An intelligent collaborative control method, system and device of integrating a helmet and a intercom

Abstract

This invention relates to the field of intelligent intercom technology and discloses an intelligent collaborative control method, system, and device integrating a helmet and a walkie-talkie. The method includes: collecting lens opening / closing status values, helmet posture angle data sets, wearing status indicators, and battery temperature values ​​from the intelligent helmet and constructing a set of scene tags and a set of status parameters; adjusting the DSP noise reduction cutoff frequency, noise suppression gain, VOX voice activation sensitivity, and RF transmission power; encapsulating the data into a Mesh heartbeat extension frame and broadcasting it to all online nodes in the network; receiving the Mesh heartbeat extension frame and selecting candidate relay nodes to perform route switching. This method solves the technical problem of communication interruption caused by passive disconnection and reconnection of the Mesh intercom network in obstructed scenarios such as curves, tunnels, and steep slopes, and ensures the continuity of communication links in multi-node cycling platoon scenarios.

Description

Technical Field

[0001] This invention relates to the field of intelligent intercom technology, and in particular to an intelligent collaborative control method, system and device integrating a helmet and an intercom. Background Technology

[0002] In existing helmet-integrated walkie-talkie products, there is no status information exchange interface between the helmet body and the walkie-talkie module. The helmet only serves as a passive mounting carrier for the walkie-talkie device and cannot provide riding status data to the communication system. As a result, the audio processing parameters and radio frequency parameters of the walkie-talkie network always operate at the factory-fixed values ​​and cannot be dynamically adjusted according to riding conditions.

[0003] In typical riding scenarios such as high-speed riding, cornering, and visor opening and closing, the acoustic environment inside the helmet and the rider's operational ability change, resulting in a decrease in the communication quality of existing intercom systems or the rider's inability to initiate intercom in a timely manner. Summary of the Invention

[0004] This invention provides an intelligent collaborative control method, system, and device integrating a helmet and a walkie-talkie. This invention solves the technical problem of communication interruption caused by passive disconnection and reconnection of the Mesh walkie-talkie network in obstructed scenarios such as curves, tunnels, and steep slopes, and ensures the continuity of communication links in multi-node cycling platoon scenarios.

[0005] In a first aspect, the present invention provides an intelligent collaborative control method integrating a helmet and a walkie-talkie, the intelligent collaborative control method comprising: Collect lens opening / closing status values, helmet posture angle data sets, wearing status indicators, and battery temperature values ​​from the smart helmet, and construct a set of scene labels and a set of status parameters. The DSP noise reduction cutoff frequency, noise suppression gain, VOX voice activation sensitivity, and RF transmission power are adjusted according to the scene tag set. At the same time, the set of state parameters is encapsulated into a Mesh heartbeat extension frame and broadcast to all online nodes in the network. The system receives the Mesh heartbeat extension frames broadcast by each online node and calculates the predicted distance between nodes and its rate of change. Based on the predicted distance between nodes and its rate of change, it selects candidate relay nodes to perform route switching.

[0006] In conjunction with the first aspect, in a first implementation of the first aspect of the present invention, the step of collecting lens opening / closing state values, helmet posture angle data sets, wearing status indicators, and battery temperature values ​​on the smart helmet and constructing a scene label set and a state parameter set includes: The opening and closing status values ​​of the lens are collected by a Hall sensor at the lens hinge in the smart helmet. The helmet's attitude angle data is collected through a six-axis inertial measurement unit located inside the top liner of the smart helmet. The wearing status indicators are collected through the pressure sensor array on the cheek pads of the smart helmet; The battery temperature value is collected through the temperature sensor in the smart helmet's battery compartment; A set of scene labels and a set of state parameters are generated based on the lens opening / closing state value, the helmet posture angle data set, the wearing status indicator, and the battery temperature value.

[0007] In conjunction with the first aspect, in a second implementation of the first aspect of the present invention, the step of acquiring helmet attitude angle data sets through a six-axis inertial measurement unit within the top liner of the smart helmet includes: Raw six-axis signals are acquired by a six-axis inertial measurement unit deployed inside the top liner of the smart helmet; The pitch and roll angles are calculated based on the three-axis angular velocity and three-axis acceleration signals in the six-axis raw signals, and the heading angle is calculated based on the vertical axis angular velocity signal in the six-axis raw signals. The pitch angle, roll angle, and yaw angle are combined to obtain the helmet attitude angle data set.

[0008] In conjunction with the first aspect, in a third implementation of the first aspect of the present invention, the step of generating a scene label set and a state parameter set based on the lens opening / closing state value, the helmet posture angle data set, the wearing state indicator, and the battery temperature value includes: The impact acceleration is calculated based on the triaxial acceleration signal, and a collision event label is generated based on the impact acceleration. The riding speed is calculated based on the triaxial acceleration signal, and the speed level is determined based on the riding speed. The tilt level is determined based on the roll angle in the helmet attitude angle data set, and the temperature level is determined based on the battery temperature value. The collision event label, the speed level, the tilt level, the temperature level, the lens opening / closing state value, and the wearing state flag are used as a scene label set, and the riding speed and the heading angle in the helmet posture angle data set are used as a state parameter set.

[0009] In conjunction with the first aspect, in the fourth implementation of the first aspect of the present invention, the step of adjusting the DSP noise reduction cutoff frequency, noise suppression gain, VOX voice activation sensitivity, and RF transmission power according to the scene tag set, and simultaneously encapsulating the state parameter set into a Mesh heartbeat extension frame and broadcasting it to all online nodes in the network, includes: When the lens opening / closing state value in the scene label set is in the open state, the first control parameter combination is output; when the lens opening / closing state value is in the closed state, the second control parameter combination is output according to the speed level in the scene label set. Adjust the DSP noise reduction cutoff frequency and noise suppression gain according to the first control parameter combination or the second control parameter combination; The VOX voice activation sensitivity is adjusted according to the tilt level and the wearing status indicator, and the radio frequency transmission power is adjusted according to the temperature level; The cycling speed and heading angle from the set of state parameters are encapsulated into a Mesh heartbeat extended frame, and the Mesh heartbeat extended frame is broadcast to all online nodes in the Mesh intercom network.

[0010] In conjunction with the first aspect, in a fifth implementation of the first aspect of the present invention, adjusting the VOX voice activation sensitivity according to the tilt level and the wearing status indicator, and adjusting the radio frequency transmission power according to the temperature level, includes: When the wearing status indicator is not worn, the VOX channel is disabled; when the wearing status indicator is worn, the VOX voice activation sensitivity is adjusted according to the tilt level and the channel holding time is extended. Using the temperature level as an index, query the radio frequency transmission power corresponding to the temperature level, and write the radio frequency transmission power into the radio frequency power control register.

[0011] In conjunction with the first aspect, in the sixth implementation of the first aspect of the present invention, the step of encapsulating the cycling speed and heading angle in the set of state parameters into a Mesh heartbeat extended frame and broadcasting the Mesh heartbeat extended frame to all online nodes in the Mesh intercom network includes: The cycling speed and heading angle from the set of state parameters, along with the node identifier, online status, and received signal strength of this node, are written into the extended data field of the Mesh heartbeat frame to obtain the Mesh heartbeat extended frame. The Mesh heartbeat extended frame is broadcast to all online nodes in the Mesh intercom network. After receiving the Mesh heartbeat extended frame, each online node parses the extended data field and writes the node identifier, the riding speed, the heading angle, the online status, and the received signal strength into its local node motion status table.

[0012] In conjunction with the first aspect, in the seventh implementation of the first aspect of the present invention, the step of receiving the Mesh heartbeat extension frames broadcast by each online node and calculating the predicted distance between nodes and its rate of change, and selecting candidate relay nodes to perform route switching based on the predicted distance between nodes and their rate of change, includes: Receive the Mesh heartbeat extension frames broadcast by each online node and extract the cycling speed and heading angle of each online node; The predicted distance between each online node is calculated based on the cycling speed and the heading angle, and the rate of change of the predicted distance between the nodes is also calculated. Candidate relay nodes are selected based on the predicted distance between nodes and their rate of change to perform route switching.

