CN111428338A - Method and system for automatically planning space use of shopping mall - Google Patents
Method and system for automatically planning space use of shopping mall Download PDFInfo
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- CN111428338A CN111428338A CN202010098164.8A CN202010098164A CN111428338A CN 111428338 A CN111428338 A CN 111428338A CN 202010098164 A CN202010098164 A CN 202010098164A CN 111428338 A CN111428338 A CN 111428338A
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Abstract
The invention discloses a method and a system for automatically planning spatial use of a market, which comprises the following steps of training a regression prediction model of the spatial use of the market: step 1.1: acquiring massive 3D data of the whole mall; step 1.2: 3D data standardization processing of a shopping mall; step 1.3: carrying out 3D data gridding processing on a shopping mall; step 1.4: calculating the central point position of the original data of each shop and the central point position corresponding to the shop; step 1.5: calculating the cube number of the center point of the shop, wherein the number is used as the cube grid number of the shop, and the step 1.6: positioning a cube number of a central point of a main entrance door of a mall; step 1.7: and establishing a regression prediction model for market use planning based on the multidimensional data. The method and the system for automatically planning the spatial use of the market grid the 3D market data with huge data, so that the complex objects become simple and the method and the system are more convenient and fast when a certain space of the market is positioned.
Description
Technical Field
The invention belongs to the technical field of automatic mall space planning, and particularly relates to a method and a system for automatically planning mall space application.
Background
Rapid development of commercial economy, improvement of people's living standard, and image of city brandThe establishment promotes the rapid development of the guiding system of the commercial signboard, no matter whether the person walks into the exclusive shops with full line and luxury and meta-assemblage,SupermarketThe mall, the pub and the dance hall are still in the bars and dance halls which are loud and glaring, the vivid identification guide system guides people to the directions, selects desirable commodities, tasting good wine and guides shopping, saves time and can propagate enterprise culture, is an indispensable important factor for improving the business image of an enterprise, and is rapidly developed, the design of the mall is more and more colorful, but the design method of the spatial application of the mall is more troublesome and the processing is more complicated.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide a method and a system for automatically planning the spatial application of a market, so as to solve the problems that the existing design method for the spatial application of the market is troublesome and the processing is complex.
In order to solve the technical problems, the invention adopts the technical scheme that:
a method for automatically planning a market space comprises the following steps of training a regression prediction model for the spatial use of the market:
step 1.1: acquiring massive 3D data of the whole mall;
step 1.2: 3D data standardization processing of a shopping mall;
step 1.3: carrying out 3D data gridding processing on a shopping mall;
step 1.4: calculating the central point position of the original data of each shop and the central point position corresponding to the shop;
step 1.5: calculating the cube number of the center point of the shop, wherein the number is used as the cube grid number of the shop;
step 1.6: positioning a cube number of a central point of a main entrance door of a mall;
step 1.7: and establishing a regression prediction model for market use planning based on the multidimensional data.
Further, in the step 1.2, the obtained mall whole bounding box, the mall 3D data and the bounding box thereof are scaled in equal proportion, and the mall 3D data is also shrunk in the same proportion as the parameters.
Further, in step 1.3, the mall bounding box is cut at equal intervals in length, width and height, and the equal intervals of the width and height are set according to the formula: the length of the long side/the pitch of the long side is defined as the pitch in the width/width direction and the pitch in the height/height direction.
Further, in step 1.3, the marketplace grid data are numbered, starting from the serial number 1, the cube at a certain corner of the bottom layer is taken, the numbers of the cubes are sequentially increased by 1 according to the counterclockwise or clockwise direction of the area, and the data are numbered from the peripheral cubes.
Further, in the step 1.4, the center point corresponding to the store is the center point corresponding to the store after the proportion obtaining processing of the step 1.2.
Further, in step 1.7, the multidimensional data includes: the system comprises a cubic grid number of a main entrance door of a market, the number of total floors of the market, cubic grid numbers of all stores of the market, the number of the floors of the stores of the market, the total cubic grid number of the market and the corresponding space application of the stores of the market.
A system for automatically planning space use of a market predicts the space use of a newly-built market based on a trained prediction model, and comprises the following specific steps:
step 2.1: acquiring newly-built 3D data of a market;
step 2.2: standardizing 3D data of a shopping mall;
step 2.3: gridding the 3D data of the shopping mall;
step 2.4: calculating multi-dimensional characteristic data of a space to be predicted in a market;
step 2.5: inputting the acquired multi-dimensional characteristic data of the space to be predicted in the market;
step 2.6: inputting the multidimensional characteristic data of the space to be predicted into the regression prediction model in the step 1 for prediction calculation to obtain the space usage of the space prediction;
step 2.7: restoring the data to original 3D space data;
step 2.8: and (4) forecasting all spatial purposes of the whole market, and planning the purposes of the market.
