Exoskeleton-based inspection control method and device, and electronic device

Abstract

The application discloses a kind of based on wearable exoskeleton's inspection control method, device and electronic equipment, the method includes: obtaining the physiological state data of user, current scene data and current task, wherein, user wears wearable exoskeleton;Physiological state data is analyzed dynamically, and physiological load characteristics are obtained;Current scene data is identified based on current task, and environmental interference factors affecting user operation are obtained;Based on physiological load characteristics and environmental interference factors, the operating parameters of wearable exoskeleton are adjusted;It can adapt to the dynamic change of user body state and on-site environment in the process of inspection, improve the rationality and adaptability of exoskeleton assistance output, can effectively reduce the physiological load of inspection user, avoid the adverse effects of environmental interference on operation, efficiency and user operation safety, comfort degree are considered.

Description

Technical Field

[0001] This invention relates to the field of intelligent assistive technology, specifically to an inspection control method, device, and electronic device based on a wearable exoskeleton. Background Technology

[0002] Extreme environments such as high-altitude and frigid climates often present problems such as scarce sunlight, drastic temperature changes, dust, or strong ultraviolet radiation, which lead to a sharp increase in the physiological load of workers and make key indicators such as heart rate and blood oxygenation prone to abnormalities. At the same time, inspection tasks require long-distance walking, frequent climbing, and repetitive bending, which significantly increases the risk of musculoskeletal fatigue and injury.

[0003] However, existing assistance systems mostly focus on information overlay or single-point target recognition. That is, existing assistance systems often only focus on the acquisition and processing of visual information, and the assistance state is in a passive response mode, making it difficult to actively adapt to complex and ever-changing working environments, let alone provide users with higher-precision personalized interactive guidance, resulting in limited assistance effects.

[0004] Therefore, existing technologies, when using wearable exoskeletons for assisted work, suffer from a lack of initiative and personalized adjustment capabilities, which makes it impossible to accurately avoid potential safety risks during inspections and to specifically alleviate the physiological burden on users. Summary of the Invention

[0005] This invention provides a patrol control method, device, and electronic device based on a wearable exoskeleton, aiming to solve the problems of existing technologies that, when assisting work with wearable exoskeletons, lack of initiative and personalized adjustment capabilities, make it impossible to accurately avoid potential safety risks during the patrol process, and make it difficult to specifically alleviate the physiological burden on users.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A patrol control method based on a wearable exoskeleton includes: The system acquires the user's physiological state data, current scene data, and current task, wherein the user is wearing a wearable exoskeleton. Dynamic changes in the physiological state data are analyzed to obtain physiological load characteristics; Based on the current task, target identification is performed on the current scene data to obtain environmental interference factors affecting the user's operation; The operating parameters of the wearable exoskeleton are adjusted based on the physiological load characteristics and the environmental disturbance factors.

[0007] Optionally, the physiological state data includes heart rate, blood oxygen saturation, altitude, exercise intensity, and temperature; the physiological load characteristics include low load, medium load, and high load; the dynamic change analysis of the physiological state data to obtain physiological load characteristics includes: Obtain the user's individualized physiological baseline; The user's standard load is determined based on the individualized physiological baseline, and a load division threshold is determined according to the standard load and a preset division coefficient. The load division threshold includes a first threshold and a second threshold, and the first threshold is less than the second threshold. The physiological state data are fused and quantified to obtain the user's actual physiological load value; The actual physiological load value is compared with the load classification threshold to determine the physiological load characteristics of the user; Specifically, when the actual physiological load value is less than the first threshold, the physiological load characteristic is determined to be low load; when the actual physiological load value is greater than or equal to the first threshold and less than the second threshold, the physiological load characteristic is determined to be medium load; and when the actual physiological load value is greater than or equal to the second threshold, the physiological load characteristic is determined to be high load.

[0008] Optionally, the step of fusing and quantifying the physiological state data to obtain the user's actual physiological load value includes: The weighting coefficients for each physiological state data are determined based on the current task and the current scenario, respectively. The physiological state data are weighted and fused based on the weighting coefficients to obtain the actual physiological load value; The weighting coefficients for heart rate and blood oxygen are related to the intensity of the current task and the complexity of the current scene, the weighting coefficient for altitude is related to the altitude of the current scene, the exercise intensity is related to the type of the current task, and the weighting coefficient for temperature is related to the ambient temperature of the current scene.

[0009] Optionally, the step of performing target identification on the current scene data based on the current task to obtain environmental interference factors affecting the user's operation includes: Construct a local 3D occupancy grid map centered on the current scene based on the current task; Target detection is performed on the local three-dimensional occupied grid map to identify environmental interference factors related to the current task, including terrain obstacles, spatial limitations, and environmental risks.

[0010] Optionally, the operating parameters include the inspection path; adjusting the operating parameters of the wearable exoskeleton based on the physiological load characteristics and the environmental disturbance factors includes: The physiological cost is determined based on the physiological load characteristics and the action type and action site corresponding to the current task; Based on the aforementioned environmental disturbance factors, the slope and terrain complexity factor, the obstacle and space limitation factor, and the road surface characteristic factor are determined respectively, and the terrain cost is determined based on the slope and terrain complexity factor, the obstacle and space limitation factor, and the road surface characteristic factor. The task cost is determined based on the path distance and time constraints corresponding to the current task. The physiological cost, terrain cost, and task cost are comprehensively evaluated to obtain a comprehensive cost, and the inspection path is adjusted based on the comprehensive cost.

[0011] Optionally, the operating parameters further include auxiliary torque and travel speed; adjusting the operating parameters of the wearable exoskeleton further includes: The user's standard assist torque and standard travel speed are determined based on the user's individualized physiological baseline; When the physiological load characteristic is low load, the standard auxiliary torque and the standard travel speed are output; When the physiological load characteristic is medium load, the first adjustment assist torque is determined based on the first assist weight coefficient and the standard assist torque, and the first adjustment travel speed is determined based on the first speed weight coefficient and the standard travel speed. When the physiological load characteristic is high load, the second adjustment assist torque is determined based on the second assist weight coefficient and the standard assist torque, and the second adjustment travel speed is determined based on the second speed weight coefficient and the standard travel speed; Wherein, the first assist weight coefficient is less than the second assist weight coefficient, and both the first assist weight coefficient and the second assist weight coefficient are greater than 1; the first speed weight coefficient is greater than the second speed weight coefficient, and both the first speed weight coefficient and the second speed weight coefficient are less than 1.

[0012] Optionally, the operating parameters further include joint stiffness; adjusting the operating parameters of the wearable exoskeleton further includes: When the physiological load characteristics are medium or high, and the current task is long-distance walking on flat ground, increase the joint stiffness; When the current task is climbing or overcoming obstacles in complex terrain, the joint stiffness is dynamically adjusted according to the gait cycle.

[0013] An inspection control device based on a wearable exoskeleton includes: The data acquisition module is used to acquire the user's physiological state data, current scene data, and current task, wherein the user is wearing a wearable exoskeleton; The physiological load analysis module is used to perform dynamic change analysis on the physiological state data to obtain physiological load characteristics; An environmental interference identification module is used to identify targets in the current scene data based on the current task, and to obtain environmental interference factors that affect the user's operation. The operating parameter adjustment module is used to adjust the operating parameters of the wearable exoskeleton based on the physiological load characteristics and the environmental interference factors.

[0014] An electronic device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the following steps: The system acquires the user's physiological state data, current scene data, and current task, wherein the user is wearing a wearable exoskeleton. Dynamic changes in the physiological state data are analyzed to obtain physiological load characteristics; Based on the current task, target identification is performed on the current scene data to obtain environmental interference factors affecting the user's operation; The operating parameters of the wearable exoskeleton are adjusted based on the physiological load characteristics and the environmental disturbance factors.

[0015] A computer-readable storage medium having a computer program stored thereon, the computer program being loaded by a processor to perform the steps in the inspection control method based on a wearable exoskeleton described above.

