Intelligent construction method, device, equipment and storage medium for emergency command cabin

Abstract

The present invention relates to the field of emergency command technology and discloses an intelligent construction method, device, equipment and storage medium for an emergency command square cabin. The method comprises the following steps: collecting environmental data of a current site of the emergency command square cabin, constructing a three-dimensional topological structure of the current site, constructing a dynamic model according to construction parameters, performing feedback control on the emergency command square cabin through a fuzzy control algorithm based on a square cabin construction plan to complete the construction, obtaining physical sensory information of personnel in the constructed emergency command square cabin, analyzing the physical sensory information of personnel through a weighted average method, generating a comfort adjustment strategy, and adjusting and controlling the environment in the emergency command square cabin through a Bayesian optimization algorithm based on the comfort adjustment strategy. By collecting environmental data and analyzing environmental parameters in combination with the three-dimensional topological structure, a square cabin construction plan can be quickly generated in a complex environment, rapid deployment of the emergency command square cabin can be achieved, and the construction efficiency and response speed of the emergency command center can be significantly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of emergency command, and in particular relates to an intelligent construction method, device, equipment and storage medium for an emergency command cabin. Background Art

[0002] Emergency command shelters are crucial infrastructure in emergency management systems, widely used in natural disasters, public emergencies, military operations, and other emergency rescue scenarios. With global climate change and the increasing frequency of natural disasters, there is an urgent need for efficient and rapidly deployable emergency command centers to coordinate on-site rescue operations. Traditional emergency command shelters rely primarily on manual construction and adjustment, often facing complex challenges such as volatile environments and densely populated areas, making efficient construction and environmental adjustments difficult to achieve in a short period of time. The increasing complexity of emergency command tasks places higher demands on the intelligence, rapid construction, and environmental adaptability of emergency command shelters.

[0003] Currently, most emergency command cabins are constructed using an inflatable structure to facilitate deployment. However, the deployment and storage process of an inflatable cabin usually requires the collaboration of multiple people, and the operation is complex and time-consuming, making rapid deployment impossible. Furthermore, after deployment, the inflatable cabin lacks the ability to resist environmental changes. Factors such as temperature, humidity, terrain, and wind speed can affect the cabin, leading to structural instability and causing problems for emergency management command.

[0004] Therefore, it is necessary to provide an intelligent construction method to solve the problem that the above-mentioned shelter lacks resistance to environmental changes after being deployed. Summary of the Invention

[0005] In view of this, the present invention aims to provide an intelligent construction method, device, equipment and storage medium for an emergency command cabin to solve the technical problems mentioned in the above background technology.

[0006] In order to achieve the above object, the technical solution provided by the present invention is as follows:

[0007] In a first aspect, the present invention provides an intelligent construction method for an emergency command shelter, comprising:

[0008] Collect environmental data of the current site of the emergency command shelter and pre-process the environmental data to obtain an environmental parameter set, where the environmental parameter set includes parameters that affect the construction and internal environment of the emergency command shelter;

[0009] Construct a three-dimensional topological structure of the current site, perform a hierarchical analysis on the environmental parameter set based on the three-dimensional topological structure, and obtain the construction parameters of the emergency command cabin. The construction parameters include the coordinates of the support points of the emergency command cabin and the force balance distribution data;

[0010] A dynamic model is constructed based on the construction parameters. The deployment path, deployment angle, and deployment torque of the emergency command shelter are calculated based on the dynamic model to form a shelter construction plan.

[0011] Based on the shelter construction plan, the emergency command shelter is built through feedback control using a fuzzy control algorithm. The interior space model of the emergency command shelter is reconstructed based on the three-dimensional point cloud data to obtain the interior space data of the shelter.

[0012] Obtain the sensory information of personnel in the constructed emergency command cabin. Based on the cabin's internal space data and environmental parameter set, the sensory information is analyzed using a weighted average method to generate a comfort adjustment strategy.

[0013] Based on the comfort adjustment strategy, the environment in the emergency command cabin is adjusted and controlled through the Bayesian optimization algorithm.

[0014] Furthermore, the step of collecting environmental data of the current site of the emergency command cabin and preprocessing the environmental data to obtain an environmental parameter set includes:

[0015] Collect multi-source data on the current site of the emergency command cabin. Use sensors in the emergency command cabin to obtain wind speed, surface slope, and temperature and humidity values ​​at the current site to form an original environmental data set.

[0016] Perform time series denoising on the original environmental dataset to obtain a time series smoothed environmental dataset;

[0017] The missing values ​​of the environmental dataset after time series smoothing are filled based on the K-nearest neighbor algorithm, and the future trend of the environmental dataset is predicted by combining the time series prediction method to obtain a complete environmental dataset;

[0018] The principal component analysis method is used to reduce the dimensionality of the complete environmental data set, and the wind speed, surface slope, temperature and humidity values ​​are extracted to form an environmental parameter set.

[0019] Furthermore, a three-dimensional topological structure of the current site is constructed, and a hierarchical analysis of the environmental parameter set is performed based on the three-dimensional topological structure to obtain the construction parameters of the emergency command cabin. The construction parameters include the coordinates of the support points of the emergency command cabin and the force balance distribution data. Specifically, the steps include:

[0020] Perform a three-dimensional scan of the current site to obtain the terrain data of the current site, and use the least squares method to fit the terrain data to obtain three-dimensional terrain data;

[0021] The 3D terrain data is reconstructed to obtain a 3D topological model. The height difference, slope and ground irregularity of the 3D topological model are extracted using a curvature-based terrain analysis method to obtain the terrain structure characteristics.

[0022] Based on the analytic hierarchy process, weights are assigned to the terrain structure characteristics and the environmental parameter set, and each type of environmental factor in the environmental parameter set is comprehensively scored to obtain the preliminary construction parameters of the emergency command cabin.

[0023] The preliminary construction parameters were combined with finite element analysis to simulate the mechanical characteristics of the support points of the emergency command shelter to obtain simulation results. The support point positions were optimized using the simulated annealing algorithm to obtain the optimized construction parameters.

[0024] The genetic algorithm is used to perform multiple iterations of the optimized construction parameters to obtain the optimal construction parameters.

[0025] Furthermore, a dynamic model is constructed based on the construction parameters, and the deployment path, deployment angle, and deployment torque of the emergency command shelter are calculated based on the dynamic model to form a shelter construction plan. The steps specifically include:

[0026] The Lagrange equation was used to establish the dynamic model of the emergency command cabin deployment based on the construction parameters. The moment distribution of the emergency command cabin under different forces was calculated to obtain a preliminary deployment path model.

[0027] Finite element analysis was performed on the force changes during the deployment of the emergency command cabin based on the dynamic model, and the stability conditions of the deployment of the emergency command cabin were obtained.

