An exoskeleton assistive device for use in the replacement of medium to large components

Abstract

The application relates to the technical field of power equipment, and provides a human exoskeleton assisting device applied to replacement of middle and large components, which comprises a waist supporting structure, a main supporting device, an auxiliary supporting device and a supporting structure connecting plate, flexible and stable support is realized through a hydraulic supporting rod and a rotating shaft. The device is also provided with an energy storage device and a control device, and the energy storage device provides electric energy for the device. The application effectively solves the problem of insufficient assistance of the existing exoskeleton assisting device when middle and large components are carried, significantly improves the operation capacity and efficiency, enhances the operation safety and user experience, and is suitable for replacement operation of middle and large components in the power field.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power equipment, and particularly relates to a human exoskeleton assisting device applied to medium-large component replacement. BACKGROUND

[0002] With the development of science and technology, especially the progress of mechanical engineering, material science and human engineering, power-assisted exoskeletons gradually move from theory to practice. Traditionally, power-assisted exoskeletons mostly use motors, pneumatics or hydraulics as power sources, but these methods have problems such as heavy weight, high cost, poor comfort and insufficient endurance. In order to solve these problems, power-free exoskeletons emerge as the times require, which use the principle of static balance to achieve assistance to the arm through mechanical structure design, without external energy, and are more in line with the concept of green, low carbon and energy sustainable development.

[0003] The human exoskeleton assisting device is often used in the field of electric power, and the workers often need to wear safety equipment such as insulating clothes and safety belts, and the equipment carried by the workers often increases the burden on the body. The existing human exoskeleton assisting device cannot provide assistance when the workers carry medium-large components. In order to solve this technical problem, the present application provides a human exoskeleton assisting device applied to medium-large component replacement. SUMMARY

[0004] In view of the deficiencies in the prior art, the present application provides a human exoskeleton assisting device applied to medium-large component replacement. In the use process of the human exoskeleton assisting device in the field of electric power, the exoskeleton device can assist the limited work capacity of humans in the running process, achieve work capacity and effect beyond the limit or longer time through modern industrialized devices.

[0005] The present application provides a human exoskeleton assisting device applied to medium-large component replacement, which comprises a waist support structure, a main support device, an auxiliary support device and a support structure connecting plate.

[0006] The support structure connecting plate is screw-connected to the two sides of the waist support structure, and the main support device is screw-connected to the outer wall of the support structure connecting plate. The support structure connecting plate is used to connect the waist support structure and the main support device, and is located between the main support device and the waist support structure.

[0007] The auxiliary support device is installed on the side wall of the main support device, and the main support device and the auxiliary support device are used to support the waist support structure. The auxiliary support device provides support force for the main support device.

[0008] The control device is installed on the outer wall of the waist support structure, and the energy storage device is installed on the top of the auxiliary support device. The control device is connected with the energy storage device, the main support device and the auxiliary support device respectively, and the energy storage device is used to provide electric energy for the control device, the main support device and the auxiliary support device.

[0009] Further, the application provides the human exoskeleton assisting device for middle and large component replacement, the main support device comprises: a first main support connecting body, a first main support rotating shaft, a first main support hydraulic support rod, a second main support rotating shaft, a second main support hydraulic support rod, a third main support rotating shaft and a main support pedal;

[0010] The first main support rotating shaft is arranged between the first main support connecting body and the first main support hydraulic support rod;

[0011] The first main support hydraulic support rod is arranged between the first main support rotating shaft and the second main support rotating shaft;

[0012] The second main support hydraulic support rod is connected between the second main support rotating shaft and the third main support rotating shaft;

[0013] The third main support rotating shaft is arranged between the second main support hydraulic support rod and the main support pedal.

[0014] Further, the application provides the human exoskeleton assisting device for middle and large component replacement, and further comprises:

[0015] The first main support hydraulic support rod is used for providing support force to the first main support connecting body;

[0016] The first main support rotating shaft is arranged between the first main support hydraulic support rod and the first main support connecting body, and is used for driving the first main support hydraulic support rod to rotate;

[0017] The second main support rotating shaft is arranged between the first main support hydraulic support rod and the second main support hydraulic support rod, and is used for driving the second main support hydraulic support rod to rotate;

[0018] The second main support hydraulic support rod is used for providing support force to the second main support rotating shaft;

[0019] The third main support rotating shaft is used for driving the main support pedal to rotate.

[0020] Further, the application provides the human exoskeleton assisting device for middle and large component replacement, the main support device comprises: a first main support connecting body, a first main support rotating shaft, a first main support hydraulic support rod, a second main support rotating shaft, a second main support hydraulic support rod, a third main support rotating shaft and a main support pedal;

[0021] The first main support rotating shaft is arranged between the first main support connecting body and the first main support hydraulic support rod;

[0022] The first main support hydraulic support rod is used for providing support force to the first main support connecting body;

[0023] The first auxiliary support rotating shaft is used to drive the first auxiliary support hydraulic support rod to rotate.

[0024] The second auxiliary support hydraulic support rod is installed between the second auxiliary support rotating shaft and the third auxiliary support rotating shaft.

[0025] The second auxiliary support rotating shaft is used to drive the second auxiliary support hydraulic support rod to rotate, and the second auxiliary support hydraulic support rod is used to provide a supporting force to the second auxiliary support rotating shaft.

[0026] The third auxiliary support rotating shaft is installed between the second auxiliary support hydraulic support rod and the auxiliary support pedal.

[0027] The third auxiliary support rotating shaft drives the auxiliary support pedal to rotate.

[0028] Further, the application provides the human exoskeleton assisting device for replacing middle and large components, and the energy storage device comprises an energy storage shell, an energy storage cover, and an energy storage cover motor.

[0029] The energy storage cover motor is installed on the top of the energy storage shell, and the energy storage cover motor is connected with the energy storage cover.

[0030] Further, the application provides the human exoskeleton assisting device for replacing middle and large components, and the waist supporting structure, the main supporting device and the auxiliary supporting device are internally installed with a sensor matrix, the sensor matrix is installed with pressure sensors and position sensors, and a control device is in communication connection with the pressure sensors and the position sensors.

[0031] The control device receives data collected by the pressure sensors and the position sensors, and the control device is also in connection with a remote control terminal and receives data transmitted by remote control.

[0032] Advantages of the application;

[0033] The human exoskeleton assisting device of the application can help the staff to replace middle and large components in the field of electric power beyond the limited work capacity, realize longer time and higher intensity work, and the stable assistance provided by the device reduces the physical burden of the staff, so that they can complete the carrying and replacement tasks of middle and large components more quickly and accurately, thereby improving the overall work efficiency.

