A multi-path transmission scheduling method and system based on wearable ECG data

Abstract

The present invention discloses a multi-path transmission scheduling method and system based on wearable ECG data, which collects ECG data through a wearable electrocardiogram monitoring device; performs filtering and denoising on the ECG data; uses wavelet packet transform to decompose the filtered and denoised ECG data to obtain a low-frequency ECG queue and a high-frequency ECG queue; obtains the priority of each path according to the path quality of each path; selects the optimal path for data transmission according to the low-frequency ECG queue, the high-frequency ECG queue and the priority of each path; constructs an energy model of the transmitted ECG data; and adjusts the path of the next transmission according to the energy consumption calculated by the energy model. The present invention solves the problem that the existing multi-path transmission scheduling method cannot perform multi-path transmission scheduling according to the wearable network transmission coverage, network quality and energy consumption, and the wearable electrocardiogram monitoring data characteristics to ensure an effective balance between transmission delay and energy consumption.

Description

Technical Field

[0001] The present invention relates to the field of network transmission technology, and in particular to a multi-path transmission scheduling method and system based on wearable ECG data. Background Art

[0002] In the current network, single-path transmission based on TCP / IP still occupies the mainstream. No matter how many available interfaces exist between the two communicating parties, only one transmission path is established during the entire transmission process. When this only path has problems, the transmission quality will be seriously affected. With the development of technology, wearable devices have multiple network interfaces such as WiFi, Bluetooth, and Cellular. When multiple networks are available, the wearable operating system defaults to the static interface selection strategy. Since WiFi has a fast transmission rate but higher basic power consumption, and BT is more energy-efficient but has a slow transmission speed and a small transmission range, single-path interface transmission cannot provide the best compromise between transmission performance and energy consumption. Considering that long-term real-time ECG monitoring has high requirements for wearable network transmission in terms of latency and energy consumption, a more suitable multi-path interface selection strategy is needed to balance performance and energy consumption. In the medical and health data transmission environment, physiological data needs to be transmitted, observed, and perceived with a high success rate to improve the efficiency of diagnostic processing. In recent years, for human area networks, priority is given according to the patient's medical condition, which can effectively adjust data transmission and congestion, thereby ensuring that ECG data can be transmitted in real time and quickly.

[0003] Invention patent CN109547505A provides a multipath TCP transmission scheduling method based on reinforcement learning, which predicts the number of data packets required in multipath transmission through a reinforcement learning model, reduces transmission time, and reduces data packet disorder, so that it can be reasonably scheduled in the shortest time. However, this method does not take into account the model calculation amount and is not suitable for multipath transmission scheduling of wearable devices. Invention patent CN109547360A provides a method and device for data transmission scheduling, which determines its maximum waiting time for sending according to the time when the router receives the data packet and the data flow status information, and then performs data packet scheduling. However, this method has a large workload for calculating data packets and does not take into account the problem that may cause buffer blockage. Invention patent CN112422678A provides a network resource transmission scheduling method and system, which allocates the target transmission capacity according to the initial state of each channel and the total capacity to be transmitted, and sets a time interval to recalculate the current state, thereby reducing the transmission time. However, this method does not take into account the special data traffic characteristics and is not effective in the face of sudden emergencies. Invention patent CN109922318A provides a multi-channel video transmission scheduling method, device, storage medium and electronic terminal, which compares the minimum transmission rate required by the capacity of the data packet to be transmitted with the current maximum transmission rate allowed, and then allocates the transmission time according to the priority. However, this method actually solves the scheduling of multiple different video streams, rather than the transmission scheduling problem of multiple paths. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, a multipath transmission scheduling method and system based on wearable ECG data provided by the present invention solves the problem that the existing multipath transmission scheduling method cannot perform multipath transmission scheduling according to the wearable network transmission coverage, network quality and energy consumption, and wearable ECG monitoring data characteristics to ensure an effective balance between transmission delay and energy consumption.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a multi-path transmission scheduling method based on wearable ECG data, comprising the following steps:

[0006] S1, collect ECG data through wearable ECG monitoring equipment;

[0007] S2, filtering and denoising the ECG data;

[0008] S3, using wavelet packet transform to decompose the ECG data after filtering and denoising to obtain a low-frequency ECG queue and a high-frequency ECG queue;

[0009] S4. Obtaining the priority of each path according to the path quality of each path;

[0010] S5. Select an optimal path for data transmission according to the low-frequency ECG queue, the high-frequency ECG queue and the priority of each path;

[0011] S6, constructing an energy model of the ECG data sent in step S5;

[0012] S7. Adjust the path of the next transmission according to the energy consumption calculated by the energy model.

