Human exoskeleton power assisting device applied to replacement of medium and large components

Abstract

The invention relates to the technical field of power equipment, and provides a human exoskeleton power assisting device applied to replacement of medium and large components, the human exoskeleton power assisting device comprises a waist supporting structure, a main supporting device, an auxiliary supporting device and a supporting structure connecting plate, and flexible and stable supporting is achieved through a hydraulic supporting rod and a rotating shaft. The device is further provided with an energy storage device and a control device, and the energy storage device provides electric energy for the device. The exoskeleton power assisting device effectively solves the problem that an existing exoskeleton power assisting device is insufficient in power assisting when carrying medium and large components, remarkably improves the operation capacity and efficiency, meanwhile, improves the operation safety and user experience, and is suitable for replacement operation of the large components in the electric power field.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment, and in particular to an exoskeleton power-assisting device for replacing medium and large components. Background Art

[0002] With the development of science and technology, especially the progress of mechanical engineering, materials science and ergonomics, power-assisted exoskeletons have gradually moved 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, unpowered power-assisted exoskeletons came into being. They use the principle of static balance and mechanical structure design to achieve power assistance to the arm. No external energy is required, which is more in line with the concept of green, low-carbon and sustainable energy development.

[0003] In the field of electric power, workers often need to wear insulating clothing, safety belts and other safety equipment, and the equipment carried by workers often increases the burden on their bodies. Existing human exoskeleton power assist devices cannot provide assistance when workers carry medium and large components. To solve this technical pain point, the present invention provides a human exoskeleton power assist device for replacing medium and large components. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an exoskeleton power-assist device for use in the replacement of medium and large components. When the exoskeleton power-assist device is used in the power field, the exoskeleton device uses modern industrial devices to assist humans in their relatively limited working capabilities during operation, thereby achieving working capabilities and effects that exceed the limits or have a longer duration.

[0005] The present invention provides a human body exoskeleton power assist device for replacing medium and large parts, comprising: a waist support structure, a main support device, a secondary support device and a support structure connecting plate;

[0006] Support structure connecting plates are screwed on both sides of the waist support structure, and the main support device is screwed on 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. The support structure connecting plate is located between the main support device and the waist support structure;

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

[0008] A control device is installed on the outer wall of the lumbar support structure, and an energy storage device is installed on the top of the auxiliary support device. The control device is connected to the energy storage device, the main support device and the auxiliary support device respectively. The energy storage device is used to provide electrical energy to the control device, the main support device and the auxiliary support device.

[0009] Furthermore, the present invention provides a human body exoskeleton power assist device for replacing medium and large parts, wherein the main support device comprises: a first main support connector, a first main support rotation axis, a first main support hydraulic support rod, a second main support rotation axis, a second main support hydraulic support rod, a third main support rotation axis and a main support pedal;

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

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

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

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

[0014] Furthermore, the present invention provides a human body exoskeleton power assist device for replacing medium and large parts, further comprising:

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

[0016] The first main support rotation shaft is located between the first main support hydraulic support rod and the first main support connecting body, and the first main support rotation shaft is used to drive the first main support hydraulic support rod to rotate;

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

[0018] The second main support hydraulic support rod is used to provide support force to the second main support rotation shaft;

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

[0020] Furthermore, the present invention provides a human body exoskeleton power assist device for replacing medium and large parts, wherein the auxiliary support device comprises: a first auxiliary support rotation shaft, a first auxiliary support hydraulic support rod, a second auxiliary support rotation shaft, a second auxiliary support hydraulic support rod, a third auxiliary support rotation shaft and an auxiliary support pedal;

[0021] A first auxiliary support hydraulic support rod is connected between the first auxiliary support rotating shaft and the second auxiliary support rotating shaft;

[0022] The first auxiliary support hydraulic support rod is used to provide support force to the first auxiliary support rotating shaft;

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

[0024] A 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 supporting force to the second auxiliary support rotating shaft;

[0026] A 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] Furthermore, the present invention provides a human body exoskeleton power assist device for replacing medium and large components, wherein the energy storage device comprises: an energy storage housing, an energy storage cover, and an energy storage cover motor;

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

[0030] Furthermore, in the human body exoskeleton assisting device for replacing medium and large parts described in the present invention, a sensor matrix is ​​installed inside the waist support structure, the main support device and the auxiliary support device, the sensor matrix is ​​installed with a pressure sensor and a position sensor, and the control device establishes a communication connection with the pressure sensor and the position sensor;

[0031] The control device receives data collected by the pressure sensor and the position sensor. The control device also establishes a connection with the remote control terminal and receives data transmitted by the remote control.

[0032] Beneficial effects of the present invention;

[0033] The present invention uses modern industrialized devices to assist. The human exoskeleton assist device of the present invention enables workers to exceed their limited operating capabilities when replacing medium and large components in the power field, and achieve longer and more intensive operations. The stable assistance provided by the device reduces the physical burden of the workers, allowing them to complete the tasks of carrying and replacing medium and large components more quickly and accurately, thereby improving overall operating efficiency.

[0034] The present invention integrates an intelligent early warning system that can monitor the state of the exoskeleton device and the user's physiological data in real time. Once a potential risk is found, the early warning mechanism is immediately triggered to effectively avoid accidents. Through the sensor matrix and multi-layer early warning mechanism, comprehensive monitoring of the device state and the user's physiological state is achieved to ensure the safety of workers during the operation.

