Intelligent digital resilience training platform and optimization method and apparatus therefor

Abstract

The present application relates to the technical field of intelligent training devices, and particularly relates to an intelligent digital resilience training platform and an optimization method and apparatus therefor. The intelligent digital resilience training platform comprises a main body frame, a data processing unit, and a dynamic feedback module, wherein the data processing unit analyzes data by means of a core processor; and the dynamic feedback module uses a sensor group to collect environmental data and adjust the state thereof, and implements multi-scenario training in combination with an environmental simulation module. The present application can improve the system adaptability and robustness by means of a modular design, can support real-time adjustment and personalized requirements in complex scenarios, and can evaluate platform performance differences by means of data analysis, thereby meeting the diversified requirements of users.

Description

A smart digital resilience training platform and its optimization method and apparatus Technical Field

[0001] This invention belongs to the field of intelligent training and digital optimization technology, specifically an intelligent digital resilience training platform and its optimization method and apparatus. Background Technology

[0002] With the rapid development of digital technology, intelligent digital resilience training platforms have shown broad application prospects in enhancing the ability of individuals and organizations to cope with complex environmental changes. However, existing related technical solutions still have shortcomings in terms of intelligence level, data processing capabilities, system integration, and adaptability of optimization methods, which limit their efficiency and effectiveness in practical applications.

[0003] A search revealed a method for testing the type I fracture toughness of the adhesive interface in composite adhesive structures, published on July 26, 2022, with publication number CN112903442B. This patent utilizes a testing method based on digital image correlation (DIC) technology to automatically record and analyze the fracture toughness of composite material interfaces, significantly improving the accuracy of crack tip location and simultaneously acquiring key parameters such as energy release rate. However, this technical solution primarily focuses on material performance testing and lacks support for multi-dimensional data integration and dynamic optimization in digital toughness training platforms. Furthermore, its application scenarios are relatively limited, exhibiting certain limitations in real-time feedback and adaptive adjustment under complex toughness training requirements.

[0004] A search revealed a patent, CN118365146B, entitled "A Digital Supply Chain Integration System and Method Based on a Smart Service Platform," published on September 17, 2024. This patent comprehensively assesses supply chain resilience by mapping the supply chain network and combining resilience and redundancy information. It then uses the TOPSIS method to identify nodes requiring focused attention, thereby achieving optimized supply chain management. While this technical solution exhibits some intelligent features in supply chain resilience assessment, its core function is limited to resilience assessment in a specific domain, failing to provide a universal digital resilience training framework. Furthermore, the solution has limited capabilities in fusing and processing multi-source heterogeneous data, and there is room for improvement in dynamic optimization strategies to meet the personalized needs of different user groups. Technical issues

[0005] The aforementioned problems indicate that existing technical solutions have significant shortcomings in the design of intelligent resilience training platforms, particularly in terms of multi-scenario adaptability, dynamic optimization capabilities, data integration depth, and personalized user support, where considerable room for improvement remains. Therefore, this invention provides an intelligent digital resilience training platform and its optimization methods and apparatus. The aim is to integrate advanced algorithms and intelligent modules to construct a highly efficient platform capable of comprehensively supporting resilience training needs across multiple domains, thereby enhancing the system's practicality and universality and meeting the urgent needs of modern society for improved digital resilience. Technical solutions

[0006] To address the shortcomings of the existing technologies, this invention provides an intelligent digital resilience training platform and its optimization method and apparatus. Through the design of a dynamic data integration module and a multi-dimensional feedback mechanism, this platform can support real-time adjustments and personalized training needs in complex scenarios, while simultaneously improving the system's adaptability and robustness in different application environments.

[0007] To achieve the above objectives, this invention provides an intelligent digital resilience training platform, comprising a main frame, a data processing unit, and a dynamic feedback module. The main frame is fixed to the ground and equipped with an adjustable pitch mechanism. The data processing unit is mounted on the main frame and is used to receive and process externally input data streams. The dynamic feedback module is located on one side of the main frame and is electrically connected to the data processing unit. The data processing unit includes a core processor, a storage module, and a communication interface. The core processor is responsible for analyzing the input data, the storage module is used to save the processed results, and the communication interface supports interaction with external devices. The dynamic feedback module includes multiple independent feedback units, each with an independent drive component and sensor group. The sensor group is used to collect real-time environmental data and transmit it to the core processor. The drive component adjusts the state of the feedback unit according to the instructions of the core processor.

