CN119047018A - AI enhanced green building design optimizing system - Google Patents

AI enhanced green building design optimizing system Download PDF

Info

Publication number
CN119047018A
CN119047018A CN202410867711.2A CN202410867711A CN119047018A CN 119047018 A CN119047018 A CN 119047018A CN 202410867711 A CN202410867711 A CN 202410867711A CN 119047018 A CN119047018 A CN 119047018A
Authority
CN
China
Prior art keywords
data
optimization
building
maintenance
unit
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202410867711.2A
Other languages
Chinese (zh)
Other versions
CN119047018B (en
Inventor
郑响升
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong Hesheng Construction Engineering Co ltd
Original Assignee
Guangdong Hesheng Construction Engineering Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong Hesheng Construction Engineering Co ltd filed Critical Guangdong Hesheng Construction Engineering Co ltd
Priority to CN202410867711.2A priority Critical patent/CN119047018B/en
Publication of CN119047018A publication Critical patent/CN119047018A/en
Application granted granted Critical
Publication of CN119047018B publication Critical patent/CN119047018B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/27—Regression, e.g. linear or logistic regression
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Geometry (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Evolutionary Biology (AREA)
  • Artificial Intelligence (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Data Mining & Analysis (AREA)
  • Computer Hardware Design (AREA)
  • Software Systems (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Genetics & Genomics (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Medical Informatics (AREA)
  • Biomedical Technology (AREA)
  • Physiology (AREA)
  • Architecture (AREA)
  • Civil Engineering (AREA)
  • Structural Engineering (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention relates to the technical field of building analysis, in particular to an AI-enhanced green building design optimization system, which comprises a building life cycle data integration module, an environment simulation and prediction module, a dynamic optimization module and a multi-objective decision support module, wherein the building life cycle data integration module integrates the full life cycle data from design, construction, operation to maintenance of a building, the environment simulation and prediction module performs environment simulation of multiple scales and multiple physical fields based on the integrated life cycle data, the dynamic optimization module performs dynamic optimization at different stages of the building life cycle, the multi-objective decision support module performs optimization decision on energy consumption, comfort, environmental influence and cost dimension based on the multi-objective decision theory and generates a comprehensive optimization scheme for balancing all objectives.

