An internet of things data transmission method and system for exoskeleton state monitoring

Abstract

This application provides an IoT data transmission method and system for exoskeleton status monitoring, relating to the field of exoskeleton IoT transmission technology. This application acquires wearer monitoring data and identifies gait intentions through an exoskeleton detection device, simultaneously predicting the relative motion trends and signal obstruction relationships between communication nodes. Then, the control nodes periodically broadcast beacons encapsulating motion vectors and link detection results. Nodes exchange beacons to construct a local network topology graph that fuses edge weights with detection results and link stability parameters. A hierarchical clustering structure is formed through distributed consensus optimization, and a highly stable cluster head is selected. Based on this structure, a multi-hop transmission path is established with the cluster head as a relay, maintaining a path concentration value characterizing transmission quality. When forwarding collaborative control commands, the next hop is probabilistically selected based on this value to complete command distribution, enabling adaptive, highly stable multi-hop transmission of collaborative control commands in exoskeleton IoT.

Description

Technical Field

[0001] This application relates to the field of exoskeleton IoT transmission technology, and in particular to an IoT data transmission method and system for exoskeleton status monitoring. Background Technology

[0002] The IoT data transmission method for exoskeleton status monitoring is a core technology of exoskeleton devices, enabling the collection of wearer monitoring data and the interactive transmission of collaborative control commands. This method forms the basis for the intelligent and collaborative operation of exoskeleton devices and has broad application prospects in fields such as rehabilitation medicine and industrial operations.

[0003] In existing technologies, IoT data transmission methods based on fixed routing are applied to exoskeleton status monitoring scenarios. This method pre-defines fixed data transmission paths for each communication node of the exoskeleton device, and all monitoring data and collaborative control commands are transmitted along the preset paths.

[0004] The gait of exoskeleton wearers causes continuous relative movement of communication nodes on the device, which can easily lead to signal blockage between nodes and dynamic changes in the communication link status. Fixed-route transmission methods cannot perceive and adapt to the movement status of nodes and link changes in real time, easily resulting in delays and packet loss in control command transmission, making it difficult to guarantee data transmission effectiveness. Therefore, existing technologies suffer from insufficient data transmission adaptability. Summary of the Invention

[0005] The purpose of this application is to provide an IoT data transmission method and system for exoskeleton status monitoring, so as to solve the problem of insufficient adaptability of IoT data transmission for exoskeleton status monitoring in the prior art.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides an Internet of Things (IoT) data transmission method for exoskeleton status monitoring, comprising:

[0007] The device acquires the wearer's monitoring data through the detection device on the exoskeleton, identifies gait intentions, and simultaneously predicts the relative movement trends and signal occlusion relationships between each communication node.

[0008] Each communication node is controlled to broadcast a beacon at a set period. The beacon encapsulates a motion vector containing at least one of the gait intention, the relative motion trend, and the signal blockage relationship, as well as the detection results of the adjacent communication link.

[0009] Each communication node constructs a local network topology by exchanging the beacons. The edge weights of the communication links in the local network topology are determined by the detection results and the link stability parameters predicted based on the motion vectors.

[0010] Distributed consensus optimization is performed based on the local network topology graph, so that each communication node converges through information interaction and state iteration to form a hierarchical cluster structure, and a highly stable node in the hierarchical cluster structure is selected as the cluster head node according to the edge weight.

[0011] Based on the hierarchical clustering structure, a multi-hop transmission path is established for the cooperative control command to be issued, and a path concentration value is maintained for each transmission path according to the historical transmission results. The multi-hop transmission path uses the cluster head node as the relay node, and the path concentration value characterizes the transmission quality.

[0012] When forwarding the cooperative control command, a probabilistic next-hop selection is performed on the multi-hop transmission path based on the path concentration value to distribute the cooperative control command to each component communication node.

[0013] Optionally, the communication nodes construct a local network topology by exchanging the beacons, including:

[0014] Each communication node receives beacons broadcast by other surrounding communication nodes and extracts the encapsulated motion vectors and detection results of nearby communication links from the beacons;

[0015] The detection results are analyzed to clarify the basic association information of the communication connection between itself and each surrounding communication node, and the relative position change and signal blockage of the device with each surrounding communication node in the subsequent set period are predicted based on the motion vector to form link stability parameters.

[0016] Each communication node assigns a unique identifier to its communication link with each of its surrounding communication nodes based on the basic association information and link stability parameters, and combines the basic association information and link stability parameters to generate comprehensive association parameters corresponding to each communication link.

[0017] Each communication node takes itself as the central node and surrounding communication nodes as associated nodes, and constructs a local network topology map within its coverage area based on the unique identifier of each communication link and the comprehensive association parameters, where the comprehensive association parameters are the edge weights.

[0018] Optionally, the step of performing distributed consensus optimization based on the local network topology graph, enabling communication nodes to converge through information interaction and state iteration to form a hierarchical cluster structure, and selecting highly stable nodes in the hierarchical cluster structure as cluster head nodes according to the edge weights, includes:

[0019] Each communication node obtains the edge weights of the communication links between itself and all its surrounding associated nodes based on the local network topology graph, and initializes its own node state information.

[0020] Each communication node sends its own node status information to surrounding associated nodes through the communication link, and receives node status information fed back by surrounding associated nodes, thus completing information interaction;

[0021] The node status information of the surrounding associated nodes is compared with its own node status information, and its own node status information is adjusted according to preset rules. The information interaction and state adjustment operations are repeatedly performed until the node status information of each communication node tends to be stable, so as to achieve state iteration convergence.

[0022] Based on the converged node state information, according to the edge weight correlation degree of the communication links between each communication node, all communication nodes are divided into multiple node groups, and each node group forms a hierarchical correlation relationship, forming a hierarchical cluster structure.

[0023] For each node group, the edge weights of the communication links between each node in the node group and other nodes are extracted, and the node with the largest sum of edge weights is selected as the cluster head node of the node group.

[0024] Optionally, after forming the hierarchical clustering structure, the following steps are also included:

[0025] Each communication node continuously monitors changes in motion vectors to determine whether the connectivity of the local network topology has changed;

[0026] If it is determined that the connectivity of the local network topology has changed, the affected communication nodes are identified, and the affected communication nodes automatically trigger the local reconstruction of the existing hierarchical cluster structure; the local reconstruction process is carried out with reference to the latest motion vectors and edge weights of each communication link.

[0027] Optionally, the step of establishing multi-hop transmission paths for the cooperative control commands to be issued based on the hierarchical clustering structure, and maintaining a path concentration value for each transmission path according to historical transmission results, includes:

[0028] Determine the starting node for sending the collaborative control command and all component communication nodes that receive the collaborative control command;

[0029] Based on a hierarchical clustering structure, starting from the starting node and with each component communication node as the end point, the cluster head nodes along the way are selected as instruction forwarding relay nodes to construct multiple transmission links from the starting point to each end point, forming a multi-hop transmission path. Each transmission link is sequentially connected by the starting node, at least one cluster head node and a component communication node.

