Intelligent building method, device and equipment for emergency command shelter and storage medium

Abstract

The invention relates to the technical field of emergency command, and discloses an intelligent building method, device and equipment of an emergency command shelter and a storage medium, and the method comprises the steps: collecting environment data of a current site of the emergency command shelter, building a three-dimensional topological structure of the current site, building a dynamic model according to building parameters, and building a three-dimensional topological structure of the current site; based on the shelter building scheme, feedback control is conducted on the emergency command shelter through a fuzzy control algorithm to complete building, personnel somatosensory information in the built emergency command shelter is obtained, a weighted average method is adopted to analyze the personnel somatosensory information, a comfort level adjusting strategy is generated, and the comfort level adjusting strategy is used for adjusting the comfort level of the emergency command shelter. Adjusting and controlling the environment in the emergency command shelter through a Bayesian optimization algorithm based on a comfort level adjusting strategy; by collecting environment data and analyzing environment parameters in combination with a three-dimensional topological structure, a building scheme of the square cabin can be quickly generated in a complex environment, quick deployment of the emergency command square cabin is realized, and the building efficiency and response speed of an emergency command center are remarkably improved.

Description

Technical Field

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

[0002] The emergency command shelter is an important infrastructure in the emergency management system and is widely used in natural disasters, public emergencies, military operations and other emergency rescue scenarios. With the global climate change and the increasing frequency of natural disasters, there is an urgent need for an emergency command center with high efficiency and rapid deployment to coordinate on-site rescue operations. Traditional emergency command shelters mainly rely on manual construction and adjustment, and often face complex problems such as large environmental changes and dense personnel, making it difficult to complete efficient construction and environmental adjustment in a short time. With the complication of emergency command tasks, higher requirements are put forward for the intelligence, rapid construction and environmental adaptability of emergency command shelters.

[0003] At present, most emergency command shelters adopt an inflatable construction method to achieve convenient deployment. However, the deployment and storage processes of inflatable shelters usually require the cooperation of multiple people, and the operation is complex and the operation time is long, resulting in the inability to achieve rapid deployment. In addition, the inflatable shelter lacks resistance to environmental changes after deployment, such as the influence of factors such as temperature and humidity, terrain, and wind speed on the shelter, resulting in the instability of the shelter structure and bringing troubles to the command of emergency management.

[0004] Therefore, an intelligent construction method is needed to solve the problem that the shelter lacks resistance to environmental changes after deployment. 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 shelter to solve the technical problems mentioned in the above background art.

[0006] To achieve the above object, the technical solutions provided by the present invention are as follows:

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

[0008] Collecting environmental data of the current site of the emergency command shelter and preprocessing the environmental data to obtain an environmental parameter set, where the environmental parameter set includes parameters affecting the construction and internal environment of the emergency command shelter;

[0009] Constructing a three-dimensional topological structure of the current site, and performing hierarchical analysis on the environmental parameter set based on the three-dimensional topological structure to obtain construction parameters of the emergency command shelter, where the construction parameters include the support point coordinates of the emergency command shelter and the stress equilibrium distribution data;

[0010] Construct a dynamic model based on the setup parameters, calculate the deployment path, deployment angle, and deployment torque of the emergency command shelter according to the dynamic model, and form a shelter setup plan;

[0011] Based on the shelter setup plan, perform feedback control on the emergency command shelter through a fuzzy control algorithm to complete the setup, and reconstruct the internal space model of the emergency command shelter after setup based on the 3D point cloud data to obtain the shelter internal space data;

[0012] Obtain the personnel body sensation information inside the emergency command shelter after setup, analyze the personnel body sensation information based on the shelter internal space data and the environmental parameter set, and adopt the weighted average method to generate a comfort adjustment strategy;

[0013] Based on the comfort adjustment strategy, adjust and control the environment inside the emergency command shelter through the Bayesian optimization algorithm.

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

[0015] Collect multi-source data of the environmental data of the current site of the emergency command shelter, obtain the wind speed, surface slope, and temperature and humidity values of the current site based on the sensors of the emergency command shelter, and form an original environmental data set;

[0016] Perform time-series denoising processing on the original environmental data set to obtain a time-series smoothed environmental data set;

[0017] Based on the K-nearest neighbor algorithm, fill in the missing values of the time-series smoothed environmental data set, and predict the future trend of the environmental data set by combining the time series prediction method to obtain a complete environmental data set;

[0018] Use the principal component analysis method to perform dimensionality reduction processing on the complete environmental data set, and extract the wind speed, surface slope, and temperature and humidity values to form an environmental parameter set.

[0019] Furthermore, construct the 3D topological structure of the current site, perform hierarchical analysis on the environmental parameter set based on the 3D topological structure to obtain the setup parameters of the emergency command shelter, and the setup parameters include the support point coordinates of the emergency command shelter and the force balance distribution data. The specific steps include:

[0020] Perform 3D scanning on 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 3D terrain data;

[0021] Reconstruct the 3D terrain data to obtain a 3D topological model, and use the terrain analysis method based on curvature to extract the height difference, slope, and ground irregularity of the 3D topological model to obtain the terrain structure characteristics;

[0022] The weight distribution is carried out on the terrain structure characteristics and the set of environmental parameters based on the analytic hierarchy process, and a comprehensive score is given to each type of environmental factor in the set of environmental parameters to obtain the preliminary construction parameters of the emergency command shelter;

[0023] The mechanical properties of the support points of the emergency command shelter are simulated and calculated by combining the preliminary construction parameters with the finite element analysis to obtain the simulation results, and the position of the support points is optimized by the simulated annealing algorithm to obtain the optimized construction parameters;

[0024] The optimal construction parameters are obtained by multi-objective and multiple iterations of the optimized construction parameters through the genetic algorithm.