[0034] The application integrates an intelligent early warning system, can monitor the state of the exoskeleton device and the physiological data of the user in real time, triggers the early warning mechanism immediately once potential risks are found, and effectively avoids accidents. Through the sensor matrix and the multi-layer early warning mechanism, the state of the device and the physiological state of the user are comprehensively monitored, and the safety of the staff in the work process is ensured.

[0035] The device of the present application can adapt to workers with different body shapes and work habits by adjusting the angle and force of the hydraulic support rod and the rotating shaft, providing personalized power support and improving user experience. Stable power support reduces the physical burden of workers, reduces fatigue and injury risk caused by long-term work, and improves work comfort.

[0036] The energy storage device of the present application uses advanced energy storage technology and materials, has high-efficiency and stable power output capacity, and improves energy utilization efficiency. Compared with traditional motors, pneumatic or hydraulic power sources, the present application uses the principle of static balance and hydraulic power system, which is more in line with the concept of green low-carbon and sustainable energy development. The various components of the device are designed in a modular manner, which facilitates disassembly, maintenance and upgrading, reducing maintenance costs and time. The control device is connected with the remote control terminal to realize remote monitoring and scheduling, improving the convenience and efficiency of management.

[0037] In summary, the human exoskeleton power assisting device for medium and large component replacement provided by the present application has significant beneficial effects in improving work capacity and efficiency, enhancing work safety, improving user experience and comfort, improving energy utilization efficiency and sustainability, and facilitating maintenance and upgrading. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed in the embodiments. Obviously, for those skilled in the art, other drawings can also be obtained without creative labor on the basis of the drawings.

[0039] Figure 1 An external structure front view schematic diagram of the human exoskeleton power assisting device for medium and large component replacement provided by the present application is provided.

[0040] Figure 2 An external structure top view schematic diagram of the human exoskeleton power assisting device for medium and large component replacement provided by the present application is provided.

[0041] Figure 3 An internal module schematic diagram of the control device of the human exoskeleton power assisting device for medium and large component replacement provided by the present application is provided.

[0042] The structure of the accompanying drawings is explained: 1, waist support structure, 2, main support device, 201, first main support connecting body, 202, first main support rotating shaft, 203, first main support hydraulic support rod, 204, second main support rotating shaft, 205, second main support hydraulic support rod, 206, third main support rotating shaft, 207, main support pedal, 3, auxiliary support device, 301, first auxiliary support rotating shaft, 302, first auxiliary support hydraulic support rod, 303, second auxiliary support rotating shaft, 304, second auxiliary support hydraulic support rod, 305, third auxiliary support rotating shaft, 306, auxiliary support pedal, 4, support structure connecting plate, 5, energy storage device, 501, energy storage shell, 502, energy storage cover, 503, energy storage cover motor, 6, control device, DETAILED DESCRIPTION

[0043] To make the purpose, technical scheme and advantages of the present application clearer, the technical scheme of the present application will be described clearly and completely below in combination with specific embodiments of the present application and corresponding drawings. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application. The technical scheme provided by each embodiment of the present application is described in detail below in combination with the drawings.

[0044] In order to better understand the purpose of the present application, the present application will be further described in detail below.

[0045] The present application provides a human exoskeleton assisting device applied to middle and large component replacement, comprising: a waist support structure 1, a main support device 2, an auxiliary support device 3 and a support structure connecting plate 4.

[0046] The waist support structure 1 has support structure connecting plates 4 screwed on both sides, and the main support device 2 is screwed on the outer wall of the support structure connecting plate 4. The support structure connecting plate 4 is used to connect the waist support structure 1 and the main support device 2, and is located between the main support device 2 and the waist support structure 1.

[0047] The main support device 2 has an auxiliary support device 3 installed on the side wall, and the main support device 2 and the auxiliary support device 3 are used to support the waist support structure 1. The auxiliary support device 3 provides support force for the main support device 2.

[0048] The control device 6 is installed on the outer wall of the waist support structure 1, and the energy storage device 5 is installed on the top of the auxiliary support device 3. The control device 6 is connected with the energy storage device 5, the main support device 2 and the auxiliary support device 3 respectively. The energy storage device 5 is used to provide electric energy to the control device 6, the main support device 2 and the auxiliary support device 3.

[0049] Specifically, the application provides the human exoskeleton assisting device for replacing middle and large components.

[0050] The first main support connecting body 201 is provided with the first main support rotating shaft 202 between the first main support connecting body 201 and the first main support hydraulic support rod 203.

[0051] The first main support rotating shaft 202 is provided with the first main support hydraulic support rod 203 between the first main support rotating shaft 202 and the second main support rotating shaft 204.

[0052] The second main support rotating shaft 204 is connected with the second main support hydraulic support rod 205 between the second main support rotating shaft 204 and the third main support rotating shaft 206.

[0053] The second main support hydraulic support rod 205 is provided with the third main support rotating shaft 206 between the second main support hydraulic support rod 205 and the main support pedal 207.

[0054] Specifically, the application provides the human exoskeleton assisting device for replacing middle and large components, and further comprises:

[0055] The first main support hydraulic support rod 203 is used for providing support force to the first main support connecting body 201.

[0056] The first main support rotating shaft 202 is located between the first main support hydraulic support rod 203 and the first main support connecting body 201, and is used for driving the first main support hydraulic support rod 203 to rotate.

[0057] The second main support rotating shaft 204 is located between the first main support hydraulic support rod 203 and the second main support hydraulic support rod 205, and is used for driving the second main support hydraulic support rod 205 to rotate.

[0058] The second main support hydraulic support rod 205 is used for providing support force to the second main support rotating shaft 204.

[0059] The third main support rotating shaft 206 is used for driving the main support pedal 207 to rotate.

[0060] Specifically, the application provides the human exoskeleton assisting device for replacing middle and large components, and the auxiliary support device comprises a first auxiliary support rotating shaft 301, a first auxiliary support hydraulic support rod 302, a second auxiliary support rotating shaft 303, a second auxiliary support hydraulic support rod 304, a third auxiliary support rotating shaft 305 and an auxiliary support pedal 306.

[0061] The first auxiliary support hydraulic support rod 302 is connected between the first auxiliary support rotating shaft 301 and the second auxiliary support rotating shaft 303.

[0062] The first auxiliary support hydraulic support rod 302 is used to provide support force to the first auxiliary support rotating shaft 301.

[0063] The first auxiliary support rotating shaft 301 is used to drive the first auxiliary support hydraulic support rod 302 to rotate.

[0064] The second auxiliary support hydraulic support rod 304 is installed between the second auxiliary support rotating shaft 303 and the third auxiliary support rotating shaft 305.

[0065] The second auxiliary support rotating shaft 303 is used to drive the second auxiliary support hydraulic support rod 304 to rotate, and the second auxiliary support hydraulic support rod 304 is used to provide support force to the second auxiliary support rotating shaft 303.

