A method and system for perceptual adjustment based on a multi-source sensor helmet

Abstract

The application discloses a kind of based on multi-source sensor helmet's perception adjustment method and system, belong to control technical field, this method is applied to the helmet including eye sensor, distance sensor, display terminal and controller.The method is executed by controller, mainly includes the following steps: first, based on the eye movement data obtained by the eye sensor, determine the current information load state parameter for representing the complexity of information received by the wearer;Then, based on the information load state parameter, dynamically control the sampling frequency of the distance sensor;Finally, based on the distance data obtained by the distance sensor, control the display content of the display terminal.The application perceives the cognitive load state of the wearer, adaptively adjusts sensor working frequency and display information, effectively reduces system power consumption under the premise of guaranteeing basic function, significantly prolongs the battery life of helmet.

Description

Technical Field

[0001] This application relates to the field of control technology, and in particular to a sensing and adjustment method and system based on a multi-source sensor helmet. Background Technology

[0002] With the rapid development of smart helmet technology, its function has evolved from a single head protection device to a highly integrated intelligent sensing platform. Modern smart helmets generally integrate multiple types of sensors, such as eye sensors for tracking the user's visual focus, distance sensors for detecting the distance to surrounding obstacles, and inertial measurement units (IMUs) for monitoring motion. These sensors work together to effectively monitor the user's blind spots and, upon detecting potential collision risks or approaching dangers, issue visual warnings to the user via displays integrated inside the helmet or on the visor, greatly enhancing situational awareness and safety during use.

[0003] In existing technologies, to maintain the reliability of the aforementioned safety functions, the sensor system and display terminal of smart helmets are typically configured to operate continuously at a fixed, usually high, frequency. The distance sensor continuously sends detection signals at fixed time intervals, while the display terminal remains constantly on, ready to present information. However, the continuous operation of these components consumes a significant amount of power, resulting in short battery life and impacting the user experience. Summary of the Invention

[0004] This application provides a sensing adjustment method and system based on a multi-source sensor helmet to improve the above-mentioned problems.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] In a first aspect, this application proposes a sensing and adjustment method based on a multi-source sensor helmet. The method is applied to a helmet including an eye sensor, a distance sensor, a display terminal, and a controller. The method is applicable to the controller, including:

[0007] Based on eye-tracking data acquired by eye sensors, the current information load state parameters of the wearer are determined. The information load state parameters are used to characterize the complexity of the information received by the wearer.

[0008] The sampling frequency of the distance sensor is controlled based on the information load status parameters.

[0009] The display terminal controls the content displayed based on the distance data acquired by the distance sensor.

[0010] Preferably, based on eye-tracking data acquired by an eye sensor, the current wearer's information load state parameters are determined. These parameters characterize the complexity of the information received by the wearer and include:

[0011] Based on the eye sensor, the total distance the wearer's eye focus moves per unit time;

[0012] Information load status parameters are determined based on total distance.

[0013] Preferably, based on eye-tracking data acquired by an eye sensor, a current wearer information load state parameter is determined based on the eye-tracking data. This information load state parameter characterizes the complexity of the information received by the wearer. The method further includes:

[0014] The number of blinks per unit time is obtained based on the eye sensor;

[0015] Information load state parameters are determined based on total distance, including:

[0016] Information load state parameters are determined based on total distance and blink count.

[0017] Preferably, controlling the sampling frequency of the distance sensor based on information load state parameters includes:

[0018] Obtain the first limiting frequency value and the second limiting frequency value, wherein the first limiting frequency value is the maximum sampling frequency of the distance sensor, the second limiting frequency value is the minimum sampling frequency of the distance sensor, and the sampling interval is between the first limiting frequency value and the second limiting frequency value.

[0019] Obtain the maximum threshold corresponding to the information load status parameter, and define the interval between zero and the maximum threshold as the information load interval;

[0020] Align the endpoints of the information load interval with the sampling interval, wherein the second limit frequency value corresponds to the zero value and the second limit frequency value corresponds to the maximum threshold.

