CN112784213A - Method and device for generating information - Google Patents
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Abstract
The invention discloses a method and a device for generating information, and relates to the technical field of computers. One embodiment of the method comprises: determining a first relation representation of second target article information and first target article information of the article to be predicted according to first basic article information, second basic article information and first reference information of the article to be predicted; determining first target article information meeting preset target constraints in a first preset article information interval of an article according to the first relation representation and constraint parameters related to preset target constraints; the first target article information provides reference information for the salesperson, and when the article information needs to be adjusted to reach the preset target, compared with the traditional mode of establishing the article information by depending on the experience of the salesperson, the accuracy and the efficiency of establishing the article information are improved, and the income loss of a merchant caused by article information adjustment deviation is reduced.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method and an apparatus for generating information.
Background
In various sales scenes, whether e-commerce or traditional retail, how to reduce inventory, clear up lost goods and obtain maximized revenue by adjusting commodity information such as commodity price is a problem to be solved; most of the current methods for adjusting the price information of commodities are set by adopting and selling personnel according to experience.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
when the commodity information needs to be changed, the salesperson determines the commodity information such as the commodity price adjustment range according to experience, so that the adjustment of the commodity information depends on the experience of the salesperson to a great extent and is not accurate enough.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for generating information, which are capable of determining first target item information of a product more accurately according to second historical item information and first historical item information and according to a constraint condition set by a user, for example, a first item information range, and guiding a salesperson to adjust the first item information, thereby effectively improving revenue.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a method of generating information, including:
acquiring first historical article information and second historical article information of an article to be predicted, and determining first basic article information, second basic article information and first reference information of the article to be predicted;
determining a first relation representation of second target item information and first target item information of the item to be predicted according to the first basic item information, the second basic item information and the first reference information;
and determining the first target article information meeting the preset target constraint in a first preset article information interval of the article according to the first relation representation and constraint parameters related to the preset target constraint.
Optionally, the method for generating information is characterized in that the preset target constraint is that a first index is maximized, and the constraint parameter is the first target item information.
Optionally, the method for generating information is characterized in that the preset target constraint is that a second target is maximized, and the constraint parameter is a difference between the first target item information and original information of the item to be predicted.
Optionally, the method for generating information is further characterized by: calculating the probability that the sum of the second target article information of the article to be predicted, which is accumulated in a preset sales range, is not less than the preset second article information of the article;
selecting the first target item information of which the probability is not less than a threshold probability from the first target item information satisfying the preset target constraint.
Optionally, the method for generating information is characterized in that the preset sales range is a set of preset sales time periods or preset sales regions.
Optionally, the method of generating information is characterized in that the first reference information conforms to a first probability distribution and the second base item information conforms to a second probability distribution.
Optionally, the method for generating information is characterized in that the first preset item information interval is defined by a first item information lower bound and a first item information upper bound;
determining the first target item information satisfying the preset target constraint within the item preset first item information interval starting from the smaller of the first item information upper bound and the first base item information when the first reference information indicates that benefit information decreases as the first item information increases;
determining the first target item information satisfying the preset target constraint within the item first preset item information interval from the first item information upper bound when the first reference information indicates that the benefit information increases as the first item information increases.
Optionally, the method for generating information is characterized in that an information analysis report is formed according to the first target item information.
To achieve the above object, according to a second aspect of an embodiment of the present invention, there is provided an apparatus for generating information, including: the system comprises a basic information processing module and a first target article information generating module; the basic information processing module is used for acquiring first historical article information and second historical article information of an article to be predicted and determining the first basic article information, the second basic article information and first reference information of the article to be predicted;
the first target article information generating module is used for determining a first relation representation of second target article information and first target article information of the article to be predicted according to the first basic article information, the second basic article information and the first reference information; and determining the first target article information meeting the preset target constraint in a first preset article information interval of the article according to the first relation representation and constraint parameters related to the preset target constraint.
Optionally, the apparatus for generating information is characterized in that the preset target constraint is that a first index is maximized, and the constraint parameter is the first target item information.