[0028] Based on the stability condition, the particle swarm optimization algorithm is used to optimize the expansion path and expansion angle to obtain the preliminary estimated values ​​of the expansion path and expansion angle;

[0029] Based on the preliminary estimated values ​​and combined with fluid dynamics, the deployment path and deployment angle are finely adjusted to obtain the deployment torque distribution model;

[0030] Based on the deployment path, deployment angle, and deployment torque, the force changes at each moment during the deployment of the emergency command shelter are calculated to construct a dynamic deployment plan.

[0031] Obtain real-time environmental data of the current site of the emergency command shelter, and compare and analyze the dynamic deployment plan with the real-time environmental data to obtain an optimized shelter construction plan.

[0032] Furthermore, the steps of completing the construction of the emergency command cabin by feedback control using a fuzzy control algorithm based on the cabin construction plan, and reconstructing the interior space model of the emergency command cabin after construction based on the three-dimensional point cloud data to obtain the cabin interior space data specifically include:

[0033] A fuzzy control model was constructed for the optimized shelter construction plan. Based on the fuzzy control model, the fuzzy logic reasoning method was used to control the deployment process of the emergency command shelter in real time, and a preliminary feedback control strategy was obtained.

[0034] Based on the preliminary feedback control strategy, the construction process of the emergency command cabin is adjusted in real time, and the deployment process control data is generated;

[0035] Error correction is performed on the deployment process control data to obtain corrected control data. The calculation results of the deployment path and deployment angle of the corrected control data are adjusted through a nonlinear optimization algorithm to obtain the precise deployment path and precise deployment angle to complete the construction of the emergency command cabin.

[0036] Scan the interior of the emergency command cabin after construction to obtain 3D point cloud data of the interior of the emergency command cabin, and align the 3D point cloud data from different perspectives to obtain aligned 3D point cloud data;

[0037] Convert the aligned 3D point cloud data into a 3D mesh model of the interior of the emergency command cabin to obtain an internal space model;

[0038] The internal space model is voxelized to obtain internal volume data, which is then partitioned and regionally optimized to form the final internal space data of the shelter.

[0039] Furthermore, the steps of obtaining the sensory information of the personnel in the constructed emergency command cabin, analyzing the sensory information of the personnel based on the cabin's internal space data and environmental parameter set and using a weighted average method to generate a comfort adjustment strategy include:

[0040] The sensors in the emergency command cabin collect the physical information of the personnel in the emergency command cabin, and pre-process the physical information to obtain preliminary physical data;

[0041] Perform dimensionality reduction processing on the preliminary somatosensory data to extract the personalized comfort factor of each person;

[0042] Based on the cabin's internal space data and environmental parameter set, a weighted analysis is performed on each person's personalized comfort factor to obtain a comprehensive comfort score for each person in each area of ​​the cabin's internal space data;

[0043] Cluster analysis is performed on the comprehensive comfort scores to obtain clustering results. Based on the clustering results, the cabin interior space data is partitioned to obtain comfort demand partitions.

[0044] Generate personalized environmental adjustment strategies based on comfort demand zoning, obtain adjustment parameters of the emergency command cabin, and form a comfort adjustment strategy.

[0045] Furthermore, the steps of regulating and controlling the environment in the emergency command cabin based on the comfort adjustment strategy and using the Bayesian optimization algorithm specifically include:

[0046] Quantify the comfort adjustment strategy to obtain the initial estimated value of the adjustment parameter;

[0047] A Bayesian optimization model is established for the initial estimated values ​​of the adjustment parameters to obtain the optimized adjustment parameters, and a probability distribution function model is constructed for the optimized adjustment parameters to obtain the prior distribution;

[0048] Iteratively update the prior distribution through Bayesian optimization to generate the posterior distribution to form a high-comfort adjustment parameter;

[0049] The constrained optimization algorithm is used to perform multivariable constraint processing on the high comfort adjustment parameters to obtain the final adjustment parameters;

[0050] A real-time adjustment plan is generated according to the final adjustment parameters, and the operation of the emergency command cabin is controlled based on the real-time adjustment plan.

[0051] In a second aspect, the present invention provides an intelligent construction device for an emergency command shelter, comprising:

[0052] The acquisition module is used to collect environmental data of the current site of the emergency command cabin and pre-process the environmental data to obtain an environmental parameter set, wherein the environmental parameter set includes parameters that affect the construction and internal environment of the emergency command cabin;

[0053] The first analysis module is used to construct a three-dimensional topological structure of the current site. Based on the three-dimensional topological structure, a hierarchical analysis is performed on the environmental parameter set to obtain the construction parameters of the emergency command cabin. The construction parameters include the coordinates of the support points of the emergency command cabin and the force balance distribution data;

[0054] The calculation module is used to build a dynamic model based on the construction parameters, calculate the deployment path, deployment angle and deployment torque of the emergency command cabin based on the dynamic model, and form a cabin construction plan;

[0055] The construction module is used to complete the construction of the emergency command cabin through feedback control using a fuzzy control algorithm based on the cabin construction plan, and reconstruct the interior space model of the emergency command cabin after construction based on the three-dimensional point cloud data to obtain the cabin interior space data;

[0056] The second analysis module is used to obtain the physical sensation information of the personnel in the emergency command cabin after construction. Based on the cabin's internal space data and environmental parameter set, the personnel physical sensation information is analyzed using the weighted average method to generate a comfort adjustment strategy;

[0057] The control module is used to adjust and control the environment in the emergency command cabin based on the comfort adjustment strategy and the Bayesian optimization algorithm.

[0058] In a third aspect, the present invention provides a computer device, comprising a processor and a memory:

[0059] The memory is used to store computer programs and send instructions of the computer programs to the processor;

[0060] The processor executes the intelligent construction method of an emergency command cabin according to the instructions of the computer program.

[0061] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, a method for intelligently constructing an emergency command cabin as described in the first aspect is implemented.

[0062] In summary, in the present invention, by collecting and pre-processing environmental data and analyzing environmental parameters in combination with a three-dimensional topological structure, it is possible to quickly and accurately generate a plan for building a cabin under complex natural environmental conditions, and to achieve rapid deployment of the emergency command cabin in the shortest possible time, significantly improving the construction efficiency and response speed of the emergency command center. At the same time, a dynamic model and fuzzy control algorithm are used to feedback control the cabin construction process, ensuring the structural stability of the cabin in various complex environments and reducing safety risks caused by environmental changes. In addition, through a comprehensive analysis based on environmental parameters and internal space data, the environment inside the emergency command cabin is dynamically adjusted to optimize the physical comfort of the personnel. The physical information of the personnel is analyzed by the weighted average method, and the environmental adjustment control is performed in combination with the Bayesian optimization algorithm. The internal environmental factors such as temperature and humidity in the cabin are intelligently adjusted according to different emergency scenarios and personnel needs to improve the comfort inside the cabin. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0064] Figure 1 A schematic diagram of the steps of an intelligent construction method for an emergency command shelter provided by an embodiment of the present invention;

[0065] Figure 2 A schematic structural diagram of an emergency command cabin provided by one embodiment of the present invention;

[0066] Figure 3 A schematic block diagram of the structure of an intelligent construction device for an emergency command shelter provided by one embodiment of the present invention;

