CN109635481B - Data analysis system for wearable equipment - Google Patents
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Abstract
The invention relates to a data analysis system for wearable equipment, which solves the technical problems of more interference sources and low analysis precision, and comprises a plurality of wearable sensors for detecting human body sensing parameters, a data recording device connected with the wearable sensors, a base station connected with the data recording device through a wireless network, and a remote processing center connected with the base station; the remote processing center comprises a central processor and a central memory, and the central memory stores an interference rejection program; the processor is used for executing the technical scheme of the interference rejection procedure, so that the problem is well solved, and the method can be used in wearable equipment application.
Description
Technical Field
The invention relates to the field of intelligent wearing equipment, in particular to a data analysis system for wearing equipment.
Background
The wearable equipment is used for detecting and alarming human body movement and physiological parameters to realize non-access, continuous and atraumatic diagnosis and monitoring of human body. Which in turn helps the person to manage the movement and monitoring. Current wearable devices are generally composed of a wearable sensor, a relay, and a remote monitoring center.
The existing data analysis system of the wearable equipment only adopts a conventional communication technology to collect data for data analysis, and has the technical problems of more interference sources and low analysis precision. Therefore, it is necessary to provide a data analysis system for a wearable device with high analysis accuracy.
Disclosure of Invention
The invention aims to solve the technical problems of more interference sources and low analysis precision in the prior art. The novel data analysis system for the wearable equipment has the characteristics of simple interference elimination, fast operation and high analysis precision.
In order to solve the technical problems, the technical scheme adopted is as follows:
a data analysis system for a wearable device, the data analysis system comprising a plurality of wearable sensors for detecting human body sensing parameters, a data recording device connected with the wearable sensors, a base station connected with the data recording device through a wireless network, and a remote processing center connected with the base station;
the remote processing center comprises a central processor and a central memory, and the central memory stores an interference rejection program; the processor is used for executing an interference rejection program to complete the following steps:
step one, defining a first detection model to detect data to obtain a data detection result A i The method comprises the steps of carrying out a first treatment on the surface of the Defining a screening and rejecting strategy model, and determining a threshold lambda according to the screening and rejecting strategy model ι And a threshold lambda h Defining a fusion mechanism and a cooperation strategy; data detection result A according to fusion mechanism and cooperation strategy i Correcting to obtain a data result correction value;
establishing an interference assessment cloud model, defining subjective weight, objective weight and maximum ambiguity, and establishing a mathematical parameter model with variable weight; calculating a comprehensive weight w according to the mathematical parameter model with variable weight, subjective weight and objective weight; measuring data uncertainty and calculating the weighting degree and time weight of the measured data; calculating the final interference threat level according to the comprehensive weight, the weighting level and the time weight, and determining the threat level T according to the final interference threat level and the maximum ambiguity i The threat degree range of the corresponding threat degree level is used for completing evaluation of the interference signal, and an interference signal evaluation result is obtained;
step three, defining threat threshold T max The threat degree level exceeds the threat degree threshold T in the data result correction value according to the interference signal evaluation result max And (3) eliminating the data result to obtain prediction data for the remote processing center to predict the human body state.
In the above solution, for optimization, further, the first detection model in the step one is
The data detection is to add direct current noise gamma (t) into R (t), wherein the intensity of the direct current noise gamma (t) is rho; />
The fusion mechanism and the cooperation strategy comprise a decision mechanism, a AND decision mechanism and a soft decision mechanism;
when (when)
When the first correction is carried out by using a decision mechanism, a first correction result is obtained: if the first correction result is H 1 Directly outputting the first correction result as a data result correction value,otherwise, performing second correction, and outputting a second correction result as a data result correction value;
when (when)
When the data is corrected, the second correction is directly carried out, and the result of the second correction is used as a data result correction value to be output;
when (when)
When the first correction is carried out by using a or decision mechanism, a first correction result is obtained: if the first correction result is H 2 Directly outputting the first correction result as a data result correction value, otherwise, performing second correction, and outputting the second correction result as the data result correction value;
wherein H is 1 Is suspected of interference, H 2 R (t) is a base station data signal received by the remote processing center from the base station, n (t) is a mean value beta and a variance is
S (t) is the mean ω and the variance +.>
J (t) is the mean μ, variance +.>
Is a signal of interference of (1); i is a positive integer less than L, mu i Is the mean value of the interference signals received by the i-th node,
is the variance of the interference signal received by the i-th node.