Optionally, the information generating apparatus is characterized in that the preset target constraint is that a second target is maximized, and the constraint parameter is a difference between the first target item information and original information of the item to be predicted.
Optionally, the information generating apparatus is configured to calculate a probability that a sum of the second target item information of the to-be-predicted item accumulated in a preset sales range is not less than a preset second item information of the item; selecting the first target item information of which the probability is not less than a threshold probability from the first target item information satisfying the preset target constraint.
Optionally, the information generating apparatus is characterized in that the preset sales range is a set of preset sales time periods or preset sales regions.
Optionally, the apparatus for generating information is characterized in that the first reference information conforms to a first probability distribution and the second base item information conforms to a second probability distribution.
Optionally, the apparatus for generating information is characterized in that the first preset item information section is defined by a first item information lower bound and a first item information upper bound;
determining the first target item information satisfying the preset target constraint within the item preset first item information interval starting from the smaller of the first item information upper bound and the first base item information when the first reference information indicates that benefit information decreases as the first item information increases;
determining the first target item information satisfying the preset target constraint within the item first preset item information interval from the first item information upper bound when the first reference information indicates that the benefit information increases as the first item information increases.
Optionally, the information generating device is characterized in that an information analysis report is formed according to the first target item information.
To achieve the above object, according to a third aspect of the embodiments of the present invention, there is provided a server for generating information, including: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out a method as claimed in any one of the above methods of generating information.
To achieve the above object, according to a fourth aspect of embodiments of the present invention, there is provided a computer-readable medium having a computer program stored thereon, characterized in that the program, when executed by a processor, implements the method as set forth in any one of the above methods of generating information.
One embodiment of the above invention has the following advantages or benefits:
acquiring first historical article information and second historical article information of an article to be predicted, and determining first basic article information, second basic article information and first reference information of the article to be predicted; determining a first relation representation of second target item information and first target item information of the item to be predicted according to the first basic item information, the second basic item information and the first reference information; and determining the first target article information meeting the preset target constraint in a first preset article information interval of the article according to the first relation representation and constraint parameters related to the preset target constraint.
Therefore, the first target item information established for achieving the target constraint is determined, and when the first item information needs to be adjusted, compared with the traditional mode of establishing the first item information such as the price by depending on the experience of the salesperson, the accuracy and the efficiency of establishing the first item information such as the price are improved, and the revenue loss of a merchant caused by the adjustment deviation of the first item information such as the price is reduced.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
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The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic flow chart diagram illustrating a method for determining information of a first target item according to a first embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method for determining information of a first target item according to a predetermined constraint objective according to a second embodiment of the present invention;
FIG. 3 is a flowchart illustrating a method for determining information of a first target item under a probability constraint according to a third embodiment of the present invention;
FIG. 4 is a schematic flow chart of a method for determining information of a preferred first target item according to a fourth embodiment of the present invention;
FIG. 5 is a schematic structural diagram of an apparatus for generating information according to an embodiment of the present invention;
FIG. 6 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 7 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
As shown in fig. 1, an embodiment of the present invention provides a method for determining information of a first target item, which may include the following steps:
step S101: the method comprises the steps of obtaining first historical article information and second historical article information of an article to be predicted, and determining first basic article information, second basic article information and first reference information of the article to be predicted.
Optionally, in this embodiment, the first historical item information indicates a historical price, the second historical item information indicates a historical sales amount, the first reference information indicates a price elasticity parameter, the first base item information indicates a base price, and the second base item information indicates a base sales amount. It can be understood that, in the following description of the steps, the historical price is the first historical item information, the historical sales amount is the second historical item information, the price elasticity parameter is the first reference information, the basic price is the first basic item information, the basic sales amount is the second basic item information, and the commodity is the item.
Specifically, according to the order data of the commodity to be predicted and the multi-dimensional sales detail data of the commodity, obtaining original data of the commodity to be predicted, wherein the original data indicates historical prices and historical sales volumes of the commodity to be predicted, and obtaining basic prices, basic sales volumes and price elasticity parameters required by pricing the commodity to be predicted according to the historical prices and the historical sales volumes; the price elasticity parameter indicates the ratio of the sales volume change rate to the price change rate and is used for measuring the relation between the price change amplitude and the sales volume change amplitude.
