Note: Descriptions are shown in the official language in which they were submitted.
COMPUTER-IMPLEMENTED BIDDING METHOD, COMPUTER EQUIPMENT AND
STORAGE MEDIUM
TECHNICAL FIELD
The present invention relates to the field of bidding, and more particularly,
to a computer-
implemented bidding method, computer equipment and a storage medium.
BACKGROUND
In the trading market, a buyer or seller publishes the brand, specification,
delivery location,
delivery time, quantity, reserve price and other information of a
demanded/supplied commodity
through a spot bidding trading system. Eligible competitors increase or
decrease their prices on
their own, and in accordance with the principle of "price priority", a
transaction is completed
at the highest buying price or the lowest selling price within a specified
time. Both parties to
the transaction sign an electronic purchase/sale contract through the trading
market, and
perform physical delivery according to the contract. This trading method is
called bidding.
For example, in a purchase scenario, the purchaser hopes that the final price
is as low as
possible. On the contrary, in an auction scenario, the auctioneer hopes that
the final price is as
high as possible. However, there are many factors that affect bidding, such as
the type and
duration of bidding, which makes it hard to reach the final price expected by
the
purchaser/auctioneer.
SUMMARY
In order to solve the above-mentioned technical problems, an objective of the
present
invention is to provide a computer-implemented bidding method, computer
equipment and a
storage medium.
In order to achieve the above objective, the present invention adopts the
following
technical solutions:
A first aspect of the present invention provides a computer-implemented
bidding method,
including:
training a CatBoost regression model through a historical bidding data set,
where the
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historical bidding data set includes bidding configuration parameters as an
input of the model
and a difference between a first quote and a final quote as an output of the
model; and
inputting current basic bidding parameters into the trained CatBoost
regression model,
and outputting values of optimized bidding configuration parameters to
configure bidding rules
for bidding participants.
In a specific example, the bidding configuration parameters are X (xi, x2, x],
xN),
where 1 < i < N, N is the number of the bidding configuration parameters,
where,
Xi to x, are basic bidding parameters, which at least include information of
the bidding
participants and information of a bidding object; and
x,+1 to x-N are optimizable bidding parameters, which at least include a
bidding duration
and a change step size for each quote.
In a specific example, the information of the bidding participants includes
one or more of
the following: a number of the bidding participants, identity information of
each of the bidding
participants, a first quote offered by each of the bidding participants, and a
number of bidding
participants who do not offer a first quote; and
the information of the bidding object includes one or more of the following: a
name of the
bidding object, a bidding place, the number of bidding objects and a preset
budget.
In a specific example, the optimizable bidding parameters further include one
or more of
the following: a parameter for characterizing whether the bidding participants
are allowed to
see each other's rankings, a parameter for characterizing whether to allow a
delay in case of an
unexpected situation, a parameter for characterizing a time of the delay and a
parameter for
characterizing whether to display the lowest quote to the bidding
participants.
In a specific example, the step of training the CatBoost regression model
through the
historical bidding data set includes:
dividing the historical bidding data set A into a training set B and a test
set C, where A =
B U
C, and each of the training
set B and the test set C includes positive samples and negative
samples; and proportions of the positive samples to the negative samples in
the training set B
and the test set C are identical;
training the CatBoost regression model through the training set B; and
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verifying the CatBoost regression model through the test set C to obtain the
trained
CatBoost regression model.
In a specific example, the step of training the CatBoost regression model
through the
training set B includes:
evaluating weights of default parameters of the model to obtain a model
parameter matrix
by taking historical bidding configuration parameters as an input and a
corresponding
difference between a historical first quote and a historical final quote as an
output, where
parameters in the model parameter matrix are sorted in the descending order of
weights.
In a specific example, the model parameter matrix includes one or more of the
following:
a learning rate (learning rate), a maximum depth of a tree (max depth), a
maximum number
of decision trees (iterations), an L2 regularization coefficient (12 leaf
reg), a loss function
(loss function), a partition number of numerical features (border count) and a
partition number
of categorical features (ctr border count).
