A control method and related device for exoskeleton-assisted equipment

Abstract

This invention discloses a control method and related device for exoskeleton-assisted equipment, relating to the field of exoskeleton technology. The method includes: acquiring the physical characteristics of an electrician carrying heavy objects based on sensors on the exoskeleton's forearm support frame; identifying the electrician's turning intentions based on sensors at various torso nodes of the exoskeleton; adjusting the stability allowable adjustment standard of the electrician's body posture based on the physical characteristics and turning intentions; acquiring real-time posture data of the electrician based on sensors at various torso nodes of the exoskeleton; and controlling the exoskeleton-assisted equipment to assist the electrician in carrying heavy objects based on the real-time posture data and the stability allowable adjustment standard. This invention significantly improves the adaptability, safety, and smoothness of human-machine collaboration of exoskeleton-assisted equipment in complex working environments.

Description

Technical Field

[0001] This application relates to the field of exoskeleton technology, and more specifically, to a control method and related device for an exoskeleton assistive device. Background Technology

[0002] In the field of modern power construction, lightweight, hydrogen-powered intelligent anti-fall exoskeletons are gradually becoming important assistive tools. These devices sense the wearer's movement intentions and body posture, providing just the right amount of assistance and intervening promptly when a potential fall risk is detected to ensure operational safety. However, in the complex and ever-changing environments of actual power construction, especially when handling specific types of heavy objects and performing specific operations, existing fall protection methods may encounter unexpected challenges and may even be counterproductive.

[0003] Currently, lightweight exoskeletons powered by hydrogen fuel cells are in use. These devices incorporate numerous sensors in their joints and support structures to detect the wearer's movement and provide synchronized walking assistance. They also feature intelligent fall protection. Internal posture sensing components, such as inertial measurement modules and plantar pressure sensors, continuously collect the worker's body posture information. The control core analyzes this information, and if key indicators such as the worker's center of gravity shift or tilt angular velocity exceed pre-set safety limits, the system determines that a fall is imminent and immediately executes pre-defined fall protection strategies. These strategies might adjust the output torque of a particular leg joint or quickly lock certain joints to generate a supporting force against the fall, helping the worker regain balance. This design provides excellent protection against common accidental imbalances such as slippery surfaces or tripping over obstacles.

[0004] However, in a specific work task, the limitations of this fall-prevention control logic began to emerge. An electrician, wearing this exoskeleton, needed to move a bundle of several meters long threaded steel bars from a storage area to the work surface. This route involved traversing a temporary passage less than a meter wide, ending at a near-90-degree corner. As the worker carried the long steel bars to the corner to turn, the bars' length resulted in a very large moment of inertia. At the instant the worker began to turn, the inertia at both ends of the bars generated a strong torque that caused the worker to tilt outwards. To counteract this torque and successfully complete the turn, an experienced worker would proactively and anticipately tilt their body significantly inwards, using their weight to generate a counterbalancing torque, thus stabilizing the steel bars and completing the turn. This is a necessary, non-upright posture adopted by the worker to maintain overall balance during the interaction between the worker and the load.

[0005] The intelligent fall protection system of the exoskeleton operates on the fundamental premise that "a significant body tilt equals an impending fall." When the system's posture sensing components detect a worker rapidly and significantly tilting inward while turning, it immediately misinterprets this as an impending severe lateral fall. The fall protection control program is then triggered, and the system immediately executes its "protective" measures. The exoskeleton's hip and ankle joint power components instantly output a powerful torque attempting to "right" the worker's body. This torque's direction is exactly opposite to the worker's deliberate tilting motion to counteract the inertia of the steel reinforcement. This sudden, unexpected external intervention completely disrupts the worker's body control. The delicate balance the worker had established to counteract the rotational torque of the steel reinforcement is instantly destroyed. He feels a powerful force forcibly twisting his body, making it impossible for him to maintain his posture against the reinforcement. This conflict of forces between the human and the machine forces the worker to contend not only with the inertial force of the steel reinforcement but also with the "assistance" force from the exoskeleton. As a result, the worker's control over the steel reinforcement rapidly weakens, and his body's stability drops drastically. Ultimately, this flawed "fall prevention" intervention not only failed to prevent falls, but also directly caused workers to lose control of the heavy steel bars, which slipped from their hands. At the same time, the workers themselves staggered a few steps due to the force of the conflict, nearly falling off the walkway, creating a situation more dangerous than a normal slip. Summary of the Invention

[0006] This application discloses a control method and related device for exoskeleton-assisted equipment, which aims to solve the problem that existing exoskeleton-assisted equipment may mistakenly identify the operator's active tilting action to maintain balance as a risk of falling when carrying heavy objects with large rotational inertia and performing turning operations in complex working environments. This application significantly improves the adaptability, safety and smoothness of human-machine collaboration of exoskeleton-assisted equipment in complex working environments.

[0007] In a first aspect, this application discloses a control method for an exoskeleton-assisted device, including:

[0008] The physical characteristics of an electrician carrying heavy objects are obtained by sensors on the forearm support frame of the exoskeleton, including the moment of inertia of the heavy objects.

[0009] Sensors at various torso nodes of the exoskeleton are used to identify the electrician's turning intentions.

[0010] Based on the physical characteristics and the turning intention, the stability adjustment criteria for adjusting the electrician's body posture include tolerance for body tilt angle, tolerance for center of gravity transfer rate, and range of motion of joints.

[0011] The electrician's real-time posture data is obtained by sensors at various torso nodes of the exoskeleton.

[0012] Based on this real-time posture data and stability, the standard control of the exoskeleton assists the electrician in moving heavy objects.

[0013] Furthermore, based on this real-time posture data and stability, the standard control of the exoskeleton assists the electrician in carrying heavy objects, including:

[0014] Based on this stability adjustment standard, it is determined whether the real-time attitude data is within the dynamic allowable range;

[0015] If it is determined that the real-time posture data is within the dynamic allowable range, the exoskeleton assistive device is controlled to operate in normal assist mode. The exoskeleton assistive device assists the electrician in moving heavy objects based on the normal assist mode.

[0016] If it is determined that the real-time posture data is not within the dynamic allowable range, the exoskeleton assistive device is controlled to operate in the corrective torque mode. The exoskeleton assistive device assists the electrician in moving heavy objects based on the corrective torque mode.

[0017] In some preferred embodiments, the acquisition of physical characteristics of an electrician carrying heavy objects based on sensors on the exoskeleton forearm support includes:

[0018] The initial pressure value applied by the electrician when moving heavy objects was collected using a mechanical sensor.

[0019] The initial angular acceleration value of the electrician when moving the heavy object was collected using an inertial measurement unit.

[0020] The rotational torque applied to the weight is calculated based on the distance from the exoskeleton hand to the center of the weight and the initial pressure value.

[0021] The moment of inertia is calculated based on the initial angular acceleration value and the rotational torque.

[0022] More specifically, the sensors at various torso nodes of the exoskeleton help identify the electrician's turning intentions, including:

[0023] The distribution of foot pressure values ​​is collected based on a pressure sensor array installed on the sole of the exoskeleton.

[0024] Angle encoders set up at the hip and knee joints collect real-time angle and angular velocity values ​​for each joint.

[0025] The electrician's turning intention was identified based on the distribution of plantar pressure, the real-time angle value, and the angular velocity value.

[0026] Preferably, determining whether the real-time attitude data is within the dynamic allowable range based on the stability adjustment criterion includes:

[0027] Determine whether the human body tilt angle in the real-time posture data exceeds the body tilt angle tolerance;

[0028] Determine whether the lateral transfer velocity of the center of gravity in the real-time attitude data exceeds the tolerance of the center of gravity transfer rate.

[0029] Determine whether the real-time range of motion of the hip and ankle joint in the real-time posture data exceeds the joint's range of motion;

[0030] If the body tilt angle exceeds the tolerance of body tilt angle, or the lateral transfer speed of the center of gravity exceeds the tolerance of the center of gravity transfer rate, or the real-time range of motion of the hip and ankle joint exceeds the range of motion of the joint, then the real-time posture data is determined to be outside the dynamic allowable range.

[0031] Based on the above, the allowable adjustment criteria for the stability of the electrician's body posture, based on this physical characteristic and the turning intention, include:

[0032] Acquire environmental disturbance information, including wind speed, wind direction, ground vibration frequency, and vibration amplitude;

[0033] Assess the potential impact of this environmental disturbance information on the overall stability of the electrician, including additional tilting moments and a tendency for the center of gravity to shift.

