CN111709062B - Method, device, equipment and medium for acquiring item placement scheme scoring - Google Patents
Method, device, equipment and medium for acquiring item placement scheme scoring Download PDFInfo
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Abstract
The disclosure provides a scoring method and device for acquiring an article placement scheme, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, wherein the scoring method comprises the following steps: acquiring a first score for representing whether the object placement scheme passes through screening or not based on a preset placement scheme screening rule, acquiring a second score for representing placement rationality of the object placement scheme by using a neural network model, acquiring an auxiliary score corresponding to the object placement scheme by using a preset auxiliary score rule, and acquiring a scheme score corresponding to the object placement scheme according to the first score, the second score and the auxiliary score; the method, the device, the electronic equipment and the storage medium can intelligently generate the scheme scores corresponding to the item placement schemes based on the position constraint relation among the placed items and the rules of prohibiting the placement of the items, provide the reference information for comparing the advantages and disadvantages of different schemes, help users to make decoration decisions and save design time.
Description
Technical Field
The disclosure relates to the technical field of artificial intelligence, and in particular relates to a scoring method and device for acquiring an article placement scheme, electronic equipment and a storage medium.
Background
With the deep development of the real estate industry, the home decoration industry is greatly developed, and the demand for home decoration is also increasing. At present, for the placement of indoor articles, an automatic home decoration design algorithm can be used for automatically generating various placement schemes of the articles in a room; however, different automatic home decoration design algorithms and the same automatic home decoration design algorithm may provide a plurality of different article placement schemes, and at present, objective evaluation methods for the article placement schemes are lacking, so that designers and consumers are difficult to compare the advantages and disadvantages of different schemes from the plurality of article placement schemes, thereby choosing and selecting the most excellent article placement scheme. Accordingly, there is a need for a technique for obtaining item placement plan scores.
Disclosure of Invention
The present disclosure has been made in order to solve the above technical problems. The embodiment of the disclosure provides a method and a device for acquiring item placement scheme scoring, electronic equipment and a storage medium.
According to one aspect of the disclosed embodiments, there is provided a method for obtaining an item placement plan score, comprising: acquiring a first score corresponding to an object placement scheme of a target room based on a preset placement scheme screening rule; wherein the first score is used to characterize whether the item placement plan passes screening; acquiring a second score corresponding to the object placement scheme by using a neural network model according to house type structure data corresponding to the target room, object attribute information of the object placed in the target room and placement information of the object placed in the object placement scheme; wherein the second score is used for representing the placement rationality of the placed objects; according to the placed objects and the corresponding placement information, acquiring at least one auxiliary score corresponding to the object placement scheme by using a preset auxiliary score rule; and obtaining a scheme score corresponding to the item placement scheme according to the first score, the second score and the auxiliary score.
Optionally, the number of the placed objects is a plurality, and the obtaining, by using the neural network model, the second score corresponding to the object placement scheme includes: according to the house type structure data, the article attribute information of each article to be placed and the placing information, the neural network model is utilized to obtain the placing probability corresponding to each article to be placed; and obtaining the second score according to the placing probability of all the placed objects.
Optionally, the obtaining the second score according to the placement probabilities of all the placed objects includes: and acquiring a mean value corresponding to the placing probability of all the placed objects by using a preset mean value algorithm, and taking the mean value as the second score.
Optionally, the placing the article includes: one or more of furniture, electrical appliances, and decorations; the article attribute information includes: one or more of item category, item style, item size, item placement attributes; the house type structure data comprises: one or more of wall surface distribution data, door and window distribution data, area data and layer height data.
Optionally, the obtaining the first score corresponding to the object placement scheme of the target room based on the preset placement scheme screening rule includes: judging whether the object placement scheme accords with the placement scheme screening rule, if so, setting the first score to be 1, and if not, setting the first score to be 0; wherein, put scheme screening rule includes: one or more of placing overlap prohibition, wall penetration prohibition, and road blocking prohibition rules.
Optionally, the obtaining a plan score corresponding to the item placement plan according to the first score, the second score and the auxiliary score includes: acquiring a weighting coefficient corresponding to the auxiliary score, and acquiring a weighting value or a weighting sum according to the auxiliary score and the corresponding weighting coefficient; obtaining the weighted value or the weighted sum and the second score as a scheme total score; and taking the product of the first score and the total score of the scheme as the scheme score.
