A multi-link deformable tent shape control method and system

Abstract

The application provides a multi-link deformable tent shape control method and system, and relates to the technical field of deformable tent control. The method comprises the following steps: obtaining external load parameters and tent structure response parameters; identifying a current load mode according to the external load parameters, associating the load mode, the structure response parameters and the current shape of the tent, recording the actual performance of the tent under the load mode, and forming historical performance data; forming shape selection rules by analyzing the historical performance data; correcting the shape selection rules according to the actual operation feedback of the tent shape adjustment; and generating control instructions and adjusting the tent shape based on the corrected shape selection rules and the current load mode. The method of the application effectively copes with the combined action of multiple loads and adapts to the performance changes of the tent structure components caused by long-term use under the premise of ensuring the safety of the structure, reducing the stress of the structure and optimizing the energy consumption.

Description

Technical Field

[0001] This invention relates to the field of deformable tent control technology, and more specifically, to a method and system for controlling the shape of a multi-link deformable tent. Background Technology

[0002] Reference Appendix Figure 2 and attached Figure 3 A deformable tent is currently available, employing a multi-link frame structure composed of multiple rigid components. This structure, driven by electricity or other means, allows for relative movement between the links, enabling flexible changes in the overall shape of the tent. These tents are widely deployed in various outdoor environments, such as high-altitude camps, polar research stations, emergency rescue sites, or temporary urban installations. In these variable and harsh outdoor environments, the tents are continuously subjected to various complex and dynamically changing external loads. Specifically, the tents encounter wind loads of different types and intensities. Wind speed and direction vary with time, terrain, and surrounding obstacles, potentially generating continuous wind pressure, sudden gusts, or complex eddy effects. Simultaneously, in cold or high-altitude regions, the tents must withstand snow loads. Snow density and distribution are often uneven, and it may melt or freeze due to changes in ambient temperature, significantly increasing the tent's weight and localized pressure. Furthermore, rainwater may accumulate on the tent's surface, creating additional localized loads. Besides external environmental loads, the movement and placement of people or items inside the tent also generate internal loads, which in turn affect the stress state of the tent structure. These external loads do not exist in isolation; they often appear in complex combinations, and their intensity, direction, and distribution are dynamically changing. For example, in mountainous areas, strong winds may be accompanied by snowfall; in urban environments, gusts may create complex wind fields between buildings, generating local high-pressure or negative-pressure zones. The complexity and uncertainty of this load environment place extremely high demands on the adaptability of the tent structure.

[0003] The multi-link structure of a tent allows for shape variability, and different shapes exhibit significantly different response characteristics to external loads. For example, streamlined shapes typically reduce wind resistance effectively, while steep roofs facilitate snow sliding and expulsion. However, altering the tent shape comes at a cost; it directly affects the tension distribution of the skin, the stress concentration at multi-link connection points, and the overall structural stiffness and stability. Therefore, intelligently selecting or adjusting to the optimal tent shape based on real-time, complex load conditions is crucial for ensuring the safe, stable, and efficient operation of tents in various environments.

[0004] Traditional tent shape control methods typically rely on a few preset shape patterns or simple shape switching based on wind speed and snow depth thresholds. However, such static or rule-based methods struggle to effectively handle complex combinations and dynamic changes in loads. This can lead to excessive structural stress under certain load conditions, posing a risk of structural failure or failing to fully realize the tent's load-bearing potential. Furthermore, frequent or unnecessary shape adjustments consume significant energy, a particularly serious problem in remote outdoor areas with limited energy access, severely impacting the tent's endurance.

[0005] To overcome these problems, the industry urgently needs a smarter and more adaptive method for controlling tent shape. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for controlling the shape of a multi-link deformable tent, which can effectively cope with the combined effects of multiple loads and adapt to the performance changes of tent structural components due to long-term use, while ensuring structural safety, reducing structural stress, and optimizing energy consumption.

[0007] In a first aspect, the present invention provides a method for controlling the shape of a multi-link deformable tent, comprising the following steps:

[0008] Obtain the external load parameters and tent structural response parameters of the tent;

[0009] Based on external load parameters, identify the current load mode, associate the load mode, structural response parameters with the current shape of the tent, record the actual performance of the tent under the load mode, and form historical performance data;

[0010] Continuously analyze historical performance data to identify the correlation between specific load patterns and the best-performing tent shape, and formulate shape selection rules;

[0011] Based on actual operational feedback after tent shape adjustments, the shape selection rules were revised.

[0012] Based on the revised shape selection rules and the current load mode, control commands for the multi-link actuator are generated, and the tent shape is adjusted according to the control commands.

[0013] The multi-link deformable tent shape control method provided by this invention enables the deformable tent to intelligently and adaptively cope with various complex and changing outdoor loads through continuous data collection, experience accumulation, pattern recognition and feedback optimization, while taking into account multiple factors such as structural safety, energy consumption and component wear, thus significantly improving the overall performance and reliability of the tent.

[0014] In a second aspect, the present invention provides a multi-link deformable tent shape control system, comprising:

[0015] The acquisition module is used to acquire the external load parameters and structural response parameters of the tent.

[0016] The identification and recording module is used to identify the current load mode based on external load parameters, associate the load mode, structural response parameters with the current shape of the tent, record the actual performance of the tent under the load mode, and form historical performance data.

[0017] The analysis module is used to continuously analyze historical performance data, identify the correlation between specific load patterns and the best-performing tent shape, and form shape selection rules.

[0018] The correction module is used to correct the shape selection rules based on actual operational feedback after the tent shape has been adjusted.

[0019] The generation control module is used to generate control commands for the multi-link actuator based on the modified shape selection rules and the current load mode, and adjust the tent shape according to the control commands.

[0020] As can be seen from the above, the multi-link deformable tent shape control method provided by this invention, through continuous data acquisition, experience accumulation, and rule optimization, achieves adaptive shape control of the deformable tent under complex and variable outdoor load environments. This significantly improves the tent's ability to cope with complex and unpredictable combinations of loads such as wind, snow, and rain, effectively reduces structural stress, ensures the structural safety and stability of the tent, and achieves intelligent balancing of multiple performance indicators of the tent (such as structural stress, load-bearing capacity, and energy consumption). This avoids the structural failure risk or resource waste that may be caused by traditional methods. At the same time, through continuous performance feedback and rule iteration, the control strategy can adapt to the performance changes of tent structural components due to long-term use (such as wear and increased gaps), extending the service life of the tent and optimizing energy consumption. Especially in outdoor environments where energy access is limited, by selecting a shape adjustment strategy with lower energy consumption, the tent's endurance is improved. Furthermore, the tent has learning capabilities, enabling it to identify and respond to new and unpredictable load combinations or change patterns, enhancing its adaptability in unknown environments.

[0021] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0022] Figure 1This is a flowchart of a multi-link deformable tent shape control method provided in an embodiment of the present invention.

[0023] Figure 2 This is a structural diagram of an existing deformable tent in its first configuration.

[0024] Figure 3 This is a structural diagram of an existing deformable tent in its second configuration.

[0025] Figure 4 This is a schematic diagram of a multi-link deformable tent shape control system provided in an embodiment of the present invention.