[0016] In this embodiment, by integrating and acquiring the physiological state data of the inspection user, the work scenario data, and the inspection task information, and by dynamically analyzing and extracting the user's physiological load characteristics and identifying environmental interference factors affecting the work, the operating parameters of the wearable exoskeleton are dynamically adjusted. This allows it to adapt to the dynamic changes in the user's physical state and the on-site environment during the inspection process, improving the rationality and adaptability of the exoskeleton's assistive output. It can effectively reduce the physiological load of the inspection user, avoid the adverse effects of environmental interference on the work, and balance the efficiency of the inspection work with the safety and comfort of the user. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this embodiment, the accompanying drawings used in the description of the embodiment will be briefly introduced below. Obviously, the accompanying 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.

[0018] Figure 1 A schematic diagram of a scenario for an embodiment of the inspection and control system based on a wearable exoskeleton provided by the present invention; Figure 2 A schematic diagram of another embodiment of the inspection control system based on wearable exoskeleton provided by the present invention; Figure 3 This is a flowchart illustrating an embodiment of the inspection control method based on a wearable exoskeleton provided by the present invention. Figure 4 This is a schematic diagram showing the result of wearing the wearable exoskeleton provided by the present invention on the human body in one embodiment. Figure 5 A data flow topology diagram for an embodiment of the AR-based wearable exoskeleton inspection and control system provided by the present invention; Figure 6 A schematic diagram of an embodiment of the inspection control device based on a wearable exoskeleton provided by the present invention; Figure 7 This is a schematic diagram of an embodiment of the electronic device provided by the present invention. Detailed Implementation

[0019] The technical solutions in this embodiment will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] In the following description, specific embodiments of the invention will be illustrated with reference to steps and symbols performed by one or more computers, unless otherwise stated. Therefore, these steps and operations will be referred to several times as being performed by a computer, and computer execution as referred to herein includes operations by a computer processing unit representing electronic signals of data in a structured format. This operation transforms the data or maintains it at a location in the computer's memory system, which can be reconfigured or otherwise alter the operation of the computer in a manner well known to those skilled in the art. The data structure maintained by the data is the physical location of the memory, which has specific characteristics defined by the data format. However, the principles of the invention described above are not intended to be limiting, and those skilled in the art will understand that many of the steps and operations described below can also be implemented in hardware.

[0021] The terms "module" or "unit" as used herein can be considered as software objects executing on the computing system. The different components, modules, engines, and services described herein can be considered as implementation objects on the computing system. The apparatus and methods described herein are preferably implemented in software, but can also be implemented in hardware, both of which are within the scope of this invention.

[0022] This invention provides a method, device, and electronic device for inspection control based on a wearable exoskeleton.

[0023] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating an embodiment of the inspection and control system based on a wearable exoskeleton provided by the present invention. The inspection and control system may include a client 100 and a server 200, which are connected via a network. The server 200 integrates an inspection and control device based on a wearable exoskeleton. The server 200 may be a work platform server (i.e., a server loaded with a work platform), such as... Figure 1 In this embodiment, the server 200 is accessed by the client 100. The server 200 is primarily used to acquire the user's physiological state data, current scene data, and current task, wherein the user wears a wearable exoskeleton. It performs dynamic change analysis on the physiological state data to obtain physiological load characteristics; identifies targets in the current scene data based on the current task to obtain environmental interference factors affecting the user's work; and adjusts the operating parameters of the wearable exoskeleton based on the physiological load characteristics and environmental interference factors.

[0024] In this embodiment, the server 200 can be a standalone server, a server network, or a server cluster. For example, the server 200 described in this embodiment includes, but is not limited to, computers, network hosts, single network servers, multiple network server sets, or cloud servers composed of multiple servers. The cloud server is composed of a large number of computers or network servers based on cloud computing. In this embodiment, communication between the server and the client can be achieved through any communication method, including but not limited to mobile communication based on the 3rd Generation Partnership Project (3GPP), Long Term Evolution (LTE), and Worldwide Interoperability for Microwave Access (WiMAX), or computer network communication based on the TCP / IP Protocol Suite (TCP / IP) and User Datagram Protocol (UDP).

[0025] It is understood that the client 100 used in this embodiment can be understood as a client device. A client device includes both receiving and transmitting hardware, that is, a device with receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. Such a client device may include: cellular or other communication devices, having a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. Specifically, the client 100 may be a desktop terminal or a mobile terminal, specifically a mobile phone, tablet computer, laptop computer, etc.

[0026] Those skilled in the art will understand that Figure 1 The application environment shown is merely one application scenario of the solution in this application and does not constitute a limitation on the application scenario of the solution in this application. Other application environments may include those that are more specific to this application. Figure 1 The number of more or fewer servers shown, or the server network connectivity relationships, for example... Figure 1 Only one server and two clients are shown in the diagram. It is understood that the inspection and control system based on wearable exoskeleton may also include one or more other servers, and / or one or more clients connected to the server network, which is not limited here.

[0027] In some embodiments of the present invention, the working platform may be an enterprise office platform, such as WeChat for Business. Taking server 200 as an example, it may further include an enterprise office platform contact server, an enterprise office platform configuration management server, and a web management server. Enterprise users or developers can access the web management server using a web browser terminal to configure the field configuration information on the enterprise office platform configuration management server, and set and store the enterprise user information of enterprise employees of the enterprise office platform on the enterprise office platform contact server.

[0028] In addition, such as Figure 2 As shown, Figure 2 This is a schematic diagram of another embodiment of the inspection control system based on a wearable exoskeleton provided by the present invention. The inspection control system based on a wearable exoskeleton may also include a storage terminal 300 for storing data, such as a storage object database. The object database stores object data, which may include application templates (such as approval templates, attendance templates, and other application templates), file data (such as Word files, Excel files, or PPT files and other files in various formats), image data (such as JPG, PNG, BMP, and other images in various formats), and so on. Correspondingly, the object database may also be divided into multiple types of data, such as an application database, a file database, or an image database.

[0029] It should be noted that, Figure 1-2 The schematic diagram of the inspection control system based on wearable exoskeleton shown is merely an example. The inspection control system and scenario based on wearable exoskeleton described in this embodiment are for the purpose of more clearly illustrating the technical solution of this embodiment and do not constitute a limitation on the technical solution provided by this invention. As those skilled in the art will know, with the evolution of the inspection control system based on wearable exoskeleton and the emergence of new business scenarios, the technical solution provided by this invention is also applicable to similar technical problems.

[0030] The following detailed description is based on specific embodiments.

[0031] In this embodiment, the description will be based on a wearable exoskeleton-based inspection control device, which can be integrated into the server 200.

[0032] This invention provides a patrol control method based on a wearable exoskeleton. Please refer to [link / reference]. Figure 3 , Figure 3 A flowchart illustrating an embodiment of the inspection control method based on a wearable exoskeleton provided by the present invention includes: S301: Acquire user's physiological state data, current scene data, and current task, wherein the user is wearing a wearable exoskeleton; S302: Perform dynamic change analysis on physiological state data to obtain physiological load characteristics; S303: Based on the current task, perform target identification on the current scene data to obtain environmental interference factors that affect the user's operation; S304: Adjust the operating parameters of the wearable exoskeleton based on physiological load characteristics and environmental interference factors.

[0033] In one specific embodiment, this application targets personnel who perform inspection operations in extreme environments such as high altitudes, frigid conditions, strong dust, and intense ultraviolet radiation. These users need to complete high-intensity tasks such as long-distance walking, frequent climbing, and repetitive bending in harsh environments, resulting in high physiological load, musculoskeletal fatigue, and a high risk of injury. They require the assistance of exoskeletons and physiological monitoring to ensure operational safety and efficiency.

[0034] Physiological status data consists of key physiological parameters collected in real time from workers using multimodal physiological sensors. These parameters primarily include heart rate (HR), blood oxygen saturation (SpO2), and body surface temperature (T1). The data is mainly acquired by heart rate / blood oxygen sensors and temperature sensors integrated into wearable devices (such as wristbands, rings, or watches), and is processed into time-series data according to a preset sampling period. This data reflects the worker's current physical condition and fitness level, serving as the core basis for assessing physiological load levels, triggering exoskeleton adaptive adjustments, and providing safety warnings.

