Multi-source environmental parameter adaptive collaborative regulation and emergency response system

Abstract

The application relates to the field of intelligent environment control and emergency management systems, and particularly discloses a multi-source environment parameter adaptive collaborative regulation and emergency response system. The system comprises a multi-source environment sensing device, a digital twin modeling engine, an intelligent decision-making hub, a bionic collaborative execution mechanism and an emergency response feedback unit; a dynamic digital twin is constructed through a high-fidelity fluid mechanics model, a regulation strategy is optimized in a virtual environment in combination with deep reinforcement learning, and a centerless collaboration of a distributed execution unit is realized by using a bionic swarm intelligence mechanism; when the actual state and the prediction deviation are out of limits, an emergency response unit triggers a correction or a safety plan in stages. The application improves the regulation accuracy, response speed and operation robustness of the system in a complex nonlinear environment through the above technical scheme.

Description

Technical Field

[0001] This invention belongs to the field of intelligent environmental control and emergency management systems, specifically relating to a multi-source environmental parameter adaptive collaborative regulation and emergency response system. Background Technology

[0002] With the continuous evolution of intelligent control and industrial automation technologies, multi-source parameter monitoring and coordinated regulation in complex physical environments have become core means to ensure the safe operation of precision manufacturing, aerospace, and large-scale energy facilities. Environmental regulation involves a deep intersection of thermodynamics, computational fluid dynamics, and automation control theory, aiming to maintain the system's steady-state operation within preset threshold ranges through dynamic intervention of key indicators such as temperature, pressure, humidity, and airflow distribution within the physical space. In the face of variable operating conditions and sudden environmental disturbances, constructing an emergency regulation mechanism with high reliability and rapid response capabilities is of practical significance for improving the defense depth and operational efficiency of complex engineering systems.

[0003] Adaptive and coordinated regulation of multi-source environmental parameters is a key research direction in the integration of environmental engineering and artificial intelligence. Its core objective is to achieve bidirectional mapping and real-time regulation between physical entities and digital models. This technology typically utilizes sensor arrays to perceive multi-dimensional environmental characteristics in real time and relies on digital twin architectures for evolutionary simulation to generate control sequences for actuators. An ideal regulation system should maintain the accuracy of physical laws, possess efficient decision-making capabilities to cope with multivariate nonlinear coupling, and be able to quickly extract decision features from massive heterogeneous data to achieve accurate characterization and proactive defense of complex environmental fields.

[0004] Traditional proportional-integral-derivative (PID) control strategies rely heavily on linearization assumptions, making it difficult to handle nonlinear coupling relationships between environmental parameters. This leads to model mismatch and control lag when facing large time delays or strong disturbances. While existing physical prediction models based on numerical simulations offer strong interpretability, their computational overhead under dynamic boundary conditions is enormous, making it difficult to meet the demands of high-frequency real-time response under sudden situations. Conventional AI-driven solutions often lack constraints from underlying physical mechanisms, making them prone to unpredictable decision deviations or even runaway risks when dealing with complex parameter mutations. Existing multi-mechanism distributed control systems lack mechanisms for collaborative evolution and resource balancing, making it easy for actuators to interfere with each other or get trapped in local optima, hindering the technological leap from passive feedback regulation to proactive predictive defense.

[0005] There is an urgent need for a multi-source environmental parameter adaptive collaborative control and emergency response system. Summary of the Invention

[0006] The purpose of this invention is to provide a multi-source environmental parameter adaptive collaborative control and emergency response system, which can solve the model mismatch and control lag problems in the above-mentioned background technology.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A multi-source environmental parameter adaptive collaborative control and emergency response system includes a multi-source environmental sensing device, a digital twin modeling engine, an intelligent decision-making center, a biomimetic collaborative execution mechanism, and an emergency response feedback unit, as follows:

[0009] The multi-source environmental sensing device is configured to collect multi-dimensional environmental parameters such as temperature, humidity, pressure, airflow speed and direction in the physical space in real time, and transmit the collected heterogeneous data to the digital twin modeling engine after time synchronization and spatial alignment.

[0010] The digital twin modeling engine constructs a high-fidelity physical model based on the principles of thermodynamics and fluid mechanics, receives real-time data streams from the multi-source environmental sensing device, dynamically updates the state of the virtual environment field, and performs environmental evolution simulation at high frequency in the virtual space to generate a future state prediction sequence.

[0011] The intelligent decision-making center integrates a deep reinforcement learning algorithm. It takes the prediction sequence output by the digital twin modeling engine as input, and through repeated trial and error and strategy optimization in the virtual environment, it learns autonomously and generates the optimal control strategy for current and future environmental disturbances. This strategy is output to the bionic collaborative execution mechanism in the form of a control instruction sequence.

[0012] The biomimetic collaborative execution mechanism includes multiple distributed execution units, each corresponding to a physical control device. The biomimetic collaborative execution mechanism introduces a biomimetic swarm intelligence mechanism, which dynamically coordinates and allocates resources for the actions of each execution unit based on a pheromone concentration model, ensuring the consistency and global optimality of the overall actions and avoiding local conflicts or resource waste.

[0013] The emergency response feedback unit continuously monitors the actual response status of the physical environment. When the deviation between the actual status and the predicted status of the digital twin model exceeds a preset threshold, it immediately triggers the emergency mode, sends a high-priority correction signal to the intelligent decision-making center, and temporarily increases the simulation frequency of the digital twin modeling engine and the learning rate of the intelligent decision-making center.

