CN107679952A - Equipment recommendation method, apparatus and storage medium based on big data - Google Patents
Equipment recommendation method, apparatus and storage medium based on big data Download PDFInfo
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- CN107679952A CN107679952A CN201710932790.0A CN201710932790A CN107679952A CN 107679952 A CN107679952 A CN 107679952A CN 201710932790 A CN201710932790 A CN 201710932790A CN 107679952 A CN107679952 A CN 107679952A
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
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Abstract
The invention discloses a kind of equipment recommendation method based on big data, the equipment recommendation method based on big data comprises the following steps：Server receives the device data for the preset kind that each platform uploads, and the device data of the preset kind includes sales data, after sale data, public sentiment data and/or Internet of Things data；The server generates the corresponding equipment portrait information of each dimension according to the device data；The server is according to equipment portrait information generating device pushed information, the generated equipment pushed information of output.The invention also discloses a kind of equipment recommendation device and storage medium based on big data.The present invention realizes the more extensive equipment recommendation of various dimensions, expands the scope of equipment recommendation, meet demand.
Description
Technical field
The present invention relates to technical field of data processing, more particularly to equipment recommendation method, apparatus based on big data and deposit
Storage media.
Background technology
With the continuous development of scientific technology, increasing electronic equipment enters daily life and work is worked as
In, for example, air conditioner, TV or computer etc..And when Products Show is done, it is mainly wide by Below-the-line popularization, media
Announcement, pocket transmission news etc. are realized.But these methods are difficult in a wide range of interior development by cost impact, and pin can not be realized
To the personal marketing of user.
To sum up the mode recommended range of current equipment recommendation is limited, can not meet to require.
The above is only used for auxiliary and understands technical scheme, does not represent and recognizes that the above is existing skill
Art.
The content of the invention
It is a primary object of the present invention to provide a kind of equipment recommendation method, apparatus and storage medium based on big data,
Aim to solve the problem that the mode recommended range of current equipment recommendation is limited, the problem of can not meeting to require.
To achieve the above object, a kind of equipment recommendation method based on big data provided by the invention, it is described based on big number
According to equipment recommendation method comprise the following steps：
Server receives the device data for the preset kind that each platform uploads, and the device data of the preset kind includes
Sales data, after sale data, public sentiment data and/or Internet of Things data；
The server generates the corresponding equipment portrait information of each dimension according to the device data；
The server is according to equipment portrait information generating device pushed information, the generated equipment push letter of output
Breath.
Alternatively, the server generates the step of the corresponding equipment portrait information of each dimension according to the device data
Suddenly include：
The server carries out word segmentation processing to the device data of reception, by the device data after word segmentation processing according to each
Dimension different demarcation is multi-class data；
The server generates corresponding equipment representation data according to the data of the corresponding division of each dimension.
Alternatively, according to equipment portrait information generating device pushed information, what output was generated sets the server
The step of standby pushed information, includes：
The server for user's dimension when, according to the equipment representation data of user's dimension generate be adapted to user individual character
Change the pushed information of equipment；
The pushed information of the personalization equipment is pushed to corresponding user side mobile terminal by the server, or
The server sends the pushed information of the personalization equipment to third party's terminal, to be pushed to other users
Equipment.
Alternatively, it is described according to equipment portrait information generating device pushed information, the generated equipment push of output
The step of information, includes：
The server for producer's side dimension when, equipment promotion is generated according to the equipment representation data of producer's side dimension
Pushed information；
The server pushes the pushed information of the equipment promotion in third-party platform.
Alternatively, according to equipment portrait information generating device pushed information, what output was generated sets the server
The step of standby pushed information, includes：
The server for product dimension when, according to the equipment representation data of product side dimension generate equipment innovate push away
Deliver letters breath；
The pushed information that the server innovates the equipment exports.
Alternatively, the device data of the preset kind is from based on user's history behavior, the preference based on user, Yong Husuo
The area at place, based on after sale, based on shops sell obtain；
Or
Based on online related, online similar, offline related, each dimension acquisition of recent hot-sale products personal style and attitude class.