[0025] Furthermore, a dynamic model is constructed according to the construction parameters, and the deployment path, deployment angle and deployment moment of the emergency command shelter are calculated according to the dynamic model, and the steps of the shelter construction plan are as follows:

[0026] The Lagrange equation is used for the construction parameters to establish a dynamic model for the deployment of the emergency command shelter, and the moment distribution of the emergency command shelter under different forces is calculated to obtain the preliminary deployment path model;

[0027] The finite element analysis is carried out on the force change during the deployment process of the emergency command shelter according to the dynamic model to obtain the stability conditions for the deployment of the emergency command shelter;

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

[0029] The deployment path and deployment angle are finely adjusted according to the preliminary estimated values in combination with fluid dynamics to obtain the deployment moment distribution model;

[0030] Based on the deployment path, deployment angle and deployment moment, the force change at each moment during the deployment process of the emergency command shelter is calculated to construct a dynamic deployment plan;

[0031] The real-time environmental data of the current site of the emergency command shelter is obtained, and the dynamic deployment plan is compared and analyzed with the real-time environmental data to obtain an optimized shelter construction plan.

[0032] Furthermore, based on the shelter construction plan, the emergency command shelter is feedback-controlled through a fuzzy control algorithm to complete the construction, and the internal space model of the constructed emergency command shelter is reconstructed based on the three-dimensional point cloud data, and the steps for obtaining the internal space data of the shelter are as follows:

[0033] Construct a fuzzy control model for the optimized construction plan of the mobile cabin, and use the fuzzy logic reasoning method based on the fuzzy control model to perform real-time control on the deployment process of the emergency command mobile cabin to obtain a preliminary feedback control strategy;

[0034] Based on the preliminary feedback control strategy, perform real-time adjustment on the construction process of the emergency command mobile cabin and generate control data for the deployment process;

[0035] Perform error correction on the control data for the deployment process to obtain corrected control data, and adjust the calculation results of the deployment path and deployment angle of the corrected control data through a non-linear optimization algorithm to obtain an accurate deployment path and an accurate deployment angle to control the emergency command mobile cabin to complete the construction;

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

[0037] Convert the aligned three-dimensional point cloud data into a three-dimensional mesh model of the interior of the emergency command mobile cabin to obtain an interior space model;

[0038] Perform voxelization processing on the interior space model to obtain interior volume data, partition the interior volume data and perform regional optimization to form the final interior space data of the mobile cabin.

[0039] Furthermore, the steps of obtaining the personnel body sensation information in the emergency command mobile cabin after construction, analyzing the personnel body sensation information based on the interior space data of the mobile cabin and the environmental parameter set, and using the weighted average method to generate a comfort adjustment strategy specifically include:

[0040] Collect the personnel body sensation information in the emergency command mobile cabin based on the sensors of the emergency command mobile cabin and perform preprocessing on the personnel body sensation information to obtain preliminary body sensation data;

[0041] Perform dimensionality reduction processing on the preliminary body sensation data to extract the personalized comfort factors of each person;

[0042] Based on the interior space data of the mobile cabin and the environmental parameter set, perform weighted analysis on the personalized comfort factors of each person to obtain the comprehensive comfort score of each person in each area of the interior space data of the mobile cabin;

[0043] Perform cluster analysis on the comprehensive comfort score to obtain a clustering result, and partition the interior space data of the mobile cabin according to the clustering result to obtain a comfort requirement partition;

[0044] Generate a personalized environment adjustment strategy according to the comfort requirement partition to obtain the adjustment parameters of the emergency command mobile cabin and form a comfort adjustment strategy.

[0045] Further, the steps of adjusting and controlling the environment in the emergency command shelter based on the comfort adjustment strategy and through the Bayesian optimization algorithm specifically include:

[0046] Quantify the comfort adjustment strategy to obtain an initial pre - estimate of the adjustment parameters;

[0047] Establish a Bayesian optimization model for the initial pre - estimate of the adjustment parameters to obtain optimized adjustment parameters, and construct a probability distribution function model for the optimized adjustment parameters to obtain the prior distribution;

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

[0049] Use a constrained optimization algorithm to perform multivariate constraint processing on the high - comfort adjustment parameters to obtain the final adjustment parameters;

[0050] Generate a real - time adjustment plan based on the final adjustment parameters, and control the operation of the emergency command shelter based on the real - time adjustment plan.

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

[0052] An acquisition module for acquiring environmental data of the current site of the emergency command shelter and pre - processing the environmental data to obtain a set of environmental parameters, where the set of environmental parameters includes parameters affecting the erection and internal environment of the emergency command shelter;

[0053] A first analysis module for constructing a three - dimensional topological structure of the current site and performing hierarchical analysis on the set of environmental parameters based on the three - dimensional topological structure to obtain erection parameters of the emergency command shelter, where the erection parameters include the support point coordinates and force - balance distribution data of the emergency command shelter;

[0054] A calculation module for constructing a dynamic model according to the erection parameters and calculating the deployment path, deployment angle, and deployment torque of the emergency command shelter based on the dynamic model to form a shelter erection plan;

[0055] An erection module for performing feedback control on the emergency command shelter based on the shelter erection plan through a fuzzy control algorithm to complete the erection, and reconstructing the internal space model of the erected emergency command shelter based on three - dimensional point cloud data to obtain shelter internal space data;

[0056] A second analysis module for obtaining the human body sensation information inside the erected emergency command shelter, analyzing the human body sensation information based on the shelter internal space data and the set of environmental parameters, and using the weighted average method to generate a comfort adjustment strategy;

[0057] A control module for adjusting and controlling the environment in the emergency command shelter based on a comfort adjustment strategy and through a Bayesian optimization algorithm.

[0058] Thirdly, the present invention provides a computer device, which includes a processor and a memory:

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

[0060] The processor executes an intelligent construction method of an emergency command shelter as described in the first aspect according to the instructions of the computer program.

[0061] Fourthly, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements an intelligent construction method of an emergency command shelter as described in the first aspect.