[0035] The current scenario data is a comprehensive dataset of the system's perception of the work environment and movement status, mainly comprising two categories: environmental parameters and motion parameters. Environmental parameters, such as altitude and ambient temperature, are collected by altimeters and thermometers mounted on wristbands and exoskeletons, reflecting the physiological stress of the external environment on the worker. Motion parameters, including gait, step count, posture changes, acceleration, and gait frequency, are collected by IMU sensors mounted on the upper and lower limb exoskeletons and pedometers on wristbands, reflecting work intensity and muscle fatigue trends. This data, fused with physiological data, provides support for physiological load assessment and dynamic path planning.

[0036] The current task refers to equipment inspection work being performed by operators in extreme environments. Specifically, it requires operators to inspect target areas according to a predetermined procedure, typically involving high-intensity activities such as long-distance walking, frequent climbing, and carrying and lifting tools. All inspection points must be completed as planned while ensuring personnel safety. The system dynamically plans inspection routes based on task priority, inspection sequence, urgency, and the operator's physiological state to ensure efficient task completion within physical limits.

[0037] The wearable exoskeleton is the mechanical actuator of this system, consisting of an upper limb assistive exoskeleton and a lower limb assistive exoskeleton. The upper limb exoskeleton provides electric assistance to the shoulder joint, reducing the muscle burden on workers during tasks such as lifting and carrying. The lower limb exoskeleton provides electric assistance to the knee joint, effectively reducing the pressure on the knee joint during walking and climbing, thereby reducing energy consumption, lowering the risk of musculoskeletal injury, and improving work efficiency and comfort. The exoskeleton receives control commands from the AR host computer to perform actions such as adjusting the assistance level, limiting speed, and switching modes, and transmits the device's operating status back in real time.

[0038] Physiological load characteristics are a comprehensive representation of the stress endured by workers under extreme environments and high-intensity work conditions. By integrating physiological, environmental, and movement parameters, a weighted comprehensive evaluation model is used to quantify this load into a physiological load index. The system classifies the load into four levels—low load, medium load, high load, and hazardous load—based on the index value. Different levels correspond to different exoskeleton assistance strategies and movement speed limits. Independent threshold limits for key physiological parameters are also set. Once a single indicator exceeds the safety red line, the highest priority emergency response is immediately triggered to ensure personnel safety. The system supports individualized physiological baseline modeling, allowing adjustment of safety thresholds based on the worker's historical data, reducing false alarms caused by individual differences.

[0039] Environmental interference factors mainly originate from extreme working environments such as high-altitude and frigid zones, including low light levels, drastic temperature changes, high altitude, dust pollution, strong ultraviolet radiation, and steep slopes and dense obstacles in complex terrain. These factors not only cause a sharp increase in the physiological load of workers, making indicators such as heart rate and blood oxygenation prone to abnormalities, but also affect the target recognition performance of AR vision sensors and increase the difficulty of path planning. The system reduces the impact of these interference factors on operational safety and efficiency through multi-sensor environmental perception, image preprocessing, and dynamic path planning based on environmental costs.

[0040] Operating parameters are real-time data on the wearable exoskeleton's own working status, mainly including motor operating parameters, assist level, remaining battery power, and fault status information. These parameters are collected in real time by the wearable exoskeleton's built-in sensors and fed back to the host computer in real time via a wireless communication module, forming a closed-loop feedback.

[0041] In this embodiment, by integrating and acquiring the physiological state data of the inspection user, the work scenario data, and the inspection task information, and by dynamically analyzing and extracting the user's physiological load characteristics and identifying environmental interference factors affecting the work, the operating parameters of the wearable exoskeleton are dynamically adjusted. This allows it to adapt to the dynamic changes in the user's physical state and the on-site environment during the inspection process, improving the rationality and adaptability of the exoskeleton's assistive output. It can effectively reduce the physiological load of the inspection user, avoid the adverse effects of environmental interference on the work, and balance the efficiency of the inspection work with the safety and comfort of the user.

[0042] In one specific embodiment, in S302, the physiological state data includes heart rate, blood oxygen, altitude, exercise intensity, and temperature; the physiological load characteristics include low load, medium load, and high load; dynamic change analysis of the physiological state data is performed to obtain the physiological load characteristics, including: obtaining the user's individualized physiological baseline; determining the user's standard load based on the individualized physiological baseline, and determining the load division threshold according to the standard load and a preset division coefficient, the load division threshold including a first threshold and a second threshold, and the first threshold being less than the second threshold; fusing and quantifying the physiological state data to obtain the user's actual physiological load value; comparing the actual physiological load value with the load division threshold to determine the user's physiological load characteristics; wherein, when the actual physiological load value is less than the first threshold, the physiological load characteristic is determined to be low load; when the actual physiological load value is greater than or equal to the first threshold and less than the second threshold, the physiological load characteristic is determined to be medium load; when the actual physiological load value is greater than or equal to the second threshold, the physiological load characteristic is determined to be high load.

[0043] Heart rate (HR) is a key physiological parameter collected in real time by a heart rate / blood oxygen sensor integrated into a wristband (or can be extended to wearable devices such as rings and watches) in a multimodal physiological sensor system. It is used to reflect the cardiac activity and current physical load status of workers under extreme environmental inspection operations. It is one of the core indicators for assessing physiological load and participates in the comprehensive assessment as the primary weighting parameter in the calculation of physiological load index. It has an independently preset limit safety threshold, and once it is exceeded, it will trigger the highest priority emergency response.

[0044] Blood oxygen saturation (SpO2) is another core physiological parameter collected synchronously by heart rate / blood oxygenation sensors. It measures the oxygen content in the blood of workers, and its value directly reflects the degree of hypoxia in workers in extreme environments such as high altitudes. A decrease in the value indicates an increased risk of physiological load. In the calculation of physiological load, inverse normalization is used, and it is a key component parameter of the comprehensive physiological load assessment model. It also has an independent limit threshold to ensure personnel safety.

[0045] Altitude (H) is collected by an altimeter mounted on the wristband by the environmental sensing unit of the multimodal physiological sensor system. It is an environmental parameter used to reflect the physiological stress of the external working environment on the workers. In extreme high-altitude environments such as plateaus, the higher the altitude, the greater the challenge to human physiological functions. Therefore, altitude is included as an independent parameter in the weighted comprehensive evaluation model of the physiological load index and participates in the quantitative calculation of physiological load.

[0046] Exercise intensity is mainly characterized by motion acceleration (A) collected by IMU motion sensors placed on the exoskeleton of the upper and lower limbs, and gait frequency (F) collected by the wristband pedometer. It is a motion parameter used to reflect the work intensity and muscle fatigue trend of the current inspection operation. Its normalized value is used in the weighted comprehensive evaluation model to calculate the final physiological load index and assist the system in judging the physical condition of the workers.

[0047] Temperature is categorized into two types of parameters in the system for physiological load assessment: one is the worker's body surface temperature (T1), collected by temperature sensors, which is a physiological parameter reflecting the body's core state; the other is the external ambient temperature (T2), collected by thermometers placed on wristbands and exoskeletons, which is an environmental parameter reflecting the physiological stress caused by extreme environments. After normalization, both types of temperature parameters are used together as a comprehensive parameter in the weighted calculation of the physiological load index.

[0048] Individualized physiological baselines are personalized physiological benchmark models established by the system for different workers. Through statistical learning of time-series physiological data collected from the same worker's historical inspection cycles, the system models the worker's resting baseline levels, daily fluctuation ranges, and load response patterns for indicators such as heart rate, blood oxygen, and body temperature. When a worker's physiological indicator deviates from the group's uniform standard for an extended period, the system recalculates the safety threshold based on the individualized baseline. This avoids false alarms or missed alarms due to individual differences, making the assessment results more closely reflect the actual physiological state. The model is continuously updated with each work cycle.

[0049] The standard load is a unified load assessment benchmark established based on group physiological indicators, corresponding to the default exoskeleton assistance strategy under low load conditions. When the physiological load of the operator is in the low load range, the system uses the control parameters corresponding to the standard load to output the standard assistance level and default travel speed, which serves as the basic assistance benchmark for the entire inspection operation process. The assistance level is dynamically adjusted based on load changes.

