Method, device, electronic device and readable medium for generating item replenishment information

Abstract

The embodiments of the present disclosure disclose a method, apparatus, electronic device, and readable medium for generating item replenishment information. A specific implementation of the method includes: obtaining an original item satisfaction data set; combining net recommendation information and item satisfaction information into a first combination information set to obtain a first combination information set; generating an item satisfaction processing information set; generating an item correlation coefficient; combining the net recommendation information and logistics distribution satisfaction information into a second combination information set to obtain a second combination information set; generating a logistics distribution satisfaction processing information set; generating a logistics distribution correlation coefficient; generating item net recommendation information based on each piece of net recommendation information included in the original item satisfaction data set; and generating item replenishment information based on the net recommendation information, the item correlation coefficient, and the logistics distribution correlation coefficient. This implementation reduces the system resources occupied by sample data, shortens query time, and thus shortens the time to determine item replenishment information.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and more particularly to a method, device, electronic device, and readable medium for generating item replenishment information. Background Art

[0002] With the development of computer technology and the maturity of logistics technology, automated item replenishment is now possible. Currently, determining item replenishment information typically involves first performing a multi-table join query on the original sample data to determine sample data. Then, based on the sample data, a type of satisfaction correlation information for the item is determined. Finally, item replenishment information is determined based on this satisfaction correlation information.

[0003] However, the inventors have discovered that when using the above method to determine item replenishment information, the following technical problems often arise:

[0004] First, the original sample data was not screened and integrated, resulting in the sample data occupying more system resources and taking a longer time to query the sample data, which in turn resulted in a longer time to determine the item replenishment information.

[0005] Second, when determining item replenishment information, the system only adjusts the replenishment information based on a single piece of satisfaction-related information that influences item flow. This results in low accuracy of the determined item replenishment information. This in turn leads to high out-of-stock rates when the item replenishment quantity is small, and high item backlogs and loss when the item replenishment quantity is large.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the Invention

[0007] The content of this disclosure is used to briefly introduce concepts that will be described in detail in the detailed description section below. The content of this disclosure is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0008] Some embodiments of the present disclosure provide a method, apparatus, electronic device, and computer-readable medium for generating item replenishment information to solve one or more of the technical problems mentioned in the background technology section above.

[0009] In a first aspect, some embodiments of the present disclosure provide a method for generating item replenishment information, the method comprising: obtaining an item satisfaction original data set of a target item within a preset time period, wherein the item satisfaction original data in the above item satisfaction original data set include net recommendation information, item satisfaction information, logistics distribution satisfaction information and item value satisfaction information; combining the net recommendation information and item satisfaction information included in each item satisfaction original data in the above item satisfaction original data set into a first combination information to obtain a first combination information set; generating an item satisfaction processing information set based on the above first combination information set, wherein the item satisfaction processing information in the above item satisfaction processing information set includes: a first combination pair and a first number of repetitions, and the above first combination pair includes net recommendation information and item satisfaction information; based on the above item satisfaction Processing information sets to generate item correlation coefficients; combining the net recommendation information and logistics distribution satisfaction information included in each item satisfaction original data in the above-mentioned item satisfaction original data set into second combination information to obtain a second combination information set; generating a logistics distribution satisfaction processing information set based on the above-mentioned second combination information set, wherein the logistics distribution satisfaction processing information in the above-mentioned logistics distribution satisfaction processing information set includes: a second combination pair and a second number of repetitions, and the above-mentioned second combination pair includes net recommendation information and logistics distribution satisfaction information; generating a logistics distribution correlation coefficient based on the above-mentioned logistics distribution satisfaction processing information set; generating item net recommendation information based on each net recommendation information included in the above-mentioned item satisfaction original data set; generating item replenishment information based on the above-mentioned net recommendation information, the above-mentioned item correlation coefficient and the above-mentioned logistics distribution correlation coefficient.

[0010] In a second aspect, some embodiments of the present disclosure provide an item replenishment information generating device, the device comprising: an acquisition unit, configured to acquire an item satisfaction raw data set of a target item within a preset time period, wherein the item satisfaction raw data in the above item satisfaction raw data set includes net recommendation information, item satisfaction information, logistics distribution satisfaction information, and item value satisfaction information; a first combination unit, configured to combine the net recommendation information and item satisfaction information included in each item satisfaction raw data in the above item satisfaction raw data set into a first combination information to obtain a first combination information set; a first generation unit, configured to generate an item satisfaction processing information set based on the above first combination information set, wherein the item satisfaction processing information in the above item satisfaction processing information set includes: a first combination pair and a first number of repetitions, wherein the above first combination pair includes net recommendation information and item satisfaction information; a second generation unit, configured to generate an item satisfaction processing information set based on the above item satisfaction processing information set, Generate an item correlation coefficient; the second combination unit is configured to combine the net recommendation information and logistics distribution satisfaction information included in each item satisfaction original data in the above-mentioned item satisfaction original data set into a second combination information to obtain a second combination information set; the third generation unit is configured to generate a logistics distribution satisfaction processing information set based on the above-mentioned second combination information set, wherein the logistics distribution satisfaction processing information in the above-mentioned logistics distribution satisfaction processing information set includes: a second combination pair and a second number of repetitions, and the above-mentioned second combination pair includes net recommendation information and logistics distribution satisfaction information; the fourth generation unit is configured to generate a logistics distribution correlation coefficient based on the above-mentioned logistics distribution satisfaction processing information set; the fifth generation unit is configured to generate item net recommendation information based on each net recommendation information included in the above-mentioned item satisfaction original data set; the sixth generation unit is configured to generate item replenishment information based on the above-mentioned net recommendation information, the above-mentioned item correlation coefficient and the above-mentioned logistics distribution correlation coefficient.

[0011] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0012] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method described in any implementation manner of the first aspect is implemented.

[0013] The aforementioned embodiments of the present disclosure have the following beneficial effects: Through the method for generating item replenishment information in some embodiments of the present disclosure, system resources occupied by sample data are reduced, the time required to query sample data is shortened, and thus the time required to determine item replenishment information is shortened. Specifically, the reason why sample data consumes more system resources, the query time is longer, and thus the time required to determine item replenishment information is longer is that the original sample data is not filtered and integrated, resulting in the sample data occupying more system resources, the query time is longer, and thus the time required to determine item replenishment information is longer. Based on this, the method for generating item replenishment information in some embodiments of the present disclosure first obtains a set of raw item satisfaction data for a target item within a preset time period. The raw item satisfaction data in this set includes net recommendation information, item satisfaction information, logistics and distribution satisfaction information, and item value satisfaction information. Thus, a set of raw item satisfaction data for the target item, including net recommendation information, item satisfaction information, logistics and distribution satisfaction information, and item value satisfaction information, is obtained. Next, the net recommendation information and item satisfaction information included in each item satisfaction data set in the raw item satisfaction data set are combined into a first combined information set, thereby obtaining a first combined information set. Based on the first combined information set, a processed item satisfaction information set is generated. The processed item satisfaction information in the processed item satisfaction information set includes a first combination pair and a first number of repetitions. The first combination pair includes net recommendation information and item satisfaction information. Thus, a processed item satisfaction information set is obtained by filtering and integrating the net recommendation information and item satisfaction information included in the original sample data. Then, an item correlation coefficient is generated based on the processed item satisfaction information set. Thus, an item correlation coefficient representing the correlation between the net recommendation information and item satisfaction information is obtained. Next, the net recommendation information and logistics and distribution satisfaction information included in each item satisfaction data set in the original item satisfaction data set are combined into a second combined information set, generating a second combined information set. Based on the second combined information set, a processed logistics and distribution satisfaction information set is generated. The processed logistics and distribution satisfaction information in the processed logistics and distribution satisfaction information set includes a second combination pair and a second number of repetitions. The second combination pair includes net recommendation information and logistics and distribution satisfaction information. Thus, a processed logistics and distribution satisfaction information set is obtained by filtering and integrating the net recommendation information and logistics and distribution satisfaction information included in the original sample data. Subsequently, a logistics distribution correlation coefficient is generated based on the processed logistics distribution satisfaction information set. This yields a logistics distribution correlation coefficient that characterizes the correlation between the net recommendation information and the logistics distribution satisfaction information. Subsequently, item net recommendation information is generated based on each piece of net recommendation information included in the original item satisfaction data set.This yields net recommendation information for the item, which can be used to measure the target user's degree of recommendation for the target item. Finally, item replenishment information is generated based on the net recommendation information, the item relevance coefficient, and the logistics and delivery relevance coefficient. This yields item replenishment information influenced by the degree of recommendation and the associated item satisfaction. By filtering and integrating the target item's net recommendation information, item satisfaction information, logistics and delivery satisfaction information, and item value satisfaction information, system resources consumed by sample data are reduced, shortening the time required to query sample data. Furthermore, by determining the correlation between net recommendation information and item satisfaction information, net recommendation information and logistics and delivery satisfaction information, and net recommendation information and item value satisfaction information, item replenishment information for the target item can be generated. This shortens the time required to determine item replenishment information. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0015] Figure 1 is a flow chart of some embodiments of a method for generating item replenishment information according to the present disclosure;

