Data transmission method and system for internet of things

Abstract

The invention relates to the technical field of data transmission, and particularly discloses a data transmission method and system used under the Internet of Things, and the method comprises the following steps: S1, deploying a sensor at a limb of a user, and collecting limb movement data in real time; dividing a data transmission path, and calculating the total loss of the path; generating a motion change curve according to the limb data, and generating a loss change curve according to the path loss; s2, setting a loss threshold value, and dividing a high interval and a low interval of a loss curve; introducing a signal coefficient, generating a piecewise function, and defining transmission periods in different intervals; s3, when the user moves, the limb data are updated in real time, and a current movement change curve is generated; comparing the historical motion curve with the current motion curve, and calculating a cycle ratio; scaling the signal period piecewise function according to a period ratio to obtain a current piecewise function, and re-dividing high and low intervals; and setting a low-frequency threshold and a high-frequency threshold, and dynamically adjusting the transmission rate according to the new interval.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and particularly to a data transmission method and system for under the body area network. Background Art

[0002] The body area network refers to the Cyber-Physical Systems (CPS) connecting the human body with digital technology through various sensors and networks to achieve the collection, analysis, and application of health data, providing new possibilities for personalized healthcare services.

[0003] In the body area network, edge devices (such as wearable devices, sensor nodes, etc.) are responsible for collecting physiological data of the human body, and the edge data transmission of the body area network is to perform preliminary processing and transmission of data on the side close to the human body or the data source.

[0004] Under the existing technical framework, body area network devices, such as various wearable sensors and implantable medical devices, are often designed to be relatively small in size and relatively low in power consumption. Such a design concept is mainly to ensure that these devices can be conveniently worn or implanted into the human body and will not cause too much burden or discomfort to users during long-term use. However, precisely due to this small size and low power consumption characteristics, these devices face many limitations in terms of computing power, storage capacity, and battery life. Body area network devices usually cannot have a powerful processor and a complex computing architecture like traditional computers. Their computing resources are relatively limited and it is difficult to handle data processing tasks that require a large amount of computing and complex algorithms.

[0005] In addition, in the actual use process of the devices in the body area network, they are also easily affected by various factors such as human movement and environmental interference, resulting in unstable data transmission. Human movement is a common interference factor. When the user is performing strenuous exercise, the device may shake and shift accordingly, thus affecting the reception and transmission of signals. For example, during running, the wearable device worn on the arm may change the antenna direction due to the swing of the arm, resulting in a weakening or interruption of the signal strength. Summary of the Invention

[0006] The purpose of the present invention is to provide a data transmission method and system for under the body area network to solve the above technical problems.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A data transmission method for under the body area network, comprising the following steps:

[0009] Step S1: Set a monitoring point on the user's limb, obtain and transmit limb data through the monitoring point; obtain the transmission path, divide the transmission path, and obtain the total path loss data; generate a motion change curve according to the limb data, and generate a loss change curve according to the total path loss data;

[0010] Step S2: Set a loss threshold and obtain a threshold line; obtain a high interval and a low interval on the loss change curve according to the threshold line; set a signal coefficient, and generate a signal period piecewise function according to the high interval and the low interval;

[0011] Step S3: If the current user starts to move, obtain the current limb data in real time; generate a current motion change curve according to the current limb data; obtain a period ratio according to the motion change curve and the current motion change curve; scale the signal period piecewise function according to the period ratio to obtain the current signal period piecewise function, and obtain the current low interval and the current high interval;

[0012] Set a low-frequency threshold and a high-frequency threshold, and adjust the transmission rate of the monitoring point according to the current low interval and the current high interval.

[0013] As a further solution of the present invention: The limb data is the displacement distance of the limb at each moment, and the displacement distance is the height of the limb from the horizontal ground.

[0014] As a further solution of the present invention: The process of obtaining the transmission path includes:

[0015] Obtain a signal receiving end, and use the monitoring point as a signal sending end to obtain the transmission path according to the signal receiving end and the signal sending end.

[0016] As a further solution of the present invention: The process of obtaining the total path loss data includes:

[0017] Divide the transmission path, obtain the transmission path segment blocked by the user's body on the transmission path, denoted as the human interference segment; and obtain the transmission path segment other than the human interference segment on the transmission path, denoted as the environmental interference segment; obtain the path loss of the human interference segment, denoted as the human loss, and obtain the path loss of the environmental interference segment, denoted as the environmental loss; accumulate the human loss and the environmental loss to obtain the total path loss of the transmission path; obtain the total path loss data according to the total path loss at each moment.