In a specific example, the step of verifying the CatBoost regression model
through the test
set C to obtain the trained CatBoost regression model includes:
inputting the test set C into the regression model defined according to the
model parameter
matrix to obtain a predicted difference; and
analyzing, based on a fitness value of the CatBoost regression model, a
relationship
between the predicted difference and a historical bidding difference to obtain
the trained
CatBoost regression model, where the fitness value is an area under the curve
(AUC), a mean
square error (MSE) or a square of a coefficient of determination (R).
In a specific example, the step of inputting the current basic bidding
parameters into the
trained CatBoost regression model, and outputting the values of the optimized
bidding
configuration parameters includes:
obtaining all combinations of values of the bidding configuration parameters
according to
the input current basic bidding parameters;
taking each of the combinations as an input of the trained CatBoost regression
model to
obtain a corresponding difference between a first quote and a final quote; and
comparing all corresponding differences between first quotes and final quotes
to obtain a
maximum difference, and obtaining values of the bidding configuration
parameters
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corresponding to the maximum difference to be output as the values of the
optimized bidding
configuration parameters.
A second aspect of the present invention provides a computer-implemented
bidding
method, which includes:
receiving the values of the optimized bidding configuration parameters
obtained
according to the method described in the first aspect of the present
invention; and
in response to a bidding organizer's configuration action based on the values
of the
optimized bidding configuration parameters, displaying options of the bidding
configuration
parameters on a bidding participant interface of a bidding system, where the
options only
include the values of the optimized bidding configuration parameters.
A third aspect of the present invention provides computer equipment, which
includes a
memory, a processor, and a computer program stored in the memory and
executable on the
processor. The computer program is executed by the processor to implement the
method
described in the first aspect of the present invention.
A fourth aspect of the present invention provides a computer-readable storage
medium,
which stores a computer program. The program is executed by a processor to
implement the
method described in the first aspect of the present invention.
A fifth aspect of the present invention provides computer equipment, which
includes a
bidding management system. The bidding management system is configured for:
receiving the values of the optimized bidding configuration parameters
obtained
according to the method described in the first aspect of the present
invention; and
displaying the bidding configuration parameters on a bidding participant
interface of a
bidding management system, where values of the bidding configuration
parameters are the
values of the optimized bidding configuration parameters.
A sixth aspect of the present invention provides a computer-readable storage
medium,
which stores a computer program. The computer program is executed by a
processor to
implement the method described in the second aspect of the present invention.
A seventh aspect of the present invention provides a bidding system, which
includes:
the computer equipment provided by the third aspect of the present invention;
and
the computer equipment provided by the fifth aspect of the present invention.
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The present invention has the following advantages.
In the technical solutions of the present invention, the CatBoost regression
model is
trained through the historical bidding data set. Then, the current basic
bidding parameters are
input into the trained CatBoost regression model to obtain the values of the
optimized bidding
configuration parameters output by the model. The bidding rules for the
bidding participants
are configured according to the values of the optimized bidding configuration
parameters, such
that an auctioneer or a purchaser completes the bidding at a favorable price.
BRIEF DESCRIPTION OF THE DRAWINGS
The specific implementations of the present invention will be further
described in detail
below with reference to the drawings.
FIG. 1 is a schematic diagram of a hardware architecture of a method according
to an
embodiment of the present application.
FIG. 2 is a flowchart of a computer-implemented bidding method according to an
embodiment of the present application.
FIG. 3 is a schematic diagram of a display interface for basic bidding
parameters among
bidding configuration parameters according to an embodiment of the present
application.
FIG. 4 is a schematic diagram of a display interface for optimizable bidding
parameters
among the bidding configuration parameters according to an embodiment of the
present
application.
FIG. 5 is a schematic diagram of a structure of a computer system for the
method
according to an embodiment of the present application.
DETAILED DESCRIPTION OF THE EMBODIMENTS
To explain the present invention more clearly, the present invention will be
further
described with reference to the preferred embodiments and drawings. The same
reference
numerals in the drawings represent the same parts. It should be understood by
those skilled in
the art that the following detailed description is intended to be
illustrative, rather than restrictive,
and the scope of protection of the present invention should not be limited
thereto.
An embodiment of the present invention is described by taking a purchase
scenario as an
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example.