[0030] Assign a unique path identifier to each multi-hop transmission path, record the forwarding order of each communication node in the multi-hop transmission path, and the historical transmission records of each communication link segment. The historical transmission records include the success status of past instruction forwarding and transmission time information.

[0031] For each multi-hop transmission path, a path concentration value representing the transmission quality is generated based on the corresponding historical transmission records.

[0032] Each time the multi-hop transmission path completes a cooperative control command forwarding operation, the historical transmission record is updated, and the corresponding path concentration value is adjusted according to the updated historical transmission record to achieve dynamic maintenance of the path concentration value.

[0033] Optionally, when forwarding the cooperative control command, performing probabilistic next-hop selection on the multi-hop transmission path based on the path concentration value includes:

[0034] The communication node currently responsible for forwarding the cooperative control command is determined as the current node. The multi-hop transmission path information corresponding to the command to be forwarded is obtained. The multi-hop transmission path information includes at least the path identifier, path concentration value, and information of the next hop node corresponding to the current node in each multi-hop transmission path.

[0035] Extract the path concentration value of each multi-hop transmission path from the acquired path information, and convert each path concentration value into the corresponding selection ratio according to the preset conversion rules, and the sum of the selection ratios of all multi-hop transmission paths is 1.

[0036] Based on the selection ratio, the probability of each next-hop node being selected is determined;

[0037] Based on the selected probability, the next hop node of the current node is selected, and the cooperative control command is sent to the next hop node;

[0038] The next-hop node that receives the instruction is taken as the new current node, and the next-hop node selection operation is repeated until the collaborative control instruction is successfully distributed to each component communication node.

[0039] Optionally, after constructing the local network topology graph, the following steps are also included:

[0040] Each communication node compares its own constructed local network topology map with the local network topology maps of its surrounding communication nodes one by one.

[0041] By comparing and identifying communication link information that is not covered in the local network topology map, the missing information is then filled in.

[0042] Secondly, this application provides an Internet of Things (IoT) data transmission system for exoskeleton status monitoring, comprising:

[0043] The acquisition module is used to acquire the wearer's monitoring data through the detection device on the exoskeleton device, identify gait intentions, and simultaneously predict the relative motion trends and signal occlusion relationships between each communication node;

[0044] The broadcast module is used to control each communication node to broadcast a beacon at a set period. The beacon encapsulates a motion vector containing at least one of the gait intention, the relative motion trend, and the signal blockage relationship, as well as the detection results of the adjacent communication link.

[0045] A construction module is used for each communication node to construct a local network topology graph by exchanging the beacons. The edge weights of the communication links in the local network topology graph are jointly determined by the detection results and the link stability parameters predicted based on the motion vectors.

[0046] The forming module is used to perform distributed consensus optimization based on the local network topology graph, so that each communication node converges to form a hierarchical cluster structure through information interaction and state iteration, and selects a highly stable node in the hierarchical cluster structure as the cluster head node according to the edge weight.

[0047] A module is established to create multi-hop transmission paths for the cooperative control commands to be issued based on the hierarchical clustering structure, and to maintain a path concentration value for each transmission path according to historical transmission results. The multi-hop transmission path uses the cluster head node as the relay node, and the path concentration value characterizes the transmission quality.

[0048] The selection module is used to perform probabilistic next-hop selection on the multi-hop transmission path based on the path concentration value when forwarding the cooperative control command, so as to distribute the cooperative control command to each component communication node.

[0049] Thirdly, this application provides an electronic device, comprising:

[0050] Memory, used to store computer programs;

[0051] A processor, configured to execute the computer program to implement the steps of the Internet of Things data transmission method for exoskeleton status monitoring as described in the first aspect above.

[0052] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the Internet of Things data transmission method for exoskeleton status monitoring as described in the first aspect above.

[0053] The IoT data transmission method for exoskeleton status monitoring provided in this application acquires wearer monitoring data and identifies gait intentions, while simultaneously predicting the movement and signal obstruction relationships between communication nodes. This allows for precise perception of wearer status and changes in the node communication environment, providing data and predictive basis for subsequent communication control. Control nodes periodically broadcast beacons encapsulating relevant motion vectors and link detection results, enabling efficient synchronization of key information between nodes and laying a unified information foundation for topology construction. Nodes exchange beacons to construct a local network topology graph and integrate multi-dimensional parameters to determine edge weights, allowing the topology structure to conform to the actual communication state and ensuring that edge weights accurately reflect link stability. Distributed consensus optimization forms a hierarchical clustering structure, and highly stable cluster head nodes are selected, enabling reasonable clustering and efficient management of network nodes and establishing a reliable communication relay core. Based on the clustering structure, multi-hop transmission paths with cluster heads as relays are established, and path concentration values ​​are maintained. This allows for planning transmission paths adapted to the network structure, achieving quantification and dynamic tracking of path transmission quality. Probabilistic selection of the next hop for forwarding collaborative control commands based on path concentration values ​​enables adaptive command forwarding, improving the reliability and efficiency of command transmission.

[0054] Furthermore, the specific process of communication nodes exchanging beacons to construct a local network topology map is defined. Each communication node first receives beacons broadcast by surrounding nodes and extracts motion vectors and neighboring communication link detection results. Then, it analyzes the detection results to clarify the basic communication association information between nodes. Simultaneously, based on the motion vectors, it predicts subsequent relative position changes and signal obstruction between nodes and forms link stability parameters. Subsequently, a unique identifier is assigned to each communication link. The basic association information and link stability parameters are combined to generate comprehensive association parameters as edge weights. Finally, each node constructs a local network topology map within its coverage area, with itself as the center and surrounding nodes as associated nodes, based on the unique link identifiers and comprehensive association parameters. This step refines the specific construction process of the local network topology map, making the topology construction operation more standardized and logically rigorous. It ensures that the extracted information and generated parameters accurately reflect the actual communication associations between nodes and the true stability of the links, guaranteeing the rationality and scientific nature of the edge weight settings. This provides accurate and reliable topology data support for subsequent operations such as network optimization, cluster construction, and path planning based on the topology map. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1A flowchart illustrating an IoT data transmission method for exoskeleton status monitoring provided in this application embodiment;

[0057] Figure 2 A flowchart illustrating another IoT data transmission method for exoskeleton status monitoring provided in this application embodiment;

[0058] Figure 3 This is a schematic diagram of the structure of an Internet of Things (IoT) data transmission system for exoskeleton status monitoring, provided as an embodiment of this application. Detailed Implementation

[0059] IoT data transmission for exoskeleton status monitoring is a core component of the intelligent operation of exoskeleton devices. It relies on various communication nodes to collect wearer monitoring data and exchange collaborative control commands, placing stringent requirements on the real-time performance and stability of data transmission. Existing technologies employ fixed-route transmission methods, pre-setting fixed transmission paths for each communication node. However, the gait of the exoskeleton wearer causes continuous relative movement between communication nodes, easily leading to signal obstruction and dynamic changes in communication link status. Fixed routes cannot detect and adapt to these dynamic changes in real time, resulting in delays and packet loss in control command transmission, making it difficult to guarantee data transmission quality. Therefore, a transmission method that can adapt to the dynamic states of communication nodes is urgently needed.

