309 results about "Built environment" patented technology

The invention relates to the technical field of building energy consumption analysis, and discloses a building energy consumption analysis method and system based on artificial intelligence. The method comprises the following steps: collecting building environment data to form an energy consumption basic data set; processing the data set to generate an energy consumption feature vector; constructing a prediction model to obtain an energy consumption predictor; a predictor is used for comparing actual data to identify abnormity and generate a report; formulating an optimization scheme based on the report to generate a control instruction; and executing instruction record change data to update the feature library to complete a closed loop. According to the invention, closed-loop management of accurate prediction, anomaly detection, optimization control and effect evaluation of building energy consumption is realized, so that the building energy utilization efficiency is improved, and energy waste is reduced.

The invention relates to the technical field of intelligent building energy-saving control, and discloses a green building equipment intelligent energy-saving control method and system based on artificial intelligence, and the method comprises the steps: collecting building environment data, and carrying out the preprocessing; and simulating the energy consumption of the building equipment by using the digital twin model, and training the baseline neural network model to obtain an initial control strategy. And adopting a genetic algorithm to optimize hyper-parameters of the baseline model, and generating a plurality of candidate energy-saving control strategies. And inputting the optimized strategy into the deep reinforcement learning model, carrying out strategy iteration, calculating the energy-saving efficiency and comfort score after each iteration, screening the optimal strategy through a reward mechanism, and improving the accuracy of energy consumption prediction of the building equipment. The hyper-parameter of the neural network is optimized by using the genetic algorithm, and the intelligent degree of the energy-saving control strategy is improved. On the basis of deep reinforcement learning, the intelligent agent can adaptively adjust an energy-saving strategy under different environmental conditions, and the user comfort is guaranteed while the optimal building energy consumption is ensured.

The invention relates to the technical field of building energy saving, in particular to a building energy-saving management system and method based on the Internet of Things. The method comprises the following steps: acquiring a target architectural design drawing; building structure feature point recognition is carried out on the target building design drawing, three-dimensional space point cloud modeling is carried out, and a building three-dimensional structure model is constructed; building environment illumination parameters and personnel flow monitoring parameters are collected based on multiple sensor nodes; environment light intensity space distribution analysis is carried out on the building environment illumination parameters, then dynamic light intensity distribution rendering is carried out, and an environment light intensity distribution rendering model is constructed; and according to the building three-dimensional structure model, carrying out regional use scene analysis on the environment light intensity distribution rendering model, and carrying out optimal time sequence illumination demand fitting so as to construct an optimal illumination demand time sequence curve of each region. The energy consumption utilization rate of the building is increased, and accurate equipment regulation and control are achieved.

The invention relates to the technical field of continuous integration and continuous delivery, and discloses a Jenkins continuous integration method based on Kubernetes. According to the method, a Jenkins server is deployed in a Kubernetes cluster to serve as a control plane, a construction environment is created through a preset multi-container Pod template, and a Pod containing a plurality of containers is dynamically created based on the Pod template, so that each container executes different construction tasks. Containers in the Pod realize collaboration through sharing a network namespace and a storage volume, and a Jenkins server monitors task execution states of the containers in the Pod and performs dynamic scheduling according to task priorities and cluster load states. And after the task is completed, automatically destroying the Pod and releasing cluster resources. According to the method and the device, dynamic expansion and contraction of a construction environment and efficient parallel execution of tasks are realized, the problem that a traditional Jenkins static resource pool is difficult to deal with load changes is solved, the resource utilization rate is improved, the expandability of the system is enhanced, and an efficient and reliable solution is provided for an enterprise-level continuous integration system.

The invention discloses a built environment and traffic accident causal effect analysis method based on bi-robust learning, and belongs to the field of intelligent traffic safety and traffic accident risk assessment. By integrating satellite remote sensing, interest point distribution and traffic accident data, a spatial-temporal feature enhanced environment index system is constructed; realizing cross-regional data collaborative analysis by adopting federal learning, and guaranteeing data compliance in combination with differential privacy encryption; a generative adversarial network is introduced to generate a high-fidelity anti-fact scene, and a dual robust learning framework is coupled to accurately quantify a condition average processing effect of a built environment on accident risks. According to the scheme, a technical tool with both theoretical preciseness and practical feasibility is provided for urban traffic planning and safety management, and a scientific basis is provided for optimizing and building environment design and reducing traffic accident risks through a multi-source data fusion and privacy cooperation mechanism; and the blank in the aspects of causal robustness, insufficient anti-fact generation precision, privacy protection and multi-source data fusion in the prior art is filled.