Step S102: and determining a first relation representation of second target item information and first target item information of the item to be predicted according to the first basic item information, the second basic item information and the first reference information.
Optionally, in this embodiment, the first target item information indicates a target price, and the second target item information indicates a target sales amount; it is understood that in the following exemplary description of the steps, the target sales amount is the second target item information, and the target price is the first target item information.
Specifically, the first relational expression is determined based on the following relational expression, as shown in formula (1), and a commodity sales and price linear demand function is constructed as shown in formula (1):
where alpha represents a price elasticity parameter, Z represents other factors that affect sales volume,
coefficient, beta, representing each factor0Representing a constant term, epsilon represents a random term, Q represents the sales volume of the commodity, log (Q) represents that the information of the sales volume of the commodity is the logarithm of the sales volume, P represents the price of the commodity, and log (P) represents that the information of the price of the commodity is the logarithm of the price;
optionally, in this embodiment, the first relationship is determined based on formula (1) to represent the following two expressions:
the first expression is that only the price elasticity parameter, the base price and the base sales are referenced, specifically, the target sales is represented by X, the target price is represented by Y, and the target sales is represented by Y, assuming that other factors affecting the sales are not changed0Denotes the base price, X0Expressing the basic sales quantity, converting according to the formula (1) to obtain the following formula (2)
The formula (2) is a first relational expression of the target sales volume and the target price of the to-be-predicted commodity, namely, a first relational expression of second target item information and first target item information of the to-be-predicted commodity is determined according to the first basic item information, the second basic item information and the first reference information.
The second expression is that, assuming that other factors affecting the sales amount are not changed, only the price elasticity parameter, the base price, and the base sales amount are referred to, specifically, the target price is represented by P, the target sales amount is represented by Q, and P is represented by P0Denotes the base price, Q0Expressing the basic sales, expressing the price elasticity parameter by alpha, and obtaining the following formula (3) according to the conversion of the formula (1)
The formula (3) is a second expression mode represented by a first relation between the target sales volume and the target price of the to-be-predicted commodity, namely, a first relation representation between second target item information and first target item information of the to-be-predicted commodity is determined according to the first basic item information, the second basic item information and the first reference information.
Further, the flexible price parameter conforms to a first probability distribution and the base sales volume conforms to a second probability distribution; the first probability distribution may be a normal distribution, a poisson distribution, a binomial distribution, a two-point distribution, a hyper-geometric distribution, a normal distribution, or the like; preferably, the second probability distribution is a truncated normal distribution, and optionally, the second probability distribution may be a normal distribution, a poisson distribution, a binomial distribution, a two-point distribution, a hyper-geometric distribution, a normal distribution, or the like, which is not limited herein. That is, the first reference information conforms to a first probability distribution, and the second base item information conforms to a second probability distribution.
Step S103: and determining the first target article information meeting the preset target constraint in a first preset article information interval of the article according to the first relation representation and constraint parameters related to the preset target constraint.
Optionally, in this embodiment, the first preset item information indicates a preset price interval, and it is understood that in the following description of each step, the preset price interval is the first preset item information.
Specifically, according to the first relational expression, taking formula (3) indicated in step S102 as an example, the target price of the target constraint is obtained according to the preset target constraint and the preset price interval.
In one embodiment, an information analysis report is further formed according to the target price of the goods to be predicted and the goods sales amount corresponding to the target price, and compared with the basic sales amount and the basic price, wherein the information analysis report includes but is not limited to the following information: the sales channel sales volume proportion, the most sales volume circle proportion improvement channel, the most sales volume circle proportion reduction channel, the most sales volume same proportion improvement channel, the average commodity price, the commodity price improvement rate, the commodity price reduction rate and the like; thus, a prompt is given to the commodity sales person to pay attention to the comprehensive factors of the commodity to be predicted, and whether the target price of the predicted commodity is the commodity price meeting the preset sales volume or not is determined.