Description

AI enhanced green building design optimizing system
Technical Field
The invention relates to the technical field of building analysis, in particular to an AI (advanced technology interface) enhanced green building design optimization system.
Background
Green building design is an important trend in the current building field, and the core idea is to reduce resource consumption and environmental pollution through energy-saving, environment-friendly, healthy and comfortable design means. However, the conventional green building design method mainly depends on experience and expertise of designers, and lacks systematic data analysis and optimization means. This is not only inefficient, but also susceptible to artifacts, and it is difficult to ensure the optimality and comprehensiveness of the design.
With the development of artificial intelligence technology, particularly the development of deep learning and big data analysis technology, new possibilities are provided for green building design. By introducing artificial intelligence, intelligent optimization and evaluation of the architectural design scheme can be realized, and the design efficiency and quality are improved. However, most of the existing systems on the market only pay attention to a certain aspect of building design, lack comprehensive optimization capability on the whole life cycle of the building, and are difficult to provide comprehensive optimization support in various stages of design, construction, operation, maintenance and the like.
The existing green building design optimization system has the following main problems:
the existing system is generally incapable of effectively integrating the full life cycle data of the building from design, construction to operation and maintenance, so that the data utilization rate is low, and the building performance cannot be comprehensively estimated.
The dynamic optimization capability is limited, and the dynamic optimization capability is lacking, so that the real-time environment data and the running condition of the building cannot be timely adjusted and optimized, and the optimization effect is not ideal.
The multi-objective optimization is lacking in that the existing system only pays attention to a single objective in the optimization process, such as energy consumption or cost, cannot balance multiple objectives such as energy consumption, comfort, environmental impact and cost, and is difficult to realize comprehensive optimization.
Aiming at the problems, the invention provides an AI-enhanced green building design optimization system, which realizes the intelligent and comprehensive optimization of green building design through building life cycle data integration, environment simulation and prediction, dynamic optimization, multi-objective decision support and other modules.
Disclosure of Invention
Based on the above objects, the present invention provides an AI-enhanced green architectural design optimization system.
An AI-enhanced green architectural design optimization system comprising:
The system comprises a building life cycle data integration module, a storage module and a storage module, wherein the building life cycle data integration module integrates full life cycle data from design, construction, operation to maintenance of a building, and the full life cycle data comprises life cycle analysis data, energy consumption data, environmental impact data and maintenance records of building materials;
the environment simulation and prediction module is used for performing environment simulation of a plurality of scales and physical fields based on integrated life cycle data, wherein the environment simulation comprises climate change prediction, air quality simulation, noise propagation simulation and water resource management simulation;
the dynamic optimization module is connected with the environment simulation and prediction module and is used for carrying out dynamic optimization at different stages of a building life cycle, wherein the dynamic optimization comprises design optimization, construction process optimization, operation management optimization and maintenance strategy optimization;
The multi-objective decision support module is connected with the dynamic optimization module and is used for carrying out optimization decision on the aspects of energy consumption, comfort level, environmental influence and cost dimension based on a multi-objective decision theory and generating a comprehensive optimization scheme for balancing all objectives.
Further, the building life cycle data integration module specifically includes:
The design data integration unit is used for collecting and integrating design drawings, bill of materials, building specifications and standards and design simulation data in the building design stage;
The construction data acquisition unit is used for acquiring and integrating a construction plan, an actual construction progress, a construction quality inspection record, construction site environment data and a material use record in a construction stage;
an operation data monitoring unit for monitoring and integrating energy consumption data (including electric power, water and gas consumption), indoor and outdoor environment data (temperature, humidity and air quality), equipment operation state and performance data in the building operation stage;
A maintenance data recording unit for recording and integrating maintenance plan, maintenance operation record, equipment replacement record and maintenance cost data in the building maintenance stage;
The data storage and management unit is connected with each unit and used for storing and managing integrated full life cycle data and ensuring the safety, consistency and accessibility of the data;
The data processing and analyzing unit is connected with the data storage and management unit and is used for processing and analyzing the full life cycle data and generating life cycle analysis data, energy consumption analysis reports, environmental impact assessment and maintenance requirement prediction of the building materials.
Further, the life cycle analysis data specifically includes:
calculating the environmental impact and resource consumption of the material in each stage of production, transportation, use and recovery, wherein the total impact formula of the life cycle is as follows: Wherein, LCA is the total influence of life cycle, EI i is the unit environmental influence of the ith stage, Q i is the material quantity or the use quantity of the ith stage, and n is the stage number;
The energy consumption analysis report specifically includes:
smoothing the energy consumption data using a moving average method, the formula being: Wherein MA t is a moving average value at the t moment, E t-i is energy consumption data at the t-i moment, and n is the size of a time window;
E t＝β0+β1×t+t, establishing an energy consumption prediction model by using regression analysis, wherein E t is the energy consumption at the t moment, beta 0 and beta 1 are regression coefficients respectively, and t is an error term;
the environmental impact assessment and maintenance requirement prediction specifically comprises:
using a weighted comprehensive evaluation method, the formula is:
wherein EI is the total environmental impact score, W i is the weight of the ith environmental factor, I i is the impact score of the ith environmental factor, and m is the number of environmental factors;
the maintenance requirement prediction specifically includes:
predicting future maintenance requirements by using an exponential smoothing method, wherein the formula is as follows:
Wherein, For the predicted demand at time t+1, D t is the actual demand at time t,For the predicted demand at time t, α is a smoothing coefficient.
Further, the environment simulation and prediction module specifically includes:
The climate change prediction unit is used for predicting climate change by using the regional climate model RCM based on the integrated life cycle data and the historical climate data, and is expressed as:
T future＝Tcurrent +DeltaT, wherein T future is the future temperature, T current is the current temperature, deltaT is the temperature variation predicted by the climate model;
Predicting climate change under different conditions by using a global climate model GCM and regional climate model RCM combination method, wherein P future＝Pcurrent × (1+R), P future is future precipitation, P current is current precipitation, and R is precipitation change rate;
the air quality simulation unit is used for simulating the air quality around the building based on life cycle data, emission source data and meteorological data, and simulating pollutant diffusion based on a Gaussian diffusion model, wherein the formula is as follows:
Wherein, C (x, y, z) is the pollutant concentration at the (x, y, z) point, Q is the emission source intensity, sigma x,σy,σz is the diffusion coefficient, and the formula is: +S, wherein u is a flow velocity vector, D is a diffusion coefficient, S is a source term;
the noise propagation simulation unit is used for simulating the propagation condition of noise inside and outside a building based on building layout, material characteristics and environmental data, and simulating noise propagation by using an acoustic wave propagation model, wherein the formula is as follows:
L p＝Lw-20log10 (r) - αr, wherein L p is the sound pressure level of a receiving point, L w is the sound power level of a sound source, r is the distance from the sound source to the receiving point, α is the air absorption coefficient, and reflection and absorption are considered in combination with an architectural acoustic model, and the formula is:
L p＝Lw-20log10 (R) - αr-R, wherein R is the sound absorption coefficient of the building material;
the water resource management simulation unit is used for simulating water resource management and a water circulation system of a building area based on water consumption data, precipitation data and geographic information, and simulating precipitation and runoff based on a hydrologic model, wherein the formula is as follows:
r=p-ET-I, where R is runoff, P is precipitation, ET is evapotranspiration, I is infiltration, and the urban water circulation system is simulated using a stormwater management model SWMM, and the simulation is expressed as:
wherein Q is water flow, n is roughness coefficient, A is cross-sectional area, R h is hydraulic radius, S is water flow gradient.
Further, the dynamic optimization module specifically includes:
The design optimizing unit is used for optimizing building layout and optimizing material selection in a building design stage, wherein the optimized building layout utilizes genetic algorithm to combine climate change prediction data so as to maximize natural ventilation and lighting, and the optimized building layout is expressed as:
Wherein F layout is a layout optimization objective function, A i is the area of the ith space, E i is the lighting effect of the ith space, W i is the ventilation effect of the ith space, and n is the number of spaces, wherein the optimization material selection is based on life cycle analysis, combines air quality simulation and environmental impact data, and selects building materials with low environmental impact, and the optimization is expressed as follows:
wherein EI total is total environmental influence, EI j is unit environmental influence of the j-th material, Q j is the use amount of the j-th material, and m is the number of the types of the materials;
A construction process optimizing unit for optimizing construction plan and optimizing material use in the construction stage of building to reduce environmental impact and improve construction efficiency, wherein the optimized construction plan uses a critical path method in combination with noise propagation simulation and air quality data, optimizes construction progress, reduces construction period and environmental impact, expressed as min Wherein T total is the total construction period, D k is the duration of the kth construction task, and p is the number of construction tasks, the optimizing material usage utilizes linear programming to optimize the material usage, reduces waste and cost, and is expressed as: Wherein, C materials is the total cost of the materials, C l is the unit cost of the first material, x l is the use amount of the first material, and q is the number of types of the materials;
The operation management optimizing unit is used for optimizing energy management and optimizing equipment operation based on environment monitoring data provided by the environment simulation and prediction module in the building operation stage so as to improve energy efficiency and comfort level, wherein the optimized energy management is based on a prediction control method so as to improve energy use efficiency and is expressed as:
