CN116543909A - Medical monitoring system, method, device and storage medium - Google Patents

Medical monitoring system, method, device and storage medium Download PDF

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CN116543909A
CN116543909A CN202310763132.9A CN202310763132A CN116543909A CN 116543909 A CN116543909 A CN 116543909A CN 202310763132 A CN202310763132 A CN 202310763132A CN 116543909 A CN116543909 A CN 116543909A
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node
detection
nodes
cluster head
medical data
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李醒
张洋
孙悦超
胡郁梅
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Dongguan University of Technology
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    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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Abstract

The invention relates to a medical monitoring system, a method, a device and a storage medium. The medical monitoring system comprises a processing module, a transmission module and a monitoring module, wherein: the processing module is used for clustering the detection nodes according to the received node information of the detection nodes, determining cluster head nodes in the detection nodes, and forwarding medical data detected by the detection nodes to the transmission module based on the cluster head nodes; the transmission module is used for transmitting the received medical data to the monitoring module through a wireless communication network; the monitoring module is used for inputting the medical data into the neural network model to obtain detection results of the medical data, and feeding back the detection results to the corresponding detection nodes. The system provided by the invention can monitor the health condition of the user in real time and feed the health condition back to related personnel, thereby ensuring the safety of the user to the maximum extent.

Description

Medical monitoring system, method, device and storage medium
Technical Field
The present invention relates to the field of disease monitoring technology, and in particular, to a medical monitoring system, method, apparatus, and storage medium.
Background
Cardiovascular disease is a sudden disease with extremely high disability rate and death rate, which causes obstruction to the development of public medical and health industry in China and seriously threatens the health of users. Therefore, it is highly desirable to provide a medical monitoring system so as to monitor the health condition of a user in real time, discover the illness state in time and give an alarm.
Disclosure of Invention
In order to solve the technical problems, the invention provides a medical monitoring system, a method, equipment and a storage medium, which can monitor the health condition of a user in real time and feed back the health condition to related personnel, thereby ensuring the safety of the user to the greatest extent.
In a first aspect, an embodiment of the present invention provides a medical monitoring system, including a processing module, a transmission module, and a monitoring module, where:
the processing module is used for clustering the detection nodes according to the received node information of the detection nodes, determining cluster head nodes in the detection nodes, and forwarding medical data detected by the detection nodes to the transmission module based on the cluster head nodes;
the transmission module is used for transmitting the received medical data to the monitoring module through a wireless communication network;
The monitoring module is used for inputting the medical data into the neural network model to obtain detection results of the medical data, and feeding back the detection results to the corresponding detection nodes.
In a second aspect, an embodiment of the present invention provides a medical monitoring method applied to a medical monitoring system as described above, where the method includes:
clustering the detection nodes according to the received node information of the detection nodes, determining a cluster head node in the detection nodes, and forwarding medical data detected by the detection nodes to the transmission module based on the cluster head node;
transmitting the received medical data to the monitoring module through a wireless communication network;
and inputting the medical data into a neural network model to obtain detection results of the medical data, and feeding back the detection results to corresponding detection nodes.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement a medical monitoring method.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements steps of a medical monitoring method.
The embodiment of the invention provides a medical monitoring system, which comprises a processing module, a transmission module and a monitoring module, wherein: the processing module is used for clustering the detection nodes according to the received node information of the detection nodes, determining cluster head nodes in the detection nodes, and forwarding medical data detected by the detection nodes to the transmission module based on the cluster head nodes; the transmission module is used for transmitting the received medical data to the monitoring module through a wireless communication network; the monitoring module is used for inputting the medical data into the neural network model to obtain detection results of the medical data, and feeding back the detection results to the corresponding detection nodes. The system provided by the invention can monitor the health condition of the user in real time and feed back the health condition to related personnel, thereby ensuring the safety of the user to the maximum extent.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic structural diagram of a medical monitoring system according to an embodiment of the present invention;
fig. 2 is a schematic view of a medical monitoring system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a detection node according to an embodiment of the present invention;
fig. 4 is a schematic flow chart of a medical monitoring method according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order that the above objects, features and advantages of the invention will be more clearly understood, a further description of the invention will be made. It should be noted that, without conflict, the embodiments of the present invention and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced otherwise than as described herein; it will be apparent that the embodiments in the specification are only some, but not all, embodiments of the invention.
With the development of computer technology and modern communication technology, new opportunities are brought to telemedicine monitoring services. The remote medical monitoring service transmits remote physiological signals and medical signals to a medical monitoring center by applying computer technology and modern communication and analyzes the remote physiological signals and medical signals, so that remote transmission and monitoring, remote consultation, medical emergency treatment, remote education and communication of medical information among individuals and hospitals and among hospitals are realized.