[0034] Adjustments to the body tilt angle tolerance, the center of gravity transfer rate tolerance, and the joint range of motion are made based on the potential impact.

[0035] Based on the adjustment factor, the physical characteristics, and the steering intention, the tolerance for body tilt angle, the tolerance for center of gravity transfer rate, and the range of motion in the stability allowable adjustment criteria are adjusted.

[0036] Furthermore, assessing the potential impact of this environmental disturbance information on the overall stability of electrical operators includes:

[0037] The resultant torque generated by wind force on the exoskeleton assistive equipment is calculated based on wind speed and direction data acquired by a multimodal environmental sensor array.

[0038] The instantaneous impact force generated by ground vibration on the exoskeleton assistive equipment is calculated based on multi-point vibration data obtained from a multimodal environmental sensor array.

[0039] The impact factors of environmental noise on the reaction speed and fine control ability of electricians are evaluated based on environmental noise data acquired by a multimodal environmental sensor array.

[0040] By weighting and superimposing the resultant torque, the instantaneous impact force, and the influencing factor, the total disturbance torque and the total disturbance energy under the combined action of multiple environmental disturbances are obtained.

[0041] The total disturbance moment and total disturbance energy are used to assess the potential impact of multiple environmental disturbances on the overall stability of the operator. These potential impacts include additional tilting moments and a tendency for the center of gravity to shift.

[0042] Secondly, this application also discloses an exoskeleton assistive device control system, which includes:

[0043] The physical feature acquisition module is used to acquire the physical characteristics of the electrician carrying heavy objects based on sensors on the forearm support frame of the exoskeleton. These physical characteristics include the moment of inertia of the heavy objects.

[0044] The steering intention recognition module is used to identify the electrician's steering intention based on sensors at various torso nodes of the exoskeleton;

[0045] A stability adjustment module is used to adjust the stability allowable adjustment criteria of the electrician's body posture based on the physical characteristics and the turning intention. The stability allowable adjustment criteria include tolerance for body tilt angle, tolerance for center of gravity transfer rate, and range of motion of joints.

[0046] The posture data acquisition module is used to acquire the real-time posture data of the electrician based on the sensors at each torso node of the exoskeleton.

[0047] The control module is used to control the exoskeleton assistive device to help the electrician move heavy objects based on the real-time posture data and the stability adjustment standard.

[0048] Thirdly, this application discloses an electronic device, which includes a processor and a memory, wherein the memory is used to store instructions, and the processor is used to call the instructions in the memory to cause the electronic device to execute the exoskeleton assistive device control method described above.

[0049] Fourthly, this application discloses a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions, which, when executed on an electronic device, cause the electronic device to perform the exoskeleton assistive device control method described above.

[0050] This application provides a control method for exoskeleton-assisted equipment. The method acquires the physical characteristics (including the moment of inertia of the load) of an electrician carrying heavy objects using sensors on the forearm support frame of the exoskeleton, and identifies the electrician's turning intentions using sensors at various torso nodes of the exoskeleton. Based on these physical characteristics and turning intentions, the method dynamically adjusts the stability tolerance standards of the electrician's body posture (including tolerance for body tilt angle, tolerance for center of gravity transfer rate, and range of motion of joints). Furthermore, by combining real-time posture data acquired by sensors at various torso nodes of the exoskeleton, the method controls the exoskeleton-assisted equipment to assist the electrician in carrying heavy objects.

[0051] By incorporating the moment of inertia of the load and the operator's turning intention as the basis for adjusting stability standards, this application enables the exoskeleton system to more accurately understand the operator's true intentions and body posture requirements in specific work scenarios, thereby avoiding unnecessary or counterproductive fall prevention interventions. For example, when an operator is carrying a long steel bar and turning, the system can identify the moment of inertia of the steel bar and the operator's intention to tilt inward, and accordingly relax the tolerance for body tilt angle, allowing the operator to make the necessary balancing posture, rather than forcibly "straightening up," thus enabling the operator to complete the carrying and turning actions smoothly and safely. Therefore, this application significantly improves the adaptability, safety, and smoothness of human-machine collaboration of exoskeleton-assisted equipment in complex work environments. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a flowchart illustrating the control method of the exoskeleton assistive device in an embodiment of the present invention;

[0054] Figure 2 This is a schematic flowchart of a method for identifying the turning intention of an electrician based on sensors at various torso nodes of an exoskeleton, according to an embodiment of the present invention.

[0055] Figure 3 This is a schematic diagram of the structure of the exoskeleton assistive device control system in an embodiment of the present invention;

[0056] Figure 4 This is a schematic diagram of the structural composition of the electronic device in an embodiment of the present invention. Detailed Implementation

[0057] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0058] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0059] Example 1

[0060] Specifically, Figure 1 A flowchart illustrating the control method for exoskeleton-assisted equipment in an embodiment of the present invention is shown, including:

[0061] S101. Based on sensors on the forearm support frame of the exoskeleton, obtain the physical characteristics of electricians carrying heavy objects.

[0062] It should be noted that the physical characteristics mentioned include the moment of inertia of the weight; exoskeleton-assisted equipment is a wearable robotic system designed to enhance human strength, endurance, or assist movement. It typically consists of a mechanical structure, drive unit, sensor system, and control system. The exoskeleton forearm support frame is the part of the exoskeleton-assisted equipment used to support the operator's forearm and contact the weight. Sensors are devices used to sense physical quantities and convert them into electrical signals, such as force sensors, inertial measurement units, pressure sensor arrays, angle encoders, etc. Physical characteristics refer to the inherent properties of the weight itself, such as mass, shape, size, and, in particular, the moment of inertia, which is of particular interest in this application. Moment of inertia is a physical quantity that measures the magnitude of an object's rotational inertia; it reflects the object's ability to resist changes in its rotational state.

[0063] This method utilizes sensors on the exoskeleton's forearm support frame to acquire the physical characteristics of the heavy objects being handled by the electrician, including the object's moment of inertia. In one embodiment, multiple sensors can be integrated into the exoskeleton's forearm support frame to acquire these physical characteristics. For example, force sensors can be used to measure the force applied by the operator while handling the heavy objects, or visual sensors (such as depth cameras) can be used to acquire the object's geometry and dimensions. The data from these sensors can be transmitted to a control unit for processing.

[0064] S102. The electrician's turning intention is identified by sensors at each torso node of the exoskeleton;

[0065] The steering intention here refers to the operator's internal command or tendency to change the direction of movement. Trunk nodes refer to the connection points or measurement points on the exoskeleton assistive device that correspond to key parts of the human torso (such as the hip joint, knee joint, and sole of the foot).

[0066] To identify an operator's steering intentions, sensors can be deployed at multiple trunk nodes of the exoskeleton. For example, an array of pressure sensors can be installed on the soles of the feet to monitor the distribution of plantar pressure, thereby inferring the direction and trend of the operator's center of gravity shift. Furthermore, angle encoders or inertial measurement units can be installed at key locations such as the hip and knee joints to obtain real-time angle and angular velocity information. This data, when combined, can be used to determine whether the operator intends to turn.

[0067] S103. The stability of the electrician's body posture, based on the physical characteristics and the turning intention, is subject to adjustment standards.

[0068] It should be noted that the stability adjustment criteria are a set of parameters preset by the system to define the safe range of the operator's body posture during dynamic movement, including tolerance for body tilt angle, tolerance for center of gravity transfer rate, and range of motion of joints. The stability adjustment criteria mentioned here include tolerance for body tilt angle, tolerance for center of gravity transfer rate, and range of motion of joints.

[0069] After acquiring the physical characteristics of the load (especially its moment of inertia) and the operator's turning intentions, the system dynamically adjusts the stability tolerance standards based on this information. For example, when the system detects that the operator is carrying a load with a large moment of inertia and has a clear turning intention, the system can appropriately relax the body tilt angle tolerance, allowing the operator to tilt more during the turning process to counteract the inertial torque of the load. Simultaneously, the center of gravity transfer rate tolerance can be adjusted according to the speed of the turn, and the joint range of motion can be corrected based on the impact of the load on joint movement.

[0070] S104. The electrician acquires real-time posture data based on the sensors at each torso node of the exoskeleton.

[0071] Real-time posture data refers to the actual posture information of the operator's body acquired by sensors at a specific moment. During the operator's handling operations, sensors on various torso nodes of the exoskeleton continuously acquire the operator's real-time posture data. This data may include, but is not limited to, the tilt angle of the torso, the position of the center of gravity, and the angles and angular velocities of each joint. This real-time data is the basis for subsequent assessment of the operator's balance.

