CN114444987A

CN114444987A - Automatic analysis method and device for house type graph

Info

Publication number: CN114444987A
Application number: CN202210372498.9A
Authority: CN
Inventors: 魏启赟; 何为; 卢冬冬; 何宛余; 杨小荻
Original assignee: Shenzhen Xkool Technology Co Ltd
Current assignee: Shenzhen Xkool Technology Co Ltd
Priority date: 2022-04-11
Filing date: 2022-04-11
Publication date: 2022-05-06
Anticipated expiration: 2042-04-11
Also published as: CN114444987B

Abstract

The embodiment of the application discloses an automatic analysis method and device of a house type graph, wherein the method comprises the following steps: acquiring a floor plan; determining a plurality of parameter indexes influenced by target dimensions in the house type graph, wherein the parameter indexes are used for reflecting the living comfort level, and the target dimensions comprise at least one of house type squareness, space orientation, lighting and ventilation, dynamic and static partitions, clean and dirty partitions, reasonable moving lines and moderate sizes; determining a base score for each of the plurality of parameter metrics; and determining the scoring result of the user-type graph in the target dimension according to the basic score of each parameter index in the plurality of parameter indexes. According to the embodiment of the application, the scoring efficiency of the house type graph can be comprehensively improved by automatically and comprehensively scoring the house type graph.

Description

Automatic analysis method and device for house type graph

Technical Field

The application relates to an automation technology, which is applied to the fields of architectural design and the like, in particular to an automatic analysis method and device for an indoor graph.

Background

In the prior art, two evaluation modes are mainly used for the family-type graph, the first evaluation mode is that a designer and the like carry out manual judgment and then score, batch processing cannot be carried out, and the scoring efficiency is low.

And secondly, training a preset house type feature recognition model through an algorithm, outputting a house type feature group and a house source feature group, performing advantage and disadvantage evaluation on a house type graph according to the house type feature group and the house source feature group according to a preset evaluation method, obtaining an advantage and disadvantage evaluation result, and generating a house evaluation report of the house type graph.

Disclosure of Invention

The embodiment of the application provides an automatic analysis method and device for a house type graph, which can comprehensively improve the scoring efficiency of the house type graph by automatically and comprehensively scoring the house type graph.

In a first aspect, an embodiment of the present application provides an automated analysis method for a user-type graph, including:

acquiring a house type graph;

determining a plurality of parameter indexes influenced by target dimensions in the house type graph, wherein the parameter indexes are used for reflecting the living comfort level, and the target dimensions comprise at least one of house type squareness, space orientation, lighting and ventilation, dynamic and static partitions, clean and dirty partitions, reasonable moving lines and moderate sizes;

determining a base score for each of the plurality of parameter metrics;

and determining the scoring result of the user-type graph in the target dimension according to the basic score of each parameter index in the plurality of parameter indexes.

In the prior art, a method for scoring a house type graph is mainly to train a preset house type feature recognition model through an algorithm, determine the quality of a house through a house type structure and a house source type, and in the method, more importantly, the scoring process is refined, the house type graph is evaluated through synthesizing relevant information of a detail level, so that the difference between the house type graph and the prior art can be more obviously embodied, and particularly, a scoring result of the house type graph on a target dimension (space orientation) is judged mainly through synthesizing basic scores of each parameter index (lighting and ventilation) in a plurality of parameter indexes (space orientation for example) influenced by the target dimension (space orientation for example). According to the embodiment of the application, the scoring efficiency of the house type graph can be comprehensively improved through automatic comprehensive scoring of the house type graph.

In a possible implementation, the determining the scoring result of the prototype graph in the target dimension according to the base score of each parameter index of the plurality of parameter indexes further includes:

analyzing the degree of influence of the target dimension on each parameter index of the plurality of parameter indexes;

determining a degree of interest of a user to each of the plurality of parameter indicators;

the determining the scoring result of the user-type graph in the target dimension according to the base score of each parameter index in the plurality of parameter indexes comprises:

and generating a scoring result of the user-type graph on the target dimension according to the basic score of each parameter index, the influence degree of each parameter index and the attention degree of the user.

In the above method, if the plurality of parameter indexes affected by the target dimension (e.g. spatial orientation) specifically include ventilation, lighting, and noise, where a basic score corresponding to the parameter index of ventilation is 5, a basic score corresponding to lighting is 0, and a basic score corresponding to noise is 5; the parameter index of ventilation is expressed as R =1 under the influence of the orientation, lighting is expressed as R =0.8 under the influence of the orientation, and noise is expressed as R =0.5 under the influence of the orientation; the user ' S attention to the parameter index of ventilation is S =0.4, the user ' S attention to the parameter index of lighting is S =0.4, and the user ' S attention to the parameter index of noise is S = 0.2; then, the final calculated composite score of the house type graph in the target dimension (such as spatial orientation) is: 5 × 1 × 0.4+0 × 0.8 × 0.4+5 × 0.5 × 0.2= 2.5. The evaluation result of the user-type graph can be more accurately determined through the target dimension (spatial orientation) through the consideration in various aspects, so that the scoring result is more accurate, and the experience of the user can be improved.

In another possible implementation, the calculating the base score of each of the plurality of parameter indicators includes:

determining a corresponding relation between the value of the target dimension and the value of the target parameter index according to natural geographic characteristics, wherein the target parameter index is any one of the parameter indexes;

and searching a corresponding numerical value of the parameter index from the corresponding relation between the value of the target dimension and the numerical value of the target parameter index according to the target value of the target dimension of the house type graph, wherein the searched numerical value of the parameter index is the basic score of the target parameter index of the house type graph.

In the method, a corresponding relationship between a target dimension (e.g. a spatial orientation) and a target parameter index (e.g. lighting) is pre-established based on natural geographic characteristics, a corresponding relationship between the target dimension (e.g. the spatial orientation) and a base score can be further established based on the corresponding relationship, and then according to a target value of the target dimension of the current floor plan, a numerical value of the corresponding parameter index is searched from the corresponding relationship between a value of the target dimension (the value of the target dimension can be expressed as the spatial orientation, or expressed as a value assigned to the spatial orientation by a written code) and a numerical value of the target parameter index, for example, a base score of a true south orientation is established as 5, a base score of a south east orientation is established as 4, a base score of a true north orientation is established as-3, if the corresponding relationship between the spatial orientation and the base score is established, the spatial orientation of the current floor plan is a true east, then the basic score of the corresponding lighting in the east direction is 2, and thus the basic score of the corresponding lighting in the east direction in the current house type figure is determined to be 2. After the corresponding relation between the target dimension (such as the spatial orientation) and the basic score is established, the basic score of the parameter index of the current house type graph can be obtained by directly searching the corresponding relation, and the scoring efficiency of the house type graph can be improved.