[0013] In conjunction with the first aspect, in the eighth implementation of the first aspect of the present invention, the step of selecting candidate relay nodes to perform route switching based on the predicted distance between the nodes and their rate of change includes: Candidate relay nodes are selected based on the predicted distance between nodes and its rate of change, and a route pre-configuration instruction is sent to the candidate relay nodes. After receiving the route pre-configuration instruction, the candidate relay nodes write relay route entries with the communication end nodes as targets in their local routing tables and mark the relay route entries as pending activation. When the received signal strength of the direct link is lower than the handover trigger threshold, a route handover notification frame is sent to the peer node, and the next hop of the peer node in the local routing table is changed from the direct link to the candidate relay node. Switch the corresponding relay route entry in the local routing table of the candidate relay node from the pending state to the active state.

[0014] Secondly, the present invention provides an intelligent collaborative control system integrating a helmet and a walkie-talkie, the intelligent collaborative control system comprising: The data acquisition module is used to collect lens opening and closing status values, helmet posture angle data sets, wearing status indicators and battery temperature values ​​on the smart helmet, and to build a set of scene labels and a set of status parameters. The online broadcast module is used to adjust the DSP noise reduction cutoff frequency, noise suppression gain, VOX voice activation sensitivity and RF transmission power according to the scene tag set, and at the same time encapsulate the state parameter set into a Mesh heartbeat extended frame and broadcast it to all online nodes in the network. The routing switching module is used to receive the Mesh heartbeat extension frames broadcast by each online node and calculate the predicted distance between nodes and its rate of change. Based on the predicted distance between nodes and its rate of change, it selects candidate relay nodes to perform routing switching.

[0015] Thirdly, the present invention provides an intelligent collaborative control device integrating a helmet and a walkie-talkie, comprising: One or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to perform the intelligent collaborative control method for the integrated helmet and walkie-talkie.

[0016] In the technical solution provided by this invention, by deploying a lens opening and closing Hall sensor, a six-axis inertial measurement unit, a cheek pad pressure sensor array, and a battery compartment temperature sensor on a smart helmet with an integrated Mesh intercom module, the helmet body is upgraded from a passive mounting carrier to an active sensing node, realizing bidirectional linkage between the physical state of the helmet and the communication parameters of the Mesh intercom network. Adaptive scheduling of DSP noise reduction cutoff frequency and noise suppression gain based on joint mapping of lens opening / closing state values ​​and speed levels solves the problem of statically fixed audio processing parameters in existing technologies, which cannot cope with high-speed wind noise and changes in the acoustic environment. Joint scheduling of VOX voice activation sensitivity threshold and channel holding time based on tilt level and wearing status indicators solves the problem of the intercom channel not being activated in time when both hands cannot operate the PTT button while riding on curves. Dead reckoning based on the riding speed and cumulative heading angle broadcast by each node enables predictive judgment of distance changes between nodes. Before the signal strength received by the direct link drops to the handover trigger threshold, the routing pre-configuration of candidate relay nodes is completed, reducing the topology handover delay from hundreds of milliseconds in the existing passive response mechanism to less than 5 milliseconds. This solves the technical problem of communication interruption caused by passive disconnection and reconnection of Mesh intercom networks in obstructed scenarios such as curves, tunnels, and steep slopes, and ensures the continuity of communication links in multi-node riding platoon scenarios.

[0017] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of an embodiment of the intelligent collaborative control method integrating a helmet and a walkie-talkie in this invention. Figure 2 This is a schematic diagram illustrating the acquisition of feature parameters of the smart helmet in an embodiment of the present invention; Figure 3 This is a schematic diagram of the helmet attitude angle data set collected in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the generation of a scene label set and a state parameter set in an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating parameter adjustment in an embodiment of the present invention; Figure 6 This is a schematic diagram illustrating the adjustment of VOX voice activation sensitivity and radio frequency transmission power in an embodiment of the present invention; Figure 7 This is a schematic diagram of broadcasting Mesh heartbeat extension frames in an embodiment of the present invention; Figure 8 This is a schematic diagram illustrating the calculation of the predicted distance between nodes and its rate of change in an embodiment of the present invention; Figure 9 This is a schematic diagram illustrating the selection of candidate relay nodes for route switching in an embodiment of the present invention; Figure 10 This is a schematic diagram of an embodiment of the intelligent collaborative control system integrating a helmet and a walkie-talkie in this invention. Figure 11 This is a schematic diagram of the overall structure of the helmet and walkie-talkie in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention 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, 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.

[0021] The terms "comprising" and "having," and any variations thereof, used in the embodiments of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0022] To facilitate understanding of this embodiment, a detailed description of the intelligent collaborative control method for integrating a helmet and a walkie-talkie disclosed in this embodiment of the invention will be provided first. For example... Figure 1 As shown, this method includes the following steps: 101. Collect lens opening / closing status values, helmet posture angle data sets, wearing status indicators, and battery temperature values ​​from the smart helmet and construct a set of scene labels and a set of status parameters; 102. Adjust the DSP noise reduction cutoff frequency, noise suppression gain, VOX voice activation sensitivity and RF transmission power according to the scene label set, and encapsulate the status parameter set into the Mesh heartbeat extension frame and broadcast it to all online nodes in the network. 103. Receive the Mesh heartbeat extension frames broadcast by each online node and calculate the predicted distance between nodes and its rate of change. Select candidate relay nodes based on the predicted distance between nodes and their rate of change to perform route switching.

[0023] In one specific embodiment, such as Figure 2 The process of executing step 101 may specifically include the following steps: 201. The opening and closing status values ​​of the lens are collected by the Hall sensor at the lens hinge in the smart helmet; 202. Collect helmet attitude angle data sets through the six-axis inertial measurement unit inside the top liner of the smart helmet; 203. The wearing status indicators are collected through the pressure sensor array of the smart helmet's cheek pads; 204. Collect battery temperature values ​​through the temperature sensor in the smart helmet's battery compartment; 205. Generate a set of scene labels and a set of state parameters based on the lens opening / closing status value, helmet posture angle data set, wearing status indicator, and battery temperature value.