[0072] S105. Based on the real-time posture data and the stability, the standard control of the exoskeleton assistive device is adjusted to assist the electrician in carrying heavy objects.

[0073] After acquiring real-time posture data, the control system compares this data with dynamically adjusted stability standards. If the real-time posture data is within the allowable range, the exoskeleton continues to provide normal assistance. If the real-time posture data exceeds the allowable range, the system determines that the operator may be at risk of imbalance and applies corrective torque as needed to help the operator regain balance. This control method ensures that the exoskeleton provides assistance while avoiding erroneous intervention when the operator actively adjusts their posture to adapt to the characteristics of the heavy load.

[0074] This application, by dynamically adjusting the stability allowance, can more accurately identify the operator's true intention and balance state when carrying heavy objects, especially those with large rotational inertia. This avoids the misjudgment and reverse intervention of traditional anti-fall systems in specific operating scenarios, thereby significantly improving the safety and assistive effect of exoskeleton-assisted equipment in complex working environments.

[0075] This method first acquires the physical characteristics of the heavy object, particularly its moment of inertia, through sensors on the forearm support frame of the exoskeleton. For example, when an electrician moves a bundle of long threaded steel bars, the system can sense the length and mass distribution of the bars and calculate its large moment of inertia. Simultaneously, sensors at various torso nodes of the exoskeleton (such as a plantar pressure sensor array and hip and knee joint angle encoders) monitor the operator's plantar pressure distribution, joint angles, and angular velocities in real time, thereby identifying the operator's steering intentions.

[0076] After identifying the physical characteristics of the heavy object and the operator's turning intention, the system dynamically adjusts the stability allowance of the body posture. For example, when the system detects that the operator is carrying a steel bar with a large moment of inertia and preparing to make a sharp 90-degree turn, it will appropriately relax the tolerance for body tilt angle, the tolerance for center of gravity transfer rate, and the range of joint motion based on the moment of inertia of the steel bar and the turning intention. This means that the system will allow the operator to actively make a greater degree of body tilt and center of gravity transfer during the turning process to counteract the inertial torque of the steel bar, without immediately judging it as an imbalance.

[0077] Subsequently, sensors at various torso nodes of the exoskeleton continuously acquire real-time posture data of the operator, including torso tilt angle, center of gravity position, and joint range of motion. The control module compares this real-time posture data with dynamically adjusted stability allowable adjustment standards. If the operator's real-time posture data, even with a significant tilt, remains within the adjusted allowable standard range, the system determines that the operator is in a controlled balance state and continues to provide normal assistance without applying a reverse "correcting" torque. Thus, the exoskeleton assists operators in smoothly completing tasks such as lifting heavy objects and turning, avoiding interference caused by misjudgments in traditional systems, thereby significantly improving operational safety and efficiency.

[0078] Compared with the closest prior art, the advantages of this application are:

[0079] First, existing technologies, when handling and turning heavy objects with large moments of inertia, have fixed or insufficiently adjusted preset stability thresholds. When the operator actively tilts their body significantly to counteract the inertia of the heavy object, existing systems incorrectly identify this as an impending fall and immediately apply a counter-current "righting" torque. This intervention not only fails to help but also disrupts the delicate balance established between the operator and the heavy object, leading to loss of control of the heavy object or a fall. This application acquires the moment of inertia of the heavy object using sensors on the forearm support frame of the exoskeleton and identifies the operator's turning intentions using sensors at various torso nodes of the exoskeleton, thereby enabling a more accurate understanding of the operator's true intentions and the physical challenges faced.

[0080] Secondly, this application further adjusts the stability criteria of the body posture dynamically based on the aforementioned physical characteristics and the stated turning intention, including tolerance for body tilt angle, tolerance for center of gravity transfer rate, and range of motion of joints. This means that when the system detects that the operator is carrying a heavy object with a large moment of rotational inertia and turning, it intelligently relaxes the tolerance for body tilt and center of gravity transfer, allowing the operator to make the necessary non-upright balancing posture. This contrasts sharply with the rigid fall prevention logic in existing technologies.

[0081] Finally, this application controls the exoskeleton-assisted equipment based on the adjusted stability allowance standard. When the operator's real-time posture data is within the dynamically adjusted allowable range, even if the posture amplitude is large, the system will not intervene incorrectly but will continue to provide appropriate assistance. Only when the real-time posture data exceeds the adjusted allowable range will the system apply a corrective torque. This adaptive control strategy enables the exoskeleton-assisted equipment to truly "understand" the operator's intentions and operational needs, avoiding human-machine conflict and significantly improving the safety and assistive effect of the exoskeleton in complex work scenarios.

[0082] It should be noted that the steps for controlling the exoskeleton assistive device to help electricians move heavy objects based on real-time posture data and stability adjustment criteria include: determining whether the real-time posture data is within the dynamic allowable range based on the stability adjustment criteria; if the real-time posture data is within the dynamic allowable range, controlling the exoskeleton assistive device to operate in normal assist mode, and the exoskeleton assistive device assisting the electrician to move heavy objects based on the normal assist mode; if the real-time posture data is not within the dynamic allowable range, controlling the exoskeleton assistive device to operate in corrective torque mode, and the exoskeleton assistive device assisting the electrician to move heavy objects based on the corrective torque mode.

[0083] Specifically, the aforementioned dynamic allowable range can be understood as the range within which an electrician's body posture data (such as tilt angle, center of gravity position, and joint range of motion) is considered stable and safe during the process of lifting heavy objects. This range is dynamically determined based on stability allowable adjustment standards, aiming to distinguish between normal, acceptable posture fluctuations and dangerous postures that may lead to instability. Normal assistance mode refers to the conventional assistance mode used by the exoskeleton assistive equipment when the operator's posture is stable. Its main purpose is to reduce the operator's physical burden and provide smooth, natural assistance to ensure the smoothness and comfort of the lifting operation. Corrective torque mode refers to an active intervention mode activated by the exoskeleton assistive equipment when the operator's posture exceeds the dynamic allowable range. In this mode, the exoskeleton assistive equipment calculates and applies an appropriate corrective torque based on real-time posture data and stability allowable adjustment standards to quickly guide the operator's body posture back to a safe and stable range, thereby preventing instability or falls.

[0084] This application's solution introduces a dynamic allowable range judgment, enabling the control strategy of the exoskeleton-assisted equipment to adaptively adjust based on the operator's real-time posture data. When the real-time posture data is within the dynamic allowable range, it indicates that the operator's posture is stable or has only slight, acceptable fluctuations. In this case, the exoskeleton-assisted equipment operates in normal assistance mode, providing conventional assistance to ensure smooth operation. However, when the real-time posture data exceeds the dynamic allowable range, it means that the operator may face the risk of instability. The exoskeleton-assisted equipment will immediately switch to a corrective torque mode, actively applying corrective torque to quickly restore the operator's body posture stability and effectively avoid potential dangers.

[0085] Through the aforementioned technical solutions, the control system of the exoskeleton-assisted equipment can more intelligently respond to the real-time posture changes of electricians. This dynamic judgment and mode switching mechanism significantly improves the safety and stability of the exoskeleton-assisted equipment during the handling of heavy objects. It avoids excessive intervention when the posture is stable, ensuring natural and smooth operation; at the same time, it can quickly provide precise corrective force when instability risks are detected, effectively preventing accidents, thereby greatly improving the work efficiency and operational safety of electricians.

[0086] In some preferred embodiments, it is assumed that an electrician is moving a heavy transformer. The exoskeleton assistive device continuously acquires real-time posture data of the operator, such as torso tilt angle, center of gravity position, and joint range of motion. The system determines whether this real-time posture data falls within the dynamic allowable range based on preset stability allowable adjustment standards. For example, if the operator's torso tilt angle is within the body tilt angle tolerance, the center of gravity transfer rate does not exceed the center of gravity transfer rate tolerance, and the joint range of motion is within the allowable range, then the posture is determined to be within the dynamic allowable range, and the exoskeleton assistive device will maintain normal assist mode, providing smooth assistance force so that the operator can easily move the transformer. However, if the operator's lateral center of gravity transfer speed suddenly exceeds the center of gravity transfer rate tolerance when turning, the system will immediately determine that the real-time posture data is not within the dynamic allowable range and quickly switch the exoskeleton assistive device to corrective torque mode. At this time, the exoskeleton assistive device will actively apply a reverse corrective torque, such as by driving motors in the hip or ankle joints, to help the operator quickly pull the center of gravity back to a safe area, preventing loss of balance and thus ensuring the safety of the moving process.

