value of the feature representing the proportion of associated types of inventory items.and / or, in the case that the second type of inventory feature comprises a feature representing a proportion of associated times of inventory materials, the first inventory feature value of the feature is obtained based on the following steps: for each type of inventory material, determining other inventory materials having an associated relationship with the type of inventory material in the sample inventory data as associated inventory materials of the type of inventory material; for each associated inventory material of the type of inventory material, determining a number of containers containing both the type of inventory material and the associated inventory material in the sample inventory data as an associated time corresponding to the associated inventory material; calculating a ratio of a maximum value of the associated times corresponding to the associated inventory materials and a number of inventories distributed with the type of inventory material as a fourth ratio corresponding to the type of inventory material; and calculating a mean value of the fourth ratios corresponding to each type of inventory material as the first inventory feature value of the feature representing the proportion of associated times of inventory materials.

[0140] In some embodiments, the association feature represents a difference between rankings of the number of demands of each type of order material in the order data and rankings of the number of provisions of the same type of inventory material in the inventory data.

[0141] In some embodiments, the feature value obtaining module comprises:

[0142] an association ranking submodule, configured to rank each type of order material in the sample order data according to a size order of the number of demands to obtain a first ranking of each type of order material, and rank each type of inventory material in the sample inventory data according to a size order of the number of provisions to obtain a second ranking of each type of inventory material; wherein the manner of ranking each type of order material in the sample order data is consistent with the manner of ranking each type of inventory material in the sample inventory data;

[0143] an association difference calculation submodule, configured to calculate, for each type of order material in the sample order data, a difference between the first ranking of the type of order material and the second ranking of an inventory material matched with the type of order material;

[0144] an association analysis submodule, configured to statistically analyze each difference calculated to obtain a random distribution to which an association feature between the order data to be generated and the inventory data to be generated conforms;

[0145] an association sampling submodule, configured to sample a third number of values as feature values of the required association feature between the order data to be generated and the inventory data to be generated to which the association feature conforms, as first association feature values, by using the obtained random distribution; wherein the third number is a first order feature value of a feature representing a total number of types of order materials involved in the order data.

[0146] In some embodiments, the material matching module is specifically configured to:

[0147] rank each type of order material in the generated order data according to the size of the required number to obtain a third ranking of each type of order material; and rank each type of inventory material in the generated inventory data according to the size of the provided number to obtain a fourth ranking of each type of inventory material; wherein the manner of ranking each type of order material in the generated order data is consistent with the manner of ranking each type of inventory material in the generated inventory data; and the manner of ranking each type of order material in the generated order data is consistent with the manner of ranking each type of order material in the sample order data;

[0148] match the order material in the generated order data and the inventory material in the generated inventory data using each first correlation characteristic value; wherein each type of order material in the matched order data corresponds to a first correlation characteristic value, and the difference between the third ranking of the type of order material and the fourth ranking of the matched inventory material is the first correlation characteristic value.

[0149] In some embodiments, the specified order features include: a feature representing the number of orders, a feature representing the number of order lines in each order, a feature representing the required number of order materials in each order line, a feature representing the total number of types of order materials involved in the order data, and a feature representing the number of orders distributed by each type of order material.

[0150] The specified inventory features include: a feature representing the total number of types of inventory materials involved in the inventory data, a feature representing the number of inventories in each container, a feature representing the provided number of inventory materials in each inventory, and a feature representing the number of containers distributed by each type of inventory material.

[0151] In some embodiments, the specified order feature further comprises: a feature representing an order material association category proportion, and / or a feature representing an order material association frequency proportion; the order material association category proportion represents: a proportion of a category of order materials having an association relationship in the order data in a total category of order materials involved in the order data; the order material association frequency proportion represents: a ratio of a maximum association frequency of a category of order materials to a number of orders in which the category of order materials is distributed; the maximum association frequency of a category of order materials represents: a maximum value of a frequency of the category of order materials being distributed in the same order as other order materials having an association relationship; and / or the specified inventory feature further comprises: a feature representing an inventory material association category proportion, and / or a feature representing an inventory material association frequency proportion; the inventory material association category proportion represents: a proportion of a category of inventory materials having an association relationship in the inventory data in a total category of inventory materials involved in the inventory data; the inventory material association frequency proportion represents: a ratio of a maximum association frequency of a category of inventory materials to a number of containers in which the category of inventory materials is distributed; the maximum association frequency of a category of inventory materials represents: a maximum value of a frequency of the category of inventory materials being distributed in the same container as other inventory materials having an association relationship.

[0152] In some embodiments, the device further comprises:

[0153] The verification module is configured to, before the generated order data satisfying the obtained first order feature value and the generated inventory data satisfying the obtained first inventory feature value, determine whether the obtained first order feature value and the obtained first inventory feature value satisfy a preset verification rule; the verification rule comprises at least one of: a first order feature value of a feature representing a total number of categories of order materials involved in the order data, which is not greater than a total sum of first order feature values of a feature representing a number of order lines in each order; a first order feature value of a feature representing a total number of categories of order materials involved in the order data, which is not less than a maximum value among first order feature values of a feature representing a number of order lines in each order; a first inventory feature value of a feature representing a total number of categories of inventory materials involved in the inventory data, which is not less than a maximum value among first inventory feature values of a feature representing a number of inventories in each container; a first order feature value of a feature representing a total number of categories of order materials involved in the order data, which is not greater than a first inventory feature value of a feature representing a total number of categories of inventory materials involved in the inventory data; a mean value of a demand number of each category of order materials, which is not greater than a mean value of a provision number of each category of inventory material.

[0154] The correction module is configured to correct the characteristic value of the specified order characteristic and / or the characteristic value of the specified inventory characteristic, if the current obtained first order characteristic value and the first inventory characteristic value do not satisfy the preset verification rule, until the first order characteristic value and the first inventory characteristic value satisfying the verification rule are obtained.

[0155] In some embodiments, the data generation module is specifically configured to:

[0156] determine the number of orders to be contained in the generated order data according to the first order characteristic value representing the number of orders;

[0157] determine the number of order lines in each order to be contained in the generated order data according to the first order characteristic value representing the number of order lines in each order;

[0158] determine the number of order materials in each order line to be contained in the generated order data according to the first order characteristic value representing the number of order materials in each order line;

[0159] determine the total number of order materials involved in the generated order data according to the first order characteristic value representing the total number of order materials involved in the order data;

[0160] determine the number of orders of each type of order material to be contained in the generated order data according to the first order characteristic value representing the number of orders of each type of order material;

[0161] allocate the type and number of order materials for each order line according to the number of order materials in each order line to be contained in the generated order data, the total number of order materials involved in the generated order data, and the number of orders of each type of order material to be contained in the generated order data;

[0162] allocate order lines for each order according to the number of order lines in each order to be contained in the generated order data and the number of orders to be contained in the generated order data, so that the allocated orders satisfy the first order characteristic value representing the proportion of associated types of order materials, and / or the first order characteristic value representing the proportion of associated times of order materials.

[0163] In some embodiments, the data generation module is specifically configured to:

[0164] calculate the number of required containers in the generated inventory data according to a first formula; wherein the first formula is:

[0165]

[0166] m1 is the first inventory characteristic value representing the total number of inventory types involved in the inventory data; m2 is the mean of the first inventory characteristic values ​​representing the number of containers for each type of inventory material distribution; m3 is the mean of the first inventory characteristic values ​​representing the number of inventory items in each container;

[0167] Determining the quantity of inventory in each container in the inventory data to be generated according to a first inventory feature value representing a feature of the quantity of inventory in each container;

[0168] determining the total number of types of inventory materials involved in the inventory data to be generated according to a first inventory characteristic value representing the total number of types of inventory materials involved in the inventory data;

[0169] Determining the provided quantity of each inventory material in the inventory data to be generated according to a first inventory characteristic value representing a characteristic of the provided quantity of each inventory material;

[0170] Determining the number of containers for each category of inventory material distribution in the inventory data to be generated according to a first inventory characteristic value representing the number of containers for each category of inventory material distribution;

[0171] Allocate the type and supply quantity of inventory materials to each inventory according to the supply quantity of inventory materials in each inventory in the inventory data to be generated, the total number of types of inventory materials involved in the inventory data to be generated, and the number of containers in which each type of inventory material is distributed in the inventory data to be generated;

[0172] According to the required number of containers in the inventory data to be generated and the number of inventories in each container in the inventory data to be generated, inventory is allocated to each container so that the allocated container satisfies the first inventory characteristic value representing the ratio of the types of associated inventory materials and / or the first inventory characteristic value representing the ratio of the number of times inventory materials are associated.

[0173] According to a third aspect of the embodiments of the present application, an electronic device is provided, including:

[0174] Memory for storing computer programs;

[0175] The processor is configured to implement any of the above-mentioned methods for generating order data and inventory data when executing the program stored in the memory.

[0176] In another aspect of the embodiments of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any of the above-mentioned methods for generating order data and inventory data is implemented.

[0177] In another aspect of the embodiments of the present application, a computer program product containing instructions, which, when executed on a computer, cause the computer to perform any of the above-mentioned order data and inventory data generation methods, is provided.

[0178] The embodiments of the present application have the following beneficial effects:

[0179] Based on the order data and inventory data generation method provided by the embodiments of the present application, the first order feature value is determined according to the preset feature value of the first order feature in the sample order data and / or the specified order feature, and the first inventory feature value is determined according to the preset feature value of the first inventory feature in the sample inventory data and / or the specified inventory feature. The first order feature value represents the constraint condition required to be met by the order data to be generated on each specified order feature. The first inventory feature value represents the constraint condition required to be met by the inventory data to be generated on each specified inventory feature. Accordingly, the electronic device can generate order data that meets the first order feature value after obtaining the first order feature value, and can generate inventory data that meets the first inventory feature value obtained after obtaining the first inventory feature value. Moreover, in the actual scenario of warehouse management, the type of material required in the order often belongs to the type of material provided in the inventory. Therefore, after independently generating the order data and the inventory data, the electronic device can match the order material in the generated order data and the inventory material in the generated inventory data according to the first association feature value of the association feature required to be met between the order data to be generated and the inventory data to be generated. The matching of a type of order material and a type of inventory material indicates that the type of order material and the type of inventory material are the same type of material. That is, for each order material in the generated order data, the inventory material in the inventory data that matches the order material is determined. In this way, the order material in the order data and the inventory material in the inventory data can be matched while ensuring that the generated order data meets the first order feature value and the generated inventory data meets the first inventory feature value, so that the generated order data and inventory data meet the characteristics of the order data and inventory data under real business, and the effectiveness of the generated order data and inventory data is improved.

[0180] Of course, implementing any product or method of the present application does not necessarily require all the advantages described above to be achieved at the same time. BRIEF DESCRIPTION OF DRAWINGS

[0181] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other embodiments can also be obtained by those skilled in the art based on these drawings.

[0182] Figure 1A first flowchart of an order data and inventory data generation method provided by an embodiment of the present application is shown in FIG. 1.

[0183] Figure 2 A flowchart of a process of obtaining sample feature values of specified order features of sample order data provided by an embodiment of the present application is shown in FIG. 3.

[0184] Figure 3 A second flowchart of an order data and inventory data generation method provided by an embodiment of the present application is shown in FIG. 4.

[0185] Figure 4 A flowchart of a process of generating order data and inventory data provided by an embodiment of the present application is shown in FIG. 5.

[0186] Figure 5 A flowchart of a process of matching order materials and inventory materials provided by an embodiment of the present application is shown in FIG. 6.

[0187] Figure 6 A structural diagram of an order data and inventory data generation device provided by an embodiment of the present application is shown in FIG. 7.

[0188] Figure 7 A structural diagram of an electronic device provided by an embodiment of the present application is shown in FIG. 8. DETAILED DESCRIPTION

[0189] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art based on the present application belong to the scope of protection of the present application.

[0190] The conventional order data and inventory data generation method only involves adjustment of the data scale, without in-depth analysis of the warehouse business characteristics, resulting in the generated data being inconsistent with the real business, and the effectiveness being not high, which cannot support subsequent optimization of the warehouse management system.

[0191] An order data and inventory data generation method is provided by an embodiment of the present application, which can be applied to an electronic device.

[0192] Referring to Figure 1 , Figure 1 A first flowchart of an order data and inventory data generation method provided by an embodiment of the present application is shown in FIG. 1. The method includes the following steps.

[0193] S101: According to the sample order data and / or the preset feature value of the first type of order feature in the specified order feature, determine the feature value of the specified order feature required to be met by the to-be-generated order data as the first order feature value; according to the sample inventory data and / or the preset feature value of the first inventory feature in the specified inventory feature, determine the feature value of the specified inventory feature required to be met by the to-be-generated inventory data as the first inventory feature value; and obtain the feature value of the association feature required to be met between the to-be-generated order data and the to-be-generated inventory data as the first association feature value.

[0194] S102: Generate order data that meets the obtained first order feature value, and generate inventory data that meets the obtained first inventory feature value.

[0195] S103: Match the order materials in the generated order data and the inventory materials in the inventory data, so that each type of order material in the matched order data and the same type of inventory material in the matched inventory data meet the first association feature value.

[0196] Based on the above processing, the first order feature value is determined according to the sample order data and / or the preset feature value of the first type of order feature in the specified order feature, the first inventory feature value is determined according to the sample inventory data and / or the preset feature value of the first inventory feature in the specified inventory feature, the first order feature value represents the constraint condition required to be met by the to-be-generated order data on each specified order feature. The first inventory feature value represents the constraint condition required to be met by the to-be-generated inventory data on each specified inventory feature. Accordingly, after the electronic device obtains the first order feature value, it can generate order data that meets the first order feature value; after obtaining the first inventory feature value, it can generate inventory data that meets the obtained first inventory feature value. And in the actual scenario of warehouse management, the type of material required in the order often belongs to the type of material provided in the inventory. Further, after independently generating order data and inventory data, the electronic device can match the order materials in the generated order data and the inventory materials in the inventory data according to the first association feature value of the association feature required to be met between the to-be-generated order data and the to-be-generated inventory data. A type of order material is matched with a type of inventory material, indicating that the type of order material and the type of inventory material are the same type of material. That is, for each order material in the generated order data, determine the inventory material in the inventory data that matches the order material. In this way, the order materials in the order data and the inventory materials in the inventory data can be matched while ensuring that the generated order data meets the first order feature value and the generated inventory data meets the first inventory feature value, so that the generated order data and inventory data meet the features of order data and inventory data in real business, improving the effectiveness of the generated order data and inventory data.

[0197] For step S101, the order data represents information of at least one order.

[0198] An order contains at least one order line. An order line contains an order material and a quantity of the order material. When an order contains multiple order lines, the multiple order lines contain different order materials.

[0199] The inventory data represents information of materials stored in the warehouse. The information of materials stored in the warehouse includes at least one inventory material, a quantity of each inventory material, and a container where each inventory material is stored.

[0200] A container is a carrier for storing materials in the inventory, for example, the container can be a shelf or a box. A container contains at least one inventory. An inventory contains an inventory material and a quantity of the inventory material. When a container contains multiple inventories, the multiple inventories contain different inventory materials.

[0201] In an implementation, the order data to be generated and the inventory data to be generated represent order data and inventory data corresponding to a specified time period.

[0202] The specified time period can be a time period set by a technician according to actual needs in advance. For example, the length of the time period can be days, weeks, or months. Correspondingly, the order data corresponding to the specified time period represents information of orders in the specified time period. The inventory data corresponding to the specified time period represents information of materials stored in the warehouse at a specified time in the specified time period. For example, the specified time in the specified time period can be any time (e.g., the end time) in the specified time period.

[0203] In some embodiments, the specified order features include a feature representing a number of orders, a feature representing a number of order lines in each order, a feature representing a quantity of order materials in each order line, a feature representing a total number of order materials involved in the order data, and a feature representing a number of orders in which each order material is distributed.

[0204] The process of specifically determining the feature values of the specified order features in an order data will be described in subsequent embodiments.

[0205] In an implementation, the specified order features further include a feature representing a proportion of associated categories of order materials, and / or a feature representing a proportion of associated times of order materials.

[0206] The proportion of associated categories of order materials represents a proportion of the number of associated order materials in the order data in the total number of order materials involved in the order data.

[0207] The order material association times proportion represents a ratio of the maximum association times of a type of order material to the number of orders in which the type of order material is distributed. The maximum association times of a type of order material represent the maximum value of the number of times that the type of order material and another type of order material distributed in the same order.

[0208] In an embodiment of the present application, the association between a type of order material and another type of order material means that the type of order material and the other type of order material are distributed in the same order.

[0209] For example, if the order data includes order 001, order 001 includes three order lines (order line 1, order line 2, and order line 3). Order line 1 includes an order material type of order material A, and order line 1 includes a demand number of 5. Order line 2 includes an order material type of order material B, and order line 2 includes a demand number of 10. Order line 3 includes an order material type of order material C, and order line 3 includes a demand number of 1. That is, order material A, order material B, and order material C are distributed in the same order (order 001), order material A and order material B have an association relationship, order material A and order material C have an association relationship, and order material B and order material C have an association relationship.

[0210] It can be understood that in the actual scenario of warehouse management, different types of order materials are not independently and uniformly distributed in all orders. For example, in the production process of household appliances, the materials required for producing washing machines usually include washing machine drums, washing machine bodies, washing machine covers, etc. The materials required for producing induction cookers usually include induction cooker panels, induction cooker bodies, heating coils, etc. It can be seen that in the orders placed by the workers of the production workshop to the warehouse management system, washing machine drums, washing machine bodies, washing machine covers, etc. are usually distributed in the same order; induction cooker panels and heating coils are usually distributed in the same order.

[0211] The order material association type proportion represents the proportion of the types of order materials having an association relationship in the total types of order materials involved in the order data. That is, the order material association type proportion reflects the characteristics of the order materials having an association relationship in the order data from the proportion of the number of types.

[0212] The order material association times proportion ratio is a ratio of the maximum association times of a type of order material to the number of orders in which the type of order material is distributed. The maximum association times of a type of order material is the maximum value of the number of times that the type of order material is distributed in the same order as other order materials with which the type of order material has an association. That is, the order material association times proportion ratio reflects the characteristics of order materials with which there is an association in order data from the proportion of occurrence frequencies.

[0213] The process of specifically determining the feature value of the feature representing the order material association type proportion and the feature value of the feature representing the order material association times proportion in an order data will be described in subsequent embodiments.

[0214] As can be seen, in the embodiments of the present application, the feature representing the order material association type proportion and the feature representing the order material association times proportion can reflect the characteristics of order materials with which there is an association in order data in different dimensions. Subsequently, the electronic device can generate order data using one or more of the feature representing the order material association type proportion and the feature representing the order material association times proportion.