[0024] Specifically, a linear Hall sensor is placed at the lens hinge, and a permanent magnet with dimensions of Φ6mm×3mm is fixed at the corresponding position on the lens frame. This causes the relative distance between the magnet and the Hall sensor to change during lens rotation, resulting in a synchronous change in the Hall sensor's output voltage U1. U1 represents the output voltage of the Hall sensor at the lens hinge, measured in volts (V). The Hall sensor's operating voltage is set to 3.3V. When the lens is fully closed, the distance between the magnet and the Hall sensor is 3 to 5mm, and the induced magnetic flux density is approximately 30 to 50 mT, corresponding to U1 falling within the 3.2 to 3.5V range. In this case, the lens is determined to be in a closed state. When the lens is fully open, the distance between the magnet and the Hall sensor exceeds 25mm, and the induced magnetic flux density is less than 5 mT, corresponding to U1 falling within the 0.5 to 0.8V range. In this case, the lens is determined to be in an open state. Considering the momentary misjudgments caused by cycling vibrations, lens bounce, and mechanical shaking, software de-jitter processing is applied to U1. Specifically, a lens state switch is only confirmed and a stable lens opening / closing state value S1 is output only when U1 remains within the same logical interval for a continuous 50ms sampling window, where S1 represents the lens opening / closing state logic value. A six-axis inertial measurement unit (IMU) is embedded in the top inner lining of the smart helmet and connected to the main control unit via a bus. The IMU outputs three-axis acceleration and three-axis angular velocity signals at a sampling rate of 100Hz. The acceleration range is set to ±8g, and the gyroscope range is set to ±2000° / s. The three-axis accelerations are denoted as a1, a2, and a3, and the three-axis angular velocities are denoted as ω1, ω2, and ω3, where a1 represents the helmet's forward axis acceleration, a2 represents the helmet's lateral axis acceleration, and a3 represents the helmet's vertical axis acceleration, all in m / s². 2ω1 represents the angular velocity of the helmet about the forward axis, ω2 represents the angular velocity of the helmet about the lateral axis, and ω3 represents the angular velocity of the helmet about the vertical axis, all in ° / s. The sampling interval is denoted as Δt, where Δt represents the time interval between two adjacent samples, taken as 0.01s. The main control unit performs complementary filtering on the six-axis raw data to obtain the changes in pitch, roll, and yaw angles in the attitude angle data set. The pitch angle is calculated using the following formula: in, This indicates the pitch angle at the current sampling moment, in degrees. This represents the pitch angle at the previous sampling time, in degrees; This represents the pitch angle increment obtained by integrating the lateral axis angular velocity within the current sampling period; This represents the resultant acceleration component consisting of lateral and vertical axial accelerations, with units of m / s². 2 ; This indicates the arctangent of the angle, and the result is returned in degrees. The roll angle is calculated using the following formula: in, Indicates the roll angle at the current sampling moment, in degrees; Represents the roll angle at the previous sampling time, in degrees; This represents the roll angle increment obtained by integrating the forward axis angular velocity within the current sampling period; This represents the resultant acceleration component consisting of the forward axis acceleration and the vertical axis acceleration, with units of m / s². 2 Change in heading angle ,in, This represents the change in heading angle within the current sampling period, in degrees. The result is... , and The data are combined to form a helmet posture angle data set. The weighting coefficient in the complementary filter is 0.98, corresponding to a time constant τ of 0.49s, where τ represents the high-pass time constant of the complementary filter in seconds. Pressure sensors are arranged on the inner sides of the cheek pads on both sides of the smart helmet to form a pressure sensor array, which collects the contact pressure between the wearer's head and the cheek pads at multiple points. Six pressure sampling channels are set up, and the main control unit performs an arithmetic average of the sampled values ​​from each channel to obtain the pressure representative value P, where P represents the average digital quantity corresponding to the comprehensive contact pressure of the cheek pad area, with a value range of 0 to 4095. When P is greater than 800 and lasts for more than 500ms, the wearing status flag S2=1 is output, where S2 represents the wearing status flag, and S2=1 indicates that the helmet is being worn; when P is less than 200 and lasts for more than 1000ms, the wearing status flag S2=0 is output, indicating that the helmet is not being worn. By setting dual thresholds and dual duration criteria, the system effectively distinguishes between normal wearing, temporary loosening, and removal / placement states, avoiding frequent erroneous switching of wearing status due to short-term pressure changes or road vibrations. A temperature sensor is placed inside the heat insulation layer of the battery compartment within the smart helmet to collect battery temperature values. The temperature sensor uses an NTC thermistor with a nominal resistance of 10kΩ and a B-parameter of 3950K at 25°C, forming a voltage divider sampling circuit with a 10kΩ precision resistor. After sampling by the analog-to-digital converter built into the main control unit, the temperature is inversely calculated according to the B-parameter equation, as shown in the following formula: in, This indicates the current resistance value of the thermistor, in Ω; This represents the converted thermodynamic temperature, in Kelvin (K). The battery temperature value is represented in °C; 298.15 represents the thermodynamic temperature reference value corresponding to 25°C, in K; 3950 represents the B-value constant of the thermistor. The temperature sampling interval is set to 1000ms to ensure that changes in the battery thermal state can be stably tracked with low processing overhead. The lens opening / closing state value S1 and the helmet attitude angle data are grouped together. Wearing status indicator S2 and battery temperature value A unified input scene recognition finite state machine is used, and multi-source fusion calculations are performed with an execution cycle of 10ms, thereby generating a scene label set and a state parameter set. The scene label set describes the current riding and wearing environment of the smart helmet, and the state parameter set represents quantifiable real-time state quantities. The scene label set includes lens open / closed tags, worn / not worn tags, tilt status tags, and temperature status tags; the state parameter set includes pitch angle... Roll angle Change in heading angle and battery temperature value .

[0025] In one specific embodiment, such as Figure 3 The process of collecting helmet attitude angle data through the six-axis inertial measurement unit inside the top liner of the smart helmet can specifically include the following steps: 301. Acquire six-axis raw signals by deploying a six-axis inertial measurement unit on the top liner of the smart helmet; 302. Calculate the pitch and roll angles based on the three-axis angular velocity and three-axis acceleration signals from the original six-axis signals, and calculate the heading angle based on the vertical axis angular velocity signals from the original six-axis signals; 303. Combine the pitch angle, roll angle and yaw angle to obtain the helmet attitude angle data set.

[0026] Specifically, the six-axis inertial measurement unit (I) is connected via I 2 The helmet communicates with the main control unit via the C-bus and continuously outputs six-axis raw signals at a sampling rate of 100Hz. The accelerometer range is set to ±8g, and the gyroscope range is set to ±2000° / s. The three-axis accelerations are denoted as a1, a2, and a3, and the three-axis angular velocities are denoted as ω1, ω2, and ω3, where a1 represents the helmet's forward axis acceleration, a2 represents the helmet's lateral axis acceleration, and a3 represents the helmet's vertical axis acceleration; all units are m / s². 2 ω1 represents the angular velocity of the helmet about the forward axis, ω2 represents the angular velocity of the helmet about the lateral axis, and ω3 represents the angular velocity of the helmet about the vertical axis, all in ° / s; the time interval between two adjacent samples is denoted as Δt, where Δt is taken as... After receiving the raw six-axis signals, the main control unit performs complementary filtering preprocessing on the raw six-axis signals to suppress long-term drift caused by simple angular velocity integration, and uses acceleration and gravity components to perform low-frequency correction of the attitude angles. After completing the calculation, the main control unit sets the pitch angle... Roll angle and change in heading angle The data is combined according to a unified data format to form a helmet attitude angle data set. .

[0027] In one specific embodiment, such as Figure 4 The process of generating a set of scene labels and a set of state parameters based on lens opening / closing status values, helmet posture angle data sets, wearing status indicators, and battery temperature values ​​can specifically include the following steps: 401. Calculate impact acceleration based on triaxial acceleration signals, generate collision event tags based on impact acceleration, calculate riding speed based on triaxial acceleration signals, and determine speed level based on riding speed; 402. Determine the tilt level based on the roll angle in the helmet attitude angle data set, and determine the temperature level based on the battery temperature value; 403. The collision event label, speed level, tilt level, temperature level, lens opening / closing status value and wearing status flag are used as the scene label set, and the heading angle in the riding speed and helmet attitude angle data set is used as the status parameter set.