[0067] Figure 4 A schematic block diagram of the structure of a computer device provided in one embodiment of the present invention;

[0068] Including: 1. Shell; 2. Bracket; 3. Display screen; 4. Folding table top. DETAILED DESCRIPTION

[0069] In order to make the purposes, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0070] See also Figure 1 The embodiment of the present invention provides an intelligent construction method for an emergency command cabin, comprising:

[0071] S100: Collecting environmental data of the current site of the emergency command cabin and preprocessing the environmental data to obtain an environmental parameter set, wherein the environmental parameter set includes parameters that affect the construction and internal environment of the emergency command cabin;

[0072] In step S100, sensors collect environmental data from the emergency command shelter's current site, which may include key parameters such as wind speed, ground slope, and temperature and humidity. Wind speed data helps assess the impact of airflow on the shelter's stability after construction. Ground slope reflects the terrain's requirements for the shelter's support structure and mechanical distribution. Temperature and humidity influence the need for adjusting the comfort level inside the shelter. These data can be categorized as parameters affecting the emergency command shelter's construction and internal environment. The collected data is filtered and corrected using a preprocessing algorithm to eliminate unrealistic data and ensure accuracy and representativeness. Wind speed data may be affected by sudden changes in transients, so a sliding average method is used for smoothing to ensure greater stability. Slope data may contain measurement errors and requires coordinate alignment and error correction. Temperature and humidity data require trend analysis combined with historical data to ensure the rationality of subsequent environmental adjustments. The resulting set of environmental parameters will serve as input for subsequent steps, providing data support for the calculation of construction parameters.

[0073] S200: Construct a three-dimensional topological structure of the current site, perform a hierarchical analysis on the environmental parameter set based on the three-dimensional topological structure, and obtain construction parameters of the emergency command cabin. The construction parameters include the support point coordinates and force balance distribution data of the emergency command cabin.

[0074] In step S200, based on the environmental parameter set collected in the previous step, the shelter construction site is scanned using scanning technologies such as LiDAR to obtain high-precision three-dimensional terrain data. Point cloud processing algorithms segment and extract features from the terrain data to construct the site's three-dimensional topology. Once the topology model is complete, the system combines the environmental parameters with the terrain model and uses the Analytic Hierarchy Process (AHP) to assess the effects of wind speed, slope, temperature, and humidity to determine the optimal coordinates for the shelter support points. Force distribution data is also calculated at this stage. The system analyzes the bearing capacity of the ground surface and optimizes the distribution of support structures based on wind speed conditions to reduce construction risks caused by uneven forces.

[0075] S300: Construct a dynamic model based on the construction parameters, calculate the deployment path, deployment angle, and deployment torque of the emergency command cabin based on the dynamic model, and form a cabin construction plan.

[0076] In step S300, a dynamic model of the cabin is established based on the construction parameters to simulate the stress conditions during the cabin's deployment. The dynamic modeling adopts the principle of rigid body dynamics and calculates the mechanical equilibrium of each part of the cabin under different deployment states through the Lagrange equation or the Newton-Euler equation. In order to optimize the deployment process, the system introduces a path planning algorithm to calculate the optimal deployment path for the cabin, ensuring that each structural unit is deployed in the optimal order to avoid mutual interference or instability caused by an unreasonable deployment order. In addition, an optimization algorithm is used to solve the deployment angle and torque to reduce energy loss during the deployment process and ensure the structural stability of the cabin after deployment. After the calculation is completed, the system generates a construction plan for the cabin, including key parameters such as the deployment path, deployment angle, and support point force data, providing guidance for actual implementation.

[0077] S400: Based on the shelter construction plan, the emergency command shelter is constructed by feedback control using a fuzzy control algorithm, and the interior space model of the constructed emergency command shelter is reconstructed based on the three-dimensional point cloud data to obtain the interior space data of the shelter.

[0078] In step S400, the expansion mechanism in the cabin is controlled to execute the construction process based on the cabin construction plan, such as a hydraulic drive device or an electric drive. During the expansion process, the state of the cabin is monitored in real time, including the expansion angle, force conditions and the stability of the support points. When the system detects that the actual expansion situation deviates from the preset plan, the expansion speed, torque and sequence are dynamically adjusted through the fuzzy control algorithm to ensure the stability of the construction process. For example, when the force on a certain support point exceeds the threshold, the system will adjust the expansion sequence or add additional support to optimize the force distribution. In addition, after the construction is completed, the system uses three-dimensional point cloud scanning technology to reconstruct the point cloud of the completed cabin to generate a three-dimensional model of the internal space, that is, to obtain the cabin internal space data.

[0079] S500: Acquire the physical sensation information of the personnel in the constructed emergency command cabin, analyze the physical sensation information of the personnel based on the cabin internal space data and environmental parameter set, and use the weighted average method to generate a comfort adjustment strategy.

[0080] In step S500, based on the cabin's internal spatial data and a set of environmental parameters, sensors within the emergency command cabin collect sensory data from cabin occupants, such as skin temperature, heart rate, and humidity perception. By analyzing this sensory data, the system uses a weighted average method to calculate a comprehensive comfort index and formulates a comfort adjustment strategy accordingly. For example, in a hot environment, if most occupants have high skin temperature and strong humidity perception, the system will increase the wind speed or lower the temperature. In a cold environment, it will increase the temperature or reduce the wind speed. Furthermore, the system will optimize the ventilation system's operating mode based on the air flow within the cabin to maintain air quality.

[0081] S600: Based on the comfort adjustment strategy and using the Bayesian optimization algorithm, the environment inside the emergency command cabin is adjusted and controlled.

[0082] In step S600, the comfort adjustment strategy dynamically adjusts environmental parameters such as temperature, humidity, and wind speed within the cabin using a Bayesian optimization algorithm. The system leverages historical data and real-time monitoring data to continuously update the adjustment model, achieving optimal environmental control with minimal energy consumption. For example, when adjusting the air conditioning system, the system predicts the impact of different temperature settings on comfort based on current temperature and humidity conditions and selects the optimal setting. Furthermore, if the environment changes, such as a drop in external temperature or an increase in wind speed, the system automatically adjusts the adjustment strategy to ensure the cabin's internal environment remains optimal. The introduction of the Bayesian optimization algorithm enables continuous learning and optimization of the environmental adjustment process, improving adjustment efficiency and reducing unnecessary energy consumption.

[0083] refer to Figure 2In one embodiment, an emergency command cabin includes an outer shell 1, a bracket 2, a display screen 3, a folding table 4, a tent, and a support frame. The outer shell 1 is connected to the bracket 2 via a hinge assembly, and the bracket 2 is connected to the support frame via a telescopic connector, allowing the bracket 2 to be hydraulically deployed and support the overall structure of the cabin. A hydraulic cylinder is provided between the bracket 2 and the support frame, and the telescopic action of the hydraulic cylinder controls the expansion and contraction of the bracket 2. The lower end of the support frame is provided with a rotatable fixed base, which is connected to the ground support structure via a universal hinge, allowing the support frame to adjust its angle to adapt to different terrain conditions and ensure the stability of the cabin.

