67 results about "Environmental modeling" patented technology

The invention discloses a reinforcement learning-based virtual power plant response optimization scheduling system and method, and relates to the technical field of virtual power plant intelligent scheduling. The system comprises an environment modeling module, an intelligent agent module, a multi-agent coordination module and a self-adaptive optimization module which are respectively used for constructing a multi-dimensional state space and a layered action space, generating and optimizing an action strategy based on an Actor-Critic network, executing a scheduling instruction through a layered multi-agent structure and realizing conflict consensus, and dynamically adapting to state space change in combination with incremental learning and meta-learning mechanisms. The system and the method have the advantages of fine state modeling, efficient action response, adaptive strategy updating, stable agent coordination and the like, and can keep the continuity, the stability and the optimality of a scheduling strategy in an operation environment in which multi-source heterogeneous power resources participate in scheduling cooperatively, market rules change frequently and load fluctuation is violent.

The invention relates to the technical field of computer control, and discloses an automatic computer control method and system based on artificial intelligence. The method comprises the following steps: firstly, acquiring multi-source heterogeneous sensor data, including environmental parameters, equipment running states and operation instruction logs; then, an environment state evolution model is constructed, an equipment health degree evaluation index is generated, and an operation instruction log is analyzed; and then, inputting the results into a multi-modal decision model to generate a dynamic control instruction set, constructing a control strategy topological graph through an adaptive optimization algorithm, and outputting an equipment regulation and control scheme. The system correspondingly comprises a multi-source data acquisition module, an environment modeling module, a health assessment module and the like. Multi-source data can be effectively processed, the health condition of equipment can be accurately evaluated, a control instruction and a regulation and control scheme can be intelligently generated, the intelligent level of computer control is improved, and the reliability and the operation efficiency of the system are improved.

The invention discloses a ship path planning method based on deep reinforcement learning. The ship path planning method is suitable for a complex sea area environment. Through environment modeling and dynamic obstacle identification, a detailed state space is constructed, and perception of the environment is enhanced by using an artificial potential field. An ID3QN-PER algorithm is adopted to train a model, an adaptive exploration strategy and a network parameter updating mechanism are designed, and navigation safety and efficiency in a dynamic environment are ensured. The method further comprises the steps of discretizing an action space, designing an obstacle avoidance strategy in combination with a COLREGS specification, smoothing a route through a B-spline technology, adjusting a navigation strategy according to real-time environment data, and improving the real-time performance and the global optimization performance of route planning.

The invention discloses a dynamic planning method for an air route of an urban low-altitude unmanned aerial vehicle. The method comprises the following steps: firstly, constructing an urban low-altitude environment model based on an airspace rasterization technology, fusing multi-dimensional constraint factors such as geographic data, meteorological conditions and airspace control information through multi-source environment information, and accurately calibrating the position of a take-off and landing point; secondly, a dynamic grid availability evaluation model is established in combination with environmental constraints and a real-time airspace state, and the navigation feasibility of each grid unit is quantitatively analyzed; then, an improved A * algorithm is combined with a grid availability evaluation result to generate a global optimal initial route; finally, a rolling time domain optimization strategy is introduced, and uncertain factors such as sudden obstacles and airspace dynamic limitation are responded in real time through periodic non-flight route re-planning after the unmanned aerial vehicle takes off. Through collaborative fusion of static environment modeling and a dynamic optimization mechanism, the problem of real-time planning of the air route of the unmanned aerial vehicle in the urban low-altitude complex environment is effectively solved, and the environmental adaptability and task reliability of the system are remarkably improved.

The invention relates to the technical field of bridge construction, in particular to an intelligent cleaning device and method for a box girder side formwork. The device comprises a mobile platform, a control device, a mechanical arm assembly, a cleaning device, an infrared induction sensor, a dust collection device, a multispectral visual recognition module, a 3D environment modeling module, a path planning module, a cleaning quality detection module and a self-adaptive learning database. The multispectral visual recognition module recognizes the stain type and the adhesion strength. The 3D environment modeling module is used for constructing a three-dimensional point cloud model of a working area in real time; the path planning module generates an optimal cleaning path based on a reinforcement learning algorithm; the cleaning quality detection module detects residual stains and triggers secondary cleaning; the adaptive learning database stores historical cleaning data and optimizes a cleaning strategy through a machine learning model. The full-process automatic cleaning operation of the box girder side form can be achieved, and the comprehensive functions of dirt recognition, autonomous path planning, efficient cleaning, real-time detection and the like are achieved.

