CN110390153B - Method, device and equipment for generating house type structure improvement scheme and storage medium - Google Patents
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Abstract
The utility model provides a method and a device for generating a house type structure improvement scheme, an electronic device and a storage medium, which relate to the technical field of artificial intelligence, wherein the method comprises the following steps: labeling the sample house type structure data to add house type characteristic information and grading information to the sample house type structure data; generating a training sample set according to the first simplified house type graph, the sample house type graph and the sample house type structure data; training a neural network according to the training sample to obtain a neural network model; acquiring a house type structure modification scheme corresponding to at least one house type modification demand characteristic, comparison information and modification description information by using a neural network model and a preset house type modification decision rule; the method, the device, the electronic equipment and the storage medium can intelligently generate the modification scheme of the house type structure meeting the modification requirement, provide modification description and comparison information with the original house type structure for each scheme, and can help a user to make a decoration decision.
Description
Technical Field
The present disclosure relates to the field of artificial intelligence technologies, and in particular, to a method and an apparatus for generating a house type structure improvement scheme, an electronic device, and a storage medium.
Background
In the house decoration process, many residents have the needs of carrying out structural transformation on house types based on original house types and combining the living needs of the residents on the basis of not damaging main structures such as heavy walls of houses and the like. The traditional house type structure modification needs professional and experienced designers to put forward modification design, and common residents often cannot perform the modification design. The family structure needs to be modified through the processes of measuring rooms by professional designers, communicating with users to form favorite styles, designing and the like, often needs a large amount of time to design drawings, occupies a large amount of designer resources, is long in design process time, complicated in things and high in price, and does not have universal applicability.
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 generating a house type structure transformation scheme, an electronic device and a storage medium.
According to an aspect of the embodiments of the present disclosure, a method for generating a house type structure improvement scheme is provided, including: acquiring sample house type structure data corresponding to a sample house type graph, and labeling the sample house type structure data to add house type feature information and scoring information to the sample house type structure data; wherein the sample house type graph comprises: the original house type graph and the modified house type graph; obtaining a first simplified house type graph corresponding to the sample house type graph based on the sample house type structure data; and processing the house type graph needing to be modified according to the first simplified house type graph, the sample house type graph and the sample house type structure data to obtain a house type structure modification scheme corresponding to at least one house type modification demand characteristic, and comparison information and modification description information of the house type structure modification scheme and the house type graph needing to be modified.
Optionally, a training sample set is generated according to the first simplified house type graph, the sample house type graph and the sample house type structure data; training a neural network according to the training sample to obtain a neural network model; and processing the house type graph to be modified by using the neural network model and a preset house type modification decision rule to obtain a house type structure modification scheme corresponding to at least one house type modification demand characteristic, and comparison information and modification description information between the house type structure modification scheme and the house type graph to be modified.
Optionally, the processing the house type graph to be modified by using the neural network model and a preset house type modification decision rule to obtain a house type structure modification scheme corresponding to at least one house type modification requirement characteristic, and the comparison information and the modification description information between the house type structure modification scheme and the house type graph to be modified include: acquiring modified house type structure data corresponding to the house type graph needing to be modified; acquiring a second simplified house type graph corresponding to the house type graph needing to be modified based on the modified house type structure data; inputting the second simplified house type diagram and the modified house type structure data into the neural network model, and acquiring wall modification scheme information which is output by the neural network model and corresponds to at least one house type modification demand characteristic and wall modification description information which corresponds to each wall modification scheme information; processing the wall body reconstruction scheme information by using the house type reconstruction decision rule to obtain a house type structure reconstruction diagram corresponding to at least one house type reconstruction demand characteristic; generating comparison information between the house type graph needing to be modified and the house type structure modification graph; and acquiring the house type reconstruction description information based on the wall reconstruction description information.
