CN111325587A

CN111325587A - Method and apparatus for generating information

Info

Publication number: CN111325587A
Application number: CN201811525298.2A
Authority: CN
Inventors: 唐琳; 陈龙; 段传超
Original assignee: Beijing Jingdong Century Trading Co Ltd; Beijing Jingdong Shangke Information Technology Co Ltd
Current assignee: Beijing Jingdong Century Trading Co Ltd; Beijing Jingdong Shangke Information Technology Co Ltd
Priority date: 2018-12-13
Filing date: 2018-12-13
Publication date: 2020-06-23

Abstract

The embodiment of the application discloses a method and a device for generating information. One embodiment of the method comprises: acquiring data of at least two dimensions of a target item, wherein the data of at least two dimensions comprises at least two of historical purchasing data, historical inventory data, historical sales data and historical profit data; determining at least two indicators of the target item based on the data in the at least two dimensions; inputting at least two indexes into corresponding index score models respectively to obtain at least two scores of the target object, wherein the index score models are used for representing the corresponding relation between the indexes and the scores; and generating evaluation information of the target item based on the at least two scores. The embodiment can perform multi-dimensional data analysis on the articles and generate the evaluation information of the articles, and is helpful for automatically selecting the articles meeting the conditions.

Description

Method and apparatus for generating information

Technical Field

The embodiment of the application relates to the technical field of computers, in particular to a method and a device for generating information.

Background

With the rapid rise of internet technology, electronic commerce gradually becomes a mainstream business transaction mode. Electronic commerce is a brand-new shopping idea, and becomes an important application field of economic globalization with the advantages of convenience and quickness.

Store operators typically manage thousands of items, but each time items need to be screened (e.g., for a next stage of sale), much effort and resources are expended, as are large sea spoons.

In the related art, articles are selected as follows: and exporting sales report data, and sorting and selecting a plurality of items (for example, the top 20 items) before the sales amount according to the sales amount to be used as the selected items for making a next stage plan.

Disclosure of Invention

The embodiment of the application provides a method and a device for generating information.

In a first aspect, an embodiment of the present application provides a method for generating information, where the method includes: acquiring data of at least two dimensions of a target item, wherein the data of at least two dimensions comprises at least two of historical purchasing data, historical inventory data, historical sales data and historical profit data; determining at least two indicators of the target item based on the data in the at least two dimensions; inputting at least two indexes into corresponding index score models respectively to obtain at least two scores of the target object, wherein the index score models are used for representing the corresponding relation between the indexes and the scores; and generating evaluation information of the target item based on the at least two scores.

In some embodiments, the at least two indicators include at least two of a fill rate, a stock out rate, a turnover number of days, and a profit margin, the fill rate being a ratio of a number of receipts to a number of orders for the target item, the stock out rate being a ratio of a number of days stock out to a number of days sales for the target item, the number of days turnover being a number of days turnover of the target item determined based on the number of inventories and the number of sales of the target item.

In some embodiments, the metric score model is determined by: acquiring a corresponding relation table of pre-defined indexes and scores; the following training steps are performed: performing polynomial fitting on the data pairs in the corresponding relation table to obtain an initial model of indexes and scores; carrying out regression analysis on the initial model, and determining whether a data pair which does not meet a preset condition exists in the corresponding relation table; and if no data pair which does not meet the preset condition exists, determining the initial model as an index score model.

In some embodiments, the metric score model is further determined by: and if the data pairs which do not meet the preset conditions exist, removing the data pairs which do not meet the preset conditions from the corresponding relation table, and continuing to execute the training step.

In some embodiments, performing regression analysis on the initial model to determine whether there is a data pair that does not satisfy the preset condition in the correspondence table includes: performing linear regression analysis on the initial model to obtain a distribution graph of data in the corresponding relation table to corresponding data points; and determining whether a data point which is more than a preset value from the preset graph exists in the distribution graph.

In some embodiments, generating ratings information for the target item based on the at least two scores includes: determining a total score of the target object based on at least two scores and a preset score weight; and generating evaluation information of the target article based on the size relation between the total score and the score threshold value.

In some embodiments, the method further comprises: in response to determining that the total score is greater than or equal to the score threshold, determining the target item as one of the items to promote.