[0026] Label Explanation:

[0027] 100. Acquisition Module; 200. Identification and Recording Module; 300. Analysis Module; 400. Correction Module; 500. Generation and Control Module. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0029] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0030] To overcome the limitations of existing technologies, a more intelligent and adaptive method for tent shape control is needed. This method not only requires real-time sensing of various environmental and structural parameters such as wind speed, wind direction, snow depth, temperature, internal pressure, multi-link joint angles, structural stress, and skin tension, but also the ability to identify current load patterns and their changing trends from this massive amount of data. Furthermore, the system should possess learning capabilities, analyzing historical data or continuously experimenting and optimizing during actual operation to determine which tent shape achieves optimal performance goals under different load modes, such as minimizing structural stress, maximizing wind or snow resistance, minimizing energy consumption, or intelligently balancing these goals.

[0031] This learning process requires the ability to adapt to new, unpredictable combinations or patterns of load, enabling the system to find appropriate coping strategies when facing unknown environments. For example, the system might need to learn how wind bends around obstacles to create localized high-pressure zones under specific terrain conditions and adjust the tent shape accordingly to distribute stress. Or it might learn how snow adhesion affects snow accumulation under specific temperature and humidity conditions and adjust the shape to facilitate snow removal.

[0032] The learned optimal shape strategy needs to be accurately translated into control commands for the multi-link actuators to achieve precise shape adjustments. This process requires careful consideration of the actuators' dynamic response characteristics and energy consumption. Furthermore, long-term use of the tent leads to wear and performance degradation of structural components, such as increased clearance at link connections, decreased control precision of the actuators, and reduced skin elasticity. These changes over time affect the correspondence between the tent's actual shape and control commands, and also alter its load-response characteristics. Therefore, the learning process must also adapt to these structural changes over time, continuously optimizing the control strategy to ensure the tent's performance throughout its entire lifespan.

[0033] For reference, see the appendix. Figure 1 This invention provides a method for controlling the shape of a multi-link deformable tent, comprising the following steps:

[0034] Obtain the external load parameters and tent structural response parameters of the tent;

[0035] Based on external load parameters, identify the current load mode, associate the load mode, structural response parameters with the current shape of the tent, record the actual performance of the tent under the load mode, and form historical performance data;

[0036] Continuously analyze historical performance data to identify the correlation between specific load patterns and the best-performing tent shape, and formulate shape selection rules;

[0037] Based on actual operational feedback after tent shape adjustments, the shape selection rules were revised.

[0038] Based on the revised shape selection rules and the current load mode, control commands for the multi-link actuator are generated, and the tent shape is adjusted according to the control commands.

[0039] Acquiring external load parameters and structural response parameters of the tent refers to the real-time data collection process of sensing the environmental conditions and structural state of the tent. This can be achieved by deploying various physical sensors, utilizing image recognition technology, or inferring from environmental forecast data. For example, in addition to wind speed and direction sensors and snow depth sensors, air pressure sensors and light sensors can also be included. Structural response parameters, besides joint angles, stress, and skin tension, can also include vibration frequency and displacement, primarily to provide the necessary data input for subsequent decision-making. Load patterns are the result of classifying and abstracting complex external load environments. They can be defined through data clustering algorithms, pattern recognition technology, or based on preset rules. For example, based on combinations of parameters such as wind speed, wind direction, snow depth, and temperature, load environments can be divided into "strong wind and snowstorm mode," "continuous gust mode," and "high temperature and heavy rainfall mode," primarily to provide a classification basis for establishing shape selection rules. Historical performance data is a collection recording the actual operating performance of tents under specific load modes and shapes. It can be stored in databases, data warehouses, or distributed file systems. For example, the data may include structural safety indicators, energy consumption indicators, and environmental adaptability indicators, primarily to provide a data foundation for subsequent learning and optimization. Shape selection rules are principles guiding the selection of tent shapes under specific load modes. These can be represented by lookup tables, decision trees, neural network models, or strategies generated based on optimization algorithms. For example, rules might explicitly state that a "streamlined, low-profile" shape should be adopted under "strong wind mode," and a "steep roof shape" under "heavy snow mode." This is primarily to enable the tent to autonomously learn and optimize its shape adaptability. Correcting shape selection rules refers to the process of adjusting and optimizing these rules based on actual tent operation feedback. This can be done through reinforcement learning, adaptive control algorithms, or expert systems. For example, when actual performance does not meet expectations, the system will adjust the weights or thresholds in the rules, primarily to ensure the long-term effectiveness and adaptability of the rules. The control commands of the multi-link actuator are the specific operational commands that drive the multi-link structure of the tent to deform. They can be motor rotation commands, hydraulic valve opening commands, or pneumatic cylinder extension commands. For example, the commands can include the precise angle values ​​of each joint, adjustment speed or torque requirements. The main purpose is to translate decisions into actual actions and achieve dynamic adjustment of the tent shape.

[0040] The working principle of this invention lies in continuously collecting information on the external environmental loads of the tent (such as wind speed, wind direction, snow depth, rainfall, and temperature) and the tent's own structural response information (such as multi-link joint angles, structural stress, skin tension, and energy consumption) through a multi-sensor array. This real-time data is correlated with the tent's current shape configuration, and the actual performance of the tent under that shape is recorded (e.g., whether the stress is excessive, its wind resistance, snow removal efficiency, and energy consumption). This correlated data is stored and continuously accumulated, forming a historical performance database.

[0041] By continuously analyzing this historical database and using data analysis algorithms (such as machine learning or statistical analysis methods), we can identify which tent shapes achieve optimal performance goals (such as minimizing stress, maximizing wind and snow resistance, or minimizing energy consumption) under specific load patterns. These identified correlations constitute a "shape selection rule set".

[0042] When a tent encounters new load patterns or its current shape performs poorly during actual operation, it will revise and improve its internal shape selection rules based on this feedback (e.g., a certain shape exhibits unexpected local high stress under a specific wind field) to ensure that a better shape can be selected when similar situations arise in the future.

[0043] Finally, based on real-time load data, when the current load pattern is identified, the optimized shape selection rule set is queried and applied to determine an optimal target tent shape. Then, the precise control commands required to adjust the tent from its current shape to the target shape are calculated and sent to the actuators, thus achieving dynamic shape adjustment of the tent. After adjustment, its performance continues to be monitored, and new performance data is used as feedback, forming a closed-loop control process of continuous learning and optimization, ensuring that the tent can adapt to constantly changing external environments and its own structural state throughout its entire lifecycle.

[0044] The core innovation of this application lies in the intelligent and dynamic optimization of the shape control of multi-link deformable tents by introducing real-time data perception, historical performance data accumulation and continuous analysis, and rule-adaptive correction mechanism based on actual operation feedback. This solves the problems of insufficient adaptability, structural safety risks and excessive energy consumption of traditional methods under complex dynamic load environments, and achieves the effect of improving tent structural safety, operating efficiency and energy utilization.

[0045] Specifically, the shape control method of this application first establishes a perception of the current environmental conditions and structural state by acquiring the external load parameters and structural response parameters of the tent in real time. Then, based on the acquired external load parameters, the current load mode is identified, and this load mode, the tent's structural response parameters, and the tent's current shape are correlated. Simultaneously, the actual performance of the tent under this load mode and shape is recorded, thereby continuously accumulating historical performance data. Based on this, these historical performance data are continuously analyzed to identify which tent shape can optimize performance under specific load modes, thereby refining and forming shape selection rules. To ensure the long-term effectiveness and adaptability of the rules, this method introduces a feedback correction mechanism: after the tent shape is adjusted according to the rules and actually operated, actual feedback information is collected, and the shape selection rules are evaluated and corrected based on this feedback. Ultimately, when a new current load pattern is identified, control commands to drive the multi-link actuator will be generated based on this modified shape selection rule and the current load pattern. The shape of the tent will be adjusted according to these commands, thereby enabling the tent to adaptively adjust to complex loads and ensuring that it can maintain structural safety, stable operation and energy utilization in various environments.

