CN111709061A - Automatic indoor article placement processing method, device and equipment and storage medium - Google Patents
Automatic indoor article placement processing method, device and equipment and storage medium Download PDFInfo
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Abstract
The utility model provides an automatic indoor article placing and processing method, an automatic indoor article placing and processing device, an electronic device and a storage medium, which relate to the technical field of artificial intelligence, wherein the method comprises the following steps: according to the house type structure data, first article attribute information of articles already placed in a target room and second article attribute information of the articles to be placed, candidate placing information corresponding to the articles to be placed is obtained by using a preset article position screening rule; acquiring placement probability corresponding to the candidate placement information by using a neural network model, acquiring recommended placement information of the to-be-placed objects based on the placement probability, and generating an object placement scheme based on the recommended placement information and the house type structure data; the method, the device, the electronic equipment and the storage medium can intelligently generate the article placing scheme corresponding to the target room by considering the position constraint relation among placed articles and the rules of forbidding placing of the articles and the like, and can provide convenience for the decision of user decoration.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence technologies, and in particular, to an indoor article automatic placement processing method and apparatus, an electronic device, and a storage medium.
Background
With the deep development of the real estate industry, the home decoration industry is greatly developed, and the decoration requirement on home is larger and larger. At present, for the placement of indoor articles, a neural network model can be utilized to automatically generate a placement scheme of the articles in a room; however, in the conventional method for generating an article placement plan, although the placement position and orientation of an article in a room can be predicted by using a neural network model, the positional correlation between the placed articles, the dependency between the articles, and the like are not considered, and thus, an abnormal situation such as overlapping of two articles or the like is likely to occur in the generated placement plan.
Disclosure of Invention
The present disclosure is proposed to solve the above technical problems. The embodiment of the disclosure provides an automatic indoor article placement processing method and device, electronic equipment and a storage medium.
According to an aspect of the disclosed embodiments, there is provided an automatic indoor article placement processing method, including: acquiring house type structure data corresponding to a target room, first article attribute information of articles already placed in the target room and second article attribute information of articles to be placed; according to the house type structure data, the first article attribute information and the second article attribute information, at least one candidate placing information corresponding to the article to be placed is obtained by using a preset article position screening rule; obtaining placement probabilities corresponding to the candidate placement information by using a neural network model, and obtaining at least one piece of recommended placement information corresponding to the to-be-placed object based on the placement probabilities; and generating an article placement scheme corresponding to the target room based on the recommended placement information and the house type structure data.
Optionally, the number of the articles to be placed is multiple, and the method further includes: setting the target room to be in an empty room state, sequentially determining a plurality of candidate placing information corresponding to each article to be placed, and sequentially acquiring the recommended placing information corresponding to each article to be placed; and setting the articles to be placed with the determined placement candidate information as the articles already placed in the target room.
Optionally, the obtaining of the recommended placement information corresponding to the article to be placed includes: respectively acquiring a plurality of placing probabilities corresponding to the plurality of candidate placing information by using the neural network model; and sequencing the placing probabilities from big to small to obtain one or more placing probabilities positioned at the head of the sequencing, and taking one or more candidate placing information corresponding to the one or more placing probabilities as the recommended placing information.
Optionally, the generating an item placement plan corresponding to the target room based on the recommended placement information and the family structure data includes: and acquiring all recommended placement information corresponding to all articles to be placed, and generating a plurality of article placement schemes according to the all recommended placement information, the association relationship among the all recommended placement information and the house type structure data.
Optionally, the first item attribute information and the second item attribute information include: one or more of an item category, an item style, an item size, and an item placement attribute; the placed article and the article to be placed comprise: one or more of furniture, appliances, and ornaments; the house type structure data includes: one or more of wall surface distribution data, door and window distribution data, area data and floor height data.
Optionally, after the article placement scheme is generated, optimizing the placement position and the placement orientation of the article to be placed in the article placement scheme based on a preset article placement strategy and according to the house type structure data; wherein the item placement strategy comprises: one or more of a wall-to-wall strategy, a floor-to-ground strategy, and a void reservation strategy.