In some embodiments, the method further comprises: in response to determining that the total score is less than the score threshold, an optimization adjustment is made to the target item based on the at least two scores.

In a second aspect, an embodiment of the present application provides an apparatus for generating information, where the apparatus includes: a data acquisition unit configured to acquire at least two-dimensional data of a target item, the at least two-dimensional data including at least two of historical procurement data, historical inventory data, historical sales data, and historical profit data; an indicator determination unit configured to determine at least two indicators of the target item based on the data of the at least two dimensions; the score acquisition unit is configured to input at least two indexes into corresponding index score models respectively to obtain at least two scores of the target object, wherein the index score models are used for representing the corresponding relation between the indexes and the scores; an information generating unit configured to generate evaluation information of the target item based on the at least two scores.

In some embodiments, the information generating unit includes: the total score determining module is configured to determine a total score of the target object based on at least two scores and a preset score weight; and the information generation module is configured to generate evaluation information of the target article based on the magnitude relation between the total score and the score threshold.

In some embodiments, the apparatus further comprises: an item determination unit configured to determine the target item as one of the items to be promoted in response to determining that the total score is greater than or equal to the score threshold.

In some embodiments, the apparatus further comprises: an optimization adjustment unit configured to perform an optimization adjustment on the target item based on the at least two scores in response to determining that the total score is less than the score threshold.

In a third aspect, an embodiment of the present application provides an electronic device, including: 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 are caused to implement the method as described in any implementation of the first aspect.

In a fourth aspect, the present application provides a computer-readable medium, on which a computer program is stored, which, when executed by a processor, implements the method as described in any implementation manner of the first aspect.

According to the method and the device for generating information, historical data of multiple dimensions of a target article are obtained, the historical data of each dimension are analyzed to obtain the index of the target article, each index is input into the corresponding index score model to obtain the score of the target article, and finally the generated score is used for generating evaluation information of the target article, so that the multi-dimensional data analysis can be performed on the article to generate the evaluation information of the article, the method and the device for generating information are beneficial to automatically selecting the article meeting the conditions, and the problem that the article is easily lost or out of stock when being selected by adopting a single index is solved.

Drawings

Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:

FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present application may be applied;

FIG. 2 is a flow diagram of one embodiment of a method for generating information according to the present application;

FIGS. 3A and 3B are schematic diagrams of an application scenario of a method for generating information according to the present application;

FIG. 4 is a schematic block diagram illustrating one embodiment of an apparatus for generating information according to the present application;

FIG. 5 is a schematic block diagram of a computer system suitable for use in implementing an electronic device according to embodiments of the present application.

Detailed Description

The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

Fig. 1 shows an exemplary system architecture 100 to which embodiments of the method for generating information or the apparatus for generating information of the present application may be applied.

As shown in fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have various communication client applications installed thereon, such as a browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.

The

terminal apparatuses

101, 102, and 103 may be hardware or software. When the

terminal devices

101, 102, 103 are hardware, they may be various electronic devices having a display screen and supporting page browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like. When the

terminal apparatuses

101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.

The server 105 may be a server that provides various services, such as a server that provides support for operation management of an article. The server 105 may obtain historical data of multiple dimensions of the item to determine multiple indicators of the item, and determine scores corresponding to the indicators to generate evaluation information of the item.

It should be noted that the method for generating information provided in the embodiment of the present application is generally performed by the server 105, and accordingly, the apparatus for generating information is generally disposed in the server 105.

The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server is software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any suitable number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to FIG. 2, a flow 200 of one embodiment of a method for generating information in accordance with the present application is shown. The method for generating information may comprise the steps of:

step 201, data of at least two dimensions of a target item is acquired.

In this embodiment, an executing agent of the method for generating information (e.g., server 105 of fig. 1) may obtain data for at least two dimensions of the target item from a local or remote location (e.g., other server). Here, the data of the at least two dimensions may include at least two of historical purchase data, historical inventory data, historical sales data, and historical profit data. For example, procurement data, inventory data, sales data, and gross profit data for item A over the past week are obtained.

At least two indicators of the target item are determined based on the data in the at least two dimensions, step 202.

In this embodiment, an executing entity (for example, the server 105 in fig. 1) of the method for generating information may analyze and process the data of at least two dimensions acquired in step 201 to obtain at least two indexes of the target item. For example, the data for each dimension may be processed to obtain an index for the target item.