[0046] In some embodiments, the steps of obtaining the external load parameters of the tent and the tent structural response parameters include:

[0047] After identifying the target areas where the tent is prone to localized stress concentration or uneven load distribution under complex combined loads, external load monitoring devices and structural response monitoring devices are deployed in the target areas and at key stress-bearing parts of the tent structure to obtain multi-point external load parameters and multi-point structural response parameters. The external load monitoring device is a physical device used to measure the external environmental loads on the tent; the structural response monitoring device is a physical device used to measure the response of the tent's own structure to external loads and its current state.

[0048] Based on the correlation between multi-point external load parameters and multi-point structural response parameters, comprehensive external load parameters and tent structural response parameters are obtained by inferring the load distribution information and structural response information of the entire tent or local areas.

[0049] Identifying target areas in a tent prone to localized stress concentration or uneven load distribution under complex combined loads involves using finite element analysis, physical model testing, or historical data analysis to identify locations in the tent skin, linkage points, or supporting structures exhibiting stress concentration, excessive deformation, or significantly uneven load distribution under specific wind, snow, and rain load combinations. This can be achieved through pre-analysis using structural simulation software or empirical judgment based on past deployment experience and on-site observation data. Furthermore, inferring load distribution and structural response information for the entire tent or specific areas based on the correlation between multi-point external load parameters and multi-point structural response parameters involves using data fusion algorithms, machine learning models, or physical models to analyze the inherent relationships between discrete measurement point data. For example, through interpolation, extrapolation, or pattern recognition, a continuous load field and structural response field can be constructed on the tent surface or inside. This can be achieved using Kriging interpolation, neural network models, or inverse calculations based on structural mechanics models.

[0050] In acquiring the external load parameters and structural response parameters of the tent, this solution first identifies target areas prone to localized stress concentration or uneven load distribution under complex combined loads, thus achieving precise focus on key data acquisition areas. This pre-identification mechanism ensures that subsequent sensor deployment is no longer blind or uniformly distributed, but rather targeted to cover the most vulnerable parts of the tent structure or those significantly impacting overall stability. Based on this, by deploying external load monitoring devices and structural response monitoring devices in these target areas and key stress-bearing locations of the tent structure, multi-point external load parameters and multi-point structural response parameters can be acquired. The external load monitoring device captures real-time load information from the tent's external environment, such as wind pressure, snow weight, or rainwater accumulation, while the structural response monitoring device simultaneously monitors changes in the tent structure's own deformation, stress, or vibration. This multi-point, distributed data acquisition method significantly improves the precision of perception of complex load environments and structural responses, overcoming the limitations of traditional methods where a single measuring point cannot comprehensively reflect the overall stress state of the tent. Furthermore, this scheme does not simply collect discrete multi-point data. Instead, based on the correlation between these multi-point external load parameters and multi-point structural response parameters, it infers the load distribution and structural response information of the tent as a whole or in local areas, thereby obtaining comprehensive external load parameters and tent structural response parameters. This inference mechanism utilizes the inherent logic and physical laws between data. For example, by interpolating data from adjacent measuring points or performing inversion calculations combined with structural mechanics models, it is possible to construct a continuous load and response field on the tent surface or inside from a limited number of discrete data points. This compensates for the lack of local information that may be caused by limitations in the number and location of sensor deployments, ensuring the comprehensiveness and accuracy of the acquired parameters. Through the above-mentioned refined and comprehensive data acquisition process, this scheme provides high-quality data input for subsequent tent shape control methods. Specifically, the acquired comprehensive external load parameters and structural response parameters can serve as an accurate basis for identifying the current load mode and can be correlated with the current tent shape to record the actual performance of the tent under specific load modes, thus forming more reliable historical performance data. This high-quality data foundation allows subsequent steps—such as continuously analyzing historical performance data, identifying the correlation between specific load patterns and the optimal tent shape, and formulating shape selection rules—to be built upon more accurate and comprehensive information. This significantly improves the accuracy and adaptability of the shape selection rules. Ultimately, control commands for the multi-link actuators generated based on the revised shape selection rules and the current load pattern can more precisely adjust the tent shape to cope with complex and ever-changing external environments, ensuring the structural safety and operational efficiency of the tent under various load conditions. This close integration of data acquisition methods with subsequent control logic enables the entire tent shape control system to achieve more intelligent and robust adaptive adjustments.

[0051] In some embodiments, the external load monitoring device includes a wind speed and direction sensor, a snow depth sensor, a temperature sensor, a humidity sensor, and a rainfall sensor.

[0052] Wind speed and direction sensors are used to capture airflow dynamics, snow depth sensors are used to quantify snow load, temperature and humidity sensors are used to provide environmental thermal and humidity conditions, and rain sensors are used to monitor precipitation intensity.

[0053] In some embodiments, the structural response monitoring device includes a multi-link joint angle sensor, a stress sensor, a skin tension sensor, and an energy consumption monitoring device.

[0054] Multi-link joint angle sensors are devices that measure the relative rotation angle between adjacent links in a multi-link structure. They can be implemented using rotary encoders, potentiometers, Hall effect sensors, or MEMS gyroscopes. Stress sensors are devices that measure the force per unit area within an object, reflecting the stress state of the material. They can be implemented using resistance strain gauges, piezoelectric sensors, or fiber optic grating sensors. Skin tension sensors are devices that measure the force in the tensile direction of flexible skin materials, reflecting the skin's tightness. They can be implemented using piezoelectric thin-film sensors, force gauges, or image-processing-based deformation analysis systems. Energy consumption monitoring equipment is a device that measures the energy consumed by a system or equipment during operation. It can be implemented using current sensors, voltage sensors combined with power calculation modules, or integrated energy metering chips.

[0055] In some embodiments, the step of continuously analyzing historical performance data to identify the correlation between specific load patterns and the tent shape with the best performance, and forming shape selection rules, includes:

[0056] A1. Extract the structural safety performance index, energy consumption performance index, and environmental adaptability performance index of tents under different tent shapes from historical performance data;

[0057] A2. Analyze the interrelationships of structural safety performance indicators, energy consumption performance indicators, and environmental adaptability performance indicators under different tent shapes under specific load modes, and determine the trade-offs between each performance indicator.

[0058] A3. Based on the trade-offs and combined with preset performance priorities or dynamic weights, comprehensively evaluate the structural safety performance indicators, energy consumption performance indicators, and environmental adaptability performance indicators to determine the tent shape that can achieve the best comprehensive performance of multiple dimensions under a specific load mode.

[0059] A4. Associate the optimal tent shape with a specific load pattern to form a shape selection rule.