[0021] Obtain the information load status parameters. If the information load status parameters are located within the information load interval, determine the actual corresponding sampling frequency value within the sampling interval based on the correspondence between the information load interval and the sampling interval.

[0022] Preferably, controlling the sampling frequency of the distance sensor based on information load state parameters includes:

[0023] If the information load state parameter is greater than the maximum threshold, the sampling frequency is determined to be equal to the first limit frequency value.

[0024] Preferably, the distance sensor includes a first sensor for monitoring a first detection area located to the left rear of the wearer, and a second sensor for monitoring a first detection area located to the right rear of the wearer. The display terminal is located in a first area to the left of the wearer's field of vision, and a second area to the right of the wearer's field of vision. Based on the distance data acquired by the distance sensor, the display terminal is controlled to display content, including:

[0025] Based on eye movement data acquired by the eye sensor at the target time, the visual field blind spot is determined, which is either the first region or the second region.

[0026] If the blind spot is the first region, then control the current first sampling frequency corresponding to the first sensor to be greater than the current second sampling frequency corresponding to the second sensor;

[0027] If the blind spot is the second region, then the current first sampling frequency corresponding to the first sensor is controlled to be less than the current second sampling frequency corresponding to the second sensor.

[0028] Preferably, based on the distance data acquired by the distance sensor, the display terminal is controlled to display content, including:

[0029] When the data is less than the preset distance value, the control display terminal will show a warning prompt.

[0030] Preferably, the helmet also includes a communication module, which is used to acquire the current speed and determine information load state parameters based on the total distance and the number of blinks, including:

[0031] The information load state parameters are determined based on the total distance, the number of blinks, and the current speed.

[0032] Secondly, this application also proposes a perception adjustment system based on a multi-source sensor helmet, including an eye sensor, a distance sensor, a display terminal, and a controller, the system being configured as follows:

[0033] Based on eye-tracking data acquired by eye sensors, the current information load state parameters of the wearer are determined. The information load state parameters are used to characterize the complexity of the information received by the wearer.

[0034] The sampling frequency of the distance sensor is controlled based on the information load status parameters.

[0035] The display terminal controls the content displayed based on the distance data acquired by the distance sensor.

[0036] Preferably, the system is configured as follows:

[0037] Based on eye-tracking data acquired by eye sensors, the current wearer's information load state parameters are determined. These parameters characterize the complexity of the information received by the wearer, including:

[0038] Based on the eye sensor, the total distance the wearer's eye focus moves per unit time;

[0039] Information load status parameters are determined based on total distance.

[0040] Preferably, the system is configured as follows:

[0041] Based on eye-tracking data acquired by eye sensors, the current wearer's information load state parameters are determined. These parameters characterize the complexity of the information received by the wearer and include:

[0042] The number of blinks per unit time is obtained based on the eye sensor;

[0043] Information load state parameters are determined based on total distance, including:

[0044] Information load state parameters are determined based on total distance and blink count.

[0045] Preferably, the system is configured as follows:

[0046] Based on information load state parameters, the sampling frequency of the distance sensor is controlled, including:

[0047] Obtain the first limiting frequency value and the second limiting frequency value, wherein the first limiting frequency value is the maximum sampling frequency of the distance sensor, the second limiting frequency value is the minimum sampling frequency of the distance sensor, and the sampling interval is between the first limiting frequency value and the second limiting frequency value.

[0048] Obtain the maximum threshold corresponding to the information load status parameter, and define the interval between zero and the maximum threshold as the information load interval;

[0049] Align the endpoints of the information load interval with the sampling interval, wherein the second limit frequency value corresponds to the zero value and the second limit frequency value corresponds to the maximum threshold.

[0050] Obtain the information load status parameters. If the information load status parameters are located within the information load interval, determine the actual corresponding sampling frequency value within the sampling interval based on the correspondence between the information load interval and the sampling interval.

[0051] Preferably, the system is configured as follows:

[0052] Based on information load state parameters, the sampling frequency of the distance sensor is controlled, including:

[0053] If the information load state parameter is greater than the maximum threshold, the sampling frequency is determined to be equal to the first limit frequency value.