Optionally, the wall improvement plan information includes: a wall body reconstruction thermodynamic diagram; wherein, the wall body transformation thermodynamic diagram includes: the method comprises the steps of obtaining information of a wall body needing to be modified in the house type graph needing to be modified and modification probability corresponding to the wall body needing to be modified.
Optionally, the processing the wall reconstruction scheme information by using the house type reconstruction decision rule, and acquiring a house type structure reconstruction diagram corresponding to at least one house type reconstruction demand characteristic includes: and traversing the wall reconstruction thermodynamic diagram by using a Monte Carlo search tree algorithm by using a preset wall reconstruction optimization limiting rule as a constraint condition, determining a wall proposed to be reconstructed in the house type diagram to be reconstructed, and acquiring the house type structure reconstruction diagram which meets the constraint condition and enables the comprehensive reconstruction probability corresponding to all the wall proposed to be reconstructed to be maximum.
Optionally, the obtaining a first simplified house type graph corresponding to the sample house type graph based on the sample house type structure data includes: obtaining first bearing wall information in the sample house type graph based on the sample house type structure data; generating the first simplified house type graph based on the first bearing wall information; wherein, only the bearing wall and the door and/or window arranged on the bearing wall are reserved in the first simplified house type drawing; the obtaining of the second simplified house type graph corresponding to the house type graph needing to be modified based on the modified house type structure data comprises: acquiring second bearing wall information in the house type graph needing to be modified based on the modified house type structure data; generating the second simplified house type graph based on the second bearing wall information; wherein only the load-bearing wall and the doors and/or windows provided thereon are retained in said second simplified floor plan.
Optionally, the neural network model comprises: an input layer neuron model, a middle layer neuron model and an output layer neuron model; the output of each layer of neuron model is used as the input of the next layer of neuron model; wherein the neural network model is a sub-network structure of a plurality of neural network layers with a fully connected structure; the middle layer neuron model is a full-connection layer.
Optionally, the sample house type structure data includes: one or more of wall distribution data, load-bearing wall distribution data, door and window distribution data, area data, floor height data and position coordinate data of the house type; the house type characteristic information comprises: one or more of the spaciousness of house type, the number of resident people, the storage degree, the lighting degree and the construction year of the house; the house type reconstruction demand characteristics include: one or more of an enhanced housing accommodation experience, an enhanced housing comfort experience.
According to another aspect of the embodiments of the present disclosure, a generation apparatus for a house type structural transformation scheme is provided, which includes: the family type labeling module is used for acquiring sample family type structure data corresponding to the sample family type graph, labeling the sample family type structure data and adding family type characteristic information and grading information to the sample family type structure data; wherein the sample house type graph comprises: the original house type graph and the modified house type graph; the house type simplifying module is used for acquiring a first simplified house type graph corresponding to the sample house type graph based on the sample house type structure data; and the house type processing module is used for processing the house type graph needing to be modified according to the first simplified house type graph, the sample house type graph and the sample house type structure data to obtain a house type structure modification scheme corresponding to at least one house type modification demand characteristic, and comparison information and modification description information of the house type structure modification scheme and the house type graph needing to be modified.
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 generation method and device of the house type structure transformation scheme provided by the embodiment of the disclosure, the electronic device and the storage medium, a training sample set is generated, and the training of the neural network is performed according to the training sample, so as to obtain a neural network model; processing a house type graph to be modified by using a neural network model and a preset house type modification decision rule, and acquiring a house type structure modification scheme corresponding to at least one house type modification demand characteristic, and comparison information and modification description information of the house type structure modification scheme and the house type graph to be modified; the method can intelligently generate the modification scheme of the house type structure meeting the modification requirement, provide modification description and comparison information with the original house type structure for each scheme, provide reasonable reference suggestions for house decoration and modification of residents, and help users to make decoration decisions.