[0028] Specifically, the main control unit reads the three-axis accelerations a1, a2, and a3 output by the six-axis inertial measurement unit, where a1 represents the helmet's forward axis acceleration, a2 represents the helmet's lateral axis acceleration, and a3 represents the helmet's vertical axis acceleration, all in m / s². 2 The impact acceleration A1 was calculated based on the triaxial acceleration signal. Where A1 represents the impact acceleration, in m / s². 2 9.8 represents the gravitational acceleration compensation value, in m / s². 2 This formula subtracts the gravitational component under static conditions, thus highlighting the dynamic impact component caused by collisions or severe jolts. The main control unit performs sliding time window processing on A1, counting the number of consecutive sampling points of A1 that continuously exceed the impact amplitude judgment threshold within the preset sampling window, and comparing the corresponding duration with a 50ms threshold; when A1 is greater than 147m / s... 2 And when the duration exceeds 50ms, a collision event label S3=1 is generated, where S3 represents the collision event label; when A1 exceeds 147m / s 2 However, if the duration does not exceed 50ms, it is judged as a road bumpy state and no collision event is triggered. The cycling speed is estimated using triaxial acceleration signals. The cycling speed is denoted as V1, where V1 represents the cycling speed; during the integration calculation phase, V1 is first calculated in m / s, then converted to km / h for classification. Speed ​​estimation is performed using forward axis acceleration integration combined with zero-speed correction; the calculation formula is as follows: ,in, This represents the estimated cycling speed at the current sampling time, in m / s. This represents the estimated cycling speed at the previous sampling time, in m / s. This represents the forward axis acceleration after filtering, in m / s². 2 , This represents the zero-offset calibration value of the forward axis acceleration, in m / s². 2 , This represents the time interval between two consecutive samples, taken as 0.01s. Satisfying 9.8 m / s 2 The deviation is less than 2m / s 2When all three-axis angular velocities are within the low angular rate range and remain so for more than 3 seconds, zero-speed correction can be performed to bring V1 to zero, thus eliminating speed drift caused by prolonged integration. After speed integration, V1 is multiplied by 3.6 to convert it to km / h and classified according to preset boundaries: when V1 is less than 15 km / h, the speed level is determined as Level 1 (low speed); when V1 is greater than or equal to 15 km / h and less than 60 km / h, the speed level is determined as Level 2 (medium speed); when V1 is greater than or equal to 60 km / h and less than 100 km / h, the speed level is determined as Level 3 (high speed); and when V1 is greater than or equal to 100 km / h, the speed level is determined as Level 4 (ultra-high speed). The main control unit reads the roll angle from the helmet attitude angle data set. ,in, This represents the roll angle at the current sampling moment, in degrees, and the tilt level is determined based on the absolute value of the roll angle. When When, the inclination level is determined to be level zero for straight travel; when When the slope level is determined to be a level one light curve; when At that time, the incline level was determined to be a level two moderate curve; when At that time, the tilt level was determined to be a level three sharp curve. This determination method directly uses the helmet's tilt posture to reflect changes in the vehicle's trajectory, ensuring that voice control parameters and link scheduling parameters are synchronized with the riding and steering conditions. Simultaneously, the main control unit reads the battery temperature value. ,in, This indicates the battery temperature value, in °C, and the temperature level is determined based on preset temperature boundaries; when Greater than or equal to When, the temperature level is determined to be the normal temperature level; when Less than and greater than or equal to When, the temperature level is determined to be a low temperature level; when Less than At that time, the temperature level was determined to be extremely cold. Through parallel processing of roll angle and temperature grading, the tilt level reflecting the riding posture and the temperature level reflecting the power supply and heating environment were obtained, respectively. The main control unit structurally encapsulated the aforementioned identification results, forming a scene label set and a state parameter set. The collision event labels were then... Speed ​​rating, tilt rating, temperature rating, lens opening / closing status value and wearing status indicator They are all written into the scene tag set, where, This indicates the logic value representing the open / closed state of the lens. Indicates the wearing status indicator. The system uses collision event labels to create a set of scene labels that describe the current operating condition of the smart helmet from six dimensions: impact safety status, speed status, posture status, thermal status, lens status, and wearing status. It also includes cycling speed... and the heading angle in the helmet attitude angle data set Write the state parameter set, where, This represents the heading angle in the helmet attitude angle data set, in degrees.

[0029] The impact acceleration is calculated based on the triaxial acceleration signal, and a collision event label is generated based on the impact acceleration. This includes: squaring and summing the sampled values ​​of each axis of the triaxial acceleration signal, taking the square root, and then subtracting the standard value of gravitational acceleration to obtain the impact acceleration; applying a sliding time window to the impact acceleration using a preset sampling window; counting the number of consecutive sampling points where the impact acceleration continuously exceeds the impact amplitude judgment threshold within the sampling window to obtain the number of continuous impact sampling points; comparing the number of continuous impact sampling points with the upper limit threshold of continuous turbulence sampling points and the lower limit threshold of continuous collision sampling points sequentially; generating a turbulence event label when the number of continuous impact sampling points does not exceed the upper limit threshold of continuous turbulence sampling points, generating a collision event label when the number of continuous impact sampling points exceeds the lower limit threshold of continuous collision sampling points, and keeping the current event label unchanged when the number of continuous impact sampling points falls between the upper limit threshold of continuous turbulence sampling points and the lower limit threshold of continuous collision sampling points; and writing the collision event label and the turbulence event label into the corresponding fields of the scene label set, where the collision event label has higher priority than the turbulence event label, and the output of the turbulence event label is overridden when the collision event label is valid.

[0030] The cycling speed is calculated based on the triaxial acceleration signal, including: subtracting the composite acceleration amplitude of the triaxial acceleration signal from the standard value of gravitational acceleration to obtain the dynamic acceleration deviation value; comparing the amplitude of each axis of the triaxial angular velocity signal with the angular velocity stationary judgment threshold to obtain the angular velocity stationary indicator for each axis; when the dynamic acceleration deviation value is lower than the acceleration stationary judgment threshold and all angular velocity stationary indicators of each axis simultaneously exceed the zero speed judgment time window, the current cycling speed integral value is cleared to zero; otherwise, the forward acceleration component in the triaxial acceleration signal is subtracted from the forward zero bias calibration value and then integrated over time to obtain the cycling speed; the cycling speed is compared with the speed classification boundary in turn to obtain the speed level.

[0031] In one specific embodiment, such as Figure 5 The process of executing step 102 may specifically include the following steps: 501. When the lens opening / closing state value in the scene label set is "open", output the first control parameter combination; when the lens opening / closing state value is "closed", output the second control parameter combination according to the speed level in the scene label set. 502. Adjust the DSP noise reduction cutoff frequency and noise suppression gain according to the first control parameter combination or the second control parameter combination; 503. Adjust the VOX voice activation sensitivity according to the tilt level and wearing status indicator, and adjust the radio frequency transmission power according to the temperature level; 504. Encapsulate the riding speed and heading angle from the state parameter set into a Mesh heartbeat extension frame, and broadcast the Mesh heartbeat extension frame to all online nodes in the Mesh intercom network.