[0016] Figure 2 is a schematic structural diagram of some embodiments of an item replenishment information generating device according to the present disclosure;

[0017] Figure 3 It is a structural diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0018] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0019] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure may be combined with each other.

[0020] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0021] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0022] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0023] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0024] Figure 1 A process 100 of some embodiments of a method for generating item replenishment information according to the present disclosure is shown. The method for generating item replenishment information includes the following steps:

[0025] Step 101: Obtain a set of original data on item satisfaction of a target item within a preset time period.

[0026] In some embodiments, the execution entity (e.g., a computing device) of the item replenishment information generation method can obtain a set of raw item satisfaction data for a target item within a preset time period from an item data information database via a wired or wireless connection. The item data information database can be a database storing information about each item in a target store. The target store can be any store. The target store is not specifically limited herein. The target item can be any type of item. The target item is not specifically limited herein. The preset time period can be a pre-set time period. For example, the preset time period can be the most recent quarter. The raw item satisfaction data in the set of raw item satisfaction data can include net recommendation information, item satisfaction information, logistics and delivery satisfaction information, and item value satisfaction information. The raw item satisfaction data can represent a set of information representing the target user's rating of the target item. The net recommendation information can be the probability that the target user recommends the target item to other users nearby. For example, the net recommendation information can be a probability of 6 out of 10 for the target user to recommend the target item to other users nearby. The target user can be the user corresponding to the raw item satisfaction data. The other users can be any user. The above-mentioned other users are not specifically limited here. The above-mentioned item satisfaction information can be information about the target user's satisfaction with the item quality of the target item. For example, the above-mentioned item satisfaction information can be that the target user is satisfied with the item quality (item quality) of the target item, which is 6 points (out of 10 points). The above-mentioned logistics and distribution satisfaction information can be information about the target user's satisfaction with the logistics and distribution timeliness of the target item. For example, the above-mentioned logistics and distribution satisfaction information can be that the target user is satisfied with the logistics and distribution timeliness of the target item, which is 6 points (out of 10 points). The above-mentioned item value satisfaction information can be information about the target user's satisfaction with the item value of the target item. For example, the above-mentioned item value satisfaction information can be that the target user is satisfied with the item value (item price) of the target item, which is 6 points (out of 10). It should be noted that the above-mentioned wireless connection method can include but is not limited to 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0027] Step 102 : combining the net recommendation information and the item satisfaction information included in each item satisfaction original data in the item satisfaction original data set into first combined information to obtain a first combined information set.

[0028] In some embodiments, the execution entity may combine the net recommendation information and the item satisfaction information included in each item satisfaction original data in the item satisfaction original data set into first combination information to obtain a first combination information set.

[0029] In practice, the execution entity may combine the net recommendation information and the item satisfaction information included in each item satisfaction raw data set into first combined information to obtain the first combined information set. Here, the combination may be concatenation.

[0030] Step 103: Generate an item satisfaction processing information set based on the first combination information set.

[0031] In some embodiments, based on the first combination information set, the execution entity may generate an item satisfaction processing information set. The item satisfaction processing information in the item satisfaction processing information set may include a first combination pair and a first repetition count. The first combination pair may include net recommendation information and item satisfaction information. The first repetition count may be the number of times the first combination pair appears in the item satisfaction processing information.

[0032] In practice, based on the first combined information set, the execution entity may generate an item satisfaction processing information set through the following steps:

[0033] In the first step, each piece of first combination information included in the first combination information set is deduplicated to obtain a first deduplicated combination information set.

[0034] In the second step, first, for each first combination of deduplicated information in the first combination of deduplicated information set, the execution entity may determine the first combination of deduplicated information as a first combination pair. Secondly, the number of occurrences of the first combination pair in the first combination information set may be determined as a first repetition count. Then, the first combination pair and the first repetition count may be combined to form item satisfaction processing information. Finally, the resulting individual item satisfaction processing information may be determined as an item satisfaction processing information set.

[0035] Step 104 : Process the information set according to the item satisfaction scores to generate an item correlation coefficient.

[0036] In some embodiments, based on the item satisfaction processing information set, the execution entity may generate an item relevance coefficient, wherein the item relevance coefficient may represent information on the degree of relevance between the item satisfaction information and the net recommendation information.

[0037] In some optional implementations of some embodiments, based on the item satisfaction processing information set, the execution entity may generate an item relevance coefficient through the following steps:

[0038] In the first step, for each item satisfaction processing information in the above item satisfaction processing information set, perform the following determination steps:

[0039] In the first sub-step, the product of the net recommendation information included in the above-mentioned item satisfaction processing information and the corresponding first repetition number is determined as the first repeated net recommendation information.

[0040] In the second sub-step, the product of the item satisfaction information included in the item satisfaction processing information and the corresponding first repetition number is determined as the first repetition satisfaction information.

[0041] In the second step, the sum of the obtained first repeated net recommendation information is determined as the first repeated item recommendation information.

[0042] In the third step, the sum of the obtained first repeated satisfaction information is determined as the first repeated item satisfaction information.

[0043] In the fourth step, the sum of the first repetition times included in the above-mentioned item satisfaction processing information set is determined as the first repetition information.

[0044] In step 5, the first repeated item recommendation average information is determined based on the first repeated item recommendation information and the first repeated information. In practice, the execution entity may determine the first repeated item recommendation average information as the ratio of the first repeated item recommendation information to the first repeated information.

[0045] Step 6: Determine the first repeated item satisfaction average information based on the first repeated item satisfaction information and the first repeated information. In practice, the execution entity may determine the first repeated item satisfaction average information as the ratio of the first repeated item satisfaction information to the first repeated information.

[0046] Step 7: For each item satisfaction processing information in the above item satisfaction processing information set, perform the following steps:

[0047] In the first sub-step, the difference between the net recommendation information included in the item satisfaction processing information and the first repeated item recommendation mean information is determined as the first net recommendation difference information.

[0048] In the second sub-step, the difference between the item satisfaction information included in the item satisfaction processing information and the first repeated item satisfaction mean information is determined as the item satisfaction difference information.