In yet another possible implementation, the determining the base score for each of the plurality of parameter indicators includes:

inputting the building characteristic parameters around the target scene into a preset parameter prediction model to obtain local geographic information around the target scene;

and determining a basic score of the target parameter index of the indoor type graph according to the deviation of the target value of the target dimension of the indoor type graph and the local geographic information.

In the method, the building characteristic parameters (such as coordinate position, height and the like) around the actual item address of the current house type map are input into a preset parameter prediction model, and information of target parameter indexes (such as air duct direction, optimal lighting direction and the like) of the item address is obtained, wherein the parameter prediction model is obtained by training a large number of building group characteristic parameters and labels (such as local air duct direction, local optimal lighting direction and the like), so that the local air duct direction and the local optimal lighting direction can be predicted to a certain extent, and if the training data input during training is more, the later prediction is more accurate. Determining the basic score of the target parameter index of the indoor graph according to the deviation between the target value of the target dimension of the indoor graph and the local geographic information, for example, comparing the target value of the spatial orientation of the indoor graph with the air duct azimuth orientation, and setting the basic score of the ventilation index of the indoor graph to be higher if the target value of the spatial orientation of the indoor graph is closer to the air duct azimuth. By the scheme, the basic score of the target parameter index (such as lighting) can be more accurately determined, so that the subsequent evaluation of the indoor type graph is more accurate.

In yet another possible implementation, the local geographic information includes a wind tunnel orientation, the target dimension is a spatial orientation, and the parameter index is a ventilation index; determining a base score of the target parameter index of the layout graph according to a deviation of a target value of the target dimension of the layout graph from the local geographic information, comprising:

comparing a target value for the spatial orientation of the floor plan to the wind tunnel azimuthal orientation;

and if the target value of the space orientation of the house-type map is closer to the air duct orientation, setting the basic score of the ventilation index of the house-type map to be higher.

In the method, the relationship is approximate to a normal distribution, the value of the probability density function f (x) is smaller as the random variable x is farther from the mathematical expectation μ, and for the interval with the same length, when the interval is farther from the mathematical expectation μ, the probability that the random variable x falls on the interval is smaller, which is equivalent to comparing the spatial orientation x of the house type diagram with the optimal lighting orientation μ in the scheme, and if the spatial orientation of the house type diagram deviates from the optimal lighting orientation, the base score of the parameter index of lighting is lower; the base score of the index parameter of lighting is higher if the spatial orientation of the house type map is closer to the optimal lighting azimuth orientation. Therefore, according to the scheme, the target value of the spatial orientation of the indoor graph is compared with the air duct orientation in an approximate normal distribution mode, and the basic score of the target parameter index is judged through non-modeling.

In yet another possible implementation, the determining the degree of attention of the user to each parameter index of the plurality of parameter indexes includes:

a degree of attention to each of the plurality of parameter indicators input by a user is received.

In the method, the degree of interest of the user has a great influence on the scoring result of the floor plan, and the S values input by different users can be the same or different. It can be understood that in real life, some people really have more interest in lighting, such as young people, and some people have more interest in noise, such as the elderly, and if the current house type diagram is designed for a nursing institution, the design can be performed from the perspective of reducing house noise, specifically, the "attention degree S of the user" corresponding to the parameter index of "noise" is set to be larger. Specifically, the attention degree of the user may be a value set by the user or the server according to historical experience, or may be designed specifically from the perspective of adapting to the personalized needs of the user.

In a further possible implementation, after the determining the scoring result of the prototype graph in the target dimension according to the base score of each parameter index of the plurality of parameter indexes, the method further includes:

and if the scoring result of the house type graph is smaller than a preset threshold value, recommending a new house type graph to the user according to the requirement condition of the user.

In the method, after determining the scoring result of the house type map in the target dimension, if the user finds that the scoring result of the current house type map is 3 points and the score which the standard house type should reach is 4 points, at this time, the scoring result of the current house type map is smaller than the preset threshold value, which indicates that the current house type map does not meet the requirement of the standard house type, at this time, the factors influencing the house type map can be optimized within a reasonable range according to the requirement of the user, for example, building characteristic parameters (such as coordinate position, height and the like) around the actual item address of the current house type map are input into a preset parameter prediction model, after obtaining the air channel position, the optimal lighting position and other information of the item address, a house type map which is not far deviated from the air channel position and the optimal lighting position of the item address is designed, for example, the air channel position of the item address is south, and the optimal lighting position is east, the orientation of the finally designed house figure can be southeast orientation, and ventilation and lighting can be simultaneously considered. Of course, when designing the house type diagram, the optimized house type diagram needs to be designed by comprehensively considering information such as the plane structure of the project address and the like in addition to information such as the air duct direction, the optimal lighting direction and the like of the project address and combining various factors. The experience of the user can be improved through the scheme.

In a second aspect, the present application provides an apparatus for automatically analyzing a house type map, the apparatus including an obtaining unit and a determining unit, and the apparatus being configured to implement the method described in the first aspect or any one of the possible embodiments of the first aspect.

It should be noted that the processor included in the apparatus described in the second aspect may be a processor dedicated to execute the methods (referred to as a special-purpose processor for convenience), or may be a processor that executes the methods by calling a computer program, such as a general-purpose processor. Optionally, at least one processor may also include both special purpose and general purpose processors.

Alternatively, the computer program may be stored in a memory. For example, the Memory may be a non-transitory (non-transitory) Memory, such as a Read Only Memory (ROM), which may be integrated with the processor on the same device or separately disposed on different devices, and the embodiment of the present application is not limited to the type of the Memory and the arrangement manner of the Memory and the processor.

In a possible embodiment, the at least one memory is located outside the analysis device.