[0062] In summary, in the present invention, 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 construction plan for the shelter under complex natural environmental conditions, achieve the rapid deployment of the emergency command shelter in the shortest time, and significantly improve the construction efficiency and response speed of the emergency command center. At the same time, a dynamic model and a fuzzy control algorithm are used to perform feedback control on the shelter construction process, ensuring the structural stability of the shelter in various complex environments and reducing the safety risks caused by environmental changes. In addition, through comprehensive analysis based on environmental parameters and internal space data, the environment in the emergency command shelter is dynamically adjusted to optimize the body sensation comfort of personnel. By analyzing personnel body sensation information through the weighted average method and combining with the Bayesian optimization algorithm for environmental adjustment control, internal environmental factors such as temperature and humidity in the shelter are intelligently adjusted according to different emergency scenarios and personnel needs, improving the comfort inside the shelter. Description of the Drawings

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0064] Figure 1 It is a step schematic diagram of an intelligent construction method of an emergency command shelter provided by an embodiment of the present invention;

[0065] Figure 2 It is a structural schematic diagram of an emergency command shelter provided by an embodiment of the present invention;

[0066] Figure 3 The structural schematic block diagram of an intelligent erection device for an emergency command shelter provided by an embodiment of the present invention;

[0067] Figure 4 The structural schematic block diagram of a computer device provided by an embodiment of the present invention;

[0068] Wherein: 1. Outer shell; 2. Bracket; 3. Display screen; 4. Folding table board. Specific embodiments

[0069] To make the objectives, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described below are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0070] Please refer to Figure 1 , an embodiment of the present invention provides an intelligent erection method for an emergency command shelter, including:

[0071] S100: Collect the environmental data of the current site of the emergency command shelter, and preprocess the environmental data to obtain an environmental parameter set, where the environmental parameter set includes parameters affecting the erection and internal environment of the emergency command shelter;

[0072] In step S100, the environmental data of the current site of the emergency command shelter is collected through sensors, which may include key parameters such as wind speed, surface slope, and temperature and humidity values. Wind speed data helps to evaluate the impact of air flow on the stability of the shelter after erection, the surface slope reflects the requirements of the terrain for the support structure and mechanical distribution of the shelter, and the temperature and humidity values affect the adjustment requirements for the internal comfort of the shelter. The above data can be classified as parameters affecting the erection and internal environment of the emergency command shelter. The collected data will be filtered and corrected through preprocessing algorithms to eliminate abnormal data that does not conform to the actual situation and ensure the accuracy and representativeness of the data. Wind speed data may be affected by instantaneous mutations, so the moving average method is used for smoothing processing to make it more stable. The slope data may have measurement errors, and coordinate alignment and error correction are required. The temperature and humidity data need to be combined with historical data for trend analysis to ensure the rationality of subsequent environmental adjustment. The finally obtained environmental parameter set will be used as the input for the subsequent steps to provide data support for the calculation of erection parameters.

[0073] S200: 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 erection parameters of the emergency command shelter, where the erection parameters include the support point coordinates and the force equilibrium distribution data of the emergency command shelter.

[0074] In step S200, based on the set of environmental parameters collected in the previous step, the construction site of the emergency command shelter can be scanned through scanning technologies such as lidar to obtain high-precision three-dimensional terrain data. Through point cloud processing algorithms, the terrain data is segmented and feature extracted to construct the three-dimensional topological structure of the current site. After the topological modeling is completed, the system combines the environmental parameters with the terrain model and uses the Analytic Hierarchy Process (AHP) to evaluate the impacts of wind speed, slope, temperature, and humidity to determine the coordinates of the optimal support points for the shelter. The data of the balanced force distribution is also calculated at this stage. The system analyzes the bearing capacity of the ground surface and optimizes the distribution of the support structure based on the wind speed conditions to reduce the construction risks caused by uneven forces.

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

[0076] In step S300, based on the construction parameters, a dynamic model of the shelter is established to simulate the force conditions during the shelter deployment process. The dynamic modeling adopts the principles of rigid body dynamics, and through Lagrange's equations or Newton-Euler equations, the mechanical equilibrium conditions of each part of the shelter in different deployment states are calculated. To optimize the deployment process, the system introduces a path planning algorithm to calculate the optimal deployment path of the shelter, 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, optimization algorithms are used to solve for the deployment angle and torque to reduce the energy consumption during the deployment process and ensure the structural stability of the shelter after deployment. After the calculation is completed, the system generates a shelter construction plan, including key parameters such as the deployment path, deployment angle, and force data of the support points, providing guidance for actual implementation.

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

[0078] In step S400, based on the mobile cabin construction plan, control the deployment mechanism in the mobile cabin to execute the construction process, such as a hydraulic drive device or an electric drive. During the deployment process, continuously monitor the status of the mobile cabin, including the deployment angle, the force condition, and the stability of the support points. When the system detects a deviation between the actual deployment situation and the preset plan, dynamically adjust the deployment speed, torque, and sequence through a 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 deployment sequence or add additional supports to optimize the force distribution. In addition, after the construction is completed, the system uses 3D point cloud scanning technology to perform point cloud reconstruction on the constructed mobile cabin and generate a 3D model of the internal space, that is, obtain the internal space data of the mobile cabin.

[0079] S500: Obtain the body sensation information of the personnel inside the constructed emergency command mobile cabin, analyze the body sensation information based on the internal space data of the mobile cabin and the environmental parameter set, and generate a comfort adjustment strategy using the weighted average method.

[0080] In step S500, based on the internal space data of the mobile cabin, combined with the environmental parameter set, use the sensors in the emergency command mobile cabin to collect the body sensation data of the personnel inside the mobile cabin, such as skin temperature, heart rate, and humidity perception. By analyzing these body sensation data, the system calculates the comprehensive comfort index using the weighted average method and formulates a comfort adjustment strategy accordingly. For example, in a high-temperature environment, if the skin temperature of most personnel is high and the humidity perception is strong, the system will increase the wind speed or lower the temperature, while in a cold environment, it will raise the temperature or reduce the wind speed. At the same time, the system will also optimize the working mode of the ventilation system according to the air flow situation inside the mobile cabin to maintain the air quality.

[0081] S600: Based on the comfort adjustment strategy, adjust and control the environment inside the emergency command mobile cabin through the Bayesian optimization algorithm.

[0082] In step S600, the comfort adjustment strategy, through the Bayesian optimization algorithm, dynamically adjusts the environmental parameters such as temperature, humidity, and wind speed inside the mobile cabin. The system uses historical data and real-time monitoring data to continuously update the adjustment model to achieve optimal environmental control with the minimum energy consumption. For example, when adjusting the air conditioning system, the system will predict the impact of different temperature settings on comfort based on the current temperature and humidity conditions and select the optimal setting value. In addition, if the environment changes, such as a drop in external air temperature or an increase in wind speed, the system will automatically adjust the adjustment strategy to ensure that the internal environment of the mobile cabin is always in the best state. The introduction of the Bayesian optimization algorithm enables the environmental adjustment process to be optimized based on continuous learning, improves the adjustment efficiency, and reduces unnecessary energy consumption.