The portable cardiovascular disease medical monitoring system based on the wireless sensor network is a modern remote medical monitoring system, and uses a medical sensor as a medical data acquisition interface for acquiring the factors affecting cardiovascular diseases, such as diastolic pressure, systolic pressure, blood sugar, cholesterol and the like of a patient. The acquired medical data is transmitted to the gateway by utilizing a wireless communication technology and then transmitted to the remote monitoring center, and a doctor analyzes and diagnoses the acquired medical data in the remote monitoring center, so that remote monitoring and remote medical treatment are realized.
The remote medical treatment is a new mode of epidemic prevention and control normalization and urban and rural medical institution construction in the post-epidemic age, the remote medical monitoring system based on the wireless sensor network provides great freedom of movement for patients, and each patient can leave home and can obtain monitoring of a hospital monitoring center at any time and any place in each home. The problems of personnel flow and aggregation are solved, and the requirement of patients for real-time monitoring is met.
However, the medical monitoring system has complex monitoring environment, and a large number of medical sensors need to perform remote medical treatment, so that the problem of unbalanced energy consumption of each medical sensor node is caused, and further, the energy consumption difference of each area where the medical sensor is located is obvious, and the transmission efficiency of each medical sensor is seriously affected.
Aiming at the technical problems, the embodiment of the invention provides a medical monitoring system which is mainly divided into a processing module, a transmission module and a monitoring module. The processing module can also be regarded as a sensing node layer, which consists of a large number of medical sensors (such as portable wireless sensors of users), wherein the medical sensors are used for measuring medical data of the users, such as diastolic pressure, systolic pressure, blood sugar, cholesterol and the like, which affect cardiovascular diseases, the sensing node layer is used for detecting, receiving and processing the medical data, after the cluster head nodes are determined in the medical sensors, the acquired user data (medical data) are uploaded to the monitoring module through a wireless communication network of the transmission module, the monitoring module judges whether the users have sudden cardiovascular diseases according to the medical data, if the system detects that the patients have sudden conditions, an emergency alarm function is triggered, and the system informs the family members and emergency centers of the patients to timely send the medical data to the hospital for treatment. And in particular by one or more of the following examples.
Fig. 1 is a schematic structural diagram of a medical monitoring system according to an embodiment of the present invention, where the medical monitoring system 100 includes a processing module 110, a transmission module 120, and a monitoring module 130, and the processing module includes:
the processing module 110 is configured to cluster each detection node according to the received node information of each detection node, determine a cluster head node among the detection nodes, and forward the medical data detected by each detection node to the transmission module based on the cluster head node.
The processing module is connected with at least one first base station, the first base station is connected with a plurality of detection nodes, and the first base station is used for receiving node information of the plurality of detection nodes.
It will be appreciated that the processing module may be regarded as a layer of sensor nodes, which in view of the complexity of the monitoring environment typically consists of a large number of detection nodes, the detection nodes and the first base stations forming the basis of the whole medical monitoring system, i.e. the processing module communicates with a large number of detection nodes via the first base stations, e.g. the processing module communicates with 5 first base stations, each of which communicates with 50 detection nodes, respectively. The detection node can be understood as a heterogeneous node, the detection node can be a medical sensor, the medical sensor can be a small portable detection device configured by a patient at home according to own needs, the detection device has an abnormal or dangerous condition alarm function, the detection device can detect medical data of the patient, and the medical data mainly comprises body temperature, pulse, heart rate, blood oxygen saturation, blood pressure (diastolic pressure, systolic pressure), blood sugar, cholesterol and the like. After receiving node information of all detection nodes in the range, the first base station performs detection node clustering through a IAP (ImprovedAffinity propagation) clustering algorithm, divides all detection nodes into different clusters, selects a cluster head node from a plurality of detection nodes of each cluster according to an adaptive cluster head round robin (ACRR) method according to each cluster after all detection nodes are clustered, and the cluster head node sorts, fuses and forwards medical data sent by all detection nodes in the cluster to a transmission module.
The transmission module 120 is configured to transmit each received medical data to the monitoring module through a wireless communication network.
The transmission module 120 includes a second base station and a system gateway, where the second base station is configured to receive, through a bus communication manner, each medical data forwarded by a corresponding cluster head node in the processing module, and the system gateway is configured to perform protocol conversion on each medical data, package the medical data into a preset data format, and transmit the medical data to the monitoring module through a wireless communication network.
It is understood that, corresponding to the sensor node layer, the transmission module may be regarded as a data transmission layer, and the transmission module includes the second base station and the system gateway, that is, the data transmission layer includes the community base station node layer and the system gateway layer, and the second base station may be regarded as a community base station. The second base station has larger work load, and not only needs to receive the data from each cluster head node, but also needs to communicate with an external network, so that the second base station adopts a field bus communication mode with strong anti-interference capability and long communication distance. The specific work of the second base station comprises: protocol conversion, communication with external networks, sending user instructions, data fusion, etc. As a core of the medical monitoring system, the second base station uses a power supply mode to ensure long-term operation thereof. The system gateway mainly realizes the mutual exchange of the body area network and the information of the monitoring module, after receiving the medical data transmitted by the second base station, the system gateway carries out protocol conversion on the medical data, then attaches the current time to the medical data, encapsulates the medical data into a data format of an application layer, transmits the data format to the monitoring module, and also receives the warning transmitted by the monitoring module, and the system gateway is a bridge for the interaction between a user and the medical monitoring system, so the user can operate the wireless sensing network through the bridge, and can be regarded as a man-machine interface of the medical monitoring system in practice.