Alternatively, the server generates the step of the corresponding equipment portrait information of each dimension according to the device data
Suddenly include：
The server extracts required device data from the device data of the preset kind received；
The server is modeled accordingly according to the device data of extraction, obtains model label；
The server is based on model label combination machine learning and predicts and recommend equipment portrait information corresponding to generation.
Alternatively, a kind of equipment recommendation method based on big data, the equipment recommendation method based on big data include
Following steps：
The equipment pushed information that air conditioner the reception server is generated；
The air conditioner sends the equipment pushed information to user's lateral terminal, to push the equipment to the user
Pushed information；
The process of the server generation equipment pushed information includes：
The device data for the preset kind that each platform uploads is received, the device data of the preset kind includes sale number
According to, data, public sentiment data and/or Internet of Things data after sale；
The corresponding equipment portrait information of each dimension is generated according to the device data；
According to equipment portrait information generating device pushed information, the generated equipment pushed information of output.
To achieve the above object, the present invention also provides a kind of equipment recommendation device based on big data, described based on big number
According to equipment recommendation device include：Memory, processor and it is stored on the memory and can runs on the processor
The equipment recommendation program based on big data, realized when the equipment recommendation program based on big data is by the computing device
Equipment recommendation method based on big data as described above.
To achieve the above object, the present invention also provides a kind of storage medium, is stored with the storage medium based on big number
According to equipment recommendation program, when the equipment recommendation program based on big data is executed by processor realize be based on as described above
The equipment recommendation method of big data.
The device data for each platform that the present invention is recommended by big data platform collecting device, carry out device data division
And equipment portrait information corresponding to generation after division, and then equipment recommendation, the equipment recommendation bag are made according to portrait result
Include Products Show, publicity recommends and equipment recommendation.The more extensive equipment recommendation of various dimensions is realized, expands the model of equipment recommendation
Enclose, meet demand.
Brief description of the drawings
Fig. 1 is the electronic devices structure schematic diagram for the hardware running environment that one embodiment of the invention scheme is related to；
Fig. 2 is the schematic flow sheet of the first embodiment of the equipment recommendation method of the invention based on big data；
Fig. 3 is to generate the corresponding equipment portrait information of each dimension according to the device data in one embodiment of the invention
Schematic flow sheet；
Fig. 4 is to be generated in one embodiment of the invention according to equipment portrait information generating device pushed information, output
Equipment pushed information schematic flow sheet；
Fig. 5 is to be given birth in another embodiment of the present invention according to equipment portrait information generating device pushed information, output
Into equipment pushed information schematic flow sheet；
Fig. 6 is to be given birth in further embodiment of this invention according to equipment portrait information generating device pushed information, output
Into equipment pushed information schematic flow sheet；
Fig. 7 is the schematic flow sheet of the second embodiment of the equipment recommendation method of the invention based on big data.
The realization, functional characteristics and advantage of the object of the invention will be described further referring to the drawings in conjunction with the embodiments.
Embodiment
It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not intended to limit the present invention.
The primary solutions of the embodiment of the present invention are：Server receives the number of devices for the preset kind that each platform uploads
According to the device data of the preset kind includes sales data, after sale data, public sentiment data and/or Internet of Things data；The clothes
Device be engaged according to the corresponding equipment portrait information of each dimension of device data generation；The server is drawn according to the equipment
As information generating device pushed information, the generated equipment pushed information of output.
Because the mode recommended range of current equipment recommendation is limited, the problem of can not meeting to require.The present invention provides a kind of
Solution, by big data platform collecting device recommend each platform device data, carry out device data division and
Equipment portrait information corresponding to generation after division, and then equipment recommendation is made according to portrait result, the equipment recommendation includes production
Product are recommended, publicity recommends and equipment recommendation.The more extensive equipment recommendation of various dimensions is realized, expands the scope of equipment recommendation,
Meet demand.