[0083] Reference Figure 2, in one embodiment, the emergency command shelter includes a housing 1, a support 2, a display screen 3, a folding tabletop 4, a tent and a support framework. The housing 1 is connected to the support 2 through a hinge assembly. The support 2 is connected to the support framework through a telescopic connecting member, enabling the support 2 to be deployed under hydraulic drive and support the overall structure of the shelter. A hydraulic cylinder is provided between the support 2 and the support framework, and the telescopic movement of the hydraulic cylinder controls the deployment and retraction of the support 2. A rotatable fixed base is provided at the lower end of the support framework and is connected to the ground support mechanism through a universal hinge, enabling the support framework to adjust the angle to adapt to different terrain conditions and ensure the stability of the shelter.

[0084] The display screen 3 is installed inside the housing 1 and is connected to the support 2 through an adjustable rotating arm. The joint structure of the rotating arm is controlled by an electric drive, enabling the display screen 3 to adjust the angle and height according to the needs of the command personnel. A switch door is also provided on the outside of the display screen 3 on the housing 1, which can be opened and closed according to the usage requirements. The folding tabletop 4 is arranged on the inner sidewall of the shelter, connected to the sidewall through a hinge mechanism, and is equipped with a gas spring support. When in use, the tabletop can be slowly unfolded by releasing the locking device and supported in a horizontal state by the gas spring. When retracted, the tabletop can be returned to the folded state and locked by gently pressing it. The tent part consists of flexible support rods and a folding mechanism. The flexible support rods are connected to the support framework through slide rails and are controlled by a winding device driven by a motor to be deployed and retracted, ensuring that the tent can automatically cover the outside area of the shelter and enclose both the display screen 3 and the folding tabletop 4 within the tent.

[0085] During the use process, when the shelter is deployed, first, the hydraulic cylinder is activated through the electric control system, causing the support 2 to be deployed synchronously along the telescopic connecting member. The support framework rises under the push of the hydraulic cylinder, and at the same time, it adapts to the ground angle through the universal hinge to ensure stable support of the shelter under different terrain conditions. As the support 2 is fully deployed, the fixed locking device automatically latches, causing the support 2 and the support framework to form a stable load-bearing framework. Subsequently, the motor drives the tent slide rails to unfold, covering the shelter structure to form a closed command space. At the same time, the rotating arm of the display screen 3 is electrically adjusted in angle, enabling the command personnel to view information from the best perspective. The folding tabletop 4 automatically unfolds slowly after being unlocked to form an operating table surface for work. During storage, the system controls the tent to be wound up, the tabletop to be folded, the display screen 3 to return to its original position in sequence, and the support 2 to be retracted through the hydraulic cylinder, causing the shelter to gradually return to a compact state and finally be locked by the fixed base for easy transportation and mobile deployment.

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

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

[0088] Perform time-series denoising processing on the original environmental data set to obtain an environmental data set with smoothed time series;

[0089] Based on the K-nearest neighbor algorithm, fill in the missing values in the environmental data set with smoothed time series, and combine the time series prediction method to predict the future trend of the environmental data set to obtain a complete environmental data set;

[0090] Use the principal component analysis method to perform dimensionality reduction processing on the complete environmental data set, and extract the wind speed, surface slope, and temperature and humidity values to form an environmental parameter set.

[0091] In the above embodiment, first, a multi-source data collection is performed on the current site through the sensor system of the emergency command shelter to obtain an original environmental data set regarding the wind speed, surface slope, and temperature and humidity. These data are collected in real time through various sensors and reflect the environmental changes in the area where the shelter is located. Then, time-series denoising processing is performed on the collected original environmental data set to eliminate instantaneous fluctuations caused by equipment errors or external interferences, making the data smoother and more reliable and ensuring the accuracy of subsequent analyses. After the denoising processing is completed, the K-nearest neighbor algorithm is used to fill in the missing values in the smoothed environmental data set, which can effectively supplement the data lost due to sensor failures or data transmission problems and ensure the integrity of the environmental data. At the same time, combined with the time series prediction method, the future trend of this data set is predicted, providing a long-term perspective for the subsequent decision-making, identifying possible environmental change trends in advance, and obtaining a complete environmental data set. Finally, the principal component analysis method is used to perform dimensionality reduction processing on the complete environmental data set, and the main environmental parameters, such as wind speed, surface slope, and temperature and humidity values, are extracted to form the final environmental parameter set. Through this series of data processing steps, the impact of noise and data loss on the environmental analysis results can be effectively reduced, the accuracy and reliability of the data can be improved, providing a more accurate and comprehensive basis for the subsequent construction, environmental adjustment, and emergency command of the shelter, thus greatly enhancing the efficiency and quality of emergency response.

[0092] In an example, construct a three-dimensional topological structure of the current site, perform hierarchical analysis on the environmental parameter set based on the three-dimensional topological structure to obtain the construction parameters of the emergency command shelter. The construction parameters include the support point coordinates of the emergency command shelter and the stress equilibrium distribution data. The specific steps are as follows:

[0093] Perform three-dimensional scanning on 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] Reconstruct the three-dimensional terrain data to obtain a three-dimensional topological model, and use a curvature-based terrain analysis method to extract the height difference, slope, and ground irregularity of the three-dimensional topological model, so as to obtain the terrain structure characteristics;

[0095] Based on the analytic hierarchy process, weight distribution is carried out on the terrain structure characteristics and the environmental parameter set, and a comprehensive score is given to each type of environmental factor in the environmental parameter set to obtain the preliminary construction parameters of the emergency command shelter;

[0096] Combine the preliminary construction parameters with finite element analysis to perform simulation calculations on the mechanical properties of the support points of the emergency command shelter to obtain simulation results, and optimize the support point positions through the simulated annealing algorithm to obtain optimized construction parameters;

[0097] Perform multi-objective and multiple iterations on the optimized construction parameters through the genetic algorithm to obtain the optimal construction parameters.