[0054] Preferably, the system is configured as follows:

[0055] The distance sensor includes a first sensor for monitoring a first detection area located to the left rear of the wearer, and a second sensor for monitoring a first detection area located to the right rear of the wearer. The display terminal is located in a first area to the left of the wearer's field of vision, and a second area to the right of the wearer's field of vision. Based on the distance data acquired by the distance sensor, the display terminal is controlled to display content, including:

[0056] Based on eye movement data acquired by the eye sensor at the target time, the visual field blind spot is determined, which is either the first region or the second region.

[0057] If the blind spot is the first region, then control the current first sampling frequency corresponding to the first sensor to be greater than the current second sampling frequency corresponding to the second sensor;

[0058] If the blind spot is the second region, then the current first sampling frequency corresponding to the first sensor is controlled to be less than the current second sampling frequency corresponding to the second sensor.

[0059] Preferably, the system is configured as follows:

[0060] Based on the distance data acquired by the distance sensor, the display terminal is controlled to display content, including:

[0061] When the data is less than the preset distance value, the control display terminal will show a warning prompt.

[0062] Preferably, the system is configured as follows:

[0063] The helmet also includes a communication module, which is used to acquire the current speed and determine information load status parameters based on the total distance and the number of blinks, including:

[0064] The information load state parameters are determined based on the total distance, the number of blinks, and the current speed.

[0065] A third aspect of the present invention provides an electronic device comprising:

[0066] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method proposed in the first aspect of the present invention.

[0067] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in the first aspect of the embodiments of the present invention.

[0068] In summary, this application has the following technical effects:

[0069] This invention discloses a perception adjustment method and system for a multi-source sensor helmet. The method is applied to a helmet comprising an eye sensor, a distance sensor, a display terminal, and a controller. The method, executed by the controller, mainly includes the following steps: First, based on eye-tracking data acquired by the eye sensor, a current information load state parameter is determined to characterize the complexity of the information received by the wearer. Then, based on the information load state parameter, the sampling frequency of the distance sensor is dynamically controlled. Finally, based on the distance data acquired by the distance sensor, the display content of the display terminal is controlled. This invention, by sensing the wearer's cognitive load state, adaptively adjusts the sensor operating frequency and displayed information, effectively reducing system power consumption and significantly extending the helmet's battery life while ensuring basic functionality. Attached Figure Description

[0070] Figure 1 This is a flowchart illustrating a sensing adjustment method based on a multi-source sensor helmet proposed in this application embodiment. Detailed Implementation

[0071] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0072] This application proposes a perception adjustment method for a helmet based on a multi-source sensor. The helmet includes an eye sensor, a distance sensor, a display terminal, and a controller. The distance sensor can be an active sensor, such as ultrasonic, laser, or millimeter-wave radar. For example, a specific model could be the MaxBotix MB1000, with a detection range of 0–5m, meeting the requirements of this application, or a millimeter-wave radar model such as TIIWR6843; however, the specific model is not limited. The display terminal can be an information output device mounted on the helmet, located in front of or to the side of the wearer's field of vision, such as a micro-OLED display, like the Kopin OLED micro-display, or a waveguide AR (augmented reality) display or projection device such as the Lumus Maximus model. Its function is to present information to the wearer in a visual form, such as navigation data, device status, or warning signals. The method is applicable to the controller; please refer to [link to relevant documentation]. Figure 1 The method includes the following steps:

[0073] S101: Based on eye movement data acquired by the eye sensor, determine the current wearer's information load state parameters. The information load state parameters are used to characterize the complexity of the information received by the wearer.

[0074] Eye sensors are hardware devices integrated inside helmets for non-invasive monitoring of the wearer's eye physiological activities. Examples include IMotions Eye Tracking Glasses, an integrated design suitable for outdoor and mobile scenarios. Alternatively, there are miniaturized eye-tracking modules like the EyeTech VT3 Mini, which feature low power consumption, are suitable for embedded systems, and support real-time tracking. Of course, the specific model is not limited here.