[0087] Specifically, the acquisition of the physical characteristics of the electrician carrying heavy objects based on the sensors on the exoskeleton forearm support frame includes: acquiring the initial pressure value applied by the electrician when carrying the heavy objects based on the mechanical sensors; acquiring the initial angular acceleration value of the electrician when carrying the heavy objects based on the inertial measurement unit; calculating the rotational torque applied to the heavy objects based on the distance from the exoskeleton hand to the center of the heavy objects and the initial pressure value; and calculating the moment of inertia based on the initial angular acceleration value and the rotational torque.

[0088] Specifically, the acquisition of the initial pressure value applied by the electrician when lifting the heavy object based on the mechanical sensor refers to the real-time monitoring and recording of the force applied to the arm by the electrician during the initial stage of lifting the heavy object by a mechanical sensor integrated on the forearm support frame of the exoskeleton. This mechanical sensor can be a piezoelectric sensor, strain gauge sensor, or torque sensor, etc., and its purpose is to obtain the initial force applied by the operator to the heavy object. The acquisition of the initial angular acceleration value by the electrician when lifting the heavy object based on the inertial measurement unit (IMU) refers to the acquisition of the angular acceleration data of the heavy object during the initial stage of lifting by an inertial measurement unit (IMU) installed on the forearm support frame of the exoskeleton or the heavy object. The IMU typically includes an accelerometer and a gyroscope, providing three-axis linear acceleration and angular velocity information to calculate the angular acceleration, the purpose of which is to quantify the rotational dynamics of the heavy object in the initial stage.

[0089] The calculation of the rotational torque applied to the weight based on the distance from the exoskeleton hand to the center of the weight and the initial pressure value refers to calculating the rotational torque applied by the operator to the weight based on the definition of torque (torque = force × lever arm), given the known distance (lever arm) between the exoskeleton hand and the geometric center of the weight, combined with the initial pressure value collected by the force sensor. This distance can be pre-calibrated or acquired in real time through vision / distance sensors, and its purpose is to convert the force applied by the operator into a rotational effect on the weight. In practical applications, the calculation of the moment of inertia based on the initial angular acceleration value and the rotational torque refers to deriving the moment of inertia of the weight by using the calculated rotational torque and the initial angular acceleration value collected by the IMU, according to the rotational form of Newton's second law (torque = moment of inertia × angular acceleration). The moment of inertia is a physical quantity that measures the magnitude of an object's rotational inertia and is crucial for the precise control of exoskeleton-assisted equipment, aiming to obtain the inherent properties of the weight during rotational motion.

[0090] This application's solution, through meticulous acquisition of initial pressure and angular acceleration values, combined with lever arm information to calculate the rotational torque, ultimately enables accurate calculation of the load's moment of inertia. This process allows the exoskeleton-assisted equipment to perceive the dynamic physical properties of the load in real time and with precision, providing crucial input parameters for subsequent assistive control. By acquiring the load's moment of inertia, the system can more accurately predict the load's response during movement, thus providing a more reliable basis for adjusting the stability allowance of body posture.

[0091] Through the aforementioned technical solution, the exoskeleton-assisted equipment can acquire the key physical characteristics of electricians handling heavy objects, particularly the moment of inertia, in a quantitative and dynamic manner. Compared to relying solely on preset parameters or rough estimates, this solution significantly improves the accuracy and real-time performance of perceiving the physical properties of heavy objects through actual measurement and calculation. Consequently, the exoskeleton-assisted equipment can more accurately understand the interaction between the operator and the heavy object, providing more refined and personalized data support for subsequent stability adjustments and assistance control, thereby enhancing the overall assistance effect and operator safety.

[0092] Specifically, Figure 2 This invention illustrates a method for identifying the turning intentions of an electrician based on sensors at various torso nodes of an exoskeleton, as shown in the following flowchart:

[0093] S201. The pressure sensor array set on the sole of the exoskeleton collects the distribution of sole pressure values;

[0094] Specifically, the pressure sensor array can be understood as one or more sets of sensors arranged in the foot region of the exoskeleton. Its purpose is to monitor the pressure distribution when the electrician's foot contacts the ground in real time. By analyzing changes in the foot pressure distribution, the operator's center of gravity shift trend and turning intention can be preliminarily determined. For example, when the operator intends to turn to the left, the pressure on the left foot may increase, the pressure on the right foot may decrease, or the center of pressure may shift to the left.

[0095] S202. Based on the angle encoders set for the hip and knee joints, the real-time angle and angular velocity values ​​of each joint are collected.

[0096] Specifically, angle encoders are sensors installed on key trunk joints such as the hip and knee joints to accurately measure the real-time flexion angles and angular velocities of these joints. This data reflects the operator's lower limb movement and posture changes, serving as crucial information for identifying steering intentions. For example, during a turn, the hip and knee joints undergo specific angular and angular velocity changes.

[0097] S203. Based on the distribution of plantar pressure values, the real-time angle values, and the angular velocity values, the electrician's turning intention is identified.

[0098] In practical applications, by comprehensively utilizing the distribution of plantar pressure values, real-time angle and angular velocity values ​​of the hip and knee joints, preset algorithms or machine learning models can be used for data fusion and pattern recognition. The aim is to more accurately and robustly determine the operator's turning intentions, such as turning left, turning right, turning in place, or maintaining a straight line, through collaborative analysis of multimodal data.

[0099] This application's solution achieves accurate identification of an electrician's turning intentions through multi-source sensor data fusion. Specifically, a plantar pressure sensor array captures early signals of the operator's center of gravity shift, providing a preliminary judgment of turning intentions. Simultaneously, hip and knee joint angle encoders provide refined data on lower limb movements, further confirming and quantifying the magnitude and speed of the turn. By comprehensively analyzing these different types of interrelated data, the potential for misjudgment or lag in data from a single sensor can be effectively avoided, thus ensuring the accuracy and timeliness of turning intention recognition.

[0100] The above technical solution enables a more comprehensive and accurate identification of the turning intentions of electricians. Compared to identification methods that rely solely on a single sensor or simple rules, this solution combines plantar pressure distribution and key joint motion data, improving the robustness and real-time performance of turning intention recognition. This provides a reliable input for the precise control of subsequent exoskeleton-assisted equipment, thereby effectively enhancing the stability and safety of operators when carrying heavy objects.

[0101] Specifically, in the above-mentioned exoskeleton-assisted equipment control method, the step of determining whether the real-time posture data is within the dynamic allowable range based on the stability allowable adjustment standard may include the following steps: determining whether the human body tilt angle in the real-time posture data exceeds the body tilt angle tolerance; determining whether the lateral transfer speed of the center of gravity in the real-time posture data exceeds the center of gravity transfer rate tolerance; determining whether the real-time range of motion of the hip and ankle joints in the real-time posture data exceeds the joint range of motion; when it is determined that the human body tilt angle exceeds the body tilt angle tolerance, or when it is determined that the lateral transfer speed of the center of gravity exceeds the center of gravity transfer rate tolerance, or when it is determined that the real-time range of motion of the hip and ankle joints exceeds the joint range of motion, then it is determined that the real-time posture data is not within the dynamic allowable range.

[0102] The body tilt angle refers to the angle of deviation of the electrician's torso from the vertical direction during the lifting of heavy objects. This angle can be calculated using real-time posture data obtained from sensors at various torso nodes of the exoskeleton. The body tilt angle tolerance is the maximum allowable tilt angle threshold determined by the stability adjustment standard. The lateral shift speed of the center of gravity refers to the rate at which the electrician's center of gravity moves horizontally during the lifting of heavy objects. This speed can also be calculated from real-time posture data. The center of gravity shift rate tolerance is the maximum allowable rate threshold for lateral shift of the center of gravity set by the stability adjustment standard. The real-time range of motion of the hip and ankle joints refers to the range of motion of the electrician's hip and ankle joints at a specific moment, while the joint range of motion is the maximum range of joint movement allowed as specified by the stability adjustment standard. The dynamic allowable range can be understood as the range within which the electrician's body posture is considered stable and safe under the current operating conditions. When any of the above judgment conditions are met, namely when the body tilt angle exceeds the body tilt angle tolerance, or the lateral transfer speed of the center of gravity exceeds the center of gravity transfer rate tolerance, or the real-time range of motion of the hip and ankle joints exceeds the joint range of motion, it indicates that the real-time posture data of the electrician has exceeded the preset dynamic allowable range, and there is a potential risk of instability.