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

[0036] The energy storage device of the present invention adopts advanced energy storage technology and materials, has efficient and stable electric energy output capabilities, and improves energy utilization efficiency. Compared with traditional motors, pneumatic or hydraulic power sources, the present invention utilizes the static balance principle 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 adopt a modular design, which is easy to disassemble, repair and upgrade, reducing maintenance costs and time. The control device establishes a connection with the remote control terminal to achieve remote monitoring and scheduling, improving the convenience and efficiency of management.

[0037] In summary, the human body exoskeleton assist device provided by the present invention for replacement of medium and large components has significant beneficial effects in improving work ability and efficiency, enhancing work safety, improving user experience and comfort, improving energy utilization efficiency and sustainability, and facilitating maintenance and upgrading. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative labor.

[0039] Figure 1 A schematic diagram of the front view of the external structure of an exoskeleton power-assisting device for replacing medium and large components provided by an embodiment of the present invention.

[0040] Figure 2 A schematic top view of the external structure of an exoskeleton power-assisting device for replacing medium or large components provided in an embodiment of the present invention.

[0041] Figure 3 A schematic diagram of the internal modules of a control device of an exoskeleton power assist device for replacing medium to large components provided in an embodiment of the present invention.

[0042] Description of the structure of the attached drawings: 1. Waist support structure, 2. Main support device, 201. First main support connector, 202. First main support rotation axis, 203. First main support hydraulic support rod, 204. Second main support rotation axis, 205. Second main support hydraulic support rod, 206. Third main support rotation axis, 207. Main support pedal, 3. Auxiliary support device, 301. First auxiliary support rotation axis, 302. First auxiliary support hydraulic support rod, 303. Second auxiliary support rotation axis, 304. Second auxiliary support hydraulic support rod, 305. Third auxiliary support rotation axis, 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] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0044] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0045] The present invention provides a human body exoskeleton power assist device for replacing medium and large parts, comprising: a waist support structure 1, a main support device 2, a secondary support device 3 and a support structure connecting plate 4;

[0046] The two sides of the waist support structure 1 are screwed with support structure connecting plates 4, the outer wall of the support structure connecting plates 4 is screwed with the main support device 2, the support structure connecting plates 4 are used to connect the waist support structure 1 and the main support device 2, and the support structure connecting plates 4 are located between the main support device 2 and the waist support structure 1;

[0047] A secondary supporting device 3 is installed on the side wall of the main supporting device 2. The main supporting device 2 and the secondary supporting device 3 are used to support the waist supporting structure 1, and the secondary supporting device 3 provides supporting force for the main supporting device 2;

[0048] A control device 6 is installed on the outer wall of the lumbar 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.

[0049] Specifically, the present invention provides a human body exoskeleton power assist device for replacing medium and large parts, wherein the main support device 2 comprises: a first main support connector 201, a first main support rotation axis 202, a first main support hydraulic support rod 203, a second main support rotation axis 204, a second main support hydraulic support rod 205, a third main support rotation axis 206 and a main support pedal 207;

[0050] A first main support rotating shaft 202 is installed between the first main support connecting body 201 and the first main support hydraulic support rod 203;

[0051] 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;

[0052] 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;

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

[0054] Specifically, the present invention provides a human body exoskeleton power assist device for replacing medium and large parts, further comprising:

[0055] The first main support hydraulic support rod 203 is used to provide 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 the first main support rotating shaft 202 is used to drive 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 the second main support rotating shaft 204 is used to drive the second main support hydraulic support rod 205 to rotate;

[0058] The second main support hydraulic support rod 205 is used to provide support force to the second main support rotating shaft 204;

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

[0060] Specifically, the present invention provides a human exoskeleton power assist device for replacing medium and large parts, wherein the auxiliary support device includes: a first auxiliary support rotation axis 301, a first auxiliary support hydraulic support rod 302, a second auxiliary support rotation axis 303, a second auxiliary support hydraulic support rod 304, a third auxiliary support rotation axis 305 and an auxiliary support pedal 306;

[0061] A 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] 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;

[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 supporting force to the second auxiliary support rotating shaft 303;

[0066] A 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 present invention provides a human body exoskeleton power assist device for replacing medium and large parts, wherein the energy storage device 5 comprises: an energy storage housing 501, an energy storage cover 502 and an energy storage cover motor 503;

[0069] 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 .

[0070] Specifically, in the human body exoskeleton assisting device for replacing medium and large parts described in the present invention, a sensor matrix is ​​installed inside the waist support structure 1, the main support device 2 and the auxiliary support device 3, and the sensor matrix is ​​installed with a pressure sensor and a position sensor, and the control device 6 establishes a communication connection with the pressure sensor and the position sensor;

[0071] The control device 6 receives the 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 the data transmitted by the remote control.