[0060] To address the aforementioned issues, this application proposes an IoT data transmission method for exoskeleton status monitoring. The core of this method lies in adaptive communication control throughout the entire process, based on the wearer's gait status and the dynamic changes in communication nodes. Specifically, firstly, gait intentions are identified by combining wearer monitoring data, while simultaneously predicting the relative movement trends and signal obstruction relationships between communication nodes. Then, key information synchronization between nodes is achieved through beacon broadcasting, constructing a local network topology that closely reflects the actual communication state. Subsequently, a hierarchical cluster structure is formed, and highly stable nodes are selected as communication relays. Multi-hop transmission paths are planned, and path transmission quality is quantified. Finally, the next-hop node for command forwarding is adaptively selected based on quality indicators.

[0061] This method abandons the traditional fixed routing mode. From node state prediction and network topology construction to path planning and command forwarding, it adapts to the dynamic movement of communication nodes and changes in link state throughout the entire process. This allows the transmission of control commands to be adjusted in real time according to the node state, effectively avoiding problems such as transmission delay and packet loss. It fundamentally solves the technical defects of insufficient data transmission adaptability in existing technologies and significantly improves the stability and real-time performance of exoskeleton IoT data transmission.

[0062] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0063] The core of this application is to provide an IoT data transmission method for exoskeleton status monitoring, and a flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes:

[0064] S101. The device on the exoskeleton acquires the wearer's monitoring data, identifies gait intentions, and simultaneously predicts the relative movement trends and signal obstruction relationships between communication nodes.

[0065] The detection device, typically integrated into the exoskeleton device, collects data related to the exoskeleton wearer. This monitoring data includes the wearer's physiological and kinematic parameters, forming the basis for determining the wearer's condition. Gait intent is the wearer's walking state derived from analyzing the monitoring data. The communication node is the terminal unit on the exoskeleton device responsible for receiving and transmitting data.

[0066] In one specific implementation, the detection device on the exoskeleton device collects various monitoring data of the wearer in real time and transmits the collected data to the device's processing unit. The processing unit analyzes and processes the received data, and identifies the wearer's current gait intention by combining preset judgment rules. For example, it determines whether the wearer is walking or turning by analyzing data such as the amplitude and frequency of limb swings. At the same time, based on the identified gait intention and combined with the current installation position information of each communication node, it predicts the relative movement direction, distance and other movement trends between each communication node in the future, as well as whether there will be signal transmission obstruction between nodes due to limb obstruction, position changes, etc., i.e., signal obstruction relationship.

[0067] S102. Control each communication node to broadcast beacons according to a set period.

[0068] Among them, the beacon is a carrier used by communication nodes to exchange information. It encapsulates a motion vector containing at least one of the following: gait intention, relative motion trend, and signal obstruction relationship, as well as the detection results of the adjacent communication link.

[0069] Among them, motion vectors are quantitative representations of gait intention, relative motion trends, or signal occlusion relationships, used to reflect the core characteristics of the relevant state. Proximity communication links refer to channels around a single communication node where communication connections can be established. The detection results are information obtained by a communication node after detecting the connection status and transmission quality of its surrounding proximity communication links.

[0070] In one specific implementation, a fixed broadcast period is preset for each communication node, and the device control unit uniformly schedules each communication node to broadcast beacons according to this period. The beacon will pre-encapsulate two types of key information: one is a motion vector, which can be obtained by quantizing one or more of gait intention, relative motion trend, and signal obstruction relationship; the other is the detection results obtained by each communication node after pre-detecting the nearby communication links around itself.

[0071] This periodic broadcasting allows each communication node to synchronize its own key information with surrounding nodes, laying the foundation for subsequent information exchange between nodes and network construction.

[0072] S103. Each communication node constructs a local network topology by exchanging beacons.

[0073] Among them, the local network topology diagram is a schematic diagram of the communication connection relationship between a communication node and its surrounding nodes with itself as the center. It is used to clearly present the association status between nodes. Its edge weight is a quantitative indicator that characterizes the reliability of the communication link and is used to distinguish the transmission guarantee capability of different links. The detection results are determined together with the link stability parameters based on dynamic vector prediction.

[0074] like Figure 2 As shown, S103 specifically includes:

[0075] S1031. Each communication node receives beacons broadcast by other surrounding communication nodes and extracts the encapsulated motion vectors and detection results of nearby communication links from the beacons.

[0076] S1032. Analyze the detection results to clarify the basic correlation information of the communication connection between itself and each surrounding communication node, and predict the relative position change and signal blockage of each surrounding communication node within the subsequent set period based on the motion vector, so as to form link stability parameters.

[0077] Among them, basic association information is fundamental data reflecting the current communication connection status between a communication node and its surrounding nodes, including whether it is connected and the current transmission rate, which is used to clarify the basic communication relationship between nodes. The core of generating link stability parameters is to combine two key dimensions, the smoothness of relative position change and the probability of signal blockage, and obtain them through quantitative evaluation. The value range is usually 0-1, and the closer the value is to 1, the stronger the subsequent stability of the link.

[0078] S1033. Each communication node assigns a unique identifier to the communication link between itself and each of its surrounding communication nodes based on the basic association information and link stability parameters, and combines the basic association information and link stability parameters to generate comprehensive association parameters corresponding to each communication link.

[0079] The unique identifier is a unique tag assigned to each communication link to distinguish communication channels between different nodes and avoid link confusion. The comprehensive correlation parameter is a composite indicator that integrates basic correlation information and link stability parameters to comprehensively assess the current state and future stability of the communication link.

[0080] S1034. Each communication node, with itself as the central node and surrounding communication nodes as associated nodes, constructs a local network topology map within its coverage area based on the unique identifier of each communication link and comprehensive associated parameters.

[0081] Among them, the comprehensive correlation parameter is the edge weight.

[0082] In this embodiment, each communication node sequentially completes the processes of beacon reception and information extraction, detection result analysis and stability prediction, link identifier allocation and comprehensive parameter generation, and topology map construction, ultimately constructing a local network topology map that fits the dynamic state of the communication nodes of the exoskeleton device.

[0083] As an example, in step S1031, each communication node on the exoskeleton device first receives beacons broadcast by other surrounding nodes and extracts the encapsulated motion vector and the detection results of the adjacent communication link from the beacons. For example, the communication node at the thigh receives beacons broadcast by the calf node and the waist node every 500ms. The motion vector of the calf node is extracted from the beacons as "swinging 15° upward relative to the thigh node within the next 1 second", and the extracted detection results are "currently communicating with the calf node, transmission rate 2Mbps; communicating with the waist node, transmission rate 1.8Mbps".

[0084] Secondly, the extracted detection results are analyzed in step S1032 to clarify the basic association information between the device and each surrounding node. Then, based on the motion vector, the relative position change and signal occlusion situation within the subsequent set period are predicted to form link stability parameters. Specifically, when generating link stability parameters, a pre-defined classification standard reference table is used to quantify and score the smoothness of relative position change and the probability of signal occlusion. The score range for each dimension is 0-1, and the weight is set to 0.5. This weight can be adjusted according to the exoskeleton usage scenario. The final parameter value is obtained by weighted summation. The classification standard reference table is shown in Table 1 below:

[0085]

[0086] Table 1 is only applicable to this example and can be modified in actual applications. This application does not impose any restrictions on it.