[0103] This application's solution performs multi-dimensional and refined judgment on the real-time posture data of electricians, including body tilt angle, lateral center of gravity transfer speed, and real-time range of motion of the hip and ankle joints, and compares it with preset stability adjustment standards for body tilt angle tolerance, center of gravity transfer rate tolerance, and joint range of motion. This multi-dimensional judgment mechanism can more comprehensively assess the operator's real-time stability status. When any key posture parameter exceeds its corresponding tolerance, it is determined that the posture data is not within the dynamic allowable range, thereby triggering the exoskeleton assistive device to switch from normal assist mode to corrective torque mode. Thus, the exoskeleton assistive device can promptly apply corrective torque, actively intervene and adjust the operator's body posture to avoid potential instability risks, and ensure the operator's safety and stability during the handling of heavy objects.

[0104] Through the above technical solution, this application achieves refined and intelligent control of exoskeleton-assisted equipment. By introducing the human body tilt angle, the lateral transfer speed of the center of gravity, and the real-time range of motion of the hip and ankle joints as specific criteria for judging whether the real-time posture data is within the dynamic allowable range, stability assessment becomes more comprehensive and accurate. This multi-dimensional judgment mechanism helps to identify the operator's instability tendency earlier, enabling the exoskeleton-assisted equipment to intervene and adjust more promptly and accurately. This effectively improves the overall safety and operational efficiency of electricians during the handling of heavy objects and reduces the risk of accidents caused by posture instability.

[0105] In response, this application further proposes the following stability adjustment criteria for adjusting the body posture of the electrician based on the aforementioned physical characteristics and the turning intention: acquiring environmental disturbance information, including wind speed, wind direction, ground vibration frequency, and vibration amplitude; assessing the potential impact of the environmental disturbance information on the overall stability of the electrician, including additional tilting torque and center of gravity shift trend; correcting the adjustment coefficients of the body tilt angle tolerance, the center of gravity transfer rate tolerance, and the joint range of motion based on the potential impact; and adjusting the body tilt angle tolerance, the center of gravity transfer rate tolerance, and the joint range of motion in the stability adjustment criteria based on the adjustment coefficients, the physical characteristics, and the turning intention.

[0106] Specifically, environmental disturbance information refers to external environmental factors that may affect the overall stability of the electrician and their worn exoskeleton-like assistive equipment. This information can include wind speed, wind direction, ground vibration frequency, and vibration amplitude. Wind speed and direction can be acquired through wind speed and direction sensors integrated into the exoskeleton or through an external environment monitoring system; ground vibration frequency and amplitude can be collected in real time through accelerometers, gyroscopes, or pressure sensor arrays installed on the exoskeleton's feet or torso. These sensors provide quantitative data on the dynamic changes in the external environment.

[0107] Furthermore, after acquiring information on environmental disturbances, it is necessary to assess the potential impact of this information on the overall stability of the electrician. These potential impacts specifically include additional tilting moments and a tendency for the center of gravity to shift. For example, higher wind speeds and specific wind directions may generate a resultant moment between the exoskeleton-assisted equipment and the operator, causing an additional tendency for the body to tilt; while ground vibrations may cause an involuntary shift of the operator's center of gravity in the horizontal direction. The assessment process may involve mechanical models, dynamic simulations, or predictive algorithms based on empirical data to quantify the specific degree and direction of these environmental disturbances' impact on the operator's stability.

[0108] Based on this, the adjustment coefficients for the body tilt angle tolerance, the center of gravity transfer rate tolerance, and the joint range of motion are adjusted according to the potential impacts. This means that when an environmental disturbance is assessed as potentially causing a large tilting moment, the adjustment coefficient corresponding to the body tilt angle tolerance will be set to a stricter value to reduce the allowable tilt range; when a significant center of gravity shift trend is assessed, the adjustment coefficient corresponding to the center of gravity transfer rate tolerance will be adjusted to limit the speed or amplitude of center of gravity transfer; when environmental disturbances may affect the fine control of joints, the adjustment coefficient for the joint range of motion will also be adjusted accordingly. These adjustment coefficients can be preset lookup table values ​​or dynamically generated by an adaptive algorithm based on real-time evaluation results.

[0109] Finally, the tolerance for body tilt angle, the tolerance for center of gravity transfer rate, and the range of joint motion in the stability allowable adjustment criteria are adjusted based on the adjustment coefficients, the physical characteristics, and the steering intention. This means that, based on the original stability adjustment based on the physical characteristics of the load and the operator's steering intention, a correction for environmental disturbance factors is introduced. For example, the adjustment coefficients can be multiplied or added to the original stability allowable adjustment criteria to obtain a final, more adaptive stability allowable adjustment criteria.

[0110] This application's solution incorporates environmental disturbance information, enabling the stability adjustment standards of exoskeleton-assisted equipment to adapt more comprehensively and dynamically to the actual working environment. Specifically, when the exoskeleton-assisted equipment operates in a complex environment, it first acquires real-time environmental disturbance information such as wind speed, wind direction, ground vibration frequency, and vibration amplitude. Subsequently, based on this information, the system uses an established mechanical model or empirical algorithm to accurately assess the potential impact of these environmental disturbances on the operator's overall stability, such as calculating additional tilting moments and center of gravity shift trends. It is precisely this prediction and quantification of external disturbances that makes subsequent stability adjustments possible. Based on this, the system dynamically corrects the adjustment coefficients for body tilt angle tolerance, center of gravity transfer rate tolerance, and joint range of motion according to the assessed potential impact. For example, when strong winds are detected that may cause the operator to tilt in a certain direction, the system will correspondingly tighten the body tilt angle tolerance in that direction and may adjust the center of gravity transfer rate tolerance to limit the operator's center of gravity movement, thereby providing more accurate and timely stability support for the operator before or during external disturbances. Ultimately, these revised adjustment coefficients, combined with the physical characteristics of the load and the operator's steering intentions, jointly determine the final stability adjustment standard, ensuring that the exoskeleton assistive equipment can provide the most suitable assistance according to the actual situation and effectively counteract the adverse effects of environmental disturbances.

[0111] In some preferred embodiments, a specific example is given below. Suppose an electrician is moving a heavy transformer on an outdoor aerial work platform, where the ambient wind speed is high and the ground is experiencing slight vibrations.

[0112] First, the wind speed and direction sensors integrated into the exoskeleton assistive device obtain real-time wind speed data, which is 5 m / s, and the wind direction is to the operator's left. Simultaneously, the accelerometer and pressure sensor array on the soles of the feet detect a slight vibration in the ground with a frequency of 10 Hz and an amplitude of 0.05g. This data is input into the control system as environmental disturbance information.

[0113] Next, the control system, based on preset aerodynamic and structural dynamic models, assesses the potential impact of these environmental disturbances on the overall stability of the operator. For example, a crosswind of 5 m / s is calculated to generate an additional tilting moment pointing to the operator's right, large enough to cause the operator's body to tilt 2 degrees to the right; simultaneously, ground vibrations may cause the operator's center of gravity to shift laterally at a speed of 0.1 m / s. These are assessed as potential tilting moments and center of gravity shift trends.

[0114] Subsequently, based on these potential effects, the system dynamically corrects stability, allowing for adjustments to the standard adjustment coefficients. For example, due to the presence of a rightward tilting moment, the right-side tolerance of the body tilt angle tolerance (e.g., originally ±5 degrees) is tightened, and the adjustment coefficient may change the right-side tolerance to ±3 degrees; simultaneously, due to the tendency of the center of gravity to shift, the adjustment coefficient of the center of gravity transfer rate tolerance (e.g., originally 0.2 m / s) may be changed to 0.15 m / s to limit the speed of lateral transfer of the center of gravity.

[0115] Finally, based on these revised adjustment coefficients, and considering the physical characteristics of the operator carrying the transformer (e.g., large moment of inertia) and their current turning intention (e.g., preparing to turn right), the tolerance for body tilt angle, center of gravity transfer rate, and joint range of motion of the exoskeleton-assisted equipment are ultimately adjusted. As a result, the control strategy of the exoskeleton-assisted equipment becomes more conservative and precise. For example, when the operator turns right, counter-assistance is provided earlier to counteract tilt caused by wind, and lateral movement of the center of gravity is more strictly limited, ensuring that the operator can maintain good balance and posture stability even in windy and vibration environments, safely completing the carrying task.