[0084] The display screen 3 is mounted inside the housing 1 and connected to the bracket 2 via an adjustable rotating arm. The joint structure of the rotating arm is controlled by an electric drive, allowing the display screen 3 to adjust its angle and height according to the needs of the commander. A switch door is also provided on the outside of the housing 1, which can be opened and closed according to the needs of use. The folding table 4 is set on the inner side wall of the cabin and is connected to the side wall via a hinge mechanism. It is equipped with a gas spring support. When in use, the table can be slowly unfolded by releasing the locking device and supported in a horizontal state by the gas spring. When folded, the table can be returned to the folded state and locked by lightly pressing the table. The tent part consists of a flexible support rod and a folding mechanism. The flexible support rod is connected to the support frame via a slide rail and is controlled to unfold and fold by a motor-driven retracting device, ensuring that the tent can automatically cover the outer area of ​​the cabin and that the display screen 3 and the folding table 4 are both enclosed within the tent.

[0085] During deployment, the shelter deploys by first activating a hydraulic cylinder through the electronic control system, causing the support frame 2 to unfold synchronously along the telescopic connector. The hydraulic cylinder propels the support frame upward, while the universal hinge adjusts to the ground angle, ensuring stable support for the shelter in varying terrain conditions. As the support frame 2 fully unfolds, the fixed locking mechanism automatically tightens, allowing the support frame 2 and the support frame to form a stable load-bearing frame. Subsequently, the motor drives the tent rails to unfold, allowing the tent to cover the shelter structure, creating an enclosed command space. Simultaneously, the rotating arm of the display screen 3 electrically adjusts its angle, allowing the commander to view information from the optimal viewing angle. Once unlocked, the folding table 4 automatically and slowly unfolds, forming a work surface. When stowed, the system sequentially controls the tent's retraction, the folding of the table, and the return of the display screen 3. The hydraulic cylinder retracts the support frame 2, gradually returning the shelter to a compact state. Finally, the fixed base locks the structure, facilitating transport and mobile deployment.

[0086] In one embodiment, the steps of collecting environmental data of the current site of the emergency command cabin and preprocessing the environmental data to obtain an environmental parameter set, wherein the environmental parameter set includes wind speed, surface slope, and temperature and humidity values, specifically include:

[0087] Collect multi-source data on the current site of the emergency command cabin. Use sensors in the emergency command cabin to obtain wind speed, surface slope, and temperature and humidity values ​​at the current site to form an original environmental data set.

[0088] Perform time series denoising on the original environmental dataset to obtain a time series smoothed environmental dataset;

[0089] The missing values ​​of the environmental dataset after time series smoothing are filled based on the K-nearest neighbor algorithm, and the future trend of the environmental dataset is predicted by combining the time series prediction method to obtain a complete environmental dataset;

[0090] The principal component analysis method is used to reduce the dimensionality of the complete environmental data set, and the wind speed, surface slope, temperature and humidity values ​​are extracted to form an environmental parameter set.

[0091] In the above embodiment, the emergency command cabin's sensor system first collects multi-source data from the current site, acquiring a raw environmental dataset of wind speed, surface slope, and temperature and humidity. This data is collected in real time by various sensors and reflects environmental changes in the area where the cabin is located. Next, the collected raw environmental dataset undergoes time-series denoising to eliminate transient fluctuations caused by equipment errors or external interference, making the data smoother and more reliable, ensuring the accuracy of subsequent analysis. After denoising, the smoothed environmental dataset is filled with missing values ​​using a K-nearest neighbor algorithm. This effectively replaces data lost due to sensor failures or data transmission issues, ensuring the integrity of the environmental data. Simultaneously, combined with time series prediction methods, future trends are predicted for this dataset, providing a long-term perspective for subsequent decision-making, identifying possible environmental change trends in advance, and obtaining a complete environmental dataset. Finally, principal component analysis is used to reduce the dimensionality of the complete environmental dataset, extracting key environmental parameters such as wind speed, surface slope, and temperature and humidity to form the final set of environmental parameters. Through this series of data processing steps, the impact of noise and data loss on environmental analysis results can be effectively reduced, the accuracy and reliability of the data can be improved, and a more accurate and comprehensive basis can be provided for the subsequent construction of the shelter, environmental adjustment and emergency command, thereby greatly improving the efficiency and quality of emergency response.

[0092] In one example, a three-dimensional topological structure of the current site is constructed, and a hierarchical analysis is performed on the environmental parameter set based on the three-dimensional topological structure to obtain the construction parameters of the emergency command cabin. The construction parameters include the coordinates of the support points of the emergency command cabin and the force balance distribution data. The steps specifically include:

[0093] Perform a three-dimensional scan of the current site to obtain the terrain data of the current site, and use the least squares method to fit the terrain data to obtain three-dimensional terrain data;

[0094] The 3D terrain data is reconstructed to obtain a 3D topological model. The height difference, slope and ground irregularity of the 3D topological model are extracted using a curvature-based terrain analysis method to obtain the terrain structure characteristics.

[0095] Based on the analytic hierarchy process, weights are assigned to the terrain structure characteristics and the environmental parameter set, and each type of environmental factor in the environmental parameter set is comprehensively scored to obtain the preliminary construction parameters of the emergency command cabin.

[0096] The preliminary construction parameters were combined with finite element analysis to simulate the mechanical characteristics of the support points of the emergency command shelter to obtain simulation results. The support point positions were optimized using the simulated annealing algorithm to obtain the optimized construction parameters.

[0097] The genetic algorithm is used to perform multiple iterations of the optimized construction parameters to obtain the optimal construction parameters.

[0098] In the above embodiment, a three-dimensional scan of the current site is performed, using lidar or drone aerial photography technology to obtain site terrain data. This data includes site elevation information and detailed surface variations, accurately reflecting the site's undulations and topographical features. The acquired terrain data is then fitted using the least squares method, minimizing the error between the data and the fitted curve to find the most appropriate fitting model. This fitting result provides the basis for subsequent three-dimensional terrain reconstruction, ensuring that the resulting terrain data closely resembles the actual ground shape. Reconstructing the fitted three-dimensional terrain data yields a detailed three-dimensional topological model. This model accurately reflects the site's topographical features, such as elevation differences, slopes, and ground irregularities. To extract topographical features, a curvature-based terrain analysis method is employed. Curvature values ​​are calculated to identify surface variations, including elevation differences and slopes. This allows the system to identify areas within the site that may impact shelter support, particularly areas with significant slopes or irregular terrain, which require additional support optimization to ensure shelter stability.