[0049] In the third sub-step, the product of the first net recommendation difference information and the item satisfaction difference information is determined as the first net recommendation satisfaction information.

[0050] The fourth sub-step is to determine the first repeated net recommendation difference information by multiplying the square of the first net recommendation difference information by the first number of repetitions included in the item satisfaction processing information.

[0051] The fifth sub-step is to determine the first repeated satisfaction difference information by multiplying the square of the above-mentioned item satisfaction difference information and the first repetition number included in the above-mentioned item satisfaction processing information.

[0052] The sixth sub-step is to determine the first repeated net recommendation satisfaction information by multiplying the first number of repetitions included in the above-mentioned first net recommendation satisfaction information and the above-mentioned item satisfaction processing information.

[0053] In the eighth step, the sum of the obtained first repeated net recommendation satisfaction information is determined as the first net recommendation satisfaction coefficient information.

[0054] In the ninth step, the obtained first repeated satisfaction difference information is determined as a first repeated satisfaction difference information set.

[0055] In step 10, the first item satisfaction coefficient information is determined based on the first repeated satisfaction difference information set. In practice, the execution entity may determine the first item satisfaction coefficient information as the power of half of the sum of the first repeated satisfaction difference information included in the first repeated satisfaction difference information set.

[0056] In the eleventh step, the obtained first-repetition net recommendation difference information is determined as a first-repetition net recommendation difference information set.

[0057] Step 12: Determine the first net recommendation coefficient information based on the first repeated net recommendation difference information set. In practice, the execution entity may determine the first net recommendation coefficient information as the power of half of the sum of the first repeated net recommendation difference information included in the first repeated net recommendation difference information set.

[0058] In the thirteenth step, the product of the first net recommendation coefficient information and the first item satisfaction coefficient information is determined as the net recommendation item satisfaction coefficient information.

[0059] In the fourteenth step, the ratio of the first net recommendation satisfaction coefficient information to the net recommendation item satisfaction coefficient information is determined as the item correlation coefficient.

[0060] Step 105 : combining the net recommendation information and the logistics distribution satisfaction information included in each item satisfaction original data in the item satisfaction original data set into second combined information to obtain a second combined information set.

[0061] In some embodiments, the execution entity may combine the net recommendation information and logistics distribution satisfaction information included in each item satisfaction original data in the item satisfaction original data set into second combination information to obtain a second combination information set.

[0062] In practice, the execution entity may combine the net recommendation information and logistics delivery satisfaction information included in each item satisfaction raw data set into a second combination information set to obtain a second combination information set. Here, the combination may be concatenation.

[0063] Step 106: Generate a logistics distribution satisfaction processing information set based on the second combined information set.

[0064] In some embodiments, based on the second combination information set, the execution entity may generate a logistics and delivery satisfaction processing information set. The logistics and delivery satisfaction processing information in the logistics and delivery satisfaction processing information set may include a second combination pair and a second repetition count. The second combination pair includes net recommendation information and logistics and delivery satisfaction information. The second repetition count may be the number of occurrences of the second combination pair in the logistics and delivery satisfaction processing information.

[0065] In practice, based on the second combined information set, the execution entity may generate a logistics distribution satisfaction processing information set through the following steps:

[0066] In the first step, each second combination information included in the second combination information set is deduplicated to obtain a second combination deduplicated information set.

[0067] In the second step, first, for each second combination of deduplicated information in the second combination of deduplicated information set, the execution entity may determine the second combination of deduplicated information as a second combination pair. Secondly, the number of times the second combination pair appears in the second combination information set may be determined as a second repetition count. Then, the second combination pair and the second repetition count may be combined to form logistics distribution satisfaction processing information. Finally, the obtained individual pieces of logistics distribution satisfaction processing information may be determined as a logistics distribution satisfaction processing information set.

[0068] Step 107: Process the information set according to the logistics delivery satisfaction and generate a logistics delivery correlation coefficient.

[0069] In some embodiments, based on the logistics delivery satisfaction processing information set, the execution entity may generate a logistics delivery correlation coefficient, wherein the logistics delivery correlation coefficient may represent the degree of correlation between the logistics delivery satisfaction information and the net recommendation information.

[0070] In some optional implementations of some embodiments, based on the logistics and delivery satisfaction processing information set, the execution entity may generate a logistics and delivery correlation coefficient through the following steps:

[0071] In the first step, for each piece of logistics delivery satisfaction processing information in the above logistics delivery satisfaction processing information set, perform the following determination steps:

[0072] In the first sub-step, the product of the net recommendation information included in the logistics delivery satisfaction processing information and the corresponding second repetition number is determined as the second repeated net recommendation information.

[0073] In the second sub-step, the product of the logistics distribution satisfaction information included in the above-mentioned logistics distribution satisfaction processing information and the corresponding second repetition number is determined as the second repetition satisfaction information.

[0074] In the second step, the sum of the obtained second repeated net recommendation information is determined as the second repeated item recommendation information.

[0075] In the third step, the sum of the obtained second repeated satisfaction information is determined as the second repeated item satisfaction information.

[0076] The fourth step is to determine the sum of the second repetition times included in the above-mentioned logistics distribution satisfaction processing information set as the second repetition information.

[0077] Step 5: Determine the second repeated item recommendation mean information based on the second repeated item recommendation information and the second repeated information.

[0078] Step 6: Determine the second repeated item satisfaction mean information based on the second repeated item satisfaction information and the second repeated information.

[0079] Step 7: For each piece of logistics distribution satisfaction processing information in the above logistics distribution satisfaction processing information set, perform the following steps:

[0080] In the first sub-step, the difference between the net recommendation information included in the logistics distribution satisfaction processing information and the second repeated item recommendation mean information is determined as the second net recommendation difference information.

[0081] The second sub-step is to determine the difference between the logistics distribution satisfaction information included in the logistics distribution satisfaction processing information and the second repeated item satisfaction mean information as the logistics distribution satisfaction difference information.

[0082] The third sub-step is to determine the second net recommendation satisfaction information by multiplying the second net recommendation difference information by the logistics distribution satisfaction difference information.

[0083] The fourth sub-step is to determine the second repeated net recommendation difference information by multiplying the square of the second net recommendation difference information by the second number of repetitions included in the item satisfaction processing information.

[0084] The fifth sub-step is to determine the second repeated satisfaction difference information by multiplying the square of the above-mentioned logistics distribution satisfaction difference information and the second repetition number included in the above-mentioned logistics distribution satisfaction processing information.

[0085] The sixth sub-step is to determine the second repeated net recommendation satisfaction information by multiplying the second net recommendation satisfaction information by the second number of repetitions included in the item satisfaction processing information.

[0086] In the eighth step, the sum of the obtained second repeated net recommendation satisfaction information is determined as the second net recommendation satisfaction coefficient information.

[0087] In the ninth step, the obtained second repeated satisfaction difference information is determined as a second repeated satisfaction difference information set.

[0088] In step 10, the second item satisfaction coefficient information is determined based on the second repeated satisfaction difference information set. In practice, the execution entity may determine the second item satisfaction coefficient information as the power of half of the sum of the second repeated satisfaction difference information included in the second repeated satisfaction difference information set.

[0089] In the eleventh step, the obtained second-repeated net recommendation difference information is determined as a second-repeated net recommendation difference information set.

[0090] Step 12: Determine the second net recommendation coefficient information based on the second repeated net recommendation difference information set. In practice, the execution entity may determine the second net recommendation coefficient information as the power of half of the sum of the second repeated net recommendation difference information included in the second repeated net recommendation difference information set.