As shown in figure 1, Fig. 1 is the electronic devices structure signal for the hardware running environment that scheme of the embodiment of the present invention is related to
Figure.
Electronic equipment of the embodiment of the present invention can be server or air conditioner, can also be and communicated with air conditioner
The equipment such as the mobile terminal of connection, PC.
As shown in figure 1, the electronic equipment can include：Processor 1001, such as CPU, network interface 1004, user interface
1003, memory 1005, communication bus 1002.Wherein, communication bus 1002 is used to realize the connection communication between these components.
User interface 1003 can include display screen (Display), input block such as keyboard (Keyboard), optional user interface
1003 can also include wireline interface, the wave point of standard.Network interface 1004 can optionally connect including the wired of standard
Mouth, wave point (such as WI-FI interfaces).Memory 1005 can be high-speed RAM memory or stable memory
(non-volatile memory), such as magnetic disk storage.Memory 1005 optionally can also be independently of aforementioned processor
1001 storage device.
It will be understood by those skilled in the art that the terminal structure shown in Fig. 1 does not form the restriction to electronic equipment, can
With including than illustrating more or less parts, either combining some parts or different parts arrangement.
As shown in figure 1, it can lead to as in a kind of memory 1005 of computer-readable storage medium including operating system, network
Believe module, Subscriber Interface Module SIM and the equipment recommendation application program based on big data.
In the terminal shown in Fig. 1, network interface 1004 is mainly used in connecting background server, is carried out with background server
Data communicate；User interface 1003 is mainly used in connecting client (user terminal), enters row data communication with client；And processor
1001 can be used for calling the equipment recommendation application program based on big data stored in memory 1005, and perform following grasp
Make：
The device data for the preset kind that each platform uploads is received, the device data of the preset kind includes sale number
According to, data, public sentiment data and/or Internet of Things data after sale；
The corresponding equipment portrait information of each dimension is generated according to the device data；
According to equipment portrait information generating device pushed information, the generated equipment pushed information of output.
Further, processor 1001 can be used for calling the equipment recommendation based on big data stored in memory 1005
Application program, and perform following operate：
Word segmentation processing is carried out to the device data of reception, the device data after word segmentation processing is drawn according to each dimension is different
It is divided into multi-class data；
Corresponding equipment representation data is generated according to the data of the corresponding division of each dimension.
Further, processor 1001 can be used for calling the equipment recommendation based on big data stored in memory 1005
Application program, and perform following operate：
For user's dimension when, generated according to the equipment representation data of user's dimension and be adapted to the personalization equipment of user to push away
Deliver letters breath；
The pushed information of the personalization equipment is pushed into corresponding user side mobile terminal, or
The pushed information of the personalization equipment is sent to third party's terminal, with to other users pushing equipment.
Further, processor 1001 can be used for calling the equipment recommendation based on big data stored in memory 1005
Application program, and perform following operate：
For producer's side dimension when, according to the equipment representation data of producer's side dimension generate equipment promotion pushed information；
The pushed information of the equipment promotion is pushed in third-party platform.
Further, processor 1001 can be used for calling the equipment recommendation based on big data stored in memory 1005
Application program, and perform following operate：
For product dimension when, according to the equipment representation data of product side dimension generate equipment innovate pushed information；
The pushed information that the equipment is innovated exports.
Further, processor 1001 can be used for calling the equipment recommendation based on big data stored in memory 1005
Application program, and perform following operate：
The device data of the preset kind is from based on user's history behavior, the preference based on user, the ground residing for user
Area, based on after sale, based on shops sell obtain；
Or
Based on online related, online similar, offline related, each dimension acquisition of recent hot-sale products personal style and attitude class.
Further, processor 1001 can be used for calling the equipment recommendation based on big data stored in memory 1005
Application program, and perform following operate：Alternatively
Required device data is extracted from the device data of the preset kind received；
Modeled accordingly according to the device data of extraction, obtain model label；
Predicted based on model label combination machine learning and recommend equipment portrait information corresponding to generation.