[0050] The preset division coefficient is a dynamic adjustment factor introduced based on the standard load. It is used to fine-tune the load division threshold according to individualized physiological baselines to adapt to the differences in physical fitness and physiological response characteristics of different workers. Specifically, the preset division coefficient can be any real number between 0.8 and 2. By adaptively scaling the load division threshold based on the standard load, it is possible to more accurately match the actual physical tolerance of workers, thereby optimizing the exoskeleton's assistance strategy while ensuring safety.

[0051] The load classification threshold is the critical value for the system to classify four physiological load levels: low, medium, high, and dangerous. Generally, the first threshold corresponds to the boundary between low and medium load, and the preset classification coefficient is selected from 1 to 1.2. The second threshold corresponds to the boundary between medium and high load, and the value range is from 1.5 to 2, which is not restricted here.

[0052] The first threshold corresponds to the boundary between low and medium workload. When the physiological workload index exceeds this threshold, the system determines that the worker has entered a medium workload state. Generally, it is the first critical point for physiological workload to move from the safe zone to the zone requiring intervention.

[0053] The second threshold corresponds to the dividing line between medium and high load. Exceeding this threshold indicates that the physiological load of the workers has increased further. The system will then activate a greater boost and stricter speed limits, and trigger an early warning. This is the critical point for determining when the physiological load enters a higher risk range.

[0054] The actual physiological workload value refers to the physiological workload index L calculated by the system through a weighted comprehensive evaluation model. It is a quantitative result of the current physiological workload of the workers and is used to quantitatively compare the relationship between the actual physiological workload value and the workload classification threshold.

[0055] The formula for calculating L is: L=α·HR*+β·SpO2*+γ·H*+δ·A*+ε·T* Where HR*, SpO2*, H*, A*, and T* are the values ​​of heart rate, blood oxygen, altitude, exercise intensity, and temperature after preprocessing and normalization, respectively, and α, β, γ, δ, and ε are the corresponding weighting coefficients.

[0056] Low load is the first load level that the system classifies based on the physiological load index L calculated by the system. When it is determined to be a low load state, it indicates that the worker's current physical condition is good and the physiological pressure is within a safe range. The system outputs the standard assistance level and default travel speed instructions to the exoskeleton to maintain the normal work rhythm without making additional adjustments or warnings.

[0057] Medium load is the second level of physiological load. When this level is reached, it indicates that the worker has developed a certain degree of physiological fatigue. The system will dynamically adjust the wearable exoskeleton to increase the assistance level by 10% to 20% and limit the movement speed to 80% of the default speed. This will reduce physical consumption and alleviate fatigue progression while ensuring work efficiency.

[0058] High load is the third level of physiological load. At this level, the physiological load of the workers has reached a high level and the degree of fatigue is significant. The system will further increase the exoskeleton assistance to 30% to 50%, limit the movement speed to 50% of the default speed, and trigger an early warning prompt on the AR host computer to remind the workers to pay attention to their physical condition and control the work rhythm.

[0059] In this embodiment, an appropriate load classification threshold is determined based on the user's individualized physiological baseline. This allows for the fusion of multi-dimensional physiological state data, including heart rate, blood oxygen, altitude, exercise intensity, and temperature, to obtain an accurate actual physiological load value. Consequently, three categories of physiological load characteristics—low, medium, and high—are reliably classified. Since the user's individualized physiological baseline is a unique physiological representation of the user, it can more accurately map the individual's true load state. Compared to a general fixed threshold classification method, this effectively improves the individual adaptability and accuracy of physiological load judgment, providing users with more scientific and personalized health and safety protection.

[0060] Furthermore, to improve the adaptability of the actual physiological load value, this embodiment introduces a dynamic weight adjustment mechanism to fuse and quantify the physiological state data to obtain the user's actual physiological load value. This includes: determining the weight coefficient of each physiological state data according to the current task and the current scenario; and performing weighted fusion of the physiological state data based on the weight coefficients to obtain the actual physiological load value. Among these, the weight coefficients of heart rate and blood oxygen are related to the intensity of the current task and the complexity of the current scenario, the weight coefficient of altitude is related to the altitude of the current scenario, the exercise intensity is related to the type of work in the current task, and the weight coefficient of temperature is related to the ambient temperature of the current scenario.

[0061] Among them, heart rate and blood oxygen directly reflect the cardiac burden and hypoxia of the workers. In the calculation of the physiological load index, the weight of these two parameters will change with the actual task and scenario: the higher the task intensity (such as continuous climbing of steep slopes or frequent handling of tools), or the greater the scenario complexity (such as many terrain obstacles or high environmental risks), the greater their contribution to the overall physiological load. Therefore, the weight coefficients α and β will increase accordingly, and the system will focus more on heart and blood oxygen indicators to assess physiological risks. Conversely, if the task is easy and the scenario is mild, the weight will be appropriately reduced.

[0062] Altitude itself represents the pressure exerted on human physiology by the high-altitude environment. Therefore, the weight γ of the altitude parameter will be dynamically adjusted according to the actual altitude of the current work location: the higher the altitude of the work area, the thinner the oxygen, and the greater the physiological impact, so the weight of the altitude parameter in the comprehensive load calculation is increased; conversely, when performing tasks in low-altitude areas, the physiological impact of altitude is small, so its weight coefficient is adjusted accordingly to make the algorithm more in line with the actual environmental pressure.

[0063] The exercise intensity parameter corresponds to the physical activity of the workers, and its weight δ will change according to the type of inspection work being performed: if the current work is a heavy physical type (such as long-term walking, frequent climbing of stairs, carrying heavy objects), the exercise intensity has a greater impact on the physiological load, so the weight is increased; if the work is mainly light physical work such as fixed-point inspection and equipment observation, the exercise consumption is small, so the weight is reduced accordingly, making the physiological load assessment more in line with the physical consumption characteristics of the current task.

[0064] The temperature parameter integrates body surface temperature and ambient temperature, and its weight ε is dynamically adjusted with the ambient temperature: when the ambient temperature deviates from the comfort range to the extreme (such as extreme low temperature at high altitudes or high temperature in the heat of summer), the impact of temperature changes on the human physiological state increases significantly, so the weight coefficient increases; when the ambient temperature is within the appropriate range, the impact on physiological load is small, and the weight coefficient decreases accordingly, ensuring that temperature factors can be fully incorporated into the load assessment under extreme temperature conditions.

[0065] In summary, this embodiment, by dynamically allocating the weight coefficients of each physiological state data in combination with the current task and the current scenario, can more accurately quantify the real physiological stress under different combinations of environments and tasks, thereby improving the accuracy and adaptability of load assessment.

[0066] In one specific embodiment, in S303, target identification is performed on the current scene data based on the current task to obtain environmental interference factors affecting the user's operation, including: constructing a local three-dimensional occupied grid map centered on the current scene based on the current task; performing target detection on the local three-dimensional occupied grid map to identify environmental interference factors related to the current task, wherein the environmental interference factors include terrain obstacles, spatial limitations and environmental risks.

[0067] The local 3D occupancy grid map serves as the environmental modeling foundation for the system's dynamic path planning. It is generated by fusing 2D target coordinates from YOLO target recognition with SLAM localization results, depth estimation results, and IMU attitude data. The system maps inspection targets, obstacles, and passable areas in the inspection scenario into 3D space, marking spatial occupancy in a grid format. Modeling only the current work area of ​​the personnel provides the environmental basis for subsequent terrain cost calculations and optimal path searches, effectively balancing environmental modeling accuracy and AR terminal computing efficiency.

[0068] Environmental interference factors mainly originate from extreme working environments such as high-altitude and frigid zones, including low light levels, drastic temperature fluctuations, dust pollution, strong ultraviolet radiation, and interference from image fogging and sudden changes in lighting conditions on AR visual recognition. These factors can lead to a sharp increase in the physiological burden on workers, making key physiological indicators such as heart rate and blood oxygenation more prone to abnormalities, thus increasing operational health risks. Furthermore, they can reduce the accuracy of target recognition by AR visual sensors and increase the difficulty of path planning. During the image preprocessing stage, the system reduces these interferences through brightness equalization, Gaussian denoising, and edge enhancement to ensure the stability of target recognition.