In a formal purchase process, a purchaser needs to create a bidding item
through
commercial purchase management software, such as an Ariba purchase management
system.
Specifically, before the creation of the bidding item, the business personnel
of the purchaser
and multiple potential suppliers (bidding participants) contact about a
product and a first quote.
The lowest first quote is called a best first quote, which is an initial set
price for bidding.
Subsequently, the purchaser inputs account information (user name and
password) on
the user terminal 101 as shown in FIG. 1 to log into the purchase management
system in the
computer equipment 107 via the network 103, and inputs bidding information
(such as the
name of the product to be purchased), and supplier information (such as name,
bank account
and other information). In addition, the purchaser manually configures bidding
configuration
parameters, such as the type of purchase, and the specific value of bidding
duration. After these
parameters are configured, a bidding order is generated, and the system
automatically sends
the bidding order to corresponding suppliers. After receiving the bidding
order, the suppliers
log into the purchase management system through their respective accounts to
bid according
to a bidding date. The suppliers start bidding online from the first quote
(more preferably, the
best first quote) until the end of the bidding, and a supplier with the lowest
quote wins the bid.
In the manual setting process of the bidding configuration parameters, due to
the
numerous parameters, the purchase parameters are often not optimized,
resulting in the
company completing the purchase process at an excessively high price.
Embodiment 1
In view of this, this embodiment of the present invention provides a computer-
implemented bidding method. In this embodiment, the bidding method may be
implemented
through hardware architecture as shown in FIG. 1.
The computer equipment 105 is provided with a bidding configuration parameter
optimization model. In this embodiment, a CatBoost regression model
(CatBoostRegressor)
based on a deep learning (DL) framework is used to optimize bidding
configuration parameters.
The model provides the optimized configuration parameters to a purchaser via
the network 103,
and the purchaser sets values of the optimized configuration parameters in the
purchase
management system in the computer equipment 107.
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The network 103 is a medium for providing a communication link between the
user
terminal 101 and the computer equipment 105 and 107. The network 103 may have
various
connection types, for example, a wired or wireless communication link or a
fiber-optic cable.
The user terminal 101 may be various electronic apparatuses with a display
screen,
including but not limited to a smart phone, a tablet computer, a portable
laptop computer and a
desktop computer.
The computer equipment 105 and 107 may be any apparatuses having a processor
and a
memory, such as servers.
It should be noted that the quantities of the user terminal, the network, and
the computer
equipment shown in FIG. 1 are only illustrative. Any quantities of user
terminals, networks and
servers may be provided according to implementation requirements.
The present invention provides a computer-implemented bidding method. In a
specific
example, the type of the product to be purchased may be a physical product,
such as a digital
product, a teaching supply, a communication apparatus, furniture or a home
appliance, or a
service, such as a legal service, which is not limited herein.
As shown in FIG. 2, the method includes the following steps.
S10: a CatBoost regression model is trained through a historical bidding data
set, where
the historical bidding data set includes bidding configuration parameters as
an input of the
model and a difference between a first quote and a final quote as an output of
the model.
In order to use the CatBoost regression model to make a prediction, it needs
to be trained
first.
The CatBoost regression model may be trained using any suitable data set. In
this
embodiment, the CatBoost regression model is trained through the historical
bidding data set.
The historical bidding data set includes the bidding configuration parameters
as the input of
the model and the difference between the first quote (the lowest value of the
first quote is
adopted if existing) and the final quote (the lowest value of the final quote
is adopted if existing)
as the output of the model.
In a possible implementation, there are N bidding configuration parameters,
which are
defined in the form of an N-dimensional vector, X (xi, x2, x,,
xN), where 1 < i < N.
Specifically, xi to x, are basic bidding parameters, which at least include
information of a
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bidding object and information of bidding participants (suppliers).
In an example, the information of the bidding object includes one or more of
the following:
a name of the bidding object, a bidding place, the number of bidding objects
and a preset budget.
In an example, the information of the bidding participants includes one or
more of the
following: the number of the bidding participants, identity information of
each of the bidding
participants, a first quote offered by each of the bidding participants, and
the number of bidding
participants who do not offer a first quote.
Specifically, x,+1 to xN are optimizable bidding parameters, and, for example,
include a
bidding duration, and a change step size for each quote.