Further, processor 1001 can be used for calling the equipment recommendation based on big data stored in memory 1005
Application program, and perform following operate：
The equipment pushed information that the reception server is generated；
The equipment pushed information is sent to user's lateral terminal, to push the equipment pushed information to the user；
The process of the server generation equipment pushed information includes：
The device data for the preset kind that each platform uploads is received, the device data of the preset kind includes sale number
According to, data, public sentiment data and/or Internet of Things data after sale；
The corresponding equipment portrait information of each dimension is generated according to the device data；
According to equipment portrait information generating device pushed information, the generated equipment pushed information of output.
Reference picture 2, the first embodiment of the present invention provides a kind of equipment recommendation method based on big data, described based on big
The executive agent of the equipment recommendation method of data be chosen as server or air conditioner or with air conditioner controlling device or other electronics
Equipment, the present embodiment describe by taking server as an example, and the equipment recommendation method based on big data includes：
Step S10, receive the device data for the preset kind that each platform uploads, the device package of the preset kind
Include sales data, after sale data, public sentiment data and/or Internet of Things data；
In the present embodiment, each platform is sales platform, after sale platform, platform of internet of things, public sentiment data platform etc., respectively
Individual platform collects the data for having device sales, data after sale, user's representation data, user's evaluating data, public sentiment data etc..Clothes
Business device either obtains device data, server transmission number according to instruction from each platform in real time at interval of preset time
The device data for each platform, receiving the preset kind that each platform uploads, the equipment of the preset kind are instructed according to obtaining
Data include sales data, after sale data, public sentiment data and/or Internet of Things data.The device data of the preset kind is from base
In user's history behavior, the preference based on user, the area residing for user, based on after sale, based on shops sell obtain；Or
Based on online related, online similar, offline related, each dimension acquisition of recent hot-sale products personal style and attitude class.Data source mainly includes：
Sale, after sale installation, Internet of Things, the data of internet, are carried out data link and are got through, i.e. server is established flat with these
The communication connection of platform.
And in data source, sale：Merchandise sales, selling time, logistics information etc.；Install after sale：Installation and maintenance, Yong Huxin
Breath, equipment state etc.；Internet of Things：Equipment control, equipment report, facility information etc.；Internet：News, forum, social activity, electric business
Deng.
Step S20, the corresponding equipment portrait information of each dimension is generated according to the device data；
Each dimension equipment portrait includes user's portrait, area portrait, shops's portrait etc., and portrait process is：To reception
Device data carries out word segmentation processing, according to each dimension different demarcation is multi-class data by the device data after word segmentation processing；Root
Corresponding equipment representation data is generated according to the data of the corresponding division of each dimension.According to dimension different demarcation into different data
Group or data acquisition system, corresponding equipment representation data is then generated for each data group or data acquisition system.
Specifically, with reference to figure 3, it is described that the corresponding equipment portrait information of each dimension is generated according to the device data
Step includes：
Step S21, required device data is extracted from the device data of the preset kind received；
Step S22, modeled accordingly according to the device data of extraction, obtain model label；
Step S23, predicted based on model label combination machine learning and recommend equipment portrait information corresponding to generation.
Equipment portrait information generation includes：Statistical analysis：According to the initial data (number of devices of each platform received
According to), statistics extracts factual data；Modeling analysis：Modeled accordingly for factual data, obtain model label；Model
Prediction：Based on model label, predict and recommend plus machine learning.
Step S30, according to equipment portrait information generating device pushed information, the generated equipment push letter of output
Breath.
By generating equipment representation data, required product, or preference is recommended to correspond to the category of feature, and use setting
Deng, or corresponding popularization is done according to demand, form advertisement information；Or the point for needing to innovate according to the specific generation of product.Will
The device data division of reception, is drawn a portrait respectively by different dimensions, has product portrait, user's portrait, area portrait, shops draws
Picture；Accurate recommendation is made for portrait result, conjecture user likes, and finally realizes accurate recommended products.