Optionally, the auxiliary scoring rule includes: article placement layout scoring rules; the auxiliary scoring includes: grading the placement layout of the articles; the method further comprises the steps of: calculating a mask diagram corresponding to the target room according to the house type structure data and the placement information of all the placed objects; determining the placement points of all the placed objects in the mask graph, and acquiring object placement layout scores according to the placement points and using the object placement layout score rules; wherein the item placement layout score is used to characterize the placement uniformity of the placed items in the target room;
optionally, the auxiliary scoring rule includes: item function scoring rules; the auxiliary scoring includes: scoring the function of the article; the method further comprises the steps of: obtaining the distance of the path between each article according to the information of all the articles; setting a use probability corresponding to each path based on the item function scoring rule as a weight coefficient corresponding to each path; and obtaining a weighted average value according to the distances of all the paths and the corresponding weight coefficients, and setting the weighted average value as the item function score.
Optionally, the neural network model includes: pre-trained graph neural network GNN model; the placement information comprises: placement position and orientation.
According to another aspect of the embodiments of the present disclosure, there is provided an apparatus for acquiring a score of an item placement plan, including: the first scoring module is used for acquiring a first score corresponding to the object placement scheme of the target room based on a preset placement scheme screening rule; wherein the first score is used to characterize whether the item placement plan passes screening; the second scoring module is used for acquiring a second score corresponding to the object placing scheme by using a neural network model according to the house type structure data corresponding to the target room, the object attribute information of the object placed in the target room and the placing information of the object placed in the object placing scheme; wherein the second score is used for representing the placement rationality of the placed objects; the third scoring module is used for acquiring at least one auxiliary score corresponding to the article placement scheme by using a preset auxiliary scoring rule according to the placed articles and the corresponding placement information; and the scheme scoring module is used for acquiring scheme scores corresponding to the object placement scheme according to the first scores, the second scores and the auxiliary scores.
Optionally, the number of the placed objects is multiple, where the second scoring module includes: the information acquisition unit is used for acquiring the placement probability corresponding to each placed article by utilizing the neural network model according to the house type structure data, the article attribute information of each placed article and the placement information; and the score determining unit is used for acquiring the second score according to the placing probability of all the placed objects.
Optionally, the score determining unit is specifically configured to obtain, using a preset average algorithm, an average value corresponding to the placement probabilities of all the placed objects, and use the average value as the second score.
Optionally, the placing the article includes: one or more of furniture, electrical appliances, and decorations; the article attribute information includes: one or more of item category, item style, item size, item placement attributes; the house type structure data comprises: one or more of wall surface distribution data, door and window distribution data, area data and layer height data.
Optionally, the first scoring module is configured to determine whether the item placement scheme meets the placement scheme screening rule, if yes, set the first score to 1, and if no, set the first score to 0; wherein, put scheme screening rule includes: one or more of placing overlap prohibition, wall penetration prohibition, and road blocking prohibition rules.
Optionally, the scheme scoring module is specifically configured to obtain a weighting coefficient corresponding to the auxiliary score, and obtain a weighted value or a weighted sum according to the auxiliary score and the corresponding weighting coefficient; obtaining the weighted value or the weighted sum and the second score as a scheme total score; and taking the product of the first score and the total score of the scheme as the scheme score.
Optionally, the auxiliary scoring rule includes: article placement layout scoring rules; the auxiliary scoring includes: grading the placement layout of the articles; the third scoring module includes: the layout scoring unit is used for calculating a mask diagram corresponding to the target room according to the house type structure data and the placement information of all the placed objects; determining the placement points of all the placed objects in the mask graph, and acquiring object placement layout scores according to the placement points and using the object placement layout score rules; wherein the item placement layout score is used to characterize the placement uniformity of the placed items within the target room.
Optionally, the auxiliary scoring rule includes: item function scoring rules; the auxiliary scoring includes: scoring the function of the article; the third scoring module includes: the function scoring unit is used for acquiring the distance of the path between the placed objects according to the placing information of all the placed objects; setting a use probability corresponding to each path based on the item function scoring rule as a weight coefficient corresponding to each path; and obtaining a weighted average value according to the distances of all the paths and the corresponding weight coefficients, and setting the weighted average value as the item function score.
Optionally, the neural network model includes: pre-trained graph neural network GNN model; the placement information comprises: placement position and orientation.
According to yet another aspect of the disclosed embodiments, there is provided a computer-readable storage medium storing a computer program for executing the above-described 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, the device, the electronic equipment and the storage medium for obtaining the item placement scheme scoring provided by the embodiment of the disclosure, scheme scoring corresponding to the item placement scheme can be intelligently generated based on the position constraint relation among the placed items and the rules of forbidden placement of the items, the reference information for comparing the advantages and disadvantages of different schemes is provided, a user is helped to make decoration decisions, designer resources and design flow time are saved, decoration design link flow can be simplified, and convenience is provided for user decoration decisions.
The technical scheme of the present disclosure is described in further detail below through the accompanying drawings and examples.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent by describing embodiments thereof in more detail with reference to the accompanying drawings. The accompanying drawings are included to provide a further understanding of 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 disclosure, not to limit the disclosure. In the drawings, like reference numerals generally refer to like parts or steps.