The monitoring module 130 is configured to input the medical data into a neural network model, obtain detection results of the medical data, and feed back the detection results to corresponding detection nodes.
It can be understood that, corresponding to the above data transmission layer, the monitoring module can be regarded as a monitoring management layer, and the functions of the monitoring module include: medical data transmitted by a wireless communication network are stored in a medical large data platform, meanwhile, the medical data acquired by the wireless communication network are input into a pre-trained cardiovascular and cerebrovascular neural network model based on a back propagation neural network (Back Propagation Neural Network, BP), the current health condition of a user is diagnosed, a detection result is obtained, the neural network model can be understood as a two-class model, namely whether sudden cardiovascular diseases occur or not, if the detection result is that the sudden cardiovascular diseases occur, the user and an emergency treatment center are notified, the user is sent to a hospital for treatment in time, and if the detection result is that the sudden vascular diseases do not occur, monitoring is continued.
Referring to fig. 2, an exemplary schematic view of a scenario of a medical monitoring system provided by the embodiment of the present invention is shown in fig. 2, where fig. 2 includes a processing module, a transmission module and a monitoring module, and as shown in fig. 2, the processing module includes a plurality of first base stations, each first base station includes a plurality of search nodes, a plurality of users using different detection nodes may reside in different residential buildings, and a possible scenario is that after at least one cluster head node is selected from all search nodes in each first base station, each cluster head node in the first base station directly receives medical data sent by other member nodes, and the member nodes do not need to send the medical data to the first base station and directly forward the medical data to the second base station by the cluster head node, or directly send the medical data to the first base station by the cluster head node and then forward the medical data to the second base station by the first base station. The transmission module comprises a plurality of second base stations and a system gateway, the second base stations are communicated through the system gateway and the monitoring module, each first base station is provided with a second base station corresponding to one, the processing module only comprises a plurality of retrieval nodes without the first base station, the plurality of retrieval nodes are directly communicated with the second base stations, and according to another possible scene, for each second base station, after at least one cluster head node is selected from all the retrieval nodes in the range of the second base station, each cluster head node in the range of the second base station directly receives medical data sent by other member nodes and forwards the medical data to the second base station directly through the cluster head node, namely, each cluster head node is provided with a corresponding second base station. The monitoring module comprises a medical big data platform, a neural network model and an online doctor, wherein the system gateway stores medical data into the medical big data platform, inputs the medical data into the neural network model for diagnosis, obtains a detection result of the medical data, and sends the detection result to the doctor for the doctor to check and detect.
It can be understood that the bottom layer of the portable cardiovascular disease telemedicine monitoring system is composed of wireless sensors (detection nodes), and medical data of factors affecting cardiovascular disease such as diastolic pressure, systolic pressure, blood sugar, cholesterol and the like of a user are acquired by the detection nodes, wherein the medical data acquired by the detection nodes are automatically detected by the user and uploaded and authorized to the telemedicine monitoring system for telemedicine. Specifically, the computing submodule clusters the detection nodes in the range of each first base station through an IAP clustering algorithm, and selects cluster head nodes from all the detection nodes in the range to forward the collected medical information, so that the node energy consumption is reduced, and the service life of the wireless sensor network is prolonged. The key core problems in the working process of the clustering Routing protocol are how to reasonably select cluster head nodes, how to quickly and effectively construct a clustering structure, how to save energy and prolong the life cycle of a network, so that aiming at the problems, a computing submodule solves the problem of unstable network caused by uneven node energy distribution in each detection node through a data main body randomization algorithm (Energy Efficient Multi-hop Routing, EEMR) of neighbor dynamic clustering, and meanwhile, a self-adaptive cluster head round-robin method is adopted to select a proper cluster head node.
Optionally, the processing module 110 includes a calculation sub-module and a forwarding sub-module.
The calculation submodule is used for calculating first distances among the plurality of detection nodes according to the node information, clustering the plurality of detection nodes according to the first distances to obtain at least one clustering area, selecting a target detection node from the plurality of detection nodes included in the clustering area to serve as a cluster head node, and simultaneously taking the rest detection nodes except the target detection node in the plurality of detection nodes included in the clustering area as member nodes.