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 of generating a residential architecture modification plan of the present disclosure;
fig. 2 is a flowchart of processing a house type diagram to be modified in an embodiment of the method for generating a house type structure modification scheme of the present disclosure;
fig. 3A is a schematic structural diagram of an embodiment of a device for generating a house type structural transformation scheme according to the present disclosure; fig. 3B is a schematic structural diagram of another embodiment of the generation device of the residential architecture renovation scheme of the present disclosure;
FIG. 4 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 realizing the method, the inventor finds that in the house decoration process, the traditional house type structural modification needs professional and experienced designers to propose modification design, the house type structural modification needs procedures of measuring rooms by professional designers, communicating style hobbies with users, designing and the like, a large amount of time is often needed for designing drawings, a large amount of designer resources are occupied, the design procedures are long in time, tedious in things and high in price, and the method is not universally applicable.
According to the generation method of the house type structure transformation scheme, a training sample set is generated, and training of a neural network is carried out according to the training sample, so that a neural network model is obtained; processing a house type graph to be modified by using a neural network model and a preset house type modification decision rule, and acquiring a house type structure modification scheme corresponding to at least one house type modification demand characteristic, and comparison information and modification description information of the house type structure modification scheme and the house type graph to be modified; the method can intelligently generate the modification scheme of the house type structure meeting the modification requirement, provide modification description and comparison information with the original house type structure for each scheme, and provide reasonable reference suggestion for house decoration and modification of residents.
Exemplary method
Fig. 1 is a flowchart of an embodiment of a method for generating a residential architecture improvement plan according to the present disclosure, where the method shown in fig. 1 includes the steps of: S101-S103. The following describes each step.
S101, obtaining sample house type structure data corresponding to the sample house type graph, labeling the sample house type structure data, and adding house type characteristic information and grading information to the sample house type structure data.
The sample house type graph comprises: original house type graphs and modified house type graphs and the like. The original house type diagram and the modified house type diagram can be sample house type diagrams generated by a CAD modeling tool and the like, the sample house type diagrams can be vector diagrams and the like, a plurality of house type elements such as walls, windows and the like exist in the sample house type diagrams, the sample house type diagrams are provided with sample house type structure data, and the sample house type structure data comprise: 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 sample house type structure data can be marked manually, the house type characteristic information comprises house type spaciousness, the number of residents, the storage degree, the lighting degree, the house construction year and the like, and the sample house type structure data can also be scored, for example, the bearing wall distribution, the door and window distribution and the like corresponding to a plurality of sample house type graphs are scored.
S102, acquiring a first simplified house type graph corresponding to the sample house type graph based on the sample house type structure data.
Obtaining the first simplified house type graph corresponding to the sample house type graph may employ various methods. For example, first bearing wall information in the sample house type graph is obtained based on the sample house type structure data, and a first simplified house type graph is generated based on the first bearing wall information, wherein the first simplified house type graph may only include the bearing wall and a house type graph of a door and/or a window arranged on the bearing wall, and is the simplest house type graph; alternatively, if the sample house type graph does not have the bearing wall information, the first simplified house type graph can be an original house type graph corresponding to the sample house type graph, and the like.
S103, the house type graph needing to be modified is processed according to the first simplified house type graph, the sample house type graph and the sample house type structure data, and a house type structure modification scheme corresponding to at least one house type modification requirement characteristic, comparison information and modification description information of the house type structure modification scheme and the house type graph needing to be modified are obtained.
There are many methods for processing the house type diagram to be modified according to the first simplified house type diagram, the sample house type diagram and the sample house type structure data. For example, a training sample set is generated according to the first simplified house type diagram, the sample house type diagram and the sample house type structure data, and the neural network is trained according to the training samples to obtain the neural network model. And taking the first simplified house type graph, the sample house type graph and the labeled sample house type structure data as training sample data of the neural network model, and training the neural network model, wherein the neural network model can be various neural network models.
And processing the house type graph to be modified by utilizing the neural network model and a preset house type modification decision rule to obtain a house type structure modification scheme corresponding to at least one house type modification demand characteristic, and comparison information and modification description information of the house type structure modification scheme and the house type graph to be modified.