[0066] The third auxiliary support rotating shaft 305 is installed between the second auxiliary support hydraulic support rod 304 and the auxiliary support pedal 306.

[0067] The third auxiliary support rotating shaft 305 drives the auxiliary support pedal 306 to rotate.

[0068] Specifically, the application provides the human exoskeleton assisting device for replacing middle and large components, and the energy storage device 5 comprises an energy storage shell 501, an energy storage cover 502 and an energy storage cover motor 503.

[0069] The energy storage cover motor 503 is installed at the top of the energy storage shell 501, and the energy storage cover motor 503 is connected with the energy storage cover 502.

[0070] Specifically, the application provides the human exoskeleton assisting device for replacing middle and large components, and the waist support structure 1, the main support device 2 and the auxiliary support device 3 are internally provided with a sensor matrix, the sensor matrix is provided with a pressure sensor and a position sensor, and a control device 6 is in communication connection with the pressure sensor and the position sensor.

[0071] The control device 6 receives data collected by the pressure sensor and the position sensor, and the control device 6 is also in connection with a remote control terminal and receives data transmitted by remote control.

[0072] Specifically, the application provides the human exoskeleton assisting device for replacing middle and large components, and the control device comprises:

[0073] The data acquisition module acquires user intention behavior data, exoskeleton device state data, environmental data, user physiological data, historical data, and warning thresholds, including human exoskeleton assistance device warning thresholds and user physiological data warning thresholds.

[0074] The prediction module divides the user intention behavior data, exoskeleton device state data, environmental data, user physiological data, and historical data into a training set and a validation set, uses the training set data to train the neural network model, and uses the data of the validation set to verify the trained model, obtaining a human exoskeleton assistance device prediction warning model.

[0075] The data acquisition module acquires human exoskeleton assistance device real-time data, including user intention behavior real-time data, exoskeleton device state real-time data, and environmental real-time data.

[0076] The data processing module substitutes the user intention behavior real-time data into the human exoskeleton assistance device prediction warning model to obtain an exoskeleton device state prediction result, compares the exoskeleton device state prediction result with the exoskeleton device state real-time data to obtain an exoskeleton device state comparison result, which includes whether the exoskeleton device state prediction result is consistent with the exoskeleton device state real-time data or not.

[0077] The warning module compares the exoskeleton device state real-time data with the human exoskeleton assistance device warning thresholds if the exoskeleton device state prediction result is inconsistent with the exoskeleton device state real-time data, generates a first human exoskeleton assistance device warning information if there is data exceeding the warning thresholds in the exoskeleton device state real-time data, sends the first human exoskeleton assistance device warning information to the user mobile terminal and the background control terminal, collects user physiological real-time data when the user mobile terminal feedback receives the first human exoskeleton assistance device warning information, compares the user physiological real-time data with the user physiological data warning thresholds, generates a second human exoskeleton assistance device warning information if the user physiological real-time data exceeds the user physiological data warning thresholds, sends the second human exoskeleton assistance device warning information to the user mobile terminal and the background control terminal, acquires user mobile terminal location information, sends the user mobile terminal location information to the background control terminal, and the background control terminal takes the user mobile terminal location information as a supplementary power location and sends the supplementary power location to the inspection equipment and the safety rescue personnel mobile terminal.

[0078] Historical data: Collect past human exoskeleton assistance device usage records and user operation data for model training and verification to improve prediction accuracy.

[0079] The early warning thresholds include human exoskeleton assistance device early warning thresholds and user physiological data early warning thresholds. These thresholds are set according to device performance specifications, safety standards and user health guidelines, etc., to determine whether the current data is within the normal range, thereby triggering the corresponding early warning mechanism.

[0080] The setting of the early warning threshold has an effect on improving the safety of the device and protecting the health of the user. For example, when the motor temperature of the exoskeleton device exceeds the preset early warning threshold, it may mean that the device has an overheating risk and needs to take immediate measures to cool down; when the heart rate or blood pressure of the user exceeds the early warning threshold, it may indicate that the user is in a state of fatigue or stress, and needs to rest or adjust the working environment.

[0081] In summary, the data acquisition module collects user intention behavior data, exoskeleton device state data, environmental data, user physiological data, historical data and preset early warning thresholds comprehensively, providing a solid foundation for subsequent prediction, processing and early warning.

[0082] The prediction module divides the user intention behavior data, exoskeleton device state data, environmental data, user physiological data and historical data into training set and validation set, uses the training set data to train the neural network model, and uses the data of the validation set to verify the trained model, to obtain the human exoskeleton assistance device prediction and early warning model;

[0083] The prediction module is responsible for training and verifying the neural network model using multiple data sources in the present application, in order to predict the future state of the human exoskeleton assistance device. The following is an explanation of the specific workflow and role of the prediction module:

[0084] The prediction module first receives multi-source data from the data acquisition module, including user intention behavior data, exoskeleton device state data, environmental data, user physiological data and historical data. These data sets are integrated and divided into training set and validation set. The training set is used to train the neural network model, and the validation set is used to verify the accuracy and generalization ability of the model.

[0085] Select a neural network model suitable for this task as the basic architecture of the prediction model. Considering the complexity of the present application and the processing ability requirement for the complex relationship between data, deep learning neural networks (such as convolutional neural network CNN, recurrent neural network RNN or its variants LSTM, GRU, etc.) are suitable choices.

[0086] The selected neural network model is trained using the training set data. During the training process, the model continuously adjusts its internal parameters (weights and biases) to minimize the difference between the predicted results and the actual results (i.e. loss function).

[0087] After the model training is completed, the model is verified using the validation set data. By comparing the model's prediction results with the actual results, the performance of the model is evaluated. According to the verification results, the model is adjusted and optimized as necessary to improve its accuracy and generalization ability. This may include adjusting the model architecture, changing the loss function, adjusting the hyperparameters, etc.

[0088] The trained and verified neural network model is deployed into the control system of the exoskeleton device. The model can receive data from the data acquisition module in real time after deployment and make predictions about the future state of the exoskeleton device.

[0089] To improve prediction accuracy, the prediction module can capture complex relationships between data by integrating multi-source data and using deep learning neural networks to model, improving the accuracy of future state prediction.

[0090] Early warning and risk prevention, accurate prediction of the state of the exoskeleton device (such as power consumption, joint angle, etc.) and the degree of user fatigue can help to discover potential problems in advance and trigger the early warning mechanism, thereby effectively preventing risks.

[0091] Optimize resource allocation, through the prediction module, the use time and task allocation of the exoskeleton device can be reasonably planned, avoiding resource waste and excessive use leading to equipment damage or user fatigue.

[0092] In summary, the prediction module plays a crucial role in the invention, not only improving the accuracy of future state prediction, but also ensuring device safety through early warning and risk prevention.

