CN114662203A - Indoor customized home effect graph collocation generation cloud platform based on virtualization - Google Patents
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Abstract
A virtualization-based indoor customized home effect graph collocation generation cloud platform, comprising: 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, and 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 all previous secondary collocation data marked as templates of the user from a database of the server; the data generation module is used for simplifying the original collocation data which are not marked as the templates to obtain secondary collocation data based on the original collocation data marked as the templates, and uploading the secondary collocation data to a server side of the cloud platform; the database is arranged in a server side of the cloud platform and used for storing 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 matching indoor customized home effect graphs based on virtualization.
Background
With the development of big data, various intelligent household devices are continuously updated and iterated. In order to more conveniently serve clients, an intelligent home cloud platform is produced.
In the related art, the smart home cloud platform uploads data of a client from a client to a server. The server is provided with a database for storing the data uploaded by the client. The server side is provided with a plurality of interfaces simultaneously so as to meet various requirements of customers.
However, the cloud platform has various disadvantages. Firstly, the data uploaded by the client is too huge, so that the storage and reading pressure of the database is large. Furthermore, due to the lack of correlation of the data of the database, especially during the storage process, it is time-consuming and less targeted to analyze the customer's preferences through the data at a later time.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to solve the problems of high database storage pressure of a cloud platform and customer requirement preference analysis, and further provides a virtualization-based indoor customized home effect graph collocation generation cloud platform.
The invention adopts the following technical scheme.
A virtualization-based indoor customized home effect graph collocation generation cloud platform, comprising: 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, and 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 all previous secondary collocation data marked as templates of the user from a database of the server;
the data generation module is used for simplifying the original collocation data which are not marked as the templates to obtain associated secondary collocation data based on the original collocation data marked as the templates, and uploading the secondary collocation data to a server side of the cloud platform;
the database is arranged in a server side of the cloud platform and used for storing secondary collocation data.
Compared with the prior art, the invention has the following advantages:
(1) the template marking module is utilized to save the storage space of the cloud platform database, and meanwhile, the operation of a user is facilitated.
(2) And by utilizing the template marking module, the complexity of a later user preference recommendation algorithm is greatly simplified.
Drawings
Fig. 1 is a cloud platform generated by matching indoor customized home effect maps based on virtualization according to an embodiment of the present disclosure.
Fig. 2 is another virtualization-based indoor customized home effect graph collocation generation cloud platform provided by an embodiment of the present disclosure.
Fig. 3 is a method for matching an effect diagram of a customized indoor furniture based on virtualization with an upload cloud platform according to an embodiment of the present disclosure.
Fig. 4 is 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 illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
In the field of smart home cloud platforms, customers often have such a need: various household collocations are carried out in the model by utilizing the selectable two-dimensional or three-dimensional model in the apk client. For example, the customer can select sofas, wine cabinets, dining tables, chairs and the like with different colors, sizes and models to be matched and replaced in the model according to the preference of the customer, so that the home effect picture is generated. In the mode, the client uploads the matching data of the household effect graph of each time of the client to the server. Wherein, the collocation data may include: the model, color, size of the sofa, the model, color, size of the cabinet, the model, color, size of the tea table, etc. Due to the requirement of good home experience, any customer can carefully select various furniture and repeatedly weigh the furniture. However, in the related art, the cloud platform stores the collocation data of the home effect map of each time of the customer in the database, which not only causes the waste of storage space (only part of the home may be changed in each collocation), but also is disadvantageous to the extraction and analysis of the later data.
Based on the defects, the invention provides a cloud platform for generating indoor customized home effect graphs based on virtualization. The cloud platform includes: 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 original collocation data includes a number 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 matched the home at time t1, which may be a matching solution recommended by the furniture vendor. Next, the customer replaces the sofa with the wine cabinet at time t2 and a new collocation effect map is generated. Then, the customer replaces the color of the tea table at time t3 again, and the collocation effect map is generated again. Again, the customer has replaced the color of the end table at time t 4. It should be noted that, for convenience of description, the "collocation effect diagram" and the "original collocation data" are interchangeable concepts.
TABLE 1
The logic processing module is arranged at the client and used for uploading the original collocation data to a server of the cloud platform after the original collocation data is processed. For convenience of description, the processed original collocation data is referred to as secondary collocation data.