Further, the first correction by the use and decision mechanism includes using a threshold lambda ι As a decision threshold, all the first correction results R i Fusion is carried out, and the fusion formula is as follows:
the first correction by the use or decision mechanism includes using a threshold lambda ι As a decision threshold, all the first correction results R i Fusion is carried out, and the fusion formula is as follows:
the uncertain is a decision result which cannot be obtained, and needs secondary cooperation.
Further, the second correction includes: using a threshold lambda h As decision threshold, according to data detection result A i Calculating T using a soft decision mechanism MRC As a result of the correction:
the soft decision mechanism comprises the steps of adopting a maximum ratio combining algorithm, wherein the weight factor of the maximum ratio combining algorithm is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,l represents the number of nodes, the number of nodes is the number of data logger, base station and remote processing center to integrate, mu i Is the average of all the interference received by the ith node,
is the variance of the interference signal received by the i-th node.
Further, calculating the comprehensive weight ω according to the variable-weight mathematical parameter model, the subjective weight, and the objective weight in the second step includes:
ω＝α*ω 1 +(1-α)*ω 2 subjective weight is ω 1 Objective weight ω 2 Is calculated from uncertainty of the interference assessment cloud model, where α=0.6;
calculating the weighting degree of the measurement data includes defining the weighting degree of the uncertainty data as a value range when the data value probability is 98% or more, the weighting degree=the data attribute value×the uncertainty data hesitation degree:
the calculation of the time weight value is to determine the time weight value y by using a normal distribution 1 ,y 2 ,L,y p
Wherein a is ij Is a data attribute value.
Further, the interference assessment cloud model is a normalized matrix:
further, the second step includes:
step a, building t based on comprehensive weight omega 0 Normalized matrix of time of day
Step b, establishing t 1 ,t 2 ,L,t P Normalized decision matrix for time of day
L,/>
Step c, establishing a final decision matrix psi, psi=YΛ based on the time weight value T ；
Y＝[y 1 ,y 2 ,L,y P ]；
Step d, determining threat level T according to the final interference threat level and the maximum ambiguity i And threat degree ranges of corresponding threat degree grades, wherein the accumulated weighting degree of the target attribute value is as follows:
h max is the maximum interpolation between weights:
representing the kronecker product.
Further, the remote processing center is further provided with a log memory, and the log memory is used for storing abnormal events.
Further, the result of the human body state prediction is classified as normal heart rate and abnormal heart rate; normal blood pressure and abnormal blood pressure; normothermia and abnormal body temperature.
The invention has the beneficial effects that: according to the invention, by adopting the method for detecting and analyzing the interference sources for the data transmission between the wearable equipment and the remote processing center, useless data signals are removed, the accuracy of data analysis is improved, and the anti-interference capability is improved. The interference detection adopts a double-threshold screening method, realizes the dynamic selection of the fusion criterion and the cooperation strategy, reduces the influence of the signal to noise ratio on the interference detection, and is not easily influenced by the uncertainty of noise. Therefore, the interference detection performance in the noise uncertainty environment is improved, and meanwhile, reasonable policy guidelines are designed to reduce the system overhead. According to the data interference evaluation and elimination method, the degree of weighting is determined to evaluate the threat degree of the interference target, the uncertainty in the description actual data is given, and the ambiguity and the randomness can be determined at the same time. Through the method, the data analysis system of the wearable equipment realizes the performance of high efficiency and high accuracy.
Drawings
The invention will be further described with reference to the drawings and examples.
Fig. 1 is a schematic diagram of a data analysis system for a wearable device in embodiment 1.