[0091] In the thirteenth step, the product of the second net recommendation coefficient information and the second item satisfaction coefficient information is determined as the net recommendation item satisfaction coefficient information.

[0092] In the fourteenth step, the ratio of the second net recommendation satisfaction coefficient information to the net recommendation item satisfaction coefficient information is determined as the logistics distribution correlation coefficient.

[0093] Step 108 : Generate item net recommendation information based on each piece of net recommendation information included in the item satisfaction original data set.

[0094] In some embodiments, the execution entity may generate net recommendation information for an item based on each piece of net recommendation information included in the item satisfaction raw data set.

[0095] In some optional implementations of some embodiments, based on each piece of net recommendation information included in the above-mentioned item satisfaction raw data set, the above-mentioned execution entity may generate the item net recommendation information through the following steps:

[0096] In the first step, each piece of net recommendation information included in the raw item satisfaction data set that is less than or equal to a first preset threshold is determined as a critic quantity information set. The first preset threshold may be a pre-set threshold. Here, the first preset threshold may be 6.

[0097] In the second step, the number of each critic quantity information included in the above critic quantity information set is determined as critic information.

[0098] In the third step, each piece of net recommendation information included in the raw item satisfaction data set that is less than or equal to a second preset threshold and greater than the first preset threshold is determined as a passive user quantity information set. The second preset threshold may be a pre-set threshold. In this example, the second preset threshold may be 8.

[0099] In the fourth step, the number of each passive participant quantity information included in the passive participant quantity information set is determined as passive participant information.

[0100] In the fifth step, each piece of net recommendation information included in the raw item satisfaction data set that is less than or equal to a third preset threshold and greater than the second preset threshold is determined as a recommender quantity information set. The third preset threshold may be a pre-set threshold. In this example, the third preset threshold may be 10.

[0101] In the sixth step, the number of each recommender quantity information included in the recommender quantity information set is determined as recommender information.

[0102] Step 7: Generate net item recommendation information based on the critic information, passive information, and recommender information. In practice, first, the execution entity may determine the sum of the critic information, passive information, and recommender information as sample information. Second, the ratio of the recommender information to the sample information may be determined as the recommender sample coefficient. Then, the ratio of the critic information to the sample information may be determined as the critic sample coefficient. Finally, the difference between the recommender sample coefficient and the critic sample coefficient may be determined as the net item recommendation information.

[0103] Step 109 : Generate item replenishment information based on the net recommendation information, the item correlation coefficient, and the logistics distribution correlation coefficient.

[0104] In some embodiments, the execution entity may generate item replenishment information based on the net recommendation information, the item relevance coefficient, and the logistics and distribution relevance coefficient.

[0105] In some optional implementations of some embodiments, based on the net recommendation information, the item relevance coefficient, and the logistics and delivery relevance coefficient, the execution entity may generate item replenishment information through the following steps:

[0106] In the first step, in response to determining that the net recommendation information is greater than a first recommendation threshold, and that the item correlation coefficient, the logistics distribution correlation coefficient, and the item value correlation coefficient are all greater than or equal to a preset correlation threshold, a first replenishment quantity is determined as the replenishment information for the target item. The first recommendation threshold may be a preset recommendation threshold. For example, the first recommendation threshold may be 0.4. The preset correlation threshold may be a preset correlation threshold. For example, the preset correlation threshold may be 0.5. The first replenishment quantity may be a replenishment quantity that satisfies a first replenishment condition. The first replenishment condition may be equal to the product of a target replenishment quantity and a first replenishment ratio. The target replenishment quantity may be the previous item replenishment quantity corresponding to the target item. The first replenishment ratio may be one hundred and thirty percent.

[0107] In a second step, in response to determining that the net recommendation information is greater than the first recommendation threshold, and two of the item correlation coefficient, the logistics distribution correlation coefficient, and the item value correlation coefficient are greater than or equal to the preset correlation threshold, a second replenishment quantity is determined as the replenishment information for the target item. The second replenishment quantity is less than the first replenishment quantity. The second replenishment quantity may be a replenishment quantity that satisfies a second replenishment condition. The second replenishment condition may be equal to the product of the target replenishment quantity and a second replenishment ratio. The second replenishment ratio may be 120%.

[0108] In a third step, in response to determining that the net recommendation information is greater than the first recommendation threshold, and that one of the item relevance coefficient, the logistics distribution relevance coefficient, and the item value relevance coefficient is greater than or equal to the preset relevance threshold, a third replenishment quantity is determined as the replenishment information for the target item. The third replenishment quantity is less than the second replenishment quantity. The third replenishment quantity may be a replenishment quantity that satisfies a third replenishment condition. The third replenishment condition may be equal to the product of the target replenishment quantity and a third replenishment ratio. The third replenishment ratio may be 110%.

[0109] In a fourth step, in response to determining that the net recommendation information is greater than or equal to the second recommendation threshold and less than or equal to the first recommendation threshold, the last item replenishment quantity corresponding to the target item is determined as the replenishment information of the target item.

[0110] In a fifth step, in response to determining that the net recommendation information is less than the second recommendation threshold, and that one of the item correlation coefficient, the logistics distribution correlation coefficient, and the item value correlation coefficient is greater than or equal to the preset correlation threshold, a fourth replenishment quantity is determined as the replenishment information for the target item. The fourth replenishment quantity is less than the third replenishment quantity. The fourth replenishment quantity may be a replenishment quantity that satisfies a fourth replenishment condition. The fourth replenishment condition may be equal to the product of the target replenishment quantity and a fourth replenishment ratio. The fourth replenishment ratio may be 90%.

[0111] In step 6, in response to determining that the net recommendation information is less than the second recommendation threshold, and two of the item correlation coefficient, the logistics distribution correlation coefficient, and the item value correlation coefficient are greater than or equal to the preset correlation threshold, a fifth replenishment quantity is determined as the replenishment information for the target item. The fifth replenishment quantity is less than the fourth replenishment quantity. The fifth replenishment quantity may be a replenishment quantity that satisfies a fifth replenishment condition. The fifth replenishment condition may be equal to the product of the target replenishment quantity and a fifth replenishment ratio. The fifth replenishment ratio may be 80%.

[0112] In step 7, in response to determining that the net recommendation information is less than the second recommendation threshold, and the item relevance coefficient, the logistics distribution relevance coefficient, and the item value relevance coefficient are all greater than or equal to the preset relevance threshold, a sixth replenishment quantity is determined as the replenishment information for the target item. The sixth replenishment quantity may be less than the fifth replenishment quantity. The sixth replenishment quantity may be a replenishment quantity that satisfies a sixth replenishment condition. The sixth replenishment condition may be equal to the product of the target replenishment quantity and a sixth replenishment ratio. The sixth replenishment ratio may be 70%.