[0075] The technology can be implemented using a near-infrared light source and a matching image sensor to track eye movements by capturing changes in corneal reflection and pupil position. Eye-tracking data consists of raw or pre-processed digital signals characterizing the state of eye movement, collected and output by eye sensors. This data specifically includes, but is not limited to: pupil center coordinates, gaze direction vector, instantaneous state of blinking events, and eyelid opening / closing.

[0076] The information load state parameter is a quantified numerical indicator derived through algorithmic processing. This parameter objectively characterizes the complexity and cognitive stress of the information received and processed by the wearer's brain in the current environment. Its value is typically positively correlated with cognitive load; that is, a higher parameter value indicates more complex or faster-paced information processing and a higher utilization rate of cognitive resources. Conversely, a lower value indicates a relaxed state with lower load. In other words, this parameter is a key intermediate variable in converting physiological signals into control logic that the system can understand.

[0077] Specifically, in this step, information load is quantified by analyzing specific eye-movement behavior features.

[0078] Specifically, as one implementation method, a key approach is to calculate the total distance the wearer's eye focus moves per unit time. Understandably, when the wearer is under high information load, they need to quickly browse and process multiple information sources in the environment, leading to frequent and rapid eye movements, thus significantly increasing this total distance. Conversely, under low information load, eye movement is relatively gradual. The specific mathematical processing can employ methods such as normalization, which are not limited here.

[0079] Information load parameters can also be determined by combining the number of blinks per unit time. Understandably, under high attentional load, an individual's blinking frequency will be significantly suppressed, i.e., the number of blinks will decrease; while under low load or fatigue state, the blinking frequency may show a regular recovery or increase.

[0080] Specifically, different weights can be assigned to the number of blinks per unit time and the total distance the eye focus moves, and these weights can be combined to derive the final information load state parameters.

[0081] The helmet also includes a communication module that can communicate with the vehicle and obtain the current speed. When the wearer is in a high-speed state, the external environment updates extremely quickly, which places higher demands on the wearer's information processing capabilities. Therefore, even with the same eye movement data, the actual cognitive load at high speeds is much higher than at rest or low speeds.

[0082] For example, firstly, the total distance and blink count can be normalized, and a basic eye load score can be calculated.

[0083] Then, the current speed is used as a weighting factor or an independent input dimension and combined with the baseline eye-tracking load score. For example, the system pre-sets a speed-weight lookup table, where higher speeds are assigned higher weights to the combined parameters, thus significantly improving the final calculated information load state parameters at high speeds.

[0084] For the specific calculation method, it can be calculated using relevant publicly available data fusion methods, such as weighted summation, fuzzy logic, or machine learning models, integrating them into a unified information load state parameter. The specific data fusion method is not limited in this application. By combining internal physiological signals with the dynamics of the external environment, a more comprehensive and accurate assessment of the wearer's true information load state is achieved.

[0085] S102: Control the sampling frequency of the distance sensor based on the information load status parameters.

[0086] Understandably, due to the independent nature of the helmet system, its power supply is a concern. Therefore, in this embodiment, the system can preset a control strategy, namely, the value of the information load state parameter is positively correlated with the sampling frequency of the distance sensor.

[0087] Specifically, in this embodiment, this can be achieved through a mapping mechanism. For example, the system pre-stores a first limiting frequency value for the distance sensor, i.e., the highest allowed sampling frequency, and a second limiting frequency value, i.e., the lowest allowed sampling frequency. These two constitute a sampling interval. Simultaneously, the system sets a maximum threshold for the information load state parameter; the range from zero to this maximum threshold constitutes the information load interval. By aligning and mapping the endpoints of these two intervals, a corresponding specific sampling frequency command within the sampling interval can be determined and output based on the specific position of the real-time acquired information load state parameter within this interval, using linear interpolation or a nonlinear function calculation.

[0088] Specifically, firstly, a first limiting frequency value and a second limiting frequency value can be obtained. The first limiting frequency value is the maximum sampling frequency of the distance sensor, and the second limiting frequency value is the minimum sampling frequency of the distance sensor. The area between the first limiting frequency value and the second limiting frequency value is the sampling interval.

