CN113362968A - Cerebral apoplexy behavior analysis and acquisition system based on cloud computing - Google Patents
Cerebral apoplexy behavior analysis and acquisition system based on cloud computing Download PDFInfo
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Abstract
The invention provides a stroke behavior analysis and acquisition system based on cloud computing, which comprises wearable equipment, an edge computing terminal and a cloud platform, wherein the wearable equipment is connected with the edge computing terminal; the wearable equipment is used for acquiring real-time state parameters; when the patient is located at a first position of the first target range, the wearable device sends the first real-time state parameter to the first edge computing terminal; the first edge calculation terminal obtains a first edge calculation result; when the first edge calculation result meets a first preset condition, the first edge calculation terminal sends the first edge calculation result and the first real-time state parameter to the cloud platform; and the cloud platform performs behavior analysis on the stroke patient based on the first edge calculation result and the first real-time state parameter, and gives a feedback suggestion based on the behavior analysis result. The cloud platform is located in a second target range; the second target range and the first target range are geospatially different ranges. The method can realize the behavior analysis of the stroke patient based on the multi-dimensional angle analysis.
Description
Technical Field
The invention belongs to the technical field of stroke analysis, and particularly relates to a stroke behavior analysis and acquisition system based on cloud computing.
Background
Stroke (stroke), also known as stroke, is a blood circulation disorder disease of brain with sudden onset of disease, which is divided into ischemic stroke and hemorrhagic stroke, wherein ischemic stroke accounts for about 80% of all strokes. The stroke is a main disease which seriously harms the life and health of middle-aged and elderly people in China, has extremely high morbidity, recurrence rate, disability rate and lethality rate, is not only dangerous to the life of a patient, but also brings heavy economic burden to the family and the society of the patient.
Cloud computing is the "brain" of the internet of things. Cloud computing essentially refers to the unified management and scheduling of a large number of computing resources connected by a network, forming a pool of computing resources (resources including network server storage application software services) to provide services to users on demand. In a specific method, cloud computing utilizes the transmission capability of the internet to enable the data processing process to be transferred from a personal computer or a server to a computer cluster on the internet to complete data processing and storage, the clusters are managed by a large-scale data processing center, the data center can distribute computing resources according to the needs of clients, the effect of a super computer is expected to be achieved, strong computing power is brought to users, and the needs of the clients are met. Cloud computing medical treatment refers to medical services which improve diagnosis and treatment levels and resource utilization efficiency through effective embedding of medical treatment technologies on the basis of new technologies such as cloud computing, internet of things and communication.
The Chinese patent application with the application number of CN201710834345.0 provides a system and a method for preventing and controlling stroke and quickly rescuing, wherein the system comprises a platform service system and a front-end system used by various users; the method comprises the following steps: uploading medical record information of a patient to a medical record data management system; performing dynamic risk assessment on the patient based on medical record information of the patient, and establishing a treatment plan; remote communication between the patient and the family members and the medical care personnel; remote morbidity joint diagnosis; and (5) making a treatment scheme and performing synergistic treatment. The stroke prevention and control and rapid rescue system and method disclosed by the invention improve the traditional hospital arrival diagnosis and treatment mode, can realize the disease judgment and the preposition of rescue and treatment, actually shorten the flow time of rescue and treatment of the disease attack, and improve the orderliness and the effectiveness of link connection.
However, the prior art does not perform analysis for the behavior of stroke patients, especially post-operative stroke rehabilitation patients, to give specific feedback recommendations; in addition, although cloud computing is further suitable for the medical industry, a full-flow analysis is performed for the behavior of a stroke that can move within a target range, data blockage may occur in cloud computing, a response cannot be given in time, and single-point or single-position individual analysis results have proven to be insufficiently comprehensive and objective.
Disclosure of Invention
In order to solve the technical problem, the invention provides a stroke behavior analysis and acquisition system based on cloud computing, which comprises wearable equipment, an edge computing terminal and a cloud platform; the wearable equipment is used for acquiring real-time state parameters; when the patient is located at a first position of the first target range, the wearable device sends the first real-time state parameter to the first edge computing terminal; the first edge calculation terminal obtains a first edge calculation result; when the first edge calculation result meets a first preset condition, the first edge calculation terminal sends the first edge calculation result and the first real-time state parameter to the cloud platform; and the cloud platform performs behavior analysis on the stroke patient based on the first edge calculation result and the first real-time state parameter, and gives a feedback suggestion based on the behavior analysis result. The cloud platform is located in a second target range; the second target range and the first target range are geospatially different ranges. The method can realize the behavior analysis of the stroke patient based on the multi-dimensional angle analysis.