In yet another possible embodiment, the at least one memory is located within the analysis device.

In yet another possible embodiment, a part of the at least one memory is located inside the analysis device, and another part of the memory is located outside the analysis device.

In this application, it is also possible that the processor and the memory are integrated in one device, i.e. that the processor and the memory are integrated together.

In a third aspect, an embodiment of the present application provides an automated analysis device for a custom graph, the device including a processor and a memory; the memory has stored therein a computer program; when the processor executes the computer program, the computing device performs the method described in any of the preceding first or second aspects.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having stored therein instructions that, when executed on at least one processor, implement the method described in any of the preceding first aspects.

In a fifth aspect, the present application provides a computer program product comprising computer instructions that, when run on at least one processor, implement the method described in any of the preceding first aspects. The computer program product may be a software installation package, which may be downloaded and executed on a computing device in case it is desired to use the method as described above.

The advantages of the technical methods provided in the second to fifth aspects of the present application may refer to the advantages of the technical solution of the first aspect, and are not described herein again.

Drawings

The drawings that are required to be used in the description of the embodiments will now be briefly described.

Fig. 1 is a scenario of analyzing and evaluating a floor plan based on an intelligent design and management platform according to an embodiment of the present application;

FIG. 2 is a schematic diagram of an architecture of an automated analysis system for a house layout provided in an embodiment of the present application;

FIG. 3 is a flow chart of a method for automated analysis of a custom graph according to an embodiment of the present application;

FIG. 4 is a schematic structural diagram of an automated analysis device 40 for house layout provided in the embodiment of the present application;

fig. 5 is a schematic structural diagram of an automated analysis device 50 of a house map provided in an embodiment of the present application.

Detailed Description

The embodiments of the present application will be described below with reference to the drawings. Fig. 1 illustrates a scenario for analyzing and evaluating a floor plan based on an intelligent design and management platform, and in fig. 1, the application scenario of the present application mainly includes converting a new technology into a new language at the bottom of the building industry, namely, a Cloud-end intelligent building information model (ABC, AI-drive BIM on Cloud), and corresponding Cloud-end tools, and establishing an intelligent design and management platform covering the whole period of the industry. The embodiment of the present application will be described later with emphasis on a scenario in which an analysis and evaluation is performed on a house-type diagram based on an intelligent design and management platform.

Referring to fig. 2, fig. 2 is a schematic structural diagram of an automated analysis system for a house layout according to an embodiment of the present application, where the system includes a server 201 and a user device 202.

The server 201 may be a server or a server cluster composed of a plurality of servers, and may specifically be a computer or an upper computer.

The user equipment 202 is a device having processing capability and data transceiving capability. The user device 202 may be a Computer, a laptop, a tablet, a palmtop, a desktop, a diagnostic device, a mobile phone, an Ultra-mobile Personal Computer (UMPC), a netbook, a Personal Digital Assistant (PDA), or the like. In the embodiment of the present Application, the user equipment 202 is an Application (APP) 203.

The user group corresponding to the user of the user device 202 may be a general user, a system administrator, and a senior designer, where the general user may include, but is not limited to, a crowd with a room purchasing requirement, a crowd interested in house design, or a crowd who has purchased a room and is ready to start designing and finishing, and the crowd may perform design or learning of an intelligent item on the application 203.

Optionally, the cloud end editing can be performed based on the server 201, and complex software does not need to be installed locally for editing, so that the workload is light, a user only needs to log in a cloud end project management interface through carriers such as a webpage and select related functions in an intelligent monomer, and an object can be constructed, created and drawn. In this embodiment of the application, after receiving the house type diagram returned by the user equipment 202, the server 201 logs in a cloud project management interface through a carrier such as a web page, and edits, analyzes and evaluates the returned house type diagram at the cloud, specifically, determines a basic score of each parameter index in the plurality of parameter indexes, and outputs a scoring result of the house type diagram in a target dimension to the user equipment 202 after determining a scoring result of the house type diagram according to the basic score of each parameter index in the plurality of parameter indexes. Optionally, the object may also be directly constructed by the user equipment 202 and created and drawn, specifically, the user may log in to the cloud project management interface through the application program 203, may log in through an account password or a WeChat, and the like, after successful logging in, the user may select a house type diagram from a local photo or other ways to upload to the application program 203, and the application program 203 scores the house type diagram through the input relevant parameter index of the house type diagram and in combination with an algorithm of the application program 203, and finally outputs a scoring result of the house type diagram in the target dimension to the user.

The method of the embodiments of the present application is described in detail below with reference to an alternative aspect of fig. 2.

Referring to fig. 3, fig. 3 is a schematic flowchart of an automated analysis method for a customized graph according to an embodiment of the present application. Alternatively, the method may be applied to the system described in fig. 2.

The method as described in fig. 3 comprises at least steps S301 to S304.

Step S301: and acquiring a house type graph.

Specifically, the house type graph has various sources, for example, a user edits the house type graph on a house type graph editing platform, the platform may be an APP, a cloud platform, a web page, or the like, and the function of each area, such as a master bedroom, a kitchen, a toilet, or the like, is indicated on the house type graph, so as to obtain the house type graph. For another example, the user selects the layout from a file stored locally, or downloads the layout from the cloud, or selects the layout from a database of the platform.

Step S302: a plurality of parameter indicators in the custom graph affected by the target dimension are determined.

Specifically, the parameter indexes are used for reflecting the living comfort of the user and reflecting the influence degree on target dimensions in the house type diagram, and the target dimensions comprise at least one of house type squareness, space orientation, lighting and ventilation, dynamic and static partition, clean and dirty partition, reasonable moving line and moderate size. For example, if the target dimension is a spatial orientation, the plurality of parameter indexes affected by the target dimension may specifically include information such as ventilation, lighting, and noise, and if the target dimension is a square or a moderate size, the plurality of parameter indexes affected by the target dimension may specifically include information such as an area and a height. For another example, if the target dimension is the contamination partition, the parameter indexes affected by the target dimension may specifically include information such as a function and a contamination degree.

Step S303: a base score for each of a plurality of parameter indicators is determined.