[0116] Specifically, the aforementioned assessment of the potential impact of environmental disturbance information on the overall stability of electricians includes: calculating the resultant torque generated by wind on the exoskeleton based on wind speed and direction data acquired by the multimodal environmental sensor array; calculating the instantaneous impact force of ground vibration on the exoskeleton based on multi-point vibration data acquired by the multimodal environmental sensor array; assessing the influence factor of environmental noise on the electrician's reaction speed and fine control ability based on environmental noise data acquired by the multimodal environmental sensor array; weighting and superimposing the resultant torque, the instantaneous impact force, and the influence factor to obtain the total disturbance torque and total disturbance energy under the combined action of multiple environmental disturbances; and assessing the potential impact of multiple environmental disturbances on the overall stability of the operator based on the total disturbance torque and the total disturbance energy, wherein the potential impact includes additional tilting torque and center of gravity shift trend.

[0117] The multimodal environmental sensor array can be understood as a collection integrating various types of sensors, such as anemometers, wind vanes, accelerometers, and microphones, to comprehensively perceive various disturbances in the working environment. Wind speed and direction data are used to calculate the resultant torque generated by wind on the exoskeleton assistive equipment through aerodynamic models. This resultant torque reflects the direct impact of wind on the operator's posture stability. Multi-point vibration data, typically collected by accelerometers or force sensors distributed on the soles or legs of the exoskeleton assistive equipment, is used to calculate the instantaneous impact force of ground vibration on the exoskeleton assistive equipment, which may cause the operator to momentarily lose stability. Environmental noise data is acquired through acoustic sensors such as microphones and combined with psychoacoustic models or cognitive load assessment methods to evaluate its impact on the electrician's reaction speed and fine control ability, as high-noise environments may distract the operator and reduce their ability to accurately adjust their posture.

[0118] This application's solution achieves refined quantification of the impact of environmental disturbances by decomposing complex environmental disturbance information into the resultant torque generated by wind, the instantaneous impact force generated by ground vibration, and the influence factor of environmental noise on the operator's cognitive ability. Specifically, wind speed and direction data are used to calculate the resultant torque directly acting on the exoskeleton assistive equipment, which directly reflects the external disturbance of wind to the operator's balance. Multi-point vibration data is used to capture the instantaneous impact force caused by uneven ground or external impacts, which can significantly change the operator's posture in a short period of time. Simultaneously, environmental noise data is used to assess its impact on the operator's reaction speed and fine control ability, because in noisy environments, operators may find it difficult to concentrate on precise posture adjustments. By weighted superposition of these disturbance factors from different sources and of different natures, a comprehensive total disturbance torque and total disturbance energy can be obtained, thus more comprehensively and accurately reflecting the comprehensive challenge of the environment to the operator's overall stability. Therefore, based on these comprehensive indicators, it is possible to more accurately predict the additional tilting torque and center of gravity shift trends that the operator may face, providing a reliable basis for subsequent corrections to stability allowance adjustment standards.

[0119] To address this, this application further proposes the above-mentioned weighted superposition of resultant torque, instantaneous impact force, and influencing factors to obtain the total disturbance torque and total disturbance energy under the combined action of multiple environmental disturbances, including: acquiring the operation stage information of the electrician, including starting, steady movement, and sharp turn; acquiring the operator's physiological state information, including heart rate, skin conductance, or electromyography signals; adjusting the weight coefficients of the resultant torque, instantaneous impact force, and influencing factors according to the operation stage information and the physiological state information; and weighted superposition of the resultant torque, instantaneous impact force, and influencing factors after adjusting the weight coefficients to obtain the total disturbance torque and total disturbance energy under the combined action of multiple environmental disturbances.

[0120] Specifically, the work phase information refers to the specific stage in the heavy-load lifting task currently being performed by the electrician, such as starting, steady movement, or sharp turns. These stages reflect significant differences in the operator's body posture and movement patterns, as well as varying sensitivities to external disturbances. For example, during sharp turns, the operator requires greater stability and may have a lower tolerance for lateral disturbances. This work phase information can be obtained through sensors (e.g., inertial measurement units, angle encoders, etc.) at various torso nodes of the exoskeleton, combined with a pre-defined motion pattern recognition algorithm.

[0121] Physiological status information can be understood as indicators of the electrician's internal physical state during operation, specifically including heart rate, skin conductance, or electromyography (EMG) signals. These physiological indicators can reflect the operator's level of fatigue, concentration, and stress response. For example, an increased heart rate or skin conductance may indicate that the operator is under stress or fatigued, at which point their resistance to external disturbances may be reduced. Physiological status information can be collected in real time using biosensors integrated into the exoskeleton assistive device.

[0122] In practical applications, adjusting the weighting coefficients of the resultant torque, instantaneous impact force, and influencing factors based on the work stage information and physiological state information means that the system dynamically modifies the relative importance of each environmental disturbance factor used for weighted superposition based on the currently identified work stage and physiological state. For example, when the operator is in a "sharp turn" phase and has a "high heart rate," the system may increase the weights of the wind resultant torque and the instantaneous ground impact force while decreasing the weight of the environmental noise influencing factor, to place greater emphasis on the assessment of physical stability risks. These weighting coefficient adjustments can be achieved through a preset rule base, machine learning model, or adaptive algorithm, with the aim of making the calculation results of the total disturbance torque and total disturbance energy closer to the operator's actual state and needs.

[0123] This application's solution dynamically adjusts the weighting coefficients of environmental disturbance factors by incorporating information on the operator's work stage and physiological state. Because operators' sensitivity and resistance to various environmental disturbances differ across work stages and physiological states, traditional fixed-weighting methods struggle to accurately assess the total disturbance. This application, by acquiring and utilizing this personalized information in real time, can more accurately reflect the actual stability challenges faced by operators in specific situations. For example, when an operator is in a "sharp turn" phase requiring high concentration and precise control, the impact on stability may be amplified even if the intensity of the environmental disturbance remains constant; similarly, when an operator's physiological state indicates fatigue, their resistance to any disturbance weakens. By dynamically adjusting the weighting coefficients, the system can more precisely quantify the impact of these situational factors on the total disturbance, making subsequent adjustments to the stability allowance for body posture more reasonable and personalized.

[0124] Specifically, the above-mentioned assessment of the potential impact of multiple environmental disturbances on the overall stability of the operator based on the total disturbance torque and the total disturbance energy includes: acquiring real-time physiological feedback information of the operator, including heart rate, respiratory rate, skin conductance or electromyography signals; acquiring real-time movement characteristic information of the operator, including trunk swaying frequency, foot pressure center swaying amplitude or joint fine adjustment frequency; assessing the operator's current fatigue level and fine control ability based on the real-time physiological feedback information and the real-time movement characteristic information; and dynamically correcting the potential impact of environmental disturbances on the overall stability of the operator based on the total disturbance torque, the total disturbance energy, the fatigue level and the fine control ability.

[0125] The real-time physiological feedback information refers to the real-time collection of physiological data of the operator through wearable sensors or physiological monitoring modules integrated into the exoskeleton assistive device. For example, heart rate can reflect the operator's physiological load and stress level; changes in respiratory rate may indicate changes in their physical state; skin conductance or electromyography signals can provide objective evidence of the operator's mental stress, muscle activity intensity, and fatigue level. This physiological data is used to quantify the operator's internal state. The real-time motion characteristic information refers to the subtle motion patterns of the operator during the process of lifting heavy objects, obtained through motion sensors (such as inertial measurement units, angle encoders, pressure sensor arrays, etc.) integrated into the exoskeleton assistive device. For example, trunk swaying frequency can reflect the operator's stability control strategy when maintaining balance; the swaying amplitude of the foot pressure center is directly related to the accuracy of their center of gravity control; and joint fine-tuning frequency can reflect the operator's ability to finely adjust joint movements. This motion characteristic data provides information on the level of control demonstrated by the operator in actual operation. The assessment of the operator's current fatigue level and fine control ability refers to the comprehensive analysis of the aforementioned real-time physiological feedback information and real-time motion characteristic information, using a pre-set model or machine learning algorithm to quantitatively assess the operator's current physiological and cognitive state. The assessment of fatigue level helps determine whether the operator is at a critical physical or mental state; the assessment of fine control ability reflects their performance potential under complex or high-precision operations. The dynamic correction of the potential impact of environmental disturbances on the operator's overall stability refers to using the assessment results of the operator's fatigue level and fine control ability as correction factors to adjust the potential impact previously assessed based on the total disturbance torque and total disturbance energy calculated from the environmental disturbances. For example, when the operator is assessed as having a high level of fatigue or a decreased fine control ability, even if the intensity of the environmental disturbance remains unchanged, its potential impact on the operator's stability will be considered greater, thus requiring a more conservative or more proactive stability adjustment strategy.