[0093] Data acquisition module, collecting real-time data of exoskeleton device, real-time data of exoskeleton device including user intention behavior real-time data, exoskeleton device state real-time data and environment real-time data;

[0094] The data acquisition module is responsible for real-time data collection related to device operation and user state in the exoskeleton device. These data are crucial for subsequent data processing, prediction and early warning mechanism. The following is a detailed explanation of the specific functions and roles of the data acquisition module:

[0095] The data acquisition module first identifies the types of data to be collected, including but not limited to user intention behavior real-time data, exoskeleton device state real-time data and environment real-time data.

[0096] Depending on the type of data to be collected, select and configure the corresponding data collection devices. For example, use electromyography sensors and motion capture system devices to collect user intention behavior data, use various sensors to monitor the state of the exoskeleton device (such as motor current, voltage, power, etc.), and use environmental sensors to collect environmental data (such as temperature, humidity, air pressure, etc.).

[0097] The data collection device transmits the real-time collected data to the database for subsequent module use. This process needs to ensure the real-time, accuracy and reliability of data transmission.

[0098] Provide real-time input for the prediction model. The data collected by the data collection module is an important input for the prediction module. By continuously inputting new real-time data, the prediction model can continuously update its prediction results, ensuring the accuracy and timeliness of the prediction.

[0099] Support dynamic early warning. Real-time data collection enables the early warning module to dynamically assess and warn based on the latest data state. Once an anomaly or potential risk is found, the early warning module can immediately trigger a response mechanism to ensure device safety and user health.

[0100] Optimize device performance. Through real-time data collection, the performance of the exoskeleton device can be monitored and evaluated in real time. This helps to discover device failures or performance degradation in a timely manner and take appropriate measures to optimize and maintain. At the same time, according to the feedback of user real-time state data, the working mode of the device can be adjusted or personalized assistance can be provided, so as to improve the user experience.

[0101] The data collection module relies on various advanced sensor technologies to obtain the required data in real time. These sensors need to have high sensitivity, high precision and high stability, etc. to ensure the accuracy of the collected data.

[0102] In order to ensure the real-time and reliability of data transmission, the data collection module needs to use efficient data transmission protocols. These protocols should support high-speed data transmission, low latency and strong error detection and correction capabilities.

[0103] The real-time data collected needs to be effectively stored and managed. Therefore, the data collection module needs to integrate a high-efficiency database management system to support fast storage, retrieval and processing of data.

[0104] In summary, the data collection module collects various types of data of the human exoskeleton assistance device in real time, providing data support for prediction, early warning and optimizing device performance.

[0105] The data processing module substitutes the user intention behavior real-time data into the human exoskeleton assistance device prediction and early warning model to obtain an exoskeleton device state prediction result, compares the exoskeleton device state prediction result with the exoskeleton device state real-time data, and obtains an exoskeleton device state comparison result, which includes consistency between the exoskeleton device state prediction result and the exoskeleton device state real-time data, and inconsistency between the exoskeleton device state prediction result and the exoskeleton device state real-time data.

[0106] The main function of the data processing module in the whole human exoskeleton assistance device system is to substitute the real-time collected user intention behavior data into the prediction and early warning model to obtain the prediction result of the exoskeleton device state, and compare the prediction result with the real-time data to generate the exoskeleton device state comparison result. The following is the analysis of the detailed function and workflow of the data processing module:

[0107] The data processing module first receives real-time data from the data acquisition module, which includes user intention behavior real-time data, exoskeleton device state real-time data, and environment real-time data, etc. Before substituting the data into the prediction and early warning model, the data processing module may need to integrate and preprocess the data to ensure the quality and consistency of the data.

[0108] The data processing module substitutes the preprocessed user intention behavior real-time data into the human exoskeleton assistance device prediction and early warning model. The prediction and early warning model generates the prediction result of the exoskeleton device state based on these data, which may include power consumption prediction, joint angle prediction, user fatigue level prediction, etc.

[0109] The data processing module compares the prediction result with the simultaneously collected exoskeleton device state real-time data. The purpose of comparison is to judge whether the prediction result is consistent with the actual state, to evaluate the accuracy and reliability of the prediction model.

[0110] According to the comparison result, the data processing module generates the exoskeleton device state comparison result. The comparison result may include two cases: one is that the prediction result is consistent with the real-time data, indicating that the current exoskeleton device state is normal; the other is that the prediction result is inconsistent with the real-time data, which may indicate that there is an abnormality or potential risk.

[0111] The data processing module receives real-time data from the data acquisition module. The received data is integrated and preprocessed, including data cleaning, formatting, normalization, etc.

[0112] Substitute the preprocessed user intention behavior real-time data into the prediction and early warning model, and the model outputs the prediction result of the exoskeleton device state.

[0113] The prediction result is compared with the exoskeleton device state real-time data. A preset comparison index and algorithm are used for calculation and judgment. An exoskeleton device state comparison result is generated according to the comparison result. The comparison result is output to a subsequent warning module or other related system components.

[0114] Through the role and workflow of the data processing module, real-time monitoring and prediction of the state of the human exoskeleton assistance device can be achieved, providing strong data support for subsequent warning and response mechanisms.

[0115] The warning module compares the exoskeleton device state real-time data with the human exoskeleton assistance device warning threshold if the exoskeleton device state prediction result is inconsistent with the exoskeleton device state real-time data. If there is data in the exoskeleton device state real-time data that exceeds the warning threshold, a first human exoskeleton assistance device warning information is generated. The first human exoskeleton assistance device warning information is sent to the user mobile terminal and the background control terminal. After the user mobile terminal feedback receives the first human exoskeleton assistance device warning information, the user physiological real-time data is collected. The user physiological real-time data is compared with the user physiological data warning threshold. If the user physiological real-time data exceeds the user physiological data warning threshold, a second human exoskeleton assistance device warning information is generated. The second human exoskeleton assistance device warning information is sent to the user mobile terminal and the background control terminal. The user mobile terminal location information is obtained. The user mobile terminal location information is sent to the background control terminal. The background control terminal takes the user mobile terminal location information as a supplementary power location. The supplementary power location is sent to the inspection equipment and the safety rescue personnel mobile terminal.

[0116] Inconsistent state detection, the warning module first receives the exoskeleton device state comparison result from the data processing module.

[0117] If it is found that the exoskeleton device state prediction result is inconsistent with the real-time data, the next threshold comparison link is immediately entered.

[0118] The exoskeleton device state real-time data is compared in detail with the preset human exoskeleton assistance device warning threshold by the warning module. If any item in the real-time data exceeds the warning threshold, it is considered as an abnormal situation and immediate action is required.