[0060] Structural safety performance indicators refer to quantitative parameters reflecting the integrity and stability of a tent structure under external loads. These can be characterized by parameters such as structural stress, structural deformation, connection point stress, or fatigue life. Energy consumption performance indicators refer to quantitative parameters reflecting the energy required for the tent to adjust or maintain a specific shape. These can be characterized by parameters such as the energy consumption of the drive mechanism, battery life, or energy efficiency. Environmental adaptability performance indicators refer to quantitative parameters reflecting the tent's ability to resist external loads and maintain internal environmental stability under specific environmental conditions. These can be characterized by parameters such as wind pressure resistance, snow pressure resistance, rainwater penetration resistance, or thermal insulation performance. Trade-offs refer to the inherent relationships of mutual influence and constraint among different performance indicators in multi-objective optimization problems. These can be established and expressed using mathematical models, empirical curves, or expert knowledge bases. Preset performance priorities refer to the predetermined order of importance or weight assigned to different performance indicators based on specific application scenarios or user needs during multi-objective comprehensive evaluation. These priorities can be determined using user configuration parameters, scenario mode selection, or system default settings. Dynamic weights refer to the real-time adjustments of importance weights assigned to different performance indicators based on real-time environmental changes, load patterns, or system states during multi-objective comprehensive evaluation. These weights can be generated using adaptive algorithms, machine learning models, or real-time decision logic. Comprehensive evaluation refers to the systematic quantitative analysis and comparison of multiple interrelated performance indicators based on their trade-offs and importance to arrive at overall performance results. This can be achieved using methods such as weighted summation, multi-attribute decision analysis, or fuzzy comprehensive evaluation. The optimal tent shape refers to the tent structure that achieves the best overall performance under a specific load pattern after comprehensive evaluation of multi-dimensional performance indicators. This shape can be determined using a predefined shape library, a shape generated through parameter optimization, or a shape recommended based on machine learning. Shape selection rules refer to the logical mapping relationship between a specific load pattern and the corresponding optimal tent shape. These rules can be stored and applied using lookup tables, decision trees, neural network models, or rule-based expert systems.

[0061] In this application's method for controlling the shape of a multi-link deformable tent, a systematic data analysis and evaluation process is employed to intelligently identify the correlation between specific load modes and the tent shape with optimal performance, and to formulate effective shape selection rules. First, by extracting structural safety performance indicators, energy consumption performance indicators, and environmental adaptability performance indicators under different tent shapes from historical performance data, this approach lays the foundation for a comprehensive evaluation of tent performance. This ensures a multi-dimensional and detailed understanding of the tent's performance under various loads, avoiding the limitations of traditional methods due to their single performance evaluation dimension.

[0062] Building upon this foundation, the system further analyzes the interrelationships of structural safety performance indicators, energy consumption performance indicators, and environmental adaptability performance indicators under specific load modes and different tent shapes, thereby determining the trade-offs between each performance indicator. This analytical step is crucial for resolving multi-objective conflicts; it reveals the potential impact of optimizing one performance indicator on others, enabling subsequent decisions to fully consider the constraints and synergies between indicators and avoid optimization strategies that sacrifice one aspect for another.

[0063] Subsequently, based on the established trade-offs and in conjunction with preset performance priorities or dynamic weights, a comprehensive evaluation is conducted on structural safety performance indicators, energy consumption performance indicators, and environmental adaptability performance indicators. This comprehensive evaluation process is the core of achieving the optimal tent shape for performance. It allows the system to be flexibly configured according to actual needs (such as prioritizing structural safety in extreme weather conditions or prioritizing energy consumption in energy-constrained areas) and to adapt to environmental changes through dynamic weights. Through this weighted comprehensive evaluation, the system can determine the tent shape that achieves the best overall performance across multiple dimensions under specific load modes, ensuring that the selected shape is the optimal choice after comprehensive consideration.

[0064] Finally, the optimal tent shape is correlated with specific load patterns to form shape selection rules. These rules transform complex analysis and evaluation results into actionable guidelines that can be directly invoked by subsequent control modules. When a certain load pattern is identified, the system can quickly and accurately determine and adjust to the corresponding optimal tent shape.

[0065] By organically combining the above steps, this solution transforms raw historical performance data into shape selection rules with intelligent decision-making capabilities. Compared to methods that generate rules based solely on a single indicator or simple threshold, this solution can deeply mine the complex correlations within multi-dimensional performance data, effectively handling conflicts and trade-offs between multiple objectives. This provides the tent with a more refined and adaptive shape adjustment strategy under complex and ever-changing external load environments. This allows the tent to simultaneously optimize energy consumption and environmental adaptability while ensuring structural safety, thereby improving the overall performance and operational stability of the tent in practical applications.

[0066] In one specific embodiment, this solution can be implemented as follows: First, to extract performance indicators of the tent under different shapes, the system can obtain data from a historical performance database. This database may contain operational records from multiple field tests, simulations, and actual deployments. For each record, the system can extract structural safety performance indicators of the tent under a specific shape (e.g., flat, dome, streamlined, or hybrid), such as the maximum stress value and maximum deformation obtained by stress sensors and deformation sensors deployed at stress points; energy consumption performance indicators, such as instantaneous power and cumulative energy consumption during shape adjustment recorded by energy consumption monitoring equipment; and environmental adaptability performance indicators, such as vibration frequency and amplitude at a specific wind speed, skin tension distribution at a specific snow depth, and internal temperature stability.

[0067] Furthermore, to analyze the interrelationships of these performance indicators under specific load modes and determine trade-offs, the system can employ data mining and machine learning algorithms for a specific load mode, such as "strong winds accompanied by moderate snow." For example, a multivariate regression model or neural network model can be constructed, taking different tent shape parameters as input and outputting corresponding structural safety, energy consumption, and environmental adaptability performance indicators. By analyzing the model's output, the potential gains or losses in other performance indicators when optimizing a particular performance indicator can be identified, thereby quantifying the interrelationships between them. For instance, it may be found that a streamlined shape prioritizing low wind resistance may lead to reduced snow resistance, while a dome shape emphasizing snow resistance may generate significant vibrations under strong winds.

[0068] Based on this, to comprehensively evaluate these performance indicators and determine the optimal tent shape, the system can perform weighted calculations according to preset performance priorities or dynamic weights. For example, under extreme weather conditions, the system can preset structural safety performance indicators to have high priority, followed by environmental adaptability performance indicators, and finally energy consumption performance indicators. The system can assign corresponding weights to these priorities, for example, structural safety weight 0.5, environmental adaptability weight 0.3, and energy consumption weight 0.2. For each tent shape, its performance indicators under a specific load mode are normalized, multiplied by the corresponding weights, and summed to obtain a comprehensive score. The tent shape with the highest score is determined as the tent shape that achieves the best comprehensive performance across multiple dimensions under that specific load mode. For example, after calculation, an "optimized streamlined dome hybrid shape" may stand out in the comprehensive score.

[0069] Finally, to establish shape selection rules, the system associates this optimal tent shape with the corresponding specific load mode. For example, a rule can be generated: "When the load mode is <strong wind with moderate snow>, it is recommended to adjust the tent shape to <optimized streamlined dome hybrid shape>." These rules can be stored in a rule base, allowing the tent's intelligent control system to quickly retrieve and execute the appropriate shape adjustment commands when it identifies the corresponding load mode in real time.

[0070] This solution comprehensively extracts multi-dimensional performance indicators, including structural safety, energy consumption, and environmental adaptability, from historical performance data and deeply analyzes the interrelationships of these indicators under different tent shapes to determine the trade-offs between performance indicators. Based on this, a comprehensive evaluation is conducted using preset performance priorities or dynamic weights to determine the tent shape that achieves the optimal overall performance across multiple dimensions under specific load modes. Finally, this optimal tent shape is correlated with the specific load mode to form an intelligent shape selection rule. This enables the tent to effectively handle conflicts and trade-offs between multiple objectives when facing complex and variable external loads, avoiding a decline in other key performance indicators due to optimization of a single performance aspect. Therefore, this solution generates a more balanced and adaptable shape selection rule, ensuring that the tent maintains performance in various environments and improving its operational stability and practicality.

