Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
CA 03159785 2022-05-02
DATA PARSING METHOD BASED ON REGIONALIZED MEMBERSHIP
MARKETING SCENE, SYSTEM AND COMPUTER EQUIPMENT
BACKGROUND OF THE INVENTION
Technical Field
[0001] The present invention relates to the field of e-commerce technology,
and more
particularly to a data parsing method based on a regionalized membership
marketing
scene, and corresponding system and computer equipment.
Description of Related Art
[0002] In the traditional retail industry, entity stores accumulate great
quantities of consumer
groups by the mode of recruiting members, and carry out various marketing
activities by
periodically pushing commodity information.
[0003] With the development of the e-commerce, the 020 (Online to Offline)
pattern has
become increasingly mature. 020 is a commercial business pattern that combines
online
transactions based on commodities or services of e-commerce websites with
actual
experiences based on commodities or services of entity stores, enabling the e-
commerce
websites to become the forestage of entity store transactions, and enabling
the entity
stores to become the backstage of e-commerce website transactions.
[0004] Over the recent years, online consumption platforms have been growing
increasingly,
companies that were originally deep-rooted in offline entity stores are
joining in the rank
of e-commerce one after the other. Rich and variegated online marketing means
have
been incessantly broadening the online membership groups. If online members
are
merged with offline entity store members, merchants will be facilitated to
know of the
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population already consumed around entity stores and potential population to
consume,
and integrated marketing will be made easier.
[0005] Patent No. 201710944600.7 has made public a transaction data processing
method, and
device and system thereof. In a scene where a user purchases a commodity at an
entity
store (namely an offline shopping scene), transaction data is generated for
the offline
commodity with the online identification of the user on a third-party/online
transaction
server, or transaction data is generated for the offline commodity with the
online price of
the offline commodity, and the transaction data is synchronized to the store
terminal and
the user terminal respectively, whereby is realized an online to offline
transaction data
processing mode, and it is made possible to utilize the online advantages to
bring about
conveniences to such aspects as administration, maintenance and manipulation
of offline
transactions, to enhance competitiveness of entity stores under the e-commerce
environment, and to promote development of the entity stores. However, this
patent fails
to address exact administration of offline entity stores, and also fails to
precisely associate
online members with offline entity stores through addresses.
SUMMARY OF THE INVENTION
[0006] The technical problem to be solved by the present invention is to
provide a data parsing
method based on a regionalized membership marketing scene to realize precise
association of online members with offline entity stores through positional
information.
[0007] Technical solutions for achieving the objective of the present
invention are as follows.
There is provided a data parsing method based on a regionalized membership
marketing
scene, and the method comprises the following steps:
[0008] so partitioning a target region into grids according to a distribution
of entity stores of
merchants that each entity store corresponds to a grid, and there is no
overlapping
between two adjacent grids;
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[0009] constructing a store information database;
[0010] constructing a user information database; and
[0011] matching address information generated by a user-triggered online
behavior, obtaining
entity store information to which a region where the user resides corresponds,
and
assigning the user to a valid marketing region of entity stores through the
online behavior
of the user.
[0012] Preferably, partitioning a target region into grids specifically
includes:
[0013] constructing radiant regions with a geographical location where the
entity stores locate as
a center, wherein the radiant regions assume a closed polygon, the radiant
regions of two
adjacent entity stores are not repetitive to each other, closed polygonal
regions
respectively constructed by all the entity stores in the target region
together constitute a
net, and partitioning of the target region into grids is realized.
[0014] Preferably, the store information database includes:
[0015] merchant entity store region geographical location information, which
includes longitude
and latitude data to which a store geographical location center point
corresponds and
longitude and latitude information of radiant region boundaries, and which is
recorded in
an online mapping tool at the same time;
[0016] entity store coding information, including numbers of the entity
stores, each entity store
having a unique entity store number; and
[0017] grid coding information, including numbers of the grids, each grid
having a unique grid
number;
[0018] wherein the above pieces of information correspond to one another.