FIG. 1 is a flow chart of one embodiment of a method of the present disclosure for obtaining item placement plan scores;
FIG. 2 is a flow chart of a method for obtaining a second score in one embodiment of a method for obtaining an item placement plan score of the present disclosure;
FIG. 3 is a flow chart of a method for obtaining item placement layout scores in one embodiment of the present disclosure for obtaining item placement plan scoring;
FIG. 4 is a flow chart of a method for obtaining item functionality scores in one embodiment of a method for obtaining item placement plan scores of the present disclosure;
FIG. 5 is a flow chart of an acquisition plan score in one embodiment of the method for acquiring an item placement plan score of the present disclosure;
FIGS. 6A and 6B are schematic diagrams of an item placement scheme;
FIG. 7A is a schematic diagram of an embodiment of a scoring device for acquiring a placement plan for an item of the present disclosure; FIG. 7B is a schematic diagram of a second scoring module in one embodiment of the present disclosure for obtaining an item placement plan scoring device; FIG. 7C is a schematic diagram of a third scoring module in one embodiment of the present disclosure for obtaining an item placement plan scoring device;
fig. 8 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 should be apparent that the described embodiments are only some of the embodiments of the present disclosure and not all of the embodiments of the present disclosure, and that the present disclosure is not limited by the example embodiments described herein.
It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless it is specifically stated otherwise.
It will be appreciated by those of skill in the art that the terms "first," "second," etc. in embodiments of the present disclosure are used merely to distinguish between different steps, devices or modules, etc., and do not represent any particular technical meaning nor necessarily logical order between them.
It should also be understood that in embodiments of the present disclosure, "plurality" may refer to two or more, and "at least one" may refer to one, two or more.
It should also be appreciated that any component, data, or structure referred to in the presently disclosed embodiments may be generally understood as one or more without explicit limitation or the contrary in the context.
In addition, the term "and/or" in this disclosure is merely an association relationship describing an association object, and indicates that three relationships may exist, such as a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" in the present disclosure generally indicates that the front and rear association objects are 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 that the same or similar features may be referred to each other, and for brevity, will not be described in detail.
Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for 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 one 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 numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
Embodiments of the present disclosure are applicable to 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 the 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 personal computers, minicomputer systems, mainframe computer systems, distributed cloud computing environments that include any of the above systems, 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 computing system storage media including memory storage devices.
Summary of the application
In the process of realizing the present disclosure, the inventor finds that, for the placement of indoor articles, various placement schemes of articles in a room can be automatically generated using an automatic home decoration design algorithm; however, different automatic home decoration design algorithms and the same automatic home decoration design algorithm may provide a plurality of different article placement schemes; at present, a relatively objective evaluation method is lacking for the object placement schemes, and a designer and a consumer are difficult to compare the advantages and disadvantages of different schemes from various object placement schemes, so that the most excellent object placement scheme is selected.
The method for obtaining the object placement scheme scoring provided by the disclosure comprises the steps of obtaining a first scoring for representing whether an object placement scheme passes screening or not based on a preset placement scheme screening rule, obtaining a second scoring for representing placement rationality of the object placement scheme by using a neural network model, obtaining an auxiliary scoring corresponding to the object placement scheme by using a preset auxiliary scoring rule, and obtaining a scheme scoring corresponding to the object placement scheme according to the first scoring, the second scoring and the auxiliary scoring; the scheme scores corresponding to the scheme of placing the articles can be intelligently generated based on the position constraint relation among the articles to be placed, the rules of prohibiting the placement of the articles and the like, and the reference information for comparing the advantages and the disadvantages of different schemes is provided.
Exemplary method
FIG. 1 is a flow chart of one embodiment of a method of the present disclosure for obtaining item placement plan scoring, the method shown in FIG. 1 comprising the steps of: S101-S104. The steps are described separately below.
S101, acquiring a first score corresponding to an object placement scheme of a target room based on a preset placement scheme screening rule; wherein, first score is used for representing whether article placement scheme passes the screening.
The target room can be a bedroom, a living room, a bathroom and the like; put article in the article placement scheme includes: furniture, electrical appliances, decorations, and the like; the number of the article placement schemes can be one or more, and the article placement schemes comprise placement diagrams and the like for showing the placement positions, orientations and the like of articles such as tables, beds and electric appliances in a target room.
S102, acquiring a second score corresponding to an article placement scheme by using a neural network model according to house type structure data corresponding to a target room, article attribute information of the placed articles in the target room and the placement information of the placed articles in the article placement scheme; wherein, the second score is used for representing the placement rationality of placing articles.