Specifically, in the above technical solution of the present invention, the system includes a wearable device and an edge computing terminal. The system further comprises a cloud platform in communication with the edge computing terminal; the cloud platform is located in a second target range; the second target range and the first target range are geospatially different ranges.
A plurality of the edge computing terminals are distributed at different positions of a first target range of the stroke patient activity;
the wearable device is used for acquiring real-time state parameters of the cerebral apoplexy patient, wherein the real-time state parameters comprise motion posture parameters and physiological activity parameters;
the stroke patient wears the wearable device;
when the stroke patient is located at a first position of the first target range, the wearable device sends the acquired first real-time state parameter of the stroke patient to a first edge computing terminal, and the first edge computing terminal is distributed at the first position;
the first edge computing terminal executes edge computing aiming at the first real-time state parameter to obtain a first edge computing result;
when the first edge calculation result meets a first preset condition, the first edge calculation terminal sends the first edge calculation result and the first real-time state parameter to the cloud platform;
the cloud platform performs behavior analysis on the stroke patient based on the first edge calculation result and the first real-time state parameter, and gives feedback suggestions based on the behavior analysis result.
The feedback suggestion is given based on the behavior analysis result, and the feedback suggestion specifically comprises the following steps:
the cloud platform sends a voice test sequence to the first edge computing terminal, the second edge computing terminal and/or the third edge computing terminal;
the first edge computing terminal, and/or the second edge computing terminal, and/or the third edge computing terminal sends the voice test sequence to the wearable device.
After the voice test sequence is sent to the wearable device, if a correct response is not received within a preset time, the edge computing terminal locks the position of the wearable device.
Further advantages of the invention will be apparent in the detailed description section in conjunction with the drawings attached hereto.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a spatial scene of a brain stroke behavior analysis and acquisition system based on cloud computing according to an embodiment of the present invention
FIG. 2 is a data processing process variation diagram for the system of FIG. 1
FIG. 3 is a first embodiment of interactive feedback between an edge computing and a cloud platform in the system of FIG. 1. FIG. 4 is a second embodiment of interactive feedback between an edge computing and a cloud platform in the system of FIG. 1
Detailed Description
The invention is further described with reference to the following drawings and detailed description.
Referring to fig. 1, a schematic spatial scene structure diagram of a cloud computing-based stroke behavior analysis acquisition system according to an embodiment of the present invention is shown.
In fig. 1, the geographic space can be divided into two different geographic space ranges, namely a first target range and a second target range, where the first target range is an activity range of the stroke patient, and the second target range is a remote centralized control range, such as a cloud platform center, a cloud data processing center, a data summarization center, and so on;
in the embodiment of fig. 1, the system includes a wearable device and an edge computing terminal; the system further comprises a cloud platform in communication with the edge computing terminal; a plurality of the edge computing terminals are distributed at different positions of a first target range of the stroke patient activity.
The cloud platform is located in a second target range; the second target range and the first target range are geospatially different ranges.
With continued reference to fig. 1. The wearable device is worn by the stroke patient and can move within the first target range, including normal post-operation exercise, auxiliary recovery, and the like, which is not specifically limited in this embodiment. Activities here also include movement of the patient at different positions within the first target range, and relatively short periods of inactivity at the same position.
Meanwhile, the wearable device is used for acquiring real-time state parameters of the cerebral apoplexy patient, wherein the real-time state parameters comprise motion posture parameters and physiological activity parameters.
As more specific examples, the motion posture parameters include gait, pace, and lower limb muscle strength of the stroke patient; the physiological activity parameters comprise heart rate, respiratory rate and blood pressure of the stroke patient.
Correspondingly, the wearable device comprises a plurality of combined sensors, and the combined sensors can acquire a plurality of real-time state parameters of the stroke patient.
On the basis of fig. 1, see fig. 2.