Optionally, based on a preset scheme screening rule, the optimized article placement scheme is subjected to screening processing.
Optionally, the obtaining, according to the house type structure data, the first article attribute information, and the second article attribute information, at least one candidate placement information corresponding to the article to be placed using a preset article position screening rule includes: calculating a mask graph corresponding to the target room according to the house type structure data, the first article attribute information and the second article attribute information; determining the candidate placing information in the mask graph by using the article position screening rule according to the first article attribute information and the second article attribute information; wherein the item location screening rule comprises: forbidding one or more of placing overlapping, forbidding wall penetrating and forbidding blocking rules.
Optionally, the neural network model comprises: pre-training a graph neural network GNN model; the placement candidate information includes: a first placement position and orientation; the recommended placement information includes: second placement position and orientation.
According to another aspect of the disclosed embodiments, there is provided an automatic indoor article placement processing device, including: the information acquisition module is used for acquiring house type structure data corresponding to a target room, first article attribute information of articles already placed in the target room and second article attribute information of articles to be placed; a candidate placement acquisition module, configured to acquire at least one candidate placement information corresponding to the to-be-placed item using a preset item position screening rule according to the house type structure data, the first item attribute information, and the second item attribute information; the recommended placement obtaining module is used for obtaining placement probabilities corresponding to the candidate placement information by using a neural network model and obtaining at least one piece of recommended placement information corresponding to the to-be-placed object based on the placement probabilities; and the placement scheme generating module is used for generating an article placement scheme corresponding to the target room based on the recommended placement information and the house type structure data.
Optionally, the number of the articles to be placed is multiple, wherein the candidate placement acquisition module is configured to set the target room to be in an empty room state, and sequentially determine multiple candidate placement information corresponding to each article to be placed; the recommended placement acquisition module is used for sequentially acquiring the recommended placement information corresponding to each article to be placed; the candidate placement acquisition module sets the articles to be placed, for which the candidate placement information has been determined, as the articles already placed in the target room.
Optionally, the recommended placement obtaining module is specifically configured to obtain, by using the neural network model, a plurality of placement probabilities corresponding to the plurality of candidate placement information respectively; and sequencing the placing probabilities from big to small to obtain one or more placing probabilities positioned at the head of the sequencing, and taking one or more candidate placing information corresponding to the one or more placing probabilities as the recommended placing information.
Optionally, the placement scheme generating module is configured to acquire all recommended placement information corresponding to all articles to be placed, and generate a plurality of article placement schemes according to the all recommended placement information, the association relationship among the all recommended placement information, and the house type structure data.
Optionally, the first item attribute information and the second item attribute information include: one or more of an item category, an item style, an item size, and an item placement attribute; the placed article and the article to be placed comprise: one or more of furniture, appliances, and ornaments; the house type structure data includes: one or more of wall surface distribution data, door and window distribution data, area data and floor height data.
Optionally, the placement scheme optimizing module is configured to, after the article placement scheme is generated, optimize a placement position and a placement orientation of the article to be placed in the article placement scheme based on a preset article placement policy and according to the house type structure data; wherein the item placement strategy comprises: one or more of a wall-to-wall strategy, a floor-to-ground strategy, and a void reservation strategy.
Optionally, the placement scheme screening module is configured to screen the optimized article placement scheme based on a preset scheme screening rule.
Optionally, the candidate placement obtaining module is specifically configured to calculate a mask map corresponding to the target room according to the house type structure data, the first item attribute information, and the second item attribute information; determining the candidate placing information in the mask graph by using the article position screening rule according to the first article attribute information and the second article attribute information; wherein the item location screening rule comprises: forbidding one or more of placing overlapping, forbidding wall penetrating and forbidding blocking rules.
Optionally, the neural network model comprises: pre-training a graph neural network GNN model; the placement candidate information includes: a first placement position and orientation; the recommended placement information includes: second placement position and orientation.