[0071] In some embodiments, the specific steps in step A3 include:

[0072] A31. Obtain performance data related to local areas of the tent from historical performance data; the performance data includes local stress, local skin tension, or local deformation.

[0073] A32. Based on the performance-related index information of local areas, and combined with the possible local defects or uneven load distribution of the tent structure under specific load modes, assess the local risks of the tent under different shapes.

[0074] A33. When comprehensively evaluating structural safety performance indicators, energy consumption performance indicators, and environmental adaptability performance indicators, the results of local risk assessment shall be included in the evaluation considerations. Based on the trade-offs, preset performance priorities or dynamic weights, and the results of local risk assessment, the multi-dimensional performance and local risks of each tent shape shall be comprehensively quantified.

[0075] A34. Based on the comprehensive quantitative results, determine the tent shape that can achieve the best comprehensive performance in multiple dimensions and minimize local risks under a specific load mode.

[0076] Trade-off relationships refer to the inherent connections between multiple interdependent performance indicators. By analyzing their interactions and the attrition-like nature of each indicator, it's determined how to balance and prioritize them under different application scenarios or load modes. This can be established using expert experience, historical data analysis, or simulation calculations based on physical models. Preset performance priorities or dynamic weights refer to the relative importance values ​​assigned to different performance indicators and risk factors during comprehensive evaluation, based on specific needs or environmental conditions. These values ​​can be pre-set or adjusted based on real-time data or specific algorithms. Comprehensive quantification of multi-dimensional performance and local risks involves integrating the tent's structural safety performance, energy consumption performance, environmental adaptability performance, and local risk assessment results through a unified mathematical model or evaluation framework, transforming them into a comparable value or score to reflect the tent's overall performance in multiple aspects of performance and risk control. This can be achieved using multi-attribute decision analysis methods, fuzzy comprehensive evaluation methods, or machine learning-based predictive models.

[0077] This application incorporates consideration of local risks when comprehensively evaluating tent shapes, ensuring that while pursuing optimal overall performance, potential local risks are also fully identified and minimized. Specifically, firstly, by acquiring historical performance data related to local areas of the tent, such as local stress, local skin tension, or local deformation, a necessary and accurate data foundation is provided for subsequent local risk assessment. It is precisely because of this detailed local data that the consideration of tent performance delves from the macroscopic to the microscopic level, enabling a comprehensive identification of potential local weaknesses. Based on this, and considering the local performance-related indicators of these local areas, combined with potential local defects or uneven load distribution in the tent structure under specific load modes, the local risks of the tent under different shapes are assessed. This assessment process not only relies on historical data but also considers the complexity and uncertainty of actual load environments, making the local risk assessment more realistic and effectively identifying risk points that may lead to local failure under specific loads, thus compensating for the shortcomings of assessments based solely on overall performance. Furthermore, when comprehensively evaluating structural safety performance, energy consumption performance, and environmental adaptability performance, the results of local risk assessment are incorporated into the evaluation. By considering trade-offs, preset performance priorities or dynamic weights, and combining local risk assessment results, a comprehensive quantification of multi-dimensional performance and local risks for each tent shape is performed. This ensures that the selection of the optimal tent shape not only considers the balance of overall performance but also gives full attention and quantitative consideration to potential local risks. This comprehensive quantification method avoids the problem of pursuing only overall optimization while ignoring local vulnerabilities, ensuring that the final selected shape achieves excellent overall performance while also guaranteeing local safety. Ultimately, based on this comprehensive quantification result, a tent shape is determined that, under specific load modes, achieves comprehensive optimization of multi-dimensional performance indicators such as structural safety, energy consumption, and environmental adaptability while simultaneously minimizing local risks. This organic combination of steps ensures that the selection of tent shape is no longer merely an optimization of macroscopic performance but also takes into account microscopic safety and robustness, improving the reliability and safety of tents in complex and variable environments.

[0078] In a specific embodiment, to achieve optimized control of the tent shape, the following approach can be taken. First, when acquiring performance data related to local areas of the tent from historical performance data, a small sensor array deployed in key stress areas of the tent, such as areas of the skin susceptible to concentrated wind pressure or snow accumulation, and multi-link connection nodes, can be used to continuously collect data on local stress, skin tension, and structural deformation. This data can be stored in a database along with the tent's shape, external load patterns, and environmental parameters, forming a complete historical performance record. Based on this, when assessing the local risks of the tent under different shapes, finite element analysis (FEA) models or computational fluid dynamics (CFD) models can be used. For example, for specific load patterns (such as strong lateral winds or asymmetric snow accumulation), local stress peaks and areas of uneven skin tension identified in historical data can be used as input, combined with preset local defect models (such as material fatigue cracks or loose connectors) or simulated uneven load distribution, to perform simulation analysis on different tent shapes. Through simulation, the stress distribution, deformation degree, and potential failure risk of various local areas of the tent under these adverse conditions can be predicted and quantified as a local risk score. Furthermore, when comprehensively evaluating structural safety performance indicators, energy consumption performance indicators, and environmental adaptability performance indicators, the aforementioned local risk assessment results can be integrated with existing macroscopic performance indicators into a multi-objective decision support system. This system can employ methods such as the Analytic Hierarchy Process (AHP) or fuzzy logic reasoning to perform a weighted sum of the normalized indicators and risk scores based on preset performance priorities (e.g., increasing the weight of structural safety and local risks under extreme weather conditions) or dynamically adjusted weights based on real-time environmental data. This yields a comprehensive quantitative score for each tent shape. Ultimately, based on these comprehensive quantitative scores, the system can automatically identify and recommend the tent shape with the highest score. For example, when facing an impending blizzard, the system will select a shape that not only has strong overall wind and snow resistance but also minimizes stress concentration risks in local areas such as skin seams and support rod connection points, based on historical data and simulation results. This ensures the tent's reliability and safety in complex and changing environments.

[0079] This application effectively identifies and avoids potential local risks overlooked in the pursuit of overall optimization by introducing local risk assessment into a comprehensive evaluation of multi-dimensional performance indicators. By acquiring performance index information for local areas and combining it with risk assessments of local defects or uneven load distribution, the selected tent shape achieves comprehensive optimization in macroscopic performance aspects such as structural safety, energy consumption, and environmental adaptability under specific load modes, while also minimizing risks at the microscopic level, such as local stress concentration and uneven skin tension. This ensures that the tent structure possesses robustness at both the local and overall levels in complex and variable environments, especially under conditions of uneven load distribution or local defects, thereby improving the tent's reliability and safety.

[0080] In some embodiments, step A33, which involves comprehensively quantifying the multi-dimensional performance and local risks of each tent shape based on trade-offs, preset performance priorities or dynamic weights, and local risk assessment results, includes:

[0081] The structural safety performance indicators, energy consumption performance indicators, environmental adaptability performance indicators, and local risk assessment results are normalized.

[0082] Based on the trade-offs, preset performance priorities, or dynamic weights, the normalized indicators are weighted and summed to obtain a comprehensive score for each tent shape.

