CN114662203B - Virtualized indoor customized house effect map collocation generation system and method based on cloud platform - Google Patents
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Abstract
An indoor customization home effect map collocation generates cloud platform based on virtualization, includes: the system comprises an information collection module, a logic processing module and a database; the information collection module is arranged in a client of the cloud platform and used for collecting original collocation data of a user, wherein the original collocation data comprises a plurality of fields; the logic processing module is arranged in a client of the cloud platform and comprises: the system comprises a template marking module, a historical data acquisition module and a data generation module; the template marking module is used for storing original collocation data marked as a template by a user; the historical data acquisition module is used for acquiring secondary collocation data of all previous marks of the user as templates from a database of the server; the data generation module simplifies the original collocation data which is not marked as a template based on the original collocation data which is marked as the template to obtain secondary collocation data, and uploads the secondary collocation data to a server side of the cloud platform; the database is arranged in the server side of the cloud platform and used for storing the secondary collocation data.
Description
Technical Field
The invention belongs to the field of intelligent home cloud platforms, and particularly relates to a cloud platform generated by collocating indoor customized home effect graphs based on virtualization.
Background
With the development of big data, various intelligent home devices continuously update iteration. For more convenient service of clients, smart home cloud platforms have grown.
In the related art, the intelligent home cloud platform uploads data of a client to a server from the client. The server is configured with a database for storing the data uploaded by the client. The server side is provided with various interfaces at the same time so as to meet various requirements of clients.
However, the above-described cloud platform has various drawbacks. First, the data uploaded by the client is too huge, so that the storage and reading pressures of the database are large. Furthermore, because of the lack of relevance of the data of the database, especially during storage, this results in a very time consuming and targeted analysis of customer preferences through the data at a later stage.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to solve the problems of high storage pressure of a database of a cloud platform and preference analysis of customer demands, and further provides an indoor customized home effect map collocation generation cloud platform based on virtualization.
The invention adopts the following technical scheme.
An indoor customization home effect map collocation generates cloud platform based on virtualization, includes: the system comprises an information collection module, a logic processing module and a database;
the information collection module is arranged in a client of the cloud platform and used for collecting original collocation data of a user, wherein the original collocation data comprises a plurality of fields;
the logic processing module is arranged in a client of the cloud platform and comprises: the system comprises a template marking module, a historical data acquisition module and a data generation module;
the template marking module is used for storing original collocation data marked as a template by a user;
the historical data acquisition module is used for acquiring secondary collocation data of all previous marks of the user as templates from a database of the server;
the data generation module simplifies the original collocation data which is not marked as a template based on the original collocation data which is marked as the template to obtain associated secondary collocation data, and uploads the secondary collocation data to a server side of the cloud platform;
the database is arranged in the server side of the cloud platform and used for storing the secondary collocation data.
Compared with the prior art, the invention has the following advantages:
(1) And the template marking module is utilized to save the storage space of the cloud platform database, and simultaneously, the operation of a user is facilitated.
(2) And the template marking module is utilized, so that the complexity of a later-stage user preference recommendation algorithm is greatly simplified.
Drawings
Fig. 1 illustrates a cloud platform generated by matching indoor customized home effect graphs based on virtualization according to an embodiment of the present disclosure.
Fig. 2 illustrates another indoor customized home effect map collocation generation cloud platform based on virtualization according to an embodiment of the present disclosure.
Fig. 3 is a method for matching and uploading a furniture effect graph customized indoors based on virtualization and a cloud platform according to an embodiment of the present disclosure.
Fig. 4 illustrates a method for generating secondary collocation data according to an embodiment of the present disclosure.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical solutions of the present invention and are not intended to limit the scope of protection of the present application.
In the field of smart home cloud platforms, customers often have such a need: and carrying out collocation of various home furnishings in the model by utilizing a two-dimensional or three-dimensional model selectable in the apk client. For example, a customer can select sofas, wine cabinets, dining tables, chairs and the like with different colors, sizes and models according to own preference, and the sofas, wine cabinets, dining tables, chairs and the like are matched and replaced in the model, so that a home effect diagram is generated. In this mode, the client will upload the collocation data of the home effect map of each time to the server. Wherein, collocation data may include: the type, color, size of sofa, the type, color, size of cabinet, the type, color, size of tea table, etc. Any one customer can select various furniture finely and repeatedly in balance with the requirements of good home experience. However, in the related art, the cloud platform stores the collocation data of the home effect graph of each time of the client into the database, which not only causes the waste of the storage space (only part of the households may change in each time of collocation), but also is unfavorable for the extraction and analysis of the later data.
