250 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 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.

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.

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 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 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.

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.

The invention discloses an intelligent fire-fighting hidden danger identification method based on multi-modal fusion, and the method comprises the steps: collecting multi-source heterogeneous data in a building environment, and carrying out the unified processing of the data, and obtaining standardized multi-modal original input data; event anchor point detection is carried out on the multi-modal input, key events are extracted, and a corresponding multi-modal event sequence is generated; constructing an event graph containing time and causal edges, and outputting an alignment event stream by using a space-time coupling causal alignment module; importing BIM and air duct structure information to establish a spatial topological graph, and fusing event streams to generate spatial constraint features; constructing a self-evolution semantic decision map based on the fusion features, and dynamically adjusting node weights and edge connection relationships; and carrying out hidden danger identification and grade division on the real-time data by using the decision map, and outputting an early warning signal and positioning information. According to the method, high-precision identification and intelligent early warning of fire-fighting hidden dangers are realized through space-time causal alignment, spatial topology fusion and self-evolution semantic decision of multi-modal data.

The invention discloses a space-time knowledge graph-based facility layout scheme generation method. The method comprises the steps of S1, constructing an urban facility layout knowledge base; s2, acquiring a reference case place set of the target grid to be optimized; s3, extracting a facility significant co-occurrence mode in the reference case place set, and taking the facility significant co-occurrence mode as a recommended facility configuration mode of the target grid to be optimized; and S4, according to the feature vector of the city built environment of the target to-be-optimized grid obtained in the S1, inputting the feature vector and the recommended facility configuration mode of the target to-be-optimized grid obtained in the S3 into the constructed facility layout generation model, and obtaining an output result which is a facility layout scheme. According to the method, the facility distribution rule knowledge and the facility layout optimization intelligence are integrated, and the facility configuration recommendation and the facility layout optimization scheme can be automatically output for a specific land parcel.

The invention discloses a street safety perception evaluation method and system based on electroencephalogram characteristic waves, and relates to the technical field of construction of environment safety sensing degree, and the method comprises the steps: obtaining an electroencephalogram signal induced by a street scene, extracting a power spectrum value of the electroencephalogram signal, and calculating the power spectrum value of the electroencephalogram signal; based on the power spectrum value of the electroencephalogram signal, calculating the asymmetry of an electroencephalogram prefrontal lobe alpha frequency band and a cognitive load and pressure balance index, and converting the asymmetry and the cognitive load and pressure balance index into electroencephalogram safety perceptibility of a street scene; acquiring safety perception element feature data of the street scene, and inputting the safety perception element feature data of the street scene and the electroencephalogram safety perceptibility of the street scene into a pre-established street safety perceptibility evaluation model for iterative training to obtain a trained street safety perceptibility evaluation model; and based on the trained street safety perceptibility evaluation model, quantifying the feature contribution of the street environment elements to the safety feeling score predicted value by adopting an SHAP method, and obtaining a street safety perception evaluation result.

The invention provides an operation data analysis method and system of a heat supply system, and relates to the technical field of building environment and energy application engineering.The method comprises the steps that if a diagnosis result shows that no abnormity exists, differential operation strategies are generated based on building historical room temperature, building characteristics and real-time meteorological parameters in a multi-source fusion data set; obtaining meteorological prediction data in a preset time period; based on the differentiated operation strategy and meteorological prediction data, predicting heat load demand change through a long short-term memory network; and calculating a target output parameter set value by taking the room temperature reaching the standard and the lowest energy consumption as an optimization target based on the change of the thermal load demand, and sending the set value to a boiler control system for execution. According to the invention, through multi-source data fusion and real-time analysis, closed-loop intelligent operation from abnormal accurate diagnosis to demand prediction regulation is realized, so that the reliability, energy efficiency and comfort level of the heat supply system are comprehensively improved.

The invention relates to the technical field of atomization dust falling, in particular to environment-friendly construction atomization dust falling equipment which comprises an electrical cabinet and an air duct rotatably mounted on the electrical cabinet, a fan is mounted in the air duct, and the environment-friendly construction atomization dust falling equipment further comprises a nozzle assembly mounted at one end of the air duct. The nozzle assembly further comprises an annular pipe, a pitching ring, a transfer ring, a transverse module, a propelling module and a foundation ring. According to the invention, through the arrangement of the atomization module and the pitching ring, when the top end of the pitching ring deviates from the ring pipe, the pitching ring pulls the nozzle at the upper half part of the ring pipe through the connecting rod, and the nozzle drives the butt joint pipe to rotate, so that the nozzle deflects upwards, and the angle between mist sprayed by the nozzle at the upper half part and jet airflow of the air duct is reduced; meanwhile, the angles of the multiple nozzles on the lower half part are pushed to deflect upwards, so that the angle between mist sprayed by the nozzles on the lower half part and jet airflow of the air duct is increased, and the staying time of the mist sprayed by the nozzles on the lower half part in the jet airflow is prolonged.