Fig. 2 is a schematic flow chart of an interference rejection procedure.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
The embodiment provides a data analysis system for a wearable device, as shown in fig. 1, the data analysis system includes a plurality of wearable sensors for detecting human body sensing parameters, a data recording device connected with the wearable sensors, a base station connected with the data recording device through a wireless network, and a remote processing center connected with the base station;
the remote processing center comprises a central processor and a central memory, and the central memory stores an interference rejection program; the processor is configured to execute an interference rejection procedure, as shown in fig. 2, and complete the following steps:
step one, defining a first detection model to detect data to obtain a data detection result A i The method comprises the steps of carrying out a first treatment on the surface of the Defining a screening and rejecting strategy model, and determining a threshold lambda according to the screening and rejecting strategy model ι And a threshold lambda h Defining a fusion mechanism and a cooperation strategy; data detection result A according to fusion mechanism and cooperation strategy i Correcting to obtain a data result correction value;
establishing an interference assessment cloud model, defining subjective weight, objective weight and maximum ambiguity, and establishing a mathematical parameter model with variable weight; calculating a comprehensive weight w according to the mathematical parameter model with variable weight, subjective weight and objective weight; measuring data uncertainty and calculating the weighting degree and time weight of the measured data; calculating the final interference threat level according to the comprehensive weight, the weighting level and the time weight, and determining the threat level T according to the final interference threat level and the maximum ambiguity i The threat degree range of the corresponding threat degree level is used for completing evaluation of the interference signal, and an interference signal evaluation result is obtained;
step three, defining threat threshold T max The threat degree level exceeds the threat degree threshold T in the data result correction value according to the interference signal evaluation result max And (3) eliminating the data result to obtain prediction data for the remote processing center to predict the human body state.
The intelligent wearable device stores the physiological indexes in an application container, realizes conversion to a cloud file system through a data virtualization and decision tool, and finally realizes analysis and prediction by a server group.
Specifically, the first detection model in the first step is
The data detection is to add direct current noise gamma (t) into R (t), wherein the intensity of the direct current noise gamma (t) is rho;
the fusion mechanism and the cooperation strategy comprise a decision mechanism, a AND decision mechanism and a soft decision mechanism;
when (when)
When the first correction is carried out by using a decision mechanism, a first correction result is obtained: if the first correction result is H 1 Directly outputting the first correction result as a data result correction value, otherwise, performing second correction, and outputting the second correction result as the data result correction value;
when (when)
When the data is corrected, the second correction is directly carried out, and the result of the second correction is used as a data result correction value to be output;
when (when)
When the first correction is carried out by using a or decision mechanism, a first correction result is obtained: if the first correction result is H 2 Directly outputting the first correction result as a data result correction value, otherwise, performing second correction, and outputting the second correction result as the data result correction value;
wherein H is 1 Is suspected of interference, H 2 R (t) is a base station data signal received by the remote processing center from the base station, n (t) is a mean value beta and a variance is
S (t) is the mean ω and the variance +.>
J (t) is the mean μ, variance +.>
Is a signal of interference of (1); i is a positive integer less than L, mu i Is the mean value of the interference signals received by the i-th node,
is received by the ith nodeVariance of the interfering signal.
Specifically, the first modification using the AND decision mechanism includes using a threshold lambda ι As a decision threshold, all the first correction results R i Fusion is carried out, and the fusion formula is as follows:
the first correction by the use or decision mechanism includes using a threshold lambda ι As a decision threshold, all the first correction results R i Fusion is carried out, and the fusion formula is as follows:
the uncertain is a decision result which cannot be obtained, and needs secondary cooperation.
Specifically, the second correction includes: using a threshold lambda h As decision threshold, according to data detection result A i Calculating T using a soft decision mechanism MRC As a result of the correction:
the soft decision mechanism comprises the steps of adopting a maximum ratio combining algorithm, wherein the weight factor of the maximum ratio combining algorithm is as follows:
wherein L represents the number of nodes, the number of nodes is the number of data logger, base station and remote processing center to be integrated, mu i Is the average of all the interference received by the ith node,
is the variance of the interference signal received by the i-th node.
Specifically, the calculating the comprehensive weight ω according to the variable-weight mathematical parameter model, the subjective weight and the objective weight in the second step includes:
ω＝α*ω 1 +(1-α)*ω 2 subjective weight is ω 1 Objective weight ω 2 Is calculated from uncertainty of the interference assessment cloud model, where α=0.6;
calculating the weighting degree of the measurement data includes defining the weighting degree of the uncertainty data as a value range when the data value probability is 98% or more, the weighting degree=the data attribute value×the uncertainty data hesitation degree:
the calculation of the time weight value is to determine the time weight value y by using a normal distribution 1 ,y 2 ,L,y p
Wherein a is ij Is a data attribute value.
Specifically, the interference assessment cloud model is a normalized matrix:
specifically, the second step includes:
step a, building t based on comprehensive weight omega 0 Normalized matrix of time of day
Step b, establishing t 1 ,t 2 ,L,t P Normalized decision matrix for time of day
L,/>
Step c, establishing a final decision matrix psi, psi=YΛ based on the time weight value T ；
Y＝[y 1 ,y 2 ,L,y P ]；
Step d, determining threat level T according to the final interference threat level and the maximum ambiguity i And threat degree ranges of corresponding threat degree grades, wherein the accumulated weighting degree of the target attribute value is as follows:
h max is the maximum interpolation between weights:
representing the kronecker product. />
Specifically, the remote processing center is further provided with a log memory, and the log memory is used for storing abnormal events. The log memory uses a setting method of a common log memory in the computer field.
Specifically, the result of the human body state prediction is classified as normal heart rate and abnormal heart rate; normal blood pressure and abnormal blood pressure; normothermia and abnormal body temperature. The human body state prediction can adopt the existing prediction method, and the embodiment is not repeated.
While the foregoing describes the illustrative embodiments of the present invention so that those skilled in the art may understand the present invention, the present invention is not limited to the specific embodiments, and all inventive innovations utilizing the inventive concepts are herein within the scope of the present invention as defined and defined by the appended claims, as long as the various changes are within the spirit and scope of the present invention.
Claims (9)
1. A data analysis system for a wearable device, characterized by: the data analysis system comprises a plurality of wearable sensors for detecting human body sensing parameters, a data recording device connected with the wearable sensors, a base station connected with the data recording device through a wireless network, and a remote processing center connected with the base station;
the remote processing center comprises a central processor and a central memory, and the central memory stores an interference rejection program; the processor is used for executing an interference rejection program to complete the following steps:
step one, defining a first detection model to detect data to obtain a data detection result A i The method comprises the steps of carrying out a first treatment on the surface of the Defining a screening and rejecting strategy model, and determining a threshold lambda according to the screening and rejecting strategy model ι And a threshold lambda h Defining a fusion mechanism and a cooperation strategy; data detection result A according to fusion mechanism and cooperation strategy i Correcting to obtain a data result correction value;
establishing an interference assessment cloud model, defining subjective weight, objective weight and maximum ambiguity, and establishing a mathematical parameter model with variable weight; calculating a comprehensive weight w according to the mathematical parameter model with variable weight, subjective weight and objective weight; measuringMeasuring the uncertainty of the data and calculating the weighting degree and time weight of the measured data; calculating the final interference threat level according to the comprehensive weight, the weighting level and the time weight, and determining the threat level T according to the final interference threat level and the maximum ambiguity i The threat degree range of the corresponding threat degree level is used for completing evaluation of the interference signal, and an interference signal evaluation result is obtained;
step three, defining threat threshold T max The threat degree level exceeds the threat degree threshold T in the data result correction value according to the interference signal evaluation result max And (3) eliminating the data result to obtain prediction data for the remote processing center to predict the human body state.
2. The data analysis system for a wearable device of claim 1, wherein: the first detection model in the first step is
The data detection is to add direct current noise gamma (t) into R (t), wherein the intensity of the direct current noise gamma (t) is rho;
the fusion mechanism and the cooperation strategy comprise a decision mechanism, a AND decision mechanism and a soft decision mechanism;
when (when)
When the first correction is carried out by using a decision mechanism, a first correction result is obtained: if the first correction result is H 1 Directly outputting the first correction result as a data result correction value, otherwise, performing second correction, and outputting the second correction result as the data result correction value;
when (when)
When the data is corrected, the second correction is directly carried out, and the result of the second correction is used as a data result correction value to be output;
when (when)
When the first correction is carried out by using a or decision mechanism, a first correction result is obtained: if the first correction result is H 2 Directly outputting the first correction result as a data result correction value, otherwise, performing second correction, and outputting the second correction result as the data result correction value;
wherein H is 1 Is suspected of interference, H 2 R (t) is a base station data signal received by the remote processing center from the base station, n (t) is a mean value beta and a variance is
S (t) is the mean ω and the variance +.>
J (t) is the mean μ, variance +.>
Is a signal of interference of (1); i is a positive integer less than L, μi is the mean value of interference signals received by the ith node,/->
Is the variance of the interference signal received by the i-th node.
3. The data analysis system for a wearable device of claim 2, wherein:
the first correction by the use and decision mechanism includes using a threshold lambda ι As a decision threshold, all the first correction results R i Fusion is carried out, and the fusion formula is as follows:
the first correction by the use or decision mechanism includes using a threshold lambda ι As a decision threshold, all the first correction results R i Fusion is carried out, and the fusion formula is as follows:
the uncertain is a decision result which cannot be obtained, and needs secondary cooperation.
4. The data analysis system for a wearable device of claim 2, wherein:
the second correction includes: using a threshold lambda h As decision threshold, according to data detection result A i Calculating T using a soft decision mechanism MRC As a result of the correction:
the soft decision mechanism comprises the steps of adopting a maximum ratio combining algorithm, wherein the weight factor of the maximum ratio combining algorithm is as follows:
wherein L represents the number of nodes, the number of nodes is the number of data logger, base station and remote processing center to be integrated, mu i Is the average of all the interference received by the ith node,
is the variance of the interference signal received by the i-th node.
5. The data analysis system for a wearable device of claim 2, wherein:
the step two of calculating the comprehensive weight omega according to the mathematical parameter model with variable weight, subjective weight and objective weight comprises the following steps:
ω＝α*ω 1 +(1-α)*ω 2 subjective weight is ω 1 Objective weight ω 2 Is calculated from uncertainty of the interference assessment cloud model, where α=0.6;
calculating the weighting degree of the measurement data includes defining the weighting degree of the uncertainty data as a value range when the data value probability is 98% or more, the weighting degree=the data attribute value×the uncertainty data hesitation degree:
the calculation of the time weight value is to determine the time weight value y by using a normal distribution 1 ,y 2 ,L,y p
Wherein a is ij Is a data attribute value.
6. The data analysis system for a wearable device of claim 5, wherein: the interference assessment cloud model is a normalized matrix:
7. the data analysis system for a wearable device of claim 6, wherein: the second step comprises the following steps:
step a, building t based on comprehensive weight omega 0 Normalized matrix of time of day
Step b, establishing t 1 ,t 2 ,L,t P Normalized decision matrix for time of day
Step c, establishing a final decision matrix psi, psi=YΛ based on the time weight value T ；
Y＝[y 1 ,y 2 ,L,y P ]；
Step d, determining threat level T according to the final interference threat level and the maximum ambiguity i And threat degree ranges of corresponding threat degree grades, wherein the accumulated weighting degree of the target attribute value is as follows:
h max is the maximum interpolation between weights:
representing the kronecker product.
8. The data analysis system for a wearable device of claim 1, wherein: the remote processing center is also provided with a log memory, and the log memory is used for storing abnormal events.
9. The data analysis system for a wearable device of claim 1, wherein: the result of the human body state prediction is classified as normal heart rate and abnormal heart rate; normal blood pressure and abnormal blood pressure; normothermia and abnormal body temperature.
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| CN103326791A (en) * | 2013-05-16 | 2013-09-25 | 吉林大学 | Physiological data collection method used for telemedicine |
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| CN108880712A (en) * | 2018-08-02 | 2018-11-23 | 重庆大学 | A kind of two steps cooperation accidental resonance energy detection algorithm |
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| CN103326791A (en) * | 2013-05-16 | 2013-09-25 | 吉林大学 | Physiological data collection method used for telemedicine |
| US9819122B1 (en) * | 2016-06-29 | 2017-11-14 | Intel Corporation | Apparel compute device connection |
| CN108365931A (en) * | 2017-01-26 | 2018-08-03 | 华为技术有限公司 | Method for wireless communications and device |
| CN108430105A (en) * | 2017-12-28 | 2018-08-21 | 衢州学院 | Distributed sensor networks cooperate with target state estimator and interference source passive location method |
| CN108880712A (en) * | 2018-08-02 | 2018-11-23 | 重庆大学 | A kind of two steps cooperation accidental resonance energy detection algorithm |
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