Wherein E total is total energy consumption, P t is power demand at T time, deltat is time interval, T is total time, the operation of the optimizing equipment is based on equipment performance data and environment monitoring data, the operation parameters of the optimizing equipment are calculated according to the formula: Wherein E efficiency is the equipment efficiency, O output is the equipment output power, and I input is the equipment input power;
The maintenance strategy optimizing unit is used for optimizing maintenance plans and strategies based on long-term environment data and equipment state data provided by the environment simulation and prediction module in the building maintenance stage so as to prolong the service life of equipment and reduce the maintenance cost, wherein the optimized maintenance plans are based on a prediction maintenance algorithm, optimize the maintenance plans and reduce the faults and the maintenance cost, and are expressed as: Wherein,
C maintenance is total maintenance cost, C t is unit maintenance cost at the t moment, M t is maintenance quantity at the t moment, the optimized maintenance strategy adopts a reliability center maintenance method, the optimized maintenance strategy improves equipment reliability, and the method is expressed as:
Wherein R total is the total reliability, R n is the reliability of the nth device, and N is the number of devices.
Further, the multi-objective decision support module specifically includes:
an objective function definition unit for defining an objective function of energy consumption, comfort, environmental impact and cost;
The weight setting unit is used for setting the weight of each objective function according to the user demand and the priority;
the multi-objective optimization algorithm unit is used for solving the optimal solution of each objective function based on the multi-objective optimization algorithm;
And the comprehensive optimization scheme generating unit is used for synthesizing the optimal solutions of the objective functions and generating a comprehensive optimization scheme for balancing the objectives.
Further, the objective function definition unit specifically includes:
and (3) defining an energy consumption objective function E by taking the minimum building energy consumption as an objective, wherein the formula is as follows: wherein P t is the power demand at time T, deltat is the time interval, and T is the total time;
comfort objective function, namely defining a comfort objective function C with the aim of maximizing the comfort in the building, wherein the formula is as follows: Wherein S i is the comfort level score of the i-th space, a i is the area of the i-th space, and n is the number of spaces;
environmental impact objective function, namely defining an environmental impact objective function I with the aim of minimizing environmental impact, wherein the formula is as follows: Wherein EI j is the unit environmental influence of the j-th material, Q j is the use amount of the j-th material, and m is the material type number;
Cost objective function, namely defining a cost objective function K by taking the minimum construction cost as a target, wherein the formula is as follows: Wherein, C k is the unit cost of the kth item, x k is the usage of the kth item, and p is the number of items.
Further, the weight setting unit sets the weight W i of each objective function according to the user requirement and the priority, wherein the formula is w= { W E,wC,wI,wK }, W E is the weight of the energy consumption target, W C is the weight of the comfort target, W I is the weight of the environmental impact target, W K is the weight of the cost target, and Σw i =1.
Further, the multi-objective optimization algorithm unit performs multi-objective optimization by using a weighted sum method, and solves the comprehensive optimization objective function F, wherein the formula is as follows:
F=w E·E+wC·C+wI·I+wK ·k, where F is a comprehensive optimization objective function, the weight w i is set by the weight setting unit, the objective functions E, C, I, K are energy consumption, comfort, environmental impact, and cost objective functions, and the optimal solution is solved by using particle swarm optimization, and the optimization parameters are updated;
And generating a comprehensive optimization scheme for balancing each target according to an Optimal Solution of the particle swarm optimization Solution, wherein the formula is Optimal solution=arg minF, and the Optimal Solution is the comprehensive optimization scheme and F is a comprehensive optimization objective function.
Further, the particle swarm optimization specifically includes:
Initializing a group of particles, setting the position and the speed of the particles, wherein each particle represents a solution to be selected, and the position and the speed vectors are x i and v i respectively:
where i represents the ith particle and n is the dimension of the problem;
evaluating fitness, namely calculating a fitness value of each particle, wherein for a multi-objective optimization problem, a fitness function is a weighted sum of a plurality of objective functions:
f(xi)＝wE·E(xi)+wC·C(xi)+wI·I(xi)+wK·K(xi), Wherein E (x i) is an energy consumption objective function, C (x i) is a comfort objective function, I (x i) is an environmental impact objective function, K (x i) is a cost objective function, and w E,wC,wI,wK is the weight of each objective function respectively;
Updating the individual optimum and the global optimum, namely updating the individual optimum position p i and the global optimum position g for each particle;
g(t+1)=Xi(t)if f(Xi(t))<f(g(t));
Updating particle velocity and position:
xi(t+1)＝xi(t)+vi(t+1);
Wherein ω is an inertial weight, c 1 and c 2 are an individual learning factor and a social learning factor, respectively, and r 1 and r 2 are random numbers between 0 and 1;
the fitness is repeatedly evaluated, the individual optimum and the global optimum are updated, and the particle speed and the position are updated until the termination condition is satisfied.
The invention has the beneficial effects that:
According to the invention, through the building life cycle data integration module, full life cycle data from design, construction, operation to maintenance are comprehensively integrated, and the full life cycle data comprise life cycle analysis data, energy consumption data, environmental impact data, maintenance records and the like of building materials. The environment simulation and prediction module is used for carrying out environment simulation of multiple scales and multiple physical fields, and data support of climate change prediction, air quality simulation, noise propagation simulation and water resource management simulation is provided. On the basis, the dynamic optimization module performs design optimization, construction process optimization, operation management optimization and maintenance strategy optimization at different stages, combines real-time environment data and simulation results, realizes the dynamic optimization of the life cycle of the building, and improves the overall performance and sustainability of the green building design.
According to the invention, a multi-objective decision support module is adopted, weights are set by defining objective functions of energy consumption, comfort level, environmental influence and cost, and multi-objective optimization is performed by using a weighted sum method. And solving the optimal solution by combining a particle swarm optimization algorithm, updating the optimization parameters, and generating a comprehensive optimization scheme for balancing each target. And the optimal solution is fed back to the dynamic optimization module through the comprehensive optimization scheme generating unit, so that the optimization decision of each stage of building design, construction, operation and maintenance is guided, and the comprehensive balance and optimization of energy consumption, comfort level, environmental influence and cost are realized. The multi-objective decision support mechanism remarkably improves the scientificity and the optimizing effect of the green building design, and fully embodies the innovation and the practical value of the system.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only of the invention and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an optimization system according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a multi-objective decision support module according to an embodiment of the invention.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
It is to be noted that unless otherwise defined, technical or scientific terms used herein should be taken in a general sense as understood by one of ordinary skill in the art to which the present invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
As shown in fig. 1-2, the AI-enhanced green architectural design optimization system includes:
The building life cycle data integration module integrates the full life cycle data of the building from design, construction, operation to maintenance, wherein the full life cycle data comprises life cycle analysis data, energy consumption data, environmental impact data and maintenance records of building materials;
the environment simulation and prediction module is used for performing environment simulation of a plurality of scales and physical fields based on integrated life cycle data, wherein the environment simulation comprises climate change prediction, air quality simulation, noise propagation simulation and water resource management simulation;
The dynamic optimization module is connected with the environment simulation and prediction module and is used for carrying out dynamic optimization at different stages of the building life cycle, wherein the dynamic optimization comprises design optimization, construction process optimization, operation management optimization and maintenance strategy optimization;
The multi-objective decision support module is connected with the dynamic optimization module and is used for carrying out optimization decision on the aspects of energy consumption, comfort level, environmental influence and cost dimension based on a multi-objective decision theory and generating a comprehensive optimization scheme for balancing all objectives;
Based on virtual reality interaction, the system is connected with a multi-target decision support module and used for displaying the implementation effect of the optimized scheme through a virtual reality technology, and a user can intuitively evaluate and adjust the scheme through immersive experience.
The building life cycle data integration module specifically comprises:
The design data integration unit is used for collecting and integrating design drawings, bill of materials, building specifications and standards and design simulation data in the building design stage;
The construction data acquisition unit is used for acquiring and integrating a construction plan, an actual construction progress, a construction quality inspection record, construction site environment data and a material use record in a construction stage;
an operation data monitoring unit for monitoring and integrating energy consumption data (including electric power, water and gas consumption), indoor and outdoor environment data (temperature, humidity and air quality), equipment operation state and performance data in the building operation stage;
A maintenance data recording unit for recording and integrating maintenance plan, maintenance operation record, equipment replacement record and maintenance cost data in the building maintenance stage;
The data storage and management unit is connected with each unit and used for storing and managing integrated full life cycle data and ensuring the safety, consistency and accessibility of the data;
The data processing and analyzing unit is connected with the data storage and management unit and is used for processing and analyzing the full life cycle data and generating life cycle analysis data, energy consumption analysis reports, environmental impact assessment and maintenance requirement prediction of the building materials.
The life cycle analysis data specifically includes:
calculating the environmental impact and resource consumption of the material in each stage of production, transportation, use and recovery, wherein the total impact formula of the life cycle is as follows: Wherein, LCA is the total influence of life cycle, EI i is the unit environmental influence of the ith stage, Q i is the material quantity or the use quantity of the ith stage, and n is the stage number;
The energy consumption analysis report specifically includes:
smoothing the energy consumption data using a moving average method, the formula being: Wherein, E t-i is energy consumption data at the t-i moment, and n is the size of a time window;
E t＝β0+β1×t+t, establishing an energy consumption prediction model by using regression analysis, wherein E t is the energy consumption at the t moment, beta 0 and beta 1 are regression coefficients respectively, and t is an error term;