[0013] Furthermore, the step S3 includes the following sub-steps:

[0014] S31, setting a sampling frequency threshold, and using wavelet packet transform to decompose the denoised ECG data;

[0015] S32. According to the sampling frequency threshold, the queues decomposed by wavelet packet transform are prioritized to obtain a low-frequency ECG queue and a high-frequency ECG queue.

[0016] The beneficial effects of the above further solution are: decomposing ECG data according to the sampling frequency threshold, reducing the amount of transmitted data and lowering data transmission delay.

[0017] Furthermore, the formula for decomposing the denoised ECG data using wavelet packet transform in step S31 is:

[0018]

[0019] Among them, μ 2n (t) is the scaling function of the number of 2n oscillations at time t, is the wavelet function with 2n+1 oscillation times at time t, μ n (2t-k) is the binary wavelet transform, h k is a low-pass filter, g k is a high-pass filter, k is the position coordinate, and n is the number of oscillations.

[0020] Furthermore, the step S4 includes the following sub-steps:

[0021] S41, calculating the path quality of each path according to the quality parameter of each path;

[0022] S42. Assign a priority to each path according to the path quality of each path to obtain the priority of each path.

[0023] Furthermore, the formula for calculating the path quality of the path in step S41 is:

[0024]

[0025] Among them, PQ is the path quality of the path, RTT is the round-trip time, and PLR is the packet loss rate.

[0026] Furthermore, the quality parameters in step S41 include: RTT and packet loss rate.

[0027] Furthermore, the paths in step S4 include: a Bluetooth path and a WiFi path.

[0028] Furthermore, the formula of the energy model in step S6 is:

[0029] e(t)=ε0ξ(t)r(t)+ζ(t)+ζ c

[0030] Among them, e(t) is the energy consumption at time t, ξ(t) is the working state of the wearable ECG monitoring device sensor at time t, r(t) is the ECG data sampling at time t, ζ(t) is the data transmission energy consumption at time t, and ζ c is the circuit energy consumption, and ε0 is the sampling power.

[0031] The beneficial effects of the above further scheme are as follows: the present invention performs multi-path priority scheduling on wearable ECG data according to the priority order after ECG data decomposition and the priority order of path quality of Bluetooth and WiFi multi-paths, and finally balances the completion time of each path to ensure that the total completion time is minimized; in the scheduling process, the energy model is used to calculate the energy consumption required for transmission in real time, set a threshold and judge whether the data transmission energy consumption is within a reasonable threshold range, thereby combining energy consumption, ECG data frequency, and path quality, and timely feedback and adjustment of the next data scheduling to ensure an effective balance between transmission delay and energy consumption.

[0032] A system for multi-path transmission scheduling method based on wearable ECG data, comprising: a wearable ECG monitoring device, a filtering and denoising unit, a wavelet packet transform unit, a priority arrangement unit and an optimal path scheduling unit;

[0033] The wearable ECG monitoring device is used to collect ECG data, the filtering and denoising unit is used to filter and denoise the ECG data, the wavelet packet transform unit is used to decompose the denoised ECG data to obtain a low-frequency ECG queue and a high-frequency ECG queue, the priority arrangement unit is used to obtain the priority of each path according to the path quality of each path, the optimal path scheduling unit is used to select the optimal path for data transmission according to the low-frequency ECG queue, the high-frequency ECG queue and the priority of each path; the energy consumption calculation unit is used to calculate the energy consumption of transmitting ECG data; the path adjustment unit is used to adjust the path of the next transmission according to the energy consumption calculated by the energy model.

[0034] In summary, the beneficial effects of the present invention are:

[0035] 1. The present invention utilizes Bluetooth and WiFi transmission paths to construct a wearable multi-path transmission network, and comprehensively considers various requirements in the wearable ECG monitoring environment, such as weak ECG signals, large data volume, and real-time transmission. It considers the priority of Bluetooth and WiFi multi-path network quality, as well as the priority of wearable ECG data, and designs a multi-path transmission scheduling method that ensures the balance between wearable ECG data transmission delay and energy consumption.

[0036] 2. The model of the present invention has a small amount of calculation and is suitable for multi-path transmission scheduling of wearable devices.

[0037] 3. The present invention uses a priority scheduling method that combines ECG data frequency with path quality to effectively schedule data transmission, reduce transmission delay, avoid data buffer congestion, and ensure the real-time performance of wearable ECG data transmission.