[0008] In one embodiment, the dynamic feedback module consists of two half-shell structures. The first side of the two half-shells is hinged together, and the second side is detachably connected by a snap-fit ​​or pin. This design facilitates quick assembly and disassembly and maintenance while ensuring the stability of the module during operation.

[0009] In one embodiment, the outer wall of the data processing unit is provided with multiple annular grooves, each containing a sealing ring. The first end of the dynamic feedback module is embedded in the annular groove of the data processing unit and presses against the sealing ring. The second end of the dynamic feedback module is connected to the main frame by bolts, which are evenly distributed circumferentially to ensure a secure connection.

[0010] In one embodiment, the core processor includes a computing chip, a cache unit, and a control circuit. The computing chip is connected to the cache unit via the control circuit. The cache unit is used to temporarily store intermediate calculation results, while the computing chip is responsible for executing complex algorithm logic. The communication interface includes a wired interface and a wireless module. The wired interface adopts a standard industrial protocol, while the wireless module supports multiple communication protocols to meet data transmission needs in different scenarios.

[0011] In one embodiment, each feedback unit of the dynamic feedback module includes a drive motor, a transmission shaft, and an actuator. The drive motor is connected to the actuator via the transmission shaft, and the actuator adjusts its own state according to the speed and direction of the drive motor. The sensor group includes a pressure sensor, a temperature sensor, and a displacement sensor. The pressure sensor monitors changes in the external environmental pressure, the temperature sensor detects fluctuations in ambient temperature, and the displacement sensor records changes in the position of the actuator.

[0012] In one embodiment, the pitch mechanism of the main frame includes an electric actuator, a guide rod, and a limiting block. The electric actuator is fixed to the bottom of the main frame by bolts, the guide rod is located on both sides of the electric actuator, and the limiting block is installed at the end of the guide rod to limit the range of pitch angle changes. The extension and retraction of the electric actuator drives the main frame to complete the pitch adjustment.

[0013] In one embodiment, the intelligent digital resilience training platform further includes a display terminal and an operation panel. The display terminal is fixed to one side of the main frame by a bracket and is used to display the analysis results of the data processing unit in real time. The operation panel is located below the display terminal and has multiple physical buttons and a touch screen. The physical buttons are used to quickly launch frequently used functions, while the touch screen supports more complex operation settings.

[0014] In one embodiment, the platform further includes an environment simulation module, which comprises a light source component, a sound effect component, and a temperature and humidity control component. The light source component is fixed to the top of the main frame by a bracket to simulate different lighting conditions; the sound effect component is installed on the side of the main frame to play preset audio signals; and the temperature and humidity control component is connected to the main frame through pipes to adjust the temperature and humidity parameters of the training environment.

[0015] To achieve the above objectives, the present invention also provides an intelligent digital resilience training method, employing the aforementioned intelligent digital resilience training platform, with the following specific steps: First, sensor groups are arranged on each feedback unit of the dynamic feedback module, and the sensor groups are connected to the core processor via cables; second, the dynamic feedback module and data processing unit are sealed together to form a complete training platform, and a display terminal and operation panel are connected; then, the operating parameters of the core processor are set through the operation panel, and the desired training mode is selected; next, the environmental simulation module is started, and the parameters of the light source component, sound effect component, and temperature and humidity control component are adjusted to simulate the target training environment; subsequently, the dynamic feedback module is started, the drive motor drives the actuator to complete the predetermined action through the transmission shaft, and the sensor groups collect environmental data in real time and transmit it to the core processor; finally, the core processor analyzes the collected data and displays the analysis results through the display terminal.

[0016] Furthermore, the training method also includes the following steps: after completing one training session, the dynamic feedback module and the environment simulation module are turned off to release excess energy in the system; then, the half-shell structure of the dynamic feedback module is disassembled, the sensor group is removed, and the data it collects is recorded; next, the parameter settings of the environment simulation module are changed, and the above steps are repeated to obtain training data under different environmental conditions; finally, the performance characteristics of the training platform in different scenarios are evaluated by comparing and analyzing the training data from multiple sessions.