As shown in fig. 2, an embodiment of the present invention provides a method for determining first target item information according to a preset constraint target, which may include the following steps:
the processing flow of steps S201 to S203 with respect to a method for generating information is consistent with the description of steps S101 to S103, and is not described herein again.
Step S204, the preset target constraint is a first index maximization, and the constraint parameter is the first target item information.
Optionally, in this embodiment, the first index indicates a total volume of deals, and it is understood that in the following exemplary description of each step, the total volume of deals is the first index.
Specifically, according to the first relational expression between the target sales volume and the target price of the commodity to be predicted, still exemplified by formula (3), the formula (3) is converted according to a preset target as the maximum of the total volume of the transaction, and the following formula (4) is obtained
subject to Plower≤P≤Pupper
Wherein P represents the target price, i.e. the target parameter, Plower≤P≤PupperRepresents a preset price interval, correspondingly, PupperDenotes the highest valence, PlowerRepresents the lowest price; and (4) determining the target price by combining the preset interval of the commodity price under the condition that the total volume of the deal is maximized according to the formula (4).
Optionally, the preset target constraint is that a second index is maximized, and the constraint parameter is a difference between the first item information and original information of the item to be predicted.
Further optionally, the second indicator indicates profit and the original information indicates cost; it is understood that in the following description of the steps, profit is the second indicator and cost is the original information.
Specifically, according to the first relation expression of the target sales volume and the target price of the commodity to be predicted, still exemplified by formula (3), the formula (3) is converted according to the preset target as profit maximization, and the following formula (5) is obtained
subject to Plower≤P≤Pupper
Wherein P represents the target price, Plower≤P≤PupperRepresents a preset price interval, correspondingly, PupperDenotes the highest valence, PlowerRepresenting the lowest price, Cost representing the Cost of the commodity, and P-Cost representing the profit, namely, the difference between the target price and the Cost of the commodity to be predicted, namely, the constraint parameter; and (4) determining the target price by combining the preset interval of the commodity price under the condition of maximizing the profit according to the formula (4).
In one embodiment, an information analysis report is further formed according to the target price of the goods to be predicted and the goods sales amount corresponding to the target price, and compared with the basic sales amount and the basic price, wherein the information analysis report includes but is not limited to the following information: the sales channel sales volume proportion, the most sales volume circle proportion improvement channel, the most sales volume circle proportion reduction channel, the most sales volume same proportion improvement channel, the average commodity price, the commodity price improvement rate, the commodity price reduction rate and the like; thus, a prompt is given to the commodity sales person to pay attention to the comprehensive factors of the commodity to be predicted, and whether the target price of the predicted commodity is the commodity price meeting the preset sales volume or not is determined.
As shown in fig. 3, an embodiment of the present invention provides a method for determining a target price of an article under a probability constraint, which may include the following steps:
the processing flow of steps S301 to S303 related to a method for generating information is consistent with the description of steps S101 to S103, and will not be described herein again.
Step S304, calculating the probability that the sum of the second target article information of the article to be predicted, which is accumulated in a preset sales range, is not less than the preset second article information of the article; selecting first target item information of which the probability is not less than a threshold probability from the first target item information meeting the preset target constraint; .
Optionally, in this embodiment, the preset second item information indicates a preset sales amount, and it is understood that in the following exemplary description of each step, the preset sales amount is the preset second item information.
Specifically, the following formula (6) is taken as an example to illustrate how to calculate the probability that the sum of the target sales amounts of the to-be-predicted commodities accumulated in the preset sales range is not less than the preset sales amount of the commodity.
Wherein QtargetRepresenting the preset sales volume of the commodity, Q representing the target sales volume of the commodity to be predicted, c representing the threshold probability, and n representing the preset sales range.
The preset sales range is a preset sales time period or a set of preset sales regions, optionally, the set of sales time periods may be days, and the set of preset sales regions may be a number of regions, that is, n is represented as n days in the preset sales time period range, and n is represented as n regions in the preset sales region range.
Further, according to the probability constraint shown in formula (6), while according to different preset target constraints shown in formula (4) or formula (5), that is, the maximum or maximization of the volume of interest, and according to the relationship between the target price indicated in formula (5) and the target sales volume, among the target prices satisfying the preset target constraints, the target price having the probability not less than the threshold probability is selected.
