CN116910871B - Feasibility assessment method and system for building design scheme - Google Patents
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Abstract
The invention discloses a feasibility evaluation method and a feasibility evaluation system for a building design scheme, which relate to the technical field of data processing, and the method comprises the following steps: acquiring a first design building of a building design scheme and a landing area of the first design building; analyzing building materials to obtain a volatile gas set; acquiring gas attribute characteristics of a volatile gas set, inputting the gas attribute characteristics into a gas influence evaluation model for evaluation, and determining a first influence index; evaluating building greening characteristics of the first design building to obtain a first greening index; carrying out dynamic time sequence prediction on the first influence index and the first greening index, and outputting a first prediction result; and generating a first feasibility report according to the first prediction result. The method solves the technical problem of low accuracy of feasibility assessment of the architectural design due to inaccurate harm prediction of harmful gas in the prior art, and achieves the technical effect of improving the accuracy of feasibility assessment of the architectural design.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a feasibility assessment method and a feasibility assessment system for a building design scheme.
Background
The design of the building scheme is used as a preliminary stage of development of the building design, has important effects on the smooth development of the subsequent stage of the engineering, can provide reasonable guidance for the economic construction of the building engineering through a scientific and reasonable building design scheme, and can accurately grasp the construction benefit of the building engineering. The feasibility assessment of the architectural design can enable the architectural engineering to have predictability, fairness and reliability. However, the existing building scheme feasibility evaluation method also has the problem that the hazard prediction of harmful gases of building materials is inaccurate.
Disclosure of Invention
The application provides a feasibility assessment method and a feasibility assessment system for a building design scheme, which are used for solving the technical problem that the feasibility assessment accuracy of the building design scheme is low due to inaccurate harm prediction of harmful gas in the prior art.
In a first aspect of the present application, there is provided a method for assessing feasibility of a building design, the method comprising: acquiring a building design scheme, and acquiring a first design building of the building design scheme and a landing area of the first design building; building material analysis is carried out on the first design building, and a volatile gas set is obtained; acquiring gas attribute characteristics of the volatile gas set, inputting the gas attribute characteristics into a gas influence evaluation model for evaluation, and determining a first influence index, wherein the gas attribute characteristics comprise gas pollution degree, gas concentration and gas persistence; evaluating building greening characteristics of the first design building to obtain a first greening index; carrying out dynamic time sequence prediction on the first influence index and the first greening index, and outputting a first prediction result, wherein the first prediction result is the gas influence degree under a preset time sequence; and generating a first feasibility report according to the first prediction result.
In a second aspect of the present application, there is provided a feasibility assessment system for a building design, the system comprising: the building design scheme acquisition module is used for acquiring a building design scheme, and acquiring a first design building of the building design scheme and a landing area of the first design building; the volatile gas collection acquisition module is used for analyzing building materials of the first design building to acquire a volatile gas collection; the first influence index determining module is used for acquiring gas attribute characteristics of the volatile gas set, inputting the gas attribute characteristics into a gas influence evaluation model for evaluation, and determining a first influence index, wherein the gas attribute characteristics comprise gas pollution degree, gas concentration and gas persistence; the first greening index acquisition module is used for evaluating building greening characteristics of the first design building to acquire a first greening index; the first prediction result output module is used for carrying out dynamic time sequence prediction on the first influence index and the first greening index and outputting a first prediction result, wherein the first prediction result is the gas influence degree under a preset time sequence; the first feasibility report generation module is used for generating a first feasibility report according to the first prediction result.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the application provides a feasibility assessment method of a building design scheme, which relates to the technical field of data processing and comprises the steps of obtaining a first design building of the building design scheme and a landing area of the first design building; analyzing building materials to obtain a volatile gas set; acquiring gas attribute characteristics of a volatile gas set, inputting the gas attribute characteristics into a gas influence evaluation model for evaluation, and determining a first influence index; evaluating building greening characteristics of the first design building to obtain a first greening index; carrying out dynamic time sequence prediction on the first influence index and the first greening index, and outputting a first prediction result; according to the first prediction result, a first feasibility report is generated, the technical problem that in the prior art, due to inaccurate harm prediction of harmful gas, the feasibility assessment accuracy of the building design scheme is low is solved, and the technical effect that the feasibility assessment accuracy of the building design scheme is improved by predicting the harm of the harmful gas in the building environment is achieved, so that the quality of the building design scheme is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a feasibility assessment method for a building design scheme according to an embodiment of the present application;
fig. 2 is a schematic flow chart of acquiring a first greening index in the feasibility assessment method of the architectural design provided in the embodiment of the present application;
fig. 3 is a schematic flow chart of adjusting the first feasibility evaluation report according to the excitation impact index in the feasibility evaluation method of the architectural design according to the embodiment of the application;
fig. 4 is a schematic structural diagram of a feasibility evaluation system for a building design according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a building design scheme acquisition module 11, a volatile gas collection acquisition module 12, a first influence index determination module 13, a first greening index acquisition module 14, a first prediction result output module 15 and a first feasibility report generation module 16.