Further, in step 2.4:
① calculating the central point of the space to be predicted;
②, judging the number of the cubic grid to which the central point of the space to be predicted belongs;
③ obtaining the number of the floor where the space to be predicted is located;
④ obtains the cube grid number at which the mall owner entry door is located.
Further, in step 2.7, the parameterized space usage is converted into a text description, and a usage name is automatically generated in the software for each space, wherein the text is located in the center of the space.
Has the advantages that: compared with the prior art, the method has the following advantages:
the method and the system for automatically planning the spatial use of the market carry out gridding processing on the 3D market data with huge data, so that complex objects become simple, and meanwhile, the method and the system are more convenient and faster when a certain space of the market is positioned, and key selection is carried out on multi-dimensional characteristic data processing.
Drawings
FIG. 1 is a flow chart of a mall spatial application regression prediction model;
FIG. 2 is a flow chart of a spatial usage planning process for forecasting a newly built mall based on a trained predictive model;
FIG. 3 is a schematic diagram of a gridding process;
FIG. 4 is a schematic diagram of a regression prediction model for use planning in a marketplace based on multidimensional data.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, fig. 2, fig. 3, and fig. 4, the method and the system for automatically planning the spatial use of the market of the present application includes training a regression prediction model of the spatial use of the market, and the specific steps are as follows:
step 1.1: acquiring massive 3D data of the whole market, wherein the massive 3D data mainly comprises data such as points, lines and planes;
step 1.2: 3D data standardization processing of a shopping mall;
step 1.3: carrying out 3D data gridding processing on a shopping mall;
step 1.4: calculating the central point position of the original data of each shop and the central point position corresponding to the shop;
step 1.5: calculating the cube number of the center point of the shop, wherein the number is used as the cube grid number of the shop;
step 1.6: positioning a cube number of a central point of a main entrance door of a mall;
step 1.7: and establishing a regression prediction model for market use planning based on the multidimensional data.
In step 1.2, the acquired mall whole bounding box, the mall 3D data and the bounding box thereof are scaled in equal proportion, and are shrunk to a bounding box length of 200 cm (or other interval values between 100 cm and 300 cm) by using a shrinking method, and the mall 3D data is also shrunk in proportion to the parameters.
In step 1.3, the mall bounding box is cut at equal intervals, as shown in fig. 3, for example, the equal interval of the long edge may be set to 10 cm, and the equal interval of the width and the height is set according to the following formula: the length of the long side/the pitch of the long side is defined as the pitch in the width/width direction and the pitch in the height/height direction.
In step 1.3, the gridding data of the shopping mall is numbered, for example, a cube at the bottommost part of the left lower corner is taken from the serial number 1, a cube at a certain corner of the bottommost layer is taken, the numbers of the cubes are sequentially increased by 1 according to the anticlockwise or clockwise direction of the area, and after a circle of cubes are arranged from the peripheral cubes, the cubes are sequentially arranged towards the inner cube, as shown in the figure; after the same layer is set, the cubes are set layer by layer to the upper layer until all the cubes are set.
In step 1.4, the center point location corresponding to the store is the center point location corresponding to the store after the proportion obtaining processing of the standardization processing in step 1.2.
In step 1.7, the multidimensional data comprises: the specific schematic diagram of the spatial application of the shopping mall store is shown in fig. 4.
The regression prediction model is as follows: a simple multidimensional linear regression model can be employed:
y ═ W1 × 1+ W2 × 2+ W3 × 3+ W4 × 4+ _ Xn; neural network models or modified multi-hidden layer deep learning networks, etc. may also be employed.
A system for automatically planning space use of a market predicts the space use of a newly-built market based on a trained prediction model, and comprises the following specific steps:
step 2.1: acquiring newly-built 3D data of a market;
step 2.2: standardizing 3D data of a shopping mall;
step 2.3: gridding the 3D data of the shopping mall;
step 2.4: calculating multi-dimensional characteristic data of a space to be predicted in a market;
step 2.5: inputting the acquired multi-dimensional characteristic data of the space to be predicted in the market;
step 2.6: inputting the multidimensional characteristic data of the space to be predicted into the regression prediction model in the step 1 for prediction calculation to obtain the space usage of the space prediction;
step 2.7: the data is restored to the original 3D space data, and the predicted parameterized space usage is restored to the original market 3D data (the mapping method is based on steps 2.2 and 2.3, and is the same as the 1 st part 1.2 and 1.3);
step 2.8: and (4) forecasting all spatial purposes of the whole market, and planning the purposes of the market.
In step 2.4:
① calculating the central point of the space to be predicted;
②, judging the number of the cubic grid to which the central point of the space to be predicted belongs;
③ obtaining the number of the floor where the space to be predicted is located;
④ obtains the cube grid number at which the mall owner entry door is located.