The invention discloses a metro station non-commuting passenger proportion calculation method based on a random forest model, and the method specifically comprises the steps: selecting a research region, and collecting metro-related built environment data and transportation data; important built-up environment variables are determined through preliminary analysis, and variables influencing non-commuter are added; establishing a prediction model by adopting a random forest algorithm, and dividing a training set and a test set; analyzing the output of the random forest model by applying an SHAP technology, and determining the importance sequence of the influence of each built-up environment factor on the proportion of non-commuting passengers and the action mechanism of the influence of each built-up environment factor; according to the size of the SHAP value and in combination with an actual scene, a numerical matrix of all built-up environment factors around the subway station in the scene is determined; and substituting a model coefficient result into the established environment factor matrix, and calculating the subway station non-commuting travel proportion. According to the method, the prediction precision of the non-commuting passenger quantity of the subway station is remarkably improved, the understanding of non-commuting travel behaviors is deepened, and powerful support is provided for urban traffic management and planning.

The invention provides a short message issuing processing method and system based on scene self-adaption. The method comprises the steps that a three-dimensional communication characteristic spectrum is constructed through a building information model and infrared thermal imaging data, short message request distribution is analyzed and predicted in combination with a signal attenuation field, cross-operator network quality data is integrated to form a converged communication quality field, and finally short message issuing is intelligently scheduled according to the network state and service requirements. According to the method, accurate short message routing optimization based on real-time communication quality and crowd distribution in a complex building environment is realized. According to the technical scheme provided by the invention, the short message delivery rate and timeliness in a complex building environment can be remarkably improved, and particularly, the communication reliability in a crowded area and a signal blind area is remarkably improved.

A system for assessing the ecological sensitivity and development potential of areas within ecologically sensitive mountain towns, consisting of: a data acquisition module configured to extract various data types, including multi-source geospatial data, environmental data, built environment data, socio-economic data and community participation data, with the extracted data being validated and stored in a database; a central processing unit comprising one or more processors connected to a memory, the processor communicatively connected to the database in which the extracted data is stored, the memory storing instructions that, when executed, configure the processor to implement the following: a data input module configured to receive data corresponding to multiple indicators from different assessment dimensions, including ecology and environment, built environment, economy, heritage and culture, mobility, location, community participation, visual aspects and safety from hazards; an indicator evaluation module configured to evaluate each indicator according to predefined sensitivity categories and assign corresponding sensitivity index values in the range of 0.1 to 1; a dimension weighting module configured to apply dimension-specific weights to calculate weighted scores for each indicator; a development potential calculation module that aggregates the weighted assessments to determine an overall sensitivity assessment and assigns this overall sensitivity assessment to a development potential rating scale; and a visualization module configured to generate visual representations and reports of the assessment results; and an output module having a user interface operatively connected to the visualization module of the central processing unit, the output module configuring the user interface to display the visualized results and the final index value obtained from the central processing unit.

The invention relates to the technical field of building energy consumption analysis, and discloses a building energy consumption analysis method and system based on multi-modal perception. According to the method and the system, building environment data, energy consumption data, personnel flow data and equipment state data are collected from a plurality of data sources, the data are cleaned, abnormal values and noise information are eliminated, the data collected at different time points are integrated through a time alignment method, and the data are stored in a database. Data from different sources and dimensions are standardized, feature extraction and modeling are carried out on the preprocessed data, time features, environment features and personnel flow features are extracted, an energy consumption change mathematical model is established, energy consumption analysis is carried out on the extracted features, total energy consumption of a building is calculated for energy efficiency evaluation, and energy efficiency evaluation is carried out. The energy consumption intensity and the energy efficiency ratio are obtained, time sequence analysis is conducted through historical data, the future building energy consumption trend is predicted, the analysis result is visualized, and energy conservation and emission reduction suggestions and optimization decisions are provided based on the visualized analysis result.

According to the remote sensing image green roof intelligent identification method and system based on the improved Mask RCNN algorithm, the feature extraction capability of the algorithm is enhanced by introducing an attention mechanism, and the edge segmentation precision of a target is improved by constructing a joint loss function, so that precise detection of a green roof in a complex built environment is realized. According to the invention, the morphological parameter analysis module is synchronously integrated, geometric feature parameters such as the area, the perimeter and the shape index of the green roof can be automatically extracted, and integrated processing of green roof identification and feature parameter extraction is realized. Compared with a traditional method, the method can effectively solve the problems that target examples are difficult to distinguish, and environmental adaptability is poor, and provides a reliable technical means for green roof monitoring and ecological environment benefit evaluation.