[0215] Based on the above processing, the order material association type proportion reflects the characteristics of order materials with which there is an association in order data from the proportion of the number of types, and the order material association times proportion reflects the characteristics of order materials with which there is an association in order data from the proportion of occurrence frequencies. The electronic device can generate order data in combination with the feature value of the feature representing the order material association type proportion and / or the feature value of the feature representing the order material association times proportion, so that the generated order data is more consistent with order data in real scenarios and improves the authenticity of the generated order data.

[0216] In some embodiments, the specified order features include specified order features of a fixed value type and specified order features of a random value type. The specified order features of the fixed value type are used to represent the scale of the order data to be generated, and the specified order features of the random value type are used to represent the randomness of the order data to be generated.

[0217] In the embodiments of the present application, the specified order features of the fixed value type are used to represent the scale of the order data to be generated. That is, the scale of the order data to be generated can be controlled through the specified order features of the fixed value type.

[0218] The feature value of a specified order feature of the fixed value type is unique for an order data. For example, for the feature representing the number of orders, the number of orders included in an order data is unique. For example, a sample order data includes 2 orders, and the feature value of the feature representing the number of orders of the sample order data is 2, and there is no other value.

[0219] For one order data, the characteristic values of the specified order characteristics of the random value type usually contain multiple values. For example, for the characteristic representing the number of order lines in each order, the order data can contain multiple orders, and the number of order lines in each order can be different or the same. For example, a sample order data contains two orders, and the number of order lines in the two orders is 2 and 3 respectively. The characteristic value of the characteristic representing the number of order lines in each order of the sample order data is 2 and 3.

[0220] In an implementation manner, the specified order characteristics of the fixed value type can include at least one of the following: a characteristic representing the number of orders, a characteristic representing the total number of order materials involved in the order data, a characteristic representing the proportion of order material associated categories, and a characteristic representing the proportion of order material associated times.

[0221] For example, the specified order characteristics of the fixed value type include a characteristic representing the number of orders. In the actual scenario of warehouse management, the number of orders in the corresponding order data within a month is usually around 100,000. Accordingly, if the user needs to generate order data corresponding to a certain month, the number of orders in the generated order data can be determined by setting a fixed value.

[0222] The specified order characteristics of the random value type are used to represent the randomness of the order data to be generated. That is, the randomness of the generated order data can be ensured by the specified order characteristics of the random value type, so that the generated order data is closer to the dynamic characteristics in the real business scenario.

[0223] In an implementation manner, the specified order characteristics of the random value type can include at least one of the following: a characteristic representing the number of order lines in each order, a characteristic representing the number of order materials in each order line, and a characteristic representing the number of orders of each type of order material distribution.

[0224] Continuing with the above example, the specified order characteristics of the random value type include a characteristic representing the number of order lines in each order. In the actual scenario of warehouse management, the number of order lines in each order in the corresponding order data within a month is often not completely consistent. Accordingly, if the user needs to generate order data corresponding to a certain month, the number of order lines in each order in the generated order data can be determined by selecting a random value.

[0225] Based on the above processing, the specified order feature of the fixed value type can adjust the size and core features of the generated data, and the specified order feature of the random value type can improve the diversity of the generated data, reflecting the difference and randomness characteristics of the real warehouse data. In this way, the order data can be generated by combining the specified order feature of the fixed value type and the specified order feature of the random value type, and the reliability of the generated order data can be ensured.

[0226] In some embodiments, the step of determining the feature value of the specified order feature required to be met by the order data to be generated as the first order feature value according to the preset feature value of the first type of order feature in the sample order data and / or the specified order feature includes:

[0227] Step S101-1: Obtain the preset feature value of the first type of order feature in the specified order feature as the first order feature value of the first type of order feature.

[0228] Step S101-2: Obtain the first order feature value for the second type of order feature in the specified order feature according to the sample order data.

[0229] Among them, the second type of order feature is an order feature in the specified order feature other than the first type of order feature.

[0230] Step S101-3: Combine the first order feature value of the first type of order feature and the first order feature value of the second type of order feature to obtain the first order feature value of the specified order feature required to be met by the order data to be generated.

[0231] In the embodiments of the present application, the specified order feature can be divided into the first type of order feature and the second type of order feature according to different ways of obtaining the feature value.

[0232] Among them, the preset feature value of the first type of order feature in the specified order feature can be input by the user. Correspondingly, the electronic device can obtain the preset feature value of the first type of order feature in the specified order feature input by the user as the first order feature value of the first type of order feature.

[0233] The first order feature value of the second type of order feature in the specified order feature is extracted from the sample order data. Correspondingly, the electronic device can obtain the sample order data of the sample time period.

[0234] Among them, the duration of the sample time period is consistent with the duration of the specified time period. The sample order data of the sample time period can be real order data of a historical time period selected by the user according to actual needs, and the specific way of selecting the sample order data of the sample time period is not limited in the present application.

[0235] For example, if the user needs to generate order data of December, the sample order data of the sample time period can be real order data generated in a certain month of the historical time period.

[0236] In an implementation, the electronic device can pre-extract the feature values of each specified order feature of the sample order data (which can be referred to as sample feature values). After obtaining the feature values of the first type of order feature input by the user (which can be referred to as input feature values), the sample feature values of the first type of order feature in the specified order features are replaced by the input feature values, and the feature values of the replaced specified order features are taken as the first order feature values.

[0237] In this way, the electronic device can pre-obtain the sample feature values of each dimension order feature (i.e., the specified order feature) of the sample order data. Furthermore, after obtaining the user's demand for part of the dimension order features (i.e., the first order feature values of the first type of order feature), the feature values of the part of the dimension order features are replaced. In this way, the generated order data can meet the actual demand of the user, improve the efficiency of obtaining each dimension order feature, and improve the efficiency of generating order data.

[0238] In another implementation, the electronic device can extract the feature values of the second type of order feature in the specified order features according to the sample order data after obtaining the feature values of the first type of order feature in the specified order features input by the user. The second type of order feature is an order feature other than the first type of order feature in the specified order features.

[0239] In this way, compared with the way of extracting the feature values of each dimension order feature (each specified order feature) in the sample order data, only the feature values of part of the dimension order features (i.e., the feature values of the second type of order feature in the specified order features) input by the user need to be extracted, so that the computational cost required to obtain the first order feature values of each specified order feature can be reduced.

[0240] In the case where the user inputs the preset feature values of all the order features in the specified order features, the electronic device can obtain the feature values of each specified order feature as the first order feature values only according to the preset feature values of the first type of order feature in the specified order features obtained. At this time, all the specified order features are the first type of order feature.

[0241] In the case where the user does not input the preset feature values of the first type of order feature in the specified order features, the electronic device can obtain the feature values of each specified order feature as the first order feature values only according to the sample order data. At this time, all the specified order features are the second type of order feature.

[0242] In some embodiments, step S101-2 includes:

[0243] For each second-type order feature, if the second-type order feature is a specified order feature of fixed value type, a value of the second-type order feature in the sample order data is determined as a first order feature value of the second-type order feature.

[0244] If the second-type order feature is a specified order feature of random value type, values of each data item of the second-type order feature in the sample order data are determined, and a frequency of occurrence of each value is determined, and a random distribution that each data item conforms to is fitted, and a first order feature value of the second-type order feature is determined according to the obtained random distribution.

[0245] In the embodiments of the present application, for a specified order feature of fixed value type, the electronic device can obtain a first order feature value of the second-type order feature by counting values of the second-type order feature in the sample order data.

[0246] For example, for a second-type order feature, if the second-type order feature is a specified order feature of fixed value type, a specific value of the second-type order feature in the sample order data can be counted by counting, as a first order feature value of the second-type order feature.

[0247] For a specified order feature of random value type, the electronic device can determine a first order feature value of the second-type order feature according to a random distribution obtained by fitting the random distribution.

[0248] For each specified order feature, according to the sample order data, a process of obtaining a feature value of the specified order feature is as follows:

[0249] (1) a feature representing the number of orders.

[0250] Correspondingly, in the case where the second-type order feature includes a feature representing the number of orders, the first order feature value of the feature is the number of orders included in the sample order data.

[0251] It can be understood that, for an order data, the number of orders included in the order data is a fixed value. That is, the type of the feature value of the feature representing the number of orders is a fixed value.

[0252] For example, the sample order data includes: order 100, order 200, and order 300.

[0253] The order lines included in order 100 are shown in Table 1-1:

[0254] Table 1-1

[0255] Order material A 20 Order material B 50 Order material C 60

[0256] The order lines included in the order 200 are shown in Table 1-2:

[0257] Table 1-2

[0258] Order material A 20 Order material B 50 Order material D 10 Order material G 10

[0259] The order lines included in the order 300 are shown in Table 1-3:

[0260] Table 1-3

[0261] Order material E 20 Order material F 20

[0262] Table 1-1 represents the information of the order 100, Table 1-2 represents the information of the order 200, and Table 1-3 represents the information of the order 300. For each order, the order includes at least one order line. An order line includes a type of order material (i.e., the left column of Table 1-1, Table 1-2 and Table 1-3) and a number of the type of order material required (i.e., the right column of Table 1-1, Table 1-2 and Table 1-3).

[0263] For the sample order data, the number of orders included in the sample order data is 3.

[0264] Correspondingly, in the case that the second type of order feature includes a feature representing the number of orders, the first order feature value of the feature representing the number of orders is 3.

[0265] (2) A feature representing the number of order lines in each order.

[0266] Correspondingly, in the case that the second type of order feature includes a feature representing the number of order lines in each order, the first order feature value of the feature is obtained based on the following steps:

[0267] Statistical analysis is performed on the number of order lines in each order included in the sample order data to obtain a random distribution to which the number of order lines in each order included in the sample order data conforms; and a first number of values are sampled based on the obtained random distribution as the first order feature value of the feature representing the number of order lines in each order.

[0268] The first number is the first order feature value of the feature representing the number of orders.

[0269] It can be understood that, for an order data, the order data includes multiple orders, and the number of orders in each order can be the same or different. That is, the feature representing the number of order lines in each order is obtained by sampling based on a random distribution, and therefore the type of the feature value of the feature representing the number of order lines in each order is a random distribution.

[0270] In the embodiments of the present application, the electronic device can fit a random distribution according to the number of order lines in each order contained in the sample order data. That is, the electronic device can select a random distribution that is most suitable for the number of order lines in each order contained in the sample order data according to the values and frequencies of each data item, and calculate the parameters (such as mean and / or standard deviation, etc.) of the fitted random distribution. The process of fitting a random distribution is not limited in the present application. For example, the electronic device can fit a random distribution by using the least square method, maximum likelihood estimation method, etc. A data item represents the number of order lines in an order.

[0271] For example, if each data item conforms to a normal distribution, the probability density function of the normal distribution can be fitted to obtain the parameters (mean and standard deviation) of the distribution. If it conforms to a Poisson distribution, the probability mass function of the Poisson distribution can be fitted to obtain the parameters (mean) of the distribution.

[0272] Further, the electronic device can use the random distribution to sample the first order feature values representing the number of order lines in each order.

[0273] Continuing with the above example, the number of order lines in each order contained in the sample order data is 3, 4, and 2, respectively.

[0274] Further, the electronic device can determine the random distribution that each data item conforms to and the parameters of the fitted random distribution according to the values and frequencies of each data item. If the first order feature value representing the number of orders is 5, that is, the order data to be generated contains 5 orders, the electronic device needs to use the obtained random distribution to sample 5 values as the first order feature values representing the number of order lines in each order. For example, the first order feature values representing the number of order lines in each order are 2, 1, 4, 3, and 3.

[0275] (3) a feature representing the number of required order materials in each order line.

[0276] Correspondingly, in the case where the second type of order feature contains a feature representing the number of required order materials in each order line, the first order feature value of the feature is obtained based on the following steps:

[0277] statistically analyze the number of required order materials in each order line contained in the sample order data to obtain a random distribution that the number of required order materials in each order line contained in the sample order data conforms to; and use the obtained random distribution to sample the second number of values as the first order feature values representing the number of required order materials in each order line.

[0278] The second number is a sum value of each first order feature value representing a feature of a number of order lines in each order.

[0279] It can be understood that, for one order data, the order data contains multiple order lines, and the number of order materials in each order line can be the same or different. That is, the feature representing the number of order materials in each order line is obtained by random distribution sampling, and therefore the type of the feature value representing the feature of the number of order materials in each order line is random distribution.

[0280] In the embodiment of the present application, the electronic device can fit a random distribution according to the number of order materials in each order line contained in the sample order data. That is, the electronic device can select a random distribution most suitable for the number of order materials in each order line contained in the sample order data according to the value and frequency of each data item, and calculate the parameters (such as mean and / or standard deviation, etc.) of the fitted random distribution. The content of the random distribution can be referred to in the above embodiment, and will not be repeated here. One data item represents the number of order materials in one order line.

[0281] Further, the electronic device can use the random distribution to sample the first order feature value representing the feature of the number of order materials in each order line.

[0282] Continuing the above example, the number of order materials in each order line contained in the sample order data is respectively: 20, 50, 60, 20, 50, 10, 10, 20, 20. Further, the electronic device can determine the random distribution that each data item conforms to and the parameters of the fitted random distribution according to the value and frequency of each data item.

[0283] If the first order feature value representing the feature of the number of order lines in each order is: 2, 1, 4, 3, 3, that is, the sum value of each first order feature value representing the feature of the number of order lines in each order is 13, that is, the order data to be generated contains 13 order lines, the electronic device needs to use the obtained random distribution to sample 13 values as the first order feature value representing the feature of the number of order materials in each order line. For example, the first order feature value representing the feature of the number of order materials in each order line is: 10, 20, 50, 40, 25, 65, 52, 43, 22, 11, 41, 12, 53.

[0284] (4) a feature representing the total number of types of order materials involved in the order data.

[0285] Correspondingly, in the case that the second type of order features includes a feature representing the total number of order materials involved in the order data, the first order feature value of the feature is: the total number of order materials involved in the sample order data.

[0286] It can be understood that, for an order data, the total number of order materials involved in the order data is a fixed value. That is, the type of the feature value of the feature representing the total number of order materials involved in the order data is a fixed value.

[0287] Continuing with the above example, the order materials involved in the sample order data include: order material A, order material B, order material C, order material D, order material G, order material E, and order material F. That is, the total number of order materials involved in the order data is 7.

[0288] Correspondingly, in the case that the second type of order features includes a feature representing the total number of order materials involved in the order data, the first order feature value of the feature representing the total number of order materials involved in the order data is 7.

[0289] (5) A feature representing the number of orders distributed in each type of order material.

[0290] Correspondingly, in the case that the second type of order features includes a feature representing the number of orders distributed in each type of order material, the first order feature value of the feature is obtained based on the following steps:

[0291] Statistical analysis is performed on the number of orders distributed in each type of order material included in the sample order data to obtain a random distribution to which the number of orders distributed in each type of order material included in the sample order data conforms; and the third number of values is sampled using the obtained random distribution as the first order feature value of the feature representing the number of orders distributed in each type of order material.

[0292] The third number is: the first order feature value of the feature representing the total number of order materials involved in the order data.

[0293] It can be understood that, for an order data, the order data includes multiple types of order materials, and the number of orders distributed in each type of order material can be the same or different. That is, the feature representing the number of orders distributed in each type of order material is obtained by sampling from a random distribution, and therefore the type of the feature value of the feature representing the number of orders distributed in each type of order material is a random distribution.

[0294] In the embodiments of the present application, the electronic device can fit to obtain a random distribution according to the order quantity of each order material distribution contained in the sample order data. That is, the electronic device can select a random distribution most suitable for the order quantity of each order material distribution contained in the sample order data according to the value and frequency of each data item, and calculate the parameters (such as mean and / or standard deviation, etc.) of the fitted random distribution. The content of the random distribution can be referred to in the above embodiments, and will not be repeated here. One data item represents the order quantity of one order material distribution.

[0295] Further, the electronic device can use the random distribution to sample to obtain the first order feature value representing the feature of the order quantity of each order material distribution.

[0296] In the above example, the order materials involved in the sample order data include: order material A, order material B, order material C, order material D, order material G, order material E, and order material F. The order quantity of each order material distribution contained in the sample order data is: 2, 2, 1, 1, 1, 1, and 1. Further, the electronic device can determine the random distribution that each data item conforms to and the parameters of the fitted random distribution according to the value and frequency of each data item.

[0297] If the first order feature value representing the total number of order materials involved in the order data is 6, that is, the total number of order materials involved in the order data to be generated is 6, the electronic device needs to use the obtained random distribution to sample 6 values as the first order feature value representing the feature of the order quantity of each order material distribution. For example, the first order feature value representing the feature of the order quantity of each order material distribution is: 1, 2, 1, 1, 2, and 3.

[0298] (6) A feature representing the proportion of associated order materials.

[0299] Correspondingly, in the case where the second order feature contains a feature representing the proportion of associated order materials, the first order feature value of the feature is obtained based on the following steps:

[0300] For each order material, determine the first ratio of the number of order materials associated with the order material in the sample order data to the total number of order materials involved in the sample order data; calculate the mean of the first ratio corresponding to each order material as the first order feature value representing the proportion of associated order materials.

[0301] It can be understood that for one order data, the feature value of the feature representing the proportion of associated order materials is a fixed value. That is, the type of the feature value of the feature representing the proportion of associated order materials is a fixed value.

[0302] In the embodiment of the present application, for each type of order material involved in the sample order data, the electronic device can determine other order materials in the sample order data that have an association relationship with the type of order material as the associated order materials of the type of order material. Wherein, an association relationship between a type of order material and another type of order material means that the type of order material and the other type of order material are distributed in the same order.

[0303] For each type of order material, the electronic device calculates a first ratio corresponding to the type of order material. Wherein, the first ratio corresponding to the i-th type of order material can be represented as: Ri / K. Ri represents the number of types of associated order materials of the i-th type of order material; K represents the total number of types of order materials involved in the sample order data.

[0304] Further, the electronic device can calculate the mean of the first ratios corresponding to each type of order material to obtain the sample feature value of the feature representing the order material association type ratio of the sample order data. That is, the first order feature value of the feature representing the order material association type ratio.

[0305] Wherein, the formula for calculating the sample feature value of the feature representing the order material association type ratio of the sample order data is as follows:

[0306]

[0307] mean represents the mean operation; P1 represents the sample feature value of the feature representing the order material association type ratio of the sample order data.

[0308] In the above example, the sample order data, order material A, order material B, order material C have an association relationship with each other; order material A, order material B, order material D, order material G have an association relationship with each other; order material E and order material F have an association relationship. The total number of types of order materials involved in the sample order data is 7.

[0309] Taking order material A as an example, the associated order materials of order material A include: order material B, order material C, order material D, and order material G. That is, the number of types of associated order materials of order material A is 4, and the first ratio corresponding to order material A is: 4 / 7.