[0038] 4. The present invention constructs an ECG data transmission priority queue based on the ECG data flow characteristics that low-frequency ECG data contains important heart condition details and high-frequency ECG data increases the amount of ECG signal details, reasonably sets the frequency threshold, and performs data scheduling according to the ECG data flow characteristics to reduce data transmission delay. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 The figure is a flowchart of a multi-path transmission scheduling method based on wearable ECG data. DETAILED DESCRIPTION

[0040] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0041] like Figure 1 As shown, a multipath transmission scheduling method based on wearable ECG data includes the following steps:

[0042] S1, collect ECG data through wearable ECG monitoring equipment;

[0043] S2, filtering and denoising the ECG data;

[0044] S3, using wavelet packet transform to decompose the ECG data after filtering and denoising to obtain a low-frequency ECG queue and a high-frequency ECG queue;

[0045] The step S3 comprises the following sub-steps:

[0046] S31, setting a sampling frequency threshold, and using wavelet packet transform to decompose the denoised ECG data;

[0047] The formula for decomposing the denoised ECG data using wavelet packet transform in step S31 is:

[0048]

[0049] Among them, μ 2n (t) is the scaling function of the number of 2n oscillations at time t, is the wavelet function with 2n+1 oscillation times at time t, μ n (2t-k) is the binary wavelet transform, h k is a low-pass filter, g k is a high-pass filter, k is the position coordinate, and n is the number of oscillations.

[0050] S32. Prioritize the queues after wavelet packet transform decomposition according to the sampling frequency threshold to obtain a low-frequency ECG queue and a high-frequency ECG queue, wherein the low-frequency ECG queue contains detailed heart condition data, and the high-frequency ECG queue, in addition to containing detailed heart condition data, also increases the amount of details of the electrocardiogram signal.

[0051] S4. Obtaining the priority of each path according to the path quality of each path;

[0052] The step S4 comprises the following sub-steps:

[0053] S41, calculating the path quality of each path according to the quality parameter of each path;

[0054] The formula for calculating the path quality of the path in step S41 is:

[0055]

[0056] Among them, PQ is the path quality of the path, RTT is the round-trip time, and PLR is the packet loss rate.

[0057] In this embodiment, the quality parameters in step S41 include: RTT and packet loss rate.

[0058] S42. Assign a priority to each path according to the path quality of each path to obtain the priority of each path.

[0059] In this embodiment, the paths in step S4 include: a Bluetooth path and a WiFi path.

[0060] S5. Select an optimal path for data transmission according to the low-frequency ECG queue, the high-frequency ECG queue and the priority of each path;

[0061] S6, constructing an energy model of the ECG data sent in step S5;

[0062] The formula of the energy model in step S5 is:

[0063] e(t)=ε0ξ(t)r(t)+ζ(t)+ζ c

[0064] Among them, e(t) is the energy consumption at time t, ξ(t) is the working state of the wearable ECG monitoring device sensor at time t, r(t) is the ECG data sampling at time t, ζ(t) is the data transmission energy consumption at time t, and ζ c is the circuit energy consumption, and ε0 is the sampling power.

[0065] S7. Adjust the path of the next transmission according to the energy consumption calculated by the energy model.

[0066] In this embodiment, the ECG queue with the highest priority selects the best path for transmission and is scheduled in descending order.

[0067] A system for multi-path transmission scheduling method based on wearable ECG data, comprising: a wearable ECG monitoring device, a filtering and denoising unit, a wavelet packet transform unit, a priority arrangement unit and an optimal path scheduling unit;

[0068] The wearable ECG monitoring device is used to collect ECG data, the filtering and denoising unit is used to filter and denoise the ECG data, the wavelet packet transform unit is used to decompose the denoised ECG data to obtain a low-frequency ECG queue and a high-frequency ECG queue, the priority arrangement unit is used to obtain the priority of each path according to the path quality of each path, the optimal path scheduling unit is used to select the optimal path for data transmission according to the low-frequency ECG queue, the high-frequency ECG queue and the priority of each path; the energy consumption calculation unit is used to calculate the energy consumption of transmitting ECG data; the path adjustment unit is used to adjust the path of the next transmission according to the energy consumption calculated by the energy model.

[0069] In summary, the beneficial effects of the present invention are as follows: the multi-path transmission scheduling method of the present invention can better "adapt" to the special environments such as the network quality, energy consumption requirements, ECG signal characteristics, etc. of the Bluetooth and WiFi multi-path of the wearable network, decompose the ECG data according to the sampling frequency threshold, reduce the amount of transmitted data, reduce the data transmission delay, calculate the energy consumption of the transmission in real time through the energy model, set the threshold and judge whether the data transmission energy consumption is within a reasonable threshold range, and further combine the energy consumption, ECG data frequency, and path quality, and timely feedback to the next data scheduling process, so as to improve resource utilization and work better in the multi-path transmission scheduling of wearable devices.

