CN108133750B - Intelligent physiological signal sensing data monitoring system - Google Patents
Intelligent physiological signal sensing data monitoring system Download PDFInfo
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Abstract
The invention provides an intelligent physiological signal sensing data monitoring system, which comprises a sensor monitoring device, a data preprocessing device and a monitoring terminal, wherein the sensor monitoring device is used for monitoring physiological signals; the sensor monitoring device collects physiological signal sensing data in real time and transmits the physiological signal sensing data to the data preprocessing device for preprocessing, and the data preprocessing device transmits the processed physiological signal sensing data to the monitoring terminal for comprehensive analysis. The invention realizes the real-time monitoring of the sensing data of various physiological signals, and the monitoring is convenient and flexible.
Description
Technical Field
The invention relates to the field of medical treatment, in particular to an intelligent physiological signal sensing data monitoring system.
Background
The human body physiological signal sensing data monitoring system of the wireless sensor technology is developed quickly, but generally only fixed physiological signal sensing data (such as single parameters of heart rate, pulse and the like) can be monitored, and a network for monitoring the physiological signal sensing data is not formed, so that whether a human body is in a normal physiological state or not can not be comprehensively judged, and an accurate conclusion can not be given; and once the monitoring scheme is changed, the monitoring scheme needs to be redesigned or purchased, so that the equipment is idle and resources are wasted. Therefore, the requirement of monitoring the personalized physiological conditions of the testee in different environments such as families, hospitals, communities and the like cannot be met.
Disclosure of Invention
In order to solve the problems, the invention provides an intelligent physiological signal sensing data monitoring system.
The purpose of the invention is realized by adopting the following technical scheme:
the physiological signal sensing data intelligent monitoring system comprises a sensor monitoring device, a data preprocessing device and a monitoring terminal; the sensor monitoring device collects physiological signal sensing data in real time and transmits the physiological signal sensing data to the data preprocessing device for preprocessing, and the data preprocessing device transmits the processed physiological signal sensing data to the monitoring terminal for comprehensive analysis.
The invention has the beneficial effects that: the long-time and continuous monitoring of multiple physiological signal sensing data is realized, and the monitoring is convenient and flexible.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a block diagram of an intelligent physiological signal sensing data monitoring system according to an embodiment of the present invention;
FIG. 2 is a block diagram of the connection of a data preprocessing apparatus according to an embodiment of the present invention;
fig. 3 is a connection block diagram of a monitoring terminal according to an embodiment of the present invention.
Reference numerals:
the system comprises a sensor monitoring device 1, a data preprocessing device 2, a monitoring terminal 3, a data preprocessing unit 10, a wireless data transmitting unit 20, a storage unit 30 and a data analyzing unit 40.
Detailed Description
The invention is further described with reference to the following examples.
Referring to fig. 1, the physiological signal sensing data intelligent monitoring system provided in this embodiment includes a sensor monitoring device 1, a data preprocessing device 2, and a monitoring terminal 3; the sensor monitoring device 1 collects physiological signal sensing data in real time and transmits the physiological signal sensing data to the data preprocessing device 2 for preprocessing, and the data preprocessing device 2 transmits the processed physiological signal sensing data to the monitoring terminal 3 for comprehensive analysis.
As shown in fig. 2, the data preprocessing device 2 includes a data preprocessing unit 10 and a wireless data transmitting unit 20; the data preprocessing unit 10 analyzes the physiological signal sensing data acquired by the sensor monitoring device 1, filters abnormal physiological signal sensing data generated by sensor errors and movement of the monitored object, and transmits the filtered physiological signal sensing data to the monitoring terminal 3 through the wireless data transmitting unit 20.
As shown in fig. 3, the monitoring terminal 3 includes a storage unit 30 and a data analysis unit 40; the storage unit 30 is provided with a knowledge base for storing the received physiological signal sensing data; the data analysis unit 40 is configured to analyze the single physiological signal sensing data, and when the physiological signal sensing data exceeds a preset standard value range, the data analysis unit 40 sends an alarm signal.