In step 2.7, the parameterized space usage is converted into a text description, and a usage name is automatically generated in software for each space, wherein the text is located in the center of the space.
The invention provides a method and a system for automatically planning market space use, and an implementation method thereof, and the specific application ways are many, and the above are only preferred embodiments of the invention, and it should be noted that, for those skilled in the art, a plurality of improvements can be made without departing from the principle of the invention, and these improvements should also be regarded as the protection scope of the invention.
Claims (9)
1. A method for automatically planning the space use of a market is characterized in that: the method comprises the following steps of training a market space purpose regression prediction model:
step 1.1: acquiring massive 3D data of the whole mall;
step 1.2: 3D data standardization processing of a shopping mall;
step 1.3: carrying out 3D data gridding processing on a shopping mall;
step 1.4: calculating the central point position of the original data of each shop and the central point position corresponding to the shop;
step 1.5: calculating the cube number of the center point of the shop, wherein the number is used as the cube grid number of the shop;
step 1.6: positioning a cube number of a central point of a main entrance door of a mall;
step 1.7: and establishing a regression prediction model for market use planning based on the multidimensional data.
2. The method for automatically planning the spatial use of a mall according to claim 1, wherein: in the step 1.2, the acquired mall integral bounding box, the mall 3D data and the bounding box thereof are scaled in equal proportion, and the mall 3D data is also shrunk in proportion to the parameters.
3. The method for automatically planning the spatial use of a mall according to claim 1, wherein: in the step 1.3, the length, the width and the height of the mall bounding box are cut at equal intervals, and the equal intervals of the width and the height are set according to the formula: the length of the long side/the pitch of the long side is defined as the pitch in the width/width direction and the pitch in the height/height direction.
4. The method for automatically planning the spatial use of a mall according to claim 1, wherein: in the step 1.3, the gridding data of the shopping mall is numbered, the cubes at a certain corner of the bottom layer are taken from the serial number 1, the numbers of the cubes are sequentially increased by 1 according to the anticlockwise or clockwise direction of the area, and the cubes are arranged from the periphery.
5. The method for automatically planning the spatial use of a mall according to claim 1, wherein: in the step 1.4, the center point corresponding to the store is the center point corresponding to the store after the proportion obtaining processing of the standardization processing in the step 1.2.
6. The method for automatically planning the spatial use of a mall according to claim 1, wherein: in step 1.7, the multidimensional data includes: the system comprises a cubic grid number of a main entrance door of a market, the number of total floors of the market, cubic grid numbers of all stores of the market, the number of the floors of the stores of the market, the total cubic grid number of the market and the corresponding space application of the stores of the market.
7. The utility model provides a system for market space usage is automatic to be planned which characterized in that: predicting the space use of a newly-built market based on a trained prediction model, and specifically comprising the following steps:
step 2.1: acquiring newly-built 3D data of a market;
step 2.2: standardizing 3D data of a shopping mall;
step 2.3: gridding the 3D data of the shopping mall;
step 2.4: calculating multi-dimensional characteristic data of a space to be predicted in a market;
step 2.5: inputting the acquired multi-dimensional characteristic data of the space to be predicted in the market;
step 2.6: inputting the multidimensional characteristic data of the space to be predicted into the regression prediction model in the step 1 for prediction calculation to obtain the space usage of the space prediction;
step 2.7: restoring the data to original 3D space data;
step 2.8: and (4) forecasting all spatial purposes of the whole market, and planning the purposes of the market.
8. Use of an automatic planning of the use of mall spaces according to claim 7, characterized in that: in the step 2.4:
① calculating the central point of the space to be predicted;
②, judging the number of the cubic grid to which the central point of the space to be predicted belongs;
③ obtaining the number of the floor where the space to be predicted is located;
④ obtains the cube grid number at which the mall owner entry door is located.
9. The system for automatically planning the use of mall space according to claim 7, wherein: in step 2.7, the parameterized space usage is converted into text description, and a usage name is automatically generated in software for each space, wherein the text is located in the center of the space.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103234539A (en) * | 2013-04-22 | 2013-08-07 | 南京师范大学 | Three-dimensional visualized indoor navigation method oriented to large shopping center |
| CN105138668A (en) * | 2015-09-06 | 2015-12-09 | 中山大学 | Urban business center and retailing format concentrated area identification method based on POI data |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103234539A (en) * | 2013-04-22 | 2013-08-07 | 南京师范大学 | Three-dimensional visualized indoor navigation method oriented to large shopping center |
| CN105138668A (en) * | 2015-09-06 | 2015-12-09 | 中山大学 | Urban business center and retailing format concentrated area identification method based on POI data |
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