[0032] Specifically, the main control unit reads the lens opening / closing state value S1, speed level, tilt level, wearing status flag S2, and temperature level from the scene tag set. Here, S1 represents the lens opening / closing state logic value, and S2 represents the wearing status flag. Based on the processing order of lens state priority, speed state subdivision, posture state supplementation, and thermal state correction, the corresponding communication parameter scheduling result is generated. When S1 is in the open state, it means that external airflow can directly enter the helmet's internal sound pickup area, and the microphone's acoustic environment is at its worst. At this time, the main control unit directly outputs the first control parameter combination, setting the high-pass filter cutoff frequency f_HPF to 300Hz and the noise suppression gain G_CVC to 18dB. Here, f_HPF represents the high-pass filter cutoff frequency in the DSP noise reduction link (in Hz), and G_CVC represents the noise suppression gain (in dB). This parameter combination no longer distinguishes between speed levels but prioritizes suppressing strong wind noise input when the lens is open. Accordingly, when S1 is closed, the main control unit outputs a second combination of control parameters based on the speed level. At speed level 1 (low speed), f_HPF is set to 100Hz and G_CVC to 6dB to preserve more voice detail. At speed level 2 (medium speed), f_HPF is set to 150Hz and G_CVC to 10dB to balance voice clarity with moderate wind noise suppression. At speed level 3 (high speed) or 4 (ultra-high speed), f_HPF is set to 200Hz and G_CVC to 15dB to enhance the suppression of helmet cavity resonance noise and high-speed airflow noise. After selecting either the first or second combination of control parameters, the main control unit outputs the second combination of control parameters via I... 2The S-interface writes the corresponding parameters into the DSP chip register, keeping the parameter activation delay within 10ms, thereby enabling real-time adjustment of the DSP noise reduction cutoff frequency and noise suppression gain. When the collision event tag is valid, G_CVC can also be forcibly switched to 0dB to preserve the original acoustic information of the scene. The main control unit performs joint scheduling of VOX voice activation sensitivity based on tilt level and wearing status flag S2, and performs synchronous scheduling of RF transmission power based on temperature level. The VOX voice activation threshold is denoted as V2, where V2 represents the VOX voice activation sensitivity threshold in dBFS. When the root mean square level of the microphone input signal exceeds V2, the intercom transmission channel is activated. When S2 indicates an unworn state, the main control unit directly disables the VOX channel to prevent environmental noise from triggering transmission when the helmet is off the head. When S2 indicates a worn state, V2 is further refined according to the tilt level. When the tilt level is straight ahead, V2 is set to -30dBFS; when the tilt level is a slight curve, V2 is set to -35dBFS; when the tilt level is a moderate or sharp curve, V2 is set to -40dBFS. Simultaneously, the VOX hold time is extended from 500ms to 1500ms to cover the short pauses caused by body weight shifts and breathing rhythm changes during cornering, preventing repeated on / off switching of the transmission channel. At the same time, the main control unit queries the RF transmission power table based on the temperature level and writes the query result to the RF power control register. The radio frequency (RF) transmission power is denoted as P1, where P1 represents the RF transmission power in mW. For normal temperature conditions, P1 is set to 100mW; for low temperature conditions, P1 is set to 80mW; and for extremely cold conditions, P1 is set to 60mW. This approach provides a power margin for battery preheating or thermal management under low and extremely cold conditions, while maintaining effective wireless communication. When the device is not worn and remains in a low-speed state for more than 30 seconds, a sleep command is sent to the Mesh RF module, putting the device into a low-power standby state. Upon re-detection of the wearing status, network re-entry is completed within 800ms. The main control unit encapsulates the riding speed V1 and heading angle θ3 from the state parameter set into a Mesh heartbeat extended frame and broadcasts it to all online nodes in the Mesh intercom network. Here, V1 represents the riding speed in km / h, and θ3 represents the heading angle in degrees. V1, θ3, along with the node identifier, online status, and received signal strength of this node, are written into the extended data field of the Mesh heartbeat frame to form the Mesh heartbeat extended frame. The extended field adopts the structure of 8 bits for device identifier, 2 bits for online status, 8 bits for received signal strength, 16 bits for riding speed, 16 bits for heading angle, 2 bits for temperature level, and 2 bits for tilt level. The resolution of the riding speed field is set to 0.1 km / h, and the resolution of the heading angle field is set to 0.01° / sampling interval.After encapsulation, the main control unit sends Mesh heartbeat extension frames to all online nodes in the network at a broadcast period of 100ms. Each online node receives and parses the extension fields, and then writes the node identifier, riding speed, heading angle, online status and received signal strength into the local node motion status table.

[0033] After querying the RF transmit power corresponding to the temperature level using the temperature level as an index and writing the RF transmit power into the RF power control register, the process also includes: jointly judging the wearing status flag and the speed level; when the wearing status flag is "not worn" and the speed level is "low speed" for a duration exceeding the preset sleep trigger time window, a deep sleep command is sent to the Mesh RF module to switch the Mesh RF module to a deep sleep state, and at the same time, the broadcast interval of the Bluetooth module is adjusted to a preset low-power broadcast interval to obtain a power sleep status flag; during the effective period of the power sleep status flag, the wearing status flag is continuously polled at the preset low-power broadcast interval; when the wearing status flag changes from "not worn" to "worn", a wake-up command is sent to the Mesh RF module and the Mesh network re-entry process is started, and the broadcast interval of the Bluetooth module is restored to the normal working interval to obtain a power wake-up status flag; based on the power wake-up status flag, the Mesh network re-entry completion time is timed; when the re-entry completion time exceeds the preset re-entry timeout threshold, the Mesh network re-entry process is re-executed until re-entry is successful, after which the power sleep status flag is cleared and the normal communication parameter scheduling process is restored.

[0034] In one specific embodiment, such as Figure 6 The process of adjusting the VOX voice activation sensitivity based on the tilt level and wearing status indicators, and adjusting the radio frequency transmission power based on the temperature level, can specifically include the following steps: 601. When the wearing status indicator is "not worn", disable the VOX channel; when the wearing status indicator is "worn", adjust the VOX voice activation sensitivity and extend the channel holding time according to the tilt level. 602. Using the temperature level as an index, query the RF transmit power corresponding to the temperature level and write the RF transmit power into the RF power control register.

[0035] Specifically, when the wearing status flag S2 indicates an unworn state, the main control unit directly disables the VOX channel enable bit and locks the voice trigger judgment state as invalid, thereby blocking environmental wind noise, road noise, and false triggering of the intercom transmission link by external voice. This processing method is suitable for helmet removal, temporary placement, and removal from head contact, and can avoid continuous occupation of the wireless channel under unworn conditions. When the wearing status indicator S2 indicates that the device is wearing the device, the main control unit restores the VOX channel enable and adjusts the VOX voice activation sensitivity V2 in stages according to the current tilt level. V2 represents the VOX voice activation sensitivity threshold in dBFS, indicating that the intercom transmission is triggered when the root mean square level of the microphone input signal reaches the corresponding threshold. When the tilt level is straight, V2 is set to -30dBFS to keep the voice trigger threshold at a high level to reduce false triggering caused by wind noise during smooth riding. When the tilt level is a slight curve, V2 is set to -35dBFS to moderately increase the voice trigger sensitivity. When the tilt level is a medium curve or a large curve, V2 is set to -40dBFS to compensate for the fluctuations in the pickup level caused by changes in head posture, airflow deflection, and vocal posture during curve riding. Meanwhile, in moderate or sharp curves, the main control unit extends the VOX channel hold time from 500ms to 1500ms, ensuring continuous conduction of the transmission link during short pauses, breathing gaps, and road disturbances, avoiding repeated interruptions during continuous dialogue. Through this processing, the VOX control link incorporates both wearing status and riding posture into the voice triggering logic, achieving forced suppression in the unwearing state and graded enhancement in the wearing state. The main control unit reads the temperature level and uses it as an index to access the pre-stored RF transmission power value in the parameter mapping table, writing the retrieved RF transmission power P1 into the RF power control register, where P1 represents the RF transmission power in mW. When the temperature rating is normal, P1 is set to 100mW to maintain the rated communication power output; when the temperature rating is low, P1 is set to 80mW to free up some power margin from the total power budget; when the temperature rating is extremely cold, P1 is set to 60mW to further reduce RF link power consumption and reserve more power space for battery thermal management in low-temperature environments. After completing the index lookup, the main control unit writes the power level code corresponding to P1 into the RF power control register via register writing, so that the RF front-end power amplifier operates at the target transmit power.The lower the temperature level, the lower the RF transmission power, thus maintaining a balance between communication link capability and power supply endurance in cold and extremely cold environments. At the same time, the 60mW level can cover the communication needs of a 600-meter obstruction scenario under the target device link budget conditions, so the network availability will not be significantly reduced due to simply reducing the RF transmission power.

[0036] Before calculating the predicted distance between online nodes based on cycling speed and heading angle, the process includes: for newly joined nodes in the Mesh intercom network, extracting the received signal strength from the Mesh heartbeat extension frame broadcast for the first time; dividing the difference between the received signal strength and the calibrated received signal strength at the reference distance by the product of the path loss exponent and 10; and raising the result to the power of 10 to obtain the initial estimated distance between the node and the local node; accumulating the heading angles in the first three consecutive Mesh heartbeat extension frames received by the node frame by frame to obtain the initial cumulative heading angle of the node; multiplying the initial estimated distance by the cosine and sine values ​​of the initial cumulative heading angle to obtain the initial x-coordinate and initial y-coordinate of the node relative to the origin of the local node's coordinates; writing the initial x-coordinate and initial y-coordinate into the relative coordinate field of the local node's motion status table to complete the initial relative coordinate assignment of the node; and incrementally updating the subsequent dead reckoning based on the initial x-coordinate and initial y-coordinate.