Based on the defects, the invention provides a cloud platform based on indoor customization home effect map collocation generation based on virtualization. The cloud platform comprises: the system comprises an information collection module, a logic processing module and a database. As shown in fig. 1.
The information collection module is arranged in the client and used for collecting original collocation data of the client. The raw collocation data includes a plurality of fields as shown in table 1.
In some embodiments, the information collection module receives the raw collocation data as shown in table 1 at times t1, t2, t3, and t4, respectively. The customer first carries out the first collocation of the home at the time t1, and the first collocation can be a collocation scheme recommended by furniture vending personnel. Next, the customer replaces the sofa and the wine cabinet at time t2, and a new collocation effect diagram is generated. Then, the customer replaces the color of the tea table at the time t3 again, and a collocation effect diagram is generated again. Furthermore, the customer replaces the color of the tea table at time t 4. It should be noted that, for convenience of description, the "collocation effect diagram" and the "original collocation data" are concepts that can be interchanged.
TABLE 1
The logic processing module is arranged at the client and used for processing the original collocation data and uploading the processed data to the server of the cloud platform. For convenience of description, the processed original collocation data is referred to as secondary collocation data.
Specifically, the logic processing module includes: the system comprises a template marking module, a historical data acquisition module, a data generation module and a field mapping module. Correspondingly, the interface of the client provides a template marking button.
The template marking module is used for storing a collocation effect graph marked as a template by a user. In some embodiments, the interface of the client provides a template marking button. The user can click on the template marking button to mark the favorite collocation effect graph as a template, so that the user can quickly view the favorite collocation effect graph. For example, the user may mark the matching effect map at time t2 as a template, in which case the user need not re-match each time, but need only perform a new matching adjustment based on the template (i.e., the matching effect map at time t 2). In other embodiments, the logic processing module may also include an algorithm module that may label some collocation effect maps as templates using a deep learning algorithm.
The historical data acquisition module is used for acquiring all the previous secondary collocation data (actually, the original collocation data) marked as templates of the user from a database of the server.
The data generation module simplifies the original collocation data which is not marked as the template based on the original collocation data marked as the template to obtain associated secondary collocation data, and uploads the secondary collocation data to the service end of the cloud platform. The processing mode specifically comprises the following steps: if the user marks the matching effect map at time t2 as a template, a new matching effect map (i.e., matching effect map at time t 3) is generated on the basis of the marking. Then the secondary collocation data may be as shown in table 2. More broadly, the method of this paragraph can be generalized to: taking the original collocation data marked as a template as first data (for example, a collocation effect diagram at the time t 2), and taking the original collocation data not marked as a template as second data (for example, a collocation effect diagram at the time t 3); the secondary collocation data resulting from the second data reduction (e.g., the secondary collocation data at time t3 as shown in table 2) is referenced by the field "reference: a timestamp of the first data (e.g., "reference: t2" in table 2), and a field composition of the second data that is different from the first data (e.g., "tea table color" field is "black").
It will be appreciated that the manner of storing each piece of data in the database should be stored in a tuple manner, so that the storage space of other options except the "tea table color" option of the collocation effect diagram at the time t3 is saved, and the additional cost is only to add a "reference" field.