[0014] Preferably, the digital twin modeling engine has a built-in computational fluid dynamics model that can accurately characterize the nonlinear coupling relationship between the temperature and humidity field and the airflow field. Its boundary conditions are dynamically adjusted according to the real-time input of the multi-source environmental sensing device, maintaining the accuracy of physical laws and the adaptability of the model under complex working conditions.

[0015] Furthermore, the intelligent decision-making center adopts a deep reinforcement learning architecture based on policy gradient. Its state space is composed of multi-dimensional environmental features output by the digital twin model, and the action space is mapped to the control parameters of each execution unit. The reward function comprehensively considers environmental stability, energy efficiency and safety margin, so that the system can continuously evolve a control strategy that balances performance and robustness during long-term operation.

[0016] Furthermore, the biomimetic collaborative execution mechanism employs a biomimetic swarm intelligence mechanism that simulates the pheromone diffusion and evaporation process in ant colony foraging behavior. Each execution unit releases virtual pheromones after performing an action, and adjacent units adjust their own action intensity and timing according to the pheromone concentration gradient, achieving distributed collaboration and load balancing without central scheduling.

[0017] Preferably, the emergency response feedback unit has a two-level response mechanism. The first-level response is for slow-drifting disturbances, and the gradual correction is achieved by fine-tuning control commands. The second-level response is for sudden and severe disturbances. When the judgment deviation exceeds a higher-level preset threshold, the current strategy is immediately frozen and a preset safety plan is activated, driving the digital twin model to perform multi-scenario parallel simulations to quickly generate alternative solutions.

[0018] Furthermore, the multi-source environmental sensing device adopts a heterogeneous sensor fusion architecture, which includes contact and non-contact sensing units. Its spatial layout follows the principle of covering key areas of the flow field and has a self-calibration function, which can automatically compensate for sensor drift during long-term operation to ensure the reliability and timeliness of input data.

[0019] Furthermore, a closed-loop iterative mechanism is formed between the digital twin modeling engine and the intelligent decision-making center. The digital twin model not only provides a training environment for AI decision-making, but its own parameters can also be corrected online based on the effect feedback after the intelligent decision-making center is verified in the real environment, realizing bidirectional enhancement of the physical model and the data-driven model.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1. The multi-source environmental parameter adaptive collaborative control and emergency response system provided by this invention solves the model mismatch problem caused by neglecting nonlinear coupling in traditional control methods by deeply embedding a high-fidelity fluid dynamics physical model into the underlying digital twin. It utilizes deep reinforcement learning to explore safe strategies in a virtual environment, preserving physical interpretability while endowing the system with adaptive evolution capabilities. It introduces a biomimetic swarm intelligence mechanism to coordinate distributed actuators, overcoming the dilemma of action interference and local optima in multivariable strongly coupled scenarios. The dynamic deviation monitoring and hierarchical intervention mechanism of the emergency response feedback unit improves the robustness and recovery speed of the system when facing sudden disturbances.

[0022] 2. This system represents a technological leap from passive response to active defense, enabling it to maintain high-precision, high-efficiency, and high-reliability coordinated control in complex nonlinear environments. It is suitable for critical fields with high environmental stability requirements, such as precision manufacturing, aerospace, and large-scale energy facilities. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention;

[0024] Figure 2 This is a schematic diagram of the core principle framework of the closed-loop iterative generation and control strategy of the digital twin modeling engine and intelligent decision-making center in this invention.

[0025] Figure 3 This is a flowchart illustrating the distributed action coordination logic of the biomimetic collaborative actuator based on swarm intelligence in this invention.

[0026] Figure 4 This is a flowchart illustrating the hierarchical monitoring and dynamic intervention logic of the emergency response feedback unit in this invention for environmental deviations.

[0027] Figure 5 This is a schematic diagram of the heterogeneous data fusion and physical field update interaction between the multi-source environmental sensing device and the digital twin modeling engine in this invention. Detailed Implementation

[0028] Example 1: Please refer to the appendix Figure 1 To be continued Figure 5 To make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments.

[0029] A multi-source environmental parameter adaptive collaborative control and emergency response system includes a multi-source environmental sensing device, a digital twin modeling engine, an intelligent decision-making center, a bionic collaborative execution mechanism, and an emergency response feedback unit.

[0030] The multi-source environmental sensing device is used to collect multi-dimensional environmental parameters such as temperature, humidity, pressure, airflow speed and direction in the physical space in real time, and transmit the collected heterogeneous data to the digital twin modeling engine after time synchronization and spatial alignment.

[0031] The digital twin modeling engine is used to construct a high-fidelity physical model based on the principles of thermodynamics and fluid mechanics, receive real-time data streams from the multi-source environmental sensing device, dynamically update the state of the virtual environment field, and perform environmental evolution simulation at high frequency in the virtual space to generate a future state prediction sequence.

[0032] The intelligent decision-making center is used to integrate deep reinforcement learning algorithms. Taking the prediction sequence output by the digital twin modeling engine as input, it learns autonomously and generates the optimal control strategy for current and future environmental disturbances through repeated trial and error and strategy optimization in the virtual environment. The strategy is output to the bionic collaborative execution mechanism in the form of a control instruction sequence.

[0033] The biomimetic collaborative execution mechanism is used to include multiple distributed execution units, each of which corresponds to a physical control device. The biomimetic collaborative execution mechanism introduces a biomimetic swarm intelligence mechanism, which dynamically coordinates and allocates resources for the actions of each execution unit based on a pheromone concentration model, ensuring the consistency and global optimality of the overall actions and avoiding local conflicts or resource waste.