[0019] Preferably, the user information database includes:
[0020] user address information, including longitude and latitude information
to which the
address information corresponds;
[0021] entity store coding information, including numbers of the entity
stores, each entity store
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having a unique entity store number; and
[0022] grid coding information, including numbers of the grids, each grid
having a unique grid
number; wherein
[0023] the above pieces of information correspond to one another.
[0024] Preferably, the user information database is classified into two types,
wherein one type is
a historical database that includes address information generated by
historical user
behaviors, entity store coding information and grid coding information;
[0025] the other type is an incremental database that includes address
information generated by
incremental user behaviors, entity store coding information and grid coding
information.
[0026] Preferably, matching address information generated by a user-triggered
online behavior
specifically includes:
[0027] performing first matching on the generated address information in the
user information
database, wherein the first matching succeeds if there is consistent address
information,
and simultaneously obtaining the entity store coding information and the grid
coding
information from the user information database;
[0028] otherwise, the first matching fails, thereafter obtaining the longitude
and latitude
information to which the address information corresponds by invoking the
online
mapping tool;
[0029] performing secondary matching on the longitude and latitude information
in the store
information database; wherein
[0030] the secondary matching succeeds if the longitude and latitude are
within the radiant region
of the entity store, simultaneously obtaining the entity store coding
information and the
grid coding information from the store information database, and inserting
relevant
information as incremental data into the incremental database of the user
information
database;
[0031] otherwise, the secondary matching fails, sending out reminder
information.
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[0032] Preferably, a target region judging step is further included to judge
whether the address
information generated by a user-triggered online behavior is in the target
region after the
first matching and/or the secondary matching have/has failed.
[0033] There is provided a data parsing system based on a regionalized
membership marketing
scene, and the system comprises:
[0034] a target region grid-partitioning module, for so partitioning a target
region into grids
according to a distribution of entity stores of merchants that each entity
store corresponds
to a grid, and there is no overlapping between two adjacent grids;
[0035] a store information database;
[0036] a user information database; and
[0037] an information matching module, for matching address information
generated by a user-
triggered online behavior, obtaining entity store information to which a
region where the
user resides corresponds, and assigning the user to a valid marketing region
of offline
stores through the online behavior of the user.
[0038] In comparison with prior-art technology, the present invention achieves
the following
apparent advantages: 1) the present invention locates offline address
information of the
user through online behaviors of the user (such as access, purchase, etc.),
converts the
text-type address to numerical value-type longitude and latitude, converts the
store valid
marketing range in the sense of businesses to grid longitude and latitude
array values to
which stores correspond in the sense of digits, and precisely associate online
members
with offline entity stores through addresses, whereby is made possible to more
exactly
administer entity stores by grids, and to facilitate the execution of
subsequent marketing
activities; 2) the present invention digitally establishes precise relation
between online
users and offline stores, and achieves the objective of merging online and
offline members,
whereby is made possible to carry out integrated online and offline marketing
on the
members, to further realize 020 fusion, and to better adapt to smart retail;
3) the present
invention locates offline address information of the user by obtaining address
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data generated by online behaviors of the user, so as to actually associate
the online user
with offline stores through the address in terms of geographical location, to
achieve the
objective of exactly assigning the online user within a range of marketable
regions of the
offline stores through the association of positional information data, whereby
is realized
gridded administration of sales regions.
[0039] The present invention will be described in greater detailed below in
conjunction with
accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0040] Fig. 1 is an overall flowchart illustrating the data parsing method
based on regionalized
membership marketing scene in the present invention;
[0041] Fig. 2 is a flowchart illustrating the step of matching address
information generated by a
user-triggered online behavior in the present invention;
[0042] Fig. 3 is a view schematically illustrating the framework of the data
parsing system based
on regionalized membership marketing scene in the present invention; and
[0043] Fig. 4 is a view illustrating embodiments of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0044] In conjunction with Fig. 1, the data parsing method based on
regionalized membership
marketing scene of the present invention comprises the following steps:
[0045] so partitioning a target region into grids according to a distribution
of entity stores of
merchants that each entity store corresponds to a grid, and there is no
overlapping
between two adjacent grids;
[0046] constructing a store information database;
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[0047] constructing a user information database; and
[0048] matching address information generated by a user-triggered online
behavior, obtaining
entity store information to which a region where the user resides corresponds,
and
assigning the user to a valid marketing region of entity stores through the
online behavior
of the user.