[0119] First warning information is generated and sent. After confirming the existence of an abnormal situation, the warning module quickly generates a first human exoskeleton assistance device warning information. The warning information is sent to the user mobile terminal and the background control terminal at the same time to ensure that the relevant parties can timely learn and respond.

[0120] User physiological data monitoring, when the user mobile terminal feedback has received the first early warning information, the early warning module will trigger the real-time monitoring of user physiological data. Through wearable devices and other means, real-time physiological data (such as heart rate, blood pressure, etc.) of the user is collected.

[0121] User physiological data and early warning threshold comparison, the collected user physiological real-time data is compared with the preset user physiological data early warning threshold. If the user's physiological data exceeds the early warning threshold, it indicates that the user may be in danger or discomfort state.

[0122] Generating and sending the second early warning information, after confirming the abnormality of user physiological data, the early warning module will immediately generate the second human exoskeleton assistance device early warning information. The early warning information will also be sent to the user mobile terminal and the background control terminal to remind the user to pay attention to their own safety, and inform the background control terminal to take necessary rescue or intervention measures.

[0123] Obtaining and sending user location information, the early warning module will obtain real-time location information from the user mobile terminal. The location information will be quickly sent to the background control terminal, so that the background can accurately grasp the current location of the user.

[0124] Supplement the power position setting and notification, after receiving the user location information, the background control terminal will mark it as a power supplement position. Then, the background will send this power supplement position information to the inspection equipment and the mobile terminal of the safety rescue personnel, so as to ensure that they can arrive at the scene in time to rescue or handle.

[0125] Through real-time monitoring and early warning of exoskeleton device state and user physiological data, the early warning module can timely discover and report potential safety hazards, so as to effectively prevent accidents.

[0126] The early warning module can ensure that the early warning information is synchronized and shared between the user mobile terminal, the background control terminal and the safety rescue personnel, improving the efficiency and accuracy of emergency response.

[0127] Through obtaining and sending user location information, the early warning module can guide the inspection equipment and safety rescue personnel to quickly arrive at the accident scene, and provide timely rescue and help for the user.

[0128] Specifically, the human exoskeleton assistance device applied to the replacement of middle and large components provided by the application comprises a data acquisition module, which comprises:

[0129] Determine the data acquisition frequency of user intention behavior data, exoskeleton device state data, environmental data, user physiological data, historical data and early warning threshold;

[0130] The action mode, force data, and speed data of the user are acquired through the electromyography sensor and the motion capture system device to obtain user intention behavior data.

[0131] The motor current, voltage, power, temperature, joint angle, motion speed, and load of the exoskeleton device are monitored to obtain exoskeleton device state data.

[0132] The temperature, humidity, air pressure, and weather parameters of the working environment are collected in real time using environmental sensors to obtain environmental data.

[0133] The heart rate, blood pressure, and body temperature of the user are collected using the sensors of the wearable device to obtain user physiological data.

[0134] Specifically, the exoskeleton assisting device for replacing middle and large components comprises a prediction module, which comprises:

[0135] The user intention behavior data, exoskeleton device state data, environmental data, user physiological data, and historical data are preprocessed, and the preprocessed data set is randomly divided into a training set and a validation set.

[0136] The exoskeleton assisting device performs a task, and the exoskeleton assisting device performs the task in the preset algorithm model knowledge base to obtain an algorithm model corresponding to the exoskeleton assisting device performing the task, wherein the algorithm model corresponding to the exoskeleton assisting device performing the task adopts a neural network model.

[0137] The neural network model is trained using the training set data, the trained model is saved in a loadable format, an exoskeleton assisting device prediction and early warning model is obtained, the exoskeleton assisting device prediction and early warning model is deployed to the control system of the exoskeleton assisting device, the exoskeleton assisting device prediction and early warning model receives data in real time and performs prediction, real-time user intention behavior data, real-time exoskeleton device state data, and real-time environmental data are input into the exoskeleton assisting device prediction and early warning model, the exoskeleton assisting device prediction and early warning model outputs a prediction result, and the prediction result comprises exoskeleton device power consumption prediction and user fatigue degree prediction.

[0138] The prediction module first receives user intention behavior data, exoskeleton device state data, environmental data, user physiological data, and historical data from the data acquisition module. These data are preprocessed, including data cleaning, format conversion, normalization, etc., to ensure the quality and consistency of the data.

[0139] The preprocessed data set is randomly divided into a training set and a validation set for subsequent model training and validation.

[0140] When the human exoskeleton assistance device receives a task to be executed, the prediction module matches in the preset algorithm model knowledge base. According to the nature and requirements of the task, the most suitable algorithm model is selected, which is a neural network model here. The neural network model has learning ability and generalization ability, and can handle complex nonlinear relationships.

[0141] The neural network model is trained using the training set data, and the model parameters are adjusted through iterative optimization algorithms to accurately predict the state of the exoskeleton device and the behavior of the user. During the training process, the prediction module uses the validation set data to verify the model to evaluate the performance and generalization ability of the model. After training, the model is saved in a loadable format for subsequent deployment in the control system of the human exoskeleton assistance device.

[0142] The trained human exoskeleton assistance device prediction and early warning model is deployed to the control system of the device. The model receives real-time data from the data acquisition module, including user intention behavior real-time data, exoskeleton device state real-time data, and environmental real-time data. These data are input into the prediction and early warning model, which makes real-time predictions and outputs the prediction results.

[0143] The prediction results include exoskeleton device power consumption prediction and user fatigue level prediction. The prediction results are of great significance for evaluating the performance of the exoskeleton device, optimizing task execution strategies, and ensuring user safety.

[0144] Through the training and learning of the neural network model, the prediction module can accurately predict the power consumption of the exoskeleton device and the fatigue level of the user, providing a scientific basis for task execution. The prediction module can receive data in real time and make predictions, ensuring the timeliness and accuracy of the prediction results. Through algorithm model matching and model training, the prediction module can automatically select the most suitable prediction model according to the requirements of different tasks, achieving intelligent prediction. The prediction module supports the expansion and update of the algorithm model knowledge base, which can be continuously optimized and upgraded with the development of technology and the diversification of tasks.

[0145] In summary, the prediction module in the human exoskeleton assistance device described in the present application provides strong technical support and safety protection for tasks such as medium and large component replacement through accurate prediction, real-time data processing, and intelligent model selection.

[0146] Specifically, the human exoskeleton assistance device for medium and large component replacement described in the present application comprises a data acquisition module, which includes:

[0147] The type of data to be collected is determined, including user intention behavior real-time data, exoskeleton device state real-time data, and environmental real-time data.

[0148] According to the type of data to be collected, determine the data collection device, configure the data transmission protocol of the data collection device, and transmit the real-time collected data to the database.

[0149] The data collection module needs to determine the type of data to be collected. In the present application, these data types mainly include user intention behavior real-time data, exoskeleton device state real-time data and environment real-time data.