[0034] The emergency response feedback unit is used to continuously monitor the actual response status of the physical environment. When the deviation between the actual status and the predicted status of the digital twin model exceeds a preset threshold, the emergency mode is immediately triggered, a high-priority correction signal is sent to the intelligent decision center, and the simulation frequency of the digital twin modeling engine and the learning rate of the intelligent decision center are temporarily increased.

[0035] The multi-source environmental sensing device includes a heterogeneous sensor array module, a signal conditioning and digitization module, a timestamp synchronization module, and a spatial coordinate mapping module. The heterogeneous sensor array module is deployed within the physical space to be monitored and specifically includes a high-precision platinum resistance temperature sensor, a capacitive thin-film humidity sensor, a piezoresistive pressure transmitter, and a multi-dimensional wind speed and direction sensor based on the ultrasonic time-of-flight method. The deployment density of the heterogeneous sensor array module is non-uniformly distributed according to the curvature changes and gradient intensity of key regions of the flow field within the physical space. High-density sensing nodes are deployed in airflow convergence areas, near-field areas of heat sources, and areas with severe pressure gradient fluctuations to capture high-frequency transient disturbances.

[0036] The signal conditioning and digitization module is connected to the heterogeneous sensor array module and is configured to perform low-noise amplification, anti-aliasing filtering, and analog-to-digital conversion on the acquired weak analog electrical signals. The signal conditioning and digitization module employs a multi-channel synchronous sampling architecture to ensure that different types of physical parameters have a consistent sampling reference in the time dimension. The timestamp synchronization module uses a precise time protocol to assign a high-precision time tag to each frame of acquired heterogeneous data, with errors controlled at the nanosecond level. The spatial coordinate mapping module stores a digital three-dimensional geometric model of the physical space and is configured to map the physical position of each sensor node to a unified Cartesian or cylindrical coordinate system, achieving precise alignment of heterogeneous data in the spatial dimension and forming a five-dimensional sensing tensor containing time, space, and attribute features.

[0037] The digital twin modeling engine includes a geometric topology building unit, a fluid dynamics solving unit, a real-time state mapping unit, and a parallel simulation and derivation unit. The geometric topology building unit defines the boundary conditions and geometric features of internal obstacles in the physical space. The fluid dynamics solving unit incorporates a computational fluid dynamics model based on the Navier-Stokes equations, which accurately characterizes the strong nonlinear coupling between the temperature and humidity fields and the airflow field within the physical space. The fluid dynamics solving unit uses the finite volume method to mesh the physical space, with mesh types including tetrahedral meshes, hexahedral meshes, and prism element meshes for the boundary layer. The boundary conditions of the fluid dynamics solving unit are dynamically adjusted based on real-time data input from the multi-source environmental sensing device. For example, the real-time collected wall temperature is used as a first-type boundary condition, and the inlet and outlet wind speeds are used as a second-type boundary condition, ensuring that the virtual model remains synchronized with the instantaneous state of the physical entity.

[0038] The real-time state mapping unit deeply fuses the sensing tensor with the simulation prediction values ​​using a Kalman filter or particle filter algorithm, correcting latent variables in the digital twin model in real time, such as local turbulence intensity coefficients or heat exchange efficiency constants. The parallel simulation inference unit is deployed on a high-performance graphics processing unit cluster, utilizing hardware acceleration technology to achieve ultra-real-time simulation, meaning the simulation time step is much smaller than the physical time step. The parallel simulation inference unit is configured to, within each decision cycle, extrapolate multiple possible development paths from the current state to the future, generating a future state prediction sequence that includes temperature trend lines, pressure distribution maps, and streamline evolution characteristics.

[0039] The intelligent decision-making center includes a deep feature extraction network, a reinforcement learning agent unit, a reward function evaluation module, and an instruction sequence generation unit. The deep feature extraction network employs a convolutional neural network or a graph neural network to reduce the dimensionality of the high-dimensional prediction sequence output by the digital twin modeling engine, extracting core feature vectors that characterize environmental stability. The reinforcement learning agent unit adopts a deep reinforcement learning architecture based on policy gradients. Its state space is composed of the feature vectors, and its action space is mapped to the control parameters of each execution unit in the biomimetic cooperative execution mechanism, such as the output frequency of the inverter, the opening percentage of the valve, or the power level of the heater.

[0040] The reward function evaluation module incorporates complex evaluation logic, configured to comprehensively consider environmental stability, energy efficiency, and safety margin indicators. The environmental stability indicator is represented by the reciprocal of the Euclidean distance between the actual parameter values ​​and the target setpoint; the energy efficiency indicator is represented by calculating the total power consumption of each execution unit and taking a negative value; and the safety margin indicator is nonlinearly weighted based on the distance of the parameters from the physical limit. At the end of each simulation round, the reward function evaluation module calculates the total reward value, guiding the reinforcement learning agent unit to optimize the control strategy through continuous trial and error in the virtual environment. The instruction sequence generation unit converts the optimized strategy into a control instruction sequence conforming to industrial communication protocols.

[0041] The biomimetic collaborative actuator includes a distributed execution control unit, a pheromone interaction virtual layer, a swarm intelligence coordination algorithm module, and physical execution components. The physical execution components include, but are not limited to, a centrifugal fan, a proportional-integral control valve, an electric heating array, and a humidification / dehumidification unit. Each physical execution component is independently connected to the distributed execution control unit. The pheromone interaction virtual layer simulates the pheromone release and sensing mechanisms in the biological world by constructing a virtual two-dimensional or three-dimensional topological network. When a distributed execution control unit performs a regulatory action, it releases positive pheromones at its corresponding position in the pheromone interaction virtual layer. The pheromone concentration decays exponentially over time and diffuses to adjacent nodes.