In some optional implementations of the embodiment, the at least two indicators may include at least two of a fill rate, a stock out rate, a number of turnaround days, and a profit margin. For example, when the data of the at least two dimensions includes historical purchase data, historical inventory data, and historical sales data, the at least two indicators may include a fill rate, a stock out rate, and a number of turnaround days.

Corresponding to this implementation, the above-mentioned at least two indicators may be determined by:

when the at least two indexes include the cargo filling rate, determining a ratio of the receiving quantity to the placing quantity in PO (Purchase Order) data as the cargo filling rate of the target item;

when the at least two indexes comprise the out-of-stock rate, the ratio of the number of days for which the target item is out of stock (the stock quantity is 0) to the sales quantity can be determined as the out-of-stock rate of the target item;

when the at least two indicators include turnaround days, the inventory count for the first day, the inventory count for the last day, and the sales count for the first day of a predetermined period (e.g., the previous week) may be determined first, and then turnaround days may be determined according to the following equation (1):

wherein t is the turnover number of days, n is the number of days in the preset period, a is the inventory number of the first day in the preset period, b is the inventory number of the last day in the preset period, and c is the sales number in the preset period.

When the at least two indexes include the profit margin, the sales amount and the cost of the target item may be first determined, and then the profit margin may be determined according to the following formula (2):

wherein p is the profit margin of the target item, c is the sales amount of the target item, and d is the cost amount of the target item.

Alternatively, when the target item has a subsidy (supplier subsidy), the profit margin may be determined according to the following equation (3):

where e is the subsidy amount returned by the supplier.

Although the above-described implementation describes a specific manner of determining the fill rate, the out-of-stock rate, the number of turnaround days, and the profit margin, the present application is not limited thereto. It should be understood that other suitable ways of determining the above-mentioned index may be used by those skilled in the art. And the present application is also not limited to the above-listed indices, and other indices may be used instead of one or more of the above-listed indices.

Step 203, inputting at least two indexes into corresponding index score models respectively to obtain at least two scores of the target object.

In this embodiment, an executing subject (for example, the server 105 in fig. 1) of the method for generating information may input the at least two indexes determined in step 202 into corresponding index score models respectively, so as to obtain a score corresponding to each index. For example, the value corresponding to the fill rate can be obtained by inputting the fill rate into the fill rate value model, the value corresponding to the out-of-stock rate can be obtained by inputting the out-of-stock rate into the out-of-stock rate value model, the value corresponding to the turnover days can be obtained by inputting the turnover days into the turnover days value model, and the value corresponding to the profit margin can be obtained by inputting the profit margin into the profit margin value model. Here, the index score model may be used to characterize the correspondence between the index and the score.

In some optional implementations of this embodiment, the index score model may be trained by:

first, a predefined table of correspondence between the index and the score is obtained.

Then, the following training steps are performed: performing polynomial fitting on the data pairs in the corresponding relation table to obtain an initial model of indexes and scores; carrying out regression analysis on the initial model, and determining whether a data pair which does not meet a preset condition exists in the corresponding relation table; and if no data pair which does not meet the preset condition exists, determining the initial model as an index score model.

And if the data pairs which do not meet the preset conditions exist, removing the data pairs which do not meet the preset conditions from the corresponding relation table, and continuing to execute the training step.

Optionally, performing regression analysis on the initial model to determine whether there is a data pair that does not satisfy the preset condition in the correspondence table, which may specifically include the following two steps:

firstly, linear regression analysis is carried out on the initial model to obtain a distribution graph of data in the corresponding relation table to corresponding data points. The distribution graph may include at least one of a residual graph, a normal fraction graph, a t-distribution graph, and a kuck distance graph.

And secondly, determining whether a data point which is more than a preset value from the preset graph exists in the distribution diagram. For example, it is determined whether a data point exceeding a preset value from a preset straight line (e.g., a straight line with a vertical coordinate value of 0) exists in the residual map, and if so, it indicates that a data pair not meeting a preset condition exists.

The following description will be given taking a filling rate score model as an example.