The invention provides an intelligent inventory robot path optimization method based on environment modeling and collaborative optimization, and relates to the technical field of intelligent warehouse management, and the method comprises the steps: scanning a warehouse region through an unmanned plane platform, and generating three-dimensional warehouse point cloud data; after point cloud denoising, plane fitting, shelf boundary extraction and channel detection processing, a two-dimensional storage plane graph is generated and transmitted to a robot data base; generating an initial inventory planning path by adopting an A * algorithm, optimizing the initial path through a simulated annealing algorithm, and generating a target inventory planning path; based on the dynamic obstacle influence factors, the RFID signal intensity and the robot residual electric quantity are combined, and a target path is corrected through a mixed algorithm of multi-target optimization and PSO dynamic weight; and the operation end checks the checking progress through the AR equipment and manually adjusts the target path, so that the checking path of the intelligent checking robot can be optimized in real time in a dynamic environment, and the checking efficiency, the coverage rate and the cruising ability of the robot are improved.

The invention discloses a method, a system, equipment and a medium for analyzing project progress based on a BIM model and an AI video, and relates to the technical field of constructional engineering management, and the method comprises the steps of collecting regional image data, and executing semantic segmentation and environment modeling. And constructing a multi-scale spatial feature network and a dynamic skeleton modeling engine, inputting the three-dimensional environment diagram, the structure recognizable region diagram and the obstacle probability distribution diagram into the multi-scale spatial feature network and the dynamic skeleton modeling engine, and outputting execution structure recognition and defect anomaly labeling. And triggering an engineering response mechanism based on the risk event scoring function and remotely and dynamically adjusting the construction path. According to the method, through a mode of combining semantic segmentation and three-dimensional environment modeling, high-precision structure identification of the construction site image is realized, and the problems of unclear structure identification and fuzzy component boundary in traditional video monitoring are effectively solved.

A navigation system and method are disclosed for operation in GPS-denied environments using quantum-inspired sensor fusion, dynamic virtual anchor points (VAPs), and predictive environmental modeling. The system represents multiple position hypothesis using wavefunction-like expansions and integrates VAP-based triangulation for drift correction. A predictive modeling module ingests solar, geomagnetic, and environmental data to proactively adjust sensor weighting. A cybersecurity module employs quantum-algebraic key generation and location-derived ephemeral keys to secure inter-device communication. The system includes an augmented reality (AR) interface to visualize and edit anchor references, and a neurofeedback module that adapts the AR interface based on real-time physiological signals from the user. The method further enables anchor optimization via AI-driven repositioning and supports low-power edge execution using approximate amplitude filtering. Additional modules may include fractal antennas, neuromorphic processors, and adaptive forecasting layers to maintain positional accuracy and user experience in subterranean, multi-floor, or magnetically complex environments.

The invention relates to the technical field of safety production management, and discloses a safety production hidden danger closed-loop management and control supervision system and a risk assessment early warning method, and the system comprises an environment modeling module which is used for building a digital model of a production unit and calculating a systematic instability coefficient based on historical data; the risk assessment module is used for quantifying the initial risk potential energy in combination with the coefficient and the hidden danger inherent attribute; the risk evolution module is used for dynamically updating risk potential energy according to a time and space coupling rule; the closed-loop verification module is used for judging the actual effectiveness of the intervention measure by comparing the management entropy before and after the intervention after the intervention measure is implemented; and the self-adaptive feedback module is used for automatically updating the systematic instability coefficient in the environment model according to the judgment result. According to the method, risk quantification, dynamic early warning, effect verification and model self-optimization are integrated, a complete closed-loop management process is formed, and the scientificity and foresight of safety production management are remarkably improved.

The invention, which relates to the field of property management, discloses an intelligent property management system comprising a property scheduling management module, a monitoring management module, a routing inspection path algorithm module and a routing inspection execution module. The property dispatching management module is used for storing positions of all cameras, sensors and property facilities, states of the property facilities and maintenance records of the property facilities, and realizing linkage through the routing inspection path algorithm module; the monitoring management module is used for deploying a camera and executing deflection and zooming of the camera, so that the monitoring range of the camera covers a target area needing to be inspected; the monitoring management module comprises a temperature and humidity sensor, a smoke sensor and an illumination sensor which are used for collecting property environment information in real time; the routing inspection path algorithm module comprises environment modeling, priority allocation, path dynamic optimization, view angle adjustment and adaptive learning; through the technologies of environment modeling, priority distribution, path dynamic optimization and the like of the inspection path algorithm module, the dynamic adjustment of the visual angle of the camera and the inspection path is realized.