[0126] This application's solution, by incorporating real-time physiological feedback and motion characteristic information from the operator, enables a more comprehensive and accurate assessment of the operator's true state in the face of environmental disturbances. Traditional assessment methods may only focus on the external environmental disturbance itself, neglecting the impact of individual differences in the operator and their real-time state on stability response. By acquiring physiological feedback information such as heart rate and respiratory rate, the operator's physiological load and mental state can be objectively reflected; for example, an elevated heart rate may indicate that the operator is under stress or fatigue. Simultaneously, by monitoring motion characteristic information such as trunk sway frequency and plantar pressure center sway amplitude, the operator's actual control ability in maintaining balance and performing fine motor operations can be intuitively reflected. The combination of this internal state information with external environmental disturbance information makes it possible to assess the operator's fatigue level and fine control ability. Therefore, when assessing the potential impact of environmental disturbances on the operator's overall stability, it is no longer solely based on the intensity of the external disturbance, but rather the operator's internal state is used as an important correction factor. When operators are fatigued or their fine control capabilities decline, even when faced with the same external disturbances, the potential threat to their stability will be dynamically adjusted to a higher level, thereby prompting exoskeleton assistive devices to adopt more timely and proactive assistance strategies to avoid potential instability risks.

[0127] Example 2

[0128] Specifically, Figure 3 The invention also discloses a schematic diagram of the exoskeleton assistive device control system in an embodiment of the invention, the system comprising:

[0129] The physical feature acquisition module is used to acquire the physical characteristics of an electrician carrying heavy objects based on sensors on the forearm support frame of the exoskeleton. The physical features include the moment of inertia of the heavy objects.

[0130] A steering intention recognition module is used to identify the steering intention of the electrician based on sensors at various torso nodes of the exoskeleton.

[0131] A stability adjustment module is used to adjust the stability allowable adjustment standard of the electrician's body posture based on the physical characteristics and the turning intention. The stability allowable adjustment standard includes the tolerance of body tilt angle, the tolerance of center of gravity transfer rate, and the range of joint movement.

[0132] The posture data acquisition module is used to acquire the real-time posture data of the electrician based on the sensors at each torso node of the exoskeleton.

[0133] The control module is used to control the exoskeleton assistive device to help the electrician carry heavy objects based on the real-time posture data and the stability allowable adjustment standard.

[0134] The system uses a physical feature acquisition module to sense the rotational inertia of the weight, a steering intention recognition module to identify the operator's steering intention, and a stability adjustment module to dynamically adjust the stability of the body posture according to the adjusted standard. Subsequently, a posture data acquisition module acquires real-time posture data, and a control module uses this adjusted standard to provide auxiliary control of the exoskeleton. As a result, this system can more accurately understand the operator's true intentions and balance state, avoiding the misjudgments and adverse interventions of traditional fall protection systems in specific operating scenarios, thus significantly improving the safety and assistive effect of the exoskeleton in complex working environments.

[0135] Specifically, the physical feature acquisition module is configured to acquire the physical characteristics of the heavy object being handled by the electrician based on sensors on the exoskeleton's forearm support frame. These physical characteristics include the moment of inertia of the heavy object. The specific process for acquiring the physical characteristics of the heavy object has already been described in the above embodiments and will not be repeated here. It should be emphasized that this physical feature acquisition module may specifically include: force sensors, vision sensors, or laser rangefinders integrated on the exoskeleton's forearm support frame. For example, a force sensor can be configured to measure the force applied to the heavy object by the operator and, combined with the contact point information between the heavy object and the exoskeleton, calculate the center of gravity and mass distribution of the heavy object. A vision sensor, such as a depth camera, can be configured to capture three-dimensional point cloud data of the heavy object and, through image processing and geometric modeling algorithms, estimate the geometric parameters and mass distribution of the heavy object, thereby calculating its moment of inertia. The data from these sensors is transmitted to the module's processor for processing to output the moment of inertia of the heavy object.

[0136] The steering intention recognition module is configured to identify the electrician's steering intention based on sensors at various trunk nodes of the exoskeleton. The specific process of identifying steering intention has already been described in the above embodiments and will not be repeated here. It is important to emphasize that this steering intention recognition module specifically includes a data processing unit that receives data from a plantar pressure sensor array, hip and knee joint angle encoders, or inertial measurement units (IMUs). The plantar pressure sensor array can monitor changes in pressure distribution on the operator's feet in real time; for example, when the operator prepares to turn left, the pressure on the inner side of the left foot and the outer side of the right foot increases. The angle encoders and IMUs can provide real-time angle, angular velocity, and angular acceleration information of the hip and knee joints. This kinematic data can reflect the operator's turning trend and intention. By fusing and analyzing this multimodal sensor data, for example using machine learning algorithms, the operator's steering intention can be accurately identified.

[0137] The stability adjustment module is configured to adjust the stability allowable adjustment criteria of the electrician's body posture based on the physical characteristics and the turning intention. These criteria include body tilt angle tolerance, center of gravity transfer rate tolerance, and joint range of motion. The specific process of adjusting the stability allowable adjustment criteria has been described in the above embodiments and will not be repeated here. It is important to emphasize that the stability adjustment module may specifically include a processor for receiving the load's moment of inertia information provided by the physical feature acquisition module and the turning intention information provided by the turning intention recognition module. For example, when the load's moment of inertia is large and the turning intention is clear, the processor can dynamically increase the body tilt angle tolerance according to a preset strategy or model, allowing the operator a larger tilt amplitude during the turning process. Simultaneously, the center of gravity transfer rate tolerance can also be adjusted according to the speed of the turning to accommodate the operator's active center of gravity transfer behavior. Adjustment of the joint range of motion ensures that the exoskeleton does not restrict the necessary joint movements required by the operator to maintain balance when carrying specific heavy objects.

[0138] The posture data acquisition module is configured to acquire real-time posture data of the electrician based on sensors at various torso nodes of the exoskeleton. The specific process of acquiring real-time posture data has been described in the above embodiments and will not be repeated here. It is important to emphasize that this posture data acquisition module may specifically include: an integrated set of multiple posture sensors, such as an inertial measurement unit (IMU) for measuring the torso's tilt angle, angular velocity, and angular acceleration, and angle encoders installed at the hip, knee, and ankle joints for accurately measuring the real-time angles of each joint. In addition, a plantar pressure sensor array can also provide real-time plantar pressure distribution data for calculating the operator's center of gravity position. This sensor data is acquired in real-time and transmitted to the control module for subsequent processing.

[0139] The control module is configured to control the exoskeleton assistive device to help the electrician move heavy objects based on the real-time posture data and the stability adjustment allowable standard. The specific process of controlling the exoskeleton assistive device has been described in the above embodiments and will not be repeated here. It should be emphasized that the control module specifically may include: a main controller, used to receive real-time posture data provided by the posture data acquisition module and compare it with the dynamically adjusted stability adjustment allowable standard provided by the stability adjustment module. Specifically, the main controller determines whether the operator's real-time posture (such as body tilt angle, center of gravity transfer rate, and joint range of motion) is within the currently allowed range. If the real-time posture data is within the allowed range, the main controller will instruct the exoskeleton assistive device to operate in normal assist mode, providing an assistive torque consistent with the operator's intention. If the real-time posture data exceeds the allowed range, the main controller will determine that there is a risk of imbalance and instruct the exoskeleton assistive device to operate in corrective torque mode, applying an appropriate corrective torque to help the operator regain balance, thereby preventing falls.

[0140] Example 3

[0141] This invention provides a computer-readable storage medium storing a computer program. When executed by a processor, this program implements the exoskeleton assistive device control method of any of the above embodiments. The computer-readable storage medium includes, but is not limited to, any type of disk (including floppy disk, hard disk, optical disk, CD-ROM, and magneto-optical disk), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic cards, or optical cards. In other words, the storage device includes any medium that can store or transmit information in a readable form by a device (e.g., a computer, a mobile phone), and can be a read-only memory, a disk, or an optical disk, etc.

[0142] Example 4

[0143] Please see Figure 4 , Figure 4 This is a schematic diagram of the structural composition of the electronic device in an embodiment of the present invention.