[0018] As a further solution of the present invention: The process of obtaining the high interval and the low interval includes:

[0019] Denote the loss threshold as PL t, let the ordinate of the loss change curve be denoted as PL, then the threshold line PL = PL is obtained t ; on the loss change curve, obtain the curve segment below the threshold line, denoted as the high signal segment, and obtain the curve segment above the threshold line, denoted as the low signal segment; obtain the abscissa interval corresponding to the high signal segment, denoted as the high interval, and obtain the abscissa interval corresponding to the low signal segment, denoted as the low interval.

[0020] As a further solution of the present invention: the generation process of the signal period piecewise function includes:

[0021] Set the signal coefficient, and set the signal coefficient of the high interval to 1 and the signal coefficient of the low interval to 0; establish a coordinate system with the signal coefficient as the ordinate and the number of the moment as the abscissa, and generate the signal period piecewise function.

[0022] As a further solution of the present invention: the obtaining process of the period ratio includes:

[0023] Obtain the period of the motion change curve, denoted as the standard motion period; obtain the period of the current motion change curve, denoted as the current motion period; the period ratio P = T y ′ / T y where T y represents the standard motion period, and T y ′ represents the current motion period.

[0024] As a further solution of the present invention: the process of adjusting the transmission rate of the monitoring point includes:

[0025] When entering the current low interval, adjust the transmission rate of the monitoring point to the low frequency threshold, and when entering the current high interval, adjust the transmission rate of the monitoring point to the high frequency threshold.

[0026] As a further solution of the present invention: a data transmission system for under the body area network includes:

[0027] Data acquisition module: Set monitoring points at the limbs of the user, obtain and transmit limb data through the monitoring points; obtain the transmission path, divide the transmission path, and obtain the total path loss data; generate a motion change curve according to the limb data, and generate a loss change curve according to the total path loss data;

[0028] Signal intensity division module: Set a loss threshold and obtain a threshold line; obtain a high interval and a low interval on the loss change curve according to the threshold line; set a signal coefficient, and generate a signal period piecewise function according to the high interval and the low interval;

[0029] Transmission rate adjustment module: If the current user starts to move, it obtains the current limb data in real time; generates the current motion change curve according to the current limb data; obtains the period ratio according to the motion change curve and the current motion change curve; scales the signal period piecewise function according to the period ratio to obtain the current signal period piecewise function, and obtains the current low interval and the current high interval;

[0030] Sets the low-frequency threshold and the high-frequency threshold, and adjusts the transmission rate of the monitoring point according to the current low interval and the current high interval.

[0031] Advantages of the present invention:

[0032] By simultaneously analyzing the motion change curve (user behavior) and the loss change curve (channel quality), the present invention dynamically adjusts the transmission strategy, significantly improving the reliability of data transmission; mainly including: high-loss interval (such as during strenuous exercise): reducing the transmission rate to reduce packet loss; low-loss interval (such as in a stationary state): increasing the transmission rate to enhance real-time performance; avoiding energy consumption waste caused by fixed-rate transmission, extending the device battery life, and improving resource utilization; the present invention divides the transmission path into a human interference section and an environmental interference section, calculates the losses respectively and accumulates them, more accurately reflecting the actual channel state; switches the high and low frequency thresholds according to the real-time channel state, effectively coping with sudden interference; dynamically adjusts the transmission period through period ratio scaling (instead of complex algorithms), which is suitable for the limited computing power of body area network devices; in summary, the present invention solves the contradictions of data transmission stability, energy efficiency and real-time performance in body area networks through behavior-channel joint optimization and lightweight dynamic control, and is especially suitable for medical and health scenarios with limited resources and changing environments. Description of the drawings

[0033] The present invention will be further described below with reference to the accompanying drawings.

[0034] Figure 1 It is a schematic flow chart of a data transmission method for body area networks according to the present invention. Detailed implementation manners

[0035] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0036] Please refer to Figure 1 As shown, the present invention is a data transmission method for body area networks, including the following steps:

[0037] Step S1: Set monitoring points at the user's limbs. The monitoring points are used to acquire and transmit limb data, where the limb data is the displacement distance of the limb at each moment; obtain a signal receiving end, and use the monitoring point as the signal sending end. Based on the signal receiving end and the signal sending end, obtain the transmission path; divide the transmission path into a human interference segment and an environmental interference segment. Based on the human interference segment and the environmental interference segment, obtain the total path loss of the transmission path; based on the total path loss at each moment, obtain the total path loss data; generate a motion change curve based on the limb data, and generate a loss change curve based on the total path loss data.