The invention discloses an unmanned aerial vehicle control system and method for water conservancy inspection, and relates to the technical field of water conservancy inspection, the unmanned aerial vehicle control system comprises an unmanned aerial vehicle main body, the unmanned aerial vehicle main body carries a multi-source sensor module, an edge calculation module, a self-adaptive navigation module and an obstacle avoidance module, and the method further comprises the steps of S1 system self-inspection, S2 environment modeling, S3 path planning and S4 self-adaptive inspection. S5, real-time processing; S6, abnormal feedback; and S7, data fusion. According to the invention, through cooperative work of the source sensor module, the edge calculation module, the adaptive navigation module and the obstacle avoidance module, the effect of efficient and accurate inspection is achieved; through optimal design of a multispectral camera, a temperature and humidity sensor, a self-adaptive navigation module and an obstacle avoidance module, the effect of adapting to routing inspection in a complex environment is achieved; through the steps of S1, system self-inspection, S2, environment modeling, S3, path planning and S4, adaptive inspection, the effects of scientifically planning the inspection process and improving the normalization and effectiveness of the inspection work are achieved.

A navigation system and method are disclosed for operation in GPS-denied environments using quantum-inspired sensor fusion, dynamic virtual anchor points (VAPs), and predictive environmental modeling. The system represents multiple position hypothesis using wavefunction-like expansions and integrates VAP-based triangulation for drift correction. A predictive modeling module ingests solar, geomagnetic, and environmental data to proactively adjust sensor weighting. A cybersecurity module employs quantum-algebraic key generation and location-derived ephemeral keys to secure inter-device communication. The system includes an augmented reality (AR) interface to visualize and edit anchor references, and a neurofeedback module that adapts the AR interface based on real-time physiological signals from the user. The method further enables anchor optimization via AI-driven repositioning and supports low-power edge execution using approximate amplitude filtering. Additional modules may include fractal antennas, neuromorphic processors, and adaptive forecasting layers to maintain positional accuracy and user experience in subterranean, multi-floor, or magnetically complex environments.

The present invention discloses a method, system, equipment and medium for analyzing engineering progress based on BIM model and AI video, which relates to the technical field of construction engineering management, including collecting regional image data, performing semantic segmentation and environmental modeling. A multi-scale spatial feature network and a dynamic skeleton modeling engine are constructed, and the three-dimensional environmental map, the structurally identifiable area map and the obstacle probability distribution map are input into the multi-scale spatial feature network and the dynamic skeleton modeling engine, and the output performs structural recognition and defect anomaly labeling. The engineering response mechanism is triggered based on the risk event scoring function, and the construction path is remotely and dynamically adjusted. The method described in the present invention realizes high-precision structural recognition of construction site images by combining semantic segmentation with three-dimensional environmental modeling, effectively solving the problems of unclear structural recognition and blurred component boundaries in traditional video surveillance.

The invention discloses a vehicle driving path planning method, and relates to the technical field of data fusion and path planning. Comprising the following steps: step 1: carrying out data acquisition and preprocessing, step 2: carrying out environment modeling according to preprocessed road network information: constructing a dynamic road network model G = (V, E), V representing nodes in a road network, E representing edges between the nodes, and a weight omega (e) of the dynamic road network model representing time cost of passing through a road section, step 3: carrying out path search, and step 4: carrying out path search. 4, performing dynamic obstacle avoidance: detecting dynamic obstacles in the surrounding environment according to sensor data, if the obstacles are detected, performing obstacle avoidance by adjusting a local path, and if the obstacles cannot be avoided by adjusting the local path, triggering global re-planning and performing path search again; and generating a vehicle control instruction according to the final path to control vehicle driving.

The invention discloses a multi-robot path planning method for an electric arc additive manufacturing system. The multi-robot path planning method comprises the specific steps that a three-dimensional model is obtained, slicing and layering are conducted, environment information is obtained, and environment modeling is conducted; determining an ideal task load; performing matrix iteration, and optimizing the current task load; constructing a minimum spanning tree, and generating a two-dimensional optimal task path of each robot; and performing three-dimensional conversion and asynchronous execution. Compared with the prior art, the method has the advantages that the form requirement of an actual additive manufacturing path is fully considered, the process defects of air holes, insufficient fusion, slag inclusion and the like in the manufacturing process are effectively reduced, and the problems of region segmentation and deposition path planning in an electric arc additive manufacturing (WAAM) task cooperatively executed by multiple robots are successfully solved; and therefore, high-quality additive manufacturing of the component is realized. Meanwhile, the path design strategy proposed in the method avoids the start-stop operation of the welding gun, ensures the parallelism and independence of each robot path in the three-dimensional space, and further improves the operation efficiency and stability of the system.