The house type graph of the target room can be generated by CAD modeling tools and the like, and can be vector graphs and the like; the house type map has a plurality of house type elements, the house type structure data corresponding to the house type map is obtained, and the house type structure data comprises: wall distribution data, bearing wall distribution data, door and window distribution data, area data, floor height data, position coordinate data and the like of a house. The article attribute information includes: item category, item style, item size, item placement properties, etc., the item placement properties include: the placement location, orientation, whether the article must be against a wall, whether the article has a front door area, whether the article can enter a front door area of other articles, etc.
The training sample set can be generated in advance according to the house type structure data, the article attribute information of the articles, the placement information of the articles in the article placement scheme and the like, and training of the neural network is carried out according to the training sample to obtain a trained neural network model; and obtaining a second score corresponding to the object placement scheme by using the trained neural network model.
S103, according to the placed objects and the corresponding placing information, obtaining at least one auxiliary score corresponding to the object placing scheme by using a preset auxiliary score rule. The auxiliary scoring rules can be multiple, and the corresponding auxiliary scoring can be multiple.
S104, obtaining scheme scores corresponding to the object placement schemes according to the first scores, the second scores and the auxiliary scores.
In one embodiment, the acquisition of the first score corresponding to the item placement plan of the target room may take a variety of approaches. For example, the placement scheme screening rules include: inhibit placement overlap, inhibit wall penetration, inhibit road blocking rules, etc. Whether the object placement scheme meets the placement scheme screening rule can be judged based on a computer vision technology and the like, if so, the first score is set to be 1, and if not, the first score is set to be 0.
The arrangement scheme screening rule is a hard rule, and is a rule which must be met by the article arrangement scheme, and the arrangement scheme screening rule can prescribe that the article arrangement positions in the article arrangement scheme cannot go out of a wall, certain articles cannot be mutually overlapped and arranged, a road cannot be blocked and the like. If the object placing position in the object placing scheme can cause abnormal results of crossing a wall, overlapping the placed object, blocking a road and the like, the object placing scheme is eliminated.
In one embodiment, the number of items placed is a plurality, fig. 2 is a flowchart of a method for obtaining a second score in one embodiment of a method for obtaining an item placement plan score according to the present disclosure, the method shown in fig. 2 includes the steps of: S201-S202. The steps are described separately below.
S201, according to the house type structure data, the article attribute information of each article to be placed and the placement information, the placement probability corresponding to each article to be placed is obtained by using a 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 a trained GNN model. The GNN model outputs the placing probability corresponding to each placed article for given house type structural data, article attribute information and placing information, wherein the placing probability is the probability that the article corresponding to the placing information of one placed article is the placed article; the placement information includes the placement position and orientation of the article, etc.
S202, obtaining a second score according to the placing probability of all the placed objects.
Multiple methods may be used to obtain the second score based on the probability of placement of all of the placed items. For example, a preset average algorithm is used for acquiring an average value corresponding to the placement probability of all the placed objects, and the average value is used as a second score; the mean algorithm may be an existing mean algorithm, a weighted mean algorithm, or the like.
In one embodiment, the auxiliary scoring rules include: article placement layout scoring rules; the auxiliary scoring includes: and (5) grading the placement layout of the articles. FIG. 3 is a flow chart of a method for obtaining item placement layout scores in one embodiment of the method for obtaining item placement plan scores of the present disclosure, the method shown in FIG. 3 comprising the steps of: S301-S302. The steps are described separately below.
S301, calculating a mask diagram corresponding to the target room according to the house type structure data and the placement information of all the placed objects. The mask map and the house type map of the target room have the same pixels and sizes, the house type map is provided with the placed objects, the value of each pixel point in the mask map is 0 or 255, and if the value of a certain pixel point is 0, the placed position of the placed objects is indicated.
S302, determining the placement points of all the placed objects in the mask graph, and acquiring object placement layout scores according to the placement points and by using object placement layout scoring rules; wherein, the article placement layout score is used for representing the placement uniformity of the placed articles in the target room.
The article placement layout scoring rules are used for measuring the overall layout rationality of the article placement scheme, and a large amount of empty space is left elsewhere because all articles are prevented from being gathered together. The item placement layout scoring rules may set scores corresponding to arrangement uniformity within the target room (item placement layout scores, which are characteristic of a layout beautification score); according to the mask map and the placement points of the objects (the placement points are the position points of the particles or the center points of the objects in the object placement scheme), the index values corresponding to the arrangement uniformity in the target room can be obtained by adopting various existing methods.
In one embodiment, the auxiliary scoring rules include: item function scoring rules; the auxiliary scoring includes: item function score. FIG. 4 is a flow chart of a method for obtaining item functionality scores in one embodiment of a method for obtaining item placement plan scores of the present disclosure, the method shown in FIG. 4 comprising the steps of: S401-S403. The steps are described separately below.
S401, obtaining the distance of paths among all the placed objects according to the placing information of all the placed objects.