Firstly, referring to a left sequence diagram of fig. 2, when the stroke patient is located at a first position of the first target range, the wearable device sends the acquired first real-time state parameter of the stroke patient to a first edge computing terminal, where the first edge computing terminal is distributed at the first position;
the first edge computing terminal executes edge computing aiming at the first real-time state parameter to obtain a first edge computing result;
when the first edge calculation result meets a first preset condition, the first edge calculation terminal sends the first edge calculation result and the first real-time state parameter to the cloud platform;
the cloud platform performs behavior analysis on the stroke patient based on the first edge calculation result and the first real-time state parameter, and gives feedback suggestions based on the behavior analysis result.
It should be noted that, in this embodiment and the following embodiments, the use of the "first" position and the "second" position does not refer to a specific position of the first target range, but any position is described as "first" or "second", just for distinguishing the existence of two different positions. As to whether there is a connection between the "first" position and the "second" position, a person skilled in the art can understand the specific context of the relevant embodiment.
Based on this understanding, reference may be continued to the right sequence diagram of fig. 2.
In the right sequence diagram of fig. 2, when the first edge calculation result does not meet the first preset condition, the data processing process when the stroke patient is determined to be located at the second position of the first target range includes:
when the stroke patient is located at a second position of the first target range, the wearable device sends the acquired second real-time state parameter of the stroke patient to a second edge computing terminal, and the second edge computing terminal is distributed at the second position;
the second edge computing terminal executes edge computing according to the second real-time state parameter to obtain a second edge computing result;
and when the second edge calculation result meets a second preset condition, the second edge calculation terminal sends the second edge calculation result and the second real-time state parameter to the cloud platform.
Of course, if the second edge calculation result does not meet the second predetermined condition, the judgment of the next position, i.e. the judgment of the third position, is continued, and fig. 2 is not continued for the sake of space.
However, it is generally understood that fig. 2 shows that the technical solution of the present invention is a continuous full-flow data acquisition and behavior analysis, rather than a single point or single judgment, as the target patient moves at various positions within the first target range, which is one of the important features of the present invention.
It should be noted that, in different implementations of the present embodiment, the "preset conditions" defined by "first" to "twenty-eight" do not necessarily represent that there is a difference or no difference between the "preset conditions". Various "preset conditions" used in the determination conditions in the above different technical solutions of the present invention may be reasonably set by a person skilled in the art according to actual situations, and the present invention is not particularly limited to this. In the following description of the specific embodiments, the related embodiments may also provide specific limitations for part of the "preset conditions", but these are merely examples of one or more of many reasonable settings, and are not exhaustive and do not limit the actual protection scope of the present invention, and any "preset conditions" that meet the actual conditions should fall within the protection scope of the present invention.
Thus, as an example, the first edge calculation result meets a first preset condition, which includes one or a combination of the following:
the gait conforms to a preset mode; the pace is lower than a first threshold; the lower limb muscle strength exceeds a second threshold value; the rate of change of one of heart rate, respiratory rate or blood pressure exceeds a third threshold.
The second edge calculation result meets a second preset condition, and specifically includes:
the second edge calculation result meets the first preset condition;
or;
the second edge calculation result does not meet the first preset condition, but the change rate of the second edge calculation result and the first edge calculation result is greater than a fourth preset threshold.
Preferably, the fourth preset threshold is associated with the first position and the second position.
On this basis, the feedback suggestion is given based on the behavior analysis result, and the feedback suggestion specifically includes:
the cloud platform sends a voice test sequence to the first edge computing terminal, the second edge computing terminal and/or the third edge computing terminal;
the first edge computing terminal, and/or the second edge computing terminal, and/or the third edge computing terminal sends the voice test sequence to the wearable device.
After the voice test sequence is sent to the wearable device, if a correct response is not received within a preset time, the edge computing terminal locks the position of the wearable device.
According to the embodiment, when the specific behavior analysis is executed, the technical scheme of the invention may need a plurality of edge calculation results of data of a plurality of positions to trigger, and may also need only one edge calculation of data of one position to trigger, so as to ensure the objectivity and continuity of the actual data.