[0310] Similarly, the electronic device can calculate the first ratio corresponding to each type of order material, and calculate the mean of the first ratios corresponding to each type of order material as the sample feature value of the feature representing the order material association type ratio of the sample order data.

[0311] Correspondingly, in the case that the second type of order features includes the feature representing the proportion of the associated categories of order materials, the electronic device can take the mean of the first ratios corresponding to each type of order materials as the first order feature value of the feature representing the proportion of the associated categories of order materials.

[0312] (7) the feature representing the proportion of the associated times of order materials.

[0313] Correspondingly, in the case that the second type of order features includes the feature representing the proportion of the associated times of order materials, the first order feature value of the feature is obtained based on the following steps:

[0314] Step 1: For each type of order materials, determine the other order materials associated with the type of order materials in the sample order data as the associated order materials of the type of order materials.

[0315] Step 2: For each associated order material of the type of order materials, determine the number of orders in the sample order data that simultaneously contain the type of order materials and the associated order material as the associated times corresponding to the associated order material.

[0316] Step 3: Calculate the ratio of the maximum value of the associated times corresponding to each associated order material to the number of orders distributed by the type of order materials as the second ratio corresponding to the type of order materials.

[0317] Step 4: Determine the mean of the second ratios corresponding to each type of order materials as the first order feature value of the feature representing the proportion of the associated times of order materials.

[0318] It can be understood that, for an order data, the feature value of the feature representing the proportion of the associated times of order materials is a fixed value. That is, the type of the feature value of the feature representing the proportion of the associated times of order materials is a fixed value.

[0319] In the embodiments of the present application, for each type of order materials involved in the sample order data, the electronic device can determine the associated order materials of the type of order materials. For each associated order material of the type of order materials, determine the number of orders in the sample order data that simultaneously contain the type of order materials and the associated order material as the associated times corresponding to the associated order material. Calculate the ratio of the maximum value of the associated times corresponding to each associated order material to the number of orders distributed by the type of order materials as the second ratio corresponding to the type of order materials.

[0320] Wherein, for the i-th type of order materials, the second ratio corresponding to the i-th type of order materials can be represented as:

[0321]

[0322] Wherein, Mij represents: the order number of the order material of the i-th type and the order material of the j-th type; the order material of the j-th type is the associated order material of the order material of the i-th type; max represents the maximum value operation; Mi represents the order number of the i-th type of order material distribution; P2i represents the second ratio corresponding to the i-th type of order material.

[0323] Further, the electronic device can calculate the mean value of the second ratio corresponding to each type of order material, to obtain the sample feature value of the feature representing the order material association times ratio of the sample order data. That is, the first order feature value of the feature representing the order material association times ratio.

[0324] Wherein, the formula for calculating the sample feature value of the feature representing the order material association times ratio of the sample order data is as follows:

[0325] P2 = mean(P2i)

[0326] mean represents the mean operation; P2 represents: the sample feature value of the feature representing the order material association times ratio of the sample order data.

[0327] In the above example, the order material A, the order material B, the order material C, and the order material D have an association relationship with each other; the order material A, the order material B, the order material D, and the order material G have an association relationship with each other; the order material E and the order material F have an association relationship. The total number of order materials involved in the sample order data is 7.

[0328] Taking the order material A as an example, the associated order materials of the order material A include: the order material B, the order material C, the order material D, and the order material G. The association times corresponding to each associated order material of the order material A are: 2, 1, 1, and 1. Correspondingly, the maximum value in the association times corresponding to each associated order material is 2, and the order number of the order material A distribution is 2. The second ratio corresponding to the order material A is: 2 / 2.

[0329] Similarly, the electronic device can calculate the second ratio corresponding to each type of order material, and calculate the mean value of the second ratio corresponding to each type of order material as the sample feature value of the feature representing the order material association times ratio of the sample order data.

[0330] Correspondingly, in the case that the second type of order feature includes the feature representing the order material association times ratio, the electronic device can calculate the mean value of the second ratio corresponding to each type of order material as the first order feature value of the feature representing the order material association times ratio.

[0331] After obtaining the first order feature value of the second type of order feature in the specified order feature, the electronic device can obtain the first order feature value of the specified order feature required to be met by the order data to be generated in combination with the first order feature value of the first type of order feature and the first order feature value of the second type of order feature.

[0332] Based on the above processing, the electronic device can obtain the first order feature value of each specified order feature required to be met by the order data to be generated in combination with the feature value of the first type of order feature input by the user and the feature value of the second type of order feature obtained from the sample order data. Subsequently, the order data generated according to the first order feature value meets the specific needs of the user while being more consistent with the order data obtained in the real scene. In this way, the quality of the generated order data can be improved.

[0333] In some embodiments, the specified inventory features include: a feature representing the total number of types of inventory materials involved in the inventory data, a feature representing the number of inventories in each container, a feature representing the number of supplies of inventory materials in each inventory, and a feature representing the number of containers in which each type of inventory material is distributed.

[0334] The process of specifically determining the feature values of each specified inventory feature in an inventory data will be described in subsequent embodiments.

[0335] In an implementation manner, the specified inventory features further include: a feature representing the associated type proportion of inventory materials, and / or a feature representing the associated times proportion of inventory materials.

[0336] The associated type proportion of inventory materials represents the proportion of the types of inventory materials having an association relationship in the inventory data in the total types of inventory materials involved in the inventory data.

[0337] The associated times proportion of inventory materials represents the ratio of the maximum association times of a type of inventory material to the number of containers in which the type of inventory material is distributed. The maximum association times of a type of inventory material represent the maximum value of the times that the type of inventory material is distributed in the same container as other inventory materials having an association relationship.

[0338] In the embodiments of the present application, the association relationship between a type of inventory material and another type of inventory material means that the type of inventory material and the other type of inventory material are distributed in the same container.

[0339] For example, if in the inventory data, the container in which inventory material A is located is container 1 and container 2, and the container in which inventory material B is located is container 1 and container 3. That is, inventory material A and inventory material B are distributed in the same container (container 1), and then inventory material A and inventory material B have an association relationship.

[0340] The inventory material associated category proportion is a proportion of a category of the inventory material with the associated relationship in the inventory data in total categories of the inventory material involved in the inventory data. That is, the inventory material associated category proportion reflects the characteristics of the inventory material with the associated relationship in the inventory data from the proportion of the category number.

[0341] The inventory material associated times proportion is a ratio of the maximum associated times of a category of the inventory material to the number of containers in which the category of the inventory material is distributed. The maximum associated times of a category of the inventory material is a maximum value of times that the category of the inventory material is distributed in the same container as other inventory materials with the associated relationship. That is, the inventory material associated times proportion reflects the characteristics of the inventory material with the associated relationship in the inventory data from the proportion of the occurrence frequency.

[0342] The process of specifically determining the feature value of the characteristic of the inventory material associated category proportion and the feature value of the characteristic of the inventory material associated times proportion in one inventory data will be described in subsequent embodiments.

[0343] It can be seen that, in the embodiments of the present application, the characteristic of the inventory material associated category proportion and the characteristic of the inventory material associated times proportion can reflect the characteristics of the inventory material with the associated relationship in the inventory data in different dimensions. Subsequently, the electronic device can generate the inventory data by using one or more of the characteristic of the inventory material associated category proportion and the characteristic of the inventory material associated times proportion.

[0344] Based on the above processing, the inventory material associated category proportion reflects the characteristics of the inventory material with the associated relationship in the inventory data from the proportion of the category number, and the inventory material associated times proportion reflects the characteristics of the inventory material with the associated relationship in the inventory data from the proportion of the occurrence frequency. The electronic device can generate the inventory data in combination with the feature value of the characteristic of the inventory material associated category proportion and / or the feature value of the characteristic of the inventory material associated times proportion, so that the generated inventory data is more consistent with the inventory data in the real scene, and the authenticity of the generated inventory data is improved.

[0345] In some embodiments, the specified inventory characteristics include a specified inventory characteristic of a fixed value type and a specified inventory characteristic of a random value type. The specified inventory characteristic of the fixed value type is used to represent the scale of the generated inventory data, and the specified inventory characteristic of the random value type is used to represent the randomness of the generated inventory data.

[0346] In the embodiments of the present application, the specified inventory characteristic of the fixed value type is used to represent the scale of the generated inventory data. That is, the scale of the generated inventory data can be controlled through the specified inventory characteristic of the fixed value type.

[0347] For one inventory data, a characteristic value of a specified inventory characteristic of a fixed value type is unique. For example, for a characteristic representing the total number of inventory materials involved in the inventory data, the total number of inventory materials involved in the inventory data is unique. For example, if a sample inventory data involves 5 types of inventory materials, the characteristic representing the total number of inventory materials involved in the inventory data of the sample inventory data is 5, and there is no other value.

[0348] For one inventory data, a characteristic value of a specified inventory characteristic of a random value type usually contains multiple values. For example, for a characteristic representing the number of containers distributed for each type of inventory material, the inventory data may contain multiple types of inventory materials, and the number of containers distributed for each type of inventory material may be different or the same. For example, if a sample inventory data contains 5 types of inventory materials, the number of containers distributed for each type of inventory material is 2, 4, 2, 3, and 5 respectively, and the characteristic value of the characteristic representing the number of containers distributed for each type of inventory material of the sample inventory data is 2, 4, 2, 3, and 5.

[0349] In an implementation manner, the specified inventory characteristic of the fixed value type can include at least one of the following: a characteristic representing the total number of inventory materials involved in the inventory data, a characteristic representing the proportion of associated categories of inventory materials, and a characteristic representing the proportion of associated times of inventory materials.

[0350] For example, the specified inventory characteristic of the fixed value type includes the characteristic representing the total number of inventory materials involved in the inventory data, and in the actual scenario of warehouse management, the total number of inventory materials involved in the inventory data is usually fixed, and accordingly, the total number of inventory materials involved in the inventory data can be determined by setting a fixed value.

[0351] The specified inventory characteristic of the random value type is used to represent the randomness of the generated inventory data. That is, through the specified inventory characteristic of the random value type, the randomness of the generated inventory data can be ensured, so that the generated inventory data is closer to the dynamic characteristics in the real business scenario.

[0352] In an implementation manner, the specified inventory characteristic of the random value type can include at least one of the following: a characteristic representing the number of inventories in each container, a characteristic representing the number of provided inventory materials in each inventory, and a characteristic representing the number of containers distributed for each type of inventory material.

[0353] For example, the specified inventory characteristic of the random value type includes the characteristic representing the number of inventories in each container, and in the actual scenario of warehouse management, the number of inventories in each container in the inventory data is usually not completely consistent, and accordingly, the number of inventories in each container in the generated inventory data can be determined by selecting a random value.

[0354] Based on the above processing, the specified inventory characteristics of the fixed value type can adjust the size and core characteristics of the generated data, and the specified inventory characteristics of the random value type can improve the diversity of the generated data, reflect the difference and randomness characteristics of the real warehouse data, so that the inventory data can be generated by combining the specified inventory characteristics of the fixed value type and the specified inventory characteristics of the random value type, and the reliability of the generated inventory data is ensured.

[0355] In some embodiments, the step of determining the feature value of the specified inventory characteristic required to be met by the generated inventory data as the first inventory feature value according to the preset feature value of the first inventory feature in the sample inventory data and / or the specified inventory characteristic includes:

[0356] Step S101-a: Obtain the preset feature value of the first inventory feature in the specified inventory characteristic as the first inventory feature value of the first type of inventory feature.

[0357] Step S101-b: Obtain the first inventory feature value of the second type of inventory feature in the specified inventory characteristic according to the sample inventory data.

[0358] Among them, the second type of inventory feature is: the inventory feature in the specified inventory characteristic except the first type of inventory feature.

[0359] Step S101-c: Combine the first inventory feature value of the first type of inventory feature and the first inventory feature value of the second type of inventory feature to obtain the first inventory feature value of the specified inventory characteristic required to be met by the generated inventory data.

[0360] In the embodiments of the present application, according to the different ways of obtaining the feature value, the specified inventory characteristics can be divided into: the first type of inventory feature and the second type of inventory feature.

[0361] Among them, the preset feature value of the first inventory feature in the specified inventory characteristic can be input by the user. Correspondingly, the electronic device can obtain the preset feature value of the first inventory feature in the specified inventory characteristic input by the user as the first inventory feature value of the first type of inventory feature.

[0362] The first order feature value of the second type of order feature in the specified order feature is extracted from the sample inventory data. Correspondingly, the electronic device can obtain the sample inventory data of the sample time period.

[0363] Among them, the sample inventory data of the sample time period can be the real inventory data of the historical time period selected by the user according to the actual demand, and the specific way of selecting the sample inventory data of the sample time period is not limited in the present application.

[0364] For example, if the user wants to predict the inventory data in December this year, the sample inventory data in the sample time period can be the real inventory data generated in December last year; or the sample inventory data in the sample time period can be the real inventory data generated in November this year.

[0365] In an implementation manner, the electronic device can pre-extract the feature values of each specified inventory feature of the sample inventory data (which can be referred to as sample feature values). After obtaining the feature values of the first type of inventory feature input by the user (which can be referred to as input feature values), the sample feature values of the first type of inventory feature in the specified inventory features are replaced by the input feature values, and the feature values of the replaced specified inventory features are taken as the first inventory feature values.

[0366] In this way, the electronic device can pre-obtain the sample feature values of each dimension inventory feature (i.e., the specified inventory feature) of the sample inventory data. Furthermore, after obtaining the demand of the user for part of the dimension inventory features (i.e., the first inventory feature values of the first type of inventory feature), the feature values of the part of the dimension inventory features are replaced. In this way, the efficiency of obtaining each dimension inventory feature and the efficiency of generating inventory data can be improved.

[0367] In another implementation manner, the electronic device can extract the feature values of the second type of inventory feature in the specified inventory features according to the sample inventory data after obtaining the feature values of the first type of inventory feature in the specified inventory features input by the user. The second type of inventory feature is the inventory feature in the specified inventory features except the first type of inventory feature.

[0368] In this way, compared with the way of extracting the feature values of each dimension inventory feature (each specified inventory feature) in the sample inventory data, only the feature values of part of the dimension inventory features (i.e., the feature values of the second type of inventory feature in the specified inventory features) input by the user need to be extracted, and thus the calculation cost required for obtaining the first inventory feature values of each specified inventory feature can be reduced.

[0369] In the case where the user inputs the preset feature values of all the inventory features in the specified inventory features, the electronic device can obtain the feature values of each specified inventory feature as the first inventory feature values only according to the preset feature values of the first inventory feature in the specified inventory features. At this time, all the specified inventory features are the first type of inventory feature.

[0370] In the case where the user does not input the preset feature values of the first inventory feature in the specified inventory features, the electronic device can obtain the feature values of each specified inventory feature as the first inventory feature values only according to the sample inventory data. At this time, all the specified inventory features are the second type of inventory feature.

[0371] In some embodiments, step S101-b comprises:

[0372] For each second-type inventory feature, if the second-type inventory feature is a specified inventory feature of fixed-value type, a value of the second-type inventory feature in the sample inventory data is determined as a first inventory feature value of the second-type inventory feature.

[0373] If the second-type inventory feature is a specified inventory feature of random-value type, values of each data item of the second-type inventory feature in the sample inventory data are determined, and a frequency of occurrence of each value is determined, and a random distribution to which each data item conforms is fitted, and a first inventory feature value of the second-type inventory feature is determined according to the obtained random distribution.

[0374] In the embodiments of the present application, for a specified inventory feature of fixed-value type, the electronic device can obtain a first inventory feature value of the second-type inventory feature by determining a value of the second-type inventory feature in the sample inventory data.

[0375] For a specified inventory feature of random-value type, the electronic device can determine a first inventory feature value of the second-type inventory feature by fitting a random distribution.

[0376] For each specified inventory feature, according to the sample inventory data, a process of obtaining a feature value of the specified inventory feature is as follows:

[0377] (8) a feature representing a total number of inventory materials involved in the inventory data.

[0378] Correspondingly, in the case where the second-type inventory feature includes a feature representing a total number of inventory materials involved in the inventory data, the first inventory feature value of the feature is: a total number of inventory materials involved in the sample inventory data.

[0379] It can be understood that, for one inventory data, a total number of inventory materials involved in the inventory data is a fixed value. That is, a type of the feature value of the feature representing a total number of inventory materials involved in the inventory data is fixed value.

[0380] For example, the sample inventory data is as shown in Table 2-1:

[0381] Table 2-1

[0382] Stock material A 5000 Container 1, container 2 Stock material B 5000 Container 1, container 3 Stock material C 4000 Container 4 Stock material D 2000 Container 4 Stock material E 2000 Container 5 Stock material F 1000 Container 6 Stock material G 1000 Container 7 Stock material H 1000 Container 8

[0383] In the table, one sample inventory data includes: at least one type of inventory material (i.e., corresponding to the left column of Table 2-1), a number of each type of inventory material (i.e., corresponding to the middle column of Table 2-1), and a container in which each type of inventory material is located (i.e., corresponding to the right column of Table 2-1).

[0384] The inventory materials involved in the sample inventory data include: inventory material A, inventory material B, inventory material C, inventory material D, inventory material G, inventory material E, inventory material F, and inventory material H. That is, the total number of inventory materials involved in the inventory data is 8.

[0385] Correspondingly, in the case where the second type of inventory feature includes a feature representing the total number of inventory materials involved in the inventory data, the first inventory feature value of the feature representing the total number of inventory materials involved in the inventory data is 8.

[0386] (9) A feature representing the number of inventories in each container.

[0387] Correspondingly, in the case where the second type of inventory feature includes a feature representing the number of inventories in each container, the first inventory feature value of the feature is obtained based on the following steps:

[0388] Statistical analysis is performed on the number of inventories in each container included in the sample inventory data to obtain a random distribution to which the number of inventories in each container included in the sample inventory data conforms; and the fourth number of values is sampled using the obtained random distribution as the first inventory feature value of the feature representing the number of inventories in each container.

[0389] The fourth number is the number of containers required in the inventory data to be generated.

[0390] It can be understood that the inventory includes a plurality of containers, and the number of inventories in each container can be the same or different. That is, the feature representing the number of inventories in each container is obtained by sampling a random distribution, and therefore the type of the feature value of the feature representing the number of inventories in each container is a random distribution.

[0391] In the embodiments of the present application, the electronic device can fit a random distribution according to the number of inventories in each container included in the sample inventory data. That is, the electronic device can select a random distribution that best fits the number of inventories in each container included in the sample inventory data according to the values and frequencies of occurrence of each data item counted, and calculate the parameters (such as mean and / or standard deviation, etc.) of the fitted random distribution. The process of fitting the random distribution is not limited in the present application. For example, the electronic device can fit the random distribution using the least squares method, maximum likelihood estimation method, etc. One data item represents the number of inventories in one container.