[0150] User intention behavior real-time data may include user actions, gestures, operation instructions, etc., for reflecting the current intention and needs of the user.

[0151] Exoskeleton device state real-time data includes device power, joint angle, torque and other key parameters, for evaluating the running state of the device.

[0152] Environmental real-time data may include temperature, humidity, light, etc. These environmental factors may affect the performance of the exoskeleton device and the safety of the user.

[0153] Determine the data collection device: according to the type of data to be collected, the data collection module needs to select the corresponding data collection device. The data collection device includes acceleration sensor, angle sensor, torque sensor, temperature and humidity sensor, optical fiber sensor and user interaction device.

[0154] After determining the data collection device, the data collection module needs to configure the data transmission protocol for these devices. Including setting the transmission format, transmission rate, verification method of data, etc., to ensure that the data can be accurately and efficiently transmitted to the database.

[0155] The data collection device collects data in real time according to the configured transmission protocol, and transmits these data to the database through wired or wireless means. The database as the storage and management center of data, is responsible for receiving, storing and providing data access service. Through accurate selection of data collection device and configuration of transmission protocol, the collected data has high accuracy.

[0156] The data collection module can collect and transmit data in real time, meeting the real-time requirements of the system. The module supports the collection and transmission of new data types, facilitating the expansion and upgrade of the system. In the process of data transmission and storage, necessary security measures are taken to protect the data from illegal access or tampering.

[0157] In summary, the data collection module in the human exoskeleton assisting device of the present application provides accurate, real-time, expandable and secure data support for the entire system by clearly defining the data type, selecting appropriate data collection devices, configuring data transmission protocols and realizing real-time data collection and transmission.

[0158] Specifically, the application relates to a human exoskeleton assisting device applied to middle-large component replacement, and a data processing module of the human exoskeleton assisting device.

[0159] The prediction result of the exoskeleton device state is obtained from the prediction module, and the prediction result of the exoskeleton device state includes power consumption prediction and joint angle prediction.

[0160] The prediction result of the exoskeleton device state is aligned with real-time data of the exoskeleton device state in time, indexes used for comparison are received and set, for each prediction result of the exoskeleton device state and real-time data of the corresponding exoskeleton device state, defined comparison indexes are used for calculation, whether the prediction result of the exoskeleton device state is consistent with the real-time data is judged according to the comparison calculation result, and an exoskeleton device state comparison result is generated according to the judgment result.

[0161] The data processing module of the human exoskeleton assisting device applied to middle-large component replacement is responsible for comparison and analysis of the prediction result of the exoskeleton device state and real-time data, so as to judge the accuracy of the prediction and generate a comparison result.

[0162] The data processing module first obtains the prediction result of the exoskeleton device state from the prediction module, and the prediction result includes key information such as power consumption prediction and joint angle prediction.

[0163] In order to improve the comparability of the prediction result and the real-time data, the data processing module needs to align the two in time. This means that for each prediction result, real-time data at the corresponding time point needs to be found.

[0164] The data processing module receives and sets indexes used for comparison. These indexes may be absolute error, relative error, mean square error, etc., which are used to quantify the difference between the prediction result and the real-time data.

[0165] For each prediction result of the exoskeleton device state and real-time data at the corresponding time point, the data processing module uses defined comparison indexes for calculation. This includes calculating the difference between the predicted value and the measured value, and calculating other related statistics as needed.

[0166] According to the comparison calculation result, the data processing module judges whether the prediction result of the exoskeleton device state is consistent with the real-time data. This usually involves setting one or more thresholds, when the difference is less than the thresholds, it is considered that the prediction result is consistent with the real-time data, otherwise, it is considered that the prediction result is not consistent with the real-time data.

[0167] Finally, the data processing module generates an exoskeleton device state comparison result according to the judgment result. These results may be presented in the form of reports, charts or numbers, which are used for subsequent analysis and decision-making.

[0168] Data alignment and synchronization ensure the consistency of prediction results and real-time data in time, providing an accurate basis for comparative analysis.

[0169] Index definition and calculation, by defining and calculating the comparison index, quantifying the difference between the prediction results and the real-time data, providing an objective basis for consistency judgment.

[0170] Consistency judgment and decision-making, according to the comparison calculation result, judge the accuracy of the prediction result, and generate the comparison result, provide necessary input information for the early warning module.

[0171] The data processing module should support the comparison of new prediction results and real-time data types, as well as the flexible setting of comparison indicators and thresholds, to adapt to the changes of different application scenarios and needs.

[0172] In summary, the data processing module of the human exoskeleton assistance device described in the present application provides reliable data processing and analysis capabilities for the entire system through accurate time alignment, reasonable comparison index setting, accurate comparison calculation, and consistency judgment and decision-making, which is an important guarantee for the exoskeleton assistance device to efficiently and accurately assist users in completing tasks such as medium and large component replacement.

[0173] Specifically, the human exoskeleton assistance device applied to medium and large component replacement according to the present application, the early warning module comprises:

[0174] When the user mobile terminal receives the first human exoskeleton assistance device early warning information, the position information acquisition function is automatically triggered, the user mobile terminal includes a smart phone and a tablet computer; using GPS, Beidou satellite positioning system or mobile network base station positioning technology, the position information of the user mobile terminal is obtained;

[0175] After the background control terminal receives the position information, the background control terminal records the position information of the user mobile terminal into the database, and marks it as a supplementary power position, the background control terminal sends the supplementary power position information to the inspection equipment, the inspection equipment includes a unmanned aerial vehicle and an inspection robot;

[0176] After the inspection equipment receives the supplementary power position, the inspection equipment loads the human exoskeleton assistance device battery, the inspection equipment generates a patrol route according to the preset path generation model, the inspection equipment executes the patrol route, the inspection equipment reaches the user mobile terminal position, the inspection equipment collects the image data of the worker using the human exoskeleton assistance device for medium and large component replacement, obtains the to-be-detected image data, and moves the human exoskeleton assistance device battery to the position of the worker, the human exoskeleton assistance device battery is used to provide power for the human exoskeleton assistance device of the worker. After the energy storage cover motor is opened, the battery is placed into the energy storage shell.

[0177] When the user mobile terminal (including smartphones and tablets) receives the first exoskeleton warning information, the system will automatically trigger a series of actions to ensure the safety of the staff and the normal operation of the equipment. The following is the detailed process:

[0178] After the user mobile terminal receives the warning information, it automatically activates the location information acquisition function immediately. Using GPS, Beidou satellite positioning system or mobile network base station positioning technology, the user's current location information is quickly and accurately acquired.

[0179] The user mobile terminal encrypts the acquired location information to ensure the security of data transmission. The encrypted location information is sent to the background control terminal through wireless network (such as 4G, 5G or Wi-Fi).