It can be understood that after the calculation submodule obtains the node information of each detection node, the first distance between any two detection nodes in the plurality of detection nodes is calculated according to the node information of each detection node. Then, the computing submodule performs clustering according to the first distance among the plurality of detection nodes, the plurality of detection nodes are divided into different clustering areas, the clustering areas can be understood as clusters or clusters, and the processing module includes a plurality of first base stations as an example for the following embodiments, each of the first base stations is divided into a plurality of clustering areas, and each of the clustering areas includes a plurality of detection nodes. After the division of the clustering areas is completed, selecting a target detection node from a plurality of detection nodes included in each clustering area, and taking the target detection node as a cluster head node in the clustering area, wherein the number of the cluster head nodes included in each clustering area can be determined according to the user requirement, for example, another cluster head node can be selected as a candidate node. After the cluster head nodes of the cluster area are determined, other detection nodes except the target detection node in the plurality of detection nodes included in the cluster area are taken as member nodes by the calculation sub-module, and therefore the cluster area comprises the plurality of member nodes and one cluster head node.
It can be appreciated that, in the monitoring area of the medical monitoring system, different patients in different communities in the city use the micro wireless medical sensor to detect medical data of diastolic blood pressure, systolic blood pressure, blood sugar, cholesterol and other factors affecting cardiovascular diseases, and the different communities can be regarded as a first base station. All medical sensor nodes (detection nodes) in the community serve as member nodes, collected data reach cluster head nodes after multi-hop routing in an ad hoc network mode, and the cluster head nodes fuse the data and forward the data to a second base station. Because of the stability requirement of the remote medical wireless sensing network, medical sensor nodes in the network form clusters (clustering areas) according to a certain rule, the clusters comprise cluster head nodes and a plurality of member nodes, data transmission communication is carried out among the clusters through base station nodes, wherein the clustering protocol clusters the medical sensor nodes of users in each community according to a certain rule, the cluster head nodes of each cluster are selected for data transmission, the medical sensor nodes reach the cluster head nodes after being routed through multiple hops, and the cluster head nodes are communicated with the base station nodes, so that the energy loss of the nodes is reduced. In addition, because the whole flexibility of the clustered routing protocol network is strong, the size of a routing table is reduced to a certain extent, the energy consumption is saved, and the life cycle of the network is prolonged.
Referring to fig. 3, fig. 3 is a schematic diagram of a detection node provided by an embodiment of the present invention, where the community monitoring range shown in fig. 3 includes a plurality of detection nodes, the detection nodes are represented by solid circles, the plurality of detection nodes are divided into a plurality of cluster areas (clusters) after being clustered by IAPs, the cluster areas are areas formed by dashed circles in fig. 3, then, cluster head nodes are selected in each cluster area, after the selection is completed, each cluster area includes one cluster head node and a plurality of member nodes, the cluster head nodes are represented by circles filled with black background, the member nodes are represented by circles filled with white background, each member node transmits detected medical data to the cluster head node, and then, each cluster head node directly performs data transmission with a corresponding second base station in a transmission module, and transmits the medical data detected by each member node to the second base station.
Wherein the node information includes average energy, remaining energy, location information, and initial energy.
The calculation submodule is used for calculating second distances among the plurality of detection nodes according to the position information, calculating first energy of the detection nodes according to the average energy and the residual energy, and calculating to obtain a first distance according to the second distances and the first energy.
It can be appreciated that, in order to further solve the problem of unbalanced node energy consumption in the clustered data acquisition algorithm, the computing submodule adopts distance and energy factors as a cluster election strategy, that is, adopts a data main body randomization algorithm (EEMR) based on neighbor dynamic clustering as a cluster election strategy, and the clustering method in the algorithm is an Improved Affinity Propagation (IAP) algorithm. Specifically, the calculating submodule calculates a second distance between any two detection nodes according to the position information, taking one detection node of the any two detection nodes as an example, calculates first energy of the detection node according to average energy and residual energy of the detection node, and calculates a product of the second distance, the first energy and a preset adjustment factor to obtain the first distance of the detection node, wherein the adjustment factor can be specifically an adjustment factor of the number of cluster heads, and the first distances of other detection nodes can be obtained by calculating by the same method, which is not described herein. After the first distance of each detection node is obtained, clustering is carried out based on the first distance of each detection node, and all detection nodes in the community are divided into different clustering areas. Specifically, the calculation formula of the second distance is shown in formula (1), and the calculation formula of the first distance is shown in formula (2).
Formula (1)
Where dist (i, j) represents a second distance between the detection node i and the detection node j, which can be understood in particular as the distance between the detection nodes approaching "affinity",position information representing the detection node i +.>Indicating the location information of the detection node j.
Formula (2)
In the method, in the process of the invention,representing a first distance between detection node i and detection node j,/>Representing the average energy of all detection nodes in the processing module at the beginning of operation,/for the detection nodes>Representing the remaining energy of the detection node j, which refers to the energy level left by the detection node j after performing the task,/o->The first energy of the detection nodes j is represented, the first energy refers to the energy attraction 'affinity' among the detection nodes, and beta represents an adjustment factor of the number of cluster heads.