[0083] The tent shapes are ranked based on their overall scores to determine the optimal tent shape.

[0084] For example, in practical applications, a tent shape optimization task can be set up, which includes multiple candidate tent shapes, such as shape A, shape B, and shape C. First, for each shape, obtain its structural safety performance indicators (e.g., maximum stress value), energy consumption performance indicators (e.g., energy consumption per unit time), environmental adaptability performance indicators (e.g., wind pressure resistance), and local risk assessment results (e.g., local stress concentration risk index) under a specific load mode. Next, these raw data are normalized. For example, the Min-Max normalization method can be used to linearly scale all indicator values ​​to the range of 0 to 1, ensuring that all indicators have the same numerical range. For indicators where smaller values ​​are better (such as energy consumption and local risk), they can be converted to 1 minus the normalized value, making larger values ​​better as well. Subsequently, based on the characteristics of the current load mode and user needs, the weights of each indicator are set. For example, in a strong wind environment, the weight of structural safety performance can be set to 0.4, the weight of energy consumption performance to 0.2, the weight of environmental adaptability performance to 0.2, and the weight of local risk assessment results to 0.2. Then, the normalized values ​​of each tent shape are multiplied by their corresponding weights and summed to obtain the overall score for that shape. For example, the overall score for shape A can be calculated as: (normalized structural safety value * 0.4) + (normalized energy consumption value * 0.2) + (normalized environmental adaptability value * 0.2) + (normalized local risk value * 0.2). This calculation is repeated for all candidate shapes. Finally, the overall scores of all tent shapes are sorted in descending order, and the tent shape with the highest overall score is determined as the optimal tent shape under the current load mode.

[0085] In some embodiments, the step of correcting the shape selection rule based on actual operational feedback after tent shape adjustment includes:

[0086] B1. Obtain information on the deviation between the actual performance and the expected performance after tent shape adjustment, as well as energy consumption data and component life data of the multi-link actuator;

[0087] B2. Based on the deviation information, combined with energy consumption data and component life data, assess the current adaptability of the shape selection rule, and evaluate the impact of rule correction operations on the energy consumption and component life of the multi-link actuator.

[0088] B3. Based on the results of the adaptability assessment, the impact of energy consumption and the impact of component life, determine the correction frequency and correction magnitude of the shape selection rules;

[0089] The shape selection rules are modified based on the correction frequency and correction magnitude.

[0090] "Assessing the current adaptability of the shape selection rule" refers to a quantitative judgment on the degree to which the current shape selection rule matches the expected target in actual operation. This can be achieved by analyzing the trend, duration, or cumulative amount of deviation between actual and expected performance. For example, the adaptability of the rule can be judged by calculating the root mean square error of the deviation or by counting the number of times the deviation exceeds a preset threshold. Furthermore, "Assessing the energy consumption and component lifespan impact of rule correction operations on multi-link actuators" refers to a predictive analysis of the potential energy consumption increase and component wear acceleration that might result from rule correction operations before making such corrections. This can be achieved using predictive models based on historical data, simulations, or simplified empirical formulas. For example, the impact can be assessed by consulting energy consumption and component stress prediction tables corresponding to different shape adjustment paths.

[0091] The above scheme details how to revise the shape selection rule based on actual operational feedback after tent shape adjustment. Its core lies in incorporating considerations of the energy consumption and component lifespan of the multi-link actuator, thereby achieving intelligent and refined rule revision. The entire revision process operates collaboratively through a series of logically rigorous steps. First, in step B1, the system acquires information on the deviation between the actual and expected performance of the tent after shape adjustment, providing direct evidence of the current rule's validity. Simultaneously, it acquires energy consumption data and component lifespan data for the multi-link actuator, providing crucial constraints and evaluation dimensions for subsequent revision decisions. It is precisely this comprehensive acquisition of multi-dimensional data that makes subsequent intelligent decision-making possible. Based on this, step B2 evaluates the current adaptability of the shape selection rule based on the acquired deviation information, combined with energy consumption and component lifespan data. This evaluation not only determines whether the rule needs revision and its urgency, but more importantly, it pre-assesses the potential impact of rule revision on the energy consumption and component lifespan of the multi-link actuator. This pre-assessment mechanism enables corrective decisions to balance performance improvement with system operating costs and reliability, avoiding indiscriminate corrections. Finally, in step B3, the system intelligently determines the correction frequency and magnitude of the shape selection rule based on the adaptive assessment results, energy consumption impact, and component lifespan impact, and corrects the shape selection rule accordingly. This multi-dimensional assessment-based correction strategy ensures that the correction of the shape selection rule effectively improves tent performance while maximizing the lifespan of the actuators and optimizing energy consumption. Overall, this solution forms a complete technical system with the previous solution. The previous solution continuously analyzed historical performance data to identify the correlation between specific load patterns and the tent shape with the best performance, thus forming shape selection rules. This solution further improves the dynamic correction mechanism of the rules. By combining actual operational feedback with key operational indicators such as energy consumption and component lifespan, this solution ensures that the correction of the shape selection rule is no longer simply performance-driven, but comprehensively considers the long-term operational efficiency and reliability of the tent. This combination enables the tent's shape control method to adapt more intelligently to complex and ever-changing outdoor environments. It can not only respond to changes in external loads in a timely manner, but also effectively manage system resource consumption and component wear while ensuring performance, thereby significantly improving the adaptability, stability, and economy of multi-link deformable tents in actual deployment.

[0092] In one specific implementation, the above-mentioned step of correcting the shape selection rule can be implemented as follows: In step B1, the tent's control system can continuously monitor and record the tent's actual performance after a specific shape adjustment. This includes, for example, structural safety indicators obtained through stress sensors and skin tension sensors, energy consumption indicators obtained through energy monitoring equipment, and environmental adaptability indicators obtained through wind speed and direction sensors, snow depth sensors, etc. These actual performance indicators are compared with the expected performance indicators determined in advance through simulation or experimentation to calculate the deviation information. Simultaneously, the energy consumption data of the multi-link actuator can be collected in real time by current and voltage sensors integrated on the drive motor and accumulated for calculation. Component lifespan data can be estimated based on the number of motion cycles recorded by the multi-link joint angle sensor, the instantaneous stress peak measured by the stress sensor, and a preset fatigue life model. In step B2, the system can determine that the current adaptability of the shape selection rule is low based on the acquired deviation information; for example, if the actual stress continues to exceed the expected safety threshold. Furthermore, to evaluate the impact of the rule correction operation on the energy consumption and component lifespan of the multi-link actuator, the system can pre-store a database of the impact of the correction operation. The database can contain estimated energy consumption and cumulative component stress values ​​corresponding to different correction magnitudes (e.g., the amount of joint angle change required to adjust from the current shape to the target shape). When an evaluation is needed, the system can query this database or run a simplified prediction algorithm to estimate energy consumption and component wear based on potential shape adjustment paths. For example, if a correction operation requires a large adjustment of multiple links, and the predicted stress values ​​of some joints along the adjustment path are close to or exceed the fatigue limit of the component, then its energy consumption impact and component life impact are evaluated as high. In step B3, based on the evaluation results of step B2, the system can use a decision matrix or fuzzy logic controller to determine the correction frequency and correction magnitude. For example, if the rule adaptability evaluation result is "low," and the energy consumption impact and component life impact evaluation results are "acceptable," the system can determine to adopt a "high frequency" and "large magnitude" correction strategy. If the rule adaptability evaluation result is "medium," but the energy consumption impact or component life impact evaluation result is "high," the system may choose a "low frequency" and "small magnitude" correction, or even temporarily refrain from correction and wait for a more suitable opportunity. Once the correction frequency and magnitude are determined, the system can actually correct the rules by adjusting the weight parameters, thresholds, or correlation functions in the shape selection rules, thereby optimizing the tent's performance in subsequent operations.