[0180] After the background control terminal receives the encrypted location information, it performs decryption processing. The decrypted location information is recorded in the database and specially marked as "supplementary power location". The background control terminal sends this location information to the inspection equipment, including unmanned aerial vehicles and inspection robots, so that they can go to the designated location.

[0181] After the inspection equipment receives the supplementary power location information, it loads the battery required by the exoskeleton. According to the preset path generation model, the optimal inspection route is generated. The inspection equipment automatically goes to the location of the user mobile terminal along the generated route.

[0182] When the inspection equipment arrives at the user mobile terminal location, it uses its image acquisition equipment (such as a camera) to collect image data of the staff using the exoskeleton for medium and large component replacement. The image data is used as the detected image data for subsequent analysis and evaluation. At the same time, the inspection equipment moves the loaded battery to the staff's location to provide power supply for the exoskeleton, ensuring that the equipment can continue to operate normally.

[0183] Through the above process, the present application not only realizes the state monitoring of the exoskeleton, thereby providing real-time battery, but also realizes the safety of the staff and the continuous operation ability of the equipment through the rapid response and on-site support of the inspection equipment.

[0184] Specifically, the exoskeleton for medium and large component replacement according to the present application comprises a warning module, which includes:

[0185] The detected image data is substituted into the preset exoskeleton image analysis model to obtain the detected image data analysis result, which includes staff action deformation data, exoskeleton running track data and exoskeleton deformation data;

[0186] The worker action deformation data is compared with the worker action deformation early warning threshold to obtain a worker action deformation data comparison result.

[0187] The human exoskeleton assistance device running trajectory data is compared with the human exoskeleton assistance device running trajectory early warning threshold to obtain a human exoskeleton assistance device running trajectory comparison result.

[0188] The human exoskeleton assistance device deformation data is compared with the human exoskeleton assistance device deformation early warning threshold to obtain a human exoskeleton assistance device deformation data comparison result.

[0189] If any one of the worker action deformation data comparison result, the human exoskeleton assistance device running trajectory comparison result, and the human exoskeleton assistance device deformation data comparison result exceeds the early warning threshold, a third human exoskeleton assistance device early warning information is generated, and the third human exoskeleton assistance device early warning information is sent to the safety rescue personnel mobile terminal.

[0190] The early warning module applied to the human exoskeleton assistance device for replacing a medium-large component realizes comprehensive monitoring and early warning of the worker action, the human exoskeleton assistance device running trajectory, and the deformation of the device itself.

[0191] The detected image data collected by the inspection equipment is substituted into a preset human exoskeleton assistance device use image analysis model. The model can automatically identify and analyze key information in the image, including the action of the worker, the running trajectory of the exoskeleton device, and the deformation of the device.

[0192] Through analysis of the model, a detected image data analysis result is obtained, specifically including worker action deformation data, human exoskeleton assistance device running trajectory data, and human exoskeleton assistance device deformation data.

[0193] The worker action deformation data is compared with a preset worker action deformation early warning threshold to determine whether the action of the worker is abnormal or deformed, and a worker action deformation data comparison result is obtained.

[0194] Meanwhile, the human exoskeleton assistance device running trajectory data is compared with a preset running trajectory early warning threshold to evaluate whether the running of the device deviates from the normal trajectory, and a human exoskeleton assistance device running trajectory comparison result is obtained.

[0195] The human exoskeleton assistance device deformation data is compared with a preset deformation early warning threshold to detect whether the device has abnormal deformation or damage, and a human exoskeleton assistance device deformation data comparison result is obtained.

[0196] If the data of any one of the above three comparison results exceeds the corresponding early warning threshold, the early warning module will immediately generate the third exoskeleton device warning information. The early warning information contains detailed abnormal data and possible dangerous situation description, so that the safety rescue personnel can quickly understand the on-site situation and respond. The early warning information is sent to the mobile terminal of the safety rescue personnel through wireless network and other communication means, to ensure that they can receive the early warning and take corresponding rescue measures in the first time.

[0197] Through comprehensive monitoring of the actions of the staff, the running trajectory of the exoskeleton device and the deformation of the device, the overall safety of the middle and large component replacement process is ensured. By using advanced image analysis model, automatic identification and intelligent analysis of image data are realized, and the accuracy and efficiency of early warning are improved. When abnormal data is detected, the early warning module can immediately generate early warning information and send it to the safety rescue personnel, providing strong support for their timely response. Through early warning and timely intervention, accidents caused by staff action deformation, abnormal device running trajectory or device deformation are effectively prevented.

[0198] In summary, the early warning module in the human exoskeleton device of the present application provides strong technical support for safety monitoring and accident prevention in the middle and large component replacement process through fine data analysis, intelligent comparison and timely early warning information transmission.

[0199] The technical scheme of the present application solves the problem that the existing human exoskeleton device cannot provide assistance when the staff is carrying middle and large components by the following means:

[0200] The waist support structure provides stable waist support for the staff as the basis of the overall device. The main support device and the auxiliary support device provide strong support force for the main support device and the auxiliary support device through the hydraulic support rod and the rotating shaft structure, to ensure that the staff can get sufficient assistance when carrying the middle and large components. The support structure connecting plate connects the waist support structure and the main support device, improving the stability of the overall structure.

[0201] The main support device and the auxiliary support device both adopt hydraulic support rods, which can adjust the support angle and force according to different working conditions through the flexible rotation of the rotating shaft, to provide accurate and stable assistance for the staff. The energy storage device provides electric energy for the hydraulic support rod and the control device, to ensure that the device can continue to operate stably.

[0202] The intelligent control system integrates multiple modules such as data acquisition, prediction, data collection, data processing, and early warning. By collecting and analyzing user intention behavior data, exoskeleton device state data, environmental data, and user physiological data, the system realizes real-time monitoring and intelligent adjustment of the device operating state. The neural network model is used to predict the device state, and once potential risks are found, the early warning mechanism is triggered immediately to ensure the safety of the workers. The control device also connects with the remote control terminal to receive data transmitted by remote control, realizing remote monitoring and scheduling.

[0203] Comprehensive safety monitoring: A sensor matrix, including pressure sensors and position sensors, is installed inside the waist support structure, main support device, and auxiliary support device to collect real-time device operating state data. Through data comparison, threshold judgment, and image analysis, the device state and user physiological state are comprehensively monitored. Once an anomaly is found, an early warning message is generated and sent to the user mobile terminal and background control terminal, and inspection equipment and safety rescue personnel are notified to go to the scene for processing. By adjusting the angle and force of the hydraulic support rod and rotating shaft, the device can adapt to workers of different body sizes and work habits, providing personalized assistance. The modular design of the components facilitates disassembly, maintenance, and upgrading.