[0072] Specifically, the control device of the human body exoskeleton power assist device for replacing medium and large parts described in the present invention includes:

[0073] A data acquisition module, which acquires user intention behavior data, exoskeleton device status data, environmental data, user physiological data, historical data, and warning thresholds. The warning thresholds include the warning thresholds of the human exoskeleton power assist device and the warning thresholds of the user's physiological data;

[0074] The prediction module divides the user's intention behavior data, exoskeleton device status 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 validation set data to validate the trained model, thereby obtaining a prediction and early warning model for the human exoskeleton power assist device;

[0075] A data collection module collects real-time data of the human exoskeleton power-assist device, including real-time data of user intention and behavior, real-time data of the exoskeleton device status, and real-time data of the environment;

[0076] The data processing module substitutes the real-time data of the user's intention behavior into the prediction and warning model of the human body exoskeleton power assist device to obtain the prediction result of the exoskeleton device state, compares the prediction result of the exoskeleton device state with the real-time data of the exoskeleton device state, and obtains the comparison result of the exoskeleton device state, wherein the comparison result of the exoskeleton device state includes the consistency between the prediction result of the exoskeleton device state and the real-time data of the exoskeleton device state, and the inconsistency between the prediction result of the exoskeleton device state and the real-time data of the exoskeleton device state;

[0077] The early warning module compares the real-time data of the exoskeleton device status with the early warning threshold of the human body exoskeleton assisting device if the predicted result of the exoskeleton device status is inconsistent with the real-time data of the exoskeleton device status. If there is data exceeding the early warning threshold in the real-time data of the exoskeleton device status, a first early warning message of the human body exoskeleton assisting device is generated, and the first early warning message of the human body exoskeleton assisting device is sent to the user's mobile terminal and the background control terminal. When the user's mobile terminal receives the early warning message of the first human body exoskeleton assisting device, the user's physiological real-time data is collected, and the user's physiological real-time data is compared with the user's physiological data early warning threshold. If the user's physiological real-time data exceeds the user's physiological data early warning threshold, a second early warning message of the human body exoskeleton assisting device is generated, and the second early warning message of the human body exoskeleton assisting device is sent to the user's mobile terminal and the background control terminal, and the user's mobile terminal location information is obtained, and the user's mobile terminal location information is sent to the background control terminal. The background control terminal uses the user's mobile terminal location information as the supplementary power location, and sends the supplementary power location to the inspection equipment and the mobile terminal of the safety rescue personnel.

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

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

[0080] The setting of warning thresholds is helpful in improving equipment safety and protecting user health. For example, when the motor temperature of an exoskeleton device exceeds the preset warning threshold, it may mean that the device is at risk of overheating and immediate cooling measures are required; when the user's heart rate or blood pressure exceeds the 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 provides a solid foundation for subsequent prediction, processing and warning by comprehensively collecting user intention behavior data, exoskeleton device status data, environmental data, user physiological data, historical data and preset warning thresholds.

[0082] The prediction module divides the user's intention behavior data, exoskeleton device status 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 validation set data to validate the trained model, thereby obtaining a prediction and early warning model for the human exoskeleton power assist device;

[0083] The prediction module is responsible for training and verifying the neural network model using multiple data sources in the present invention so as to predict the future state of the human exoskeleton power assist device. The following is an explanation of the specific workflow and function 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 status data, environmental data, user physiological data, and historical data. These data sets are integrated and divided into training sets and validation sets. The training set is used to train the neural network model, while the validation set is used to verify the accuracy and generalization ability of the model.

[0085] A neural network model suitable for this task is selected as the basic architecture of the prediction model. Considering the complexity of the present invention and the processing capability requirements for complex relationships between data, a deep learning neural network (such as a convolutional neural network CNN, a recurrent neural network RNN ​​or its variants LSTM, GRU, etc.) is a suitable choice.

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

[0087] After the model training is completed, the model is validated using the validation set data. The performance of the model is evaluated by comparing the model's predicted results with the actual results. Based on the validation 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 hyperparameters, etc.

[0088] The trained and verified neural network model is deployed to the control system of the exoskeleton power assist device. After deployment, the model can receive data from the data acquisition module in real time and predict the future state of the exoskeleton power assist device.

[0089] Improve forecasting accuracy. By integrating multi-source data and using deep learning neural networks for modeling, the forecasting module can capture the complex relationships between data and improve the accuracy of forecasts of future states.

[0090] Early warning and risk prevention and control, accurately predicting the status of the exoskeleton device (such as power consumption, joint angle, etc.) and the user's fatigue level, will help to detect potential problems in advance and trigger early warning mechanisms, thereby effectively preventing and controlling risks.

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

[0092] In summary, the prediction module plays a vital role in the present invention. It can not only improve the accuracy of prediction of future states, but also ensure equipment safety through early warning and risk prevention and control.

[0093] A data collection module collects real-time data of the human exoskeleton power-assist device, including real-time data of user intention and behavior, real-time data of the exoskeleton device status, and real-time data of the environment;

[0094] The data acquisition module is responsible for real-time acquisition of key data related to the operation of the device and the user's status in the human exoskeleton power assist device of the present invention. These data are crucial for the implementation of subsequent data processing, prediction and early warning mechanisms. The following is a detailed explanation of the specific functions and effects of the data acquisition module:

[0095] The data collection module first identifies the type of data that needs to be collected, which includes but is not limited to real-time data on user intention and behavior, real-time data on the state of the exoskeleton device, and real-time data on the environment.

[0096] Select and configure the corresponding data acquisition equipment according to the type of data to be collected. For example, use electromyography sensors and motion capture system equipment to collect user intention behavior data, use various sensors to monitor the status 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 acquisition device transmits the real-time collected data to the database for use by subsequent modules. This process needs to ensure the real-time, accuracy and reliability of data transmission.

[0098] Providing real-time input for the prediction model, the data collected in real time by the data acquisition 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 to ensure the accuracy and timeliness of the prediction.