[0037] In one specific embodiment, such as Figure 7 The process of encapsulating the riding speed and heading angle from the state parameter set into a Mesh heartbeat extended frame and broadcasting the Mesh heartbeat extended frame to all online nodes in the Mesh intercom network can specifically include the following steps: 701. Write the cycling speed and heading angle from the status parameter set, along with the node identifier, online status, and received signal strength of this node, into the extended data field of the Mesh heartbeat frame to obtain the Mesh heartbeat extended frame; 702. Broadcast the Mesh heartbeat extension frame to all online nodes in the Mesh intercom network. After receiving the Mesh heartbeat extension frame, each online node parses the extension data field and writes the node identifier, riding speed, heading angle, online status and received signal strength into the local node motion status table.

[0038] Specifically, the main control unit reads the cycling speed V1 and heading angle θ3 from the status parameter set, then reads the node identifier, online status, and received signal strength of this node, and writes the above information into the extended data field of the Mesh heartbeat frame according to a predetermined field order, forming the Mesh heartbeat extended frame. Here, V1 represents the cycling speed in km / h, θ3 represents the heading angle in degrees, the received signal strength represents the strength value of the wireless signal received by the current node from a neighboring node in dBm, and the online status is used to characterize the current state of this node in the online, pending confirmation, or offline management process. The extended data field is set to a combined structure of 8 bits for device identifier, 2 bits for online status, 8 bits for received signal strength, 16 bits for cycling speed, 16 bits for heading angle, 2 bits for temperature level, and 2 bits for tilt level. The resolution of the cycling speed field is set to 0.1 km / h, and the resolution of the heading angle field is set to 0.01° / sampling interval. After loading the fields, the main control unit encapsulates the extended data fields, the basic heartbeat header, the length field, and the checksum field to obtain a Mesh heartbeat extended frame that can be directly transmitted in the Mesh intercom network. The main control unit broadcasts the Mesh heartbeat extended frame to all online nodes in the Mesh intercom network at a 100ms interval, enabling each online node to periodically obtain the latest motion status information of other nodes. Upon receiving the Mesh heartbeat extended frame, each online node parses the extended data fields and sequentially extracts the node identifier, riding speed V1, heading angle θ3, online status, and received signal strength. The extracted results are written into the local node motion status table NST, where NST represents the node motion status table, used to record the motion status and link status of each node in the network. A single node record in the local node motion status table is defined as NST[i] = {Node_ID}. i V 1i θ 3i T 1i L 1i RSSI i , t 1i Pos i [x, y]}, where Node_ID i V represents the node identifier of the i-th node. 1i Let θ represent the cycling speed at the i-th node. 3i T represents the heading angle of the i-th node. 1i L represents the temperature level of the i-th node. 1i RSSI represents the tilt level of the i-th node. i t represents the received signal strength corresponding to the i-th node. 1i Indicates the most recent update time, Pos i[x, y] represents the estimated two-dimensional coordinates of the i-th node relative to the current node. To maintain the validity of the node motion state table, each online node, while writing new received data, calculates the difference between the most recent update time and the current time in the node motion state table to obtain the heartbeat timeout duration, and compares the heartbeat timeout duration with a preset timeout threshold. When a node has not updated for more than 500ms, the corresponding node is marked as pending confirmation; when it has not updated for more than 2000ms, the corresponding node is marked as offline and removed from the active list. For nodes in the pending confirmation state, their last recorded riding speed V1 and heading angle θ3 are temporarily retained for short-term dead reckoning, but their relay priority is downgraded; for nodes in the offline state, they stop participating in the subsequent inter-node predicted distance calculation.

[0039] The process includes receiving Mesh heartbeat extension frames broadcast by online nodes and maintaining a node motion state table locally. It also involves: calculating the difference between the most recent update time and the current time for each node in the motion state table to obtain the heartbeat timeout duration for each node; comparing the heartbeat timeout duration with a first timeout threshold and a second timeout threshold sequentially; marking the corresponding node as pending confirmation when the heartbeat timeout duration exceeds the first timeout threshold, and marking the corresponding node as offline when the heartbeat timeout duration exceeds the second timeout threshold; retaining the last recorded cycling speed and heading angle of nodes marked as pending confirmation for dead reckoning, but setting their priority as candidate relay nodes to the lowest level; removing nodes marked as offline from the active list of the node motion state table and terminating their participation in all dead reckoning calculations; performing dead reckoning based on the cycling speed and cumulative heading angle of each node in the active list of the node motion state table to obtain the predicted distance between effective nodes and its rate of change, and inputting the predicted distance between effective nodes and its rate of change into the candidate relay node filtering process.

[0040] In one specific embodiment, such as Figure 8 The process of executing step 103 may specifically include the following steps: 801. Receive Mesh heartbeat extension frames broadcast by each online node and extract the cycling speed and heading angle of each online node; 802. Calculate the predicted distance between each online node based on the cycling speed and heading angle, and calculate the rate of change of the predicted distance between nodes; 803. Select candidate relay nodes and perform route switching based on the predicted distance between nodes and their rate of change.

[0041] Specifically, each online node receives Mesh heartbeat extension frames periodically broadcast by other nodes in the network, and extracts the corresponding node's cycling speed V1 and heading angle θ3 from the extended data field. V1 represents the cycling speed in km / h, and θ3 represents the heading angle in degrees. Combining the node identifier, online status, and received signal strength, the latest status of each node is written into the local node motion state table NST. NST represents the node motion state table, and a single node record in the table is written as NST[i] = {node identifier, cycling speed V1, heading angle θ3, temperature level, tilt level, received signal strength, last update time, estimated coordinates}. If a node does not update a heartbeat extension frame within 500ms, the node is marked as pending confirmation, and its most recent cycling speed V1 and heading angle θ3 are retained for short-term prediction. If a node still does not update a heartbeat extension frame within 2000ms, it is marked as offline and removed from the active list to prevent failed nodes from continuing to participate in distance prediction calculations. The topology prediction engine performs dead reckoning on active node pairs in the node motion state table, with an execution cycle of 200ms. (Due to cycling speed...) The speed is stored in km / h within the heartbeat extended frame, so the cycling speed is updated before the coordinates are updated. Converted to m / s, that is Where 3.6 represents the speed unit conversion factor; at the same time, the heading angle is... Converting degrees to radians, i.e. ,in, This represents pi. After unit conversion, update the estimated coordinates of the i-th online node relative to the current node using the following formula: , ,in, This represents the estimated lateral coordinates of the i-th online node at the current moment, in meters. This represents the estimated vertical coordinates of the i-th online node at the current moment, in meters. and These represent the estimated horizontal and vertical coordinates of the i-th online node in the previous prediction period, respectively, both in meters. This represents the cycling speed of the i-th online node, in km / h. Let represent the heading angle of the i-th online node in degrees, and 0.2 represent the prediction execution cycle in seconds. Based on the estimated coordinates of each node, calculate the predicted distance between any two online nodes i and j. The calculation formula is: ; in, This represents the predicted distance between node i and node j at the current time, in meters (m). The rate of change is calculated based on the predicted distances between nodes at the current time and in the previous prediction period. The calculation formula is: ,in, This represents the rate of change of the predicted distance between nodes i and j, in m / s. A positive value indicates that the two nodes are moving away from each other, while a negative value indicates that the two nodes are moving closer together. To determine in advance whether the link is approaching its effective communication limit in the future, the predicted distance is calculated 3 seconds later. ,in, This represents the predicted future distance between node i and node j at the end of the 3-second prediction time domain, in meters (m), where 3 represents the prediction duration in seconds (s). The topology prediction engine predicts distances between nodes. rate of change and future predicted distance Select candidate relay nodes and perform route switching. Determine the upper limit of the effective communication distance based on the tilt level corresponding to the current riding posture. ,in, This represents the effective communication distance threshold, in meters (m); when the tilt level is in a straight-line state... Taking 900m as an example, when the incline is in a curve condition... Take 600m. If the node With nodes Between and This indicates that the direct link between the two nodes will continue to deteriorate and approach the effective communication distance threshold within the next 3 seconds, at which point the candidate relay node selection process can be triggered; where 0.85 represents the prediction trigger margin coefficient. The topology prediction engine searches in the node motion state table NST for nodes that are online and simultaneously meet the following conditions: and nodes Among the candidate relay nodes, This represents a candidate relay node, and 0.6 represents the relay stability margin coefficient. After determining the candidate relay node, the current node sends a route pre-configuration command to the candidate relay node, causing the candidate relay node to pre-write a relay route entry targeting the two communication endpoints in its local routing table and mark the relay route entry as pending activation. Then, it continuously monitors the received signal strength of the direct link, and when the node... Detected with node When the received signal strength of the direct link between the two nodes drops below -85dBm, a route switching notification frame is sent to the peer node, and the next hop of the peer node in the local routing table is changed from the direct link to the candidate relay node. After receiving the route switching notification frame, the candidate relay node changes the corresponding relay route entry in its local routing table from the pending state to the active state, thus completing the route switching.

