CN111709062A - Method, device, equipment and medium for obtaining item placement scheme score - Google Patents
Method, device, equipment and medium for obtaining item placement scheme score Download PDFInfo
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Abstract
The present disclosure provides a method and an apparatus for obtaining a score of an article placement scheme, an electronic device and a storage medium, and relates to the technical field of artificial intelligence, wherein the method comprises: acquiring a first score for representing whether the article placing scheme passes screening or not based on a preset placing scheme screening rule, acquiring a second score for representing the placing rationality of the article placing scheme by using a neural network model, acquiring an auxiliary score corresponding to the article placing scheme by using a preset auxiliary scoring rule, and acquiring a scheme score corresponding to the article placing 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 scheme scores corresponding to the article placing schemes based on the position constraint relation among placed articles and rules of forbidden placing of the articles, provide 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 present disclosure relates to the field of artificial intelligence technologies, and in particular, to a method and an apparatus for obtaining a score of an article placement plan, 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, various placement schemes of the articles in a room can be automatically generated by 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, and at present, a relatively objective evaluation method is not available for the article placement schemes, so that designers and consumers are difficult to compare the advantages and disadvantages of different schemes from the plurality of article placement schemes, and thus the best article placement scheme is selected. Therefore, a technical solution for obtaining the score of the goods placement solution is needed.
Disclosure of Invention
The present disclosure is proposed to solve the above technical problems. The embodiment of the disclosure provides a method and a device for obtaining a grading of an article placement scheme, an electronic device and a storage medium.
According to an aspect of the embodiments of the present disclosure, there is provided a method for obtaining an item placement plan score, including: acquiring a first score corresponding to an article placing scheme of a target room based on a preset placing scheme screening rule; wherein the first score is used for representing whether the item placement scheme passes the screening; acquiring a second score corresponding to the article placement scheme by using a neural network model according to the house type structure data corresponding to the target room, the 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 placing reasonability of the placed object; according to the placed articles and the corresponding placement information, at least one auxiliary score corresponding to the article placement scheme is obtained by using a preset auxiliary score rule; and acquiring a scheme score corresponding to the article placing scheme according to the first score, the second score and the auxiliary score.
Optionally, the number of the placed items is multiple, and the obtaining a second score corresponding to the item placement scheme by using the neural network model includes: according to the house type structure data, the article attribute information of each placed article and the placement information, obtaining a placement probability corresponding to each placed article by using the neural network model; and obtaining the second score according to the placing probabilities of all the placed articles.
Optionally, the obtaining the second score according to the placement probabilities of all the placed items includes: and acquiring a mean value corresponding to the placing probabilities of all the placed articles by using a preset mean value algorithm, and taking the mean value as the second score.
Optionally, the presenting article comprises: one or more of furniture, appliances, and ornaments; the item attribute information includes: one or more of an item category, an item style, an item size, an item placement attribute; 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 obtaining a first score corresponding to the item placement scheme of the target room based on a preset placement scheme screening rule includes: judging whether the article placing scheme accords with the placing scheme screening rule, if so, setting the first score to be 1, and if not, setting the first score to be 0; wherein, the placing scheme screening rule comprises: forbidding one or more of placing overlapping, forbidding wall penetrating and forbidding blocking 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 weighted value or a weighted sum according to the auxiliary score and the corresponding weighting coefficient; acquiring the weighted value or the sum of the weighted sum and the second score as a total score of the scheme; taking the product of the first score and the total score of the solution as the solution score.