Specifically, the corresponding relation between the value of the target dimension (such as the spatial orientation) and the numerical value of the target parameter index (any parameter index in lighting, noise and ventilation) is determined according to the natural geographic characteristics, the target parameter index is used as lighting, for example, most areas in China are north of the return line of north, the sunshine is from northeast to northwest in summer, and the winter is from southeast to southwest in winter, so that windows facing south receive less light in summer and more light in winter, and can feel warm in winter and cool in summer than windows facing north. The windows facing east and west can receive sunlight at most in half a day every day, the other half day is completely free of sunlight because of the backlight relation, and because the two directions are directly subjected to light, the light is too strong to generate certain interference when sunlight exists, the windows facing south and north can receive light more evenly, because many houses in China are built in the south and north directions, and the planning also requires that the building distance between the building and the building is wider in the south and north directions than in the east and west directions, and the building distance is wide and the natural lighting is better. Generally, a house facing south can receive sunshine almost all day, a house facing east can receive sunshine only in the morning, a house facing west can receive sunshine only in the afternoon, and a house facing north can hardly receive sunshine all day, so that, the correspondence between the spatial orientation and the lighting (parameter index) may be established in advance, and based on this correspondence, the correspondence between the spatial orientation and the base score may be further established, that is, according to the target value of the target dimension of the user-type graph, the corresponding parameter index value is searched from the corresponding relation between the target dimension value (the target dimension value can be expressed as a space orientation, or can be expressed as a space orientation assignment through written codes) and the target parameter index value, and the searched numerical value of the parameter index is the basic score of the target parameter index of the indoor graph. For example, as shown in table 1, the base score of the north orientation is 5, the base score of the south-east orientation is 4, and the base score of the north orientation is-3, if the correspondence between the spatial orientation and the base score is established and the spatial orientation of the home type diagram is the true east, the base score of the east orientation corresponding to lighting in table 1 can be queried to be 2, so that the base score of the east orientation corresponding to lighting in the home type diagram is determined to be 2.

TABLE 1

Spatial orientation	Base score
		Zhengnan	5
Southeast China	4
		Southwest	3
Zhengdong (east China)	2
		Zhengxi brand medicine	1
Northeast	0
		Northwest of China	0
True north	-3

Optionally, the building characteristic parameters around the target scene are input into a preset parameter prediction model to obtain local geographic information around the target scene, and the basic score of the target parameter index of the customized graph is determined according to the deviation between the target value of the target dimension of the customized graph and the local geographic information.

Specifically, if the local geographic information includes the air duct orientation, the target dimension corresponds to a spatial orientation, the parameter index corresponds to a ventilation index, and the air duct orientation, the spatial orientation, and the ventilation index correspond to each other. For another example, if the local geographic information includes the optimal lighting azimuth, the target dimension is a spatial orientation, and the parameter index is a lighting index, where the optimal lighting azimuth, the spatial orientation, and the lighting index correspond to each other.

Determining the basic score of the target parameter index of the indoor graph according to the deviation between the target value of the target dimension of the indoor graph and the local geographic information, for example, comparing the target value of the spatial orientation of the indoor graph with the air duct azimuth orientation, and setting the basic score of the ventilation index of the indoor graph to be higher if the target value of the spatial orientation of the indoor graph is closer to the air duct azimuth.

Building characteristic parameters (such as coordinate positions, heights and the like) around an actual item address of the current house type map are input into a preset parameter prediction model, and information such as air duct direction, optimal lighting direction and the like of the item address is obtained, wherein the parameter prediction model is obtained by training a large number of building group characteristic parameters and labels (such as local air duct direction, local optimal lighting direction and the like), so that the local air duct direction and the local optimal lighting direction can be predicted to a certain extent, and if more training data are input during training, the later prediction is more accurate.

Further, the spatial orientation of the home map is compared with the air duct orientation, and if the spatial orientation of the home map is consistent with the air duct orientation, the basic score of the index parameter of ventilation is set to be the highest, such as 5 scores; the farther the orientation of the floor plan deviates from the orientation of the air duct, the lower the base score of the ventilation index parameter; conversely, the closer the orientation of the floor plan is to the orientation of the wind tunnel, the higher the base score of this index parameter of ventilation. Similarly, for example, the spatial orientation of the house type map is compared with the optimal lighting orientation, and if the spatial orientation of the house type map is consistent with the optimal lighting orientation, the basic score of the index parameter of lighting is set to be the highest, such as 5 scores; the farther the spatial orientation of the house type diagram deviates from the optimal lighting azimuth orientation, the lower the basic score of the index parameter of lighting is; conversely, the closer the spatial orientation of the housekeeping map is to the optimal lighting azimuth orientation, the higher the base score of the lighting indicator parameter. The above relationship may be approximated to a normal distribution, with the probability density function f (x) having smaller values the further the random variable x is from the mathematically expected μ, and the curve having an inflection point at x = μ ± a, the curve having an asymptote with the Ox axis.

This means that for the interval with the same length, the farther the interval is from the mathematically expected μ, the smaller the probability that the random variable x falls on the interval is, which is equivalent to comparing the spatial orientation x of the house type diagram with the optimal lighting orientation μ in this scheme, and if the spatial orientation of the house type diagram deviates from the optimal lighting orientation, the lower the basic score of the index parameter of lighting is; the base score of the index parameter of lighting is higher as the spatial orientation of the house layout is closer to the optimal lighting azimuth orientation. Therefore, the basic score corresponding to each parameter index can be calculated in this way.

Step S304: and determining the scoring result of the customized graph in the target dimension according to the basic score of each parameter index in the plurality of parameter indexes.

Specifically, the ways of determining the scoring result of the customized graph in the target dimension include, but are not limited to, the following two ways.

Firstly, if the plurality of parameter indexes affected by the target dimension (such as spatial orientation) specifically include ventilation, lighting and noise, wherein the basic score corresponding to the parameter index of ventilation is 3, the basic score corresponding to lighting is 0, and the basic score corresponding to noise is 2; if the house type graph is the house type structure of the old home, the priority coefficient of the parameter index of ventilation is 0.7, the priority coefficient of lighting is 0.8, and the priority coefficient of noise is 0.9; then, the final calculated composite score of the house type graph in the target dimension (such as spatial orientation) is: 3 × 0.7+ 0.8+2 × 0.9=3.9, but the scoring result obtained in the first mode is obtained by rough calculation and may deviate from the actual result.