[0042] Before selecting candidate relay nodes to perform route switching based on the predicted distance between nodes and its rate of change, the process includes: prioritizing collision event tags in the scene tag set; when a collision event tag is in a collision state, skipping the candidate relay node selection process, forcibly switching the radio frequency transmission power to the maximum rated power, and obtaining a collision alarm broadcast trigger command; according to the collision alarm broadcast trigger command, encapsulating the node identifier, impact acceleration value, battery temperature value, and current timestamp into a collision alarm frame, and broadcasting the collision alarm frame to all online nodes in the Mesh intercom network with a preset number of repetitions and a preset repetition interval; after receiving the collision alarm frame, each online node forwards the node identifier and impact acceleration value in the collision alarm frame to the mobile terminal paired with the node via the Bluetooth channel, and obtains the collision alarm forwarding result.

[0043] In one specific embodiment, such as Figure 9 The process of selecting candidate relay nodes and performing route switching based on the predicted distance between nodes and their rate of change can specifically include the following steps: 901. Select candidate relay nodes based on the predicted distance between nodes and its rate of change, and send a route pre-configuration instruction to the candidate relay nodes. After receiving the route pre-configuration instruction, the candidate relay nodes write relay route entries with the communication end nodes as the destination in their local routing table and mark the relay route entries as pending activation. 902. When the received signal strength of the direct link is lower than the handover trigger threshold, a route handover notification frame is sent to the peer node, and the next hop of the peer node in the local routing table is changed from the direct link to the candidate relay node. 903. Switch the corresponding relay route entry in the local routing table of the candidate relay node from the pending state to the active state.

[0044] Specifically, when the future link state between node i and node j is determined to be continuously deteriorating, the topology prediction engine searches the local node motion state table for a candidate relay node k that meets the relay constraints. These constraints include the candidate relay node k being online, and the predicted future distance between the candidate relay node k and both communication endpoints i and j being less than 0.6 times the effective communication distance threshold. This ensures that the candidate relay node k maintains stable communication with both endpoints within the pre-configuration validity period. After determining the candidate relay node k, the current node sends a route pre-configuration instruction frame to it. This frame uses the ROUTE_PRECONFIG format to carry information such as the target node pair, the pre-configuration validity period, and the relay priority. The pre-configuration validity period T1 is set to 5 seconds, where T1 represents the effective retention time of the relay route entry in seconds. After receiving the route pre-configuration instruction frame, candidate relay node k pre-writes relay route entries targeting the two communication endpoints i and j into its local routing table and marks these entries as pending activation (S4). S4 represents the status flag of the relay route entry; S4=0 indicates pending activation, and S4=1 indicates activation. The current node continuously monitors the received signal strength R1 of the direct link with the peer node. R1 represents the received signal strength of the direct link, in dBm. When R1 falls below the handover trigger threshold R2, it determines that the direct link has entered a handover window where communication is possible but quality deteriorates rapidly. R2 is the handover trigger threshold, set to -85 dBm. At this time, the current node sends a route handover notification frame to the peer node. This frame uses the ROUTE_SWITCH format, causing both communication endpoints to synchronously enter the handover process. Simultaneously, the current node immediately updates the next-hop information of the peer node in its local routing table, rewriting the previous next-hop pointing to the direct link to candidate relay node k, ensuring that subsequent data to be sent is preferentially relayed through node k. Setting R2 to -85dBm allows for handover initiation before the direct link completely fails. This is because when R1 is near -85dBm, the link still has reliable control frame transmission capability, ensuring the successful delivery of the route handover notification frame. Delaying the handover until near the receiver's sensitivity limit increases the control frame error rate, potentially leading to unreliable handover command transmission. Upon receiving the route handover notification frame, candidate relay node k retrieves the relay route entry corresponding to the communication endpoints i and j from its local routing table. It then updates the status flag S4 of the relay route entry from pending activation to active activation, changing S4 from 0 to 1, thus officially putting the relay route entry into data forwarding mode. Candidate relay node k then receives, determines, and forwards service data frames from node i or j according to the activated relay route entry, completing the relay communication loop between the two endpoints.

[0045] The above describes the intelligent collaborative control method integrating a helmet and a walkie-talkie in embodiments of the present invention. The following describes the intelligent collaborative control system integrating a helmet and a walkie-talkie in embodiments of the present invention. Please refer to [link / reference]. Figure 10 One embodiment of the intelligent collaborative control system integrating a helmet and a walkie-talkie in this invention includes: The data acquisition module 1001 is used to collect lens opening and closing status values, helmet posture angle data sets, wearing status indicators and battery temperature values ​​on the smart helmet, and to construct a scene label set and a status parameter set. The online broadcast module 1002 is used to adjust the DSP noise reduction cutoff frequency, noise suppression gain, VOX voice activation sensitivity and RF transmission power according to the scene tag set, and at the same time encapsulate the status parameter set into the Mesh heartbeat extension frame and broadcast it to all online nodes in the network. The routing switching module 1003 is used to receive the Mesh heartbeat extension frames broadcast by each online node and calculate the predicted distance between nodes and its rate of change. Based on the predicted distance between nodes and its rate of change, it selects candidate relay nodes to perform routing switching.

[0046] An embodiment of the present invention provides an intelligent collaborative control device integrating a helmet and a walkie-talkie, comprising: One or more processors; Memory; One or more applications, wherein the applications are stored in memory and configured to be executed by one or more processors, the applications being configured to execute an integrated helmet and walkie-talkie intelligent collaborative control method.

[0047] Figure 11 This is the overall structure of the helmet and walkie-talkie of the present invention. Among them, 111 is a smart helmet, and 112 is a Mesh walkie-talkie; 1101 is the main shell of the smart helmet 111, with a ventilation opening on the top; 1102 is a lens located at the front of the main shell, with a rounded rectangular full-coverage structure; 1103 is a chin guard located at the bottom of the main shell, with a ventilation slot at the bottom edge; 1104 is a Mesh walkie-talkie module integrated into the side of the main shell, which has a speaker grid and circular control buttons, and communicates bidirectionally with the Mesh walkie-talkie 112 via a signal link indicated by a bidirectional arrow; the Mesh walkie-talkie 112 has an antenna on the top, and its front has a speaker grid area, a display screen area, and an operation button area from top to bottom, including a PTT button (1105), a VOL button (1106), and a MESH button (1107); the Mesh walkie-talkie module 1104 and the Mesh walkie-talkie 112 communicate via a link indicated by a bidirectional arrow for status signal transmission and communication parameter scheduling.