In a specific example, the historical bidding data may be acquired from the
Internet, for
example, by crawler technology.
However, the format of data acquired from the Internet is often irregular and
does not
conform to the format definition of the historical data. For example, the data
format in various
fields is inconsistent. In addition, these data, for example, may lack some
key information (such
as the final quote). Therefore, before training the model, the method further
includes cleaning
the acquired data.
Optionally, the missing information of each piece of historical data is
completed. In a
specific example, if the amount of missing information of certain historical
data exceeds half,
the historical data is deleted. If the amount of missing information of the
historical data does
not exceed half, the historical data is completed by using an average, a
median or a row/column
mode of the information of the historical data.
In another embodiment, the historical bidding data may be acquired from the
purchaser
who plans the purchase. For example, the purchaser transmits the historical
bidding data of
bidders participating in its past purchase project to the CatBoost model
through the user
terminal 101, so as to train the model. In this embodiment, the historical
data for training the
model is pertinent, especially in case that a bidding participant in the
planned purchase has also
participated in the bidding of the company's past purchase project. In this
way, through the
training of the model, the bidding habit of the bidding participant, such as
the step size for each
quote, can be acquired.
It should be noted here that the type of the bidding product in the historical
bidding data
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set is not limited to the same product (such as computer) to be purchased. It
may be other digital
product, teaching supply, communication apparatus, furniture or home
appliance. More
preferably, the historical bidding data set includes a historical bidding data
set of the bidding
participant on other occasions for the product (such as computer) to be
purchased.
In a specific example, 15000 pieces of historical bidding data are acquired to
train the
CatBoost regression model. The historical bidding data set includes, for
example, 15000 sets
of bidding configuration parameters. Each set of configuration parameters
includes a total of
40 common bidding parameters, such as bidding duration, and is expressed as a
40-dimensional
vector Xi (Xi, X2, X3, X4, X5, X5+1, ..., X40), where xi to xs are basic
bidding parameters, and x6 to
X40 are optimizable bidding parameters. A difference between a first quote and
a final quote
corresponding to N (xi, x2, x3, x4, x5, x5+1, ..., x40) is Y, where 1 < j <
15000.
The 15000 sets of X (xi, x2, x3, x4, xs, x5+1, ..., x40) are taken as an input
of the CatBoost
regression model, and the difference Yi between the first quote and the final
quote
corresponding to each set of X (xi, x2, x3, x4, xs, x5+1, ..., x40) is taken
as an output of the
CatBoost regression model, so as to obtain a trained CatBoost regression
model.
In a possible implementation, the step of training the CatBoost regression
model through
the historical bidding data set includes the following.
S100: the historical bidding data set A is divided into a training set B and a
test set C,
where A = B U C, and each of the training set B and the test set C includes
positive samples
and negative samples; and proportions of the positive samples to the negative
samples in the
training set B and the test set C are identical.
In a specific example, the historical bidding data sets are classified
according to the type
of product, and a data set A of each type of product is divided into a
training set B and a test
set C. For example, 75% of the data set A of each type of product is taken as
the training set B,
and 25% of the data set A of each type of product is taken as the test set B.
Each of the training
set B and the test set C includes positive samples and negative samples, and
the proportions of
the positive samples to the negative samples in the training set B and the
test set C are identical.
S105: the CatBoost regression model is trained through the training set B.
In a possible implementation, a model parameter matrix includes one or more of
the
following: a learning rate (learning rate), a maximum depth of a tree (max
depth), a maximum
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number of decision trees (iterations), an L2 regularization coefficient (12
leaf reg), a loss
function (loss function), a partition number of numerical features (border
count) and a
partition number of categorical features (ctr border count).
The analysis of a large amount of data shows that for different bidding
products and
different bidding participants with different bidding habits, the weights of
the parameters
included in the model parameter matrix are different. The order of importance
of the parameters
directly affects the prediction accuracy of the model. In view of this, in a
possible
implementation, the step of training the CatBoost regression model through the
training set B
includes the following.