In a second mode, for comprehensively evaluating the scoring result of the indoor graph in the target dimension, besides determining the basic score of each parameter index in the multiple parameter indexes, the influence degree of the target dimension on each parameter index in the multiple parameter indexes can be analyzed, for example, if a south-facing house can receive sunlight almost all day long, the influence degree of the south-facing house on the optimal lighting direction is maximum, and the influence degree can be set to be 5 minutes; if the east house can receive the sunshine only in the morning and the west house can receive the sunshine only in the afternoon, the influence degree of the houses with the east and south space on the optimal lighting direction is moderate, and the influence degree can be set to be 3 minutes; if a house facing north can hardly receive sunlight all day, the house facing north has the least influence on the optimal lighting direction, and the influence can be set to 1 point.

After the influence degree of the target dimension on each of the multiple parameter indexes is determined, the attention degree of the user on each of the multiple parameter indexes needs to be determined, for example, the attention degree of the user may be a value set by the user or the server according to historical experience, or the user may be subjected to targeted design from the perspective of adapting to personalized requirements of the user, for example, receiving "attention degree S" input by the user in real time, where S values input by different users may be the same or different. It can be understood that in real life, some people really have more interest in lighting, such as young people, and some people have more interest in noise, such as the elderly, and if the current house type diagram is designed for a nursing institution, the design can be performed from the perspective of reducing house noise, specifically, the "attention degree S of the user" corresponding to the parameter index of "noise" is set to be larger.

Generating a scoring result of the indoor type graph on the target dimension according to the basic score of each parameter index, the influenced degree of each parameter index and the attention degree of the user, for example, if a plurality of parameter indexes influenced by the target dimension (such as spatial orientation) specifically include ventilation, lighting and noise, wherein the basic score corresponding to the parameter index of ventilation is 5, the basic score corresponding to lighting is 0, and the basic score corresponding to noise is 5; the parameter index of ventilation is expressed as R =1 under the influence of the orientation, lighting is expressed as R =0.8 under the influence of the orientation, and noise is expressed as R =0.5 under the influence of the orientation; the user ' S attention to the parameter index of ventilation is S =0.4, the user ' S attention to the parameter index of lighting is S =0.4, and the user ' S attention to the parameter index of noise is S = 0.2; then, the final calculated composite score of the house type graph in the target dimension (such as spatial orientation) is: 5 × 1 × 0.4+0 × 0.8 × 0.4+5 × 0.5 × 0.2= 2.5. The scoring result which is output by considering in two aspects in the above way is more accurate.

Optionally, after determining a scoring result of the house type graph in the target dimension according to the basic score of each parameter index in the plurality of parameter indexes, if the scoring result of the house type graph is smaller than a preset threshold, recommending a new house type graph to the user according to a requirement condition of the user.

Specifically, after determining the scoring result of the house type map in the target dimension, the user finds that the scoring result of the current house type map is 3 points, the scoring that the standard house type should reach is 4 points, at this time, the scoring result of the current house type map is smaller than the preset threshold value, which indicates that the current house type map does not meet the requirement of the standard house type, at this time, the factors influencing the house type map can be optimized within a reasonable range according to the requirement of the user, for example, building characteristic parameters (such as coordinate position, height and the like) around the actual project address of the current house type map are input into a preset parameter prediction model, after obtaining the air duct position, the optimal lighting position and other information of the project address, a house type map whose orientation and the air duct position and the optimal lighting position of the project address deviate from each other little is designed, for example, the orientation of the project address is south, the optimal lighting position is east, the orientation of the finally designed house figure can be southeast orientation, and ventilation and lighting can be simultaneously considered. Of course, when designing the house type diagram, the optimized house type diagram needs to be designed by comprehensively considering information such as the plane structure of the project address and the like in addition to information such as the air duct direction, the optimal lighting direction and the like of the project address and combining various factors.

In the prior art, a method for scoring a house type graph mainly includes training a preset house type feature recognition model through an algorithm, determining the quality of a house through a house type structure and a house source type, and in the method, the achievement of a scoring process is refined, the house type graph is evaluated through synthesizing relevant information of a detail level, so that the difference between the house type graph and the prior art can be more obviously embodied, and particularly, a scoring result of the house type graph on a target dimension (space orientation) is judged mainly through synthesizing basic scores of each parameter index (lighting and ventilation) in a plurality of parameter indexes (space orientation for example) influenced by the target dimension (space orientation for example). According to the embodiment of the application, the scoring efficiency of the house type graph can be comprehensively improved through automatic comprehensive scoring of the house type graph.

The method of the embodiments of the present application is explained in detail above, and the apparatus of the embodiments of the present application is provided below.

It is to be understood that a plurality of apparatuses, such as an analysis apparatus, provided in the embodiments of the present application include a hardware structure, a software module, or a combination of a hardware structure and a software structure, which performs respective functions, in order to implement the functions in the above-described method embodiments.

Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. A person skilled in the art may implement the foregoing method embodiments in different usage scenarios by using different device implementations, and the different implementation manners of the device should not be considered as exceeding the scope of the embodiments of the present application.

The embodiment of the application can divide the functional modules of the device. For example, each functional module may be divided for each function, or two or more functions may be integrated into one functional module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. It should be noted that, in the embodiment of the present application, the division of the module is schematic, and is only one logic function division, and there may be another division manner in actual implementation.

For example, in the case where the respective functional blocks of the apparatus are divided in an integrated manner, the present application exemplifies several possible processing apparatuses.

Referring to fig. 4, fig. 4 is a schematic structural diagram of an automated analysis device 40 for a house layout provided in an embodiment of the present application, where the analysis device 40 may be a server or a device in the server, such as a chip, a software module, an integrated circuit, and the like. The analysis device 40 is used to implement the aforementioned automated analysis method based on a house map, for example, the automated analysis method based on a house map as shown in fig. 3.

In a possible embodiment, the analysis device 40 may include an acquisition unit 401 and a determination unit 402.