Further, for example, taking formula (4) as an example, since the price elasticity parameter and the basic sales volume are subject to distribution, one calculation is based on one sampling of the price elasticity parameter and the basic sales volume data, and thus the target sales volume obtained by each calculation is correspondingly different; accumulating to form the sum of the target sales in a plurality of days or a plurality of areas according to the target sales obtained by multiple calculations; comparing the multiple simulation calculation results with the sum of the target data to obtain the probability; alternatively, the simulation calculation may employ a monte carlo method.
In one embodiment, an information analysis report is further formed according to the target price of the goods to be predicted and the goods sales amount corresponding to the target price, and compared with the basic sales amount and the basic price, wherein the information analysis report includes but is not limited to the following information: the sales channel sales volume proportion, the most sales volume circle proportion improvement channel, the most sales volume circle proportion reduction channel, the most sales volume same proportion improvement channel, the average commodity price, the commodity price improvement rate, the commodity price reduction rate and the like; thus, the commodity sales staff is prompted to pay attention to the comprehensive factors of the commodity to be predicted, and then the target price of the predicted commodity is determined.
As shown in fig. 4, an embodiment of the present invention provides a method for determining preferred first target item information, which may include the steps of:
the processing flow of steps S401 to S404 regarding a method of generating information is consistent with the description of steps S301 to S304, and will not be described again here.
Step S405, the first preset article information interval is defined by a first article information lower bound and a first article information upper bound; when the first reference information indicates that the profit information decreases as the first item information increases, determining first target item information satisfying the preset target constraint within the item preset first item information interval starting from the smaller value of the first item information upper bound and the first base item information; when the first reference information indicates that the profit information increases as the first item information increases, first target item information satisfying the preset target constraint within the item first preset item information section is determined from the first item information upper bound.
Optionally, the first preset item information interval indicates a preset price interval, and the first item information indicates a price, it can be understood that in the following exemplary description of each step, the preset price interval is the first preset item information interval, and the price is the first item information.
In particular, when the flexible price parameter indicates not to be below a critical value, the yield increases with increasing price; when the price elasticity parameter indicates that it is below a critical value, the profit decreases with increasing price, for example with a critical value of-1, as shown below, α being the price elasticity parameter:
according to the above rule, the probability calculation rule indicated in step S404, and the preset price interval of the commodity, among the target prices satisfying the preset target constraint, a target price with the probability not less than a threshold probability is selected, which is exemplified as follows:
when alpha is more than or equal to-1, the income rises along with the rise of the price, and the price change direction for optimizing the income is to improve the price; therefore, the target price which can meet the preset constraint target is searched downwards from the highest price in the preset price interval, namely, the target price which meets the preset target constraint in the commodity preset price interval is determined from the price upper bound.
When alpha is less than-1, the profit is reduced along with the increase of the price, the price change direction of the optimized profit is the price reduction, the target price of the commodity is obtained according to a formula (4) or a formula (5), and the target price capable of meeting the preset target is searched downwards according to the smaller value of the target price of the commodity and the maximum value of the preset price interval, namely, when the elastic price parameter indicates that the profit is reduced along with the increase of the price, the target price meeting the preset target constraint in the preset price interval of the commodity is determined from the smaller value of the upper price limit and the basic price.
Further, taking the number as an example, taking the product A as an example, the basic price of the product A is 100 yuan, 20 products need to be sold with the probability higher than 80% in 2 days, the price elasticity parameter alpha is known to be-2, and the basic price P is known to be0100, base sales Q0Is 5, the preset price interval is [60,110 ]]. From a price elasticity parameter of less than-1, it is known that the profit decreases as the price increases, and therefore the smaller value 100 of either 100 and the upper price boundary, i.e., 110, is taken as the starting point to search down for the target price. Root of herbaceous plantSimulating the sum of sales volume for 2 days according to the formula (3), and determining whether the probability of the obtained target price is higher than a probability threshold value through simulation calculation for preset times, wherein if the probability threshold value is 80%, the obtained price is determined to be the target price under the condition that the probability threshold value is higher than 80%, and if the probability threshold value is lower than 80%, the calculation process is repeated with the price reduced.