The house type transformation demand can be for the enhancement accomodate living experience, the enhancement comfortable experience of living etc.. The method comprises the steps of obtaining a house type graph which needs to be decorated and modified by a user, processing the house type graph which needs to be modified by utilizing a neural network model and preset house type modification decision rules, obtaining a house type structure modification scheme corresponding to accommodation experience and comfort experience enhancement, providing comparison information and modification description information of the house type structure modification scheme and the house type graph which needs to be modified for the user, wherein the modification description information can be a description for wall modification and the like, and can provide one or more house type structure modification schemes meeting different house type modification demand characteristics, and the comparison information and the modification description information of the house type structure modification scheme and the house type graph which needs to be modified.
Fig. 2 is a flowchart of processing a house type graph to be modified in an embodiment of a method for generating a house type structure modification scheme according to the present disclosure, where the method shown in fig. 2 includes the steps of: S201-S206. The following describes each step.
S201, obtaining the modified house type structure data corresponding to the house type graph needing to be modified.
The user can input or select the user-type graph needing to be modified through the front-end page. The house type graph which is input by the user and needs to be modified can be a house type graph generated through a CAD modeling tool and the like, the house type graph which needs to be modified can be a vector graph and the like, and modified house type structure data can be obtained from the house type graph which needs to be modified. The reconstruction of the house type structure data comprises the following steps: 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. And if the house type graph needing to be modified input by the user is not the house type graph generated by a CAD modeling tool and the like, searching the sample house type graph corresponding to the house type graph needing to be modified from the stored sample house type graphs, and acquiring modified house type structure data from the sample house type graphs.
S202, acquiring a second simplified house type graph corresponding to the house type graph needing to be modified based on the modified house type structure data.
Various methods may be used to obtain the second simplified house type map corresponding to the house type map to be reconstructed. For example, second bearing wall information in the house type graph needing to be modified is obtained based on the modified house type structure data, and a second simplified house type graph is generated based on the second bearing wall information. The second simplified house type drawing only comprises a bearing wall and a door and/or a window arranged on the bearing wall, and is the simplest house type drawing; or, if the house type graph needing to be modified does not have the bearing wall information, the second simplified house type graph can be an original house type graph corresponding to the house type graph needing to be modified, and the like.
And S203, inputting the second simplified house type diagram and the modified house type structure data into the neural network model, and acquiring wall modification scheme information which is output by the neural network model and corresponds to at least one house type modification demand characteristic and wall modification description information which corresponds to each wall modification scheme information.
And inputting the second simplified house type diagram and the modified house type structure data into the neural network model, acquiring wall modification scheme information which is output by the neural network model and corresponds to the enhanced accommodation experience, the enhanced living comfort experience and the like, and acquiring wall modification description information which is output by the neural network model and corresponds to each piece of wall modification scheme information, wherein the wall modification description information comprises information of the position, the length and the like of an added or removed wall, description information of the added or removed wall and the like.
And S204, processing the wall body transformation scheme information by using the house type transformation decision rule to obtain a house type structure transformation diagram corresponding to at least one house type transformation demand characteristic.
And processing the wall body transformation scheme information which is output by the neural network model and corresponds to the enhanced accommodation living experience, the enhanced living comfort experience and the like by utilizing the family type transformation decision rule to obtain a family type structure transformation diagram which corresponds to the enhanced accommodation living experience, the enhanced living comfort experience and the like.
And S205, generating comparison information between the house type graph needing to be modified and the house type structure modification graph.
The comparison information can be various, and can be a wall body difference comparison schematic diagram and the like. Wall difference information between the house type graph to be modified and the house type structure modification graph can be generated based on information such as the position and the length of the wall to be added or removed, and a wall difference comparison schematic diagram is generated based on the wall difference information.
And S206, acquiring the house type reconstruction description information based on the wall reconstruction description information. For example, the house type renovation instruction information is generated based on the instruction information of the added or removed wall body, and the house type renovation instruction information may include: the purpose of adding the wall body a is to increase the accommodation area and the like.