[0042] The swarm intelligence coordination algorithm module simulates the foraging and path optimization logic in ant colony optimization. Each execution unit monitors the pheromone concentration gradient within its own perception range and received through the network in real time. If the parameter deviation in a certain area is large, the execution units in that area will release a high concentration of "help" pheromones to attract execution units in neighboring areas to participate in coordination and regulation. By adjusting their own action intensity and timing, dynamic balance of local load is achieved. This decentralized distributed coordination mechanism ensures that the system can still maintain basic coordinated regulation capabilities when some execution units fail or communication is blocked, avoiding local oscillations or global control conflicts.

[0043] The emergency response feedback unit includes a real-time deviation monitoring module, a priority arbitration logic module, a response mode switching switch, and a knowledge base update unit. The real-time deviation monitoring module continuously compares the real data collected by the multi-source environmental sensing device with the simulation data generated by the digital twin modeling engine. When the absolute deviation or rate of change deviation between the two exceeds a first preset threshold and the duration exceeds a preset observation window, the priority arbitration logic module determines that the system enters a Level 1 emergency response state. In the Level 1 emergency response state, the response mode switching switch drives the intelligent decision-making center into a fine-tuning mode, which corrects the strategy online by increasing exploratory noise or adjusting the learning rate.

[0044] If the deviation further increases and exceeds the second preset threshold, the priority arbitration logic module determines that the system enters a level-two emergency response state. The response mode switching switch immediately freezes the current reinforcement learning strategy and forcibly switches to the corresponding protection action in the preset safety plan library, such as emergency full-volume forced ventilation or graded cutoff of heat sources. The emergency response feedback unit sends an acceleration signal to the digital twin modeling engine, enabling it to enter a multi-scenario parallel simulation mode to simulate the evolution results under various extreme conditions in a short period of time and find the optimal risk avoidance path. After the emergency handling is completed, the knowledge base update unit stores the feature data, processing process, and final result of the abnormal event in the long short-term memory database for subsequent model training and strategy optimization.

[0045] The multi-source environmental sensing device further features a self-calibration function. This self-calibration function is achieved by setting several reference points in the physical space, where high-stability standard sensors with metrological certification are deployed. The spatial coordinate mapping module periodically compares the output differences between ordinary sensor nodes and adjacent reference points, calculates the sensor drift, and generates compensation coefficients through polynomial fitting or support vector machine regression algorithms. This automatically updates the correction parameters in the signal conditioning and digitization module, ensuring the long-term reliability of the input data.

[0046] The computational fluid dynamics model built into the digital twin modeling engine can accurately characterize the nonlinear coupling relationship between the temperature and humidity field and the airflow field. Specifically, it introduces a buoyancy term into the solver to simulate the influence of natural convection on the flow field, and employs a two-equation turbulence model to describe the eddy characteristics under complex geometries. The boundary conditions are dynamically adjusted based on the real-time input from the multi-source environmental sensing device, enabling the model to adapt to fluctuations in operating conditions caused by personnel entry and exit, equipment start-up and shutdown, or changes in the outdoor environment.

[0047] The intelligent decision-making center employs a deep reinforcement learning architecture based on policy gradients, in which the reward function also includes a stability penalty term. When the frequency or magnitude of control commands is too high, this penalty term reduces the total reward value, guiding the system to learn a smooth, orderly control strategy with minimal mechanical wear. The intelligent decision-making center possesses incremental learning capabilities, enabling it to continuously fine-tune neural network parameters based on the slow, seasonal evolution of the environment, achieving adaptive evolution of the system.

[0048] The biomimetic swarm intelligence mechanism in the described biomimetic collaborative actuator also incorporates dynamic adjustment of the "pheromone evaporation coefficient." When environmental fluctuations are drastic, the system automatically reduces the evaporation coefficient, prolonging the pheromone's duration and enhancing the strong coupling and collaboration between execution units. When the environment tends towards a steady state, the evaporation coefficient is increased, allowing each unit to rely more on local sensing data for fine-tuning and reducing communication overhead.

[0049] In the specific logic of system operation, a closed-loop iterative mechanism is formed between the digital twin modeling engine and the intelligent decision-making center. The digital twin model not only provides a massive number of "trial and error" samples for the reinforcement learning agent, avoiding the security risks associated with training on physical entities; the actual control effect verified by the intelligent decision-making center in the physical environment is also transmitted back to the digital twin engine as a feedback signal to verify the boundary parameters or source term coefficients of the physical model, realizing bidirectional enhancement and accuracy fitting of the physical model and the data-driven model.

[0050] Example 2: A multi-source environmental parameter adaptive collaborative control and emergency response system. Based on Example 1, this example enhances the edge computing capabilities and expands the hierarchical scheduling logic of the system architecture for large-scale distributed industrial field application scenarios.

[0051] In this embodiment, the multi-source environmental sensing device adopts a distributed deployment scheme based on an industrial wireless sensor network. Each sensing node integrates an ultra-low-power microprocessor, a multi-modal sensor chip, and a communication module compliant with low-power wide-area network standards. The sensing nodes have preliminary data cleaning and anomaly detection capabilities, and can automatically remove outlier data caused by sensor transient noise or electrical interference. The timestamp synchronization module uses BeiDou / GPS timing signals as a global reference source, and controls the time deviation of thousands of sensor nodes distributed over several hectares to within 10 microseconds through wireless synchronization pulses.