According to yet another aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium storing a computer program for executing the above-mentioned method.
According to still another aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including: a processor; a memory for storing the processor-executable instructions; the processor is used for executing the method.
Based on the method and the device for automatically placing and processing the indoor articles, the electronic equipment and the storage medium, provided by the embodiment of the disclosure, the position constraint relationship among the placed articles and rules of forbidden placement of the articles can be considered, an article placement scheme corresponding to a target room is intelligently generated, and the reasonability of the article placement scheme can be ensured; the method saves designer resources and time for designing the process, provides reasonable reference suggestions for placing articles, can simplify the decoration design process and can provide convenience for the decision of user decoration.
The technical solution of the present disclosure is further described in detail by the accompanying drawings and examples.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent by describing in more detail embodiments of the present disclosure with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the disclosure, and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the principles of the disclosure and not to limit the disclosure. In the drawings, like reference numbers generally represent like parts or steps.
Fig. 1 is a flowchart of an embodiment of an indoor article automatic placement processing method according to the present disclosure;
fig. 2 is a flowchart of generating placement candidate information in an embodiment of an indoor article automatic placement processing method according to the present disclosure;
fig. 3 is a flowchart of obtaining recommended placement information in an embodiment of an indoor article automatic placement processing method according to the present disclosure;
fig. 4 is a flowchart of an article placement scheme generated in an embodiment of the automatic indoor article placement processing method according to the present disclosure;
fig. 5A to 5E are schematic diagrams of an article placement scheme generated by using the indoor article automatic placement processing method of the present disclosure;
fig. 6A is a schematic structural diagram of an embodiment of an automatic indoor article placement processing apparatus according to the present disclosure; fig. 6B is a schematic structural diagram of another embodiment of an automatic indoor article placement and processing device according to the present disclosure;
FIG. 7 is a block diagram of one embodiment of an electronic device of the present disclosure.
Detailed Description
Example embodiments according to the present disclosure will be described in detail below with reference to the accompanying drawings. It is to be understood that the described embodiments are merely a subset of the embodiments of the present disclosure and not all embodiments of the present disclosure, with the understanding that the present disclosure is not limited to the example embodiments described herein.
It should be noted that: the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.
It will be understood by those of skill in the art that the terms "first," "second," and the like in the embodiments of the present disclosure are used merely to distinguish one element from another, and are not intended to imply any particular technical meaning, nor is the necessary logical order between them.
It is also understood that in embodiments of the present disclosure, "a plurality" may refer to two or more than two and "at least one" may refer to one, two or more than two.
It is also to be understood that any reference to any component, data, or structure in the embodiments of the disclosure, may be generally understood as one or more, unless explicitly defined otherwise or stated otherwise.
In addition, the term "and/or" in the present disclosure is only one kind of association relationship describing the associated object, and means that there may be three kinds of relationships, such as a and/or B, and may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" in the present disclosure generally indicates that the former and latter associated objects are in an "or" relationship.
It should also be understood that the description of the various embodiments of the present disclosure emphasizes the differences between the various embodiments, and the same or similar parts may be referred to each other, so that the descriptions thereof are omitted for brevity.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Embodiments of the present disclosure may be implemented in electronic devices such as terminal devices, computer systems, servers, etc., which are operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known terminal devices, computing systems, environments, and/or configurations that may be suitable for use with an electronic device, such as a terminal device, computer system, or server, include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set top boxes, programmable consumer electronics, network pcs, minicomputer systems, mainframe computer systems, distributed cloud computing environments that include any of the above, and the like.
Electronic devices such as terminal devices, computer systems, servers, etc. may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. The computer system/server may be implemented in a distributed cloud computing environment. In a distributed cloud computing environment, tasks may be performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.