Optionally, the auxiliary scoring rule includes: an article placement layout scoring rule; the auxiliary scoring comprises: grading the placement layout of the articles; the method further comprises the following steps: calculating a mask graph corresponding to the target room according to the house type structure data and the placing information of all the placed articles; determining the placing points of all the placed articles in the mask graph, and obtaining the article placing layout scores according to the placing points and by using the article placing layout scoring rules; wherein the item placement layout score is used to characterize the uniformity of placement of the placed items within the target room;
optionally, the auxiliary scoring rule includes: item function scoring rules; the auxiliary scoring comprises: scoring the function of the item; the method further comprises the following steps: obtaining the distance of the path between every two placed objects according to the placement information of all the placed objects; setting a use probability corresponding to each path as a weight coefficient corresponding to each path based on the item function scoring rule; 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 comprises: pre-training a graph neural network GNN model; the placement information includes: the placement position and orientation.
According to another aspect of the embodiments of the present disclosure, there is provided an apparatus for obtaining an item placement plan score, including: the first grading module is used for obtaining a first grade corresponding to the article placing scheme of the target room based on a preset placing scheme screening rule; wherein the first score is used for representing whether the item placement scheme passes the screening; a second scoring module, configured to obtain, by using a neural network model, a second score corresponding to the article placement scheme according to the house type structure data corresponding to the target room, the 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 placing reasonability of the placed object; the third grading module is used for acquiring at least one auxiliary grade corresponding to the article placing scheme by using a preset auxiliary grading rule according to the placed articles and the corresponding placing information; and the scheme scoring module is used for acquiring a scheme score corresponding to the article placing scheme according to the first score, the second score and the auxiliary score.
Optionally, the number of the put items is multiple, wherein the second scoring module includes: an information obtaining unit, configured to obtain, according to the house type structure data, the article attribute information of each placed article, and the placement information, a placement probability corresponding to each placed article by using the neural network model; and the score determining unit is used for acquiring the second score according to the placing probabilities of all the placed articles.
Optionally, the score determining unit is specifically configured to obtain a mean value corresponding to the placement probabilities of all the placed articles by using a preset mean value algorithm, and use the mean value as the second score.
Optionally, the presenting article comprises: one or more of furniture, appliances, and ornaments; the item attribute information includes: one or more of an item category, an item style, an item size, an item placement attribute; 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 first scoring module is configured to determine whether the article 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, the placing scheme screening rule comprises: forbidding one or more of placing overlapping, forbidding wall penetrating and forbidding blocking 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; acquiring the weighted value or the sum of the weighted sum and the second score as a total score of the scheme; taking the product of the first score and the total score of the solution as the solution score.
Optionally, the auxiliary scoring rule includes: an article placement layout scoring rule; the auxiliary scoring comprises: grading the placement layout of the articles; the third scoring module comprises: the layout scoring unit is used for calculating a mask graph corresponding to the target room according to the house type structure data and the placing information of all the placed articles; determining the placing points of all the placed articles in the mask graph, and obtaining the article placing layout scores according to the placing points and by using the article placing layout scoring rules; wherein the item placement layout score is used to characterize the uniformity of placement of the placed items within the target room.
Optionally, the auxiliary scoring rule includes: item function scoring rules; the auxiliary scoring comprises: scoring the function of the item; the third scoring module comprises: the function scoring unit is used for acquiring the distance of the path between every two placed articles according to the placement information of all the placed articles; setting a use probability corresponding to each path as a weight coefficient corresponding to each path based on the item function scoring rule; 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 comprises: pre-training a graph neural network GNN model; the placement information includes: the 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 device for obtaining the grading of the goods placement schemes, the electronic equipment and the storage medium, provided by the embodiments of the present disclosure, the scheme grading corresponding to the goods placement schemes can be intelligently generated based on the position constraint relationship among the placed goods and the rules of forbidden placement of the goods, reference information for comparing the advantages and disadvantages of different schemes is provided, a user is helped to make a decoration decision, designer resources and design flow time are saved, a decoration design link flow can be simplified, and convenience can be provided for the user to make a decoration decision.