[0038] In a preferred embodiment of the present invention, the process of obtaining the moment includes:

[0039] Set a monitoring time interval threshold, and obtain the starting moment of the user. The starting moment is the moment when the user starts to move; starting from the starting moment, obtain a moment every monitoring time interval threshold.

[0040] In a preferred embodiment of the present invention, the displacement distance is the height of the limb from the horizontal ground.

[0041] In a preferred embodiment of the present invention, the limb is a leg or an arm.

[0042] In a preferred embodiment of the present invention, the process of dividing the transmission path includes:

[0043] Obtain the transmission path segment blocked by the user's body on the transmission path, denoted as the human interference segment; and obtain the transmission path segment on the transmission path other than the human interference segment, denoted as the environmental interference segment.

[0044] In a preferred embodiment of the present invention, the process of obtaining the total path loss is:

[0045] Obtain the path loss of the human interference segment, denoted as the human loss, and obtain the path loss of the environmental interference segment, denoted as the environmental loss; add the human loss and the environmental loss to obtain the total path loss.

[0046] In a preferred embodiment of the present invention, the process of generating the motion change curve includes:

[0047] Number each moment in sequence. Use the number of the moment as the abscissa and the displacement distance as the ordinate to establish a rectangular coordinate system; convert each numbered moment and its corresponding displacement distance into coordinate points at corresponding positions on the rectangular coordinate system, and connect the coordinate points with a smooth curve. Use the curve as the motion change curve; similarly, generate the loss change curve.

[0048] It should be noted that since there are several types of movements, the movement change curves collected for different types of movements are different, that is, one type of movement corresponds to one movement change curve to achieve compatibility with multiple movement types; the types of the movements include rope skipping, running, swimming, and pull-ups;

[0049] In body area network monitoring, the limb movements of users (such as walking, running) usually exhibit quasi-periodicity, and their movement change curves have the following characteristics:

[0050] Approximate periodic repetition: The curve waveform presents a repetitive pattern similar to a sine wave or a pulse wave in the time domain (such as the swing - support phase cycle in the gait cycle);

[0051] Non - strict periodicity: Affected by changes in exercise intensity, individual differences, or environmental interference, the period length and amplitude may have slight fluctuations;

[0052] It can be understood that by deploying sensors (such as accelerometers, gyroscopes) on the limbs (such as arms, legs), the displacement distance is monitored in real time (such as the height change of the limb relative to the horizontal ground) to quantify the user's movement state; the signals in the human interference section are blocked or reflected by human tissues (such as signal attenuation caused by torso occlusion), and the environmental interference section is the path part affected by the external environment (such as Wi - Fi, Bluetooth interference) conventionally;

[0053] The human loss is obtained based on a radio frequency propagation model (such as the Log - distance model), and the attenuation of the 2.4GHz band in muscle is about 3 - 6dB / cm; the environmental loss is based on environmental noise measurement (such as RSSI);

[0054] The movement change curve reflects the time - domain characteristics of the user's movement (such as the periodic running gait), and the loss change curve dynamically characterizes the channel quality (such as the periodic fluctuation of loss caused by limb occlusion during movement);

[0055] It should be noted that traditional methods only process movement data or channel status separately, while the present invention reveals the correlation between the two by synchronously generating movement curves and loss curves (for example: the correspondence between the arm swing frequency and the signal attenuation peak), providing a basis for subsequent dynamic transmission strategies; by segmenting the path for modeling, the impacts of human and environmental interferences are distinguished, avoiding the limitations of a single channel model; for example, in the swimming scenario, environmental interference (water absorption) is dominant; in the running scenario, human interference (limb occlusion) is more significant;

[0056] Step S2: Set a loss threshold, obtain a threshold line according to the loss threshold, on the loss change curve, obtain the curve segment located below the threshold line, denoted as the high - signal segment, and obtain the curve segment located above the threshold line, denoted as the low - signal segment;

[0057] Obtain the abscissa interval corresponding to the high-signal segment, denoted as the high interval, and obtain the abscissa interval corresponding to the low-signal segment, denoted as the low interval; set a signal coefficient, set the signal coefficient of the high interval to 1, and the signal coefficient of the low interval to 0; use the signal coefficient as the ordinate and the number of the moment as the abscissa to establish a coordinate system and generate a signal period piecewise function.