Specifically, the logic processing module includes: the device comprises a template marking module, a historical data acquisition module, a data generation module and a field mapping module. Accordingly, the client's interface provides a template markup button.
The template marking module is used for storing the collocation effect graph marked as the template by the user. In some embodiments, the client's interface provides a template markup button. The user can click the template mark button to mark the favorite collocation effect icon as the template, so that the user can quickly check the favorite collocation effect icon. For example, the user may mark the collocation effect icon at time t2 as a template, in which case, the user does not need to re-collocate and adjust every time, but only needs to perform new collocation and adjustment based on the template (i.e., the collocation effect icon at time t 2). In other embodiments, the logic processing module may further include an algorithm module that may use a deep learning algorithm to record some of the collocation effect icons as templates.
The historical data acquisition module is used for acquiring all the secondary collocation data (actually, the primary collocation data) marked as the template of the user from the database of the server.
And the data generation module simplifies the original collocation data which are not marked as the templates to obtain associated secondary collocation data based on the original collocation data marked as the templates, and uploads the secondary collocation data to the server side of the cloud platform together. The treatment method may specifically be: if the user records the collocation effect icon at time t2 as a template, and generates a new collocation effect map (i.e. the collocation effect map at time t 3) based on the template. The secondary collocation data may be as shown in table 2. More broadly, the method of this paragraph can be summarized as: using the original collocation data marked as the template as first data (e.g. collocation effect graph at t2 time), and using the original collocation data not marked as the template as second data (e.g. collocation effect graph at t3 time); the secondary collocation data (e.g., secondary collocation data at time t3 as shown in table 2) obtained by the second data reduction is referenced by the field: the timestamp of the first data (e.g., "reference: t 2" in table 2), and the field composition of the second data that is different from the first data (e.g., "end table color" field is "black").
It can be understood that each piece of data in the database should be stored in a tuple manner, so that the storage space of other options of the collocation effect map at time t3 except for the option of "tea table color" is saved, and the overhead is only to add a "reference" field.
TABLE 2
It should be noted that, if the user marks the collocation effect icon at time t1 as a template, and also marks the collocation effect icon at time t4 as a template, there are two marked templates. On the basis, a collocation effect map of t2 time is generated. Since the collocation effect map at time t1 and the collocation effect map at time t2 change 5 options, the way for the data generation module to process the original collocation data may also be specifically: firstly, calculating the similarity between the collocation effect graph at t2 time and each collocation effect graph marked as a template; then, the template with the highest similarity (i.e., the difference is smaller) 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 summarized as: the raw collocation data that will be labeled as a template includes: first data (e.g., the collocation effect map at time t 1) and second data (e.g., the collocation effect map at time t 4), wherein the original collocation data not labeled as a template is third data (e.g., the collocation effect map at time t 2); respectively calculating the similarity of the third data and the first data and the similarity of 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 referred to by the field: a timestamp of the first data ", and a field in which the third data is 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 referred by the field: the timestamp of the second data (e.g., as in table 3: "quote: t 4"), and the field composition of the third data as different from the second data (e.g., as in table 3: "sofa color: yellow").
That is, although from the perspective of the client, the collocation effect map at time t2 is generated based on the collocation effect map at time t 1. But the secondary collocation data transmitted to the server side is a collocation effect graph of 'quote't 4 time. 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
Specifically, the following similarity calculation scheme is adopted in the present disclosure:
(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 relating to color is the RGB value. For example, a three-dimensional number vector corresponding to black is (0, 0, 0), and a three-dimensional number vector corresponding to white is (255, 255, 255). The abstract field related to the model number corresponds to a digital vector corresponding to the second-level abstract field. For example, the c1 model corresponds to a 2-dimensional number vector (103, 124), since the c1 model is a model of the tea table, and the factors (except for color) considered in matching the effect map of the tea table are generally only the shape of the tea table and the size of the tea table. Therefore, in the related art, each model can be assigned as a numerical vector by learning a large amount of user selection data for each model through an optimization algorithm.
(2) And converting the collocation effect graphs into vectors according to the number fields, and calculating the similarity between the 2 collocation effect graphs.