[0113] Steps 1 through 7 and their related content, as an inventive feature of an embodiment of the present disclosure, address the second technical issue mentioned in the background art: "In determining item replenishment information, adjusting item replenishment information based solely on satisfaction-related information that influences item flow results in low accuracy of the determined item replenishment information. This in turn results in a high stock-out rate when the item replenishment quantity is small, and an item backlog and high item loss when the item replenishment quantity is large." The factors that contribute to low accuracy of the determined item replenishment information, high stock-out rate when the item replenishment quantity is small, and an item backlog and high item loss when the item replenishment quantity is large are often as follows: In determining item replenishment information, adjusting item replenishment information based solely on satisfaction-related information that influences item flow results in low accuracy of the determined item replenishment information. This in turn results in a high stock-out rate when the item replenishment quantity is small, and an item backlog and high item loss when the item replenishment quantity is large. If these factors are addressed, the accuracy of the determined item replenishment information can be improved, the stock-out rate can be reduced, and the item loss can be reduced. To achieve this effect, first, in response to determining that the net recommendation information is greater than a first recommendation threshold and that the item relevance coefficient, the logistics and distribution relevance coefficient, and the item value relevance coefficient are all greater than or equal to a preset relevance threshold, a first replenishment quantity is determined as the replenishment information for the target item. This allows for replenishment information to be obtained when the net recommendation information is greater than the first recommendation threshold and all three types of relevance information meet the preset relevance threshold. Then, in response to determining that the net recommendation information is greater than the first recommendation threshold and that two of the item relevance coefficient, the logistics and distribution relevance coefficient, and the item value relevance coefficient are greater than or equal to the preset relevance threshold, a second replenishment quantity is determined as the replenishment information for the target item. The second replenishment quantity is less than the first replenishment quantity. This allows for replenishment information to be obtained when the net recommendation information is greater than the first recommendation threshold and both types of relevance information meet the preset relevance threshold. Subsequently, in response to determining that the net recommendation information is greater than the first recommendation threshold and that one of the item relevance coefficient, the logistics and distribution relevance coefficient, and the item value relevance coefficient is greater than or equal to the preset relevance threshold, a third replenishment quantity is determined as the replenishment information for the target item. The third replenishment quantity is less than the second replenishment quantity. Thus, when the net recommendation information is greater than the first recommendation threshold, replenishment information can be obtained in which the correlation information satisfies the preset correlation threshold. Next, in response to determining that the net recommendation information is greater than or equal to the second recommendation threshold and less than or equal to the first recommendation threshold, the last replenishment quantity of the target item is determined as the replenishment information for the target item. Thus, replenishment information can be obtained that indicates that the net recommendation information tends to zero and that the obtained correlation coefficient has no reference value for the replenishment quantity of the target item.Then, in response to determining that the net recommendation information is less than the second recommendation threshold, and one of the item correlation coefficient, the logistics distribution correlation coefficient, and the item value correlation coefficient is greater than or equal to the preset correlation threshold, the fourth replenishment quantity is determined as the replenishment information of the target item. The fourth replenishment quantity is less than the third replenishment quantity. Thus, when the net recommendation information is less than the second recommendation threshold, replenishment information in which one type of correlation information satisfies the preset correlation threshold can be obtained. Afterwards, in response to determining that the net recommendation information is less than the second recommendation threshold, and two of the item correlation coefficient, the logistics distribution correlation coefficient, and the item value correlation coefficient are greater than or equal to the preset correlation threshold, the fifth replenishment quantity is determined as the replenishment information of the target item. The fifth replenishment quantity is less than the fourth replenishment quantity. Thus, when the net recommendation information is less than the second recommendation threshold, replenishment information in which both types of correlation information satisfy the preset correlation threshold can be obtained. In response to determining that the net recommendation information is less than the second recommendation threshold, and the item relevance coefficient, the logistics distribution relevance coefficient, and the item value relevance coefficient are all greater than or equal to the preset relevance threshold, a sixth replenishment quantity is determined as the replenishment information for the target item. The sixth replenishment quantity is less than the fifth replenishment quantity. Thus, when the net recommendation information is less than the second recommendation threshold, replenishment information can be obtained in which all three types of relevance information meet the preset relevance thresholds. Furthermore, by adjusting the item replenishment information based on the three types of satisfaction relevance information that influence item flow, the accuracy of the determined item replenishment information can be improved. This reduces out-of-stock rates and minimizes item loss.

[0114] Optionally, before generating the item replenishment information based on the net recommendation information, the item relevance coefficient, and the logistics and delivery relevance coefficient, the execution entity may further perform the following steps:

[0115] In the first step, the net recommendation information and item value satisfaction information included in each item satisfaction raw data set are combined into a third combination information to obtain a third combination information set. Here, the combination can be splicing.

[0116] Step 2: Generate an item value satisfaction processing information set based on the third combination information set. The item value satisfaction processing information in the item value satisfaction processing information set may include a third combination pair and a third repetition count. The third combination pair includes net recommendation information and item value satisfaction information.

[0117] The third step is to process the information set based on the above-mentioned item value satisfaction and generate the item value correlation coefficient.

[0118] In some optional implementations of some embodiments, based on the item value satisfaction processing information set, the execution entity may generate an item value relevance coefficient through the following steps:

[0119] In the first step, for each item value satisfaction processing information in the above item value satisfaction processing information set, perform the following determination steps:

[0120] In the first sub-step, the product of the net recommendation information included in the item value satisfaction processing information and the corresponding third repetition number is determined as the third repetition net recommendation information.

[0121] In the second sub-step, the product of the item value satisfaction information included in the item value satisfaction processing information and the corresponding third repetition number is determined as the third repetition satisfaction information.

[0122] In the second step, the sum of the obtained third-repeated net recommendation information is determined as the third-repeated item recommendation information.

[0123] In the third step, the sum of the obtained third repeated satisfaction information is determined as the third repeated item satisfaction information.

[0124] In the fourth step, the sum of the third repetition times included in the above-mentioned item value satisfaction processing information set is determined as the third repetition information.

[0125] In step 5, the third repeated item recommendation average information is determined based on the third repeated item recommendation information and the third repeated information. In practice, the execution entity may determine the third repeated item recommendation average information as the ratio of the third repeated item recommendation information to the third repeated information.

[0126] Step 6: Determine the average satisfaction score of the third repeated item based on the satisfaction score information of the third repeated item and the third repeated information. In practice, the execution entity may determine the average satisfaction score of the third repeated item as the ratio of the satisfaction score information of the third repeated item to the third repeated information.

[0127] Step 7: For each item value satisfaction processing information in the above item value satisfaction processing information set, perform the following steps:

[0128] In the first sub-step, the difference between the net recommendation information included in the item value satisfaction processing information and the third repeated item recommendation mean information is determined as the third net recommendation difference information.

[0129] In the second sub-step, the difference between the item value satisfaction information included in the item value satisfaction processing information and the third repeated item satisfaction mean information is determined as the item value satisfaction difference information.

[0130] The third sub-step is to determine the third net recommendation satisfaction information by multiplying the third net recommendation difference information by the item value satisfaction difference information.

[0131] The fourth sub-step is to determine the third repeated net recommendation difference information by multiplying the square of the third net recommendation difference information by the third number of repetitions included in the item value satisfaction processing information.

[0132] The fifth sub-step is to determine the third repeated satisfaction difference information as the product of the square of the above-mentioned item value satisfaction difference information and the third repetition number included in the above-mentioned item value satisfaction processing information.

[0133] The sixth sub-step is to determine the third repeated net recommendation satisfaction information by multiplying the third number of repetitions of the third net recommendation satisfaction information and the third number of repetitions of the item value satisfaction difference information.

[0134] In the eighth step, the sum of the obtained third repeated net recommendation satisfaction information is determined as the third net recommendation satisfaction coefficient information.

[0135] In the ninth step, the obtained third-repeated satisfaction difference information is determined as a third-repeated satisfaction difference information set.

[0136] In step 10, the third item satisfaction coefficient information is determined based on the third repeated satisfaction difference information set. In practice, the execution entity may determine the third item satisfaction coefficient information as the power of half of the sum of the third repeated satisfaction difference information included in the third repeated satisfaction difference information set.

[0137] In the eleventh step, the obtained third-repetition net recommendation difference information is determined as a third-repetition net recommendation difference information set.

[0138] Step 12: Determine the third net recommendation coefficient information based on the third repeated net recommendation difference information set. In practice, the execution entity may determine the third net recommendation coefficient information as the power of half of the sum of the third repeated net recommendation difference information included in the third repeated net recommendation difference information set.