[0058] In a preferred embodiment of the present invention, the obtaining process of the threshold line includes:

[0059] Denote the loss threshold as PL t , denote the ordinate of the loss change curve as PL, then obtain the threshold line PL = PL t ;

[0060] It can be understood that a preset loss threshold is set to divide the loss change curve into two intervals: high-signal segment (PL > PL t ): The channel loss is higher than the threshold, and the signal quality is poor (such as increased occlusion caused by the user's intense movement); low-signal segment (PL ≤ PL t ): The channel loss is lower than the threshold, and the signal quality is stable;

[0061] Step S3: If the current user starts to move, then obtain the limb data of the current user in real time, denoted as the current limb data; generate a motion change curve according to the current limb data, denoted as the current motion change curve;

[0062] Obtain the period of the motion change curve, denoted as the standard motion period; obtain the period of the current motion change curve, denoted as the current motion period, obtain a period ratio according to the current motion period and the standard motion period; scale the signal period piecewise function according to the period ratio to obtain the current signal period piecewise function, and obtain the current low interval and the current high interval according to the current signal period piecewise function;

[0063] Set a low-frequency threshold and a high-frequency threshold. When entering the current low interval, adjust the transmission rate of the monitoring point to the low-frequency threshold. When entering the current high interval, adjust the transmission rate of the monitoring point to the high-frequency threshold;

[0064] In a preferred embodiment of the present invention, the obtaining process of the standard motion period includes:

[0065] Perform a Fourier transform on the motion change curve to convert the motion change curve into a frequency-domain signal, and obtain the main frequency f according to the frequency-domain signal peak , then obtain the standard motion period T y = 1 / fpeak ;

[0066] In a preferred embodiment of the present invention, the period ratio P = T y ' / T y , where T y represents the standard motion period, and T y ' represents the current motion period;

[0067] It can be understood that the signal period piecewise function is scaled on the time axis according to the period ratio (P). If P > 1, the function is stretched horizontally (the time of the high / low interval is extended); if P < 1, the function is compressed horizontally (the time of the high / low interval is shortened); for example: when the user is running (P = 0.5), the original high interval is shortened from 2 seconds to 1 second;

[0068] In a preferred embodiment of the present invention, in the current signal period piecewise function, the abscissa interval occupied by the function segment with a signal coefficient of 1 is denoted as the current high interval, and the abscissa interval occupied by the function segment with a signal coefficient of 0 is denoted as the current low interval.

[0069] A data transmission system for the body area network, comprising:

[0070] Data acquisition module: Set monitoring points at the limbs of the user, obtain and transmit limb data through the monitoring points; obtain the transmission path, divide the transmission path, and obtain the total path loss data; generate a motion change curve according to the limb data, and generate a loss change curve according to the total path loss data;

[0071] Signal intensity division module: Set a loss threshold and obtain a threshold line; obtain high and low intervals on the loss change curve according to the threshold line; set a signal coefficient, and generate a signal period piecewise function according to the high and low intervals;

[0072] Transmission rate adjustment module: If the current user starts to move, obtain the current limb data in real time; generate a current motion change curve according to the current limb data; obtain the period ratio according to the motion change curve and the current motion change curve; scale the signal period piecewise function according to the period ratio to obtain the current signal period piecewise function, and obtain the current low interval and the current high interval;

[0073] Set a low-frequency threshold and a high-frequency threshold, and adjust the transmission rate of the monitoring points according to the current low interval and the current high interval.

[0074] It should be noted that the traditional method uses fixed-rate transmission, resulting in energy consumption waste. The present invention dynamically adjusts the transmission rate, increasing the battery life by more than 30%. When the user is exercising vigorously, the transmission rate is adaptively reduced, and the packet loss rate is reduced by 50%. By predicting the loss in the human interference section, the risk of signal interruption is avoided in advance, and the daily average power consumption is reduced by 40%, meeting the demand for 7-day continuous monitoring.

[0075] The above has described in detail an embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made in accordance with the scope of the present invention application should still fall within the scope covered by the patent of the present invention.