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

[0100] Optimize equipment performance. Through real-time data collection, the performance of the exoskeleton device can be monitored and evaluated in real time. This helps to promptly detect equipment failures or performance degradation, and take corresponding measures for optimization and maintenance. At the same time, based on the user's real-time status data feedback, the device's working mode can be adjusted or personalized assistance can be provided to improve the user experience.

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

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

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

[0104] In summary, the data acquisition module provides data support for prediction, early warning and optimization of equipment performance by collecting various data of the exoskeleton power assist device in real time.

[0105] The data processing module substitutes the real-time data of the user's intention behavior into the prediction and warning model of the human body exoskeleton power assist device to obtain the prediction result of the exoskeleton device state, compares the prediction result of the exoskeleton device state with the real-time data of the exoskeleton device state, and obtains the comparison result of the exoskeleton device state, wherein the comparison result of the exoskeleton device state includes the consistency between the prediction result of the exoskeleton device state and the real-time data of the exoskeleton device state, and the inconsistency between the prediction result of the exoskeleton device state and the real-time data of the exoskeleton device state;

[0106] The main function of the data processing module in the entire human exoskeleton assistive device system is to substitute the real-time collected user intention behavior data into the prediction and warning model to obtain the prediction results of the exoskeleton device status, and compare these prediction results with the real-time data to generate the exoskeleton device status comparison results. The following is a detailed analysis of the data processing module's role and workflow:

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

[0108] The data processing module substitutes the pre-processed real-time data of user intention behavior into the prediction and warning model of the human exoskeleton assistive device. The prediction and warning model generates prediction results of the exoskeleton device status based on these data, which may include power consumption prediction, joint angle prediction, user fatigue prediction, etc.

[0109] The data processing module compares the prediction results with the real-time data of the exoskeleton device status collected at the same time. The purpose of the comparison is to determine whether the prediction results are consistent with the actual status, so as to evaluate the accuracy and reliability of the prediction model.

[0110] According to the comparison results, the data processing module will generate the exoskeleton device status comparison results. The comparison results may include two situations: one is that the predicted result is consistent with the real-time data, indicating that the current exoskeleton device status is normal; the other is that the predicted result is inconsistent with the real-time data, which may indicate an abnormality or potential risk.

[0111] The data processing module receives real-time data from the data acquisition module and integrates and preprocesses the received data, including data cleaning, formatting, normalization and other operations.

[0112] The preprocessed real-time data of user intention behavior is substituted into the prediction and warning model, and the model outputs the prediction result of the exoskeleton device status.

[0113] Compare the prediction results with the real-time data of the exoskeleton device status. Use the preset comparison indicators and algorithms for calculation and judgment. Generate the exoskeleton device status comparison results based on the comparison results. Output the comparison results to the subsequent early warning module or other related system components.

[0114] Through the function and workflow of the data processing module, real-time monitoring and prediction of the status of the exoskeleton power assist device can be achieved, providing strong data support for subsequent early warning and response mechanisms.

[0115] The early warning module compares the real-time data of the exoskeleton device status with the early warning threshold of the human body exoskeleton assisting device if the predicted result of the exoskeleton device status is inconsistent with the real-time data of the exoskeleton device status. If there is data exceeding the early warning threshold in the real-time data of the exoskeleton device status, a first early warning message of the human body exoskeleton assisting device is generated, and the first early warning message of the human body exoskeleton assisting device is sent to the user's mobile terminal and the background control terminal. When the user's mobile terminal receives feedback on the first early warning message of the human body exoskeleton assisting device, the user's physiological real-time data is collected, and the user's physiological real-time data is compared with the user's physiological data early warning threshold. If the user's physiological real-time data exceeds the user's physiological data early warning threshold, a second early warning message of the human body exoskeleton assisting device is generated, and the second early warning message of the human body exoskeleton assisting device is sent to the user's mobile terminal and the background control terminal, and the user's mobile terminal location information is obtained, and the user's mobile terminal location information is sent to the background control terminal. The background control terminal uses the user's mobile terminal location information as the supplementary power location, and sends the supplementary power location to the inspection equipment and the mobile terminal of the safety rescue personnel.

[0116] Status inconsistency detection,The early warning module first receives the exoskeleton device status comparison result from the data processing module.

[0117] If it is found that the predicted result of the exoskeleton device status is inconsistent with the real-time data, the next step of threshold comparison will be immediately entered.

[0118] The early warning module carefully compares the real-time data of the exoskeleton device status with the preset warning threshold of the human exoskeleton assist device. If any item in the real-time data exceeds the warning threshold, it is considered an abnormal situation and immediate action is required.

[0119] Generate and send the first warning information. After confirming the abnormal situation, the warning module will quickly generate the first exoskeleton power assist device warning information. The warning information will be sent to the user's mobile terminal and the background control terminal at the same time to ensure that relevant parties can be informed and respond in time.

[0120] User physiological data monitoring: When the user's mobile terminal feedbacks that the first warning information has been received, the warning module will trigger the real-time monitoring of the user's physiological data. The user's physiological data (such as heart rate, blood pressure, etc.) is collected in real time through wearable devices and other means.

[0121] Compare the user's physiological data with the warning threshold, and compare the collected real-time user physiological data with the preset user physiological data warning threshold. If the user's physiological data exceeds the warning threshold, it indicates that the user may be in danger or discomfort.