After the calculation submodule completes the division of the clustering areas, the calculation submodule is further used for selecting cluster head nodes in each clustering area, wherein:
for each detection node, calculating the ratio of the residual energy to the initial energy to obtain the absolute energy ratio of the detection node; determining the sum value of the residual energy of a plurality of detection nodes included in the clustering area, and calculating the ratio of the residual energy to the sum value to obtain the relative energy ratio of the detection nodes; calculating the relative distance ratio of the detection node according to the position information and the position information of the first base station corresponding to the clustering area; determining a first number of detection nodes included in the clustering area, and calculating the ratio of the number of nodes which do not continuously act as cluster head nodes in the clustering area to the first number to obtain a relative node ratio; calculating to obtain a cluster head ratio of the detection node according to the absolute energy ratio, the relative distance ratio and the relative node ratio; and selecting a target detection node from a plurality of detection nodes included in the clustering area according to the cluster head ratio as a cluster head node.
It can be understood that in the EEMR algorithm, after the first base station receives node information of all the detection nodes, the computing submodule operates an IAP clustering algorithm to perform network clustering, and in addition, the EEMR algorithm proposes an ACRR (Adaptive cluster-head Round Robin) method for local dynamic election of cluster heads, where the ACRR is a cluster head polling strategy based on node adaptability, and related formulas of the cluster head node election strategies are shown in the following formulas (3) to (7):
formula (3)
Formula (4)
Formula (5)
Formula (6)
Formula (7)
Wherein H (j) represents the cluster head ratio of the detection node j, H 1 Representing the absolute energy ratio (absolute duty cycle) of the detection node j,representing the remaining energy of the detection node j, i.e. the energy remaining by the detection node j after the end of the r-th round of medical data collection, E 0 Represents the initial energy of the detection node j, namely the energy of the initial configuration of the detection node j, H 2 Representing the relative energy ratio (relative duty cycle) of the detection node j +>Representing the sum value of the residual energy of a plurality of detection nodes included in the cluster area to which the detection node j belongs, namely the energy of the cluster to which the detection node j belongs, H 3 Representing a detection sectionThe relative distance ratio of the point j, i.e. the relative distance ratio of the distance between the detection node j and the belonging first base station, dist (i, sink) represents the distance between the detection node j and the first base station,/- >Represents the furthest distance from the first base station in the cluster to which the detection node j belongs, H 4 Representing the relative node ratio, i.e. the ratio of nodes in the cluster to which the detection node j is continuously not functioning as a cluster head, once the detection node j is functioning as a cluster head r m And setting 0.
The cluster area comprises the cluster head node and a plurality of member nodes.
Wherein the computation submodule is used for:
aiming at each member node, calculating to obtain the node capacity of the member node according to the residual energy of the cluster head node, the initial energy of the cluster head node, the distance from the member node to the cluster head node and a preset data transmission deflection angle; and determining a target member node as a relay node from the plurality of member nodes according to the node capability, wherein the relay node is used for forwarding the medical data detected by the member node to the cluster head node.
It can be understood that the kernel of the EEMR algorithm is to avoid the control energy consumption caused by frequent clustering, so that the calculation submodule adopts a layered multi-hop data forwarding strategy based on the capability of the detection node, that is, after determining the cluster head node, a relay node is selected from the cluster head nodes according to the node capability of each member node, and the relay node can be understood as a forwarding node for forwarding the medical data of other member nodes to the cluster head node, that is, the relay node for performing data multi-hop transmission is determined by the member node capability, for example, the node with larger member node capability can be used as the relay node to bear the jumping task of other member nodes. For example, as shown in fig. 3, the cluster area 300 includes a cluster head node 310, a relay node 320 and a plurality of detection nodes, the detection nodes 330 in the plurality of detection nodes transmit medical data to the relay node 320, and then the relay node 320 transmits the medical data to the cluster head node 310, that is, in a transmission direction shown by an arrow in fig. 3, the medical data of the detection nodes 330 is jumped to the cluster head node 310 through the relay node 320, so that node energy consumption caused by frequent clustering can be effectively reduced, and further energy consumption when member nodes jump can be reduced. The node capacity is calculated as shown in formula (8):
Formula (8)
Where f (j) represents the node capability of the detection node j,representing the remaining energy, E, of the cluster head node s init Representing the initial energy of the cluster head node s, dist (j, s) representing the distance between the cluster head node s and the detection node j, R h Is the communication radius, θ is the data transmission deflection angle, λ i (j=1, 2) represents a weight coefficient of each competency factor.
The forwarding sub-module is used for carrying out fusion processing on the medical data detected by the member nodes based on the cluster head nodes and forwarding the medical data to the transmission module.
It can be understood that the forwarding submodule in the processing module is applied to the medical data detected by the member node in the same cluster area as the cluster head node based on the cluster head node, the cluster head node performs fusion processing on each medical data, and then sends the medical data after fusion processing to the corresponding second base station.
The monitoring module comprises a training submodule, wherein the training submodule is used for training the constructed neural network to obtain the neural network model.
Wherein, training submodule is specifically used for:
inputting the acquired learning sample into the neural network to obtain a prediction result; calculating a gradient square based on the learning sample and the prediction result, calculating a mean value according to the gradient square and a first preset parameter, and calculating a variance according to the gradient square and a second preset parameter; and calculating to obtain a parameter difference value according to a preset learning rate, the mean value and the variance, and training the neural network according to the parameter difference value to obtain the neural network model.
