Communication data sampling method and system for electronic device

Abstract

The application relates to the technical field of electronic communication equipment, in particular to a communication data sampling method and system for electronic equipment. The method divides a communication signal curve into local signal segments, and obtains the importance of the local signal segments; the data points of the communication signal curve are classified, the motion risk degree of the data points of each category is obtained according to the number of the data points of each category in the local signal segments and the amplitude of the data points; the motion data points are screened out by combining the motion risk degree of the data points of each category in the local signal segments and the importance; the optimal sampling rate is selected according to the number of the motion data points obtained under each to-be-tested sampling rate, the blood oxygen concentration sequence of the motion time period is obtained based on the optimal sampling rate, and the elements in the sequence are compressed and transmitted. The number of the important motion information, i.e. the motion data points, obtained based on the to-be-tested sampling rate is used to select the optimal sampling rate, so that the reliability and stability of data transmission are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electronic communication equipment, and particularly relates to a communication data sampling method and system for electronic equipment. BACKGROUND

[0002] Electronic equipment is a kind of intelligent terminal equipment integrating sensors, wireless communication and Bluetooth modules, and having basic functions such as signal processing detection and data transmission. Generally, electronic equipment integrates multiple functional modules in order to be closer to life scenes. In the process of monitoring motion by using electronic equipment, due to the fact that electronic equipment is designed to be exquisite and has low power consumption, data loss is prone to occur in the process of communication transmission, so that timely tracking and monitoring of motion behavior and motion physiological state of a wearer cannot be achieved. Meanwhile, the amount of original data collected by the sensor of electronic equipment is relatively large, and power consumption and transmission speed need to be considered in the process of data transmission, so that a suitable sampling rate needs to be set before data compression; if the sampling rate is not set properly, important data will be lost in the process of data transmission, and the reliability and stability of data transmission are reduced. SUMMARY

[0003] In order to solve the technical problem that improper setting of a sampling rate causes important data to be lost in the process of data transmission and reduces the reliability of data transmission, the purpose of the present application is to provide a communication data sampling method and system for electronic equipment, and the technical solution adopted is as follows:

[0004] In a first aspect, an embodiment of the present application provides a communication data sampling method for electronic equipment, which comprises the following steps:

[0005] Obtaining a communication signal curve corresponding to electronic communication equipment in a historical time period;

[0006] Dividing the communication signal curve into different local signal segments; obtaining the importance of each local signal segment according to the time interval between the time instants corresponding to adjacent extreme points of each local signal segment and the difference between the amplitudes of any two data points;

[0007] Dividing the data points of the communication signal curve into different categories; obtaining the motion risk degree of the data points of each category according to the number of data points of each category in different local signal segments and the amplitude of the data points of each category;

[0008] Screening motion data points from the data points of the communication signal curve in combination with the difference between the importance of each local signal segment and the importance of the remaining local signal segments and the motion risk degree of the data points of each category of each local signal segment;

[0009] Set different to-be-tested sampling rates, sample data points of the communication signal curve at each to-be-tested sampling rate respectively, and select an optimal sampling rate from the to-be-tested sampling rates according to the number of motion data points obtained under each to-be-tested sampling rate;

[0010] Based on the optimal sampling rate, a blood oxygen concentration data sequence in a current motion time period is obtained, and elements in the blood oxygen concentration data sequence are compressed and transmitted.

[0011] Further, the calculation formula of the importance of each local signal segment is as follows:

[0012] In the formula, W is the importance of each local signal segment; t i is the corresponding time of the i th extreme point of each local signal segment; t i+1 is the corresponding time of the i+1 th extreme point of each local signal segment; I is the total number of extreme points of each local signal segment; ∈ is a preset positive number; U is the total number of data points of each local signal segment; h u is the amplitude of the u th data point of each local signal segment; h v is the amplitude of the v th data point of each local signal segment; h j is the amplitude of the j th data point of each local signal segment; || is an absolute value function.