Detailed Description
The application provides a feasibility assessment method of a building design scheme, which is used for solving the technical problem of low feasibility assessment accuracy of the building design scheme due to inaccurate harm prediction of harmful gas in the prior art.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, the present application provides a method for evaluating feasibility of a building design, the method comprising:
s100: acquiring a building design scheme, and acquiring a first design building of the building design scheme and a landing area of the first design building;
specifically, a target building design scheme for feasibility evaluation is obtained, a first design building of the target building design scheme and a floor area of the first design building are obtained, the first design building is a building in the target building scheme, information such as types, using functions and using crowd of the building can be obtained to serve as a reference standard for feasibility evaluation, and the floor area of the first design building is position coordinates of the first design building, an area and a floor area of the first design building and the like, so that environment parameters can be conveniently obtained later.
S200: building material analysis is carried out on the first design building, and a volatile gas set is obtained;
specifically, by referring to the design scheme and the drawing of the first design building, extracting the types and the processing procedures of building materials used in each part and each procedure of the building in the scheme, respectively carrying out environmental analysis on each building material, analyzing the stability, the durability, the ageing resistance, the content of harmful substances, the volatility and the like of materials of each building material one by one, screening out the types of harmful gases such as formaldehyde, benzene and the like contained in the building material and the types of the harmful gases generated in the construction process, and obtaining a volatile gas set which can be used as one of indexes for carrying out the feasibility evaluation of the scheme.
S300: acquiring gas attribute characteristics of the volatile gas set, inputting the gas attribute characteristics into a gas influence evaluation model for evaluation, and determining a first influence index, wherein the gas attribute characteristics comprise gas pollution degree, gas concentration and gas persistence;
specifically, the gas attribute characteristics of each gas in the volatile gas collection are obtained, wherein the gas attribute characteristics comprise gas pollution degree, gas concentration and gas persistence, the gas pollution degree refers to the hazard degree of the gas, the gas concentration refers to the proportion of harmful gas released by building materials in air in unit time, and the gas persistence refers to the existence time of the harmful gas in the air, and comprises the time required by complete volatilization of the gas in the building materials.
Further, the gas influence evaluation model is a model for evaluating environmental influence according to the type and concentration of harmful gas in the environment, and the construction process may be: based on the architecture of the BP neural network, a gas influence evaluation model is constructed, influence data of various harmful gases on the environment is obtained through big data, the influence data are randomly divided into a training data set, a verification data set and a test data set, and the gas influence evaluation model is trained, verified and tested based on the training data set, the verification data set and the test data set until the model converges and meets the preset accuracy requirement. And inputting the gas attribute characteristics into a gas influence evaluation model for evaluation, and outputting a first influence index, wherein the first influence index is the influence index of harmful gas released by building materials on the ambient air, and can be used as basic data for subsequent building environment prediction.