TABLE 2
It should be noted that, if the user marks the matching effect diagram at the time t1 as a template, and marks the matching effect diagram at the time t4 as a template, two marked templates exist at this time. On the basis, a collocation effect diagram of t2 time is generated. Because the matching effect diagram at time t1 and the matching effect diagram at time t2 change 5 options, the manner in which the data generating module processes the original matching data may be specifically: firstly, calculating the similarity between a collocation effect diagram at t2 time and each collocation effect diagram marked as a template; then, the template with the highest similarity (i.e. smaller difference) is selected as the template of the collocation effect map at time t 2. As shown in table 3. More broadly, the method of this paragraph can be generalized to: the raw collocation data to be marked as templates includes: the first data (e.g., the matching effect graph at time t 1) and the second data (e.g., the matching effect graph at time t 4), the original matching data not marked as a template is the third data (e.g., the matching effect graph at time t 2); respectively calculating the similarity between the third data and the first data and the similarity between the third data and the second data; if the similarity between the third data and the first data is higher than the similarity between the third data and the second data, the secondary collocation data associated with the third data is referenced by the field: a timestamp "of the first data, the third data being different from the first data; if the similarity between the third data and the first data is not higher than the similarity between the third data and the second data, the secondary collocation data associated with the third data is referenced by the field: a timestamp of the second data (e.g., as in table 3: "reference: t 4"), and a field composition of the third data that is different from the second data (e.g., as in table 3: "sofa color: yellow").
That is, although the above-described collocation effect map at time t2 is generated based on the collocation effect map at time t1 from the viewpoint of the client. However, the secondary collocation data transmitted to the server is a collocation effect diagram of the time of 'reference't 4. This has the advantage of facilitating later user preference recommendation algorithms. It is understood that the time t4 may be greater than the time t1 or less than the time t 1.
TABLE 3 Table 3
Specifically, the present disclosure employs the following similarity calculation scheme:
(1) And acquiring a mapping table from a database of the server by using a field mapping module, wherein a plurality of groups of preset field values are stored in the mapping table, and each field value consists of an abstract field and a corresponding digital vector.
The number vector corresponding to the abstract field related to color is an RGB number. For example, the three-dimensional number vector corresponding to black is (0, 0), and the three-dimensional number vector corresponding to white is (255, 255, 255). The abstract fields related to the model correspond to the number vectors corresponding to the two-level abstract fields. For example, the model c1 corresponds to the 2-dimensional number vector (103, 124), since the model c1 is the model of the tea table, and the effect map of the tea table is typically only the shape of the tea table and the size of the tea table (except for color). Accordingly, in the related art, each model may be assigned as a digital vector by learning a large number of user selection data for each model through an optimization algorithm.
(2) And converting the collocation effect graphs into vectors according to the digital fields, and calculating the similarity between the 2 collocation effect graphs.
Let the numerical vectors corresponding to "s 1", "blue", "j 1", "purple", "z 1", "cyan" be (178), (0, 255), (240, 231), (255, 0, 255), (201, 20, 34, 77), (0, 255, 255), respectively. Then, the collocation effect map at time t1 may be represented by vector X1, x1= (178,0,0, 255, 240, 231, 255,0, 255, 103, 124, 255, 255, 255, 201, 20, 34, 77,0, 255, 255). The same method can calculate the collocation effect graph X2 at the time t 2. More broadly, the method of this paragraph can be generalized to: converting each field in the secondary collocation data (e.g., collocation effect diagram at time t 1) into a digital vector according to the mapping table; the numerical vectors of the secondary collocation data are combined into one vector (e.g., X1) according to the order of the fields in the secondary collocation data (e.g., according to the order of the fields in table 1: sofa model, sofa color, … …, chair color, respectively).
It is understood that the vector dimensions of X1 and X2 are the same. Finally, similarity o between the matching effect diagram at time t1 and the matching effect diagram at time t2 X1X2 The calculation formula of (2) is as follows:
wherein sigma X1 、σ X2 The standard deviations of the samples are X1 and X2, respectively.
The database is in a server side and can be in distributed storage for storing the processed collocation data.
Further, the server side further includes: the recommendation module and the screening module are shown in fig. 2. The recommendation module is used for pushing similar collocation schemes of other users on the intelligent home cloud platform. The screening module is used for screening users of the same secondary collocation data, and in some embodiments, only secondary collocation data marked as a template is often screened. And in the secondary collocation data uploaded to the server, each piece of data is added with a field: number of references. The reference number of the secondary collocation data of the collocation effect graph which is not marked as the template is 0, and the reference number of the secondary collocation data of the collocation effect graph which is marked as the template is the number of times the reference field in the secondary collocation data is referenced.