[0098] In the above embodiment, the current site is scanned three-dimensionally, and lidar or drone aerial photography technology is used to obtain the terrain data of the site. The data includes the elevation information of the site and the detailed changes on its surface, which can accurately reflect the undulation and terrain characteristics of the site. The least squares method is used to fit the obtained terrain data, and the most suitable fitting model is found by minimizing the error between the data and the fitting curve. During this process, the fitting result will provide a basis for subsequent three-dimensional terrain reconstruction to ensure that the obtained terrain data is as close as possible to the actual ground shape. By reconstructing the fitted three-dimensional terrain data, a detailed three-dimensional topological model is obtained. This model accurately reflects various terrain characteristics of the site, such as height difference, slope, and ground irregularity. To extract the terrain characteristics, a curvature-based terrain analysis method is adopted. By calculating the curvature value, the changes on the terrain surface are identified, including the changes in height difference and slope, so that the system can identify the areas in the site that may affect the support of the shelter, especially the areas with large slopes or irregular terrains, which require additional support optimization to ensure the stability of the shelter.

[0099] Based on the topographic structure features and the set of environmental parameters, the analytic hierarchy process is used to allocate weights. By comprehensively evaluating different parameters, the importance of the stability of the mobile cabin during the construction process is determined. In this step, the set of environmental parameters includes data such as wind speed, surface slope, temperature and humidity, etc., while the topographic structure features provide the topographic basis for the influence of these parameters. By weighting these parameters, the system can assign appropriate weights to each environmental factor (such as wind speed, temperature and humidity), and conduct a comprehensive score to evaluate the relative importance of each factor during the construction of the mobile cabin. Based on the preliminary construction parameters, the mechanical properties of the support points of the mobile cabin are simulated by combining the finite element analysis method. By discretizing the structure, the stress conditions of the mobile cabin during the construction process are simulated. It can provide the mechanical responses of each support point under different environmental conditions, such as stress distribution and stress concentration. Through simulation, the structural performance of the mobile cabin under the influence of different wind speeds, surface slopes and other factors can be predicted. On this basis, the simulated annealing algorithm is used to optimize the position of the support points of the simulation results. Based on the optimized support point positions and force balance data, the genetic algorithm is used for multi-objective optimization. Through multiple iterations and selection operations, the construction parameters are continuously optimized. The genetic algorithm will optimize multiple objectives, such as the best position of the support points, mechanical stability, construction speed and other dimensions. In each iteration, the system will evaluate the effect of the current construction parameters and select the best-performing individuals for reproduction and mutation, so as to generate a better construction plan.

[0100] In one embodiment, a dynamic model is constructed according to the construction parameters, and the deployment path, deployment angle and deployment torque of the emergency command mobile cabin are calculated according to the dynamic model. The steps of forming the mobile cabin construction plan specifically include:

[0101] The Lagrange equation is used for the construction parameters to establish a dynamic model for the deployment of the emergency command mobile cabin, calculate the torque distribution of the emergency command mobile cabin under different forces, and obtain a preliminary deployment path model;

[0102] According to the dynamic model, a finite element analysis is carried out on the force change during the deployment process of the emergency command mobile cabin to obtain the stability conditions for the deployment of the emergency command mobile cabin;

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

[0104] According to the preliminary estimated values, combined with fluid dynamics, the deployment path and deployment angle are refined and adjusted to obtain a deployment torque distribution model;

[0105] Based on the deployment path, deployment angle and deployment torque, calculate the force change at each moment during the deployment process of the emergency command mobile cabin, and construct a dynamic deployment plan;

[0106] Obtain the 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 embodiment, based on the construction parameters, the Lagrange equation is used to establish the dynamic model of the emergency command shelter deployment to analyze the torque distribution of the shelter under different forces. This model models the mass, moment of inertia and external forces of each structural unit of the shelter, solves its motion trajectory and force state during the deployment process, and thus obtains a preliminary deployment path model. On this basis, the finite element analysis is further used to simulate and calculate the force changes during the deployment process to evaluate the structural stability of the shelter at each stage, identify possible stress concentration areas and weak points. The finite element analysis can provide high-precision structural response calculations, enabling the shelter to remain stable in complex environments and avoiding deformation or structural instability problems caused by local overload during the deployment process. According to the finite element analysis results, the stability conditions for the shelter deployment are determined, providing a constraint basis for the subsequent optimization of the deployment path and deployment angle.

[0108] After obtaining the preliminary stability conditions, the particle swarm optimization algorithm is used to optimize the calculation of the deployment path and deployment angle. Considering factors such as minimizing energy consumption, minimizing deployment time, and mechanical balance, preliminary estimated values of the deployment path and angle are obtained. Then, combined with the fluid dynamics analysis, the influence of wind load on the shelter during the deployment process is corrected to ensure that there will be no structural vibration or deployment deviation due to wind speed changes during the deployment process, and the deployment path and angle are finely adjusted accordingly. Finally, a deployment torque distribution model is obtained. On this basis, the force changes of each part of the shelter at each moment during the deployment process are calculated to construct a dynamic deployment plan, making the deployment process smoother and safer. Finally, the environmental data of the current site of the shelter is obtained in real time, and it is compared and analyzed with the dynamic deployment plan to adjust the parameters of the plan, so that the shelter can adapt to the current environmental conditions, such as wind speed, slope change, etc., during the actual construction process, and finally an optimized shelter construction plan is generated. Through this complete optimization process, the shelter can achieve stable and efficient automatic deployment in different environments, improving the reliability and deployment efficiency of emergency response.

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

[0110]

[0111] Among them, represents the optimized shelter construction plan, argmin represents the process of finding the optimized shelter construction plan, represents the deployment angle, represents the deployment path, and respectively represent the start time and end time of the deployment of the emergency command shelter; , and respectively represent the weight coefficients, which are used to adjust the influence degree of different optimization objectives; represents the time derivative, which is used to represent the rate of change of a variable with time and acts on to calculate the rate of change of the dynamic error, represents the gradient of the Lagrangian function with respect to , reflecting the influence of the angle changes of the components of the emergency command shelter on the mechanical characteristics of the system, is the derivative of , that is, the rate of change of the deployment angle with time, which is the angular velocity, represents the external applied torque; represents the force distribution calculated by finite element analysis; represents the optimal force distribution under stability conditions; represents the optimal deployment path.