Further, step S300 in the embodiment of the present application further includes:
s310: performing feature recognition on a building living group of the first design building, and outputting group features;
s320: performing index weight adjustment on the first influence index according to the group characteristics to generate a fusion weight network layer;
s330: and carrying out model optimization on the gas influence evaluation model according to the fusion weight network layer.
Specifically, feature recognition is performed on a building living group of the first design building, namely, a using group of the target building, the facing living group is determined through the using function of the target building, for example, the facing living group of the aged, children, hospitals and the like is the aged, the patients, the sensitivity degree of different using groups to gas is different, and the requirements on the quality of the ambient air are also different, so that feature recognition is performed on different building living groups to obtain corresponding group features, further, corresponding index weights are distributed for each influence factor of the first influence index according to different group features to generate a fusion weight network layer, the fusion weight network layer is embedded into the gas influence evaluation model, and the gas influence evaluation model is optimized, so that the function of evaluating the gas influence of different groups can be met.
S400: evaluating building greening characteristics of the first design building to obtain a first greening index;
further, as shown in fig. 2, step S400 in the embodiment of the present application further includes:
s410: collecting greening characteristics of the first design building, including a type of a green belt, a greening growth rate and a greening floor area;
s420: inputting the type of the green belt, the greening growth rate and the greening occupied area into a greening evaluation model, and outputting a first greening index according to the greening evaluation model, wherein the first greening index is used for marking the absorption degree of evaluation gas;
s430: the greening evaluation model is obtained by collecting mapping samples of the type of the green belt and the gas absorptivity and training according to the mapping samples until convergence.
Specifically, the greening characteristics of the first design building are collected, the greening characteristics comprise types, greening growth rates and greening occupied areas of the greenbelt in the building range of the first design building, the greening areas refer to vertical projection areas of all vegetation such as trees, shrubs and lawns in a calculation area, the types of the greenbelt, the greening growth rates and the greening occupied areas are input into a greening evaluation model, a first greening index is output after evaluation by the greening evaluation model, and the first greening index is used for identifying the absorbance of evaluation gas, namely, the index reflecting the absorbance of the greenbelt in the building range of the first design building to harmful gas.
Further, the greening evaluation model is a model for evaluating the absorption degree of the green belt in the building range to harmful gas generated by the building, and the construction process can be as follows: firstly, collecting a plurality of green belt types and the corresponding harmful gas absorptivity based on big data, taking the green belt types and the corresponding harmful gas absorptivity as mapping samples of the green belt types and the harmful gas absorptivity, taking the mapping samples as construction data, constructing a green evaluation model by combining a BP neural network, and performing supervised training on the green evaluation model based on the mapping samples of the green belt types and the harmful gas absorptivity until the model converges and meets the preset accuracy, so as to obtain the green evaluation model.
S500: carrying out dynamic time sequence prediction on the first influence index and the first greening index, and outputting a first prediction result, wherein the first prediction result is the gas influence degree under a preset time sequence;
specifically, the dynamic time sequence prediction is performed according to the first influence index and the first greening index, that is, the speed of generating harmful gas by the first design building and the absorptivity of the harmful gas in the building area are performed according to greening in the building area, the air quality in the building area in a future period after the building is completed is dynamically predicted, a first prediction result is output, the first prediction result is the gas influence degree under the preset time sequence, the preset time sequence is the future period after the building is completed, the preset time sequence can be set according to the use requirement of the building, the reduction amount of the harmful gas in each period is predicted according to the absorptivity of the harmful gas in the green belt, and the influence degree of the harmful gas in the building area is predicted according to the reduction amount and the release amount of the harmful gas in each period, so that the influence degree of the harmful gas in the building area is used as the gas influence degree, the gas influence degree of building materials in the target building design scheme can reflect the gas danger and the influence degree on the living environment, and the feasibility of the target building scheme is evaluated from the angle of living environment.
S600: and generating a first feasibility report according to the first prediction result.