[0013] Further, the method for dividing the data points of the communication signal curve into different categories comprises:

[0014] The data points of the communication signal curve with the same amplitude are taken as data points of the same category.

[0015] Further, the calculation formula of the motion risk degree of each category of data points is as follows:

[0016] In the formula, T y is the motion risk degree of the y th category of data points; L is the total number of local signal segments; G y is the total number of data points of the y th category of the communication signal curve; g r,y is the total number of data points of the y th category of the r th local signal segment; h y is the amplitude of the y th category of data points; p is a preset blood oxygen risk threshold; ∈ is a preset positive number; exp is an exponential function with the natural constant e as the base number; || is an absolute value function.

[0017] Further, the method for screening the motion data points from the data points of the communication signal curve comprises:

[0018] obtaining a retention degree of each data point of each local signal segment according to the difference between the importance of each local signal segment and the rest of the local signal segments, and the motion risk degree of each category of data points of each local signal segment and the importance of each local signal segment;

[0019] For each data point of each local signal segment, the data point with the retention degree greater than a preset retention threshold is taken as a motion data point.

[0020] Further, the calculation formula of the retention degree of each data point of each local signal segment is as follows:

[0021] In the formula, is the retention degree of the zth data point of the rth local signal segment; W r is the importance of the rth local signal segment; W f is the importance of the fth local signal segment other than the rth local signal segment; L is the total number of local signal segments; T r,z is the motion risk degree of the data points of the category to which the zth data point of the rth local signal segment belongs; Norm is a normalization function; || is an absolute value function.

[0022] Further, the method for selecting the optimal sampling rate from the to-be-tested sampling rates comprises:

[0023] The data points of the communication signal curve are sampled at each to-be-tested sampling rate respectively, and the total number of motion data points in the data points sampled at each to-be-tested sampling rate is taken as a judgment index of each to-be-tested sampling rate;

[0024] The to-be-tested sampling rate corresponding to the largest judgment index is taken as the optimal sampling rate.

[0025] Further, the method for compressing the elements in the blood oxygen concentration data sequence is run-length encoding.

[0026] Further, the preset retention threshold is 0.6.

[0027] In a second aspect, another embodiment of the present application provides a communication data sampling system for an electronic device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of any one of the above methods are implemented.

[0028] The present application has the following beneficial effects:

[0029] In the embodiment of the present application, in order to improve the accuracy of subsequent analysis, the communication signal curve is divided into local signal segments, the time interval between the adjacent extreme points of the local signal segment presents the size of the change frequency of the blood oxygen saturation, and the difference between the amplitudes of any two data points reflects the change degree of the blood oxygen signal in the local signal segment. The importance degree obtained by comprehensively analyzing the above two factors can more accurately reflect the movement risk degree. The local signal segment may contain some data points that are important for expressing the movement situation, and such data points can better express the blood oxygen information of the movement risk. Since the arbitrary blood oxygen saturation appears periodic change with the breathing process, compared with the normal state, the local signal segment under the movement state contains more periods, and the number of local signal segments of each category of data points reflects the movement state degree of the category of data points. In addition, the amplitude of the data point is lower during the movement, so the analysis of the number of each category of data points in different local signal segments and the amplitude of the data point makes the obtained movement risk degree more accurate. The difference between the importance degrees of different local signal segments reflects the prominence degree of the local signal segment, and further reflects the possibility of the movement state. The movement risk degree presents the possibility of each category of data points representing the movement risk situation. The comprehensive analysis of the above two factors makes the screened movement data points more accurate. According to the number of movement data points obtained by the to-be-measured sampling rate, the optimal sampling rate is selected, and the data is compressed and transmitted by using the optimal sampling rate. The data accuracy and stability are ensured, the load and power consumption of data transmission are reduced, the communication cost is reduced to a certain extent, and the real-time performance and accuracy of the movement alarm are improved. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor based on these drawings.