[0017] The evaluation of the training platform's performance characteristics in different scenarios includes the following steps: First, the sensor data obtained under a set pitch angle is divided into multiple segments along the time axis, with each segment corresponding to an independent training cycle; second, a data segment is selected, input into the core processor for secondary analysis, and the analysis results are recorded; then, the pitch angle of the dynamic feedback module is adjusted, and the above steps are repeated until all data segments have been analyzed; finally, by comparing the analysis results under different pitch angles, the performance differences of the training platform in the spatial dimension are determined.

[0018] Furthermore, this invention also provides an intelligent digital resilience training device, which includes the aforementioned intelligent digital resilience training platform and its related components. The main frame of the device is made of high-strength aluminum alloy, which has good corrosion resistance and mechanical strength; the outer shell of the data processing unit is made of engineering plastic, which has excellent insulation performance and heat dissipation capacity; the semi-shell structure of the dynamic feedback module is made of stainless steel to ensure durability during long-term use. Beneficial effects

[0019] In summary, this invention, through the collaborative design of the dynamic feedback module and the data processing unit, combined with the flexible configuration of the environment simulation module, addresses the shortcomings of existing technologies in terms of multi-scenario adaptability, dynamic optimization capabilities, and data integration depth. Furthermore, the modular design concept enables the platform to possess strong scalability and ease of maintenance, meeting the personalized needs of different user groups. Attached Figure Description

[0020] Figure 1 is a schematic diagram of the overall structure of the intelligent digital resilience training platform of the present invention;

[0021] Figure 2 is a schematic diagram of the disassembly of the semi-shell structure of the dynamic feedback module;

[0022] Figure 3 is a schematic diagram of the data processing unit;

[0023] Figure 4 is a schematic diagram of the structure of a single feedback unit in the dynamic feedback module;

[0024] Figure 5 is a structural schematic diagram of the pitching mechanism of the main frame;

[0025] Figure 6 is a schematic diagram of the light source assembly;

[0026] Figure 7 is a schematic diagram of the light source assembly.

[0027] Figure 8 is an enlarged view of point A in Figure 1.

[0028] The attached figures are labeled as follows:

[0029] 1. Main frame; 2. Data processing unit; 3. Dynamic feedback module; 4. Core processor; 5. Storage module; 6. Communication interface; 7. Feedback unit; 8. Drive motor; 9. Transmission shaft; 10. Actuator; 11. Sensor group; 12. Electric push rod; 13. Guide rod; 14. Limit block; 15. Display terminal; 16. Operation panel; 17. Light source assembly; 18. Sound effect assembly; 19. Temperature and humidity control assembly. The best embodiment of the present invention

[0030] This invention provides an intelligent digital resilience training platform and its optimization method and device, the specific implementation of which is as follows. A detailed description is provided with reference to the reference numerals in Figures 1 to 8 and the specific structural relationships of each component. The main frame 1 is the basic support structure of the entire platform, made of high-strength aluminum alloy, possessing good corrosion resistance and mechanical strength. The main frame 1 is fixed to the ground, and its angle adjustment function is achieved through a pitch mechanism at its bottom. The pitch mechanism includes an electric push rod 12, a guide rod 13, and a limiting block 14. The electric push rod 12 is fixed to the bottom of the main frame 1 by bolts. The guide rod 13 is located on both sides of the electric push rod 12 to ensure the stability of the movement direction. The limiting block 14 is installed at the end of the guide rod 13 to limit the range of pitch angle variation. The extension and retraction of the electric push rod 12 drives the main frame 1 to complete the pitch adjustment, thereby adapting to the training needs in different scenarios.

[0031] The data processing unit 2 is mounted on the main frame 1, and its outer shell is made of engineering plastic, possessing excellent insulation and heat dissipation capabilities. The data processing unit 2 includes a core processor 4, a storage module 5, and a communication interface 6. The core processor 4 is responsible for analyzing and processing the input data, the storage module 5 is used to store the processed results, and the communication interface 6 supports interaction with external devices. As shown in Figure 4, the core processor 4 internally includes a computing chip, a cache unit, and a control circuit. The computing chip is connected to the cache unit through the control circuit. The cache unit is used to temporarily store intermediate calculation results, and the computing chip is responsible for executing complex algorithm logic. The communication interface 6 includes a wired interface and a wireless module. The wired interface adopts a standard industrial protocol, and the wireless module supports multiple communication protocols to meet the data transmission needs of different scenarios.