Specifically, according to the first prediction result, the feasibility of the target building scheme is evaluated from the perspective of the air quality of the living environment, the predicted air environmental quality of the target building scheme is compared with the air quality requirement of the living group, whether the current target building design scheme is feasible or not is judged, and an environmental quality feasibility study report of the target building is generated, and the environment quality feasibility study report is used as a first feasibility report, so that the adaptation degree of the design scheme and the design level of the design scheme in the aspect of environmental protection can be improved for the environment-sensitive crowd.
Further, as shown in fig. 3, step S600 in the embodiment of the present application further includes:
s610: connecting a GIS map, acquiring region coordinates of the landing region, equidistantly expanding according to the coordinate edges of the landing region, identifying the range of a neighborhood region, and acquiring a neighborhood building region;
s620: collecting building information in the neighborhood building area and outputting neighborhood building attributes;
s630: identifying excitation influence degree of the volatile gas set according to the neighborhood building attribute, and outputting excitation influence indexes;
s640: and adjusting the first feasibility assessment report according to the excitation influence index.
Specifically, a GIS map is connected, and region coordinates of the landing region, including the land range coordinates of the whole target building design scheme, are collected through the GIS map, wherein the GIS map refers to a map for displaying various geographic data on one map based on a geographic information system technology. And equidistantly expanding the building range according to the coordinate edge of the landing area, identifying the neighborhood area range around the target building, and acquiring other building areas around the target building as neighborhood building areas.
Further, the building information in the neighborhood building area is collected, neighborhood building attributes including the type, the use function, the greening rate and the like of the neighborhood building are output, so that whether pollution type enterprises such as sewage treatment plants and chemical plants exist around the target building or not is judged, whether green belts of surrounding building green belts are weak or not is judged, excitation influence degree identification is further carried out on the volatile gas set according to the neighborhood building attributes, the excitation influence degree refers to influence degree of the neighborhood building attributes on air quality of the first design building, namely influence degree of the surrounding building on air quality of the target building, the influence degree may be negative or positive, for example, negative influence caused by chemical plants, sewage treatment plants and the like, park, wetland and the like is judged, corresponding excitation influence indexes such as air purification indexes, air pollution indexes and the like are output according to the excitation influence degree, the first feasibility evaluation report is adjusted according to the excitation influence indexes, and accuracy of the first feasibility evaluation report is improved.
Further, step S640 in the embodiment of the present application further includes:
s641: dynamically monitoring the content of each volatile gas in the first influence index to generate a gas change curve set, wherein the abscissa of the gas change curve set is a time sequence period, and the ordinate is the gas content;
s642: performing influence degree conversion on the gas change curve set by using the gas influence evaluation model, and outputting an influence degree change curve;
s643: outputting a greening index change curve according to the first greening index;
s644: and taking the excitation influence index as a first excitation factor, taking the index on the greening index change curve as a second excitation factor, carrying out Markov chain prediction on the influence degree change curve, and outputting the first prediction result.
Specifically, the content of each volatile gas in the first impact index is dynamically monitored, gas change curves are drawn according to dynamic change data of the content of each volatile gas respectively, and a gas change curve set is generated, wherein in the gas change curve set, the abscissa of each gas change curve is a time sequence period, and the ordinate is the gas content. And respectively inputting all the gas content data sequences in the gas change curve set into the gas influence evaluation model, outputting a gas influence degree sequence by the gas influence evaluation model, generating an influence degree change curve according to the gas influence degree sequence, and similarly, obtaining a greening index change curve according to the first greening index.
Further, the excitation influence index is used as a first excitation factor, the index on the greening index change curve is used as a second excitation factor, namely, the influence of surrounding buildings on the air quality is used as the first excitation factor, the influence of greening in a target building area on the air quality is used as the second excitation factor, the influence degree change curve is subjected to Markov chain prediction, the first prediction result is output, the Markov chain prediction is a prediction method for predicting the change condition of each future moment according to the current condition of an event, future values are quantitatively calculated through a mathematical formula, the variable values of the future moment are predicted, and the first prediction result, namely, the gas influence under a preset time sequence can be obtained by inputting the first excitation factor and the second excitation factor which change along with the time sequence.