The invention relates to the technical field of building environment temperature control, and discloses a temperature control equipment intelligent adjusting method and system based on machine learning, and the method comprises the steps: building a space thermal field sensing network, obtaining multi-dimensional temperature field data, and generating a temperature gradient distribution matrix; and the positions of cold and hot spots are calibrated by combining infrared temperature measurement data, an airflow path is predicted, and air supply parameters are optimized. Meanwhile, building heat capacity characteristics are analyzed, and a thermal response dynamic model is constructed. And the air supply direction is adjusted for the local supercooling area. And based on the temperature change rate and the air supply parameters, the equipment start-stop time is optimized. Through real-time temperature sampling, continuous updating of temperature gradient distribution, cyclic execution of cold and hot spot calibration, airflow prediction and air supply optimization, accurate control of space temperature is realized. According to the invention, the accuracy and energy efficiency of building space temperature adjustment can be obviously improved, and an innovative solution is provided for an intelligent temperature control system.

The invention discloses a full-equipment energy consumption optimization management system in a building, and relates to the technical field of intelligent control, and the system is provided with a multi-modal data collection module which collects building environment parameters and equipment operation data through a distributed sensor network, and generates a multi-modal sensing matrix; an environment state prediction module is set to construct a dynamic knowledge graph based on a multi-modal sensing matrix, a graph attention network is used, an energy flow matrix of equipment topology is output, micro-environment state prediction data is generated through prediction of a finite element solver, a scheduling scheme solving module is set, a mixed integer programming model is constructed based on the micro-environment state prediction data, and a scheduling scheme is set. And solving the mixed integer programming model to obtain a scheduling scheme of the energy consumption equipment, setting a control instruction optimization module, and driving the edge execution equipment to operate based on the scheduling scheme. The global scheduling optimization of the whole building energy consumption equipment is realized, the energy consumption is effectively reduced, and the environmental comfort is improved.

The invention discloses a roof radiant cooling and independent fresh air dehumidification system mixed operation comprehensive control method, and belongs to the technical field of building environment control and heating ventilation air conditioning. The method comprises the steps that the radiant cooling capacity and the dehumidification capacity of a surface air cooler are calculated through a heat balance equation, and an optimization objective that a top plate radiation system bears 70% sensible heat load and a fresh air system bears 90% humidity load is set; an anti-condensation strategy is triggered by combining dew point temperature monitoring, a control variable is adjusted by taking minimization of energy consumption as a target in a rolling time domain, and it is ensured that the indoor temperature ranges from 23 DEG C to 26 DEG C, the humidity ranges from 40% to 60%, the black ball temperature difference is smaller than or equal to 2 DEG C, and the condensation risk is smaller than or equal to 6%. The method is suitable for energy-saving temperature and humidity regulation and control of large public buildings, and efficient cooperative processing of sensible heat and latent heat loads is achieved.

The invention relates to the technical field of urban rail transit station-city collaborative planning, in particular to an ontology-based station-city collaborative data integration and planning prediction method, which comprises the following steps of: obtaining rail transit station passenger flow data, resident travel behavior data and station periphery built environment index data; forming a space-time sample sequence according to the unified space-time granularity of the site walking service area; constructing an urban rail transit station-city cooperation ontology, and carrying out semantic annotation and semantic fusion on the space-time sample sequence to generate a feature sequence; inputting the feature sequence into a multi-task space-time diagram convolutional neural network prediction model to output a passenger flow prediction result and establish an environment index prediction result; and calculating a feature contribution degree based on a Shapley additive interpretation value, optimizing a background sample set by using a genetic algorithm to determine a key action element set, outputting a planning index threshold and an intervention measure parameter, and realizing an interpretable station-city collaborative prediction and planning decision closed loop.

The innovation is the Landscape Syntax Algorithm, a new analytical framework to transcend the age-old limitations of space syntax. It integrates the highly versatile idea of landscape. Space syntax is largely objective and deals with the arrangement of space, along with little or no consideration for the more subjective parts of human aspects and experiences. Thus, the Landscape Syntax Algorithm fills that gap by providing a model that marries objective spatial measures with the more subjective cognitive and experiential aspects of landscape. This is carried out by infusing perceptual cognition and history / contextual layers within spatial analysis. Hence, there is a thorough reading of the relation between human experience and the built world. It opens up the opportunity to unify the most objective spatial configuration and subjective perceptual cognition to provide a powerful resource for the architect, urban planner, or researcher interested in analyzing and designing spaces that connect to the human experience in a more profound and mature sense. This invention enables quantification and analysis of landscape as moving and interactive phenomena towards a much more human-oriented contextual design of space.