[0069] Terrain obstacles are obstacles in the inspection environment that affect passage, including steep slopes, steps, and densely packed obstacles that make passage difficult. They constitute a major component of terrain cost in dynamic path planning. The system uses the YOLO target recognition algorithm to detect the location and density of obstacles, and calculates the terrain cost by combining slope information. The path planning algorithm adjusts the terrain cost weight based on the worker's lower limb fatigue level. When the lower limbs are fatigued and walking is difficult, the system prioritizes avoiding areas with high obstacle density and steep slopes, choosing paths with gentle slopes and low passage difficulty to reduce walking energy consumption and the risk of falls and injuries.

[0070] Spatial constraints refer to the spatial limitations imposed on travel paths by terrain and obstacles within the inspection area. In this system, this is primarily reflected through a local 3D occupied grid map and terrain cost: grids occupied by obstacles are impassable and marked as inaccessible areas, forcing the path search algorithm to plan routes only within accessible grids. Simultaneously, terrain slope and obstacle density increase travel costs. Path planning must select the path with the lowest overall cost while ensuring spatial accessibility. Spatial constraints are the core influencing factor of terrain cost, determining the feasible range of planarable paths.

[0071] Environmental risks are potential threats to the health and safety of workers in extreme environments. These mainly include altitude sickness caused by high altitude, hypothermia or heatstroke caused by extreme temperatures, skin damage caused by strong ultraviolet radiation, and the risk of falls due to complex terrain. This system calculates physiological load by integrating environmental parameters (altitude, ambient temperature) and dynamically adjusts path planning and exoskeleton assistance strategies based on real-time physiological status. When risks increase, the weight of physiological costs is increased, safer paths are planned, and even emergency return is triggered, thereby reducing the threat of environmental risks to workers and ensuring the safety of inspection operations.

[0072] In this embodiment, the scene data is processed in conjunction with the current inspection task. By constructing a local three-dimensional occupancy grid map centered on the current scene and carrying out target detection, various environmental interference factors such as terrain obstacles, spatial limitations, and environmental risks that affect the user's operation can be accurately identified. This can provide accurate environmental basis for the subsequent adaptive adjustment of the wearable exoskeleton and ensure the safe and smooth conduct of the inspection operation.

[0073] In one specific embodiment, in S304, the operating parameters include the inspection path; adjusting the operating parameters of the wearable exoskeleton based on physiological load characteristics and environmental interference factors includes: determining the physiological cost based on the physiological load characteristics and the action type and action part corresponding to the current task; determining the slope and terrain complexity factor, obstacle and space restriction factor, and road surface characteristic factor respectively based on environmental interference factors, and determining the terrain cost based on the slope and terrain complexity factor, obstacle and space restriction factor, and road surface characteristic factor; determining the task cost based on the path distance and time constraint corresponding to the current task; comprehensively evaluating the physiological cost, terrain cost, and task cost to obtain the comprehensive cost, and adjusting the inspection path based on the comprehensive cost.

[0074] The inspection path refers to the walking route planned by the system for operators to travel from their current location to various inspection target points. The optimal safe path is calculated by a dynamic path planning algorithm based on a local 3D occupancy grid map, integrating terrain cost, physiological cost, and task cost. The system dynamically adjusts the path according to the operator's real-time physiological state and environmental changes: when the upper limbs are fatigued, the shortest return path is prioritized; when the lower limbs are fatigued, the "slowest path" with a gentle slope and few obstacles is prioritized, completing all inspection tasks while ensuring safety and achieving a dynamic balance between physical strength and task completion.

[0075] The action type mainly refers to the types of inspection operations that wearable exoskeletons assist workers in completing. These can be divided into upper limb actions (carrying, lifting, repetitive bending to operate equipment, etc.) and lower limb actions (long-distance walking, climbing steep slopes and steps, going up and down stairs, etc.). Different action types have different limb load characteristics: when the upper limbs are under high load, upper limb assistance and shortest path planning are triggered; when the lower limbs are under high load, lower limb assistance and adjustment are triggered and high-difficulty terrain is avoided first. The system determines the action type and fatigue level based on sensor data placed on the corresponding limbs.

[0076] The movement area refers to the limb that currently generates the main physiological load and fatigue, mainly divided into the upper limb and lower limb: the upper limb includes the shoulder joint and arm muscle groups, corresponding to upper limb tasks such as lifting and carrying, and fatigue is mainly monitored through upper limb exoskeleton IMU sensors and heart rate and blood oxygenation data; the lower limb includes the knee joint and leg muscle groups, corresponding to lower limb movements such as walking and climbing, and fatigue is monitored through lower limb exoskeleton IMU, gait frequency, stride length, and other data. The system adjusts the path planning strategy according to the fatigued movement area to meet the safety and load reduction needs of different parts of the body.

[0077] Physiological cost (C2) is a core component of the dynamic path planning comprehensive cost function. It characterizes the current physiological load level, fatigue trend, and safety risk of the workers. Its value is quantified by the physiological load index L output by the physiological load adaptive assessment algorithm. A higher physiological cost indicates a worse physical condition and a higher safety risk. The system will correspondingly increase the weight λ2 of the physiological cost in the comprehensive cost, allowing the path search to prioritize routes that meet safety requirements (shortest return distance or most energy-efficient terrain).

[0078] Slope and terrain complexity are the primary factors in calculating terrain cost (C1), mainly characterizing the steepness and complexity of the terrain in the inspection area: the steeper the slope and the more complex the terrain, the greater the difficulty of passage, the greater the energy consumption of the lower limbs, and the higher the terrain cost, which will be prioritized for avoidance in path planning. When the lower limbs are fatigued, the system will increase the weight of this factor in the terrain cost, guiding the path to choose gentler areas and reducing energy consumption and risk of injury during walking.

[0079] Obstacles and spatial constraints are another component of terrain cost (C1), characterizing the spatial constraints of passable areas: the higher the obstacle density and the narrower the passable space, the greater the difficulty and risk of path planning through that area, and the higher the corresponding terrain cost. The system detects obstacle locations and densities using YOLO object recognition and marks passable areas using a grid map. This factor ensures that path planning selects the route with the fewest obstacles within the feasible space, reducing detours and additional physical exertion.

[0080] The road surface characteristic factor is a supplementary factor to the terrain cost (C1), mainly characterizing the traffic quality of the inspected road surface, such as road surface smoothness, slip risk, and softness. The worse the road surface conditions, the higher the energy consumption during walking, the greater the risk of falling, and the higher the corresponding terrain cost. This factor, together with the slope factor and obstacle factor, comprehensively describes the impact of terrain on traffic cost, making the terrain cost calculation more consistent with the actual complex inspection environment.

[0081] Terrain cost (C1) is a component of the comprehensive path cost function that describes the difficulty of passage. It is calculated by weighting factors of slope and terrain complexity, obstacles and spatial constraints, and road surface characteristics. It is used to quantify the difficulty of passage and physical exertion in different path areas. The higher the terrain cost, the more difficult the path is and the higher the demand on the lower limbs. Path planning will prioritize areas with lower terrain costs. When the lower limbs are fatigued, the system will further increase the weight of terrain cost λ1 to more strictly avoid high-difficulty terrain.

[0082] Path distance refers to the total length of the path from the current location to the target inspection point or safe return point. It is reflected in both task cost and physiological cost. When it is necessary to return to base as soon as possible or shorten the journey, the system will increase the weight of physiological / task cost and prioritize the option with a shorter path distance to reduce the walking consumption of the operators and ensure that they can reach the safe area quickly. When the physiological state is good, the distance constraint will be appropriately relaxed and the comfortable path with lower terrain cost will be prioritized.

[0083] Time constraints are the core factor influencing task cost, referring to the deadline for completing all inspection tasks and the time priority of each inspection point. If the task time is tight and the inspection order is strict, the weight of distance and target priority in the task cost will be increased to ensure timely completion of the task. If the time is flexible, more consideration will be given to physiological and terrain costs, and more effort-saving paths will be chosen to balance task progress and personnel physical strength.