Optionally, the method for determining the target price according to the upper price bound or the smaller value of the upper price bound and the base price as a starting point may adopt a dichotomy, and specifically, the following is exemplified:
assuming that the example of the preset price interval is [60, 80], when the determination is started by taking the upper price bound as a starting point, namely, the determination is started from 80, and when the probability threshold cannot be met by 80, the average value of the upper bound and the lower bound is obtained according to the upper bound and the lower bound of the preset price interval, namely, 70; determining 70 whether the target price is reached, if the target price can be reached, determining a new price interval as 70,80, and repeating the calculation process according to the target price precision to obtain the target price of the commodity.
In one embodiment, an information analysis report is further formed according to the target price of the goods to be predicted and the goods sales amount corresponding to the target price, and compared with the basic sales amount and the basic price, wherein the information analysis report includes but is not limited to the following information: the sales channel sales volume proportion, the most sales volume circle proportion improvement channel, the most sales volume circle proportion reduction channel, the most sales volume same proportion improvement channel, the average commodity price, the commodity price improvement rate, the commodity price reduction rate and the like; thus, a prompt is given to the commodity sales person to pay attention to the comprehensive factors of the commodity to be predicted, and whether the target price of the predicted commodity is the commodity price meeting the preset sales volume or not is determined.
As shown in fig. 5, an embodiment of the present invention provides an apparatus 500 for generating information, including: a basic information processing module 501 and a first target article information generating module 502; wherein the content of the first and second substances,
the basic information processing module is used for acquiring first historical article information and second historical article information of an article to be predicted and determining first basic article information, second basic article information and first reference information of the article to be predicted;
the first target article information generating module is used for determining a first relation representation of second target article information and first target article information of the article to be predicted according to the first basic article information, the second basic article information and the first reference information; and determining the first target article information meeting the preset target constraint in a first preset article information interval of the article according to the first relation representation, constraint parameters related to the preset target constraint and the second target article information.
Optionally, in the apparatus for generating information, the preset target constraint is that a first index is maximized, and the constraint parameter is the first target item price.
Optionally, the preset target constraint of the information generating device is that a second target is maximized, and the constraint parameter is a difference between the first target item information and original information of the item to be predicted.
Optionally, the information generating device is configured to calculate a probability that a sum of second target item information of the to-be-predicted item accumulated within a preset sales range is not less than preset second item information of the item; and selecting the first target item information of which the probability is not less than the threshold probability from the first target item information meeting the preset target constraint.
Optionally, in the information generating device, the preset sales range is a set of preset sales time periods or preset sales regions.
Optionally, the means for generating information, the first reference information conforms to a first probability distribution and the second base item information conforms to a second probability distribution.
Optionally, the information generating device is configured to define the first preset item information section by a first item information lower bound and a first item information upper bound; when the first reference information indicates that the profit information decreases as the first item information increases, determining first target item information satisfying the preset target constraint within the item preset first item information interval starting from the smaller value of the first item information upper bound and the first base item information; when the first reference information indicates that the profit information increases as the first item information increases, first target item information satisfying the preset target constraint within the item first preset item information section is determined from the first item information upper bound.
Optionally, the information generating device forms an information analysis report according to the first target item information.
The embodiment of the invention also provides a commodity pricing server, which comprises: one or more processors; the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors are enabled to realize the method provided by any one of the above embodiments.
Embodiments of the present invention further provide a computer-readable medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method provided in any of the above embodiments.
Fig. 6 illustrates an exemplary system architecture 600 of a method or apparatus for generating information to which embodiments of the present invention may be applied.
As shown in fig. 6, the system architecture 600 may include terminal devices 601, 602, 603, a network 604, and a server 605. The network 604 serves to provide a medium for communication links between the terminal devices 601, 602, 603 and the server 605. Network 604 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use the terminal devices 601, 602, 603 to interact with the server 605 via the network 604 to receive or send messages or the like. Various communication client applications, such as a web browser application, a search application, an instant messaging tool, a mailbox client, and the like, may be installed on the terminal devices 601, 602, and 603.