The embodiment of the invention realizes long-time and continuous monitoring of the sensing data of various physiological signals, and the monitoring is convenient and flexible.
Preferably, the sensor monitoring device 1 includes a sink node and a plurality of sensor nodes, wherein the plurality of sensor nodes are randomly deployed in a set physiological signal sensing data monitoring area.
The sensor node collects physiological signal sensing data and transmits the physiological signal sensing data to the sink node. The sink node is connected with the data preprocessing device 2 in a communication mode. The sensor nodes and the sink nodes jointly form a wireless sensor network for collecting and transmitting physiological signal sensing data.
In one embodiment, in an initial stage of a network, a sensor node broadcasts a path detection packet to the network, at this time, the path detection packet carries an Identity (ID) of the sensor node, current remaining energy and bandwidth requirements, the sensor node receiving the path detection packet adds the ID of the sensor node, the current remaining energy and information of a single-hop link with a previous-hop sensor node into the received path detection packet, and then sends the path detection packet to a next-hop node until the path detection packet reaches a sink node.
According to the embodiment, the relevant information of the routing path from the sensor node to the sink node is acquired in a mode that the sensor node sends the path detection packet to the sink node, and the method is simple and efficient.
In one embodiment, a sensor node originating a path detection packet is set as a source node, in a receiving stage, a sink node starts timing when receiving the path detection packet for the first time, continues to receive a plurality of path detection packets originating from each source node within a set time period, stores the received path detection packets, and does not receive the path detection packets after timeout.
In the processing stage, the sink node processes the path detection packet to obtain a plurality of routing paths from the source node u to the sink node,and falls into the initial path set phiuU is 1, …, n, n is the number of source nodes; for the initial path set phiuThe sink node takes a plurality of routing paths from the source node u to the sink node which meet basic conditions as initial particle swarms, optimizes the initial particle swarms by adopting an improved multi-cluster particle swarms optimization algorithm to obtain a plurality of preferred routing paths from the source node u to the sink node, and sends corresponding preferred routing path information to the source node u along each preferred routing path.
Wherein, the basic conditions are as follows:
in the formula (I), the compound is shown in the specification,
indicating the lifetime of the lambda-th routing path from the source node u to the sink node, wherein,
the current residual energy of the sensor node with the minimum energy in the lambda-th routing path from the source node u to the sink node; eelecIs a radio frequency transmission coefficient, epsilonampFor power amplifier power consumption coefficient of amplifier, dmaxIs the distance between two adjacent sensor nodes with the maximum distance in the lambda-th routing path from the source node u to the sink node, LTIs a preset life time lower limit value;
Hλfor the total delay of the 3 rd routing path from the source node u to the sink node, HTIs a preset maximum time delay value; f (H)λ,HT) To judge the function, when Hλ＞HTWhen f (H)λ,HT) When H is equal to 0λ≤HTWhen f (H)λ,HT)＝1。
In the embodiment, the sink node determines the routing path from each source node to the sink node, so that the burden of the source node in determining the routing to the sink node is reduced.
In the related art, when a multi-population particle swarm optimization algorithm is adopted to optimize routing paths, all routing paths from a sensor node to a sink node are usually used as initial particle swarms, and the complexity of particle search is increased undoubtedly under the condition that the number of routing paths is large. When the initial particle group used for path optimization is determined, the routing paths which do not meet the basic conditions are creatively removed in advance, and the remaining routing paths are used as the initial particle group, so that the complexity of particle search is reduced, and the speed of routing path optimization is increased. Two factors of energy and time delay are considered when basic conditions are set, and a routing path with an excessively short life cycle and a relatively long total time delay is directly screened out, so that basic requirements on energy, stability and instantaneity can be met when a route from a sensor node to a sink node is subsequently established, the life cycle of a wireless sensor network is prolonged, and the stability of physiological signal sensing data acquisition and transmission is guaranteed.