Further, step S640 in the embodiment of the present application further includes:
s645: performing linear weighted fitting on the first excitation factor and the second excitation factor, and outputting a first excitation function;
s646: predicting the influence degree change curve by using the first excitation function, and judging that the expected probability smaller than the preset influence degree is reached when the influence degree change curve is in the preset time sequence;
s647: if the expected probability meets the preset expected probability, acquiring a report passing the generation feasibility;
s648: and if the expected probability does not meet the preset expected probability, generating a report that the feasibility is not passed.
Specifically, linear weighted fitting is performed on the first excitation factor and the second excitation factor, corresponding weights are distributed according to the importance degree of the first excitation factor and the second excitation factor on the gas influence degree, a mathematical function is fitted by combining the corresponding relation between each excitation factor and the gas influence degree, a first excitation function is obtained, the first excitation factor and the second excitation factor which change along with time sequence are respectively input into the first excitation function, the influence degree change curve is predicted, the influence degree change curve is used for judging that when the influence degree change curve is in the preset time sequence, the expected probability that the gas influence degree reaches less than the preset influence degree, namely the probability that the air quality of the target building reaches the preset air quality standard is judged, the preset influence degree refers to the maximum value of the gas influence degree of harmful gas generated by the acceptable building material, if the expected probability meets the preset expected probability, the air quality of the target building meets the use requirement, the report that the generation feasibility is passed is obtained, and if the expected probability does not meet the expected probability, the air quality of the target building does not meet the expected requirement is not shown by the preset report.
Further, the embodiment of the present application further includes step S700, where step S700 further includes:
s710: acquiring the building height of the first design building;
s720: when the building height exceeds a preset building height, directly predicting the first influence index, and outputting a direct prediction result based on the preset time sequence, wherein the direct prediction result comprises the gas influence degree under the preset time sequence;
s730: and when the predicted gas influence degree of the direct prediction result is larger than the preset gas influence degree, generating first reminding information.
Specifically, the building height of the first design building is obtained, the building height of the first design building is compared with a preset building height, the preset building height is a preset building height threshold, when the building height exceeds the preset building height, the absorption rate of the harmful gas by greening in a building area is too small, the auxiliary absorption capacity of the harmful gas is weakened, the first greening index, namely the second excitation factor, can be not considered, the excitation influence index is used for directly predicting the first influence index, a direct prediction result based on the preset time sequence is output, the gas influence degree is included in the preset time sequence, when the predicted gas influence degree of the direct prediction result is larger than the preset gas influence degree, the influence of the harmful gas on the air quality of the target building cannot be naturally reduced along with time, first reminding information is generated, the accuracy of the first feasibility assessment report is adjusted according to the first reminding information, and the accuracy of the first feasibility assessment report is improved.
In summary, the embodiments of the present application have at least the following technical effects:
the method comprises the steps of obtaining a first design building of a building design scheme and a landing area of the first design building; analyzing building materials to obtain a volatile gas set; acquiring gas attribute characteristics of a volatile gas set, inputting the gas attribute characteristics into a gas influence evaluation model for evaluation, and determining a first influence index; evaluating building greening characteristics of the first design building to obtain a first greening index; carrying out dynamic time sequence prediction on the first influence index and the first greening index, and outputting a first prediction result; and generating a first feasibility report according to the first prediction result.
The technical effect of improving the feasibility assessment accuracy of the building design scheme and further improving the quality of the building design scheme by predicting the harmfulness of harmful gases in the building environment is achieved.