environmental impact assessment and maintenance requirement prediction specifically includes:
using a weighted comprehensive evaluation method, the formula is:
wherein EI is the total environmental impact score, W i is the weight of the ith environmental factor, I i is the impact score of the ith environmental factor, and m is the number of environmental factors;
The maintenance requirement prediction specifically includes:
predicting future maintenance requirements by using an exponential smoothing method, wherein the formula is as follows:
Wherein, For the predicted demand at time t+1, D t is the actual demand at time t,For the predicted demand at time t, α is a smoothing coefficient.
The environment simulation and prediction module specifically comprises:
The climate change prediction unit is used for predicting climate change by using the regional climate model RCM based on the integrated life cycle data and the historical climate data, and is expressed as:
T future＝Tcurrent +DeltaT, wherein T future is the future temperature, T current is the current temperature, deltaT is the temperature variation predicted by the climate model;
Predicting climate change under different conditions by using a global climate model GCM and regional climate model RCM combination method, wherein P future＝Pcurrent × (1+R), P future is future precipitation, P current is current precipitation, and R is precipitation change rate;
the air quality simulation unit is used for simulating the air quality around the building based on life cycle data, emission source data and meteorological data, and simulating pollutant diffusion based on a Gaussian diffusion model, wherein the formula is as follows:
Wherein, C (x, y, z) is the pollutant concentration at the (x, y, z) point, Q is the emission source intensity, sigma x,σy,σz is the diffusion coefficient, and the formula is: Wherein u is a flow velocity vector, D is a diffusion coefficient, and S is a source term;
the noise propagation simulation unit is used for simulating the propagation condition of noise inside and outside a building based on building layout, material characteristics and environmental data, and simulating noise propagation by using an acoustic wave propagation model, wherein the formula is as follows:
L p＝Lw-20log10 (r) - αr, wherein L p is the sound pressure level of a receiving point, L w is the sound power level of a sound source, r is the distance from the sound source to the receiving point, α is the air absorption coefficient, and reflection and absorption are considered in combination with an architectural acoustic model, and the formula is:
L p＝Lw-20log10 (R) - αr-R, wherein R is the sound absorption coefficient of the building material;
the water resource management simulation unit is used for simulating water resource management and a water circulation system of a building area based on water consumption data, precipitation data and geographic information, and simulating precipitation and runoff based on a hydrologic model, wherein the formula is as follows:
r=p-ET-I, where R is runoff, P is precipitation, ET is evapotranspiration, I is infiltration, and the urban water circulation system is simulated using a stormwater management model SWMM, and the simulation is expressed as:
wherein Q is water flow, n is roughness coefficient, A is cross-sectional area, R h is hydraulic radius, S is water flow gradient.
The dynamic optimization module specifically comprises:
the design optimizing unit is used for optimizing building layout and optimizing material selection in the building design stage, and the optimized building layout utilizes a genetic algorithm to combine climate change prediction data so as to maximize natural ventilation and lighting, and is expressed as:
Wherein F layout is a layout optimization objective function, A i is the area of the ith space, E i is the lighting effect of the ith space, W i is the ventilation effect of the ith space, n is the number of spaces, the optimization material selection is based on life cycle analysis, air quality simulation and environmental impact data are combined, the low-environmental-impact building material is selected, and the optimization is expressed as:
wherein EI total is total environmental influence, EI j is unit environmental influence of the j-th material, Q j is the use amount of the j-th material, and m is the number of the types of the materials;
the construction process optimizing unit is used for optimizing a construction plan and optimizing material use in a building construction stage so as to reduce environmental influence and improve construction efficiency, wherein the optimizing construction plan uses a key path method to combine noise propagation simulation and air quality data, optimize construction progress and reduce construction period and environmental influence, and the optimizing construction process optimizing unit is expressed as: Wherein T total is the total construction period, D k is the duration of the kth construction task, p is the number of construction tasks, and optimizing material use by using linear programming reduces waste and cost, expressed as: Wherein, C materials is the total cost of the materials, C l is the unit cost of the first material, x l is the use amount of the first material, and q is the number of types of the materials;
The operation management optimizing unit is used for optimizing energy management and optimizing equipment operation based on environment monitoring data provided by the environment simulation and prediction module in the building operation stage so as to improve energy efficiency and comfort, optimizing an energy management method based on prediction control and improving energy use efficiency, and is expressed as:
Wherein E total is total energy consumption, P t is power demand at T time, deltat is time interval, T is total time, optimizing device operation is based on device performance data and environment monitoring data, optimizing device operation parameters, and the formula is: Wherein E efficiency is the equipment efficiency, O output is the equipment output power, and I input is the equipment input power;
The maintenance strategy optimizing unit is used for optimizing maintenance plans and strategies based on the long-term environment data and the equipment state data provided by the environment simulation and prediction module in the building maintenance stage so as to prolong the service life of equipment and reduce the maintenance cost, and optimizing the maintenance plans based on the prediction maintenance algorithm so as to reduce faults and maintenance cost, and is expressed as: Wherein, C maintenance is the total maintenance cost, C t is the unit maintenance cost at the t moment, M t is the maintenance amount at the t moment, the optimized maintenance strategy adopts a reliability center maintenance method, the optimized maintenance strategy improves the reliability of the equipment, and the method is expressed as:
Wherein R total is the total reliability, R n is the reliability of the nth device, and N is the number of devices.
The objective function of the multi-objective decision support module is used for generating the comprehensive optimization scheme, and the dynamic optimization part (design optimization, construction process optimization, operation management optimization and maintenance strategy optimization) performs specific optimization work in each stage. The comprehensive optimization scheme of the multi-objective decision support module can guide and adjust the specific optimization processes, so that the optimization results in each stage can better balance a plurality of targets such as energy consumption, comfort level, environmental influence, cost and the like, and therefore, the multi-objective decision support module specifically comprises:
an objective function definition unit for defining an objective function of energy consumption, comfort, environmental impact and cost;
The weight setting unit is used for setting the weight of each objective function according to the user demand and the priority;
the multi-objective optimization algorithm unit is used for solving the optimal solution of each objective function based on the multi-objective optimization algorithm;
And the comprehensive optimization scheme generating unit is used for synthesizing the optimal solutions of the objective functions and generating a comprehensive optimization scheme for balancing the objectives.
The objective function definition unit specifically includes:
and (3) defining an energy consumption objective function E by taking the minimum building energy consumption as an objective, wherein the formula is as follows: wherein P t is the power demand at time T, deltat is the time interval, and T is the total time;
comfort objective function, namely defining a comfort objective function C with the aim of maximizing the comfort in the building, wherein the formula is as follows: Wherein S i is the comfort level score of the i-th space, a i is the area of the i-th space, and n is the number of spaces;
environmental impact objective function, namely defining an environmental impact objective function I with the aim of minimizing environmental impact, wherein the formula is as follows: Wherein EI j is the unit environmental influence of the j-th material, Q j is the use amount of the j-th material, and m is the material type number;
Cost objective function, namely defining a cost objective function K by taking the minimum construction cost as a target, wherein the formula is as follows: Wherein, C k is the unit cost of the kth item, x k is the usage of the kth item, and p is the number of items.
The weight setting unit sets the weight W i of each objective function according to the user demand and the priority, wherein the formula is W= { W E,wC,wI,wK }, W E is the weight of the energy consumption target, W C is the weight of the comfort target, W I is the weight of the environmental impact target, W K is the weight of the cost target, and Σw i =1.
The multi-objective optimization algorithm unit performs multi-objective optimization by using a weighted sum method, and solves a comprehensive optimization objective function F, wherein the formula is as follows:
F=w E·E+wC·C+wI·I+wK ·k, where F is a comprehensive optimization objective function, the weight w i is set by the weight setting unit, the objective functions E, C, I, K are energy consumption, comfort, environmental impact, and cost objective functions, and the optimal solution is solved by using particle swarm optimization, and the optimization parameters are updated;
And generating a comprehensive optimization scheme for balancing each target according to an Optimal Solution of the particle swarm optimization Solution, wherein the formula is Optimal solution=arg minF, and the Optimal Solution is the comprehensive optimization scheme and F is a comprehensive optimization objective function.
Particle swarm optimization specifically comprises:
Initializing a group of particles, setting the position and the speed of the particles, wherein each particle represents a solution to be selected, and the position and the speed vectors are x i and v i respectively:
where i represents the ith particle and n is the dimension of the problem;
evaluating fitness, namely calculating a fitness value of each particle, wherein for a multi-objective optimization problem, a fitness function is a weighted sum of a plurality of objective functions:
f(xi)＝wE·E(xi)+wC·C(xi)+wI·I(xi)+wK·K(xi), Wherein E (x i) is an energy consumption objective function, C (x i) is a comfort objective function, I (x i) is an environmental impact objective function, K (x i) is a cost objective function, and w E,wC,wI,wK is the weight of each objective function respectively;
Updating the individual optimum and the global optimum, namely updating the individual optimum position p i and the global optimum position g for each particle;
g(t+1)=Xi(t)if f(Xi(t))<f(g(t));
Updating particle velocity and position:
vi(t+1)＝ωvi(t)+c1r1(pi(t)-xi(t))+c2r2(g(t)-xi(t));
xi(t+1)＝xi(t)+vi(t+1);
Wherein ω is an inertial weight, c 1 and c 2 are an individual learning factor and a social learning factor, respectively, and r 1 and r 2 are random numbers between 0 and 1;
The fitness is repeatedly evaluated, the individual and global optima are updated, the particle speed and position are updated until the termination condition is met (maximum number of iterations is reached or the fitness value converges).
It will be appreciated by persons skilled in the art that the above discussion of any embodiment is merely exemplary and is not intended to imply that the scope of the invention is limited to these examples, that combinations of technical features in the above embodiments or in different embodiments may also be implemented in any order, and that many other variations of the different aspects of the invention as described above exist, which are not provided in detail for the sake of brevity.