The distance between the paths of the articles can be obtained according to the mask diagram and the placement points of the articles, and the paths between the articles can be the paths between the placement points of the articles.
S402, setting a use probability corresponding to each path based on the item function scoring rule as a weight coefficient corresponding to each path.
S403, obtaining a weighted average value according to the distances of all paths and the corresponding weight coefficients, and setting the weighted average value as the item function score.
The article function grading rule is used for setting use probability and the like corresponding to each path, wherein the use probability is the probability of a user walking in the path; the item function score is a functional score that measures the rationality of the item function. For example, in the article placement scheme, there are three placement articles A, B and C, and the distances between the three paths between the three placement articles A, B and C are 3,4,5; setting the use probabilities corresponding to the three paths as 0.3,0.5,0.6 respectively based on the item function scoring rule, and taking the use probabilities as weight coefficients corresponding to the three paths respectively; a weighted average is obtained as the bin function score, which is (3 x 0.3+4 x 0.4+5 x 0.6)/3=1.83.
In one embodiment, there are a number of ways to obtain a plan score corresponding to an item placement plan. FIG. 5 is a flow chart of an acquisition plan scoring method of one embodiment of the present disclosure for acquiring an item placement plan scoring method, the method shown in FIG. 5 comprising the steps of: S501-S503. The steps are described separately below.
S501, obtaining a weighting coefficient corresponding to the auxiliary score, and obtaining a weighting value or a weighting sum according to the auxiliary score and the corresponding weighting coefficient. If there are a plurality of auxiliary scores, weighting information corresponding to each auxiliary score is acquired, and a weighted sum is calculated.
S502, taking the weighted value or the weighted sum and the sum of the second scores as a scheme total score.
And S503, taking the product of the first score and the total score of the scheme as the scheme score.
For example, the protocol score is s1 x (s2+k3 x s3+k4 x s 4); wherein s1 is the first score, (s2+k3×s3+k4×s4) is the total score of the scheme; s2 is a second score, s3 is an article placement layout score, s4 is an article function score, and k3 and k4 are weighting coefficients respectively.
In one embodiment, as shown in fig. 6A and 6B, the house type structural data corresponding to the target room includes structural data of a door, a window, a wall, etc., and fig. 6A and 6B each provide an item placement scheme for placing item list list_funnitures= [ "bed", "wardrobe", "dressing table", "desk" ]. The arrangement scheme screening rule is a rule which the article arrangement scheme must meet, and the arrangement scheme screening rule can prescribe that the article arrangement positions in the article arrangement scheme cannot go out of a wall, certain articles cannot be mutually overlapped and arranged, a road cannot be blocked and the like. Judging whether the object placement scheme accords with the placement scheme screening rule by using the existing computer vision algorithm, if so, setting the first score as 1, and if not, setting the first score as 0.
And acquiring the placement probability corresponding to each placed article according to the house type structural data, the article attribute information of each placed article and the placement information by utilizing a pre-trained graph neural network GNN model. For example, for each item in list_items, deleting the item in the item placement scheme in turn, and predicting the placement probability of the item by using the GNN model; and after predicting all the articles in the list_furnitures to obtain the placement probability, acquiring a mean value corresponding to the placement probability of all the placed articles by using a mean value algorithm, and taking the mean value as a second score.
According to the house type structure data and the placement information of all the placed articles, calculating a mask diagram corresponding to a target room, determining placement points of all the articles in list_furnitures in the mask diagram, and obtaining article placement layout scores according to the placement points and using article placement layout scoring rules.
Calculating the distance of paths among all the articles in list_funnitures (the paths are paths between two articles when a user arrives at the other article from one article), and setting the use probability corresponding to each path based on the article function scoring rule as a weight coefficient corresponding to each path; and obtaining a weighted average value according to the distances of all paths and the corresponding weight coefficients, and setting the weighted average value as the item function score.
The plan scores for calculating the item placement plan of fig. 6A and 6B were s1 (s2+k3+s3+k4)
s 4), wherein s1 is the first score, (s2+k3×s3+k4×s4) is the total score of the scheme; s2 is a second score, s3 is an article placement layout score, s4 is an article function score, and k3 and k4 are weighting coefficients respectively; after the item placement schemes of fig. 6A and 6B are obtained, the item placement scheme of fig. 6A with the highest scheme score is taken as the preferred scheme.
Exemplary apparatus
In one embodiment, as shown in fig. 7A, the present disclosure provides an apparatus for obtaining an item placement plan score, comprising: a first scoring module 701, a second scoring module 702, a third scoring module 703, and a solution scoring module 704. The first scoring module 701 obtains a first score corresponding to the object placement scheme of the target room based on a preset placement scheme screening rule; wherein, first score is used for representing whether article placement scheme passes the screening.