The invention discloses a multi-mode travel flow collaborative prediction method, system and device based on multi-task learning. The method comprises the following steps: acquiring related data of multiple travel modes in a to-be-tested area; performing preprocessing and time-space alignment on related data of various travel modes, extracting flow similar characteristics, built environment characteristics and time sequence characteristics of OD flows based on an OD flow matrix, and extracting high-dimensional characteristics of built environment and mobile flows based on the built environment characteristics and the time sequence characteristics; constructing a spatial adjacency graph based on a spatial adjacency relation, constructing a traffic feature graph based on traffic similar features, constructing an environment feature graph based on extracted high-dimensional features, and further constructing a view of each travel mode; constructing a traffic prediction pre-training model, and training the traffic prediction pre-training model to obtain a traffic prediction model; and inputting the OD flow to be measured into the flow prediction model for prediction to obtain a prediction result at the next moment. The multi-traffic OD flow in the subsequent time period is predicted based on the historical OD flow and the built environment data, and the result precision is high.

The invention discloses a land utilization classification method based on multi-source data intelligent fusion, and the method comprises the steps: obtaining multi-source geographic space data, including remote sensing image data, streetscape image data, building vector data, road network vector data, interest point data, mobile phone signaling data and microblog sign-in data, of a research region, and forming a land utilization classification experiment data set; remote sensing image features and streetscape image features of each research unit are constructed, environment features, interest point features and population distribution features are established, and a feature set of land utilization classification tasks is formed; combining the constructed features in pairs to generate feature pairs formed by features of different modalities, performing feature alignment on the features of different modalities through a comparative learning method, and adding the aligned features into a feature set of a land utilization classification task; and land utilization classification is carried out based on an XGBoost integrated learning model, and model validity inspection is carried out by adopting five-fold cross validation. According to the invention, the accuracy of land utilization classification is improved.

The invention discloses a water energy accumulator and ground source heat pump combined control method, relates to the technical field of building environment and equipment engineering, accurately captures a time sequence change rule of a building cold load based on the load prediction capability of a time convolution network, overcomes the defect that a traditional method is insufficient in response to sudden fluctuation, and improves the control efficiency. The system can plan an energy storage strategy in advance; an electricity price-load change rate sensitivity index is introduced, and through an adaptive weight adjustment mechanism, economic operation and load tracking requirements are dynamically balanced, so that strategy stiffness caused by fixed priorities is avoided, and the utilization efficiency of time-of-use electricity price signals is remarkably improved; according to the joint optimization design of the mixed integer linear programming model, the equipment operation constraint and the cost target are comprehensively considered, the collaborative decision of the energy accumulator charging and discharging strategy and the heat pump frequency setting is realized, and the direct energy supply dependence in the midday high electricity price period is effectively reduced.

This invention discloses a full lifecycle management system and method for scientific computing programs. The system includes a build environment subsystem and a production environment subsystem. The former provides computer resources for the build process of the scientific computing program throughout its lifecycle, while the latter provides computer resources for the testing and deployment processes. This invention, through a full lifecycle management method for scientific computing programs, operates on corresponding computing resources, encompassing a series of steps including querying, triggering scheduling, build execution, result distribution, and test deployment. It automatically generates the executable file of the scientific computing program and configures its runtime dependencies. Simultaneously, it automatically generates a corresponding description file recording the entire lifecycle process. Furthermore, based on version management of these description files and their sets, it achieves full lifecycle traceability and cross-platform migration and deployment of scientific computing programs in a high-performance computing environment.

The invention relates to the technical field of illumination design layout, in particular to an illumination design layout method and system based on a BIM model. The method comprises the following steps: collecting building environment data and basic illumination data, carrying out geometric topology analysis to determine a building environment structure, fusing the building environment structure based on a BIM model, constructing an environment structure semantic framework, building a simulation building environment field, analyzing a basic illumination condition through illumination simulation, identifying an illumination weak area, and determining the illumination weak area. The method comprises the following steps: firstly, building environment structures are constructed, then, lighting requirements of all environment areas are analyzed, lighting areas needing to be strengthened are screened out, then, light data in the areas are detected, light refraction traceability is carried out to identify light shielding objects, finally, lighting design optimization is carried out according to the light shielding objects and the building environment structures, and the final optimized lighting design layout is formed. According to the invention, the lighting design layout optimization is realized, the uniformity and effectiveness of lighting are ensured, and the comfort and functionality of the internal space of the building are improved.