[0032] The dynamic feedback module 3 is located on one side of the main frame 1 and is electrically connected to the data processing unit 2. Referring to Figure 2, the dynamic feedback module 3 consists of two half-shell structures. The first side of the two half-shells is hinged, and the second side is detachably connected by a buckle or pin. This design facilitates quick assembly and disassembly and maintenance while ensuring the stability of the module during operation. Referring to Figure 3, the outer wall of the data processing unit 2 has multiple annular grooves, each containing a sealing ring. The first end of the dynamic feedback module 3 is embedded in the annular groove of the data processing unit 2 and presses against the sealing ring. The second end of the dynamic feedback module 3 is connected to the main frame 1 by bolts evenly distributed circumferentially to ensure a secure connection. The dynamic feedback module 3 includes multiple independent feedback units 7, each with an independent drive assembly and sensor group 11. Referring to Figure 5, each feedback unit 7 includes a drive motor 8, a transmission shaft 9, and an actuator 10. The drive motor 8 is connected to the actuator 10 via the transmission shaft 9, and the actuator 10 adjusts its own state according to the speed and direction of the drive motor 8. The sensor group 11 includes a pressure sensor, a temperature sensor, and a displacement sensor. The pressure sensor is used to monitor changes in the pressure of the external environment, the temperature sensor is used to detect fluctuations in the ambient temperature, and the displacement sensor records changes in the position of the actuator 10.

[0033] The display terminal 15 is fixed to one side of the main frame 1 by a bracket and is used to display the analysis results of the data processing unit 2 in real time. The operation panel 16 is located below the display terminal 15. The operation panel 16 has multiple physical buttons and a touch screen. The physical buttons are used to quickly launch frequently used functions, while the touch screen supports more complex operation settings. The environment simulation module includes a light source component 17, a sound effect component 18, and a temperature and humidity control component 19. As shown in Figure 7, the light source component 17 is fixed to the top of the main frame 1 by a bracket and is used to simulate different lighting conditions; the sound effect component 18 is installed on the side of the main frame 1 and is used to play preset audio signals; the temperature and humidity control component 19 is connected to the main frame 1 through pipes and is used to adjust the temperature and humidity parameters of the training environment.

[0034] The platform's operating principle and process are as follows. First, sensor groups 11 are arranged on each feedback unit 7 of the dynamic feedback module 3, and the sensor groups 11 are connected to the core processor 4 via cables. Then, the dynamic feedback module 3 is sealed and connected to the data processing unit 2 to form a complete training platform, and the display terminal 15 and operation panel 16 are connected. The operating parameters of the core processor 4 are set through the operation panel 16, and the desired training mode is selected. The environment simulation module is started, and the parameters of the light source component 17, sound effect component 18, and temperature and humidity control component 19 are adjusted to simulate the target training environment. Next, the dynamic feedback module 3 is started, and the drive motor 8 drives the actuator 10 to complete the predetermined action through the transmission shaft 9. The sensor groups 11 collect environmental data in real time and transmit it to the core processor 4. The core processor 4 analyzes the collected data and displays the analysis results through the display terminal 15.

[0035] After completing one training cycle, the dynamic feedback module 3 and the environment simulation module are shut down to release excess energy in the system. Then, the semi-shell structure of the dynamic feedback module 3 is disassembled, the sensor group 11 is removed, and its collected data is recorded. The parameter settings of the environment simulation module are changed, and the above steps are repeated to obtain training data under different environmental conditions. The performance characteristics of the training platform in different scenarios are evaluated through comparative analysis of multiple training data. The specific evaluation steps are as follows: First, the data from the sensor group 11 obtained under the set pitch angle condition is divided into multiple segments along the time axis, each segment corresponding to an independent training cycle. A data segment is selected, input into the core processor 4 for secondary analysis, and the analysis results are recorded. Then, the pitch angle of the dynamic feedback module 3 is adjusted, and the above steps are repeated until all data segments have been analyzed. Finally, by comparing the analysis results under different pitch angles, the performance differences of the training platform in the spatial dimension are determined.

