CN110689935A - Internet of things emergency service platform and early warning method - Google Patents
Internet of things emergency service platform and early warning method Download PDFInfo
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- CN110689935A CN110689935A CN201910933309.9A CN201910933309A CN110689935A CN 110689935 A CN110689935 A CN 110689935A CN 201910933309 A CN201910933309 A CN 201910933309A CN 110689935 A CN110689935 A CN 110689935A
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- G—PHYSICS
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- 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 belongs to the field of public health data management systems, and particularly relates to an Internet of things emergency service platform and an early warning method. The emergency monitoring based on the cloud computing and the disease prediction model based on the big data analysis not only obtain more rescue time than the traditional emergency processing method, but also can accurately predict the disease incidence possibility. The patent is based on sensors to capture personal daily health information, and based on fog computing and mobile application monitoring of emergency situations, and based on cloud computing and big data analysis disease prediction. Through comparison of test results, the emergency monitoring based on the cloud computing and the disease prediction model based on big data analysis are proved, so that more rescue time is obtained compared with the traditional emergency processing method, and the disease incidence possibility can be accurately predicted.
Description
Technical Field
The invention belongs to the field of public health data management systems, and particularly relates to an Internet of things emergency service platform and an early warning method.
Background
With the rapid development of modern high and new technologies and the change of consciousness of people's healthy life, the demand of personal health care services is gradually increased. The rapid development of information communication technology and medical biotechnology not only improves the level of personal medical care service, but also enables people to enter the age of long life. In order to provide personal medical services, fog and cloud computing provide an effective solution for ubiquitous health information transfer, and a health big data analysis model can provide daily personal health condition analysis reports.
However, injury death and death from sudden illness often cause great pain to the family. There is currently no effective rescue method to save precious lives, nor is there a method to predict the likelihood of disease onset.
Statistically, the ten leading causes of death are cardiovascular disease, cancer, cerebrovascular disease, nociceptive death, respiratory disease, diabetes, neuropsychiatric disease, respiratory infection, digestive disease, and genitourinary disease. The order of causes of death is different from the age group. The first three causes of death of children (1-14 years old) are injury, cancer and congenital malformation, accounting for 74.28%; the first three death causes of young people (15-44 years old) are injuries, cancers and cardiovascular diseases, and account for 75.97%; cardiovascular disease, cancer and cerebrovascular disease are the first three causes of death in the middle-aged and elderly (over 45 years old) groups. 88.07% of the total weight of the product. According to the statistical analysis result of death causes, 29 percent of cancer, 20 percent of cardiovascular disease, 19 percent of injury, 11 percent of cerebrovascular disease and 9 percent of congenital malformation. Among them, cancers and congenital abnormalities are mostly genetic diseases. The emergency monitoring and disease prediction service model provided by the patent mainly analyzes and provides a result report for the emergency monitoring of injury, cerebrovascular diseases and heart deficiency diseases and the possibility of disease attack.
The invention discloses a Chinese patent application 201811454499.8, and belongs to the technical field of data processing. The medical big data analysis processing method comprises the main steps of data acquisition and transmission, data preprocessing, prediction model construction and testing and optimization of the prediction model to obtain the prediction model used for expressing the disease probability of a predicted object or the probability that the predicted object has a certain disease, and the Internet of things transmission of data information is realized through a communication module.
The method has the defects that the initial received signals are not optimized and graded, so that the response is not timely, and the health of patients is harmed.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an Internet of things emergency service platform with timely response, small error and high accuracy and an early warning method.
The invention is realized in the way, and the emergency service platform of the Internet of things is characterized in that: the utility model provides an thing networking emergency service platform which characterized in that: comprises a sensor layer, a fog computing module, a core network, a cloud computing module and a mobile edge computing module which are connected by a network, wherein,
the system comprises a fog calculation module, a monitoring module and a monitoring module, wherein the fog calculation module comprises a receiving unit, a system management unit and an output unit, the receiving unit receives data information from a sensor layer, the system management unit analyzes the data information, judges whether the data information is urgent or not, sends the urgent data information to the mobile edge calculation module through the output unit, and uploads non-urgent data to a core network;
the cloud computing module comprises a personal health record management system, a big data analysis platform, a health management file system, an emergency analysis service unit and a disease prediction service unit, wherein the personal health record management system receives and analyzes data from the mobile edge computing module, and feeds the data back to a medical institution of the mobile edge computing module after analysis;
and the mobile edge calculation module comprises a family user, a medical institution and/or a health management center.