The invention discloses a wind power plant fire intelligent sensing and early warning method based on multi-sensor fusion, and belongs to the technical field of fire sensing and early warning, and the method comprises the steps: obtaining data information collected by a plurality of sensors in real time through the plurality of sensors disposed in a wind power plant, and carrying out the data preprocessing, according to the method, the importance weights of the environmental parameters are dynamically adjusted, the importance degrees of the parameters are reflected in real time according to environmental changes, the accuracy of environmental condition monitoring and modeling is improved, the space-time correlation model is formed in combination with the multi-dimensional time sequence matrix and the space correlation matrix, the uncertainty of the environmental state is comprehensively evaluated, and the accuracy of environmental condition monitoring and modeling is improved. The dynamic environment modeling and prediction are carried out, the time and space factors are fully considered, the fire key features are extracted by adaptively adjusting the weight according to the change of the dynamic environment model and the environment parameters to the fire sensitivity, the information related to the fire can be captured in a more targeted manner, and the accuracy and effectiveness of fire feature extraction are improved.

The invention provides a civil aircraft safety perception method and system based on virtual flight, and relates to the technical field of aircraft safety perception. A dynamic and hierarchical three-dimensional wind shear field model is constructed through space-time alignment and fusion of multi-source meteorological data; deep coupling of wind disturbance and aircraft dynamic response is realized based on a small disturbance theory and a flight dynamics model; continuous and dynamic quantification of risks is realized by combining and analyzing flight parameters and a safety threshold value dynamically adjusted based on wind disturbance intensity and utilizing multi-parameter risk level weighted integration; according to the method, the risk coefficient is coupled with the three-dimensional terrain and the virtual flight trajectory, hierarchical color mapping is adopted to carry out visual space visualization, and a complete closed loop from environment modeling to risk early warning is formed, so that the identification accuracy and timeliness of the flight risk in the wind shear environment are greatly improved, and the scientificity and practicability of security situation awareness are enhanced.

The invention relates to the technical field of endoscopic microbial culture, and discloses a dynamic perfusion control system and method for an endoscopic microbial culture agent, and the system comprises a multi-channel data collection module which is used for obtaining multi-dimensional culture agent parameters including concentration distribution, perfusion rate and metabolite in real time, wherein the concentration distribution data comprises first environment distribution data generated by the multi-region perfusion device and second dynamic feedback data collected by the optical sensor array; the dynamic regulation and control module performs gradient equilibrium correction processing on the parameters and inputs the parameters into the perfusion analysis processing layer for feature modeling, the perfusion analysis processing layer comprises a data preprocessing module and an environment modeling module, and the environment modeling module comprises an environment parameter fusion layer, a gradient equilibrium layer and a perfusion decision-making layer which are connected in sequence. And finally, generating a perfusion regulation and control instruction according to an output result. According to the invention, real-time monitoring and dynamic accurate regulation and control of multi-dimensional parameters are realized, and the quality and efficiency of endoscopic microorganism culture are improved.

In various examples, an environment visualization pipeline may determine whether to generate or otherwise enable a visualization using an environmental modeling pipeline that models an environment as a 3D bowl or using an environmental modeling pipeline that models the environment using some other 3D representation, such as a detected 3D surface topology. The determination may made based on various factors, such as ego-machine state, (e.g., one or more detected features indicative of a designated operational scenario, proximity to a detected object, speed of ego-machine, etc.), estimated image quality of a corresponding environment visualization, and / or other factors. Accordingly, an environment around an ego-machine, such as a vehicle, robot, and / or other type of object, may be visualized in systems such as parking visualization systems, Surround View Systems, and / or others.

The invention provides a space debris bistatic detection data fusion method based on a Bayesian rule, and the method comprises the following steps: 1, building a detection model: obtaining a detection probability and detection data distribution through a space-based in-situ simulation detection model and a foundation narrow-beam radar simulation detection model based on the orbit parameters of given space debris; step 2, inputting detection data and performing inversion; and step 3, calculating the number of fragments by using a space debris bistatic detection data fusion method based on a Bayesian rule. According to the method, the limitation of a single observation mode is overcome, and the reliability of environment modeling is improved; compared with a single data source, the data obtained by the two detection modes can complement each other, and the obtained information is more complete; and theoretical support is provided for cooperation of fragment monitoring. Data fusion supports data sharing of space monitoring institutions of different countries and regions to cope with challenges brought by space debris together.