[0036] Figure 8 illustrates the overall application status of this intelligent digital resilience training device, showcasing its operation in a real-world training scenario. The main frame 1 is constructed of high-strength aluminum alloy, offering excellent corrosion resistance and mechanical strength. The data processing unit 2's outer shell is made of engineering plastic, providing superior insulation and heat dissipation. The semi-shell structure of the dynamic feedback module 3 is made of stainless steel, ensuring durability over long-term use. Through the collaborative design of the dynamic feedback module 3 and the data processing unit 2, combined with the flexible configuration of the environment simulation module, the device addresses the shortcomings of existing technologies in multi-scenario adaptability, dynamic optimization capabilities, and data integration depth. The modular design concept enables the platform to possess strong scalability and ease of maintenance, meeting the personalized needs of different user groups. Embodiments of the present invention

[0037] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principles of this invention are further supplemented below with a specific application scenario.

[0038] First, the main frame 1 is fixed to the ground, and its pitch angle is adjusted by the electric actuator 12. The extension and retraction of the electric actuator 12 drives the main frame 1 to complete the angle adjustment, while the guide rod 13 ensures the stability of the movement direction, and the limit block 14 limits the range of pitch angle changes. This design allows the platform to adapt to the needs of different training scenarios, such as resilience training in simulated slopes or uneven terrain. The target pitch angle is set through the operation panel 16, and the controller starts the electric actuator 12 according to the preset value. The core processor 4 receives angle feedback data from the sensor group 11 in real time to ensure that the pitch mechanism accurately reaches the designated position.

[0039] Subsequently, sensor groups 11 are arranged on each feedback unit 7 of the dynamic feedback module 3 and connected to the core processor 4 via cables. The pressure sensor, temperature sensor, and displacement sensor in the sensor group 11 collect information on pressure changes, temperature fluctuations, and the position of the actuator 10 in the external environment, respectively. This data is transmitted to the core processor 4 via the communication interface 6 and analyzed by the computing chip. The computing chip uses a cache unit to temporarily store intermediate calculation results and combines them with a built-in algorithm to generate optimized instructions, which are then sent to the drive motor 8. The drive motor 8 drives the actuator 10 to complete predetermined actions via the transmission shaft 9, thereby achieving dynamic response to the training environment. For example, in simulating an emergency escape scenario, the actuator 10 can quickly adjust its state according to environmental changes to provide real-time feedback.

[0040] Next, the environmental simulation module is activated, and the parameters of the light source component 17, sound effect component 18, and temperature and humidity control component 19 are adjusted to construct the target training environment. The light source component 17 simulates day-night cycles or specific weather conditions by adjusting light intensity and color temperature; the sound effect component 18 plays preset audio signals, such as alarm sounds or natural sounds, to enhance immersion; the temperature and humidity control component 19 delivers regulated air to the main frame 1 through pipes, changing the temperature and humidity levels of the training area. The core processor 4 coordinates the working status of each component according to the settings on the operation panel 16, ensuring that the environmental simulation module and the dynamic feedback module 3 operate synchronously. For example, in emergency response training simulating a high-temperature and high-humidity environment, the temperature and humidity control component 19 raises the ambient temperature to 35°C and adjusts the humidity to 80%, while the light source component 17 simulates strong light and the sound effect component 18 plays loud background noise, providing users with realistic training conditions.

[0041] During training, the display terminal 15 shows the analysis results of the core processor 4 in real time, including environmental parameter change curves, the motion trajectory of the actuator 10, and user performance evaluation data. The operation panel 16 allows users to quickly switch training modes via physical buttons or make complex parameter settings via the touch screen. For example, users can select the "multi-scenario adaptability test" mode via the touch screen, and the system will automatically adjust the parameters of the environmental simulation module and record the response data of the dynamic feedback module 3 under different conditions.

[0042] After completing one training session, the dynamic feedback module 3 and the environment simulation module are shut down to release excess energy in the system. Then, the semi-shell structure of the dynamic feedback module 3 is disassembled, the sensor group 11 is removed, and its collected data is recorded. The core processor 4 performs secondary analysis on the data to evaluate the training effect. For example, the data from the sensor group 11 obtained under a set pitch angle condition is divided into multiple segments along the time axis, each segment corresponding to an independent training cycle. A data segment is selected and input into the core processor 4. The computing chip combines this data with historical data from the storage module 5 to generate a comparison report, determining the performance differences of the training platform in the spatial dimension.