[0052] In this embodiment, the spatial coordinate mapping module incorporates a dynamic spatial perception function based on synchronous positioning and mapping technology. When the location of equipment in the physical space changes or new actuators are added, the system can automatically perceive changes in the geometric features of the environment through a mobile mapping terminal and automatically update the geometric topology information in the digital twin modeling engine. This dynamic mapping mechanism improves the deployment efficiency of the system in production line transformation or flexible manufacturing environments.

[0053] The digital twin modeling engine is architecturally divided into an edge real-time model unit and a cloud-based high-fidelity model unit. The edge real-time model unit is deployed in an edge computing gateway close to the physical site, employing simplified reduced-order models or intrinsic orthogonal decomposition algorithms to perform fast approximate solutions to the environmental field in seconds or even sub-seconds, meeting the basic requirements of real-time control. The cloud-based high-fidelity model unit is deployed on a private or public cloud server cluster, running a complete full-scale, high-resolution computational fluid dynamics simulation. The two maintain consistency through an asynchronous communication mechanism: the cloud model periodically sends correction coefficients to the edge model, while the edge model periodically uploads key feature data to the cloud, achieving efficient allocation of computing resources.

[0054] The intelligent decision-making center introduces a federated learning mechanism at the policy generation level. In a system with multiple similar environmental regions, each region's intelligent decision-making sub-center trains its model using only local data and periodically uploads the trained neural network gradients or weight updates to the central scheduling module. The central scheduling module then weights and aggregates these parameters before distributing a general global policy to each region. This mechanism protects the data privacy of each process region while achieving the sharing of collective knowledge and improving the model's generalization ability.

[0055] In this embodiment, the biomimetic collaborative actuator is enhanced with a robotic arm execution unit and a mobile control robot. The biomimetic swarm intelligence mechanism is configured not only to coordinate the actions of fixed equipment but also to manage the spatial pose of the mobile actuator. For example, when extreme local overheating occurs in a certain area and the fixed fan cannot cover it, the pheromone concentration model will guide the nearest mobile control robot to move towards that coordinate and adjust the angle of its cooling nozzles to achieve dynamic resource compensation in the spatial dimension.

[0056] The emergency response feedback unit, designed for a distributed architecture, employs a three-tiered response chain. In addition to the first and second-tier responses described in Example 1, a third-tier global disaster response mode is added. When the emergency response feedback unit detects catastrophic events such as widespread sensor failure, backbone network paralysis, or critical power system failure, it immediately triggers physical isolation logic. Each distributed execution unit automatically switches to autonomous control mode, executing emergency avoidance actions based on the locally cached last state and pre-set hardware logic (such as bimetallic thermostats and mechanical safety valves), ensuring the basic safety and stability of physical entities can be maintained even in the event of a complete loss of system command.

[0057] The heterogeneous sensor fusion architecture of the multi-source environmental sensing device also incorporates acoustic and visual sensors. The acoustic sensor monitors the aerodynamic noise spectrum characteristics generated when airflow passes through complex pipes or equipment gaps, inverting local pressure fluctuations and structural vibration states. The visual sensor (such as an infrared thermal imager) provides a large-area, non-contact thermal map of temperature distribution. The data processing unit is configured to utilize a multi-task learning network to fuse one-dimensional time-series signals, two-dimensional visual thermal maps, and three-dimensional acoustic field information to construct a holographic mapping of multiple physical quantities within the physical space.

[0058] The physical model in the digital twin modeling engine also integrates a device aging and degradation model. The modeling engine is configured to estimate the degree of valve wear, fan scaling, or heating element coking in real time by tracking the deviation between the actual and theoretical output of the actuators under different environmental pressures. This real-time correction of the physical model enables the intelligent decision-making center to generate precise control strategies that conform to the actual hardware condition, achieving a deep integration of preventative maintenance and adaptive control.

[0059] The deep reinforcement learning architecture of the intelligent decision-making center employs a hierarchical reinforcement learning approach. Lower-level agents are responsible for executing specific parameter controls (such as voltage and frequency regulation), while higher-level agents are responsible for determining macroscopic control objectives (such as maintaining stable dew point temperature or minimizing the total entropy increase of the system). This hierarchical task decomposition reduces the difficulty of solving large-scale control problems and improves the system's convergence speed to complex nonlinear disturbances.

[0060] The pheromone model in the biomimetic collaborative actuator incorporates "opposites repulsion" logic to simulate resource competition. When strong regulation of the same area by multiple actuators may lead to system overshoot, the system releases virtual signals with repulsive properties to guide the actuators to retreat in an orderly manner. This achieves the desired regulation effect with minimal actuator actions while ensuring response speed.

[0061] Example 3: A multi-source environmental parameter adaptive collaborative control and emergency response system. This example focuses on large-scale energy facilities or aerospace environment simulation rooms with extremely high safety requirements, emphasizing the system's hardware redundancy, formal verification capabilities, and extremely high fidelity of multi-physics coupling.

[0062] In this embodiment, all key sensors of the multi-source environmental sensing device employ a triple redundancy design. Each sensing node contains three completely independent sensing units. The signal conditioning and digitization module incorporates a two-to-two or three-to-two voting logic, which can automatically shield individual sensors that malfunction or exhibit abnormal drift, ensuring the absolute reliability of the sensing source. The sensor housing is made of a special corrosion-resistant and electromagnetic interference-resistant material, enabling accurate capture of physical parameters under extreme temperature, humidity, and strong radiation environments.