The second scoring module 702 obtains a second score corresponding to the item placement scheme by using the neural network model according to the house type structure data corresponding to the target room, the item attribute information of the item placed in the target room and the placement information of the item placed in the item placement scheme; wherein, the second score is used for representing the placement rationality of placing articles. The third scoring module 703 obtains at least one auxiliary score corresponding to the item placement scheme according to the placed item and the corresponding placement information using a preset auxiliary scoring rule. The plan scoring module 704 obtains a plan score corresponding to the item placement plan based on the first score, the second score, and the auxiliary score.
In one embodiment, placing the item includes: furniture, electrical appliances, decorations, and the like; the article attribute information includes: item category, item style, item size, item placement properties, etc.; the house type structure data includes: wall surface distribution data, door and window distribution data, area data, layer height data and the like.
The first scoring module 701 judges whether the object placement scheme meets the placement scheme screening rule, if so, the first score is set to 1, and if not, the first score is set to 0; wherein, put scheme screening rule includes: one or more of placing overlap prohibition, wall penetration prohibition, and road blocking prohibition rules.
The scheme scoring module 704 obtains a weighting coefficient corresponding to the auxiliary score, and obtains a weighting value or a weighting sum according to the auxiliary score and the corresponding weighting coefficient; the solution scoring module 704 obtains the weighted value or sum of the weighted values and the second score as a solution total score; the scenario scoring module 704 takes the product of the first score and the scenario total score as a scenario score.
In one embodiment, as shown in FIG. 7B, the second scoring module 702 includes: an information acquisition unit 7021 and a score determination unit 7022. The number of the placed articles is plural, and the information acquisition unit 7021 acquires the placement probability corresponding to each placed article by using the neural network model according to the house type structure data, the article attribute information of each placed article, and the placement information. The score determining unit 7022 obtains a second score according to the placement probabilities of all the placed items. The score determining unit 7022 may acquire a mean value corresponding to the placement probabilities of all the placed items using a preset mean algorithm, and use the mean value as the second score.
In one embodiment, as shown in fig. 7C, the third scoring module 703 includes: a layout scoring unit 7031 and a function scoring unit 7032. The auxiliary scoring rules include: article placement layout scoring rules; the auxiliary scoring includes: and (5) grading the placement layout of the articles. The layout scoring unit 7031 calculates a mask map corresponding to the target room according to the house type structure data and the placement information of all the placed articles; the layout scoring unit 7031 determines the placement points of all the placed objects in the mask map, and obtains object placement layout scores according to the placement points and by using object placement layout scoring rules; wherein, the article placement layout score is used for representing the placement uniformity of the placed articles in the target room.
The auxiliary scoring rules include: item function scoring rules; the auxiliary scoring includes: item function score. The function scoring unit 7032 acquires the distance of the path between each of the placed articles according to the placing information of all the placed articles; the function scoring unit 7032 sets the use probability corresponding to each path based on the item function scoring rule as a weight coefficient corresponding to each path; the function scoring unit 7032 obtains a weighted average value from the distances of all paths and the corresponding weight coefficients, and sets the weighted average value as the item function score.
Fig. 8 is a block diagram of one embodiment of an electronic device of the present disclosure, as shown in fig. 8, the electronic device 81 including one or more processors 811 and memory 812.
The processor 811 may be a Central Processing Unit (CPU) or other form of processing unit having data processing and/or instruction execution capabilities and may control other components in the electronic device 81 to perform the desired functions.
Memory 812 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, etc. Non-volatile memory, for example, may include: read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on a computer readable storage medium and the processor 811 may execute the program instructions to implement the method for obtaining item placement plan scoring and/or other desired functions of the various embodiments of the present disclosure above. Various contents such as an input signal, a signal component, a noise component, and the like may also be stored in the computer-readable storage medium.
In one example, the electronic device 81 may further include: input devices 813, output devices 814, and the like, interconnected by a bus system and/or other forms of connection mechanisms (not shown). In addition, the input device 813 may also include, for example, a keyboard, a mouse, and the like. The output device 814 may output various information to the outside. The output devices 814 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, etc.
Of course, only some of the components of the electronic device 81 relevant to the present disclosure are shown in fig. 8 for simplicity, components such as buses, input/output interfaces, and the like being omitted. In addition, the electronic device 81 may include any other suitable components depending on the particular application.
In addition to the methods and apparatus described above, 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 a method for obtaining item placement plan scoring according to various embodiments of the present disclosure described in the "exemplary methods" section 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, partly on a remote computing device, or entirely on the remote computing device or server.
Moreover, 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 a method for obtaining item placement plan scoring according to various embodiments of the present disclosure described in the "exemplary methods" section of the present disclosure above.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is 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 the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The basic principles of the present disclosure have been described above in connection with specific embodiments, however, it should be noted that the advantages, benefits, effects, etc. mentioned in the present disclosure are merely examples and not limiting, and these advantages, benefits, effects, etc. are not to be considered as necessarily possessed by the various embodiments of the present disclosure. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, since the disclosure is not necessarily limited to practice with the specific details described.