[0048] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0049] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0050] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent collaborative control integrating a helmet and a walkie-talkie, characterized in that, include: Collect lens opening / closing status values, helmet posture angle data sets, wearing status indicators, and battery temperature values ​​from the smart helmet, and construct a set of scene labels and a set of status parameters. The DSP noise reduction cutoff frequency, noise suppression gain, VOX voice activation sensitivity, and RF transmission power are adjusted according to the scene tag set. At the same time, the set of state parameters is encapsulated into a Mesh heartbeat extension frame and broadcast to all online nodes in the network. The system receives the Mesh heartbeat extension frames broadcast by each online node and calculates the predicted distance between nodes and its rate of change. Based on the predicted distance between nodes and its rate of change, it selects candidate relay nodes to perform route switching.

2. The intelligent collaborative control method for integrating a helmet and a walkie-talkie according to claim 1, characterized in that, The system collects lens opening / closing status values, helmet posture angle data sets, wearing status indicators, and battery temperature values ​​from the smart helmet and constructs a scene label set and a status parameter set, including: The opening and closing status values ​​of the lens are collected by a Hall sensor at the lens hinge in the smart helmet. The helmet's attitude angle data is collected through a six-axis inertial measurement unit located inside the top liner of the smart helmet. The wearing status indicators are collected through the pressure sensor array on the cheek pads of the smart helmet; The battery temperature value is collected through the temperature sensor in the smart helmet's battery compartment; A set of scene labels and a set of state parameters are generated based on the lens opening / closing state value, the helmet posture angle data set, the wearing status indicator, and the battery temperature value.

3. The intelligent collaborative control method for integrating a helmet and a walkie-talkie according to claim 2, characterized in that, The set of helmet attitude angle data collected through the six-axis inertial measurement unit inside the top liner of the smart helmet includes: Raw six-axis signals are acquired by a six-axis inertial measurement unit deployed inside the top liner of the smart helmet; The pitch and roll angles are calculated based on the three-axis angular velocity and three-axis acceleration signals in the six-axis raw signals, and the heading angle is calculated based on the vertical axis angular velocity signal in the six-axis raw signals. The pitch angle, roll angle, and yaw angle are combined to obtain the helmet attitude angle data set.

4. The intelligent collaborative control method for integrating a helmet and a walkie-talkie according to claim 3, characterized in that, The process of generating a scene label set and a state parameter set based on the lens opening / closing state value, the helmet posture angle data set, the wearing status indicator, and the battery temperature value includes: The impact acceleration is calculated based on the triaxial acceleration signal, and a collision event label is generated based on the impact acceleration. The riding speed is calculated based on the triaxial acceleration signal, and the speed level is determined based on the riding speed. The tilt level is determined based on the roll angle in the helmet attitude angle data set, and the temperature level is determined based on the battery temperature value. The collision event label, the speed level, the tilt level, the temperature level, the lens opening / closing state value, and the wearing state flag are used as a scene label set, and the riding speed and the heading angle in the helmet posture angle data set are used as a state parameter set.

5. The intelligent collaborative control method for integrating a helmet and a walkie-talkie according to claim 4, characterized in that, The step of adjusting the DSP noise reduction cutoff frequency, noise suppression gain, VOX voice activation sensitivity, and RF transmission power according to the scene tag set, and simultaneously encapsulating the state parameter set into a Mesh heartbeat extended frame and broadcasting it to all online nodes in the network, includes: When the lens opening / closing state value in the scene label set is in the open state, the first control parameter combination is output; when the lens opening / closing state value is in the closed state, the second control parameter combination is output according to the speed level in the scene label set. Adjust the DSP noise reduction cutoff frequency and noise suppression gain according to the first control parameter combination or the second control parameter combination; The VOX voice activation sensitivity is adjusted according to the tilt level and the wearing status indicator, and the radio frequency transmission power is adjusted according to the temperature level; The cycling speed and heading angle from the set of state parameters are encapsulated into a Mesh heartbeat extended frame, and the Mesh heartbeat extended frame is broadcast to all online nodes in the Mesh intercom network.

6. The intelligent collaborative control method for integrating a helmet and a walkie-talkie according to claim 5, characterized in that, The step of adjusting the VOX voice activation sensitivity according to the tilt level and the wearing status indicator, and adjusting the radio frequency transmission power according to the temperature level, includes: When the wearing status indicator is not worn, the VOX channel is disabled; when the wearing status indicator is worn, the VOX voice activation sensitivity is adjusted according to the tilt level and the channel holding time is extended. Using the temperature level as an index, query the radio frequency transmission power corresponding to the temperature level, and write the radio frequency transmission power into the radio frequency power control register.

7. The intelligent collaborative control method for integrating a helmet and a walkie-talkie according to claim 6, characterized in that, The step of encapsulating the riding speed and heading angle from the set of state parameters into a Mesh heartbeat extended frame and broadcasting the Mesh heartbeat extended frame to all online nodes in the Mesh intercom network includes: The cycling speed and heading angle from the set of state parameters, along with the node identifier, online status, and received signal strength of this node, are written into the extended data field of the Mesh heartbeat frame to obtain the Mesh heartbeat extended frame. The Mesh heartbeat extended frame is broadcast to all online nodes in the Mesh intercom network. After receiving the Mesh heartbeat extended frame, each online node parses the extended data field and writes the node identifier, the riding speed, the heading angle, the online status, and the received signal strength into its local node motion status table.

8. The intelligent collaborative control method for integrating a helmet and a walkie-talkie according to claim 1, characterized in that, The process of receiving the Mesh heartbeat extension frames broadcast by each online node and calculating the predicted distance between nodes and its rate of change, and selecting candidate relay nodes to perform route switching based on the predicted distance between nodes and their rate of change, includes: Receive the Mesh heartbeat extension frames broadcast by each online node and extract the cycling speed and heading angle of each online node; The predicted distance between each online node is calculated based on the cycling speed and the heading angle, and the rate of change of the predicted distance between the nodes is also calculated. Candidate relay nodes are selected based on the predicted distance between nodes and its rate of change, and a route pre-configuration instruction is sent to the candidate relay nodes. After receiving the route pre-configuration instruction, the candidate relay nodes write relay route entries with the communication end nodes as targets in their local routing tables and mark the relay route entries as pending activation. When the received signal strength of the direct link is lower than the handover trigger threshold, a route handover notification frame is sent to the peer node, and the next hop of the peer node in the local routing table is changed from the direct link to the candidate relay node. Switch the corresponding relay route entry in the local routing table of the candidate relay node from the pending state to the active state.

9. An intelligent collaborative control system integrating a helmet and a walkie-talkie, characterized in that, The intelligent collaborative control method for integrating a helmet and a walkie-talkie as described in any one of claims 1-8 includes: The data acquisition module is used to collect lens opening and closing status values, helmet posture angle data sets, wearing status indicators and battery temperature values ​​on the smart helmet, and to build a set of scene labels and a set of status parameters. The online broadcast module is used to adjust the DSP noise reduction cutoff frequency, noise suppression gain, VOX voice activation sensitivity and RF transmission power according to the scene tag set, and at the same time encapsulate the state parameter set into a Mesh heartbeat extended frame and broadcast it to all online nodes in the network. The routing switching module is used to receive the Mesh heartbeat extension frames broadcast by each online node and calculate the predicted distance between nodes and its rate of change. Based on the predicted distance between nodes and its rate of change, it selects candidate relay nodes to perform routing switching.

10. An intelligent collaborative control device integrating a helmet and a walkie-talkie, characterized in that, include: One or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to perform the intelligent collaborative control method for an integrated helmet and walkie-talkie as described in any one of claims 1-8.

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