It is assumed that the numerical vectors corresponding to "s 1 type", "blue", "j 1 type", "purple", "z 1 type", and "cyan" are (178), (0, 0, 255), (240, 231), (255, 0, 255), (201, 20, 34, 77), and (0, 255, 255), respectively. Then, the collocation effect map at time t1 can be represented by vector X1, where X1 is (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 map X2 at time t 2. More broadly, the method of this paragraph can be summarized as: converting each field in the secondary collocation data (e.g., collocation effect map at time t 1) into a digital vector according to a mapping table; the digital vectors of the secondary collocation data are merged 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, which are, respectively, sofa model, sofa color, chair color).
It is understood that the vector dimensions of X1 and X2 are the same. Finally, the similarity o between the collocation effect graph at time t1 and the collocation effect graph at time t2X1X2The calculation formula of (a) is as follows:
wherein σX1、σX2Sample standard deviations of X1 and X2, respectively.
The database may be distributed storage in the server, and is used to store the processed collocation data.
Furthermore, the server further comprises: a recommendation module and a screening module, as shown in fig. 2. The recommendation module is used for pushing similar matching schemes of other users on the smart home cloud platform. The screening module is used for screening users with the same secondary collocation data, and in some embodiments, the screening module is often used for screening only secondary collocation data marked as a template. And in the secondary collocation data matched with the data and uploaded to the server, each piece of data is added with a field: the number of references. The reference number of the secondary collocation data of the collocation effect graph not marked as the template is 0, and the reference number of the secondary collocation data of the collocation effect graph marked as the template is the number of times that the 'reference' field in the secondary collocation data is referred to.
In some common user preference recommendation algorithms, the collocation effect map (or the secondary collocation data) may be regarded as "goods", and the "reference number" may be regarded as "rating of goods". Therefore, the collocation effect maps of other users are recommended to the user. For example: intelligent household cloud platform stores user Y1、Y2、Y3、Y4、Y5Collocation effect diagram D1、D2、D3、D4And user YiCollocation effect diagram DjIs given a "score of goods" of GijWherein, i is 1, 2, 3, 4, 5, j is 1, 2, 3, 4. Then order Gi＝(Gi1，Gi2，Gi3，Gi4). Then any two users YsAnd YtDegree of correlation between T (Y)s，Yt) The following formula identifies s, t as 1, 2, 3, 4, 5, and s ≠ t.
For example, when user Y1When the intelligent furniture cloud platform is used, the service end of the intelligent furniture cloud platform can respectively calculate Y1And Y2、Y1And Y3、Y1And Y4、Y1And Y5From T (Y) to1，Y2)、T(Y1，Y3)、T(Y1，Y4)、T(Y1，Y5) Of the values with the highest correlation, e.g. T (Y)1，Y3). The smart home cloud platform may assign user Y3Recommending the other collocation effect graphs to the user Y1。
And according to a user preference recommendation algorithm, calculating the relevance by taking the reference quantity field as the score of the corresponding secondary collocation data so as to select other users with the maximum relevance and recommend other collocation effect graphs of the other users with the maximum relevance.
Furthermore, in the secondary collocation data uploaded to the server, each piece of data is added with a field: the length of the residence time. The stay time length is used for recording the time length from the generation of the effect diagram to the end of the effect diagram. The end of the effect graph can be a time point when the user performs new collocation or a time point when the user closes the client. The meaning of the stay time period is that the stay time period can be regarded as "the score of the goods" in combination with the above "cited number". In some embodiments, the product of "stay time" and "quote amount" may be taken as "score of goods". More broadly, the method of this paragraph can be summarized as: the secondary collocation data also comprises a stay time length field, the stay time length is used for recording the time length from the generation of the effect diagram to the completion of the effect diagram of the client, and the product of the stay time length and the reference number is used as the score of the corresponding secondary collocation data.
The invention also discloses a method for uploading the effect diagram of the indoor customized furniture matched with the cloud platform based on virtualization, which is matched with the cloud platform system, and comprises the following steps as shown in fig. 3:
step S1, collecting a plurality of original collocation data of the client;
step S2, the original collocation data marked as the template is taken as secondary collocation data to be directly uploaded to a server;
step S3, acquiring all the secondary collocation data marked as templates of the user from the database of the server;
and step S4, based on the original collocation data marked as the template, simplifying the original collocation data not marked as the template to obtain associated secondary collocation data, and uploading the secondary collocation data to a server of the cloud platform for storage.