[0043] Finally, the parameter settings of the environment simulation module are changed, and the above steps are repeated to obtain training data under different environmental conditions. By comparing and analyzing the training data from multiple training iterations, the platform's dynamic adjustment capabilities and personalized support functions can be further optimized. For example, the environmental complexity can be gradually increased in multiple training iterations to observe changes in the user's adaptability under different conditions, thus providing a scientific basis for the design of subsequent training programs.

[0044] In summary, this invention, through the collaborative design of the dynamic feedback module 3 and the data processing unit 2, combined with the flexible configuration of the environment simulation module, achieves real-time adjustment and personalized training requirements in complex scenarios. The modular design concept not only enhances the system's scalability and maintenance convenience but also significantly improves the platform's performance in multi-scenario adaptability, dynamic optimization capabilities, and data integration depth, meeting the urgent needs of modern society for enhanced digital resilience.

Claims

1. An intelligent digital resilience training platform, characterized in that, It includes a main frame, a data processing unit, and a dynamic feedback module; the main frame is fixed to the ground and is equipped with a pitching mechanism; The data processing unit is mounted on the main frame and includes a core processor, a storage module and a communication interface; the dynamic feedback module is located on one side of the main frame and is electrically connected to the data processing unit, and includes multiple independent feedback units, each of which has a drive component and a sensor group.

2. The intelligent digital resilience training platform according to claim 1, characterized in that, The dynamic feedback module consists of two half-shell structures. The first side of the two half-shells is hinged together, and the second side is detachably connected by a buckle or a pin.

3. The intelligent digital resilience training platform according to claim 1, characterized in that, The outer wall of the data processing unit is provided with multiple annular grooves, and a sealing ring is embedded in each annular groove; the first end of the dynamic feedback module is embedded in the annular groove and presses the sealing ring, and the second end is connected to the main frame by bolts, with the bolts evenly distributed along the circumference.

4. The intelligent digital resilience training platform according to claim 1, characterized in that, The core processor includes a computing chip, a cache unit, and a control circuit; the computing chip is connected to the cache unit through the control circuit; the communication interface includes a wired interface and a wireless module.

5. The intelligent digital resilience training platform according to claim 1, characterized in that, Each feedback unit of the dynamic feedback module includes a drive motor, a transmission shaft, and an actuator; the drive motor is connected to the actuator via the transmission shaft; the sensor group includes a pressure sensor, a temperature sensor, and a displacement sensor.

6. The intelligent digital resilience training platform according to claim 1, characterized in that, The pitch mechanism includes an electric push rod, a guide rod, and a limiting block; the electric push rod is fixed to the bottom of the main frame by bolts, the guide rod is located on both sides of the electric push rod, and the limiting block is installed at the end of the guide rod.

7. The intelligent digital resilience training platform according to claim 1, characterized in that, It also includes a display terminal and an operation panel; the display terminal is fixed to one side of the main frame by a bracket; the operation panel is located below the display terminal and includes physical buttons and a touch screen.

8. The intelligent digital resilience training platform according to claim 1, characterized in that, It also includes an environment simulation module, which includes a light source component, a sound effect component, and a temperature and humidity control component; the light source component is fixed to the top of the main frame by a bracket; the sound effect component is installed on the side of the main frame; and the temperature and humidity control component is connected to the main frame by a pipe.

9. A method for training intelligent digital resilience, characterized in that, The intelligent digital resilience training platform according to any one of claims 1 to 8 includes the following steps: Step 1, arranging sensor groups on each feedback unit of the dynamic feedback module and connecting the sensor groups to the core processor via cables; Step 2, sealing the dynamic feedback module and the data processing unit to form a complete training platform, and connecting the display terminal and the operation panel; Step 3, setting the operating parameters of the core processor and selecting the training mode through the operation panel; Step 4, starting the environmental simulation module and adjusting the parameters of the light source component, sound effect component, and temperature and humidity control component; Step 5, starting the dynamic feedback module, driving the motor to drive the actuator to complete the action through the transmission shaft, and the sensor group collecting environmental data and transmitting it to the core processor; Step 6, the core processor analyzing the collected data and displaying the analysis results through the display terminal.

10. An intelligent digital resilience training device, characterized in that, The system includes the intelligent digital resilience training platform as described in any one of claims 1 to 8; the main frame is made of high-strength aluminum alloy; the outer shell of the data processing unit is made of engineering plastic; and the semi-shell structure of the dynamic feedback module is made of stainless steel.

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