In particular, cardiovascular disease is a chronic disease with extremely high disability rate and mortality rate, which is a hindrance to the development of public medical and health industry in China, and most research is now focused on the treatment of the disease, but the work of disease prevention is neglected. In addition, along with the rapid development of big data analysis, the machine learning method can obtain higher accuracy when processing complex data, so that the medical monitoring system performs prediction research on cardiovascular diseases on the basis of machine learning, and predicts cardiovascular diseases by starting from 11 factors of age, gender, systolic pressure, diastolic pressure, height, weight, smoking, exercise, drinking, cholesterol and blood sugar, so that a doctor can be helped to judge whether the user suffers from cardiovascular diseases or not, effective auxiliary decision support can be provided for the doctor in medical data processing, and real-time effective monitoring on the health state of the user is realized.
The monitoring module comprises a training sub-module and a prediction module, wherein the training sub-module is used for training the constructed neural network based on the 11 factors to obtain a neural network model, and the prediction module is used for diagnosing the health state of the user based on medical data by utilizing the trained neural network model. Specifically, in order to improve the training speed of the neural network and improve the accuracy of the training result, the training submodule adopts a BP neural network of an optimal gradient self-adaptive optimization algorithm as a deep learning system, the BP neural network is short for a back propagation neural network (Back Propagation Neural Network), the front part of the BP neural network is an input layer, the middle of the BP neural network comprises a plurality of hidden layers, the rear part of the BP neural network is an output layer, all the layers are in a full connection mode, if the neurons are in the same layer, connection is not allowed between the neurons, the neurons of each layer only can output an activation signal to the neurons of the next layer, and correction errors are reversely transferred to the upper layer.
As can be appreciated, the training sub-module initializes the BP neural network first, then inputs the learning sample into the BP neural network, calculates the input and output of each layer of neurons, preferably, the BP neural network has an input layer node of 11 and an output layer node of 2; and calculating an output error according to the learning sample and a prediction result output by the network, carrying out back propagation according to the output error, updating the weight of the BP neural network again, calculating a global error, judging whether to stop learning according to the precision requirement, and obtaining a trained neural network model if the precision is met.
It can be appreciated that the selection of the learning rate is critical to the accuracy of the neural network, and a higher learning rate can result in a larger error or irregular dispersion of the network, and a too low learning rate can reduce the training efficiency of the network. Compared with the fixed learning rate, the training submodule selects Adam as an optimization algorithm to realize self-adaptive adjustment of the learning rate, and a new self-adaptive algorithm TAdam is adopted for solving the problem that the accuracy of the model is reduced due to the fact that the learning rate is too fast in Adam adjustment.
It can be appreciated that the medical monitoring system needs extremely high accuracy to predict the health condition of the user, and introduces the concept of the adaptive friction coefficient into Adam algorithm to obtain a new adaptive algorithm, namely a TAdam algorithm, wherein the update rule of the TAdam algorithm is as shown in formula (9) to formula (12):
Formula (9)
Formula (10)
Formula (11)
Formula (12)
In the method, in the process of the invention,represents the square of the gradient, m t Mean, v t Representing variance, beta 1 Representing a first preset parameter, beta 2 Representing a second preset parameter, beta 1 And beta 2 In particular an exponential decay coefficient, delta theta t Expressed as a parameter difference, eta is a learning rate, and is generally set to 0.001, theta t Is a model parameter.
It can be appreciated that the main update point of the TAdam algorithm is the update rule of the gradient second-order momentum, v in the TAdam algorithm t And v t-1 The difference between them depends only onWhen v t-1 Far greater than->In the process, the TAdam algorithm can increase the effective learning rate in a more gentle mode, and the convergence rate of the model and the accuracy of the model can be remarkably improved by controlling the increasing speed of the learning rate.
It can be understood that the monitoring module uses cardiovascular diseases as dependent variables to carry out detailed descriptive statistical analysis on whether the cardiovascular diseases are obtained by the user, the components are used for BP neural network models of cardiovascular diseases, and the deep statistical analysis is carried out on the cardiovascular diseases by establishing the neural network models, and the cardiovascular diseases are predicted so as to realize real-time monitoring.
The embodiment of the invention provides a medical monitoring system which comprises a processing module, a transmission module and a monitoring module, wherein the transmission module is used for collecting medical data detected by each detection node and transmitting the medical data to the monitoring module through the transmission module for health condition diagnosis so as to monitor whether a user has sudden vascular diseases or not in real time, so that the patient can be sent to a hospital for treatment in time. The processing module further comprises a calculation submodule, the calculation submodule can solve the problem that the energy consumption difference of the areas among different communities is obvious when the detection nodes are released by clustering and clustering the detection nodes, and meanwhile, after all clusters are formed, all clusters independently and distributively execute a self-adaptive cluster head round robin mechanism so as to dynamically update the cluster heads, so that the transmission rate is improved, the energy consumption of the nodes is reduced, and the service life of the wireless sensing network is prolonged.