[0031] Figure 1 The step flow chart of the communication data sampling method for an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following describes the communication data sampling method and system for an electronic device according to the present application, the specific implementation, structure, features and effects thereof in detail. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0034] The specific scenario to which the present application is directed: Generally, electronic communication equipment will integrate physiological detection functions to achieve the capture and detection of human physiological information. These function modules mainly include heart rate sensors, blood oxygen sensors, pressure sensors, and temperature sensors, etc. In the process of data transmission, the sampling rate is reasonably adjusted to avoid data transmission congestion, and the communication efficiency and stability are further improved.

[0035] The specific scheme of the communication data sampling method and system for electronic equipment provided by the present application will be specifically described below with reference to the accompanying drawings.

[0036] Please refer to Figure 1 , which shows the step flowchart of the communication data sampling method for electronic equipment provided by an embodiment of the present application. The method comprises:

[0037] Step S1: Obtain the communication signal curve corresponding to the electronic communication equipment in the historical time period.

[0038] Specifically, a high-precision blood oxygen sensor is integrated in the electronic communication equipment to ensure that the blood oxygen sensor is in a normal working state; the blood oxygen saturation is collected in real time by the blood oxygen sensor in the electronic communication equipment, and in the monitoring process, when the blood oxygen saturation decreases to a dangerous level, the electronic communication equipment communicates with the mobile phone application and sends an alarm information.

[0039] The display signal of the blood oxygen sensor in the electronic communication equipment in the historical time period is taken as the communication signal curve, the horizontal axis of the communication signal curve is time, and the vertical axis is blood oxygen saturation.

[0040] In order to improve the transmission efficiency and reduce the power consumption, it is necessary to compress the blood oxygen saturation data in the motion time period, and information may be lost in the compression process. In order to maximize the retention of effective data after compression, the present application needs to set a suitable sampling rate to sample the blood oxygen saturation data, discard some unimportant information, maximize the retention of effective information, and improve the data transmission speed.

[0041] Step S2: Divide the communication signal curve into different local signal segments; according to the time interval between the corresponding time of each local signal segment adjacent extreme value point and the difference between the amplitudes of any two data points, obtain the importance of each local signal segment.

[0042] To improve the accuracy of subsequent analysis, the communication signal curve is uniformly segmented to obtain different local signal segments, and the total number of local signal segments is denoted as L, which is empirically 10 in the embodiment of the present application, and the implementer can set it according to the specific circumstances. It should be noted that the time lengths of all local signal segments corresponding to the time periods are equal.

[0043] The time interval between the time instants corresponding to the adjacent extreme points of the local signal segment presents the size of the change frequency of the blood oxygen saturation, and the difference between the amplitudes of any two data points of the local signal segment embodies the change intensity of the blood oxygen signal in the local signal segment. The importance degree obtained by comprehensively analyzing the above two factors can more accurately reflect the importance of the electronic device communication monitoring information.

[0044] The calculation formula of the importance degree of each local signal segment is as follows:

[0045]

[0046] In the formula, W is the importance degree of each local signal segment; t i is the corresponding time instant of the i-th extreme point of each local signal segment; t i+1 is the corresponding time instant of the i+1-th extreme point of each local signal segment; I is the total number of extreme points of each local signal segment; ∈ is a preset positive number, which is empirically 0.01, and functions to prevent the denominator from being 0 to cause the fraction to be meaningless; U is the total number of data points of each local signal segment; h u is the amplitude of the u-th data point of each local signal segment; h v is the amplitude of the v-th data point of each local signal segment; h j is the amplitude of the j-th data point of each local signal segment; || is an absolute value function.