[0093] Through the above technical solution, this application effectively addresses the challenge of intelligently determining the correction frequency and magnitude of shape selection rules in outdoor environments where deformable tents face wear risks and limited energy access for multi-link components. This balances the rule's adaptation speed with system energy consumption and component lifespan. Specifically, this solution comprehensively acquires actual performance deviations of the tent, energy consumption data of the multi-link actuators, and component lifespan data. Based on this, it comprehensively evaluates the rule's adaptability and the impact of correction operations on energy consumption and component lifespan, thereby intelligently determining the most suitable correction frequency and magnitude. This avoids excessively frequent or sensitive corrections that increase energy consumption and component wear, while also preventing insufficient corrections that prevent the rules from adapting promptly to long-term changes in the tent's performance or new load patterns. Therefore, this solution ensures that the tent's shape selection rules dynamically adapt to changes in the external environment while maximizing the lifespan of the multi-link actuators and optimizing system energy consumption. This significantly improves the long-term operational stability, reliability, and economy of multi-link deformable tents in complex outdoor environments.

[0094] In some embodiments, step B2, specifically evaluating the impact of the rule correction operation on the energy consumption and component lifespan of the multi-link actuator, includes the following steps:

[0095] Based on the target tent shape determined by the rule correction operation and the current tent shape, determine the shape adjustment path of the multi-link actuator, and predict the transient load of the multi-link actuator at each point on the shape adjustment path;

[0096] Obtain wear status information of specific components of a multi-link actuator;

[0097] Based on the predicted transient load values ​​of the multi-link actuator at each point along the shape adjustment path and the wear status information of specific components, the instantaneous energy consumption value of the multi-link actuator and the instantaneous stress value of specific components during the shape adjustment process are calculated.

[0098] Identify the peak values ​​in instantaneous energy consumption and instantaneous stress, and combine them with wear status information of specific components to assess the risk of peak values ​​to overload or accelerated component failure in multi-link actuators;

[0099] Based on the risk assessment results, the assessment results of the impact of rule correction operations on the energy consumption and component life of multi-link actuators are generated.

[0100] The shape adjustment path refers to the continuous spatial motion trajectory of each link and joint of the multi-link actuator as it transitions from the current tent shape to the target tent shape. This path can be determined using inverse kinematics algorithms, path planning algorithms, or empirical data fitting. Transient load refers to the dynamic force or torque experienced by the multi-link actuator at a specific moment along the shape adjustment path. This load can be obtained using dynamic simulation models, real-time sensor measurements, or prediction methods based on physical models. Wear status information refers to the current physical wear or aging condition of specific components (e.g., joints, bearings, gears, or links) of the multi-link actuator. This wear can be obtained using wear sensors, vibration analysis, acoustic emission detection, or life prediction models based on historical operating data. Instantaneous energy consumption value refers to the instantaneous power integral value of the electrical or mechanical energy consumed by the drive system (e.g., motor) of the multi-link actuator at any moment during the shape adjustment process. This instantaneous energy consumption value can be obtained using power sensors, current and voltage sensors, or calculation methods based on motor models. The instantaneous stress value refers to the internal force per unit area borne by a specific component of the multi-link actuator at any moment during the shape adjustment process. It can be obtained using strain gauges, finite element analysis, or calculation methods based on material mechanics models. The peak value refers to the maximum instantaneous energy consumption or instantaneous stress value reached within a certain period. It can be identified using signal processing algorithms, threshold detection, or statistical analysis methods. The risk of overload or accelerated component failure refers to the possibility that the multi-link actuator or its specific components may experience performance degradation, functional loss, or significantly shortened lifespan due to instantaneous high loads or cumulative damage during the shape adjustment process. It can be assessed using reliability assessment models, fault tree analysis, or prediction methods based on fatigue life curves.

[0101] This solution provides a refined and predictive method for assessing the potential impact of rule correction operations on the energy consumption and component lifespan of multi-link actuators. This addresses the potential negative impacts on actuators when correcting shape selection rules, ensuring that the system optimizes tent performance while maintaining the long-term reliability and energy efficiency of the actuators. The assessment process first precisely plans the motion trajectory of the multi-link actuator from its current state to the target state—the shape adjustment path—based on the target tent shape determined by the rule correction operation and the current tent shape. Based on this, the system further predicts the transient loads experienced by the multi-link actuator at different times along this path. By predicting transient loads, the system can anticipate the dynamic stress conditions the actuator may face during shape adjustment, which is crucial for subsequent assessments of energy consumption and component stress. This avoids the shortcomings of assessments based solely on static or average loads, laying the foundation for more accurate risk assessment. Simultaneously, this solution incorporates consideration of the wear status information of specific actuator components. Wear status directly reflects the health and remaining lifespan of components and is crucial for assessing component performance under future loads. By acquiring this information, the assessment is no longer based on assumptions about ideal or brand-new components, but rather incorporates the actual aging conditions of the components, making the assessment of the impact on component lifespan more realistic and improving the accuracy and reliability of the assessment. Subsequently, this solution combines the aforementioned transient load prediction values ​​with the wear status information of specific components for more refined calculations. The transient load prediction values ​​can be used to calculate the instantaneous energy consumption of the actuator during dynamic adjustment, which reflects energy usage efficiency and peak demand more accurately than simply calculating total energy consumption. Simultaneously, combining the wear status information to calculate the instantaneous stress value of specific components can more accurately reflect the actual stress level of the component under transient loads at its current wear level, providing crucial data for subsequent risk assessment. This solution focuses on identifying peak values ​​in the instantaneous energy consumption and instantaneous stress values, as peak values ​​are often key factors leading to system overload or component failure. By identifying these peak values ​​and again combining them with the wear status information of specific components, the system can assess the potential risks of these extreme conditions to overall actuator overload or accelerated failure of specific components. This risk assessment, based on peak values ​​and actual wear conditions, effectively avoids unexpected failures caused by ignoring instantaneous high loads, ensuring the safety of rule-correction operations. Finally, this solution integrates all the detailed predictions, calculations, and risk assessment results to form the final evaluation result. This result is multi-dimensional and refined; it not only quantifies the energy consumption impact of rule-correction operations on the actuator, but more importantly, it deeply assesses the potential impact on component lifespan, particularly the risks of overload and accelerated failure.This comprehensive evaluation provides solid data support for determining the frequency and magnitude of rule corrections, ensuring that the revised shape selection rules optimize tent performance while maximizing the long-term stable operation and energy efficiency of the multi-link actuators. By comprehensively considering transient loads, instantaneous energy consumption, instantaneous stress, and component wear conditions along the shape adjustment path, this approach provides a deeper and more accurate insight than simple evaluations. This allows for not only optimizing tent performance when correcting shape selection rules but also proactively predicting and mitigating the risks of overload and accelerated failure that multi-link actuators may face during dynamic adjustments. This refined evaluation mechanism enables the entire tent shape control method to achieve a balance between performance optimization and system reliability when dealing with complex dynamic loads, thereby improving the tent's long-term adaptability and operational stability in harsh environments.