Claims (10)

1.AI增强绿色建筑设计优化系统，其特征在于，包括：1. AI enhanced green building design optimization system, characterized by including: 建筑生命周期数据整合模块，整合建筑从设计、施工、运营到维护的全生命周期数据，所述全生命周期数据包括建筑材料的生命周期分析数据、能源消耗数据、环境影响数据、维护记录；The building life cycle data integration module integrates the full life cycle data of the building from design, construction, operation to maintenance. The full life cycle data includes the life cycle analysis data of building materials, energy consumption data, environmental impact data, and maintenance records; 环境模拟与预测模块，基于整合的生命周期数据进行多尺度、多物理场的环境模拟，所述环境模拟包括气候变化预测、空气质量模拟、噪音传播模拟、水资源管理模拟；Environmental simulation and prediction module, which performs multi-scale, multi-physics field environmental simulation based on integrated life cycle data, including climate change prediction, air quality simulation, noise propagation simulation, and water resource management simulation; 动态优化模块，与所述环境模拟与预测模块连接，在建筑生命周期的不同阶段进行动态优化，所述动态优化包括设计优化、施工过程优化、运营管理优化和维护策略优化；A dynamic optimization module, connected to the environmental simulation and prediction module, performs dynamic optimization at different stages of the building life cycle, including design optimization, construction process optimization, operation management optimization and maintenance strategy optimization; 多目标决策支持模块，与所述动态优化模块连接，用于基于多目标决策理论在能耗、舒适度、环境影响和成本维度上进行优化决策，并生成平衡各目标的综合优化方案。The multi-objective decision support module is connected to the dynamic optimization module and is used to make optimization decisions in the dimensions of energy consumption, comfort, environmental impact and cost based on the multi-objective decision theory, and to generate a comprehensive optimization plan that balances various objectives. 2.根据权利要求1所述的AI增强绿色建筑设计优化系统，其特征在于，所述建筑生命周期数据整合模块具体包括：2. The AI enhanced green building design optimization system according to claim 1, wherein the building life cycle data integration module specifically comprises: 设计数据集成单元，用于在建筑设计阶段，收集和整合设计图纸、材料清单、建筑规范和标准、设计仿真数据；Design data integration unit, used to collect and integrate design drawings, material lists, building codes and standards, and design simulation data during the building design phase; 施工数据采集单元，用于在建筑施工阶段，采集和整合施工计划、实际施工进度、施工质量检查记录、施工现场环境数据和材料使用记录；Construction data collection unit, used to collect and integrate construction plans, actual construction progress, construction quality inspection records, construction site environmental data and material usage records during the construction phase; 运营数据监测单元，用于在建筑运营阶段，监测和整合能源消耗数据、室内外环境数据、设备运行状态和性能数据；Operational data monitoring unit, used to monitor and integrate energy consumption data, indoor and outdoor environmental data, equipment operating status and performance data during the building operation phase; 维护数据记录单元，用于在建筑维护阶段，记录和整合维护计划、维护操作记录、设备更换记录和维护成本数据；Maintenance data recording unit, used to record and integrate maintenance plans, maintenance operation records, equipment replacement records and maintenance cost data during the building maintenance phase; 数据存储与管理单元，与各单元连接，用于存储和管理整合的全生命周期数据，确保数据的安全性、一致性和可访问性；The data storage and management unit is connected to each unit to store and manage the integrated full life cycle data to ensure the security, consistency and accessibility of the data; 数据处理与分析单元，与数据存储与管理单元连接，用于对全生命周期数据进行处理和分析，生成建筑材料的生命周期分析数据、能源消耗分析报告、环境影响评估和维护需求预测。The data processing and analysis unit is connected to the data storage and management unit, and is used to process and analyze the full life cycle data, generate life cycle analysis data of building materials, energy consumption analysis reports, environmental impact assessments and maintenance demand forecasts. 3.根据权利要求2所述的AI增强绿色建筑设计优化系统，其特征在于，所述生命周期分析数据具体包括：3. The AI enhanced green building design optimization system according to claim 2, wherein the life cycle analysis data specifically includes: 计算材料在生产、运输、使用和回收各阶段的环境影响和资源消耗，生命周期总影响公式为：其中，LCA为生命周期总影响，EIi为第i阶段的单位环境影响，Qi为第i阶段的材料数量或使用量，n为阶段数；Calculate the environmental impact and resource consumption of materials at each stage of production, transportation, use and recycling. The total life cycle impact formula is: Where LCA is the total life cycle impact, EI i is the unit environmental impact of the i-th stage, Qi is the amount of material or usage in the i-th stage, and n is the number of stages; 所述能源消耗分析报告具体包括：The energy consumption analysis report specifically includes: 使用移动平均方法平滑能源消耗数据，公式为：其中，MAt为第t时刻的移动平均值，Et-i为第t-i时刻的能耗数据，n为时间窗口大小；The energy consumption data is smoothed using the moving average method, and the formula is: Among them, MA t is the moving average at the tth moment, E ti is the energy consumption data at the tith moment, and n is the time window size; 模式识别：使用回归分析建立能耗预测模型：Et＝β0+β1×t+t，其中，Et为第t时刻的能耗，β0和β1分别为回归系数，t为误差项；Pattern recognition: Use regression analysis to establish an energy consumption prediction model: E t = β 0 + β 1 × t + t , where E t is the energy consumption at the tth moment, β 0 and β 1 are regression coefficients, and t is the error term; 所述环境影响评估和维护需求预测具体包括：The environmental impact assessment and maintenance demand forecast specifically include: 使用加权综合评价法，公式为：Using the weighted comprehensive evaluation method, the formula is: 其中，EI为总环境影响评分，Wi为第i个环境因素的权重，Ii为第i个环境因素的影响评分,m为环境因素的数量； Among them, EI is the total environmental impact score, Wi is the weight of the i-th environmental factor, Ii is the impact score of the i-th environmental factor, and m is the number of environmental factors; 所述维护需求预测具体包括：The maintenance demand prediction specifically includes: 使用指数平滑法预测未来维护需求，公式为：Use exponential smoothing to predict future maintenance needs, the formula is: 其中，为第t+1时刻的预测需求，Dt为第t时刻的实际需求，为第t时刻的预测需求，α为平滑系数。 in, is the predicted demand at time t+1, Dt is the actual demand at time t, is the forecast demand at time t, and α is the smoothing coefficient. 4.根据权利要求1所述的AI增强绿色建筑设计优化系统，其特征在于，所述环境模拟与预测模块具体包括：4. The AI enhanced green building design optimization system according to claim 1, wherein the environmental simulation and prediction module specifically comprises: 气候变化预测单元，用于基于整合的生命周期数据和历史气候数据，利用区域气候模型RCM进行气候变化预测，表示为：The climate change prediction unit is used to predict climate change using the regional climate model RCM based on the integrated life cycle data and historical climate data, expressed as: Tfuture＝Tcurrent+ΔT，其中，Tfuture为未来温度，Tcurrent为当前温度，ΔT为气候模型预测的温度变化量；T future =T current +ΔT, where T future is the future temperature, T current is the current temperature, and ΔT is the temperature change predicted by the climate model; 使用全球气候模型GCM和区域气候模型RCM结合方法，预测不同情景下的气候变化：Pfuture＝Pcurrent×(1+R)，其中，Pfuture为未来降水量，Pcurrent为当前降水量，R为降水变化率；The combined method of the global climate model GCM and the regional climate model RCM is used to predict climate change under different scenarios: P future = P current × (1 + R), where P future is future precipitation, P current is current precipitation, and R is the precipitation change rate; 空气质量模拟单元，用于基于生命周期数据、排放源数据和气象数据，模拟建筑周边的空气质量，基于高斯扩散模型模拟污染物扩散，公式为：The air quality simulation unit is used to simulate the air quality around the building