[0392] The content of the random distribution can be referred to the above embodiments and will not be repeated here.

[0393] Further, the electronic device can sample the first inventory feature value of the feature representing the number of inventories in each container using the random distribution.

[0394] Continuing the above example, the sample inventory data contains 8 containers, and the number of inventory in each container is respectively: 2, 1, 2, 2, 1, 1, 1, 1.

[0395] Further, the electronic device can determine a random distribution that each data item conforms to and a parameter of the random distribution that each data item conforms to according to the value and frequency of occurrence of each data item. If the number of required containers in the to-be-generated inventory data is 6, the electronic device needs to sample 6 values by using the obtained random distribution, as the first inventory feature values representing the number of inventory in each container. For example, the first inventory feature values representing the number of inventory in each container are: 2, 2, 1, 3, 1, 1.

[0396] (10) represents the number of provided inventory materials in each inventory.

[0397] Correspondingly, in the case where the second type of inventory feature contains a feature representing the number of provided inventory materials in each inventory, the first inventory feature value of the feature is obtained based on the following steps:

[0398] statistically analyze the number of provided inventory materials in each inventory contained in the sample inventory data, to obtain a random distribution that the number of provided inventory materials in each inventory contained in the sample inventory data conforms to; and sample a fifth number of values by using the obtained random distribution, as the first inventory feature values representing the number of provided inventory materials in each inventory.

[0399] The fifth number is the sum of the first inventory feature values representing the number of inventory in each container.

[0400] It can be understood that, for one inventory data, the inventory data contains a plurality of containers, and the plurality of containers contain a plurality of inventories. The number of provided inventory materials in each inventory can be the same or different. That is, the feature representing the number of provided inventory materials in each inventory is obtained by sampling a random distribution, and therefore the type of the feature value representing the number of provided inventory materials in each inventory is a random distribution.

[0401] In the embodiment of the present application, the electronic device can fit a random distribution according to the number of provided inventory materials in each inventory contained in the sample inventory data. That is, the electronic device can select a random distribution that is most suitable for the number of provided inventory materials in each inventory contained in the sample inventory data according to the value and frequency of occurrence of each data item that is statistically obtained, and calculate the parameter (such as mean and / or standard deviation, etc.) of the random distribution that is conformed to. The content of the random distribution can be referred to the above embodiment, and will not be described here. One data item represents the number of provided inventory materials in one inventory.

[0402] Furthermore, the electronic device may utilize the random distribution to sample and obtain a first inventory characteristic value representing a characteristic of the provided quantity of inventory materials in each inventory.

[0403] Continuing with the previous example (based on Table 2-1), if the number of inventory material A provided in container 1 is 2000, the number of inventory material A provided in container 2 is 3000, the number of inventory material B provided in container 1 is 1000, and the number of inventory material B provided in container 2 is 4000, then the number of inventory materials provided in each inventory included in the sample inventory data is: 2000, 3000, 1000, 4000, 4000, 2000, 2000, 1000, 1000, 1000. Furthermore, the electronic device can determine the random distribution that each data item conforms to and the parameters of the random distribution based on the value and frequency of occurrence of each data item.

[0404] If the number of containers required in the inventory data to be generated is 6, and the first inventory feature value representing the number of inventory items in each container is 2, 2, 1, 3, 1, 1, then the fifth number is 10. In other words, the electronic device needs to use the obtained random distribution to sample 10 values ​​as the first inventory feature value representing the number of inventory items provided in each inventory item.

[0405] (11) Characteristics representing the number of containers distributed for each type of inventory material.

[0406] Accordingly, when the second type of inventory feature includes a feature representing the number of containers distributed for each type of inventory material, the first inventory feature value of the feature is obtained based on the following steps:

[0407] A statistical analysis is performed on the number of containers distributed for each type of inventory material contained in the sample inventory data to obtain a random distribution that conforms to the number of containers distributed for each type of inventory material contained in the sample inventory data; using the obtained random distribution, a sixth number of values ​​is sampled to obtain a first inventory characteristic value representing the characteristic of the number of containers distributed for each type of inventory material.

[0408] The sixth number is: a first inventory characteristic value representing a characteristic of the total number of types of inventory materials involved in the inventory data.

[0409] It is understood that for a given inventory data set, the inventory data may contain multiple categories of inventory items, and the number of containers distributed within each category of inventory items may be the same or different. In other words, the feature representing the number of containers distributed within each category of inventory items is obtained through random sampling. Therefore, the feature value representing the feature representing the number of containers distributed within each category of inventory items is of a random distribution type.

[0410] In the embodiments of the present application, the electronic device can fit to obtain a random distribution according to the container numbers of each type of inventory material distribution contained in the sample inventory data. That is, the electronic device can select a random distribution most suitable for the container numbers of each type of inventory material distribution contained in the sample inventory data according to the values and frequencies of each data item, and calculate the parameters (such as mean and / or standard deviation, etc.) of the fitted random distribution. The content of the random distribution can refer to the above embodiments, and will not be repeated here. One data item represents the container number of one type of inventory material distribution.

[0411] Further, the electronic device can use the random distribution to sample to obtain the first inventory feature value representing the characteristics of the container number of each type of inventory material distribution.

[0412] In the above example, the inventory materials involved in the sample inventory data include: inventory material A, inventory material B, inventory material C, inventory material D, inventory material G, inventory material E, inventory material F, and inventory material H. The container numbers of each type of inventory material distribution contained in the sample inventory data are: 2, 2, 1, 1, 1, 1, 1, and 1. Further, the electronic device can determine the random distribution that each data item conforms to and the parameters of the fitted random distribution according to the values and frequencies of each data item.

[0413] If the first inventory feature value representing the total number of types of inventory materials involved in the inventory data is 6, that is, the total number of types of inventory materials involved in the to-be-generated inventory data is 6, the electronic device needs to use the obtained random distribution to sample 6 values as the first inventory feature value representing the characteristics of the container number of each type of inventory material distribution. For example, the first inventory feature value representing the characteristics of the container number of each type of inventory material distribution is: 1, 2, 1, 1, 2, and 2.

[0414] (12) A feature representing the proportion of associated types of inventory materials.

[0415] In the case where the second type of inventory feature contains a feature representing the proportion of associated types of inventory materials, the first inventory feature value of the feature is obtained based on the following steps:

[0416] For each type of inventory material, determine the third ratio of the number of types of inventory materials associated with the type of inventory material in the total number of types of inventory materials involved in the sample inventory data. Calculate the mean of the third ratios corresponding to each type of inventory material as the first inventory feature value representing the proportion of associated types of inventory materials.

[0417] It can be understood that, for one inventory data, the characteristic value of the characteristic representing the proportion of the associated category of the inventory material is a fixed value. That is, the type of the characteristic value of the characteristic representing the proportion of the associated category of the inventory material is a fixed value.

[0418] In the embodiment of the present application, for each category of inventory material involved in the sample inventory data, the electronic device can determine other inventory materials in the sample inventory data that have an association relationship with the category of inventory material as the associated inventory material of the category of inventory material. Wherein, the association relationship between a category of inventory material and another category of inventory material means that the category of inventory material and the other category of inventory material are distributed in the same container.

[0419] For each category of inventory material, a third ratio corresponding to the category of inventory material is calculated. Wherein, the process of calculating the third ratio corresponding to a category of inventory material can refer to the process of calculating the first ratio corresponding to a category of order material in the above embodiment, which will not be repeated here.

[0420] In the above example, in the sample inventory data, inventory material A and inventory material B have an association relationship; inventory material C and inventory material D have an association relationship. And the total number of categories of inventory materials involved in the sample inventory data is 8.

[0421] Taking inventory material A as an example, the associated inventory material of inventory material A includes: inventory material B. That is, the number of categories of associated inventory material of inventory material A is 1, and the third ratio corresponding to inventory material A is: 1 / 8.

[0422] Similarly, the electronic device can calculate the third ratio corresponding to each category of inventory material, and calculate the mean of the third ratio corresponding to each category of inventory material as the sample characteristic value of the characteristic representing the proportion of the associated category of the inventory material.

[0423] Correspondingly, in the case that the second type of inventory characteristic includes the characteristic representing the proportion of the associated category of the inventory material, the electronic device can take the mean of the third ratio corresponding to each category of inventory material as the first inventory characteristic value of the characteristic representing the proportion of the associated category of the inventory material.

[0424] (13) the characteristic representing the proportion of the associated number of inventory materials.

[0425] Correspondingly, in the case that the second type of inventory characteristic includes the characteristic representing the proportion of the associated number of inventory materials, the first inventory characteristic value of the characteristic is obtained based on the following steps:

[0426] Step 1: For each category of inventory material, determine other inventory materials in the sample inventory data that have an association relationship with the category of inventory material as the associated inventory material of the category of inventory material.

[0427] Step 2: For each associated inventory material of the inventory material, determine the number of containers in the sample inventory data that contain both the inventory material and the associated inventory material as the association times of the associated inventory material.

[0428] Step 3: Calculate the ratio of the maximum value in the association times of each associated inventory material to the number of containers in which the inventory material is distributed as the fourth ratio corresponding to the inventory material.

[0429] Step 4: Calculate the average of the fourth ratios corresponding to each inventory material to determine the first inventory feature value representing the feature of the association times proportion of inventory materials.

[0430] It can be understood that, for an inventory data, the feature value representing the feature of the association times proportion of inventory materials is a fixed value. That is, the type of the feature value representing the feature of the association times proportion of inventory materials is a fixed value.

[0431] In the embodiments of the present application, for each inventory material involved in the sample inventory data, the electronic device can determine the associated inventory material of the inventory material. For each associated inventory material of the inventory material, determine the number of containers in the sample inventory data that contain both the inventory material and the associated inventory material as the association times of the associated inventory material. Calculate the ratio of the maximum value in the association times of each associated inventory material to the number of containers in which the inventory material is distributed as the fourth ratio corresponding to the inventory material. The process of calculating the fourth ratio corresponding to a type of inventory material can refer to the process of calculating the second ratio corresponding to a type of order material in the above embodiments, which will not be repeated here.

[0432] Further, the electronic device can calculate the average of the fourth ratios corresponding to each inventory material to obtain the sample feature value representing the feature of the association times proportion of inventory materials of the sample inventory data. That is, the first inventory feature value representing the feature of the association times proportion of inventory materials.

[0433] Based on the above processing, the electronic device can obtain the first inventory feature value of each specified inventory feature required to be met for the to-be-generated inventory data by combining the feature value of the first type of inventory feature input by the user and the feature value of the second type of inventory feature obtained from the sample inventory data. Subsequently, the inventory data generated according to the first inventory feature value meets the specific needs of the user while being more consistent with the inventory data obtained in the real scene. In this way, the quality of the generated inventory data can be improved.

[0434] It can be understood that, in the actual scenario of warehouse management, the type of material required in the order often belongs to the type of material provided in the inventory.

[0435] In addition, for the material with a small number of demands, in order to avoid repeated purchasing, the purchasing frequency of the material can be reduced by increasing the number of the material. For the material with a large number of demands, the number of the material can be set close to and higher than the number of the material, so as to reduce the risk of out-of-stock and avoid the increase of storage cost caused by excessive inventory. Therefore, the above-mentioned preparation method can cause the ranking difference between the number of demands of the same material and the ranking of the number of the material.

[0436] Correspondingly, in order to determine the correspondence between the generated order data of each type of order material and the generated inventory data of each type of inventory material, the electronic device can obtain the feature value of the association feature between the to-be-generated order data and the to-be-generated inventory data (i.e., the first association feature value).

[0437] The association feature between the order data and the inventory data can be referred to as an order-inventory matching feature. The association feature between the to-be-generated order data and the to-be-generated inventory data represents the difference between the ranking of the number of demands of each type of order material in the to-be-generated order data and the ranking of the number of supplies of the same type of inventory material in the to-be-generated inventory data. Subsequently, the electronic device can use the first association feature value to match each type of order material in the generated order data with each type of inventory material in the generated inventory data.

[0438] In some embodiments, the above-mentioned step of obtaining the feature value of the association feature between the to-be-generated order data and the to-be-generated inventory data as the first association feature value includes:

[0439] Step one: according to the size order of the number of demands, the order materials in the sample order data are sorted to obtain the first ranking of each type of order material, and according to the size order of the number of supplies, the inventory materials in the sample inventory data are sorted to obtain the second ranking of each type of inventory material.

[0440] The sorting method of the order materials in the sample order data is consistent with the sorting method of the inventory materials in the sample inventory data.

[0441] Step two: for each type of order material in the sample order data, the difference between the first ranking of the order material and the second ranking of the inventory material matched with the order material is calculated.

[0442] Step three: the calculated differences are statistically analyzed to obtain the random distribution to which the association feature between the to-be-generated order data and the to-be-generated inventory data conforms.

[0443] Step four: using the obtained random distribution, sampling to obtain a third number of numerical values as the feature values of the required associated characteristics between the to-be-generated order data and the to-be-generated inventory data, as the first associated feature values.

[0444] wherein the third number is a first order feature value of a feature representing the total number of types of order materials involved in the order data.

[0445] In the embodiments of the present application, after obtaining the sample order data, the electronic device can sort each type of order material in the sample order data in order of the size of the required number, to obtain a first ranking of each type of order material.

[0446] Similarly, after obtaining the sample inventory data, the electronic device can sort each type of inventory material in the sample inventory data in order of the size of the provided number, to obtain a second ranking of each type of inventory material.

[0447] For example, the size order can be in the order of large first and small second, or in the order of small first and large second.

[0448] For a type of order material, the inventory material matched with the type of order material represents that the two are the same type of material. Correspondingly, for each type of order material in the sample order data, the electronic device can calculate the difference between the first ranking of the type of order material and the second ranking of the inventory material matched with the type of order material.

[0449] For example, for order material A in the sample order data, the first ranking of order material A is 1. The second ranking of the inventory material matched with order material A is 3, and the electronic device can calculate the difference between the first ranking of the type of order material and the second ranking of the inventory material matched with the type of order material (+2).

[0450] For order material B in the sample order data, the first ranking of order material B is 2. The second ranking of the inventory material matched with order material B is 1, and the electronic device can calculate the difference between the first ranking of the type of order material and the second ranking of the inventory material matched with the type of order material (-1).

[0451] Correspondingly, the electronic device can statistically analyze the calculated differences to obtain a random distribution of the associated characteristics between the to-be-generated order data and the to-be-generated inventory data. Wherein, the content of the random distribution can refer to the above embodiments, which will not be repeated here.

[0452] Further, the electronic device can sample a third number of values from the obtained random distribution as the characteristic value of the required matching association characteristic between the to-be-generated order data and the to-be-generated inventory data as the first association characteristic value.

[0453] The third number is a first order characteristic value representing a characteristic of a total number of order materials involved in the order data.

[0454] Based on the above processing, the electronic device can use the difference between the ranking of the same material in the sample order data and the ranking in the sample inventory data to construct a random distribution. Further, the electronic device can obtain the characteristic value of the association characteristic (i.e., the first association characteristic value) between the to-be-generated order data and the to-be-generated inventory data. Subsequently, the electronic device can use the first association characteristic value to match each type of order material in the generated order data with each type of inventory material in the generated inventory data.

[0455] In an implementation manner, the electronic device can obtain a preset characteristic value of the required matching association characteristic between the to-be-generated order data and the to-be-generated inventory data input by the user as the first association characteristic value.

[0456] That is, the first association characteristic value of the required matching association characteristic between the to-be-generated order data and the to-be-generated inventory data can be input by the user according to actual needs.

[0457] Based on the above processing, even in the absence of sample order data and / or sample inventory data, the electronic device can achieve matching of order materials in order data and inventory materials in inventory data. In this way, the flexibility of generating order data and inventory data can be improved to meet the diversified needs of order data and inventory data in different business scenarios.

[0458] Referring to Figure 2 , Figure 2 A flowchart for obtaining a sample characteristic value of a specified order characteristic of sample order data is provided for the embodiments of the present application.

[0459] S201: Input sample data of orders and inventories.

[0460] That is, the electronic device obtains sample order data and sample inventory data.

[0461] S202: For a fixed value type of characteristic, count the characteristic parameter value from the sample data.

[0462] That is, for the specified order characteristics and the specified inventory characteristics in which the type of the characteristic value is a fixed value, the electronic device can directly count the characteristic values of each specified order characteristic and specified inventory characteristic from the sample order data and the sample inventory data.

[0463] S203: For the characteristic of random distribution type, the value and the frequency of the data item are counted from the sample data, and the random distribution is fitted.

[0464] That is, for the specified order characteristics and the specified inventory characteristics, the type of the characteristic value is the random distribution of the specified order characteristics and the specified inventory characteristics, and the electronic device can directly count the value and the frequency of each data item from the sample order data and the sample inventory data, and fit the random distribution corresponding to each characteristic.

[0465] S204: Obtain the characteristic value of each dimension characteristic of the sample data.

[0466] That is, the sample characteristic value of each specified sample order characteristic in the sample order data is obtained, and the sample characteristic value of each specified inventory order characteristic in the sample inventory data is obtained.

[0467] In some embodiments, the electronic device can obtain the first order characteristic value of each specified order characteristic, the first inventory characteristic value of each specified inventory characteristic, and the characteristic value of the required associated characteristic between the order data to be generated and the inventory data to be generated as the first associated characteristic value.

[0468] In the embodiments of the present application, the first order characteristic value of each specified order characteristic, the first inventory characteristic value of each specified inventory characteristic, and the first associated characteristic value of the required associated characteristic between the order data to be generated and the inventory data to be generated can be input by the user according to the actual demand.

[0469] Based on the above processing, since the first order characteristic value of each specified order characteristic, the first inventory characteristic value of each specified inventory characteristic, and the characteristic value of the required associated characteristic required for generating the order data and the inventory data are all input by the user, even without sample order data and sample inventory data, the electronic device can also realize the generation of order data and inventory data. In this way, the flexibility of generating order data and inventory data can be improved, and the diversified demand for order data and inventory data in different business scenarios can be met.

[0470] In combination with the description in the above embodiments, in one embodiment, the order data and the inventory data can be generated based on each characteristic shown in Table 3. Referring to Table 3, Table 3 is a characteristic table of the characteristics used when generating order data and inventory data:

[0471] Table 3

[0472]

[0473] In some embodiments, before step S102, the method further comprises:

[0474] Step S104: determining whether the current obtained first order characteristic value and the first inventory characteristic value satisfy the preset verification rule.

[0475] Step S105: if the current obtained first order characteristic value and the first inventory characteristic value do not satisfy the preset verification rule, correcting the characteristic value of the specified order characteristic, and / or the characteristic value of the specified inventory characteristic, until the first order characteristic value and the first inventory characteristic value satisfying the verification rule are obtained.