The obtaining unit 401 is configured to obtain a house type graph;

the determining unit 402 is configured to determine a plurality of parameter indexes affected by a target dimension in the house type graph, where the parameter indexes are used to reflect comfort of living, and the target dimension includes at least one of house type squareness, space orientation, lighting and ventilation, dynamic and static partitions, dirty partition, reasonable moving line, and moderate size;

the determining unit 402, further configured to determine a base score of each parameter index of the plurality of parameter indexes;

the determining unit 402 is further configured to determine a scoring result of the layout in the target dimension according to a base score of each parameter index of the plurality of parameter indexes.

In another possible embodiment, the method further includes:

the analysis unit is used for analyzing the influence degree of the target dimension on each parameter index in the plurality of parameter indexes;

the determining unit 402, further configured to determine a degree of attention of a user to each parameter index of the plurality of parameter indexes;

the generating unit is used for generating a scoring result of the prototype diagram on the target dimension according to the basic score of each parameter index, the influence degree of each parameter index and the attention degree of the user.

In the embodiment of the present application, if the plurality of parameter indexes affected by the target dimension (for example, spatial orientation) specifically include ventilation, lighting, and noise, where a basic score corresponding to the parameter index of ventilation is 5, a basic score corresponding to lighting is 0, and a basic score corresponding to noise is 5; the parameter index of ventilation is expressed as R =1 under the influence of the orientation, lighting is expressed as R =0.8 under the influence of the orientation, and noise is expressed as R =0.5 under the influence of the orientation; the user ' S attention to the parameter index of ventilation is S =0.4, the user ' S attention to the parameter index of lighting is S =0.4, and the user ' S attention to the parameter index of noise is S = 0.2; then, the final calculated composite score of the house type graph in the target dimension (such as spatial orientation) is: 5 × 1 × 0.4+0 × 0.8 × 0.4+5 × 0.5 × 0.2= 2.5. The evaluation result of the user-type graph can be more accurately determined through the target dimension (spatial orientation) through the consideration in various aspects, so that the scoring result is more accurate, and the experience of the user can be improved.

In yet another possible implementation manner, the determining unit 402 is further configured to determine, according to a natural geographic characteristic, a correspondence between a value of the target dimension and a value of the target parameter index, where the target parameter index is any one parameter index in the plurality of parameter indexes;

the searching unit is configured to search, according to the target value of the target dimension of the house type graph, a corresponding value of the parameter index from a corresponding relationship between the value of the target dimension and the value of the target parameter index, where the searched value of the parameter index is a basic score of the target parameter index of the house type graph.

In the embodiment of the present application, a corresponding relationship between a target dimension (e.g., a spatial orientation) and a target parameter indicator (e.g., lighting) is pre-established based on natural geographic characteristics, a corresponding relationship between the target dimension (e.g., the spatial orientation) and a base score can be further established based on the corresponding relationship, and then according to a target value of the target dimension of the current floor plan, a numerical value of the corresponding parameter indicator is searched from a corresponding relationship between a value of the target dimension (the value of the target dimension can be represented as the spatial orientation, or is represented as a value assigned to the spatial orientation by a written code) and a numerical value of the target parameter indicator, for example, a base score of a true south orientation is 5, a base score of a south east orientation is 4, a base score of a true north orientation is-3, if the corresponding relationship between the spatial orientation and the base score is already established, a spatial orientation of the current floor plan is a true east, then the basic score of the corresponding lighting in the east direction is 2, and thus the basic score of the corresponding lighting in the east direction in the current house type figure is determined to be 2. After the corresponding relation between the target dimension (such as the spatial orientation) and the basic score is established, the basic score of the parameter index of the current house type graph can be obtained by directly searching the corresponding relation, and the scoring efficiency of the house type graph can be improved.

In yet another possible implementation, the input unit is configured to input the building characteristic parameters around the target scene into a preset parameter prediction model, so as to obtain local geographic information around the target scene;

the determining unit 402 is further configured to determine a base score of the target parameter index of the layout plan according to a deviation between a target value of the target dimension of the layout plan and the local geographic information.

In the embodiment of the application, the building characteristic parameters (such as coordinate position, height and the like) around the actual item address of the current house type map are input into a preset parameter prediction model, and information of target parameter indexes (such as air duct direction, optimal lighting direction and the like) of the item address is obtained, wherein the parameter prediction model is obtained by training a large number of building group characteristic parameters and labels (such as local air duct direction, local optimal lighting direction and the like), so that the parameter prediction model can predict the local air duct direction and the local optimal lighting direction to a certain extent, and the later prediction is more accurate if more training data are input during training. Determining the basic score of the target parameter index of the indoor graph according to the deviation between the target value of the target dimension of the indoor graph and the local geographic information, for example, comparing the target value of the spatial orientation of the indoor graph with the air duct azimuth orientation, and setting the basic score of the ventilation index of the indoor graph to be higher if the target value of the spatial orientation of the indoor graph is closer to the air duct azimuth. By the scheme, the basic score of the target parameter index (such as lighting) can be more accurately determined, so that the subsequent evaluation of the indoor type graph is more accurate.

In yet another possible implementation, the local geographic information includes a wind tunnel orientation, the target dimension is a spatial orientation, and the parameter index is a ventilation index; comparing a target value for the spatial orientation of the floor plan to the wind tunnel azimuthal orientation;

the setting unit is configured to set a base score of the ventilation index of the floor plan to be higher if a target value of the spatial orientation of the floor plan is closer to the air duct azimuth orientation.

In the embodiment of the present application, the above relationship is similar to a normal distribution, the value of the probability density function f (x) is smaller as the random variable x is farther from the mathematically expected μ, and for the interval with the same length, the probability that the random variable x falls on the interval is smaller as the interval is farther from the mathematically expected μ, which is equivalent to comparing the spatial orientation x of the house type diagram with the optimal lighting orientation μ in the present scheme, and if the spatial orientation of the house type diagram deviates from the optimal lighting orientation, the base score of the parameter index of lighting is lower; the base score of the index parameter of lighting is higher if the spatial orientation of the house type map is closer to the optimal lighting azimuth orientation. Therefore, according to the scheme, the target value of the spatial orientation of the indoor graph is compared with the air duct orientation in an approximate normal distribution mode, and the basic score of the target parameter index is judged through non-modeling.

In yet another possible implementation, the receiving unit is configured to receive a degree of attention, input by a user, to each of the plurality of parameter indicators.