[0089] Then, the maximum threshold corresponding to the information load state parameter is obtained, and the interval between zero and the maximum threshold is defined as the information load interval. The endpoints of the information load interval are aligned with the sampling interval, where the second limit frequency value corresponds to zero and the second limit frequency value corresponds to the maximum threshold. Finally, the information load state parameter is obtained. If the information load state parameter is located within the information load interval, the actual corresponding sampling frequency value is determined in the sampling interval according to the correspondence between the information load interval and the sampling interval.

[0090] Of course, if the information load state parameter is greater than the maximum threshold, the value of the sampling frequency is determined to be equal to the first limit frequency value.

[0091] Understandably, in low-information-load conditions, such as when the wearer is stationary or in a simple environment, a lower sampling frequency can significantly reduce the activation time and frequency of the distance sensor, thereby directly reducing the system's average power consumption and extending battery life. Conversely, in high-information-load conditions, such as when the wearer is moving at high speed in a complex traffic environment, the system prioritizes safety and uses a high sampling frequency to ensure sensitive perception of environmental changes.

[0092] S103: Based on the distance data obtained by the distance sensor, control the display terminal to display the content.

[0093] Based on real-time distance data analysis, the controller generates instructions to dynamically manage the type, quantity, layout, brightness, or prominence of information displayed on the terminal. Its specific implementation methods mainly include, but are not limited to, the following two:

[0094] a) Triggering a warning prompt: When distance data indicates the presence of an obstacle in a specific direction (such as behind or to the side) and its distance is less than a preset safe distance threshold, the controller will immediately instruct the display terminal to output a warning prompt. This prompt can be manifested as a flashing icon in a specific area, a highlighted symbol, a color change (such as turning red), or a text warning (such as "Approaching from behind"), designed to prioritize and prominently remind the wearer of potential risks.

[0095] b) Simplifying Display Content for Energy Saving: Conversely, when distance data consistently indicates a safe surrounding environment (distances in all directions are greater than the safety threshold), the controller can determine that a high-intensity safety warning is not currently necessary. In this case, the system can instruct the display terminal to simplify its display content, for example: turning off auxiliary areas used to display warning information, reducing overall or partial display brightness, retaining only the most essential navigation information (such as speed and direction), or even switching to a low refresh rate sleep mode. This environment-aware display content management directly reduces the display terminal's energy consumption, significantly contributing to extending helmet battery life.

[0096] In some other embodiments, the distance sensor may include a first sensor for monitoring a first detection area located to the left rear of the wearer, and a second sensor for monitoring a first detection area located to the right rear of the wearer, with the display terminal located in a first area to the left of the wearer's field of vision and a second area located to the right of the wearer's field of vision.

[0097] Understandably, the first sensor is specifically responsible for monitoring the first detection area located to the left rear of the wearer. The second sensor is specifically responsible for monitoring the second detection area located to the right rear of the wearer.

[0098] The process based on distance data acquired by a distance sensor may also include the following steps:

[0099] S201: Based on the eye movement data acquired by the eye sensor at the target time, determine the visual field blind spot, which is either the first region or the second region.

[0100] Understandably, based on eye movement data acquired by the eye sensor at the target time—that is, the current or most recent sampling period—such as gaze direction and focal position, the algorithm determines in real time the display area that the wearer's current visual attention is not effectively covered. Specifically, the visual field blind spot is defined as one of the first and second regions. For example, when eye movement data indicates that the wearer is continuously looking at the second region on the right, the first region on the left becomes the current visual field blind spot because it is off-focus.

[0101] S202: If the blind spot is the first region, then control the current first sampling frequency corresponding to the first sensor to be greater than the current second sampling frequency corresponding to the second sensor;

[0102] S203: If the blind spot is the second region, then control the current first sampling frequency corresponding to the first sensor to be less than the current second sampling frequency corresponding to the second sensor.

[0103] Understandably, if the blind spot is the first area, i.e., when the user is looking to the right, then the left rear is a weak point in perception and reaction. In this case, the system instructs the first sensor's current first sampling frequency to be greater than the second sensor's current second sampling frequency. This increases the monitoring density of the left rear blind spot to compensate for potential hazards that might be delayed in detection because the user is not directly looking at it.