[0476] The verification rule includes at least one of the following:

[0477] Rule 1: the first order characteristic value representing the total number of order materials involved in the order data is not greater than the total value of each first order characteristic value representing the number of order lines in each order.

[0478] Rule 2: the first order characteristic value representing the total number of order materials involved in the order data is not less than the maximum value in each first order characteristic value representing the number of order lines in each order.

[0479] Rule 3: the first inventory characteristic value representing the total number of inventory materials involved in the inventory data is not less than the maximum value in each first inventory characteristic value representing the number of inventories in each container.

[0480] Rule 4: the first order characteristic value representing the total number of order materials involved in the order data is not greater than the first inventory characteristic value representing the total number of inventory materials involved in the inventory data.

[0481] Rule 5: the average of the demand number of each type of order material is not greater than the average of the supply number of each type of inventory material.

[0482] It can be understood that the first order characteristic value and the first inventory characteristic value can include user input characteristic values. However, the user input characteristic values may cause errors in the generated order data or inventory data, or do not conform to the rules of real data.

[0483] For example, if the first order feature value of the feature representing the total number of order materials involved in the order data input by the user is greater than the first inventory feature value of the feature representing the total number of inventory materials involved in the inventory data, the total number of order materials in the generated order data is greater than the total number of inventory materials involved in the generated inventory data. In a real scenario, the total number of inventory materials involved in the inventory data is usually greater than the total number of order materials involved in the order data. That is, the generated order data and inventory data do not conform to the rules of real data. When the generated order data and inventory data are used for subsequent operation processing (such as using the generated data to perform stress testing on the warehouse management system), it may cause invalid test conditions.

[0484] In the embodiments of the present application, the electronic device can determine whether the first order feature value and the first inventory feature value currently obtained satisfy a preset verification rule.

[0485] For rule 1, the sum of the first order feature values of the feature representing the number of order lines in each order represents the total number of order lines included in the order data to be generated.

[0486] It can be understood that the total number of order materials involved in the order data to be generated is necessarily not greater than the total number of order lines. Otherwise, it will result in that at least one type of order material cannot be allocated to the order line to which it belongs.

[0487] For rule 2, the total number of order materials involved in the order data to be generated is not less than the maximum value in the first order feature values of the feature representing the number of order lines in each order. Otherwise, at least one order material will be allocated to the same order multiple times.

[0488] For rule 3, the first inventory feature value of the feature representing the total number of inventory materials involved in the inventory data is not less than the maximum value in the first inventory feature values of the feature representing the number of inventories in each container. Otherwise, at least one inventory material will be allocated to the same container multiple times.

[0489] For rule 4, the first order feature value of the feature representing the total number of order materials involved in the order data is not greater than the first inventory feature value of the feature representing the total number of inventory materials involved in the inventory data. Otherwise, for at least one order material in the generated order data, it is impossible to determine the inventory material matched with the order material.

[0490] For rule 5, the mean of the demand number of each type of order material is not greater than the mean of the supply number of each type of inventory material. Otherwise, it can lead to the generated data that the demand number of the same material is greater than the supply number of the material. That is, the material in the inventory cannot meet the demand of the order.

[0491] If the obtained first order feature value and the first inventory feature value do not satisfy the preset verification rule, the feature value of the specified order feature and / or the feature value of the specified inventory feature that do not satisfy the verification rule are corrected.

[0492] For example, for each feature that does not satisfy the verification rule (including the specified order feature and the specified inventory feature), if the feature value type of the feature is a random distribution, the electronic device can resample the feature value representing the feature according to the random distribution corresponding to the feature.

[0493] If the feature value type of the feature is a fixed value, the electronic device can directly adjust (such as increase or decrease) the feature value of the feature to make the adjusted feature value satisfy the preset verification rule.

[0494] Based on the above processing, the electronic device can perform legality detection on the obtained first order feature and the first inventory feature. That is, whether the obtained first order feature value and the first inventory feature value satisfy the preset verification rule is verified. Correspondingly, when there is a feature value of an order feature and / or an inventory feature that does not satisfy the verification rule, the feature value of the order feature and / or the inventory feature that does not satisfy the verification rule can be corrected. In this way, the reliability of the generated order data and inventory data can be improved.

[0495] For step S102, order data satisfying the obtained first order feature value is generated, and inventory data satisfying the obtained first inventory feature value is generated.

[0496] In some embodiments, the above step of generating order data satisfying the obtained first order feature value includes:

[0497] Step one: according to the first order feature value representing the number of orders, the number of orders contained in the to-be-generated order data is determined.

[0498] Step two: according to the first order feature value representing the number of order lines in each order, the number of order lines in each order contained in the to-be-generated order data is determined.

[0499] Step three: according to the first order feature value representing the demand number of order materials in each order line, the demand number of order materials in each order line contained in the to-be-generated order data is determined.

[0500] Step four: determining the total number of order materials involved in the order data to be generated according to the first order feature value representing the total number of order material types involved in the order data.

[0501] Step five: determining the order number of each order material distribution in the order data to be generated according to the first order feature value representing the order number of each order material distribution.

[0502] Step six: allocating the order material type and the order quantity for each order line according to the order quantity of order material in each order line included in the order data to be generated, the total number of order materials involved in the order data to be generated, and the order number of each order material distribution in the order data to be generated.

[0503] Step seven: allocating the order line for each order according to the number of order lines in each order included in the order data to be generated and the number of orders included in the order data to be generated, so that the allocated order satisfies the first order feature value representing the order material associated type proportion, and / or, the first order feature value representing the order material associated times proportion.

[0504] In the embodiments of the present application, the electronic device first determines the number of order lines in each order included in the order data to be generated, the order quantity of order material in each order line included in the order data to be generated, and the total number of order materials involved in the order data to be generated according to the first order feature value.

[0505] Further, the electronic device can allocate the order material type and the order quantity for each order line according to the order quantity of order material in each order line included in the order data to be generated, the total number of order materials involved in the order data to be generated, and the order number of each order material distribution in the order data to be generated. That is, the order line, the order material, and the order quantity are bound to each other.

[0506] The order line for each order is allocated according to the number of order lines in each order included in the order data to be generated and the number of orders included in the order data to be generated, so that the allocated order satisfies the first order feature value representing the order material associated type proportion, and / or, the first order feature value representing the order material associated times proportion. That is, the electronic device can bind the order line and the order to each other according to the constraint of the first order feature value representing the order material associated type proportion, and / or, the first order feature value representing the order material associated times proportion.

[0507] Based on the above processing, the first order feature value can independently represent the order data to be generated, and accordingly, the electronic device can independently generate the order data according to the first order feature value. In this way, the data can be flexibly generated according to the actual demand.

[0508] In some embodiments, the step of generating the inventory data satisfying the obtained first inventory feature value comprises:

[0509] Step one: calculating the required container number in the inventory data to be generated according to the first formula.

[0510] The first formula is:

[0511]

[0512] m1 is a first inventory feature value representing the total number of types of inventory materials involved in the inventory data; m2 is the mean of each first inventory feature value representing the number of containers in which each type of inventory material is distributed; and m3 is the mean of each first inventory feature value representing the number of inventories in each container.

[0513] Step two: determining the number of inventories in each container in the inventory data to be generated according to the first inventory feature value representing the number of inventories in each container.

[0514] Step three: determining the total number of types of inventory materials involved in the inventory data to be generated according to the first inventory feature value representing the total number of types of inventory materials involved in the inventory data.

[0515] Step four: determining the number of inventory materials provided in each inventory in the inventory data to be generated according to the first inventory feature value representing the number of inventory materials provided in each inventory.

[0516] Step five: determining the number of containers in which each type of inventory material is distributed in the inventory data to be generated according to the first inventory feature value representing the number of containers in which each type of inventory material is distributed.

[0517] Step six: allocating the type and number of inventory materials to each inventory according to the number of inventory materials provided in each inventory in the inventory data to be generated, the total number of types of inventory materials involved in the inventory data to be generated, and the number of containers in which each type of inventory material is distributed in the inventory data to be generated.

[0518] Step seven: allocating the inventory to each container according to the required container number in the inventory data to be generated and the number of inventories in each container in the inventory data to be generated, so that the allocated container satisfies the first inventory feature value representing the proportion of associated types of inventory materials, and / or the first inventory feature value representing the proportion of associated times of inventory materials.

[0519] In the embodiment of the present application, the electronic device first determines, according to the first inventory characteristic value, the number of required containers in the to-be-generated inventory data, the number of inventories in each container in the to-be-generated inventory data, the total number of inventory materials involved in the to-be-generated inventory data, the number of inventory materials provided in each inventory in the to-be-generated inventory data, and the number of containers in which each type of inventory material is distributed in the to-be-generated inventory data.

[0520] Further, the electronic device can allocate, according to the number of inventory materials provided in each inventory in the to-be-generated inventory data, the total number of inventory materials involved in the to-be-generated inventory data, and the number of containers in which each type of inventory material is distributed in the to-be-generated inventory data, the type and number of inventory materials for each inventory. That is, the inventory, inventory material, and number of provided are bound to each other.

[0521] Correspondingly, the electronic device can allocate, according to the number of required containers in the to-be-generated inventory data and the number of inventories in each container in the to-be-generated inventory data, the inventory for each container, so that the container obtained by allocation satisfies the first inventory characteristic value representing the characteristic of the associated type ratio of the inventory material, and / or the first inventory characteristic value representing the characteristic of the associated frequency ratio of the inventory material. That is, the electronic device can bind the container and the inventory to each other according to the constraint of the first inventory characteristic value representing the characteristic of the associated type ratio of the inventory material, and / or the first inventory characteristic value representing the characteristic of the associated frequency ratio of the inventory material.

[0522] Based on the above processing, the first inventory characteristic value can independently represent the to-be-generated inventory data, and correspondingly, the electronic device can independently generate the inventory data according to the first inventory characteristic value. In this way, the data can be flexibly generated according to actual needs.

[0523] In addition, for the characteristic value type of the randomly distributed characteristic (order characteristic and inventory characteristic), the electronic device can determine the characteristic value of this part of the characteristic by using the sampling result of the random distribution. In this way, the obtained characteristic value can have the characteristics of difference and randomness of real data. Further, the generated data is more consistent with the characteristics of real data.

[0524] It can be understood that the process of generating order data is independent of the process of generating inventory data. That is, the first order characteristic value can independently represent the to-be-generated order data, and the first inventory characteristic value can independently represent the to-be-generated inventory data, and correspondingly, the order data and the inventory data can be decoupled and generated. In this way, the order data and the inventory data generation method provided by the present application has strong scalability and wide application range.

[0525] In an implementation, for the generated order data, the electronic device can number each order material in the generated order data to obtain an initial number of each type of order material in the generated order data. For example, the electronic device can sequentially number each type of order material in an incremental manner starting from 1 in order of the size of the quantity demanded.

[0526] Similarly, for the generated inventory data, the electronic device can number each inventory material in the generated inventory data to obtain an initial number of each type of inventory material in the generated inventory data. For example, the electronic device can sequentially number each type of inventory material in an incremental manner starting from 1 in order of the size of the quantity provided.

[0527] For step S103, the order materials in the generated order data and the inventory materials in the inventory data are matched so that each type of order material in the matched order data and the same type of inventory material in the matched inventory data meet the first correlation characteristic value.

[0528] In an implementation, the correlation characteristic represents the difference between the ranking of the quantity demanded of each type of order material in the order data and the ranking of the quantity provided of the same type of inventory material in the inventory data.

[0529] In this way, based on the correlation characteristic, the order materials in the generated order data and the inventory materials in the inventory data are matched, which can make the difference between the ranking of the quantity demanded of each type of order material in the matched order data and the ranking of the quantity provided of the same type of inventory material in the matched inventory data meet the first correlation characteristic value. The specific manner of determining the first correlation characteristic value can refer to the above embodiments, which will not be described here.

[0530] In some embodiments, referring to Figure 3 , Figure 3 A second flowchart of the order data and inventory data generation method provided by the embodiments of the present application is provided. Step S103 includes:

[0531] S1031: In order of the size of the quantity demanded, each type of order material in the generated order data is sorted to obtain a third ranking of each type of order material; and in order of the size of the quantity provided, each type of inventory material in the generated inventory data is sorted to obtain a fourth ranking of each type of inventory material.

[0532] The manner of sorting each type of order material in the generated order data is consistent with the manner of sorting each type of inventory material in the generated inventory data; and the manner of sorting each type of order material in the generated order data is consistent with the manner of sorting each type of order material in the sample order data.

[0533] S1032: Match the order materials in the generated order data and the inventory materials in the generated inventory data by using the first correlation characteristic value.

[0534] The first correlation characteristic value is the difference between the third ranking of the order material and the fourth ranking of the matched inventory material.

[0535] In the embodiments of the present application, the process of obtaining the third ranking of each order material can refer to the process of obtaining the first ranking of each order material in the above-mentioned embodiments, which will not be repeated here.

[0536] The process of obtaining the fourth ranking of each inventory material can refer to the process of obtaining the second ranking of each inventory material in the above-mentioned embodiments, which will not be repeated here.

[0537] Further, for each order material in each generated order data, the electronic device can obtain a first correlation characteristic value corresponding to the order material and the third ranking of the order material, and calculate the ranking of the inventory material matched with the order material. Correspondingly, the difference between the third ranking of the order material and the fourth ranking of the matched inventory material is the first correlation characteristic value.

[0538] For example, if the third ranking of the order material is 1 and the first correlation characteristic value corresponding to the order material is +1, the ranking of the inventory material matched with the order material is 2. Correspondingly, the electronic device can determine the inventory material with the fourth ranking of 2 as the inventory material matched with the order material.

[0539] In an implementation manner, after determining the inventory material matched with the order material, the electronic device records the correspondence between the number of the order material in the generated order data and the number of the inventory material in the generated inventory data. Alternatively, the electronic device can replace the number of each order material with the number of the inventory material matched with the order material.

[0540] Based on the above processing, the materials in the order data and the inventory data can be associated by using the material ranking difference. That is, by the number of order materials and the number of inventory materials, the matching of the materials in the order data and the materials in the inventory data is realized. In this way, the feature that the number of provided materials and the number of required materials exist difference in the real warehouse scenario can be retained.

[0541] In one implementation, after obtaining the third ranking of each order material in the generated order data and the fourth ranking of each order material in the generated order data, for each order material, the inventory material with the same ranking order can be directly determined as the inventory material matched with the order material.

[0542] For example, if the third ranking of an order material is 1, the electronic device can determine the inventory material with the fourth ranking of 1 as the inventory material matched with the order material.

[0543] Based on the above processing, after generating the order data and the inventory data, the order material in the generated order data and the inventory material in the generated inventory data can be matched directly according to the size order of the demand number of each order material and the size order of the provision number of each inventory material. In this way, the matching required calculation amount can be reduced and the matching efficiency can be improved without matching based on each first correlation feature value.

[0544] Referring to Figure 4 , Figure 4 A flowchart of generating order data and inventory data is provided for the embodiments of the present application.

[0545] S401: inputting sample data of orders and inventories and feature parameters of expected generated order and inventory data.

[0546] That is, the electronic device obtains sample order data, sample inventory data, a feature value input by a user for a first order feature in specified order features (a first order feature value of the first order feature), and a feature value input by the user for a first inventory feature in specified inventory features (a first order feature value of the first inventory feature).

[0547] S402: analyzing feature values of each feature of the sample data of orders and inventories.

[0548] That is, according to the sample order data, sample feature values of each specified order feature are obtained. According to the sample inventory data, sample feature values of each specified inventory feature are obtained.

[0549] S403: replacing the feature values of the part of features of the sample data with the input feature values to obtain feature values of the expected generated data.

[0550] That is, according to the sample feature values of the sample order data, a first order feature value for a second order feature in the specified order features is obtained. The first order feature value of the first order feature and the first order feature value of the second order feature are combined to obtain the first order feature value of the specified order feature required to be met by the generated order data.

[0551] According to the sample characteristic value of the sample library inventory data, a first inventory characteristic value of a second type of inventory characteristic in the specified inventory characteristic is obtained. The first inventory characteristic value of the first type of inventory characteristic and the first inventory characteristic value of the second type of inventory characteristic are combined to obtain the first inventory characteristic value of the specified inventory characteristic required to be met by the generated inventory data.

[0552] S404: Verify the legitimacy of the characteristic value of the expected generated data, and process and repair the characteristic value that does not meet the verification rule.

[0553] That is, it is judged whether the current obtained first order characteristic value and the first inventory characteristic value meet the preset verification rule. If the current obtained first order characteristic value and the first inventory characteristic value do not meet the preset verification rule, the characteristic value of the specified order characteristic and / or the characteristic value of the specified inventory characteristic that does not meet the verification rule is corrected until the first order characteristic value and the first inventory characteristic value that meet the verification rule are obtained.

[0554] S405: According to the characteristic value of the expected generated data, a virtual order, inventory data meeting the characteristic value is generated.

[0555] That is, order data meeting the obtained first order characteristic value is generated, and inventory data meeting the obtained first inventory characteristic value is generated.

[0556] Referring to Figure 5 , Figure 5 A flowchart of an order material and inventory material matching process provided by an embodiment of the present application.

[0557] S501: Obtain a first associated characteristic value of an order-inventory matching characteristic, order temporary data, and inventory temporary data.

[0558] That is, the first associated characteristic value of the order-inventory matching characteristic, that is, the characteristic value of the associated characteristic between the generated order data and the generated inventory data.

[0559] The order temporary data and the inventory temporary data represent the order data and the inventory data generated according to the above step S102.

[0560] S502: Sort each order material in the order temporary data according to the demand number to obtain the ranking of each order material; sort each inventory material in the inventory temporary data according to the provided number to obtain the ranking of each inventory material.

[0561] That is, according to the size order of the demand number, each type of order material in the generated order data is sorted to obtain a third ranking of each type of order material; and according to the size order of the provided number, each type of inventory material in the generated inventory data is sorted to obtain a fourth ranking of each type of inventory material.

[0562] S503: traversing the order material according to the ranking of the order material.

[0563] That is, according to the third ranking of each type of order material, the order material currently required to be matched is determined.

[0564] S504: calculating the inventory material corresponding to the current order material according to the first correlation characteristic value, and saving the corresponding relationship.

[0565] That is, using each first correlation characteristic value, the order material in the generated order data and the inventory material in the generated inventory data are matched.

[0566] Among them, each type of order material in the matched order data corresponds to a first correlation characteristic value, and the difference between the third ranking of the order material and the fourth ranking of the matched inventory material is the first correlation characteristic value.