[0047] It should be noted that after exercise, symptoms such as rapid breathing and increased heart rate occur, causing the blood oxygen saturation to change faster, i.e., the frequency of the blood oxygen signal becomes larger; t i+1 -t i represents the size of the change frequency of the blood oxygen saturation in the local signal segment, and when is smaller, the change frequency of the blood oxygen signal in the local signal segment corresponding time period is faster, indicating that the importance degree of the local signal segment to the exercise danger is higher, and the importance degree W is larger.

[0048] is the proportion of the amplitude of the u-th data point of the local signal segment in the total sum of the amplitudes of all data points, is the proportion of the amplitude of the v-th data point of the local signal segment in the total sum of the amplitudes of all data points; The mean value of the difference between the proportion of the amplitude of the two data points of the local signal segment is presented, when The greater the mean value of the difference between the proportion of the amplitude of the two data points of the local signal segment is presented, the more the amplitude type of the data points of the local signal segment, the more intense the change of the blood oxygen saturation of the local signal segment, the greater the possibility of the local signal segment embodying the motion information, the higher the importance degree of the local signal segment to the motion danger, the greater the importance degree W, which indicates that the data points of the local signal segment are more important in the preprocessing process of data transmission and need to be reserved more.

[0049] Step S3: dividing the data points of the communication signal curve into different categories; obtaining the motion danger degree of each category of data points according to the number of data points of each category in different local signal segments and the amplitude of data points of each category.

[0050] Specifically, there are some data points in the local signal segment that are important for expressing the motion condition, and such data points can better express the blood oxygen information of the motion danger; in the embodiment of the present application, the data points of the same amplitude of the communication signal curve are taken as the data points of the same category.

[0051] The blood oxygen saturation of the human body will change periodically with the breathing process, and if in a normal state, the blood oxygen saturation of different periods in the motion time period is relatively similar, because the time length of the time period corresponding to all local signal segments is equal, so that the number of periods in the local signal segment is basically equal, and then the number of data points of each category in different local signal segments is relatively similar; if due to the rapid breathing after the motion, the number of periods in the time period corresponding to the local signal segment is increased, resulting in that the number of data points of each category in the individual local signal segment is increased. The greater the amplitude of the data points of each category is close to the preset blood oxygen danger threshold, the greater the possibility of the data points of the category presenting the motion information. Therefore, the motion danger degree of each category of data points is obtained according to the number of data points of each category in different local signal segments and the amplitude of data points of each category.

[0052] It should be noted that in the embodiment of the present application, the dangerous threshold of blood oxygen saturation, i.e. the preset blood oxygen danger threshold p, is determined according to the physiological parameters and medical standards, and the implementer can set it according to the specific situation.

[0053] The calculation formula of the motion danger degree of each category of data points is as follows:

[0054]

[0055] In the formula, T y is the motion danger degree of the yth category of data points; L is the total number of local signal segments; G y is the total number of the yth category of data points of the communication signal curve; g r,y is the total number of the yth category of data points of the rth local signal segment; h'y is the amplitude of the data point of the yth category; p is a preset blood oxygen risk threshold; ∈ is a preset positive number, which is empirically 0.01, and the role is to prevent is 0, it is considered that |h y -p| is meaningless; exp is an exponential function with the natural constant e as the base; and || is an absolute value function.

[0056] In the time period corresponding to the local signal segment, compared with the number of data points with the same amplitude under normal circumstances, the number of data points with the same amplitude increases due to the faster frequency of the blood oxygen signal after exercise; which is equivalent to obtaining the heterogeneity index of the data point of the yth category, indicating the uniformity of the distribution of the data point of the yth category in all local signal segments, the value range of is between 0 and 1; when is closer to 1, the higher the degree of uniform distribution of the data point of the yth category in the L local signal segments, the greater the possibility that the data point of the yth category represents normal conditions, the smaller the risk of exercise, and the smaller the exercise risk degree T y ; when is closer to 0, the greater the possibility that the data point of the yth category concentrates in a certain local signal segment, the greater the possibility that the data point of the yth category represents the risk of exercise, the greater the risk of exercise, and the greater the exercise risk degree T y . The heterogeneity index is a known technology and will not be described here.