based on life cycle data, emission source data and meteorological data, and simulate the diffusion of pollutants based on the Gaussian diffusion model. The formula is: 其中，为点处的污染物浓度，Q为排放源强度，σx,σy,σz分别为扩散系数，结合CFD模拟空气流动和污染物扩散情况，公式为：其中，u为流速向量，D为扩散系数，S为源项； in, for The pollutant concentration at the point, Q is the emission source intensity, σ x ,σ y ,σ z are diffusion coefficients, and the formula is: Among them, u is the velocity vector, D is the diffusion coefficient, and S is the source term; 噪音传播模拟单元，用于基于建筑布局、材料特性和环境数据，模拟噪音在建筑内外的传播情况，使用声波传播模型模拟噪音传播，公式为：The noise propagation simulation unit is used to simulate the propagation of noise inside and outside the building based on the building layout, material properties and environmental data. The noise propagation is simulated using the sound wave propagation model. The formula is: Lp＝Lw-20log10(r)-αr，其中，Lp为接收点的声压级，Lw为声源的声功率级，r为声源到接收点的距离，α为空气吸收系数，结合建筑声学模型考虑反射和吸收，公式为：L p =L w -20log 10 (r)-αr, where L p is the sound pressure level at the receiving point, L w is the sound power level of the sound source, r is the distance from the sound source to the receiving point, and α is the air absorption coefficient. Considering reflection and absorption in combination with the architectural acoustic model, the formula is: Lp＝Lw-20log10(r)-αr-R，其中，R为建筑物材料的吸声系数；L p =L w -20log 10 (r)-αr-R, where R is the sound absorption coefficient of the building material; 水资源管理模拟单元，用于基于用水数据、降水量数据和地理信息，模拟建筑区域的水资源管理和水循环系统，基于水文模型模拟降水和径流，公式为：The water resource management simulation unit is used to simulate the water resource management and water cycle system of the building area based on water use data, precipitation data and geographic information, and simulate precipitation and runoff based on the hydrological model. The formula is: R＝P-ET-I，其中，R为径流量，P为降水量，ET为蒸散量，I为入渗量，使用暴雨水管理模型SWMM模拟城市水循环系统，模拟表示为：R = P-ET-I, where R is runoff, P is precipitation, ET is evapotranspiration, and I is infiltration. The stormwater management model SWMM is used to simulate the urban water cycle system. The simulation is expressed as: 其中，Q为水流量，n为糙率系数，A为横截面积，Rh为水力半径，S为水流坡度。 Among them, Q is the water flow, n is the roughness coefficient, A is the cross-sectional area, R h is the hydraulic radius, and S is the water flow slope. 5.根据权利要求4所述的AI增强绿色建筑设计优化系统，其特征在于，所述动态优化模块具体包括：5. The AI enhanced green building design optimization system according to claim 4, wherein the dynamic optimization module specifically comprises: 设计优化单元，用于在建筑设计阶段，优化建筑布局、优化材料选择，所述优化建筑布局利用遗传算法结合气候变化预测数据，以最大化自然通风和采光，表示为：The design optimization unit is used to optimize the building layout and material selection during the building design stage. The optimized building layout uses a genetic algorithm combined with climate change prediction data to maximize natural ventilation and daylighting, which is expressed as: 其中，Flayout为布局优化目标函数，Ai为第i个空间的面积，Ei为第i个空间的采光效果，Wi为第i个空间的通风效果，n为空间数量；所述优化材料选择基于生命周期分析结合空气质量模拟和环境影响数据，选择低环境影响的建筑材料，优化表示为： Among them, F layout is the layout optimization objective function, Ai is the area of the i-th space, Ei is the lighting effect of the i-th space, Wi is the ventilation effect of the i-th space, and n is the number of spaces; the optimized material selection is based on life cycle analysis combined with air quality simulation and environmental impact data to select building materials with low environmental impact, and the optimization is expressed as: 其中，EItotal为总环境影响，EIj为第j种材料的单位环境影响，Qj为第j种材料的使用量,m为材料种类数； Among them, EI total is the total environmental impact, EI j is the unit environmental impact of the jth material, Q j is the amount of the jth material used, and m is the number of material types; 施工过程优化单元，用于在建筑施工阶段，优化施工计划、优化材料使用，以减少环境影响和提高施工效率，所述优化施工计划使用关键路径法结合噪音传播模拟和空气质量数据，优化施工进度，减少工期和环境影响，表示为：min其中，Ttotal为总工期，Dk为第k项施工任务的持续时间，p为施工任务数量；所述优化材料使用利用线性规划优化材料使用，减少浪费和成本，表示为：其中，Cmaterials为材料总成本，cl为第l种材料的单位成本，xl为第l种材料的使用量，q为材料种类数；The construction process optimization unit is used to optimize the construction plan and material usage during the construction phase to reduce environmental impact and improve construction efficiency. The optimized construction plan uses the critical path method combined with noise propagation simulation and air quality data to optimize the construction progress, reduce construction period and environmental impact, expressed as: min Wherein, T total is the total construction period, D k is the duration of the kth construction task, and p is the number of construction tasks; the optimized material use utilizes linear programming to optimize material use and reduce waste and cost, which is expressed as: Among them, C materials is the total material cost, c l is the unit cost of the lth material, x l is the usage of the lth material, and q is the number of material types; 运营管理优化单元，用于在建筑运营阶段，基于环境模拟与预测模块提供的环境监测数据，优化能源管理、优化设备运行，以提高能效和舒适度，所述优化能源管理基于预测控制的方法，提高能源使用效率，表示为：The operation management optimization unit is used to optimize energy management and equipment operation to improve energy efficiency and comfort during the building operation phase based on the environmental monitoring data provided by the environmental simulation and prediction module. The optimized energy management is based on the predictive control method to improve energy efficiency, which is expressed as: 其中，Etotal为总能耗，Pt为第t时刻的功率需求，Δt为时间间隔，T为时间总数；所述优化设备运行基于设备性能数据和环境监测数据，优化设备运行参数，公式为： 其中，Eefficiency为设备效率，Ooutput为设备输出功率，Iinput为设备输入功率； Wherein, E total is the total energy consumption, P t is the power demand at the tth moment, Δt is the time interval, and T is the total time. The optimization equipment operation is based on the equipment performance data and the environmental monitoring data to optimize the equipment operation parameters, and the formula is: Where, E efficiency is the equipment efficiency, O output is the equipment output power, and I input is the equipment input power; 维护策略优化单元，用于在建筑维护阶段，基于环境模拟与预测模块提供的长期环境数据和设备状态数据，优化维护计划和策略，以延长设备寿命和降低维护成本，所述优化维护计划基于预测维护算法，优化维护计划，减少故障和维护成本，表示为：其中，Cmaintenance为总维护成本，ct为第t时刻的单位维护成本，Mt为第t时刻的维护量；所述优化维护策略采用可靠性中心维护方法，优化维护策略，提高设备可靠性，表示为：The maintenance strategy optimization unit is used to optimize the maintenance plan and strategy during the building maintenance phase based on the long-term environmental data and equipment status data provided by the environmental simulation and prediction module to extend the equipment life and reduce maintenance costs. The optimized maintenance plan is based on a predictive maintenance algorithm to optimize the maintenance plan and reduce failures and maintenance costs, which is expressed as: Where C maintenance is the total maintenance cost, c t is the unit maintenance cost at the tth moment, and M t is the maintenance amount at the tth moment. The optimized maintenance strategy adopts the reliability center maintenance method to optimize the maintenance strategy and improve the equipment reliability, which is expressed as: 其中，Rtotal为总可靠性，Rn为第n个设备的可靠性，N为设备数量。 Where R total is the total reliability, R n is the reliability of the nth device, and N is the number of devices. 