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 flow chart of one embodiment of a method for obtaining an item placement solution score according to the present disclosure;
fig. 2 is a flowchart of obtaining a second score in an embodiment of the method for obtaining an item placement plan score according to the present disclosure;
fig. 3 is a flowchart for obtaining an item placement layout score in an embodiment of the scoring method for obtaining an item placement plan according to the present disclosure;
fig. 4 is a flowchart of obtaining an item function score in an embodiment of the method for obtaining an item placement plan score according to the present disclosure;
fig. 5 is a flowchart of obtaining a plan score in an embodiment of a method for obtaining an item placement plan score according to the present disclosure;
FIGS. 6A and 6B are schematic views of an article placement scheme;
fig. 7A is a schematic structural diagram of an embodiment of the scoring apparatus for obtaining an item placement plan according to the present disclosure; fig. 7B is a schematic structural diagram of a second scoring module in an embodiment of the scoring apparatus for obtaining an item placement plan according to the present disclosure; fig. 7C is a schematic structural diagram of a third scoring module in an embodiment of the scoring apparatus for obtaining an item placement plan according to the present disclosure;
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 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 process of implementing the present disclosure, the inventors found that, for the placement of indoor articles, various placement schemes of the 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 give a plurality of different article placement schemes; at present, a relatively objective evaluation method is lacked for an article placement scheme, and designers and consumers are difficult to compare the advantages and disadvantages of different schemes from multiple article placement schemes, so that the best and elegant article placement scheme is selected.
The scoring method for obtaining the article placement scheme comprises the steps of obtaining a first score for representing whether the article placement scheme passes screening or not based on a preset placement scheme screening rule, obtaining a second score for representing the placement reasonability of the article placement scheme by using a neural network model, obtaining an auxiliary score corresponding to the article placement scheme by using a preset auxiliary scoring rule, and obtaining a scheme score corresponding to the article placement scheme according to the first score, the second score and the auxiliary score; the method and the device can intelligently generate scheme scores corresponding to the article placing schemes based on the position constraint relation among placed articles and rules of forbidden placing of the articles, and provide reference information for comparing the advantages and the disadvantages of different schemes.
Exemplary method
Fig. 1 is a flowchart of an embodiment of a method for obtaining an item placement plan score 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 a first score corresponding to an article placement scheme of a target room based on a preset placement scheme screening rule; wherein, the first score is used for representing whether the goods placement scheme passes the screening.
The target room can be a bedroom, a living room, a bathroom and the like; the article placing scheme comprises the following steps: furniture, appliances, ornaments, etc.; the number of the article placing schemes can be one or more, and the article placing schemes comprise placing figures and the like for displaying the placing positions, the orientations and the like of articles such as tables, beds, electric appliances and the like in a target room.
S102, acquiring a second score corresponding to the article placement scheme by using a neural network model according to the house type structure data corresponding to the target room, the article attribute information of the placed articles in the target room and the placement information of the placed articles in the article placement scheme; and the second score is used for representing the placing rationality of the placed articles.
The house type diagram of the target room 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 article attribute information includes: 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.
A training sample set can be generated in advance according to house type structure data, article attribute information of placed articles, placing information of the placed articles in an article placing scheme and the like, and training of a neural network is performed according to the training samples to obtain a trained neural network model; and acquiring a second score corresponding to the article placement scheme by using the trained neural network model.
S103, according to the placed articles and the corresponding placement information, at least one auxiliary score corresponding to the article placement scheme is obtained by using a preset auxiliary score rule. The auxiliary scoring rules may be various, and the corresponding auxiliary scoring may be various.
And S104, acquiring a scheme score corresponding to the article placement scheme according to the first score, the second score and the auxiliary score.
In one embodiment, obtaining the first score corresponding to the item placement plan of the target room may employ a variety of methods. For example, the placement scheme filtering rules include: forbidding placing overlapping, forbidding wall penetrating, forbidding blocking rules and the like. Whether the article placing scheme meets the placing scheme screening rule or not can be judged based on computer vision technology and the like, if yes, the first score is set to be 1, and if not, the first score is set to be 0.