In this example, a correspondence table of the filling rate and the score (for example, a correspondence table of the filling rate and the score preset by the operation and maintenance staff) is first obtained. And then, performing polynomial fitting on the value pairs in the corresponding relation table by utilizing an lm () function (a function commonly used in the R language and used for fitting a regression model) in the R language to obtain an initial model of the filling rate value. For example:

y＝α×x+β (4)

then, performing regression diagnosis on the initial model, for example, using a plot () (a general function, where the generated image type depends on the type or category of the first parameter) function to generate four images of the initial model, namely, a Residual map (i.e., Residual vs fixed map), a normal fraction map (NormalQQ-plot), a t-profile (Scale-Location map) containing a Scale parameter and a position parameter, and a Cook's distance map, so as to determine whether an abnormal value pair (e.g., a data pair corresponding to a data point whose Residual exceeds a preset Residual range) exists in the correspondence table.

And step 204, generating evaluation information of the target item based on the at least two scores.

In this embodiment, the executing subject (e.g., server 105 of fig. 1) of the method for generating information may generate evaluation information of the target item from the at least two scores generated in step 203. Here, the evaluation information may be used to characterize the operation health of the target item (reference basis for whether the target item needs to adjust the operation).

As an example, the at least two scores may be sequentially compared with corresponding score thresholds, and evaluation information of the target item may be generated according to the comparison result.

In some optional implementations of this embodiment, step 204 may specifically include:

the method comprises the following steps of firstly, determining the total score of a target article based on at least two scores and a preset score weight.

And secondly, generating evaluation information of the target article based on the size relation between the total score and the score threshold.

For example,step 203 determines four indicators of the target item: rate of charge x₁The out-of-stock rate x₂Number of days to turnover x₃Profit margin x₄The preset weights of the four indexes are respectively omega₁、ω₂、ω₃、ω₄Then the total score y of the target item_sumComprises the following steps:

y_sum＝ω₁×x₁+ω₂×x₂+ω₃×x₃+ω₄×x₄(5)

if the total score y of the target item_sumIf the value is larger than the score threshold value (for example, 80), evaluation information of the operation health of the target article can be generated; if the total score y of the target item_sumAnd if the value is smaller than the score threshold value, evaluation information of unhealthy operation of the target object can be generated.

In some optional implementations of this embodiment, the method for generating information may further include: in response to the fact that the total score of the target object is larger than or equal to the score threshold, the target object is determined to be one of the objects to be popularized (for example, the target object is added into an object selection pool), so that automatic object selection can be achieved, the complex work of manually selecting the objects is avoided, and the selected objects are more accurate due to the comprehensive consideration of indexes of at least two dimensions.

In some optional implementations of this embodiment, the method for generating information may further include: and performing optimization adjustment on the target item based on the at least two scores in response to determining that the total score of the target item is less than the score threshold value. For example, if the fill rate is below a predetermined fill rate threshold, then a problem (e.g., a purchase procedure problem or a supplier problem) may be identified and a specific solution may be formulated to increase the fill rate of the target item. For another example, if the turnover number of days is higher than the preset number of days threshold, it may be determined whether to perform price reduction or return goods according to specific situations, so as to reduce the inventory and thus reduce the loss.

With continued reference to fig. 3A and 3B, one application scenario for a method for generating information according to the present application is illustrated.

In this application scenario, first, order information, stock quantity, sales volume, sales amount, cost amount, and subsidy amount of the supplier for a certain item (for example, an item whose item has changed to 00111) in the last week are acquired, and it is determined that the stock fill rate of the item is 98%, the stock out rate is 28.5%, the turnaround days are 16, and the profit margin is 8%.

Then, a predefined filling rate score correspondence table (as shown in table one below), a shortage rate score correspondence table (not shown), a turnover days score correspondence table (not shown), and a profit margin score correspondence table (not shown) are obtained. Derivation formula lm (y) from R₁～x₁) Fitting the data pairs in the table I to obtain the goods filling rate x₁And the score y₁Formula (i.e., the initial model shown in fig. 3A):

y₁＝a₃×x₁ ³+a₂×x₁ ²+a₁×x₁+a₀(6)

wherein, a₀、a₁、a₂、a₃Are regression coefficients.

Table-corresponding relation table of goods-filling rate and score

Then, the initial model of fig. 3A was subjected to residual analysis. For example, four images for model diagnosis (i.e., Residual vs fixed image, Normal QQ-plot image, Scale-Location image, and Cook's distance image) can be sequentially generated by using the function plot (), and the Residual of the first piece of data (50%, 0) in the first table is found to be larger than the preset Residual range (i.e., the piece of data is abnormal data).