[0087] For example, after parsing the detection results of the thigh node, the basic association information is clearly defined as "connected to the calf node, transmission rate 2Mbps, connected to the waist node, transmission rate 1.8Mbps". Based on the motion vector of the calf node "swinging upward 15° within the next 1 second", referring to the table, the relative position change amplitude is ≤20°, with a score of 1.0 relative to Table 1. At the same time, it is predicted that there will be no limb obstruction during this movement, with a signal obstruction probability score of 0.8. The link stability parameter is calculated by weighted summation as 1.0×0.5+0.8×0.5=0.9. For the waist node, due to the wearer's slight body twisting, the relative position change amplitude is predicted to be ≤20° within the next 1 second, with a relative position change smoothness score of 1.0. However, there will be brief obstruction, with a signal obstruction probability score of 0.4. The calculated link stability parameter is 1.0×0.5+0.4×0.5=0.7.

[0088] Then, in step S1033, a unique identifier is assigned to each communication link, and the basic association information and link stability parameters are combined to generate comprehensive association parameters. Here, a well-known weighted summation algorithm is used for the combination process, setting the weight of the quantized value of the basic association information to 0.4 and the weight of the link stability parameters to 0.6. These two weights can be adjusted according to the actual scenario. First, the transmission rate in the basic association information is quantized, using the device's maximum transmission rate of 2Mbps as a benchmark. A transmission rate of 2Mbps is quantized as 1, and 1.8Mbps is quantized as 0.9. Then, the comprehensive association parameters are calculated.

[0089] The comprehensive correlation parameters of the thigh-to-calf link are calculated to be 1×0.4+0.9×0.6=0.94;

[0090] The comprehensive correlation parameters of the thigh-to-waist link are calculated to be 0.9×0.4+0.7×0.6=0.78.

[0091] At the same time, a unique identifier L1 is assigned to the link from thigh to calf, and a unique identifier L2 is assigned to the link from thigh to waist, in order to distinguish the two links.

[0092] Finally, in step S1034, each communication node constructs a local network topology based on itself as the central node and surrounding nodes as associated nodes, according to the unique link identifier and comprehensive association parameters (i.e., edge weights). For example, the thigh node, centered on itself, connects to the calf node through identifier L1 (edge ​​weight 0.94) and to the waist node through identifier L2 (edge ​​weight 0.78), forming a clear local network topology that intuitively presents the communication association between itself and surrounding nodes and the reliability of each link.

[0093] This application accurately constructs a local network topology map that fits the dynamic movement state of the exoskeleton communication nodes, clearly distinguishes the reliability of each communication link, and provides a realistic foundation for subsequent network clustering and path planning, ensuring that data transmission adapts to the dynamic changes of nodes.

[0094] Optionally, following S103, the following may also be included:

[0095] Each communication node compares its own constructed local network topology map with the local network topology maps of surrounding communication nodes one by one; through comparison, it identifies communication link information not covered in its own local network topology map and completes it.

[0096] In one specific implementation, after each communication node completes the construction of its own local network topology map, it actively initiates a topology map interaction request with surrounding communication nodes, sequentially receives local network topology map data sent by surrounding nodes, and then performs a precise comparison of its own topology map with each received topology map link by link and node by node, identifies communication link information not covered in its own topology map and completes the information, forming a more complete local topology map.

[0097] As an example, continuing the application scenario of lower limb rehabilitation exoskeletons, the thigh communication node has constructed its own local topology map, containing two links: thigh to calf and thigh to waist. The calf communication node's local topology map contains two links: calf to thigh and calf to ankle. The waist communication node's local topology map contains two links: waist to thigh and waist to back. The thigh node first initiates a topology map interaction request to the calf node, receives the calf node's topology map, compares it, and finds that its own topology map lacks the link information from calf to ankle. Subsequently, the thigh node completes topology map interaction with the waist node, and after comparison, identifies the waist to back link information not covered in its own topology map. The thigh node supplements its own local topology map with these two missing link information. The completed topology map not only includes the links directly related to itself, but also covers the key links between surrounding nodes.

[0098] This step effectively eliminates the detection blind spots in the local topology map of a single node, allowing the topology map to more comprehensively reflect the actual link relationships between various communication nodes of the exoskeleton device, and further improves the rationality of subsequent network clustering and the accuracy of transmission path planning.

[0099] S104. Perform distributed consensus optimization based on the local network topology graph, so that each communication node converges through information interaction and state iteration to form a hierarchical cluster structure, and select the highly stable node in the hierarchical cluster structure as the cluster head node according to the edge weight.

[0100] Distributed consensus optimization is an optimization method in which multiple communication nodes gradually adjust their states through bidirectional information interaction to achieve a consistent understanding. This is used to achieve collaborative adaptation among nodes in dynamic movement scenarios of exoskeleton devices. Hierarchical clustering structure divides all communication nodes into multiple node groups, with clear hierarchical relationships between these groups. This simplifies node management and improves data transmission efficiency. The cluster head node is the core node in each node group responsible for data relay forwarding and state coordination, reducing transmission redundancy and ensuring link stability.

[0101] S104 specifically includes:

[0102] S1041. Each communication node obtains the edge weights of the communication links between itself and all surrounding associated nodes based on the local network topology graph, and initializes its own node state information.

[0103] Among them, node status information is the core data reflecting the communication capabilities and connection status of a communication node, including the sum of edge weights of its associated links, the current load of the node, and other information, which is used for state comparison and collaborative adjustment between nodes.

[0104] S1042. Each communication node sends its own node status information to surrounding associated nodes through the communication link, and receives node status information fed back by surrounding associated nodes, thus completing information exchange.

[0105] S1043. Compare the received node status information of surrounding associated nodes with its own node status information, adjust its own node status information according to preset rules, and repeatedly perform information interaction and state adjustment operations until the node status information of each communication node tends to stabilize, so as to achieve state iteration convergence.

[0106] Among them, the preset rules are state adjustment criteria formulated based on the collaborative needs of the exoskeleton network. They are used to standardize the optimization direction of node state information and ensure that the state of each node eventually reaches a consistent and stable state.

[0107] S1044. Based on the converged node state information, according to the edge weight correlation degree of the communication links between each communication node, all communication nodes are divided into multiple node groups, and each node group forms a hierarchical correlation relationship, constituting a hierarchical cluster structure.

[0108] S1045. For each node group, extract the edge weights of the communication links between each node in the node group and other nodes, and select the node with the largest sum of edge weights as the cluster head node of the node group.

[0109] In this embodiment, each communication node first completes state initialization and information interaction, then achieves state convergence through repeated iterations, and finally constructs a hierarchical clustering network structure adapted to exoskeleton scenarios by clustering based on the converged state and selecting cluster head nodes.