For each type of product in the historical bidding data, weights of default
parameters of
the CatBoost model are evaluated to obtain a model parameter matrix regarding
the bidding
product by taking the corresponding historical bidding configuration
parameters as an input
and the corresponding difference between a historical first quote and a
historical final quote as
an output, where parameters in the model parameter matrix are sorted in a
descending order of
weights.
Similarly, for each of the bidding participants in the historical bidding
data, weights of
default parameters of the CatBoost model are evaluated to obtain a model
parameter matrix
regarding the bidding participant by taking the corresponding historical
bidding configuration
parameters as an input and the corresponding difference between a historical
first quote and a
historical final quote as an output, where parameters in the model parameter
matrix are sorted
in a descending order of weights.
Further, the model parameter matrices obtained by the above two methods are
merged to
obtain a model parameter matrix regarding the bidding participant and the
product, where the
parameters in the model parameter matrix are sorted in a descending order of
weights.
Specifically, in the embodiment of the present invention, R2 is used as a
weight ranking
optimization parameter of the CatBoost regression model. According to the
training data set,
the default values of the model parameters (such as the parameters included in
the model
parameter matrix) are used for calculating to obtain an inversion value. R2 is
calculated based
on a relationship between the inversion value as well as the predicted
difference and a historical
bidding difference. The values of the model parameters are then changed.The
importance of
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the model parameters increases as the change in R2 increases. The model
parameters are sorted
in the descending order of importance to construct an optimized model
parameter matrix for
subsequent calculations.
S110: the CatBoost regression model is verified through the test set C to
obtain the trained
CatBoost regression model.
In a possible implementation, the step of verifying the CatBoost regression
model through
the test set C to obtain the trained CatBoost regression model includes:
inputting the test set C into the regression model defined according to the
model parameter
matrix to obtain a predicted difference; and
analyzing, based on a fitness value of the CatBoost regression model, a
relationship
between the predicted difference and the historical bidding difference to
obtain the trained
CatBoost regression model, where the fitness value is an area under the curve
(AUC), a mean
square error (MSE) or a square of a coefficient of determination (R).
For example, taking R2 as an example, a threshold, such as 0.9, may be set.
Then the value
of R2 is calculated, and if the value of R2 exceeds 0.9, it is considered that
the CatBoost
regression model is well trained, and a CatBoost regression model with the
optimized model
parameters is obtained.
520: Current basic bidding parameters are input into the trained CatBoost
regression
model, and values of optimized bidding configuration parameters are output to
configure
bidding rules for bidding participants.
In an embodiment, this step includes:
S200: all combinations of values of the bidding configuration parameters are
obtained
according to the input current basic bidding parameters.
In a specific example, a purchaser needs to purchase 10 computers in the Asia
Pacific
(APAC) region. The purchaser inputs current basic bidding parameters on a
visual interactive
interface of the model (as shown in FIG. 3): name of bidding object: computer;
bidding place:
APAC; number of bidding participants: 5; number of bidding participants who do
not offer a
first quote: 2; number of bidding object: 10; first quote: 50000; and preset
budget: 45000.
The staff of the purchaser clicks a submit button to send the current basic
bidding
parameters to the CatBoost model.
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The combinations of the values of these 7 basic parameters and the remaining
33
parameters among the above 40 parameters except these 7 basic parameters are
enumerated. In
other words, the values of the 7 basic parameters are determined, but each of
the remaining 33
parameters has multiple options of values. For example, to characterize
whether the bidding
process allows a delay, there are two options, i.e., yes/no. To characterize
the delay time, there
are more options of values. To characterize whether bidding participants are
allowed to see
each other's rankings, there are two options, i.e., yes/no. To characterize
how multiple bidding
projects proceed, there are three options, i.e., parallel, serial and
alternate. To characterize
whether a bidding delay is allowed, there are two options, i.e., yes/no. To
characterize the
ranking of bidding participants allowed to trigger a delay, for example, if it
is set to 3, the top
3 bidding participants are allowed to trigger a delay To characterize the
delay time, for example,
it may be recorded in minutes. To characterize how long a delay is allowed
before the end of
the bidding, for example, it may be recorded in minutes. If a bidding
participant ranked within
the ranking of bidding participants allowed to trigger a delay makes a quote
within this time
period or a new quote appears within this time period, the delay is triggered
and the delay lasts
for the above delay time. To characterize whether to display leading quotes to
all participants,
there are two options, i.e., yes/no. The options of the values of these 33
parameters are selected,
such that a variety of combinations including the values of the 40 bidding
configuration
parameters are formed.