Next, (50%, 0) is removed from the correspondence table of the filling rate and the score, and the in-out fitting and residual analysis are continued to obtain the model shown in fig. 3B (as a trained filling rate score model):

y₁＝102.09×x₁-1.2665 (7)

similarly, a trained out-of-stock score model (where x is₂To the out-of-stock rate, y₂In scores):

y₂＝-215.95×x₂ ²-33.931×x₂+95.684 (8)

trained turn-around days score model (where x₃Number of days of turnover, y₃In scores):

y₃＝0.001×x₃ ³+0.1574×x₃ ²-8.7791×x₃+189.63 (9)

trained profit margin score model (where x₄To increase profit margin, y₄In scores):

y₄＝0.2778×x₄ ³-4.5238×x₄ ²+3.7698×x₄+100 (10)

finally, as shown in table two, the total score of the item was 86.58 according to the preset weight of each index score. Since the total score value is greater than the score threshold value (80), evaluation information of the operation health of the article can be generated, and the article can be added into a selection pool for the next stage of sale.

Table two stock shortage rate and score corresponding relation table

According to the method for generating information provided by the embodiment of the application, the historical data of the target object in multiple dimensions is obtained, the historical data of each dimension is analyzed to obtain the index of the target object, each index is input into the corresponding index score model to obtain the score of the target object, and finally the generated score is used for generating the evaluation information of the target object, so that the multi-dimensional data analysis can be performed on the object to generate the evaluation information of the object, and the method is helpful for automatically selecting the object meeting the conditions.

With further reference to fig. 4, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an apparatus for generating information, which corresponds to the embodiment of the method shown in fig. 2, and which may be specifically applied in a server.

As shown in fig. 4, the apparatus 400 for generating information of the present embodiment may include a data acquisition unit 401, an index determination unit 402, a score acquisition unit 403, and an information generation unit 404. Wherein the data acquisition unit 401 is configured to acquire at least two dimensions of data of the target item, the at least two dimensions of data including at least two of historical procurement data, historical inventory data, historical sales data, and historical profit data; the indicator determination unit 402 is configured to determine at least two indicators of the target item based on the data of the at least two dimensions; the score obtaining unit 403 is configured to input at least two indexes into corresponding index score models respectively, and obtain at least two scores of the target item, where the index score models are used for representing the corresponding relationship between the indexes and the scores; and the information generating unit 404 is configured to generate the evaluation information of the target item based on the at least two scores.

In this embodiment, for specific implementation of the data obtaining unit 401, the index determining unit 402, the score obtaining unit 403, and the information generating unit 404 of the apparatus 400 for generating information, reference may be made to relevant description about step 201 to step 204 in the embodiment corresponding to fig. 2, and details are not repeated here.

In some optional implementations of the embodiment, the at least two indicators may include at least two of a fill rate, a stock out rate, a number of turnaround days, and a profit margin.

In some optional implementation manners of this embodiment, the information generating unit 404 may include a total score determining module and an information generating module. Wherein the total score determination module is configured to determine a total score of the target item based on the at least two scores and a preset score weight; and the information generation module is configured to generate the evaluation information of the target article based on the magnitude relation between the total score and the score threshold.

In some optional implementations of this embodiment, the apparatus 400 may further include an item determination unit. Wherein the item determination unit is configured to: in response to determining that the total score is greater than or equal to the score threshold, determining the target item as one of the items to promote.

In some optional implementations of this embodiment, the apparatus 400 may further include an optimization adjustment unit. Wherein the optimization adjustment unit is configured to: in response to determining that the total score is less than the score threshold, an optimization adjustment is made to the target item based on the at least two scores.

According to the device for generating information, the historical data of the target object in multiple dimensions are acquired, the historical data of each dimension is analyzed to obtain the index of the target object, each index is input into the corresponding index score model to obtain the score of the target object, and finally the generated score is used for generating the evaluation information of the target object, so that the multi-dimensional data analysis can be performed on the object to generate the evaluation information of the object, and the automatic selection of the object meeting the conditions is facilitated.