Summary of the application
In the course of implementing the present disclosure, the inventors found that, in the conventional method for generating an article placement plan, although the placement positions and orientations of articles in a room can be predicted by using a neural network model, the abnormal situations such as overlapping of two articles in the generated placement plan are likely to occur, for example, because the position constraint relationship between the placed articles and the placement prohibition rule of the articles (for example, a toilet table is not generally placed in the direction of the bed tail, a wash basin is closest to a toilet door, etc.) are not considered.
According to the indoor article automatic placement processing method, candidate placement information corresponding to articles to be placed is obtained by using a preset article position screening rule according to house type structure data, first article attribute information of the articles already placed in a target room and second article attribute information of the articles to be placed; the method comprises the steps of obtaining placement probabilities corresponding to candidate placement information by using a neural network model, obtaining recommended placement information of articles to be placed based on the placement probabilities, generating an article placement scheme based on the recommended placement information and house type structure data, and intelligently generating an article placement scheme corresponding to a target house type by considering rules such as position constraint relations among the placed articles and prohibition of placement of the articles.
Exemplary method
Fig. 1 is a flowchart of an embodiment of an indoor article automatic placement processing method according to the present disclosure, where the method shown in fig. 1 includes the steps of: S101-S104. The following describes each step.
S101, acquiring house type structure data corresponding to a target room, first article attribute information of articles already placed in the target room and second article attribute information of articles to be placed.
The target room can be a bedroom, a living room, a bathroom and the like; the house type graph can be generated through a CAD modeling tool and the like, and can be a vector diagram and the like; there are multiple house type elements in the house type picture, obtain the house type structure data that the house type picture corresponds to, the house type structure data includes: the data of the wall surface distribution, the load-bearing wall distribution, the door and window distribution, the area data, the floor height data, the position coordinate data and the like of the house type.
The placed and to-be-placed items include: furniture, appliances, ornaments, etc.; the first item attribute information and the second item attribute information include: article category, article style, article size, article placement attributes, and the like, the article placement attributes including: the placement, orientation, whether the item must be against a wall, whether the item has a door front area, whether the item can access door front areas of other items, etc.
S102, obtaining at least one candidate placing information corresponding to the article to be placed by using a preset article position screening rule according to the house type structure data, the first article attribute information and the second article attribute information. The article position screening rules may include a variety of rules, such as placement overlap prohibition, wall penetration prohibition, block prohibition, and the like.
S103, obtaining placing probability corresponding to the candidate placing information by using the neural network model, and obtaining at least one piece of recommended placing information corresponding to the to-be-placed object based on the placing probability.
A training sample set can be generated in advance according to the house type structure data, the first article attribute information, the second article attribute information, the placing position and the like, and the training of the neural network is carried out according to the training samples to obtain a trained neural network model; and the placement probability corresponding to the candidate placement information of the article to be placed can be obtained by utilizing the trained neural network model.
And S104, generating an article placement scheme corresponding to the target room based on the recommended placement information and the house type structure data.
The number of the article placing schemes can be one or more; the article arrangement scheme comprises arrangement drawings and the like for displaying the arrangement position, orientation and the like of articles such as tables, beds, electric appliances and the like in a target room.
In one embodiment, the number of the articles to be placed is multiple, the target room is set to be in an empty room state, multiple candidate placing information corresponding to each article to be placed is sequentially determined, and recommended placing information corresponding to each article to be placed is sequentially acquired; when the candidate placing information is determined, the articles to be placed with the determined candidate placing information are set as the articles already placed in the target room. The candidate placing information comprises a first placing position, orientation and the like of the article; the recommended placement information includes a second placement position and orientation of the article, and the like.
Fig. 2 is a flowchart of generating placement candidate information in an embodiment of an indoor article automatic placement processing method according to the present disclosure, where the method shown in fig. 2 includes the steps of: S201-S202. The following describes each step.
S201, calculating a mask graph corresponding to the target room according to the house type structure data, the first item attribute information and the second item attribute information.
The mask graph and the house graph of the target room have the same pixels and the same size, the house graph contains placed objects and the to-be-placed objects needing to calculate the candidate placement information, the value of each pixel point in the mask graph is 0 or 255, and if the value of a certain pixel point is 0, the to-be-placed object can be placed at the position.