[0099] Based on the terrain structural characteristics and a set of environmental parameters, the analytic hierarchy process (AHP) was used to assign weights. Through a comprehensive evaluation of these parameters, the importance of these parameters to the shelter's stability during construction was determined. In this step, the environmental parameter set includes data such as wind speed, surface slope, and temperature and humidity. The terrain structural characteristics provide the topographical basis for the influence of these parameters. By weighting these parameters, the system assigns appropriate weights to each environmental factor (such as wind speed, temperature, and humidity) and generates a comprehensive score to assess the relative importance of each factor in the shelter's construction. Based on the preliminary construction parameters, the finite element analysis method was used to simulate the mechanical properties of the shelter's support points. By discretizing the structure, the stresses acting on the shelter during construction were simulated. This method provides the mechanical response of each support point under different environmental conditions, such as force distribution and stress concentration. The simulations predict the shelter's structural performance under varying wind speeds, surface slope, and other factors. Based on these results, a simulated annealing algorithm was used to optimize the support point positions. Based on the optimized support point positions and force balance data, a genetic algorithm was used for multi-objective optimization. Through multiple iterations and selection operations, the genetic algorithm continuously optimizes building parameters, focusing on multiple objectives such as optimal support point placement, mechanical stability, and building speed. In each iteration, the system evaluates the effectiveness of the current building parameters and selects the best-performing individuals for reproduction and mutation, generating a more optimal building solution.

[0100] In one embodiment, the steps of constructing a dynamic model based on the construction parameters, calculating the deployment path, deployment angle, and deployment torque of the emergency command shelter based on the dynamic model, and forming a shelter construction plan specifically include:

[0101] The Lagrange equation was used to establish the dynamic model of the emergency command cabin deployment based on the construction parameters. The moment distribution of the emergency command cabin under different forces was calculated to obtain a preliminary deployment path model.

[0102] Finite element analysis was performed on the force changes during the deployment of the emergency command cabin based on the dynamic model, and the stability conditions of the deployment of the emergency command cabin were obtained.

[0103] Based on the stability condition, the particle swarm optimization algorithm is used to optimize the expansion path and expansion angle to obtain the preliminary estimated values ​​of the expansion path and expansion angle;

[0104] Based on the preliminary estimated values ​​and combined with fluid dynamics, the deployment path and deployment angle are finely adjusted to obtain the deployment torque distribution model;

[0105] Based on the deployment path, deployment angle, and deployment torque, the force changes at each moment during the deployment of the emergency command shelter are calculated to construct a dynamic deployment plan.

[0106] Obtain real-time environmental data of the current site of the emergency command shelter, and compare and analyze the dynamic deployment plan with the real-time environmental data to obtain an optimized shelter construction plan.

[0107] In the above-described embodiment, a dynamic model of the emergency command cabin deployment was established using the Lagrange equations based on the construction parameters to analyze the torque distribution of the cabin under different forces. This model modeled the mass, moment of inertia, and external forces of each cabin structural unit, solving its motion trajectory and stress state during deployment, thereby obtaining a preliminary deployment path model. On this basis, finite element analysis was further used to simulate the force changes during deployment to assess the cabin's structural stability at each stage and identify potential stress concentration areas and weak points. Finite element analysis can provide high-precision structural response calculations, ensuring that the cabin remains stable in complex environments and avoiding deformation or structural instability caused by local overloads during deployment. Based on the finite element analysis results, the stability conditions for the cabin deployment are determined, providing a constraint basis for the subsequent optimization of the deployment path and deployment angle.

[0108] After obtaining preliminary stability conditions, the particle swarm optimization algorithm was used to optimize the deployment path and angle. This algorithm comprehensively considered factors such as minimizing energy consumption, minimizing deployment time, and mechanical balance, resulting in preliminary estimates of the deployment path and angle. Next, combined with fluid dynamics analysis, wind loads on the shelter during deployment were corrected to ensure that wind speed fluctuations would not cause structural vibration or deployment deviation. The deployment path and angle were then fine-tuned, ultimately resulting in a deployment moment distribution model. Based on this, the force variations in each component of the shelter at each moment during deployment were calculated, and a dynamic deployment plan was constructed to ensure a smoother and safer deployment process. Finally, environmental data for the shelter's current site was collected in real time and compared with the dynamic deployment plan. The plan's parameters were adjusted to adapt the shelter to current environmental conditions, such as wind speed and slope variations, during actual construction. Ultimately, an optimized shelter construction plan was generated. This comprehensive optimization process enables stable and efficient automated deployment of the shelter in diverse environments, improving the reliability and deployment efficiency of emergency response.

[0109] Furthermore, the calculation expression of the above embodiment is:

[0110]

[0111] in, represents the optimized shelter construction plan, and argmin represents the process of finding the optimized shelter construction plan. represents the expansion angle, Represents the expansion path, and They represent the start and end time of the deployment of the emergency command cabin respectively; 、 and Respectively represent weight coefficients, which are used to adjust the influence of different optimization objectives; Represents the time derivative, which is used to express the rate of change of a variable over time and acts on The above is used to calculate the rate of change of the dynamic error, Represents the Lagrangian function pair The gradient reflects the impact of the angle changes of the components of the emergency command cabin on the mechanical properties of the system. for The derivative of , that is, the rate at which the expansion angle changes with time, is the angular velocity, represents the external torque; Represents the force distribution calculated by finite element analysis; Represents the optimal force distribution under stability conditions; represents the optimal expansion path.

[0112] In one embodiment, the steps of completing the construction of the emergency command shelter by feedback control using a fuzzy control algorithm based on the shelter construction plan, and reconstructing the interior space model of the constructed emergency command shelter based on the three-dimensional point cloud data to obtain the shelter interior space data specifically include:

[0113] A fuzzy control model was constructed for the optimized shelter construction plan. Based on the fuzzy control model, the fuzzy logic reasoning method was used to control the deployment process of the emergency command shelter in real time, and a preliminary feedback control strategy was obtained.

[0114] Based on the preliminary feedback control strategy, the construction process of the emergency command cabin is adjusted in real time, and the deployment process control data is generated;

[0115] Error correction is performed on the deployment process control data to obtain corrected control data. The calculation results of the deployment path and deployment angle of the corrected control data are adjusted through a nonlinear optimization algorithm to obtain the precise deployment path and precise deployment angle to complete the construction of the emergency command cabin.

[0116] Scan the interior of the emergency command cabin after construction to obtain 3D point cloud data of the interior of the emergency command cabin, and align the 3D point cloud data from different perspectives to obtain aligned 3D point cloud data;

[0117] Convert the aligned 3D point cloud data into a 3D mesh model of the interior of the emergency command cabin to obtain an internal space model;

[0118] The internal space model is voxelized to obtain internal volume data, which is then partitioned and regionally optimized to form the final internal space data of the shelter.

[0119] In the above embodiment, based on the optimized shelter construction plan, a fuzzy control algorithm is used to perform real-time feedback control on the deployment process of the emergency command shelter to ensure the efficiency and accuracy of the shelter construction process. After constructing a fuzzy control model, based on the model, the fuzzy logic reasoning method is used to adjust the shelter deployment process in real time to generate a preliminary feedback control strategy. These strategies dynamically adjust the construction process based on real-time data to ensure that the deployment path and angle of each component of the shelter are consistent with the predetermined target. By further optimizing the preliminary feedback control strategy, a nonlinear optimization algorithm is used to perform error correction on the deployment path and deployment angle to obtain an accurate deployment path and angle, thereby controlling the precise construction of the shelter.