The house type structure improvement drawing, the comparison information and the house type improvement instruction information can be provided for the user and selected by the user. By analyzing and comparing the house structures before and after modification, reasonable reference suggestions are provided for house decoration and modification of residents, and the effect of helping users to make decoration decisions is achieved.
In one embodiment, the wall modification plan information is multiple. For example, a wall reconstruction thermodynamic diagram or the like corresponding to the enhanced housing experience, the enhanced housing comfort experience or the like, which is output for the neural network model. The wall reconstruction thermodynamic diagram comprises information of the wall to be reconstructed in the house type diagram to be reconstructed and reconstruction probability corresponding to the wall to be reconstructed. The information of the wall needing to be modified comprises: information on the position, length, etc. of the added or removed wall.
Various methods can be adopted for processing the wall reconstruction scheme information by using the house type reconstruction decision rule. For example, a preset wall reconstruction optimization limiting rule is used as a constraint condition, a Monte Carlo search tree algorithm is used for traversing a wall reconstruction thermodynamic diagram, a wall body suggested to be reconstructed is determined in a house type diagram to be reconstructed, and a house type structure reconstruction diagram which meets the constraint condition and enables comprehensive reconstruction probability corresponding to all the wall bodies suggested to be reconstructed to be maximum is obtained.
The preset wall body transformation optimization restriction rule can have various rules. For example, the axis point position of the wall is optimized through an integer programming algorithm, and the axis point position of the wall based on the optimized is used as a constraint condition. And constructing the Monte Carlo search tree for the nodes by using the wall in the wall reconstruction thermodynamic diagram. The Monte Carlo tree search method is also called a random sampling or statistical test method, and is a calculation method based on probability and statistical theory method because the Monte Carlo tree search method can truly simulate the actual physical process, and is a method for solving a plurality of calculation problems by using random numbers. The solved problem is associated with a certain probabilistic model to obtain an approximate solution to the problem.
The Monte Carlo search tree is traversed through a Monte Carlo search algorithm, and a proposed wall body is selected from the wall body reconstruction thermodynamic diagram, namely a house type structure reconstruction diagram which meets constraint conditions and enables the comprehensive reconstruction probability corresponding to all the proposed wall bodies to be the maximum is selected from the constructed Monte Carlo search tree, for example, the comprehensive reconstruction probability can be the sum, weighted sum and the like of the reconstruction probabilities of all the selected proposed wall bodies.
In one embodiment, the neural network model may be built using a variety of algorithmic models, such as a floornet algorithmic model. The neural network model includes an input layer neuron model, a middle layer neuron model and an output layer neuron model, an output of each layer of neuron model is used as an input of the next layer of neuron model, the neural network model may be a sub-network structure of a plurality of neural network layers having a full connection structure, and the middle layer neuron model is a full connection layer.
And generating a training sample set according to the first simplified house type graph, the sample house type graph (including labeled house type characteristic information and grading information) and the sample house type structure data, and training a neural network according to the training sample to obtain a neural network model. And inputting a second simplified house type diagram and modified house type structure data corresponding to the house type diagram to be modified into the neural network model, and outputting a wall modification thermodynamic diagram corresponding to the enhanced accommodation and living experience, the enhanced living comfort experience and the like and wall modification description information corresponding to each wall to be modified in the wall modification thermodynamic diagram through the neural network model.
Exemplary devices
In one embodiment, as shown in fig. 3A, the present disclosure provides a generation apparatus for a residential architecture renovation scheme, including: a house type labeling module 301, a house type simplifying module 302 and a house type processing module 305. The house type labeling module 301 obtains sample house type structure data corresponding to the sample house type graph, labels the sample house type structure data, and adds house type feature information and scoring information to the sample house type structure data, and the sample house type graph includes: original house type graphs and modified house type graphs and the like.