The network connection is one or a combination of a wired network, a cellular wireless network, a mobile communication network and a satellite communication network.
The sensor layer comprises a sphygmomanometer, a motion quantity measuring instrument, a blood glucose meter, an electroencephalograph, an electrocardiograph, an oxygen saturation instrument, a body composition measuring instrument and a weight and height measuring instrument.
The sensor layer further comprises a photoelectric sensor and a man-machine conversation device, wherein the man-machine conversation device comprises a button, handheld mobile communication equipment and wearable electronic equipment.
The system management unit of the fog computing module comprises a personal health record system and an emergency monitoring service management system, wherein the personal health record system is connected with the personal health record management system of the cloud computing module, and the emergency monitoring service management system is connected with the emergency analysis service unit of the cloud computing module.
The disease prediction service unit is connected with a rescue service center of the health management center.
An early warning method based on the Internet of things emergency service platform according to the service platform is characterized in that,
step 1, using manual input, automatic reading of health information by a sensor, electronic medical record scanning or machine learning mode input data information, and establishing a big database, wherein the big database at least comprises safety data, emergency threshold data and disease influence factor standard values;
step 2, a system management unit of the fog calculation module analyzes input data, judges whether data information is urgent or not, sends the urgent data information to the mobile edge calculation module through an output unit in a low-delay mode, and uploads non-urgent data to a core network;
step 3, the mobile edge calculation module distributes the urgent data information to a medical institution, a health management center, medical staff, family members of patients and patients;
and 4, enabling the sensor layer around the patient to comprise a sphygmomanometer, a motion quantity measuring instrument, a glucometer, an electroencephalograph, an electrocardiograph, an oxygen saturation instrument, a body composition measuring instrument and a weight and height measuring instrument, and uploading collected data to a fog calculating module for the next cycle.
After the non-urgent data are uploaded to the core network, the data are compared with the safety data and the emergency threshold data, an APC model based on a time sequence is established, and disease prediction is realized through the coefficient of each queue.
The disease occurrence probability analysis formula of the APC model based on the time series is
Wherein, E (r)ijk) Is a certain characteristic [ namely a certain age group (i), a certain time (j) and a certain birth queue (k) ] crowd expected value of the occurrence possibility of the disease; thetaijkExpected value of disease occurrence probability, N, representing the i-age group observed in the j-th periodijkRepresenting the population in the corresponding age group, period and birth cohort; mu is the intercept of the regression model, alphaiIs the influence of the ith age cohort, βjIs the influence of the jth epoch, γkIs the influence of the kth birth queue, εijkIs a random error.
The invention has the advantages and positive effects that:
the patent is based on sensors to capture personal daily health information, and based on fog computing and mobile application monitoring of emergency situations, and based on cloud computing and big data analysis disease prediction. Through comparison of test results, the emergency monitoring based on the cloud computing and the disease prediction model based on big data analysis are proved, so that more rescue time is obtained compared with the traditional emergency processing method, and the disease incidence possibility can be accurately predicted.
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FIG. 1 is a schematic block diagram of the present invention;
FIG. 2 is a system architecture diagram of the present invention;
FIG. 3 is a service module configuration diagram of the present invention;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
Example 1:
as shown in fig. 1-3, an internet of things emergency service platform is characterized in that: an Internet of things emergency service platform comprises a sensor layer, a fog computing module, a core network, a cloud computing module and a mobile edge computing module which are connected by a network, wherein,
the system comprises a fog calculation module, a monitoring module and a monitoring module, wherein the fog calculation module comprises a receiving unit, a system management unit and an output unit, the receiving unit receives data information from a sensor layer, the system management unit analyzes the data information, judges whether the data information is urgent or not, sends the urgent data information to the mobile edge calculation module through the output unit, and uploads non-urgent data to a core network;
the cloud computing module comprises a personal health record management system, a big data analysis platform, a health management file system, an emergency analysis service unit and a disease prediction service unit, wherein the personal health record management system receives and analyzes data from the mobile edge computing module, and feeds the data back to a medical institution of the mobile edge computing module after analysis;
and the mobile edge calculation module comprises a family user, a medical institution and/or a health management center.