Example two
Based on the same inventive concept as the feasibility assessment method of a building design in the foregoing embodiments, as shown in fig. 4, the present application provides a feasibility assessment system of a building design, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
a building design acquiring module 11, where the building design acquiring module 11 is configured to acquire a building design, and acquire a first design building of the building design and a landing area of the first design building;
the volatile gas collection acquisition module 12, wherein the volatile gas collection acquisition module 12 is used for analyzing building materials of the first design building to acquire a volatile gas collection;
the first impact index determining module 13 is configured to obtain a gas attribute feature of the volatile gas set, input the gas attribute feature into a gas impact assessment model for assessment, and determine a first impact index, where the gas attribute feature includes a gas pollution level, a gas concentration, and a gas persistence;
the first greening index acquisition module 14 is used for evaluating building greening characteristics of the first design building to acquire a first greening index;
the first prediction result output module 15 is configured to perform dynamic time sequence prediction on the first impact indicator and the first greening indicator, and output a first prediction result, where the first prediction result is a gas impact degree under a preset time sequence;
the first feasibility report generating module 16 is configured to generate a first feasibility report according to the first prediction result by the first feasibility report generating module 16.
Further, the first impact indicator determining module 13 is further configured to perform the following steps:
performing feature recognition on a building living group of the first design building, and outputting group features;
performing index weight adjustment on the first influence index according to the group characteristics to generate a fusion weight network layer;
and carrying out model optimization on the gas influence evaluation model according to the fusion weight network layer.
Further, the first greening index obtaining module 14 is further configured to perform the following steps:
collecting greening characteristics of the first design building, including a type of a green belt, a greening growth rate and a greening floor area;
inputting the type of the green belt, the greening growth rate and the greening occupied area into a greening evaluation model, and outputting a first greening index according to the greening evaluation model, wherein the first greening index is used for marking the absorption degree of evaluation gas;
the greening evaluation model is obtained by collecting mapping samples of the type of the green belt and the gas absorptivity and training according to the mapping samples until convergence.
Further, the first feasibility report generating module 16 is further configured to perform the following steps:
connecting a GIS map, acquiring region coordinates of the landing region, equidistantly expanding according to the coordinate edges of the landing region, identifying the range of a neighborhood region, and acquiring a neighborhood building region;
collecting building information in the neighborhood building area and outputting neighborhood building attributes;
identifying excitation influence degree of the volatile gas set according to the neighborhood building attribute, and outputting excitation influence indexes;
and adjusting the first feasibility assessment report according to the excitation influence index.
Further, the first feasibility report generating module 16 is further configured to perform the following steps:
dynamically monitoring the content of each volatile gas in the first influence index to generate a gas change curve set, wherein the abscissa of the gas change curve set is a time sequence period, and the ordinate is the gas content;
performing influence degree conversion on the gas change curve set by using the gas influence evaluation model, and outputting an influence degree change curve;
outputting a greening index change curve according to the first greening index;
and taking the excitation influence index as a first excitation factor, taking the index on the greening index change curve as a second excitation factor, carrying out Markov chain prediction on the influence degree change curve, and outputting the first prediction result.
Further, the first feasibility report generating module 16 is further configured to perform the following steps:
performing linear weighted fitting on the first excitation factor and the second excitation factor, and outputting a first excitation function;
predicting the influence degree change curve by using the first excitation function, and judging that the expected probability smaller than the preset influence degree is reached when the influence degree change curve is in the preset time sequence;
if the expected probability meets the preset expected probability, acquiring a report passing the generation feasibility;
and if the expected probability does not meet the preset expected probability, generating a report that the feasibility is not passed.
Further, the system further comprises:
the building height acquisition module is used for acquiring the building height of the first design building;
the direct prediction result output module is used for directly predicting the first influence index when the building height exceeds a preset building height and outputting a direct prediction result based on the preset time sequence, wherein the direct prediction result comprises the gas influence degree under the preset time sequence;
the first reminding information generation module is used for generating first reminding information when the predicted gas influence degree of the direct predicted result is larger than the preset gas influence degree.
It should be noted that the sequence of the embodiments of the present application is merely for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the present application is not intended to limit the invention to the particular embodiments of the present application, but to limit the scope of the invention to the particular embodiments of the present application.