For convenience of description, the plurality of different positions are divided into a current position, a previous position, and a next position, the corresponding data are referred to as current data, previous data, and next position, and the corresponding edge calculation result and real-time state parameter may be referred to as a current edge calculation result, a current real-time state parameter, a current edge calculation terminal, a next position edge calculation terminal, and the like, which may be specifically referred to as schematic diagrams of different scenarios in fig. 3 to 4.
In summary, the edge computing terminal of the present invention is corresponding to the position one by one, and can only obtain the real-time status parameters of the target patient at the corresponding position (range), which also makes full use of the characteristics of the edge computing.
In fig. 3, as shown in the upper part of the figure, when the stroke patient is located at the current position of the first target range, the wearable device sends the acquired current real-time state parameters of the stroke patient to current edge computing terminals, where the current edge computing terminals are distributed at the current position;
the current edge computing terminal executes edge computing according to the current real-time state parameters to obtain a current edge computing result;
when the current edge calculation result meets a first preset condition, the current edge calculation terminal sends the current edge calculation result and the current real-time state parameter to the cloud platform;
the cloud platform performs behavior analysis on the cerebral apoplexy patient based on the current edge calculation result and the current real-time state parameter, gives a feedback suggestion based on the behavior analysis result, and sends the feedback suggestion to the current edge calculation terminal at the current position;
as a further improvement, when the current edge computing terminal at the current position acquires the current real-time state parameters, considering that the target patient moves in real time in the first target range, the target patient is located at the current position; when the cloud platform gives a feedback recommendation, the target patient may be in the next position, and therefore, as shown in the lower part of fig. 3, the cloud platform should send the feedback recommendation to the edge computing terminal in the next position for subsequent processing.
FIG. 3 shows a process that requires only one edge calculation of data for one location to trigger behavior analysis.
In contrast, see fig. 4. FIG. 4 illustrates a process that requires multiple edge calculations of data at multiple locations to trigger behavior analysis.
For convenience of description, fig. 4 exemplifies two positions, i.e., a previous position and a current position, and relates to an edge terminal of a next position.
When the cerebral apoplexy patient is located at the current position of the first target range, the wearable device sends the acquired current real-time state parameters of the cerebral apoplexy patient to a current edge computing terminal, and the current edge computing terminal is distributed at the current position;
the current edge computing terminal executes edge computing according to the current real-time state parameters to obtain a current edge computing result;
when the current edge calculation result meets a first preset condition, the current edge calculation terminal sends the current edge calculation result and the current real-time state parameter to the cloud platform;
meanwhile, the cloud platform also needs to receive a previous edge calculation result, namely an edge calculation result of a previous position, and the edge calculation result is output by an edge calculation terminal corresponding to the previous position according to a previous real-time state parameter when the target patient is at the previous position;
the cloud platform performs behavior analysis on the cerebral apoplexy patient based on the current edge calculation result and the previous edge calculation result, gives a feedback suggestion based on the behavior analysis result, and sends the feedback suggestion to the current edge calculation terminal at the current position;
as a further improvement, when the current edge computing terminal at the current position acquires the current real-time state parameters, considering that the target patient moves in real time in the first target range, the target patient is located at the current position; when the cloud platform gives a feedback recommendation, the target patient may be in the next position, and therefore, as shown in the lower part of fig. 3, the cloud platform should send the feedback recommendation to the edge computing terminal in the next position for subsequent processing.
It can be seen that in the scenario illustrated in fig. 4, the cloud platform needs to aggregate the first edge calculation result and the second edge calculation result to perform the behavior analysis.
In conjunction with fig. 3-4, the above process can be described generally as: the cloud platform sends a voice test sequence to the first edge computing terminal, the second edge computing terminal and/or the third edge computing terminal;
the first edge computing terminal, and/or the second edge computing terminal, and/or the third edge computing terminal sends the voice test sequence to the wearable device.
The feedback suggestion is given based on the behavior analysis result, and the method further comprises the following steps:
after the voice test sequence is sent to the wearable device, if a correct response is not received within a preset time, the edge computing terminal locks the position of the wearable device.