[0112] In one embodiment, based on the shelter construction plan, the emergency command shelter is feedback-controlled to complete the construction through a fuzzy control algorithm, and the steps of reconstructing the internal space model of the emergency command shelter after construction based on the three-dimensional point cloud data to obtain the internal space data of the shelter specifically include:

[0113] Construct a fuzzy control model for the optimized shelter construction plan, and perform real-time control on the deployment process of the emergency command shelter based on the fuzzy control model using the fuzzy logic reasoning method to obtain a preliminary feedback control strategy;

[0114] Perform real-time adjustment on the construction process of the emergency command shelter based on the preliminary feedback control strategy, and generate control data for the deployment process;

[0115] Perform error correction on the control data for the deployment process to obtain corrected control data, and adjust the calculation results of the deployment path and deployment angle of the corrected control data through a non-linear optimization algorithm to obtain an accurate deployment path and accurate deployment angle to control the emergency command shelter to complete the construction;

[0116] Scan the inside of the emergency command shelter after construction to obtain the three-dimensional point cloud data inside the emergency command shelter, and align the three-dimensional point cloud data from different perspectives to obtain aligned three-dimensional point cloud data;

[0117] Convert the aligned three-dimensional point cloud data into a three-dimensional mesh model inside the emergency command shelter to obtain the internal space model;

[0118] Perform voxelization processing on the internal space model to obtain internal volume data, partition the internal volume data and perform regional optimization 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, and an accurate deployment path and angle are obtained, thereby controlling the precise construction of the shelter.

[0120] After the construction of the shelter is completed, in order to further obtain the internal space data of the emergency command shelter, the three-dimensional point cloud data is obtained by scanning the interior of the shelter, and the three-dimensional point cloud data from different perspectives are aligned to ensure the accuracy of the data. The aligned three-dimensional point cloud data is converted into a three-dimensional grid model inside the shelter, thereby reconstructing the internal space structure of the shelter. Voxel processing technology is used to convert the three-dimensional grid model into detailed internal volume data, and then these data are regionally partitioned and optimized to form the final internal space data of the shelter. Not only can the internal structure of the shelter be accurately obtained, but it can also provide reliable data support for subsequent space utilization and optimization, ensuring that the emergency command shelter has a good spatial layout and functionality after its construction is completed.

[0121] In one embodiment, the steps of obtaining the body sensory information of personnel in the constructed emergency command cabin, analyzing the body sensory information of 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 include:

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

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

[0124] Based on the internal space data and environmental parameter set of the shelter, a weighted analysis is performed on the personalized comfort factor of each person, and the comprehensive comfort score of each person in each area of ​​the internal space data of the shelter is obtained;

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

[0126] Generate personalized environment adjustment strategies according to the comfort requirements of different zones, obtain the adjustment parameters of the emergency command shelter, and form comfort adjustment strategies.

[0127] In the above embodiment, by obtaining the body sensation information of the personnel inside the built emergency command shelter, and combining the internal space data and environmental parameter set of the shelter, the weighted average method is used to analyze the body sensation information of the personnel to generate an optimized comfort adjustment strategy. Specifically, sensors arranged inside the shelter are used to collect the body sensation information of the personnel in the shelter in real time, including multiple comfort-related parameters such as temperature, humidity, and air quality, and these data are preprocessed to remove outliers and noise to obtain preliminary body sensation data. On this basis, for the physiological characteristics and preferences of different individuals, the preliminary body sensation data is subjected to dimensionality reduction processing to extract the personalized comfort factors of each person, so as to ensure that the comfort requirements of different personnel can be taken into account during the analysis process. And based on the internal space data and environmental parameter set of the shelter, a weighted analysis is performed on the personalized comfort factors to calculate the comprehensive comfort score in different areas inside the shelter, ensuring that the comfort evaluation of different positions is more accurate.

[0128] In order to further optimize the internal environment adjustment of the shelter, the clustering analysis method is used to classify the comprehensive comfort score, identify the comfort characteristics of different personnel groups, and accordingly divide the internal space of the shelter into zones to form comfort requirement zones. Based on the zoning results, combined with the personnel characteristics and environmental parameters in different zones, personalized environment adjustment strategies are generated, including air-conditioning temperature adjustment, air circulation optimization, lighting brightness adjustment, etc., to improve the overall comfort. According to the generated comfort adjustment strategy, the adjustment parameters of the emergency command shelter are output to form a comfort adjustment strategy, enabling the internal environment of the shelter to be intelligently adjusted according to the personnel needs, realizing the intelligent optimization of the internal environment of the emergency command shelter, and helping to improve the comfort of personnel in a long-term operation environment.

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

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

[0131] Establish a Bayesian optimization model for the initial estimated value of the adjustment parameters to obtain optimized adjustment parameters, and construct a probability distribution function model for the optimized adjustment parameters to obtain the prior distribution;

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

[0133] Use the constraint optimization algorithm to perform multivariate constraint processing on the high-comfort adjustment parameters to obtain the final adjustment parameters;

[0134] Generate a real-time adjustment plan based on the final adjustment parameters, and control the operation of the emergency command shelter based on the real-time adjustment plan.

[0135] In the above embodiment, the comfort adjustment strategy is quantified to obtain an initial estimated value of the adjustment parameter, which represents the preliminary adjustment level of various environmental factors in the shelter, such as temperature, humidity, air circulation, etc. The Bayesian optimization method is used to establish an optimization model to further optimize the initial adjustment parameters, which can predict and update the probability distribution function of the adjustment parameters, and obtain a prior distribution, which reflects the possible value range of each adjustment parameter under the 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 adjustment parameters with high comfort. The advantage of Bayesian optimization is that by continuously iterating and updating the prior and posterior distributions, it can provide more accurate adjustment values in an environment with high uncertainty, and finally achieve the goal of optimizing comfort. The constrained optimization algorithm is used to perform multivariate constraint processing on these high-comfort adjustment parameters to ensure that the environmental adjustment plan can meet various constraint conditions in actual operation. Finally, the optimized and constrained adjustment parameters are generated, and a real-time adjustment plan is formulated based on the final adjustment parameters, so as to perform real-time control on the environment of the shelter.