It is described to be drawn a portrait information generating device pushed information according to the equipment specifically, with reference to figure 4, what output was generated
The step of equipment pushed information, includes：
Step S31, for user's dimension when, according to the equipment representation data of user's dimension generate be adapted to user personalization
The pushed information of equipment；
Step S32, the pushed information of the personalization equipment is pushed into corresponding user side mobile terminal, or
Step S33, the pushed information of the personalization equipment is sent to third party's terminal, to be set to other users push
It is standby.
For a user, can also by the above-mentioned means, user can obtain the suitable personalized product recommendation of oneself
Specific crowd is given Products Show, degree of recognition and buying experience of the user to product can be improved.
It is described according to equipment portrait information generating device pushed information, the generated equipment push of output with reference to figure 5
The step of information, includes：
Step S34, for producer's side dimension when, equipment promotion is generated according to the equipment representation data of producer's side dimension and pushed away
Deliver letters breath；
Step S35, the pushed information of the equipment promotion is pushed in third-party platform.
For producer, by recommending, advertisement can be pointedly launched, publicizes product, hold the portrait letter of product
Breath.
It is described according to equipment portrait information generating device pushed information, the generated equipment push of output with reference to figure 6
The step of information, includes：
Step S36, for product dimension when, according to the equipment representation data of product side dimension generate equipment innovate push
Information；
Step S37, the pushed information that the equipment is innovated export.
In product side, targetedly solve user's pain spot, enhanced products bright spot, hold the potential demand of user, instruct city
Field decision-making, product adjustment is made according to product portrait in time, constantly brought forth new ideas.
The device data for each platform that the present embodiment is recommended by big data platform collecting device, carry out device data and draw
Equipment portrait information corresponding to generation after dividing and dividing, and then equipment recommendation, the equipment recommendation are made according to portrait result
Including Products Show, publicity recommends and equipment recommendation.The more extensive equipment recommendation of various dimensions is realized, expands equipment recommendation
Scope, meet demand.
In one embodiment, the present invention also provides a kind of equipment recommendation device based on big data, described to be based on big data
Equipment recommendation device include：Memory, processor and it is stored on the memory and can runs on the processor
Equipment recommendation program based on big data, the equipment recommendation program based on big data are realized such as during the computing device
Lower step：
The device data for the preset kind that each platform uploads is received, the device data of the preset kind includes sale number
According to, data, public sentiment data and/or Internet of Things data after sale；
The corresponding equipment portrait information of each dimension is generated according to the device data；
According to equipment portrait information generating device pushed information, the generated equipment pushed information of output.
The device data for each platform that the present invention is recommended by big data platform collecting device, carry out device data division
And equipment portrait information corresponding to generation after division, and then equipment recommendation, the equipment recommendation bag are made according to portrait result
Include Products Show, publicity recommends and equipment recommendation.The more extensive equipment recommendation of various dimensions is realized, expands the model of equipment recommendation
Enclose, meet demand.
With reference to figure 7, the equipment recommendation method based on big data comprises the following steps：
Step S101, the equipment pushed information that air conditioner the reception server is generated；
Step S102, the air conditioner send the equipment pushed information to user's lateral terminal, to be pushed away to the user
Send the equipment pushed information.In the present embodiment, the process of the equipment pushed information of server generation is in above-described embodiment
Process, this is no longer going to repeat them.And air conditioner is connected with the server communication, the generation of air conditioner the reception server is set
Standby pushed information, the equipment pushed information is pushed, for example, pushing to consumer end, consumer end will be adapted to oneself
Product be pushed to oneself friend or sellers equipment pushed information is pushed to customer；Believe for another example equipment is pushed
Breath is pushed to product research person or production producer, and manufacturer finds innovative point according to equipment pushed information, to product
Renewal meets user's request；Or sale producer is given, user preferences are specified, carry out the marketing etc. of equipment.
The equipment pushed information that the present embodiment is produced by air conditioner the reception server, carry out pushing away for equipment recommendation information
Recommend so that equipment pushes more diversified, and equipment push is more accurate.