The method, the device, the electronic equipment and the storage medium for obtaining the object placement scheme in the embodiment are used for obtaining the first score for representing whether the object placement scheme passes the screening or not based on the preset placement scheme screening rule, obtaining the second score for representing the placement rationality of the object placement scheme by using the neural network model, and obtaining the auxiliary score corresponding to the object placement scheme by using the preset auxiliary scoring rule; obtaining a scheme score corresponding to the object placement scheme according to the first score, the second score and the auxiliary score; the method can intelligently generate scheme scores corresponding to the item placement schemes based on the position constraint relation among the placed items and the rules of the prohibited placement of the items, provide reference information for comparing the advantages and disadvantages of different schemes, help a user to make decoration decisions, save designer resources and design flow time, simplify decoration design link flows, provide convenience for user decoration decisions and reduce decoration cost.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different manner from other embodiments, so that the same or similar parts between the embodiments are mutually referred to. For system embodiments, the description is relatively simple as it essentially corresponds to method embodiments, and reference should be made to the description of method embodiments for relevant points.
The block diagrams of the devices, apparatuses, devices, systems referred to in this disclosure are merely illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the devices, apparatus, devices, and systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, 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, firmware. The above-described sequence of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the sequence specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present disclosure may also be implemented 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 apparatus, devices and methods of the present disclosure, components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered equivalent to 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, this description is not intended to limit embodiments of the disclosure to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, changes, additions, and sub-combinations thereof.
Claims (18)
1. A method for obtaining item placement plan scores, comprising:
acquiring a first score corresponding to an object placement scheme of a target room based on a preset placement scheme screening rule; wherein the first score is used to characterize whether the item placement plan passes screening; judging whether the object placement scheme accords with the placement scheme screening rule, if so, setting the first score to be 1, and if not, setting the first score to be 0;
acquiring a second score corresponding to the object placement scheme by using a neural network model according to house type structure data corresponding to the target room, object attribute information of the object placed in the target room and placement information of the object placed in the object placement scheme;
wherein the second score is used for representing the placement rationality of the placed objects; according to the house type structure data, the article attribute information of each article to be placed and the placing information, the neural network model is utilized to obtain the placing probability corresponding to each article to be placed; acquiring the second score according to the placing probability of all the placed objects;
According to the placed objects and the corresponding placement information, acquiring at least one auxiliary score corresponding to the object placement scheme by using a preset auxiliary score rule;
and obtaining a scheme score corresponding to the item placement scheme according to the first score, the second score and the auxiliary score.
2. The method of claim 1, the obtaining the second score according to the placement probabilities of all of the placed items comprising:
and acquiring a mean value corresponding to the placing probability of all the placed objects by using a preset mean value algorithm, and taking the mean value as the second score.
3. The method of claim 1, wherein,
the placing of the articles comprises: one or more of furniture, electrical appliances, and decorations;
the article attribute information includes: one or more of item category, item style, item size, item placement attributes;
the house type structure data comprises: one or more of wall surface distribution data, door and window distribution data, area data and layer height data.
4. The method according to claim 1,
wherein, put scheme screening rule includes: one or more of placing overlap prohibition, wall penetration prohibition, and road blocking prohibition rules.
5. The method of claim 1, the obtaining a plan score corresponding to the item placement plan based on the first score, the second score, and the auxiliary score comprising:
acquiring a weighting coefficient corresponding to the auxiliary score, and acquiring a weighting value or a weighting sum according to the auxiliary score and the corresponding weighting coefficient;
obtaining the weighted value or the weighted sum and the second score as a scheme total score;
and taking the product of the first score and the total score of the scheme as the scheme score.
6. The method of claim 1, wherein the auxiliary scoring rules comprise: article placement layout scoring rules; the auxiliary scoring includes: grading the placement layout of the articles; the method further comprises the steps of:
calculating a mask diagram corresponding to the target room according to the house type structure data and the placement information of all the placed objects;
determining the placement points of all the placed objects in the mask graph, and acquiring object placement layout scores according to the placement points and using the object placement layout score rules; wherein the item placement layout score is used to characterize the placement uniformity of the placed items within the target room.
7. The method of claim 1, wherein the auxiliary scoring rules comprise: item function scoring rules; the auxiliary scoring includes: scoring the function of the article; the method further comprises the steps of:
obtaining the distance of the path between each article according to the information of all the articles;
setting a use probability corresponding to each path based on the item function scoring rule as a weight coefficient corresponding to each path;
and obtaining a weighted average value according to the distances of all the paths and the corresponding weight coefficients, and setting the weighted average value as the item function score.