The terminal devices 601, 602, 603 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
It should be noted that the method for generating information provided by the embodiment of the present invention is generally executed by the server 605, and accordingly, the apparatus for generating information is generally disposed in the server 605.
It should be understood that the number of terminal devices, networks, and servers in fig. 6 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 7, shown is a block diagram of a computer system 700 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU)701, which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for the operation of the system 700 are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.
In particular, according to the embodiments 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 can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 701.
It should be noted that the computer readable medium shown in the present invention 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 invention, 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 the present invention, 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.
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 invention. 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 or flowchart illustration, and combinations of blocks in the block diagrams 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 modules and/or units described in the embodiments of the present invention may be implemented by software, and may also be implemented by hardware. The described modules and/or units may also be provided in a processor, and may be described as: a processor comprises a basic information processing module and a first target object information generating module. Where the names of the modules do not constitute a limitation on the modules themselves in some cases, for example, the first target item information generation module may also be described as a "module that determines the first target item information from the first base item information, the second base item information, and the first reference information".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: acquiring first historical article information and second historical article information of an article to be predicted, and determining first basic article information, second basic article information and first reference information of the article to be predicted; determining a first relation representation of second target item information and first target item information of the item to be predicted according to the first basic item information, the second basic item information and the first reference information;
and determining the first target article information meeting the preset target constraint in a first preset article information interval of the article according to the first relation representation and constraint parameters related to the preset target constraint.
According to the technical scheme of the embodiment of the invention, the first target item information of the commodity can be accurately determined according to the second historical item information and the first historical item information and the constraint condition set by the user, such as the first item information range, so that the salesperson is guided to adjust the first item information, and the benefit is effectively improved.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (11)
1. A method of generating information, comprising:
acquiring first historical article information and second historical article information of an article to be predicted, and determining first basic article information, second basic article information and first reference information of the article to be predicted;
determining a first relation representation of second target item information and first target item information of the item to be predicted according to the first basic item information, the second basic item information and the first reference information;
and determining the first target article information meeting the preset target constraint in a first preset article information interval of the article according to the first relation representation and constraint parameters related to the preset target constraint.
2. The method of claim 1,
the preset target constraint is that a first index is maximized, and the constraint parameter is the first target article information.
3. The method of claim 1,
the preset target constraint is that a second index is maximized, and the constraint parameter is the difference between the first target article information and the original information of the article to be predicted.
4. The method of claim 1, further comprising:
calculating the probability that the sum of the second target article information of the article to be predicted, which is accumulated in a preset sales range, is not less than the preset second article information of the article;
selecting the first target item information of which the probability is not less than a threshold probability from the first target item information satisfying the preset target constraint.
5. The method of claim 4,
the preset sales range is a set of preset sales time periods or preset sales regions.
6. The method of claim 1,
the first reference information conforms to a first probability distribution and the second base item information conforms to a second probability distribution.
7. The method of claim 1,
the first preset article information interval is defined by a first article information lower bound and a first article information upper bound;
determining the first target item information satisfying the preset target constraint within the item preset first item information interval starting from the smaller of the first item information upper bound and the first base item information when the first reference information indicates that benefit information decreases as the first item information increases;
determining the first target item information satisfying the preset target constraint within the item first preset item information interval from the first item information upper bound when the first reference information indicates that the benefit information increases as the first item information increases.
8. The method of claims 1-7,
and forming an information analysis report according to the first target article information.
9. An apparatus for generating information, comprising: the system comprises a basic information processing module and a first target article information generating module; wherein the content of the first and second substances,
the basic information processing module is used for acquiring first historical article information and second historical article information of an article to be predicted and determining first basic article information, second basic article information and first reference information of the article to be predicted;
the first target article information generating module is used for determining a first relation representation of second target article information and first target article information of the article to be predicted according to the first basic article information, the second basic article information and the first reference information;
and determining the first target article information meeting the preset target constraint in a first preset article information interval of the article according to the first relation representation and constraint parameters related to the preset target constraint.
10. A server that generates information, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-8.
11. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-8.
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