[0567] S505: determining whether all order materials are matched with inventory materials. If yes, S506 is executed; if no, S503 is executed.

[0568] S506: replacing the material number of the order line with the corresponding inventory number according to the corresponding relationship.

[0569] That is, according to the matching result, the order material in the order data and the inventory material in the inventory data are uniformly numbered.

[0570] Based on the same inventive concept, the embodiment of the present application provides an order data and inventory data generation device. Referring to Figure 6 , Figure 6 The structure diagram of an order data and inventory data generation device provided by the embodiment of the present application, the device comprises:

[0571] The characteristic value acquisition module 601 is configured to determine the characteristic value of the specified order characteristic required to be met by the order data to be generated as the first order characteristic value according to the preset characteristic value of the first order characteristic in the sample order data and / or the specified order characteristic, determine the characteristic value of the specified inventory characteristic required to be met by the inventory data to be generated as the first inventory characteristic value according to the preset characteristic value of the first inventory characteristic in the sample inventory data and / or the specified inventory characteristic, and acquire the characteristic value of the correlation characteristic required to be met between the order data to be generated and the inventory data to be generated as the first correlation characteristic value;

[0572] The data generation module 602 is configured to generate order data satisfying the obtained first order characteristic value, and generate inventory data satisfying the obtained first inventory characteristic value.

[0573] The material matching module 603 is configured to match the order materials in the generated order data and the inventory materials in the inventory data, so that each type of order material in the matched order data and each type of inventory material in the matched inventory data meet the first associated characteristic value.

[0574] In some embodiments, the specified order characteristics include: a specified order characteristic of a fixed value type, and a specified order characteristic of a random value type; the specified order characteristic of the fixed value type is used to represent the scale of the order data to be generated, and the specified order characteristic of the random value type is used to represent the randomness of the order data to be generated; and / or, the specified inventory characteristics include: a specified inventory characteristic of a fixed value type, and a specified inventory characteristic of a random value type; the specified inventory characteristic of the fixed value type is used to represent the scale of the inventory data to be generated, and the specified inventory characteristic of the random value type is used to represent the randomness of the inventory data to be generated.

[0575] In some embodiments, the characteristic value acquisition module 601 includes:

[0576] The first order characteristic value acquisition submodule is configured to acquire a preset characteristic value of a first type of order characteristic in the specified order characteristics as a first order characteristic value of the first type of order characteristic.

[0577] The second order characteristic value acquisition submodule is configured to obtain a first order characteristic value of a second type of order characteristic in the specified order characteristics according to the sample order data; the second type of order characteristic is an order characteristic other than the first type of order characteristic in the specified order characteristics.

[0578] The first combination submodule is configured to combine the first order characteristic value of the first type of order characteristic and the first order characteristic value of the second type of order characteristic to obtain a first order characteristic value of the specified order characteristics required to be met by the order data to be generated.

[0579] In some embodiments, the second order characteristic value acquisition submodule is specifically configured to:

[0580] For each second type of order characteristic, if the second type of order characteristic is a specified order characteristic of a fixed value type, the value of the sample order data for the second type of order characteristic is determined as the first order characteristic value of the second type of order characteristic.

[0581] If the second type of order characteristic is a specified order characteristic of a random value type, the values of each data item of the second type of order characteristic in the sample order data and the frequency of occurrence of each value are determined, and a random distribution that each data item meets is fitted, and the first order characteristic value of the second type of order characteristic is determined according to the obtained random distribution.

[0582] In some embodiments, in the case that the second type of order feature comprises a feature representing the number of orders, the first order feature value of the feature is: the number of orders comprised in the sample order data; and / or, in the case that the second type of order feature comprises a feature representing the number of order lines in each order, the first order feature value of the feature is obtained based on the following steps: statistically analyzing the number of order lines in each order comprised in the sample order data to obtain a random distribution to which the number of order lines in each order comprised in the sample order data conforms; and using the obtained random distribution to sample a first number of numerical values as the first order feature values of the feature representing the number of order lines in each order; wherein the first number is the first order feature value of the feature representing the number of orders; and / or, in the case that the second type of order feature comprises a feature representing the number of orders of each order line, the first order feature value of the feature is obtained based on the following steps: statistically analyzing the number of orders of each order line comprised in the sample order data to obtain a random distribution to which the number of orders of each order line comprised in the sample order data conforms; and using the obtained random distribution to sample a second number of numerical values as the first order feature values of the feature representing the number of orders of each order line; wherein the second number is the sum of the first order feature values of the feature representing the number of order lines in each order; and / or, in the case that the second type of order feature comprises a feature representing the total number of order materials involved in the order data, the first order feature value of the feature is: the total number of order materials involved in the sample order data; and / or, in the case that the second type of order feature comprises a feature representing the number of orders of each order material distribution, the first order feature value of the feature is obtained based on the following steps: statistically analyzing the number of orders of each order material distribution comprised in the sample order data to obtain a random distribution to which the number of orders of each order material distribution comprised in the sample order data conforms; and using the obtained random distribution to sample a third number of numerical values as the first order feature values of the feature representing the number of orders of each order material distribution; wherein the third number is the first order feature value of the feature representing the total number of order materials involved in the order data; and / or, in the case that the second type of order feature comprises a feature representing the proportion of associated categories of order materials, the first order feature value of the feature is obtained based on the following steps: for each order material, determining a first proportion of the total number of order materials involved in the sample order data, which is occupied by the number of order materials associated with the order material in the sample order data; wherein an order material is associated with another order material if the order material and the other order material are distributed in the same order.The average of the first ratios of each type of order material is calculated as a first order feature value representing the proportion of the type of order material; and / or, in the case that the second type of order feature comprises a feature representing the proportion of the number of associations, the first order feature value of the feature is obtained based on the following steps: for each type of order material, determining other order materials in the sample order data that have an association relationship with the type of order material as associated order materials of the type of order material; for each associated order material of the type of order material, determining the number of orders in the sample order data that simultaneously contain the type of order material and the associated order material as the number of associations corresponding to the associated order material; calculating the ratio of the maximum value of the number of associations corresponding to each associated order material to the number of orders distributed with the type of order material as a second ratio corresponding to the type of order material; and determining the average of the second ratios corresponding to each type of order material as the first order feature value representing the proportion of the number of associations.

[0583] In some embodiments, the feature value obtaining module 601 comprises:

[0584] The first inventory feature value obtaining submodule is configured to obtain a preset feature value of a first inventory feature in the specified inventory features as a first inventory feature value of a first type of inventory feature.

[0585] The second inventory feature value obtaining submodule is configured to obtain, according to the sample inventory data, a first inventory feature value of a second type of inventory feature in the specified inventory features; wherein the second type of inventory feature is an inventory feature other than the first type of inventory feature in the specified inventory features.

[0586] The second combining submodule is configured to combine the first inventory feature value of the first type of inventory feature and the first inventory feature value of the second type of inventory feature to obtain a first inventory feature value of the specified inventory features that the to-be-generated inventory data needs to meet.

[0587] In some embodiments, the second inventory feature value obtaining submodule is specifically configured to:

[0588] For each second type of inventory feature, if the second type of inventory feature is a specified inventory feature of a fixed value type, the value of the sample inventory data for the second type of inventory feature is determined as the first inventory feature value of the second type of inventory feature.

[0589] If the second type of inventory feature is a specified inventory feature of a random value type, the values of each data item of the sample inventory data for the second type of inventory feature and the frequency of occurrence of each value are determined, and a random distribution that each data item conforms to is fitted, and the first inventory feature value of the second type of inventory feature is determined according to the obtained random distribution.

[0590] In some embodiments, in the case that the second type of inventory feature comprises a feature representing the total number of types of inventory items involved in the inventory data, the first inventory feature value of this feature is: the total number of types of inventory items involved in the sample inventory data; and / or, in the case that the second type of inventory feature comprises a feature representing the number of inventories in each container, the first inventory feature value of this feature is obtained based on the following steps: performing statistical analysis on the number of inventories in each container included in the sample inventory data to obtain a random distribution to which the number of inventories in each container included in the sample inventory data conforms; and using the obtained random distribution to sample a fourth number of values as the first inventory feature values of the feature representing the number of inventories in each container; wherein the fourth number is: the number of containers required in the inventory data to be generated; and / or, in the case that the second type of inventory feature comprises a feature representing the number of provides of inventory items in each inventory, the first inventory feature value of this feature is obtained based on the following steps: performing statistical analysis on the number of provides of inventory items in each inventory included in the sample inventory data to obtain a random distribution to which the number of provides of inventory items in each inventory included in the sample inventory data conforms; and using the obtained random distribution to sample a fifth number of values as the first inventory feature values of the feature representing the number of provides of inventory items in each inventory; wherein the fifth number is: the total sum of the first inventory feature values of the feature representing the number of inventories in each container; and / or, in the case that the second type of inventory feature comprises a feature representing the number of containers distributed with each type of inventory item, the first inventory feature value of this feature is obtained based on the following steps: performing statistical analysis on the number of containers distributed with each type of inventory item included in the sample inventory data to obtain a random distribution to which the number of containers distributed with each type of inventory item included in the sample inventory data conforms; and using the obtained random distribution to sample a sixth number of values as the first inventory feature values of the feature representing the number of containers distributed with each type of inventory item; wherein the sixth number is: the first inventory feature value of the feature representing the total number of types of inventory items involved in the inventory data; and / or, in the case that the second type of inventory feature comprises a feature representing the proportion of associated types of inventory items, the first inventory feature value of this feature is obtained based on the following steps: for each type of inventory item, determining a third ratio of the number of types of inventory items associated with this type of inventory item in the sample inventory data to the total number of types of inventory items involved in the sample inventory data, wherein a type of inventory item is associated with another type of inventory item if the two types of inventory items are distributed in the same container; and calculating the mean of the third ratios corresponding to each type of inventory item as the first inventory feature value of the feature representing the proportion of associated types of inventory items.and / or, in the case that the second type of inventory feature comprises a feature representing a proportion of associated times of inventory materials, the first inventory feature value of the feature is obtained based on the following steps: for each type of inventory material, determining other inventory materials having an associated relationship with the type of inventory material in the sample inventory data as associated inventory materials of the type of inventory material; for each associated inventory material of the type of inventory material, determining a number of containers containing both the type of inventory material and the associated inventory material in the sample inventory data as an associated time corresponding to the associated inventory material; calculating a ratio of a maximum value of the associated times corresponding to the associated inventory materials and a number of inventories distributed with the type of inventory material as a fourth ratio corresponding to the type of inventory material; and calculating a mean value of the fourth ratios corresponding to each type of inventory material as the first inventory feature value of the feature representing the proportion of associated times of inventory materials.

[0591] In some embodiments, the association feature represents a difference between rankings of the number of demands of each type of order material in the order data and rankings of the number of provisions of the same type of inventory material in the inventory data.

[0592] In some embodiments, the feature value obtaining module 601 comprises:

[0593] an association ranking submodule, configured to rank each type of order material in the sample order data according to a size order of the number of demands to obtain a first ranking of each type of order material, and rank each type of inventory material in the sample inventory data according to a size order of the number of provisions to obtain a second ranking of each type of inventory material; wherein the manner of ranking each type of order material in the sample order data is consistent with the manner of ranking each type of inventory material in the sample inventory data;

[0594] an association difference calculation submodule, configured to calculate, for each type of order material in the sample order data, a difference between the first ranking of the type of order material and the second ranking of the inventory material matched with the type of order material;

[0595] an association analysis submodule, configured to statistically analyze each difference calculated to obtain a random distribution to which an association feature between the order data to be generated and the inventory data to be generated conforms;

[0596] an association sampling submodule, configured to sample a third number of values as feature values of the required association feature between the order data to be generated and the inventory data to be generated to be generated, as first association feature values, by using the obtained random distribution; wherein the third number is a first order feature value of a feature representing a total number of types of order materials involved in the order data.

[0597] In some embodiments, the material matching module 603 is specifically configured to:

[0598] ranking each type of order material in the generated order data according to the size of the required number, to obtain a third ranking of each type of order material; and ranking each type of inventory material in the generated inventory data according to the size of the provided number, to obtain a fourth ranking of each type of inventory material; wherein the manner of ranking each type of order material in the generated order data is consistent with the manner of ranking each type of inventory material in the generated inventory data; and the manner of ranking each type of order material in the generated order data is consistent with the manner of ranking each type of order material in the sample order data;

[0599] matching the order material in the generated order data and the inventory material in the generated inventory data by using each first correlation characteristic value; wherein each type of order material in the matched order data corresponds to a first correlation characteristic value, and the difference between the third ranking of the type of order material and the fourth ranking of the matched inventory material is the first correlation characteristic value.

[0600] In some embodiments, the specified order features include: a feature representing the number of orders, a feature representing the number of order lines in each order, a feature representing the required number of order materials in each order line, a feature representing the total number of types of order materials involved in the order data, and a feature representing the number of orders in which each type of order material is distributed;

[0601] The specified inventory features include: a feature representing the total number of types of inventory materials involved in the inventory data, a feature representing the number of inventories in each container, a feature representing the provided number of inventory materials in each inventory, and a feature representing the number of containers in which each type of inventory material is distributed.

[0602] In some embodiments, the specified order feature further comprises: a feature representing a proportion of associated types of order materials, and / or a feature representing a proportion of associated times of order materials; the proportion of associated types of order materials represents a proportion of types of order materials having an associated relationship in the order data in the total types of order materials involved in the order data; the proportion of associated times of order materials represents a proportion of the maximum associated times of a type of order materials to the number of orders in which the type of order materials is distributed; the maximum associated times of a type of order materials represents a maximum value of times that the type of order materials is distributed in the same order with other order materials having an associated relationship; and / or the specified inventory feature further comprises: a feature representing a proportion of associated types of inventory materials, and / or a feature representing a proportion of associated times of inventory materials; the proportion of associated types of inventory materials represents a proportion of types of inventory materials having an associated relationship in the inventory data in the total types of inventory materials involved in the inventory data; the proportion of associated times of inventory materials represents a proportion of the maximum associated times of a type of inventory materials to the number of containers in which the type of inventory materials is distributed; the maximum associated times of a type of inventory materials represents a maximum value of times that the type of inventory materials is distributed in the same container with other inventory materials having an associated relationship.

[0603] In some embodiments, the device further comprises:

[0604] a verification module configured to determine whether the obtained first order feature value and the obtained first inventory feature value satisfy a preset verification rule before generating the order data satisfying the obtained first order feature value and generating the inventory data satisfying the obtained first inventory feature value; the verification rule comprises at least one of: a first order feature value of a feature representing a total number of types of order materials involved in the order data is not greater than a total sum of first order feature values of a feature representing a number of order lines in each order; the first order feature value of the feature representing the total number of types of order materials involved in the order data is not less than a maximum value among the first order feature values of the feature representing the number of order lines in each order; a first inventory feature value of a feature representing a total number of types of inventory materials involved in the inventory data is not less than a maximum value among first inventory feature values of a feature representing a number of inventories in each container; the first order feature value of the feature representing the total number of types of order materials involved in the order data is not greater than the first inventory feature value of the feature representing the total number of types of inventory materials involved in the inventory data; a mean value of a demand number of each type of order materials is not greater than a mean value of a supply number of each type of inventory material.

[0605] The correction module is configured to correct the characteristic value of the specified order characteristic and / or the characteristic value of the specified inventory characteristic, if the current obtained first order characteristic value and the first inventory characteristic value do not satisfy the preset verification rule, until the first order characteristic value and the first inventory characteristic value satisfying the verification rule are obtained.

[0606] In some embodiments, the data generation module 602 is specifically configured to:

[0607] determine the number of orders to be contained in the generated order data according to the first order characteristic value representing the number of orders;

[0608] determine the number of order lines in each order to be contained in the generated order data according to the first order characteristic value representing the number of order lines in each order;

[0609] determine the number of order materials in each order line to be contained in the generated order data according to the first order characteristic value representing the number of order materials in each order line;

[0610] determine the total number of order materials involved in the generated order data according to the first order characteristic value representing the total number of order materials involved in the order data;

[0611] determine the number of orders of each type of order material to be distributed in the generated order data according to the first order characteristic value representing the number of orders of each type of order material to be distributed;

[0612] allocate the type and number of order materials for each order line according to the number of order materials in each order line to be contained in the generated order data, the total number of order materials involved in the generated order data, and the number of orders of each type of order material to be distributed in the generated order data;

[0613] allocate order lines for each order according to the number of order lines in each order to be contained in the generated order data and the number of orders to be contained in the generated order data, so that the allocated orders satisfy the first order characteristic value representing the proportion of associated types of order materials, and / or the first order characteristic value representing the proportion of associated times of order materials.

[0614] In some embodiments, the data generation module 602 is specifically configured to:

[0615] calculate the number of required containers in the generated inventory data according to a first formula; wherein the first formula is:

[0616]

[0617] m1 is a first inventory characteristic value representing a total number of types of inventory materials involved in the inventory data; m2 is a mean value of the first inventory characteristic values representing a number of containers in which each type of inventory material is distributed; m3 is a mean value of the first inventory characteristic values representing a number of inventories in each container;

[0618] determining, according to the first inventory characteristic values representing the number of inventories in each container, the number of inventories in each container in the inventory data to be generated;

[0619] determining, according to the first inventory characteristic values representing a total number of types of inventory materials involved in the inventory data, the total number of types of inventory materials involved in the inventory data to be generated;

[0620] determining, according to the first inventory characteristic values representing a number of inventories of each type of inventory material, the number of inventories of each type of inventory material in the inventory data to be generated;

[0621] determining, according to the first inventory characteristic values representing a number of containers in which each type of inventory material is distributed, the number of containers in which each type of inventory material is distributed in the inventory data to be generated;

[0622] allocating, according to the number of inventories of each type of inventory material in the inventory data to be generated, the total number of types of inventory materials involved in the inventory data to be generated, and the number of containers in which each type of inventory material is distributed in the inventory data to be generated, a type and a number of inventories of each type of inventory material for each inventory;

[0623] allocating, according to the number of containers in the inventory data to be generated, and the number of inventories in each container in the inventory data to be generated, an inventory for each container, so that the container allocated satisfies the first inventory characteristic value representing a characteristic of a proportion of associated types of inventory materials, and / or the first inventory characteristic value representing a characteristic of a proportion of associated times of inventory materials.

[0624] The embodiment of the present application further provides an electronic device, as shown in the accompanying drawings, comprising: Figure 7

[0625] a memory 701 for storing a computer program;

[0626] a processor 702 for executing the program stored in the memory 701, and realizing the steps of any of the order data and inventory data generation methods.

[0627] The electronic device can further comprise a communication bus and / or a communication interface, and the processor 702, the communication interface and the memory 701 can communicate with each other through the communication bus.

[0628] ​The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0629] The communication interface is used for communication between the above electronic device and other devices.