Claims

1. A multipath transmission scheduling method based on wearable ECG data, characterized in that: The following steps are involved: S1, collect ECG data through wearable ECG monitoring equipment; S2, filtering and denoising the ECG data; S3, using wavelet packet transform to decompose the ECG data after filtering and denoising to obtain a low-frequency ECG queue and a high-frequency ECG queue; The step S3 comprises the following sub-steps: S31, setting a sampling frequency threshold, and using wavelet packet transform to decompose the denoised ECG data; S32, according to the sampling frequency threshold, the queues decomposed by wavelet packet transform are prioritized to obtain a low-frequency ECG queue and a high-frequency ECG queue; S4. Obtaining the priority of each path according to the path quality of each path; S5. Select an optimal path for data transmission according to the low-frequency ECG queue, the high-frequency ECG queue and the priority of each path; S6, constructing an energy model of the ECG data sent in step S5; The formula of the energy model in step S6 is: e(t)=ε0ξ(t)r(t)+ζ(t)+ζ c Among them, e(t) is the energy consumption at time t, ξ(t) is the working state of the wearable ECG monitoring device sensor at time t, r(t) is the ECG data sampling at time t, ζ(t) is the data transmission energy consumption at time t, and ζ c is the circuit energy consumption, ε0 is the sampling power; S7. Adjust the path of the next transmission according to the energy consumption calculated by the energy model.

2. The multipath transmission scheduling method based on wearable ECG data according to claim 1, characterized in that: The formula for decomposing the denoised ECG data using wavelet packet transform in step S31 is: Among them, μ 2n (t) is the scaling function of the number of 2n oscillations at time t, is the wavelet function with 2n+1 oscillation times at time t, μ n (2t-k) is the binary wavelet transform, h k is a low-pass filter, g k is a high-pass filter, k is the position coordinate, and n is the number of oscillations.

3. The multipath transmission scheduling method based on wearable ECG data according to claim 1, characterized in that: The step S4 comprises the following sub-steps: S41, calculating the path quality of each path according to the quality parameter of each path; S42. Assign a priority to each path according to the path quality of each path to obtain the priority of each path.

4. The multipath transmission scheduling method based on wearable ECG data according to claim 3 is characterized in that: The formula for calculating the path quality of the path in step S41 is: Among them, PQ is the path quality of the path, RTT is the round-trip time, and PLR is the packet loss rate.

5. The multipath transmission scheduling method based on wearable ECG data according to claim 3, characterized in that: The quality parameters in step S41 include: RTT and packet loss rate.

6. The multi-path transmission scheduling method based on wearable ECG data according to claim 1, characterized in that: The paths in step S4 include: a Bluetooth path and a WiFi path.

7. A system for multi-path transmission scheduling method based on wearable ECG data, characterized in that: include: Wearable ECG monitoring device, filtering and denoising unit, wavelet packet transform unit, priority arrangement unit, optimal path scheduling unit, energy consumption calculation unit and path adjustment unit; The wearable ECG monitoring device is used to collect ECG data, the filtering and denoising unit is used to filter and denoise the ECG data, and the wavelet packet transform unit is used to decompose the denoised ECG data to obtain a low-frequency ECG queue and a high-frequency ECG queue, and the decomposition method includes: Set the sampling frequency threshold and use wavelet packet transform to decompose the denoised ECG data; According to the sampling frequency threshold, the queues decomposed by wavelet packet transform are prioritized to obtain a low-frequency ECG queue and a high-frequency ECG queue; The priority arrangement unit is used to obtain the priority of each path according to the path quality of each path. The optimal path scheduling unit is used to select the optimal path for data transmission according to the low-frequency ECG queue, the high-frequency ECG queue and the priority of each path; the energy consumption calculation unit is used to calculate the energy consumption of transmitting ECG data, and the calculation formula is: e(t)=ε0ξ(t)r(t)+ζ(t)+ζ c Among them, e(t) is the energy consumption at time t, ξ(t) is the working state of the wearable ECG monitoring device sensor at time t, r(t) is the ECG data sampling at time t, ζ(t) is the data transmission energy consumption at time t, and ζ c is the circuit energy consumption, ε0 is the sampling power; The path adjustment unit is used to adjust the path of the next transmission according to the energy consumption calculated by the energy model.

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