[0139] In the thirteenth step, the product of the third net recommendation coefficient information and the third item satisfaction coefficient information is determined as the net recommended item value satisfaction coefficient information.

[0140] In the fourteenth step, the ratio of the third net recommendation satisfaction coefficient information to the net recommendation item value satisfaction coefficient information is determined as the item value correlation coefficient.

[0141] Optionally, the execution entity may also control associated transport equipment to perform item dispatch operations based on the item replenishment information. The associated transport equipment may be a vehicle capable of dispatching target items. For example, the associated transport equipment may be an unmanned transport vehicle.

[0142] In practice, the execution entity can control the unmanned transport vehicle to transport the target items in the quantity specified in the item replenishment information to the target store, thereby replenishing the target items.

[0143] The aforementioned embodiments of the present disclosure have the following beneficial effects: Through the method for generating item replenishment information in some embodiments of the present disclosure, system resources occupied by sample data are reduced, the time required to query sample data is shortened, and thus the time required to determine item replenishment information is shortened. Specifically, the reason why sample data consumes more system resources, the query time is longer, and thus the time required to determine item replenishment information is longer is that the original sample data is not filtered and integrated, resulting in the sample data occupying more system resources, the query time is longer, and thus the time required to determine item replenishment information is longer. Based on this, the method for generating item replenishment information in some embodiments of the present disclosure first obtains a set of raw item satisfaction data for a target item within a preset time period. The raw item satisfaction data in this set includes net recommendation information, item satisfaction information, logistics and distribution satisfaction information, and item value satisfaction information. Thus, a set of raw item satisfaction data for the target item, including net recommendation information, item satisfaction information, logistics and distribution satisfaction information, and item value satisfaction information, is obtained. Next, the net recommendation information and item satisfaction information included in each item satisfaction data set in the raw item satisfaction data set are combined into a first combined information set, thereby obtaining a first combined information set. Based on the first combined information set, a processed item satisfaction information set is generated. The processed item satisfaction information in the processed item satisfaction information set includes a first combination pair and a first number of repetitions. The first combination pair includes net recommendation information and item satisfaction information. Thus, a processed item satisfaction information set is obtained by filtering and integrating the net recommendation information and item satisfaction information included in the original sample data. Then, an item correlation coefficient is generated based on the processed item satisfaction information set. Thus, an item correlation coefficient representing the correlation between the net recommendation information and item satisfaction information is obtained. Next, the net recommendation information and logistics and distribution satisfaction information included in each item satisfaction data set in the original item satisfaction data set are combined into a second combined information set, generating a second combined information set. Based on the second combined information set, a processed logistics and distribution satisfaction information set is generated. The processed logistics and distribution satisfaction information in the processed logistics and distribution satisfaction information set includes a second combination pair and a second number of repetitions. The second combination pair includes net recommendation information and logistics and distribution satisfaction information. Thus, a processed logistics and distribution satisfaction information set is obtained by filtering and integrating the net recommendation information and logistics and distribution satisfaction information included in the original sample data. Subsequently, a logistics distribution correlation coefficient is generated based on the processed logistics distribution satisfaction information set. This yields a logistics distribution correlation coefficient that characterizes the correlation between the net recommendation information and the logistics distribution satisfaction information. Subsequently, item net recommendation information is generated based on each piece of net recommendation information included in the original item satisfaction data set.This yields net recommendation information for the item, which can be used to measure the target user's degree of recommendation for the target item. Finally, item replenishment information is generated based on the net recommendation information, the item relevance coefficient, and the logistics and delivery relevance coefficient. This yields item replenishment information influenced by the degree of recommendation and the associated item satisfaction. By filtering and integrating the target item's net recommendation information, item satisfaction information, logistics and delivery satisfaction information, and item value satisfaction information, system resources consumed by sample data are reduced, shortening the time required to query sample data. Furthermore, by determining the correlation between net recommendation information and item satisfaction information, net recommendation information and logistics and delivery satisfaction information, and net recommendation information and item value satisfaction information, item replenishment information for the target item can be generated. This shortens the time required to determine item replenishment information.

[0144] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of an item replenishment information generating device. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0145] like Figure 2As shown, the item replenishment information generating device 200 of some embodiments includes: an acquisition unit 201, a first combination unit 202, a first generation unit 203, a second generation unit 204, a second combination unit 205, a third generation unit 206, a fourth generation unit 207, a fifth generation unit 208 and a sixth generation unit 209. The acquisition unit 201 is configured to acquire a set of original data on item satisfaction of a target item within a preset time period, wherein the original data on item satisfaction in the above-mentioned original data set includes net recommendation information, item satisfaction information, logistics distribution satisfaction information, and item value satisfaction information; the first combination unit 202 is configured to combine the net recommendation information and item satisfaction information included in each original data on item satisfaction in the above-mentioned original data set into a first combination information to obtain a first combination information set; the first generation unit 203 is configured to generate an item satisfaction processing information set based on the above-mentioned first combination information set, wherein the item satisfaction processing information in the above-mentioned item satisfaction processing information set includes: a first combination pair and a first number of repetitions, and the above-mentioned first combination pair includes net recommendation information and item satisfaction information; the second generation unit 204 is configured to generate an item correlation coefficient based on the above-mentioned item satisfaction processing information set; the second combination unit 205 is configured to generate an item correlation coefficient based on the above-mentioned item satisfaction processing information set; the second combination unit 206 is configured to generate an item correlation coefficient based on the above-mentioned item satisfaction processing information set; the second combination unit 207 is configured to generate an item correlation coefficient based on the above-mentioned item satisfaction processing information set; the second combination unit 208 is configured to generate an item correlation coefficient based on the above-mentioned item satisfaction processing information set; the second combination unit 209 ... first combination unit 209 is configured to generate 05 is configured to combine the net recommendation information and logistics distribution satisfaction information included in each item satisfaction original data in the above-mentioned item satisfaction original data set into second combination information to obtain a second combination information set; the third generation unit 206 is configured to generate a logistics distribution satisfaction processing information set based on the above-mentioned second combination information set, wherein the logistics distribution satisfaction processing information in the above-mentioned logistics distribution satisfaction processing information set includes: a second combination pair and a second number of repetitions, and the above-mentioned second combination pair includes net recommendation information and logistics distribution satisfaction information; the fourth generation unit 207 is configured to generate a logistics distribution correlation coefficient based on the above-mentioned logistics distribution satisfaction processing information set; the fifth generation unit 208 is configured to generate item net recommendation information based on each net recommendation information included in the above-mentioned item satisfaction original data set; the sixth generation unit 209 is configured to generate item replenishment information based on the above-mentioned net recommendation information, the above-mentioned item correlation coefficient and the above-mentioned logistics distribution correlation coefficient.

[0146] It is understood that the various units recorded in the item replenishment information generating device 200 and the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the replenishment information generating device 200 and the units included therein, and will not be described in detail here.

[0147] Reference below Figure 3, which shows a schematic diagram of the structure of an electronic device 300 (e.g., a computing device) suitable for implementing some embodiments of the present disclosure. The electronic devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0148] like Figure 3 As shown, the electronic device 300 may include a processing device 301 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. Various programs and data required for the operation of the electronic device 300 are also stored in the RAM 303. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0149] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as needed.

[0150] In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from a network via the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are performed.