[0084] Task cost (C3) is a component of the comprehensive path cost function, used to characterize the degree to which the path plan satisfies the inspection task objective. It is mainly related to the objective priority, inspection order, task urgency and time constraints: the higher the priority and the more urgent the inspection objective, the lower the corresponding task cost. Path planning will prioritize going to such objectives and balance the task completion requirements and personnel safety under the premise that the physiological state allows.

[0085] The overall cost (denoted by the formula C = λ1·C1 + λ2·C2 + λ3·C3, where λ1 + λ2 + λ3 = 1) is the total cost function used in dynamic path planning to evaluate the merits of candidate paths. Terrain cost reflects the difficulty of passage, physiological cost reflects personnel safety risks, and task cost reflects task completion requirements. By adjusting the weighting coefficients λ1, λ2, and λ3 to accommodate different fatigue locations and physiological load states, the path with the lowest overall cost is ultimately selected as the optimal inspection path, achieving synergistic optimization of safety, efficiency, and task requirements.

[0086] In this embodiment, intelligent adjustment is achieved for the core operating parameter of the inspection path. It can obtain the physiological cost by combining the physiological load characteristics of the inspection personnel, the action type and action parts of the corresponding task. At the same time, based on the interference factors of the inspection environment, three types of factors are extracted to calculate the terrain cost: slope and terrain complexity, obstacle space limitation and road surface characteristics. Then, the task cost is obtained by combining the path distance and time constraints of the current inspection task. Finally, the inspection path is optimized and adjusted through the comprehensive evaluation of the three types of costs. This allows the inspection path to adapt to the physical condition of the inspection personnel, the complex environment on site and the requirements of the inspection task. It effectively improves the adaptability and rationality of wearable exoskeleton-assisted inspection, and takes into account both inspection efficiency and control of the physical load of the inspection personnel.

[0087] On the other hand, the operating parameters also include auxiliary torque and travel speed; adjusting the operating parameters of the wearable exoskeleton also includes: determining the user's standard auxiliary torque and standard travel speed based on the user's individualized physiological baseline; outputting the standard auxiliary torque and standard travel speed when the physiological load characteristics are low; determining the first adjusted auxiliary torque based on the first assist weight coefficient and the standard auxiliary torque when the physiological load characteristics are medium, and determining the first adjusted travel speed based on the first speed weight coefficient and the standard travel speed when the physiological load characteristics are high; determining the second adjusted auxiliary torque based on the second assist weight coefficient and the standard auxiliary torque, and determining the second adjusted travel speed based on the second speed weight coefficient and the standard travel speed when the physiological load characteristics are high; wherein, the first assist weight coefficient is less than the second assist weight coefficient, and both the first assist weight coefficient and the second assist weight coefficient are greater than 1; the first speed weight coefficient is greater than the second speed weight coefficient, and both the first speed weight coefficient and the second speed weight coefficient are less than 1.

[0088] The auxiliary torque is the electrically assisted torque provided by the wearable exoskeleton to the worker's limbs. It is divided into auxiliary torque for the upper limb shoulder joint and auxiliary torque for the lower limb knee joint, depending on the joint. It is used to reduce the load on muscles and joints during work: when the upper limbs are performing high-intensity tasks such as lifting and carrying, the auxiliary torque will share the force on the shoulder joint; when the lower limbs are walking long distances or climbing, the auxiliary torque will support the knee joint, reducing energy consumption and the risk of injury. The magnitude of the auxiliary torque is dynamically adjusted according to the physiological load level; the higher the load level, the greater the auxiliary torque.

[0089] Walking speed refers to the maximum walking speed allowed for workers by the wearable exoskeleton for the lower limbs, which is dynamically limited by the AR host computer system based on the physiological load level. The higher the physiological load, the stricter the system's limit on walking speed, controlling the rate of energy consumption by reducing speed, avoiding rapid accumulation of fatigue, and ensuring work safety; walking at standard speed is only allowed under low load conditions.

[0090] The standard auxiliary torque is the baseline auxiliary torque output by the exoskeleton when the operator's physiological load is determined to be low. It serves as the basic value for exoskeleton assistance throughout the entire inspection operation. At this point, the operator's physical condition is good, and no additional assistance is required. The system's output of the standard auxiliary torque is sufficient to meet the needs of daily inspection operations, maintaining normal work intensity and efficiency.

[0091] The standard walking speed is the default walking speed allowed for workers under low load conditions, serving as the benchmark for all speed limits. When the physiological load is in the low load range, the system does not impose additional speed restrictions, allowing workers to walk normally at the standard walking speed to ensure the efficiency of inspection tasks. Under medium and high load conditions, the system will use the standard walking speed as a benchmark and proportionally reduce the maximum allowable walking speed.

[0092] The first assist weighting coefficient corresponds to the increase in the exoskeleton's assist torque under medium load conditions. The value range of the first assist weighting coefficient is 1.1~1.3, representing the increase based on the standard assist torque. When the load is determined to be medium, the final assist torque = standard assist torque × first assist weighting coefficient. By appropriately increasing the assist, the operator can alleviate initial fatigue and delay further increases in load.

[0093] The first speed weighting coefficient is the percentage of travel speed restricted under medium load conditions, corresponding to 80% of the default speed, representing the percentage retained based on the standard travel speed. When entering a medium load state, the actual maximum allowable travel speed = standard travel speed × first speed weighting coefficient (i.e., 80%). By moderately reducing speed, energy consumption is reduced, and with the help of assistance, a balance between fatigue and efficiency is achieved. In other embodiments, the first speed weighting coefficient can also take other values ​​between 70% and 90%, which are not limited here.

[0094] The second assist weighting coefficient corresponds to the increase in exoskeleton assist torque under high load conditions, with a value range of 1.3 to 1.5, which is higher than the first assist weighting coefficient under medium load conditions. When workers enter a high load state, their physiological fatigue level increases significantly, requiring a greater increase in assist: final assist torque = standard assist torque × second assist weighting coefficient, thereby significantly reducing limb load and avoiding sports injuries.

[0095] The second speed weighting coefficient is a strict limitation on movement speed under high load conditions, corresponding to 50% of the default speed, which is lower than the first speed weighting coefficient under medium load conditions. When the physiological load reaches the high load level, the maximum allowable movement speed = standard movement speed × second speed weighting coefficient (i.e., 50%). By significantly reducing the movement speed, the rate of physical exertion is further controlled. At the same time, with a greater boost, the safety of the workers is ensured. In other embodiments, the first speed weighting coefficient can also take other values ​​between 40% and 60%, which are not limited here.

[0096] In this embodiment, standard operating parameters are determined based on the individualized physiological baseline of the inspection personnel. By combining the physiological load characteristics of personnel at different levels, the auxiliary torque and travel speed of the exoskeleton can be dynamically matched and adjusted through differentiated weighting coefficients. This can not only accurately adapt to the assistance needs of inspection personnel under different physical conditions, avoiding excessive physical exertion, but also realize personalized dynamic adjustment of the exoskeleton's assistance output, effectively improving the comfort and endurance of the wearing inspection process, and better adapting to the requirements of long-term inspection operations.

[0097] Furthermore, the operating parameters also include joint stiffness; adjusting the operating parameters of the wearable exoskeleton also includes: increasing joint stiffness when the physiological load characteristics are medium or high load and the current task is long-distance flat walking; and dynamically adjusting joint stiffness according to the gait cycle when the current task is complex terrain climbing or obstacle crossing.

[0098] Among them, joint stiffness is a mechanical characteristic parameter of the knee joint of the lower limb exoskeleton, describing the degree of stiffness of the joint in resisting deformation. The higher the stiffness, the "harder" the joint and the stronger the support force; the lower the stiffness, the "softer" the joint and the better the flexibility. The knee joint stiffness is dynamically adjusted according to the current walking environment and different stages of the gait cycle: the stiffness is increased when it is necessary to support the body weight, and the stiffness is decreased when flexible swinging is required, so as to adapt to different walking terrains and gait stages and balance support stability and movement flexibility.