5205: the trained CatBoost regression model outputs differences between
corresponding
first quotes and predicted final quotes based on the combinations.
S210: all the differences between the corresponding first quotes and predicted
final quotes
are compared to obtain a maximum difference, and obtain values of the bidding
configuration
parameters corresponding to the maximum difference to be output as the values
of the
optimized bidding configuration parameters.
As shown in FIG. 4, the optimized configuration parameters are displayed
(partly) on the
interactive interface of the model as follows:
Whether bidding participants are allowed to see each other's rankings: No;
Bidding duration: 20 minutes;
Step size for each quote: 10%;
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How multiple bidding projects proceed: Serial;
Whether the bidding process allows a delay: Yes;
Ranking of bidding participants allowed to trigger a delay: 3;
Delay time: 5 minutes;
How long a delay is allowed before the end of bidding: 6 minutes; and
Whether to display leading quotes to all participants: Yes.
For example, the staff of the purchaser may export an Excel format file
including the
values of the optimized configuration parameters by clicking an output button
on the interactive
interface.
In the technical solutions of the present invention, the CatBoost regression
model is
trained through the historical bidding data set. Then, the current basic
bidding parameters are
input into the trained CatBoost regression model to obtain the values of the
optimized bidding
configuration parameters output by the model. The bidding rules for the
bidding participants
are configured according to the values of the optimized bidding configuration
parameters, such
that the purchaser completes the bidding at a favorable price.
Embodiment 2
The present invention provides a computer-implemented bidding method, which is
implemented by the computer equipment 107 shown in FIG. 1, and includes the
following.
S30: the values of the optimized bidding configuration parameters obtained
according to
the method of Embodiment 1 are received.
In a specific example, the staff of the purchaser imports the aforementioned
Excel table
into the Ariba purchase management system in the computer equipment 107, and
inputs the
basic bidding parameters into the purchase management system through the
interactive
interface (not shown).
S32: bidding configuration parameters are displayed on a bidding participant
interface of
a purchase management system, where values of the bidding configuration
parameters are the
values of the optimized bidding configuration parameters.
In this embodiment, the bidding participants participate in the bidding
according to
prescribed bidding rules. For example, bidding duration: 20 minutes; step size
for each quote:
10%; the bidding participants are not allowed to see each other's rankings;
multiple bidding
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projects proceed in series; the top 3 bidding participants are allowed to
delay 5 minutes in the
last 6 minutes of the 20 minutes; and all participants are allowed to see
leading quotes. Finally,
a bidding participant with the lowest quote wins the bid.
In the technical solutions of the present invention, the CatBoost regression
model is
trained through the historical bidding data set. Then, the current basic
bidding parameters are
input into the trained CatBoost regression model to obtain the values of the
optimized bidding
configuration parameters output by the model. The bidding rules for the
bidding participants
are configured according to the values of the optimized bidding configuration
parameters, such
that the purchaser completes the bidding at a favorable price.
Embodiment 3
The computer equipment 105 and 107 shown in FIG. 1 may include the
architecture shown
in FIG. 5, which is configured to implement the computer-implemented bidding
methods
provided by Embodiments 1 and 2, respectively. The architecture includes a
central processing
unit (CPU), which performs various suitable actions and processing according
to a program
stored in a read-only memory (ROM) or a program loaded from a storage part to
a random
access memory (RAM). Various programs and data required for operations of a
computer
system are further stored in the RAM. The CPU, the ROM and the RAM are
connected to each
other through a bus. An input/output (I/O) interface is also connected to the
bus.
The following components are connected to the I/O interface: an input part
including a
keyboard, a mouse and others.; an output part including a liquid crystal
display (LCD), a
loudspeaker and others; the storage part including a hard disk and others; and
a communication
part including a network interface card such as a local area network (LAN)
card or a modem.