In some common user preference recommendation algorithms, the collocation effect map (or secondary collocation data) may be considered as "merchandise" and the "reference number" as "merchandise scoring". Thereby recommending collocation effect graphs of other users for the users. For example: smart home cloud platform stores user Y 1 、Y 2 、Y 3 、Y 4 、Y 5 Collocation effect diagram D of (2) 1 、D 2 、D 3 、D 4 And user Y i Collocation effect diagram D of (2) j The "score of commodity" of (2) is G ij Wherein i=1, 2,3,4,5, j=1, 2,3,4. Then let G i ＝(G i1 ，G i2 ，G i3 ，G i4 ). Then any two users Y s And Y is equal to t Degree of correlation between T (Y) s ,Y t ) Is determined by the following formula, where s, t=1, 2,3,4,5, and s+.t.
For example, when user Y 1 When the intelligent household cloud platform is used, the service end of the intelligent furniture cloud platform can respectively calculate Y 1 And Y is equal to 2 、Y 1 And Y is equal to 3 、Y 1 And Y is equal to 4 、Y 1 And Y is equal to 5 From T (Y 1 ,Y 2 )、T(Y 1 ,Y 3 )、T(Y 1 ,Y 4 )、T(Y 1 ,Y 5 ) The value of the highest degree of association is selected, for example, T (Y 1 ,Y 3 ). The intelligent home cloud platform can send the user Y 3 Is recommended to the user Y 1 。
And according to a user preference recommendation algorithm, the reference number field is used as the score of the corresponding secondary collocation data to perform association degree calculation so as to select other users with the largest association degree, and other collocation effect graphs of the other users with the largest association degree are recommended.
Furthermore, in the secondary collocation data uploaded to the server, each piece of data is further added with a field: a residence time period. The dwell time is used to record the time from the start of generating the effect graph to the end of the effect graph. The end of the effect graph may be the time point when the user performs new collocation, or may be the time point when the user closes the client. The meaning of the stay time is that the stay time can be regarded as "score of commodity" in combination with the above-described "reference number". In some embodiments, the product of the "stay time period" and the "reference number" may be used as the "score of the commodity". More broadly, the method of this paragraph can be generalized to: the secondary collocation data also comprises a stay time field, wherein the stay time is used for recording the time from the beginning of generating the effect graph to the end of the effect graph, and the product of the stay time and the reference number is used as the score of the corresponding secondary collocation data.
The invention also discloses a method for uploading the cloud platform based on the matching of the virtualized indoor customized furniture effect graph, which is matched with the cloud platform system, as shown in fig. 3, and comprises the following steps:
step S1, collecting a plurality of original collocation data of clients;
step S2, the original collocation data marked as a template is directly uploaded to a server as secondary collocation data;
step S3, acquiring secondary collocation data of all previous marks of the user as templates from a database of a server;
and S4, simplifying the original collocation data which are not marked as templates based on the original collocation data which are marked as templates to obtain associated secondary collocation data, and uploading the secondary collocation data to a server side of the cloud platform for storage.
Further, in step S1, the plurality of original collocation data includes: first data and second data marked as templates, and third data not marked as templates; in contrast, as shown in fig. 4, step S4 is based on the original matching data marked as the template, and the simplification of the original matching data not marked as the template to obtain the secondary matching data specifically includes:
step S41, calculating the similarity between the third data and the first data and the similarity between the third data and the second data respectively;
in step S42, if the similarity between the third data and the first data is higher than the similarity between the third data and the second data, the second collocation data associated with the third data is referenced by the field: a timestamp "of the first data, the third data being different from the first data; if the similarity between the third data and the first data is not higher than the similarity between the third data and the second data, the secondary collocation data associated with the third data is referenced by the field: a timestamp "of the second data, and a field of the third data that is different from the second data.
While the applicant has described and illustrated the embodiments of the present invention in detail with reference to the drawings, it should be understood by those skilled in the art that the above embodiments are only preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not to limit the scope of the present invention, but any improvements or modifications based on the spirit of the present invention should fall within the scope of the present invention.