The placing scheme screening rules are hard rules, and are rules which must be met by the article placing scheme, and the placing positions of articles in the article placing scheme can not be out of the wall, certain articles can not be placed in an overlapped mode, and the like. If the article placing position in the article placing scheme can cause abnormal results such as wall crossing, overlapping with the placed article, road blocking and the like, the article placing scheme is excluded.
In one embodiment, the number of the placed items is multiple, fig. 2 is a flowchart for obtaining a second score in one embodiment of the scoring method for an item placement plan of the present disclosure, and the method shown in fig. 2 includes the steps of: S201-S202. The following describes each step.
S201, obtaining placing probability corresponding to each placed article by using a neural network model according to the house type structure data, the article attribute information of each placed article and the placing information.
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. The GNN model outputs placement probability corresponding to each placed article for given house type structure data, article attribute information and placement information, wherein the placement probability is the probability of placing an article for which the article corresponding to the placement information of one placed article is; the placing information includes placing positions and orientations of the articles, and the like.
S202, obtaining a second score according to the placing probability of all placed articles.
The second score may be obtained according to the placement probability of all the placed items using various methods. For example, a preset mean value algorithm is used to obtain a mean value corresponding to the placement probability of all the placed articles, and the mean value is used as a second score; the average algorithm may be an existing average algorithm, a weighted average algorithm, or the like.
In one embodiment, the secondary scoring rules include: an article placement layout scoring rule; the auxiliary scoring comprises the following steps: and grading the placement layout of the articles. Fig. 3 is a flowchart of obtaining an item placement layout score according to an embodiment of the method for obtaining an item placement plan score according to the present disclosure, where the method shown in fig. 3 includes the steps of: S301-S302. The following describes each step.
S301, calculating a mask graph corresponding to the target room according to the house type structure data and the placing information of all placed articles. The mask graph and the house type graph of the target room have the same pixels and the same size, the house type graph contains the placed objects, 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 placement position of the placed object is represented.
S302, determining placing points of all placed articles in the mask image, and obtaining article placing layout scores according to the placing points and by using an article placing layout scoring rule; and the article placing layout score is used for representing the arrangement uniformity of the placed articles in the target room.
The article placement layout scoring rule is used for measuring the overall layout rationality of an article placement scheme, and avoiding that all articles are gathered together and leave a large amount of vacant space elsewhere. The article placement layout scoring rules can set scores corresponding to the uniformity of the arrangement in the target room (article placement layout scoring, the nature of the article placement layout scoring is a layout beautification score); according to the mask diagram and the placement point of the article (the placement point is the position point of the mass point or the central point of the article in the article placement scheme), index values for representing the arrangement uniformity in the target room can be obtained by adopting various existing methods.
In one embodiment, the secondary scoring rules include: item function scoring rules; the auxiliary scoring comprises the following steps: and (4) scoring the function of the article. Fig. 4 is a flowchart of an embodiment of the method for obtaining an item placement plan score according to the present disclosure, where the method shown in fig. 4 includes the steps of: S401-S403. The following describes each step.
S401, the distance of the path between every two placed articles is obtained according to the placement information of all placed articles.
The distance of each path between the placed objects can be obtained according to the mask diagram and the placing points of the objects, and the path between the placed objects can be the path between the placing points of the objects.
S402, setting the use probability corresponding to each path based on the item function scoring rule as a weight coefficient corresponding to each path.
And S403, 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.
The article function scoring rule is used for setting the use probability and the like corresponding to each path, and the use probability is the probability of the user walking in the path; the item function score is a function score that measures the rationality of the item function. For example, there are three placed items A, B and C in the item placement scheme, and the distances of three paths between three placed items A, B and C are 3, 4, and 5; setting the use probabilities corresponding to the three paths to be 0.3, 0.5 and 0.6 respectively based on the article function scoring rule as weight coefficients corresponding to the three paths respectively; a weighted average of (3 × 0.3+4 × 0.4+5 × 0.6)/3 ═ 1.83 was obtained as the item function score.