S202, determining a plurality of candidate placing information in the mask graph by using an article position screening rule according to the first article attribute information and the second article attribute information.
Calculating a plurality of discrete candidate placing information according to the mask graph, the first article attribute information and the second article attribute information, wherein each candidate placing information comprises a first placing position and an orientation, and the orientation can be four orientations, namely an upper orientation, a lower orientation, a left orientation and a right orientation; the candidate placing information is used as a potential option of the placing position of the article to be placed; the article position screening rule comprises the following steps: placing overlap prohibition, wall penetration prohibition, road blocking prohibition and the like, the article position screening rule can be used for checking candidate placing information, and if the candidate placing information can cause abnormal results such as wall crossing, overlapping with placed articles, road blocking and the like, the candidate placing information is deleted.
Fig. 3 is a flowchart of obtaining recommended placement information in an embodiment of an indoor article automatic placement processing method according to the present disclosure, where the method shown in fig. 3 includes the steps of: S301-S302. The following describes each step.
S301, a plurality of placing probabilities corresponding to the plurality of candidate placing information are respectively obtained by utilizing the neural network model.
The Neural network model can be various, such as a graph Neural network gnn (graphical Neural network) model and the like; the GNN network is based on the existing fixed point Query algorithm (Position Query), and the GNN model is trained in advance to obtain the trained GNN model. And outputting the placing probability corresponding to the candidate placing information of the article to be placed for the given house type diagram and the placed article by the GNN model, wherein the placing probability is the probability that the article corresponding to the candidate placing information is the article to be placed.
S302, the placing probabilities are ranked from big to small, one or more placing probabilities located at the head of the ranking are obtained, and one or more candidate placing information corresponding to the one or more placing probabilities are used as recommended placing information.
K values can be set, K is 1,2,3 and the like, and K candidate placing information corresponding to K maximum placing probabilities is selected as K recommended placing information.
Fig. 4 is a flowchart of generating an article placement scheme in an embodiment of the method for automatically placing indoor articles according to the present disclosure, where the method shown in fig. 4 includes the steps of: S401-S403. The following describes each step.
S401, all recommended placement information corresponding to all articles to be placed is obtained, and a plurality of article placement schemes are generated according to all recommended placement information, the incidence relation among all recommended placement information and house type structure data.
When a plurality of candidate placing information corresponding to each article to be placed is determined, the article to be placed, for which the candidate placing information has been determined, is set as the article already placed in the target room, the article to be placed may have a plurality of recommended placing information, and a plurality of recommended placing information of the next article to be placed may be obtained based on each recommended placing information, so that the recommended placing information and the plurality of recommended placing information of the next article to be placed have an association relationship, and similarly, the association relationship between all the recommended placing information may be determined.
S402, after the article placing scheme is generated, based on a preset article placing strategy and according to the house type structure data, the placing position and the placing direction of the article to be placed in the article placing scheme are optimized.
The article placement strategy comprises: wall-attaching strategies, ground-attaching strategies, void-retention strategies, and the like. The wall attaching strategy can be a wall attaching algorithm and the like, and the ground attaching strategy can be a ground attaching algorithm and the like; the space retention strategy may be a space retention strategy between the articles, which may be varied, for example, if two articles are adjacent but leave an insufficient gap therebetween to allow passage, adjusted to bring the two articles together.
And S403, based on a preset scheme screening rule, screening the optimized article placement scheme.
The scheme screening rules can be some preset forcing rules, and if the article placing scheme accords with the scheme screening rules, the scheme is deleted; all article placing schemes can be evaluated manually or automatically, and the article placing schemes with high evaluation scores are recommended preferentially.
In one embodiment, the house type structure data corresponding to the target room includes structure data of doors, windows, walls, and the like, and the list _ furniture [ "bed", "wardrobe", "dresser", "table" ]isgenerated based on the article to be placed. As shown in fig. 5A to 5E, the target room is set to be in an empty room state, a plurality of candidate placement information corresponding to each article to be placed is sequentially determined, recommended placement information corresponding to each article to be placed is sequentially obtained, and an article placement scheme is generated.