6.根据权利要求5所述的AI增强绿色建筑设计优化系统，其特征在于，所述多目标决策支持模块，具体包括：6. The AI enhanced green building design optimization system according to claim 5, characterized in that the multi-objective decision support module specifically includes: 目标函数定义单元，用于定义能耗、舒适度、环境影响和成本的目标函数；An objective function definition unit is used to define objective functions of energy consumption, comfort, environmental impact and cost; 权重设定单元，用于根据用户需求和优先级设定各目标函数的权重；A weight setting unit, used to set the weight of each objective function according to user needs and priorities; 多目标优化算法单元，用于基于多目标优化算法求解各目标函数的最优解；A multi-objective optimization algorithm unit, used to solve the optimal solution of each objective function based on a multi-objective optimization algorithm; 综合优化方案生成单元，用于综合各目标函数的最优解，生成平衡各目标的综合优化方案。The comprehensive optimization scheme generation unit is used to synthesize the optimal solutions of various objective functions and generate a comprehensive optimization scheme that balances various objectives. 7.根据权利要求6所述的AI增强绿色建筑设计优化系统，其特征在于，所述目标函数定义单元具体包括：7. The AI enhanced green building design optimization system according to claim 6, wherein the objective function definition unit specifically comprises: 能耗目标函数：以最小化建筑能耗为目标，定义能耗目标函数E，公式为：其中，Pt为第t时刻的功率需求，Δt为时间间隔，T为时间总数；Energy consumption objective function: With the goal of minimizing building energy consumption, define the energy consumption objective function E, the formula is: Where Pt is the power demand at time t, Δt is the time interval, and T is the total time; 舒适度目标函数：以最大化建筑内舒适度为目标，定义舒适度目标函数C，公式为：其中，Si为第i个空间的舒适度评分，Ai为第i个空间的面积，n为空间数量；Comfort objective function: To maximize the comfort level in the building, define the comfort objective function C, the formula is: Among them, S i is the comfort score of the i-th space, A i is the area of the i-th space, and n is the number of spaces; 环境影响目标函数：以最小化环境影响为目标，定义环境影响目标函数I，公式为：其中，EIj为第j种材料的单位环境影响，Qj为第j种材料的使用量，m为材料种类数；Environmental impact objective function: With the goal of minimizing environmental impact, define the environmental impact objective function I, the formula is: Among them, EI j is the unit environmental impact of the jth material, Q j is the usage of the jth material, and m is the number of material types; 成本目标函数：以最小化建筑成本为目标，定义成本目标函数K，公式为：其中，Ck为第k项目的单位成本，xk为第k项目的使用量，p为项目数量。Cost objective function: With the goal of minimizing construction cost, the cost objective function K is defined, and the formula is: Where C k is the unit cost of the kth item, x k is the usage of the kth item, and p is the number of items. 8.根据权利要求7所述的AI增强绿色建筑设计优化系统，其特征在于，所述权重设定单元根据用户需求和优先级设定各目标函数的权重wi，公式为:W＝{wE,wC,wI,wK}，其中，wE为能耗目标的权重，wC为舒适度目标的权重，wI为环境影响目标的权重，wK为成本目标的权重，且∑wi＝1。8. The AI enhanced green building design optimization system according to claim 7 is characterized in that the weight setting unit sets the weight w i of each objective function according to user needs and priorities, and the formula is: W = {w E ,w C ,w I ,w K }, wherein w E is the weight of the energy consumption target, w C is the weight of the comfort target, w I is the weight of the environmental impact target, w K is the weight of the cost target, and ∑w i = 1. 9.根据权利要求8所述的AI增强绿色建筑设计优化系统，其特征在于，所述多目标优化算法单元使用加权和法进行多目标优化，求解综合优化目标函数F，公式为：9. The AI enhanced green building design optimization system according to claim 8 is characterized in that the multi-objective optimization algorithm unit uses a weighted sum method to perform multi-objective optimization and solve the comprehensive optimization objective function F, and the formula is: F＝wE·E+wC·C+wI·I+wK·K，其中，F为综合优化目标函数，权重wi由权重设定单元设定，目标函数E,C,I,K分别为能耗、舒适度、环境影响和成本目标函数，使用粒子群优化求解最优解，更新优化参数；F = w E ·E + w C ·C + w I ·I + w K ·K, where F is the comprehensive optimization objective function, the weight w i is set by the weight setting unit, the objective functions E, C, I, and K are energy consumption, comfort, environmental impact, and cost objective functions, respectively. Particle swarm optimization is used to solve the optimal solution and update the optimization parameters; 根据粒子群优化求解的最优解，生成平衡各目标的综合优化方案，公式为：OptimalSolution＝arg minF，其中，OptimalSolution为综合优化方案，F为综合优化目标函数。According to the optimal solution obtained by particle swarm optimization, a comprehensive optimization plan that balances various objectives is generated. The formula is: OptimalSolution = arg minF, where OptimalSolution is the comprehensive optimization plan and F is the comprehensive optimization objective function. 10.根据权利要求9所述的AI增强绿色建筑设计优化系统，其特征在于，所述粒子群优化具体包括：10. The AI enhanced green building design optimization system according to claim 9, wherein the particle swarm optimization specifically comprises: 初始化粒子群，设定粒子的位置和速度,每个粒子代表一个待选解决方案，其位置和速度向量分别为xi和vi：Initialize the particle swarm, set the position and velocity of the particles, each particle represents a candidate solution, and its position and velocity vectors are xi and vi respectively: 其中，i表示第i个粒子，n为问题的维度； Among them, i represents the i-th particle, and n is the dimension of the problem; 评价适应度：计算每个粒子的适应度值，对于多目标优化问题，适应度函数是多个目标函数的加权和：Evaluate fitness: Calculate the fitness value of each particle. For multi-objective optimization problems, the fitness function is the weighted sum of multiple objective functions: f(xi)＝wE·E(xi)+wC·C(xi)+wI·I(xi)+wK·K(xi)，其中，E(xi)为能耗目标函数，C(xi)为舒适度目标函数，I(xi)为环境影响目标函数，K(xi)为成本目标函数，wE,wC,wI,wK分别为各目标函数的权重；f( xi ) = wE ·E( xi )+ wC ·C(xi)+ wI ·I( xi )+wK· K ( xi ), where E( xi ) is the energy consumption objective function, C( xi ) is the comfort objective function, I( xi ) is the environmental impact objective function, K( xi ) is the cost objective function, and wE , wC , wI , and wK are the weights of each objective function respectively; 更新个体最优和全局最优：对于每个粒子，更新个体最优位置pi和全局最优位置g；Update individual optimal and global optimal: For each particle, update the individual optimal position pi and the global optimal position g; g(t+1)＝Xi(t)if f(Xi(t))<f(g(t))；g(t+1)=X i (t)if f(X i (t))<f(g(t)); 更新粒子速度和位置：Update particle velocity and position: vi(t+1)＝ωvi(t)+c1r1(pi(t)-xi(t))+c2r2(g(t)-xi(t))；v i (t+1)=ωv i (t)+c 1 r 1 (p i (t)-x i (t))+c 2 r 2 (g (t)-x i (t)); xi(t+1)＝xi(t)+vi(t+1)；x i (t+1)=x i (t)+v i (t+1); 其中，ω为惯性权重，c1和c2分别为个体学习因子和社会学习因子，r1和r2为0到1之间的随机数；Among them, ω is the inertia weight, c 1 and c 2 are the individual learning factor and social learning factor, r 1 and r 2 are random numbers between 0 and 1; 重复评价适应度、更新个体最优和全局最优、更新粒子速度和位置，直到满足终止条件。Repeatedly evaluate fitness, update individual optimal and global optimal, and update particle speed and position until the termination condition is met.
CN202410867711.2A 2024-07-01 2024-07-01 AI-enhanced green building design optimization system Active CN119047018B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202410867711.2A CN119047018B (en) 2024-07-01 2024-07-01 AI-enhanced green building design optimization system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202410867711.2A CN119047018B (en) 2024-07-01 2024-07-01 AI-enhanced green building design optimization system