The network connection is one or a combination of a wired network, a cellular wireless network, a mobile communication network and a satellite communication network.
The sensor layer comprises a sphygmomanometer, a motion quantity measuring instrument, a blood glucose meter, an electroencephalograph, an electrocardiograph, an oxygen saturation instrument, a body composition measuring instrument and a weight and height measuring instrument.
The sensor layer further comprises a photoelectric sensor and a man-machine conversation device, wherein the man-machine conversation device comprises a button, handheld mobile communication equipment and wearable electronic equipment.
The system management unit of the fog computing module comprises a personal health record system and an emergency monitoring service management system, wherein the personal health record system is connected with the personal health record management system of the cloud computing module, and the emergency monitoring service management system is connected with the emergency analysis service unit of the cloud computing module.
The disease prediction service unit is connected with a rescue service center of the health management center.
The system service platform can realize the health management service based on the personal health record and the health management service based on the electronic health record. The health management service based on the personal health record comprises preference data analysis, spectrogram data analysis, case diagnosis analysis, disease prediction and emergency situation monitoring, and can be provided according to web service and mobile service of intelligent sensors and mobile equipment. The health management service based on the electronic health record comprises living habit on-line expert consultation, medical image management consultation, family health management consultation, disease analysis and treatment in disease diagnosis and monitoring and emergency rescue, and the providing mode can be according to web service and face-to-face service of medical institutions.
Example 2:
an early warning method based on an Internet of things emergency service platform according to the service platform comprises the following steps:
step 1, using manual input, automatic reading of health information by a sensor, electronic medical record scanning or machine learning mode input data information, and establishing a big database, wherein the big database at least comprises safety data, emergency threshold data and disease influence factor standard values;
step 2, a system management unit of the fog calculation module analyzes input data, judges whether data information is urgent or not, sends the urgent data information to the mobile edge calculation module through an output unit in a low-delay mode, and uploads non-urgent data to a core network;
step 3, the mobile edge calculation module distributes the urgent data information to a medical institution, a health management center, medical staff, family members of patients and patients;
and 4, enabling the sensor layer around the patient to comprise a sphygmomanometer, a motion quantity measuring instrument, a glucometer, an electroencephalograph, an electrocardiograph, an oxygen saturation instrument, a body composition measuring instrument and a weight and height measuring instrument, and uploading collected data to a fog calculating module for the next cycle.
After the non-urgent data are uploaded to the core network, the data are compared with the safety data and the emergency threshold data, an APC model based on a time sequence is established, and disease prediction is realized through the coefficient of each queue.
The disease occurrence probability analysis formula of the APC model based on the time series is
Wherein, E (r)ijk) Is a certain characteristic [ namely a certain age group (i), a certain time (j) and a certain birth queue (k) ] crowd expected value of the occurrence possibility of the disease; thetaijkExpected value of disease occurrence probability, N, representing the i-age group observed in the j-th periodijkRepresenting the population in the corresponding age group, period and birth cohort; mu is the intercept of the regression model, alphaiIs the influence of the ith age cohort, βjIs the influence of the jth epoch, γkIs the influence of the kth birth queue, εijkIs a random error.
Example 3:
the following comparative analyses were performed in actual cases:
when the moving distance is 5 km, the time consumed by the traditional first aid and the proposed first aid mode is compared in the process of rescuing patients with sudden cardiovascular diseases
(1) And (3) finding an emergency:
in the traditional emergency rescue mode, the patient is discovered by others and is in contact with an emergency center. The passive discovery process is generally about 1 minute or more. The status of emergency is unknown. Such as a traumatic emergency, an emergency caused by a disease, etc.