[0110] As an example, continuing with the lower limb rehabilitation exoskeleton scenario, this scenario includes five communication nodes: thigh, calf, ankle, waist, and back. After completion, the topology graph covers the links and edge weights between each node, with the edge weight from thigh to calf being 0.94, the edge weight from thigh to waist being 0.78, the edge weight from calf to ankle being 0.92, and the edge weight from waist to back being 0.85.

[0111] First, through step S1041, each communication node extracts the edge weights of all associated links from its own local topology graph and initializes the node state information. The node state value is set to the average value of the associated link edge weights, and the default load value is 0.5. The state information consists of the state value and the load.

[0112] For example, if the connection link between the thigh node is thigh to calf (edge ​​weight 0.94) and thigh to waist (edge ​​weight 0.78), then the state value is... The initial state information is (0.86, 0.5); the calf node's associated links are calf to thigh (edge ​​weight 0.94) and calf to ankle (edge ​​weight 0.92), then the state value is... The initial state information is (0.93, 0.5); the same applies to the waist node, ankle node, and back node, which are initialized with state information of (0.815, 0.5), (0.92, 0.5), and (0.85, 0.5) respectively.

[0113] Secondly, through step S1042, each node sends its own status information to surrounding associated nodes via the communication link and receives feedback information. For example, the thigh node sends its own status information (0.86, 0.5) to the calf node and the waist node, while simultaneously receiving feedback from the calf node (0.93, 0.5) and the waist node (0.815, 0.5); other nodes synchronously complete information exchange, such as the calf node receiving status information from the thigh node and the ankle node, and the waist node receiving status information from the thigh node and the back node.

[0114] Then, in step S1043, each node compares the received status information of surrounding nodes with its own status information and adjusts its own status value according to a preset rule. The preset rule adopts a weighted average algorithm, and the adjustment formula is as follows:

[0115] (1)

[0116] in, As its own weight, it is set to 0.6 in this example. This represents the weight of the surrounding nodes; in this example, it is 0.4.

[0117] For example, the thigh node first calculates the average state value of its surrounding nodes. Substituting into equation (1), we get: new state value = 0.6 × 0.86 + 0.4 × 0.8725 = 0.865, and update the state information to (0.865, 0.5). Then each node performs information interaction and state adjustment again until the fluctuation range of the state values ​​of all nodes is ≤ 0.01, that is, it tends to be stable. The final converged state information is as follows: thigh (0.87, 0.5), calf (0.92, 0.5), ankle (0.92, 0.5), waist (0.83, 0.5), back (0.84, 0.5).

[0118] Next, in step S1044, based on the converged state information, the links are clustered according to the degree of association of the edge weights. Edge weights ≥ 0.8 are defined as strong associations and are grouped into the same group; edge weights < 0.8 are defined as weak associations and are divided into different levels.

[0119] For example, the link weight between the calf node and the ankle node is 0.92, which is greater than 0.8, indicating a strong association, and it is classified into the first group; the link weight between the thigh node and the calf node is 0.94, which is greater than 0.8, indicating a strong association, and the link weight between the thigh node and the waist node is 0.78, which is less than 0.8, indicating a weak association, and the thigh node is classified into the second group, which is at a higher level than the first group; the link weight between the waist node and the back node is 0.85, which is greater than 0.8, indicating a strong association, and it is classified into the third group, which is at the same level as the second group and is affected by the second group, ultimately forming a hierarchical clustering structure of "second group (thigh) - first group (calf, ankle), second group (thigh) - third group (waist, back)".

[0120] Finally, in step S1045, the sum of edge weights of all nodes in each group is calculated, and the node with the maximum value is selected as the cluster head. For example, in the first group, the sum of edge weights of the calf nodes is 0.94 (with the thigh) + 0.92 (with the ankle) = 1.86, and the sum of edge weights of the ankle nodes is 0.92 (with the calf) = 0.92, so the calf nodes are selected as the cluster head of the first group; the second group only has thigh nodes, so it is directly selected as the cluster head; in the third group, the sum of edge weights of the waist nodes is 0.78 (with the thigh) + 0.85 (with the back) = 1.63, and the sum of edge weights of the back nodes is 0.85 (with the waist) = 0.85, so the waist nodes are selected as the cluster head of the third group.

[0121] This application constructs a hierarchical clustering structure that adapts to the dynamic state of exoskeleton communication nodes, selects highly stable cluster head nodes, simplifies the node management process, improves network collaboration efficiency, and provides orderly network support for the reliable transmission of subsequent collaborative control commands.

[0122] Optionally, after the hierarchical clustering structure is formed in S104, the following steps are also included: each communication node continuously monitors the changes in motion vectors to determine whether the connectivity of the local network topology has changed; if the connectivity of the local network topology has changed, the affected communication nodes are identified, and the affected communication nodes automatically trigger the local reconstruction of the formed hierarchical clustering structure; the local reconstruction process is carried out with reference to the latest motion vectors and the edge weights of each communication link.

[0123] In one specific implementation, after completing the construction of the hierarchical cluster structure, each communication node continuously collects and monitors the motion vector change data of itself and surrounding nodes. Based on the magnitude and trend of the motion vector changes, the connectivity status of each communication link in the local network topology is evaluated in real time to determine whether the overall connectivity of the topology has changed, such as link disconnection, new link establishment, or significant fluctuations in edge weights. If it is determined that the connectivity has changed, the affected communication nodes involved in the topology change are accurately identified through rapid information exchange between nodes. These affected nodes automatically initiate local reconstruction commands without triggering the participation of all nodes in the network. The reconstruction process strictly refers to the latest motion vectors and the real-time edge weights of each communication link to adjust the node groups within the affected range, while preserving the cluster structure of the unaffected areas.

[0124] As an example, continuing the application scenario of lower limb rehabilitation exoskeletons, the wearer was originally in a normal walking state, forming a hierarchical cluster structure of "second group (thigh) - first group (lower leg, ankle), second group (thigh) - third group (waist, back)". When the wearer suddenly switched to a rapid turning gait, the limb occlusion between the waist node and the back node intensified. Based on the latest motion vector prediction, the signal occlusion probability between the two increased significantly, and the link edge weight dropped from 0.85 to 0.3. The connectivity of this link in the local network topology graph was determined to be weak, and the topology connectivity changed. At this time, the system identified the waist node and the back node as the affected communication nodes, and these two nodes automatically triggered the local reconstruction process. During reconstruction, referring to the latest motion vector, the waist node rotated 30° relative to the back node within 1 second and the real-time edge weight was 0.3. The back node was temporarily adjusted to the thigh group, and the sum of the edge weights of each node in the thigh group was recalculated to confirm that the thigh node was still the cluster head of the group. The unaffected lower leg and ankle group cluster structure remained unchanged. The above example is only one example of this application. In practical applications, the connectivity determination threshold and reconstruction triggering conditions can be adjusted according to the needs. This application does not limit this.

[0125] This step enables dynamic local adaptation of the hierarchical cluster structure, avoiding resource consumption and transmission interruptions caused by global reconstruction. It ensures that the network structure of the exoskeleton device remains stable and efficient when the wearer's gait changes, providing a guarantee for the continuous and reliable transmission of collaborative control commands.