[0137] Referring to Figure 3 , the embodiment of the present invention also provides an intelligent construction device for an emergency command shelter, including:

[0138] The acquisition module 100 is used to acquire the environmental data of the current site of the emergency command shelter, and preprocess 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;

[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 shelter. The construction parameters include the support point coordinates and force balance distribution data of the emergency command shelter;

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

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

[0142] The second analysis module 500 is used to obtain the personnel's body sensation information in the emergency command shelter after construction, analyze the personnel's body sensation information based on the internal space data and environmental parameter set of the shelter, and adopt the weighted average method to generate a comfort adjustment strategy;

[0143] The control module 600 is used to adjust and control the environment in the emergency command shelter 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 shelter construction plan under complex natural environmental conditions, realize the rapid deployment of the emergency command shelter in the shortest time, and significantly improve the construction efficiency and response speed of the emergency command center. At the same time, a dynamic model and a fuzzy control algorithm are used to perform feedback control on the shelter construction process to ensure the structural stability of the shelter in various complex environments and reduce the safety risks caused by environmental changes. In addition, the environment in the emergency command shelter is dynamically adjusted through comprehensive analysis based on environmental parameters and internal space data to optimize the personnel's body sensation comfort. The personnel's body sensation information is analyzed by the weighted average method, and the environment is adjusted and controlled in combination with the Bayesian optimization algorithm. The internal environmental factors such as temperature and humidity in the shelter are intelligently adjusted according to different emergency scenarios and personnel needs to improve the comfort inside the shelter.

[0145] Refer to Figure 4 , in the embodiment of the present application, a computer device is further provided. The computer device can be a server, and its internal structure can be as Figure 4 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, 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 shelter. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it realizes an intelligent construction method for an emergency command shelter.

[0146] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements an intelligent construction method for an emergency command shelter, including the steps of: collecting environmental data of the current site of the emergency command shelter and preprocessing the environmental data to obtain a set of environmental parameters, where the set of environmental parameters includes wind speed, surface slope, and temperature and humidity values; 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 to obtain construction parameters of the emergency command shelter, where the construction parameters include the support point coordinates and force balance distribution data of the emergency command shelter; constructing a dynamic model according to the construction parameters, calculating the deployment path, deployment angle, and deployment torque of the emergency command shelter based on the dynamic model to form a shelter construction plan; performing feedback control on the emergency command shelter to complete the construction through a fuzzy control algorithm based on the shelter construction plan, and reconstructing the internal space model of the constructed emergency command shelter based on three-dimensional point cloud data to obtain shelter internal space data; obtaining the personal body sensation information inside the constructed emergency command shelter, analyzing the personal body sensation information based on the shelter internal space data and the set of environmental parameters, and using the weighted average method to generate a comfort adjustment strategy; and adjusting and controlling the environment inside the emergency command shelter based on the comfort adjustment strategy through a Bayesian optimization algorithm.

[0147] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided in the present application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can 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 data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0148] In the above embodiments, the descriptions of the various embodiments each have their own focuses. For parts not described in detail or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0149] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0150] In the embodiments disclosed in this application, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are only illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate 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 cabin, characterized in that Including: Collect the environmental data of the current site of the emergency command shelter, and preprocess 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; 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 shelter, where the construction parameters include the support point coordinates and force equilibrium distribution data of the emergency command shelter; Construct a dynamic model according to the construction parameters, and calculate the deployment path, deployment angle, and deployment torque of the emergency command shelter according to the dynamic model to form a shelter construction plan; Based on the shelter construction plan, perform feedback control on the emergency command shelter through a fuzzy control algorithm to complete the construction, and reconstruct the internal space model of the constructed emergency command shelter based on the three-dimensional point cloud data to obtain the shelter internal space data; Obtain the personnel body sensation information inside the constructed emergency command shelter, analyze the personnel body sensation information based on the shelter internal space data and the environmental parameter set, and use the weighted average method to generate a comfort adjustment strategy; Based on the comfort adjustment strategy, adjust and control the environment inside the emergency command shelter through the Bayesian optimization algorithm.

2. The intelligent construction method of the emergency command shelter according to claim 1, characterized in that, In the step of collecting the environmental data of the current site of the emergency command shelter and preprocessing the environmental data to obtain an environmental parameter set, it includes: Perform multi-source data collection on the environmental data of the current site of the emergency command shelter, and obtain the wind speed, surface slope, and temperature and humidity values of the current site based on the sensors of the emergency command shelter to form an original environmental data set; Perform time series denoising processing on the original environmental data set to obtain a time series smoothed environmental data set; Fill in the missing values of the time series smoothed environmental data set based on the K-nearest neighbor algorithm, and predict the future trend of the environmental data set in combination with the time series prediction method to obtain a complete environmental data set; Use the principal component analysis method to perform dimensionality reduction processing on the complete environmental data set, and extract the wind speed, surface slope, and temperature and humidity values to form an environmental parameter set.

3. The intelligent construction method of an emergency command shelter according to claim 1, characterized in that, The step of constructing the three-dimensional topological structure of the current site, performing hierarchical analysis on the environmental parameter set based on the three-dimensional topological structure to obtain the construction parameters of the emergency command shelter, where the construction parameters include the support point coordinates and force equilibrium distribution data of the emergency command shelter, specifically includes: Perform three-dimensional scanning on 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; Reconstruct the three-dimensional terrain data to obtain a three-dimensional topological model, and use the terrain analysis method based on curvature to extract the height difference, slope, and ground irregularity of the three-dimensional topological model to obtain the terrain structure characteristics; Based on the analytic hierarchy process, weight distribution is carried out for the terrain structure features and the set of environmental parameters, and a comprehensive score is given to each type of environmental factor in the set of environmental parameters to obtain the preliminary construction parameters of the emergency command shelter; Combining the preliminary construction parameters with finite element analysis, the mechanical properties of the support points of the emergency command shelter are simulated and calculated to obtain the simulation results, and the support point positions are optimized through the simulated annealing algorithm to obtain the optimized construction parameters; Through genetic algorithm, multi-objective and multiple iterations are carried out on the optimized construction parameters to obtain the optimal construction parameters.