[0063] In this embodiment, the digital twin modeling engine achieves full physical coupling. In addition to thermodynamics and fluid dynamics, it also incorporates structural mechanics, electromagnetic fields, and chemical reaction kinetics models. For environments involving flammable and explosive gases or sophisticated chemical processes, the modeling engine can calculate the diffusion rate of chemical components and the local explosion limit risk value in real time. The fluid dynamics solution unit employs a massively parallel lattice Boltzmann method, capable of handling fluid problems with extremely complex geometric boundaries and cross-scale characteristics, such as permeation and pinhole jetting phenomena in porous media.

[0064] The intelligent decision-making center incorporates a formal verification layer within its neural network architecture. Before control commands are output to the physical actuators, this verification layer performs a safety boundary scan on the command sequence based on constraint solving techniques or model checking algorithms. If the strategy generated by reinforcement learning might mathematically lead to physical parameters exceeding safety limits, the verification layer will forcibly prune the commands to within a preset safety envelope, thus mathematically eliminating the risk of unpredictable actions from black-box AI algorithms.

[0065] The biomimetic collaborative actuator adopts an industrial real-time bus architecture with dual communication links, including fiber optic Ethernet links and electrical signal links. The controllers of each actuator unit employ high-concurrency processing technology based on field-programmable gate arrays (FPGAs), enabling pheromone diffusion calculations in swarm intelligence algorithms with millisecond-level response latency. The physical control equipment in the actuator employs a redundant backup configuration; for example, multiple fan arrays are connected in parallel along critical ventilation paths, achieving dynamic hot standby switching and fault load rebalancing through a swarm intelligence mechanism.

[0066] The emergency response feedback unit in this embodiment possesses multi-timescale early warning capabilities. In addition to monitoring real-time deviations, it also includes a long-term trend prediction module based on a long short-term memory network. This prediction module is configured to predict potential performance degradation or failure risks of the environmental system hours or even days in advance through in-depth analysis of historical operating data, triggering preventative maintenance instructions or strategy optimization processes before a failure occurs.

[0067] The multi-source environmental sensing device also integrates a fiber Bragg grating sensor. This sensor utilizes the reflection characteristics of light waves within the fiber to sense environmental changes, contains no electronic components, and possesses intrinsic safety and resistance to electromagnetic pulse interference. The fiber optic sensing array is woven into the walls or supporting structures of the physical space, enabling continuous distributed temperature and strain monitoring and providing extremely high-resolution boundary state feedback for the digital twin engine.

[0068] The digital twin modeling engine supports concurrent simulations across multiple scenarios. During decision-making simulations, the engine can run hundreds of different perturbation scenarios across thousands of independent computing cores, such as leaks of varying scales and sudden changes in heat sources of varying intensities. The intelligent decision-making center then evaluates these simulation results using a Monte Carlo tree search algorithm, selecting the robust control scheme with the highest overall score across all potential risk scenarios.

[0069] The reward function design of the intelligent decision-making center incorporates a "knowledge interpretability" term. This term quantifies the degree to which the decision-making process matches known physical laws by analyzing the activation patterns of the hidden layers within the neural network. If the decision scheme appears physically illogical (even if it performs well in the short term), the system will provide a certain negative feedback reward, forcing the AI ​​model to converge in a direction with better physical interpretability, thereby improving the system's reliability under extreme and unknown conditions.

[0070] The biomimetic collaborative actuator incorporates an "energy competition" model simulating an ecosystem. Each actuator is assigned a virtual "energy budget," aiming to guide the actuator through swarm intelligence algorithms to find the collaborative path with the lowest energy loss while achieving the control objectives. This model demonstrates self-organizing optimization capabilities when dealing with complex systems with thousands of actuators.

[0071] The emergency response feedback unit is equipped with a dedicated hardware emergency management server. This server operates independently of the main control system and has its own power supply system and communication frequency band. When the main control network is attacked by hackers, experiences a large-scale power outage, or suffers physical damage, this server can take over the minimum environmental maintenance logic and control the emergency control equipment to perform basic cooling, depressurization, or toxic gas extraction actions.

[0072] Example 4: A multi-source environmental parameter adaptive collaborative control and emergency response system. This example is designed for small precision laboratories or mobile controlled environment cabins (such as mobile hospitals and modular laboratories), emphasizing the system's compact integration, low power consumption characteristics, and adaptability to dynamic mobile conditions.

[0073] In this embodiment, the multi-source environmental sensing device employs a highly integrated all-in-one microelectromechanical system (MEMS) sensor array. Temperature, humidity, pressure, and airflow velocity sensors are integrated onto a single-chip-sized ceramic substrate and deployed in narrow device gaps via high-density flexible circuit boards. The sensor array supports plug-and-play functionality, and the system can automatically identify newly connected sensor types and automatically incorporate them into the spatial mapping coordinate system.

[0074] The digital twin modeling engine employs a simplified physical model assisted by artificial intelligence. By pre-running numerous full computational fluid dynamics examples on a supercomputer, an eigenvalue library for a specific small space is generated. In real-time operation, the modeling engine no longer runs the complete Navier-Stokes equations; instead, it uses a neural network-based algebraic solver, employing lookup tables and nonlinear interpolation techniques to achieve high-precision environmental field reconstruction with extremely low hardware power consumption.

[0075] The intelligent decision-making center is deployed on a high-performance embedded system-on-a-chip and employs a lightweight reinforcement learning network with quantized compression. This network reduces floating-point computation and memory usage while maintaining the accuracy of key decisions, enabling it to operate stably for extended periods on battery-powered mobile devices.