In the embodiment of the application, the degree of interest of the user has a great influence on the scoring result of the layout chart, and the S values input by different users may be the same or different. It can be understood that in real life, some people really have more interest in lighting, such as young people, and some people have more interest in noise, such as the elderly, and if the current house type diagram is designed for a nursing institution, the design can be performed from the perspective of reducing house noise, specifically, the "attention degree S of the user" corresponding to the parameter index of "noise" is set to be larger. Specifically, the attention degree of the user may be a value set by the user or the server according to historical experience, or may be designed specifically from the perspective of adapting to the personalized needs of the user.

In yet another possible embodiment, the method further includes:

and if the scoring result of the house type graph is smaller than a preset threshold value, the output unit is used for recommending a new house type graph to the user according to the requirement condition of the user.

In the embodiment of the present application, after determining the scoring result of the house type graph in the target dimension, if the user finds that the scoring result of the current house type graph is 3 points and the score that the standard house type should reach is 4 points, at this time, the scoring result of the current house type graph is smaller than the preset threshold value, which indicates that the current house type graph does not meet the requirement of the standard house type, at this time, the factors affecting the house type graph can be optimized within a reasonable range according to the requirement of the user, for example, building characteristic parameters (such as coordinate position, height and the like) around the actual item address of the current house type graph are input to a preset parameter prediction model, and after obtaining the air channel position, the optimal lighting position and other information of the item address, an house type graph whose orientation is not far away from the air channel position and the optimal lighting position of the item address is designed, for example, the air channel position of the item address is southward, and the air channel position of the item address is not far away from the standard house type graph, The best lighting direction is towards east, so that the orientation of the finally designed house-type picture can be towards south east, and ventilation and lighting can be simultaneously considered. Of course, when designing the house type diagram, the optimized house type diagram needs to be designed by comprehensively considering information such as the plane structure of the project address and the like in addition to information such as the air duct direction, the optimal lighting direction and the like of the project address and combining various factors. The experience of the user can be improved through the scheme.

Referring to fig. 5, fig. 5 is a schematic structural diagram of an automated analysis device 50 of a home graph according to an embodiment of the present application, where the analysis device 50 may be a server or a device in the server, such as a chip, a software module, an integrated circuit, and the like. The analysis device 50 may comprise at least one processor 501. Optionally, at least one memory 503 may also be included. Further optionally, the analysis device 50 may further comprise a communication interface 502. Still further optionally, a bus 504 may be included, wherein the processor 501, the communication interface 502, and the memory 503 are coupled via the bus 504.

The processor 501 is a module for performing arithmetic operation and/or logical operation, and may specifically be one or a combination of multiple Processing modules, such as a Central Processing Unit (CPU), a picture Processing Unit (GPU), a Microprocessor (MPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Complex Programmable Logic Device (CPLD), a coprocessor (assisting the Central Processing Unit to complete corresponding Processing and Application), and a Micro Control Unit (MCU).

The communication interface 502 may be used to provide information input or output to the at least one processor. And/or, the communication interface 502 may be used to receive and/or transmit data externally, and may be a wired link interface such as an ethernet cable, and may also be a wireless link (Wi-Fi, bluetooth, general wireless transmission, vehicle-mounted short-range communication technology, other short-range wireless communication technology, and the like) interface. Optionally, the communication interface 502 may also include a transmitter (e.g., a radio frequency transmitter, an antenna, etc.), or a receiver, etc. coupled to the interface.

The memory 503 is used to provide a storage space in which data such as an operating system and computer programs can be stored. The Memory 503 may be one or a combination of Random Access Memory (RAM), Read-only Memory (ROM), Erasable Programmable Read-only Memory (EPROM), or portable Read-only Memory (CD-ROM), among others.

The at least one processor 501 in the analysis device 50 is configured to perform the method described above, for example, the method described in the embodiment illustrated in fig. 3.

Alternatively, the processor 501 may be a processor dedicated to performing the methods (referred to as a special-purpose processor for convenience), or may be a processor for executing the methods by calling a computer program, such as a general-purpose processor. Optionally, the at least one processor may also include both special purpose and general purpose processors. Optionally, in case the computing device comprises at least one processor 501, the above mentioned computer program may be stored in the memory 503.

Optionally, the at least one processor 501 in the analysis device 50 is configured to execute a calling computer instruction to perform the following operations:

acquiring a house type graph;

determining a base score for each of the plurality of parameter metrics;

In the embodiment of the application, the method for scoring the house type graph mainly includes training a preset house type feature recognition model through an algorithm, determining the quality of a house through a house type structure and a house source type, more importantly, the scoring process is finely processed, the house type graph is evaluated through synthesizing relevant information of a detail level, the difference between the house type graph and the prior art can be more obviously embodied, and particularly, the scoring result of the house type graph on a target dimension (space orientation) is judged mainly through synthesizing basic scores of each parameter index (lighting and ventilation) in a plurality of parameter indexes (space orientation for example) influenced by the target dimension (space orientation for example). According to the embodiment of the application, the scoring efficiency of the house type graph can be comprehensively improved through automatic comprehensive scoring of the house type graph.

Optionally, the processor 501 is further configured to:

In the embodiment of the present application, a corresponding relationship between a target dimension (e.g., a spatial orientation) and a target parameter indicator (e.g., lighting) is pre-established based on natural geographic characteristics, a corresponding relationship between the target dimension (e.g., the spatial orientation) and a base score can be further established based on the corresponding relationship, and then according to a target value of the target dimension of the current floor plan, a numerical value of the corresponding parameter indicator is searched from a corresponding relationship between a value of the target dimension (the value of the target dimension can be represented as the spatial orientation, or is represented as a value assigned to the spatial orientation by a written code) and a numerical value of the target parameter indicator, for example, a base score of a true south orientation is 5, a base score of a south east orientation is 4, a base score of a true north orientation is-3, if the corresponding relationship between the spatial orientation and the base score is already established, a spatial orientation of the current floor plan is a true east, then the basic score of the corresponding lighting in the east direction is 2, and thus the basic score of the corresponding lighting in the east direction in the current house type figure is determined to be 2. After the corresponding relation between the target dimension (such as the space orientation) and the basic score is established, the basic score of the parameter index of the current floor plan can be obtained by directly searching the corresponding relation, and the scoring efficiency of the floor plan can be improved.