[0151] It should be noted that the computer-readable medium described in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0152] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (Hypertext Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0153] The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist independently without being assembled into the electronic device. The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: obtains a set of original data on item satisfaction of the target item within a preset time period, wherein the original data on item satisfaction in the above-mentioned original data set includes net recommendation information, item satisfaction information, logistics distribution satisfaction information and item value satisfaction information; combines the net recommendation information and item satisfaction information included in each original data on item satisfaction in the above-mentioned original data set into a first combination information to obtain a first combination information set; generates an item satisfaction processing information set based on the above-mentioned first combination information set, wherein the item satisfaction processing information in the above-mentioned item satisfaction processing information set includes: a first combination pair and a first number of repetitions, wherein the above-mentioned first combination pair includes net recommendation information and item satisfaction information; based on The above-mentioned item satisfaction processing information set is used to generate an item correlation coefficient; the net recommendation information and logistics distribution satisfaction information included in each item satisfaction original data in the above-mentioned item satisfaction original data set are combined into a second combination information to obtain a second combination information set; based on the above-mentioned second combination information set, a logistics distribution satisfaction processing information set is generated, wherein the logistics distribution satisfaction processing information in the above-mentioned logistics distribution satisfaction processing information set includes: a second combination pair and a second number of repetitions, and the above-mentioned second combination pair includes net recommendation information and logistics distribution satisfaction information; based on the above-mentioned logistics distribution satisfaction processing information set, a logistics distribution correlation coefficient is generated; based on the above-mentioned net recommendation information included in the above-mentioned item satisfaction original data set, item net recommendation information is generated; based on the above-mentioned net recommendation information, the above-mentioned item correlation coefficient and the above-mentioned logistics distribution correlation coefficient, item replenishment information is generated.

[0154] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0155] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0156] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The units described may also be provided in a processor. For example, they may be described as follows: a processor including an acquisition unit, a first combination unit, a first generation unit, a second generation unit, a second combination unit, a third generation unit, a fourth generation unit, a fifth generation unit, and a sixth generation unit. The names of these units do not, in some cases, constitute limitations on the units themselves. For example, the acquisition unit may also be described as a "unit for acquiring a set of raw data on item satisfaction of a target item within a preset time period."

[0157] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0158] The above description is only an illustration of some preferred embodiments of the present disclosure and the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A method for generating item replenishment information, comprising: Obtaining a set of original item satisfaction data for a target item within a preset time period, wherein the original item satisfaction data in the set includes net recommendation information, item satisfaction information, logistics and delivery satisfaction information, and item value satisfaction information; combining the net recommendation information and the item satisfaction information included in each item satisfaction original data in the item satisfaction original data set into first combined information to obtain a first combined information set; Generate an item satisfaction processing information set based on the first combination information set, wherein the item satisfaction processing information in the item satisfaction processing information set includes: a first combination pair and a first repetition number, the first combination pair including net recommendation information and item satisfaction information, and the first repetition number being the number of times the first combination pair appears in the item satisfaction processing information; Processing the information set according to the item satisfaction level to generate an item relevance coefficient; combining the net recommendation information and the logistics distribution satisfaction information included in each item satisfaction original data set in the item satisfaction original data set into second combined information to obtain a second combined information set; Generate a logistics and delivery satisfaction processing information set based on the second combination information set, wherein the logistics and delivery satisfaction processing information in the logistics and delivery satisfaction processing information set includes: a second combination pair and a second repetition number, the second combination pair including net recommendation information and logistics and delivery satisfaction information, and the second repetition number is the number of times the second combination pair appears in the logistics and delivery satisfaction processing information; Processing the information set according to the logistics delivery satisfaction, generating a logistics delivery correlation coefficient; Generating item net recommendation information according to each piece of net recommendation information included in the item satisfaction raw data set; combining the net recommendation information and the item value satisfaction information included in each item satisfaction original data in the item satisfaction original data set into third combined information to obtain a third combined information set; Generate an item value satisfaction processing information set based on the third combination information set, wherein the item value satisfaction processing information in the item value satisfaction processing information set includes: a third combination pair and a third repetition number, the third combination pair including net recommendation information and item value satisfaction information, and the third repetition number is the number of times the third combination pair appears in the logistics and delivery satisfaction processing information; Processing the information set according to the item value satisfaction, generating an item value correlation coefficient; Item replenishment information is generated according to the net recommendation information, the item relevance coefficient, and the logistics distribution relevance coefficient.

2. The method according to claim 1, wherein The method further comprises: According to the item replenishment information, the associated transportation equipment is controlled to perform item dispatching operations.

3. The method according to claim 1, wherein Generating item net recommendation information according to each piece of net recommendation information included in the item satisfaction raw data set includes: Determining each piece of net recommendation information included in the item satisfaction original data set and having a value less than or equal to a first preset threshold as a critic quantity information set; determining the number of each critic quantity information included in the critic quantity information set as critic information; Determining each piece of net recommendation information included in the item satisfaction raw data set, which is less than or equal to a second preset threshold and greater than the first preset threshold, as a passive user quantity information set; determining the number of each passive participant quantity information included in the passive participant quantity information set as passive participant information; Determining, among the net recommendation information included in the item satisfaction raw data set, each piece of net recommendation information that is less than or equal to a third preset threshold and greater than the second preset threshold as a recommender quantity information set; determining the number of each recommender quantity information included in the recommender quantity information set as recommender information; Item net recommendation information is generated based on the critic information, the passive information, and the recommender information.

4. The method according to claim 1, wherein The step of processing the information set according to the item satisfaction level to generate an item correlation coefficient includes: For each item satisfaction processing information in the item satisfaction processing information set, the following determination steps are performed: Determine the product of the net recommendation information included in the item satisfaction processing information and the corresponding first repetition number as the first repeated net recommendation information; determining the product of the item satisfaction information included in the item satisfaction processing information and the corresponding first repetition number as first repetition satisfaction information; Determine the sum of the obtained first repeated net recommendation information as the first repeated item recommendation information; Determine the sum of the obtained first repeated satisfaction information as the first repeated item satisfaction information; Determining the sum of each first repetition number included in the item satisfaction processing information set as first repetition information; Determining first repeated item recommendation mean information according to the first repeated item recommendation information and the first repeated information; determining first repeated item satisfaction mean information based on the first repeated item satisfaction information and the first repeated information; For each item satisfaction processing information in the item satisfaction processing information set, perform the following steps: determining a difference between the net recommendation information included in the item satisfaction processing information and the first repeated item recommendation mean information as first net recommendation difference information; determining a difference between the item satisfaction information included in the item satisfaction processing information and the first repeated item satisfaction mean information as item satisfaction difference information; Determine the product of the first net recommendation difference information and the item satisfaction difference information as first net recommendation satisfaction information; Determine the product of the square of the first net recommendation difference information and the first repetition number included in the item satisfaction processing information as the first repetition net recommendation difference information; determining the product of the square of the item satisfaction difference information and the first number of repetitions included in the item satisfaction processing information as first repetition satisfaction difference information; determining the product of the first net recommendation satisfaction information and the first repetition number included in the item satisfaction processing information as first repeated net recommendation satisfaction information; Determine the sum of the obtained first repeated net recommendation satisfaction information as the first net recommendation satisfaction coefficient information; Determine the obtained first repeated satisfaction difference information as a first repeated satisfaction difference information set; Determining first item satisfaction coefficient information based on the first repeated satisfaction difference information set; Determine the obtained first repeated net recommendation difference information as a first repeated net recommendation difference information set; determining first net recommendation coefficient information based on the first repeated net recommendation difference information set; determining the product of the first net recommendation coefficient information and the first item satisfaction coefficient information as the net recommended item satisfaction coefficient information; The ratio of the first net recommendation satisfaction coefficient information to the net recommendation item satisfaction coefficient information is determined as the item relevance coefficient.