The invention relates to a laser radar environment modeling and navigation system based on artificial intelligence, and the system comprises the steps: obtaining three-dimensional point cloud data in a complex environment, and obtaining an initial data set; preprocessing the initial data set by adopting a data enhancement mode to obtain a target data set; denoising and complementing the initial data set by adopting a deep learning model based on a generative adversarial network to obtain a target data set; dividing the target data set into a training set and a test set; distinguishing static obstacles and dynamic objects in the training set by adopting an attention enhancement algorithm; identifying a state space in a complex environment by adopting a path planning algorithm; based on the combination of the convolutional neural network and the recurrent neural network, the environmental complexity is evaluated and the control parameters are dynamically adjusted, so that the environmental modeling precision of the system is improved, the path planning efficiency and real-time performance are improved, the navigation control adaptive capability is improved, and the engineering and generalization effects of the system are improved.

The present invention relates to the technical fields of image processing and environmental modeling and simulation, and provides a cloud planning strategy for an unmanned system in open-pit mining areas. The method includes: obtaining the target positions to be reached by each vehicle of a formation fleet with n vehicles currently and the boundary of the planning area. If the current vehicle i > n, the planning ends; otherwise, through path planning and speed planning, the motion trajectory of the i-th vehicle is obtained. If there is no conflict, it is added to the directed weighted graph; otherwise, the conflict node is used as the boundary condition of the time interval, speed planning is performed on the existing planned path to obtain a trajectory and it is planned. Through evaluation, the optimal path is obtained. The present invention increases the 2D problem of multi-vehicle path planning to the 3D problem of multi-vehicle trajectory planning, and obtains the optimal multi-vehicle collaborative planning result considering both time and space dimensions, solves the heavy dependence of the current unmanned system in mining areas on high-precision maps, and has higher operating efficiency.

The invention discloses a motion path planning system and method of a crawler crane, and belongs to the field of intelligent driving of crawler cranes. Comprising a path planning module loaded into a workstation, a data acquisition module and an unmanned aerial vehicle. The method is easy to operate, the labor intensity of workers is effectively reduced, and the workers only need to use the unmanned aerial vehicle to shoot the environment, import automatic drawing software to generate a three-dimensional model and read the initial pose of the crane according to the GPS, so that the optimal path of the crane can be automatically calculated. And through a data acquisition module, an RTK module of the unmanned aerial vehicle, environment modeling software and the like, it is ensured that the generation result of the three-dimensional model is rapid and accurate. And based on the developed path algorithm, the optimal path is automatically generated, so that the efficiency, safety and accuracy of the hoisting process are improved.

The embodiment of the invention provides a rail transit intelligent ray tracing channel modeling method based on local phase error calibration, and aims to solve the problem of inaccurate channel modeling caused by environment modeling errors and dynamic phase distortion in a traditional ray tracing technology in a high-speed dense rail transit scene. The method comprises the following steps: firstly, constructing an intelligent railway communication system three-dimensional geometric model comprising a transmitting end and a receiving end, generating multipath parameters by using ray tracing simulation, and establishing a deterministic frequency response model based on the multipath parameters; secondly, an independent and identically distributed von Mises phase error # imgabs0 # is superposed in the deterministic model, and a random channel model is formed; and finally, optimizing a material electromagnetic parameter vector by using an actually measured data set, minimizing the difference between ray tracing prediction and actually measured data, and improving the generalization ability of the model at an unknown position.

The present invention provides an intelligent inventory robot path optimization method based on environmental modeling and collaborative optimization, which relates to the technical field of intelligent warehouse management. The method comprises the following steps: scanning a warehouse area through an unmanned aerial vehicle platform and generating three-dimensional warehouse point cloud data; generating a two-dimensional warehouse plan after point cloud denoising, plane fitting, shelf boundary extraction, and channel detection processing, and transmitting the result to a robot data base; using an A* algorithm to generate an initial inventory planning path, and optimizing the initial path through a simulated annealing algorithm to generate a target inventory planning path; correcting the target path through a hybrid algorithm of multi-objective optimization and PSO dynamic weights based on dynamic obstacle influencing factors, combined with RFID signal strength and the remaining battery power of the robot; and viewing the inventory progress through an AR device at the operator's end and manually adjusting the target path. This allows the intelligent inventory robot's inventory path to be optimized in real time in a dynamic environment, thereby improving the robot's inventory efficiency, coverage, and endurance.