Optionally, the processor 501 is further configured to:

In the embodiment of the application, the building characteristic parameters (such as coordinate position, height and the like) around the actual item address of the current house type map are input into a preset parameter prediction model, and information of target parameter indexes (such as air duct direction, optimal lighting direction and the like) of the item address is obtained, wherein the parameter prediction model is obtained by training a large number of building group characteristic parameters and labels (such as local air duct direction, local optimal lighting direction and the like), so that the parameter prediction model can predict the local air duct direction and the local optimal lighting direction to a certain extent, and the later prediction is more accurate if more training data are input during training. Determining a base score of the target parameter index of the user-type graph according to the deviation between the target value of the target dimension of the user-type graph and the local geographic information, for example, comparing the target value of the spatial orientation of the user-type graph with the air duct orientation, and setting the base score of the ventilation index of the user-type graph to be higher if the target value of the spatial orientation of the user-type graph is closer to the air duct orientation. By the scheme, the basic score of the target parameter index (such as lighting) can be more accurately determined, so that the subsequent evaluation of the indoor type graph is more accurate.

Optionally, the processor 501 is further configured to:

the local geographic information comprises the azimuth orientation of the air duct, the target dimension is the spatial orientation, and the parameter index is a ventilation index;

comparing the target value of the spatial orientation of the floor plan with the air duct azimuthal orientation;

Optionally, the processor 501 is further configured to:

and if the scoring result of the house type graph is smaller than a preset threshold value, recommending a new house type graph to the user according to the requirement condition of the user. In the embodiment of the present application, after determining the scoring result of the house type graph in the target dimension, if the user finds that the scoring result of the current house type graph is 3 points and the score that the standard house type should reach is 4 points, at this time, the scoring result of the current house type graph is smaller than the preset threshold value, which indicates that the current house type graph does not meet the requirement of the standard house type, at this time, the factors affecting the house type graph can be optimized within a reasonable range according to the requirement of the user, for example, building characteristic parameters (such as coordinate position, height and the like) around the actual item address of the current house type graph are input to a preset parameter prediction model, and after obtaining the air channel position, the optimal lighting position and other information of the item address, an house type graph whose orientation is not far away from the air channel position and the optimal lighting position of the item address is designed, for example, the air channel position of the item address is southward, and the air channel position of the item address is not far away from the standard house type graph, The best lighting direction is towards east, so that the orientation of the finally designed house-type picture can be towards south east, and ventilation and lighting can be simultaneously considered. Of course, when designing the house type diagram, the optimized house type diagram needs to be designed by comprehensively considering information such as the plane structure of the project address and the like in addition to information such as the air duct direction, the optimal lighting direction and the like of the project address and combining various factors. The experience of the user can be improved through the scheme.

The present application also provides a computer-readable storage medium having stored therein instructions that, when executed on at least one processor, implement the aforementioned method for automated analysis of a custom graph, such as the method described in fig. 3.

The present application also provides a computer program product comprising computer instructions which, when executed by a computing device, implement the aforementioned method for automated analysis of a house-type diagram, such as the method described in fig. 3.

In the embodiments of the present application, words such as "for example" or "like" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "for example" or "such as" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the words "for example" or "such as" are intended to present relevant concepts in a concrete fashion.

In the present application, the embodiments refer to "at least one" and "a plurality" and two or more. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, or c, may represent: a. b, c, (a and b), (a and c), (b and c), or (a and b and c), wherein a, b and c can be single or multiple. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a alone, A and B together, and B alone, wherein A, B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.

And unless stated to the contrary, the ordinal numbers such as "first", "second", etc. are used in the embodiments of the present application to distinguish a plurality of objects and are not used to limit the sequence, timing, priority, or importance of the plurality of objects. For example, a first device and a second device are for convenience of description only and do not represent differences in structure, importance, etc. of the first device and the second device, and in some embodiments, the first device and the second device may be the same device.

As used in the above embodiments, the term "when … …" may be interpreted to mean "if … …" or "after … …" or "in response to determination … …" or "in response to detection … …", depending on the context. The above description is only exemplary of the present application and is not intended to limit the present application, and any modifications, equivalents, improvements, etc. made within the spirit and principles of the present application are intended to be included within the scope of the present application.

It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for automated analysis of a house graph, comprising:

acquiring a house type graph;

determining a base score for each of the plurality of parameter metrics;

2. The method of claim 1, wherein determining the scoring result of the prototype graph at the target dimension according to the base score of each of the plurality of parameter metrics further comprises:

determining a degree of attention of a user for each of the plurality of parameter metrics;

3. The method of claim 1, wherein determining the base score for each of the plurality of parameter metrics comprises:

4. The method of claim 1 or 3, wherein determining the base score for each of the plurality of parameter metrics comprises:

5. The method of claim 4, wherein the local geographic information includes a wind tunnel orientation, the target dimension is a spatial orientation, and the parameter indicator is a ventilation indicator; determining a base score of the target parameter index of the layout graph according to a deviation of a target value of the target dimension of the layout graph from the local geographic information, comprising:

6. The method of claim 2, wherein determining the degree of user interest in each of the plurality of parameter metrics comprises:

7. The method of claim 1, further comprising, after the determining the scoring result of the prototype graph in the target dimension according to the base score of each of the plurality of parameter metrics:

8. An automated house pattern analysis apparatus, comprising an acquisition unit and a determination unit, wherein:

the acquiring unit is used for acquiring a house type graph;

the determining unit is used for determining a plurality of parameter indexes influenced by target dimensions in the house type graph, wherein the parameter indexes are used for reflecting the living comfort level, and the target dimensions comprise at least one of house type squareness, space orientation, lighting and ventilation, dynamic and static partitions, dirt and dirt partition, reasonable moving line and moderate size;

the determining unit is further configured to determine a base score of each of the plurality of parameter indicators;

the determining unit is further configured to determine a scoring result of the layout at the target dimension according to a base score of each of the plurality of parameter indexes.

9. An automated analysis device for house graphs, the device comprising a processor and a memory, the memory for storing computer instructions, the processor for invoking the computer instructions to implement the method of any one of claims 1-7.

10. A computer-readable storage medium having stored therein instructions which, when executed on at least one processor, implement the method of any one of claims 1-7.