[0104] If the blind spot is the second region, the situation is reversed. The system instructs the first sensor to set its current first sampling frequency to be lower than the second sensor's current second sampling frequency in order to enhance the monitoring of the right rear blind spot.

[0105] Understandably, by dynamically allocating higher sensor resources to areas behind the user's line of sight that are not directly visible, the system can detect objects approaching from blind spots earlier and more reliably, issuing timely warnings in the corresponding display area, thus significantly improving safety. Simultaneously, while focusing on monitoring blind spots, the system proactively reduces the sampling frequency of sensors in the corresponding directions of non-blind spots. Since the user is currently looking in that direction, their own visual system already provides a considerable level of environmental awareness; moderately reducing the sensor frequency does not introduce significant risks but immediately results in reduced power consumption.

[0106] This application proposes a perception adjustment method for a multi-source sensor helmet, applicable to a helmet comprising an eye sensor, a distance sensor, a display terminal, and a controller. The method, executed by the controller, mainly includes the following steps: First, based on eye-tracking data acquired by the eye sensor, a current information load state parameter is determined to characterize the complexity of the information received by the wearer; then, based on the information load state parameter, the sampling frequency of the distance sensor is dynamically controlled; finally, based on the distance data acquired by the distance sensor, the display content of the display terminal is controlled. This invention, by sensing the wearer's cognitive load state, adaptively adjusts the sensor operating frequency and displayed information, effectively reducing system power consumption and significantly extending the helmet's battery life while ensuring basic functionality.

[0107] Based on the same inventive concept, this application also proposes a perception adjustment system based on a multi-source sensor helmet, including an eye sensor, a distance sensor, a display terminal, and a controller. This system is configured as follows:

[0108] Based on eye-tracking data acquired by eye sensors, the current information load state parameters of the wearer are determined. The information load state parameters are used to characterize the complexity of the information received by the wearer.

[0109] The sampling frequency of the distance sensor is controlled based on the information load status parameters.

[0110] The display terminal controls the content displayed based on the distance data acquired by the distance sensor.

[0111] The system is configured as follows:

[0112] Based on eye-tracking data acquired by eye sensors, the current wearer's information load state parameters are determined. These parameters characterize the complexity of the information received by the wearer, including:

[0113] Based on the eye sensor, the total distance the wearer's eye focus moves per unit time;

[0114] Information load status parameters are determined based on total distance.

[0115] The system is configured as follows:

[0116] Based on eye-tracking data acquired by eye sensors, the current wearer's information load state parameters are determined. These parameters characterize the complexity of the information received by the wearer and include:

[0117] The number of blinks per unit time is obtained based on the eye sensor;

[0118] Information load state parameters are determined based on total distance, including:

[0119] Information load state parameters are determined based on total distance and blink count.

[0120] The system is configured as follows:

[0121] Based on information load state parameters, the sampling frequency of the distance sensor is controlled, including:

[0122] Obtain the first limiting frequency value and the second limiting frequency value, wherein the first limiting frequency value is the maximum sampling frequency of the distance sensor, the second limiting frequency value is the minimum sampling frequency of the distance sensor, and the sampling interval is between the first limiting frequency value and the second limiting frequency value.

[0123] Obtain the maximum threshold corresponding to the information load status parameter, and define the interval between zero and the maximum threshold as the information load interval;

[0124] Align the endpoints of the information load interval with the sampling interval, wherein the second limit frequency value corresponds to the zero value and the second limit frequency value corresponds to the maximum threshold.

[0125] Obtain the information load status parameters. If the information load status parameters are located within the information load interval, determine the actual corresponding sampling frequency value within the sampling interval based on the correspondence between the information load interval and the sampling interval.

[0126] The system is configured as follows:

[0127] Based on information load state parameters, the sampling frequency of the distance sensor is controlled, including:

[0128] If the information load state parameter is greater than the maximum threshold, the sampling frequency is determined to be equal to the first limit frequency value.