[0049] Partitioning a target region into grids specifically includes:
[0050] constructing radiant regions centering on a geographical location where
the entity stores
locate, wherein each of the radiant regions assume a closed polygon, the
radiant regions
of two adjacent entity stores are non-overlapping with each other, closed
polygonal
regions respectively constructed by all the entity stores in the target region
together
constitute a net, thereby partitioning of the target region into grids is
realized. It is
specifically possible to base on the circumstances of geographical location
where the
stores locate and the surrounding communities to partition the radiant regions
to which
the stores correspond, the radiant region is actually a closed polygon, plural
polygons are
joined to form a net, such radiant regions are referred to as grids,
overlapping should be
avoided in the partitioning into grids, the various grids should not overlap
one another
and one grid can only correspond to one store. When the radiant regions are
constructed,
partitioning can be made in conjunction with the consumption circumstance of
the local
regions and the population distribution circumstance, as long as it can be
ensured that the
grids formed by all the entity stores can cover the entire target region. With
respect to
stores with stronger sales capabilities, the radiant regions can be adequately
enlarged,
with respect to stores with weaker sales capabilities or with respect to new
stores, the
radiant regions can be adaptively shrunk. The target region is a region which
the merchant
prepares to cover by sales, and can be a country, a province, a city or a
county, etc.
[0051] The store information database includes:
[0052] merchant entity store region geographical location information, which
includes longitude
and latitude data to which a store geographical location center point
corresponds and
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longitude and latitude information of radiant region boundaries, and which is
recorded in
an online mapping tool at the same time; the online mapping tool is such an
existent
online map as Amap map, Baidu map, Beidou map, etc.;
[0053] entity store coding information, including numbers of the entity
stores, each entity store
having a unique entity store number; the arranging method of store numbers can
be
determined by the merchant itself, as long as it is ensured that one entity
store corresponds
to one number; and
[0054] grid coding information, including numbers of the grids, each grid
having a unique grid
number.
[0055] The merchant entity store region geographical location information, the
entity store
coding information and the grid coding information correspond to one another
on a one-
to-one basis, and the other two pieces of information can be matched out
through one
piece of information therefrom.
[0056] The user information database includes:
[0057] user address information, including longitude and latitude information
to which the
address information corresponds;
[0058] entity store coding information, including numbers of the entity
stores, each entity store
having a unique entity store number; and
[0059] grid coding information, including numbers of the grids, each grid
having a unique grid
number;
[0060] the user address information, the entity store coding information and
the grid coding
information correspond to one another on a one-by-one basis, and the other two
pieces of
information can be matched out through one piece of information therefrom. The
user
information database functions to accelerate matching speed and shorten
matching time.
[0061] The user information database is classified into two types, one type is
a historical database
that includes address information generated by historical user behaviors,
entity store
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coding information and grid coding information. The historical user behaviors
indicate
address information previously input by the user while purchasing commodities
or
browsing commodities, and the format thereof is usually province I city I
district I street
I community I house number. One user can correspond to plural pieces of
address
information.
[0062] The other type is an incremental database that includes address
information generated by
incremental user behaviors, entity store coding information and grid coding
information,
including address information generated by new users, and also including new
address
information data added by existent users.
[0063] The merchant entity store region geographical location information, the
entity store
coding information and the grid coding information in the store information
database
have one-to-one correspondence relations. Each piece of merchant entity store
region
geographical location information only corresponds to one piece of entity
store coding
information, and also only corresponds to one piece of grid coding information
at the
same time.