And taking the next article to be placed from the list _ furniture, and calculating a mask graph corresponding to the target room according to the house type structure data, the first article attribute information and the second article attribute information, wherein the size of the mask graph is consistent with the house type size of the target room, and the value of each point in the mask is 0 or 255. And determining a plurality of candidate placing information in the mask graph by using an article position screening rule according to the first article attribute information and the second article attribute information, and deleting the candidate placing information which can cause abnormal results such as wall crossing, overlapping with placed articles, road blocking and the like.
Respectively acquiring a plurality of placing probabilities corresponding to a plurality of candidate placing information by using a pre-trained graph neural network GNN model; and sequencing the placing probabilities from big to small, acquiring two placing probabilities positioned at the head part of the sequencing, and generating a list selected _ offers by taking two candidate placing information corresponding to the two placing probabilities as recommended placing information.
And sequentially and circularly executing the method, acquiring all recommended placement information corresponding to all articles to be placed in the llist _ furniture, and generating a plurality of article placement schemes according to all recommended placement information, the association relationship among all recommended placement information and the house type structure data. Optimizing the placing position and the placing direction of the articles to be placed in each article placing scheme and carrying out screening treatment.
Exemplary devices
In one embodiment, as shown in fig. 6A, the present disclosure provides an automatic indoor article placement processing apparatus, including: an information acquisition module 601, a candidate placement acquisition module 602, a recommended placement acquisition module 603, and a placement plan generation module 604.
The information obtaining module 601 obtains the house type structure data corresponding to the target room, the first item attribute information of the items already placed in the target room, and the second item attribute information of the items to be placed. The candidate placement obtaining module 602 obtains at least one candidate placement information corresponding to the to-be-placed item by using a preset item position screening rule according to the house type structure data, the first item attribute information, and the second item attribute information.
The recommended placement acquisition module 603 acquires a placement probability corresponding to the candidate placement information using the neural network model, and acquires at least one piece of recommended placement information corresponding to the item to be placed based on the placement probability. The placement plan generation module 604 generates an item placement plan corresponding to the target room based on the recommended placement information and the house type structure data. The neural network model comprises: pre-training a graph neural network GNN model; the placement candidate information includes: a first placement position and orientation; the recommended placement information includes: second placement position and orientation.
In one embodiment, the first item attribute information and the second item attribute information include: item type, item style, item size, item placement attribute; the placed and to-be-placed items include: one or more of furniture, appliances, and ornaments; the house type structure data includes: one or more of wall surface distribution data, door and window distribution data, area data and floor height data.
The candidate placement acquisition module 602 calculates a mask map corresponding to the target room according to the house type structure data, the first item attribute information, and the second item attribute information. The candidate placement acquisition module 602 determines a plurality of candidate placement information in the mask map by using an article position screening rule according to the first article attribute information and the second article attribute information; wherein, article position screening rule includes: forbidding one or more of placing overlapping, forbidding wall penetrating and forbidding blocking rules.
The number of the articles to be placed is plural, the candidate placement acquisition module 602 sets the target room to be in an empty room state, and sequentially determines a plurality of candidate placement information corresponding to each article to be placed. The recommended placement acquisition module 603 sequentially acquires recommended placement information corresponding to each article to be placed; the candidate placement acquisition module 602 sets the to-be-placed object for which the candidate placement information has been determined as the to-be-placed object in the target room.
The recommended placement acquisition module 603 acquires a plurality of placement probabilities corresponding to the plurality of candidate placement information, respectively, by using a neural network model; the recommended placement obtaining module 603 ranks the placement probabilities from large to small, obtains one or more placement probabilities located at the head of the ranking, and uses one or more candidate placement information corresponding to the one or more placement probabilities as recommended placement information.