[0204] In summary, the technical scheme of the present application solves the problem that existing human exoskeleton assistance devices cannot provide assistance when workers are carrying large components by optimizing the structure design, using a hydraulic power system, integrating an intelligent control system, achieving comprehensive safety monitoring, and improving flexibility and adaptability.

Claims

1. A human exoskeleton assistive device for replacing medium to large components, characterized in that, include: Waist support structure (1), main support device (2), secondary support device (3) and support structure connecting plate (4); The waist support structure (1) has a support structure connecting plate (4) screwed on both sides. The outer wall of the support structure connecting plate (4) is screwed with a main support device (2). The support structure connecting plate (4) is used to connect the waist support structure (1) and the main support device (2). The support structure connecting plate (4) is located between the main support device (2) and the waist support structure (1). The main support device (2) has a secondary support device (3) installed on its side wall. The main support device (2) and the secondary support device (3) are used to support the waist support structure (1). The secondary support device (3) provides support force to the main support device (2). A control device (6) is installed on the outer wall of the waist support structure (1), and an energy storage device (5) is installed on the top of the auxiliary support device (3). The control device (6) is connected to the energy storage device (5), the main support device (2), and the auxiliary support device (3) respectively. The energy storage device (5) is used to provide electrical energy to the control device (6), the main support device (2), and the auxiliary support device (3). The energy storage device (5) includes: an energy storage shell (501), an energy storage cover (502), and an energy storage cover motor (503); An energy storage cover motor (503) is installed on the top of the energy storage housing (501), and the energy storage cover motor (503) is connected to the energy storage cover (502); The waist support structure (1), the main support device (2) and the auxiliary support device (3) are equipped with a sensor matrix. The sensor matrix is ​​equipped with a pressure sensor and a position sensor. The control device (6) establishes a communication connection with the pressure sensor and the position sensor. The control device (6) receives data collected by the pressure sensor and the position sensor. The control device (6) also establishes a connection with the remote control terminal and receives data transmitted remotely. The control device includes: The data acquisition module acquires user intent and behavior data, exoskeleton device status data, environmental data, user physiological data, historical data, and warning thresholds. The warning thresholds include warning thresholds for human exoskeleton assistive devices and warning thresholds for user physiological data. The prediction module divides user intent behavior data, exoskeleton device status data, environmental data, user physiological data, and historical data into training sets and validation sets. The training set data is used to train the neural network model, and the validation set data is used to validate the trained model, resulting in a prediction and early warning model for human exoskeleton assistive devices. The data acquisition module collects real-time data from the human exoskeleton assistive device, including real-time data on user intent and behavior, real-time data on the status of the exoskeleton device, and real-time data on the environment. The data processing module inputs real-time user intent and behavior data into the prediction and early warning model of the human exoskeleton assistive device to obtain the exoskeleton device status prediction result. The exoskeleton device status prediction result is compared with the real-time exoskeleton device status data to obtain the exoskeleton device status comparison result. The exoskeleton device status comparison result includes the exoskeleton device status prediction result being consistent with the exoskeleton device status data, and the exoskeleton device status prediction result being inconsistent with the exoskeleton device status data. The early warning module, if the predicted status of the exoskeleton device is inconsistent with the real-time status data, compares the real-time status data with the early warning threshold of the exoskeleton assistive device. If any data in the real-time status data exceeds the early warning threshold, a first early warning message for the exoskeleton assistive device is generated and sent to the user's mobile terminal and the backend control terminal. After the user's mobile terminal receives the first early warning message, it collects the user's real-time physiological data and compares it with the user's physiological data early warning threshold. If the user's real-time physiological data exceeds the user's physiological data early warning threshold, a second early warning message for the exoskeleton assistive device is generated and sent to the user's mobile terminal and the backend control terminal. The module also obtains the user's mobile terminal location information and sends it to the backend control terminal. The backend control terminal uses the user's mobile terminal location information as the location for replenishing power and sends the location information to the inspection equipment and the mobile terminals of the safety rescue personnel.

2. The exoskeleton assistive device for replacing medium and large components as described in claim 1, characterized in that, The main support device (2) includes: a first main support connector (201), a first main support rotating shaft (202), a first main support hydraulic support rod (203), a second main support rotating shaft (204), a second main support hydraulic support rod (205), a third main support rotating shaft (206), and a main support pedal (207). A first main support rotating shaft (202) is installed between the first main support connector (201) and the first main support hydraulic support rod (203). A first main support hydraulic support rod (203) is installed between the first main support rotating shaft (202) and the second main support rotating shaft (204). A second main support hydraulic support rod (205) is connected between the second main support rotating shaft (204) and the third main support rotating shaft (206). A third main support rotating shaft (206) is installed between the second main support hydraulic support rod (205) and the main support pedal (207).

3. The exoskeleton assistive device for replacing medium and large components as described in claim 2, characterized in that, The first main support hydraulic support rod (203) is used to provide support force to the first main support connector (201); The first main support rotating shaft (202) is located between the first main support hydraulic support rod (203) and the first main support connecting body (201). The first main support rotating shaft (202) is used to drive the first main support hydraulic support rod (203) to rotate. The second main support rotating shaft (204) is located between the first main support hydraulic support rod (203) and the second main support hydraulic support rod (205). The second main support rotating shaft (204) is used to drive the second main support hydraulic support rod (205) to rotate. The second main support hydraulic support rod (205) is used to provide support force to the second main support rotating shaft (204); The third main support rotating shaft (206) is used to drive the main support pedal (207) to rotate.

4. The exoskeleton assistive device for replacing medium and large components as described in claim 1, characterized in that, The auxiliary support device includes: a first auxiliary support rotating shaft (301), a first auxiliary support hydraulic support rod (302), a second auxiliary support rotating shaft (303), a second auxiliary support hydraulic support rod (304), a third auxiliary support rotating shaft (305), and an auxiliary support pedal (306). A first secondary support hydraulic support rod (302) is connected between the first secondary support rotating shaft (301) and the second secondary support rotating shaft (303). The first auxiliary support hydraulic support rod (302) is used to provide support force to the first auxiliary support rotating shaft (301); The first auxiliary support rotating shaft (301) is used to drive the first auxiliary support hydraulic support rod (302) to rotate; A second auxiliary support hydraulic support rod (304) is installed between the second auxiliary support rotating shaft (303) and the third auxiliary support rotating shaft (305). The second auxiliary support rotating shaft (303) is used to drive the second auxiliary support hydraulic support rod (304) to rotate, and the second auxiliary support hydraulic support rod (304) is used to provide support force to the second auxiliary support rotating shaft (303); A third auxiliary support rotating shaft (305) is installed between the second auxiliary support hydraulic support rod (304) and the auxiliary support pedal (306). The third secondary support rotating shaft (305) drives the secondary support pedal (306) to rotate.

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