In one embodiment, there may be a variety of ways to obtain a solution score corresponding to an item placement solution. Fig. 5 is a flowchart of a method for obtaining a plan score according to an embodiment of the method for obtaining a plan score of an item placement plan of the present disclosure, where the method shown in fig. 5 includes the steps of: S501-S503. The following describes each step.
S501, acquiring a weighting coefficient corresponding to the auxiliary score, and acquiring a weighted value or a weighted sum according to the auxiliary score and the corresponding weighting coefficient. If there are a plurality of auxiliary scores, weighting information corresponding to the respective auxiliary scores is acquired, and a weighted sum is calculated.
And S502, taking the sum of the weighted value or the weighted sum and the second score as the total score of the scheme.
And S503, taking the product of the first score and the total score of the scheme as the score of the scheme.
For example, the protocol score is s1 (s2+ k3 s3+ k4 s 4); wherein s1 is the first score, (s2+ k3 s3+ k4 s4) is the total score of the regimen; s2 is the second score, s3 is the placement score, s4 is the function score, and k3 and k4 are the weighting coefficients.
In one embodiment, as shown in fig. 6A and 6B, the house type structure data corresponding to the target room includes structure data of doors, windows, walls, etc., and fig. 6A and 6B each provide an item placement scheme, and a list _ furniture [ "bed", "wardrobe", "dresser", "table" ]. The placing scheme screening rules are rules which the article placing scheme must meet, and the placing positions of articles in the article placing scheme can not be out of the wall, certain articles can not be placed in an overlapped mode, and the like based on the placing scheme screening rules. And judging whether the article placing scheme accords with the placing scheme screening rule or not by using the existing computer vision algorithm, if so, setting the first score to be 1, and if not, setting the first score to be 0.
And obtaining the placing probability corresponding to each placed article according to the house type structure data, the article attribute information of each placed article and the placing information by using a pre-trained graph neural network GNN model. For example, for each item in list _ furniture, the item is deleted in the item placement scheme in turn, and the GNN model is used to predict the placement probability of the item; after all the articles in the list _ furniture are predicted and the placing probabilities are obtained, the mean value corresponding to the placing probabilities of all the placed articles is obtained by using a mean value algorithm, and the mean value is used as a second score.
Calculating a mask graph corresponding to the target room according to the house type structure data and the placing information of all placed articles, determining placing points of all articles in the list _ furnitures in the mask graph, and obtaining article placing layout scores according to the placing points and by using an article placing layout scoring rule.
Calculating the distance of paths among all the articles in the list _ furniture (the paths are the paths between two articles when the user arrives at another 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 the paths and the corresponding weight coefficients, and setting the weighted average value as the item function score.
The protocol score calculated for the item placement scheme of fig. 6A and 6B was s1 (s2+ k3 s3+ k 4)
s4), wherein s1 is the first score, (s2+ k3 × s3+ k4 × s4) is the total score of the regimen; s2 is a second score, s3 is a score of placement layout of the articles, s4 is a score of functions of the articles, and k3 and k4 are weighting coefficients respectively; after the plan scores of the item placement plans of fig. 6A and 6B are obtained, the item placement plan of fig. 6A with the highest plan score is taken as the preferred plan.
Exemplary devices
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 scheme scoring module 704. The first scoring module 701 acquires a first score corresponding to an article placement scheme of a target room based on a preset placement scheme screening rule; wherein, the first score is used for representing whether the goods placement scheme passes the screening.
The second scoring module 702 obtains a second score corresponding to the article placement scheme by using the neural network model according to the house type structure data corresponding to the target room, the article attribute information of the placed articles in the target room, and the placement information of the placed articles in the article placement scheme; and the second score is used for representing the placing rationality of the placed articles. The third scoring module 703 obtains at least one auxiliary score corresponding to the article placement scheme using a preset auxiliary scoring rule according to the placed articles and the corresponding placement information. The plan scoring module 704 obtains a plan score corresponding to the item placement plan according to the first score, the second score and the auxiliary score.