The house type simplification module 302 obtains a first simplified house type graph corresponding to the sample house type graph based on the sample house type structure data. The house type processing module 305 processes the house type graph to be modified according to the first simplified house type graph, the sample house type graph and the sample house type structure data, and obtains a house type structure modification scheme corresponding to at least one house type modification requirement characteristic, and comparison information and modification description information between the house type structure modification scheme and the house type graph to be modified.
In one embodiment, as shown in fig. 3B, the apparatus for generating a house type structural transformation scheme further includes: a sample generation module 303 and a model training module 304. The sample generation module 303 generates a training sample set according to the first simplified house type graph, the sample house type graph and the sample house type structure data. The model training module 304 performs training of the neural network according to the training samples to obtain a neural network model.
The house type processing module 305 processes the house type graph to be modified by using the neural network model and the preset house type modification decision rule, and obtains a house type structure modification scheme corresponding to at least one house type modification requirement characteristic, and comparison information and modification description information between the house type structure modification scheme and the house type graph to be modified.
In one embodiment, the house type labeling module 301 obtains the modified house type structure data corresponding to the house type graph to be modified. The house type simplification module 302 acquires a second simplified house type graph corresponding to the house type graph needing to be improved based on the improved house type structure data. The house type processing module 305 inputs the second simplified house type diagram and the modified house type structure data into the neural network model, and obtains wall modification scheme information corresponding to at least one house type modification requirement characteristic output by the neural network model and wall modification description information corresponding to each wall modification scheme information.
The house type processing module 305 processes the wall type modification scheme information by using the house type modification decision rule, obtains a house type structure modification corresponding to at least one house type modification demand characteristic, generates comparison information between a house type diagram to be modified and a house type structure modification diagram, and obtains house type modification instruction information based on the wall type modification instruction information.
In one embodiment, the house type simplification module 302 obtains first bearing wall information in the sample house type graph based on the sample house type structure data, and generates a first simplified house type graph based on the first bearing wall information, wherein only the bearing wall and the door and/or window arranged on the bearing wall are reserved in the first simplified house type graph. The house type simplification module 302 obtains information of a second bearing wall in the house type graph to be improved based on the improved house type structure data, and generates a second simplified house type graph based on the information of the second bearing wall, wherein only the bearing wall and a door and/or a window arranged on the bearing wall are reserved in the second simplified house type graph.
In one embodiment, the wall retrofit scenario information includes: wall body reconstruction thermodynamic diagrams and the like; the wall body transformation thermodynamic diagram comprises: the method comprises the steps of obtaining information of a wall body needing to be modified in a house type graph needing to be modified, modification probability corresponding to the wall body needing to be modified and the like. The house-type processing module 305 uses a preset wall modification optimization restriction rule as a constraint condition, traverses the wall modification thermodynamic diagram by using a monte carlo search tree algorithm, determines the wall body which is proposed to be modified in the house-type diagram which needs to be modified, and acquires the house-type structure modification diagram which meets the constraint condition and enables the comprehensive modification probability corresponding to all the wall bodies which are proposed to be modified to be the maximum.
Fig. 4 is a block diagram of one embodiment of an electronic device of the present disclosure, as shown in fig. 4, the electronic device 41 includes one or more processors 411 and memory 412.
The processor 411 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 41 to perform desired functions.
Memory 412 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 the processor 411 to implement the above generation method of the residential architecture renovation scheme of the various embodiments of the disclosure and/or other desired functions. Various contents such as an input signal, a signal component, a noise component, etc. may also be stored in the computer-readable storage medium.
In one example, the electronic device 41 may further include: an input device 413, and an output device 414, etc., interconnected by a bus system and/or other form of connection mechanism (not shown). The input device 413 may also include, for example, a keyboard, a mouse, and the like. The output device 414 can output various information to the outside. The output devices 414 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 41 relevant to the present disclosure are shown in fig. 4, omitting components such as buses, input/output interfaces, and the like. In addition, the electronic device 41 may include any other suitable components, depending on the particular application.