The placement scheme generating module 604 obtains all recommended placement information corresponding to all the articles to be placed, and generates a plurality of article placement schemes according to all the recommended placement information, the association relationship among all the recommended placement information, and the house type structure data.
In one embodiment, as shown in fig. 6B, the indoor article automatic placement processing device includes: a placement plan optimization module 605 and a placement plan screening module 606. After generating the article placement scheme, the placement scheme optimization module 605 optimizes the placement position and placement orientation of the articles to be placed in the article placement scheme based on a preset article placement strategy and according to the house type structure data; wherein, the article put the tactics and include: one or more of a wall-to-wall strategy, a floor-to-ground strategy, and a void reservation strategy. The placement scheme screening module 606 screens the optimized article placement scheme based on preset scheme screening rules.
FIG. 7 is a block diagram of one embodiment of an electronic device of the present disclosure, as shown in FIG. 7, the electronic device 71 includes one or more processors 711 and memory 712.
The processor 711 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 71 to perform desired functions.
The memory 712 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory, for example, may include: random Access Memory (RAM) and/or cache memory (cache), etc. The nonvolatile memory, for example, may include: read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer readable storage medium and executed by the processor 711 to implement the above automatic indoor item placement processing methods of the various embodiments of the present disclosure and/or other desired functions. Various contents such as an input signal, a signal component, a noise component, etc. may also be stored in the computer-readable storage medium.
In one example, the electronic device 71 may further include: input devices 713 and output devices 714, among other components, interconnected by a bus system and/or other form of connection mechanism (not shown). The input device 713 may also include, for example, a keyboard, a mouse, and the like. The output device 714 can output various information to the outside. The output devices 714 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, among others.
Of course, for simplicity, only some of the components of the electronic device 71 relevant to the present disclosure are shown in fig. 7, omitting components such as buses, input/output interfaces, and the like. In addition, the electronic device 71 may include any other suitable components, depending on the particular application.
In addition to the above-described methods and apparatus, embodiments of the present disclosure may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the method for automatic placement of indoor items according to various embodiments of the present disclosure described in the "exemplary methods" section above of this specification.
The computer program product may write program code for performing the operations of embodiments of the present disclosure in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present disclosure may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform the steps in the method for automatically presenting indoor items according to various embodiments of the present disclosure described in the "exemplary methods" section above in this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium may include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing describes the general principles of the present disclosure in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present disclosure are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present disclosure. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the disclosure is not intended to be limited to the specific details so described.
According to the indoor article automatic placement processing method and device, the electronic device and the storage medium in the embodiment, the candidate placement information corresponding to the articles to be placed is obtained by using the preset article position screening rule according to the house type structure data, the first article attribute information of the articles already placed in the target room and the second article attribute information of the articles to be placed; acquiring placement probability corresponding to the candidate placement information by using a neural network model, acquiring recommended placement information of the to-be-placed objects based on the placement probability, and generating an object placement scheme based on the recommended placement information and the house type structure data; the position constraint relation among the placed objects and rules such as prohibition of placing the objects can be considered, an object placing scheme corresponding to the target room is generated intelligently, and the reasonability of the object placing scheme can be ensured; the system saves designer resources and design flow time, provides reasonable reference suggestions for article placement, can help a user to make decoration decisions, saves designer resources and design flow time, can simplify decoration design link flow, can provide convenience for user decoration decisions, and reduces decoration cost.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts in the embodiments are referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The block diagrams of devices, apparatuses, systems referred to in this disclosure are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, and systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," comprising, "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
The methods and apparatus of the present disclosure may be implemented in a number of ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
It is also noted that in the devices, apparatuses, and methods of the present disclosure, each component or step can be decomposed and/or recombined. These decompositions and/or recombinations are to be considered equivalents of the present disclosure.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects, and the like, will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the disclosure to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
Claims (10)
1. An automatic indoor article placing and processing method comprises the following steps:
acquiring house type structure data corresponding to a target room, first article attribute information of articles already placed in the target room and second article attribute information of articles to be placed;
according to the house type structure data, the first article attribute information and the second article attribute information, at least one candidate placing information corresponding to the article to be placed is obtained by using a preset article position screening rule;
obtaining placement probabilities corresponding to the candidate placement information by using a neural network model, and obtaining at least one piece of recommended placement information corresponding to the to-be-placed object based on the placement probabilities;
and generating an article placement scheme corresponding to the target room based on the recommended placement information and the house type structure data.