In addition, the embodiment of the present invention also proposes a kind of storage medium, it is stored with the storage medium based on big data
Equipment recommendation program, the equipment recommendation program based on big data are realized following operation during the computing device：
The device data for the preset kind that each platform uploads is received, the device data of the preset kind includes sale number
According to, data, public sentiment data and/or Internet of Things data after sale；
The corresponding equipment portrait information of each dimension is generated according to the device data；
According to equipment portrait information generating device pushed information, the generated equipment pushed information of output.
Further, the equipment recommendation program based on big data is realized following operation during the computing device：
Word segmentation processing is carried out to the device data of reception, the device data after word segmentation processing is drawn according to each dimension is different
It is divided into multi-class data；
Corresponding equipment representation data is generated according to the data of the corresponding division of each dimension.
Further, the equipment recommendation program based on big data is realized following operation during the computing device：
For user's dimension when, generated according to the equipment representation data of user's dimension and be adapted to the personalization equipment of user to push away
Deliver letters breath；
The pushed information of the personalization equipment is pushed into corresponding user side mobile terminal, or
The pushed information of the personalization equipment is sent to third party's terminal, with to other users pushing equipment.
Further, the equipment recommendation program based on big data is realized following operation during the computing device：
For producer's side dimension when, according to the equipment representation data of producer's side dimension generate equipment promotion pushed information；
The pushed information of the equipment promotion is pushed in third-party platform.
Further, the equipment recommendation program based on big data is realized following operation during the computing device：
For product dimension when, according to the equipment representation data of product side dimension generate equipment innovate pushed information；
The pushed information that the equipment is innovated exports.
Further, the equipment recommendation program based on big data is realized following operation during the computing device：
The device data of the preset kind is from based on user's history behavior, the preference based on user, the ground residing for user
Area, based on after sale, based on shops sell obtain；
Or
Based on online related, online similar, offline related, each dimension acquisition of recent hot-sale products personal style and attitude class.
Further, the equipment recommendation program based on big data is realized following operation during the computing device：
Alternatively
Required device data is extracted from the device data of the preset kind received；
Modeled accordingly according to the device data of extraction, obtain model label；
Predicted based on model label combination machine learning and recommend equipment portrait information corresponding to generation.
Further, the equipment recommendation program based on big data is realized following operation during the computing device：
The equipment pushed information that the reception server is generated；
The equipment pushed information is sent to user's lateral terminal, to push the equipment pushed information to the user；
The process of the server generation equipment pushed information includes：
The device data for the preset kind that each platform uploads is received, the device data of the preset kind includes sale number
According to, data, public sentiment data and/or Internet of Things data after sale；
The corresponding equipment portrait information of each dimension is generated according to the device data；
According to equipment portrait information generating device pushed information, the generated equipment pushed information of output.
It should be noted that herein, term " comprising ", "comprising" or its any other variant are intended to non-row
His property includes, so that process, method, article or system including a series of elements not only include those key elements, and
And also include the other element being not expressly set out, or also include for this process, method, article or system institute inherently
Key element.In the absence of more restrictions, the key element limited by sentence "including a ...", it is not excluded that including this
Other identical element also be present in the process of key element, method, article or system.
The embodiments of the present invention are for illustration only, do not represent the quality of embodiment.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can add the mode of required general hardware platform to realize by software, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.Based on such understanding, technical scheme is substantially done to prior art in other words
Going out the part of contribution can be embodied in the form of software product, and the computer software product is stored in one as described above
In storage medium (such as ROM/RAM, magnetic disc, CD), including some instructions to cause a station terminal equipment (can be mobile phone,
Computer, server, air conditioner, or network equipment etc.) perform method described in each embodiment of the present invention.
The preferred embodiments of the present invention are these are only, are not intended to limit the scope of the invention, it is every to utilize this hair
The equivalent structure or equivalent flow conversion that bright specification and accompanying drawing content are made, or directly or indirectly it is used in other related skills
Art field, is included within the scope of the present invention.