In one implementation, the improved multi-population particle swarm optimization algorithm is:
(1) calculating the fitness of each particle in the initial particle swarm;
(2) estimating the number K of required routing paths according to the bandwidth requirement of a source node, carrying out K-means clustering on the initial particle swarm, and dividing the particles into K populations according to a clustering result;
(3) various groups find out the global optimal position of the groups;
(4) the flight speed of each particle population is calculated according to the following improved speed formula:
in the formula, Vi(t +1) represents the velocity of particle i at time t +1, Vi(t) represents the velocity of particle i at time t, Xi(t) is the position of the particle i at time t, QibestFor the historically optimal position, Q, experienced by the particle i itselfgbestRepresents the global optimum position, Q, of the population in which the particle i is locatedgbest(j) Represents the global optimal position of the jth population,wherein j is 1, …, K, alpha1、α2、93Are all acceleration constants, β1、β2、β3Are all in the interval [0,1 ]]Random numbers are uniformly distributed in the system, and w is an inertia weight;
(5) each population of particles changes its position according to the following position formula:
Xi(t+1)＝Xi(t)+Vi(t+1)
in the formula, Xi(t +1) represents the position of particle i at time t + 1;
(6) the optimization goal is achieved, and the optimal routing path Q of each population is outputgbest(j) I.e., the preferred routing paths from the source node u to the sink node, ends, otherwise returns to (1).
Wherein, the calculation formula for defining the fitness is as follows:
in the formula, Qu(λ) denotes the adapted value of the λ -th routing path from the source node u to the sink node, Au(λ) is the total link cost of the λ -th routing path from the source node u to the sink node; b isu(lambda) is the bandwidth of the lambda-th routing path from the source node u to the sink node, and is determined by the minimum bandwidth in the path;
the current residual energy of the sensor node with the minimum energy in the lambda-th routing path from the source node u to the sink node; a. themin、Bmin、EminThe link cost upper limit, the maximum link cost value, the bandwidth lower limit, the energy lower limit and eta set for meeting the network service quality requirement1,η2,η3Are all weight coefficients.
The embodiment defines an improved multi-population particle swarm optimization algorithm, considers the intersection of global optimal positions of various populations, improves the speed formula of particles, enables the particles of different populations to fly towards different directions, avoids mutual intersection, reduces the probability of population particle intersection, ensures that no common sensor node exists between any two optimal routing paths, and considers bandwidth, link cost and energy factors when designing a fitness function so as to meet the network service quality requirement for transmitting physiological signal sensing data.
In one embodiment, the sensor node transmits the collected physiological signal sensing data to the sink node according to a parallel multipath routing protocol.
The parallel multipath routing protocol is as follows:
(1) setting a sensor node which starts physiological signal sensing data as a source node, and acquiring a plurality of preferred routing paths from the source node to a sink node from a plurality of preferred routing path information sent by the sink node by the source node, and classifying the preferred routing paths into a path set phi;
(2) the source node calculates the weight value of each preferred routing path according to the following formula:
in the formula, QiRepresents the weight value of the preferred routing path i in the path set phi, wherein i ∈ phi, Ei-minIs the residual energy of the sensor node with the minimum current residual energy in the preferred routing path i, Ej-minThe residual energy of the sensor node with the minimum current residual energy in the jth preferred routing path in the path set phi is Di-sinkFor the total length of the preferred routing path i, Dj-sinkIs the total length of the jth preferred routing path, a1,a2All are preset weight coefficients;
(3) when the physiological signal sensing data volume acquired by the sensor node in the unit time period is lower than a set data volume threshold, the sensor node selects a preferred routing path with the largest weight value for transmission, and when the physiological signal sensing data volume acquired by the sensor node in the unit time period is higher than the set data volume threshold, the sensor node divides the physiological signal sensing data according to the weight value proportion and distributes the physiological signal sensing data to each preferred routing path for transmission, wherein the physiological signal sensing data volume divided by the preferred routing path i is as follows:
in the formula phiiThe physiological signal sensing data quantity divided into the preferred routing path i, phi is the total physiological signal sensing data quantity to be transmitted to the sink node, QjThe weight value for the jth preferred routing path in the path set Φ.