[0099] Long-distance walking on flat ground is the most common lower limb movement scenario in high-altitude inspection operations. It refers to workers moving long distances in areas with relatively flat terrain, gentle slopes, and low obstacle density. The key characteristic is that lower limb energy consumption accumulates with walking distance, easily leading to chronic fatigue in the knee joints and leg muscles. The system addresses this scenario by maintaining moderate joint stiffness in the exoskeleton, providing continuous and stable basic assistance, controlling the rate of energy consumption, and delaying fatigue accumulation.

[0100] Climbing or overcoming obstacles in complex terrain refers to walking movements involving steep slopes, steps, or dense obstacles. These movements place higher demands on lower limb knee joint stability and muscle explosive power, and also carry a greater risk of fall injury. To address this scenario, the system increases joint stiffness during the support phase to provide stronger support, and decreases stiffness during the swing phase to increase freedom of movement, assisting workers in completing movements more safely and reducing stress and fatigue on the knee joint.

[0101] The gait cycle refers to the complete movement cycle from the heel striking the ground on one side to the heel striking the ground again during walking. It can generally be divided into a stance phase (the weight-bearing phase) and a swing phase (the leg swinging phase). Each phase has different requirements for lower limb joint support and flexibility. This system automatically adjusts the knee joint stiffness based on the gait phase detected by the IMU sensor: increasing stiffness in the stance phase (weight-bearing phase) to ensure support stability, and decreasing stiffness in the swing phase (leg swing phase) to facilitate flexible leg swing, making the exoskeleton assistance more in line with the natural walking pattern.

[0102] Dynamic adjustment of joint stiffness combines the terrain recognition results (slope, obstacles, road surface type) output by the AR host computer and the gait phase detected by the IMU sensor to change the knee joint stiffness parameters in real time: increase stiffness in the support phase, high-difficulty terrain and weight-bearing phase, and decrease stiffness in the swing phase, flat terrain and leg swing phase, so that the mechanical properties of the exoskeleton can adapt to the current terrain conditions and gait phase in real time, ensuring support stability without restricting movement flexibility, and improving wearing comfort and assist efficiency.

[0103] In this embodiment, when the current task is climbing or overcoming obstacles in complex terrain, the joint stiffness is dynamically adjusted according to the gait cycle: the joint stiffness is increased in the support phase to enhance support stability, and the joint stiffness is decreased in the swing phase to improve the smoothness of passage. The joint stiffness is adjusted in a targeted manner according to the physiological load level of the inspector and the type of inspection task. When the personnel are in a medium to high load state and are carrying out long-distance flat ground walking inspection, the joint stiffness is increased to ensure support stability. When carrying out inspection operations such as climbing or overcoming obstacles in complex terrain, the joint stiffness is dynamically adjusted according to the gait cycle to adapt to the gait requirements. This allows the assistive characteristics of the exoskeleton to better match different inspection operation conditions, effectively improving the adaptability and practicality of wearable exoskeleton-assisted inspection operations.

[0104] It should be noted that the above embodiments are merely preferred embodiments of the present invention. The inspection path, auxiliary torque, travel speed, and joint stiffness are independent operating parameters that can be flexibly combined and adjusted according to the actual application scenario. The specific combination method and parameter value range can be flexibly set according to the actual working conditions and are not limited here. Equivalent substitutions or improvements made by those skilled in the art to the above parameter control logic, sensor layout, or hardware structure without departing from the core concept of the present invention should all be included within the protection scope of the present invention.

[0105] To visually demonstrate the effect of wearable exoskeletons on the human body, please refer to [link / reference]. Figure 4 , Figure 4 This is a schematic diagram illustrating an embodiment of the wearable exoskeleton provided by the present invention worn on a human body. The worker wears AR glasses, which serve as the head-mounted terminal of the AR host computer system. These glasses have a built-in display module and a spatial perception and positioning module, used to acquire first-view inspection scene images, provide AR information display, and complete spatial positioning. The torso and limbs wear an integrated assistive exoskeleton system, divided into an upper limb exoskeleton and a lower limb exoskeleton. The upper limb exoskeleton is positioned on the upper limbs, providing electric assistance to the shoulder joints to reduce the burden of carrying and lifting. Simultaneously, an upper limb IMU (Inertial Measurement Unit) is installed at the corresponding position on the upper limb exoskeleton for data acquisition. The system collects upper limb movement posture and motion data; the lower limb exoskeleton is deployed in the lower limbs, primarily providing electric assistance to the knee joints to reduce knee joint load during walking and climbing. It also houses lower limb IMUs to collect lower limb movement data such as gait frequency, stride length, and knee joint angular velocity, detecting lower limb fatigue. Additionally, workers wear wristbands integrating thermometers and heart rate / blood oxygen sensors, which collect real-time data on heart rate, blood oxygen saturation, body surface temperature, and ambient temperature. Together with the upper and lower limb IMUs and environmental sensing units, this forms a multimodal physiological sensor system, providing raw data support for physiological load assessment. The entire system achieves integrated wearable functionality combining AR visual perception, physiological state monitoring, and exoskeleton-assisted execution.

[0106] Further, please refer to Figure 5 , Figure 5This is a data flow topology diagram of an embodiment of the AR-based wearable exoskeleton inspection control system provided by the present invention. The wireless data interaction logic between the three-level units of the entire control system is as follows: The multimodal physiological sensor system, as the perception layer, uploads the collected heart rate / blood oxygen data, altitude / temperature data, and human electromechanical motion information to the comprehensive statistical analysis module included in the AR host computer system (within the dashed box) via a wireless transmission link; The AR host computer, as the decision-making center, sends control commands such as generator acceleration / deceleration and assist adjustment to the wearable exoskeleton system after fusion calculation and physiological load assessment; At the same time, the wearable exoskeleton, as the actuator, sends back operating information such as equipment fault status and remaining power to the AR host computer, forming a complete closed-loop bidirectional communication of "perception-decision-execution-feedback". This ensures that the system can dynamically adjust the control strategy according to the physiological state of the personnel and the status of the equipment, realizing full-link adaptive collaborative control.

[0107] To facilitate better implementation of the inspection control method based on a wearable exoskeleton provided by this invention, this invention also provides an apparatus based on the aforementioned inspection control method based on a wearable exoskeleton. The meanings of the terms used are the same as in the aforementioned inspection control method based on a wearable exoskeleton, and specific implementation details can be found in the descriptions of the method embodiments.

[0108] Please see Figure 6 , Figure 6 This is a schematic diagram of an embodiment of the inspection control device based on a wearable exoskeleton provided by the present invention. The inspection control device 600 based on the wearable exoskeleton may include: The data acquisition module 601 is used to acquire the user's physiological state data, current scene data, and current task, wherein the user wears a wearable exoskeleton; The physiological load analysis module 602 is used to perform dynamic change analysis on physiological state data to obtain physiological load characteristics; The environmental interference identification module 603 is used to identify targets in the current scene data based on the current task, and to obtain the environmental interference factors that affect the user's operation. The operating parameter adjustment module 604 is used to adjust the operating parameters of the wearable exoskeleton based on physiological load characteristics and environmental interference factors.

[0109] The present invention also provides an electronic device, such as... Figure 7 As shown, Figure 7 This is a schematic diagram of an embodiment of the electronic device provided by the present invention, specifically: The electronic device may include components such as a processor 701 with one or more processing cores, a memory 702 with one or more computer-readable storage media, a power supply 703, and an input unit 704. Those skilled in the art will understand that... Figure 7The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 701 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 702, and by calling data stored in the memory 702, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Optionally, the processor 701 may include one or more processing cores; preferably, the processor 701 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operation of the storage medium, user interface, and application programs, while the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into the processor 701.

[0110] The memory 702 can be used to store software programs and modules. The processor 701 executes various functional applications and data processing by running the software programs and modules stored in the memory 702. The memory 702 may mainly include a program storage area and a data storage area. The program storage area may store application programs required for operating the storage medium and at least one function (such as sound playback function, image playback function, etc.); the data storage area may store data created according to the use of the electronic device. In addition, the memory 702 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 702 may also include a memory controller to provide the processor 701 with access to the memory 702.

[0111] The electronic device also includes a power supply 703 that supplies power to various components. Preferably, the power supply 703 can be logically connected to the processor 701 via a power management storage medium, thereby enabling functions such as charging, discharging, and power consumption management through the power management storage medium. The power supply 703 may also include one or more DC or AC power supplies, recharge storage media, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0112] The electronic device may also include an input unit 704, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0113] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 701 in the electronic device loads the executable files corresponding to the processes of one or more applications into the memory 702 according to the following instructions, and the processor 701 runs the applications stored in the memory 702 to realize various functions, as follows: acquiring the user's physiological state data, current scene data, and current task, wherein the user wears a wearable exoskeleton; performing dynamic change analysis on the physiological state data to obtain physiological load characteristics; performing target recognition on the current scene data based on the current task to obtain environmental interference factors affecting the user's work; and adjusting the operating parameters of the wearable exoskeleton based on the physiological load characteristics and environmental interference factors.

[0114] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0115] Therefore, the present invention provides a computer-readable storage medium storing a computer program thereon, the computer program being loaded by a processor to execute the steps in any of the inspection control methods based on wearable exoskeletons provided by the present invention. For example, the computer program, when loaded by a processor, can execute the following steps: The system acquires the user's physiological state data, current scene data, and current task, with the user wearing a wearable exoskeleton. It performs dynamic change analysis on the physiological state data to obtain physiological load characteristics. Based on the current task, it performs target identification on the current scene data to obtain environmental interference factors affecting the user's work. Based on the physiological load characteristics and environmental interference factors, it adjusts the operating parameters of the wearable exoskeleton.

[0116] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0117] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0118] Since the computer program stored in the computer-readable storage medium can execute the steps in any of the inspection control methods based on wearable exoskeletons provided by the present invention, it can achieve the beneficial effects that any of the inspection control methods based on wearable exoskeletons provided by the present invention can achieve, as detailed in the preceding embodiments, and will not be repeated here.

[0119] The above provides a detailed description of the inspection control method, device, and electronic device based on a wearable exoskeleton provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A patrol control method based on a wearable exoskeleton, characterized in that, include: The system acquires the user's physiological state data, current scene data, and current task, wherein the user is wearing a wearable exoskeleton. Dynamic changes in the physiological state data are analyzed to obtain physiological load characteristics; Based on the current task, target identification is performed on the current scene data to obtain environmental interference factors affecting the user's operation; The operating parameters of the wearable exoskeleton are adjusted based on the physiological load characteristics and the environmental disturbance factors.

2. The inspection control method based on wearable exoskeleton according to claim 1, characterized in that, The physiological data include heart rate, blood oxygen, altitude, exercise intensity, and temperature; The physiological load characteristics include low load, medium load, and high load; the dynamic change analysis of the physiological state data to obtain physiological load characteristics includes: Obtain the user's individualized physiological baseline; The user's standard load is determined based on the individualized physiological baseline, and a load division threshold is determined according to the standard load and a preset division coefficient. The load division threshold includes a first threshold and a second threshold, and the first threshold is less than the second threshold. The physiological state data is fused and quantified to obtain the user's actual physiological load value; The actual physiological load value is compared with the load classification threshold to determine the physiological load characteristics of the user; Specifically, when the actual physiological load value is less than the first threshold, the physiological load characteristic is determined to be low load; when the actual physiological load value is greater than or equal to the first threshold and less than the second threshold, the physiological load characteristic is determined to be medium load; and when the actual physiological load value is greater than or equal to the second threshold, the physiological load characteristic is determined to be high load.

3. The inspection control method based on wearable exoskeleton according to claim 2, characterized in that, The process of fusing and quantifying the physiological state data to obtain the user's actual physiological load value includes: The weighting coefficients for each physiological state data are determined based on the current task and the current scenario, respectively. The physiological state data are weighted and fused based on the weighting coefficients to obtain the actual physiological load value; The weighting coefficients for heart rate and blood oxygen are related to the intensity of the current task and the complexity of the current scene, the weighting coefficient for altitude is related to the altitude of the current scene, the exercise intensity is related to the type of the current task, and the weighting coefficient for temperature is related to the ambient temperature of the current scene.

4. The inspection control method based on wearable exoskeleton according to claim 1, characterized in that, The step of identifying targets in the current scene data based on the current task to obtain environmental interference factors affecting user operations includes: Construct a local 3D occupancy grid map centered on the current scene based on the current task; Target detection is performed on the local three-dimensional occupied grid map to identify environmental interference factors related to the current task, including terrain obstacles, spatial limitations, and environmental risks.

5. The inspection control method based on wearable exoskeleton according to claim 1, characterized in that, The operating parameters include the inspection path; The adjustment of the operating parameters of the wearable exoskeleton based on the physiological load characteristics and the environmental disturbance factors includes: The physiological cost is determined based on the physiological load characteristics and the action type and action site corresponding to the current task; Based on the aforementioned environmental disturbance factors, the slope and terrain complexity factor, the obstacle and space limitation factor, and the road surface characteristic factor are determined respectively, and the terrain cost is determined based on the slope and terrain complexity factor, the obstacle and space limitation factor, and the road surface characteristic factor. The task cost is determined based on the path distance and time constraints corresponding to the current task. The physiological cost, the terrain cost, and the task cost are comprehensively evaluated to obtain a comprehensive cost, and the inspection path is adjusted based on the comprehensive cost.

6. The inspection control method based on wearable exoskeleton according to claim 5, characterized in that, The operating parameters also include auxiliary torque and travel speed; adjusting the operating parameters of the wearable exoskeleton further includes: The user's standard assist torque and standard travel speed are determined based on the user's individualized physiological baseline; When the physiological load characteristic is low load, the standard auxiliary torque and the standard travel speed are output; When the physiological load characteristic is medium load, the first adjustment assist torque is determined based on the first assist weight coefficient and the standard assist torque, and the first adjustment travel speed is determined based on the first speed weight coefficient and the standard travel speed. When the physiological load characteristic is high load, the second adjustment assist torque is determined based on the second assist weight coefficient and the standard assist torque, and the second adjustment travel speed is determined based on the second speed weight coefficient and the standard travel speed; Wherein, the first assist weight coefficient is less than the second assist weight coefficient, and both the first assist weight coefficient and the second assist weight coefficient are greater than 1; the first speed weight coefficient is greater than the second speed weight coefficient, and both the first speed weight coefficient and the second speed weight coefficient are less than 1.

7. The inspection control method based on wearable exoskeleton according to claim 5, characterized in that, The operating parameters also include joint stiffness; adjusting the operating parameters of the wearable exoskeleton further includes: When the physiological load characteristics are medium or high, and the current task is long-distance walking on flat ground, increase the joint stiffness; When the current task is climbing or overcoming obstacles in complex terrain, the joint stiffness is dynamically adjusted according to the gait cycle.

8. An inspection control device based on a wearable exoskeleton, characterized in that, include: The data acquisition module is used to acquire the user's physiological state data, current scene data, and current task, wherein the user is wearing a wearable exoskeleton; The physiological load analysis module is used to perform dynamic change analysis on the physiological state data to obtain physiological load characteristics; An environmental interference identification module is used to identify targets in the current scene data based on the current task, and to obtain environmental interference factors that affect the user's operation. The operating parameter adjustment module is used to adjust the operating parameters of the wearable exoskeleton based on the physiological load characteristics and the environmental interference factors.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the following steps: The system acquires the user's physiological state data, current scene data, and current task, wherein the user is wearing a wearable exoskeleton. Dynamic changes in the physiological state data are analyzed to obtain physiological load characteristics; Based on the current task, target identification is performed on the current scene data to obtain environmental interference factors affecting the user's operation; The operating parameters of the wearable exoskeleton are adjusted based on the physiological load characteristics and the environmental disturbance factors.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which is loaded by a processor to execute the steps in the inspection control method based on a wearable exoskeleton as described in any one of claims 1 to 7.

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