[0630] The memory can include a Random Access Memory (RAM) and can also include a Non-Volatile Memory (NVM), for example, at least one disk memory. Optionally, the memory can also be at least one storage device located away from the aforementioned processor.

[0631] The processor mentioned above can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0632] In another embodiment provided in the present application, a computer readable storage medium is also provided, and the computer readable storage medium stores a computer program. The computer program is executed by a processor to implement the steps of any of the above order data and inventory data generation methods.

[0633] In another embodiment provided in the present application, a computer program product containing instructions is also provided, and when the computer program product is run on a computer, the computer is caused to execute any of the above order data and inventory data generation methods.

[0634] In the embodiments described above, all or some of the steps can be implemented by hardware, software, firmware or any combination thereof. When implemented by software, all or some of the steps can be implemented in the form of one or more computer programs or program elements. The computer programs reside (at least temporarily) in a memory of a computer during execution. The memory can be a RAM memory, a flash memory, a ROM memory, an EPROM memory, or any other suitable memory. The memory can be fixed or removable. The computer programs can be stored in a computer readable storage medium (or media) before being loaded into the memory of the computer. The computer readable storage medium (or media) can be a magnetic or optical recording medium, e.g., a floppy disk, a hard disk, a DVD, a CD, a memory stick, etc. The computer programs can be distributed over networkeded computers or can be stored and executed by a server computer.

[0635] It should be noted that, in the present document, relational terms are used solely to distinguish one entity or action from another entity or action without necessarily implying any actual relationship or order between such entities or actions. Also, the terms "comprises", "comprising", or any other variations 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 can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element preceded by "comprises... a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the recited element.

[0636] Each of the embodiments in the present document is described in a related manner, and the same or similar parts among the embodiments can be referred to each other. Each of the embodiments focuses on the difference from other embodiments. In particular, for the device, electronic device, and computer readable storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiments.

[0637] The above merely provides the preferred embodiment of the present application, and not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for generating order data and inventory data, characterized in that: The method comprises: Determining, based on the preset characteristic values ​​of the first type of order characteristics in the sample order data and / or the specified order characteristics, the characteristic values ​​of the specified order characteristics that the order data to be generated must conform to as the first order characteristic values; determining, based on the preset characteristic values ​​of the first inventory characteristics in the sample inventory data and / or the specified inventory characteristics, the characteristic values ​​of the specified inventory characteristics that the inventory data to be generated must conform to as the first inventory characteristic values; and obtaining, as the first correlation characteristic values, the characteristic values ​​of the association characteristics that must conform between the order data to be generated and the inventory data to be generated; generating order data satisfying the obtained first order characteristic value, and generating inventory data satisfying the obtained first inventory characteristic value; The order materials in the generated order data and the inventory materials in the inventory data are matched, so that each type of order material in the matched order data and the same type of inventory materials in the matched inventory data meet the first correlation characteristic value.

2. The method according to claim 1, characterized in that The specified order features include: a specified order feature of a fixed value type, and a specified order feature of a random value type; the specified order feature of the fixed value type is used to characterize the scale of the order data to be generated, and the specified order feature of the random value type is used to characterize the randomness of the order data to be generated; and / or, The designated inventory features include: designated inventory features of fixed value type, and designated inventory features of random value type; The specified inventory feature of the fixed value type is used to characterize the scale of the inventory data to be generated, and the specified inventory feature of the random value type is used to characterize the randomness of the inventory data to be generated.

3. The method according to claim 2, characterized in that The step of determining, based on the sample order data and / or the preset feature values ​​of the first type of order features in the specified order features, the feature values ​​of the specified order features that the order data to be generated must comply with as the first order feature values, includes: Obtaining a preset feature value of the first type of order feature in the specified order features as the first order feature value of the first type of order feature; Obtaining, based on the sample order data, a first order feature value for a second type of order feature in the specified order features; wherein the second type of order feature is: order features in the specified order features other than the first type of order features; The first order characteristic value of the first type of order characteristic and the first order characteristic value of the second type of order characteristic are combined to obtain the first order characteristic value of the specified order characteristic that the order data to be generated needs to comply with.

4. The method according to claim 3, characterized in that Obtaining, based on the sample order data, a first order feature value for a second type of order feature in the specified order feature, includes: For each second-type order feature, if the second-type order feature is a specified order feature of a fixed value type, determining the value of the sample order data for the second-type order feature as the first order feature value of the second-type order feature; If the second type of order feature is a specified order feature of a random value type, then determine the values ​​of each data item of the sample order data for the second type of order feature, as well as the frequency of occurrence of each value, and fit the random distribution that each data item conforms to. Determine the first order feature value of the second type of order feature based on the obtained random distribution.

5. The method according to claim 4, characterized in that In the case where the second type of order feature includes a feature indicating the number of orders, the first order feature value of the feature is: the number of orders included in the sample order data; and / or, When the second type of order feature includes a feature indicating the number of order lines in each order, the first order feature value of the feature is obtained based on the following steps: Performing statistical analysis on the number of order lines in each order included in the sample order data to obtain a random distribution to which the number of order lines in each order included in the sample order data conforms; Using the obtained random distribution, sampling a first number of values ​​as first order characteristic values ​​representing a characteristic of the number of order lines in each order; wherein the first number is the first order characteristic value representing a characteristic of the number of orders; and / or, When the second-category order feature includes a feature indicating the required quantity of order items in each order line, the first order feature value of the feature is obtained based on the following steps: Performing statistical analysis on the demand quantity of order materials in each order line included in the sample order data to obtain a random distribution conforming to the demand quantity of order materials in each order line included in the sample order data; Using the obtained random distribution, a second number of values ​​is sampled to obtain as first order characteristic values ​​representing the characteristic of the required quantity of order items in each order line; wherein the second number is the sum of the first order characteristic values ​​representing the characteristic of the number of order lines in each order; and / or, In the case where the second type of order feature includes a feature representing the total number of types of order materials involved in the order data, the first order feature value of the feature is: the total number of types of order materials involved in the sample order data; and / or, When the second type of order feature includes a feature indicating the number of orders for each type of order material distribution, the first order feature value of the feature is obtained based on the following steps: Performing statistical analysis on the number of orders distributed by various order materials included in the sample order data to obtain a random distribution to which the number of orders distributed by various order materials included in the sample order data conforms; Using the obtained random distribution, a third number of values ​​is sampled to obtain as a first order characteristic value representing a characteristic of the number of orders for each type of order material distribution; wherein the third number is: a first order characteristic value representing a characteristic of the total number of types of order materials involved in the order data; and / or, When the second type of order feature includes a feature indicating a ratio of order material associated categories, the first order feature value of the feature is obtained based on the following steps: For each type of order material, determining a first ratio of types of order materials associated with the type of order material in the sample order data to the total types of order materials involved in the sample order data; wherein an associated relationship between one type of order material and another type of order material indicates that the order material of the type and the order material of the other type are distributed in the same order; Calculate the average of the first ratios corresponding to each type of order material as the first order characteristic value representing the ratio of the order material-related categories; and / or, When the second type of order feature includes a feature indicating a ratio of order-material association times, the first order feature value of the feature is obtained based on the following steps: For each type of order material, determine other order materials in the sample order data that have an associated relationship with the order material of this type, as associated order materials of the order material of this type; For each associated order material of the type of order material, determine the number of orders in the sample order data that include both the type of order material and the associated order material, as the number of associations corresponding to the associated order material; Calculate the maximum value of the association counts corresponding to each associated order material and the ratio of the maximum value to the number of orders distributed for this type of order material as the second ratio corresponding to this type of order material; The average of the second ratios corresponding to each type of order material is determined as the first order characteristic value representing the ratio of the number of association times of the order materials.

6. The method according to claim 2, characterized in that The step of determining, based on the sample inventory data and / or the preset characteristic value of the first inventory characteristic in the designated inventory characteristic, the characteristic value of the designated inventory characteristic that the inventory data to be generated must conform to as the first inventory characteristic value, includes: Obtaining a preset feature value of a first inventory feature in the specified inventory features as the first inventory feature value of the first category of inventory features; Obtaining, based on the sample inventory data, a first inventory feature value for a second category of inventory features in the specified inventory features; wherein the second category of inventory features is: inventory features in the specified inventory features other than the first category of inventory features; The first inventory feature value of the first type of inventory feature and the first inventory feature value of the second type of inventory feature are combined to obtain the first inventory feature value of the specified inventory feature that the inventory data to be generated needs to comply with.

7. The method according to claim 6, characterized in that Obtaining, based on the sample inventory data, a first inventory feature value for a second type of inventory feature in the designated inventory feature includes: For each second-category inventory feature, if the second-category inventory feature is a designated inventory feature of a fixed value type, determining a value of the sample inventory data for the second-category inventory feature as a first inventory feature value of the second-category inventory feature; If the second-type inventory feature is a designated inventory feature of a random value type, then the values ​​of each data item of the sample inventory data for the second-type inventory feature and the frequency of occurrence of each value are determined, and a random distribution that each data item conforms to is fitted. The first inventory feature value of the second-type inventory feature is determined based on the obtained random distribution.

8. The method according to claim 7, characterized in that In the case where the second type of inventory feature includes a feature representing the total number of types of inventory materials involved in the inventory data, the first inventory feature value of the feature is: the total number of types of inventory materials involved in the sample inventory data; and / or, In the case where the second type of inventory feature includes a feature indicating the quantity of inventory in each container, the first inventory feature value of the feature is obtained based on the following steps: Performing statistical analysis on the number of items in each container included in the sample inventory data to obtain a random distribution to which the number of items in each container included in the sample inventory data conforms; Using the obtained random distribution, a fourth number of values ​​is sampled to obtain a first inventory feature value representing the number of inventory items in each container; wherein the fourth number is the number of containers required in the inventory data to be generated; and / or, In the case where the second-category inventory feature includes a feature indicating the provided quantity of inventory materials in each inventory, the first inventory feature value of the feature is obtained based on the following steps: Performing statistical analysis on the number of inventory materials provided in each inventory included in the sample inventory data to obtain a random distribution to which the number of inventory materials provided in each inventory included in the sample inventory data conforms; Using the obtained random distribution, a fifth number of values ​​is sampled to obtain as a first inventory characteristic value representing a characteristic of the provided quantity of inventory materials in each inventory; wherein the fifth number is: the sum of the first inventory characteristic values ​​representing a characteristic of the quantity of inventory in each container; and / or, When the second type of inventory feature includes a feature representing the number of containers distributed for each type of inventory material, the first inventory feature value of the feature is obtained based on the following steps: Performing statistical analysis on the number of containers distributed among various types of inventory materials included in the sample inventory data to obtain a random distribution conforming to the number of containers distributed among various types of inventory materials included in the sample inventory data; Using the obtained random distribution, a sixth number of values ​​is sampled to obtain as a first inventory characteristic value representing a characteristic of the number of containers distributed within each type of inventory material; wherein the sixth number is: a first inventory characteristic value representing a characteristic of the total number of types of inventory materials involved in the inventory data; and / or, When the second-category inventory feature includes a feature representing a ratio of associated categories of inventory materials, the first inventory feature value of the feature is obtained based on the following steps: For each category of inventory materials, determining a third ratio of the types of inventory materials associated with the inventory materials of the category in the sample inventory data to the total types of inventory materials involved in the sample inventory data; wherein the association between one category of inventory materials and another category of inventory materials indicates that the inventory materials of the category and the inventory materials of the another category are distributed in the same container; Calculate the average of the third ratios corresponding to each type of inventory material as the first inventory characteristic value representing the ratio of the associated types of inventory materials; and / or, When the second type of inventory feature includes a feature representing the ratio of the number of times inventory materials are associated, the first inventory feature value of the feature is obtained based on the following steps: For each type of inventory material, determine other inventory materials in the sample inventory data that have an associated relationship with the inventory materials of this type, as associated inventory materials of this type of inventory material; For each associated inventory material of the type, determining the number of containers in the sample inventory data that contain both the type of inventory material and the associated inventory material, as the number of associations corresponding to the associated inventory material; Calculate the maximum value of the association counts corresponding to each associated inventory material and the ratio of the maximum value to the inventory quantity of the inventory material of this type as the fourth ratio corresponding to the inventory material of this type; The average of the fourth ratios corresponding to each type of inventory material is calculated and determined as a first inventory characteristic value representing a ratio of the number of association times of the inventory materials.

9. The method according to claim 1, characterized in that The association feature represents the difference between the ranking of the demand quantity of each type of order material in the order data and the ranking of the supply quantity of the same type of inventory materials in the inventory data.

10. The method according to claim 9, characterized in that The step of obtaining a characteristic value of an association characteristic required to be satisfied between the order data to be generated and the inventory data to be generated, as a first association characteristic value, includes: Sorting each type of order material in the sample order data in order of required quantity to obtain a first ranking for each type of order material, and sorting each type of inventory material in the sample inventory data in order of provided quantity to obtain a second ranking for each type of inventory material; wherein the method for sorting each type of order material in the sample order data is consistent with the method for sorting each type of inventory material in the sample inventory data; For each type of order item in the sample order data, calculate the difference between the first ranking of the order item of the type and the second ranking of the inventory items matching the order item of the type; Performing statistical analysis on the calculated differences to obtain a random distribution corresponding to the correlation characteristics between the order data to be generated and the inventory data to be generated; Using the obtained random distribution, a third number of values ​​is sampled as the characteristic value of the required association characteristic between the order data to be generated and the inventory data to be generated, as the first association characteristic value; wherein, the third number is the first order characteristic value representing the characteristic of the total number of types of order materials involved in the order data.

11. The method according to claim 9, characterized in that The matching of the order items in the generated order data and the inventory items in the inventory data so that each type of order item in the matched order data and the same type of inventory items in the matched inventory data meet the first correlation characteristic value includes: sorting the various order materials in the generated order data in order of required quantity to obtain a third ranking for each order material; and sorting the various inventory materials in the generated inventory data in order of provided quantity to obtain a fourth ranking for each inventory material; wherein the manner of sorting the various order materials in the generated order data is consistent with the manner of sorting the various inventory materials in the generated inventory data; and the manner of sorting the various order materials in the generated order data is consistent with the manner of sorting the various order materials in the sample order data; The order materials in the generated order data and the inventory materials in the generated inventory data are matched using each first association feature value; wherein each type of order material in the matched order data corresponds to a first association feature value, and the difference between the third ranking of the order material of that type and the fourth ranking of the matched inventory material is the first association feature value.

12. The method according to claim 1, characterized in that The specified order features include: a feature indicating the number of orders, a feature indicating the number of order lines in each order, a feature indicating the required number of order items in each order line, a feature indicating the total number of types of order items involved in the order data, and a feature indicating the number of orders distributed within each type of order item; The specified inventory characteristics include: characteristics representing the total number of types of inventory materials involved in the inventory data, characteristics representing the number of inventory in each container, characteristics representing the number of inventory materials provided in each inventory, and characteristics representing the number of containers distributed for each type of inventory material.

13. The method according to claim 12, characterized in that The specified order characteristics also include: a characteristic indicating the ratio of order material association types, and / or a characteristic indicating the ratio of order material association times; The ratio of order material related types indicates the ratio of the types of order materials with related relationships in the order data to the total types of order materials involved in the order data. The order item association ratio indicates the ratio of the maximum number of associations for a certain type of order item to the number of orders for which this type of order item is associated. The maximum number of associations for a certain type of order item indicates the maximum number of times this type of order item and other associated order items are distributed in the same order. and / or, The specified inventory characteristics also include: characteristics indicating the proportion of related types of inventory materials, and / or characteristics indicating the proportion of the number of times inventory materials are related; The ratio of related inventory material types indicates the ratio of the types of related inventory materials in the inventory data to the total types of inventory materials involved in the inventory data. The inventory material association frequency ratio indicates the ratio of the maximum association frequency of a type of inventory material to the number of containers in which this type of inventory material is distributed. The maximum association frequency of a type of inventory material indicates the maximum number of times this type of inventory material and other associated inventory materials are distributed in the same container.

14. The method according to claim 12 or 13, characterized in that Before generating order data that satisfies the obtained first order characteristic value and generating inventory data that satisfies the obtained first inventory characteristic value, the method further includes: Determine whether the currently obtained first order characteristic value and first inventory characteristic value meet the preset verification rules; The verification rule includes at least one of the following: The first order characteristic value representing the total number of order items involved in the order data is not greater than the sum of the first order characteristic values ​​representing the number of order lines in each order. The first order characteristic value representing the total number of types of order materials involved in the order data is not less than the maximum value of the first order characteristic values ​​representing the number of order lines in each order; The first inventory characteristic value representing the total number of types of inventory materials involved in the inventory data is not less than the maximum value of the first inventory characteristic values ​​representing the number of inventory in each container; A first order characteristic value representing a characteristic of the total number of types of order materials involved in the order data is not greater than a first inventory characteristic value representing a characteristic of the total number of types of inventory materials involved in the inventory data; The average quantity of demand for each type of order material is not greater than the average quantity of supply for each type of inventory material; If the currently obtained first order characteristic value and first inventory characteristic value do not meet the preset verification rules, the characteristic value of the unsatisfied specified order characteristic and / or the characteristic value of the specified inventory characteristic are corrected until the first order characteristic value and first inventory characteristic value that meet the verification rules are obtained.

15. The method according to claim 13, characterized in that Generating order data that satisfies the obtained first order characteristic value includes: determining the number of orders included in the order data to be generated according to a first order feature value representing a feature of the number of orders; Determining the number of order lines in each order included in the order data to be generated according to a first order characteristic value representing the number of order lines in each order; Determining the required quantity of order materials in each order line included in the order data to be generated according to a first order characteristic value representing the required quantity of order materials in each order line; determining the total number of types of order materials involved in the order data to be generated according to a first order characteristic value representing the characteristic of the total number of types of order materials involved in the order data; Determining the number of orders for each type of order material distribution in the order data to be generated according to a first order characteristic value representing the characteristic of the number of orders for each type of order material distribution; Assign the type and required quantity of order materials to each order line based on the required quantity of order materials in each order line included in the order data to be generated, the total number of order material types involved in the order data to be generated, and the number of orders distributed among each type of order materials in the order data to be generated. According to the number of order lines in each order contained in the order data to be generated and the number of orders contained in the order data to be generated, order lines are allocated to each order so that the allocated orders meet the first order characteristic value representing the proportion of order material association types and / or the first order characteristic value representing the proportion of order material association times.