The specification and drawings are merely exemplary of the application and are to be regarded as covering any and all modifications, variations, combinations, or equivalents that are within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (5)
1. A method of assessing feasibility of a building design, the method comprising:
acquiring a building design scheme, and acquiring a first design building of the building design scheme and a landing area of the first design building;
building material analysis is carried out on the first design building, and a volatile gas set is obtained;
acquiring gas attribute characteristics of the volatile gas set, inputting the gas attribute characteristics into a gas influence evaluation model for evaluation, and determining a first influence index, wherein the gas attribute characteristics comprise gas pollution degree, gas concentration and gas persistence;
evaluating building greening characteristics of the first design building to obtain a first greening index;
carrying out dynamic time sequence prediction on the first influence index and the first greening index, and outputting a first prediction result, wherein the first prediction result is the gas influence degree under a preset time sequence;
generating a first feasibility report according to the first prediction result;
the method further comprises the steps of:
connecting a GIS map, acquiring region coordinates of the landing region, equidistantly expanding according to the coordinate edges of the landing region, identifying the range of a neighborhood region, and acquiring a neighborhood building region;
collecting building information in the neighborhood building area and outputting neighborhood building attributes;
identifying excitation influence degree of the volatile gas set according to the neighborhood building attribute, and outputting excitation influence indexes;
adjusting the first feasibility assessment report according to the excitation impact index;
adjusting the first feasibility assessment report according to the excitation impact index, wherein the method comprises the following steps:
dynamically monitoring the content of each volatile gas in the first influence index to generate a gas change curve set, wherein the abscissa of the gas change curve set is a time sequence period, and the ordinate is the gas content;
performing influence degree conversion on the gas change curve set by using the gas influence evaluation model, and outputting an influence degree change curve;
outputting a greening index change curve according to the first greening index;
taking the excitation influence index as a first excitation factor, taking an index on the greening index change curve as a second excitation factor, carrying out Markov chain prediction on the influence degree change curve, and outputting the first prediction result;
performing linear weighted fitting on the first excitation factor and the second excitation factor, and outputting a first excitation function;
predicting the influence degree change curve by using the first excitation function, and judging that the expected probability smaller than the preset influence degree is reached when the influence degree change curve is in the preset time sequence;
if the expected probability meets the preset expected probability, acquiring a report passing the generation feasibility;
and if the expected probability does not meet the preset expected probability, generating a report that the feasibility is not passed.
2. The method of claim 1, wherein the method further comprises:
performing feature recognition on a building living group of the first design building, and outputting group features;
performing index weight adjustment on the first influence index according to the group characteristics to generate a fusion weight network layer;
and carrying out model optimization on the gas influence evaluation model according to the fusion weight network layer.
3. The method of claim 1, wherein evaluating building greening characteristics of the first design building to obtain a first greening metric comprises:
collecting greening characteristics of the first design building, including a type of a green belt, a greening growth rate and a greening floor area;
inputting the type of the green belt, the greening growth rate and the greening occupied area into a greening evaluation model, and outputting a first greening index according to the greening evaluation model, wherein the first greening index is used for marking the absorption degree of evaluation gas;
the greening evaluation model is obtained by collecting mapping samples of the type of the green belt and the gas absorptivity and training according to the mapping samples until convergence.
4. The method of claim 1, wherein the method further comprises:
acquiring the building height of the first design building;
when the building height exceeds a preset building height, directly predicting the first influence index, and outputting a direct prediction result based on the preset time sequence, wherein the direct prediction result comprises the gas influence degree under the preset time sequence;
and when the predicted gas influence degree of the direct prediction result is larger than the preset gas influence degree, generating first reminding information.