The invention provides an unmanned aerial vehicle autonomous obstacle avoidance method and system based on an A star algorithm, belongs to the technical field of unmanned aerial vehicle flight control, and solves the problems of path planning and autonomous obstacle avoidance of an unmanned aerial vehicle in a complex environment. 2, performing unmanned aerial vehicle path planning in the constructed environment map based on an A star algorithm; 3, constructing a simulation platform, and verifying the A star algorithm planning path by using the constructed simulation platform; and step 4, evaluating a verification result, and outputting an unmanned aerial vehicle planning path conforming to evaluation parameters.

The invention discloses a method and system for measuring and calculating the coupling degree of a green space structure and a built environment, and relates to the technical field of urban planning, landscape gardens and artificial intelligence. Through establishment of a coupling structure, based on a random forest regression model, a significant relation between urban observation data and a green space structure-built environment correlation index is established; urban green space performance evaluation is converted into a series of coupling values, the problems that an urban green space performance evaluation system is huge, indexes are redundant and quantification is difficult are solved, and the benefit evaluation accuracy and the measurement and calculation efficiency are improved.

A comprehensive spatial AI platform for neural 3D reconstruction and built environment analysis integrates foundation models, deep learning methods, and spatial reasoning capabilities to provide expert knowledge of the physical world. The platform combines symbolic AI and machine learning to facilitate 3D semantics for insurance, real estate, construction, robotics, and other business applications. The system processes captured images, videos, or point clouds through neural networks with low compute requirements, incorporating device-agnostic advanced spatial intelligence that delivers geometric, semantic, and relational data. The platform includes proprietary training innovations, comprehensive measurement and semantic understanding, external sensor integration, human-in-the-loop quality assurance, and API integration for programmatic access. Advanced spatial reasoning capabilities enable property damage assessments, construction progress tracking, robotics navigation, and real-time applications including room dimension validation and automated repair estimates, supporting enterprise workflows across various industry segments while enabling productivity improvements and new value-added spatial AI use cases.

The invention discloses a buried pipe temperature response calculation method considering underground water seepage and ground temperature gradient, and belongs to the field of building environment and energy application engineering.The buried pipe temperature response calculation method comprises the steps that a shallow buried pipe heat exchanger is regarded as a finite long-line heat source model, and when underground water seepage passes, the shallow buried pipe heat exchanger is regarded as a finite long-line heat source seepage model; an energy control equation containing a soil heat conduction item, an underground water seepage convection item and a ground temperature gradient correlation item at the same time is established, and initial temperature distribution based on ground temperature gradient, ground surface fixed wall temperature and boundary conditions of an underground limited boundary are set; and finally, obtaining an analytical solution expression of the change of the temperature at any position of the underground soil along with the time through analytical solution. According to the method, the defect that a traditional model ignores key environmental factors is overcome, the heat transfer process of heat conduction and convection coupling can be accurately simulated, the accuracy of temperature field prediction and the reliability of long-term performance evaluation are remarkably improved, meanwhile, the complexity of numerical calculation is avoided, and the calculation efficiency is improved.

The invention relates to the technical field of building environment monitoring, and discloses an intelligent building environment monitoring system and method, and the method comprises the steps: S1, collecting the related environment information of each preset monitoring point in a warehouse through a first environment collection module; s2, comparing each acquired environment information parameter with a corresponding preset early warning threshold value, and if any environment information parameter exceeds the corresponding preset early warning threshold value, generating an abnormal early warning signal of the monitoring point; according to the method, warehouse monitoring point environment parameters are collected in real time through distributed sensors, single-point threshold comparison is performed firstly to trigger preliminary early warning, then environment characteristic values are calculated through multi-parameter weighting and compared with a historical abnormal range, high-density sampling analysis is performed on a characteristic value abnormal region through an inspection robot, and the abnormal region is subjected to early warning. The method overcomes the defects that a traditional warehouse environment monitoring mode is high in false alarm rate, lacks comprehensive evaluation, cannot perform dynamic inspection and the like.