On the basis of the above embodiment, fig. 4 is a schematic flow chart of a medical monitoring method according to an embodiment of the present invention, which is applied to the medical monitoring system, and specifically includes the following steps S410 to S430 shown in fig. 4:
s410, clustering the detection nodes according to the received node information of the detection nodes, determining a cluster head node in the detection nodes, and forwarding medical data detected by the detection nodes to the transmission module based on the cluster head node.
And S420, transmitting the received medical data to the monitoring module through a wireless communication network.
S430, inputting the medical data into a neural network model to obtain detection results of the medical data, and feeding back the detection results to corresponding detection nodes.
It can be appreciated that the implementation manners of S410 to S430 are referred to the above embodiments, and are not described herein.
Optionally, clustering each detection node according to the received node information of each detection node, and determining a cluster head node in each detection node, including:
calculating a first distance among the plurality of detection nodes according to the node information, clustering the plurality of detection nodes according to the first distance to obtain at least one clustering area, selecting a target detection node from the plurality of detection nodes included in the clustering area as a cluster head node, and simultaneously taking the rest detection nodes except the target detection node in the plurality of detection nodes included in the clustering area as member nodes.
Optionally, forwarding, based on the cluster head node, the medical data detected by each detection node to the transmission module, including:
and based on the cluster head node, the medical data detected by the member node are fused and then forwarded to the transmission module.
Optionally, the node information includes average energy, remaining energy, and location information.
Optionally, calculating a first distance between the plurality of detection nodes according to the node information includes:
and calculating a second distance among the plurality of detection nodes according to the position information, calculating first energy of the detection nodes according to the average energy and the residual energy, and calculating a first distance according to the second distance and the first energy.
Optionally, the node information further includes initial energy.
Optionally, selecting a target detection node from a plurality of detection nodes included in the clustering area as a cluster head node, including:
for each detection node, calculating the ratio of the residual energy to the initial energy to obtain the absolute energy ratio of the detection node; determining the sum value of the residual energy of a plurality of detection nodes included in the clustering area, and calculating the ratio of the residual energy to the sum value to obtain the relative energy ratio of the detection nodes; calculating the relative distance ratio of the detection node according to the position information and the position information of the first base station corresponding to the clustering area; determining a first number of detection nodes included in the clustering area, and calculating the ratio of the number of nodes which do not continuously act as cluster head nodes in the clustering area to the first number to obtain a relative node ratio; calculating to obtain a cluster head ratio of the detection node according to the absolute energy ratio, the relative distance ratio and the relative node ratio; and selecting a target detection node from a plurality of detection nodes included in the clustering area according to the cluster head ratio as a cluster head node.
Optionally, the cluster area includes the cluster head node and a plurality of member nodes, and after determining the cluster head node and the member nodes, the method further includes:
aiming at each member node, calculating to obtain the node capacity of the member node according to the residual energy of the cluster head node, the initial energy of the cluster head node, the distance from the member node to the cluster head node and a preset data transmission deflection angle; and determining a target member node as a relay node from the plurality of member nodes according to the node capability, wherein the relay node is used for forwarding the medical data detected by the member node to the cluster head node.
Optionally, the training process of the neural network model in S430 is as follows:
inputting the acquired learning sample into the neural network to obtain a prediction result; calculating a gradient square based on the learning sample and the prediction result, calculating a mean value according to the gradient square and a first preset parameter, and calculating a variance according to the gradient square and a second preset parameter; and calculating to obtain a parameter difference value according to a preset learning rate, the mean value and the variance, and training the neural network according to the parameter difference value to obtain the neural network model.
It can be understood that the specific implementation steps of the medical monitoring method are referred to the above embodiments, and are not described herein.
The medical monitoring method provided by the embodiment of the invention can be used for rapidly and accurately diagnosing the health condition of the user, and if the diagnosis result is sudden vascular diseases, the family members and the emergency treatment center of the patient are notified, and the patient is sent to a hospital for treatment in time.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. Referring now in particular to fig. 5, a schematic diagram of an electronic device 500 suitable for use in implementing embodiments of the present invention is shown. The electronic device 500 in the embodiment of the present invention may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), wearable electronic devices, and the like, and fixed terminals such as digital TVs, desktop computers, smart home devices, and the like. The electronic device shown in fig. 5 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.
As shown in fig. 5, the electronic device 500 may include a processing means (e.g., a central processor, a graphics processor, etc.) 501 that may perform various suitable actions and processes to implement the medical monitoring method according to embodiments of the present invention according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
In general, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 507 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 508 including, for example, magnetic tape, hard disk, etc.; and communication means 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 shows an electronic device 500 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present invention, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow chart, thereby implementing the medical monitoring method as described above. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or from the storage means 508, or from the ROM 502. The above-described functions defined in the method of the embodiment of the present invention are performed when the computer program is executed by the processing means 501.