Further, in step S1, the original collocation data includes: the first data and the second data marked as the template and the third data not marked as the template; in matching with the above, as shown in fig. 4, the step S4 is to simplify the original matching data not marked as the template based on the original matching data marked as the template to obtain the secondary matching data, which 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 secondary collocation data associated with the third data is referred to by the field ": a timestamp of the first data ", and a field in which the third data is 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 referred by the field: a timestamp of the second data ", and a field in which the third data is different from the second data.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely 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 for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.
Claims (10)
1. A virtualization-based indoor customized home effect graph collocation generation cloud platform, comprising: 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, and 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 all previous secondary collocation data marked as templates of the user from a database of the server;
the data generation module is used for simplifying the original collocation data which are not marked as the templates to obtain associated secondary collocation data based on the original collocation data marked as the templates, and uploading the secondary collocation data to a server side of the cloud platform;
the database is arranged in a server side of the cloud platform and used for storing secondary collocation data.
2. The virtualization-based indoor customized home effect graph collocation generating cloud platform of claim 1, wherein the data generating module is configured to simplify, based on the original collocation data marked as the template, the original collocation data not marked as the template to obtain secondary collocation data, and specifically comprises:
the original collocation data marked as the template is first data, and the original collocation data not marked as the template is second data;
the secondary collocation data resulting from the second data reduction is referenced by the field: a timestamp of the first data, and a field in which the second data is different from the first data.
3. The cloud platform for generating matching of indoor customized home effect maps based on virtualization of claim 1, wherein the data generation module is configured to simplify the original matching data not marked as the template to obtain the secondary matching data based on the original matching data marked as the template, and specifically comprises:
the original collocation data marked as the template comprises: the first data and the second data, and the original collocation data which is not marked as the template is third data;
respectively calculating the similarity of the third data and the first data and the similarity of 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 referred to by the field: a timestamp of the first data ", and a field in which the third data is 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 referred by the field: a timestamp of the second data ", and a field in which the third data is different from the second data.
4. The virtualized indoor customized home effect graph collocation generation cloud platform of claim 3, wherein 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 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 a mapping table;
merging the number vectors of the first data and the third data into a vector X1 and a vector X2, respectively, according to the order of the fields;
similarity o of the first data and the third datax1x2Comprises the following steps:
wherein σX1、σX2Sample standard deviations of X1 and X2, respectively.
5. The virtualization-based indoor customized home effect map collocation generation cloud platform of claim 4, wherein the fields comprise colors, and the numerical vectors of the color fields in the mapping table are three-dimensional RGB vector values.
6. The virtualization-based indoor customized home effect graph collocation generation cloud platform according to claim 1, wherein the database stores and stores secondary collocation data in a tuple manner.
7. The virtualized indoor customized home effect graph collocation generation cloud platform of claim 1, further comprising: the system comprises a recommending module and a screening module which are arranged at a server side, wherein the recommending module is used for pushing similar matching schemes of other users on the intelligent home cloud platform, and the screening module is used for screening the users with the same secondary matching data;
the secondary collocation data further comprises a reference number field, wherein 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 marked as the template is the number of times that the reference field in the secondary collocation data is referred;
and according to a user preference recommendation algorithm, calculating the relevance by taking the reference quantity field as the score of the corresponding secondary collocation data so as to select other users with the maximum relevance and recommend other collocation effect graphs of the other users with the maximum relevance.
8. The virtualization-based indoor customized home effect graph collocation generating cloud platform of claim 7, wherein the secondary collocation data further comprises a stay time length field, the stay time length is used for recording the time length from the generation of the effect graph to the completion of the effect graph of a customer, and the product of the stay time length and the reference number is used as the score of the corresponding secondary collocation data.
9. The virtualization-based indoor customized home effect graph collocation generation cloud platform of claim 1, wherein an interface of the client provides a template marking button for marking a collocation effect icon as a template.
10. A method for matching a virtualized indoor customized furniture effect graph with an uploading cloud platform, applied to the cloud platform of claims 1-9, comprising the steps of:
step S1, collecting a plurality of original collocation data of the client;
step S2, the original collocation data marked as the template is taken as secondary collocation data to be directly uploaded to a server;
step S3, obtaining all previous secondary collocation data marked as templates of the user from a database of the server;
and step S4, based on the original collocation data marked as the template, simplifying the original collocation data not marked as the template 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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