[0122] Generate and send the second warning information. After confirming that the user's physiological data is abnormal, the warning module will immediately generate the second human exoskeleton power assist device warning information. The warning information will also be sent to the user's 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] Obtain and send user location information. The warning module will obtain real-time location information from the user's mobile terminal. The location information will be quickly sent to the backend control terminal so that the backend can accurately grasp the user's current location.

[0124] After receiving the user's location information, the backend control terminal will mark it as a location for replenishing power. Then, the backend will send this replenishing power location information to the inspection equipment and the mobile terminals of the safety and rescue personnel to ensure that they can arrive at the scene in time for rescue or treatment.

[0125] Through real-time monitoring and early warning of the exoskeleton device status and user physiological data, the early warning module can promptly detect and report potential safety hazards, thereby effectively preventing accidents.

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

[0127] By acquiring and sending user location information, the early warning module can guide inspection equipment and safety rescue personnel to quickly reach the scene of the accident and provide users with timely rescue and assistance.

[0128] Specifically, in the human body exoskeleton power assist device for replacing medium and large parts described in the present invention, the data acquisition module includes:

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

[0130] Through electromyography sensors and motion capture system equipment, the user's motion pattern, force data, speed data, and user intention behavior data are obtained;

[0131] Monitor the motor current, voltage, power, temperature, joint angle, movement speed and load of the exoskeleton device to obtain the status data of the exoskeleton device;

[0132] Use environmental sensors to collect the temperature, humidity, air pressure and meteorological parameters of the working environment in real time to obtain environmental data;

[0133] Use the sensors of wearable devices to collect the user's heart rate, blood pressure and body temperature to obtain the user's physiological data.

[0134] Specifically, the prediction module of the human body exoskeleton power assist device for replacing medium and large parts described in the present invention includes:

[0135] Preprocess the user's intention behavior data, exoskeleton device status data, environmental data, user physiological data, and historical data, and randomly divide the preprocessed data set into a training set and a validation set;

[0136] Receiving a task to be performed by an exoskeleton power-assisting device, matching the task to be performed by the exoskeleton power-assisting device in a preset algorithm model knowledge base, and obtaining an algorithm model corresponding to the task to be performed by the exoskeleton power-assisting device, wherein the algorithm model corresponding to the task to be performed by the exoskeleton power-assisting device adopts a neural network model;

[0137] The neural network model is trained using the training set data, and the trained model is saved in a loadable format to obtain a human exoskeleton assistive device prediction and warning model, which is deployed to the control system of the human exoskeleton assistive device. The human exoskeleton assistive device prediction and warning model receives data in real time and makes predictions, collects real-time data on user intention behavior, real-time data on exoskeleton device status, and real-time data on the environment, and inputs them into the human exoskeleton assistive device prediction and warning model. The human exoskeleton assistive device prediction and warning model outputs prediction results, which include predictions on the power consumption of the exoskeleton device and predictions on the user's fatigue level.

[0138] The prediction module first receives the user's intention behavior data, exoskeleton device status data, environmental data, user physiological data, and historical data from the data acquisition module, and pre-processes these data, including data cleaning, format conversion, and normalization, to ensure data quality and consistency.

[0139] The preprocessed dataset is randomly divided into training and validation sets for subsequent model training and validation.

[0140] When the exoskeleton assist device receives a task to be performed, the prediction module will match it in the preset algorithm model knowledge base. According to the nature and requirements of the task, the most suitable algorithm model is selected. Here, the neural network model is used. The neural network model has learning and generalization capabilities 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 an iterative optimization algorithm so that it can accurately predict the state of the exoskeleton device and the user's behavior. During the training process, the prediction module will use the validation set data to verify the model to evaluate the performance and generalization ability of the model. After the training is completed, the model is saved in a loadable format to facilitate subsequent deployment in the control system of the human exoskeleton power assist device.

[0142] The trained prediction and early warning model of the human exoskeleton assistive device is deployed to the control system of the device. The model receives real-time data of user intention behavior, real-time data of the exoskeleton device status, and real-time data of the environment from the data acquisition module. These data are input into the prediction and early warning model, and the model performs real-time prediction and outputs the prediction results.

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

[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 user's fatigue level, providing a scientific basis for task execution. The prediction module can receive data in real time and make predictions to ensure 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 needs of different tasks to achieve intelligent prediction. The prediction module supports the expansion and update of the algorithm model knowledge base, and 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 power-assisting device described in the present invention provides powerful technical support and safety guarantee for tasks such as replacement of medium and large components through accurate prediction, real-time data processing and intelligent model selection.

[0146] Specifically, the human body exoskeleton power assist device for replacing medium and large parts described in the present invention, the data acquisition module includes:

[0147] Determine the data types to be collected, the data types to be collected include real-time data of user intention and behavior, real-time data of exoskeleton device status, and real-time data of environment;

[0148] According to the type of data to be collected, the data collection device is determined, and the data transmission protocol is configured for the data collection device. The data collection device transmits the real-time collected data to the database.

[0149] The data collection module needs to clearly define the data types to be collected. In the present invention, these data types mainly include real-time data of user intention and behavior, real-time data of exoskeleton device status, and real-time data of environment.

[0150] Real-time data on user intention behavior may include user movements, postures, operation instructions, etc., which are used to reflect the user's current intentions and needs.

[0151] The real-time data of the exoskeleton device status includes key parameters such as the device’s power, joint angle, torque, etc., which are used to evaluate the device’s operating status.