In one embodiment, presenting the item comprises: furniture, appliances, ornaments, etc.; the article attribute information includes: item type, item style, item size, item placement attributes, and the like; the house type structure data includes: wall distribution data, door and window distribution data, area data, floor height data and the like.
The first scoring module 701 judges whether the article 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 and include: forbidding one or more of placing overlapping, forbidding wall penetrating and forbidding blocking rules.
The scheme scoring module 704 obtains a weighting coefficient corresponding to the auxiliary score, and obtains a weighted value or a weighted sum according to the auxiliary score and the corresponding weighting coefficient; the scheme scoring module 704 obtains the weighted value or the sum of the weighted sum and the second score as a total score of the scheme; the project scoring module 704 takes the product of the first score and the total score of the project as a project score.
In one embodiment, as shown in fig. 7B, second scoring module 702 includes: information acquisition unit 7021 and score determination unit 7022. The number of the placed articles is plural, and information obtaining unit 7021 obtains, according to the house type structure data, the article attribute information of each placed article, and the placement information, the placement probability corresponding to each placed article by using the neural network model. Score determining unit 7022 obtains a second score according to the placement probabilities of all the placed items. Score determining unit 7022 may obtain a mean value corresponding to the placement probabilities of all the placed items by using a preset mean value 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: an article placement layout scoring rule; the auxiliary scoring comprises the following steps: and grading the placement layout of the articles. The layout scoring unit 7031 calculates a mask diagram corresponding to the target room according to the house type structure data and the placement information of all the placed articles; a layout scoring unit 7031 determines placement points of all placed articles in the mask map, and obtains article placement layout scores according to the placement points and using an article placement layout scoring rule; and the article placing layout score is used for representing the arrangement uniformity of the placed articles in the target room.
The auxiliary scoring rules include: item function scoring rules; the auxiliary scoring comprises the following steps: and (4) scoring the function of the article. The function scoring unit 7032 obtains the distance of the path between each placed article according to the placement information of all placed articles; the function scoring unit 7032 sets the use probability corresponding to each path as a weight coefficient corresponding to each path based on the article function scoring rule; the function scoring unit 7032 obtains a weighted average value from the distances of all the 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 includes 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 capability and/or instruction execution capability, and may control other components in the electronic device 81 to perform 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 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 a computer-readable storage medium and executed by processor 811 to implement the above described methods for obtaining item placement plan scoring and/or other desired functionality of the various embodiments of the present disclosure. 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 81 may further include: an input device 813, an output device 814, etc., which are interconnected by a bus system and/or other form of connection mechanism (not shown). 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, among others.
Of course, for simplicity, only some of the components of the electronic device 81 relevant to the present disclosure are shown in fig. 8, and components such as buses, input/output interfaces, and the like are omitted. In addition, the electronic device 81 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 a method for obtaining an item placement plan scoring method according to various embodiments of the present disclosure as described in the "exemplary methods" section of this specification above.
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 obtaining an item placement plan scoring method 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.
In the method and the device for obtaining the scores of the article placement schemes, the electronic device and the storage medium in the above embodiments, a first score for representing whether the article placement scheme passes the screening is obtained based on a preset placement scheme screening rule, a second score for representing the placement rationality of the article placement scheme is obtained by using a neural network model, and an auxiliary score corresponding to the article placement scheme is obtained by using a preset auxiliary score rule; acquiring a scheme score corresponding to the article placing scheme according to the first score, the second score and the auxiliary score; the scheme scores corresponding to the article placement schemes can be intelligently generated based on the position constraint relation among placed articles and the rules of prohibition of placement of the articles, 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 flows can be simplified, convenience can be provided for the decoration decisions of the user, and decoration cost is reduced.