Claims (10)
- A kind of 1. equipment recommendation method based on big data, it is characterised in that the equipment recommendation method bag based on big data Include following steps：Server receives the device data for the preset kind that each platform uploads, and the device data of the preset kind includes sale Data, after sale data, public sentiment data and/or Internet of Things data；The server generates the corresponding equipment portrait information of each dimension according to the device data；The server is according to equipment portrait information generating device pushed information, the generated equipment pushed information of output.
- 2. the equipment recommendation method based on big data as claimed in claim 1, it is characterised in that the server is according to Device data, which generates the step of each dimension corresponding equipment portrait information, to be included：The server carries out word segmentation processing to the device data of reception, by the device data after word segmentation processing according to each dimension Different demarcation is multi-class data；The server generates corresponding equipment representation data according to the data of the corresponding division of each dimension.
- 3. the equipment recommendation method based on big data as claimed in claim 2, it is characterised in that the server is according to Equipment is drawn a portrait information generating device pushed information, is included the step of output generated equipment pushed information：The server for user's dimension when, according to the equipment representation data of user's dimension generate be adapted to user personalization set Standby pushed information；The pushed information of the personalization equipment is pushed to corresponding user side mobile terminal by the server, orThe server sends the pushed information of the personalization equipment to third party's terminal, to be set to other users push It is standby.
- 4. the equipment recommendation method based on big data as claimed in claim 2, it is characterised in that the server is according to Equipment is drawn a portrait information generating device pushed information, is included the step of output generated equipment pushed information：The server for producer's side dimension when, according to the equipment representation data of producer's side dimension generate equipment promotion push Information；The server pushes the pushed information of the equipment promotion in third-party platform.
- 5. the equipment recommendation method based on big data as claimed in claim 2, it is characterised in that the server is according to Equipment is drawn a portrait information generating device pushed information, is included the step of output generated equipment pushed information：The server for product dimension when, according to the equipment representation data of product side dimension generate equipment innovate push believe Breath；The pushed information that the server innovates the equipment exports.
- 6. the equipment recommendation method based on big data as claimed in claim 1, it is characterised in that the server preset kind Device data from based on user's history behavior, the preference based on user, the area residing for user, based on after sale, based on shops Sale obtains；OrThe server is based on online related, online similar, offline related, each dimension acquisition of recent hot-sale products personal style and attitude class.
- 7. the equipment recommendation method based on big data as claimed in claim 1, it is characterised in that the server is according to Device data, which generates the step of each dimension corresponding equipment portrait information, to be included：The server extracts required device data from the device data of the preset kind received；The server is modeled accordingly according to the device data of extraction, obtains model label；The server is based on model label combination machine learning and predicts and recommend equipment portrait information corresponding to generation.
- A kind of 8. equipment recommendation method based on big data, it is characterised in that the equipment recommendation method bag based on big data Include following steps：The equipment pushed information that air conditioner the reception server is generated；The air conditioner sends the equipment pushed information to user's lateral terminal, is pushed with pushing the equipment to the user Information；The process of the server generation equipment pushed information includes：Receive the device data for the preset kind that each platform uploads, the device data of the preset kind include sales data, Data, public sentiment data and/or Internet of Things data after sale；The corresponding equipment portrait information of each dimension is generated according to the device data；According to equipment portrait information generating device pushed information, the generated equipment pushed information of output.
- A kind of 9. equipment recommendation device based on big data, it is characterised in that the equipment recommendation device bag based on big data Include：Memory, processor and it is stored in the equipment based on big data that can be run on the memory and on the processor Recommended program, the equipment recommendation program based on big data are realized during the computing device as appointed in claim 1 to 8 The equipment recommendation method based on big data described in one.
- A kind of 10. storage medium, it is characterised in that the equipment recommendation program based on big data is stored with the storage medium, The base as any one of claim 1 to 7 is realized when the equipment recommendation program based on big data is executed by processor In the equipment recommendation method of big data.
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Application publication date: 20180209 |