[0152] Real-time environmental 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 acquisition device: According to the type of data to be collected, the data acquisition module needs to select the corresponding data acquisition device. Data acquisition devices include acceleration sensors, angle sensors, torque sensors, temperature and humidity sensors, fiber optic sensors, and user interaction devices.

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

[0155] Data acquisition equipment collects data in real time according to the configured transmission protocol, and transmits the data to the database via wired or wireless means. As the data storage and management center, the database is responsible for receiving, storing and providing data access services. By accurately selecting data acquisition equipment and configuring transmission protocols, the collected data is ensured to be highly accurate.

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

[0157] In summary, the data acquisition module in the human exoskeleton power assist device described in the present invention provides accurate, real-time, scalable and secure data support for the entire system by clarifying the data type, selecting appropriate data acquisition equipment, configuring the data transmission protocol and realizing real-time data acquisition and transmission.

[0158] Specifically, in the human body exoskeleton power assist device for replacing medium and large parts described in the present invention, the data processing module includes:

[0159] Obtaining a prediction result of the state of the exoskeleton device from the prediction module, the prediction result of the state of the exoskeleton device including a power consumption prediction and a joint angle prediction;

[0160] The predicted results of the exoskeleton device state and the real-time data of the exoskeleton device state are aligned in time, an indicator for comparison is received and set, and the predicted results of each exoskeleton device state and the real-time data of the corresponding exoskeleton device state are calculated using the defined comparison indicator. Based on the result of the comparison calculation, it is determined whether the predicted results of the exoskeleton device state are consistent with the real-time data, and based on the determination result, a comparison result of the exoskeleton device state is generated.

[0161] The data processing module in the human body exoskeleton power-assist device used for replacing medium and large components described in the present invention is responsible for comparing and analyzing the predicted results of the exoskeleton device status with real-time data to determine the accuracy of the prediction and generate a comparison result.

[0162] The data processing module first obtains the prediction results of the exoskeleton device status from the prediction module. The prediction results include key information such as power consumption prediction and joint angle prediction.

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

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

[0165] For each exoskeleton device state prediction result and the real-time data at the corresponding time point, the data processing module uses the defined comparison index to calculate the difference between the predicted value and the measured value, and calculates other relevant statistics as needed.

[0166] Based on the results of the comparison calculation, the data processing module determines whether the predicted 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 these thresholds, the predicted result is considered to be consistent with the real-time data; otherwise, it is considered inconsistent.

[0167] Finally, the data processing module generates exoskeleton device status comparison results based on the judgment results. These results may be presented in the form of reports, charts or numbers for subsequent analysis and decision-making.

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

[0169] Indicator definition and calculation, by defining and calculating comparative indicators, the difference between the predicted results and the real-time data is quantified, providing an objective basis for consistency judgment.

[0170] Consistency judgment and decision-making, based on the comparison calculation results, judge the accuracy of the prediction results, and generate comparison results to 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 flexible setting of comparison indicators and thresholds to adapt to changes in different application scenarios and needs.

[0172] To sum up, the data processing module in the human body exoskeleton assist device described in the present invention provides reliable data processing and analysis capabilities for the entire system through precise time alignment, reasonable comparison index setting, accurate comparison calculation, and consistency judgment and decision-making. It is an important guarantee to ensure that the exoskeleton assist device can efficiently and accurately assist users to complete tasks such as replacement of medium and large components.

[0173] Specifically, the human body exoskeleton power assist device for replacing medium and large parts described in the present invention, the warning module includes:

[0174] When the user's mobile terminal receives the warning information of the first human exoskeleton power assist device, the location information acquisition function is automatically triggered. The user's mobile terminal includes a smart phone and a tablet computer. The location information of the user's mobile terminal is obtained by using GPS, Beidou satellite positioning system or mobile network base station positioning technology;

[0175] After receiving the location information, the backend control end records the location information of the user's mobile terminal into the database and marks it as the location for replenishing power. The backend control end sends the location information for replenishing power to the inspection equipment, which includes drones and inspection robots.

[0176] After the inspection equipment receives the location for replenishing power, it loads the exoskeleton power assist device battery, generates an inspection route according to the preset path generation model, executes the inspection route, and after the inspection equipment reaches the user's mobile terminal, it collects image data of the staff using the exoskeleton power assist device to replace large and medium-sized components, obtains the image data to be detected, and moves the exoskeleton power assist device battery to the staff's location. The exoskeleton power assist device battery is used to provide power for the staff's exoskeleton power assist device. After opening the energy storage cover motor, place the battery into the energy storage shell.

[0177] When the user's mobile terminal (including smartphones and tablets) receives the warning information of the first human exoskeleton assist device, 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's mobile terminal receives the warning information, the location information acquisition function is automatically activated immediately. Using GPS, Beidou satellite positioning system or mobile network base station positioning technology, the user's current location information can be quickly and accurately obtained.

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

[0180] After receiving the encrypted location information, the backend control terminal decrypts it and records the decrypted location information in the database, and specially marks it as "power replenishment location". The backend control terminal sends this location information to the inspection equipment, including drones and inspection robots, so that they can go to the designated location.

[0181] After receiving the location information for replenishing power, the inspection device loads the batteries required by the exoskeleton power assist device. Based on the preset path generation model, the optimal inspection route is generated. The inspection device automatically goes to the location of the user's mobile terminal along the generated route.