[0064] Matching address information generated by a user-triggered online
behavior specifically
includes:
[0065] performing first matching on the generated address information in the
user information
database, wherein the first matching succeeds if there is consistent address
information,
and simultaneously obtaining the entity store coding information and the grid
coding
information from the user information database;
[0066] otherwise, the first matching fails, thereafter obtaining the longitude
and latitude
information to which the address corresponds by invoking the online mapping
tool;
[0067] performing secondary matching on the longitude and latitude information
in the store
information database; wherein
[0068] the secondary matching succeeds if the longitude and latitude are
within the radiant region
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of the entity store, simultaneously obtaining the entity store coding
information and the
grid coding information from the store information database, and inserting
relevant
information as incremental data into the incremental database of the user
information
database;
[0069] otherwise, the secondary matching fails, sending out reminder
information.
[0070] During the matching process, first matching is firstly performed in the
user information
database, the online mapping tool is invoked only after the first matching has
failed to
obtain longitude and latitude information to which the address corresponds,
and the
longitude and latitude information is used to perform secondary matching in
the store
information database. Matching time can thus be greatly shortened, and entity
store
related information to which the address information generated by the user-
triggered
online behavior corresponds can be quickly obtained.
[0071] With reference to Fig. 2, performing secondary matching on the
longitude and latitude
information in the store information database is specifically as follows:
[0072] if the longitude and latitude are within the radiant region of the
entity store (the longitude
and latitude are within the longitude and latitude range of the radiant region
boundaries,
in other words, the address is located within the radiant range of the entity
store), the
matching succeeds, the entity store coding information and the grid coding
information
are simultaneously obtained from the store information database; since the
merchant
entity store region geographical location information, the entity store coding
information
and the grid coding information in the store information database have one-to-
one
correspondence relations, through the aforementioned longitude and latitude
information
can be obtained the one unique piece of entity store coding information and
the one
unique piece of grid coding information, and the relevant information is taken
as
incremental data to be inserted into the incremental database of the user
information
database, to enlarge data in the database, and to facilitate the next quick
matching. If the
longitude and latitude are not within the radiant region of the entity store,
the secondary
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matching fails, and reminder information is sent out.
[0073] A target region judging step is further included to judge whether the
address information
generated by a user-triggered online behavior is in the target region after
the first
matching and/or the secondary matching have/has failed.
[0074] Target region judgement is performed after the first matching has
failed, or after the
secondary matching has failed, or after the first matching and the secondary
matching
have both failed. In practice, when newly added address information exceeds
the radiant
region of the entity store, for instance, the target region serviced by a
merchant is within
the geographical range of Province A, but a newly added address is Province C,
the range
radiated by all the entity stores of the merchant is exceeded, it is required
at this time to
send out reminder information for processing by the backstage.
[0075] Preferably, target region judgment is performed after the secondary
matching has failed,
to judge whether the address information generated by an online platform
member is in
the target region, if not, corresponding information record is null, and
reminder
information is sent out; if yes, the matching step is executed again, and a
new round of
matching is performed.
[0076] The present invention locates offline address information of the user
through online
behaviors of the user (such as access, purchase, etc.), converts the text-type
address to
numerical value-type longitude and latitude, converts the store valid
marketing range in
the sense of businesses to grid longitude and latitude array values to which
stores
correspond in the sense of digits, and precisely associate online members with
offline
entity stores through addresses, whereby is made possible to more exactly
administer
entity stores by grids, and to facilitate the execution of subsequent
marketing activities.
[0077] With reference to Fig. 3, there is provided a data parsing system based
on a regionalized
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membership marketing scene, and the system comprises:
[0078] A target region grid-partitioning module that is employed for so
partitioning a target
region into grids according to a distribution of entity stores of merchants
that each entity
store corresponds to a grid, and there is no overlapping between two adjacent
grids;
partitioning a target region into grids specifically includes:
[0079] constructing radiant regions centering on a geographical location where
the entity stores
locate, wherein the radiant regions assume a closed polygon, the radiant
regions of two
adjacent entity stores are non-overlapping with each other, closed polygonal
regions
respectively constructed by all the entity stores in the target region
together constitute a
net, and partitioning of the target region into grids is realized.