[0057] Under normal circumstances, the amplitude of the data point of the communication signal curve should be much higher than the preset blood oxygen risk threshold p, and when |h y -p| is smaller, the data point of the yth category is closer to the preset blood oxygen risk threshold p, indicating that the data point of the yth category is more likely to belong to the blood oxygen data of the exercise risk, and the exercise risk degree T y is greater.

[0058] Step S4: combining the difference between each local signal segment and the importance of the remaining local signal segments, and the exercise risk degree of each category of data points of each local signal segment, to screen out exercise data points from the data points of the communication signal curve.

[0059] The difference between each local signal segment and the importance of the remaining signal segments reflects the prominence of each local signal segment, and because the normal state duration is usually greater than the exercise duration in the historical time period, the local signal segment with greater prominence is more likely to be in the exercise state; the exercise risk degree presents the possibility that each category of data points represents the risk of exercise, and the comprehensive analysis of the above two factors makes the screened exercise data points more accurate.

[0060] Preferably, the specific screening method of the motion data points is:

[0061] The difference between the importance of each local signal segment and the rest of the local signal segments, and the motion risk degree of each category of data points of each local signal segment, are combined to obtain the retention degree of each data point of each local signal segment. The calculation formula of the retention degree of each data point of each local signal segment is as follows:

[0062]

[0063] In the formula, is the retention degree of the zth data point of the rth local signal segment; W r is the importance of the rth local signal segment; W f is the importance of the fth local signal segment other than the rth local signal segment; L is the total number of local signal segments; T r,z is the motion risk degree of the data points of the category to which the zth data point of the rth local signal segment belongs; Norm is a normalization function; || is an absolute value function.

[0064] It should be noted that when is smaller, the rth local signal segment and the rest of the local signal segments are closer in terms of importance to motion, indicating that the data points of the rth local signal segment are less likely to be blood oxygen data during motion risk; when is larger, the rth local signal segment is more prominent in terms of importance to motion information compared to the rest of the local signal segments, and there are two cases, the first being that the importance of the rth local signal segment is much greater than that of the rest of the local signal segments, indicating that the data points of the rth local signal segment are more likely to be blood oxygen data during motion risk; the second being that the importance of the rth local signal segment is much less than that of the rest of the local signal segments, as the duration of normal state is usually longer than the duration of motion in the historical time period, and the importance of the local signal segments under normal state is relatively close, resulting in the second case not occurring. is larger, W r is larger, indicating that the data points in the rth local signal segment belong to the first case and are more prominent in terms of importance to motion information, the data points of the rth local signal segment are more likely to be blood oxygen data during motion risk, the demand for retention is higher, the retention degree of the data points of the rth local signal segment is larger, and the retention degree is larger.

[0065] When the motion risk degree T r,zThe greater the value is, the greater the possibility that the zth data point of the rth local signal segment belongs to blood oxygen data in a motion dangerous situation is, the higher the accuracy of the data point reflecting the motion situation is, the more the data transmission process needs to retain the data point, and the greater the retention degree The greater the value is.

[0066] It should be noted that the retention degrees of the data points in the same category in the same local signal segment are equal.

[0067] The data point with the greater retention degree has a greater possibility of belonging to motion data, and for each data point of each local signal segment, the data point with a retention degree greater than a preset retention threshold is regarded as a motion data point; the motion data point is a key data point in a motion dangerous situation.

[0068] It should be noted that the preset retention threshold is 0.6 in the embodiment of the present application, and the implementer can set it according to the specific situation.

[0069] Step S5: Different to-be-tested sampling rates are set, and the data points of the communication signal curve are sampled at each to-be-tested sampling rate respectively; and the optimal sampling rate is selected from the to-be-tested sampling rates according to the number of motion data points obtained at each to-be-tested sampling rate.