[0126] S105. Based on the hierarchical clustering structure, establish multi-hop transmission paths for the cooperative control commands to be issued, and maintain path concentration values ​​for each transmission path according to historical transmission results.

[0127] S105 specifically includes:

[0128] S1051. Determine the starting node for sending the cooperative control command and all component communication nodes that receive the cooperative control command.

[0129] The coordinated control commands are used to regulate the coordinated movements of various components of the exoskeleton device, including control information such as joint extension and contraction, and force adjustment. These are the core commands for the normal operation of the exoskeleton device. The starting node is the source node for sending coordinated control commands, typically the main control node of the exoskeleton device. Component communication nodes are the communication nodes on each functional component of the exoskeleton, used to receive and execute coordinated control commands.

[0130] S1052. Based on a hierarchical clustering structure, starting from the starting node and ending at each component communication node, the cluster head nodes along the route are selected as instruction forwarding relay nodes to construct multiple transmission links from the starting point to each ending point, forming a multi-hop transmission path. Each transmission link is connected sequentially by the starting node, at least one cluster head node, and component communication nodes.

[0131] Among them, the multi-hop transmission path is a transmission channel in which the instruction starts from the starting node, is relayed through at least one cluster head node, and finally reaches the component communication node. It is used to adapt to the scenario of multi-node distributed layout of exoskeleton devices.

[0132] S1053. Assign a unique path identifier to each multi-hop transmission path, record the forwarding order of each communication node in the multi-hop transmission path, and the historical transmission records of each communication link segment.

[0133] The unique path identifier is a unique marker assigned to each multi-hop transmission path to distinguish different command transmission channels and avoid path confusion. Historical transmission records, containing information on past command forwarding successes and transmission times, are core data recording the past command forwarding performance of each path, used to evaluate path transmission quality and generate path density values.

[0134] S1054. For each multi-hop transmission path, generate a path concentration value that characterizes the transmission quality based on the corresponding historical transmission records.

[0135] Among them, the path concentration value is a transmission quality indicator quantified based on historical transmission results. The closer the value is to 1, the better the transmission quality, which is used to guide the selection of instruction forwarding paths.

[0136] S1055. Whenever a multi-hop transmission path completes a cooperative control command forwarding operation, the historical transmission record is updated, and the corresponding path concentration value is adjusted according to the updated historical transmission record to achieve dynamic maintenance of the path concentration value.

[0137] In this embodiment, the process of first determining the command transmission and receiving nodes, then constructing multi-hop transmission paths based on the clustering structure, assigning identifiers to the paths and recording historical transmission information, and dynamically maintaining the path concentration value after generation, realizes the command transmission path planning and quality control adapted to the exoskeleton clustering network.

[0138] As an example, continuing with the lower limb rehabilitation exoskeleton scenario, the hierarchical clustering structure in this scenario includes: the second group cluster head is the thigh node, the first group cluster head is the calf node and its members are ankle nodes, and the third group cluster head is the waist node and its members are back nodes. In this case, the starting node is set to the thigh node, the collaborative control command is "adjust the extension and contraction force of the calf joint," and the corresponding component communication nodes are the calf node and the ankle node.

[0139] First, through step 1051, it is determined that the starting node of the collaborative control command is the thigh node, that is, the main control node is the thigh node, and the component communication nodes that receive the command are the lower leg joint and the ankle node, which are used to directly execute joint adjustment and coordination feedback action states, respectively.

[0140] Secondly, through step 1052, cluster head nodes are selected as relays based on the clustering structure to construct multi-hop transmission paths. For example, for the calf node, since it is the cluster head of the first group, two paths can be constructed: path 1 is from the thigh node to the calf node, 1 hop, passing through its own cluster head; path 2 is from the thigh node to the waist node to the calf node, 2 hops, passing through cross-group cluster heads. For the ankle node, it needs to pass through the calf node of the first group cluster head, and two paths are constructed: path 3 is from the thigh node to the calf node to the ankle node, 2 hops; path 4 is from the thigh node to the waist node to the calf node and then to the ankle node, 3 hops. These four paths together form a set of multi-hop transmission paths.

[0141] Then, in step 1053, a unique path identifier is assigned to each path, and the forwarding order and historical transmission records are recorded. For example, path 1 is assigned identifier P1, the forwarding order is from thigh to calf, the initial historical transmission record shows 3 successful data transmissions in the past 3 transmissions, and an average transmission time of 20ms; path 2 is identified by identifier P2, the forwarding order is from thigh to waist to calf, the initial historical record shows 2 successful data transmissions in the past 3 transmissions, 1 failure, and an average transmission time of 35ms; path 3 is identified by identifier P3, the forwarding order is from thigh to calf to ankle, the initial historical record shows 3 successful data transmissions in the past 3 transmissions, and an average transmission time of 25ms; path 4 is identified by identifier P4, the forwarding order is from thigh to waist to calf to ankle, the initial historical record shows 2 successful data transmissions in the past 3 transmissions, 1 failure, and an average transmission time of 40ms.

[0142] Next, in step 1054, a path concentration value is generated based on historical transmission records. A weighted summation algorithm is then used to calculate the path concentration value by weighting and summing the successful forwarding percentage and the time optimization value. In this example, the successful forwarding percentage has a weight of 0.6, and the time optimization percentage has a weight of 0.4. The maximum average time is 40ms.

[0143] The calculation process for each path is as follows:

[0144] Path 1: Path concentration value ;

[0145] Path 2: Path concentration value ;

[0146] Path 3: Path concentration value ;

[0147] Path 4: Path concentration value .

[0148] Finally, in step 1055, after completing one instruction forwarding, the historical record is updated and the concentration value is adjusted. For example, if the instruction forwarding using path 1 is successful and takes 18ms, its historical record is updated to "4 successful attempts, average transmission time". "; The concentration value was recalculated." This enables dynamic maintenance of path concentration values.

[0149] This application plans a multi-hop transmission path adapted to the cluster structure for collaborative control commands. By dynamically maintaining the path concentration value, it achieves precise control over the transmission quality, ensuring the reliability and adaptability of command transmission, and adapting to the dynamic operation scenarios of exoskeleton devices.

[0150] S106. When forwarding cooperative control commands, perform probabilistic next-hop selection on the multi-hop transmission path based on the path concentration value to distribute the cooperative control commands to the communication nodes of each component.

[0151] The probabilistic next-hop selection strategy assigns selection probabilities to different transmission paths based on path concentration values. It prioritizes high-quality paths while retaining alternative paths, balancing the stability and flexibility of command transmission and avoiding transmission interruptions caused by the failure of a single path. The current node is the communication node responsible for forwarding coordinated control commands at a given moment and can be dynamically updated during command transmission.

[0152] S106 specifically includes:

[0153] S1061. Determine the communication node currently responsible for forwarding the cooperative control command as the current node, and obtain the multi-hop transmission path information corresponding to the command to be forwarded.

[0154] The multi-hop transmission path information includes at least the path identifier, path concentration value, and information about the next-hop node corresponding to the current node in each multi-hop transmission path. The next-hop node is the next communication node to which the current node needs to forward the cooperative control command on a certain multi-hop transmission path, and it can be a cluster head node or a component communication node.