16. The method according to claim 13, characterized in that Generating inventory data that satisfies the obtained first inventory characteristic value includes: The number of containers required in the inventory data to be generated is calculated according to the first formula; wherein the first formula is: m1 is the first inventory characteristic value representing the total number of inventory types involved in the inventory data; m2 is the mean of the first inventory characteristic values ​​representing the number of containers for each type of inventory material distribution; m3 is the mean of the first inventory characteristic values ​​representing the number of inventory items in each container; Determining the quantity of inventory in each container in the inventory data to be generated according to a first inventory feature value representing a feature of the quantity of inventory in each container; determining the total number of types of inventory materials involved in the inventory data to be generated according to a first inventory characteristic value representing the total number of types of inventory materials involved in the inventory data; Determining the provided quantity of each inventory material in the inventory data to be generated according to a first inventory characteristic value representing a characteristic of the provided quantity of each inventory material; Determining the number of containers for each category of inventory material distribution in the inventory data to be generated according to a first inventory characteristic value representing the number of containers for each category of inventory material distribution; Allocate the type and supply quantity of inventory materials to each inventory according to the supply quantity of inventory materials in each inventory in the inventory data to be generated, the total number of types of inventory materials involved in the inventory data to be generated, and the number of containers in which each type of inventory material is distributed in the inventory data to be generated; According to the required number of containers in the inventory data to be generated and the number of inventories in each container in the inventory data to be generated, inventory is allocated to each container so that the allocated container satisfies the first inventory characteristic value representing the ratio of the types of associated inventory materials and / or the first inventory characteristic value representing the ratio of the number of times inventory materials are associated.

17. A device for generating order data and inventory data, characterized in that: The device comprises: a feature value acquisition module configured to determine, based on the sample order data and / or the preset feature value of the first type of order feature in the specified order feature, the feature value of the specified order feature that the to-be-generated order data must conform to, as the first order feature value; determine, based on the sample inventory data and / or the preset feature value of the first inventory feature in the specified inventory feature, the feature value of the specified inventory feature that the to-be-generated inventory data must conform to, as the first inventory feature value; and obtain, as the first association feature value, the feature value of the association feature that the to-be-generated order data and the to-be-generated inventory data must conform to; a data generation module, configured to generate order data satisfying the obtained first order characteristic value, and generate inventory data satisfying the obtained first inventory characteristic value; The material matching module is used to match the order materials in the generated order data with the inventory materials in the inventory data, so that each type of order material in the matched order data and the same type of inventory materials in the matched inventory data meet the first association characteristic value.

18. The device according to claim 17, characterized in that The specified order features include: a specified order feature of a fixed value type, and a specified order feature of a random value type; the specified order feature of the fixed value type is used to characterize the scale of the order data to be generated, and the specified order feature of the random value type is used to characterize the randomness of the order data to be generated; And / or, the designated inventory feature includes: a designated inventory feature of a fixed value type, and a designated inventory feature of a random value type; The specified inventory feature of the fixed value type is used to characterize the scale of the inventory data to be generated, and the specified inventory feature of the random value type is used to characterize the randomness of the inventory data to be generated; and / or, The characteristic value acquisition module includes: A first order feature value acquisition submodule is configured to acquire a preset feature value of a first type of order feature in a specified order feature as a first order feature value of the first type of order feature; A second order feature value acquisition submodule is configured to obtain, based on the sample order data, a first order feature value for a second type of order feature in the specified order features; wherein the second type of order feature is: order features in the specified order features other than the first type of order features; a first combining submodule, configured to combine the first order characteristic value of the first type of order characteristic and the first order characteristic value of the second type of order characteristic to obtain the first order characteristic value of the specified order characteristic that the order data to be generated must comply with; and / or, The second order characteristic value acquisition submodule is specifically used to: For each second-type order feature, if the second-type order feature is a specified order feature of a fixed value type, determining the value of the sample order data for the second-type order feature as the first order feature value of the second-type order feature; If the second type of order feature is a specified order feature of a random value type, then determining the value of each data item of the sample order data for the second type of order feature and the frequency of occurrence of each value, and fitting to obtain a random distribution that each data item conforms to, and determining the first order feature value of the second type of order feature based on the obtained random distribution; and / or, In the case where the second type of order feature includes a feature representing the number of orders, the first order feature value of the feature is: the number of orders included in the sample order data; and / or, in the case where the second type of order feature includes a feature representing the number of order lines in each order, the first order feature value of the feature is obtained based on the following steps: performing statistical analysis on the number of order lines in each order included in the sample order data to obtain a random distribution that the number of order lines in each order included in the sample order data conforms to; using the obtained random distribution, sampling a first number of values ​​as the first order feature value of the feature representing the number of order lines in each order; wherein the first number is the number representing the number of order lines in each order. and / or, in a case where the second type of order feature includes a feature representing the required number of order items in each order line, the first order feature value of the feature is obtained based on the following steps: performing statistical analysis on the required number of order items in each order line included in the sample order data to obtain a random distribution conforming to the required number of order items in each order line included in the sample order data; using the obtained random distribution, sampling a second number of values ​​as the first order feature value representing the feature of the required number of order items in each order line; wherein the second number is: the sum of the first order feature values ​​representing the feature of the number of order lines in each order; And / or, in the case where the second type of order feature includes a feature representing the total number of types of order materials involved in the order data, the first order feature value of the feature is: the total number of types of order materials involved in the sample order data; and / or, in the case where the second type of order feature includes a feature representing the number of orders distributed for each type of order material, the first order feature value of the feature is obtained based on the following steps: performing statistical analysis on the number of orders distributed for each type of order material included in the sample order data to obtain a random distribution that conforms to the number of orders distributed for each type of order material included in the sample order data; using the obtained random distribution, sampling to obtain a third number of values ​​as a representation of each type of order material The first order characteristic value of the characteristic representing the total number of order types involved in the order data; wherein the third number is: the first order characteristic value of the characteristic representing the total number of types of order materials involved in the order data; and / or, in the case where the second type of order characteristics includes a characteristic representing the ratio of associated types of order materials, the first order characteristic value of the characteristic is obtained based on the following steps: for each type of order material, determining, in the sample order data, a first ratio of the types of order materials that have an associated relationship with the order materials of that type to the total types of order materials involved in the sample order data; wherein the existence of an associated relationship between one type of order material and another type of order material indicates that the order materials of that type and the order materials of the other type are distributed in the same order;Calculate the mean of the first ratios corresponding to each type of order material as the first order characteristic value representing the ratio of order material association types; and / or, in a case where the second type of order characteristic includes a characteristic representing the ratio of order material association times, the first order characteristic value of the characteristic is obtained based on the following steps: for each type of order material, determine other order materials in the sample order data that have an association relationship with the order material of this type as the associated order materials of this type; for each associated order material of this type of order material, determine the number of orders in the sample order data that simultaneously include the order material of this type and the associated order material as the number of associations corresponding to the associated order material; calculate the maximum value of the number of associations corresponding to each associated order material and the ratio of the number of orders distributed over the order material of this type as the second ratio corresponding to the order material of this type; determine the mean of the second ratios corresponding to each type of order material as the first order characteristic value representing the ratio of order material association times; and / or, The characteristic value acquisition module includes: A first inventory feature value acquisition submodule is configured to acquire a preset feature value of a first inventory feature among the specified inventory features as a first inventory feature value of the first category of inventory features; A second inventory feature value acquisition submodule is configured to obtain, based on the sample inventory data, a first inventory feature value for a second type of inventory feature in the specified inventory features; wherein the second type of inventory feature is: inventory features in the specified inventory features other than the first type of inventory feature; a second combining submodule, configured to combine the first inventory feature value of the first category of inventory features and the first inventory feature value of the second category of inventory features to obtain the first inventory feature value of the specified inventory feature that the inventory data to be generated must comply with; and / or, The second inventory characteristic value acquisition submodule is specifically used to: For each second-category inventory feature, if the second-category inventory feature is a designated inventory feature of a fixed value type, determining a value of the sample inventory data for the second-category inventory feature as a first inventory feature value of the second-category inventory feature; If the second-type inventory feature is a designated inventory feature of a random value type, determining the value of each data item of the sample inventory data for the second-type inventory feature and the frequency of occurrence of each value, fitting a random distribution that each data item conforms to, and determining the first inventory feature value of the second-type inventory feature based on the obtained random distribution; and / or, In the case where the second type of inventory feature includes a feature representing the total number of types of inventory materials involved in the inventory data, the first inventory feature value of the feature is: the total number of types of inventory materials involved in the sample inventory data; and / or, in the case where the second type of inventory feature includes a feature representing the number of inventory in each container, the first inventory feature value of the feature is obtained based on the following steps: performing statistical analysis on the number of inventory in each container included in the sample inventory data to obtain a random distribution that the number of inventory in each container included in the sample inventory data conforms to; using the obtained random distribution, sampling to obtain a fourth number of values ​​as the first value of the feature representing the number of inventory in each container Inventory characteristic value; wherein the fourth number is: the number of containers required in the inventory data to be generated; and / or, in the case where the second type of inventory characteristic includes a characteristic representing the number of inventory materials provided in each inventory, the first inventory characteristic value of the characteristic is obtained based on the following steps: performing statistical analysis on the number of inventory materials provided in each inventory included in the sample inventory data to obtain a random distribution that the number of inventory materials provided in each inventory included in the sample inventory data conforms to; using the obtained random distribution, sampling a fifth number of values ​​as the first inventory characteristic value representing the number of inventory materials provided in each inventory; wherein the fifth number is: the number of inventory materials in each container The sum of the first inventory characteristic values ​​of the number of characteristics; and / or, in the case where the second type of inventory characteristics includes a characteristic representing the number of containers for each type of inventory material distribution, the first inventory characteristic value of the characteristic is obtained based on the following steps: performing statistical analysis on the number of containers for each type of inventory material distribution contained in the sample inventory data to obtain a random distribution that conforms to the number of containers for each type of inventory material distribution contained in the sample inventory data; using the obtained random distribution, sampling to obtain a sixth number of values ​​as the first inventory characteristic value representing the characteristic of the number of containers for each type of inventory material distribution; wherein the sixth number is: a characteristic representing the total number of types of inventory materials involved in the inventory data and / or, in the case where the second type of inventory feature includes a feature representing a ratio of associated types of inventory materials, the first inventory feature value of the feature is obtained based on the following steps: determining, for each type of inventory material, a third ratio of the types of inventory materials associated with the inventory materials of the type in the sample inventory data to the total types of inventory materials involved in the sample inventory data; wherein the association between one type of inventory material and another type of inventory material indicates that the inventory materials of the type and the inventory materials of the other type are distributed in the same container; and calculating the average of the third ratios corresponding to each type of inventory material as the first inventory feature value representing the ratio of associated types of inventory materials;And / or, in the case where the second type of inventory feature includes a feature representing a ratio of the number of associations of inventory materials, the first inventory feature value of the feature is obtained based on the following steps: for each type of inventory material, determining other inventory materials in the sample inventory data that have an association relationship with the inventory materials of this type as associated inventory materials of this type; for each associated inventory material of this type of inventory material, determining the number of containers in the sample inventory data that simultaneously contain the inventory materials of this type and the associated inventory materials as the number of associations corresponding to the associated inventory material; calculating the maximum value of the number of associations corresponding to each associated inventory material and the ratio of the maximum value to the number of inventory distributed in this type of inventory material as the fourth ratio corresponding to this type of inventory material; calculating the average of the fourth ratios corresponding to each type of inventory material and determining the average value as the first inventory feature value representing the ratio of the number of associations of inventory materials; and / or, The correlation feature represents the difference between the ranking of the demand quantity of each type of order material in the order data and the ranking of the supply quantity of the same type of inventory material in the inventory data; and / or, The characteristic value acquisition module includes: The associated sorting submodule is configured to sort the various order materials in the sample order data in order of required quantity to obtain a first ranking for each order material category, and to sort the various inventory materials in the sample inventory data in order of provided quantity to obtain a second ranking for each inventory material category; wherein the method for sorting the various order materials in the sample order data is consistent with the method for sorting the various inventory materials in the sample inventory data; an associated difference calculation submodule, configured to calculate, for each type of order material in the sample order data, a difference between a first ranking of the order material of the type and a second ranking of inventory materials matching the order material of the type; The association analysis submodule is used to perform statistical analysis on the calculated differences to obtain the random distribution of the association characteristics between the order data to be generated and the inventory data to be generated; an association sampling submodule, configured to sample a third number of values ​​using the obtained random distribution to obtain the values ​​as characteristic values ​​of the association characteristic required to be satisfied between the to-be-generated order data and the to-be-generated inventory data, as well as the first association characteristic value; wherein the third number is the first order characteristic value representing the total number of types of order materials involved in the order data; and / or, The material matching module is specifically used to: sorting the various order materials in the generated order data in order of required quantity to obtain a third ranking for each order material; and sorting the various inventory materials in the generated inventory data in order of provided quantity to obtain a fourth ranking for each inventory material; wherein the manner of sorting the various order materials in the generated order data is consistent with the manner of sorting the various inventory materials in the generated inventory data; and the manner of sorting the various order materials in the generated order data is consistent with the manner of sorting the various order materials in the sample order data; Matching the order items in the generated order data with the inventory items in the generated inventory data using each first correlation feature value; wherein each type of order item in the matched order data corresponds to a first correlation feature value, and the difference between the third ranking of the order item in that type and the fourth ranking of the matched inventory item is the first correlation feature value; and / or, The specified order features include: a feature indicating the number of orders, a feature indicating the number of order lines in each order, a feature indicating the required number of order items in each order line, a feature indicating the total number of types of order items involved in the order data, and a feature indicating the number of orders distributed within each type of order item; The specified inventory features include: a feature indicating the total number of types of inventory materials involved in the inventory data, a feature indicating the number of inventory materials in each container, a feature indicating the number of inventory materials provided in each inventory, and a feature indicating the number of containers in which each type of inventory material is distributed; and / or, The specified order characteristics also include: a characteristic indicating the ratio of order material associated types, and / or a characteristic indicating the ratio of order material association times; the order material associated type ratio indicates: the ratio of the types of order materials with associated relationships in the order data to the total types of order materials involved in the order data; the order material association times ratio indicates: the ratio of the maximum number of associations of a type of order materials to the number of orders distributed with this type of order materials; the maximum number of associations of a type of order materials indicates: the maximum number of times this type of order materials and other associated order materials are distributed in the same order; and / or, the specified inventory characteristics also include: a characteristic indicating the ratio of inventory material associated types, and / or a characteristic indicating the ratio of inventory material association times; the inventory material associated type ratio indicates: the ratio of the types of inventory materials with associated relationships in the inventory data to the total types of inventory materials involved in the inventory data; the inventory material association times ratio indicates: the ratio of the maximum number of associations of a type of inventory materials to the number of containers distributed with this type of inventory materials; the maximum number of associations of a type of inventory materials indicates: the maximum number of times this type of inventory materials and other associated inventory materials are distributed in the same container; and / or, The device further comprises: A verification module is configured to determine whether the currently obtained first order characteristic value and the first inventory characteristic value satisfy a preset verification rule before generating order data satisfying the obtained first order characteristic value and generating inventory data satisfying the obtained first inventory characteristic value; wherein the verification rule includes at least one of the following: the first order characteristic value representing the characteristic of the total number of types of order materials involved in the order data is not greater than the sum of the first order characteristic values ​​representing the characteristic of the number of order lines in each order; the first order characteristic value representing the characteristic of the total number of types of order materials involved in the order data is not less than the maximum value of the first order characteristic values ​​representing the characteristic of the number of order lines in each order; the first inventory characteristic value representing the characteristic of the total number of types of inventory materials involved in the inventory data is not less than the maximum value of the first inventory characteristic values ​​representing the characteristic of the number of inventory in each container; the first order characteristic value representing the characteristic of the total number of types of order materials involved in the order data is not greater than the first inventory characteristic value representing the characteristic of the total number of types of inventory materials involved in the inventory data; the average demand quantity of each type of order material is not greater than the average supply quantity of each type of inventory material; a correction module, configured to, if the currently obtained first order characteristic value and first inventory characteristic value do not satisfy a preset validation rule, correct the characteristic value of the specified order characteristic and / or the characteristic value of the specified inventory characteristic that do not satisfy the validation rule, until the first order characteristic value and first inventory characteristic value that satisfy the validation rule are obtained; and / or, The data generation module is specifically used to: determining the number of orders included in the order data to be generated according to a first order feature value representing a feature of the number of orders; Determining the number of order lines in each order included in the order data to be generated according to a first order characteristic value representing the number of order lines in each order; Determining the required quantity of order materials in each order line included in the order data to be generated according to a first order characteristic value representing the required quantity of order materials in each order line; determining the total number of types of order materials involved in the order data to be generated according to a first order characteristic value representing the characteristic of the total number of types of order materials involved in the order data; Determining the number of orders for each type of order material distribution in the order data to be generated according to a first order characteristic value representing the characteristic of the number of orders for each type of order material distribution; Assign the type and required quantity of order materials to each order line based on the required quantity of order materials in each order line included in the order data to be generated, the total number of order material types involved in the order data to be generated, and the number of orders distributed among each type of order materials in the order data to be generated. Allocating order lines to each order according to the number of order lines in each order included in the order data to be generated and the number of orders included in the order data to be generated, so that the allocated orders satisfy a first order characteristic value representing a ratio of order material association types and / or a first order characteristic value representing a ratio of order material association times; and / or, The data generation module is specifically used to: The number of containers required in the inventory data to be generated is calculated according to the first formula; wherein the first formula is: m1 is the first inventory characteristic value representing the total number of inventory types involved in the inventory data; m2 is the mean of the first inventory characteristic values ​​representing the number of containers for each type of inventory material distribution; m3 is the mean of the first inventory characteristic values ​​representing the number of inventory items in each container; Determining the quantity of inventory in each container in the inventory data to be generated according to a first inventory feature value representing a feature of the quantity of inventory in each container; determining the total number of types of inventory materials involved in the inventory data to be generated according to a first inventory characteristic value representing the total number of types of inventory materials involved in the inventory data; Determining the provided quantity of each inventory material in the inventory data to be generated according to a first inventory characteristic value representing a characteristic of the provided quantity of each inventory material; Determining the number of containers for each category of inventory material distribution in the inventory data to be generated according to a first inventory characteristic value representing the number of containers for each category of inventory material distribution; Allocate the type and supply quantity of inventory materials to each inventory according to the supply quantity of inventory materials in each inventory in the inventory data to be generated, the total number of types of inventory materials involved in the inventory data to be generated, and the number of containers in which each type of inventory material is distributed in the inventory data to be generated; According to the required number of containers in the inventory data to be generated and the number of inventories in each container in the inventory data to be generated, inventory is allocated to each container so that the allocated container satisfies the first inventory characteristic value representing the ratio of the types of associated inventory materials and / or the first inventory characteristic value representing the ratio of the number of times inventory materials are associated.

19. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the method according to any one of claims 1 to 16 when executing a program stored in a memory.

20. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 16 is implemented.

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