[0120] After the shelter was completed, in order to further obtain the internal spatial data of the emergency command shelter, the interior of the completed shelter was scanned to obtain 3D point cloud data. The 3D point cloud data from different perspectives was aligned to ensure data accuracy. The aligned 3D point cloud data was converted into a 3D mesh model of the shelter's interior, thereby reconstructing the internal spatial structure of the shelter. Voxel processing technology was used to convert the 3D mesh model into detailed internal volume data, which was then regionalized and optimized to form the final internal spatial data of the shelter. This not only accurately captures the internal structure of the shelter but also provides reliable data support for subsequent space utilization and optimization, ensuring that the emergency command shelter has a good spatial layout and functionality after its completion.

[0121] In one embodiment, the steps of obtaining sensory information of personnel in the constructed emergency command cabin, analyzing the sensory information of personnel using a weighted average method based on the cabin's internal space data and a set of environmental parameters, and generating a comfort adjustment strategy specifically include:

[0122] The sensors in the emergency command cabin collect the physical information of the personnel in the emergency command cabin, and pre-process the physical information to obtain preliminary physical data;

[0123] Perform dimensionality reduction processing on the preliminary somatosensory data to extract the personalized comfort factor of each person;

[0124] Based on the cabin's internal space data and environmental parameter set, a weighted analysis is performed on each person's personalized comfort factor to obtain a comprehensive comfort score for each person in each area of ​​the cabin's internal space data;

[0125] Cluster analysis is performed on the comprehensive comfort scores to obtain clustering results. Based on the clustering results, the cabin interior space data is partitioned to obtain comfort demand partitions.

[0126] Generate personalized environmental adjustment strategies based on comfort demand zoning, obtain adjustment parameters of the emergency command cabin, and form a comfort adjustment strategy.

[0127] In the above embodiment, by obtaining the physical information of the personnel inside the emergency command cabin after construction, and combining the internal space data of the cabin and the set of environmental parameters, the weighted average method is used to analyze the physical information of the personnel to generate an optimized comfort adjustment strategy. Specifically, the physical information of the personnel inside the cabin is collected in real time using sensors arranged inside the cabin, including multiple comfort-related parameters such as temperature, humidity, and air quality, and these data are pre-processed to remove outliers and noise to obtain preliminary physical data. On this basis, the preliminary physical data is subjected to dimensionality reduction processing based on the physiological characteristics and preferences of different individuals, and the personalized comfort factor of each person is extracted, thereby ensuring that the comfort needs of different personnel can be taken into account during the analysis process. Based on the internal space data of the cabin and the set of environmental parameters, the personalized comfort factor is weightedly analyzed to calculate the comprehensive comfort score of different areas inside the cabin, ensuring that the comfort assessment of different locations is more accurate.

[0128] To further optimize the cabin's internal environmental conditioning, a cluster analysis method was used to classify the comprehensive comfort scores, identifying the comfort characteristics of different personnel groups. Based on this, the cabin's interior space was divided into zones, forming comfort demand zones. Based on these zones, and combining the characteristics of the personnel and environmental parameters within each zone, a personalized environmental conditioning strategy was generated, including air conditioning temperature adjustment, air circulation optimization, and lighting brightness adjustment, to enhance overall comfort. Based on the generated comfort conditioning strategy, the adjustment parameters for the emergency command cabin were output, forming a comfort conditioning strategy that intelligently adjusts the cabin's internal environment to the personnel's needs. This achieves intelligent optimization of the emergency command cabin's internal environment and helps improve personnel comfort during prolonged work environments.

[0129] In one embodiment, the steps of adjusting and controlling the environment in the emergency command cabin based on the comfort adjustment strategy and using the Bayesian optimization algorithm specifically include:

[0130] Quantify the comfort adjustment strategy to obtain the initial estimated value of the adjustment parameter;

[0131] A Bayesian optimization model is established for the initial estimated values ​​of the adjustment parameters to obtain the optimized adjustment parameters, and a probability distribution function model is constructed for the optimized adjustment parameters to obtain the prior distribution;

[0132] Iteratively update the prior distribution through Bayesian optimization to generate the posterior distribution to form a high-comfort adjustment parameter;

[0133] The constrained optimization algorithm is used to perform multivariable constraint processing on the high comfort adjustment parameters to obtain the final adjustment parameters;

[0134] A real-time adjustment plan is generated according to the final adjustment parameters, and the operation of the emergency command cabin is controlled based on the real-time adjustment plan.

[0135] In the above embodiment, the comfort adjustment strategy is quantified to obtain initial estimates of the adjustment parameters, representing the initial adjustment levels of various environmental factors within the cabin, such as temperature, humidity, and air circulation. Using Bayesian optimization to establish an optimization model, further optimizing these initial adjustment parameters can predict and update the probability distribution function of the adjustment parameters, resulting in a prior distribution that reflects the possible value range of each adjustment parameter under current environmental conditions.

[0136] Through the iterative update process of Bayesian optimization, a posterior distribution is generated based on the prior distribution, thereby deriving a set of high-comfort adjustment parameters. The advantage of Bayesian optimization is that by continuously iteratively updating the prior and posterior distributions, it can provide more precise adjustment values ​​in environments with high uncertainty, ultimately achieving the goal of optimizing comfort. A constrained optimization algorithm is used to apply multivariate constraints to these high-comfort adjustment parameters to ensure that the environmental adjustment solution can meet various constraints in actual operation. Ultimately, the optimized and constrained adjustment parameters are generated, and a real-time adjustment solution is formulated based on the final adjustment parameters, thereby achieving real-time control of the cabin environment.

[0137] Reference Figure 3 The embodiment of the present invention further provides an intelligent construction device for an emergency command cabin, comprising:

[0138] The acquisition module 100 is used to collect environmental data of the current site of the emergency command cabin and pre-process the environmental data to obtain an environmental parameter set, wherein the environmental parameter set includes parameters that affect the construction and internal environment of the emergency command cabin;

[0139] The first analysis module 200 is used to construct a three-dimensional topological structure of the current site, and perform hierarchical analysis on the environmental parameter set based on the three-dimensional topological structure to obtain the construction parameters of the emergency command cabin, which include the coordinates of the support points of the emergency command cabin and the force balance distribution data;

[0140] The calculation module 300 is used to construct a dynamic model based on the construction parameters, calculate the deployment path, deployment angle, and deployment torque of the emergency command shelter based on the dynamic model, and form a shelter construction plan;

[0141] The construction module 400 is used to complete the construction of the emergency command cabin by feedback control based on the cabin construction plan through a fuzzy control algorithm, and reconstruct the interior space model of the emergency command cabin after construction based on the three-dimensional point cloud data to obtain the cabin interior space data;

[0142] The second analysis module 500 is used to obtain the physical sensation information of the personnel in the emergency command cabin after construction, analyze the physical sensation information of the personnel based on the cabin internal space data and the environmental parameter set, and generate a comfort adjustment strategy;

[0143] The control module 600 is used to adjust and control the environment in the emergency command cabin based on the comfort adjustment strategy and through the Bayesian optimization algorithm.