5. The method according to claim 1, wherein The step of processing the information set according to the logistics delivery satisfaction to generate a logistics delivery correlation coefficient includes: For each piece of logistics delivery satisfaction processing information in the logistics delivery satisfaction processing information set, the following determination steps are performed: Determine the product of the net recommendation information included in the logistics delivery satisfaction processing information and the corresponding second repetition number as the second repeated net recommendation information; Determine the product of the logistics distribution satisfaction information included in the logistics distribution satisfaction processing information and the corresponding second repetition number as the second repetition satisfaction information; Determine the sum of the obtained second repeated net recommendation information as the second repeated item recommendation information; Determining the sum of the obtained second repeated satisfaction information as the second repeated item satisfaction information; Determining the sum of the second repetition times included in the logistics delivery satisfaction processing information set as second repetition information; determining second repeated item recommendation mean information according to the second repeated item recommendation information and the second repeated information; determining second repeated item satisfaction mean information based on the second repeated item satisfaction information and the second repeated information; For each piece of logistics delivery satisfaction processing information in the logistics delivery satisfaction processing information set, perform the following steps: Determine the difference between the net recommendation information included in the logistics delivery satisfaction processing information and the second repeated item recommendation mean information as second net recommendation difference information; determining the difference between the logistics distribution satisfaction information included in the logistics distribution satisfaction processing information and the second repeated item satisfaction mean information as logistics distribution satisfaction difference information; Determine the product of the second net recommendation difference information and the logistics delivery satisfaction difference information as the second net recommendation satisfaction information; Determine the product of the square of the second net recommendation difference information and the second repetition number included in the item satisfaction processing information as the second repetition net recommendation difference information; Determine the second repetition satisfaction difference information by multiplying the square of the logistics delivery satisfaction difference information by the second repetition number included in the logistics delivery satisfaction processing information; determining the product of the second net recommendation satisfaction information and the second repetition number included in the item satisfaction processing information as the second repeated net recommendation satisfaction information; Determine the sum of the obtained second repeated net recommendation satisfaction information as the second net recommendation satisfaction coefficient information; Determining the obtained second repeated satisfaction difference information as a second repeated satisfaction difference information set; Determining second item satisfaction coefficient information based on the second repeated satisfaction difference information set; Determine the obtained second-repeated net recommendation difference information as a second-repeated net recommendation difference information set; determining second net recommendation coefficient information according to the second repeated net recommendation difference information set; determining the product of the second net recommendation coefficient information and the second item satisfaction coefficient information as the net recommended item satisfaction coefficient information; The ratio of the second net recommendation satisfaction coefficient information to the net recommendation item satisfaction coefficient information is determined as the logistics distribution correlation coefficient.

6. The method according to claim 1, wherein The step of processing the information set according to the item value satisfaction to generate an item value correlation coefficient includes: For each item value satisfaction processing information in the item value satisfaction processing information set, the following determination steps are performed: Determine the product of the net recommendation information included in the item value satisfaction processing information and the corresponding third repetition number as the third repetition net recommendation information; determining the product of the item value satisfaction information included in the item value satisfaction processing information and the corresponding third repetition number as third repetition satisfaction information; Determine the sum of the obtained third-repeated net recommendation information as the third-repeated item recommendation information; Determining the sum of the obtained third repeated satisfaction information as the third repeated item satisfaction information; determining the sum of each third repetition number included in the item value satisfaction processing information set as third repetition information; determining third repeated item recommendation mean information according to the third repeated item recommendation information and the third repeated information; determining third repeated item satisfaction mean information based on the third repeated item satisfaction information and the third repeated information; For each item value satisfaction processing information in the item value satisfaction processing information set, perform the following steps: determining a difference between the net recommendation information included in the item value satisfaction processing information and the third repeated item recommendation mean information as third net recommendation difference information; determining a difference between the item value satisfaction information included in the item value satisfaction processing information and the third repeated item satisfaction mean information as item value satisfaction difference information; Determine the product of the third net recommendation difference information and the item value satisfaction difference information as the third net recommendation satisfaction information; Determine the product of the square of the third net recommendation difference information and the third repetition number included in the item value satisfaction processing information as the third repetition net recommendation difference information; Determine the third repetition satisfaction difference information by multiplying the square of the item value satisfaction difference information and the third repetition number included in the item value satisfaction processing information; Determine the third repeated net recommendation satisfaction information by multiplying the third net recommendation satisfaction information by the third number of repetitions included in the item value satisfaction difference information; Determine the sum of the obtained third repeated net recommendation satisfaction information as the third net recommendation satisfaction coefficient information; Determine the obtained third repeated satisfaction difference information as a third repeated satisfaction difference information set; Determining third item satisfaction coefficient information based on the third repeated satisfaction difference information set; Determine each obtained third-repetition net recommendation difference information as a third-repetition net recommendation difference information set; determining third net recommendation coefficient information based on the third repeated net recommendation difference information set; Determine the product of the third net recommendation coefficient information and the third item satisfaction coefficient information as the net recommended item value satisfaction coefficient information; The ratio of the third net recommendation satisfaction coefficient information to the net recommendation item value satisfaction coefficient information is determined as the item value correlation coefficient.

7. An item replenishment information generating device, comprising: an acquisition unit configured to acquire a set of raw item satisfaction data for a target item within a preset time period, wherein the raw item satisfaction data in the set includes net recommendation information, item satisfaction information, logistics and delivery satisfaction information, and item value satisfaction information; a first combining unit configured to combine the net recommendation information and the item satisfaction information included in each item satisfaction original data in the item satisfaction original data set into first combined information to obtain a first combined information set; a first generating unit configured to generate an item satisfaction processing information set based on the first combination information set, wherein the item satisfaction processing information in the item satisfaction processing information set includes: a first combination pair and a first repetition number, the first combination pair including net recommendation information and item satisfaction information, and the first repetition number being the number of times the first combination pair appears in the item satisfaction processing information; a second generating unit configured to generate an item relevance coefficient based on the item satisfaction processing information set; The second combining unit is configured to combine the net recommendation information and the logistics and delivery satisfaction information included in each item satisfaction original data in the item satisfaction original data set into second combined information to obtain a second combined information set; a third generating unit configured to generate a logistics delivery satisfaction processing information set based on the second combination information set, wherein the logistics delivery satisfaction processing information in the logistics delivery satisfaction processing information set includes: a second combination pair and a second repetition number, the second combination pair including net recommendation information and logistics delivery satisfaction information, and the second repetition number being the number of times the second combination pair appears in the logistics delivery satisfaction processing information; a fourth generating unit configured to generate a logistics delivery correlation coefficient according to the logistics delivery satisfaction processing information set; a fifth generating unit, configured to generate item net recommendation information according to each piece of net recommendation information included in the item satisfaction raw data set; a third combining unit configured to combine the net recommendation information and the item value satisfaction information included in each item satisfaction original data in the item satisfaction original data set into third combined information to obtain a third combined information set; a sixth generating unit configured to generate an item value satisfaction processing information set based on the third combination information set, wherein the item value satisfaction processing information in the item value satisfaction processing information set includes: a third combination pair and a third repetition number, the third combination pair including net recommendation information and item value satisfaction information, and the third repetition number being the number of times the third combination pair appears in the logistics and delivery satisfaction processing information; a seventh generating unit, configured to process the information set according to the item value satisfaction score to generate an item value relevance coefficient; An eighth generating unit is configured to generate item replenishment information according to the net recommendation information, the item relevance coefficient, and the logistics distribution relevance coefficient.

8. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer-readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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