[0070] In the embodiment of the present application, in the process of data compression, the minimum sampling rate cannot be lower than half of the sampling rate of the communication signal curve, and therefore, different to-be-tested sampling rates are selected on the basis of more than half of the sampling rate of the communication signal curve. It should be noted that the data points obtained by sampling based on the to-be-tested sampling rate need to have motion data points; and the implementer can set the number and size of the to-be-tested sampling rates according to the specific situation.

[0071] In another embodiment of the present application, in the process of data compression, according to the Nyquist theorem, in order to accurately reconstruct the signal, the sampling rate should be at least twice the highest frequency of the signal, and therefore, the highest frequency f of the communication signal curve is obtained, the minimum value of the sampling rate is 2xf, and the to-be-tested sampling rate needs to be greater than or equal to 2xf. In the embodiment, 20 to-be-tested sampling rates are set, and it should be noted that the data points obtained by sampling based on the to-be-tested sampling rate need to have motion data points; and the implementer can set the number and size of the to-be-tested sampling rates according to the specific situation. The calculation method of the highest frequency of the signal is a known technology, and is not described here.

[0072] Preferably, the specific method for obtaining the optimal sampling rate is: sampling the data points of the communication signal curve at each to-be-tested sampling rate respectively, and taking the total number of motion data points obtained by sampling at each to-be-tested sampling rate as the judgment index of the to-be-tested sampling rate; taking the to-be-tested sampling rate corresponding to the largest judgment index as the optimal sampling rate. The optimal sampling rate can improve the rate and reduce the power consumption in the signal transmission process, and make the transmission of key signal data more timely and effective.

[0073] It should be noted that, the more the total number of motion data points obtained by sampling at the to-be-tested sampling rate, the more important motion information contained in the data collected at the to-be-tested sampling rate, and the more reliable the data compression transmission at the to-be-tested sampling rate is.

[0074] Step S6: obtaining the blood oxygen concentration data sequence in the current motion time period based on the optimal sampling rate, and compressing and transmitting the elements in the blood oxygen concentration data sequence.

[0075] The communication signal curve in the current motion time period is obtained, the data points of the signal curve are sampled at the optimal sampling frequency, the amplitudes of the sampled data points, i.e. the blood oxygen saturation, are arranged in time sequence, and the blood oxygen concentration data sequence in the current motion time period is obtained. The elements in the blood oxygen concentration data sequence are blood oxygen saturation. In the embodiment of the present application, Huffman coding is selected to compress the elements in the blood oxygen concentration data sequence, and the compressed data is transmitted to the mobile phone through the Bluetooth of the electronic communication device.

[0076] Thus, the present application is completed.

[0077] In summary, in the embodiment of the present application, the communication signal curve is divided into local signal segments, the importance of the local signal segments is obtained, the data points of the communication signal curve are classified, the motion risk degree of the data points of each category is obtained according to the number of data points of each category in the local signal segment and the amplitude of the data points, the motion data points are screened out by combining the motion risk degree and the importance of the data points of each category in the local signal segment, the optimal sampling rate is selected according to the number of motion data points obtained at each to-be-tested sampling rate, and the blood oxygen concentration sequence in the motion time period is obtained based on the optimal sampling rate, and the elements in the sequence are compressed and transmitted. The present application selects the optimal sampling rate based on the number of important motion information, i.e. the motion data points, obtained at the to-be-tested sampling rate, and improves the reliability and stability of data transmission.

[0078] Based on the same inventive concept as the above method embodiments, the embodiments of the present application also provide a communication data sampling system for an electronic device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of the above-mentioned communication data sampling method embodiments for an electronic device are implemented, such as Figure 1 The communication data sampling method for an electronic device has been described in detail in the above embodiments and will not be described here.

[0079] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0080] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments.