[0144] In this embodiment, by collecting and preprocessing environmental data and analyzing environmental parameters in combination with a three-dimensional topological structure, it is possible to quickly and accurately generate a plan for building a shelter under complex natural environmental conditions, and to achieve rapid deployment of the emergency command shelter in the shortest possible time, significantly improving the construction efficiency and response speed of the emergency command center. At the same time, a dynamic model and fuzzy control algorithm are used to feedback control the shelter construction process, ensuring the structural stability of the shelter in various complex environments and reducing safety risks caused by environmental changes. In addition, through a comprehensive analysis based on environmental parameters and internal space data, the environment inside the emergency command shelter is dynamically adjusted to optimize the physical comfort of personnel. The weighted average method is used to analyze the physical information of personnel, and the Bayesian optimization algorithm is used for environmental adjustment and control. The temperature and humidity and other internal environmental factors in the shelter are intelligently adjusted according to different emergency scenarios and personnel needs to improve the comfort inside the shelter.

[0145] Reference Figure 4 In the embodiment of the present application, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as the database of the emergency command cabin. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for intelligently building an emergency command cabin is realized.

[0146] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, an intelligent construction method for an emergency command cabin is implemented, comprising the steps of: collecting environmental data of the current site of the emergency command cabin, and pre-processing the environmental data to obtain an environmental parameter set, wherein the environmental parameter set includes wind speed, surface slope, and temperature and humidity values; constructing a three-dimensional topological structure of the current site, and performing a hierarchical analysis on the environmental parameter set based on the three-dimensional topological structure to obtain construction parameters of the emergency command cabin, wherein the construction parameters include the coordinates of the support points of the emergency command cabin and force balance distribution data; and constructing a dynamic model according to the construction parameters. According to the dynamic model, the deployment path, deployment angle and deployment torque of the emergency command cabin are calculated to form a cabin construction plan; based on the cabin construction plan, the emergency command cabin is feedback controlled by the fuzzy control algorithm to complete the construction, and the internal space model of the emergency command cabin after construction is reconstructed based on the three-dimensional point cloud data to obtain the cabin internal space data; the physical sensation information of the personnel in the emergency command cabin after construction is obtained, based on the cabin internal space data and the environmental parameter set, and the weighted average method is used to analyze the physical sensation information of the personnel to generate a comfort adjustment strategy; based on the comfort adjustment strategy, the environment in the emergency command cabin is adjusted and controlled by the Bayesian optimization algorithm.

[0147] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media provided herein and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0148] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0149] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0150] In the embodiments disclosed in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0151] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An intelligent construction method for an emergency command shelter, characterized in that: include: Collecting environmental data of the current site of the emergency command cabin and preprocessing the environmental data to obtain an environmental parameter set, wherein the environmental parameter set includes parameters that affect the construction and internal environment of the emergency command cabin; Constructing a three-dimensional topological structure of the current site, and performing a hierarchical analysis on the set of environmental parameters based on the three-dimensional topological structure to obtain construction parameters of the emergency command cabin, wherein the construction parameters include support point coordinates and force balance distribution data of the emergency command cabin; Constructing a dynamic model based on the construction parameters, and calculating the deployment path, deployment angle, and deployment torque of the emergency command cabin based on the dynamic model to form a cabin construction plan; Based on the shelter construction plan, the emergency command shelter is feedback-controlled by a fuzzy control algorithm to complete the construction, and an interior space model of the emergency command shelter after construction is reconstructed based on the three-dimensional point cloud data to obtain the interior space data of the shelter; Acquire the physical sensation information of the personnel in the emergency command cabin after construction, analyze the physical sensation information of the personnel based on the cabin internal space data and the set of environmental parameters and adopt a weighted average method to generate a comfort adjustment strategy; Based on the comfort adjustment strategy and using a Bayesian optimization algorithm, the environment in the emergency command cabin is adjusted and controlled; The steps of constructing a three-dimensional topological structure of the current site, performing hierarchical analysis on the set of environmental parameters based on the three-dimensional topological structure, and obtaining construction parameters of the emergency command cabin, wherein the construction parameters include support point coordinates and force balance distribution data of the emergency command cabin, specifically include: Performing a three-dimensional scan on the current site to obtain terrain data of the current site, and fitting the terrain data using a least squares method to obtain three-dimensional terrain data; Reconstructing the three-dimensional terrain data to obtain a three-dimensional topological model, and extracting the height difference, slope and ground irregularity of the three-dimensional topological model using a curvature-based terrain analysis method to obtain terrain structural features; Based on the hierarchical analysis method, weights are assigned to the terrain structure characteristics and the environmental parameter set, and each type of environmental factor in the environmental parameter set is comprehensively scored to obtain the preliminary construction parameters of the emergency command cabin; The preliminary construction parameters are combined with finite element analysis to simulate the mechanical characteristics of the support points of the emergency command shelter to obtain simulation results, and the support point positions of the simulation results are optimized by a simulated annealing algorithm to obtain optimized construction parameters; The optimized construction parameters are iterated multiple times with multiple objectives by using a genetic algorithm to obtain the optimal construction parameters.

2. The intelligent construction method of the emergency command shelter according to claim 1 is characterized in that: The step of collecting environmental data of the current site of the emergency command cabin and preprocessing the environmental data to obtain an environmental parameter set includes: Perform multi-source data collection on the environmental data of the current site of the emergency command cabin, and obtain the wind speed, surface slope, and temperature and humidity values ​​of the current site based on the sensors of the emergency command cabin to form an original environmental data set; Performing time series denoising on the original environmental data set to obtain a time series smoothed environmental data set; Filling missing values ​​in the environmental dataset after time series smoothing based on the K-nearest neighbor algorithm, and predicting the future trend of the environmental dataset in combination with the time series prediction method to obtain a complete environmental dataset; The principal component analysis method is used to reduce the dimension of the complete environmental data set, and the wind speed, surface slope, temperature and humidity values ​​are extracted to form an environmental parameter set.

3. The intelligent construction method of an emergency command shelter according to claim 1, characterized in that: The steps of constructing a dynamic model according to the construction parameters, calculating the deployment path, deployment angle, and deployment torque of the emergency command shelter according to the dynamic model, and forming a shelter construction plan specifically include: A dynamic model of the deployment of the emergency command cabin is established using the Lagrange equation for the construction parameters, and the moment distribution of the emergency command cabin under different forces is calculated to obtain a preliminary deployment path model; Performing finite element analysis on the force changes during the deployment of the emergency command cabin according to the dynamic model to obtain stability conditions for the deployment of the emergency command cabin; Based on the stability condition, the deployment path and the deployment angle are optimized and calculated using a particle swarm optimization algorithm to obtain preliminary estimated values ​​of the deployment path and the deployment angle; According to the preliminary estimated value and combined with fluid dynamics, the deployment path and deployment angle are finely adjusted to obtain a deployment torque distribution model; Based on the deployment path, deployment angle, and deployment torque, the force changes at each moment during the deployment of the emergency command shelter are calculated to construct a dynamic deployment plan; The real-time environmental data of the current site of the emergency command cabin is obtained, and the dynamic deployment plan is compared and analyzed with the real-time environmental data to obtain an optimized cabin construction plan.