[0129] The system is configured as follows:

[0130] The distance sensor includes a first sensor for monitoring a first detection area located to the left rear of the wearer, and a second sensor for monitoring a first detection area located to the right rear of the wearer. The display terminal is located in a first area to the left of the wearer's field of vision, and a second area to the right of the wearer's field of vision. Based on the distance data acquired by the distance sensor, the display terminal is controlled to display content, including:

[0131] Based on eye movement data acquired by the eye sensor at the target time, the visual field blind spot is determined, which is either the first region or the second region.

[0132] If the blind spot is the first region, then control the current first sampling frequency corresponding to the first sensor to be greater than the current second sampling frequency corresponding to the second sensor;

[0133] If the blind spot is the second region, then the current first sampling frequency corresponding to the first sensor is controlled to be less than the current second sampling frequency corresponding to the second sensor.

[0134] The system is configured as follows:

[0135] Based on the distance data acquired by the distance sensor, the display terminal is controlled to display content, including:

[0136] When the data is less than the preset distance value, the control display terminal will show a warning prompt.

[0137] The system is configured as follows:

[0138] The helmet also includes a communication module, which is used to acquire the current speed and determine information load status parameters based on the total distance and the number of blinks, including:

[0139] The information load state parameters are determined based on the total distance, the number of blinks, and the current speed.

[0140] This application proposes a perception adjustment system for a multi-source sensor helmet, comprising an eye sensor, a distance sensor, a display terminal, and a controller. The system is configured to: first, determine a current information load state parameter characterizing the complexity of the information received by the wearer based on eye-tracking data acquired by the eye sensor; then, dynamically control the sampling frequency of the distance sensor based on the information load state parameter; and finally, control the display content of the display terminal based on distance data acquired by the distance sensor. This invention, by sensing the wearer's cognitive load state and adaptively adjusting the sensor operating frequency and displayed information, effectively reduces system power consumption and significantly extends the helmet's battery life while ensuring basic functionality.

[0141] Based on the same inventive concept, embodiments of this application also propose an electronic device, which includes:

[0142] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the perception modulation method based on a multi-source sensor helmet according to the embodiments of this application.

[0143] Furthermore, to achieve the above objectives, embodiments of this application also propose a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the sensing and adjustment method based on a multi-source sensor helmet according to embodiments of this application.

[0144] The following is a detailed introduction to the various components of the electronic device:

[0145] In this context, the processor is the control center of the electronic device. It can be a single processor or a collective term for multiple processing elements. For example, a processor can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0146] Alternatively, the processor can perform various functions of the electronic device by running or executing software programs stored in memory and by calling data stored in memory.

[0147] The memory is used to store the software program that executes the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can be referred to the above method embodiment, which will not be repeated here.

[0148] The memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. The memory can be integrated with the processor or exist independently and coupled to the processor through an interface circuit of an electronic device; this embodiment of the invention does not specifically limit this.

[0149] A transceiver is used to communicate with network devices or with terminal devices.

[0150] A transceiver can include a receiver and a transmitter. The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0151] The transceiver can be integrated with the processor or exist independently and coupled to the processor through the router's interface circuit. This embodiment of the invention does not impose specific limitations on this.

[0152] Furthermore, the technical effects of the electronic device can be referred to the technical effects of the data transmission method in the above method embodiments, and will not be repeated here.

[0153] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0154] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0155] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0156] Those skilled in the art will 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, or a combination of computer software and electronic hardware. 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.

Claims

1. A sensing and adjustment method based on a multi-source sensor helmet, characterized in that, The method is applied to a helmet, the helmet including an eye sensor, a distance sensor, a display terminal, and a controller, the method being applicable to the controller, including: Based on the eye movement data acquired by the eye sensor, the current wearer's information load state parameter is determined based on the eye movement data. The information load state parameter is used to characterize the complexity of the information received by the wearer. Based on the information load status parameters, control the sampling frequency of the distance sensor; Based on the distance data acquired by the distance sensor, the display terminal is controlled to display content.

2. The sensing and adjustment method based on a multi-source sensor helmet according to claim 1, characterized in that, Based on the eye movement data acquired by the eye sensor, the current wearer's information load state parameters are determined. These information load state parameters characterize the complexity of the information received by the wearer, including: The total distance the wearer's eye focus moves per unit time is obtained based on the eye sensor. The information load status parameters are determined based on the total distance.