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. A method for obtaining an item placement plan score, comprising:
acquiring a first score corresponding to an article placing scheme of a target room based on a preset placing scheme screening rule; wherein the first score is used for representing whether the item placement scheme passes the screening;
acquiring a second score corresponding to the article placement scheme by using a neural network model according to the house type structure data corresponding to the target room, the 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 placing reasonability of the placed object;
according to the placed articles and the corresponding placement information, at least one auxiliary score corresponding to the article placement scheme is obtained by using a preset auxiliary score rule;
and acquiring a scheme score corresponding to the article placing scheme according to the first score, the second score and the auxiliary score.
2. The method of claim 1, wherein the number of the placed items is plural, and the obtaining a second score corresponding to the item placement plan using the neural network model comprises:
according to the house type structure data, the article attribute information of each placed article and the placement information, obtaining a placement probability corresponding to each placed article by using the neural network model;
and obtaining the second score according to the placing probabilities of all the placed articles.
3. The method of claim 2, wherein the obtaining the second score according to the placement probabilities of all the placed items comprises:
and acquiring a mean value corresponding to the placing probabilities of all the placed articles by using a preset mean value algorithm, and taking the mean value as the second score.
4. The method of claim 1, wherein,
the put article includes: one or more of furniture, appliances, and ornaments;
the item attribute information includes: one or more of an item category, an item style, an item size, an item placement attribute;
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.
5. The method of claim 1, wherein the obtaining a first score corresponding to an item placement plan of a target room based on preset placement plan filtering rules comprises:
judging whether the article placing scheme accords with the placing scheme screening rule, if so, setting the first score to be 1, and if not, setting the first score to be 0;
wherein, the placing scheme screening rule comprises: forbidding one or more of placing overlapping, forbidding wall penetrating and forbidding blocking rules.
6. The method of claim 1, wherein said obtaining a plan score corresponding to the item placement plan based on the first score, the second score, and the auxiliary score comprises:
acquiring a weighting coefficient corresponding to the auxiliary score, and acquiring a weighted value or a weighted sum according to the auxiliary score and the corresponding weighting coefficient;
acquiring the weighted value or the sum of the weighted sum and the second score as a total score of the scheme;
taking the product of the first score and the total score of the solution as the solution score.
7. The method of claim 1, wherein the secondary scoring rules comprise: an article placement layout scoring rule; the auxiliary scoring comprises: grading the placement layout of the articles; the method further comprises the following steps:
calculating a mask graph corresponding to the target room according to the house type structure data and the placing information of all the placed articles;
determining the placing points of all the placed articles in the mask graph, and obtaining the article placing layout scores according to the placing points and by using the article placing layout scoring rules; wherein the item placement layout score is used to characterize the uniformity of placement of the placed items within the target room.
8. An apparatus for obtaining an item placement plan score, comprising:
the first grading module is used for obtaining a first grade corresponding to the article placing scheme of the target room based on a preset placing scheme screening rule; wherein the first score is used for representing whether the item placement scheme passes the screening;
a second scoring module, configured to obtain, by using a neural network model, a second score corresponding to the article placement scheme according to the house type structure data corresponding to the target room, the 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 placing reasonability of the placed object;
the third grading module is used for acquiring at least one auxiliary grade corresponding to the article placing scheme by using a preset auxiliary grading rule according to the placed articles and the corresponding placing information;
and the scheme scoring module is used for acquiring a scheme score corresponding to the article placing scheme according to the first score, the second score and the auxiliary score.
9. A computer-readable storage medium, the storage medium storing a computer program for performing the method of any of the preceding claims 1-7.
10. An electronic device, the electronic device comprising:
a processor; a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the method of any one of claims 1-7.
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| CN202010561583.0A CN111709062B (en) | 2020-06-18 | 2020-06-18 | Method, device, equipment and medium for acquiring item placement scheme scoring |
| US17/324,023 US20210365603A1 (en) | 2020-05-19 | 2021-05-18 | Artificial intelligence systems and methods for interior furnishing |
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