[0076] The biomimetic collaborative actuator mainly consists of a miniature axial flow fan, an electronic expansion valve, and a semiconductor cooling chip. The biomimetic swarm intelligence mechanism is optimized for miniaturized actuators and employs a resource allocation algorithm that simulates the feeding logic of slime mold. When dealing with local parameter loss caused by dead airflow angles in confined spaces, this algorithm can quickly guide the miniature actuator to perform localized, pulse-like, precise interventions through virtual path enhancement.

[0077] The emergency response feedback unit has an extremely high dynamic response speed. In the instant the mobile cabin experiences turbulence, tilting, or damage to its seals, the system's built-in accelerometer and pressure gradient sensor can trigger sub-millisecond emergency correction logic. The response mode switching switch immediately adjusts the physical output of the actuators, maintaining environmental stability in the core precision instrument area through pressure compensation or flow field redirection.

[0078] The multi-source environmental sensing device also integrates a miniature infrared gas analysis unit, capable of real-time monitoring of the concentration of specific chemical components (such as volatile organic compounds and carbon dioxide concentration) within the controlled space. The sensing data is fed back to a digital twin engine to simulate mass exchange processes within the environment.

[0079] The digital twin modeling engine possesses "multi-compartment linkage" modeling capabilities. When multiple mobile environment modules are combined together via soft connection channels, the system can automatically detect the establishment of physical connections and dynamically stitch together the digital models of each module to form a unified fluid dynamics domain. The intelligent decision-making center then adjusts accordingly to a multi-agent collaborative mode to achieve balanced parameter control across modules.

[0080] The intelligent decision-making center integrates a hyperparameter adjuster based on Bayesian optimization. In mobile operating conditions, due to the frequent changes in the external environment (such as outdoor temperature and air pressure), this adjuster can optimize the exploration rate and discount factor of the reinforcement learning model in real time, so that the control strategy is always in the optimal energy efficiency range under the current external background.

[0081] The micro-actuator in the biomimetic collaborative actuator adopts wireless power transmission and control integration technology, which reduces the complex wiring inside the cabin and lowers the risk of environmental interference caused by electrical faults.

[0082] The emergency response feedback unit also features a special "self-healing strategy" generation module. When a localized insulation failure or minor leak is detected on the cabin wall, this module can drive a bionic actuator to form a pressure shield curtain in a specific direction near the leak point, delaying the deterioration of the internal environment and buying time for personnel maintenance or equipment evacuation.

[0083] Those skilled in the art should understand that the embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-source environmental parameter adaptive collaborative control and emergency response system, characterized in that, include: A multi-source environmental sensing device is configured to collect multi-dimensional environmental parameters such as temperature, humidity, pressure, airflow speed and direction in the physical space in real time, synchronize the collected heterogeneous data in time and space, and output a five-dimensional sensing tensor. The digital twin modeling engine is connected to the multi-source environmental sensing device and is configured to build a high-fidelity physical model based on thermodynamics and fluid mechanics principles. It receives the five-dimensional sensing tensor to dynamically update the state of the virtual environment field and performs environmental evolution simulation in the virtual space to generate a future state prediction sequence. The intelligent decision-making center is connected to the digital twin modeling engine and is configured to integrate deep reinforcement learning algorithms. Taking the future state prediction sequence as input, it generates the optimal control strategy for environmental disturbances in the virtual environment through strategy optimization and outputs it in the form of a control command sequence. A biomimetic collaborative execution mechanism, connected to the intelligent decision-making center, includes multiple distributed execution units and is configured to introduce a biomimetic swarm intelligence mechanism to dynamically coordinate and allocate resources for the actions of each distributed execution unit based on a pheromone concentration model. The emergency response feedback unit is connected to the multi-source environmental sensing device, the digital twin modeling engine, the intelligent decision-making center, and the physical environment, respectively. It is configured to continuously monitor the actual response status of the physical environment. When the deviation between the actual status and the predicted status of the digital twin model exceeds a preset threshold, the emergency mode is triggered to correct the control strategy. The intelligent decision-making center includes: A deep feature extraction network is configured to perform dimensionality reduction processing on the future state prediction sequence using a convolutional neural network or a graph neural network to extract core feature vectors characterizing environmental stability. The reinforcement learning agent unit adopts a deep reinforcement learning architecture based on policy gradient. Its state space is composed of the core feature vector, and its action space is mapped to the control parameters of each of the distributed execution units. The control parameters include the inverter output frequency, valve opening percentage, or heater power level. The reward function evaluation module is configured to comprehensively consider environmental stability indicators, energy efficiency indicators, and safety margin indicators, calculate the total reward value at the end of each simulation round, and guide the reinforcement learning agent unit to optimize the control strategy. An instruction sequence generation unit is configured to convert the optimized strategy into a control instruction sequence that conforms to an industrial communication protocol; The evaluation logic of the reward function evaluation module is described as follows: The environmental stability index is characterized by calculating the reciprocal of the Euclidean distance between the actual parameter value and the target set value; The energy efficiency index is represented by calculating the total power consumption of each execution unit and taking a negative value; the safety margin index is non-linearly weighted based on the distance of the parameters from the physical limit value; The reward function evaluation module also introduces a stability penalty term. When the action frequency or action amplitude of the control command sequence exceeds a preset range, the total reward value is reduced through the stability penalty term to guide the system to generate a smooth and orderly control strategy.