[0080] A store information database that includes:
[0081] merchant entity store region geographical location information, which
includes longitude
and latitude data to which a store geographical location center point
corresponds and
longitude and latitude information of radiant region boundaries, and which is
recorded in
an online mapping tool at the same time;
[0082] entity store coding information, including numbers of the entity
stores, each entity store
having a unique entity store number; and
[0083] grid coding information, including numbers of the grids, each grid
having a unique grid
number.
[0084] A user information database that includes:
[0085] user address information, including longitude and latitude information
to which the
address information corresponds;
[0086] entity store coding information, including numbers of the entity
stores, each entity store
having a unique entity store number; and
[0087] grid coding information, including numbers of the grids, each grid
having a unique grid
number; wherein
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[0088] the above pieces of information correspond to one another.
[0089] The user information database is classified into two types, one type is
a historical database
that includes address information generated by historical user behaviors,
corresponding
entity store region geographical location information, entity store coding
information and
grid coding information;
[0090] the other type is an incremental database that includes address
information generated by
incremental user behaviors, corresponding entity store region geographical
location
information, entity store coding information and grid coding information.
[0091] An information matching module that is employed for matching address
information
generated by a user-triggered online behavior, obtaining entity store
information to which
a region where the user resides corresponds, and assigning the user to a valid
marketing
region of offline stores through the online behavior of the user.
[0092] Matching address information generated by a user-triggered online
behavior specifically
includes:
[0093] performing first matching on the generated address information in the
user information
database, wherein the first matching succeeds if there is consistent address
information,
and simultaneously obtaining the entity store coding information and the grid
coding
information from the user information database;
[0094] otherwise, the first matching fails, thereafter obtaining the longitude
and latitude
information to which the address corresponds by invoking the online mapping
tool;
[0095] performing secondary matching on the longitude and latitude information
in the store
information database; wherein
[0096] the secondary matching succeeds if the longitude and latitude are
within the radiant region
of the entity store, simultaneously obtaining the entity store coding
information and the
grid coding information from the store information database, and inserting
relevant
information as incremental data into the incremental database of the user
information
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database;
[0097] otherwise, the secondary matching fails, sending out reminder
information.
[0098] The system further comprises a target region judging module for judging
whether address
information generated by an online platform member is in the target region, if
not, sending
out reminder information, if yes, invoking the information matching module to
perform
anew round of matching.
[0099] There is provided a computer equipment that comprises a memory, a
processor and a
computer program stored on the memory and operable on the processor, and the
following
steps are realized when the processor executes the computer program:
[0100] so partitioning a target region into grids according to a distribution
of entity stores of
merchants that each entity store corresponds to a grid, and there is no
overlapping
between two adjacent grids;
[0101] constructing a store information database;
[0102] constructing a user information database; and
[0103] matching address information generated by a user-triggered online
behavior, obtaining
entity store information to which a region where the user resides corresponds,
and
assigning the user to a valid marketing region of entity stores through the
online behavior
of the user.
[0104] There is provided a computer-readable storage medium storing a computer
program
thereon, and the following steps are realized when the computer program is
executed by
a processor:
[0105] so partitioning a target region into grids according to a distribution
of entity stores of
merchants that each entity store corresponds to a grid, and there is no
overlapping
between two adjacent grids;
[0106] constructing a store information database;
[0107] constructing a user information database; and
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[0108] matching address information generated by a user-triggered online
behavior, obtaining
entity store information to which a region where the user resides corresponds,
and
assigning the user to a valid marketing region of entity stores through the
online behavior
of the user.
[0109] The present invention digitally establishes precise relation between
online users and
offline entity stores, and achieves the objective of merging online and
offline members,
whereby is made possible to carry out integrated online and offline marketing
on the
members, to further realize 020 fusion, and to better adapt to smart retail.