Claims (6)
1. A cloud platform-based virtualized indoor customized house effect map collocation generating system is characterized in that: comprising the following steps: the system comprises an information collection module, a logic processing module and a database;
the information collection module is arranged in a client of the cloud platform and used for collecting original collocation data of a user, wherein the original collocation data comprises a plurality of fields;
the logic processing module is arranged in a client of the cloud platform and comprises: the system comprises a template marking module, a historical data acquisition module and a data generation module;
the template marking module is used for storing original collocation data marked as a template by a user;
the historical data acquisition module is used for acquiring secondary collocation data of all previous marks of the user as templates from a database of the server;
the data generation module simplifies the original collocation data which is not marked as a template based on the original collocation data which is marked as the template to obtain associated secondary collocation data, and uploads the secondary collocation data to a server side of the cloud platform;
the data generating module simplifies the original collocation data which is not marked as the template based on the original collocation data marked as the template to obtain secondary collocation data specifically comprises: the original collocation data marked as the template comprises: the first data and the second data, and original collocation data which is not marked as a template is third data; respectively calculating the similarity between the third data and the first data and the similarity between the third data and the second data; if the similarity between the third data and the first data is higher than the similarity between the third data and the second data, the secondary collocation data associated with the third data is referenced by the field: a timestamp "of the first data, the third data being different from the first data; if the similarity between the third data and the first data is not higher than the similarity between the third data and the second data, the secondary collocation data associated with the third data is referenced by the field: a timestamp "of the second data, the third data being different from the second data;
the database is arranged in the server side of the cloud platform and used for storing secondary collocation data;
the logic processing module further comprises: the field mapping module is used for acquiring a mapping table from a database of the server; the calculating of the similarity between the third data and the first data specifically includes:
converting each field in the first data and the third data into a digital vector according to the mapping table;
combining the digital vectors of the first data and the third data into a vector X1 and a vector X2 respectively according to the sequence of the fields;
the fields comprise colors, and the digital vector of the color field in the mapping table is a three-dimensional RGB vector value;
similarity o between first data and third data X1X2 The method comprises the following steps:
wherein sigma X1 、σ X2 The standard deviations of the samples are X1 and X2, respectively.
2. The cloud platform-based virtualized indoor customized home effect graph collocation generation system is characterized in that: the database stores and saves the secondary collocation data in a tuple mode.
3. The cloud platform-based virtualized indoor customized home effect graph collocation generation system is characterized in that: further comprises: the recommendation module is used for pushing similar collocation schemes of other users on the intelligent home cloud platform, and the screening module is used for screening users with the same secondary collocation data;
the secondary collocation data also comprises a reference number field, wherein the reference number of the secondary collocation data of the collocation effect graph which is not marked as a template is 0, and the reference number of the secondary collocation data of the collocation effect graph which is marked as a template is the number of times the reference field is referenced in the secondary collocation data;
and according to a user preference recommendation algorithm, the reference number field is used as the score of the corresponding secondary collocation data to perform association degree calculation so as to select other users with the largest association degree, and other collocation effect graphs of the other users with the largest association degree are recommended.
4. The cloud platform-based virtualized indoor customized home effect graph collocation generation system of claim 3, wherein: the secondary collocation data also comprises a stay time field, wherein the stay time is used for recording the time from the beginning of generating the effect graph to the end of the effect graph, and the product of the stay time and the reference number is used as the score of the corresponding secondary collocation data.
5. The cloud platform-based virtualized indoor customized home effect graph collocation generation system is characterized in that: the interface of the client provides a template marking button for marking the collocation effect graph as a template.
6. A cloud platform-based virtualized indoor customized house effect map collocation generating method applied to the system as claimed in any one of claims 1-5, characterized in that: the method comprises the following steps:
step S1, collecting a plurality of original collocation data of clients;
step S2, the original collocation data marked as a template is directly uploaded to a server as secondary collocation data;
step S3, acquiring secondary collocation data of all previous marks of the user as templates from a database of a server;
and S4, simplifying the original collocation data which are not marked as templates based on the original collocation data which are marked as templates to obtain associated secondary collocation data, and uploading the secondary collocation data to a server side of the cloud platform for storage.
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