2. The multi-source environmental parameter adaptive collaborative control and emergency response system according to claim 1, characterized in that, The multi-source environmental sensing device includes: The heterogeneous sensor array module is deployed in the key flow field area in the physical space. The heterogeneous sensor array module includes a high-precision platinum resistance temperature sensor, a capacitive thin film humidity sensor, a piezoresistive pressure transmitter, and a multi-dimensional wind speed and direction sensor based on the ultrasonic time difference method. The signal conditioning and digitization module is connected to the heterogeneous sensor array module and is configured to perform low-noise amplification, anti-aliasing filtering and analog-to-digital conversion on the acquired analog electrical signals, and to establish a consistent sampling reference using a multi-channel synchronous sampling architecture. The timestamp synchronization module is configured to add time stamps to each frame of heterogeneous data collected using a precise time protocol, aligning different types of physical parameters in the time dimension. The spatial coordinate mapping module stores a digital three-dimensional geometric model of the physical space and is configured to map the physical position of each sensor node to a unified coordinate system, thereby achieving precise alignment of heterogeneous data in the spatial dimension and forming the five-dimensional sensing tensor containing temporal features, spatial features, and attribute features.

3. The multi-source environmental parameter adaptive collaborative control and emergency response system according to claim 2, characterized in that, The multi-source environmental sensing device also has a self-calibration function, which is achieved by setting multiple reference points in the physical space, and high-stability standard sensors are deployed at the reference points. The spatial coordinate mapping module periodically compares the output differences between ordinary sensor nodes and adjacent reference points, calculates the sensor drift, and generates compensation coefficients through a support vector machine regression algorithm to automatically update the correction parameters in the signal conditioning and digitization module.

4. The multi-source environmental parameter adaptive collaborative control and emergency response system according to claim 3, characterized in that, The digital twin modeling engine includes: Geometric topology building blocks, configured with geometric features used to define the boundary conditions and internal obstacles of the physical space; The fluid dynamics solution unit has a built-in computational fluid dynamics model based on the Navier-Stokes equations, and is configured to perform mesh generation of the physical space using the finite volume method and dynamically adjust the boundary conditions based on the five-dimensional sensing tensor. The real-time state mapping unit is configured to deeply fuse the five-dimensional sensing tensor with the simulation prediction value through a Kalman filter algorithm or a particle filter algorithm, and correct the local turbulence intensity coefficient or heat exchange efficiency constant in the digital twin model in real time. The parallel simulation and extrapolation unit, deployed on a hardware acceleration cluster, is configured to extrapolate multiple development paths into the future within each decision cycle, generating the predicted sequence of future states that includes temperature trends, pressure distribution, and streamline evolution characteristics.

5. The multi-source environmental parameter adaptive collaborative control and emergency response system according to claim 4, characterized in that, The fluid dynamics solution unit introduces a buoyancy term during the solution process to simulate the influence of natural convection on the flow field, and uses a two-equation turbulence model to describe the eddy characteristics under complex geometries. The real-time state mapping unit uses the real-time collected wall temperature as the first type of boundary condition and the inlet and outlet wind speed as the second type of boundary condition, so that the virtual model and the physical entity keep their real-time states synchronized.

6. The multi-source environmental parameter adaptive collaborative control and emergency response system according to claim 5, characterized in that, The biomimetic collaborative actuator includes: The physical execution component includes a centrifugal fan, a proportional-integral control valve, an electric heating array, and a humidification and dehumidification unit, with each physical execution component independently connected to one of the distributed execution units; A pheromone interaction virtual layer is configured to construct a virtual topology network to simulate pheromone release and sensing mechanisms. When any of the distributed execution units performs a control action, positive pheromone is released at the corresponding position in the pheromone interaction virtual layer. The positive pheromone decays exponentially over time and diffuses to adjacent nodes. The swarm intelligence coordination algorithm module is configured with path optimization logic to simulate the ant colony algorithm. Each of the distributed execution units detects the pheromone concentration gradient within its own perception range and received through the network in real time, and achieves dynamic balancing of local load by adjusting its own action intensity and timing.

7. The multi-source environmental parameter adaptive collaborative control and emergency response system according to claim 6, characterized in that, The biomimetic collaborative execution mechanism adapts to environmental fluctuations by dynamically adjusting the pheromone evaporation coefficient. When the rate of change of environmental parameters exceeds a preset fluctuation threshold, the system automatically reduces the pheromone evaporation coefficient to prolong the pheromone effect time, thereby enhancing the collaborative strength among the distributed execution units. When the environment tends to be in a steady state, the pheromone evaporation coefficient is increased, so that each of the distributed execution units can adjust based on local sensing data; The pheromone interaction virtual layer also introduces a virtual signal with a repulsive attribute. When multiple distributed execution units regulate the same area, causing a risk of system overshoot, the distributed execution units are guided to retreat in an orderly manner through the virtual signal.

8. The multi-source environmental parameter adaptive collaborative control and emergency response system according to claim 7, characterized in that, The emergency response feedback unit includes: The deviation real-time monitoring module is configured to compare the real data collected by the multi-source environmental sensing device with the simulation data generated by the digital twin modeling engine, and calculate the absolute deviation or rate of change deviation. The priority arbitration logic module is configured to determine the emergency response level based on the interval to which the absolute deviation or the rate of change deviation belongs. When the deviation is between the first preset threshold and the second preset threshold, it is determined to be a level one emergency response. When the deviation exceeds the second preset threshold, it is determined to be a level two emergency response. The response mode switching switch is configured to drive the intelligent decision-making center into fine-tuning mode to correct the strategy in the first-level emergency response state, freeze the current reinforcement learning strategy and switch to the preset safety plan library in the second-level emergency response state, and drive the digital twin modeling engine into multi-scenario parallel inference mode. The knowledge base update unit is configured to store the characteristic data, processing procedures, and final results of the abnormal event into a long short-term memory database after the emergency response is completed, for subsequent model training and policy optimization.

Images