In this embodiment, the sensor node shunts the physiological signal sensing data to a plurality of preferred routing paths for transmission under the condition of a large amount of physiological signal sensing data, so that the transmission efficiency of the physiological signal sensing data can be effectively improved.
The embodiment further sets a parallel multipath routing protocol, wherein a calculation formula of the weight value is set based on energy and distance factors. Since the energy affects the stability of the routing path, the total length of the routing path reflects the energy consumption of the physiological signal sensing data transmitted along the routing path, and a calculation formula of the weight value shows that the optimal routing path with a larger weight value has more excellent performance in the aspect of transmitting the physiological signal sensing data. According to the embodiment, when the physiological signal sensing data volume is large, the physiological signal sensing data is divided according to the weight value proportion and then distributed to the optimal routing paths for transmission, so that the energy consumption of the sensor nodes on the optimal routing paths can be balanced, and the service life of the wireless sensor network can be prolonged beneficially.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (5)
1. The physiological signal sensing data intelligent monitoring system is characterized by comprising a sensor monitoring device, a data preprocessing device and a monitoring terminal; the sensor monitoring device collects physiological signal sensing data in real time and transmits the physiological signal sensing data to the data preprocessing device for preprocessing, and the data preprocessing device transmits the processed physiological signal sensing data to the monitoring terminal for comprehensive analysis;
the sensor monitoring device comprises a sink node and a plurality of sensor nodes, wherein the sensor nodes collect physiological signal sensing data and transmit the physiological signal sensing data to the sink node; the sink node is in communication connection with the data preprocessing device;
setting a sensor node for starting a path detection packet as a source node, in a receiving stage, starting timing by a sink node when the sink node receives the path detection packet for the first time, continuously receiving a plurality of path detection packets started from each source node within a set time period, storing the received path detection packets, and not receiving the path detection packets after timeout;
in the processing stage, the sink node processes the path detection packet to obtain a plurality of routing paths from the source node u to the sink node and the routing paths are classified into an initial path set phiuN, n is the number of source nodes; for the initial path set phiuThe sink node takes a plurality of routing paths from the source node u to the sink node which meet basic conditions as initial particle swarms, optimizes the initial particle swarms by adopting an improved multi-cluster particle swarms optimization algorithm to obtain a plurality of preferred routing paths from the source node u to the sink node, and sends corresponding preferred routing path information to the source node u along each preferred routing path;
wherein, the basic conditions are as follows:
in the formula (I), the compound is shown in the specification,
indicating the lifetime of the lambda-th routing path from the source node u to the sink node, wherein,
the current residual energy of the sensor node with the minimum energy in the lambda-th routing path from the source node u to the sink node; eelecIs a radio frequency transmission coefficient, epsilonampFor power amplifier power consumption coefficient of amplifier, dmaxIs the distance between two adjacent sensor nodes with the maximum distance in the lambda-th routing path from the source node u to the sink node, LTIs a preset life time lower limit value;
Hλfor the total time delay of the lambda-th routing path from the source node u to the sink node, HTIs a preset maximum time delay value; f (H)λ，HT) To judge the function, when Hλ＞HTWhen f (H)λ，HT) When H is equal to 0λ≤HTWhen f (H)λ，HT)＝1；
The improved multi-population particle swarm optimization algorithm comprises the following steps:
(1) calculating the fitness of each particle in the initial particle swarm;
(2) estimating the number K of required routing paths according to the bandwidth requirement of a source node, carrying out K-means clustering on the initial particle swarm, and dividing the particles into K populations according to a clustering result;
(3) various groups find out the global optimal position of the groups;
(4) the flight speed of each particle population is calculated according to the following improved speed formula:
in the formula, Vi(t +1) represents the velocity of particle i at time t +1, Vi(t) represents the velocity of particle i at time t, Xi(t) is the particle i at time tPosition, QibestFor the historically optimal position, Q, experienced by the particle i itselfgbestRepresents the global optimum position, Q, of the population in which the particle i is locatedgbest(j) Represents the global optimal position of the jth population, where j 11、α2、α3Are all acceleration constants, β1、β2、β3Are all in the interval [0,1 ]]Random numbers are uniformly distributed in the system, and w is an inertia weight;
(5) each population of particles changes its position according to the following position formula:
Xi(t+1)＝Xi(t)+Vi(t+1)
in the formula, Xi(t +1) represents the position of particle i at time t + 1;
(6) the optimization goal is achieved, and the optimal routing path Q of each population is outputgbest(j) I.e. a plurality of preferred routing paths from the source node u to the sink node, and ending, otherwise, returning to (1);
wherein, the calculation formula for defining the fitness is as follows:
in the formula, Qu(λ) denotes the adapted value of the λ -th routing path from the source node u to the sink node, Au(λ) is the total link cost of the λ -th routing path from the source node u to the sink node; b isu(lambda) is the bandwidth of the lambda-th routing path from the source node u to the sink node, and is determined by the minimum bandwidth in the path;
the current residual energy of the sensor node with the minimum energy in the lambda-th routing path from the source node u to the sink node; a. themin、Bmin、EminThe link cost upper limit, the maximum link cost value, the bandwidth lower limit, the energy lower limit and eta set for meeting the network service quality requirement1，η2，η3Are all weight coefficients.
2. The physiological signal sensing data intelligent monitoring system according to claim 1, wherein a plurality of sensor nodes are randomly deployed in a set physiological signal sensing data monitoring area.
3. The intelligent physiological signal sensing data monitoring system as recited in claim 1, wherein the sensor node transmits the collected physiological signal sensing data to the sink node according to a parallel multipath routing protocol,
the parallel multipath routing protocol is as follows:
(1) setting a sensor node which starts physiological signal sensing data as a source node, and acquiring a plurality of preferred routing paths from the source node to a sink node from a plurality of preferred routing path information sent by the sink node by the source node, and classifying the preferred routing paths into a path set phi;
(2) the source node calculates the weight value of each preferred routing path according to the following formula:
in the formula, QiRepresents the weight value of the preferred routing path i in the path set phi, wherein i ∈ phi, Ei-minIs the residual energy of the sensor node with the minimum current residual energy in the preferred routing path i, Ej-minThe residual energy of the sensor node with the minimum current residual energy in the jth preferred routing path in the path set phi is Di-sinkFor the total length of the preferred routing path i, Dj-sinkIs the total length of the jth preferred routing path, a1，a2All are preset weight coefficients;
(3) when the physiological signal sensing data quantity acquired by the sensor node in the unit time period is lower than a set data quantity threshold value, the sensor node selects a preferred routing path with the largest weight value for transmission, and when the physiological signal sensing data quantity acquired by the sensor node in the unit time period is higher than the set data quantity threshold value, the sensor node divides the physiological signal sensing data according to the weight value proportion and distributes the physiological signal sensing data to each preferred routing path for transmission.
4. The intelligent physiological signal sensing data monitoring system according to any one of claims 1 to 3, wherein the data preprocessing device comprises a data preprocessing unit and a wireless data transmitting unit; the data preprocessing unit analyzes the physiological signal sensing data acquired by the sensor monitoring device, filters abnormal physiological signal sensing data generated by sensor errors and movement of the monitored object, and sends the filtered physiological signal sensing data to the monitoring terminal through the wireless data sending unit.
5. The physiological signal sensing data intelligent monitoring system according to any one of claims 1-3, wherein the monitoring terminal comprises a storage unit, a data analysis unit; the storage unit is provided with a knowledge base and is used for storing the received physiological signal sensing data; the data analysis unit is used for analyzing the single physiological signal sensing data, and when the physiological signal sensing data exceeds a preset standard value range, the data analysis unit sends out an alarm signal.
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| CN107450344A (en) * | 2017-09-21 | 2017-12-08 | 深圳市鑫汇达机械设计有限公司 | A kind of intelligent domestic system based on wireless sensor network |
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