[0155] S1062. Extract the path concentration value of each multi-hop transmission path from the acquired path information, and convert each path concentration value into the corresponding selection ratio according to the preset conversion rule, and the sum of the selection ratios of all multi-hop transmission paths is 1.

[0156] The selection percentage is the proportion of each multi-hop transmission path in the probabilistic selection, which directly corresponds to the probability of the path being selected and is used to quantify the priority of different paths.

[0157] S1063. Based on the selection ratio, determine the probability of each next-hop node being selected.

[0158] S1064. Based on the selection probability, filter the next hop node of the current node and send the cooperative control command to the next hop node.

[0159] S1065. Take the next-hop node that receives the instruction as the new current node, and repeat the next-hop node selection operation until the collaborative control instruction is successfully distributed to the communication nodes of each component.

[0160] In this embodiment, the overall process of determining the current node and obtaining path information, calculating the path selection ratio, allocating the next-hop node selection probability, filtering forwarding nodes and iteratively executing the process achieves probabilistic adaptive forwarding of collaborative control commands.

[0161] As an example, continuing with the lower limb rehabilitation exoskeleton scenario, the collaborative control command in this scenario is "adjust the extension and contraction force of the lower leg joint," with the starting node being the thigh node, and the component communication nodes being the lower leg node and the ankle node; the multi-hop transmission path includes:

[0162] P1: Thigh to calf, concentration value 0.8;

[0163] P2: From thigh to waist to calf, concentration value 0.452;

[0164] P3: Thigh to calf to ankle, concentration value 0.75.

[0165] First, in step S1061, the thigh node responsible for forwarding the instruction is identified as the current node. All multi-hop transmission path data corresponding to the instruction are obtained from the path information stored in the node, including path identifiers P1, P2 and P3, the concentration values ​​of each path are 0.8, 0.452 and 0.75 respectively, and the next hop node of the current node in each path: the next hop of P1 is the calf node, the next hop of P2 is the waist node, and the next hop of P3 is the calf node.

[0166] Secondly, in step S1062, the concentration values ​​of the three paths are extracted. A normalization algorithm is used as the preset conversion rule to convert the concentration values ​​into selection percentages, ensuring that the sum of the percentages for all paths is 1. The conversion process is as follows: First, the sum of the path concentration values ​​for all paths is calculated as 0.8 + 0.452 + 0.75 = 2.002; then, the selection percentages for each path are calculated as follows: P1 selection percentage 0.4, P2 selection percentage 0.226, and P3 selection percentage 0.374; the sum of these three percentages ≈ 0.4 + 0.226 + 0.374 = 1, which meets the rule requirements.

[0167] Then, in step S1063, the probability of each next-hop node being selected is determined based on the selection ratio. The current node has two next-hop nodes: the lower leg node (corresponding to P1 and P3) and the waist node (corresponding to P2). The probability of the lower leg node being selected = selection ratio of P1 + selection ratio of P3 ≈ 0.4 + 0.374 = 0.774; the probability of the waist node being selected = selection ratio of P2 ≈ 0.226.

[0168] Next, in step S1064, the next hop node is selected based on the selection probability. Since the lower leg node has a higher selection probability, the current upper leg node sends the collaborative control command to the lower leg node. If the lower leg node is unavailable in this selection, the alternative plan is triggered, and the waist node is selected for forwarding.

[0169] Finally, in step S1065, the lower leg node that received the instruction is designated as the new current node. The lower leg node is itself a component communication node and can directly execute instructions; it is also the current node of the P3 path and needs to forward instructions to the ankle node. At this point, steps S1061-S1064 are repeated until the instruction is successfully distributed to both the lower leg and ankle component communication nodes.

[0170] This application implements adaptive probabilistic forwarding of collaborative control commands, enabling the command transmission path to dynamically match changes in network status. It prioritizes high-quality paths to ensure transmission stability while retaining alternative paths to avoid single-point failure risks, ultimately improving the reliability and adaptability of command transmission for exoskeleton devices.

[0171] Figure 3 This is a schematic diagram illustrating a specific implementation of an IoT data transmission system for exoskeleton status monitoring provided in this application embodiment, with reference to... Figure 3 The system may include:

[0172] The acquisition module 31 is used to acquire the wearer's monitoring data through the detection device on the exoskeleton device, identify gait intentions, and simultaneously predict the relative motion trends and signal occlusion relationships between each communication node;

[0173] The broadcast module 32 is used to control each communication node to broadcast a beacon according to a set period. The beacon encapsulates a motion vector containing at least one of the following: gait intention, relative motion trend, and signal blockage relationship, as well as the detection results of the adjacent communication link.

[0174] Module 33 is used by each communication node to construct a local network topology graph by exchanging beacons. The edge weights of communication links in the local network topology graph are determined by the detection results and the link stability parameters based on motion vector prediction.

[0175] The forming module 34 is used to perform distributed consensus optimization based on the local network topology graph, so that each communication node converges through information interaction and state iteration to form a hierarchical cluster structure, and selects the high stability node in the hierarchical cluster structure as the cluster head node according to the edge weight.

[0176] Module 35 is established to create multi-hop transmission paths for cooperative control commands to be issued based on a hierarchical clustering structure, and to maintain path concentration values ​​for each transmission path based on historical transmission results. The multi-hop transmission path uses the cluster head node as the relay node, and the path concentration value characterizes the transmission quality.

[0177] Selection module 36 is used to perform probabilistic next-hop selection on a multi-hop transmission path based on the path concentration value when forwarding cooperative control instructions, so as to distribute the cooperative control instructions to the communication nodes of each component.

[0178] The IoT data transmission system for exoskeleton status monitoring in this application embodiment is used to implement the aforementioned IoT data transmission method for exoskeleton status monitoring. Therefore, the specific implementation of the IoT data transmission system for exoskeleton status monitoring can be found in the embodiment section of the IoT data transmission method for exoskeleton status monitoring above. The specific implementation can be referred to the description of the corresponding embodiments, and will not be repeated here.

[0179] This application also provides an electronic device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the above-described Internet of Things data transmission method for exoskeleton status monitoring.

[0180] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described IoT data transmission methods for exoskeleton status monitoring.

[0181] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0182] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the Internet of Things data transmission method for exoskeleton status monitoring.

[0183] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0184] The foregoing has provided a detailed description of an IoT data transmission method and system for exoskeleton status monitoring. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. An Internet of Things (IoT) data transmission method for exoskeleton status monitoring, characterized in that, include: The device acquires the wearer's monitoring data through the detection device on the exoskeleton, identifies gait intentions, and simultaneously predicts the relative movement trends and signal occlusion relationships between each communication node. Each communication node is controlled to broadcast a beacon at a set period. The beacon encapsulates a motion vector containing at least one of the gait intention, the relative motion trend, and the signal blockage relationship, as well as the detection results of the adjacent communication link. Each communication node constructs a local network topology by exchanging the beacons. The edge weights of the communication links in the local network topology are determined by the detection results and the link stability parameters predicted based on the motion vectors. Distributed consensus optimization is performed based on the local network topology graph, so that each communication node converges through information interaction and state iteration to form a hierarchical cluster structure, and a highly stable node in the hierarchical cluster structure is selected as the cluster head node according to the edge weight. Based on the hierarchical clustering structure, a multi-hop transmission path is established for the cooperative control command to be issued, and a path concentration value is maintained for each transmission path according to the historical transmission results. The multi-hop transmission path uses the cluster head node as the relay node, and the path concentration value characterizes the transmission quality. When forwarding the cooperative control command, a probabilistic next-hop selection is performed on the multi-hop transmission path based on the path concentration value to distribute the cooperative control command to each component communication node.