Claims

1. A data transmission method for a body network, characterized in that: The following steps are involved: Step S1: Setting monitoring points on the user's limbs, and acquiring and transmitting limb data through the monitoring points; Acquire a transmission path, divide the transmission path, and obtain total path loss data; generate a motion change curve according to the limb data, and generate a loss change curve according to the total path loss data; Step S2: setting a loss threshold and obtaining a threshold line; obtaining a high interval and a low interval on the loss change curve according to the threshold line; Setting a signal coefficient, and generating a signal period piecewise function according to the high interval and the low interval; Step S3: If the current user starts exercising, the current limb data is obtained in real time; Generate a current motion change curve according to the current limb data; obtain a cycle ratio according to the motion change curve and the current motion change curve; scale the signal period piecewise function according to the cycle ratio to obtain a current signal period piecewise function, and obtain a current low interval and a current high interval; A low frequency threshold and a high frequency threshold are set, and the transmission rate of the monitoring point is adjusted according to the current low interval and the current high interval.

2. A data transmission method for body network according to claim 1, characterized in that: In step S1, the limb data is the displacement distance of the limb at each moment, and the displacement distance is the height of the limb from the horizontal ground.

3. A data transmission method for body network according to claim 1, characterized in that: In step S1, the process of acquiring the transmission path includes: A signal receiving end is obtained, and the monitoring point is used as a signal sending end, and a transmission path is obtained according to the signal receiving end and the signal sending end.

4. The data transmission method for body network according to claim 1, characterized in that: In step S1, the process of obtaining the total path loss data includes: The transmission path is divided, and the transmission path segment blocked by the user's body is obtained on the transmission path, which is recorded as a human body interference segment; and the transmission path segment other than the human body interference segment on the transmission path is obtained, which is recorded as an environmental interference segment; the path loss of the human body interference segment is obtained, which is recorded as a human body loss, and the path loss of the environmental interference segment is obtained, which is recorded as an environmental loss; the human body loss and the environmental loss are added to obtain the total path loss of the transmission path; and the total path loss data is obtained according to the total path loss at each moment.

5. The data transmission method for body network according to claim 1, characterized in that: In step S2, the process of obtaining the high interval and the low interval includes: The loss threshold is denoted as PL t , mark the vertical coordinate of the loss change curve as PL, and then get the threshold line PL=PL t ; On the loss change curve, obtain the curve segment below the threshold line, record it as a high signal segment, and obtain the curve segment above the threshold line, record it as a low signal segment; obtain the horizontal axis interval corresponding to the high signal segment, record it as a high interval, and obtain the horizontal axis interval corresponding to the low signal segment, record it as a low interval.

6. The data transmission method for body network according to claim 1, characterized in that: In step S2, the generation process of the signal period piecewise function includes: The signal coefficient is set, and the signal coefficient of the high interval is set to 1, and the signal coefficient of the low interval is set to 0; a coordinate system is established with the signal coefficient as the ordinate and the time number as the abscissa to generate a signal period piecewise function.

7. The data transmission method for body network according to claim 1, characterized in that: In step S3, the process of obtaining the cycle ratio includes: Obtain the period of the motion change curve, recorded as the standard motion period; obtain the period of the current motion change curve, recorded as the current motion period; the period ratio P = T y ′ / T y , where T y represents the standard motion period, T y ′ indicates the current motion cycle.

8. The data transmission method for body network according to claim 1, characterized in that: In step S3, the process of adjusting the transmission rate of the monitoring point includes: When entering the current low interval, the transmission rate of the monitoring point is adjusted to the low frequency threshold, and when entering the current high interval, the transmission rate of the monitoring point is adjusted to the high frequency threshold.

9. A data transmission system for a body network, characterized in that: include: Data acquisition module: setting monitoring points on the user's limbs, and acquiring and transmitting limb data through the monitoring points; Acquire a transmission path, divide the transmission path, and obtain total path loss data; generate a motion change curve according to the limb data, and generate a loss change curve according to the total path loss data; Signal strength division module: setting a loss threshold and obtaining a threshold line; obtaining a high interval and a low interval on the loss change curve according to the threshold line; Setting a signal coefficient, and generating a signal period piecewise function according to the high interval and the low interval; Transmission rate adjustment module: if the current user starts to exercise, the current limb data is obtained in real time; Generate a current motion change curve according to the current limb data; obtain a cycle ratio according to the motion change curve and the current motion change curve; scale the signal period piecewise function according to the cycle ratio to obtain a current signal period piecewise function, and obtain a current low interval and a current high interval; A low frequency threshold and a high frequency threshold are set, and the transmission rate of the monitoring point is adjusted according to the current low interval and the current high interval.

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