The computer readable medium of the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.
Alternatively, the electronic device may perform other steps described in the above embodiments when the above one or more programs are executed by the electronic device.
Computer program code for carrying out operations of the present invention may be written in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present invention may be implemented in software or in hardware. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or gateway that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or gateway. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or gateway comprising the element.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown and described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A medical monitoring system, comprising a processing module, a transmission module, and a monitoring module, wherein:
the processing module is used for clustering the detection nodes according to the received node information of the detection nodes, determining cluster head nodes in the detection nodes, and forwarding medical data detected by the detection nodes to the transmission module based on the cluster head nodes;
the transmission module is used for transmitting the received medical data to the monitoring module through a wireless communication network;
the monitoring module is used for inputting the medical data into the neural network model to obtain detection results of the medical data, and feeding back the detection results to the corresponding detection nodes.
2. The system of claim 1, wherein the processing module comprises a computation sub-module and a forwarding sub-module, wherein the processing module is coupled to at least one first base station, the first base station is coupled to a plurality of detection nodes, and the first base station is configured to receive node information of the plurality of detection nodes;
the calculation submodule is used for calculating first distances among the plurality of detection nodes according to the node information, clustering the plurality of detection nodes according to the first distances to obtain at least one clustering area, selecting a target detection node from the plurality of detection nodes included in the clustering area as a cluster head node, and taking other detection nodes except the target detection node in the plurality of detection nodes included in the clustering area as member nodes;
The forwarding sub-module is used for carrying out fusion processing on the medical data detected by the member nodes based on the cluster head nodes and forwarding the medical data to the transmission module.
3. The system of claim 2, wherein the node information includes average energy, residual energy, and location information, wherein the computation submodule is configured to compute a second distance between the plurality of detection nodes based on the location information, compute a first energy of the detection nodes based on the average energy and the residual energy, and compute a first distance based on the second distance and the first energy.
4. The system of claim 3, wherein the node information further comprises an initial energy, the computing submodule to:
for each detection node, calculating the ratio of the residual energy to the initial energy to obtain the absolute energy ratio of the detection node;
determining the sum value of the residual energy of a plurality of detection nodes included in the clustering area, and calculating the ratio of the residual energy to the sum value to obtain the relative energy ratio of the detection nodes;
calculating the relative distance ratio of the detection node according to the position information and the position information of the first base station corresponding to the clustering area;
Determining a first number of detection nodes included in the clustering area, and calculating the ratio of the number of nodes which do not continuously act as cluster head nodes in the clustering area to the first number to obtain a relative node ratio;
calculating to obtain a cluster head ratio of the detection node according to the absolute energy ratio, the relative distance ratio and the relative node ratio;
and selecting a target detection node from a plurality of detection nodes included in the clustering area according to the cluster head ratio as a cluster head node.
5. The system of claim 4, wherein the cluster region comprises the cluster head node and a plurality of member nodes, the computing submodule to:
aiming at each member node, calculating to obtain the node capacity of the member node according to the residual energy of the cluster head node, the initial energy of the cluster head node, the distance from the member node to the cluster head node and a preset data transmission deflection angle;
and determining a target member node as a relay node from the plurality of member nodes according to the node capability, wherein the relay node is used for forwarding the medical data detected by the member node to the cluster head node.
6. The system of claim 1, wherein the transmission module comprises a second base station and a system gateway, the second base station is configured to receive each medical data forwarded by a corresponding cluster head node in the processing module through a bus communication manner, and the system gateway is configured to perform protocol conversion on each medical data, package the medical data into a preset data format, and transmit the medical data to the monitoring module through a wireless communication network.
7. The system of claim 1, wherein the monitoring module comprises a training submodule for training the constructed neural network to obtain the neural network model;
the training submodule is specifically used for:
inputting the acquired learning sample into the neural network to obtain a prediction result;
calculating a gradient square based on the learning sample and the prediction result, calculating a mean value according to the gradient square and a first preset parameter, and calculating a variance according to the gradient square and a second preset parameter;
and calculating to obtain a parameter difference value according to a preset learning rate, the mean value and the variance, and training the neural network according to the parameter difference value to obtain the neural network model.
8. A medical monitoring method applied to the medical monitoring system of any one of claims 1-7, the method comprising:
clustering the detection nodes according to the received node information of the detection nodes, determining a cluster head node in the detection nodes, and forwarding medical data detected by the detection nodes to the transmission module based on the cluster head node;
transmitting the received medical data to the monitoring module through a wireless communication network;
and inputting the medical data into a neural network model to obtain detection results of the medical data, and feeding back the detection results to corresponding detection nodes.
9. An electronic device, comprising:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the medical monitoring method of claim 8.
10. A computer readable storage medium, on which a computer program is stored which, when being executed by a processor, implements the steps of the medical monitoring method according to claim 8.
CN202310763132.9A 2023-06-27 2023-06-27 Medical monitoring system, method, device and storage medium Pending CN116543909A (en)

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