The invention discloses a college dormitory noise multi-dimensional evaluation method based on psychoacoustic and sound field simulation, and belongs to the field of building environment and noise control. The objective of the invention is to solve the problem that an existing dormitory noise evaluation method is single in index and neglects subjective perception and spatial distribution heterogeneity. The method comprises the following steps: acquiring physical data and original audio of dormitory noise through multi-point synchronous acquisition; calculating loudness, sharpness, roughness, fluctuation intensity and comprehensive psychoacoustic annoyance (PA) by using a psychoacoustic model, and scientifically quantifying subjective interference of noise; meanwhile, sound field simulation is carried out based on the accurately established three-dimensional dormitory model, a sound pressure level distribution cloud picture is generated, and a standing wave phenomenon and a sound energy accumulation area are visually revealed; and finally, generating a targeted noise reduction optimization strategy by integrating subjective and objective and spatial analysis results. According to the method, full-chain diagnosis of physical characteristics-subjective perception-spatial distribution of dormitory noise is realized, and a scientific basis is provided for accurate and efficient noise treatment.

The invention provides a smart building environment adaptive regulation and control method based on digital twinning and AI, and relates to the technical field of artificial intelligence, and the method comprises the following steps: extracting dynamic features based on multi-source system data after time alignment, so as to construct a dynamic feature set; inputting multi-source system data into the digital twin, performing iterative simulation on key environment parameters of a plurality of time steps in the future by taking a current building equipment state as a boundary condition, and outputting a multi-step prediction sequence; inputting into a model prediction controller, and solving to obtain an equipment regulation and control instruction sequence in a future first preset duration by taking the lowest total energy consumption in a prediction period as an optimization target and taking the condition that the key environment parameters do not exceed a comfort interval as a constraint condition; and the first equipment regulation and control instruction in the equipment regulation and control instruction sequence is issued to the field execution mechanism, so that self-adaptive regulation and control of the building environment are realized, the defect that passive response is performed after environmental parameters exceed the standard is overcome, and the quick response capability of the system to sudden environmental changes is remarkably improved.

The invention discloses a method for identifying a to-be-optimized space of a built environment based on a form hierarchical model. The method comprises the steps of collecting and preprocessing data of the built environment, preliminarily performing form hierarchical operation of the built environment, correcting the form hierarchical operation of the built environment, preliminarily identifying the to-be-optimized space, identifying the hierarchical operation of the to-be-optimized space, and interacting and feeding back identification results. According to the method, data acquisition is carried out through a laser radar system, an aerial photography unmanned aerial vehicle, a street scene acquisition vehicle and a near-earth satellite, and recognition and display of a to-be-optimized space of a built environment are carried out in combination with multi-task Bayesian federal learning, an XGBoost model, a Vision Transformer and a convolutional neural network. According to the method, the rapid identification of the to-be-optimized space of the built environment in the urban planning field can be assisted, and the rationality and accuracy of the identification result of the to-be-optimized space of the built environment are improved through form classification.

The invention discloses a building air conditioner energy consumption-electric charge-comfort level intelligent control method based on MMOE and ParetoMTL, and the method comprises the following steps: (1) collecting the cooling load data of a large commercial building air conditioner, carrying out the abnormal value detection, and completing the normalization preprocessing; (2) constructing an environment-price-behavior multi-source time sequence data set and performing grading standardization; (3) dividing a training set, a verification set and a test set according to building grouping; (4) completing feature fusion and realizing task decoupling under cooperative constraint by using an MMOE mechanism; (5) jointly training a three-branch expert network, an MLP gating network and a PCGrad shared parameter optimization model; (6) generating a multi-task prediction and Pareto solution set on the test set; and (7) evaluating prediction performance and trade-off efficiency by using a plurality of indexes. According to the method, multi-target conflict, physical consistency and Pareto solution set missing difficulty can be effectively solved, and a new scheme is provided for demand response HVAC intelligent control.

The invention relates to the technical field of building intelligent management, and discloses a BIM-based indoor optimization design method and system, and the method comprises the following steps: a) building a building information model through BIM software, and collecting the space size, material parameters, structure parameters, environment data and user demand data of a building; and b) an indoor space optimization target is set according to design specifications and user requirements, design constraint conditions are formulated, and the design constraint conditions comprise building specifications, fire protection requirements, human flow lines, functional requirements and ergonomic parameters. According to the invention, through multi-source data fusion, high-precision and low-delay data acquisition is realized; through hybrid modeling, the balance between the accuracy of energy consumption prediction and the calculation efficiency is realized; through reinforcement learning and fuzzy control, adaptive optimization of building environment adjustment is realized; through the modular architecture and the block chain, high expansibility and data security of the system are realized.