Referring now to FIG. 5, a block diagram of a computer system 500 suitable for use in implementing an electronic device (e.g., server 105 of FIG. 1) of an embodiment of the present application is shown. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.

As shown in fig. 5, the computer system 500 includes one or more Central Processing Units (CPUs) 501, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as an Organic Light Emitting Diode (OLED) display, a Liquid Crystal Display (LCD), and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program performs the above-described functions defined in the method of the present application when executed by the Central Processing Unit (CPU) 501.

It should be noted that the computer readable medium described herein can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a data acquisition unit, an index determination unit, a score acquisition unit, and an information generation unit. Where the names of the units do not in some cases constitute a limitation on the units themselves, for example, a data acquisition unit may also be described as a "unit that acquires data for at least two dimensions of a target item".

As another aspect, the present application also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be present separately and not assembled into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to: acquiring data of at least two dimensions of a target item, wherein the data of at least two dimensions comprises at least two of historical purchasing data, historical inventory data, historical sales data and historical profit data; determining at least two indicators of the target item based on the data in the at least two dimensions; inputting at least two indexes into corresponding index score models respectively to obtain at least two scores of the target object, wherein the index score models are used for representing the corresponding relation between the indexes and the scores; and generating evaluation information of the target item based on the at least two scores.

The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.

Claims

1. A method for generating information, comprising:

acquiring data of at least two dimensions of a target item, wherein the data of at least two dimensions comprises at least two of historical purchasing data, historical inventory data, historical sales data and historical profit data;

determining at least two indicators of the target item based on the data of the at least two dimensions;

inputting the at least two indexes into corresponding index score models respectively to obtain at least two scores of the target object, wherein the index score models are used for representing the corresponding relation between the indexes and the scores;

and generating evaluation information of the target item based on the at least two scores.

2. The method of claim 1, wherein the at least two metrics include at least two of a fill rate, an out-of-stock rate, a turnover number of days, and a profit margin, the fill rate being a ratio of a quantity of received goods to a quantity of orders for the target item, the out-of-stock rate being a ratio of a number of out-of-stock days to a number of sales days for the target item, the turnover number of days being a number of turnover days for the target item determined based on the quantity of inventory and the quantity of sales for the target item.

3. The method of claim 1, wherein the metric score model is determined by:

acquiring a corresponding relation table of pre-defined indexes and scores;

the following training steps are performed: performing polynomial fitting on the data pairs in the corresponding relation table to obtain an initial model of indexes and scores; carrying out regression analysis on the initial model, and determining whether a data pair which does not meet a preset condition exists in the corresponding relation table; and if no data pair which does not meet the preset condition exists, determining the initial model as an index score model.

4. The method of claim 3, wherein the metric score model is further determined by:

5. The method of claim 3, wherein the performing regression analysis on the initial model to determine whether there are data pairs in the correspondence table that do not satisfy a preset condition comprises:

performing linear regression analysis on the initial model to obtain a distribution graph of data in the corresponding relation table to corresponding data points;

and determining whether a data point which is more than a preset value from the preset graph exists in the distribution graph.

6. The method of claim 1, wherein the generating ratings information for the target item based on the at least two scores comprises:

determining a total score of the target object based on the at least two scores and a preset score weight;

and generating the evaluation information of the target item based on the size relation between the total score and a score threshold value.

7. The method of claim 6, wherein the method further comprises:

in response to determining that the total score is greater than or equal to the score threshold, determining the target item as one of the items to promote.

8. The method of claim 7, wherein the method further comprises:

in response to determining that the total score is less than the score threshold, making an optimization adjustment to the target item based on the at least two scores.

9. An apparatus for generating information, comprising:

a data acquisition unit configured to acquire at least two-dimensional data of a target item, the at least two-dimensional data including at least two of historical procurement data, historical inventory data, historical sales data, and historical profit data;

an indicator determination unit configured to determine at least two indicators of the target item based on the data of the at least two dimensions;

the score acquisition unit is configured to input the at least two indexes into corresponding index score models respectively to obtain at least two scores of the target object, wherein the index score models are used for representing the corresponding relation between the indexes and the scores;

an information generating unit configured to generate evaluation information of the target item based on the at least two scores.

10. An electronic device, comprising:

one or more processors;

a storage device having one or more programs stored thereon,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-8.

11. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-8.