Obviously, in the technical scheme of the invention, the behavior analysis of the stroke patient is the activity throughout the whole target range, and not only the data of individual time points are considered, but also the data continuity and the whole trend in the whole process are comprehensively considered, so that the behavior analysis result is more objective and comprehensive, and the obtained feedback suggestion is more accurate; meanwhile, the combined processing of edge calculation and platform is adopted, so that transmission blockage can be avoided, data delay (especially considering the activity state of a patient) is reduced as much as possible, and related measures can be taken as soon as possible.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The utility model provides a stroke behavior analysis collection system based on cloud calculates, the system includes wearable equipment and marginal computing terminal, its characterized in that:
the system further comprises a cloud platform in communication with the edge computing terminal;
a plurality of the edge computing terminals are distributed at different positions of a first target range of the stroke patient activity;
the wearable device is used for acquiring real-time state parameters of the cerebral apoplexy patient, wherein the real-time state parameters comprise motion posture parameters and physiological activity parameters;
the stroke patient wears the wearable device;
when the stroke patient is located at a first position of the first target range, the wearable device sends the acquired first real-time state parameter of the stroke patient to a first edge computing terminal, and the first edge computing terminal is distributed at the first position;
the first edge computing terminal executes edge computing aiming at the first real-time state parameter to obtain a first edge computing result;
when the first edge calculation result meets a first preset condition, the first edge calculation terminal sends the first edge calculation result and the first real-time state parameter to the cloud platform;
the cloud platform performs behavior analysis on the stroke patient based on the first edge calculation result and the first real-time state parameter, and gives feedback suggestions based on the behavior analysis result.
2. The cloud-computing-based stroke behavior analysis and acquisition system of claim 1, wherein:
the motion posture parameters comprise gait, pace and lower limb muscle strength of the stroke patient;
the physiological activity parameters comprise heart rate, respiratory rate and blood pressure of the stroke patient.
3. The cloud-computing-based stroke behavior analysis and acquisition system of claim 1, wherein:
when the stroke patient is located at a second position of the first target range, the wearable device sends the acquired second real-time state parameter of the stroke patient to a second edge computing terminal, and the second edge computing terminal is distributed at the second position;
the second edge computing terminal executes edge computing according to the second real-time state parameter to obtain a second edge computing result;
and when the second edge calculation result meets a second preset condition, the second edge calculation terminal sends the second edge calculation result and the second real-time state parameter to the cloud platform.
4. The cloud-computing-based stroke behavior analysis and acquisition system of claim 2, wherein:
the first edge calculation result meets a first preset condition, and the first edge calculation result comprises one or a combination of the following conditions:
the gait conforms to a preset mode; the pace is lower than a first threshold; the lower limb muscle strength exceeds a second threshold value; the rate of change of one of heart rate, respiratory rate or blood pressure exceeds a third threshold.
5. The cloud-computing-based stroke behavior analysis and acquisition system of claim 3, wherein:
the second edge calculation result meets a second preset condition, and specifically includes:
the second edge calculation result meets the first preset condition;
or;
the second edge calculation result does not meet the first preset condition, but the change rate of the second edge calculation result and the first edge calculation result is greater than a fourth preset threshold.
6. The cloud-computing-based stroke behavior analysis and acquisition system of claim 5, wherein:
the fourth preset threshold is associated with the first position and the second position.
7. The cloud-computing-based stroke behavior analysis and acquisition system of claim 3, wherein:
and the cloud platform collects the first edge calculation result and the second edge calculation result and executes the behavior analysis.
8. The cloud-computing-based stroke behavior analysis and acquisition system as claimed in any one of claims 1 to 7, wherein:
the feedback suggestion is given based on the behavior analysis result, and the feedback suggestion specifically comprises the following steps:
the cloud platform sends a voice test sequence to the first edge computing terminal, the second edge computing terminal and/or the third edge computing terminal;
the first edge computing terminal, and/or the second edge computing terminal, and/or the third edge computing terminal sends the voice test sequence to the wearable device.
9. The cloud-computing-based stroke behavior analysis and acquisition system of claim 8, wherein:
the feedback suggestion is given based on the behavior analysis result, and the method further comprises the following steps:
after the voice test sequence is sent to the wearable device, if a correct response is not received within a preset time, the edge computing terminal locks the position of the wearable device.
10. The cloud-computing-based stroke behavior analysis acquisition system of any one of claims 1-7 or 9, wherein:
the cloud platform is located in a second target range;
the second target range and the first target range are geospatially different ranges.
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