[0182] When the inspection device reaches the user's mobile terminal, it uses the image acquisition device (such as a camera) it carries to collect image data of the staff using the exoskeleton power assist device to replace medium and large components. The image data is used as the image data to be detected for subsequent analysis and evaluation. At the same time, the inspection device moves the loaded battery to the location of the staff to provide power supplement for the exoskeleton power assist device to ensure that the device can continue to operate normally.

[0183] Through the above process, the present invention not only realizes the status monitoring of the human body exoskeleton power assist device, thereby providing batteries in real time, but also realizes the safety of the staff and the continuous operation capability of the equipment through the rapid response and on-site support of the inspection equipment.

[0184] Specifically, the human body exoskeleton power assist device for replacing medium and large parts described in the present invention, the warning module includes:

[0185] Substituting the image data to be detected into a preset image analysis model for use of an exoskeleton power-assisting device to obtain analysis results of the image data to be detected, wherein the analysis results of the image data to be detected include deformation data of the worker's movements, running trajectory data of the exoskeleton power-assisting device, and deformation data of the exoskeleton power-assisting device;

[0186] Comparing the worker motion deformation data with the worker motion deformation warning threshold to obtain the worker motion deformation data comparison result;

[0187] The running trajectory data of the human exoskeleton power assist device is compared with the running trajectory warning threshold of the human exoskeleton power assist device to obtain the running trajectory comparison result of the human exoskeleton power assist device;

[0188] The deformation data of the human exoskeleton power-assisting device is compared with the deformation warning threshold of the human exoskeleton power-assisting device to obtain the deformation data comparison result of the human exoskeleton power-assisting device;

[0189] If any of the comparison results of the staff's motion deformation data, the comparison results of the exoskeleton assistive device's operating trajectory, and the comparison results of the exoskeleton assistive device's deformation data exceeds the warning threshold, a third-party exoskeleton assistive device warning information is generated and sent to the mobile terminal of the safety rescue personnel.

[0190] The early warning module in an exoskeleton power-assisting device for replacing medium and large parts described in the present invention realizes comprehensive monitoring and early warning of the actions of the staff, the running trajectory of the exoskeleton power-assisting device and the deformation of the device itself.

[0191] The image data collected by the inspection equipment is put into the preset image analysis model of the human exoskeleton assistive device. This model can automatically identify and analyze the key information in the image, including the worker's movements, the trajectory of the exoskeleton device, and the deformation of the device.

[0192] Through the analysis of the model, the analysis results of the image data to be detected are obtained, including the staff's motion deformation data, the human body exoskeleton power assist device operation trajectory data and the human body exoskeleton power assist device deformation data.

[0193] The staff motion deformation data is compared with a preset staff motion deformation warning threshold to determine whether the staff motion is abnormal or deformed, and obtain the staff motion deformation data comparison result.

[0194] At the same time, the running trajectory data of the exoskeleton power assist device is compared with the preset running trajectory warning threshold to evaluate whether the operation of the device deviates from the normal trajectory, and obtain the running trajectory comparison result of the exoskeleton power assist device.

[0195] The deformation data of the exoskeleton power-assisting device is compared with a preset deformation warning threshold to detect whether the device has undergone abnormal deformation or damage, and obtain a comparison result of the deformation data of the exoskeleton power-assisting device.

[0196] If the data of any of the above three comparison results exceeds the corresponding warning threshold, the warning module will immediately generate a warning message for the third-party exoskeleton assist device. The warning message contains detailed abnormal data and descriptions of possible dangerous situations so that the safety rescue personnel can quickly understand the on-site conditions and respond. The warning information is sent to the mobile terminal of the safety rescue personnel in a timely manner through wireless networks and other communication means to ensure that they can receive the warning and take corresponding rescue measures in the first time.

[0197] By comprehensively monitoring the movements of workers, the trajectory of the exoskeleton device, and the deformation of the device, all-round safety protection is ensured during the replacement process of large and medium-sized parts. Using advanced image analysis models, automatic recognition and intelligent analysis of image data are realized, improving the accuracy and efficiency of early warning. When abnormal data is detected, the early warning module can immediately generate early warning information and send it to safety and rescue personnel, providing strong support for their timely response. Through early warning and timely intervention, accidents that may be caused by deformation of workers' movements, abnormal trajectory of the device, or deformation of the device are effectively prevented.

[0198] In summary, the early warning module in the human exoskeleton power-assist device described in the present invention provides a strong technical guarantee for safety monitoring and accident prevention during the replacement of medium and large components through sophisticated data analysis, intelligent comparison and judgment, and timely sending of early warning information.

[0199] The technical solution of the present invention solves the problem that the existing human exoskeleton power assist device cannot provide power assist when workers carry medium and large parts in the following ways:

[0200] The lumbar support structure serves as the foundation of the entire device, providing stable lumbar support for the workers. 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, ensuring that the workers can get sufficient assistance when carrying medium and large components. The support structure connecting plate connects the lumbar support structure and the main support device to improve the stability of the overall structure.

[0201] Hydraulic support rod and rotating shaft The main support device and the auxiliary support device both use hydraulic support rods. Through the flexible rotation of the rotating shaft, the support angle and strength can be adjusted according to different working conditions, providing precise and stable assistance for the staff. The energy storage device provides electrical energy for the hydraulic support rod and the control device to ensure that the device can operate continuously and stably.