Publications (2)

Publication Number Publication Date
CN119047018A true CN119047018A (en) 2024-11-29
CN119047018B CN119047018B (en) 2025-03-07

Family

ID=93582316

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202410867711.2A Active CN119047018B (en) 2024-07-01 2024-07-01 AI-enhanced green building design optimization system

Country Status (1)

Country Link
CN (1) CN119047018B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119783221A (en) * 2024-12-31 2025-04-08 中体(山东)工程项目管理咨询有限公司 Building engineering design method based on BIM

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117852126A (en) * 2023-12-07 2024-04-09 中铁十六局集团有限公司 Sustainable environment-friendly management control method and system for building
CN118171369A (en) * 2024-04-10 2024-06-11 贵州电子科技职业学院 Building design method based on artificial intelligence

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117852126A (en) * 2023-12-07 2024-04-09 中铁十六局集团有限公司 Sustainable environment-friendly management control method and system for building
CN118171369A (en) * 2024-04-10 2024-06-11 贵州电子科技职业学院 Building design method based on artificial intelligence

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
TONG QIU 等: "An adaptation resilience assessment framework for key components of prefabricated underground stations", 《TUNNELLING ANDUNDERGROUNDSPACETECHNOLOGY136》, 28 March 2023 (2023-03-28), pages 1 - 17 *
侯慧 等: "基于深度强化学习区间多目标优化的智能建筑低碳优化调度", 《电力系统自动化》, vol. 47, no. 21, 10 November 2023 (2023-11-10), pages 47 - 57 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119783221A (en) * 2024-12-31 2025-04-08 中体(山东)工程项目管理咨询有限公司 Building engineering design method based on BIM

Also Published As

Publication number Publication date
CN119047018B (en) 2025-03-07

Similar Documents

Publication Publication Date Title
Thrampoulidis et al. A machine learning-based surrogate model to approximate optimal building retrofit solutions
Kim et al. Sequence-to-sequence deep learning model for building energy consumption prediction with dynamic simulation modeling
CN112561728B (en) Comprehensive energy consumption optimization method, medium and device based on attention mechanism LSTM
CN114692265B (en) A zero-carbon building optimization design method based on deep reinforcement learning
CN110046743A (en) Energy Consumption of Public Buildings prediction technique and system based on GA-ANN
CN107909220A (en) Electric heating load prediction method
CN112241607B (en) Water resource planning method based on simulation and considering climate change
CN112415924A (en) Energy-saving optimization method and system for air conditioning system
CN112149890A (en) Comprehensive energy load prediction method and system based on user energy label
CN113902213B (en) A grid planning method based on carbon emissions and interaction between sources, grids, loads and storage for electricity spot market
Song et al. An indoor temperature prediction framework based on hierarchical attention gated recurrent unit model for energy efficient buildings
CN112686571A (en) Comprehensive intelligent energy optimization scheduling method and system based on dynamic adaptive modeling
CN114065646B (en) Energy consumption prediction method based on hybrid optimization algorithm, cloud computing platform and system
CN117495119A (en) Intelligent prediction method for carbon emission in building operation stage of neural network optimization algorithm
CN119047018B (en) AI-enhanced green building design optimization system
CN118485261A (en) An Optimal Dispatching System for Cascade Reservoirs
CN117454168A (en) Building energy consumption prediction method and system
CN116255665A (en) Heat supply combined control method and system based on load prediction of heat supply network system
CN108346009A (en) A kind of power generation configuration method and device based on user model self study
Ali et al. Simulation and energy management in smart environment using ensemble of GA and PSO
Yeon et al. Lower and upper threshold limit for artificial neural network based chilled and condenser water temperatures set-point control in a chilled water system
CN118171788B (en) Collaborative optimization method for schedulable flexible resources and power grid planning layout
Kumaresan et al. Smart building transferable energy scheduling employing reward shaping deep reinforcement learning with demand side energy management
CN118885910A (en) Air conditioning energy consumption prediction method, system and storage medium based on improved LSTM network
CN118723732A (en) A distributed scheduling method and system for elevator groups based on machine learning

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant