93 results about "Environmental modelling" patented technology

The invention discloses an environment modeling method and system for realizing intelligent security and protection, and relates to the technical field of intelligent security and protection, and the method comprises the steps: collecting a visual image and depth data of a monitoring region, and generating a monitoring scene feature matrix; performing hierarchical feature extraction on the monitoring scene feature matrix by using a deep convolutional neural network, and constructing a scene semantic feature map; projecting the scene semantic feature map to a three-dimensional monitoring space according to the depth data, and constructing a three-dimensional scene grid model; extracting spatial topological characteristics of each grid node in the three-dimensional scene grid model, and calculating association weights among the grid nodes; and constructing a scene dynamic knowledge graph based on the spatial topological features and the associated weights, and carrying out modeling analysis on abnormal events. According to the method, through cooperative collection of the multi-view vision sensor and the depth sensor, in combination with information entropy weighted standardization and principal component analysis, accurate generation of the monitoring scene feature matrix is realized, and the problems of information redundancy and feature coupling in traditional vision data processing are effectively solved.

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 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 multi-type robot collaborative operation method based on dynamic environment perception modeling, which realizes optimization and dynamic adjustment of task allocation and forms a complete solution by combining three-dimensional environment modeling and a multi-robot collaborative communication mechanism through a multi-modal data acquisition and fusion technology. Multi-modal data fusion improves the comprehensiveness and real-time performance of environment perception, dynamic environment modeling ensures the accuracy and updating efficiency of a model, and multi-robot communication and task collaboration optimize the resource allocation and execution effect; according to the method, the problems of information isolation, single task allocation and insufficient collaborative operation flexibility in a traditional robot system are improved, efficient collaboration and resource optimization among multiple robots are realized in a complex environment according to the proposed technical scheme, the sensing ability, the task response speed and the overall operation efficiency of the system are remarkably improved, and the collaborative operation efficiency is improved. The system is widely applicable to complex scenes such as inspection, rescue and industrial production.

The invention relates to an Internet of Vehicles edge computing multi-target unloading method and system fusing dynamic environment modeling and improved SARSA, and belongs to the field of intelligent traffic and edge computing fusion. The method and the system comprise MEC environment perception and multi-dimensional state construction, dynamic reward feedback oriented to multi-dimensional performance indexes, intelligent decision model construction and learning based on improved SARSA, and antagonism training oriented to real disturbance. A high-fidelity environment model is constructed through a space-time attention mechanism, an SARSA algorithm is improved to realize hierarchical qualification trace attenuation and collaborative Q table updating, a multi-target hierarchical reward engine is combined to implement differential optimization on an emergency task and a conventional task, and an adversarial training mechanism is introduced to improve robustness. The core problems of high mobility, task diversity, resource limitation and the like in the Internet of Vehicles are effectively solved, the comprehensive performance is optimal in multiple dimensions of delay, energy consumption, resource utilization rate and the like, and the actual landing of the edge computing technology of the Internet of Vehicles is promoted.

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 environment modeling, in particular to a carbon accounting method and system based on artificial intelligence, and the method comprises the following steps: obtaining a carbon emission source rate, a wind direction and airflow intensity, setting a time step length, calculating the spatial distribution of a carbon diffusion trajectory, and calling an airflow influence range to calculate a diffusion path of carbon molecules. According to the method, through multi-source data fusion and dynamic calculation, a carbon emission diffusion path is accurately simulated, the joint calculation of the wind direction, the airflow intensity and the emission rate ensures the accuracy of diffusion trend depiction, the extraction of carbon concentration data is combined with the dominant emission direction, the data pertinence is enhanced, a missing region is filled through gradient difference calculation, and the accuracy of the carbon emission diffusion path is improved. Data integrity is improved, a diffusion path is optimized through error calculation and threshold judgment, the emission rate and airflow intensity are dynamically adjusted, the carbon concentration trend prediction precision is improved, space correlation factors are combined with a region adjustment mechanism, the accounting range is adjusted in a self-adaptive mode, errors are prevented from being divided, and time sequence calculation of carbon emission is optimized.

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 discloses a modeling method for a wind wave flow coupling environment of an FPSO system based on modelica, and relates to the technical field of model convergence, and the method comprises the steps: setting different time steps, carrying out simulation, recording the response of a key physical quantity, and calculating a time step sensitivity index; performing different grid division and simulation on the model, and calculating a grid independence index; and recording the dynamic response of the FPSO platform, and calculating the instability index of the simulation result. A time step sensitivity index, a grid independence index and a simulation result instability index are combined to calculate a convergence anomaly coefficient, the convergence anomaly coefficient is compared with a preset threshold value, whether the convergence of the model is qualified or not is judged, the standardized convergence of the FPSO system wind wave flow coupling model based on Modelica can be verified in the process, it is ensured that the convergence performance of the model is detected in time, and the reliability of the model is improved. Error accumulation is avoided, the accuracy and reliability of a simulation result are improved, and therefore potential influences on engineering decision and safety evaluation are reduced.

The invention relates to the technical field of fixed-wing aircraft fire extinguishing, in particular to a fixed-wing aircraft fire extinguishing task planning and efficiency evaluation system. Comprising a dynamic environment modeling module, an intelligent task planning module, a fire extinguishing agent distribution prediction module, a high-precision bomb dropping control module, an efficiency real-time evaluation module, a heterogeneous data collaboration module, a self-adaptive model iteration module and a man-machine collaborative decision-making module. The dynamic environment modeling module is used for real-time meteorological data fusion and three-dimensional terrain reconstruction. Based on a quantum annealing multi-objective optimization and multiphase flow coupling correction method, a flight path considering efficiency and precision is generated through environmental data fusion and path optimization, then fire extinguishing agent distribution is corrected and predicted based on physical simulation and data driving, and finally accurate execution of a bomb dropping trajectory is ensured through anti-interference guidance. A'planning-prediction-execution 'closed loop is formed, and the problems that a traditional scheme is low in efficiency and the fire extinguishing task of the fixed-wing aircraft cannot be planned and evaluated are solved.

The invention belongs to the technical field of robots, and particularly relates to a global trajectory strategy reinforcement learning method and a robot control system, and the method comprises the following specific steps: S1, environment modeling and state definition: firstly collecting environment data, then defining a state space of a robot according to the collected environment information, the position, the posture, the sensor data and the task target information of the robot are coded and converted into a state vector which can be processed by an algorithm; s2, setting an action space: determining all actions which can be executed by the robot in a scene, and discretizing or continuously processing the actions to form the action space; according to the method, the path planning response speed can be increased, collision can be effectively avoided, and the adaptive capacity of the robot in a dynamic environment can be greatly enhanced.

The invention relates to the technical field of unmanned system navigation and path planning, in particular to a self-adaptive unmanned navigation path planning system and method, which are suitable for path generation and optimization in a complex dynamic environment, especially for dynamic obstacle avoidance and path adjustment application of an unmanned ship, an unmanned aerial vehicle and other autonomous navigation equipment. According to the system, efficient autonomous navigation of an unmanned system in a complex dynamic environment is realized through cooperative work of environment modeling, global path generation and local path optimization.

The method comprises the following steps: constructing an intelligent research and judgment model, carrying out environment modeling on the intelligent research and judgment model, constructing a network attack path according to the intelligent research and judgment model, and identifying a network attack by using a known signature of the network attack or a rule-based detection technology; classifying and marking the data of the network security equipment by using an SVM classification algorithm, training an intelligent study and judgment model, establishing a threat identification strategy of the network attack, and identifying the features of the network attack; performing context learning and state prediction by using the intelligent study and judgment model, and optimizing a threat identification strategy of network attacks; a data field of a normal HTTP request is slightly disturbed, an adversarial sample is generated by using a threat of a known network attack, adversarial training is performed in combination with a forged HTTP request, and a deceptive network attack is identified. According to the method, cross validation and correlation analysis of high-quality and multi-source threat intelligence are realized, and the attack defense capability is improved.

The invention relates to the technical field of mine electric locomotives, and discloses a mine electric locomotive unmanned comprehensive control method based on artificial intelligence. The method comprises the following steps: S1, multi-modal environment perception: acquiring static and dynamic data of a mine environment by a multi-modal sensor, and processing the data through a sensor fusion technology; s2, real-time environment modeling: fusing multi-modal data to construct a mine three-dimensional real-time environment model; and S3, dynamic target trajectory prediction: predicting the motion trajectory of the dynamic target based on the deep learning model. And through a deep learning model, carrying out modeling and prediction on a historical motion track of a dynamic target. Through a multi-head self-attention mechanism, the model can accurately capture the time correlation and the multi-target interaction relationship in the target trajectory, the probability distribution of the future trajectory is generated, and important support is provided for obstacle avoidance decision and path optimization in a complex dynamic environment.

The invention belongs to the technical field of navigation, and discloses an inland river bridge group water area ship navigation collision avoidance method and system, and the method comprises the steps: carrying out the environment modeling based on an electronic river map; designing a bridge group collision avoidance path planning algorithm; and designing a bridge group navigation mark auxiliary yaw early warning system. An inland river bridge group water area ship navigation collision avoidance system is established, and environment information is obtained based on an electronic river map and processed into an environment model capable of being directly utilized. A ship-bridge group is used as a research object, a ship-bridge group collision-free path is established based on a voronoi method, factor constraints are considered, and a ship bridge group operation collision avoidance strategy is researched. The navigation track of the ship is predicted, navigation mark acousto-optic early warning prompt is supplemented for the ship yawing or having the collision risk, and the bridge group water area ship yawing early warning function is achieved.

The invention relates to the technical field of sun visors, in particular to an environment modeling method and system applied to sun shading of an intelligent sun visor, and the method comprises the steps: obtaining a target adjustment moment, an environment monitoring unit and the intelligent sun visor, obtaining a historical environment data set based on the environment monitoring unit, obtaining a historical data sequence according to the historical environment data set, and obtaining a target adjustment moment; the weather forecast interface unit receives the prediction data sequence, the real-time monitoring unit obtains real-time environment data, the environment modal is analyzed based on the historical data sequence, the prediction data sequence and the real-time environment data, and a sun shield control instruction of the intelligent sun shield is generated according to the environment modal. And according to the sun shield control instruction, controlling the pose of the sun shield, and completing environment modeling applied to the intelligent sun shield under sun shading. According to the invention, the sun shield can be intelligently controlled according to the environmental data.

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 dynamic obstacle avoidance control method for an unmanned ship, and the method comprises the steps: multi-sensor data fusion and environment modeling, dynamic obstacle trajectory prediction, deep reinforcement learning path planning, nonlinear model prediction control, and final planning algorithm summarization. Obstacle avoidance control is carried out through multi-sensor data fusion and environment modeling, dynamic obstacle trajectory prediction, deep reinforcement learning path planning, nonlinear model prediction control and final planning algorithm summarization, and the method comprises multi-modal sensor fusion, LSTM trajectory prediction, deep reinforcement learning decision and nonlinear model prediction control. High-precision real-time obstacle avoidance under a complex water area is achieved, the whole method combines dynamic modeling and optimization control, the path safety, efficiency and energy utilization rate are remarkably improved, and the method is suitable for the fields of agricultural unmanned feeding, unmanned water quality intelligent detection and the like.

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 provides a wind power equipment transportation scene construction method and system, and relates to the technical field of environment modeling and emulation.The method comprises the steps that environment data in a pre-transportation environment are obtained according to a multi-source sensor, denoising and credibility grading are conducted on the environment data, and graded environment data are obtained; according to the graded environment data, scene distortion factors in the environment data are identified and corrected, and a preliminary static scene is generated according to the corrected environment data; the method comprises the following steps: capturing dynamic change factors in a pre-transportation environment in real time by taking a static scene with geometric and texture information as a reference, identifying a dynamic area influencing a transportation path according to the dynamic change factors, and updating and generating a real-time dynamic scene through the dynamic area. According to the invention, through real-time monitoring and dynamic updating of the environment change, it is ensured that the transportation path and scene always accord with the latest environment condition, the transportation risk caused by the environment change is reduced, and the safety in the transportation process is improved.

The invention provides a mobile robot path planning method based on an improved ant colony algorithm, and relates to the technical field of robot path planning, and the method is technically characterized in that the method comprises the following steps: carrying out environment modeling by adopting a grid method, and constructing a multi-target performance evaluation function; an improved ant colony algorithm is adopted to carry out optimal path search, path planning is carried out on a grid map from a starting node to a target node, and the improvement of the ant colony algorithm comprises the steps of distributing pheromone concentration by adopting a non-uniform initialization method; improving a state transition rule; differentiating a pheromone updating strategy; and smoothing the path. An initial pheromone distribution optimization strategy is introduced, heuristic information based on a triangular inequality, a steering angle suppression function and a state transition rule of artificial potential field force are combined, a differential pheromone updating strategy based on a multi-target performance evaluation index is adopted, and finally a planned path is smoothed. Therefore, the convergence speed, the path quality and the environment adaptability of the algorithm are remarkably improved.

The invention belongs to the field of unmanned ship navigation, and relates to an unmanned ship marine obstacle environment modeling method based on rough set data fusion. Comprising the following steps: (1) constructing a two-dimensional grid map by taking the position of an unmanned ship as a center; (2) the unmanned ship is equipped with m different types of obstacle sensors, and the two-dimensional grid map is divided into n areas according to different detection capabilities of the obstacle sensors; (3) establishing n information tables for n areas in the two-dimensional grid map; (4) performing condition attribute reduction on the information table of the n areas in the two-dimensional grid map by utilizing the dependency degree of the decision attribute in the information table on each condition attribute; (5) utilizing n simplified information tables in the two-dimensional grid map to classify and conclude a decision fusion rule for judging whether the grid is an obstacle area or not; and (6) judging whether each grid is an obstacle area or not, and completing modeling. According to the method, a new thought is provided for marine obstacle environment modeling of the unmanned ship, and the detection precision is improved.

The invention relates to automatic driving, in particular to a dynamic environment modeling and active adaptation system for an automatic driving vehicle, which comprises a multi-band cognitive radar module and a millimeter wave radar integrated with multiple bands, adopts an intelligent switching and cooperative working mechanism, and automatically optimizes the duty cycle proportion of the bands according to the environment condition. Radar waves of different frequency bands are transmitted and received, and multi-dimensional information of a target is obtained; the data processing and dynamic environment modeling module adopts a multi-sensor data fusion algorithm to fuse target multi-dimensional information acquired by radars of different frequency bands, performs dynamic environment modeling on the periphery of the vehicle, and updates a dynamic environment model in real time; the decision-making and active adaptation control module is used for making a reasonable driving decision, planning a path and carrying out active adaptation control by adopting a deep learning and artificial intelligence algorithm; according to the technical scheme provided by the invention, the defects that an accurate and real-time dynamic environment model is difficult to construct and the vehicle driving state cannot be timely and reasonably adjusted can be effectively overcome.

The invention provides a path planning method, system and equipment for underwater rock drilling operation of a high-frequency breaking hammer, and relates to the technical field of underwater rock drilling. A multi-source data modeling technology and a position indicator area positioning technology are fused, microscopic details of a rock drilling target are complemented through optical data, equipment motion constraints are calibrated through position indicator data, and three-dimensional environment modeling of a macroscopic operation area in combination with the microscopic rock drilling target and the equipment motion constraints is achieved; a multi-objective optimization model is constructed, and an objective function for aggregating the path length, the path fluctuation, the energy consumption and the breaking hammer slip rate is a composite cost function; solving a global optimal operation path corresponding to the composite cost function by adopting IPSO; obtaining the global optimal path through speed updating mechanism optimization, adaptive inertia weight adjustment, asynchronous change learning factor setting and natural selection population iteration; the data volume and the calculation complexity of environment modeling are reduced, and the accuracy and the practicability of environment perception and modeling are improved.

The invention discloses an urban road sky-ground integrated automatic driving collaborative awareness and planning system in severe weather, and the system comprises a multi-mode perception fusion and environment modeling module which generates a dynamic-static fusion BEV map through the fusion and complementation of a vehicle-end multi-mode sensor and low-orbit satellite perception data; the dynamic global path planning module is used for constructing a dynamic cost BEV map and generating a global optimal path by using an improved A * algorithm; the dynamic spatio-temporal reasoning planning module dynamically updates the dynamic cost BEV map based on the surrounding vehicle prediction trajectory and the occupied area, and predicts the local optimal path of the own vehicle; and the space-ground collaborative closed-loop optimization module dynamically updates global and local path planning results through real-time data interaction between the vehicle end and the satellite end. According to the method, the sensing precision, the path planning efficiency and the dynamic adaptive capacity of the urban road automatic driving system under the complex dynamic environment and severe weather conditions are remarkably 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 discloses a domain model and platform engine docking system and method for a multi-agent calculation experiment system. The method is based on a domain knowledge analysis module, an agent modeling docking module and an environment modeling docking module, and rule evolution modeling comprises the following steps: the domain knowledge analysis module analyzes a domain knowledge document input by a user through a large language model; automatically extracting experimental system boundaries, key roles, environmental elements and behavior logic relationships, and outputting domain knowledge information matched and analyzed with each docking module; the agent modeling module establishes a relationship with a role model in an artificial society based on the analyzed domain knowledge information; the environment modeling docking is based on a standard geographic space and an artificial network establishment relationship; the rule evolution modeling establishes a relationship with an artificial network through a strategy feedback dynamic interaction rule based on individual learning, group emergence and a regulation and control evolution mechanism; the platform engine is used for mapping analysis domain knowledge information to a calculation experiment platform through a standardized interface; according to the method, a complete technical path is provided for efficient construction and simulation analysis of an artificial social system through a structured, standardized and adjustable model construction and docking mechanism.

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 the technical field of cold chain transportation environment control, and discloses a temperature and humidity control system for cold chain transportation, and the system comprises an environment modeling module which is used for constructing a heat conduction model of a cold chain space based on a random field theory and a finite element method, and outputting a stiffness matrix containing random parameters; the self-adaptive sensing network module is used for receiving the characteristic information of the stiffness matrix, dynamically optimizing sensor distribution positions, collecting multi-node temperature and humidity data and adding space-time labels; and the distributed data processing module is used for receiving the sensing data. According to the invention, through random field theoretical modeling and parameter-state joint estimation, the temperature field prediction precision is significantly improved and the system environment adaptability is enhanced; a distributed sensing network architecture and a heterogeneous calculation acceleration technology are combined, so that the cooperative control with optimal energy efficiency is realized while millisecond-level real-time response is ensured, and the performance bottleneck of a traditional cold chain temperature control system is comprehensively broken through.