[0081] The above-mentioned is only the preferred embodiment of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A communication data sampling method for an electronic device, characterized by, The method comprises: acquiring a communication signal curve corresponding to the electronic communication device in a historical time period; dividing the communication signal curve into different local signal segments; acquiring an importance degree of each local signal segment according to a time interval between adjacent extreme points of each local signal segment and a difference between amplitudes of any two data points; dividing the data points of the communication signal curve into different categories; obtaining a motion risk degree of the data points of each category according to a number of the data points of each category in different local signal segments and amplitudes of the data points of each category; screening motion data points from the data points of the communication signal curve in combination with a difference between the importance degree of each local signal segment and the importance degrees of the remaining local signal segments and the motion risk degree of the data points of each category of each local signal segment; setting different to-be-tested sampling rates, sampling the data points of the communication signal curve at each to-be-tested sampling rate, and selecting an optimal sampling rate from the to-be-tested sampling rates according to a number of the motion data points obtained at each to-be-tested sampling rate; acquiring a blood oxygen concentration data sequence in a current motion time period based on the optimal sampling rate, and compressively transmitting elements in the blood oxygen concentration data sequence; a calculation formula of the importance degree of each local signal segment is as follows: ; wherein W is the importance of each local signal segment; is the corresponding time of the i-th extreme point of each local signal segment; is the corresponding time of the i+1-th extreme point of each local signal segment; I is the total number of extreme points of each local signal segment; is a preset positive number; U is the total number of data points of each local signal segment; is the amplitude of the u-th data point of each local signal segment; is the amplitude of the v-th data point of each local signal segment; is the amplitude of the j-th data point of each local signal segment; is an absolute value function.

2. The communication data sampling method for an electronic device according to claim 1, wherein a method for dividing the data points of the communication signal curve into different categories comprises: regarding data points of the same amplitude of the communication signal curve as data points of the same category.

3. The method of claim 1, wherein the method comprises: a calculation formula of the motion risk degree of the data points of each category is as follows: ; wherein is the motion risk degree of the yth category of data points; L is the total number of local signal segments; is the total number of the yth category of data points of the communication signal curve; is the total number of the yth category of data points of the rth local signal segment; is the amplitude of the yth category of data points. p is a preset blood oxygen risk threshold value; is a preset positive number; exp is an exponential function with the natural constant e as the base number; is an absolute value function.

4. The communication data sampling method for an electronic device according to claim 1, wherein a method for screening motion data points from the data points of the communication signal curve comprises: acquiring a retention degree of each data point of each local signal segment in combination with the motion risk degree of the data points of each category of each local signal segment and the importance degree; regarding, for each data point of each local signal segment, a data point with a retention degree greater than a preset retention threshold as a motion data point.

5. The communication data sampling method for an electronic device according to claim 4, wherein a calculation formula of the retention degree of each data point of each local signal segment is as follows: wherein, is the significance of the zth data point of the rth local signal segment; is the significance of the rth local signal segment; is the significance of the fth local signal segment other than the rth local signal segment; L is the total number of local signal segments; is the motion risk of data points of the category to which the zth data point of the rth local signal segment belongs; Norm is a normalization function; is an absolute value function.

6. The communication data sampling method for an electronic device according to claim 1, wherein a method for selecting an optimal sampling rate from to-be-tested sampling rates comprises: sampling the data points of the communication signal curve at each to-be-tested sampling rate, and taking a total number of motion data points in the data points obtained by sampling at each to-be-tested sampling rate as a judgment index of each to-be-tested sampling rate; regarding a to-be-tested sampling rate corresponding to the largest judgment index as the optimal sampling rate.

7. The method of claim 1, wherein the method comprises: The method for compressing elements in the blood oxygen concentration data sequence is run-length encoding.

8. The communication data sampling method for an electronic device according to claim 4, wherein The preset retention threshold is 0.

6.

9. A communication data sampling system for an electronic device, characterized by comprising: The system comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the communication data sampling method for an electronic device according to any one of claims 1-8 when executing the computer program.

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