The communication part performs communication processing through a network
such as the
Internet. A drive is also connected to the I/O interface as needed. A
removable medium, such
as a magnetic disk, an optical disk, a magneto-optical disk, or a
semiconductor memory, is
provided on the drive as needed, such that a computer program read therefrom
is installed into
the storage part as needed.
Particularly, according to this embodiment, the process described above with
reference to
the flowchart may be implemented as a computer software program. For example,
this
embodiment includes a computer program product including a computer program
tangibly
CA 03139962 2021-11-29 14
carried by a computer-readable medium. The computer program includes a program
code for
executing the method shown in the flowchart. In this embodiment, the computer
program may
be downloaded from a network through the communication part and installed,
and/or be
installed from the removable medium.
The flowcharts and schematic diagrams in the drawings illustrate the
architecture,
functions and operations of possible implementations of the system, method,
and computer
program product in this embodiment. Each block in the flowchart or schematic
diagram may
represent a module, a program segment or a part of code, and the module, the
program segment
or the part of code includes one or more executable instructions used to
implement a specified
logical function. It should also be noted that, in some alternative
implementations, the functions
marked in the blocks may occur in a different order from that marked in the
drawings. For
example, two successively shown blocks actually may be executed in parallel
substantially, or
may be executed in reverse order sometimes, depending on the functions
involved. It should
also be noted that each block in the flowchart and/or schematic diagram and
combinations of
the blocks in the flowchart and/or schematic diagram may be implemented by a
dedicated
hardware-based system for executing specified functions or operations, or may
be implemented
by a combination of dedicated hardware and computer instructions.
Embodiment 4
As another aspect, this embodiment further provides a non-volatile computer
storage
medium. The non-volatile computer storage medium may be included in the
apparatus of the
above embodiment, or may separately exist without being installed in the
terminal. One or
more programs are stored in the non-volatile computer storage medium When the
one or more
programs are executed by the apparatus shown in FIG. 5, the computer-
implemented bidding
method provided in Embodiment 1 or Embodiment 2 is implemented.
Those skilled in the art should understand that although the above embodiments
are
described in a purchase scenario, the teachings of the present invention may
obviously be used
in other bidding scenario, such as auction. In the auction scenario, the final
price is the highest
price, and the purchase system is replaced with a commercial auction system.
It should be noted that, in the description of the present invention,
orientations or position
relationships indicated by terms such as "upper" and "lower" are described
based on the
CA 03139962 2021-11-29 15
orientations or position relationships shown in the drawings. These terms are
merely used to
facilitate and simplify the description, rather than to indicate or imply that
the mentioned
apparatus or elements must have a specific orientation and must be constructed
and operated
in a specific orientation. Therefore, these terms should not be understood as
a limitation to the
present invention. Unless otherwise clearly specified and limited, terms such
as "mounted",
"connected with", and "connected to" should be understood in a broad sense.
For example, a
connection may be a fixed connection, a detachable connection, or an
integrated connection; it
may be a mechanical connection, or an electrical connection; it may be a
direct connection, an
indirect connection via an intermediate medium, or an intercommunication
between two
components. Those having ordinary skill in the art may understand specific
meanings of the
above terms in the present invention based on a specific situation.
It should be noted that, in the description of the present invention,
relational terms such
as first and second are merely used to distinguish one entity or operation
from another entity
or operation without necessarily requiring or implying any actual such
relationship or order
between such entities or operations. In addition, terms "include/comprise",
"contain", or any
other variations thereof are intended to cover a non-exclusive inclusion, so
that a process, a
method, an article, or an apparatus including a series of elements not only
includes those
elements, but also includes other elements that are not explicitly listed, or
also includes inherent
elements of the process, the method, the article, or the apparatus. In case
there are no more
restrictions, an element limited by the statement "include/comprise a ..."
does not exclude the
presence of additional identical elements in the process, the method, the
article, or the apparatus
that includes the element.
Apparently, the above embodiments of the present invention are merely
illustrative of the
present invention, rather than to limit the implementations of the present
invention. Those
skilled in the art may make changes or variations in other forms based on the
above description.
It is not possible to give an exhaustive list of all the implementations
herein, but all obvious
changes and variations derived from the technical solutions of the present
invention shall fall
within the scope of protection of the present invention.
CA 03139962 2021-11-29 16