[0110] The present invention is described in greater detailed below in
conjunction with specific
steps.
[0111] A data parsing method based on a regionalized membership marketing
scene comprises
the following steps:
[0112] Step 1 ¨ store data preparation, in which longitude and latitude data
to which various
entity store numbers of the merchant and various entity store geographical
location center
points correspond is recorded in the store information database, and input in
the online
mapping tool;
[0113] Step 2 ¨ target region grid-partition, in which radiant regions to
which the stores
correspond are partitioned according to circumstances of the geographical
location where
the stores locate and the surrounding communities, the radiant region is
actually a closed
polygon, plural polygons are joined like a net, so the radiant regions are
referred to as
grids, overlapping should be avoided in the partitioning into grids, the
various grids
should not overlap one another and one grid can only correspond to one store;
[0114] during grid-partitioning, a corresponding store is firstly selected or
input, the
geographical location of the store is located by the online mapping tool
according to
longitude and latitude of the store, a polygon is drawn around the store
according to the
sales coverage region, the drawing result is thereafter submitted. The system
locates the
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longitude and latitude values of the various endpoints of the polygon, and
records the
corresponding store number, longitude and latitude value array data, and grid
number
(these can be automatically generated by the system according to a certain
coding
specification) in the store information database.
[0115] Step 3 ¨ address data preparation, in which, in order to prevent the
same address from
being repetitively obtained by invoking the online mapping tool, and to
prevent the
possible circumstances of delays and stutters returning from external
platforms during the
process of real-time invoking, longitude and latitude values to which existent
addresses
correspond are disposed in advance in the user information database.
[0116] 1 Stock data preparation: address information of platform members is
obtained, the format
thereof is usually province I city I district I street I community I house
number, the
online mapping tool is invoked to obtain longitudes and latitudes to which the
various
addresses correspond, and correspondence relations between the addresses and
the
longitudes and latitudes are disposed in the historical database for standby
subsequent
matching with address data generated by user behaviors.
[0117] 2Incremental data preparation: if a newly added address is generated by
user behaviors,
address data after maintenance is obtained in real time, the online mapping
tool is invoked
in real time to obtain the longitude and latitude to which the address
corresponds, and the
same is inserted as incremental data into the incremental database.
[0118] Step 4 ¨ user behavior address longitude and latitude matching, in
which, after address
information has been generated by a user-triggered online behavior (for
instance, an
address where the user resides will be located after a user accessing behavior
has been
authorized, and a receiving address of the user will be obtained after a user
purchasing
behavior has occurred), addresses disposed in the user information database
are matched
according to the address of the user behavior, to obtain longitude and
latitude values to
which the address of the user behavior corresponds, and the entity store
coding
information and grid coding information. If no relevant information is matched
out, step
is executed.
[0119] Step 5 ¨ after the longitude and latitude values to which the address
of the user behavior
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corresponds have been obtained, the longitude and latitude values are
secondarily
matched in the store information database according to relevant tool assembly
(such as
the IsPtInPoly static method of JAVA) to obtain a grid number to which the
corresponding
array corresponds.
[0120] Since one grid number corresponds to one store number, the
corresponding store number
can be remapped via the grid number. Accordingly, the user is assigned to the
valid
marketing region of offline stores through the online behavior of the user,
whereby are
authentically achieved the objectives of merging online and offline members
and refined
and gridded administration.
[0121] The present invention is described in greater detail below in
conjunction with an
embodiment.
[0122] Embodiment
[0123] A purchasing behavior generated by a user is taken for example in this
embodiment, with
reference to Fig. 4, the following steps are specifically included:
[0124] 51. The store number to which store A corresponds is A001, the
longitude and latitude to
which the store central position corresponds are (32.1, 118.4), the store
number to which
store B corresponds is A002, the longitude and latitude to which the store
central position
corresponds are (32.5, 118.7), a sheet of correspondence relation table is
established to
record mapping relations between the store numbers and the longitudes and
latitudes, and
this table is marked as Table 1.