8. The method according to claim 1 to 7, wherein,
the neural network model includes: pre-trained graph neural network GNN model;
the placement information comprises: placement position and orientation.
9. An apparatus for obtaining item placement plan scores, comprising:
the first scoring module is used for acquiring a first score corresponding to the object placement scheme of the target room based on a preset placement scheme screening rule; wherein the first score is used to characterize whether the item placement plan passes screening; judging whether the object placement scheme accords with the placement scheme screening rule, if so, setting the first score to be 1, and if not, setting the first score to be 0;
The second scoring module is used for acquiring a second score corresponding to the object placing scheme by using a neural network model according to the house type structure data corresponding to the target room, the object attribute information of the object placed in the target room and the placing information of the object placed in the object placing scheme;
wherein the second score is used for representing the placement rationality of the placed objects; the second scoring module includes:
the information acquisition unit is used for acquiring the placement probability corresponding to each placed article by utilizing the neural network model according to the house type structure data, the article attribute information of each placed article and the placement information;
the score determining unit is used for obtaining the second score according to the placing probability of all the placed objects;
the third scoring module is used for acquiring at least one auxiliary score corresponding to the article placement scheme by using a preset auxiliary scoring rule according to the placed articles and the corresponding placement information;
and the scheme scoring module is used for acquiring scheme scores corresponding to the object placement scheme according to the first scores, the second scores and the auxiliary scores.
10. The apparatus of claim 9, wherein,
the scoring determining unit is specifically configured to obtain, using a preset average algorithm, an average value corresponding to the placement probabilities of all the placed objects, and use the average value as the second score.
11. The apparatus of claim 9, wherein,
the placing of the articles comprises: one or more of furniture, electrical appliances, and decorations;
the article attribute information includes: one or more of item category, item style, item size, item placement attributes;
the house type structure data comprises: one or more of wall surface distribution data, door and window distribution data, area data and layer height data.
12. The apparatus of claim 9, wherein,
the placement scheme screening rule comprises the following steps: one or more of placing overlap prohibition, wall penetration prohibition, and road blocking prohibition rules.
13. The apparatus of claim 9, wherein,
the scheme scoring module is specifically configured to obtain a weighting coefficient corresponding to the auxiliary score, and obtain a weighting value or a weighting sum according to the auxiliary score and the corresponding weighting coefficient; obtaining the weighted value or the weighted sum and the second score as a scheme total score; and taking the product of the first score and the total score of the scheme as the scheme score.
14. The apparatus of claim 9, wherein the auxiliary scoring rules comprise: article placement layout scoring rules; the auxiliary scoring includes: grading the placement layout of the articles;
the third scoring module includes:
the layout scoring unit is used for calculating a mask diagram corresponding to the target room according to the house type structure data and the placement information of all the placed objects; determining the placement points of all the placed objects in the mask graph, and acquiring object placement layout scores according to the placement points and using the object placement layout score rules; wherein the item placement layout score is used to characterize the placement uniformity of the placed items within the target room.
15. The apparatus of claim 9, wherein the auxiliary scoring rules comprise: item function scoring rules; the auxiliary scoring includes: scoring the function of the article;
the third scoring module includes:
the function scoring unit is used for acquiring the distance of the path between the placed objects according to the placing information of all the placed objects; setting a use probability corresponding to each path based on the item function scoring rule as a weight coefficient corresponding to each path; and obtaining a weighted average value according to the distances of all the paths and the corresponding weight coefficients, and setting the weighted average value as the item function score.
16. The apparatus of any one of claim 9 to 15, wherein,
the neural network model includes: pre-trained graph neural network GNN model;
the placement information comprises: placement position and orientation.
17. A computer readable storage medium storing a computer program for performing the method of any one of the preceding claims 1-8.
18. An electronic device, the electronic device comprising:
a processor; a memory for storing the processor-executable instructions;
the processor being configured to read the executable instructions from the memory and execute the instructions to implement the method of any of the preceding claims 1-8.
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| CN113221225A (en) * | 2021-05-27 | 2021-08-06 | 北京有竹居网络技术有限公司 | Article layout method, article layout device, storage medium and electronic equipment |
| CN113449356B (en) * | 2021-09-01 | 2021-12-07 | 深圳须弥云图空间科技有限公司 | Indoor automatic layout method, indoor automatic layout device, computer equipment and computer-readable storage medium |
| CN113505429B (en) * | 2021-09-09 | 2021-12-07 | 深圳须弥云图空间科技有限公司 | Indoor design scheme acquisition method and device, computer equipment and storage medium |
| CN113642090B (en) * | 2021-10-14 | 2022-04-01 | 深圳润世华软件和信息技术服务有限公司 | Building layout generation method and device, computer equipment and storage medium |
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