4. 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 and calculating the deployment path, deployment angle and deployment moment of the emergency command shelter based on the dynamic model to form a shelter construction plan specifically include: Using the Lagrangian equation for the construction parameters to establish the dynamic model of the deployment of the emergency command shelter, calculating the moment distribution of the emergency command shelter under different forces, and obtaining the preliminary deployment path model; According to the dynamic model, finite element analysis is carried out on the force change during the deployment process of the emergency command shelter to obtain the stability condition of the deployment of the emergency command shelter; Based on the stability condition, the particle swarm optimization algorithm is used to optimize and calculate the deployment path and deployment angle to obtain the preliminary estimated values of the deployment path and deployment angle; Based on the preliminary estimated values, combined with fluid dynamics, the deployment path and deployment angle are refined to obtain the deployment moment distribution model; Based on the deployment path, deployment angle and deployment moment, calculate the force change at each moment during the deployment process of the emergency command shelter, and construct a dynamic deployment plan; Obtain the 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.

5. The intelligent construction method of an emergency command shelter according to claim 4, characterized in that The steps of completing the construction of the emergency command shelter through feedback control based on the shelter construction plan by means of a fuzzy control algorithm and reconstructing the internal space model of the built emergency command shelter based on the three-dimensional point cloud data to obtain the shelter internal space data specifically include: Construct a fuzzy control model for the optimized shelter construction plan, and based on the fuzzy control model, use the fuzzy logic reasoning method to carry out real-time control on the deployment process of the emergency command shelter to obtain the preliminary feedback control strategy; Based on the preliminary feedback control strategy, the construction process of the emergency command shelter is adjusted in real time, and the deployment process control data is generated; Carry out error correction on the deployment process control data to obtain the corrected control data, and adjust the calculation results of the deployment path and deployment angle of the corrected control data through the non-linear optimization algorithm to obtain the accurate deployment path and accurate deployment angle to control the completion of the construction of the emergency command shelter; Scan the inside of the built emergency command shelter to obtain the three-dimensional point cloud data inside the emergency command shelter, and align the three-dimensional point cloud data from different perspectives to obtain the aligned three-dimensional point cloud data; Convert the aligned three-dimensional point cloud data into a three-dimensional grid model inside the emergency command shelter to obtain an internal space model; Perform voxelization processing on the internal space model to obtain internal volume data, partition the internal volume data and perform regional optimization to form the final shelter internal space data.

6. The intelligent construction method of an emergency command shelter according to claim 1, characterized in that The step of obtaining the personnel body sensation information inside the erected emergency command shelter, analyzing the personnel body sensation information based on the shelter internal space data and the environmental parameter set, and generating a comfort adjustment strategy by using the weighted average method specifically includes: Collect the personnel body sensation information inside the emergency command shelter based on the sensors of the emergency command shelter, and preprocess the personnel body sensation information to obtain preliminary body sensation data; Perform dimensionality reduction processing on the preliminary body sensation data to extract the personalized comfort factors of each person; Based on the shelter internal space data and the environmental parameter set, perform weighted analysis on the personalized comfort factors of each person to obtain the comprehensive comfort score of each person in each area in the shelter internal space data; Perform clustering analysis on the comprehensive comfort score to obtain a clustering result, and partition the shelter internal space data according to the clustering result to obtain a comfort requirement partition; Generate a personalized environment adjustment strategy according to the comfort requirement partition to obtain the adjustment parameters of the emergency command shelter, and form a comfort adjustment strategy.

7. The intelligent construction method of an emergency command shelter according to claim 6, characterized in that, The step of adjusting and controlling the environment inside the emergency command shelter based on the comfort adjustment strategy and through the Bayesian optimization algorithm specifically includes: Perform quantization processing on the comfort adjustment strategy to obtain an initial pre-estimation value of the adjustment parameters; Establish a Bayesian optimization model for the initial pre-estimation value of the adjustment parameters to obtain optimized adjustment parameters, and construct a probability distribution function model for the optimized adjustment parameters to obtain a prior distribution; Iteratively update the prior distribution through Bayesian optimization to generate a posterior distribution to form high-comfort adjustment parameters; Perform multivariate constraint processing on the high-comfort adjustment parameters by using a constraint optimization algorithm to obtain final adjustment parameters; Generate a real-time adjustment plan according to the final adjustment parameters, and control the operation of the emergency command shelter based on the real-time adjustment plan.

8. An intelligent erection device for an emergency command shelter, characterized in that, Including: An acquisition module, configured to acquire the environmental data of the current site of the emergency command shelter, and preprocess the environmental data to obtain an environmental parameter set, where the environmental parameter set includes parameters affecting the erection and internal environment of the emergency command shelter; A first analysis module, configured 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 erection parameters of the emergency command shelter, where the erection parameters include the support point coordinates and the force balance distribution data of the emergency command shelter; A calculation module, configured to construct a dynamic model according to the erection parameters, and calculate the deployment path, deployment angle and deployment torque of the emergency command shelter according to the dynamic model to form a shelter erection plan; A construction module, configured to perform feedback control on the emergency command shelter through a fuzzy control algorithm based on the shelter construction plan to complete the construction, and reconstruct the internal space model of the constructed emergency command shelter based on the three-dimensional point cloud data to obtain the shelter internal space data; A second analysis module, configured to obtain the personnel body sensation information in the constructed emergency command shelter, analyze the personnel body sensation information based on the shelter internal space data and the environmental parameter set, and generate a comfort adjustment strategy by using the weighted average method; A control module, configured to adjust and control the environment in the emergency command shelter based on the comfort adjustment strategy through a Bayesian optimization algorithm.

9. A computer device, characterized in that, The device includes a processor and a memory: The memory is used to store a computer program and send the instructions of the computer program to the processor; The processor executes a method for intelligent construction of an emergency command shelter according to any one of claims 1-7 based on the instructions of the computer program.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by the processor, a method for intelligent construction of an emergency command shelter according to any one of claims 1-7 is implemented.

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