[0144] This invention also provides an electronic device, such as... Figure 4As shown, the electronic device includes a memory 41, a processor 43, and a computer program 42 stored in the memory 41 and executable on the processor 43. Those skilled in the art will understand that... Figure 3 The illustrated electronic device does not constitute a limitation on all devices and may include more or fewer components than illustrated, or combine certain components. Memory 41 can be used to store computer program 42 and various functional modules. Processor 43 runs the computer program 42 stored in memory 41, thereby performing various functional applications and data processing of the device. Memory can be internal memory or external memory, or both. Internal memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, or random access memory. External memory may include hard disks, floppy disks, ZIP disks, USB flash drives, magnetic tapes, etc. Processor 43 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, a single-chip microcomputer, or a processor 43, or any conventional processor, etc. The processors and memories disclosed in this invention include, but are not limited to, these types of processors and memories. The processors and memories disclosed in this invention are merely examples and not intended to be limiting.

[0145] As one embodiment, the electronic device includes: one or more processors 43, a memory 41, and one or more computer programs 42, wherein the one or more computer programs 42 are stored in the memory 41 and configured to be executed by the one or more processors 43, and the one or more computer programs 42 are configured to execute the exoskeleton assistive device control method in any of the above embodiments. For the specific implementation process, please refer to Embodiment 1 above, which will not be repeated here.

[0146] In this embodiment of the invention, the present application introduces the moment of inertia of the load and the operator's turning intention as the basis for adjusting the stability standard. This allows the exoskeleton system to more accurately understand the operator's true intentions and body posture requirements in specific work scenarios, thereby avoiding unnecessary or counterproductive fall prevention interventions. For example, when an operator is carrying a long steel bar and turning, the system can identify the moment of inertia of the steel bar and the operator's intention to tilt inward. Based on this, the system relaxes the tolerance for body tilt angle, allowing the operator to make the necessary balancing posture, rather than forcibly "straightening" the operator. This enables the operator to complete the carrying and turning actions smoothly and safely. Therefore, this application significantly improves the adaptability, safety, and smoothness of human-machine collaboration of exoskeleton-assisted equipment in complex work environments.

[0147] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A control method for an exoskeleton-assisted device, characterized in that, include: The physical characteristics of an electrician carrying heavy objects are obtained based on sensors on the forearm support frame of the exoskeleton, including the moment of inertia of the heavy objects. Sensors at various torso nodes of the exoskeleton are used to identify the electrician's turning intentions. The stability adjustment criteria for adjusting the electrician's body posture based on the physical characteristics and the turning intention include tolerance for body tilt angle, tolerance for center of gravity transfer rate, and range of motion of joints. The electrician's real-time posture data is acquired by sensors at various torso nodes of the exoskeleton. Based on the real-time posture data and the stability, the standard control of the exoskeleton assistive device is allowed to help the electrician carry heavy objects. The method of adjusting the standard control of the exoskeleton assisting the electrician in moving heavy objects based on the real-time posture data and the stability includes: Based on the stability allowable adjustment standard, determine whether the real-time attitude data is within the dynamic allowable range; If it is determined that the real-time posture data is within the dynamic allowable range, the exoskeleton assistive device is controlled to operate in normal assist mode, and the exoskeleton assistive device assists the electrician in moving heavy objects based on the normal assist mode. If it is determined that the real-time posture data is not within the dynamic allowable range, the exoskeleton assistive device is controlled to operate in the corrective torque mode. The exoskeleton assistive device assists the electrician in moving heavy objects based on the corrective torque mode.

2. The control method for exoskeleton assistive equipment according to claim 1, characterized in that, The physical characteristics of an electrician carrying heavy objects, obtained from sensors on the exoskeleton forearm support frame, include: The initial pressure value applied by the electrician when moving heavy objects is collected based on the mechanical sensor. The initial angular acceleration value of the electrician when moving heavy objects is collected based on the inertial measurement unit; The rotational torque applied to the weight is calculated based on the distance from the exoskeleton hand to the center of the weight and the initial pressure value. The moment of inertia is calculated based on the initial angular acceleration value and the rotational torque.

3. The control method for exoskeleton-assisted equipment according to claim 1, characterized in that, The sensors based on various torso nodes of the exoskeleton used to identify the electrician's turning intentions include: The distribution of foot pressure values ​​is collected based on a pressure sensor array installed on the sole of the exoskeleton. Angle encoders set up at the hip and knee joints collect real-time angle and angular velocity values ​​for each joint. The electrician's turning intention is identified based on the distribution of plantar pressure values, the real-time angle values, and the angular velocity values.

4. The control method for exoskeleton assistive equipment according to claim 1, characterized in that, The step of determining whether the real-time attitude data is within the dynamic allowable range based on the stability allowable adjustment criterion includes: Determine whether the human body tilt angle in the real-time posture data exceeds the body tilt angle tolerance; Determine whether the lateral shift speed of the center of gravity in the real-time attitude data exceeds the tolerance of the center of gravity shift rate. Determine whether the real-time range of motion of the hip and ankle joint in the real-time posture data exceeds the joint's range of motion; If the body tilt angle exceeds the tolerance of body tilt angle, or the lateral transfer speed of the center of gravity exceeds the tolerance of the center of gravity transfer rate, or the real-time range of motion of the hip and ankle joints exceeds the range of motion of the joints, then the real-time posture data is determined to be outside the dynamic allowable range.

5. The control method for exoskeleton assistive equipment according to claim 1, characterized in that, The stability adjustment criteria for adjusting the electrician's body posture based on the physical characteristics and the turning intention include: Acquire environmental disturbance information, including wind speed, wind direction, ground vibration frequency, and vibration amplitude; The potential impact of the environmental disturbance information on the overall stability of the electrician operator is assessed, including additional tilting moments and center of gravity shift trends. The adjustment coefficients for the body tilt angle tolerance, the center of gravity transfer rate tolerance, and the joint range of motion are modified based on the potential impact. The tolerance for body tilt angle, the tolerance for center of gravity transfer rate, and the range of joint motion in the stability allowable adjustment criteria are adjusted based on the adjustment coefficient, the physical characteristics, and the steering intention.

6. The control method for the exoskeleton assistive device according to claim 5, characterized in that, The assessment of the potential impact of the environmental disturbance information on the overall stability of electrical operators includes: The resultant torque generated by wind force on the exoskeleton assistive equipment is calculated based on wind speed and direction data acquired by a multimodal environmental sensor array. The instantaneous impact force generated by ground vibration on the exoskeleton assistive equipment is calculated based on multi-point vibration data obtained from a multimodal environmental sensor array. The impact factors of environmental noise on the reaction speed and fine control ability of electricians are evaluated based on environmental noise data acquired by a multimodal environmental sensor array. The resultant torque, the instantaneous impact force, and the influencing factors are weighted and superimposed to obtain the total disturbance torque and total disturbance energy under the combined action of multiple environmental disturbances; The potential impact of multiple environmental disturbances on the overall stability of the operator is assessed based on the total disturbance moment and the total disturbance energy. These potential impacts include additional tilting moments and center of gravity shift trends.

7. A control system for an exoskeleton-assisted device, characterized in that, The system includes: The physical characteristics acquisition module is used to acquire the physical characteristics of the electrician carrying heavy objects based on sensors on the forearm support frame of the exoskeleton. The physical characteristics include the moment of inertia of the heavy objects. A steering intention recognition module is used to identify the steering intention of the electrician based on sensors at various torso nodes of the exoskeleton. A stability adjustment module is used to adjust the stability allowable adjustment standard of the electrician's body posture based on the physical characteristics and the turning intention. The stability allowable adjustment standard includes the tolerance of body tilt angle, the tolerance of center of gravity transfer rate, and the range of joint movement. The posture data acquisition module is used to acquire the real-time posture data of the electrician based on the sensors at each torso node of the exoskeleton. The control module is used to control the exoskeleton assistive device to assist the electrician in moving heavy objects based on the real-time posture data and the stability adjustment standard. The method of adjusting the standard control of the exoskeleton assisting the electrician in moving heavy objects based on the real-time posture data and the stability includes: Based on the stability allowable adjustment standard, determine whether the real-time attitude data is within the dynamic allowable range; If it is determined that the real-time posture data is within the dynamic allowable range, the exoskeleton assistive device is controlled to operate in normal assist mode, and the exoskeleton assistive device assists the electrician in moving heavy objects based on the normal assist mode. If it is determined that the real-time posture data is not within the dynamic allowable range, the exoskeleton assistive device is controlled to operate in the corrective torque mode. The exoskeleton assistive device assists the electrician in moving heavy objects based on the corrective torque mode.

8. An electronic device, the electronic device comprising a processor and a memory, characterized in that, The memory is used to store instructions, and the processor is used to call the instructions in the memory to cause the electronic device to execute the exoskeleton assistive device control method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed on an electronic device, cause the electronic device to perform the exoskeleton assistive device control method as described in any one of claims 1 to 6.

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