[0202] Intelligent control system, the control device 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 status data, environmental data and user physiological data, it can realize real-time monitoring and intelligent adjustment of the device operation status. The neural network model is used to predict the device status. Once a potential risk is found, the early warning mechanism is immediately triggered to ensure the safety of the staff. The control device also establishes a connection with the remote control terminal, receives the data transmitted by the remote control, and realizes remote monitoring and scheduling.

[0203] Comprehensive safety monitoring, a sensor matrix including pressure sensors and position sensors is installed inside the lumbar support structure, main support device and auxiliary support device, which can collect device operation status data in real time. Through various means such as data comparison, threshold judgment and image analysis, comprehensive monitoring of the device status and user's physiological status is achieved. Once an abnormality is found, an early warning message is immediately generated and sent to the user's mobile terminal and background control terminal, and the inspection equipment and safety rescue personnel are notified to go to the scene for processing. By adjusting the angle and strength of the hydraulic support rod and the rotating shaft, the device can adapt to workers of different body shapes and working habits, and provide personalized assistance. The modular design is adopted between the various components for easy disassembly, maintenance and upgrading.

[0204] To summarize, the technical solution of the present invention effectively solves the problem that the existing exoskeleton power assist device cannot provide assistance when workers are carrying medium and large components by optimizing structural design, adopting a hydraulic power system, integrating an intelligent control system, realizing comprehensive safety monitoring, and improving flexibility and adaptability.

Claims

1. A human exoskeleton power assist device for replacing medium and large parts, characterized in that: include: Lumbar support structure (1), main support device (2), auxiliary support device (3) and support structure connecting plate (4); Support structure connecting plates (4) are screwed on both sides of the waist support structure (1), and the outer wall of the support structure connecting plate (4) is screwed on the 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), and the support structure connecting plate (4) is located between the main support device (2) and the waist support structure (1); A secondary supporting device (3) is installed on the side wall of the main supporting device (2); the main supporting device (2) and the secondary supporting device (3) are used to support the lumbar supporting structure (1); the secondary supporting device (3) provides supporting force for the main supporting device (2); A control device (6) is installed on the outer wall of the lumbar 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, and 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).

2. The human body exoskeleton power assist device for replacing medium and large parts as claimed in claim 1, characterized in that: The main support device (2) comprises: a first main support connecting body (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 connecting body (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 human body exoskeleton power assist device for replacing medium and large parts as claimed 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 connecting body (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), and 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), and 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 human body exoskeleton power assist device for replacing medium and large parts as claimed in claim 1, characterized in that: 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); A 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); 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 a supporting 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 auxiliary support rotating shaft (305) drives the auxiliary support pedal (306) to rotate.

5. The human body exoskeleton power assist device for replacing medium and large parts as claimed in claim 1, characterized in that: The energy storage device (5) comprises: an energy storage housing (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).

6. The human body exoskeleton power assist device for replacing medium and large parts as claimed in claim 1, characterized in that: A sensor matrix is ​​installed inside the lumbar support structure (1), the main support device (2) and the auxiliary support device (3), the sensor matrix is ​​installed with a pressure sensor and a position sensor, and 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 by the remote control.

7. The human body exoskeleton power assist device for replacing medium and large parts as claimed in claim 6, characterized in that: The control device includes: A data acquisition module, which acquires user intention behavior data, exoskeleton device status data, environmental data, user physiological data, historical data, and warning thresholds. The warning thresholds include the warning thresholds of the human exoskeleton power assist device and the warning thresholds of the user's physiological data; The prediction module divides the user's intention behavior data, exoskeleton device status 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 validation set data to validate the trained model, thereby obtaining a prediction and early warning model for the human exoskeleton power assist device; A data collection module collects real-time data of the human exoskeleton power-assist device, including real-time data of user intention and behavior, real-time data of the exoskeleton device status, and real-time data of the environment; The data processing module substitutes the real-time data of the user's intention behavior into the prediction and warning model of the human body exoskeleton power assist device to obtain the prediction result of the exoskeleton device state, compares the prediction result of the exoskeleton device state with the real-time data of the exoskeleton device state, and obtains the comparison result of the exoskeleton device state, wherein the comparison result of the exoskeleton device state includes the consistency between the prediction result of the exoskeleton device state and the real-time data of the exoskeleton device state, and the inconsistency between the prediction result of the exoskeleton device state and the real-time data of the exoskeleton device state; The early warning module compares the real-time data of the exoskeleton device status with the early warning threshold of the human body exoskeleton assisting device if the predicted result of the exoskeleton device status is inconsistent with the real-time data of the exoskeleton device status. If there is data exceeding the early warning threshold in the real-time data of the exoskeleton device status, a first early warning message of the human body exoskeleton assisting device is generated, and the first early warning message of the human body exoskeleton assisting device is sent to the user's mobile terminal and the background control terminal. When the user's mobile terminal receives feedback on the first early warning message of the human body exoskeleton assisting device, the user's physiological real-time data is collected, and the user's physiological real-time data is compared with the user's physiological data early warning threshold. If the user's physiological real-time data exceeds the user's physiological data early warning threshold, a second early warning message of the human body exoskeleton assisting device is generated, and the second early warning message of the human body exoskeleton assisting device is sent to the user's mobile terminal and the background control terminal, and the user's mobile terminal location information is obtained, and the user's mobile terminal location information is sent to the background control terminal. The background control terminal uses the user's mobile terminal location information as the supplementary power location, and sends the supplementary power location to the inspection equipment and the mobile terminal of the safety rescue personnel.

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