[0102] Reference Appendix Figure 4 The present invention provides a multi-link deformable tent shape control system, comprising:

[0103] The acquisition module 100 is used to acquire the external load parameters and the tent structural response parameters.

[0104] The identification and recording module 200 is used to identify the current load mode based on external load parameters, associate the load mode, structural response parameters with the current shape of the tent, record the actual performance of the tent under the load mode, and form historical performance data.

[0105] Analysis module 300 is used to continuously analyze historical performance data, identify the correlation between specific load patterns and the best-performing tent shape, and form shape selection rules.

[0106] The correction module 400 is used to correct the shape selection rules based on the actual operational feedback after the tent shape adjustment.

[0107] The generation control module 500 is used to generate control commands for the multi-link actuator based on the modified shape selection rules and the current load mode, and adjust the tent shape according to the control commands.

[0108] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0109] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-link deformable tent shape control method, characterized by, The method comprises the following steps: Obtaining external load parameters and structural response parameters of the tent; According to the external load parameters, identifying the current load mode, and associating the load mode, the structural response parameters and the current shape of the tent, recording the actual performance of the tent under the load mode, and forming historical performance data; Continuously analyzing the historical performance data to identify the correlation between the specific load mode and the best tent shape in performance, and forming a shape selection rule; According to the actual operation feedback of the tent shape adjustment, correcting the shape selection rule; Based on the corrected shape selection rule and the current load mode, a control command of the multi-link executive mechanism is generated, and the tent shape is adjusted according to the control command.

2. The multi-link deformable tent shape control method of claim 1, wherein, The step of obtaining external load parameters and structural response parameters of the tent comprises: After determining the target area of the tent under complex combined load which is prone to local stress concentration or uneven load distribution, external load monitoring devices and structural response monitoring devices are deployed at the target area and key stress parts of the tent structure to obtain multi-point external load parameters and multi-point structural response parameters; the external load monitoring device is a physical device for measuring the external environmental load of the tent; the structural response monitoring device is a physical device for measuring the response of the tent structure to external load and its current state; According to the correlation between the multi-point external load parameters and the multi-point structural response parameters, the load distribution information and the structural response information of the whole tent or the local area are inferred to obtain comprehensive external load parameters and structural response parameters of the tent.

3. The multi-link deformable tent shape control method of claim 2, wherein, The external load monitoring device includes wind speed and direction sensor, snow depth sensor, temperature sensor, humidity sensor and rainfall sensor.

4. The multi-link deformable tent shape control method of claim 2, wherein, The structural response monitoring device includes multi-link joint angle sensor, stress sensor, skin tension sensor and energy consumption monitoring equipment.

5. The multi-link deformable tent shape control method of claim 1, wherein, The step of continuously analyzing the historical performance data to identify the correlation between the specific load mode and the best tent shape in performance, and forming a shape selection rule comprises: A1. Extract the structural safety performance index, energy consumption performance index and environmental adaptation performance index of the tent under different tent shapes from the historical performance data; A2. Analyze the mutual influence relationship of the structural safety performance index, energy consumption performance index and environmental adaptation performance index under different tent shapes under the specific load mode, and determine the trade-off relationship between the performance indexes; A3. According to the trade-off relationship, and combining with the preset performance priority or dynamic weight, the structural safety performance index, energy consumption performance index and environmental adaptation performance index are comprehensively evaluated to determine the tent shape which can realize the comprehensive optimization of multi-dimensional performance indexes under the specific load mode; A4. Associating the tent shape with the specific load mode to form a shape selection rule.

6. The multi-link deformable tent shape control method of claim 5, wherein, The specific steps in step A3 include: A31. Obtain the index information related to the local area performance of the tent in the historical performance data; A32. According to the index information related to the local area performance, and combining with the possible local defects or uneven load distribution of the tent structure under the specific load mode, the local risk of the tent under different shapes is evaluated; A33. In the comprehensive evaluation of the structure safety performance index, the energy consumption performance index, and the environmental adaptation performance index, the local risk assessment result is taken into account, and according to the trade-off relationship, the preset performance priority or dynamic weight, and the local risk assessment result, the multi-dimensional performance and local risk of each tent shape are comprehensively quantified; A34. According to the comprehensive quantification result, the tent shape capable of realizing the comprehensive optimization of multi-dimensional performance index and the minimization of local risk under a specific load mode is determined.

7. The multi-link deformable tent shape control method of claim 6, wherein, In step A33, the step of comprehensively quantifying the multi-dimensional performance and local risk of each tent shape according to the trade-off relationship, the preset performance priority or dynamic weight, and the local risk assessment result comprises: normalizing the structure safety performance index, the energy consumption performance index, the environmental adaptation performance index, and the local risk assessment result; weighting and summing the normalized indexes according to the trade-off relationship, the preset performance priority or dynamic weight, to obtain the comprehensive score of each tent shape; sorting the tent shapes according to the comprehensive score to determine the optimal tent shape.

8. The multi-link deformable tent shape control method of claim 1, wherein, According to the actual operation feedback of the tent shape adjustment, the step of correcting the shape selection rule comprises: B1. Obtain the deviation information between the actual performance and the expected performance of the tent shape adjustment, and the energy consumption data and component service life data of the multi-link actuator; B2. According to the deviation information, combined with the energy consumption data and component service life data, evaluate the current adaptability of the shape selection rule, and evaluate the energy consumption influence and component life influence of the rule correction operation on the multi-link actuator; B3. Based on the adaptability evaluation result, the energy consumption influence and the component life influence, determine the correction frequency and correction amplitude of the shape selection rule; According to the correction frequency and correction amplitude, correct the shape selection rule.

9. The multi-link deformable tent shape control method of claim 8, wherein, In step B2, the specific steps of evaluating the energy consumption influence and component life influence of the rule correction operation on the multi-link actuator comprise: determining the shape adjustment path of the multi-link actuator according to the target tent shape determined by the rule correction operation and the current tent shape, and predicting the multi-link actuator transient load at each point on the shape adjustment path; obtaining the wear state information of a specific component of the multi-link actuator; According to the multi-link actuator transient load prediction value at each point on the shape adjustment path and the wear state information of the specific component, calculate the instantaneous energy consumption value of the multi-link actuator and the instantaneous stress value of the specific component during the shape adjustment process; identify the peak values in the instantaneous energy consumption value and the instantaneous stress value, and combine the wear state information of the specific component to evaluate the risk of the peak values to the overload of the multi-link actuator or the accelerated failure of the component; According to the risk evaluation result, generate the evaluation result of the energy consumption influence and component life influence of the rule correction operation on the multi-link actuator.

10. A multi-link deformable tent shape control system, characterized by, comprise: an acquisition module configured to acquire external load parameters and tent structure response parameters of the tent; The identification recording module is configured to identify a current load mode according to the external load parameter, associate the load mode, the structural response parameter and a current shape of the tent, record actual performance of the tent under the load mode, and form historical performance data; The analysis module is configured to continuously analyze the historical performance data, identify a correlation rule between a specific load mode and a best shape of the tent in performance, and form a shape selection rule; The correction module is configured to correct the shape selection rule according to actual operation feedback of the adjusted shape of the tent; The control module is configured to generate a control instruction of the multi-link actuator based on the corrected shape selection rule and the current load mode, and adjust the shape of the tent according to the control instruction.

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