4. The intelligent construction method of an emergency command shelter according to claim 3, characterized in that: The steps of performing feedback control on the emergency command cabin through a fuzzy control algorithm based on the cabin construction plan to complete the construction, and reconstructing the interior space model of the emergency command cabin after construction based on the three-dimensional point cloud data to obtain the cabin interior space data specifically include: A fuzzy control model is constructed for the optimized shelter construction plan, and a fuzzy logic reasoning method is used based on the fuzzy control model to control the deployment process of the emergency command shelter in real time to obtain a preliminary feedback control strategy; Based on the preliminary feedback control strategy, the construction process of the emergency command cabin is adjusted in real time, and deployment process control data is generated; Error correction is performed on the deployment process control data to obtain corrected control data, and the calculation results of the deployment path and deployment angle of the corrected control data are adjusted by a nonlinear optimization algorithm to obtain a precise deployment path and precise deployment angle to control the emergency command shelter to complete construction; Scanning the interior of the constructed emergency command cabin to obtain three-dimensional point cloud data of the interior of the emergency command cabin, and aligning the three-dimensional point cloud data from different perspectives to obtain aligned three-dimensional point cloud data; Converting the aligned three-dimensional point cloud data into a three-dimensional grid model of the interior of the emergency command cabin to obtain an interior space model; The internal space model is voxelized to obtain internal volume data, and the internal volume data is partitioned and regionally optimized to form final cabin internal space data.

5. The intelligent construction method of an emergency command shelter according to claim 1, characterized in that: The step of obtaining the physical sensation information of the personnel in the emergency command cabin after construction, analyzing the physical sensation information of the personnel based on the cabin internal space data and the environmental parameter set and using the weighted average method to generate a comfort adjustment strategy specifically includes: Collecting physical sensation information of personnel in the emergency command cabin based on sensors of the emergency command cabin, and preprocessing the physical sensation information of personnel to obtain preliminary physical sensation data; Performing dimensionality reduction processing on the preliminary somatosensory data to extract the personalized comfort factor of each person; Based on the cabin interior space data and the set of environmental parameters, a weighted analysis is performed on the personalized comfort factor of each person to obtain a comprehensive comfort score for each person in each area of ​​the cabin interior space data; Performing cluster analysis on the comprehensive comfort score to obtain clustering results, and partitioning the interior space data of the shelter according to the clustering results to obtain comfort demand partitions; A personalized environmental adjustment strategy is generated according to the comfort demand zoning, and adjustment parameters of the emergency command cabin are obtained to form a comfort adjustment strategy.

6. The intelligent construction method of an emergency command shelter according to claim 5, characterized in that: The step of regulating and controlling the environment in the emergency command cabin based on the comfort adjustment strategy and using the Bayesian optimization algorithm specifically includes: Quantifying the comfort adjustment strategy to obtain an initial estimated value of the adjustment parameter; Establishing a Bayesian optimization model for the initial estimated value of the adjustment parameter to obtain the optimized adjustment parameter, and constructing a probability distribution function model for the optimized adjustment parameter to obtain a priori distribution; Iteratively updating the prior distribution through Bayesian optimization to generate a posterior distribution to form a high-comfort adjustment parameter; Using a constrained optimization algorithm to perform multivariable constraint processing on the high comfort adjustment parameters to obtain final adjustment parameters; A real-time adjustment plan is generated according to the final adjustment parameters, and the operation of the emergency command cabin is controlled based on the real-time adjustment plan.

7. An intelligent construction device for an emergency command cabin, characterized in that: include: A collection module is used to collect environmental data of the current site of the emergency command cabin and pre-process the environmental data to obtain an environmental parameter set, wherein the environmental parameter set includes parameters that affect the construction and internal environment of the emergency command cabin; A first analysis module is configured to construct a three-dimensional topological structure of the current site, and perform a hierarchical analysis on the set of environmental parameters based on the three-dimensional topological structure to obtain construction parameters of the emergency command cabin, wherein the construction parameters include the coordinates of the support points of the emergency command cabin and the force balance distribution data; A calculation module is used to construct a dynamic model based on the construction parameters, calculate the deployment path, deployment angle and deployment torque of the emergency command cabin based on the dynamic model, and form a cabin construction plan; A construction module is used to complete the construction of the emergency command cabin by feedback control using a fuzzy control algorithm based on the cabin construction plan, and reconstruct the interior space model of the emergency command cabin after construction based on the three-dimensional point cloud data to obtain cabin interior space data; The second analysis module is used to obtain the physical sensation information of the personnel in the emergency command cabin after construction, analyze the physical sensation information of the personnel based on the cabin internal space data and the environmental parameter set, and generate a comfort adjustment strategy; A control module, configured to adjust and control the environment in the emergency command cabin based on the comfort adjustment strategy and using a Bayesian optimization algorithm; The steps of constructing a three-dimensional topological structure of the current site, performing hierarchical analysis on the set of environmental parameters based on the three-dimensional topological structure, and obtaining construction parameters of the emergency command cabin, wherein the construction parameters include support point coordinates and force balance distribution data of the emergency command cabin, specifically include: Performing a three-dimensional scan on the current site to obtain terrain data of the current site, and fitting the terrain data using a least squares method to obtain three-dimensional terrain data; Reconstructing the three-dimensional terrain data to obtain a three-dimensional topological model, and extracting the height difference, slope and ground irregularity of the three-dimensional topological model using a curvature-based terrain analysis method to obtain terrain structural features; Based on the hierarchical analysis method, weights are assigned to the terrain structure characteristics and the environmental parameter set, and each type of environmental factor in the environmental parameter set is comprehensively scored to obtain the preliminary construction parameters of the emergency command cabin; The preliminary construction parameters are combined with finite element analysis to simulate the mechanical characteristics of the support points of the emergency command shelter to obtain simulation results, and the support point positions of the simulation results are optimized by a simulated annealing algorithm to obtain optimized construction parameters; The optimized construction parameters are iterated multiple times with multiple objectives by using a genetic algorithm to obtain the optimal construction parameters.

8. A computer device, characterized in that: The device includes a processor and a memory: The memory is used to store the computer program and send instructions of the computer program to the processor; The processor executes the intelligent construction method of the emergency command cabin according to any one of claims 1 to 6 according to the instructions of the computer program.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the method for intelligently constructing an emergency command shelter according to any one of claims 1 to 6 is implemented.

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