In the emergency rescue mode of the invention, the heart pulse test sensor and software of the mobile communication equipment judge the emergency and transmit the relevant data to the fog layer (the instrument for linking the sensor or the mobile communication equipment and the like to receive data) for service, and the fog layer serves as a gateway service, so the network delay is little. The emergency information may be received within 30 seconds and analyzed and transmitted to the emergency service center.
In the process, the data received by the instrument mainly comprise systolic pressure and diastolic pressure, and the standard values of the sex and the age group are used as judgment bases of dangerous conditions. When the systolic pressure is larger than or equal to the value obtained by adding 10 to the age group reference value and the diastolic pressure is smaller than or equal to the value obtained by subtracting 10 from the age group reference value, the emergency condition is taken, early warning is sent to the platform, and relevant data are sent.
TABLE 1 reference value of normal blood pressure of Chinese people of all ages
(2) The help seeking process comprises the following steps:
in the conventional emergency rescue mode, first, emergency services are dialed from the neighboring hospital 120 and the help is asked, and then the family is contacted. In this process, contacting emergency services requires contacting the hospital and providing location information, so at least 1 minute or more is required. The contact of the family is slow or impossible under the condition that the patient is in a coma and the like. And the patient can not be contacted with the family, and the patient can not be directly transported to the adjacent hospital for rescue.
In the emergency rescue mode of the invention, the service platform automatically contacts the adjacent hospitals 120 for emergency services immediately after receiving the abnormal condition and allows the hospitals to send ambulances in time. And instantly contacting the family according to the member information registered by the patient on the platform and the related information of the emergency contact. The help seeking process is automatically and intelligently processed, so that the required time is less than or equal to 30 seconds.
In order to save the time required by the patient to move to the hospital, the platform service can be linked with a drip vehicle service platform in time and can obtain escort service after being authenticated by special conditions. This process takes approximately 1 minute or so and can be serviced by special rescue vehicles with rescue experience.
(3) Moving process:
in a conventional emergency rescue mode, after receiving 120 a service call for help, the hospital dispatches the vehicle from the hospital and carries the patient to the hospital for the required round trip travel time. In general, it takes about 30 minutes or more.
In the emergency rescue mode, the hospital sends the ambulance immediately after obtaining the information of asking for help and prepares medical staff according to the physical condition of the patient. The patient is timely serviced by a particular ambulance and is in real-time contact with the 120 ambulance and shares geographical locations to reduce travel time and to timely deliver patient care to the encountering 120 ambulance. This shortens the travel, approaching one-way convoying, so that the time required for the moving process is nearly half, half being 18 minutes or less.
(4) The rescue process comprises the following steps:
in the conventional emergency rescue mode, the rescuer can judge the condition of the patient after meeting the patient and report the condition to the hospital to prepare for rescue in response. The time required before the rescue is performed is at least 3 minutes.
In the emergency rescue mode, medical staff can make rescue preparation in advance according to the health information of the patient on the rescue service platform and the personal health information stored by the cloud platform, and can prepare various treatment schemes in advance. The rescue preparation process does not delay any time.
The experiment proves that:
1. results of emergency services experiments
TABLE 2.5 comparison of results of emergency response experiments within kilometers
2. Disease prediction results based on APC model
TABLE 3 disease prediction results based on APC model
(p < 0.0001;. p < 0.001;. p < 0.05; standard deviation: square root of coefficient variance. calculation of coefficients is based on observed information matrix in maximum likelihood estimation.)
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (9)
1. The utility model provides an thing networking emergency service platform which characterized in that: comprises a sensor layer, a fog computing module, a core network, a cloud computing module and a mobile edge computing module which are connected by a network, wherein,
the system comprises a fog calculation module, a monitoring module and a monitoring module, wherein the fog calculation module comprises a receiving unit, a system management unit and an output unit, the receiving unit receives data information from a sensor layer, the system management unit analyzes the data information, judges whether the data information is urgent or not, sends the urgent data information to the mobile edge calculation module through the output unit, and uploads non-urgent data to a core network;
the cloud computing module comprises a personal health record management system, a big data analysis platform, a health management file system, an emergency analysis service unit and a disease prediction service unit, wherein the personal health record management system receives and analyzes data from the mobile edge computing module, and feeds the data back to a medical institution of the mobile edge computing module after analysis;
and the mobile edge calculation module comprises a family user, a medical institution and/or a health management center.