3. The sensing and adjustment method based on a multi-source sensor helmet according to claim 2, characterized in that, Based on the eye movement data acquired by the eye sensor, a current wearer information load state parameter is determined based on the eye movement data. The information load state parameter is used to characterize the complexity of the information received by the wearer, and further includes: The number of blinks of the wearer per unit time is obtained based on the eye sensor; Determining the information load state parameters based on the total distance includes: The information load state parameters are determined based on the total distance and the number of blinks.

4. The sensing and adjustment method based on a multi-source sensor helmet according to claim 1, characterized in that, The step of controlling the sampling frequency of the distance sensor based on the information load state parameters includes: Obtain a first limiting frequency value and a second limiting frequency value, wherein the first limiting frequency value is the maximum sampling frequency of the distance sensor, the second limiting frequency value is the minimum sampling frequency of the distance sensor, and the area between the first limiting frequency value and the second limiting frequency value is the sampling interval; Obtain the maximum threshold corresponding to the information load status parameter, and define the interval between zero and the maximum threshold as the information load interval; Align the endpoints of the information load interval with the sampling interval, wherein the second limit frequency value corresponds to the zero value and the second limit frequency value corresponds to the maximum threshold; The information load status parameter is obtained. If the information load status parameter is located within the information load interval, the actual corresponding value of the sampling frequency is determined in the sampling interval according to the correspondence between the information load interval and the sampling interval.

5. The sensing and adjustment method based on a multi-source sensor helmet according to claim 4, characterized in that, The step of controlling the sampling frequency of the distance sensor based on the information load state parameters includes: If the information load state parameter is greater than the maximum threshold, the value of the sampling frequency is determined to be equal to the first limit frequency value.

6. The sensing and adjustment method based on a multi-source sensor helmet according to claim 1, characterized in that, The distance sensor includes a first sensor for monitoring a first detection area located to the left rear of the wearer, and a second sensor for monitoring a first detection area located to the right rear of the wearer. The display terminal is located in a first area to the left of the wearer's field of vision and a second area to the right of the wearer's field of vision. Based on the distance data acquired by the distance sensor, the display terminal is controlled to display content, including: Based on the eye movement data acquired by the eye sensor at the target time, a visual field blind spot is determined, wherein the visual field blind spot is one of the first region or the second region; If the blind spot is the first region, then the current first sampling frequency corresponding to the first sensor is controlled to be greater than the current second sampling frequency corresponding to the second sensor; If the blind spot is the second region, then the current first sampling frequency corresponding to the first sensor is controlled to be less than the current second sampling frequency corresponding to the second sensor.

7. The sensing and adjustment method based on a multi-source sensor helmet according to claim 1, characterized in that, Based on the distance data acquired by the distance sensor, the display terminal is controlled to display content, including: When the data is less than a preset distance value, the display terminal is controlled to display a warning message.

8. The sensing and adjustment method based on a multi-source sensor helmet according to claim 3, characterized in that, The helmet also includes a communication module, which is used to acquire the current speed and determine the information load status parameters based on the total distance and the number of blinks, including: The information load state parameters are determined based on the total distance, the number of blinks, and the current speed.

9. A sensing and adjustment system based on a multi-source sensor helmet, characterized in that, The system includes an eye sensor, a distance sensor, a display terminal, and a controller, and the system is configured to: Based on the eye movement data acquired by the eye sensor, the current wearer's information load state parameter is determined based on the eye movement data. The information load state parameter is used to characterize the complexity of the information received by the wearer. Based on the information load status parameters, control the sampling frequency of the distance sensor; Based on the distance data acquired by the distance sensor, the display terminal is controlled to display content.

10. An electronic device, characterized in that, include: At least one processor; And, a memory communicatively connected to at least one of the processors; The memory stores instructions that can be executed by at least one of the processors, which are executed by at least one of the processors to enable at least one of the processors to perform a perception modulation method based on a multi-source sensor helmet as claimed in any one of claims 1-8.

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