The present invention discloses a method, device, and related products for modeling an obstacle SLT risk field environment. The method comprises: establishing a current-frame SLT spatial coordinate system in a Frenet coordinate system with the vehicle's position as the origin, wherein the S axis of the SLT spatial coordinate system is based on the current lane of the vehicle's current frame, and the current-frame ST grid maps corresponding to each discrete lane are arranged along the L axis according to a preset arrangement order; and determining the current-frame occupied area of ​​each obstacle in the current-frame ST grid map corresponding to each discrete lane and the current-frame occupancy probability of each grid in the current-frame occupied area based on the predicted trajectory of the current-frame obstacle, thereby obtaining the SLT spatial risk field of the current-frame obstacle. The environmental modeling method of the present invention establishes a three-dimensional SLT spatial risk field for obstacles based on the Frenet coordinate system, which can be used by a decision-making and planning module for joint horizontal and vertical planning evaluation.

The invention relates to the technical field of unmanned aerial vehicle automatic inspection, provides an unmanned aerial vehicle take-off and landing platform rapid deployment and path planning method for a building curtain wall, constructs a gradient risk field model fusing geometric structures, environmental risks, visual perception and task regulations, and realizes unified quantitative evaluation of operation environmental risks. And based on the risk field, generating a global safe energy-saving path on the curved surface of the curtain wall through mixed search and path smoothing optimization guided by a risk navigation potential function. Through real-time updating of a local risk field and model prediction control, autonomous obstacle avoidance and smooth return flight of the unmanned aerial vehicle to sudden obstacles are realized. Meanwhile, a dynamic scheduling mechanism considering multi-target cost is introduced, and collaborative optimization of multiple unmanned aerial vehicles and take-off and landing platform resources is achieved. According to the method, full-link closed loop from environment modeling, global planning, real-time control to system scheduling is realized, and the safety, continuity and overall operation efficiency of automatic routing inspection of the super high-rise building curtain wall are improved.

The present invention provides a method and device for planning the path for lifting large-diameter long pipe-section piles, which relate to the technical field of cranes. The present invention obtains the motion state parameters of the pipe-section piles in real time, and constructs a spatial operation diagram including obstacles and safety buffer zones in combination with the three-dimensional environmental perception data of the lifting area; based on the current motion direction, multiple groups of candidate paths composed of speed and steering angle are generated within the fan-shaped area at set angle intervals; paths that conflict with obstacles are eliminated through collision detection, and a set of valid paths is retained; based on multi-dimensional feature analysis of the safety item index, smoothness index and progress index, a scoring function is constructed with preset weights, and the optimal path is dynamically selected; path planning is performed at fixed time intervals until the pile arrives at the target point smoothly; the present invention solves the obstacle avoidance and efficiency problems of lifting large-diameter pipe piles in complex scenarios through real-time environmental modeling and dynamic path planning, and significantly improves lifting safety and operation accuracy.

The invention discloses a smart home ad hoc network system and method based on sound wave communication and dynamic topology. The system comprises smart home equipment nodes, a sound wave communication module, an environment sensing sensor, an environment modeling engine module, a dynamic topology manager, a security encryption module and a topology discovery protocol module. A sound wave / radio frequency transmitting module is arranged in the smart home equipment node; the intelligent household equipment node is connected with the sound wave communication module, the environment perception sensor and the security encryption module. According to the system, network construction can be automatically completed after the equipment is powered on, the manual operation error rate is reduced, self-adaptive routing and topological optimization of a smart home equipment network can be achieved, the network stability and performance are improved, the labor cost and time consumption are reduced, high-safety communication can be achieved by combining the sound wave communication technology and the voiceprint encryption technology, and the communication efficiency is improved. And the password leakage risk is reduced.

The present invention provides an air conditioning control method, device, electronic device, storage medium, and product, including: obtaining user location information and indoor environmental modeling information; and determining the blowing angle of an air deflector based on the user location information and the environmental modeling information. The air conditioning control method, device, electronic device, storage medium, and product provided by the present invention utilize environmental modeling information to assist in air blowing, flexibly adjusting the air blowing angle of the air conditioner, thereby meeting the user's air blowing needs.