In addition to the above-described methods and apparatus, embodiments of the present disclosure may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the method of generating a house keeping retrofit solution according to various embodiments of the present disclosure described in the above-mentioned "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 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 of generating a house structure improvement plan according to various embodiments of the present disclosure described in the "exemplary methods" section above in this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium may include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing describes the general principles of the present disclosure in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present disclosure are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present disclosure. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the disclosure is not intended to be limited to the specific details so described.
According to the method and the device for generating the house type structure transformation scheme, the electronic device and the storage medium in the embodiment, the training sample set is generated according to the first simplified house type diagram, the sample house type diagram and the sample house type structure data; training a neural network according to the training sample to obtain a neural network model; processing a house type graph to be modified by using a neural network model and a preset house type modification decision rule, and acquiring a house type structure modification scheme corresponding to at least one house type modification demand characteristic, and comparison information and modification description information of the house type structure modification scheme and the house type graph to be modified; the house type structure reconstruction scheme meeting the reconstruction requirement can be intelligently generated, reconstruction description and comparison information with the original house type structure are provided for each scheme, reasonable reference suggestions are provided for house decoration reconstruction of residents, a user can be helped to make decoration decisions, designer resources and design flow time are saved, the decoration design link flow can be simplified, convenience can be provided for the decision of user decoration, and the 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 (9)
1. A method for generating a house type structure improvement scheme comprises the following steps:
acquiring sample house type structure data corresponding to a sample house type graph, and labeling the sample house type structure data to add house type feature information and scoring information to the sample house type structure data; wherein the sample house type graph comprises: the original house type graph and the modified house type graph;
obtaining a first simplified house type graph corresponding to the sample house type graph based on the sample house type structure data;
generating a training sample set according to the first simplified house type graph, the sample house type graph and the sample house type structure data;
training a neural network according to the training sample to obtain the neural network model;
acquiring modified house type structure data corresponding to a house type graph to be modified;
acquiring a second simplified house type graph corresponding to the house type graph needing to be modified based on the modified house type structure data;
inputting the second simplified house type diagram and the modified house type structure data into the neural network model, and acquiring wall modification scheme information which is output by the neural network model and corresponds to at least one house type modification demand characteristic and wall modification description information which corresponds to each wall modification scheme information;
processing the wall body reconstruction scheme information by using the house type reconstruction decision rule to obtain a house type structure reconstruction diagram corresponding to at least one house type reconstruction demand characteristic;
generating comparison information between the house type graph needing to be modified and the house type structure modification graph;
and acquiring the house type reconstruction description information based on the wall reconstruction description information.
2. The method of claim 1, wherein,
the wall body transformation scheme information comprises: a wall body reconstruction thermodynamic diagram;
wherein, the wall body transformation thermodynamic diagram includes: the method comprises the steps of obtaining information of a wall body needing to be modified in the house type graph needing to be modified and modification probability corresponding to the wall body needing to be modified.
3. The method of claim 2, wherein the processing the wall improvement plan information using the house improvement decision rule to obtain a house improvement graph corresponding to at least one house improvement demand characteristic comprises:
and traversing the wall reconstruction thermodynamic diagram by using a Monte Carlo search tree algorithm by using a preset wall reconstruction optimization limiting rule as a constraint condition, determining at least one optimal reconstruction wall in the house type diagram to be reconstructed, and acquiring the house type structure reconstruction diagram which meets the constraint condition and enables the comprehensive reconstruction probability corresponding to all the optimal reconstruction walls to be maximum.
4. The method of claim 1, the obtaining a first simplified house type graph corresponding to the sample house type graph based on the sample house type structure data comprising:
obtaining first bearing wall information in the sample house type graph based on the sample house type structure data;
generating the first simplified house type graph based on the first bearing wall information; wherein, only the bearing wall and the door and/or window arranged on the bearing wall are reserved in the first simplified house type drawing;
the obtaining of the second simplified house type graph corresponding to the house type graph needing to be modified based on the modified house type structure data comprises:
acquiring second bearing wall information in the house type graph needing to be modified based on the modified house type structure data;
generating the second simplified house type graph based on the second bearing wall information; wherein only the load-bearing wall and the doors and/or windows provided thereon are retained in said second simplified floor plan.