5. A feasibility assessment system for a building design, the system comprising:
the building design scheme acquisition module is used for acquiring a building design scheme, and acquiring a first design building of the building design scheme and a landing area of the first design building;
the volatile gas collection acquisition module is used for analyzing building materials of the first design building to acquire a volatile gas collection;
the first influence index determining module is used for acquiring gas attribute characteristics of the volatile gas set, inputting the gas attribute characteristics into a gas influence evaluation model for evaluation, and determining a first influence index, wherein the gas attribute characteristics comprise gas pollution degree, gas concentration and gas persistence;
the first greening index acquisition module is used for evaluating building greening characteristics of the first design building to acquire a first greening index;
the first prediction result output module is used for carrying out dynamic time sequence prediction on the first influence index and the first greening index and outputting a first prediction result, wherein the first prediction result is the gas influence degree under a preset time sequence;
the first feasibility report generation module is used for generating a first feasibility report according to the first prediction result;
wherein the first feasibility report generating module is further configured to perform the following steps:
connecting a GIS map, acquiring region coordinates of the landing region, equidistantly expanding according to the coordinate edges of the landing region, identifying the range of a neighborhood region, and acquiring a neighborhood building region;
collecting building information in the neighborhood building area and outputting neighborhood building attributes;
identifying excitation influence degree of the volatile gas set according to the neighborhood building attribute, and outputting excitation influence indexes;
adjusting the first feasibility assessment report according to the excitation impact index;
dynamically monitoring the content of each volatile gas in the first influence index to generate a gas change curve set, wherein the abscissa of the gas change curve set is a time sequence period, and the ordinate is the gas content;
performing influence degree conversion on the gas change curve set by using the gas influence evaluation model, and outputting an influence degree change curve;
outputting a greening index change curve according to the first greening index;
taking the excitation influence index as a first excitation factor, taking an index on the greening index change curve as a second excitation factor, carrying out Markov chain prediction on the influence degree change curve, and outputting the first prediction result;
performing linear weighted fitting on the first excitation factor and the second excitation factor, and outputting a first excitation function;
predicting the influence degree change curve by using the first excitation function, and judging that the expected probability smaller than the preset influence degree is reached when the influence degree change curve is in the preset time sequence;
if the expected probability meets the preset expected probability, acquiring a report passing the generation feasibility;
and if the expected probability does not meet the preset expected probability, generating a report that the feasibility is not passed.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101791422A (en) * | 2010-02-21 | 2010-08-04 | 韩恒吉 | Regulating system for decomposing, purifying and carrying polluted gas in building space |
| CN106647452A (en) * | 2016-11-16 | 2017-05-10 | 深圳市铁汉方环境科技有限公司 | Intelligent control device based on three-dimensional greening and visual control system thereof |
| CN109377069A (en) * | 2018-11-05 | 2019-02-22 | 天津大学 | A kind of green building integrated environment impact evaluation method based on Life Circle |
| TWI727557B (en) * | 2019-12-20 | 2021-05-11 | 逢甲大學 | Net zero-energy green building simulation system using biomass energy and operation method thereof |
| CN115825336A (en) * | 2022-10-31 | 2023-03-21 | 中国林业科学研究院木材工业研究所 | Method for predicting concentration of pollutants in indoor air after house decoration |
-
2023
- 2023-07-21 CN CN202310902328.1A patent/CN116910871B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101791422A (en) * | 2010-02-21 | 2010-08-04 | 韩恒吉 | Regulating system for decomposing, purifying and carrying polluted gas in building space |
| CN106647452A (en) * | 2016-11-16 | 2017-05-10 | 深圳市铁汉方环境科技有限公司 | Intelligent control device based on three-dimensional greening and visual control system thereof |
| CN109377069A (en) * | 2018-11-05 | 2019-02-22 | 天津大学 | A kind of green building integrated environment impact evaluation method based on Life Circle |
| TWI727557B (en) * | 2019-12-20 | 2021-05-11 | 逢甲大學 | Net zero-energy green building simulation system using biomass energy and operation method thereof |
| CN115825336A (en) * | 2022-10-31 | 2023-03-21 | 中国林业科学研究院木材工业研究所 | Method for predicting concentration of pollutants in indoor air after house decoration |
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