The invention discloses a building environment real-time regulation and control method, device and equipment based on the Internet of Things and a medium, and relates to the technical field of intelligent building control. Light, temperature and humidity data of a building environment are collected in real time through Internet of Things sensing equipment, frequency domain features of an environment data set are extracted through Fourier transform, classification and recognition are carried out in combination with a preset environment regulation and control model, and a dynamic weight algorithm is adopted to optimize and generate an environment control signal. And through a self-adaptive PID control algorithm of Kalman filtering denoising and reinforcement learning optimization, a deviation integral is calculated in real time to generate a precise regulation and control instruction, and environment regulation and control equipment is synchronously driven. Frequency domain feature analysis, dynamic weight adjustment and adaptive control technologies are fused, multi-parameter coordinated regulation and control of the building environment are realized, and the real-time performance, the stability and the energy efficiency ratio of environment regulation and control are effectively improved.

The application provides a kind of intelligent building environment adaptive regulation and control method based on digital twinning and AI, it is related to artificial intelligence technical field, including the following steps: based on time alignment after multi-source system data, extract dynamic characteristics to construct dynamic feature set;Multi-source system data is input to digital twinning, with the current building equipment state as boundary condition, iteratively simulates the key environmental parameters of multiple time steps in the future, and outputs multi-step prediction sequence;Again input model predictive controller, with the minimum overall energy consumption in the prediction period as the optimization goal, with the key environmental parameters not exceeding the comfort interval as the constraint condition, the equipment control instruction sequence in the first preset time in the future is solved;The first equipment control instruction in the equipment control instruction sequence is issued to the on-site actuator to realize the adaptive regulation and control of building environment, solve the disadvantage that the environment parameter is over-standard and then responds passively, significantly improve the rapid response capability of the system to sudden environmental changes.

The embodiment of the invention provides a business system container environment automatic configuration method and device and computer equipment. The method comprises the steps that a building task is created; a standard environment yaml file stored on the container cloud platform is backed up through a gitlab and pulled to a local server; judging whether a to-be-built environment exists on the container cloud platform or not; if yes, modifying the reference environment yaml file according to the to-be-built environment yaml file by utilizing a machine learning model; if not, modifying the yaml file of the reference environment by utilizing a machine learning model according to the associated information of the reference environment and the to-be-built environment; the modified standard environment yaml file is uploaded to an environment project to be set up of the gitlab; and configuring an operation and maintenance tool to deploy the environment to be established by using the modified reference environment yaml file. According to the method, the configuration of the existing container environment can be reused, and modification is carried out by utilizing the machine learning model, so that the labor cost is reduced, and the building efficiency is improved.

The invention discloses an intelligent building monitoring system and method, belongs to the technical field of building environment control systems, and solves the problems of energy waste and emergency response lag caused by the fact that an existing system is difficult to dynamically coordinate multi-region environment parameters. According to the technical scheme, the method comprises the steps that building space multi-dimensional environment parameters are collected through a distributed infrared sensor array, a temperature sensor array and a carbon dioxide concentration sensor array; the central controller normalizes the data, constructs a space model of a temperature gradient distribution diagram, a personnel density thermodynamic diagram and an air quality index matrix, generates an air conditioner linkage instruction based on a preset temperature difference threshold value to adjust the air supply volume ratio of adjacent areas, and generates an emergency evacuation channel opening instruction based on a personnel density threshold value; the equipment control module executes air conditioning, lighting and security linkage operation, and the lighting unit regulates and controls the brightness in a grading mode according to the curvature radius of the personnel track. The system is mainly used for realizing building environment precise regulation and control and intelligent security and protection collaborative management.

The embodiment of the invention discloses a digital warehouse park goods watching method, device and equipment and a medium, relates to the technical field of intelligent monitoring, and is used for solving the problems that an existing pledge goods watching mode is high in labor cost and poor in real-time performance. The method comprises the following steps: drawing a digital fence of a current digital warehouse park according to pledge cargo storage information and building environment data of the current digital warehouse park; acquiring video image data in the area of the digital fence so as to identify current cargo information corresponding to the video image data; and comparing the warehousing cargo information corresponding to the digital fence with the current cargo information, determining cargo change data, and generating a real-time cargo watching prompt according to the cargo change data.