2. The internet-of-things emergency services platform of claim 1, wherein the network connection is one or a combination of a wired network, a cellular wireless network, a mobile communication network, and a satellite communication network.
3. The internet of things emergency services platform of claim 1, wherein the sensor layer comprises a sphygmomanometer, an exercise quantity measuring instrument, a blood glucose meter, an electroencephalograph, an electrocardiograph, an oxygen saturation instrument, a body composition measuring instrument and a body weight and height measuring instrument.
4. The internet of things emergency services platform of claim 3, wherein the sensor layer further comprises a photoelectric sensor and a man-machine interaction device, wherein the man-machine interaction device comprises a button, a handheld mobile communication device and a wearable electronic device.
5. The internet of things emergency service platform of claim 1, wherein the system management unit of the fog computing module comprises a personal health record system and an emergency monitoring service management system, the personal health record system is connected with the personal health record management system of the cloud computing module, and the emergency monitoring service management system is connected with the emergency analysis service unit of the cloud computing module.
6. The internet of things emergency service platform of claim 1, wherein the disease prediction service unit is connected to a rescue service center of a health management center.
7. An early warning method based on the Internet of things emergency service platform according to the service platform is characterized in that,
step 1, using manual input, automatic reading of health information by a sensor, electronic medical record scanning or machine learning mode input data information, and establishing a big database, wherein the big database at least comprises safety data, emergency threshold data and disease influence factor standard values;
step 2, a system management unit of the fog calculation module analyzes input data, judges whether data information is urgent or not, sends the urgent data information to the mobile edge calculation module through an output unit in a low-delay mode, and uploads non-urgent data to a core network;
step 3, the mobile edge calculation module distributes the urgent data information to a medical institution, a health management center, medical staff, family members of patients and patients;
and 4, enabling the sensor layer around the patient to comprise a sphygmomanometer, a motion quantity measuring instrument, a glucometer, an electroencephalograph, an electrocardiograph, an oxygen saturation instrument, a body composition measuring instrument and a weight and height measuring instrument, and uploading collected data to a fog calculating module for the next cycle.
8. The Internet of things emergency service platform-based early warning method as claimed in claim 7, wherein after the non-urgent data is uploaded to the core network, the non-urgent data is compared with the safety data and the urgent threshold data to establish an APC model based on a time sequence, and disease prediction is realized through a coefficient of each queue.
9. The Internet of things emergency service platform based early warning method as claimed in claim 8, wherein the disease occurrence probability analysis formula of the APC model based on time series is
Wherein, E (r)ijk) Is a certain characteristic [ namely a certain age group (i), a certain time (j) and a certain birth queue (k) ] crowd expected value of the occurrence possibility of the disease; thetaijkExpected value of disease occurrence probability, N, representing the i-age group observed in the j-th periodijkRepresenting the population in the corresponding age group, period and birth cohort; mu is the intercept of the regression model, alphaiIs the influence of the ith age cohort, βjIs the influence of the jth epoch, γkIs the influence of the kth birth queue, εijkIs a random error.
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| CN112201345A (en) * | 2020-10-10 | 2021-01-08 | 上海奇博自动化科技有限公司 | Method for analyzing cattle diseases based on motion sensor |
| CN113408813A (en) * | 2021-06-30 | 2021-09-17 | 重庆东登科技有限公司 | Switching system of water emergency platform |
| CN113408813B (en) * | 2021-06-30 | 2022-03-15 | 重庆东登科技有限公司 | Switching system of water emergency platform |
| CN115689836A (en) * | 2023-01-04 | 2023-02-03 | 山东沐华医疗科技有限公司 | Home medical contract signing, basic public health and chronic disease management integrated service platform and management method |
| CN115689836B (en) * | 2023-01-04 | 2023-06-23 | 山东沐华医疗科技有限公司 | Household doctor signing, basic public guard and chronic disease management integrated service platform and management method |
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