5. The method of claim 1, wherein,
the neural network model includes: an input layer neuron model, a middle layer neuron model and an output layer neuron model; the output of each layer of neuron model is used as the input of the next layer of neuron model;
wherein the neural network model is a sub-network structure of a plurality of neural network layers with a fully connected structure; the middle layer neuron model is a full-connection layer.
6. The method of claim 1, wherein,
the sample house type structure data and the modified house type structure data comprise: one or more of wall distribution data, load-bearing wall distribution data, door and window distribution data, area data, floor height data and position coordinate data of the house type;
the house type characteristic information comprises: one or more of the spaciousness of house type, the number of resident people, the storage degree, the lighting degree and the construction year of the house;
the house type reconstruction demand characteristics include: one or more of an enhanced housing accommodation experience, an enhanced housing comfort experience.
7. A generation device of a house type structure improvement scheme comprises:
the family type labeling module is used for acquiring sample family type structure data corresponding to the sample family type graph, labeling the sample family type structure data and adding family type characteristic information and grading information to the sample family type structure data; wherein the sample house type graph comprises: the original house type graph and the modified house type graph;
the house type simplifying module is used for acquiring a first simplified house type graph corresponding to the sample house type graph based on the sample house type structure data;
the sample generation module is used for generating a training sample set according to the first simplified house type graph, the sample house type graph and the sample house type structure data;
the model training module is used for training the neural network according to the training sample to obtain a neural network model;
the house type marking module is also used for acquiring the modified house type structure data corresponding to the house type graph needing to be modified;
the house type simplifying module is also used for acquiring a second simplified house type graph corresponding to the house type graph needing to be modified based on the modified house type structure data;
the house type processing module is used for inputting the second simplified house type graph and the modified house type structure data into the neural network model, and acquiring wall body modification scheme information which is output by the neural network model and corresponds to at least one house type modification demand characteristic and wall body modification description information which corresponds to each wall body modification scheme information; and processing the wall body reconstruction scheme information by using the house type reconstruction decision rule, acquiring the house type structure reconstruction corresponding to at least one house type reconstruction demand characteristic, generating comparison information between a house type graph to be reconstructed and a house type structure reconstruction graph, and acquiring house type reconstruction description information based on the wall body reconstruction description information.
8. A computer-readable storage medium, the storage medium storing a computer program for performing the method of any of the preceding claims 1-6.
9. 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 1-6.
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| CN201910637659.0A CN110390153B (en) | 2019-07-15 | 2019-07-15 | Method, device and equipment for generating house type structure improvement scheme and storage medium |
| US16/929,082 US10956626B2 (en) | 2019-07-15 | 2020-07-14 | Artificial intelligence systems and methods for interior design |
| JP2022502901A JP7325602B2 (en) | 2019-07-15 | 2020-07-15 | Artificial intelligence system and method for interior design |
| AU2020315029A AU2020315029B2 (en) | 2019-07-15 | 2020-07-15 | Artificial intelligence systems and methods for interior design |
| CA3147320A CA3147320A1 (en) | 2019-07-15 | 2020-07-15 | Artificial intelligence systems and methods for interior design |
| PCT/CN2020/102215 WO2021008566A1 (en) | 2019-07-15 | 2020-07-15 | Artificial intelligence systems and methods for interior design |
| US17/180,377 US20210173967A1 (en) | 2019-07-15 | 2021-02-19 | Artificial intelligence systems and methods for interior design |
| US17/180,415 US20210173968A1 (en) | 2019-07-15 | 2021-02-19 | Artificial intelligence systems and methods for interior design |
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