2. The method of claim 1, wherein the number of the articles to be placed is plural, the method further comprising:
setting the target room to be in an empty room state, sequentially determining a plurality of candidate placing information corresponding to each article to be placed, and sequentially acquiring the recommended placing information corresponding to each article to be placed;
and setting the articles to be placed with the determined placement candidate information as the articles already placed in the target room.
3. The method of claim 2, wherein the obtaining of the recommended placement information corresponding to the to-be-placed item comprises:
respectively acquiring a plurality of placing probabilities corresponding to the plurality of candidate placing information by using the neural network model;
and sequencing the placing probabilities from big to small to obtain one or more placing probabilities positioned at the head of the sequencing, and taking one or more candidate placing information corresponding to the one or more placing probabilities as the recommended placing information.
4. The method of claim 2, the generating an item placement solution corresponding to the target room based on the recommended placement information and the household structure data comprising:
and acquiring all recommended placement information corresponding to all articles to be placed, and generating a plurality of article placement schemes according to the all recommended placement information, the association relationship among the all recommended placement information and the house type structure data.
5. An automatic processing apparatus that puts of indoor article includes:
the information acquisition module is used for acquiring house type structure data corresponding to a target room, first article attribute information of articles already placed in the target room and second article attribute information of articles to be placed;
a candidate placement acquisition module, configured to acquire at least one candidate placement information corresponding to the to-be-placed item using a preset item position screening rule according to the house type structure data, the first item attribute information, and the second item attribute information;
the recommended placement obtaining module is used for obtaining placement probabilities corresponding to the candidate placement information by using a neural network model and obtaining at least one piece of recommended placement information corresponding to the to-be-placed object based on the placement probabilities;
and the placement scheme generating module is used for generating an article placement scheme corresponding to the target room based on the recommended placement information and the house type structure data.
6. The device of claim 5, wherein the number of the articles to be placed is multiple, wherein,
the candidate placement acquisition module is used for setting the target room to be in an empty room state and sequentially determining a plurality of candidate placement information corresponding to each article to be placed;
the recommended placement acquisition module is used for sequentially acquiring the recommended placement information corresponding to each article to be placed;
the candidate placement acquisition module sets the articles to be placed, for which the candidate placement information has been determined, as the articles already placed in the target room.
7. The apparatus of claim 6, wherein,
the recommended placement obtaining module is specifically configured to obtain, by using the neural network model, a plurality of placement probabilities corresponding to the plurality of candidate placement information, respectively; and sequencing the placing probabilities from big to small to obtain one or more placing probabilities positioned at the head of the sequencing, and taking one or more candidate placing information corresponding to the one or more placing probabilities as the recommended placing information.
8. The apparatus of claim 6, wherein,
the placement scheme generation module is used for acquiring all recommended placement information corresponding to all articles to be placed, and generating a plurality of article placement schemes according to the all recommended placement information, the incidence relation among the all recommended placement information and the house type structure data.
9. A computer-readable storage medium, the storage medium storing a computer program for performing the method of any of the preceding claims 1-4.
10. An electronic device, the electronic device comprising:
a processor; a memory for storing the processor-executable instructions;
the processor is used for reading the executable instructions from the memory and executing the instructions to realize the method of any one of the claims 1 to 4.
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| CN202010560309.1A CN111709061B (en) | 2020-06-18 | 2020-06-18 | Automatic indoor article placement processing method, device and equipment and storage medium |
| US17/324,023 US20210365603A1 (en) | 2020-05-19 | 2021-05-18 | Artificial intelligence systems and methods for interior furnishing |
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