[0125] Table 1
[0126]
Store Number Store Latitude Store Longitude
A001 32.1 118.4
A002 32.5 118.7
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[0127] S3. Grids are drawn around store A via the online mapping tool
according to the actual
sales region of the store, longitudes and latitudes of the various endpoints
of the grids
constitute a longitude and latitude array, and the correspondence relations
are stored as
Table 2.
[0128] Table 2
[0129]
Store Number Grid Latitude Gird Longitude Grid
Number
A001 31.8 116.4 W001
A001 32.6 116.1 W001
A001 32.5 120.7 W001
A001 31.3 119.4 W001
[0130] S4. There is address A in an information sheet of frequently used
delivery addresses of a
certain user, the corresponding longitude and latitude are (32.2, 118.6),
these fall within
the grid range of store A, the mapping relation between address A and store
A001 is
recorded in a datasheet, which is marked as Table 3.
[0131] Table 3
[0132]
Address Address Address Grid to which Store to
which
Latitude Longitude Address
Address
Corresponds
Corresponds
A 32.2 118.6 W001 A001
[0133] S5. A certain user generates two orders, one is an order of address A,
another is an order
of address B, and address B is not in Table 3. The two order addresses are
then matched
with Table 3, the order of address A is matched with store A001, the order of
address B
cannot be matched, address B is then transmitted to the online mapping tool to
obtain the
longitude and latitude to which address B corresponds, it is parsed that the
longitude and
18
Date recue/date received 2022-05-02
CA 03159785 2022-05-02
latitude fall within the grid range of store B, the mapping relation between
address B and
store A002 is then inserted into Table 3 and the store number A002 is marked
on the order
of address B, processing of the detailed datasheet is thus completed.
[0134] The number of online buyers and offline buyers, etc., to which store
A001 corresponds
can be subsequently obtained by clustering or summarizing the various orders,
and
relevant attributes and preferences of these buyers can be checked.
[0135] The present invention locates offline address information of the user
through online
behaviors of the user (such as access, purchase, etc.), converts the text-type
address to
numerical value-type longitude and latitude, and converts the store valid
marketing range
in the sense of businesses to grid longitude and latitude array values to
which stores
correspond in the sense of digits, so as to achieve the objectives of refined
and gridded
administration and integrated marketing with respect to online and offline
members by
parsing the exact geographical information data.
[0136] As comprehensible to persons ordinarily skilled in the art, the entire
or partial flows in
the methods according to the aforementioned embodiments can be completed via a
computer program instructing relevant hardware, the computer program can be
stored in
a nonvolatile computer-readable storage medium, and the computer program can
include
the flows as embodied in the aforementioned various methods when executed. Any
reference to the memory, storage, database or other media used in the various
embodiments provided by the present application can all include nonvolatile
and/or
volatile memory/memories. The nonvolatile memory can include a read-only
memory
(ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM),
an electrically erasable and programmable ROM (EEPROM) or a flash memory. The
volatile memory can include a random access memory (RAM) or an external cache
memory. To serve as explanation rather than restriction, the RAM is obtainable
in many
forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM
19
Date recue/date received 2022-05-02
CA 03159785 2022-05-02
(SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM),
synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM
(RDRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM
(RDRAM), etc.
[0137] Technical features of the aforementioned embodiments are randomly
combinable, while
all possible combinations of the technical features in the aforementioned
embodiments
are not exhausted for the sake of brevity, but all these should be considered
to fall within
the scope recorded in the description as long as such combinations of the
technical
features are not mutually contradictory.
[0138] The foregoing embodiments are merely directed to several modes of
execution of the
present application, and their descriptions are relatively specific and
detailed, but they
should not be hence misunderstood as restrictions to the inventive patent
scope. As should
be pointed out, persons with ordinary skill in the art may further make
various
modifications and improvements without departing from the conception of the
present
application, and all these should pertain to the protection scope of the
present application.
Accordingly, the patent protection scope of the present application shall be
based on the
attached Claims.
Date recue/date received 2022-05-02