2. The method according to claim 1, characterized in that, The communication nodes construct a local network topology by exchanging the beacons, including: Each communication node receives beacons broadcast by other surrounding communication nodes and extracts the encapsulated motion vectors and detection results of nearby communication links from the beacons; The detection results are analyzed to clarify the basic association information of the communication connection between itself and each surrounding communication node, and the relative position change and signal blockage of the device with each surrounding communication node in the subsequent set period are predicted based on the motion vector to form link stability parameters. Each communication node assigns a unique identifier to its communication link with each of its surrounding communication nodes based on the basic association information and link stability parameters, and combines the basic association information and link stability parameters to generate comprehensive association parameters corresponding to each communication link. Each communication node takes itself as the central node and surrounding communication nodes as associated nodes, and constructs a local network topology map within its coverage area based on the unique identifier of each communication link and the comprehensive association parameters, where the comprehensive association parameters are the edge weights.

3. The method according to claim 1, characterized in that, The process of performing distributed consensus optimization based on the local network topology graph, enabling communication nodes to converge through information interaction and state iteration to form a hierarchical cluster structure, and selecting highly stable nodes in the hierarchical cluster structure as cluster head nodes according to the edge weights, includes: Each communication node obtains the edge weights of the communication links between itself and all its surrounding associated nodes based on the local network topology graph, and initializes its own node state information. Each communication node sends its own node status information to surrounding associated nodes through the communication link, and receives node status information fed back by surrounding associated nodes, thus completing information interaction; The node status information of the surrounding associated nodes is compared with its own node status information, and its own node status information is adjusted according to preset rules. The information interaction and state adjustment operations are repeatedly performed until the node status information of each communication node tends to be stable, so as to achieve state iteration convergence. Based on the converged node state information, according to the edge weight correlation degree of the communication links between each communication node, all communication nodes are divided into multiple node groups, and each node group forms a hierarchical correlation relationship, forming a hierarchical cluster structure. For each node group, the edge weights of the communication links between each node in the node group and other nodes are extracted, and the node with the largest sum of edge weights is selected as the cluster head node of the node group.

4. The method according to claim 1, characterized in that, After forming a hierarchical clustering structure, it also includes: Each communication node continuously monitors changes in motion vectors to determine whether the connectivity of the local network topology has changed; If it is determined that the connectivity of the local network topology has changed, the affected communication nodes are identified, and the affected communication nodes automatically trigger the local reconstruction of the existing hierarchical cluster structure; the local reconstruction process is carried out with reference to the latest motion vectors and edge weights of each communication link.

5. The method according to claim 1, characterized in that, Based on the hierarchical clustering structure, a multi-hop transmission path is established for the cooperative control commands to be issued, and a path concentration value is maintained for each transmission path according to historical transmission results, including: Determine the starting node for sending the collaborative control command and all component communication nodes that receive the collaborative control command; Based on a hierarchical clustering structure, starting from the starting node and with each component communication node as the end point, the cluster head nodes along the way are selected as instruction forwarding relay nodes to construct multiple transmission links from the starting point to each end point, forming a multi-hop transmission path. Each transmission link is sequentially connected by the starting node, at least one cluster head node and a component communication node. Assign a unique path identifier to each multi-hop transmission path, record the forwarding order of each communication node in the multi-hop transmission path, and the historical transmission records of each communication link segment. The historical transmission records include the success status of past instruction forwarding and transmission time information. For each multi-hop transmission path, a path concentration value representing the transmission quality is generated based on the corresponding historical transmission records. Each time the multi-hop transmission path completes a cooperative control command forwarding operation, the historical transmission record is updated, and the corresponding path concentration value is adjusted according to the updated historical transmission record to achieve dynamic maintenance of the path concentration value.

6. The method according to claim 1, characterized in that, When forwarding the cooperative control command, performing probabilistic next-hop selection on the multi-hop transmission path based on the path concentration value includes: The communication node currently responsible for forwarding the cooperative control command is determined as the current node. The multi-hop transmission path information corresponding to the command to be forwarded is obtained. The multi-hop transmission path information includes at least the path identifier, path concentration value, and information of the next hop node corresponding to the current node in each multi-hop transmission path. Extract the path concentration value of each multi-hop transmission path from the acquired path information, and convert each path concentration value into the corresponding selection ratio according to the preset conversion rules, and the sum of the selection ratios of all multi-hop transmission paths is 1. Based on the selection ratio, the probability of each next-hop node being selected is determined; Based on the selected probability, the next hop node of the current node is selected, and the cooperative control command is sent to the next hop node; The next-hop node that receives the instruction is taken as the new current node, and the next-hop node selection operation is repeated until the collaborative control instruction is successfully distributed to each component communication node.

7. The method according to claim 1, characterized in that, After constructing the local network topology graph, the following is also included: Each communication node compares its own constructed local network topology map with the local network topology maps of its surrounding communication nodes one by one. By comparing and identifying communication link information that is not covered in the local network topology map, the missing information is then filled in.

8. An Internet of Things (IoT) data transmission system for exoskeleton status monitoring, characterized in that, include: The acquisition module is used to acquire the wearer's monitoring data through the detection device on the exoskeleton device, identify gait intentions, and simultaneously predict the relative motion trends and signal occlusion relationships between each communication node; The broadcast module is used to control each communication node to broadcast a beacon at a set period. The beacon encapsulates a motion vector containing at least one of the gait intention, the relative motion trend, and the signal blockage relationship, as well as the detection results of the adjacent communication link. A construction module is used for each communication node to construct a local network topology graph by exchanging the beacons. The edge weights of the communication links in the local network topology graph are jointly determined by the detection results and the link stability parameters predicted based on the motion vectors. The forming module is used to perform distributed consensus optimization based on the local network topology graph, so that each communication node converges to form a hierarchical cluster structure through information interaction and state iteration, and selects a highly stable node in the hierarchical cluster structure as the cluster head node according to the edge weight. A module is established to create multi-hop transmission paths for the cooperative control commands to be issued based on the hierarchical clustering structure, and to maintain a path concentration value for each transmission path according to historical transmission results. The multi-hop transmission path uses the cluster head node as the relay node, and the path concentration value characterizes the transmission quality. The selection module is used to perform probabilistic next-hop selection on the multi-hop transmission path based on the path concentration value when forwarding the cooperative control command, so as to distribute the cooperative control command to each component communication node.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the Internet of Things data transmission method for exoskeleton status monitoring as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the Internet of Things (IoT) data transmission method for exoskeleton status monitoring as described in any one of claims 1 to 7.

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